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BigCode 1.41k

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/misabhishek/gcp-iam-recommender/blob/main/iam_recommender_basics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "# Before you begin\n\n\n1. Have a GCP projrect ready. \n3. [Enable Iam Recommender](https://console.cloud.google.com/flows/enableapi?apiid=recommender.googleapis.com) APIs for the project.", "_____no_output_____" ], [ "### Provide your credentials to the runtime", "_____no_output_____" ] ], [ [ "from google.colab import auth\nauth.authenticate_user()\nprint('Authenticated')", "_____no_output_____" ] ], [ [ "## Understand GCP IAM Recommender", "_____no_output_____" ], [ "**Declare the Cloud project ID which will be used throughout this notebook**", "_____no_output_____" ] ], [ [ "project_id = \"Enter-your-project\"", "_____no_output_____" ] ], [ [ "**A helper function to execute `gcloud` commands**", "_____no_output_____" ] ], [ [ "import json\nimport subprocess\ndef execute_command(command):\n return json.loads(subprocess.check_output(filter(lambda x: x, command.split(\" \"))).decode(\"utf-8\"))", "_____no_output_____" ], [ "recommender_command = f\"\"\"gcloud recommender recommendations list \\\n --location=global \\\n --recommender=google.iam.policy.Recommender \\\n --project={project_id} \\\n --format=json\n \"\"\"", "_____no_output_____" ], [ "recommendations = execute_command(recommender_command)", "_____no_output_____" ], [ "recommendations[7]", "_____no_output_____" ] ], [ [ "### Getting insight for the recommendations", "_____no_output_____" ] ], [ [ "insight_command = f\"\"\"gcloud recommender insights list \\\n --project={project_id} \\\n --location=global \\\n --insight-type=google.iam.policy.Insight \\\n --format=json\n \"\"\"", "_____no_output_____" ], [ "insights = execute_command(insight_command)", "_____no_output_____" ], [ "insights[0]", "_____no_output_____" ] ], [ [ "# Generate diff view", "_____no_output_____" ] ], [ [ "recommendation_name = \"Enter-the-recommendation-name\"", "_____no_output_____" ], [ "#@title A helper to generate diff view. It uses IAM roles api also.\nimport pandas as pd\ndef generate_diff_view(recommendation_name):\n role_to_permission_command = \"gcloud iam roles describe {} --format=json\"\n\n recommendation = [r for r in recommendations if r[\"name\"] == recommendation_name][0]\n insight_name = recommendation[\"associatedInsights\"][0][\"insight\"]\n\n added_roles = []\n removed_role = []\n for op in recommendation[\"content\"][\"operationGroups\"][0][\"operations\"]:\n if op[\"action\"] == \"add\":\n added_roles.append(op[\"pathFilters\"][\"/iamPolicy/bindings/*/role\"])\n if op[\"action\"] == \"remove\":\n removed_role.append(op[\"pathFilters\"][\"/iamPolicy/bindings/*/role\"])\n\n cur_permissions = set(execute_command(\n role_to_permission_command.format(removed_role[0]))[\"includedPermissions\"])\n\n recommended_permisisons = set() \n for r in added_roles:\n recommended_permisisons.update(execute_command(\n role_to_permission_command.format(r))[\"includedPermissions\"])\n \n removed_permisisons = cur_permissions - recommended_permisisons\n \n insight = [insight for insight in insights \n if insight[\"name\"] == insight_name][0]\n used_permissions = set(k[\"permission\"] for k in \n insight[\"content\"][\"exercisedPermissions\"])\n inferred_permissions = set(k[\"permission\"] for k in \n insight[\"content\"][\"inferredPermissions\"])\n \n unused_but_still_common_permissions = (recommended_permisisons - used_permissions \n - inferred_permissions)\n \n types = ([\"used\"] * len(used_permissions) \n + [\"ml-inferred\"] * len(inferred_permissions)\n + [\"common\"] * len(unused_but_still_common_permissions)\n + [\"removed\"] * len(removed_permisisons))\n \n permissions = [*used_permissions, *inferred_permissions, \n *unused_but_still_common_permissions, *removed_permisisons]\n\n return pd.DataFrame({\"type\": types, \"permission\": permissions})", "_____no_output_____" ], [ "diff_view = generate_diff_view(recommendation_name)", "_____no_output_____" ], [ "diff_view", "_____no_output_____" ], [ "diff_view[\"type\"].value_counts()", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\nfrom numpy import linalg as npla\nimport matplotlib.pyplot as plt\nfrom scipy import integrate", "_____no_output_____" ], [ "def eigen(A):\n eigenValues, eigenVectors = npla.eig(A)\n idx = np.argsort(eigenValues)\n eigenValues = eigenValues[idx]\n eigenVectors = eigenVectors[:,idx]\n return (eigenValues, eigenVectors)", "_____no_output_____" ], [ "L=15.0 # box length is 2L; [-L,L]\nm=1 # particle mass \nhbar=1 # 1 in atomic units", "_____no_output_____" ], [ "def potential(x): # 1-D molecule\n potential = -6/np.sqrt((x+4)**2+1)-4/np.sqrt((x-4)**2+1)\n return potential\n \ndef fn_V(x):\n psi_i=np.sqrt(1/L)*np.sin((i+1)*(x-L)*np.pi/(2*L))\n psi_j=np.sqrt(1/L)*np.sin((j+1)*(x-L)*np.pi/(2*L))\n Nx=x.size\n pot=np.zeros(Nx)\n for ix in range(Nx):\n pot[ix]=potential(x[ix])\n \n fn_V=psi_i * pot * psi_j\n return fn_V\n\nfor iN in range(0,6):\n \n N=2**iN # No. of basis functions\n\n V=np.zeros([N,N])\n T=np.zeros([N,N])\n H=np.zeros([N,N])\n \n for i in range(N):\n for j in range(N):\n Int_V=integrate.quadrature(fn_V, -L, L,maxiter=1000)\n V[i][j]=Int_V[0] \n T[i][i]=(i+1)**2 * hbar**2 * np.pi**2 / (8 * m * L**2)\n\n H=T+V\n\n E,V=eigen(H)\n\n print(\"Number of basis: \", N, \", ground state energy is:\", E[0])", "Number of basis: 1 , ground state energy is: -3.0563258068171444\nNumber of basis: 2 , ground state energy is: -3.156640541974056\nNumber of basis: 4 , ground state energy is: -3.924186324234983\nNumber of basis: 8 , ground state energy is: -4.547315446207627\nNumber of basis: 16 , ground state energy is: -5.2692879329887035\nNumber of basis: 32 , ground state energy is: -5.486268540734763\n" ], [ "x=np.linspace(-L, L, 101)\nVharm=np.zeros(101)\nfor ix in range(101):\n Vharm[ix]=potential(x[ix])\n\nfor k in range(16): \n psi0=np.zeros(101)\n for i in range(N):\n psi0=psi0+V[i][k]*np.sqrt(1/L)*np.sin((i+1)*(x-L)*np.pi/(2*L))\n \n plt.plot(x,np.abs(psi0)**2+E[k])\n\n\nplt.plot(x,Vharm)\nplt.xlabel(\"$x$\", fontsize=14)\nplt.ylabel(\"$|\\psi(x)|^2$\", fontsize=14)\nplt.savefig('psi_1Dmol.png') \n#plt.legend(['n = 0','n = 1','n = 2','n = 3','n = 4'])\nplt.xlim(-L,L)\n#plt.ylim(-10,10)\nplt.show()", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from presidio_analyzer import AnalyzerEngine, PatternRecognizer\nfrom presidio_anonymizer import AnonymizerEngine\nfrom presidio_anonymizer.entities import AnonymizerConfig", "_____no_output_____" ] ], [ [ "# Analyze Text for PII Entities\n\n<br>Using Presidio Analyzer, analyze a text to identify PII entities. \n<br>The Presidio analyzer is using pre-defined entity recognizers, and offers the option to create custom recognizers.\n\n<br>The following code sample will:\n<ol>\n<li>Set up the Analyzer engine - load the NLP module (spaCy model by default) and other PII recognizers</li>\n<li> Call analyzer to get analyzed results for \"PHONE_NUMBER\" entity type</li>\n</ol>", "_____no_output_____" ] ], [ [ "text_to_anonymize = \"His name is Mr. Jones and his phone number is 212-555-5555\"", "_____no_output_____" ], [ "analyzer = AnalyzerEngine()\nanalyzer_results = analyzer.analyze(text=text_to_anonymize, entities=[\"PHONE_NUMBER\"], language='en')\n\nprint(analyzer_results)", "_____no_output_____" ] ], [ [ "# Create Custom PII Entity Recognizers\n\n<br>Presidio Analyzer comes with a pre-defined set of entity recognizers. It also allows adding new recognizers without changing the analyzer base code,\n<b>by creating custom recognizers. \n<br>In the following example, we will create two new recognizers of type `PatternRecognizer` to identify titles and pronouns in the analyzed text.\n<br>A `PatternRecognizer` is a PII entity recognizer which uses regular expressions or deny-lists.\n\n<br>The following code sample will:\n<ol>\n<li>Create custom recognizers</li>\n<li>Add the new custom recognizers to the analyzer</li>\n<li>Call analyzer to get results from the new recognizers</li>\n</ol>\n", "_____no_output_____" ] ], [ [ "titles_recognizer = PatternRecognizer(supported_entity=\"TITLE\",\n deny_list=[\"Mr.\",\"Mrs.\",\"Miss\"])\n\npronoun_recognizer = PatternRecognizer(supported_entity=\"PRONOUN\",\n deny_list=[\"he\", \"He\", \"his\", \"His\", \"she\", \"She\", \"hers\" \"Hers\"])\n\nanalyzer.registry.add_recognizer(titles_recognizer)\nanalyzer.registry.add_recognizer(pronoun_recognizer)\n\nanalyzer_results = analyzer.analyze(text=text_to_anonymize,\n entities=[\"TITLE\", \"PRONOUN\"],\n language=\"en\")\nprint(analyzer_results)\n", "_____no_output_____" ] ], [ [ "Call Presidio Analyzer and get analyzed results with all the configured recognizers - default and new custom recognizers", "_____no_output_____" ] ], [ [ "analyzer_results = analyzer.analyze(text=text_to_anonymize, language='en')\n\nanalyzer_results", "_____no_output_____" ] ], [ [ "# Anonymize Text with Identified PII Entities\n\n<br>Presidio Anonymizer iterates over the Presidio Analyzer result, and provides anonymization capabilities for the identified text.\n<br>The anonymizer provides 5 types of anonymizers - replace, redact, mask, hash and encrypt. The default is **replace**\n\n<br>The following code sample will:\n<ol>\n<li>Setup the anonymizer engine </li>\n<li>Create an anonymizer request - text to anonymize, list of anonymizers to apply and the results from the analyzer request</li>\n<li>Anonymize the text</li>\n</ol>", "_____no_output_____" ] ], [ [ "anonymizer = AnonymizerEngine()\n\nanonymized_results = anonymizer.anonymize(\n text=text_to_anonymize,\n analyzer_results=analyzer_results, \n anonymizers_config={\"DEFAULT\": AnonymizerConfig(\"replace\", {\"new_value\": \"<ANONYMIZED>\"}), \n \"PHONE_NUMBER\": AnonymizerConfig(\"mask\", {\"type\": \"mask\", \"masking_char\" : \"*\", \"chars_to_mask\" : 12, \"from_end\" : True}),\n \"TITLE\": AnonymizerConfig(\"redact\", {})}\n)\n\nprint(anonymized_results)", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code" ], [ "markdown" ], [ "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "empty" ] ] ]

[ "empty" ]

[ [ "empty" ] ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "empty" ] ] ]

[ "empty" ]

[ [ "empty" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# ML/DL techniques for Tabular Modeling PART I\n> In this part, I have explained Decision Trees.\n- toc: true \n- badges: true\n- comments: true", "_____no_output_____" ] ], [ [ "#hide\n# !pip install -Uqq fastbook\n\nimport fastbook\nfastbook.setup_book()", "/home/nitish/miniconda3/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n return torch._C._cuda_getDeviceCount() > 0\n" ], [ "#hide\nfrom fastbook import *\nfrom kaggle import api\nfrom pandas.api.types import is_string_dtype, is_numeric_dtype, is_categorical_dtype\nfrom fastai.tabular.all import *\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.tree import DecisionTreeRegressor\nfrom dtreeviz.trees import *\nfrom IPython.display import Image, display_svg, SVG\nimport numpy as np\nimport matplotlib.pyplot as plt\n\npd.options.display.max_rows = 20\npd.options.display.max_columns = 8", "_____no_output_____" ], [ "#hide\n\n# api.competition_download_cli('bluebook-for-bulldozers')\n# file_extract('bluebook-for-bulldozers.zip')\ndf = pd.read_csv('/home/nitish/Downloads/bluebook-bulldozers/TrainAndValid.csv', low_memory=False)", "_____no_output_____" ] ], [ [ "## Introduction\nTabular Modelling takes data in the form of a table, where generally we want to learn about a column's value from all the other columns' values. The column we want to learn is known as a dependent variable and others are known as independent variables. The learning could be both like a classification problem or regression problem. We will look into various machine learning models such as decision trees, random forests, etc, also we'll look for what deep learning has to offer in tabular modeling.", "_____no_output_____" ], [ "## Dataset\nI will be using [Kaggle competition](https://www.kaggle.com/c/bluebook-for-bulldozers) dataset on all the models so that it will be easier to understand and compare different models. I have loaded it into a dataframe df.", "_____no_output_____" ] ], [ [ "df.head()", "_____no_output_____" ] ], [ [ "The key fields are in train.csv are:\n\n- SalesID: the unique identifier of the sale\n- MachineID: the unique identifier of a machine. A machine can be sold multiple times\n- saleprice: what the machine sold for at auction (only provided in train.csv)\n- saledate: the date of the sale\n\nFor this competition, we need to predict the log of the sale price of bulldozers sold at auctions. We will try to build different ML and DL models which will be predicting $log$(sale price).", "_____no_output_____" ], [ "## Decision Trees\nA decision tree makes a split in data based on the values of a column. For example, suppose we have data for different persons for their age, whether they eat healthy, whether they exercise, etc, and want to predict whether they are fit or unfit based on the data then we can use the following decision tree.", "_____no_output_____" ], [ "![](images/blog5_1.png \"Credit:fast.ai\")", "_____no_output_____" ], [ "At each level, the data is divided into 2 groups for the next level, e.g. at the first level, whether age<30 or not divides the whole dataset into 2 smaller datasets, and similary the data is split again until we reach leaf node of 2 classes: FIT or UNFIT. \n\nIn the real world, data is way more complex containing a lot of columns. E.g., in our dataframe df, there are 53 columns. So the question arises which column to chose for each split and what should be the value at which it is split. The answer is to try for every column and each value present in a column for the split. So if there are n columns and each column have x different values then we need to try n\\*x splits and chose the best one on some criteria. When trying a split, then whole data will be divided into 2 groups for that level, so we can take the average of the sale price of a group as the predicted sale price for all the rows in that group, and can calculate rmse distance between predictions and actual sale price. This will give us a number, which is our loss value, if bigger tells our predictions are far from the actual sale price and vice-versa. So the algorithm for building a decision tree could be written as:\n1. Loop through all the columns in the training dataset.\n1. Loop through all the possible values for a column. If the column contains categorical data then chose the condition as \"equal to\" a category and \"not equal to\" a category. If the column contains continuous data then for all the distinct values split on \"less than equal to\" and \"greater than\" the value.\n1. Find the average sale price for each of the groups, this is our prediction. Calculate rmse from the actual values of the saleprice.\n1. The rmse of a split could be set as the sum of rmse for all groups after the split.\n1. After looping through all the columns and all possible splits for each column chose the split with the least rmse.\n1. Continue the same process recursively on the child groups until some stopping criteria are reached like maximum number of the leaf nodes, minimum number of data items per group, etc.", "_____no_output_____" ], [ "Below is given an example of a decision tree. In the root node, the value is simply the average of all the training dataset which would be the most simple prediction we could calculate for a new datapoint is to simply give a prediction of 10.1 every time. Mean Square Error (mse) is 0.48, and there is a total of 404710 samples, which is actually the total number of samples in the training dataset.\n\nNow for the split, it has tried all the columns and all the possible values for a column, and it came with $Coupler\\_System \\leq 0.5$ split. This would split the whole dataset into two smaller datasets. When the condition is True it resulted in 360847 samples, with mse of 0.42 and an average value of sale price as 10.21. When the condition is False it resulted in 43863 samples, with mse of 0.12 and an average sale price value of 9.21. It could be seen that this split has improved our prediction, and our model has learnt some pattern because now the weighted average mse is (360847 * 0.42 + 43863 * 0.12)/(404710) = 0.38 < 0.48.\n\nSimilarly, splitting the \"True condition child\" on $YearMade \\leq 0.42$ further decreases the mse, which means our predictions are further closer to the actual values\n", "_____no_output_____" ], [ "![](images/blog5_2.png \"Credit:fast.ai\")", "_____no_output_____" ], [ "## Overfitting and Underfitting in decision trees\nUnderfitting in the decision trees will be when we make very few splits, or no splits at all, e.g., in the root node the average value is 10.1, and if we use this value as prediction, then it's clearly a naive solution to a complex problem, which is therefore an underfitting. This is the case of high bias and low variance.\n\nOverfitting will be when there are way too many splits such that in extreme case there is one training sample per leaf node, which is actually the model has memorized the training dataset. It is overfitting because although the mse will be 0 for the training dataset, it will be very high for the validation dataset, as the model will fail to generalize on unseen datapoints. This is the case of low bias and high variance.", "_____no_output_____" ], [ "Data Generation step:", "_____no_output_____" ] ], [ [ "x = np.linspace(0, 10, 110)\ny = x + np.random.randn(110)\nmy_list = [0]*30 + [1]*80\nrandom.shuffle(my_list)\nmy_list = [True if i==1 else False for i in my_list]\ntr_x, tr_y = x[np.where(my_list)[0]],y[np.where(my_list)[0]]\nmy_list = [not elem for elem in my_list]\nval_x, val_y = x[np.where(my_list)[0]],y[np.where(my_list)[0]] \ntr_x = tr_x.reshape(tr_x.shape[0],1)\nval_x = val_x.reshape(val_x.shape[0],1)", "_____no_output_____" ] ], [ [ "### Underfitting Case\nIn the underfitting case, I have set max_leaf_nodes=2, so that bias will be high.", "_____no_output_____" ] ], [ [ "m = DecisionTreeRegressor(max_leaf_nodes=2)\n\nm.fit(tr_x, tr_y);\nfig, ax = plt.subplots(figsize=(16,8))\nax.scatter(x,y, marker='+', label='actual data')\nax.scatter(tr_x, m.predict(tr_x), label='predicted data on training dataset')\nax.scatter(val_x, m.predict(val_x), label='predicted data on validation dataset')\n\nax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(1))\nax.grid(which='major', axis='both', linestyle=':', linewidth = 1, color='b')\n\n\nax.set_xlabel(\"x\", labelpad=5, fontsize=26, fontname='serif', color=\"blue\")\nax.set_ylabel(\"y\", labelpad=5, fontsize=26, fontname='serif', color=\"blue\")\nax.legend(prop={\"size\":15})", "_____no_output_____" ] ], [ [ "In the above example, I have generated a dataset with $x = y + \\epsilon$, where $\\epsilon \\in N(0,1)$ is the random noise. I have generated this data because it's 2-d data, much simpler, and easy to visualize than the complex Kaggle dataset.\n\nA decision tree is implemented which tries to learn the relationship between x and y and predicts y from x. Training data is randomly chosen 80 samples from 110 samples, and the remaining 30 are in validation data. Stopping criteria is set as the max number of leaf nodes = 10. In the above figure, the orange ones are training samples and the green ones are validation samples.", "_____no_output_____" ] ], [ [ "print(f'Training rmse is {m_rmse(m, tr_x, tr_y)}, and validation rmse is {m_rmse(m, val_x, val_y)}')\ndraw_tree(m, pd.DataFrame([tr_x,tr_y], columns=['tr_y']))", "Training rmse is 1.6552681120862984, and validation rmse is 1.787770216476971\n" ] ], [ [ "### Overfitting Case\nIn the overfitting case, I have set max_leaf_nodes=100. This leads to a huge decision tree with each leaf node containing one training example only. Therefore, bias will be zero, the variance will be high and there will be overfitting.", "_____no_output_____" ] ], [ [ "m = DecisionTreeRegressor(max_leaf_nodes=100)\n\nm.fit(tr_x, tr_y);\nfig, ax = plt.subplots(figsize=(16,8))\nax.scatter(x,y, marker='+', label='actual data')\nax.scatter(tr_x, m.predict(tr_x), label='predicted data on training dataset')\nax.scatter(val_x, m.predict(val_x), label='predicted data on validation dataset')\n\nax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(1))\nax.grid(which='major', axis='both', linestyle=':', linewidth = 1, color='b')\n\n\nax.set_xlabel(\"x\", labelpad=5, fontsize=26, fontname='serif', color=\"blue\")\nax.set_ylabel(\"y\", labelpad=5, fontsize=26, fontname='serif', color=\"blue\")\nax.legend(prop={\"size\":15})", "_____no_output_____" ], [ "print(f'Training rmse is {m_rmse(m, tr_x, tr_y)}, and validation rmse is {m_rmse(m, val_x, val_y)}')\ndraw_tree(m, pd.DataFrame([tr_x,tr_y], columns=['tr_y']))", "Training rmse is 0.0, and validation rmse is 1.2925307400522905\n" ] ], [ [ "### Balanced Case\nIn the balanced case, I have set max_leaf_nodes=10. This leads to a nice decision tree implementation with better generalization power than both above cases, which could be confirmed by seeing training and validation losses.", "_____no_output_____" ] ], [ [ "m = DecisionTreeRegressor(max_leaf_nodes=10)\n\nm.fit(tr_x, tr_y);\nfig, ax = plt.subplots(figsize=(16,8))\nax.scatter(x,y, marker='+', label='actual data')\nax.scatter(tr_x, m.predict(tr_x), label='predicted data on training dataset')\nax.scatter(val_x, m.predict(val_x), label='predicted data on validation dataset')\n\nax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(1))\nax.grid(which='major', axis='both', linestyle=':', linewidth = 1, color='b')\n\n\nax.set_xlabel(\"x\", labelpad=5, fontsize=26, fontname='serif', color=\"blue\")\nax.set_ylabel(\"y\", labelpad=5, fontsize=26, fontname='serif', color=\"blue\")\nax.legend(prop={\"size\":15})", "_____no_output_____" ], [ "#hide\ndef rmse(preds, target):\n return np.sqrt(np.mean((preds-target)**2))\n\ndef m_rmse(m, x, y):\n return rmse(m.predict(x), y)", "_____no_output_____" ] ], [ [ "It seems like a good fit because it's neither overfitting nor underfitting.", "_____no_output_____" ], [ "Below is the training and validation losses and complete decision tree as generated by the algorithm.", "_____no_output_____" ] ], [ [ "print(f'Training rmse is {m_rmse(m, tr_x, tr_y)}, and validation rmse is {m_rmse(m, val_x, val_y)}')\ndraw_tree(m, pd.DataFrame([tr_x,tr_y], columns=['tr_y']))", "Training rmse is 0.7629283571148474, and validation rmse is 1.0146270166850742\n" ] ], [ [ "## Extrapolation problem\nThe decision tree suffers from a serious drawback when trying to predict them on data outside the domain of the current dataset. Suppose we have split the dataset into training and validation such as included the first 80 datapoints in the training and the remaining 30 datapoints in the validation dataset, like:\n```python\ntr_x, tr_y = x[:80], y[:80]\nval_x, val_y = x[80:], y[80:]\n```", "_____no_output_____" ] ], [ [ "m = DecisionTreeRegressor(max_leaf_nodes=10)\ntr_x, tr_y = x[:80], y[:80]\nval_x, val_y = x[80:], y[80:]\ntr_x = tr_x.reshape(80,1)\nval_x = val_x.reshape(30,1)\nm.fit(tr_x, tr_y);\nfig, ax = plt.subplots(figsize=(16,8))\nax.scatter(x,y, marker='+', label='actual data')\nax.scatter(tr_x, m.predict(tr_x), label='predicted data on training dataset')\nax.scatter(val_x, m.predict(val_x), label='predicted data on validation dataset')\n\nax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(1))\nax.grid(which='major', axis='both', linestyle=':', linewidth = 1, color='b')\n\n\nax.set_xlabel(\"x\", labelpad=5, fontsize=26, fontname='serif', color=\"blue\")\nax.set_ylabel(\"y\", labelpad=5, fontsize=26, fontname='serif', color=\"blue\")\nax.legend(prop={\"size\":15})", "_____no_output_____" ] ], [ [ "In the above figure, because the validation data is in the range $x>7.2$ something, and training data has only seen datapoints which are in the range $0\\leq x\\leq7.2$, therefore validation data is out of the domain, and hence a poor extrapolation is done by decision trees. Because of this problem, and also high variance in predictions, a single decision tree is rarely used in practice. High Variance means high variance in the predictions, and it is because a little up and down in the training data could have changed the decision tree completely, and so the predictions will vary. A linear regression model has much less variance than a decision tree but bias is also higher than a decision tree.", "_____no_output_____" ], [ "## Conclusion\nWe have covered the most basic ML method for tabular data modeling. In the next parts, I will cover Random Forests and some DL methods. Also, there is no point in training a decision tree model on the Kaggle dataset because it will give poor results as the data is complex, and it needs some more sophisticated algorithms.", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Visualization principles\n\n1. Log scale\n2. Jitter\n3. Set the scale\n4. Text on plot\n", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport matplotlib as mpl", "_____no_output_____" ] ], [ [ "## Plotting binary variables\n\nNot directly connected to today's lesson. But many of you asked.\nLet look at a case were we have 2 binary variables: 'sex' and 'survived'", "_____no_output_____" ] ], [ [ "titanic = sns.load_dataset(\"titanic\")\ntitanic.head()\ntitanic.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 15 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 survived 891 non-null int64 \n 1 pclass 891 non-null int64 \n 2 sex 891 non-null object \n 3 age 714 non-null float64 \n 4 sibsp 891 non-null int64 \n 5 parch 891 non-null int64 \n 6 fare 891 non-null float64 \n 7 embarked 889 non-null object \n 8 class 891 non-null category\n 9 who 891 non-null object \n 10 adult_male 891 non-null bool \n 11 deck 203 non-null category\n 12 embark_town 889 non-null object \n 13 alive 891 non-null object \n 14 alone 891 non-null bool \ndtypes: bool(2), category(2), float64(2), int64(4), object(5)\nmemory usage: 80.6+ KB\n" ] ], [ [ "Use a barplot (two variables) or a countplot (one variable)", "_____no_output_____" ] ], [ [ "sns.barplot(x=\"sex\", y=\"survived\", hue=\"class\", data=titanic)\nplt.show()", "_____no_output_____" ], [ "sns.countplot(x=\"sex\", hue=\"class\", data=titanic)\nplt.show()", "_____no_output_____" ] ], [ [ "Or use a catplot for categorical data:", "_____no_output_____" ] ], [ [ "sns.catplot(x=\"sex\", y=\"survived\", hue=\"class\", kind=\"bar\", data=titanic)\nplt.show()", "_____no_output_____" ], [ "sns.catplot(x=\"sex\", hue=\"class\", kind=\"count\", data=titanic)\nplt.show()", "_____no_output_____" ] ], [ [ "## Log scale", "_____no_output_____" ] ], [ [ "diamonds = sns.load_dataset(\"diamonds\")\ndiamonds.head()", "_____no_output_____" ], [ "sns.histplot(diamonds.price[diamonds.cut == 'Ideal'])", "_____no_output_____" ] ], [ [ "##### One option:", "_____no_output_____" ] ], [ [ "sns.histplot(diamonds.price[diamonds.cut == 'Ideal'], log_scale = True)", "_____no_output_____" ] ], [ [ "##### Another option:", "_____no_output_____" ] ], [ [ "sns.histplot(np.log2(diamonds.price[diamonds.cut == 'Ideal']))", "_____no_output_____" ] ], [ [ "### Stack histogram:", "_____no_output_____" ] ], [ [ "sns.histplot(\n diamonds,\n x=\"price\", hue=\"cut\",\n multiple=\"stack\", \n)", "_____no_output_____" ], [ "sns.set_theme(style=\"ticks\")\n\nf, ax = plt.subplots(figsize=(7, 5))\nsns.despine(f)\n\nsns.histplot(\n diamonds,\n x=\"price\", hue=\"cut\",\n multiple=\"stack\",\n palette=\"colorblind\",\n edgecolor=\".3\",\n linewidth=.5, \n log_scale = True\n)\nax.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())\nax.set_xticks([500, 1000, 2000, 5000, 10000])\nplt.show()", "_____no_output_____" ] ], [ [ "## Jitter in python\n\nGoogle it: [Jitter in python](https://www.google.com/search?q=jitter+in+python&sxsrf=ALeKk01NFy18kBeX8CmyToZAT-l4YIlJeQ%3A1621252840686&ei=6FqiYPSmKYzdkwXckaGgCw&oq=jitter&gs_lcp=Cgdnd3Mtd2l6EAMYADIECCMQJzIFCAAQkQIyBQgAEMsBMgUIABDLATICCAAyAggAMgUIABDLATICCAAyAggAMgIIADoECAAQQzoFCAAQsQM6CAgAELEDEJECOggILhCxAxCDAToFCC4QsQM6BwgAEIcCEBQ6AgguUJ8gWIcuYJg1aAFwAngAgAGdAYgB1giSAQMwLjiYAQCgAQGqAQdnd3Mtd2l6wAEB&sclient=gws-wiz)\n\nDocumentation contains such a good example we'll just [follow it](https://seaborn.pydata.org/generated/seaborn.stripplot.html)", "_____no_output_____" ] ], [ [ "tips = sns.load_dataset(\"tips\")", "_____no_output_____" ], [ "tips.head()", "_____no_output_____" ], [ "sns.stripplot(x=\"day\", y=\"total_bill\", data=tips)", "_____no_output_____" ] ], [ [ "Use a smaller amount of jitter:", "_____no_output_____" ] ], [ [ "sns.stripplot(x=\"day\", y=\"total_bill\", data=tips, jitter=0.05)", "_____no_output_____" ] ], [ [ "Jitter plus a boxplot:", "_____no_output_____" ] ], [ [ "ax = sns.boxplot(x=\"tip\", y=\"day\", data=tips)\nax = sns.stripplot(x=\"tip\", y=\"day\", data=tips, color=\".3\")\nax = sns.boxplot(x=\"tip\", y=\"day\", data=tips)", "_____no_output_____" ] ], [ [ "## Set the scale\n\nGoogle it: [set scale seaborn](https://www.google.com/search?q=set+scale+seaborn&sxsrf=ALeKk02NiH79RWrRRXIqusuG-vHfuyIm2A%3A1621254123926&ei=61-iYOiGOMyxkwWAiZjICQ&oq=set+scale+sea&gs_lcp=Cgdnd3Mtd2l6EAMYADIECCMQJzIGCAAQFhAeMgYIABAWEB4yBggAEBYQHjoHCCMQsAMQJzoHCAAQRxCwAzoCCAA6BQghEKABOggIABAIEA0QHlC8EVjyJmCULGgEcAJ4AIABogGIAbgHkgEDMC43mAEAoAEBqgEHZ3dzLXdpesgBCcABAQ&sclient=gws-wiz)", "_____no_output_____" ], [ "##### One option:", "_____no_output_____" ] ], [ [ "ax = sns.stripplot(x=\"day\", y=\"total_bill\", data=tips, jitter=0.05)\nax.set(ylim=(0, 100))", "_____no_output_____" ] ], [ [ "##### Another option:", "_____no_output_____" ] ], [ [ "plt.ylim(0, 400)\nax = sns.stripplot(x=\"day\", y=\"total_bill\", data=tips, jitter=0.05)", "_____no_output_____" ] ], [ [ "## Add labels onto the plot\n\nGoogle it: [add text to plot seaborn](https://www.google.com/search?q=add+text+to+plot+seaborn&sxsrf=ALeKk01vym2w-SfYoAOBXBgUbDCr0I04Uw%3A1621255993821&ei=OWeiYObWMdCTkwXRoIngCw&oq=add+text+to+plot+seaborn&gs_lcp=Cgdnd3Mtd2l6EAMyAggAMgYIABAWEB4yBggAEBYQHjoHCCMQsAMQJzoHCAAQRxCwAzoECAAQQzoGCAAQBxAeUJAcWKgzYJs1aAFwAngAgAGeAYgBmgqSAQQwLjEwmAEAoAEBqgEHZ3dzLXdpesgBCcABAQ&sclient=gws-wiz&ved=0ahUKEwim1-ec4dDwAhXQyaQKHVFQArwQ4dUDCA4&uact=5_)", "_____no_output_____" ] ], [ [ "penguins = sns.load_dataset(\"penguins\")\npenguins.head()", "_____no_output_____" ] ], [ [ "With a legend:", "_____no_output_____" ] ], [ [ "ax = sns.scatterplot(data=penguins, x=\"bill_length_mm\", y=\"bill_depth_mm\", hue = 'species', palette = 'colorblind')", "_____no_output_____" ] ], [ [ "Without a legend but with text:", "_____no_output_____" ] ], [ [ "ax = sns.scatterplot(data=penguins, x=\"bill_length_mm\", y=\"bill_depth_mm\", hue = 'species', palette = 'colorblind', legend=False)\n\nstyle = dict(size=12, color='black')\nax.text(35, 15, \"Adelie\", **style)\nax.text(55, 20, \"Chinstrap\", **style)\nax.text(52, 14, \"Gentoo\", **style)\nplt.show()", "_____no_output_____" ] ] ]

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[ [ [ "This notebook will set up colab so that you can run the SYCL blur lab for the module \"Introduction to SYCYL programming\" created by the TOUCH project. (https://github.com/TeachingUndergradsCHC/modules/tree/master/Programming/sycl). The initial setup instructions are created following slides by Aksel Alpay\nhttps://www.iwocl.org/wp-content/uploads/iwocl-syclcon-2020-alpay-32-slides.pdf\nand the hipSCYL documentation https://github.com/illuhad/hipSYCL/blob/develop/doc/installing.md .\n\nBegin by setting your runtime to use a CPU (Select \"Change runtime type\" in the Runtime menu and choose \"CPU\".) Then run the first couple of instructions below. Run them one at a time, waiting for each to finish before beginning the next. This will take several minutes.", "_____no_output_____" ], [ "Update repositories and then get and build llvm so can build hipSYCL.", "_____no_output_____" ] ], [ [ "!apt update -qq;\n!apt-get update -qq;\n!add-apt-repository -y ppa:ubuntu-toolchain-r/test\n!apt update\n!apt install gcc-11 g++-11\n!bash -c \"$(wget -O - https://apt.llvm.org/llvm.sh)\"\n!apt-get install libboost-all-dev libclang-13-dev cmake python -qq;\n!git clone --recurse-submodules https://github.com/illuhad/hipSYCL", "_____no_output_____" ], [ "!apt-get upgrade", "_____no_output_____" ] ], [ [ "Now build hipSYCL", "_____no_output_____" ] ], [ [ "\n!mkdir hipSYCL_build\n%cd hipSYCL_build\n!export CC=/usr/bin/gcc-11\n!export CXX=/usr/bin/g++-11\n!cmake -DCMAKE_INSTALL_PREFIX=/content/hipSYCL_install -DCMAKE_C_COMPILER=/usr/bin/gcc-11 -DCMAKE_CXX_COMPILER=/usr/bin/g++-11 /content/hipSYCL\n!make install\n%cd ..", "_____no_output_____" ] ], [ [ "Get the examples", "_____no_output_____" ] ], [ [ "!git clone https://github.com/TeachingUndergradsCHC/modules\n%cd modules/Programming/sycl", "_____no_output_____" ] ], [ [ "Examine hello.cpp", "_____no_output_____" ] ], [ [ "!cat hello.cpp", "_____no_output_____" ] ], [ [ "Now compile hello.cpp", "_____no_output_____" ] ], [ [ "!/content/hipSYCL_install/bin/syclcc --hipsycl-platform=cpu -o hello hello.cpp", "_____no_output_____" ] ], [ [ "Then run it", "_____no_output_____" ] ], [ [ "!./hello", "_____no_output_____" ] ], [ [ "Now try the addVector program, first view it", "_____no_output_____" ] ], [ [ "\n!cat addVectors.cpp", "_____no_output_____" ] ], [ [ "Then compile it", "_____no_output_____" ] ], [ [ "!/content/hipSYCL_install/bin/syclcc --hipsycl-platform=cpu -o addVectors addVectors.cpp", "_____no_output_____" ] ], [ [ "Finally run it", "_____no_output_____" ] ], [ [ "!./addVectors", "_____no_output_____" ] ], [ [ "Next, examine the files that you'll need for the blur project. These are the library code for managing bmp files (stb_image.h and stb_image_write.h), the image that you'll be using (I provide 640x426.bmp, but you could use another file instead) and the program itself noRed.cpp. Then compile it", "_____no_output_____" ] ], [ [ "!/content/hipSYCL_install/bin/syclcc --hipsycl-platform=cpu -o noRed noRed.cpp\n\n", "_____no_output_____" ] ], [ [ "Now run the code", "_____no_output_____" ] ], [ [ "!./noRed", "_____no_output_____" ] ], [ [ "Original Image", "_____no_output_____" ] ], [ [ "from IPython.display import display\nfrom PIL import Image\n\n\npath=\"/content/modules/Programming/sycl/640x426.bmp\"\ndisplay(Image.open(path))", "_____no_output_____" ] ], [ [ "Final Image", "_____no_output_____" ] ], [ [ "from IPython.display import display\nfrom PIL import Image\n\n\npath=\"/content/modules/Programming/sycl/out.bmp\"\ndisplay(Image.open(path))", "_____no_output_____" ] ] ]

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[ [ [ "import matplotlib.pyplot as plt\nimport numpy\n%matplotlib inline", "_____no_output_____" ], [ "x = numpy.genfromtxt(\"data.csv\")", "_____no_output_____" ], [ "plt.hist(x)", "_____no_output_____" ] ] ]

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[ [ [ "# Classification of Chest and Abdominal X-rays", "_____no_output_____" ], [ "Code Source: Lakhani, P., Gray, D.L., Pett, C.R. et al. J Digit Imaging (2018) 31: 283. https://doi.org/10.1007/s10278-018-0079-6\n\nThe code to download and prepare dataset had been modified form the original source code.", "_____no_output_____" ] ], [ [ "# load requirements for the Keras library\nfrom keras import applications\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras import optimizers\nfrom keras.models import Sequential\nfrom keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D\nfrom keras.models import Model\nfrom keras.optimizers import Adam", "_____no_output_____" ], [ "!rm -rf /content/*", "_____no_output_____" ], [ "# Download dataset\n!wget https://github.com/paras42/Hello_World_Deep_Learning/raw/9921a12c905c00a88898121d5dc538e3b524e520/Open_I_abd_vs_CXRs.zip", "_____no_output_____" ], [ "!ls /content", "_____no_output_____" ], [ "# unzip\n!unzip /content/Open_I_abd_vs_CXRs.zip", "_____no_output_____" ], [ "# dimensions of our images\nimg_width, img_height = 299, 299\n\n# directory and image information\ntrain_data_dir = 'Open_I_abd_vs_CXRs/TRAIN/'\nvalidation_data_dir = 'Open_I_abd_vs_CXRs/VAL/'\n\n# epochs = number of passes of through training data\n# batch_size = number of images processes at the same time\ntrain_samples = 65\nvalidation_samples = 10\nepochs = 20\nbatch_size = 5", "_____no_output_____" ], [ "# build the Inception V3 network, use pretrained weights from ImgaeNet\n# remove top funnly connected layers by imclude_top=False\n\nbase_model = applications.InceptionV3(weights='imagenet', include_top=False,\n input_shape=(img_width, img_height,3))", "_____no_output_____" ], [ "# build a classifier model to put on top of the convolutional model\n# This consists of a global average pooling layer and a fully connected layer with 256 nodes\n# Then apply dropout and signoid activation\n\nmodel_top = Sequential()\nmodel_top.add(GlobalAveragePooling2D(input_shape=base_model.output_shape[1:],\n data_format=None)),\nmodel_top.add(Dense(256, activation='relu'))\nmodel_top.add(Dropout(0.5))\nmodel_top.add(Dense(1, activation='sigmoid'))\nmodel = Model(inputs=base_model.input, outputs=model_top(base_model.output))\n\n# Compile model using Adam optimizer with common values and binary cross entropy loss\n# USe low learning rate (lr) for transfer learning\nmodel.compile(optimizer=Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0),\n loss='binary_crossentropy',\n metrics=['accuracy'])", "_____no_output_____" ], [ "# Some on-the-fly augmentation options\ntrain_datagen = ImageDataGenerator(\n rescale = 1./255, # Rescale pixel values to 0-1 to aid CNN processing\n shear_range = 0.2, # 0-1 range for shearing\n zoom_range = 0.2, # 0-1 range for zoom\n rotation_range = 20, # 0.180 range, degrees of rotation\n width_shift_range = 0.2, # 0-1 range horizontal translation\n height_shift_range = 0.2, # 0-1 range vertical translation\n horizontal_flip = True # set True or false\n)\n\nval_datagen = ImageDataGenerator(\n rescale=1./255 # Rescale pixel values to 0-1 to aid CNN processing\n)", "_____no_output_____" ], [ "# Directory, image size, batch size already specied above\n# Class mode is set to 'binary' for a 2-class problem\n# Generator randomly shuffles and presents images in batches to the network\n\ntrain_generator = train_datagen.flow_from_directory(\n train_data_dir,\n target_size=(img_height, img_width),\n batch_size=batch_size,\n class_mode='binary'\n)\n\nvalidation_generator = val_datagen.flow_from_directory(\n validation_data_dir,\n target_size=(img_height, img_width),\n batch_size=batch_size,\n class_mode='binary'\n)", "_____no_output_____" ], [ "# Fine-tune the pretrained Inception V3 model using the data generator\n# Specify steps per epoch (number of samples/batch_size)\n\nhistory = model.fit_generator(\n train_generator,\n steps_per_epoch=train_samples//batch_size,\n epochs=epochs,\n validation_data=validation_generator,\n validation_steps=validation_samples//batch_size\n)", "_____no_output_____" ], [ "# import matplotlib library, and plot training curve\nimport matplotlib.pyplot as plt\nprint(history.history.keys())\n\nplt.figure()\nplt.plot(history.history['acc'],'orange', label='Training accuracy')\nplt.plot(history.history['val_acc'],'blue', label='Validation accuracy')\nplt.plot(history.history['loss'],'red', label='Training loss')\nplt.plot(history.history['val_loss'],'green', label='validation loss')\nplt.legend()\nplt.show()", "_____no_output_____" ], [ "# import numpy and keras preprocessing libraries\nimport numpy as np\nfrom keras.preprocessing import image\n\n# load, resize, and display test images\nimg_path = 'Open_I_abd_vs_CXRs/TEST/abd2.png'\nimg_path2 = 'Open_I_abd_vs_CXRs/TEST/chest2.png'\nimg = image.load_img(img_path, target_size=(img_width, img_height))\nimg2 = image.load_img(img_path2, target_size=(img_width, img_height))\nplt.imshow(img)\nplt.show()\n\n# convert image to numpy array, so Keras can render a prediction\nimg = image.img_to_array(img)\n\n# expand array from 3 dimensions (height, width, channels) to 4 dimensions (batch size, height, width, channels)\n# rescale pixel values to 0-1\nx = np.expand_dims(img, axis=0) * 1./255\n\n# get prediction on test image\nscore = model.predict(x)\nprint('Predicted:', score, 'Chest X-ray' if score < 0.5 else 'Abd X-ray')\n\n# display and render a prediction for the 2nd image\nplt.imshow(img2)\nplt.show()\nimg2 = image.img_to_array(img2)\nx = np.expand_dims(img2, axis=0) * 1./255\nscore = model.predict(x)\nprint('Predicted:', score, 'Chest X-ray' if score < 0.5 else 'Abd X-ray')\n", "_____no_output_____" ] ] ]

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[ [ [ "<a href=\"https://colab.research.google.com/github/hadisotudeh/zestyAI_challenge/blob/main/Zesty_AI_Data_Scientist_Assignment_%7C_Hadi_Sotudeh.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "<center> <h1><b>Zesty AI Data Science Interview Task - Hadi Sotudeh</b></h1> </center>", "_____no_output_____" ], [ "To perform this task, I had access to the [`2009 RESIDENTIAL ENERGY CONSUMPTION SURVEY`](https://www.eia.gov/consumption/residential/data/2009/index.php?view=microdata) to predict `electricity consumption`.\n</br>\n</br>\nLibraries available in Python such as `scikit-learn` and `fastai` were employed to perform this machine learning regression task.\n</br>\n</br>\nFirst, I need to install the notebook dependencies, import the relevant libraries, download the dataset, and have them available in Google Colab (next cell).", "_____no_output_____" ], [ "## Install Dependencies, Import Libraries, and Download the dataset", "_____no_output_____" ] ], [ [ "%%capture\n\n# install dependencies\n!pip install fastai --upgrade", "_____no_output_____" ], [ "# Import Libraries\n\n# general libraries\nimport warnings\nimport os\nfrom datetime import datetime\nfrom tqdm import tqdm_notebook as tqdm\n\n# machine learning libraries\nimport pandas as pd\nimport matplotlib\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom fastai.tabular.all import *\nfrom sklearn.ensemble import RandomForestRegressor\nfrom pandas_profiling import ProfileReport\nimport joblib\nfrom xgboost import XGBRegressor\nfrom lightgbm import LGBMRegressor\n\n# model interpretation library\nfrom sklearn.inspection import plot_partial_dependence", "_____no_output_____" ], [ "%%capture\n\n#download the dataset\n! wget https://www.eia.gov/consumption/residential/data/2009/csv/recs2009_public.csv", "_____no_output_____" ] ], [ [ "## Set Global parameters", "_____no_output_____" ], [ "The electric consumption is located in the `KWH` field of the dataset.", "_____no_output_____" ] ], [ [ "#show plots inside the jupyter notebook\n%matplotlib inline\n\n# pandas settings to show more columns are rows in the jupyter notebook\npd.set_option('display.max_rows', 500)\npd.set_option('display.max_columns', 50000)\n\n# don't show warnings\nwarnings.filterwarnings('ignore')\n\n# dataset file path\ndataset = \"recs2009_public.csv\"\n\n# target variable to predict\ndep_var = \"KWH\"", "_____no_output_____" ] ], [ [ "## Read the dataset from CSV files, Perform Data Cleaning, and Feature Engineering", "_____no_output_____" ], [ "Following a typical machine learning project, I first clean up the dataset to prevent data-leakage related features or non-relevant features.</br></br>It is important to mention that I did not first look at each column to figure out which feature to keep or not. What I did first was to train a model and iteratively look at the feature importances and check their meanings in the dataset documentation to figure out what features to remove to prevent data leakage.</br></br>In addition, a group of features with high correlations were identified and only one of them in each group was kept. ", "_____no_output_____" ] ], [ [ "# read the train file\ndf = pd.read_csv(dataset)\n\n# remove data-leakage and non-relevant features\nnon_essential_features = [\"KWHSPH\",\"KWHCOL\",\"KWHWTH\",\"KWHRFG\",\"KWHOTH\",\"BTUEL\",\"BTUELSPH\",\"BTUELCOL\",\n \"BTUELWTH\",\"BTUELRFG\",\"BTUELOTH\",\"DOLLAREL\",\"DOLELSPH\",\"DOLELCOL\",\"DOLELWTH\",\n \"DOLELRFG\",\"DOLELOTH\",\"TOTALBTUOTH\",\"TOTALBTURFG\",\"TOTALDOL\",\"ELWATER\",\n \"TOTALBTUWTH\",\"TOTALBTU\",\"ELWARM\",\"TOTALBTUCOL\",\"TOTALDOLCOL\",\n \"REPORTABLE_DOMAIN\",\"TOTALDOLWTH\",\"TOTALBTUSPH\",\"TOTCSQFT\",\"TOTALDOLSPH\",\n \"BTUNG\", \"BTUNGSPH\", \"BTUNGWTH\",\"BTUNGOTH\",\"DOLLARNG\",\"DOLNGSPH\",\"DOLNGWTH\",\"DOLNGOTH\",\n \"DIVISION\"\n ]\ndf.drop(columns = non_essential_features, inplace=True)\n\n# take the log of dependent variable ('price'). More details are in the training step.\ndf[dep_var] = np.log(df[dep_var])", "_____no_output_____" ] ], [ [ "I created train and validation sets with random selection (80% vs.20% rule) from the dataset[link text](https://) file in the next step.", "_____no_output_____" ] ], [ [ "splits = RandomSplitter(valid_pct=0.2)(range_of(df))\n\nprocs = [Categorify, FillMissing]\n\ncont, cat = cont_cat_split(df, 1, dep_var=dep_var)\n\nto = TabularPandas(df, procs, cat, cont, y_names=dep_var, splits = splits)", "_____no_output_____" ] ], [ [ "The following cell shows 5 random instances of the dataset (after cleaning and feature engineering).", "_____no_output_____" ] ], [ [ "to.show(5)", "_____no_output_____" ] ], [ [ "## Train the ML Model", "_____no_output_____" ], [ "Since model interpretation is also important for me, I chose RandomForest for both prediction and interpretation and knowledge discovery.", "_____no_output_____" ] ], [ [ "def rf(xs, y, n_estimators=40, max_features=0.5, min_samples_leaf=5, **kwargs):\n \"randomforst regressor\"\n return RandomForestRegressor(n_jobs=-1, n_estimators=n_estimators, max_features=max_features, min_samples_leaf=min_samples_leaf, oob_score=True).fit(xs, y)", "_____no_output_____" ], [ "xs,y = to.train.xs,to.train.y\nvalid_xs,valid_y = to.valid.xs,to.valid.y\n\nm = rf(xs, y)", "_____no_output_____" ] ], [ [ "The predictions are evaluated based on [Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview/evaluation) (Taking logs means that errors in predicting high electricity consumptions and low ones will affect the result equally). \n</br>\n</br>", "_____no_output_____" ] ], [ [ "def r_mse(pred,y): \n return round(math.sqrt(((pred-y)**2).mean()), 6)\n\ndef m_rmse(m, xs, y): \n return r_mse(m.predict(xs), y)", "_____no_output_____" ] ], [ [ "Print the Mean Root Squared Error of the logarithmic `KWH` on the train set:", "_____no_output_____" ] ], [ [ "m_rmse(m, xs, y)", "_____no_output_____" ] ], [ [ "Print the Mean Root Squared Error of the logarithmic `KWH` on the validation set:", "_____no_output_____" ] ], [ [ "m_rmse(m, valid_xs, valid_y)", "_____no_output_____" ] ], [ [ "Calculate Feature Importance and remove non-important features and re-train the model.", "_____no_output_____" ] ], [ [ "def rf_feat_importance(m, df):\n return pd.DataFrame({'cols':df.columns, 'imp':m.feature_importances_}).sort_values('imp', ascending=False)\n\n# show the top 10 features\nfi = rf_feat_importance(m, xs)\nfi[:10]", "_____no_output_____" ] ], [ [ "Only keep features with importance of more than 0.005 for re-training.", "_____no_output_____" ] ], [ [ "to_keep = fi[fi.imp>0.005].cols\nprint(f\"features to keep are : {list(to_keep)}\")", "features to keep are : ['TOTALDOLOTH', 'PELHOTWA', 'TOTALDOLRFG', 'ACROOMS', 'FUELHEAT', 'TEMPHOMEAC', 'FUELH2O', 'TYPEHUQ', 'REGIONC', 'WASHLOAD', 'TEMPNITEAC', 'DRYRFUEL', 'PELHEAT', 'CDD30YR', 'USECENAC', 'CUFEETNG', 'TEMPGONEAC', 'CUFEETNGOTH', 'BEDROOMS', 'CUFEETNGWTH']\n" ] ], [ [ "Some of the features to keep for re-training are:\n\n1. `TOTALDOLOTH`: Total cost for appliances, electronics, lighting, and miscellaneous\n\n2. `PELHOTWA`: Who pays for electricity used for water heating\n\n3. `ACROOMS`: Number of rooms cooled\n\n4. `TOTALDOLRFG`: Total cost for refrigerators, in whole dollars\n\n5. `REGIONC`: Census Region\n\n6. `TEMPNITEAC`: Temperature at night (summer)", "_____no_output_____" ] ], [ [ "xs_imp = xs[to_keep]\nvalid_xs_imp = valid_xs[to_keep]\n\nm = rf(xs_imp, y)", "_____no_output_____" ] ], [ [ "Print the loss function of the re-trained model on train and validation sets.", "_____no_output_____" ] ], [ [ "m_rmse(m, xs_imp, y), m_rmse(m, valid_xs_imp, valid_y)", "_____no_output_____" ] ], [ [ "Check the correlation among the final features and adjust the set of features to remove at the beginning of the code.", "_____no_output_____" ] ], [ [ "from scipy.cluster import hierarchy as hc\n\ndef cluster_columns(df, figsize=(10,6), font_size=12):\n corr = np.round(scipy.stats.spearmanr(df).correlation, 4)\n corr_condensed = hc.distance.squareform(1-corr)\n z = hc.linkage(corr_condensed, method='average')\n fig = plt.figure(figsize=figsize)\n hc.dendrogram(z, labels=df.columns, orientation='left', leaf_font_size=font_size)\n plt.show()\n\ncluster_columns(xs_imp)", "_____no_output_____" ] ], [ [ "Store the re-trained model.", "_____no_output_____" ] ], [ [ "joblib.dump(m, 'model.joblib') ", "_____no_output_____" ] ], [ [ "## Interpret the Model and Do Knowledge Discovery", "_____no_output_____" ], [ "When I plot the feature importances of the trained model, I can clearly see that `TOTALDOLOTH` (Total cost for appliances, electronics, lighting, and miscellaneous uses in whole dollars) is the most important factor for the model to make its decisions.", "_____no_output_____" ] ], [ [ "def plot_fi(fi):\n return fi.plot('cols', 'imp', 'barh', figsize=(12,7), legend=False)\n\nplot_fi(rf_feat_importance(m, xs_imp));", "_____no_output_____" ] ], [ [ "In this section, I make use of the [Partial Dependence Plots](https://christophm.github.io/interpretable-ml-book/pdp.html) to interpret the learned function (ML model) and understand how this function makes decisions and predicts house prices for sale.</br></br>The 1D-feature plots show by changing one unit (increase or decrease) of the feature shown in the x-axis, how much the predicted dependent variable (`log KWH`) changes on average.", "_____no_output_____" ] ], [ [ "explore_cols = ['TOTALDOLOTH','TOTALDOLRFG','ACROOMS','TEMPHOMEAC','TEMPNITEAC','CDD30YR','CUFEETNGOTH','WASHLOAD','CUFEETNG']\n\nexplore_cols_vals = [\"Total cost for appliances, electronics, lighting, and miscellaneous uses, in whole dollars\",\n \"Total cost for refrigerators, in whole dollars\",\n \"Number of rooms cooled\",\n \"Temperature when someone is home during the day (summer)\",\n \"Temperature at night (summer)\",\n \"Cooling degree days, 30-year average 1981-2010, base 65F\",\n \"Natural Gas usage for other purposes (all end-uses except SPH and WTH), in hundred cubic feet\",\n \"Frequency clothes washer used\",\n \"Total Natural Gas usage, in hundred cubic feet\"]\n\nfor index, col in enumerate(explore_cols):\n fig,ax = plt.subplots(figsize=(12, 4))\n plot_partial_dependence(m, valid_xs_imp, [col], grid_resolution=20, ax=ax);\n x_label = explore_cols_vals[index]\n plt.xlabel(x_label)", "_____no_output_____" ] ], [ [ "The 2D-feature plots show by changing one unit (increase or decrease) of the features shown in the x and y axes, how much the dependent variable changes.\n</br>\n</br>\nHere, the plot shows how much the model (learned function) changes its `log KWH` prediction on average when the two dimensions on the x and y axes change.", "_____no_output_____" ] ], [ [ "paired_features = [(\"TEMPNITEAC\",\"TEMPHOMEAC\"),(\"CUFEETNG\",\"CUFEETNGOTH\")]\npaired_features_vals = [(\"Temperature at night (summer)\",\"Temperature when someone is home during the day (summer)\"),\n (\"Total Natural Gas usage, in hundred cubic feet\",\"Natural Gas usage for other purposes (all end-uses except SPH and WTH), in hundred cubic feet\")]\n\n\nfor index, pair in enumerate(paired_features):\n fig,ax = plt.subplots(figsize=(8, 8))\n plot_partial_dependence(m, valid_xs_imp, [pair], grid_resolution=20, ax=ax);\n \n x_label = paired_features_vals[index][0]\n y_label = paired_features_vals[index][1]\n\n plt.xlabel(x_label)\n plt.ylabel(y_label)", "_____no_output_____" ] ], [ [ "## THE END!", "_____no_output_____" ] ] ]

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[ [ [ "# Day and Night Image Classifier\n---\n\nThe day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images.\n\nWe'd like to build a classifier that can accurately label these images as day or night, and that relies on finding distinguishing features between the two types of images!\n\n*Note: All images come from the [AMOS dataset](http://cs.uky.edu/~jacobs/datasets/amos/) (Archive of Many Outdoor Scenes).*\n", "_____no_output_____" ], [ "### Import resources\n\nBefore you get started on the project code, import the libraries and resources that you'll need.", "_____no_output_____" ] ], [ [ "import cv2 # computer vision library\nimport helpers\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n%matplotlib inline", "_____no_output_____" ] ], [ [ "## Training and Testing Data\nThe 200 day/night images are separated into training and testing datasets. \n\n* 60% of these images are training images, for you to use as you create a classifier.\n* 40% are test images, which will be used to test the accuracy of your classifier.\n\nFirst, we set some variables to keep track of some where our images are stored:\n\n image_dir_training: the directory where our training image data is stored\n image_dir_test: the directory where our test image data is stored", "_____no_output_____" ] ], [ [ "# Image data directories\nimage_dir_training = \"day_night_images/training/\"\nimage_dir_test = \"day_night_images/test/\"", "_____no_output_____" ] ], [ [ "## Load the datasets\n\nThese first few lines of code will load the training day/night images and store all of them in a variable, `IMAGE_LIST`. This list contains the images and their associated label (\"day\" or \"night\"). \n\nFor example, the first image-label pair in `IMAGE_LIST` can be accessed by index: \n``` IMAGE_LIST[0][:]```.\n", "_____no_output_____" ] ], [ [ "# Using the load_dataset function in helpers.py\n# Load training data\nIMAGE_LIST = helpers.load_dataset(image_dir_training)\n", "_____no_output_____" ] ], [ [ "## Construct a `STANDARDIZED_LIST` of input images and output labels.\n\nThis function takes in a list of image-label pairs and outputs a **standardized** list of resized images and numerical labels.", "_____no_output_____" ] ], [ [ "# Standardize all training images\nSTANDARDIZED_LIST = helpers.standardize(IMAGE_LIST)", "_____no_output_____" ] ], [ [ "## Visualize the standardized data\n\nDisplay a standardized image from STANDARDIZED_LIST.", "_____no_output_____" ] ], [ [ "# Display a standardized image and its label\n\n# Select an image by index\nimage_num = 0\nselected_image = STANDARDIZED_LIST[image_num][0]\nselected_label = STANDARDIZED_LIST[image_num][1]\n\n# Display image and data about it\nplt.imshow(selected_image)\nprint(\"Shape: \"+str(selected_image.shape))\nprint(\"Label [1 = day, 0 = night]: \" + str(selected_label))\n", "Shape: (600, 1100, 3)\nLabel [1 = day, 0 = night]: 1\n" ] ], [ [ "# Feature Extraction\n\nCreate a feature that represents the brightness in an image. We'll be extracting the **average brightness** using HSV colorspace. Specifically, we'll use the V channel (a measure of brightness), add up the pixel values in the V channel, then divide that sum by the area of the image to get the average Value of the image.\n", "_____no_output_____" ], [ "---\n### Find the average brigtness using the V channel\n\nThis function takes in a **standardized** RGB image and returns a feature (a single value) that represent the average level of brightness in the image. We'll use this value to classify the image as day or night.", "_____no_output_____" ] ], [ [ "# Find the average Value or brightness of an image\ndef avg_brightness(rgb_image):\n # Convert image to HSV\n hsv = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2HSV)\n\n # Add up all the pixel values in the V channel\n sum_brightness = np.sum(hsv[:,:,2])\n area = 600*1100.0 # pixels\n \n # find the avg\n avg = sum_brightness/area\n \n return avg", "_____no_output_____" ], [ "# Testing average brightness levels\n# Look at a number of different day and night images and think about \n# what average brightness value separates the two types of images\n\n# As an example, a \"night\" image is loaded in and its avg brightness is displayed\nimage_num = 190\ntest_im = STANDARDIZED_LIST[image_num][0]\n\navg = avg_brightness(test_im)\nprint('Avg brightness: ' + str(avg))\nplt.imshow(test_im)", "Avg brightness: 35.217\n" ] ], [ [ "# Classification and Visualizing Error\n\nIn this section, we'll turn our average brightness feature into a classifier that takes in a standardized image and returns a `predicted_label` for that image. This `estimate_label` function should return a value: 0 or 1 (night or day, respectively).", "_____no_output_____" ], [ "---\n### TODO: Build a complete classifier \n\nComplete this code so that it returns an estimated class label given an input RGB image.", "_____no_output_____" ] ], [ [ "# This function should take in RGB image input\ndef estimate_label(rgb_image):\n \n # Extract average brightness feature from an RGB image \n avg = avg_brightness(rgb_image)\n \n # Use the avg brightness feature to predict a label (0, 1)\n predicted_label = 0\n threshold = 100\n if(avg > threshold):\n # if the average brightness is above the threshold value, we classify it as \"day\"\n predicted_label = 1\n # else, the pred-cted_label can stay 0 (it is predicted to be \"night\")\n \n return predicted_label \n ", "_____no_output_____" ] ], [ [ "## Testing the classifier\n\nHere is where we test your classification algorithm using our test set of data that we set aside at the beginning of the notebook!\n\nSince we are using a pretty simple brightess feature, we may not expect this classifier to be 100% accurate. We'll aim for around 75-85% accuracy usin this one feature.\n\n\n### Test dataset\n\nBelow, we load in the test dataset, standardize it using the `standardize` function you defined above, and then **shuffle** it; this ensures that order will not play a role in testing accuracy.\n", "_____no_output_____" ] ], [ [ "import random\n\n# Using the load_dataset function in helpers.py\n# Load test data\nTEST_IMAGE_LIST = helpers.load_dataset(image_dir_test)\n\n# Standardize the test data\nSTANDARDIZED_TEST_LIST = helpers.standardize(TEST_IMAGE_LIST)\n\n# Shuffle the standardized test data\nrandom.shuffle(STANDARDIZED_TEST_LIST)", "_____no_output_____" ] ], [ [ "## Determine the Accuracy\n\nCompare the output of your classification algorithm (a.k.a. your \"model\") with the true labels and determine the accuracy.\n\nThis code stores all the misclassified images, their predicted labels, and their true labels, in a list called `misclassified`.", "_____no_output_____" ] ], [ [ "# Constructs a list of misclassified images given a list of test images and their labels\ndef get_misclassified_images(test_images):\n # Track misclassified images by placing them into a list\n misclassified_images_labels = []\n\n # Iterate through all the test images\n # Classify each image and compare to the true label\n for image in test_images:\n\n # Get true data\n im = image[0]\n true_label = image[1]\n\n # Get predicted label from your classifier\n predicted_label = estimate_label(im)\n\n # Compare true and predicted labels \n if(predicted_label != true_label):\n # If these labels are not equal, the image has been misclassified\n misclassified_images_labels.append((im, predicted_label, true_label))\n \n # Return the list of misclassified [image, predicted_label, true_label] values\n return misclassified_images_labels\n", "_____no_output_____" ], [ "# Find all misclassified images in a given test set\nMISCLASSIFIED = get_misclassified_images(STANDARDIZED_TEST_LIST)\n\n# Accuracy calculations\ntotal = len(STANDARDIZED_TEST_LIST)\nnum_correct = total - len(MISCLASSIFIED)\naccuracy = num_correct/total\n\nprint('Accuracy: ' + str(accuracy))\nprint(\"Number of misclassified images = \" + str(len(MISCLASSIFIED)) +' out of '+ str(total))", "Accuracy: 0.925\nNumber of misclassified images = 12 out of 160\n" ] ], [ [ "---\n<a id='task9'></a>\n### Visualize the misclassified images\n\nVisualize some of the images you classified wrong (in the `MISCLASSIFIED` list) and note any qualities that make them difficult to classify. This will help you identify any weaknesses in your classification algorithm.", "_____no_output_____" ] ], [ [ "# Visualize misclassified example(s)\nnum = 0\ntest_mis_im = MISCLASSIFIED[num][0]\n\n## TODO: Display an image in the `MISCLASSIFIED` list \nplt.imshow(test_mis_im)\n## TODO: Print out its predicted label - \n## to see what the image *was* incorrectly classified as\nprint(\"Misslabeled as: \",MISCLASSIFIED[num][1])", "Misslabeled as: 0\n" ] ], [ [ "---\n<a id='question2'></a>\n## (Question): After visualizing these misclassifications, what weaknesses do you think your classification algorithm has?", "_____no_output_____" ], [ "**Answer:** Write your answer, here.", "_____no_output_____" ], [ "# 5. Improve your algorithm!\n\n* (Optional) Tweak your threshold so that accuracy is better.\n* (Optional) Add another feature that tackles a weakness you identified!\n---\n", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "## Bengaluru House Price", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\npd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)", "_____no_output_____" ], [ "df1=pd.read_csv(\"Dataset/Bengaluru_House_Data.csv\")\ndf1.head()", "_____no_output_____" ] ], [ [ "### Data Cleaning", "_____no_output_____" ] ], [ [ "df1.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 13320 entries, 0 to 13319\nData columns (total 9 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 area_type 13320 non-null object \n 1 availability 13320 non-null object \n 2 location 13319 non-null object \n 3 size 13304 non-null object \n 4 society 7818 non-null object \n 5 total_sqft 13320 non-null object \n 6 bath 13247 non-null float64\n 7 balcony 12711 non-null float64\n 8 price 13320 non-null float64\ndtypes: float64(3), object(6)\nmemory usage: 936.7+ KB\n" ], [ "df1.isnull().sum()", "_____no_output_____" ], [ "df1.groupby('area_type')['area_type'].agg('count')", "_____no_output_____" ], [ "df2=df1.drop(['area_type','availability','society','balcony'], axis='columns')", "_____no_output_____" ], [ "df2.head()", "_____no_output_____" ], [ "df2.isnull().sum()", "_____no_output_____" ], [ "df2.shape", "_____no_output_____" ], [ "df2['location'].fillna(df2['location'].mode().values[0],inplace=True)", "_____no_output_____" ], [ "df2['size'].fillna(df2['size'].mode().values[0],inplace=True)", "_____no_output_____" ], [ "df2['bath'].fillna(df2['bath'].mode().values[0],inplace=True)", "_____no_output_____" ], [ "df2.isnull().sum()", "_____no_output_____" ], [ "df2['size'].unique()", "_____no_output_____" ], [ "df2['bhk']=df2['size'].apply(lambda x: int(x.split(' ')[0]))", "_____no_output_____" ], [ "df2=df2.drop(['size'],axis='columns')", "_____no_output_____" ], [ "df2.head()", "_____no_output_____" ], [ "df2['bhk'].unique()", "_____no_output_____" ], [ "df2['total_sqft'].unique()", "_____no_output_____" ] ], [ [ "###### Dimension Reduction", "_____no_output_____" ] ], [ [ "def infloat(x):\n try:\n float(x)\n except:\n return False\n return True\n ", "_____no_output_____" ], [ "df2[~df2['total_sqft'].apply(infloat)].head(10)", "_____no_output_____" ], [ "def convert(x):\n token=x.split('-')\n if(len(token)==2):\n return (float(token[0])+float(token[1]))/2\n try:\n return float(x)\n except:\n return 1600\n ", "_____no_output_____" ], [ "df2['total_sqft']=df2['total_sqft'].apply(convert)", "_____no_output_____" ], [ "df2.head()", "_____no_output_____" ], [ "df2.loc[410]", "_____no_output_____" ], [ "df2.isnull().sum()", "_____no_output_____" ], [ "df2['total_sqft'].agg('mean')", "_____no_output_____" ], [ "df2['bath'].unique()", "_____no_output_____" ], [ "df3=df2.copy()", "_____no_output_____" ], [ "df3['price_per_sqft']=(df3['price']*100000/df3['total_sqft']).round(2)\ndf3.head()", "_____no_output_____" ], [ "df3.location.unique()", "_____no_output_____" ], [ "\nstats=df3.groupby('location')['location'].agg('count').sort_values(ascending=False)\nstats\n", "_____no_output_____" ], [ "location_stat_less_than_10=stats[stats<=10]", "_____no_output_____" ], [ "location_stat_less_than_10", "_____no_output_____" ], [ "df3['location']=df3['location'].apply(lambda x:'others' if x in location_stat_less_than_10 else x)", "_____no_output_____" ], [ "len(df3.location.unique())", "_____no_output_____" ], [ " df3.head(10)", "_____no_output_____" ], [ "df3[df3['total_sqft']/df3['bhk']<300].head()", "_____no_output_____" ], [ "df3.shape", "_____no_output_____" ], [ "df4=df3[~(df3['total_sqft']/df3['bhk']<300)]", "_____no_output_____" ], [ "df4.shape", "_____no_output_____" ], [ "df4.price_per_sqft.describe()", "_____no_output_____" ], [ "def remove(df):\n df_out = pd.DataFrame()\n for key, subdf in df.groupby('location'):\n m=np.mean(subdf.price_per_sqft)\n st=np.std(subdf.price_per_sqft)\n reduced_df=subdf[(subdf.price_per_sqft >(m-st)) & (subdf.price_per_sqft<=(m+st))]\n df_out = pd.concat([df_out, reduced_df],ignore_index=True)\n return df_out\n \ndf5=remove(df4)\ndf5.shape", "_____no_output_____" ], [ "def draw(df,location):\n bhk2=df[ (df.location==location) & (df.bhk==2)]\n bhk3=df[ (df.location==location) & (df.bhk==3)]\n plt.rcParams['figure.figsize']=(15,10)\n plt.scatter(bhk2.total_sqft,bhk2.price,color='blue')\n plt.scatter(bhk3.total_sqft,bhk3.price,color='green',marker='+')\n \n \n ", "_____no_output_____" ], [ "draw(df5,'Rajaji Nagar')", "_____no_output_____" ], [ "import matplotlib\nmatplotlib.rcParams['figure.figsize']=(15,10)\nplt.hist(df5.price_per_sqft,rwidth=.8)", "_____no_output_____" ], [ "df5.bath.unique()", "_____no_output_____" ], [ "df5[df5.bath>df5.bhk+2]", "_____no_output_____" ], [ "df6=df5[df5.bath<df5.bhk+2]", "_____no_output_____" ], [ "df6.shape", "_____no_output_____" ], [ "df6.head()", "_____no_output_____" ], [ "df6=df6.drop(['price_per_sqft'],axis='columns')", "_____no_output_____" ], [ "df6.head()", "_____no_output_____" ], [ "dummies=pd.get_dummies(df6.location)\ndummies.head(3)", "_____no_output_____" ], [ "dummies.shape", "_____no_output_____" ], [ "df7=pd.concat([df6,dummies.drop('others',axis='columns')],axis='columns')", "_____no_output_____" ], [ "df7.shape", "_____no_output_____" ], [ "df7.head(3)", "_____no_output_____" ], [ "df8=df7.drop('location',axis='columns')", "_____no_output_____" ], [ "df8.head(3)", "_____no_output_____" ], [ "df8.shape", "_____no_output_____" ], [ "x=df8.drop('price',axis='columns')\nx.head(2)", "_____no_output_____" ], [ "y=df8['price']", "_____no_output_____" ], [ "y.head()", "_____no_output_____" ], [ "from sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test=train_test_split(x,y, test_size=0.2,random_state=10)", "_____no_output_____" ], [ "from sklearn.linear_model import LinearRegression\nlr=LinearRegression()\nlr.fit(x_train,y_train)", "_____no_output_____" ], [ "y_pred=lr.predict(x_test)", "_____no_output_____" ], [ "from sklearn.metrics import r2_score\nr2_score(y_pred,y_test)", "_____no_output_____" ], [ "lr.score(x_test,y_test)", "_____no_output_____" ], [ "from sklearn.model_selection import ShuffleSplit\nfrom sklearn.model_selection import cross_val_score\n\ncv=ShuffleSplit(n_splits=5, test_size=.2,random_state=0)\n\ncross_val_score(LinearRegression(),x,y,cv=cv)\n", "_____no_output_____" ], [ "from sklearn.ensemble import RandomForestRegressor\nrfg=RandomForestRegressor(n_estimators=50)\nrfg.fit(x_train,y_train)\nr2_score(y_test,rfg.predict(x_test))", "_____no_output_____" ], [ "rfg.score(x_test,y_test)", "_____no_output_____" ], [ "cross_val_score(RandomForestRegressor(),x,y,cv=cv)", "_____no_output_____" ], [ "x.columns", "_____no_output_____" ], [ "X=x\n", "_____no_output_____" ], [ "def predict_price(location,sqft,bath,bhk):\n loc_index = np.where(X.columns==location)[0][0]\n \n x=np.zeros(len(X.columns))\n x[0]=sqft\n x[1]=bath\n x[2]=bhk\n if loc_index>=0:\n x[loc_index]=1\n \n return lr.predict([x])[0]", "_____no_output_____" ], [ "predict_price('1st Phase JP Nagar',1000,4,5)", "_____no_output_____" ], [ "predict_price('Indira Nagar',1000,2,2)", "_____no_output_____" ], [ "import pickle\nwith open('banglore_home_price_model.pickle','wb') as f:\n pickle.dump(lr,f)\n ", "_____no_output_____" ], [ "import json\ncolumns={\n 'data_columns' : [col.lower() for col in X.columns]\n}\nwith open(\"columns.json\",\"w\") as f:\n f.write(json.dumps(columns))", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "##### Copyright 2019 The TensorFlow Authors.\n", "_____no_output_____" ] ], [ [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.", "_____no_output_____" ] ], [ [ "# Multi-worker training with Keras\n\n<table class=\"tfo-notebook-buttons\" align=\"left\">\n <td>\n <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n </td>\n <td>\n <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/distribute/multi_worker_with_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n </td>\n <td>\n <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/distribute/multi_worker_with_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n </td>\n <td>\n <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/distribute/multi_worker_with_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n </td>\n</table>", "_____no_output_____" ], [ "## Overview\n\nThis tutorial demonstrates multi-worker distributed training with Keras model using `tf.distribute.Strategy` API, specifically `tf.distribute.MultiWorkerMirroredStrategy`. With the help of this strategy, a Keras model that was designed to run on single-worker can seamlessly work on multiple workers with minimal code change.\n\n[Distributed Training in TensorFlow](../../guide/distributed_training.ipynb) guide is available for an overview of the distribution strategies TensorFlow supports for those interested in a deeper understanding of `tf.distribute.Strategy` APIs.\n", "_____no_output_____" ], [ "## Setup\n\nFirst, some necessary imports.", "_____no_output_____" ] ], [ [ "import json\nimport os\nimport sys", "_____no_output_____" ] ], [ [ "Before importing TensorFlow, make a few changes to the environment.\n\nDisable all GPUs. This prevents errors caused by the workers all trying to use the same GPU. For a real application each worker would be on a different machine.", "_____no_output_____" ] ], [ [ "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"", "_____no_output_____" ] ], [ [ "Reset the `TF_CONFIG` environment variable, you'll see more about this later.", "_____no_output_____" ] ], [ [ "os.environ.pop('TF_CONFIG', None)", "_____no_output_____" ] ], [ [ "Be sure that the current directory is on python's path. This allows the notebook to import the files written by `%%writefile` later.\n", "_____no_output_____" ] ], [ [ "if '.' not in sys.path:\n sys.path.insert(0, '.')", "_____no_output_____" ] ], [ [ "Now import TensorFlow.", "_____no_output_____" ] ], [ [ "import tensorflow as tf", "_____no_output_____" ] ], [ [ "### Dataset and model definition", "_____no_output_____" ], [ "Next create an `mnist.py` file with a simple model and dataset setup. This python file will be used by the worker-processes in this tutorial:", "_____no_output_____" ] ], [ [ "%%writefile mnist.py\n\nimport os\nimport tensorflow as tf\nimport numpy as np\n\ndef mnist_dataset(batch_size):\n (x_train, y_train), _ = tf.keras.datasets.mnist.load_data()\n # The `x` arrays are in uint8 and have values in the range [0, 255].\n # You need to convert them to float32 with values in the range [0, 1]\n x_train = x_train / np.float32(255)\n y_train = y_train.astype(np.int64)\n train_dataset = tf.data.Dataset.from_tensor_slices(\n (x_train, y_train)).shuffle(60000).repeat().batch(batch_size)\n return train_dataset\n\ndef build_and_compile_cnn_model():\n model = tf.keras.Sequential([\n tf.keras.Input(shape=(28, 28)),\n tf.keras.layers.Reshape(target_shape=(28, 28, 1)),\n tf.keras.layers.Conv2D(32, 3, activation='relu'),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(128, activation='relu'),\n tf.keras.layers.Dense(10)\n ])\n model.compile(\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),\n metrics=['accuracy'])\n return model", "_____no_output_____" ] ], [ [ "Try training the model for a small number of epochs and observe the results of a single worker to make sure everything works correctly. As training progresses, the loss should drop and the accuracy should increase.", "_____no_output_____" ] ], [ [ "import mnist\n\nbatch_size = 64\nsingle_worker_dataset = mnist.mnist_dataset(batch_size)\nsingle_worker_model = mnist.build_and_compile_cnn_model()\nsingle_worker_model.fit(single_worker_dataset, epochs=3, steps_per_epoch=70)", "_____no_output_____" ] ], [ [ "## Multi-worker Configuration\n\nNow let's enter the world of multi-worker training. In TensorFlow, the `TF_CONFIG` environment variable is required for training on multiple machines, each of which possibly has a different role. `TF_CONFIG` is a JSON string used to specify the cluster configuration on each worker that is part of the cluster.\n\nHere is an example configuration:", "_____no_output_____" ] ], [ [ "tf_config = {\n 'cluster': {\n 'worker': ['localhost:12345', 'localhost:23456']\n },\n 'task': {'type': 'worker', 'index': 0}\n}", "_____no_output_____" ] ], [ [ "Here is the same `TF_CONFIG` serialized as a JSON string:", "_____no_output_____" ] ], [ [ "json.dumps(tf_config)", "_____no_output_____" ] ], [ [ "There are two components of `TF_CONFIG`: `cluster` and `task`.\n\n* `cluster` is the same for all workers and provides information about the training cluster, which is a dict consisting of different types of jobs such as `worker`. In multi-worker training with `MultiWorkerMirroredStrategy`, there is usually one `worker` that takes on a little more responsibility like saving checkpoint and writing summary file for TensorBoard in addition to what a regular `worker` does. Such a worker is referred to as the `chief` worker, and it is customary that the `worker` with `index` 0 is appointed as the chief `worker` (in fact this is how `tf.distribute.Strategy` is implemented).\n\n* `task` provides information of the current task and is different on each worker. It specifies the `type` and `index` of that worker. ", "_____no_output_____" ], [ "In this example, you set the task `type` to `\"worker\"` and the task `index` to `0`. This machine is the first worker and will be appointed as the chief worker and do more work than the others. Note that other machines will need to have the `TF_CONFIG` environment variable set as well, and it should have the same `cluster` dict, but different task `type` or task `index` depending on what the roles of those machines are.\n", "_____no_output_____" ], [ "For illustration purposes, this tutorial shows how one may set a `TF_CONFIG` with 2 workers on `localhost`. In practice, users would create multiple workers on external IP addresses/ports, and set `TF_CONFIG` on each worker appropriately.\n\nIn this example you will use 2 workers, the first worker's `TF_CONFIG` is shown above. For the second worker you would set `tf_config['task']['index']=1`", "_____no_output_____" ], [ "Above, `tf_config` is just a local variable in python. To actually use it to configure training, this dictionary needs to be serialized as JSON, and placed in the `TF_CONFIG` environment variable.", "_____no_output_____" ], [ "### Environment variables and subprocesses in notebooks", "_____no_output_____" ], [ "Subprocesses inherit environment variables from their parent. So if you set an environment variable in this `jupyter notebook` process:", "_____no_output_____" ] ], [ [ "os.environ['GREETINGS'] = 'Hello TensorFlow!'", "_____no_output_____" ] ], [ [ "You can access the environment variable from a subprocesses:", "_____no_output_____" ] ], [ [ "%%bash\necho ${GREETINGS}", "_____no_output_____" ] ], [ [ "In the next section, you'll use this to pass the `TF_CONFIG` to the worker subprocesses. You would never really launch your jobs this way, but it's sufficient for the purposes of this tutorial: To demonstrate a minimal multi-worker example.", "_____no_output_____" ], [ "## Choose the right strategy\n\nIn TensorFlow there are two main forms of distributed training:\n\n* Synchronous training, where the steps of training are synced across the workers and replicas, and\n* Asynchronous training, where the training steps are not strictly synced.\n\n`MultiWorkerMirroredStrategy`, which is the recommended strategy for synchronous multi-worker training, will be demonstrated in this guide.\nTo train the model, use an instance of `tf.distribute.MultiWorkerMirroredStrategy`.\n\n`MultiWorkerMirroredStrategy` creates copies of all variables in the model's layers on each device across all workers. It uses `CollectiveOps`, a TensorFlow op for collective communication, to aggregate gradients and keep the variables in sync. The [`tf.distribute.Strategy` guide](../../guide/distributed_training.ipynb) has more details about this strategy.", "_____no_output_____" ] ], [ [ "strategy = tf.distribute.MultiWorkerMirroredStrategy()", "_____no_output_____" ] ], [ [ "Note: `TF_CONFIG` is parsed and TensorFlow's GRPC servers are started at the time `MultiWorkerMirroredStrategy()` is called, so the `TF_CONFIG` environment variable must be set before a `tf.distribute.Strategy` instance is created. Since `TF_CONFIG` is not set yet the above strategy is effectively single-worker training.", "_____no_output_____" ], [ "`MultiWorkerMirroredStrategy` provides multiple implementations via the [`CommunicationOptions`](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/CommunicationOptions) parameter. `RING` implements ring-based collectives using gRPC as the cross-host communication layer. `NCCL` uses [Nvidia's NCCL](https://developer.nvidia.com/nccl) to implement collectives. `AUTO` defers the choice to the runtime. The best choice of collective implementation depends upon the number and kind of GPUs, and the network interconnect in the cluster.", "_____no_output_____" ], [ "## Train the model\n\nWith the integration of `tf.distribute.Strategy` API into `tf.keras`, the only change you will make to distribute the training to multiple-workers is enclosing the model building and `model.compile()` call inside `strategy.scope()`. The distribution strategy's scope dictates how and where the variables are created, and in the case of `MultiWorkerMirroredStrategy`, the variables created are `MirroredVariable`s, and they are replicated on each of the workers.\n", "_____no_output_____" ] ], [ [ "with strategy.scope():\n # Model building/compiling need to be within `strategy.scope()`.\n multi_worker_model = mnist.build_and_compile_cnn_model()", "_____no_output_____" ] ], [ [ "Note: Currently there is a limitation in `MultiWorkerMirroredStrategy` where TensorFlow ops need to be created after the instance of strategy is created. If you see `RuntimeError: Collective ops must be configured at program startup`, try creating the instance of `MultiWorkerMirroredStrategy` at the beginning of the program and put the code that may create ops after the strategy is instantiated.", "_____no_output_____" ], [ "To actually run with `MultiWorkerMirroredStrategy` you'll need to run worker processes and pass a `TF_CONFIG` to them.\n\nLike the `mnist.py` file written earlier, here is the `main.py` that each of the workers will run:", "_____no_output_____" ] ], [ [ "%%writefile main.py\n\nimport os\nimport json\n\nimport tensorflow as tf\nimport mnist\n\nper_worker_batch_size = 64\ntf_config = json.loads(os.environ['TF_CONFIG'])\nnum_workers = len(tf_config['cluster']['worker'])\n\nstrategy = tf.distribute.MultiWorkerMirroredStrategy()\n\nglobal_batch_size = per_worker_batch_size * num_workers\nmulti_worker_dataset = mnist.mnist_dataset(global_batch_size)\n\nwith strategy.scope():\n # Model building/compiling need to be within `strategy.scope()`.\n multi_worker_model = mnist.build_and_compile_cnn_model()\n\n\nmulti_worker_model.fit(multi_worker_dataset, epochs=3, steps_per_epoch=70)", "_____no_output_____" ] ], [ [ "In the code snippet above note that the `global_batch_size`, which gets passed to `Dataset.batch`, is set to `per_worker_batch_size * num_workers`. This ensures that each worker processes batches of `per_worker_batch_size` examples regardless of the number of workers.", "_____no_output_____" ], [ "The current directory now contains both Python files:", "_____no_output_____" ] ], [ [ "%%bash\nls *.py", "_____no_output_____" ] ], [ [ "So json-serialize the `TF_CONFIG` and add it to the environment variables:", "_____no_output_____" ] ], [ [ "os.environ['TF_CONFIG'] = json.dumps(tf_config)", "_____no_output_____" ] ], [ [ "Now, you can launch a worker process that will run the `main.py` and use the `TF_CONFIG`:", "_____no_output_____" ] ], [ [ "# first kill any previous runs\n%killbgscripts", "_____no_output_____" ], [ "%%bash --bg\npython main.py &> job_0.log", "_____no_output_____" ] ], [ [ "There are a few things to note about the above command:\n\n1. It uses the `%%bash` which is a [notebook \"magic\"](https://ipython.readthedocs.io/en/stable/interactive/magics.html) to run some bash commands.\n2. It uses the `--bg` flag to run the `bash` process in the background, because this worker will not terminate. It waits for all the workers before it starts.\n\nThe backgrounded worker process won't print output to this notebook, so the `&>` redirects its output to a file, so you can see what happened.\n\nSo, wait a few seconds for the process to start up:", "_____no_output_____" ] ], [ [ "import time\ntime.sleep(10)", "_____no_output_____" ] ], [ [ "Now look what's been output to the worker's logfile so far:", "_____no_output_____" ] ], [ [ "%%bash\ncat job_0.log", "_____no_output_____" ] ], [ [ "The last line of the log file should say: `Started server with target: grpc://localhost:12345`. The first worker is now ready, and is waiting for all the other worker(s) to be ready to proceed.", "_____no_output_____" ], [ "So update the `tf_config` for the second worker's process to pick up:", "_____no_output_____" ] ], [ [ "tf_config['task']['index'] = 1\nos.environ['TF_CONFIG'] = json.dumps(tf_config)", "_____no_output_____" ] ], [ [ "Now launch the second worker. This will start the training since all the workers are active (so there's no need to background this process):", "_____no_output_____" ] ], [ [ "%%bash\npython main.py", "_____no_output_____" ] ], [ [ "Now if you recheck the logs written by the first worker you'll see that it participated in training that model:", "_____no_output_____" ] ], [ [ "%%bash\ncat job_0.log", "_____no_output_____" ] ], [ [ "Unsurprisingly this ran _slower_ than the the test run at the beginning of this tutorial. Running multiple workers on a single machine only adds overhead. The goal here was not to improve the training time, but only to give an example of multi-worker training.", "_____no_output_____" ] ], [ [ "# Delete the `TF_CONFIG`, and kill any background tasks so they don't affect the next section.\nos.environ.pop('TF_CONFIG', None)\n%killbgscripts", "_____no_output_____" ] ], [ [ "## Multi worker training in depth\n\nSo far this tutorial has demonstrated a basic multi-worker setup. The rest of this document looks in detail other factors which may be useful or important for real use cases.", "_____no_output_____" ], [ "### Dataset sharding\n\nIn multi-worker training, dataset sharding is needed to ensure convergence and performance.\n\nThe example in the previous section relies on the default autosharding provided by the `tf.distribute.Strategy` API. You can control the sharding by setting the `tf.data.experimental.AutoShardPolicy` of the `tf.data.experimental.DistributeOptions`. To learn more about auto-sharding see the [Distributed input guide](https://www.tensorflow.org/tutorials/distribute/input#sharding).\n\nHere is a quick example of how to turn OFF the auto sharding, so each replica processes every example (not recommended):\n", "_____no_output_____" ] ], [ [ "options = tf.data.Options()\noptions.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF\n\nglobal_batch_size = 64\nmulti_worker_dataset = mnist.mnist_dataset(batch_size=64)\ndataset_no_auto_shard = multi_worker_dataset.with_options(options)", "_____no_output_____" ] ], [ [ "### Evaluation\n\nIf you pass `validation_data` into `model.fit`, it will alternate between training and evaluation for each epoch. The evaluation taking `validation_data` is distributed across the same set of workers and the evaluation results are aggregated and available for all workers. Similar to training, the validation dataset is automatically sharded at the file level. You need to set a global batch size in the validation dataset and set `validation_steps`. A repeated dataset is also recommended for evaluation.\n\nAlternatively, you can also create another task that periodically reads checkpoints and runs the evaluation. This is what Estimator does. But this is not a recommended way to perform evaluation and thus its details are omitted.", "_____no_output_____" ], [ "### Prediction\nCurrently `model.predict` doesn't work with `MultiWorkerMirroredStrategy.`", "_____no_output_____" ], [ "### Performance\n\nYou now have a Keras model that is all set up to run in multiple workers with `MultiWorkerMirroredStrategy`. You can try the following techniques to tweak performance of multi-worker training with `MultiWorkerMirroredStrategy`.\n\n* `MultiWorkerMirroredStrategy` provides multiple [collective communication implementations](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/CommunicationImplementation). `RING` implements ring-based collectives using gRPC as the cross-host communication layer. `NCCL` uses [Nvidia's NCCL](https://developer.nvidia.com/nccl) to implement collectives. `AUTO` defers the choice to the runtime. The best choice of collective implementation depends upon the number and kind of GPUs, and the network interconnect in the cluster. To override the automatic choice, specify `communication_options` parameter of `MultiWorkerMirroredStrategy`'s constructor, e.g. `communication_options=tf.distribute.experimental.CommunicationOptions(implementation=tf.distribute.experimental.CollectiveCommunication.NCCL)`.\n* Cast the variables to `tf.float` if possible. The official ResNet model includes [an example](https://github.com/tensorflow/models/blob/8367cf6dabe11adf7628541706b660821f397dce/official/resnet/resnet_model.py#L466) of how this can be done.\n", "_____no_output_____" ], [ "### Fault tolerance\n\nIn synchronous training, the cluster would fail if one of the workers fails and no failure-recovery mechanism exists. Using Keras with `tf.distribute.Strategy` comes with the advantage of fault tolerance in cases where workers die or are otherwise unstable. You do this by preserving training state in the distributed file system of your choice, such that upon restart of the instance that previously failed or preempted, the training state is recovered.\n\nWhen a worker becomes unavailable, other workers will fail (possibly after a timeout). In such cases, the unavailable worker needs to be restarted, as well as other workers that have failed.\n\nNote:\nPreviously, the `ModelCheckpoint` callback provided a mechanism to restore training state upon restart from job failure for multi-worker training. The TensorFlow team are introducing a new [`BackupAndRestore`](#scrollTo=kmH8uCUhfn4w) callback, to also add the support to single worker training for a consistent experience, and removed fault tolerance functionality from existing `ModelCheckpoint` callback. From now on, applications that rely on this behavior should migrate to the new callback.", "_____no_output_____" ], [ "#### ModelCheckpoint callback\n\n`ModelCheckpoint` callback no longer provides fault tolerance functionality, please use [`BackupAndRestore`](#scrollTo=kmH8uCUhfn4w) callback instead.\n\nThe `ModelCheckpoint` callback can still be used to save checkpoints. But with this, if training was interrupted or successfully finished, in order to continue training from the checkpoint, the user is responsible to load the model manually.\n\nOptionally the user can choose to save and restore model/weights outside `ModelCheckpoint` callback.", "_____no_output_____" ], [ "### Model saving and loading\n\nTo save your model using `model.save` or `tf.saved_model.save`, the destination for saving needs to be different for each worker. On the non-chief workers, you will need to save the model to a temporary directory, and on the chief, you will need to save to the provided model directory. The temporary directories on the worker need to be unique to prevent errors resulting from multiple workers trying to write to the same location. The model saved in all the directories are identical and typically only the model saved by the chief should be referenced for restoring or serving. You should have some cleanup logic that deletes the temporary directories created by the workers once your training has completed.\n\nThe reason you need to save on the chief and workers at the same time is because you might be aggregating variables during checkpointing which requires both the chief and workers to participate in the allreduce communication protocol. On the other hand, letting chief and workers save to the same model directory will result in errors due to contention.\n\nWith `MultiWorkerMirroredStrategy`, the program is run on every worker, and in order to know whether the current worker is chief, it takes advantage of the cluster resolver object that has attributes `task_type` and `task_id`. `task_type` tells you what the current job is (e.g. 'worker'), and `task_id` tells you the identifier of the worker. The worker with id 0 is designated as the chief worker.\n\nIn the code snippet below, `write_filepath` provides the file path to write, which depends on the worker id. In the case of chief (worker with id 0), it writes to the original file path; for others, it creates a temporary directory (with id in the directory path) to write in:", "_____no_output_____" ] ], [ [ "model_path = '/tmp/keras-model'\n\ndef _is_chief(task_type, task_id):\n # Note: there are two possible `TF_CONFIG` configuration.\n # 1) In addition to `worker` tasks, a `chief` task type is use;\n # in this case, this function should be modified to \n # `return task_type == 'chief'`.\n # 2) Only `worker` task type is used; in this case, worker 0 is\n # regarded as the chief. The implementation demonstrated here\n # is for this case.\n # For the purpose of this colab section, we also add `task_type is None` \n # case because it is effectively run with only single worker.\n return (task_type == 'worker' and task_id == 0) or task_type is None\n\ndef _get_temp_dir(dirpath, task_id):\n base_dirpath = 'workertemp_' + str(task_id)\n temp_dir = os.path.join(dirpath, base_dirpath)\n tf.io.gfile.makedirs(temp_dir)\n return temp_dir\n\ndef write_filepath(filepath, task_type, task_id):\n dirpath = os.path.dirname(filepath)\n base = os.path.basename(filepath)\n if not _is_chief(task_type, task_id):\n dirpath = _get_temp_dir(dirpath, task_id)\n return os.path.join(dirpath, base)\n\ntask_type, task_id = (strategy.cluster_resolver.task_type,\n strategy.cluster_resolver.task_id)\nwrite_model_path = write_filepath(model_path, task_type, task_id)", "_____no_output_____" ] ], [ [ "With that, you're now ready to save:", "_____no_output_____" ] ], [ [ "multi_worker_model.save(write_model_path)", "_____no_output_____" ] ], [ [ "As described above, later on the model should only be loaded from the path chief saved to, so let's remove the temporary ones the non-chief workers saved:", "_____no_output_____" ] ], [ [ "if not _is_chief(task_type, task_id):\n tf.io.gfile.rmtree(os.path.dirname(write_model_path))", "_____no_output_____" ] ], [ [ "Now, when it's time to load, let's use convenient `tf.keras.models.load_model` API, and continue with further work. Here, assume only using single worker to load and continue training, in which case you do not call `tf.keras.models.load_model` within another `strategy.scope()`.", "_____no_output_____" ] ], [ [ "loaded_model = tf.keras.models.load_model(model_path)\n\n# Now that the model is restored, and can continue with the training.\nloaded_model.fit(single_worker_dataset, epochs=2, steps_per_epoch=20)", "_____no_output_____" ] ], [ [ "### Checkpoint saving and restoring\n\nOn the other hand, checkpointing allows you to save model's weights and restore them without having to save the whole model. Here, you'll create one `tf.train.Checkpoint` that tracks the model, which is managed by a `tf.train.CheckpointManager` so that only the latest checkpoint is preserved. ", "_____no_output_____" ] ], [ [ "checkpoint_dir = '/tmp/ckpt'\n\ncheckpoint = tf.train.Checkpoint(model=multi_worker_model)\nwrite_checkpoint_dir = write_filepath(checkpoint_dir, task_type, task_id)\ncheckpoint_manager = tf.train.CheckpointManager(\n checkpoint, directory=write_checkpoint_dir, max_to_keep=1)", "_____no_output_____" ] ], [ [ "Once the `CheckpointManager` is set up, you're now ready to save, and remove the checkpoints non-chief workers saved.", "_____no_output_____" ] ], [ [ "checkpoint_manager.save()\nif not _is_chief(task_type, task_id):\n tf.io.gfile.rmtree(write_checkpoint_dir)", "_____no_output_____" ] ], [ [ "Now, when you need to restore, you can find the latest checkpoint saved using the convenient `tf.train.latest_checkpoint` function. After restoring the checkpoint, you can continue with training.", "_____no_output_____" ] ], [ [ "latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\ncheckpoint.restore(latest_checkpoint)\nmulti_worker_model.fit(multi_worker_dataset, epochs=2, steps_per_epoch=20)", "_____no_output_____" ] ], [ [ "#### BackupAndRestore callback\n\nBackupAndRestore callback provides fault tolerance functionality, by backing up the model and current epoch number in a temporary checkpoint file under `backup_dir` argument to `BackupAndRestore`. This is done at the end of each epoch.\n\nOnce jobs get interrupted and restart, the callback restores the last checkpoint, and training continues from the beginning of the interrupted epoch. Any partial training already done in the unfinished epoch before interruption will be thrown away, so that it doesn't affect the final model state.\n\nTo use it, provide an instance of `tf.keras.callbacks.experimental.BackupAndRestore` at the `tf.keras.Model.fit()` call.\n\nWith MultiWorkerMirroredStrategy, if a worker gets interrupted, the whole cluster pauses until the interrupted worker is restarted. Other workers will also restart, and the interrupted worker rejoins the cluster. Then, every worker reads the checkpoint file that was previously saved and picks up its former state, thereby allowing the cluster to get back in sync. Then the training continues.\n\n`BackupAndRestore` callback uses `CheckpointManager` to save and restore the training state, which generates a file called checkpoint that tracks existing checkpoints together with the latest one. For this reason, `backup_dir` should not be re-used to store other checkpoints in order to avoid name collision.\n\nCurrently, `BackupAndRestore` callback supports single worker with no strategy, MirroredStrategy, and multi-worker with MultiWorkerMirroredStrategy.\nBelow are two examples for both multi-worker training and single worker training.", "_____no_output_____" ] ], [ [ "# Multi-worker training with MultiWorkerMirroredStrategy.\n\ncallbacks = [tf.keras.callbacks.experimental.BackupAndRestore(backup_dir='/tmp/backup')]\nwith strategy.scope():\n multi_worker_model = mnist.build_and_compile_cnn_model()\nmulti_worker_model.fit(multi_worker_dataset,\n epochs=3,\n steps_per_epoch=70,\n callbacks=callbacks)", "_____no_output_____" ] ], [ [ "If you inspect the directory of `backup_dir` you specified in `BackupAndRestore`, you may notice some temporarily generated checkpoint files. Those files are needed for recovering the previously lost instances, and they will be removed by the library at the end of `tf.keras.Model.fit()` upon successful exiting of your training.\n\nNote: Currently BackupAndRestore only supports eager mode. In graph mode, consider using [Save/Restore Model](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#model_saving_and_loading) mentioned above, and by providing `initial_epoch` in `model.fit()`.", "_____no_output_____" ], [ "## See also\n1. [Distributed Training in TensorFlow](https://www.tensorflow.org/guide/distributed_training) guide provides an overview of the available distribution strategies.\n2. [Official models](https://github.com/tensorflow/models/tree/master/official), many of which can be configured to run multiple distribution strategies.\n3. The [Performance section](../../guide/function.ipynb) in the guide provides information about other strategies and [tools](../../guide/profiler.md) you can use to optimize the performance of your TensorFlow models.\n", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import os \nos.environ['PYSPARK_SUBMIT_ARGS'] = '--jars spark-streaming-kafka-0-8-assembly_2.11-2.2.1.jar pyspark-shell' \n", "_____no_output_____" ], [ "from pyspark.streaming.kafka import KafkaUtils\nfrom pyspark import SparkContext\nfrom pyspark.streaming import StreamingContext\nimport sys\nimport json", "_____no_output_____" ], [ "sc = SparkContext.getOrCreate()\nsc.stop()\n\n\n", "_____no_output_____" ], [ "sc = SparkContext(appName = \"PythonStreamingReciever\")\nssc = StreamingContext(sc, 5)", "_____no_output_____" ], [ "kafkaStream = KafkaUtils.createStream(ssc, 'localhost:2181', 'spark-streaming', {'province':1})\nlines = kafkaStream.map(lambda x:x[1])\ncounts = lines.flatMap(lambda line:line.split(\" \")).map(lambda word:(word,1)).reduceByKey(lambda a,b:a+b)\ncounts.pprint()", "_____no_output_____" ], [ "from kafka import KafkaProducer\nproducer = KafkaProducer(bootstrap_servers = 'localhost:9092')\n", "_____no_output_____" ], [ "def process(rdd):\n print(rdd)\n message = json.dumps(rdd.map(lambda x:[str(x[0]),str(x[1])]).collect())\n\n producer.send('result', message.encode('utf-8'))\n", "_____no_output_____" ], [ "counts.foreachRDD(process)\nssc.start()", "_____no_output_____" ], [ "ssc.awaitTermination()", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from numpy import log", "_____no_output_____" ], [ "def cross_entropy(y, a):\n return -1*(y*log(a) + (1-y)*log(1-a))", "_____no_output_____" ], [ "cross_entropy(0, 0.01)", "_____no_output_____" ], [ "cross_entropy(1, 0.99)", "_____no_output_____" ], [ "cross_entropy(0, 0.3)", "_____no_output_____" ], [ "cross_entropy(0, 0.6)", "_____no_output_____" ], [ "cross_entropy(0, 0.9)", "_____no_output_____" ] ] ]

[ "code" ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "\n\n\n\n[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/RE_POSOLOGY.ipynb)\n\n\n", "_____no_output_____" ], [ "# **Detect posology relations**", "_____no_output_____" ], [ "To run this yourself, you will need to upload your license keys to the notebook. Otherwise, you can look at the example outputs at the bottom of the notebook. To upload license keys, open the file explorer on the left side of the screen and upload `workshop_license_keys.json` to the folder that opens.", "_____no_output_____" ], [ "## 1. Colab Setup", "_____no_output_____" ], [ "Import license keys", "_____no_output_____" ] ], [ [ "import os\nimport json\n\nwith open('/content/spark_nlp_for_healthcare.json', 'r') as f:\n license_keys = json.load(f)\n\nlicense_keys.keys()\n\nsecret = license_keys['SECRET']\nos.environ['SPARK_NLP_LICENSE'] = license_keys['SPARK_NLP_LICENSE']\nos.environ['AWS_ACCESS_KEY_ID'] = license_keys['AWS_ACCESS_KEY_ID']\nos.environ['AWS_SECRET_ACCESS_KEY'] = license_keys['AWS_SECRET_ACCESS_KEY']\nsparknlp_version = license_keys[\"PUBLIC_VERSION\"]\njsl_version = license_keys[\"JSL_VERSION\"]\n\nprint ('SparkNLP Version:', sparknlp_version)\nprint ('SparkNLP-JSL Version:', jsl_version)", "_____no_output_____" ] ], [ [ "Install dependencies", "_____no_output_____" ] ], [ [ "# Install Java\n! apt-get update -qq\n! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null\n! java -version\n\n# Install pyspark\n! pip install --ignore-installed -q pyspark==2.4.4\n\n# Install Spark NLP\n! pip install --ignore-installed spark-nlp==$sparknlp_version\n! python -m pip install --upgrade spark-nlp-jsl==$jsl_version --extra-index-url https://pypi.johnsnowlabs.com/$secret", "_____no_output_____" ] ], [ [ "Import dependencies into Python", "_____no_output_____" ] ], [ [ "os.environ['JAVA_HOME'] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\nos.environ['PATH'] = os.environ['JAVA_HOME'] + \"/bin:\" + os.environ['PATH']\n\nimport pandas as pd\nfrom pyspark.ml import Pipeline\nfrom pyspark.sql import SparkSession\nimport pyspark.sql.functions as F\n\nimport sparknlp\nfrom sparknlp.annotator import *\nfrom sparknlp_jsl.annotator import *\nfrom sparknlp.base import *\nimport sparknlp_jsl\n", "_____no_output_____" ] ], [ [ "Start the Spark session", "_____no_output_____" ] ], [ [ "spark = sparknlp_jsl.start(secret)", "_____no_output_____" ] ], [ [ "## 2. Select the Relation Extraction model and construct the pipeline", "_____no_output_____" ], [ "Select the models:\n\n\n* Posology Relation Extraction models: **posology_re**\n\n\n\n\nFor more details: https://github.com/JohnSnowLabs/spark-nlp-models#pretrained-models---spark-nlp-for-healthcare", "_____no_output_____" ] ], [ [ "# Change this to the model you want to use and re-run the cells below.\nRE_MODEL_NAME = \"posology_re\"\nNER_MODEL_NAME = \"ner_posology_large\"", "_____no_output_____" ] ], [ [ "Create the pipeline", "_____no_output_____" ] ], [ [ "document_assembler = DocumentAssembler() \\\n .setInputCol('text')\\\n .setOutputCol('document')\n\nsentence_detector = SentenceDetector() \\\n .setInputCols(['document'])\\\n .setOutputCol('sentences')\n\ntokenizer = Tokenizer()\\\n .setInputCols(['sentences']) \\\n .setOutputCol('tokens')\n\npos_tagger = PerceptronModel()\\\n .pretrained(\"pos_clinical\", \"en\", \"clinical/models\") \\\n .setInputCols([\"sentences\", \"tokens\"])\\\n .setOutputCol(\"pos_tags\")\n\ndependency_parser = DependencyParserModel()\\\n .pretrained(\"dependency_conllu\", \"en\")\\\n .setInputCols([\"sentences\", \"pos_tags\", \"tokens\"])\\\n .setOutputCol(\"dependencies\")\n\nembeddings = WordEmbeddingsModel.pretrained('embeddings_clinical', 'en', 'clinical/models')\\\n .setInputCols([\"sentences\", \"tokens\"])\\\n .setOutputCol(\"embeddings\")\n\nclinical_ner_model = NerDLModel().pretrained(NER_MODEL_NAME, 'en', 'clinical/models').setInputCols(\"sentences\", \"tokens\", \"embeddings\")\\\n .setOutputCol(\"clinical_ner_tags\") \n\nclinical_ner_chunker = NerConverter()\\\n .setInputCols([\"sentences\", \"tokens\", \"clinical_ner_tags\"])\\\n .setOutputCol(\"clinical_ner_chunks\")\n\nclinical_re_Model = RelationExtractionModel()\\\n .pretrained(RE_MODEL_NAME, 'en', 'clinical/models')\\\n .setInputCols([\"embeddings\", \"pos_tags\", \"clinical_ner_chunks\", \"dependencies\"])\\\n .setOutputCol(\"clinical_relations\")\\\n .setMaxSyntacticDistance(4)\n #.setRelationPairs()#[\"problem-test\", \"problem-treatment\"]) # we can set the possible relation pairs (if not set, all the relations will be calculated)\n\npipeline = Pipeline(stages=[\n document_assembler, \n sentence_detector,\n tokenizer,\n pos_tagger,\n dependency_parser,\n embeddings,\n clinical_ner_model,\n clinical_ner_chunker,\n clinical_re_Model])\n\nempty_df = spark.createDataFrame([['']]).toDF(\"text\")\npipeline_model = pipeline.fit(empty_df)\nlight_pipeline = LightPipeline(pipeline_model)", "_____no_output_____" ] ], [ [ "## 3. Create example inputs", "_____no_output_____" ] ], [ [ "# Enter examples as strings in this array\ninput_list = [\n\"\"\"The patient is a 40-year-old white male who presents with a chief complaint of \"chest pain\". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain.\"\"\",\n]", "_____no_output_____" ] ], [ [ "# 4. Run the pipeline", "_____no_output_____" ] ], [ [ "df = spark.createDataFrame(pd.DataFrame({\"text\": input_list}))\nresult = pipeline_model.transform(df)\nlight_result = light_pipeline.fullAnnotate(input_list[0])", "_____no_output_____" ] ], [ [ "# 5. Visualize", "_____no_output_____" ], [ "helper function for visualization", "_____no_output_____" ] ], [ [ "def get_relations_df (results, rel='clinical_relations'):\n rel_pairs=[]\n for rel in results[rel]:\n rel_pairs.append((\n rel.result, \n rel.metadata['entity1'], \n rel.metadata['entity1_begin'],\n rel.metadata['entity1_end'],\n rel.metadata['chunk1'], \n rel.metadata['entity2'],\n rel.metadata['entity2_begin'],\n rel.metadata['entity2_end'],\n rel.metadata['chunk2'], \n rel.metadata['confidence']\n ))\n\n rel_df = pd.DataFrame(rel_pairs, columns=['relation','entity1','entity1_begin','entity1_end','chunk1','entity2','entity2_begin','entity2_end','chunk2', 'confidence'])\n\n return rel_df[rel_df.relation!='O']", "_____no_output_____" ], [ "get_relations_df(light_result[0])", "_____no_output_____" ] ] ]

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[ [ [ "from python_dict_wrapper import wrap\nimport sys\nsys.path.append('../')", "_____no_output_____" ], [ "import torch", "_____no_output_____" ], [ "sys.path.append(\"../../CPC/dpc\")\nsys.path.append(\"../../CPC/backbone\")", "_____no_output_____" ], [ "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy\n\ndef find_dominant_orientation(W):\n Wf = abs(np.fft.fft2(W))\n orient_sel = 1 - Wf[0, 0] / Wf.sum()\n Wf[0, 0] = 0\n Wf = np.fft.fftshift(Wf)\n dt = W.shape[0] // 2\n xi, yi = np.meshgrid(np.arange(-dt, dt+1), np.arange(-dt, dt+1))\n \n # Check whether we should split this horizontally or vertically\n if Wf[xi == 0].sum() > Wf[yi == 0].sum():\n # Use a top-down split \n xi_ = xi * (xi >= 0)\n yi_ = yi * (xi >= 0)\n x0 = (xi_ * Wf).sum() / ((xi >= 0) * Wf).sum()\n y0 = (yi_ * Wf).sum() / ((xi >= 0) * Wf).sum()\n else:\n xi_ = xi * (yi >= 0)\n yi_ = yi * (yi >= 0)\n x0 = (xi_ * Wf).sum() / ((yi >= 0) * Wf).sum()\n y0 = (yi_ * Wf).sum() / ((yi >= 0) * Wf).sum()\n return np.arctan2(y0, x0), orient_sel\n\ndef get_spatial_slice(W, theta):\n dx = W.shape[0] // 2\n dt = W.shape[2] // 2\n xi, yi, zi = np.meshgrid(np.arange(W.shape[0]), \n np.arange(W.shape[1]),\n np.arange(W.shape[2]))\n \n xi_, zi_ = np.meshgrid(np.arange(W.shape[0]), \n np.arange(W.shape[2]))\n Ws = []\n for i in range(W.shape[3]):\n interp = scipy.interpolate.LinearNDInterpolator(np.array([xi.ravel(), \n yi.ravel(), \n zi.ravel()]).T, W[:, :, :, i].ravel())\n probe = np.array([dx + (xi_ - dx) * np.cos(theta),\n dx + (xi_ - dx) * np.sin(theta),\n zi_]).T\n Ws.append(interp(probe))\n \n return np.stack(Ws, axis=2)\n\ndef plot_static_shot(W):\n #assert W.shape[0] == 64\n W = W / abs(W).max(axis=4).max(axis=3).max(axis=2).max(axis=1).reshape(-1, 1, 1, 1, 1) / 2 + .5\n t = W.shape[2] // 2\n \n best_thetas = []\n orient_sels = []\n for i in range(W.shape[0]):\n theta, orient_sel = find_dominant_orientation(W[i, :, t, :, :].transpose(1, 2, 0).sum(2))\n best_thetas.append(theta)\n orient_sels.append(orient_sel)\n \n best_thetas = np.array(best_thetas)\n orient_sels = np.array(orient_sels)\n \n sort_idx = np.argsort(orient_sels)[::-1]\n best_thetas = best_thetas[sort_idx]\n orient_sels = orient_sels[sort_idx]\n W = W[sort_idx, :, :, :, :]\n \n plt.figure(figsize=(8, 8))\n for i in range(W.shape[0]):\n plt.subplot(8, 8, i + 1)\n plt.imshow(W[i, :, t, :, :].transpose(1, 2, 0))\n theta = best_thetas[i]\n #plt.plot([3 + 3 * np.sin(theta), 3 - 3 * np.sin(theta)], [3 + 3 * np.cos(theta), 3 - 3 * np.cos(theta)], 'r-')\n plt.axis(False)\n #plt.suptitle(f'xy filters, sliced at t = {t}')\n plt.show()\n \n dt = W.shape[-1] // 2\n xi, yi = np.meshgrid(np.arange(-dt, dt+1), np.arange(-dt, dt+1))\n \n plt.figure(figsize=(8, 8))\n for i in range(W.shape[0]):\n W_ = W[i, :, :, :, :].transpose((3, 2, 1, 0))\n plt.subplot(8, 8, i + 1)\n theta = best_thetas[i]\n W_ = get_spatial_slice(W_, theta)\n plt.imshow(W_)\n plt.axis(False)\n plt.show()\n\nfrom models import get_feature_model\nargs = wrap({'features': 'cpc_02',\n 'ckpt_root': '../pretrained',\n 'slowfast_root': '../../slowfast',\n 'ntau': 1,\n 'subsample_layers': False})\n\nmodel, _, _ = get_feature_model(args)\n \nplot_static_shot(model.s1.conv1.weight.detach().cpu().numpy())\n\nargs = wrap({'features': 'cpc_01',\n 'ckpt_root': '../pretrained',\n 'slowfast_root': '../../slowfast',\n 'ntau': 1,\n 'subsample_layers': False})\n\n\nmodel, _, _ = get_feature_model(args)\n \nplot_static_shot(model.s1.conv1.weight.detach().cpu().numpy())\n\nargs = wrap({'features': 'airsim_04',\n 'ckpt_root': '../pretrained',\n 'slowfast_root': '../../slowfast',\n 'ntau': 1,\n 'subsample_layers': False})\n\n\nmodel, _, _ = get_feature_model(args)\n \nplot_static_shot(model.s1.conv1.weight.detach().cpu().numpy())", "Using DPC-RNN model\nfinal feature map has size 2x2\n" ], [ "data = model.s1.conv1.weight.detach().cpu().numpy()\nF = data.mean(axis=1).reshape((64, 5, 7*7))\nsepindexes = []\n\nfor i in range(F.shape[0]):\n U, S, V = np.linalg.svd(F[i, :, :])\n sepindex = S[0] ** 2 / (S ** 2).sum() \n sepindexes.append(sepindex)\n\n \nplt.figure(figsize=(2,2))\nplt.hist(sepindexes, np.arange(11)/10)\nplt.xlabel('Separability index')\nplt.ylabel('Count')\n\nplt.plot([.71, .71], [0, 17], 'k--')\nplt.plot([.71], [17], 'kv')\n\nimport seaborn as sns\nsns.despine()", "_____no_output_____" ] ] ]

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[ [ [ "# Лекция 7. Разреженные матрицы и прямые методы для решения больших разреженных систем", "_____no_output_____" ], [ "## План на сегодняшнюю лекцию\n\n- Плотные неструктурированные матрицы и распределённое хранение\n- Разреженные матрицы и форматы их представления\n- Быстрая реализация умножения разреженной матрицы на вектор \n- Метод Гаусса для разреженных матриц: упорядоченность\n- Заполнение и графы: сепараторы\n- Лапласиан графа", "_____no_output_____" ], [ "## Плотные матрицы большой размерности\n\n- Если размер матрицы очень большой, то она не помещается в память\n- Возможные способы работы с такими матрицами\n - Если матрица **структурирована**, например блочно Тёплицева с Тёплицевыми блоками (в следующих лекциях), тогда возможно сжатое хранение\n - Для неструктурированных матриц помогает **распределённая память**\n - MPI для обработки распределённо хранимых матриц", "_____no_output_____" ], [ "### Распределённая память и MPI\n\n- Разбиваем матрицу на блоки и храним их на различных машинах\n- Каждая машина имеет своё собственное адресное пространство и не может повредить данные на других машинах\n- В этом случае машины передают друг другу данные для агрегирования результата вычислений\n- [MPI (Message Passing Interface)](https://en.wikipedia.org/wiki/Message_Passing_Interface) – стандарт в параллельных вычислениях с распределённой памятью", "_____no_output_____" ], [ "### Пример: умножение матрицы на вектор\n\n- Предположим, вы хотите посчитать произведение $Ax$ и матрица $A$ не помещается в памяти\n- В этом случае вы можете разбить матрицу на блоки и поместить их на разные машины\n- Возможные стратегии:\n - Одномерное деление на блоки использует только строки\n - Двумерное деление на блоки использует и строки и столбцы", "_____no_output_____" ], [ "#### Пример одномерного деления на блоки\n<img src=\"./1d_block.jpg\">", "_____no_output_____" ], [ "#### Общее время вычисления произведения матрицы на вектор для одномерного разбиения на блоки\n\n- Каждая машина хранит $n / p $ полных строк и $n / p$ элементов вектора $x$\n- Общее число операций $n^2 / p$\n- Общее время для отправки и записи данных $t_s \\log p + t_w n$, где $t_s$ – единица времени на отправку и $t_w$ – единица времени на запись", "_____no_output_____" ], [ "#### Пример двумерного деления на блоки\n\n<img src=\"./2d_block.png\" width=400>", "_____no_output_____" ], [ "#### Общее время вычисления умножения матрицы на вектор с использованием двумерного разбиения на блоки\n\n- Каждая машина хранит блок размера $n / \\sqrt{p} $ и $n / \\sqrt{p}$ элементов вектора\n- Общее число операций $n^2 / p$\n- Общее время для отправки и записи данных примерно равно $t_s \\log p + t_w (n/\\sqrt{p}) \\log p$, где $t_s$ – единица времени на отправку и $t_w$ – единица времени на запись", "_____no_output_____" ], [ "### Пакеты с поддержкой распределённого хранения данных\n\n- [ScaLAPACK](http://www.netlib.org/scalapack/)\n- [Trilinos](https://trilinos.org/)\n\nВ Python вы можете использовать [mpi4py](https://mpi4py.readthedocs.io/en/stable/) для параллельной реализации ваших алгоритмов.\n\n- PyTorch поддерживает распределённое обучение и хранение данных, см подробности [тут](https://pytorch.org/tutorials/intermediate/dist_tuto.html) ", "_____no_output_____" ], [ "### Резюме про работу с большими плотными неструктурированными матрицами\n\n- Распределённое хранение матриц\n- MPI\n- Пакеты, которые используют блочные вычисления\n- Различные подходы к блочным вычислениям", "_____no_output_____" ], [ "## Разреженные матрицы\n\n- Ограничением в решении задач линейной алгебры с плотными матрицами является память, требуемая для хранения плотных матриц, $N^2$ элементов.\n\n- Разреженные матрицы, где большинство элементов нулевые позволяют по крайней мере хранить их в памяти.\n\n- Основные вопросы: можем ли мы решать следующие задачи для разреженных матриц?\n - решение линейных систем\n - вычисление собственных значений и собственных векторов\n - вычисление матричных функций", "_____no_output_____" ], [ "## Приложения разреженных матриц\n\nРазреженные матрицы возникают в следующих областях:\n\n- математическое моделирование и решение уравнений в частных производных\n- обработка графов, например анализ социальных сетей\n- рекомендательные системы\n- в целом там, где отношения между объектами \"разрежены\".", "_____no_output_____" ], [ "### Разреженные матрицы помогают в вычислительной теории графов \n\n- Графы представляют в виде матриц смежности, которые чаще всего разрежены\n- Численное решение задач теории графов сводится к операциям с этими разреженными матрицами\n - Кластеризация графа и выделение сообществ\n - Ранжирование\n - Случайные блуждатели\n - И другие....\n- Пример: возможно, самый большой доступный граф гиперссылок содержит 3.5 миллиарда веб-страниц и 128 миллиардов гиперссылок, больше подробностей см. [тут](http://webdatacommons.org/hyperlinkgraph/) \n- Различные графы среднего размера для тестирования ваших алгоритмов доступны в [Stanford Large Network Dataset Collection](https://snap.stanford.edu/data/)", "_____no_output_____" ], [ "### Florida sparse matrix collection\n\n- Большое количество разреженных матриц из различных приложений вы можете найти в [Florida sparse matrix collection](http://www.cise.ufl.edu/research/sparse/matrices/).", "_____no_output_____" ] ], [ [ "from IPython.display import IFrame\nIFrame('http://yifanhu.net/GALLERY/GRAPHS/search.html', 500, 500)", "_____no_output_____" ] ], [ [ "### Разреженные матрицы и глубокое обучение\n\n- DNN имеют очень много параметров\n- Некоторые из них могут быть избыточными\n- Как уменьшить число параметров без серьёзной потери в точности?\n- [Sparse variational dropout method](https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn) даёт существенно разреженные фильтры в DNN почти без потери точности!", "_____no_output_____" ], [ "## Построение разреженных матриц\n\n- Мы можем генерировать разреженные матрицы с помощью пакета **scipy.sparse**\n\n- Можно задать матрицы очень большого размера\n\nПолезные функции при создании разреженных матриц:\n- для созданий диагональной матрицы с заданными диагоналями ```spdiags```\n- Кронекерово произведение (определение будет далее) разреженных матриц ```kron```\n- также арифметические операции для разреженных матриц перегружены", "_____no_output_____" ], [ "### Кронекерово произведение\n\nДля матриц $A\\in\\mathbb{R}^{n\\times m}$ и $B\\in\\mathbb{R}^{l\\times k}$ Кронекерово произведение определяется как блочная матрица следующего вида\n\n$$\n A\\otimes B = \\begin{bmatrix}a_{11}B & \\dots & a_{1m}B \\\\ \\vdots & \\ddots & \\vdots \\\\ a_{n1}B & \\dots & a_{nm}B\\end{bmatrix}\\in\\mathbb{R}^{nl\\times mk}.\n$$\n\nОсновные свойства:\n- билинейность\n- $(A\\otimes B) (C\\otimes D) = AC \\otimes BD$\n- Пусть $\\mathrm{vec}(X)$ оператор векторизации матрицы по столбцам. Тогда \n$\\mathrm{vec}(AXB) = (B^T \\otimes A) \\mathrm{vec}(X).$", "_____no_output_____" ] ], [ [ "import numpy as np\nimport scipy as sp\nimport scipy.sparse\nfrom scipy.sparse import csc_matrix, csr_matrix\nimport matplotlib.pyplot as plt\nimport scipy.linalg\nimport scipy.sparse.linalg\n%matplotlib inline\nn = 5\nex = np.ones(n);\nlp1 = sp.sparse.spdiags(np.vstack((ex, -2*ex, ex)), [-1, 0, 1], n, n, 'csr'); \ne = sp.sparse.eye(n)\nA = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\nA = csc_matrix(A)\nplt.spy(A, aspect='equal', marker='.', markersize=5)", "_____no_output_____" ] ], [ [ "### Шаблон разреженности\n\n- Команда ```spy``` рисует шаблон разреженности данной матрицы: пиксель $(i, j)$ отображается на рисунке, если соответствующий элемент матрицы ненулевой.\n\n- Шаблон разреженности действительно очень важен для понимания сложности алгоритмов линейной алгебры для разреженных матриц. \n\n- Зачастую шаблона разреженности достаточно для анализа того, насколько \"сложно\" работать с этой матрицей.", "_____no_output_____" ], [ "### Определение разреженных матриц\n\n- Разреженные матрицы – это матрицы, такие что количество ненулевых элементов в них существенно меньше общего числа элементов в матрице. \n\n- Из-за этого вы можете выполнять базовые операции линейной алгебры (прежде всего решать линейные системы) гораздо быстрее по сравнению с использованием плотных матриц.", "_____no_output_____" ], [ "## Что нам необходимо, чтобы увидеть, как это работает\n\n- **Вопрос 1:** Как хранить разреженные матрицы в памяти?\n\n- **Вопрос 2:** Как умножить разреженную матрицу на вектор быстро?\n\n- **Вопрос 3:** Как быстро решать линейные системы с разреженными матрицами?", "_____no_output_____" ], [ "### Хранение разреженных матриц\n\nСуществет много форматов хранения разреженных матриц, наиболее важные:\n\n- COO (координатный формат)\n- LIL (список списков)\n- CSR (compressed sparse row)\n- CSC (compressed sparse column)\n- блочные варианты\n\nВ ```scipy``` представлены конструкторы для каждого из этих форматов, например\n\n```scipy.sparse.lil_matrix(A)```.", "_____no_output_____" ], [ "#### Координатный формат (COO)\n\n- Простейший формат хранения разреженной матрицы – координатный. \n- В этом формате разреженная матрица – это набор индексов и значений в этих индексах.\n\n```python\ni, j, val\n```\n\nгде ```i, j``` массивы индексов, ```val``` массив элементов матрицы. <br>\n\n- Таким образом, нам нужно хранить $3\\cdot$**nnz** элементов, где **nnz** обозначает число ненулевых элементов в матрице.\n\n**Q:** Что хорошего и что плохого в использовании такого формата?", "_____no_output_____" ], [ "#### Основные недостатки\n\n- Он неоптимален по памяти (почему?)\n- Он неоптимален для умножения матрицы на вектор (почему?)\n- Он неоптимален для удаления элемента (почему?)\n\nПервые два недостатка решены в формате CSR.\n\n**Q**: какой формат решает третий недостаток? ", "_____no_output_____" ], [ "#### Compressed sparse row (CSR)\n\nВ формате CSR матрица хранится также с помощью трёх массивов, но других:\n\n```python\nia, ja, sa\n```\n\nгде:\n\n- **ia** (начало строк) массив целых чисел длины $n+1$ \n- **ja** (индексы столбцов) массив целых чисел длины **nnz** \n- **sa** (элементы матрицы) массив действительных чисел длины **nnz**\n\n<img src=\"https://www.karlrupp.net/wp-content/uploads/2016/02/csr_storage_sparse_marix.png\" width=60% />\n\nИтак, всего необходимо хранить $2\\cdot{\\bf nnz} + n+1$ элементов.", "_____no_output_____" ], [ "### Разреженные матрицы в PyTorch и Tensorflow\n\n- PyTorch поддерживает разреженные матрицы в формате COO\n- Неполная поддержка вычисления градиентов в операциях с такими матрицами, список и обсуждение см. [тут](https://github.com/pytorch/pytorch/issues/9674)\n- Tensorflow также поддерживает разреженные матрицы в COO формате\n- Список поддерживаемых операций приведён [здесь](https://www.tensorflow.org/api_docs/python/tf/sparse) и поддержка вычисления градиентов также ограничена", "_____no_output_____" ], [ "### CSR формат позволяет быстро умножить разреженную матрицу на вектор (SpMV)\n\n```python\n\n for i in range(n):\n \n for k in range(ia[i]:ia[i+1]):\n \n y[i] += sa[k] * x[ja[k]]\n```", "_____no_output_____" ] ], [ [ "import numpy as np\nimport scipy as sp\nimport scipy.sparse\nimport scipy.sparse.linalg\nfrom scipy.sparse import csc_matrix, csr_matrix, coo_matrix\nimport matplotlib.pyplot as plt\n%matplotlib inline\nn = 1000\nex = np.ones(n);\nlp1 = sp.sparse.spdiags(np.vstack((ex, -2*ex, ex)), [-1, 0, 1], n, n, 'csr'); \ne = sp.sparse.eye(n)\nA = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\nA = csr_matrix(A)\nrhs = np.ones(n * n)\nB = coo_matrix(A)\n%timeit A.dot(rhs)\n%timeit B.dot(rhs)", "7.91 ms ± 453 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n10.8 ms ± 536 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] ], [ [ "Видно, что **CSR** быстрее, и чем менее структурирован шаблон разреженности, тем выше выигрыш в скорости.", "_____no_output_____" ], [ "### Разреженные матрицы и эффективность\n\n- Использование разреженных матриц приводит к уменьшению сложности\n- Но они не очень подходят для параллельных/GPU реализаций \n- Они не показывают максимальную эффективность из-за случайного доступа к данным. \n- Обычно, пиковая производительность порядка $10\\%-15\\%$ считается хорошей. ", "_____no_output_____" ], [ "### Вспомним как измеряется эффективность операций\n\n- Стандартный способ измерения эффективности операций линейной алгебры – это использование **flops** (число опраций с плавающей точкой в секунду)\n\n- Измерим эффективность умножения матрицы на вектор в случае плотной и разреженной матрицы", "_____no_output_____" ] ], [ [ "import numpy as np\nimport time\nn = 4000\na = np.random.randn(n, n)\nv = np.random.randn(n)\nt = time.time()\nnp.dot(a, v)\nt = time.time() - t\nprint('Time: {0: 3.1e}, Efficiency: {1: 3.1e} Gflops'.\\\n format(t, ((2 * n ** 2)/t) / 10 ** 9))", "Time: 1.2e-02, Efficiency: 2.7e+00 Gflops\n" ], [ "n = 4000\nex = np.ones(n);\na = sp.sparse.spdiags(np.vstack((ex, -2*ex, ex)), [-1, 0, 1], n, n, 'csr'); \nrhs = np.random.randn(n)\nt = time.time()\na.dot(rhs)\nt = time.time() - t\nprint('Time: {0: 3.1e}, Efficiency: {1: 3.1e} Gflops'.\\\n format(t, (3 * n) / t / 10 ** 9))", "Time: 1.3e-04, Efficiency: 9.0e-02 Gflops\n" ] ], [ [ "### Случайный доступ к данным и промахи в обращении к кешу\n\n- Сначала все элементы матрицы и вектора хранятся в оперативной памяти (RAM – Random Access Memory)\n- Если вы хотите вычислить произведение матрицы на вектор, часть элементов матрицы и вектора перемещаются в кеш (быстрой памяти малого объёма), см. [лекцию об алгоритме Штрассена и умножении матриц](https://github.com/amkatrutsa/nla2020_ozon/blob/master/lectures/lecture4/lecture4.ipynb)\n- После этого CPU берёт данные из кеша, обрабатывает их и возвращает результат снова в кеш\n- Если CPU требуются данные, которых ещё нет в кеше, это называется промах в обращении к кешу (cache miss)\n- Если случается промах в обращении к кешу, необходимые данные перемещаются из оперативной памяти в кеш\n\n**Q**: что если в кеше нет свободного места?\n\n\n- Чем больше промахов в обращении к кешу, тем медленнее выполняются вычисления", "_____no_output_____" ], [ "### План кеша и LRU\n\n<img src=\"./cache_scheme.png\" width=\"500\">", "_____no_output_____" ], [ "#### Умножение матрицы в CSR формате на вектор\n\n```python\n\n for i in range(n):\n \n for k in range(ia[i]:ia[i+1]):\n \n y[i] += sa[k] * x[ja[k]]\n \n```\n\n- Какая часть операций приводит к промахам в обращении к кешу?\n- Как эту проблему можно решить?", "_____no_output_____" ], [ "### Переупорядочивание уменьшает количество промахов в обращении к кешу\n\n- Если ```ja``` хранит последовательно элементы, тогда они могут быть перемещены в кеш одновременно и количество промахов в обращении к кешу уменьшится\n- Так происходит, когда разреженная матрица является **ленточной** или хотя бы блочно-диагональной\n- Мы можем превратить данную разреженную матрицу в ленточную или блочно-диагональную с помощью *перестановок* \n\n- Пусть $P$ матрица перестановок строк матрицы и $Q$ матрица перестановок столбцов матрицы\n- $A_1 = PAQ$ – матрица с шириной ленты меньшей, чем у матрицы $A$\n- $y = Ax \\to \\tilde{y} = A_1 \\tilde{x}$, где $\\tilde{x} = Q^{\\top}x$ и $\\tilde{y} = Py$\n- [Separated block diagonal form](http://albert-jan.yzelman.net/PDFs/yzelman09-rev.pdf) призван минимизировать количество промахов в обращении к кешу\n- Он также может быть расширен на двумерный случай, где разделяются не только строки, но и столбцы", "_____no_output_____" ], [ "#### Пример\n\n- SBD в одномерном случае\n<img src=\"./sbd.png\" width=\"400\">", "_____no_output_____" ], [ "## Методы решения линейных систем с разреженными матрицами\n\n- Прямые методы\n - LU разложение\n - Различные методы переупорядочивания для минимизации заполнения факторов\n- Крыловские методы", "_____no_output_____" ] ], [ [ "n = 10\nex = np.ones(n);\nlp1 = sp.sparse.spdiags(np.vstack((ex, -2*ex, ex)), [-1, 0, 1], n, n, 'csr'); \ne = sp.sparse.eye(n)\nA = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\nA = csr_matrix(A)\nrhs = np.ones(n * n)\nsol = sp.sparse.linalg.spsolve(A, rhs)\n_, (ax1, ax2) = plt.subplots(1, 2)\nax1.plot(sol)\nax1.set_title('Not reshaped solution')\nax2.contourf(sol.reshape((n, n), order='f'))\nax2.set_title('Reshaped solution')", "_____no_output_____" ] ], [ [ "## LU разложение разреженной матрицы\n\n- Почему разреженная линейная система может быть решена быстрее, чем плотная? С помощью какого метода? \n\n- В LU разложении матрицы $A$ факторы $L$ и $U$ могут быть также разреженными:\n\n$$A = L U$$\n\n- А решение линейной системы с разреженной треугольной матрицей может быть вычислено очень быстро. \n\n<font color='red'> Заметим, что обратная матрица от разреженной матрицы НЕ разрежена! </font>\n", "_____no_output_____" ] ], [ [ "n = 7\nex = np.ones(n);\na = sp.sparse.spdiags(np.vstack((ex, -2*ex, ex)), [-1, 0, 1], n, n, 'csr'); \nb = np.array(np.linalg.inv(a.toarray()))\nprint(a.toarray())\nprint(b)", "[[-2. 1. 0. 0. 0. 0. 0.]\n [ 1. -2. 1. 0. 0. 0. 0.]\n [ 0. 1. -2. 1. 0. 0. 0.]\n [ 0. 0. 1. -2. 1. 0. 0.]\n [ 0. 0. 0. 1. -2. 1. 0.]\n [ 0. 0. 0. 0. 1. -2. 1.]\n [ 0. 0. 0. 0. 0. 1. -2.]]\n[[-0.875 -0.75 -0.625 -0.5 -0.375 -0.25 -0.125]\n [-0.75 -1.5 -1.25 -1. -0.75 -0.5 -0.25 ]\n [-0.625 -1.25 -1.875 -1.5 -1.125 -0.75 -0.375]\n [-0.5 -1. -1.5 -2. -1.5 -1. -0.5 ]\n [-0.375 -0.75 -1.125 -1.5 -1.875 -1.25 -0.625]\n [-0.25 -0.5 -0.75 -1. -1.25 -1.5 -0.75 ]\n [-0.125 -0.25 -0.375 -0.5 -0.625 -0.75 -0.875]]\n" ] ], [ [ "## А факторы...\n\n- $L$ и $U$ обычно разрежены\n- В случае трёхдиагональной матрицы они даже бидиагональны!", "_____no_output_____" ] ], [ [ "from scipy.sparse.linalg import splu\nT = splu(a.tocsc(), permc_spec=\"NATURAL\")\nplt.spy(T.L)", "_____no_output_____" ] ], [ [ "Отметим, что ```splu``` со значением параметра ```permc_spec``` по умолчанию даёт перестановку, которая не даёт бидиагональные факторы:", "_____no_output_____" ] ], [ [ "from scipy.sparse.linalg import splu\nT = splu(a.tocsc())\nplt.spy(T.L)\nprint(T.perm_c)", "[0 1 2 3 5 4 6]\n" ] ], [ [ "## Двумерный случай\n\nВ двумерном случае всё гораздо хуже:", "_____no_output_____" ] ], [ [ "n = 20\nex = np.ones(n);\nlp1 = sp.sparse.spdiags(np.vstack((ex, -2*ex, ex)), [-1, 0, 1], n, n, 'csr'); \ne = sp.sparse.eye(n)\nA = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\nA = csc_matrix(A)\nT = scipy.sparse.linalg.spilu(A)\nplt.spy(T.L, marker='.', color='k', markersize=8)", "_____no_output_____" ] ], [ [ "Для правильной перестановки в двумерном случае число ненулевых элементов в $L$ растёт как $\\mathcal{O}(N \\log N)$. Однако сложность равна $\\mathcal{O}(N^{3/2})$.", "_____no_output_____" ], [ "## Разреженные матрицы и теория графов\n\n- Число ненулей в факторах из LU разложения тесно связано с теорией графов.\n\n- Пакет ``networkx`` можно использовать для визуализации графов, имея только матрицу смежности. ", "_____no_output_____" ] ], [ [ "import networkx as nx\nn = 10\nex = np.ones(n);\nlp1 = sp.sparse.spdiags(np.vstack((ex, -2*ex, ex)), [-1, 0, 1], n, n, 'csr'); \ne = sp.sparse.eye(n)\nA = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\nA = csc_matrix(A)\nG = nx.Graph(A)\nnx.draw(G, pos=nx.spectral_layout(G), node_size=10)", "_____no_output_____" ] ], [ [ "## Заполнение (fill-in)\n\n- Заполнение матрицы – это элементы, которые были **нулями**, но стали **ненулями** в процессе выполнения алгоритма.\n\n- Заполнение может быть различным для различных перестановок. Итак, до того как делать факторизацию матрицы нам необходимо переупорядочить её элементы так, чтобы заполнение факторов было наименьшим.\n\n**Пример**\n\n$$A = \\begin{bmatrix} * & * & * & * & *\\\\ * & * & 0 & 0 & 0 \\\\ * & 0 & * & 0 & 0 \\\\ * & 0 & 0& * & 0 \\\\ * & 0 & 0& 0 & * \\end{bmatrix} $$\n\n- Если мы исключаем элементы сверху вниз, тогда мы получим плотную матрицу.\n- Однако мы можем сохранить разреженность, если исключение будет проводиться снизу вверх.\n- Подробности на следующих слайдах", "_____no_output_____" ], [ "## Метод Гаусса для разреженных матриц\n\n- Дана матрица $A$ такая что $A=A^*>0$. \n- Вычислим её разложение Холецкого $A = LL^*$.\n\nФактор $L$ может быть плотным даже если $A$ разреженная:\n\n$$\n\\begin{bmatrix} * & * & * & * \\\\ * & * & & \\\\ * & & * & \\\\ * & & & * \\end{bmatrix} = \n\\begin{bmatrix} * & & & \\\\ * & * & & \\\\ * & * & * & \\\\ * & * & * & * \\end{bmatrix}\n\\begin{bmatrix} * & * & * & * \\\\ & * & * & * \\\\ & & * & * \\\\ & & & * \\end{bmatrix}\n$$\n\n**Q**: как сделать факторы разреженными, то есть минимизировать заполнение?", "_____no_output_____" ], [ "## Метод Гаусса и перестановка\n\n- Нам нужно найти перестановку индексов такую что факторы будут разреженными, то есть мы будем вычислять разложение Холецкого для матрицы $PAP^\\top$, где $P$ – матрица перестановки.\n\n- Для примера с предыдущего слайда\n\n$$\nP \\begin{bmatrix} * & * & * & * \\\\ * & * & & \\\\ * & & * & \\\\ * & & & * \\end{bmatrix} P^\\top = \n\\begin{bmatrix} * & & & * \\\\ & * & & * \\\\ & & * & * \\\\ * & * & * & * \\end{bmatrix} = \n\\begin{bmatrix} * & & & \\\\ & * & & \\\\ & & * & \\\\ * & * & * & * \\end{bmatrix}\n\\begin{bmatrix} * & & & * \\\\ & * & & * \\\\ & & * & * \\\\ & & & * \\end{bmatrix}\n$$\n\nгде\n\n$$\nP = \\begin{bmatrix} & & & 1 \\\\ & & 1 & \\\\ & 1 & & \\\\ 1 & & & \\end{bmatrix}\n$$\n\n- Такая форма матрицы даёт разреженные факторы в LU разложении", "_____no_output_____" ] ], [ [ "import numpy as np\nimport scipy.sparse as spsp\nimport scipy.sparse.linalg as spsplin\nimport scipy.linalg as splin\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nA = spsp.coo_matrix((np.random.randn(10), ([0, 0, 0, 0, 1, 1, 2, 2, 3, 3], \n [0, 1, 2, 3, 0, 1, 0, 2, 0, 3])))\nprint(\"Original matrix\")\nplt.spy(A)\nplt.show()\nlu = spsplin.splu(A.tocsc(), permc_spec=\"NATURAL\")\nprint(\"L factor\")\nplt.spy(lu.L)\nplt.show()\nprint(\"U factor\")\nplt.spy(lu.U)\nplt.show()\nprint(\"Column permutation:\", lu.perm_c)\nprint(\"Row permutation:\", lu.perm_r)", "Original matrix\n" ] ], [ [ "### Блочный случай\n\n$$\nPAP^\\top = \\begin{bmatrix} A_{11} & & A_{13} \\\\ & A_{22} & A_{23} \\\\ A_{31} & A_{32} & A_{33}\\end{bmatrix}\n$$\n\nтогда\n\n$$\nPAP^\\top = \\begin{bmatrix} A_{11} & 0 & 0 \\\\ 0 & A_{22} & 0 \\\\ A_{31} & A_{32} & A_{33} - A_{31}A_{11}^{-1} A_{13} - A_{32}A_{22}^{-1}A_{23} \\end{bmatrix} \\begin{bmatrix} I & 0 & A_{11}^{-1}A_{13} \\\\ 0 & I & A_{22}^{-1}A_{23} \\\\ 0 & 0 & I\\end{bmatrix}\n$$\n\n- Блок $ A_{33} - A_{31}A_{11}^{-1} A_{13} - A_{32}A_{22}^{-1}A_{23}$ является дополнением по Шуру для блочно-диагональной матрицы $\\begin{bmatrix} A_{11} & 0 \\\\ 0 & A_{22} \\end{bmatrix}$\n- Мы свели задачу к решению меньших линейных систем с матрицами $A_{11}$ и $A_{22}$ ", "_____no_output_____" ], [ "### Как найти перестановку?\n\n- Основная идея взята из теории графов\n- Разреженную матрицы можно рассматривать как **матрицу смежности** некоторого графа: вершины $(i, j)$ связаны ребром, если соответствующий элемент матрицы не ноль.\n", "_____no_output_____" ], [ "### Пример\n\nГрафы для матрицы $\\begin{bmatrix} * & * & * & * \\\\ * & * & & \\\\ * & & * & \\\\ * & & & * \\end{bmatrix}$ и для матрицы $\\begin{bmatrix} * & & & * \\\\ & * & & * \\\\ & & * & * \\\\ * & * & * & * \\end{bmatrix}$ имеют следующий вид:\n\n<img src=\"./graph_dense.png\" width=300 align=\"center\"> и <img src=\"./graph_sparse.png\" width=300 align=\"center\">\n\n* Почему вторая упорядоченность лучше, чем первая?", "_____no_output_____" ], [ "### Сепаратор графа\n\n**Определение.** Сепаратором графа $G$ называется множество вершин $S$, таких что их удаление оставляет как минимум две связные компоненты.\n\nСепаратор $S$ даёт следующий метод нумерации вершин графа $G$:\n- Найти сепаратор $S$, удаление которого оставляет связные компоненты $T_1$, $T_2$, $\\ldots$, $T_k$\n- Номера вершин в $S$ от $N − |S| + 1$ до $N$\n- Рекурсивно, номера вершин в каждой компоненте: \n - в $T_1$ от $1$ до $|T_1|$\n - в $T_2$ от $|T_1| + 1$ до $|T_1| + |T_2|$\n - и так далее\n- Если компонента достаточно мала, то нумерация внутри этой компоненты произвольная", "_____no_output_____" ], [ "### Сепаратор и структура матрицы: пример\n\nСепаратор для матрицы двумерного лапласиана\n\n$$\n A_{2D} = I \\otimes A_{1D} + A_{1D} \\otimes I, \\quad A_{1D} = \\mathrm{tridiag}(-1, 2, -1),\n$$\n\nимеет следующий вид\n\n<img src='./separator.png' width=300> </img>", "_____no_output_____" ], [ "Если мы пронумеруем сначала индексы в $\\alpha$, затем в $\\beta$, и наконец индексы в сепараторе $\\sigma$ получим следующую матрицу\n\n$$\nPAP^\\top = \\begin{bmatrix} A_{\\alpha\\alpha} & & A_{\\alpha\\sigma} \\\\ & A_{\\beta\\beta} & A_{\\beta\\sigma} \\\\ A_{\\sigma\\alpha} & A_{\\sigma\\beta} & A_{\\sigma\\sigma}\\end{bmatrix},\n$$\n\nкоторая имеет подходящую структуру.\n\n- Таким образом, задача поиска перестановки была сведена к задаче поиска сепаратора графа!", "_____no_output_____" ], [ "### Nested dissection\n\n- Для блоков $A_{\\alpha\\alpha}$, $A_{\\beta\\beta}$ можем продолжить разбиение рекурсивно\n\n- После завершения рекурсии нужно исключить блоки $A_{\\sigma\\alpha}$ и $A_{\\sigma\\beta}$. \n\n- Это делает блок в положении $A_{\\sigma\\sigma}\\in\\mathbb{R}^{n\\times n}$ **плотным**.\n\n- Вычисление разложения Холецкого этого блока стоит $\\mathcal{O}(n^3) = \\mathcal{O}(N^{3/2})$, где $N = n^2$ – общее число вершин.\n\n- В итоге сложность $\\mathcal{O}(N^{3/2})$", "_____no_output_____" ], [ "## Пакеты для nested dissection\n\n- MUltifrontal Massively Parallel sparse direct Solver ([MUMPS](http://mumps.enseeiht.fr/))\n- [Pardiso](https://www.pardiso-project.org/)\n- [Umfpack как часть пакета SuiteSparse](http://faculty.cse.tamu.edu/davis/suitesparse.html)\n\nУ них есть интефейс для C/C++, Fortran и Matlab ", "_____no_output_____" ], [ "### Резюме про nested dissection\n\n- Нумерация свелась к поиску сепаратора\n- Подход разделяй и властвуй\n- Рекурсивно продолжается на два (или более) подмножества вершин после разделения\n- В теории nested dissection даёт оптимальную сложность (почему?)\n- На практике этот метод лучше других только на очень больших задачах", "_____no_output_____" ], [ "## Сепараторы на практике\n\n- Вычисление сепаратора – это **нетривиальная задача!**\n\n- Построение методов разбиения графа было активной сферой научных исследований долгие годы\n\nСуществующие подходы:\n\n- Спектральное разбиение (использует собственные векторы **Лапласиана графа**) – подробности далее\n- Геометрическое разбиение (для сеток с заданными координатами вершин) [обзор и анализ](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.4886&rep=rep1&type=pdf)\n- Итеративные перестановки ([(Kernighan-Lin, 1970)](http://xilinx.asia/_hdl/4/eda.ee.ucla.edu/EE201A-04Spring/kl.pdf), [(Fiduccia-Matheysses, 1982](https://dl.acm.org/citation.cfm?id=809204))\n- Поиск в ширину [(Lipton, Tarjan 1979)](http://www.cs.princeton.edu/courses/archive/fall06/cos528/handouts/sepplanar.pdf)\n- Многоуровневая рекурсивная бисекция (наиболее практичная эвристика) ([обзор](https://people.csail.mit.edu/jshun/6886-s18/lectures/lecture13-1.pdf) и [статья](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.499.4130&rep=rep1&type=pdf)). Пакет для подобного рода разбиений называется METIS, написан на C, и доступен [здесь](http://glaros.dtc.umn.edu/gkhome/views/metis)", "_____no_output_____" ], [ "## Спектральное разбиение графа\n\n- Идея спектрального разбиения восходит к работам Мирослава Фидлера, который изучал связность графов ([статья](https://dml.cz/bitstream/handle/10338.dmlcz/101168/CzechMathJ_23-1973-2_11.pdf)).\n\n- Нам нужно разбить вершинеы графа на 2 множества\n\n- Рассмотрим метки вершин +1/-1 и **функцию потерь**\n\n$$E_c(x) = \\sum_{j} \\sum_{i \\in N(j)} (x_i - x_j)^2, \\quad N(j) \\text{ обозначает множество соседей вершины } j. $$\n\nНам нужно сбалансированное разбиение, поэтому\n\n$$\\sum_i x_i = 0 \\quad \\Longleftrightarrow \\quad x^\\top e = 0, \\quad e = \\begin{bmatrix}1 & \\dots & 1\\end{bmatrix}^\\top,$$\n\nи поскольку мы ввели метки +1/-1, то выполнено\n\n$$\\sum_i x^2_i = n \\quad \\Longleftrightarrow \\quad \\|x\\|_2^2 = n.$$", "_____no_output_____" ], [ "## Лапласиан графа\n\nФункция потерь $E_c$ может быть записана в виде (проверьте почему)\n\n$$E_c = (Lx, x)$$\n\nгде $L$ – **Лапласиан графа**, который определяется как симметричная матрица с элементами\n\n$$L_{ii} = \\mbox{степень вершины $i$},$$\n\n$$L_{ij} = -1, \\quad \\mbox{если $i \\ne j$ и существует ребро},$$\n\nи $0$ иначе.\n\n- Строчные суммы в матрице $L$ равны нулю, поэтому существует собственное значение $0$, которое даёт собственный вектор из всех 1.\n- Собственные значения неотрицательны (почему?).", "_____no_output_____" ], [ "## Разбиение как задача оптимизации\n\n- Минимизация $E_c$ с упомянутыми ограничениями приводит к разбиению, которое минимизирует число вершин в сепараторе, но сохраняет разбиение сбалансированным\n\n- Теперь мы запишем релаксацию целочисленной задачи квадратичного программирования в форме непрерывной задачи квадратичного программирования\n\n$$E_c(x) = (Lx, x)\\to \\min_{\\substack{x^\\top e =0, \\\\ \\|x\\|_2^2 = n}}$$", "_____no_output_____" ], [ "## Вектор Фидлера\n\n- Решение этой задачи минимизации – собственный вектор матрицы $L$, соответствующий **второму** минимальному собственному значению (он называется вектором Фидлера)\n- В самом деле,\n\n$$\n \\min_{\\substack{x^\\top e =0, \\\\ \\|x\\|_2^2 = n}} (Lx, x) = n \\cdot \\min_{{x^\\top e =0}} \\frac{(Lx, x)}{(x, x)} = n \\cdot \\min_{{x^\\top e =0}} R(x), \\quad R(x) \\text{ отношение Релея}\n$$\n\n- Поскольку $e$ – собственный вектор, соответствующий наименьшему собственному значению, то на подпространстве $x^\\top e =0$ мы получим второе минимальное собственное значение.\n\n- Знак $x_i$ обозначает разбиение графа.\n\n- Осталось понять, как вычислить этот вектор. Мы знаем про степенной метод, но он ищет собственный вектор для максимального по модулю собственного значения.\n- Итерационные методы для задачи на собственные значения будут рассмотрены далее в курсе...\n", "_____no_output_____" ] ], [ [ "import numpy as np\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport networkx as nx\nkn = nx.read_gml('karate.gml')\nprint(\"Number of vertices = {}\".format(kn.number_of_nodes()))\nprint(\"Number of edges = {}\".format(kn.number_of_edges()))\nnx.draw_networkx(kn, node_color=\"red\") #Draw the graph", "Number of vertices = 34\nNumber of edges = 78\n" ], [ "Laplacian = nx.laplacian_matrix(kn).asfptype()\nplt.spy(Laplacian, markersize=5)\nplt.title(\"Graph laplacian\")\nplt.axis(\"off\")\nplt.show()\neigval, eigvec = spsplin.eigsh(Laplacian, k=2, which=\"SM\")\nprint(\"The 2 smallest eigenvalues =\", eigval)", "_____no_output_____" ], [ "plt.scatter(np.arange(len(eigvec[:, 1])), np.sign(eigvec[:, 1]))\nplt.show()\nprint(\"Sum of elements in Fiedler vector = {}\".format(np.sum(eigvec[:, 1].real)))", "_____no_output_____" ], [ "nx.draw_networkx(kn, node_color=np.sign(eigvec[:, 1]))", "_____no_output_____" ] ], [ [ "### Резюме по примеру использования спектрального разбиения графа\n\n- Мы вызвали функцию из SciPy для поиска фиксированного числа собственных векторов и собственных значений, которые минимальны (возможны другие опции)\n- Детали методов, которые реализованы в этих функциях, обсудим уже скоро\n- Вектор Фидлера даёт простой способ разбиения графа\n- Для разбиения графа на большее количество частей следует использовать собственные векторы Лапласиана как векторы признаков и запустить какой-нибудь алгоритм кластеризации, например $k$-means", "_____no_output_____" ], [ "### Вектор Фидлера и алгебраическая связность графа\n\n**Определение.** Алгебраическая связность графа – это второе наименьшее собственное значение матрицы Лапласиана графа.\n\n**Утверждение.** Алгебраическая связность графа больше 0 тогда и только тогда, когда граф связный.", "_____no_output_____" ], [ "## Minimal degree orderings\n\n- Идея в том, чтобы исклоючить строки и/или столбцы с малым числом ненулей, обновить заполнение и повторить.\n\n- Эффективная реализация является отдельной задачей (добавление/удаление элементов).\n\n- На практике часто лучше всего для задач среднего размера\n\n- SciPy [использует](https://docs.scipy.org/doc/scipy-1.3.0/reference/generated/scipy.sparse.linalg.splu.html) этот подход для различных матриц ($A^{\\top}A$, $A + A^{\\top}$) ", "_____no_output_____" ], [ "## Главное в сегодняшней лекции\n\n- Плотные матрицы большого размера и распределённые вычисления\n- Разреженные матрицы, приложения и форматы их хранения\n- Эффективные способы умножения разреженной матрицы на вектор\n- LU разложение разреженной матрицы: заполнение и перестановки строк\n- Минимизация заполнения: сепараторы и разбиение графа\n- Nested dissection\n- Спектральное разбиение графа: Лапласиан графа и вектор Фидлера", "_____no_output_____" ] ], [ [ "from IPython.core.display import HTML\ndef css_styling():\n styles = open(\"./styles/custom.css\", \"r\").read()\n return HTML(styles)\ncss_styling()", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ [ [ "# The stereology module\n\nThe main purpose of stereology is to extract quantitative information from microscope images relating two-dimensional measures obtained on sections to three-dimensional parameters defining the structure. The aim of stereology is not to reconstruct the 3D geometry of the material (as in tomography) but to estimate a particular 3D feature. In this case, we aim to approximate the actual (3D) grain size distribution from the apparent (2D) grain size distribution obtained in sections.\n\nGrainSizeTools script includes two stereological methods: 1) the Saltykov, and 2) the two-step methods. Before looking at its functionalities, applications and limitations, let's import the example dataset.", "_____no_output_____" ] ], [ [ "# Load the script first (change the path to GrainSizeTools_script.py accordingly!)\n%run C:/Users/marco/Documents/GitHub/GrainSizeTools/grain_size_tools/GrainSizeTools_script.py", "module plot imported\nmodule averages imported\nmodule stereology imported\nmodule piezometers imported\nmodule template imported\n\n======================================================================================\nWelcome to GrainSizeTools script\n======================================================================================\nA free open-source cross-platform script to visualize and characterize grain size\npopulation and estimate differential stress via paleopizometers.\n\nVersion: v3.0.2 (2020-12-30)\nDocumentation: https://marcoalopez.github.io/GrainSizeTools/\n\nType get.functions_list() to get a list of the main methods\n\n" ], [ "# Import the example dataset\nfilepath = 'C:/Users/marco/Documents/GitHub/GrainSizeTools/grain_size_tools/DATA/data_set.txt'\ndataset = pd.read_csv(filepath, sep='\\t')\ndataset['diameters'] = 2 * np.sqrt(dataset['Area'] / np.pi) # estimate ECD", "_____no_output_____" ] ], [ [ "## The Saltykov method\n\n> **What is it?**\n>\n> It is a stereological method that approximates the actual grain size distribution from the histogram of the apparent grain size distribution. The method is distribution-free, meaning that no assumption is made upon the type of statistical distribution, making the method very versatile.\n>\n> **What do I use it for?**\n>\n> Its main use (in geosciences) is to estimate the volume fraction of a specific range of grain sizes.\n>\n> **What are its limitations?**\n>\n> The method presents several limitations for its use in rocks\n>\n> - It assumes that grains are non-touching spheres uniformly distributed in a matrix (e.g. bubbles within a piece of glass). This never holds for polycrystalline rocks. To apply the method, the grains should be at least approximately equiaxed, which is normally fulfilled in recrystallized grains.\n> - Due to the use of the histogram, the number of classes determines the accuracy and success of the method. There is a trade-off here because the smaller the number of classes, the better the numerical stability of the method, but the worse the approximation of the targeted distribution and vice versa. The issue is that no method exists to find an optimal number of classes and this has to be set by the user. The use of the histogram also implies that we cannot obtain a complete description of the grain size distribution.\n> - The method lacks a formulation for estimating errors during the unfolding procedure.\n> - You cannot obtain an estimate of the actual average grain size (3D) as individual data is lost when using the histogram (i.e. The Saltykov method reconstructs the 3D histogram, not every apparent diameter in the actual one as this is mathematically impossible).\n>\n\nTODO: explain the details of the method", "_____no_output_____" ] ], [ [ "fig1, (ax1, ax2) = stereology.Saltykov(dataset['diameters'], numbins=11, calc_vol=50)", "=======================================\nvolume fraction (up to 50 microns) = 41.65 %\n=======================================\n=======================================\nbin size = 14.24\n=======================================\n" ], [ "fig1.savefig(\"saltykov_plot.png\", dpi=150)", "_____no_output_____" ] ], [ [ "Now let's assume that we want to use the class densities estimated by Saltykov's method to calculate the specific volume of each or one of the classes. We have two options here.", "_____no_output_____" ] ], [ [ "stereology.Saltykov?", "\u001b[1;31mSignature:\u001b[0m\n\u001b[0mstereology\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSaltykov\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mdiameters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mnumbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mcalc_vol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mtext_file\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mreturn_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m \u001b[0mleft_edge\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;31mDocstring:\u001b[0m\nEstimate the actual (3D) distribution of grain size from the population\nof apparent diameters measured in a thin section using a Saltykov-type\nalgorithm (Saltykov 1967; Sahagian and Proussevitch 1998).\n\nThe Saltykov method is optimal to estimate the volume of a particular grain\nsize fraction as well as to obtain a qualitative view of the appearance of\nthe actual 3D grain size population, either in uni- or multimodal populations.\n\nParameters\n----------\ndiameters : array_like\n the apparent diameters of the grains.\n\nnumbins : positive integer, optional\n the number of bins/classes of the histogram. If not declared,\n is set to 10 by default.\n\ncalc_vol : positive scalar or None, optional\n if the user specifies a diameter, the function will return the volume\n occupied by the grain fraction up to that diameter.\n\ntext_file : string or None, optional\n if the user specifies a name, the function will store a csv file\n with that name containing the Saltykov output.\n\nreturn_data : bool, optional\n if True the function will return the position of the midpoints and\n the frequencies.\n\nleft_edge : positive scalar or 'min', optional\n set the left edge of the histogram. Default is zero.\n\nCall functions\n--------------\n- unfold_population\n- Saltykov_plot\n\nExamples\n--------\n>>> Saltykov(diameters)\n>>> Saltykov(diameters, numbins=16, calc_vol=40)\n>>> Saltykov(diameters, text_file='foo.csv')\n>>> mid_points, frequencies = Saltykov(diameters, return_data=True)\n\nReferences\n----------\nSaltykov SA (1967) http://doi.org/10.1007/978-3-642-88260-9_31\nSahagian and Proussevitch (1998) https://doi.org/10.1016/S0377-0273(98)00043-2\n\nReturn\n------\nStatistical descriptors, a plot, and/or a file with the data (optional)\n\u001b[1;31mFile:\u001b[0m c:\\users\\marco\\documents\\github\\grainsizetools\\grain_size_tools\\stereology.py\n\u001b[1;31mType:\u001b[0m function\n" ] ], [ [ "The input parameter ``text_file`` allows you to save a text file with the data in tabular format, you only have to declare the name of the file and the file type, either txt or csv (as in the function documentation example). Alternatively, you can use the Saltykov function to directly return the density and the midpoint values of the classes as follows:", "_____no_output_____" ] ], [ [ "mid_points, densities = stereology.Saltykov(dataset['diameters'], numbins=11, return_data=True)\nprint(densities)", "[1.31536871e-03 2.17302235e-02 2.25631643e-02 1.45570771e-02\n 6.13586532e-03 2.24830266e-03 1.29306084e-03 3.60326809e-04\n 0.00000000e+00 0.00000000e+00 4.11071036e-05]\n" ] ], [ [ "As you may notice, these density values do not add up to 1 or 100.", "_____no_output_____" ] ], [ [ "np.sum(densities)", "_____no_output_____" ] ], [ [ "This is because the script normalized the frequencies of the different classes so that the integral over the range (not the sum) is one (see FAQs for an explanation on this). If you want to calculate the relative proportion for each class you must multiply the value of the densities by the bin size. After doing this, you can check that the relative densities sum one (i.e. they are proportions relative to one).", "_____no_output_____" ] ], [ [ "corrected_densities = densities * 14.236\nnp.sum(corrected_densities)", "_____no_output_____" ] ], [ [ "So for example if you have a volume of rock of say 100 cm2 and you want to estimate what proportion of that volume is occupied by each grain size class/range, you could estimate it as follows:", "_____no_output_____" ] ], [ [ "# I use np.around to round the values\nnp.around(corrected_densities * 100, 2)", "_____no_output_____" ] ], [ [ "## The two-step method\n\n> **What is it?**\n>\n> It is a stereological method that approximates the actual grain size distribution. The method is distribution-dependent, meaning that it is assumed that the distribution of grain sizes follows a lognormal distribution. The method fit a lognormal distribution on top of the Saltykov output, hence the name two-step method.\n>\n> **What do I use it for?**\n>\n> Its main use is to estimate the shape of the lognormal distribution, the average grain size (3D), and the volume fraction of a specific range of grain sizes (not yet implemented).\n>\n> **What are its limitations?**\n>\n> The method is partially based on the Saltykov method and therefore inherits some of its limitations. The method however do not require to define a specific number of classes.", "_____no_output_____" ] ], [ [ "fig2, ax = stereology.calc_shape(dataset['diameters'])", "=======================================\nPREDICTED OPTIMAL VALUES\nNumber of classes: 11\nMSD (lognormal shape) = 1.63 ± 0.06\nGeometric mean (scale) = 36.05 ± 1.27\n=======================================\n" ], [ "fig2.savefig(\"2step_plot.png\", dpi=150)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# Convolutional Neural Network\n\n### Author: Ivan Bongiorni, Data Scientist at GfK.\n\n[LinkedIn profile](https://www.linkedin.com/in/ivan-bongiorni-b8a583164/)\n\nIn this Notebook I will implement a **basic CNN in TensorFlow 2.0**. I will use the famous **Fashion MNIST** dataset, [published by Zalando](https://github.com/zalandoresearch/fashion-mnist) and made [available on Kaggle](https://www.kaggle.com/zalando-research/fashionmnist). Images come already preprocessed in 28 x 28 black and white format.\n\nIt is a multiclass classification task on the following labels:\n0. T-shirt/top\n1. Trouser\n2. Pullover\n3. Dress\n4. Coat\n5. Sandal\n6. Shirt\n7. Sneaker\n8. Bag\n9. Ankle boot ", "_____no_output_____" ], [ "", "_____no_output_____" ], [ "Summary:\n\n0. Import data + Dataprep\n0. CNN architecture\n0. Training with Mini Batch Gradient Descent\n0. Test", "_____no_output_____" ] ], [ [ "# Import necessary modules\n\nimport numpy as np\nimport pandas as pd\n\nimport tensorflow as tf\nprint(tf.__version__)\n\nfrom sklearn.utils import shuffle\n\nfrom matplotlib import pyplot as plt\nimport seaborn", "2.0.0-beta0\n" ] ], [ [ "# 0. Import data + Dataprep\n\nThe dataset comes already divided in 60k and 10k Train and Test images. I will now import Training data, and leave Test for later. In order to dataprep image data, I need to reshape the pixel into `(, 28, 28, 1)` arrays; the 1 at the end represents the channel: 1 for black and white images, 3 (red, green, blue) for colored images. Pixel data are also scaled to the `[0, 1]` interval.", "_____no_output_____" ] ], [ [ "df = pd.read_csv('fashion-mnist_train.csv')\n\n# extract labels, one-hot encode them\nlabel = df.label\nlabel = pd.get_dummies(label)\nlabel = label.values\nlabel = label.astype(np.float32)\n\ndf.drop('label', axis = 1, inplace = True)\ndf = df.values\ndf = df.astype(np.float32)\n\n# reshape and scale data\ndf = df.reshape((len(df), 28, 28, 1))\ndf = df / 255.", "_____no_output_____" ] ], [ [ "# 1. CNN architecture\n\nI will feed images into a set of **convolutional** and **max-pooling layers**:\n\n- Conv layers are meant to extract relevant informations from pixel data. A number of *filters* scroll through the image, learning the most relevant informations to extract.\n- Max-Pool layers instead are meant to drastically reduce the number of pixel data. For each (2, 2) window size, Max-Pool will save only the pixel with the highest activation value. Max-Pool is meant to make the model lighter by removing the least relevant observations, at the cost of course of loosing a lot of data!\n\nSince I'm focused on the implementation, rather than on the theory behind it, please refer to [this good article](https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d) on how Conv and Max-Pool work in practice. If you are a die-hard, check [this awesome page from a CNN Stanford Course](http://cs231n.github.io/convolutional-networks/?source=post_page---------------------------#overview).\n\nCon and Max-Pool will extract and reduce the size of the input, so that the following feed-forward part could process it. The first convolutional layer requires a specification of the input shape, corresponding to the shape of each image.\n\nSince it's a multiclass classification tasks, softmax activation must be placed at the output layer in order to transform the Network's output into a probability distribution over the ten target categories.", "_____no_output_____" ] ], [ [ "from tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, BatchNormalization, Dropout\nfrom tensorflow.keras.activations import relu, elu, softmax\n\n\nCNN = Sequential([\n \n Conv2D(32, kernel_size = (3, 3), activation = elu, \n kernel_initializer = 'he_normal', input_shape = (28, 28, 1)), \n MaxPool2D((2, 2)), \n \n Conv2D(64, kernel_size = (3, 3), kernel_initializer = 'he_normal', activation = elu), \n BatchNormalization(), \n \n Conv2D(128, kernel_size = (3, 3), kernel_initializer = 'he_normal', activation = elu), \n BatchNormalization(), \n Dropout(0.2), \n \n \n Flatten(), \n \n \n Dense(400, activation = elu), \n BatchNormalization(), \n Dropout(0.2), \n \n Dense(400, activation = elu), \n BatchNormalization(), \n Dropout(0.2), \n \n Dense(10, activation = softmax)\n \n])\n", "_____no_output_____" ], [ "CNN.summary()", "Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nconv2d (Conv2D) (None, 26, 26, 32) 320 \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0 \n_________________________________________________________________\nconv2d_1 (Conv2D) (None, 11, 11, 64) 18496 \n_________________________________________________________________\nbatch_normalization (BatchNo (None, 11, 11, 64) 256 \n_________________________________________________________________\nconv2d_2 (Conv2D) (None, 9, 9, 128) 73856 \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 9, 9, 128) 512 \n_________________________________________________________________\ndropout (Dropout) (None, 9, 9, 128) 0 \n_________________________________________________________________\nflatten (Flatten) (None, 10368) 0 \n_________________________________________________________________\ndense (Dense) (None, 400) 4147600 \n_________________________________________________________________\nbatch_normalization_2 (Batch (None, 400) 1600 \n_________________________________________________________________\ndropout_1 (Dropout) (None, 400) 0 \n_________________________________________________________________\ndense_1 (Dense) (None, 400) 160400 \n_________________________________________________________________\nbatch_normalization_3 (Batch (None, 400) 1600 \n_________________________________________________________________\ndropout_2 (Dropout) (None, 400) 0 \n_________________________________________________________________\ndense_2 (Dense) (None, 10) 4010 \n=================================================================\nTotal params: 4,408,650\nTrainable params: 4,406,666\nNon-trainable params: 1,984\n_________________________________________________________________\n" ] ], [ [ "# 2. Training with Mini Batch Gradient Descent\n\nThe training part is no different from mini batch gradient descent training of feed-forward classifiers. I wrote [a Notebook on this technique](https://github.com/IvanBongiorni/TensorFlow2.0_Tutorial/blob/master/TensorFlow2.0_02_MiniBatch_Gradient_Descent.ipynb) in which I explain it in more detail.\n\nAssuming you already know how it works, I will define a function to fetch mini batches into the Network and process with training in eager execution.", "_____no_output_____" ] ], [ [ "@tf.function\ndef fetch_batch(X, y, batch_size, epoch):\n start = epoch*batch_size\n \n X_batch = X[start:start+batch_size, :, :]\n y_batch = y[start:start+batch_size, :]\n \n return X_batch, y_batch", "_____no_output_____" ], [ "# To measure execution time\nimport time\nstart = time.time()", "_____no_output_____" ] ], [ [ "There is one big difference with respect to previous training exercises. Since this Network has a high number of parameters (approx 4.4 million) it will require comparatively longer training times. For this reason, I will measure training not just in *epochs*, but also in *cycles*.\n\nAt each training cycle, I will shuffle the dataset and feed it into the Network in mini batches until it's completed. At the following cycle, I will reshuffle the data using a different random seed and repeat the process. (What I called cycles are nothing but Keras' \"epochs\".)\n\nUsing 50 cycles and batches of size 120 on a 60.000 images dataset, I will be able to train my CNN for an overall number of 25.000 epochs.", "_____no_output_____" ] ], [ [ "from tensorflow.keras.losses import CategoricalCrossentropy\nfrom tensorflow.keras.metrics import CategoricalAccuracy\n\nloss = tf.keras.losses.CategoricalCrossentropy()\naccuracy = tf.keras.metrics.CategoricalAccuracy()\n\noptimizer = tf.optimizers.Adam(learning_rate = 0.0001)\n\n\n### TRAINING\n\ncycles = 50\nbatch_size = 120\n\nloss_history = []\naccuracy_history = []\n\nfor cycle in range(cycles):\n \n df, label = shuffle(df, label, random_state = cycle**2)\n \n for epoch in range(len(df) // batch_size):\n \n X_batch, y_batch = fetch_batch(df, label, batch_size, epoch)\n \n with tf.GradientTape() as tape:\n current_loss = loss(CNN(X_batch), y_batch)\n \n gradients = tape.gradient(current_loss, CNN.trainable_variables)\n optimizer.apply_gradients(zip(gradients, CNN.trainable_variables))\n \n loss_history.append(current_loss.numpy())\n \n current_accuracy = accuracy(CNN(X_batch), y_batch).numpy()\n accuracy_history.append(current_accuracy)\n accuracy.reset_states()\n \n print(str(cycle + 1) + '.\\tTraining Loss: ' + str(current_loss.numpy()) \n + ',\\tAccuracy: ' + str(current_accuracy)) \n#\nprint('\\nTraining complete.')\nprint('Final Loss: ' + str(current_loss.numpy()) + '. Final accuracy: ' + str(current_accuracy))", "1.\tTraining Loss: 2.963158,\tAccuracy: 0.825\n2.\tTraining Loss: 2.2184157,\tAccuracy: 0.875\n3.\tTraining Loss: 1.6165245,\tAccuracy: 0.90833336\n4.\tTraining Loss: 1.8334527,\tAccuracy: 0.89166665\n5.\tTraining Loss: 1.2200023,\tAccuracy: 0.93333334\n6.\tTraining Loss: 1.9942344,\tAccuracy: 0.89166665\n7.\tTraining Loss: 2.1937194,\tAccuracy: 0.8833333\n8.\tTraining Loss: 1.8208188,\tAccuracy: 0.89166665\n9.\tTraining Loss: 1.6057307,\tAccuracy: 0.9\n10.\tTraining Loss: 1.036152,\tAccuracy: 0.94166666\n11.\tTraining Loss: 1.1800997,\tAccuracy: 0.925\n12.\tTraining Loss: 0.9550361,\tAccuracy: 0.9583333\n13.\tTraining Loss: 0.71287704,\tAccuracy: 0.96666664\n14.\tTraining Loss: 1.1355927,\tAccuracy: 0.93333334\n15.\tTraining Loss: 0.42137903,\tAccuracy: 0.975\n16.\tTraining Loss: 1.2537751,\tAccuracy: 0.925\n17.\tTraining Loss: 0.6678084,\tAccuracy: 0.96666664\n18.\tTraining Loss: 0.67579913,\tAccuracy: 0.96666664\n19.\tTraining Loss: 0.93005073,\tAccuracy: 0.95\n20.\tTraining Loss: 0.9756758,\tAccuracy: 0.95\n21.\tTraining Loss: 0.5439663,\tAccuracy: 0.96666664\n22.\tTraining Loss: 0.48298022,\tAccuracy: 0.975\n23.\tTraining Loss: 0.71461415,\tAccuracy: 0.96666664\n24.\tTraining Loss: 0.59260106,\tAccuracy: 0.96666664\n25.\tTraining Loss: 0.52771163,\tAccuracy: 0.975\n26.\tTraining Loss: 0.70353717,\tAccuracy: 0.96666664\n27.\tTraining Loss: 0.8188721,\tAccuracy: 0.95\n28.\tTraining Loss: 0.5126863,\tAccuracy: 0.975\n29.\tTraining Loss: 0.45719293,\tAccuracy: 0.975\n30.\tTraining Loss: 0.54228246,\tAccuracy: 0.96666664\n31.\tTraining Loss: 0.55225444,\tAccuracy: 0.96666664\n32.\tTraining Loss: 0.9566125,\tAccuracy: 0.94166666\n33.\tTraining Loss: 0.29893246,\tAccuracy: 0.98333335\n34.\tTraining Loss: 0.70337826,\tAccuracy: 0.9583333\n35.\tTraining Loss: 0.5612643,\tAccuracy: 0.96666664\n36.\tTraining Loss: 0.59767526,\tAccuracy: 0.96666664\n37.\tTraining Loss: 0.67508984,\tAccuracy: 0.96666664\n38.\tTraining Loss: 0.67434996,\tAccuracy: 0.9583333\n39.\tTraining Loss: 0.0228432,\tAccuracy: 1.0\n40.\tTraining Loss: 0.2526902,\tAccuracy: 0.9916667\n41.\tTraining Loss: 0.17682979,\tAccuracy: 0.9916667\n42.\tTraining Loss: 0.4235972,\tAccuracy: 0.975\n43.\tTraining Loss: 0.13609551,\tAccuracy: 0.9916667\n44.\tTraining Loss: 0.5388846,\tAccuracy: 0.96666664\n45.\tTraining Loss: 0.293708,\tAccuracy: 0.98333335\n46.\tTraining Loss: 0.04402418,\tAccuracy: 1.0\n47.\tTraining Loss: 0.5923631,\tAccuracy: 0.96666664\n48.\tTraining Loss: 0.7310451,\tAccuracy: 0.9583333\n49.\tTraining Loss: 0.41430146,\tAccuracy: 0.975\n50.\tTraining Loss: 0.14125405,\tAccuracy: 0.9916667\n\nTraining complete.\nFinal Loss: 0.14125405. Final accuracy: 0.9916667\n" ], [ "end = time.time()\nprint(end - start) # around 3.5 hours :(", "12431.102645158768\n" ], [ "plt.figure(figsize = (15, 4)) # adjust figures size\nplt.subplots_adjust(wspace=0.2) # adjust distance\n\nfrom scipy.signal import savgol_filter\n\n# loss plot\nplt.subplot(1, 2, 1)\nplt.plot(loss_history)\nplt.plot(savgol_filter(loss_history, len(loss_history)//3, 3))\nplt.title('Loss')\nplt.xlabel('epochs')\nplt.ylabel('Categorical Cross-Entropy')\n\n# accuracy plot\nplt.subplot(1, 2, 2)\nplt.plot(accuracy_history)\nplt.plot(savgol_filter(accuracy_history, len(loss_history)//3, 3))\nplt.title('Accuracy')\nplt.xlabel('epochs')\nplt.ylabel('Accuracy')\n\nplt.show()", "_____no_output_____" ] ], [ [ "# 3. Test\n\nLet's now test the model on the Test set. I will repeat the dataprep part on it:", "_____no_output_____" ] ], [ [ "# Dataprep Test data\n\ntest = pd.read_csv('fashion-mnist_test.csv')\n\ntest_label = pd.get_dummies(test.label)\ntest_label = test_label.values\ntest_label = test_label.astype(np.float32)\n\ntest.drop('label', axis = 1, inplace = True)\ntest = test.values\ntest = test.astype(np.float32)\n\ntest = test / 255.\n\ntest = test.reshape((len(test), 28, 28, 1))\n\nprediction = CNN.predict(test)\n\nprediction = np.argmax(prediction, axis=1)\ntest_label = np.argmax(test_label, axis=1) # reverse one-hot encoding", "_____no_output_____" ], [ "from sklearn.metrics import confusion_matrix\n\nCM = confusion_matrix(prediction, test_label)\nprint(CM)\nprint('\\nTest Accuracy: ' + str(np.sum(np.diag(CM)) / np.sum(CM)))", "[[855 2 13 13 4 1 92 0 4 0]\n [ 0 980 2 2 0 2 4 0 1 0]\n [ 19 1 848 6 23 0 41 0 2 0]\n [ 18 13 9 926 9 0 23 0 1 0]\n [ 2 1 74 35 936 0 74 1 3 0]\n [ 0 0 0 0 0 963 1 9 0 6]\n [ 92 2 50 16 26 0 757 0 4 0]\n [ 1 0 0 0 0 17 0 966 2 32]\n [ 13 1 4 1 2 3 8 0 983 5]\n [ 0 0 0 1 0 14 0 24 0 957]]\n\nTest Accuracy: 0.9171\n" ], [ "seaborn.heatmap(CM, annot=True)\nplt.show()", "_____no_output_____" ] ], [ [ "My Convolutional Neural Network classified 91.7% of Test data correctly. The confusion matrix showed that category no. 6 (Shirt) has been misclassified the most. The next goal is to correct it; one possible solution would be to increase regularization, another to build an ensemble of models.", "_____no_output_____" ], [ "Thanks for coming thus far. In the next Notebooks I will implement more advanced Convolutional models, among other things.", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Data Processing", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd", "_____no_output_____" ], [ "df_orig = pd.DataFrame([['big', 2.00, 'black', 'coffee'],\n ['medium', 1.90, 'black', 'coffee'],\n ['small', 1.80, 'black', 'coffee'],\n ['small', 1.20, 'brown', 'tea'],\n ['medium', 1.30, 'brown', 'tea']], \n columns=['size', 'price', 'colour', 'drink'])\ndf_orig", "_____no_output_____" ] ], [ [ "## Categorical Data\n- **nominal**\n- **ordinal**", "_____no_output_____" ], [ "## Encoding Norminal: `LabelEncoder`", "_____no_output_____" ] ], [ [ "df = df_orig.copy()", "_____no_output_____" ], [ "from sklearn.preprocessing import LabelEncoder\nclass_le = LabelEncoder()\ny = class_le.fit_transform(df['drink']) # y can now be used as labels\nprint(y[0:5])\nclass_le.classes_", "[0 0 0 1 1]\n" ] ], [ [ "## Encoding Norminal into One-Hot: `LabelEncoder`", "_____no_output_____" ] ], [ [ "df = df_orig.copy()\nfrom sklearn.preprocessing import OneHotEncoder\ncolour_le = LabelEncoder()\ncolour_le.fit_transform(df['colour'].values)\ncolour_le.classes_", "_____no_output_____" ], [ "df['colour']", "_____no_output_____" ], [ "X = pd.get_dummies(df[['price', 'colour']])\nX", "_____no_output_____" ] ], [ [ "## Hmm ... Encoding Nominal: `OneHotEncoder` ??", "_____no_output_____" ] ], [ [ "df = df_orig.copy()\n\nfrom sklearn.preprocessing import OneHotEncoder\ncolour_le = LabelEncoder()\ndf['colour'] = colour_le.fit_transform(df['colour'].values)\ncolour_le.classes_", "_____no_output_____" ], [ "df['colour']", "_____no_output_____" ], [ "from sklearn.preprocessing import OneHotEncoder\n#ohe = OneHotEncoder(categorical_features=[2], sparse=False) # feature column number 0. ['size'] won't work\n#ohe.fit_transform(df['colour'].values)\n\nohe = OneHotEncoder(categorical_features=[0], sparse=True) # feature column number 0. ['size'] won't work\nohe.fit_transform(df['colour'].values).toarray()\nX = pd.get_dummies(df[['price', 'colour']])\nX", "C:\\WinPython-64bit-3.4.4.3Qt5\\python-3.4.4.amd64\\lib\\site-packages\\sklearn\\utils\\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n DeprecationWarning)\n" ] ], [ [ "## Encoding Ordinal: `.map`\nOrder does has a meaning.", "_____no_output_____" ] ], [ [ "df = df_orig.copy()\nsize_map = {'big':3, 'medium':2, 'small':1}\ndf['size'] = df['size'].map(size_map)\ndf", "_____no_output_____" ] ], [ [ "## Below won't preserve the intended order!!", "_____no_output_____" ] ], [ [ "df = df_orig.copy()\nnp.unique(df['size'])", "_____no_output_____" ], [ "size_map = {category:idx for idx, category in enumerate(np.unique(df['size']))}\nsize_map", "_____no_output_____" ] ], [ [ "## Data Scaling 1 of 2: Normalisation\nSee `classification-k-nn.ipynb`", "_____no_output_____" ], [ "## Data Scaling 2 of 2: Standardization\nsee `classification-knn.ipynb`", "_____no_output_____" ] ] ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "# quant-econ Solutions: Modeling Career Choice", "_____no_output_____" ], [ "Solutions for http://quant-econ.net/py/career.html", "_____no_output_____" ] ], [ [ "%matplotlib inline", "_____no_output_____" ], [ "import numpy as np\nimport matplotlib.pyplot as plt\nfrom quantecon import DiscreteRV, compute_fixed_point\nfrom career import CareerWorkerProblem", "_____no_output_____" ] ], [ [ "## Exercise 1", "_____no_output_____" ], [ "\nSimulate job / career paths. \n\nIn reading the code, recall that `optimal_policy[i, j]` = policy at\n$(\\theta_i, \\epsilon_j)$ = either 1, 2 or 3; meaning 'stay put', 'new job' and\n'new life'.\n\n", "_____no_output_____" ] ], [ [ "wp = CareerWorkerProblem()\nv_init = np.ones((wp.N, wp.N))*100\nv = compute_fixed_point(wp.bellman_operator, v_init, verbose=False)\noptimal_policy = wp.get_greedy(v)\nF = DiscreteRV(wp.F_probs)\nG = DiscreteRV(wp.G_probs)\n\ndef gen_path(T=20):\n i = j = 0 \n theta_index = []\n epsilon_index = []\n for t in range(T):\n if optimal_policy[i, j] == 1: # Stay put\n pass\n elif optimal_policy[i, j] == 2: # New job\n j = int(G.draw())\n else: # New life\n i, j = int(F.draw()), int(G.draw())\n theta_index.append(i)\n epsilon_index.append(j)\n return wp.theta[theta_index], wp.epsilon[epsilon_index]\n\ntheta_path, epsilon_path = gen_path()\n\nfig, axes = plt.subplots(2, 1, figsize=(10, 8))\nfor ax in axes:\n ax.plot(epsilon_path, label='epsilon')\n ax.plot(theta_path, label='theta')\n ax.legend(loc='lower right')\n\nplt.show()\n\n", "_____no_output_____" ] ], [ [ "## Exercise 2", "_____no_output_____" ], [ "The median for the original parameterization can be computed as follows", "_____no_output_____" ] ], [ [ "\nwp = CareerWorkerProblem()\nv_init = np.ones((wp.N, wp.N))*100\nv = compute_fixed_point(wp.bellman_operator, v_init)\noptimal_policy = wp.get_greedy(v)\nF = DiscreteRV(wp.F_probs)\nG = DiscreteRV(wp.G_probs)\n\ndef gen_first_passage_time():\n t = 0\n i = j = 0\n while 1:\n if optimal_policy[i, j] == 1: # Stay put\n return t\n elif optimal_policy[i, j] == 2: # New job\n j = int(G.draw())\n else: # New life\n i, j = int(F.draw()), int(G.draw())\n t += 1\n\nM = 25000 # Number of samples\nsamples = np.empty(M)\nfor i in range(M): \n samples[i] = gen_first_passage_time()\nprint(np.median(samples))\n", "Iteration Distance Elapsed (seconds)\n---------------------------------------------\n5 4.073e+00 9.388e-02 \n10 3.151e+00 1.871e-01 \n15 2.438e+00 2.811e-01 \n20 1.887e+00 3.741e-01 \n25 1.460e+00 4.675e-01 \n30 1.130e+00 5.603e-01 \n35 8.741e-01 6.537e-01 \n40 6.764e-01 7.467e-01 \n45 5.234e-01 8.400e-01 \n50 4.050e-01 9.336e-01 \n7.0\n" ] ], [ [ "To compute the median with $\\beta=0.99$ instead of the default value $\\beta=0.95$,\nreplace `wp = CareerWorkerProblem()` with `wp = CareerWorkerProblem(beta=0.99)`\n\nThe medians are subject to randomness, but should be about 7 and 11\nrespectively. Not surprisingly, more patient workers will wait longer to settle down to their final job\n\n", "_____no_output_____" ], [ "## Exercise 3", "_____no_output_____" ], [ "Here’s the code to reproduce the original figure", "_____no_output_____" ] ], [ [ "from matplotlib import cm\n\nwp = CareerWorkerProblem()\nv_init = np.ones((wp.N, wp.N))*100\nv = compute_fixed_point(wp.bellman_operator, v_init)\noptimal_policy = wp.get_greedy(v)\n\nfig, ax = plt.subplots(figsize=(6,6))\ntg, eg = np.meshgrid(wp.theta, wp.epsilon)\nlvls=(0.5, 1.5, 2.5, 3.5)\nax.contourf(tg, eg, optimal_policy.T, levels=lvls, cmap=cm.winter, alpha=0.5)\nax.contour(tg, eg, optimal_policy.T, colors='k', levels=lvls, linewidths=2)\nax.set_xlabel('theta', fontsize=14)\nax.set_ylabel('epsilon', fontsize=14)\nax.text(1.8, 2.5, 'new life', fontsize=14)\nax.text(4.5, 2.5, 'new job', fontsize=14, rotation='vertical')\nax.text(4.0, 4.5, 'stay put', fontsize=14)\n\n", "Iteration Distance Elapsed (seconds)\n---------------------------------------------\n5 4.073e+00 9.415e-02 \n10 3.151e+00 1.866e-01 \n15 2.438e+00 2.793e-01 \n20 1.887e+00 3.721e-01 \n25 1.460e+00 4.647e-01 \n30 1.130e+00 5.568e-01 \n35 8.741e-01 6.499e-01 \n40 6.764e-01 7.424e-01 \n45 5.234e-01 8.348e-01 \n50 4.050e-01 9.276e-01 \n" ] ], [ [ "Now we want to set `G_a = G_b = 100` and generate a new figure with these parameters. \n\nTo do this replace:\n\n wp = CareerWorkerProblem()\n\nwith:\n\n wp = CareerWorkerProblem(G_a=100, G_b=100)\n\nIn the new figure, you will see that the region for which the worker will stay put has grown because the distribution for $\\epsilon$ has become more concentrated around the mean, making high-paying jobs less realistic\n", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]

[ [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "cd /tf/src/data/gpt-2/", "/tf/src/data/gpt-2\n" ], [ "!pip3 install -r requirements.txt", "Collecting fire>=0.1.3 (from -r requirements.txt (line 1))\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/d9/69/faeaae8687f4de0f5973694d02e9d6c3eb827636a009157352d98de1129e/fire-0.2.1.tar.gz (76kB)\n\u001b[K |████████████████████████████████| 81kB 1.1MB/s eta 0:00:01\n\u001b[?25hCollecting regex==2017.4.5 (from -r requirements.txt (line 2))\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/36/62/c0c0d762ffd4ffaf39f372eb8561b8d491a11ace5a7884610424a8b40f95/regex-2017.04.05.tar.gz (601kB)\n\u001b[K |████████████████████████████████| 604kB 2.7MB/s eta 0:00:01\n\u001b[?25hCollecting requests==2.21.0 (from -r requirements.txt (line 3))\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/7d/e3/20f3d364d6c8e5d2353c72a67778eb189176f08e873c9900e10c0287b84b/requests-2.21.0-py2.py3-none-any.whl (57kB)\n\u001b[K |████████████████████████████████| 61kB 6.1MB/s eta 0:00:011\n\u001b[?25hCollecting tqdm==4.31.1 (from -r requirements.txt (line 4))\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/4b/c38b5144cf167c4f52288517436ccafefe9dc01b8d1c190e18a6b154cd4a/tqdm-4.31.1-py2.py3-none-any.whl (48kB)\n\u001b[K |████████████████████████████████| 51kB 6.1MB/s eta 0:00:011\n\u001b[?25hCollecting toposort==1.5 (from -r requirements.txt (line 5))\n Downloading https://files.pythonhosted.org/packages/e9/8a/321cd8ea5f4a22a06e3ba30ef31ec33bea11a3443eeb1d89807640ee6ed4/toposort-1.5-py2.py3-none-any.whl\nRequirement already satisfied: six in /usr/lib/python3/dist-packages (from fire>=0.1.3->-r requirements.txt (line 1)) (1.11.0)\nRequirement already satisfied: termcolor in /usr/local/lib/python3.6/dist-packages (from fire>=0.1.3->-r requirements.txt (line 1)) (1.1.0)\nCollecting certifi>=2017.4.17 (from requests==2.21.0->-r requirements.txt (line 3))\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/69/1b/b853c7a9d4f6a6d00749e94eb6f3a041e342a885b87340b79c1ef73e3a78/certifi-2019.6.16-py2.py3-none-any.whl (157kB)\n\u001b[K |████████████████████████████████| 163kB 231kB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: idna<2.9,>=2.5 in /usr/lib/python3/dist-packages (from requests==2.21.0->-r requirements.txt (line 3)) (2.6)\nCollecting urllib3<1.25,>=1.21.1 (from requests==2.21.0->-r requirements.txt (line 3))\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/01/11/525b02e4acc0c747de8b6ccdab376331597c569c42ea66ab0a1dbd36eca2/urllib3-1.24.3-py2.py3-none-any.whl (118kB)\n\u001b[K |████████████████████████████████| 122kB 3.1MB/s eta 0:00:01\n\u001b[?25hCollecting chardet<3.1.0,>=3.0.2 (from requests==2.21.0->-r requirements.txt (line 3))\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/bc/a9/01ffebfb562e4274b6487b4bb1ddec7ca55ec7510b22e4c51f14098443b8/chardet-3.0.4-py2.py3-none-any.whl (133kB)\n\u001b[K |████████████████████████████████| 143kB 170kB/s eta 0:00:01\n\u001b[?25hBuilding wheels for collected packages: fire, regex\n Building wheel for fire (setup.py) ... \u001b[?25ldone\n\u001b[?25h Stored in directory: /root/.cache/pip/wheels/31/9c/c0/07b6dc7faf1844bb4688f46b569efe6cafaa2179c95db821da\n Building wheel for regex (setup.py) ... \u001b[?25ldone\n\u001b[?25h Stored in directory: /root/.cache/pip/wheels/75/07/38/3c16b529d50cb4e0cd3dbc7b75cece8a09c132692c74450b01\nSuccessfully built fire regex\nInstalling collected packages: fire, regex, certifi, urllib3, chardet, requests, tqdm, toposort\nSuccessfully installed certifi-2019.6.16 chardet-3.0.4 fire-0.2.1 regex-2017.4.5 requests-2.21.0 toposort-1.5 tqdm-4.31.1 urllib3-1.24.3\n\u001b[33mWARNING: You are using pip version 19.1.1, however version 19.2.1 is available.\nYou should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ], [ "import fire\nimport json\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport regex as re\nfrom functools import lru_cache\nimport tqdm\nfrom tensorflow.core.protobuf import rewriter_config_pb2\nimport glob\nimport pickle\n\ntf.__version__", "_____no_output_____" ] ], [ [ "# Encoding", "_____no_output_____" ] ], [ [ "\"\"\"Byte pair encoding utilities\"\"\"\n\n\n@lru_cache()\ndef bytes_to_unicode():\n \"\"\"\n Returns list of utf-8 byte and a corresponding list of unicode strings.\n The reversible bpe codes work on unicode strings.\n This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.\n When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.\n This is a signficant percentage of your normal, say, 32K bpe vocab.\n To avoid that, we want lookup tables between utf-8 bytes and unicode strings.\n And avoids mapping to whitespace/control characters the bpe code barfs on.\n \"\"\"\n bs = list(range(ord(\"!\"), ord(\"~\")+1))+list(range(ord(\"¡\"), ord(\"¬\")+1))+list(range(ord(\"®\"), ord(\"ÿ\")+1))\n cs = bs[:]\n n = 0\n for b in range(2**8):\n if b not in bs:\n bs.append(b)\n cs.append(2**8+n)\n n += 1\n cs = [chr(n) for n in cs]\n return dict(zip(bs, cs))\n\ndef get_pairs(word):\n \"\"\"Return set of symbol pairs in a word.\n\n Word is represented as tuple of symbols (symbols being variable-length strings).\n \"\"\"\n pairs = set()\n prev_char = word[0]\n for char in word[1:]:\n pairs.add((prev_char, char))\n prev_char = char\n return pairs\n\nclass Encoder:\n def __init__(self, encoder, bpe_merges, errors='replace'):\n self.encoder = encoder\n self.decoder = {v:k for k,v in self.encoder.items()}\n self.errors = errors # how to handle errors in decoding\n self.byte_encoder = bytes_to_unicode()\n self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}\n self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))\n self.cache = {}\n\n # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions\n self.pat = re.compile(r\"\"\"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+\"\"\")\n\n def bpe(self, token):\n if token in self.cache:\n return self.cache[token]\n word = tuple(token)\n pairs = get_pairs(word)\n\n if not pairs:\n return token\n\n while True:\n bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))\n if bigram not in self.bpe_ranks:\n break\n first, second = bigram\n new_word = []\n i = 0\n while i < len(word):\n try:\n j = word.index(first, i)\n new_word.extend(word[i:j])\n i = j\n except:\n new_word.extend(word[i:])\n break\n\n if word[i] == first and i < len(word)-1 and word[i+1] == second:\n new_word.append(first+second)\n i += 2\n else:\n new_word.append(word[i])\n i += 1\n new_word = tuple(new_word)\n word = new_word\n if len(word) == 1:\n break\n else:\n pairs = get_pairs(word)\n word = ' '.join(word)\n self.cache[token] = word\n return word\n\n def encode(self, text):\n bpe_tokens = []\n for token in re.findall(self.pat, text):\n token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))\n bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))\n return bpe_tokens\n\n def decode(self, tokens):\n text = ''.join([self.decoder[token] for token in tokens])\n text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)\n return text\n\ndef get_encoder(model_name, models_dir):\n with open(os.path.join(models_dir, model_name, 'encoder.json'), 'r') as f:\n encoder = json.load(f)\n with open(os.path.join(models_dir, model_name, 'vocab.bpe'), 'r', encoding=\"utf-8\") as f:\n bpe_data = f.read()\n bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\\n')[1:-1]]\n return Encoder(\n encoder=encoder,\n bpe_merges=bpe_merges,\n )", "_____no_output_____" ] ], [ [ "# Model", "_____no_output_____" ] ], [ [ "class HParams():\n n_vocab=50257\n n_ctx=1024\n n_embd=768\n n_head=12\n n_layer=12\n \n def __init__(self, n_vocab, n_ctx, n_embd, n_head, n_layer):\n self.n_vocab = n_vocab\n self.n_ctx = n_ctx\n self.n_embd = n_embd\n self.n_head = n_head\n self.n_layer = n_layer", "_____no_output_____" ], [ "def default_hparams():\n return HParams(\n n_vocab=50257,\n n_ctx=1024,\n n_embd=768,\n n_head=12,\n n_layer=12,\n )\n\ndef shape_list(x):\n \"\"\"Deal with dynamic shape in tensorflow cleanly.\"\"\"\n static = x.shape.as_list()\n dynamic = tf.shape(input=x)\n return [dynamic[i] if s is None else s for i, s in enumerate(static)]\n\ndef gelu(x):\n return 0.5 * x * (1 + tf.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))\n\ndef norm(x, scope, *, axis=-1, epsilon=1e-5):\n \"\"\"Normalize to mean = 0, std = 1, then do a diagonal affine transform.\"\"\"\n with tf.compat.v1.variable_scope(scope):\n n_state = x.shape[-1]\n g = tf.compat.v1.get_variable('g', [n_state], initializer=tf.compat.v1.constant_initializer(1), use_resource=False)\n b = tf.compat.v1.get_variable('b', [n_state], initializer=tf.compat.v1.constant_initializer(0), use_resource=False)\n u = tf.reduce_mean(input_tensor=x, axis=axis, keepdims=True)\n s = tf.reduce_mean(input_tensor=tf.square(x-u), axis=axis, keepdims=True)\n x = (x - u) * tf.math.rsqrt(s + epsilon)\n x = x*g + b\n return x\n\ndef split_states(x, n):\n \"\"\"Reshape the last dimension of x into [n, x.shape[-1]/n].\"\"\"\n *start, m = shape_list(x)\n return tf.reshape(x, start + [n, m//n])\n\ndef merge_states(x):\n \"\"\"Smash the last two dimensions of x into a single dimension.\"\"\"\n *start, a, b = shape_list(x)\n return tf.reshape(x, start + [a*b])\n\ndef conv1d(x, scope, nf, *, w_init_stdev=0.02):\n with tf.compat.v1.variable_scope(scope):\n *start, nx = shape_list(x)\n w = tf.compat.v1.get_variable('w', [1, nx, nf], initializer=tf.compat.v1.random_normal_initializer(stddev=w_init_stdev), use_resource=False)\n b = tf.compat.v1.get_variable('b', [nf], initializer=tf.compat.v1.constant_initializer(0), use_resource=False)\n c = tf.reshape(tf.matmul(tf.reshape(x, [-1, nx]), tf.reshape(w, [-1, nf]))+b, start+[nf])\n return c\n\ndef attention_mask(nd, ns, *, dtype):\n \"\"\"1's in the lower triangle, counting from the lower right corner.\n\n Same as tf.matrix_band_part(tf.ones([nd, ns]), -1, ns-nd), but doesn't produce garbage on TPUs.\n \"\"\"\n i = tf.range(nd)[:,None]\n j = tf.range(ns)\n m = i >= j - ns + nd\n return tf.cast(m, dtype)\n\n\ndef attn(x, scope, n_state, *, past, hparams):\n assert x.shape.ndims == 3 # Should be [batch, sequence, features]\n assert n_state % hparams.n_head == 0\n if past is not None:\n assert past.shape.ndims == 5 # Should be [batch, 2, heads, sequence, features], where 2 is [k, v]\n\n def split_heads(x):\n # From [batch, sequence, features] to [batch, heads, sequence, features]\n return tf.transpose(a=split_states(x, hparams.n_head), perm=[0, 2, 1, 3])\n\n def merge_heads(x):\n # Reverse of split_heads\n return merge_states(tf.transpose(a=x, perm=[0, 2, 1, 3]))\n\n def mask_attn_weights(w):\n # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.\n _, _, nd, ns = shape_list(w)\n b = attention_mask(nd, ns, dtype=w.dtype)\n b = tf.reshape(b, [1, 1, nd, ns])\n w = w*b - tf.cast(1e10, w.dtype)*(1-b)\n return w\n\n def multihead_attn(q, k, v):\n # q, k, v have shape [batch, heads, sequence, features]\n w = tf.matmul(q, k, transpose_b=True)\n w = w * tf.math.rsqrt(tf.cast(v.shape[-1], w.dtype))\n\n w = mask_attn_weights(w)\n w = tf.nn.softmax(w, axis=-1)\n a = tf.matmul(w, v)\n return a\n\n with tf.compat.v1.variable_scope(scope):\n c = conv1d(x, 'c_attn', n_state*3)\n q, k, v = map(split_heads, tf.split(c, 3, axis=2))\n present = tf.stack([k, v], axis=1)\n if past is not None:\n pk, pv = tf.unstack(past, axis=1)\n k = tf.concat([pk, k], axis=-2)\n v = tf.concat([pv, v], axis=-2)\n a = multihead_attn(q, k, v)\n a = merge_heads(a)\n a = conv1d(a, 'c_proj', n_state)\n return a, present\n\n\ndef mlp(x, scope, n_state, *, hparams):\n with tf.compat.v1.variable_scope(scope):\n nx = x.shape[-1]\n h = gelu(conv1d(x, 'c_fc', n_state))\n h2 = conv1d(h, 'c_proj', nx)\n return h2\n\ndef block(x, scope, *, past, hparams):\n with tf.compat.v1.variable_scope(scope):\n nx = x.shape[-1]\n a, present = attn(norm(x, 'ln_1'), 'attn', nx, past=past, hparams=hparams)\n x = x + a\n m = mlp(norm(x, 'ln_2'), 'mlp', nx*4, hparams=hparams)\n x = x + m\n return x, present\n\ndef past_shape(*, hparams, batch_size=None, sequence=None):\n return [batch_size, hparams.n_layer, 2, hparams.n_head, sequence, hparams.n_embd // hparams.n_head]\n\ndef expand_tile(value, size):\n \"\"\"Add a new axis of given size.\"\"\"\n value = tf.convert_to_tensor(value=value, name='value')\n ndims = value.shape.ndims\n return tf.tile(tf.expand_dims(value, axis=0), [size] + [1]*ndims)\n\ndef positions_for(tokens, past_length):\n batch_size = tf.shape(input=tokens)[0]\n nsteps = tf.shape(input=tokens)[1]\n return expand_tile(past_length + tf.range(nsteps), batch_size)\n\n\ndef model(hparams, X, past=None, scope='model', reuse=tf.compat.v1.AUTO_REUSE):\n with tf.compat.v1.variable_scope(scope, reuse=reuse):\n results = {}\n batch, sequence = shape_list(X)\n\n wpe = tf.compat.v1.get_variable('wpe', [hparams.n_ctx, hparams.n_embd],\n initializer=tf.compat.v1.random_normal_initializer(stddev=0.01), use_resource=False)\n wte = tf.compat.v1.get_variable('wte', [hparams.n_vocab, hparams.n_embd],\n initializer=tf.compat.v1.random_normal_initializer(stddev=0.02), use_resource=False)\n past_length = 0 if past is None else tf.shape(input=past)[-2]\n h = tf.gather(wte, X) + tf.gather(wpe, positions_for(X, past_length))\n# print(h.shape)\n\n # Transformer\n presents = []\n pasts = tf.unstack(past, axis=1) if past is not None else [None] * hparams.n_layer\n assert len(pasts) == hparams.n_layer\n for layer, past in enumerate(pasts):\n h, present = block(h, 'h%d' % layer, past=past, hparams=hparams)\n presents.append(present)\n results['present'] = tf.stack(presents, axis=1)\n h = norm(h, 'ln_f')\n results['hidden_state'] = h\n\n # Language model loss. Do tokens <n predict token n?\n h_flat = tf.reshape(h, [batch*sequence, hparams.n_embd])\n logits = tf.matmul(h_flat, wte, transpose_b=True)\n logits = tf.reshape(logits, [batch, sequence, hparams.n_vocab])\n results['logits'] = logits\n return results", "_____no_output_____" ] ], [ [ "# Sample from Model", "_____no_output_____" ] ], [ [ "def top_k_logits(logits, k):\n if k == 0:\n # no truncation\n return logits\n\n def _top_k():\n values, _ = tf.nn.top_k(logits, k=k)\n min_values = values[:, -1, tf.newaxis]\n return tf.compat.v1.where(\n logits < min_values,\n tf.ones_like(logits, dtype=logits.dtype) * -1e10,\n logits,\n )\n return tf.cond(\n pred=tf.equal(k, 0),\n true_fn=lambda: logits,\n false_fn=lambda: _top_k(),\n )\n\n\ndef sample_sequence(*, hparams, length, start_token=None, batch_size=None, context=None, past=None, temperature=1, top_k=0):\n if start_token is None:\n assert context is not None, 'Specify exactly one of start_token and context!'\n else:\n assert context is None, 'Specify exactly one of start_token and context!'\n context = tf.fill([batch_size, 1], start_token)\n\n def step(hparams, tokens, past=None):\n lm_output = model(hparams=hparams, X=tokens, past=past, reuse=tf.compat.v1.AUTO_REUSE)\n\n logits = lm_output['logits'][:, :, :hparams.n_vocab]\n presents = lm_output['present']\n presents.set_shape(past_shape(hparams=hparams, batch_size=batch_size))\n return {\n 'logits': logits,\n 'presents': presents,\n 'hidden_state': lm_output['hidden_state']\n }\n\n def body(past, prev, output, embedding):\n next_outputs = step(hparams, prev, past=past)\n logits = next_outputs['logits'][:, -1, :] / tf.cast(temperature, dtype=tf.float32)\n logits = top_k_logits(logits, k=top_k)\n samples = tf.random.categorical(logits=logits, num_samples=1, dtype=tf.int32)\n return [\n next_outputs['presents'] if past is None else tf.concat([past, next_outputs['presents']], axis=-2),\n samples,\n tf.concat([output, samples], axis=1),\n next_outputs['hidden_state']\n ]\n\n past, prev, output, h = body(past, context, context, context)\n\n def cond(*args):\n return True\n\n return output, past, h", "_____no_output_____" ] ], [ [ "# Embedding Methods", "_____no_output_____" ] ], [ [ "import math\nclass Embedder:\n \n def __init__(self, chkpt_path, chunk_size):\n tf.compat.v1.disable_eager_execution()\n self.g = tf.Graph()\n with self.g.as_default():\n self.context = tf.compat.v1.placeholder(tf.int32, [1, None])\n\n self.sess = tf.compat.v1.Session(graph=self.g)\n \n self.MAX_CHUNK = chunk_size\n self.enc = get_encoder(\"117M\", \"models\")\n hparams = default_hparams()\n with self.g.as_default():\n self.output, self.past, self.hidden_state = sample_sequence(\n hparams=hparams, length=None,\n context=self.context,\n past=None,\n batch_size=1,\n temperature=1, top_k=1\n )\n \n if chkpt_path is not None:\n self.restore(chkpt_path)\n \n def restore(self, chkpt_path):\n with self.g.as_default():\n saver = tf.compat.v1.train.Saver()\n chkpt = tf.train.latest_checkpoint(chkpt_path)\n saver.restore(self.sess, chkpt)\n \n def __call__(self, method):\n with self.g.as_default():\n\n p = None\n for i in range(math.ceil(len(method) / self.MAX_CHUNK)):\n chunk = method[i * self.MAX_CHUNK : (i + 1) * self.MAX_CHUNK]\n context_tokens = self.enc.encode(chunk)\n\n if p is None:\n out, p, h = self.sess.run([self.output, self.past, self.hidden_state], feed_dict={\n self.context: [context_tokens]\n }, options = tf.compat.v1.RunOptions(report_tensor_allocations_upon_oom = True))\n else:\n out, p, h = self.sess.run([self.output, self.past, self.hidden_state], feed_dict={\n self.context: [context_tokens],\n self.past: p\n }, options = tf.compat.v1.RunOptions(report_tensor_allocations_upon_oom = True))\n\n return h", "_____no_output_____" ], [ "emb = Embedder(\"/tf/src/data/gpt-2/checkpoint/run3\", 1024)\nwith open(\"/tf/src/data/methods/DATA00M_[god-r]/after.java.~186835~\", 'r') as fp:\n method = fp.read()", "_____no_output_____" ], [ "emb(method).shape", "_____no_output_____" ] ] ]

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[ [ [ "# Задание 2.2 - Введение в PyTorch\n\nДля этого задания потребуется установить версию PyTorch 1.0\n\nhttps://pytorch.org/get-started/locally/\n\nВ этом задании мы познакомимся с основными компонентами PyTorch и натренируем несколько небольших моделей.<br>\nGPU нам пока не понадобится.\n\nОсновные ссылки: \nhttps://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html \nhttps://pytorch.org/docs/stable/nn.html \nhttps://pytorch.org/docs/stable/torchvision/index.html ", "_____no_output_____" ] ], [ [ "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.datasets as dset\nfrom torch.utils.data.sampler import SubsetRandomSampler, Sampler\n\nfrom torchvision import transforms\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport numpy as np", "_____no_output_____" ] ], [ [ "## Как всегда, начинаем с загрузки данных\n\nPyTorch поддерживает загрузку SVHN из коробки.", "_____no_output_____" ] ], [ [ "# First, lets load the dataset\ndata_train = dset.SVHN('./data/', split='train',\n transform=transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.43,0.44,0.47],\n std=[0.20,0.20,0.20]) \n ])\n )\ndata_test = dset.SVHN('./data/', split='test', \n transform=transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.43,0.44,0.47],\n std=[0.20,0.20,0.20]) \n ]))", "_____no_output_____" ] ], [ [ "Теперь мы разделим данные на training и validation с использованием классов `SubsetRandomSampler` и `DataLoader`.\n\n`DataLoader` подгружает данные, предоставляемые классом `Dataset`, во время тренировки и группирует их в батчи.\nОн дает возможность указать `Sampler`, который выбирает, какие примеры из датасета использовать для тренировки. Мы используем это, чтобы разделить данные на training и validation.\n\nПодробнее: https://pytorch.org/tutorials/beginner/data_loading_tutorial.html", "_____no_output_____" ] ], [ [ "batch_size = 64\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(device)\n\ndata_size = data_train.data.shape[0]\nvalidation_split = .2\nsplit = int(np.floor(validation_split * data_size))\nindices = list(range(data_size))\nnp.random.shuffle(indices)\n\ntrain_indices, val_indices = indices[split:], indices[:split]\n\ntrain_sampler = SubsetRandomSampler(train_indices)\nval_sampler = SubsetRandomSampler(val_indices)\n\ntrain_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, \n sampler=train_sampler)\nval_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size,\n sampler=val_sampler)", "cuda:0\n" ] ], [ [ "В нашей задаче мы получаем на вход изображения, но работаем с ними как с одномерными массивами. Чтобы превратить многомерный массив в одномерный, мы воспользуемся очень простым вспомогательным модулем `Flattener`.", "_____no_output_____" ] ], [ [ "sample, label = data_train[0]\nprint(\"SVHN data sample shape: \", sample.shape)\n# As you can see, the data is shaped like an image\n\n# We'll use a special helper module to shape it into a tensor\nclass Flattener(nn.Module):\n def forward(self, x):\n batch_size, *_ = x.shape\n return x.view(batch_size, -1)", "SVHN data sample shape: torch.Size([3, 32, 32])\n" ] ], [ [ "И наконец, мы создаем основные объекты PyTorch:\n- `nn_model` - собственно, модель с нейросетью\n- `loss` - функцию ошибки, в нашем случае `CrossEntropyLoss`\n- `optimizer` - алгоритм оптимизации, в нашем случае просто `SGD`", "_____no_output_____" ] ], [ [ "nn_model = nn.Sequential(\n Flattener(),\n nn.Linear(3*32*32, 100),\n nn.ReLU(inplace=True),\n nn.Linear(100, 10), \n )\nnn_model.type(torch.FloatTensor)\n\n# We will minimize cross-entropy between the ground truth and\n# network predictions using an SGD optimizer\nloss = nn.CrossEntropyLoss().type(torch.FloatTensor)\noptimizer = optim.SGD(nn_model.parameters(), lr=1e-2, weight_decay=1e-1)", "_____no_output_____" ] ], [ [ "## Тренируем!\n\nНиже приведена функция `train_model`, реализующая основной цикл тренировки PyTorch.\n\nКаждую эпоху эта функция вызывает функцию `compute_accuracy`, которая вычисляет точность на validation, эту последнюю функцию предлагается реализовать вам.", "_____no_output_____" ] ], [ [ "# This is how to implement the same main train loop in PyTorch. Pretty easy, right?\n\ndef train_model(model, train_loader, val_loader, loss, optimizer, num_epochs): \n loss_history = []\n train_history = []\n val_history = []\n model.to(device)\n for epoch in range(num_epochs):\n model.train() # Enter train mode\n \n loss_accum = 0\n correct_samples = 0\n total_samples = 0\n for i_step, (x, y) in enumerate(train_loader):\n x = x.to(device)\n y = y.to(device)\n prediction = model(x) \n loss_value = loss(prediction, y)\n optimizer.zero_grad()\n loss_value.backward()\n optimizer.step()\n \n _, indices = torch.max(prediction, 1)\n correct_samples += torch.sum(indices == y)\n total_samples += y.shape[0]\n \n loss_accum += loss_value\n\n ave_loss = loss_accum / (i_step + 1)\n train_accuracy = float(correct_samples) / total_samples\n val_accuracy = compute_accuracy(model, val_loader)\n \n loss_history.append(float(ave_loss))\n train_history.append(train_accuracy)\n val_history.append(val_accuracy)\n \n print(\"Average loss: %f, Train accuracy: %f, Val accuracy: %f\" % (ave_loss, train_accuracy, val_accuracy))\n \n return loss_history, train_history, val_history\n \ndef compute_accuracy(model, loader):\n \"\"\"\n Computes accuracy on the dataset wrapped in a loader\n \n Returns: accuracy as a float value between 0 and 1\n \"\"\"\n model.eval() # Evaluation mode\n trueAnswerCounter = 0 \n totalAnswerCounter = 0 \n with torch.no_grad():\n for i_step, (x, y) in enumerate(loader):\n x = x.to(device)\n y = y.to(device)\n prediction = torch.argmax(model(x) , 1) \n for i in range(len(prediction)):\n if prediction[i] == y[i]:\n trueAnswerCounter += float(1)\n totalAnswerCounter += float(len(prediction))\n \n del prediction\n\n return float(trueAnswerCounter/totalAnswerCounter)\n\nloss_history, train_history, val_history = train_model(nn_model, train_loader, val_loader, loss, optimizer, 3)", "Average loss: 1.837043, Train accuracy: 0.405078, Val accuracy: 0.548222\nAverage loss: 1.463647, Train accuracy: 0.577927, Val accuracy: 0.598457\nAverage loss: 1.383073, Train accuracy: 0.616217, Val accuracy: 0.628217\n" ] ], [ [ "## После основного цикла\n\nПосмотрим на другие возможности и оптимизации, которые предоставляет PyTorch.\n\nДобавьте еще один скрытый слой размера 100 нейронов к модели", "_____no_output_____" ] ], [ [ "# Since it's so easy to add layers, let's add some!\n\n# TODO: Implement a model with 2 hidden layers of the size 100\nnn_model = nn.Sequential(\n Flattener(),\n nn.Linear(3*32*32, 100),\n nn.ReLU(inplace=True),\n nn.Linear(100, 100), \n nn.ReLU(inplace=True),\n nn.Linear(100, 10), \n )\nnn_model.type(torch.FloatTensor)\n\noptimizer = optim.SGD(nn_model.parameters(), lr=1e-2, weight_decay=1e-1)\nloss_history, train_history, val_history = train_model(nn_model, train_loader, val_loader, loss, optimizer, 5)", "Average loss: 2.179205, Train accuracy: 0.201515, Val accuracy: 0.232885\nAverage loss: 1.987170, Train accuracy: 0.291813, Val accuracy: 0.346939\nAverage loss: 1.779322, Train accuracy: 0.389875, Val accuracy: 0.422292\nAverage loss: 1.700140, Train accuracy: 0.426373, Val accuracy: 0.424886\nAverage loss: 1.678671, Train accuracy: 0.436764, Val accuracy: 0.444884\n" ] ], [ [ "Добавьте слой с Batch Normalization", "_____no_output_____" ] ], [ [ "# We heard batch normalization is powerful, let's use it!\n# TODO: Add batch normalization after each of the hidden layers of the network, before or after non-linearity\n# Hint: check out torch.nn.BatchNorm1d\n\nnn_model = nn.Sequential(\n Flattener(),\n nn.Linear(3*32*32, 100),\n nn.BatchNorm1d(100),\n nn.ReLU(inplace=True),\n nn.Linear(100, 100), \n nn.BatchNorm1d(100),\n nn.ReLU(inplace=True),\n nn.Linear(100, 10), \n nn.BatchNorm1d(10),\n )\n\noptimizer = optim.SGD(nn_model.parameters(), lr=1e-3, weight_decay=1e-1)\nloss_history, train_history, val_history = train_model(nn_model, train_loader, val_loader, loss, optimizer, 5)", "Average loss: 1.825798, Train accuracy: 0.404737, Val accuracy: 0.566446\nAverage loss: 1.395718, Train accuracy: 0.617343, Val accuracy: 0.679612\nAverage loss: 1.298632, Train accuracy: 0.664096, Val accuracy: 0.698246\nAverage loss: 1.250279, Train accuracy: 0.690271, Val accuracy: 0.706641\nAverage loss: 1.218383, Train accuracy: 0.706583, Val accuracy: 0.715924\n" ], [ "def my_train_model(model, train_loader, val_loader, loss, optimizer, num_epochs, scheduler): \n loss_history = []\n train_history = []\n val_history = []\n model.to(device)\n for epoch in range(num_epochs):\n model.train() # Enter train mode\n \n loss_accum = 0\n correct_samples = 0\n total_samples = 0\n for i_step, (x, y) in enumerate(train_loader):\n x = x.to(device)\n y = y.to(device)\n prediction = model(x) \n loss_value = loss(prediction, y)\n optimizer.zero_grad()\n loss_value.backward()\n optimizer.step()\n \n _, indices = torch.max(prediction, 1)\n correct_samples += torch.sum(indices == y)\n total_samples += y.shape[0]\n \n loss_accum += loss_value\n\n ave_loss = loss_accum / (i_step + 1)\n train_accuracy = float(correct_samples) / total_samples\n val_accuracy = compute_accuracy(model, val_loader)\n \n scheduler.step(ave_loss)\n del loss_accum,correct_samples, total_samples\n \n loss_history.append(float(ave_loss))\n train_history.append(train_accuracy)\n val_history.append(val_accuracy)\n \n print(\"Average loss: %f, Train accuracy: %f, Val accuracy: %f\" % (ave_loss, train_accuracy, val_accuracy))\n \n return loss_history, train_history, val_history", "_____no_output_____" ] ], [ [ "Добавьте уменьшение скорости обучения по ходу тренировки.", "_____no_output_____" ] ], [ [ "# Learning rate annealing\n# Reduce your learning rate 2x every 2 epochs\n# Hint: look up learning rate schedulers in PyTorch. You might need to extend train_model function a little bit too!\n\nnn_model = nn.Sequential(\n Flattener(),\n nn.Linear(3*32*32, 100),\n nn.BatchNorm1d(100),\n nn.ReLU(inplace=True),\n nn.Linear(100, 10), \n nn.BatchNorm1d(10)\n )\n\noptimizer = optim.SGD(nn_model.parameters(), lr=1e-3, weight_decay=1e-1)\nscheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer , factor = 0.5 , patience= 3 , verbose= True)\nloss_history, train_history, val_history = my_train_model(nn_model, train_loader, val_loader, loss, optimizer, 5, scheduler)\n", "Average loss: 1.806619, Train accuracy: 0.427038, Val accuracy: 0.588561\nAverage loss: 1.406469, Train accuracy: 0.619988, Val accuracy: 0.684254\nAverage loss: 1.321503, Train accuracy: 0.667935, Val accuracy: 0.712716\nAverage loss: 1.282513, Train accuracy: 0.689452, Val accuracy: 0.717289\nAverage loss: 1.259725, Train accuracy: 0.706190, Val accuracy: 0.725002\n" ] ], [ [ "# Визуализируем ошибки модели\n\nПопробуем посмотреть, на каких изображениях наша модель ошибается.\nДля этого мы получим все предсказания модели на validation set и сравним их с истинными метками (ground truth).\n\nПервая часть - реализовать код на PyTorch, который вычисляет все предсказания модели на validation set. \nЧтобы это сделать мы приводим код `SubsetSampler`, который просто проходит по всем заданным индексам последовательно и составляет из них батчи. \n\nРеализуйте функцию `evaluate_model`, которая прогоняет модель через все сэмплы validation set и запоминает предсказания модели и истинные метки.", "_____no_output_____" ] ], [ [ "class SubsetSampler(Sampler):\n r\"\"\"Samples elements with given indices sequentially\n\n Arguments:\n indices (ndarray): indices of the samples to take\n \"\"\"\n\n def __init__(self, indices):\n self.indices = indices\n\n def __iter__(self):\n return (self.indices[i] for i in range(len(self.indices)))\n\n def __len__(self):\n return len(self.indices)\n \n \ndef evaluate_model(model, dataset, indices):\n \"\"\"\n Computes predictions and ground truth labels for the indices of the dataset\n \n Returns: \n predictions: np array of ints - model predictions\n grount_truth: np array of ints - actual labels of the dataset\n \"\"\"\n model.eval() # Evaluation mode\n model.to(device)\n val_set = torch.utils.data.DataLoader(dataset, batch_size=len(indices),sampler=SubsetSampler(indices))\n \n # TODO: Evaluate model on the list of indices and capture predictions\n # and ground truth labels\n # Hint: SubsetSampler above could be useful!\n with torch.no_grad():\n for i_step, (x, y) in enumerate(val_set):\n x = x.to(device)\n y = y.to(device)\n predictions = torch.argmax(model(x) , 1) \n ground_truth = y\n return np.array(predictions.cpu()), np.array(ground_truth.cpu())\n\n# Evaluate model on validation\npredictions, gt = evaluate_model(nn_model, data_train, val_indices)\nassert len(predictions) == len(val_indices)\nassert len(gt) == len(val_indices)\nassert gt[100] == data_train[val_indices[100]][1]\nassert np.any(np.not_equal(gt, predictions))", "_____no_output_____" ] ], [ [ "## Confusion matrix\nПервая часть визуализации - вывести confusion matrix (https://en.wikipedia.org/wiki/Confusion_matrix ).\n\nConfusion matrix - это матрица, где каждой строке соответствуют классы предсказанный, а столбцу - классы истинных меток (ground truth). Число с координатами `i,j` - это количество сэмплов класса `j`, которые модель считает классом `i`.\n\n\n\nДля того, чтобы облегчить вам задачу, ниже реализована функция `visualize_confusion_matrix` которая визуализирует такую матрицу. \nВам осталось реализовать функцию `build_confusion_matrix`, которая ее вычислит.\n\nРезультатом должна быть матрица 10x10.", "_____no_output_____" ] ], [ [ "def visualize_confusion_matrix(confusion_matrix):\n \"\"\"\n Visualizes confusion matrix\n \n confusion_matrix: np array of ints, x axis - predicted class, y axis - actual class\n [i][j] should have the count of samples that were predicted to be class i,\n but have j in the ground truth\n \n \"\"\"\n # Adapted from \n # https://stackoverflow.com/questions/2897826/confusion-matrix-with-number-of-classified-misclassified-instances-on-it-python\n assert confusion_matrix.shape[0] == confusion_matrix.shape[1]\n size = confusion_matrix.shape[0]\n fig = plt.figure(figsize=(10,10))\n plt.title(\"Confusion matrix\")\n plt.ylabel(\"predicted\")\n plt.xlabel(\"ground truth\")\n res = plt.imshow(confusion_matrix, cmap='GnBu', interpolation='nearest')\n cb = fig.colorbar(res)\n plt.xticks(np.arange(size))\n plt.yticks(np.arange(size))\n for i, row in enumerate(confusion_matrix):\n for j, count in enumerate(row):\n plt.text(j, i, count, fontsize=14, horizontalalignment='center', verticalalignment='center')\n \ndef build_confusion_matrix(predictions, ground_truth):\n \"\"\"\n Builds confusion matrix from predictions and ground truth\n\n predictions: np array of ints, model predictions for all validation samples\n ground_truth: np array of ints, ground truth for all validation samples\n \n Returns:\n np array of ints, (10,10), counts of samples for predicted/ground_truth classes\n \"\"\"\n \n confusion_matrix = np.zeros((10,10), int)\n \n print(predictions)\n print(ground_truth)\n \n for i in range(predictions.shape[0]):\n confusion_matrix[predictions[i]][ground_truth[i]] += 1\n \n return confusion_matrix\n\n\nconfusion_matrix = build_confusion_matrix(predictions, gt)\nvisualize_confusion_matrix(confusion_matrix)", "[3 2 1 ... 1 1 2]\n[0 7 1 ... 1 1 2]\n" ] ], [ [ "Наконец, посмотрим на изображения, соответствующие некоторым элементам этой матрицы.\n\nКак и раньше, вам дана функция `visualize_images`, которой нужно воспрользоваться при реализации функции `visualize_predicted_actual`. Эта функция должна вывести несколько примеров, соответствующих заданному элементу матрицы.\n\nВизуализируйте наиболее частые ошибки и попробуйте понять, почему модель их совершает.", "_____no_output_____" ] ], [ [ "data_train_images = dset.SVHN('./data/', split='train')\n\ndef visualize_images(indices, data, title='', max_num=10):\n \"\"\"\n Visualizes several images from the dataset\n \n indices: array of indices to visualize\n data: torch Dataset with the images\n title: string, title of the plot\n max_num: int, max number of images to display\n \"\"\"\n to_show = min(len(indices), max_num)\n fig = plt.figure(figsize=(10,1.5))\n fig.suptitle(title)\n for i, index in enumerate(indices[:to_show]):\n plt.subplot(1,to_show, i+1)\n plt.axis('off')\n sample = data[index][0]\n plt.imshow(sample)\n \ndef visualize_predicted_actual(predicted_class, gt_class, predictions, groud_truth, val_indices, data):\n \"\"\"\n Visualizes images of a ground truth class which were predicted as the other class \n \n predicted: int 0-9, index of the predicted class\n gt_class: int 0-9, index of the ground truth class\n predictions: np array of ints, model predictions for all validation samples\n ground_truth: np array of ints, ground truth for all validation samples\n val_indices: np array of ints, indices of validation samples\n \"\"\"\n\n # TODO: Implement visualization using visualize_images above\n # predictions and ground_truth are provided for validation set only, defined by val_indices\n # Hint: numpy index arrays might be helpful\n # https://docs.scipy.org/doc/numpy/user/basics.indexing.html#index-arrays\n # Please make the title meaningful!\n indices = np.where((predictions == predicted_class) & (groud_truth == gt_class))\n visualize_images(val_indices[indices], data)\n\nvisualize_predicted_actual(6, 8, predictions, gt, np.array(val_indices), data_train_images)\nvisualize_predicted_actual(1, 7, predictions, gt, np.array(val_indices), data_train_images)", "_____no_output_____" ] ], [ [ "# Переходим к свободным упражнениям!\n\nНатренируйте модель как можно лучше - экспериментируйте сами!\nЧто следует обязательно попробовать:\n- перебор гиперпараметров с помощью валидационной выборки\n- другие оптимизаторы вместо SGD\n- изменение количества слоев и их размеров\n- наличие Batch Normalization\n\nНо ограничиваться этим не стоит!\n\nТочность на тестовой выборке должна быть доведена до **80%**", "_____no_output_____" ] ], [ [ "nn_model = nn.Sequential(\n Flattener(),\n nn.Linear(3*32*32, 100),\n nn.BatchNorm1d(100),\n nn.ReLU(inplace=True),\n nn.Linear(100, 100),\n nn.BatchNorm1d(100),\n nn.ReLU(inplace=True),\n nn.Linear(100, 10)\n )\n\noptimizer = optim.Adam(nn_model.parameters(), lr=0.8e-3, weight_decay=1e-4)\nsheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer , factor = 0.5 , patience= 3 , verbose= True)\n\nloss_history, train_history, val_history = my_train_model(nn_model, train_loader, val_loader, loss, optimizer, 30, sheduler)", "Average loss: 1.363839, Train accuracy: 0.557451, Val accuracy: 0.693605\nAverage loss: 0.950863, Train accuracy: 0.701191, Val accuracy: 0.735854\nAverage loss: 0.836577, Train accuracy: 0.738406, Val accuracy: 0.772985\nAverage loss: 0.757244, Train accuracy: 0.762584, Val accuracy: 0.780152\nAverage loss: 0.706240, Train accuracy: 0.778436, Val accuracy: 0.799263\nAverage loss: 0.673274, Train accuracy: 0.789612, Val accuracy: 0.809842\nAverage loss: 0.642621, Train accuracy: 0.798946, Val accuracy: 0.809023\nAverage loss: 0.627442, Train accuracy: 0.801949, Val accuracy: 0.802676\nAverage loss: 0.604402, Train accuracy: 0.811589, Val accuracy: 0.815166\nAverage loss: 0.593587, Train accuracy: 0.813074, Val accuracy: 0.821514\nAverage loss: 0.580327, Train accuracy: 0.817493, Val accuracy: 0.823289\nAverage loss: 0.563763, Train accuracy: 0.824574, Val accuracy: 0.828339\nAverage loss: 0.558496, Train accuracy: 0.824370, Val accuracy: 0.815917\nAverage loss: 0.546168, Train accuracy: 0.827526, Val accuracy: 0.831616\nAverage loss: 0.539990, Train accuracy: 0.829215, Val accuracy: 0.824790\nAverage loss: 0.530318, Train accuracy: 0.833362, Val accuracy: 0.831684\nAverage loss: 0.524641, Train accuracy: 0.835051, Val accuracy: 0.828817\nAverage loss: 0.515824, Train accuracy: 0.838703, Val accuracy: 0.837759\nAverage loss: 0.510912, Train accuracy: 0.838481, Val accuracy: 0.831070\nAverage loss: 0.504318, Train accuracy: 0.840614, Val accuracy: 0.832162\nAverage loss: 0.500301, Train accuracy: 0.840682, Val accuracy: 0.838987\nAverage loss: 0.496784, Train accuracy: 0.843173, Val accuracy: 0.840898\nAverage loss: 0.488715, Train accuracy: 0.845357, Val accuracy: 0.840557\nAverage loss: 0.487820, Train accuracy: 0.844965, Val accuracy: 0.840625\nAverage loss: 0.482398, Train accuracy: 0.846756, Val accuracy: 0.834278\nAverage loss: 0.480014, Train accuracy: 0.848173, Val accuracy: 0.835574\nAverage loss: 0.477898, Train accuracy: 0.848787, Val accuracy: 0.840830\nAverage loss: 0.470878, Train accuracy: 0.848463, Val accuracy: 0.834824\nAverage loss: 0.470596, Train accuracy: 0.850322, Val accuracy: 0.841308\nAverage loss: 0.463579, Train accuracy: 0.851397, Val accuracy: 0.833595\n" ], [ "# Как всегда, в конце проверяем на test set\ntest_loader = torch.utils.data.DataLoader(data_test, batch_size=batch_size)\ntest_accuracy = compute_accuracy(nn_model, test_loader)\nprint(\"Test accuracy: %2.4f\" % test_accuracy)", "Test accuracy: 0.8045\n" ] ] ]

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[ [ [ "# Regularization\n\nWelcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that it has never seen!\n\n**You will learn to:** Use regularization in your deep learning models.\n\nLet's first import the packages you are going to use.", "_____no_output_____" ] ], [ [ "# import packages\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec\nfrom reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters\nimport sklearn\nimport sklearn.datasets\nimport scipy.io\nfrom testCases import *\n\n%matplotlib inline\nplt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\nplt.rcParams['image.interpolation'] = 'nearest'\nplt.rcParams['image.cmap'] = 'gray'", "/home/jovyan/work/week5/Regularization/reg_utils.py:85: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n assert(parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l-1])\n/home/jovyan/work/week5/Regularization/reg_utils.py:86: SyntaxWarning: assertion is always true, perhaps remove parentheses?\n assert(parameters['W' + str(l)].shape == layer_dims[l], 1)\n" ] ], [ [ "**Problem Statement**: You have just been hired as an AI expert by the French Football Corporation. They would like you to recommend positions where France's goal keeper should kick the ball so that the French team's players can then hit it with their head. \n\n<img src=\"images/field_kiank.png\" style=\"width:600px;height:350px;\">\n<caption><center> <u> **Figure 1** </u>: **Football field**<br> The goal keeper kicks the ball in the air, the players of each team are fighting to hit the ball with their head </center></caption>\n\n\nThey give you the following 2D dataset from France's past 10 games.", "_____no_output_____" ] ], [ [ "train_X, train_Y, test_X, test_Y = load_2D_dataset()", "_____no_output_____" ] ], [ [ "Each dot corresponds to a position on the football field where a football player has hit the ball with his/her head after the French goal keeper has shot the ball from the left side of the football field.\n- If the dot is blue, it means the French player managed to hit the ball with his/her head\n- If the dot is red, it means the other team's player hit the ball with their head\n\n**Your goal**: Use a deep learning model to find the positions on the field where the goalkeeper should kick the ball.", "_____no_output_____" ], [ "**Analysis of the dataset**: This dataset is a little noisy, but it looks like a diagonal line separating the upper left half (blue) from the lower right half (red) would work well. \n\nYou will first try a non-regularized model. Then you'll learn how to regularize it and decide which model you will choose to solve the French Football Corporation's problem. ", "_____no_output_____" ], [ "## 1 - Non-regularized model\n\nYou will use the following neural network (already implemented for you below). This model can be used:\n- in *regularization mode* -- by setting the `lambd` input to a non-zero value. We use \"`lambd`\" instead of \"`lambda`\" because \"`lambda`\" is a reserved keyword in Python. \n- in *dropout mode* -- by setting the `keep_prob` to a value less than one\n\nYou will first try the model without any regularization. Then, you will implement:\n- *L2 regularization* -- functions: \"`compute_cost_with_regularization()`\" and \"`backward_propagation_with_regularization()`\"\n- *Dropout* -- functions: \"`forward_propagation_with_dropout()`\" and \"`backward_propagation_with_dropout()`\"\n\nIn each part, you will run this model with the correct inputs so that it calls the functions you've implemented. Take a look at the code below to familiarize yourself with the model.", "_____no_output_____" ] ], [ [ "def model(X, Y, learning_rate = 0.3, num_iterations = 30000, print_cost = True, lambd = 0, keep_prob = 1):\n \"\"\"\n Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n \n Arguments:\n X -- input data, of shape (input size, number of examples)\n Y -- true \"label\" vector (1 for blue dot / 0 for red dot), of shape (output size, number of examples)\n learning_rate -- learning rate of the optimization\n num_iterations -- number of iterations of the optimization loop\n print_cost -- If True, print the cost every 10000 iterations\n lambd -- regularization hyperparameter, scalar\n keep_prob - probability of keeping a neuron active during drop-out, scalar.\n \n Returns:\n parameters -- parameters learned by the model. They can then be used to predict.\n \"\"\"\n \n grads = {}\n costs = [] # to keep track of the cost\n m = X.shape[1] # number of examples\n layers_dims = [X.shape[0], 20, 3, 1]\n \n # Initialize parameters dictionary.\n parameters = initialize_parameters(layers_dims)\n\n # Loop (gradient descent)\n\n for i in range(0, num_iterations):\n\n # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n if keep_prob == 1:\n a3, cache = forward_propagation(X, parameters)\n elif keep_prob < 1:\n a3, cache = forward_propagation_with_dropout(X, parameters, keep_prob)\n \n # Cost function\n if lambd == 0:\n cost = compute_cost(a3, Y)\n else:\n cost = compute_cost_with_regularization(a3, Y, parameters, lambd)\n \n # Backward propagation.\n assert(lambd==0 or keep_prob==1) # it is possible to use both L2 regularization and dropout, \n # but this assignment will only explore one at a time\n if lambd == 0 and keep_prob == 1:\n grads = backward_propagation(X, Y, cache)\n elif lambd != 0:\n grads = backward_propagation_with_regularization(X, Y, cache, lambd)\n elif keep_prob < 1:\n grads = backward_propagation_with_dropout(X, Y, cache, keep_prob)\n \n # Update parameters.\n parameters = update_parameters(parameters, grads, learning_rate)\n \n # Print the loss every 10000 iterations\n if print_cost and i % 10000 == 0:\n print(\"Cost after iteration {}: {}\".format(i, cost))\n if print_cost and i % 1000 == 0:\n costs.append(cost)\n \n # plot the cost\n plt.plot(costs)\n plt.ylabel('cost')\n plt.xlabel('iterations (x1,000)')\n plt.title(\"Learning rate =\" + str(learning_rate))\n plt.show()\n \n return parameters", "_____no_output_____" ] ], [ [ "Let's train the model without any regularization, and observe the accuracy on the train/test sets.", "_____no_output_____" ] ], [ [ "parameters = model(train_X, train_Y)\nprint (\"On the training set:\")\npredictions_train = predict(train_X, train_Y, parameters)\nprint (\"On the test set:\")\npredictions_test = predict(test_X, test_Y, parameters)", "Cost after iteration 0: 0.6557412523481002\nCost after iteration 10000: 0.16329987525724216\nCost after iteration 20000: 0.13851642423255986\n" ] ], [ [ "The train accuracy is 94.8% while the test accuracy is 91.5%. This is the **baseline model** (you will observe the impact of regularization on this model). Run the following code to plot the decision boundary of your model.", "_____no_output_____" ] ], [ [ "plt.title(\"Model without regularization\")\naxes = plt.gca()\naxes.set_xlim([-0.75,0.40])\naxes.set_ylim([-0.75,0.65])\nplot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)", "_____no_output_____" ] ], [ [ "The non-regularized model is obviously overfitting the training set. It is fitting the noisy points! Lets now look at two techniques to reduce overfitting.", "_____no_output_____" ], [ "## 2 - L2 Regularization\n\nThe standard way to avoid overfitting is called **L2 regularization**. It consists of appropriately modifying your cost function, from:\n$$J = -\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} \\tag{1}$$\nTo:\n$$J_{regularized} = \\small \\underbrace{-\\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(}\\small y^{(i)}\\log\\left(a^{[L](i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right) \\large{)} }_\\text{cross-entropy cost} + \\underbrace{\\frac{1}{m} \\frac{\\lambda}{2} \\sum\\limits_l\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2} }_\\text{L2 regularization cost} \\tag{2}$$\n\nLet's modify your cost and observe the consequences.\n\n**Exercise**: Implement `compute_cost_with_regularization()` which computes the cost given by formula (2). To calculate $\\sum\\limits_k\\sum\\limits_j W_{k,j}^{[l]2}$ , use :\n```python\nnp.sum(np.square(Wl))\n```\nNote that you have to do this for $W^{[1]}$, $W^{[2]}$ and $W^{[3]}$, then sum the three terms and multiply by $ \\frac{1}{m} \\frac{\\lambda}{2} $.", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: compute_cost_with_regularization\n\ndef compute_cost_with_regularization(A3, Y, parameters, lambd):\n \"\"\"\n Implement the cost function with L2 regularization. See formula (2) above.\n \n Arguments:\n A3 -- post-activation, output of forward propagation, of shape (output size, number of examples)\n Y -- \"true\" labels vector, of shape (output size, number of examples)\n parameters -- python dictionary containing parameters of the model\n \n Returns:\n cost - value of the regularized loss function (formula (2))\n \"\"\"\n m = Y.shape[1]\n W1 = parameters[\"W1\"]\n W2 = parameters[\"W2\"]\n W3 = parameters[\"W3\"]\n \n cross_entropy_cost = compute_cost(A3, Y) # This gives you the cross-entropy part of the cost\n \n ### START CODE HERE ### (approx. 1 line)\n L2_regularization_cost = (lambd/(2*m)) * (np.sum(np.square(W1)) + np.sum(np.square(W2)) + np.sum(np.square(W3)))\n ### END CODER HERE ###\n \n cost = cross_entropy_cost + L2_regularization_cost\n \n return cost", "_____no_output_____" ], [ "A3, Y_assess, parameters = compute_cost_with_regularization_test_case()\n\nprint(\"cost = \" + str(compute_cost_with_regularization(A3, Y_assess, parameters, lambd = 0.1)))", "cost = 1.78648594516\n" ] ], [ [ "**Expected Output**: \n\n<table> \n <tr>\n <td>\n **cost**\n </td>\n <td>\n 1.78648594516\n </td>\n \n </tr>\n\n</table> ", "_____no_output_____" ], [ "Of course, because you changed the cost, you have to change backward propagation as well! All the gradients have to be computed with respect to this new cost. \n\n**Exercise**: Implement the changes needed in backward propagation to take into account regularization. The changes only concern dW1, dW2 and dW3. For each, you have to add the regularization term's gradient ($\\frac{d}{dW} ( \\frac{1}{2}\\frac{\\lambda}{m} W^2) = \\frac{\\lambda}{m} W$).", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: backward_propagation_with_regularization\n\ndef backward_propagation_with_regularization(X, Y, cache, lambd):\n \"\"\"\n Implements the backward propagation of our baseline model to which we added an L2 regularization.\n \n Arguments:\n X -- input dataset, of shape (input size, number of examples)\n Y -- \"true\" labels vector, of shape (output size, number of examples)\n cache -- cache output from forward_propagation()\n lambd -- regularization hyperparameter, scalar\n \n Returns:\n gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n \"\"\"\n \n m = X.shape[1]\n (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n \n dZ3 = A3 - Y\n \n ### START CODE HERE ### (approx. 1 line)\n dW3 = 1./m * np.dot(dZ3, A2.T) + lambd/m * W3\n ### END CODE HERE ###\n db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n \n dA2 = np.dot(W3.T, dZ3)\n dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n ### START CODE HERE ### (approx. 1 line)\n dW2 = 1./m * np.dot(dZ2, A1.T) + lambd/m * W2\n ### END CODE HERE ###\n db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n \n dA1 = np.dot(W2.T, dZ2)\n dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n ### START CODE HERE ### (approx. 1 line)\n dW1 = 1./m * np.dot(dZ1, X.T) + lambd/m * W1\n ### END CODE HERE ###\n db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n \n gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n \n return gradients", "_____no_output_____" ], [ "X_assess, Y_assess, cache = backward_propagation_with_regularization_test_case()\n\ngrads = backward_propagation_with_regularization(X_assess, Y_assess, cache, lambd = 0.7)\nprint (\"dW1 = \"+ str(grads[\"dW1\"]))\nprint (\"dW2 = \"+ str(grads[\"dW2\"]))\nprint (\"dW3 = \"+ str(grads[\"dW3\"]))", "dW1 = [[-0.25604646 0.12298827 -0.28297129]\n [-0.17706303 0.34536094 -0.4410571 ]]\ndW2 = [[ 0.79276486 0.85133918]\n [-0.0957219 -0.01720463]\n [-0.13100772 -0.03750433]]\ndW3 = [[-1.77691347 -0.11832879 -0.09397446]]\n" ] ], [ [ "**Expected Output**:\n\n<table> \n <tr>\n <td>\n **dW1**\n </td>\n <td>\n [[-0.25604646 0.12298827 -0.28297129]\n [-0.17706303 0.34536094 -0.4410571 ]]\n </td>\n </tr>\n <tr>\n <td>\n **dW2**\n </td>\n <td>\n [[ 0.79276486 0.85133918]\n [-0.0957219 -0.01720463]\n [-0.13100772 -0.03750433]]\n </td>\n </tr>\n <tr>\n <td>\n **dW3**\n </td>\n <td>\n [[-1.77691347 -0.11832879 -0.09397446]]\n </td>\n </tr>\n</table> ", "_____no_output_____" ], [ "Let's now run the model with L2 regularization $(\\lambda = 0.7)$. The `model()` function will call: \n- `compute_cost_with_regularization` instead of `compute_cost`\n- `backward_propagation_with_regularization` instead of `backward_propagation`", "_____no_output_____" ] ], [ [ "parameters = model(train_X, train_Y, lambd = 0.7)\nprint (\"On the train set:\")\npredictions_train = predict(train_X, train_Y, parameters)\nprint (\"On the test set:\")\npredictions_test = predict(test_X, test_Y, parameters)", "Cost after iteration 0: 0.6974484493131264\nCost after iteration 10000: 0.2684918873282239\nCost after iteration 20000: 0.2680916337127301\n" ] ], [ [ "Congrats, the test set accuracy increased to 93%. You have saved the French football team!\n\nYou are not overfitting the training data anymore. Let's plot the decision boundary.", "_____no_output_____" ] ], [ [ "plt.title(\"Model with L2-regularization\")\naxes = plt.gca()\naxes.set_xlim([-0.75,0.40])\naxes.set_ylim([-0.75,0.65])\nplot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)", "_____no_output_____" ] ], [ [ "**Observations**:\n- The value of $\\lambda$ is a hyperparameter that you can tune using a dev set.\n- L2 regularization makes your decision boundary smoother. If $\\lambda$ is too large, it is also possible to \"oversmooth\", resulting in a model with high bias.\n\n**What is L2-regularization actually doing?**:\n\nL2-regularization relies on the assumption that a model with small weights is simpler than a model with large weights. Thus, by penalizing the square values of the weights in the cost function you drive all the weights to smaller values. It becomes too costly for the cost to have large weights! This leads to a smoother model in which the output changes more slowly as the input changes. \n\n<font color='blue'>\n**What you should remember** -- the implications of L2-regularization on:\n- The cost computation:\n - A regularization term is added to the cost\n- The backpropagation function:\n - There are extra terms in the gradients with respect to weight matrices\n- Weights end up smaller (\"weight decay\"): \n - Weights are pushed to smaller values.", "_____no_output_____" ], [ "## 3 - Dropout\n\nFinally, **dropout** is a widely used regularization technique that is specific to deep learning. \n**It randomly shuts down some neurons in each iteration.** Watch these two videos to see what this means!\n\n<!--\nTo understand drop-out, consider this conversation with a friend:\n- Friend: \"Why do you need all these neurons to train your network and classify images?\". \n- You: \"Because each neuron contains a weight and can learn specific features/details/shape of an image. The more neurons I have, the more featurse my model learns!\"\n- Friend: \"I see, but are you sure that your neurons are learning different features and not all the same features?\"\n- You: \"Good point... Neurons in the same layer actually don't talk to each other. It should be definitly possible that they learn the same image features/shapes/forms/details... which would be redundant. There should be a solution.\"\n!--> \n\n\n<center>\n<video width=\"620\" height=\"440\" src=\"images/dropout1_kiank.mp4\" type=\"video/mp4\" controls>\n</video>\n</center>\n<br>\n<caption><center> <u> Figure 2 </u>: Drop-out on the second hidden layer. <br> At each iteration, you shut down (= set to zero) each neuron of a layer with probability $1 - keep\\_prob$ or keep it with probability $keep\\_prob$ (50% here). The dropped neurons don't contribute to the training in both the forward and backward propagations of the iteration. </center></caption>\n\n<center>\n<video width=\"620\" height=\"440\" src=\"images/dropout2_kiank.mp4\" type=\"video/mp4\" controls>\n</video>\n</center>\n\n<caption><center> <u> Figure 3 </u>: Drop-out on the first and third hidden layers. <br> $1^{st}$ layer: we shut down on average 40% of the neurons. $3^{rd}$ layer: we shut down on average 20% of the neurons. </center></caption>\n\n\nWhen you shut some neurons down, you actually modify your model. The idea behind drop-out is that at each iteration, you train a different model that uses only a subset of your neurons. With dropout, your neurons thus become less sensitive to the activation of one other specific neuron, because that other neuron might be shut down at any time. \n\n### 3.1 - Forward propagation with dropout\n\n**Exercise**: Implement the forward propagation with dropout. You are using a 3 layer neural network, and will add dropout to the first and second hidden layers. We will not apply dropout to the input layer or output layer. \n\n**Instructions**:\nYou would like to shut down some neurons in the first and second layers. To do that, you are going to carry out 4 Steps:\n1. In lecture, we dicussed creating a variable $d^{[1]}$ with the same shape as $a^{[1]}$ using `np.random.rand()` to randomly get numbers between 0 and 1. Here, you will use a vectorized implementation, so create a random matrix $D^{[1]} = [d^{[1](1)} d^{[1](2)} ... d^{[1](m)}] $ of the same dimension as $A^{[1]}$.\n2. Set each entry of $D^{[1]}$ to be 0 with probability (`1-keep_prob`) or 1 with probability (`keep_prob`), by thresholding values in $D^{[1]}$ appropriately. Hint: to set all the entries of a matrix X to 0 (if entry is less than 0.5) or 1 (if entry is more than 0.5) you would do: `X = (X < 0.5)`. Note that 0 and 1 are respectively equivalent to False and True.\n3. Set $A^{[1]}$ to $A^{[1]} * D^{[1]}$. (You are shutting down some neurons). You can think of $D^{[1]}$ as a mask, so that when it is multiplied with another matrix, it shuts down some of the values.\n4. Divide $A^{[1]}$ by `keep_prob`. By doing this you are assuring that the result of the cost will still have the same expected value as without drop-out. (This technique is also called inverted dropout.)", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: forward_propagation_with_dropout\n\ndef forward_propagation_with_dropout(X, parameters, keep_prob = 0.5):\n \"\"\"\n Implements the forward propagation: LINEAR -> RELU + DROPOUT -> LINEAR -> RELU + DROPOUT -> LINEAR -> SIGMOID.\n \n Arguments:\n X -- input dataset, of shape (2, number of examples)\n parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n W1 -- weight matrix of shape (20, 2)\n b1 -- bias vector of shape (20, 1)\n W2 -- weight matrix of shape (3, 20)\n b2 -- bias vector of shape (3, 1)\n W3 -- weight matrix of shape (1, 3)\n b3 -- bias vector of shape (1, 1)\n keep_prob - probability of keeping a neuron active during drop-out, scalar\n \n Returns:\n A3 -- last activation value, output of the forward propagation, of shape (1,1)\n cache -- tuple, information stored for computing the backward propagation\n \"\"\"\n \n np.random.seed(1)\n \n # retrieve parameters\n W1 = parameters[\"W1\"]\n b1 = parameters[\"b1\"]\n W2 = parameters[\"W2\"]\n b2 = parameters[\"b2\"]\n W3 = parameters[\"W3\"]\n b3 = parameters[\"b3\"]\n \n # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n Z1 = np.dot(W1, X) + b1\n A1 = relu(Z1)\n ### START CODE HERE ### (approx. 4 lines) # Steps 1-4 below correspond to the Steps 1-4 described above. \n D1 = np.random.rand(A1.shape[0], A1.shape[1]) # Step 1: initialize matrix D1 = np.random.rand(..., ...)\n D1 = D1 < keep_prob # Step 2: convert entries of D1 to 0 or 1 (using keep_prob as the threshold)\n A1 = A1*D1 # Step 3: shut down some neurons of A1\n A1 = A1/keep_prob # Step 4: scale the value of neurons that haven't been shut down\n ### END CODE HERE ###\n Z2 = np.dot(W2, A1) + b2\n A2 = relu(Z2)\n ### START CODE HERE ### (approx. 4 lines)\n D2 = np.random.rand(A2.shape[0],A2.shape[1]) # Step 1: initialize matrix D2 = np.random.rand(..., ...)\n D2 = (D2 < keep_prob)\n # Step 2: convert entries of D2 to 0 or 1 (using keep_prob as the threshold)\n A2 = A2*D2 # Step 3: shut down some neurons of A2\n A2 = A2/keep_prob # Step 4: scale the value of neurons that haven't been shut down\n ### END CODE HERE ###\n Z3 = np.dot(W3, A2) + b3\n A3 = sigmoid(Z3)\n \n cache = (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3)\n \n return A3, cache", "_____no_output_____" ], [ "X_assess, parameters = forward_propagation_with_dropout_test_case()\n\nA3, cache = forward_propagation_with_dropout(X_assess, parameters, keep_prob = 0.7)\nprint (\"A3 = \" + str(A3))", "A3 = [[ 0.36974721 0.00305176 0.04565099 0.49683389 0.36974721]]\n" ] ], [ [ "**Expected Output**: \n\n<table> \n <tr>\n <td>\n **A3**\n </td>\n <td>\n [[ 0.36974721 0.00305176 0.04565099 0.49683389 0.36974721]]\n </td>\n \n </tr>\n\n</table> ", "_____no_output_____" ], [ "### 3.2 - Backward propagation with dropout\n\n**Exercise**: Implement the backward propagation with dropout. As before, you are training a 3 layer network. Add dropout to the first and second hidden layers, using the masks $D^{[1]}$ and $D^{[2]}$ stored in the cache. \n\n**Instruction**:\nBackpropagation with dropout is actually quite easy. You will have to carry out 2 Steps:\n1. You had previously shut down some neurons during forward propagation, by applying a mask $D^{[1]}$ to `A1`. In backpropagation, you will have to shut down the same neurons, by reapplying the same mask $D^{[1]}$ to `dA1`. \n2. During forward propagation, you had divided `A1` by `keep_prob`. In backpropagation, you'll therefore have to divide `dA1` by `keep_prob` again (the calculus interpretation is that if $A^{[1]}$ is scaled by `keep_prob`, then its derivative $dA^{[1]}$ is also scaled by the same `keep_prob`).\n", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: backward_propagation_with_dropout\n\ndef backward_propagation_with_dropout(X, Y, cache, keep_prob):\n \"\"\"\n Implements the backward propagation of our baseline model to which we added dropout.\n \n Arguments:\n X -- input dataset, of shape (2, number of examples)\n Y -- \"true\" labels vector, of shape (output size, number of examples)\n cache -- cache output from forward_propagation_with_dropout()\n keep_prob - probability of keeping a neuron active during drop-out, scalar\n \n Returns:\n gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n \"\"\"\n \n m = X.shape[1]\n (Z1, D1, A1, W1, b1, Z2, D2, A2, W2, b2, Z3, A3, W3, b3) = cache\n \n dZ3 = A3 - Y\n dW3 = 1./m * np.dot(dZ3, A2.T)\n db3 = 1./m * np.sum(dZ3, axis=1, keepdims = True)\n dA2 = np.dot(W3.T, dZ3)\n ### START CODE HERE ### (≈ 2 lines of code)\n dA2 = dA2*D2 # Step 1: Apply mask D2 to shut down the same neurons as during the forward propagation\n dA2 = dA2/keep_prob # Step 2: Scale the value of neurons that haven't been shut down\n ### END CODE HERE ###\n dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n dW2 = 1./m * np.dot(dZ2, A1.T)\n db2 = 1./m * np.sum(dZ2, axis=1, keepdims = True)\n \n dA1 = np.dot(W2.T, dZ2)\n ### START CODE HERE ### (≈ 2 lines of code)\n dA1 = dA1*D1 # Step 1: Apply mask D1 to shut down the same neurons as during the forward propagation\n dA1 = dA1/keep_prob # Step 2: Scale the value of neurons that haven't been shut down\n ### END CODE HERE ###\n dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n dW1 = 1./m * np.dot(dZ1, X.T)\n db1 = 1./m * np.sum(dZ1, axis=1, keepdims = True)\n \n gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\"dA2\": dA2,\n \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2, \"dA1\": dA1, \n \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n \n return gradients", "_____no_output_____" ], [ "X_assess, Y_assess, cache = backward_propagation_with_dropout_test_case()\n\ngradients = backward_propagation_with_dropout(X_assess, Y_assess, cache, keep_prob = 0.8)\n\nprint (\"dA1 = \" + str(gradients[\"dA1\"]))\nprint (\"dA2 = \" + str(gradients[\"dA2\"]))", "dA1 = [[ 0.36544439 0. -0.00188233 0. -0.17408748]\n [ 0.65515713 0. -0.00337459 0. -0. ]]\ndA2 = [[ 0.58180856 0. -0.00299679 0. -0.27715731]\n [ 0. 0.53159854 -0. 0.53159854 -0.34089673]\n [ 0. 0. -0.00292733 0. -0. ]]\n" ] ], [ [ "**Expected Output**: \n\n<table> \n <tr>\n <td>\n **dA1**\n </td>\n <td>\n [[ 0.36544439 0. -0.00188233 0. -0.17408748]\n [ 0.65515713 0. -0.00337459 0. -0. ]]\n </td>\n \n </tr>\n <tr>\n <td>\n **dA2**\n </td>\n <td>\n [[ 0.58180856 0. -0.00299679 0. -0.27715731]\n [ 0. 0.53159854 -0. 0.53159854 -0.34089673]\n [ 0. 0. -0.00292733 0. -0. ]]\n </td>\n \n </tr>\n</table> ", "_____no_output_____" ], [ "Let's now run the model with dropout (`keep_prob = 0.86`). It means at every iteration you shut down each neurons of layer 1 and 2 with 24% probability. The function `model()` will now call:\n- `forward_propagation_with_dropout` instead of `forward_propagation`.\n- `backward_propagation_with_dropout` instead of `backward_propagation`.", "_____no_output_____" ] ], [ [ "parameters = model(train_X, train_Y, keep_prob = 0.86, learning_rate = 0.3)\n\nprint (\"On the train set:\")\npredictions_train = predict(train_X, train_Y, parameters)\nprint (\"On the test set:\")\npredictions_test = predict(test_X, test_Y, parameters)", "Cost after iteration 0: 0.6543912405149825\n" ] ], [ [ "Dropout works great! The test accuracy has increased again (to 95%)! Your model is not overfitting the training set and does a great job on the test set. The French football team will be forever grateful to you! \n\nRun the code below to plot the decision boundary.", "_____no_output_____" ] ], [ [ "plt.title(\"Model with dropout\")\naxes = plt.gca()\naxes.set_xlim([-0.75,0.40])\naxes.set_ylim([-0.75,0.65])\nplot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)", "_____no_output_____" ] ], [ [ "**Note**:\n- A **common mistake** when using dropout is to use it both in training and testing. You should use dropout (randomly eliminate nodes) only in training. \n- Deep learning frameworks like [tensorflow](https://www.tensorflow.org/api_docs/python/tf/nn/dropout), [PaddlePaddle](http://doc.paddlepaddle.org/release_doc/0.9.0/doc/ui/api/trainer_config_helpers/attrs.html), [keras](https://keras.io/layers/core/#dropout) or [caffe](http://caffe.berkeleyvision.org/tutorial/layers/dropout.html) come with a dropout layer implementation. Don't stress - you will soon learn some of these frameworks.\n\n<font color='blue'>\n**What you should remember about dropout:**\n- Dropout is a regularization technique.\n- You only use dropout during training. Don't use dropout (randomly eliminate nodes) during test time.\n- Apply dropout both during forward and backward propagation.\n- During training time, divide each dropout layer by keep_prob to keep the same expected value for the activations. For example, if keep_prob is 0.5, then we will on average shut down half the nodes, so the output will be scaled by 0.5 since only the remaining half are contributing to the solution. Dividing by 0.5 is equivalent to multiplying by 2. Hence, the output now has the same expected value. You can check that this works even when keep_prob is other values than 0.5. ", "_____no_output_____" ], [ "## 4 - Conclusions", "_____no_output_____" ], [ "**Here are the results of our three models**: \n\n<table> \n <tr>\n <td>\n **model**\n </td>\n <td>\n **train accuracy**\n </td>\n <td>\n **test accuracy**\n </td>\n\n </tr>\n <td>\n 3-layer NN without regularization\n </td>\n <td>\n 95%\n </td>\n <td>\n 91.5%\n </td>\n <tr>\n <td>\n 3-layer NN with L2-regularization\n </td>\n <td>\n 94%\n </td>\n <td>\n 93%\n </td>\n </tr>\n <tr>\n <td>\n 3-layer NN with dropout\n </td>\n <td>\n 93%\n </td>\n <td>\n 95%\n </td>\n </tr>\n</table> ", "_____no_output_____" ], [ "Note that regularization hurts training set performance! This is because it limits the ability of the network to overfit to the training set. But since it ultimately gives better test accuracy, it is helping your system. ", "_____no_output_____" ], [ "Congratulations for finishing this assignment! And also for revolutionizing French football. :-) ", "_____no_output_____" ], [ "<font color='blue'>\n**What we want you to remember from this notebook**:\n- Regularization will help you reduce overfitting.\n- Regularization will drive your weights to lower values.\n- L2 regularization and Dropout are two very effective regularization techniques.", "_____no_output_____" ] ] ]

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[ [ [ "# Table of Contents\n* [1c. Fixed flux spinodal decomposition on a T shaped domain](#1c.-Fixed-flux-spinodal-decomposition-on-a-T-shaped-domain)\n\t* [Use Binder For Live Examples](#Use-Binder-For-Live-Examples)\n\t* [Define $f_0$](#Define-$f_0$)\n\t* [Define the Equation](#Define-the-Equation)\n\t* [Solve the Equation](#Solve-the-Equation)\n\t* [Run the Example Locally](#Run-the-Example-Locally)\n\t* [Movie of Evolution](#Movie-of-Evolution)\n", "_____no_output_____" ], [ "# 1c. Fixed flux spinodal decomposition on a T shaped domain", "_____no_output_____" ], [ "## Use Binder For Live Examples", "_____no_output_____" ], [ "[](http://mybinder.org/repo/wd15/fipy-hackathon1)", "_____no_output_____" ], [ "The free energy is given by,\n\n$$ f_0\\left[ c \\left( \\vec{r} \\right) \\right] =\n - \\frac{A}{2} \\left(c - c_m\\right)^2\n + \\frac{B}{4} \\left(c - c_m\\right)^4\n + \\frac{c_{\\alpha}}{4} \\left(c - c_{\\alpha} \\right)^4\n + \\frac{c_{\\beta}}{4} \\left(c - c_{\\beta} \\right)^4 $$\n\nIn FiPy we write the evolution equation as \n\n$$ \\frac{\\partial c}{\\partial t} = \\nabla \\cdot \\left[\n D \\left( c \\right) \\left( \\frac{ \\partial^2 f_0 }{ \\partial c^2} \\nabla c - \\kappa \\nabla \\nabla^2 c \\right)\n \\right] $$\n\nLet's start by calculating $ \\frac{ \\partial^2 f_0 }{ \\partial c^2} $ using sympy. It's easy for this case, but useful in the general case for taking care of difficult book keeping in phase field problems.", "_____no_output_____" ] ], [ [ "%matplotlib inline\n\nimport sympy\nimport fipy as fp\nimport numpy as np", "_____no_output_____" ], [ "A, c, c_m, B, c_alpha, c_beta = sympy.symbols(\"A c_var c_m B c_alpha c_beta\")", "_____no_output_____" ], [ "f_0 = - A / 2 * (c - c_m)**2 + B / 4 * (c - c_m)**4 + c_alpha / 4 * (c - c_alpha)**4 + c_beta / 4 * (c - c_beta)**4", "_____no_output_____" ], [ "print f_0", "-A*(-c_m + c_var)**2/2 + B*(-c_m + c_var)**4/4 + c_alpha*(-c_alpha + c_var)**4/4 + c_beta*(-c_beta + c_var)**4/4\n" ], [ "sympy.diff(f_0, c, 2)", "_____no_output_____" ] ], [ [ "The first step in implementing any problem in FiPy is to define the mesh. For [Problem 1a]({{ site.baseurl }}/hackathons/hackathon1/problems.ipynb/#1.a-Square-Periodic) the solution domain is just a square domain, but the boundary conditions are periodic, so a `PeriodicGrid2D` object is used. No other boundary conditions are required.", "_____no_output_____" ] ], [ [ "mesh = fp.Grid2D(dx=0.5, dy=0.5, nx=40, ny=200) + (fp.Grid2D(dx=0.5, dy=0.5, nx=200, ny=40) + [[-40],[100]])", "_____no_output_____" ] ], [ [ "The next step is to define the parameters and create a solution variable.", "_____no_output_____" ] ], [ [ "c_alpha = 0.05\nc_beta = 0.95\nA = 2.0\nkappa = 2.0\nc_m = (c_alpha + c_beta) / 2.\nB = A / (c_alpha - c_m)**2\nD = D_alpha = D_beta = 2. / (c_beta - c_alpha)\nc_0 = 0.45\nq = np.sqrt((2., 3.))\nepsilon = 0.01\n\nc_var = fp.CellVariable(mesh=mesh, name=r\"$c$\", hasOld=True)", "_____no_output_____" ] ], [ [ "Now we need to define the initial conditions given by,\n\nSet $c\\left(\\vec{r}, t\\right)$ such that\n\n$$ c\\left(\\vec{r}, 0\\right) = \\bar{c}_0 + \\epsilon \\cos \\left( \\vec{q} \\cdot \\vec{r} \\right) $$", "_____no_output_____" ] ], [ [ "r = np.array((mesh.x, mesh.y))\nc_var[:] = c_0 + epsilon * np.cos((q[:, None] * r).sum(0))\nviewer = fp.Viewer(c_var)", "_____no_output_____" ] ], [ [ "## Define $f_0$", "_____no_output_____" ], [ "To define the equation with FiPy first define `f_0` in terms of FiPy. Recall `f_0` from above calculated using Sympy. Here we use the string representation and set it equal to `f_0_var` using the `exec` command.", "_____no_output_____" ] ], [ [ "out = sympy.diff(f_0, c, 2)", "_____no_output_____" ], [ "exec \"f_0_var = \" + repr(out)", "_____no_output_____" ], [ "#f_0_var = -A + 3*B*(c_var - c_m)**2 + 3*c_alpha*(c_var - c_alpha)**2 + 3*c_beta*(c_var - c_beta)**2\nf_0_var", "_____no_output_____" ] ], [ [ "## Define the Equation", "_____no_output_____" ] ], [ [ "eqn = fp.TransientTerm(coeff=1.) == fp.DiffusionTerm(D * f_0_var) - fp.DiffusionTerm((D, kappa))\neqn", "_____no_output_____" ] ], [ [ "## Solve the Equation", "_____no_output_____" ], [ "To solve the equation a simple time stepping scheme is used which is decreased or increased based on whether the residual decreases or increases. A time step is recalculated if the required tolerance is not reached.", "_____no_output_____" ] ], [ [ "elapsed = 0.0\nsteps = 0\ndt = 0.01\ntotal_sweeps = 2\ntolerance = 1e-1\ntotal_steps = 10", "_____no_output_____" ], [ "c_var[:] = c_0 + epsilon * np.cos((q[:, None] * r).sum(0))\nc_var.updateOld()\nfrom fipy.solvers.pysparse import LinearLUSolver as Solver\nsolver = Solver()\n\nwhile steps < total_steps:\n res0 = eqn.sweep(c_var, dt=dt, solver=solver)\n\n for sweeps in range(total_sweeps):\n res = eqn.sweep(c_var, dt=dt, solver=solver)\n\n if res < res0 * tolerance:\n steps += 1\n elapsed += dt\n dt *= 1.1\n c_var.updateOld()\n else:\n dt *= 0.8\n c_var[:] = c_var.old\n\nviewer.plot()\nprint 'elapsed_time:',elapsed", "_____no_output_____" ] ], [ [ "## Run the Example Locally", "_____no_output_____" ], [ "The following cell will dumpy a file called `fipy_hackathon1c.py` to the local file system to be run. The images are saved out at each time step.", "_____no_output_____" ] ], [ [ "%%writefile fipy_hackathon_1c.py\n\nimport fipy as fp\nimport numpy as np\n\nmesh = fp.Grid2D(dx=0.5, dy=0.5, nx=40, ny=200) + (fp.Grid2D(dx=0.5, dy=0.5, nx=200, ny=40) + [[-40],[100]])\n\nc_alpha = 0.05\nc_beta = 0.95\nA = 2.0\nkappa = 2.0\nc_m = (c_alpha + c_beta) / 2.\nB = A / (c_alpha - c_m)**2\nD = D_alpha = D_beta = 2. / (c_beta - c_alpha)\nc_0 = 0.45\nq = np.sqrt((2., 3.))\nepsilon = 0.01\n\nc_var = fp.CellVariable(mesh=mesh, name=r\"$c$\", hasOld=True)\n\nr = np.array((mesh.x, mesh.y))\nc_var[:] = c_0 + epsilon * np.cos((q[:, None] * r).sum(0))\n\nf_0_var = -A + 3*B*(c_var - c_m)**2 + 3*c_alpha*(c_var - c_alpha)**2 + 3*c_beta*(c_var - c_beta)**2\n\neqn = fp.TransientTerm(coeff=1.) == fp.DiffusionTerm(D * f_0_var) - fp.DiffusionTerm((D, kappa))\n\nelapsed = 0.0\nsteps = 0\ndt = 0.01\ntotal_sweeps = 2\ntolerance = 1e-1\ntotal_steps = 600\n\nc_var[:] = c_0 + epsilon * np.cos((q[:, None] * r).sum(0))\n\nc_var.updateOld()\n\nfrom fipy.solvers.pysparse import LinearLUSolver as Solver\n\nsolver = Solver()\n\nviewer = fp.Viewer(c_var)\nwhile steps < total_steps:\n res0 = eqn.sweep(c_var, dt=dt, solver=solver)\n\n for sweeps in range(total_sweeps):\n res = eqn.sweep(c_var, dt=dt, solver=solver)\n\n print ' '\n print 'steps',steps\n print 'res',res\n print 'sweeps',sweeps\n print 'dt',dt\n\n\n if res < res0 * tolerance:\n steps += 1\n elapsed += dt\n dt *= 1.1\n if steps % 1 == 0:\n viewer.plot('image{0}.png'.format(steps))\n c_var.updateOld()\n else:\n dt *= 0.8\n c_var[:] = c_var.old", "Overwriting fipy_hackathon_1c.py\n" ] ], [ [ "## Movie of Evolution", "_____no_output_____" ], [ "The movie of the evolution for 600 steps.\n\nThe movie was generated with the output files of the form `image*.png` using the following commands,\n\n $ rename 's/\\d+/sprintf(\"%05d\",$&)/e' image*\n $ ffmpeg -f image2 -r 6 -i 'image%05d.png' output.mp4", "_____no_output_____" ] ], [ [ "from IPython.display import YouTubeVideo\nscale = 1.5\nYouTubeVideo('aZk38E7OxcQ', width=420 * scale, height=315 * scale, rel=0)", "_____no_output_____" ] ] ]

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[ [ "markdown", "markdown", "markdown", "markdown", "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "##### 아래 URL의 NBA 데이터를 크롤링하여 판다스 데이터 프레임으로 나타내세요. \n- http://stats.nba.com/teams/traditional/?sort=GP&dir=-1", "_____no_output_____" ] ], [ [ "from selenium import webdriver", "_____no_output_____" ], [ "import pandas as pd", "_____no_output_____" ], [ "def Nba():\n\n try:\n\n driver = webdriver.Chrome()\n driver.get(\"http://stats.nba.com/teams/traditional/?sort=GP&dir=-1\")\n\n # column_list\n columns = driver.find_elements_by_css_selector(\".nba-stat-table__overflow > table > \\\n thead > tr > th\")[1:28]\n column_list = [\" \",]\n for column in columns:\n column = column.text\n column_list.append(column)\n\n # making dataframe\n df = pd.DataFrame(columns=column_list, index = [\" \" for i in range(30)])\n\n # data\n data = driver.find_elements_by_css_selector\\\n (\".nba-stat-table__overflow > table > tbody > tr > td\")\n data_list = []\n\n for datum in data:\n datum = datum.text.replace(\"\\n\", \" \")\n data_list.append(datum)\n\n grouped = list(zip(*[iter(data_list)] * 28))\n\n for i in range(30):\n for j in range(28):\n df.iloc[i, j] = grouped[i][j]\n\n driver.quit()\n\n except:\n\n driver.quit()\n\n return df\n", "_____no_output_____" ], [ "Nba()", "_____no_output_____" ] ], [ [ "##### 셀레니움을 이용하여 네이버 IT/과학 기사의 10 페이지 까지의 최신 제목 리스트를 크롤링하세요.\n- http://news.naver.com/main/main.nhn?mode=LSD&mid=shm&sid1=105", "_____no_output_____" ] ], [ [ "def News():\n \n try:\n\n driver = webdriver.Chrome()\n driver.get(\"http://news.naver.com/main/main.nhn?mode=LSD&mid=shm&sid1=105\")\n\n # news = pd.DataFrame(columns = [\"Number of Articles\",\"Article Headline\"])\n\n for i in range(10):\n results = driver.find_elements_by_css_selector(\"#section_body > ul > li > dl > dt:nth-child(2) > a\")\n for result in results:\n result = result.text\n print(result)\n \n driver.find_element_by_css_selector(\"#paging > a:nth-child({})\".format (i+2)).click()\n\n driver.quit()\n \n except:\n driver.quit()\n", "_____no_output_____" ], [ "News()", "웹젠, ‘일하기 좋은 회사’로 바뀐다\n권영수 LGU+ “5G장비, 화웨이 가장 앞서…5G 킬러서비스는 고민”\n권영수 \"5G 장비서 中 화웨이 가장 앞서\"\nLG유플러스, 화웨이 5G 장비 '호평'…도입 유력\n'화웨이' 만난 권영수 LGU+ 부회장 \"기술력 가장 앞선다\"\n권영수 LG유플러스 부회장, \"이변 없는 한 화웨이 도입\"...5G 서비스 구현 고민\n[전문]한경바이오헬스포럼 제5차 조찬간담회 토론 내용\nLGU+, 5G 화웨이 장비 도입 유력해졌다\n브랜든 김 삼성넥스트 투자총괄 “탈중앙화 시대, 제2의 우버·페이스북 나올 것”\n카카오M '이병헌·김태리' 소속사 지분 사들인 까닭은?\n권영수 LGU+ 부회장 “5G 장비, 화웨이가 가장 앞서”\n\"10년 넘게 걸리는 신약개발, AI로 단축… 의료 빅데이터 활용이 관건\"\nKTㆍLG 앞에서…5G 야심 드러낸 中 화웨이\n“알뜰폰 구하기”… 올해도 전파사용료 면제 유력\n[MWC 상하이 2018] 드론·VR게임… ‘5G의 미래’ 엿본다\n[알아봅시다] 보이지 않는 위협 전자파 막는 차폐 소재\n황창규 - 권영수, 5G 상용화 `리더십 행보`\n삼성이냐 화웨이냐… 5G 장비 도입 앞두고 고민 깊은 이통3사\n삼성디스플레이 '와이옥타' 패널 中 오포에 첫 판매\n휴대폰 명의도용 피해 '뚝'···부정가입방지시스템 빛봤다\n" ], [ "# 다음 페이지로 넘어가는 것에서 문제가 있음", "_____no_output_____" ] ] ]

[ "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# IMPORTS", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np \nimport seaborn as sns \nimport matplotlib.pyplot as plt ", "_____no_output_____" ] ], [ [ "# READ THE DATA", "_____no_output_____" ] ], [ [ "data = pd.read_csv('./input/laptops.csv', encoding='latin-1')\ndata.head(10)", "_____no_output_____" ] ], [ [ "# MAIN EDA BLOCK", "_____no_output_____" ] ], [ [ "print(f'Data Shape\\nRows: {data.shape[0]}\\nColumns: {data.shape[1]}')\nprint('=' * 30)\ndata.info()", "Data Shape\nRows: 1303\nColumns: 13\n==============================\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1303 entries, 0 to 1302\nData columns (total 13 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Unnamed: 0 1303 non-null int64 \n 1 Company 1303 non-null object \n 2 Product 1303 non-null object \n 3 TypeName 1303 non-null object \n 4 Inches 1303 non-null float64\n 5 ScreenResolution 1303 non-null object \n 6 Cpu 1303 non-null object \n 7 Ram 1303 non-null object \n 8 Memory 1303 non-null object \n 9 Gpu 1303 non-null object \n 10 OpSys 1303 non-null object \n 11 Weight 1303 non-null object \n 12 Price_euros 1303 non-null float64\ndtypes: float64(2), int64(1), object(10)\nmemory usage: 132.5+ KB\n" ], [ "data.describe()", "_____no_output_____" ], [ "data['Product'] = data['Product'].str.split('(').apply(lambda x: x[0])", "_____no_output_____" ], [ "data['CPu_Speed'] = data['Cpu'].str.split(' ').apply(lambda x: x[-1]).str.replace('GHz', '')\ndata['Cpu_Vender'] = data['Cpu'].str.split(' ').apply(lambda x: x[0])\ndata['Cpu_Type'] = data['Cpu'].str.split(' ').apply(lambda x: x[1:4] if x[1]=='Celeron' and 'Pentium' and 'Xeon' else (x[1:3] if (x[1]=='Core' or x[0]=='AMD') else x[0]))\ndata['Cpu_Type'] = data['Cpu_Type'].apply(lambda x: ' '.join(x))\ndata['Cpu_Type']\ndata.head(10)", "_____no_output_____" ], [ "split_mem = data['Memory'].str.split(' ', 1, expand=True)\ndata['Storage_Type'] = split_mem[1]\ndata['Memory'] = split_mem[0]\ndata['Memory'].unique()\ndata.head(10)", "_____no_output_____" ], [ "data['Ram'] = data['Ram'].str.replace('GB', '')\n\ndf_mem = data['Memory'].str.split('(\\d+)', expand=True)\ndata['Memory'] = pd.to_numeric(df_mem[1])\ndata.rename(columns={'Memory': 'Memory (GB or TB)'}, inplace=True)", "_____no_output_____" ], [ "def mem(x):\n if x == 1:\n return 1024\n elif x == 2:\n return 2048", "_____no_output_____" ], [ "data['Memory (GB or TB)'] = data['Memory (GB or TB)'].apply(lambda x: 1024 if x==1 else x)\ndata['Memory (GB or TB)'] = data['Memory (GB or TB)'].apply(lambda x: 2048 if x==2 else x)\ndata.rename(columns={'Memory (GB or TB)': 'Storage (GB)'}, inplace=True)\ndata.head(10)", "_____no_output_____" ], [ "data['Weight'] = data['Weight'].str.replace('kg', '')\ndata.head(10)", "_____no_output_____" ], [ "gpu_distr_list = data['Gpu'].str.split(' ')\ndata['Gpu_Vender'] = data['Gpu'].str.split(' ').apply(lambda x: x[0])\ndata['Gpu_Type'] = data['Gpu'].str.split(' ').apply(lambda x: x[1:])\ndata['Gpu_Type'] = data['Gpu_Type'].apply(lambda x: ' '.join(x))\ndata.head(10)", "_____no_output_____" ], [ "data['Touchscreen'] = data['ScreenResolution'].apply(lambda x: 1 if 'Touchscreen' in x else 0)\ndata['Ips'] = data['ScreenResolution'].apply(lambda x: 1 if 'IPS' in x else 0)", "_____no_output_____" ], [ "def cat_os(op_s):\n if op_s =='Windows 10' or op_s == 'Windows 7' or op_s == 'Windows 10 S':\n return 'Windows'\n elif op_s =='macOS' or op_s == 'Mac OS X':\n return 'Mac'\n else:\n return 'Other/No OS/Linux'\n \ndata['OpSys'] = data['OpSys'].apply(cat_os)", "_____no_output_____" ], [ "data = data.reindex(columns=[\"Company\", \"TypeName\", \"Inches\", \"Touchscreen\", \n \"Ips\", \"Cpu_Vender\", \"Cpu_Type\",\"Ram\", \"Storage (GB)\", \n \"Storage Type\", \"Gpu_Vender\", \"Gpu_Type\", \"Weight\", \"OpSys\", \"Price_euros\" ])", "_____no_output_____" ], [ "data.head(10)", "_____no_output_____" ], [ "data['Ram'] = data['Ram'].astype('int')\ndata['Storage (GB)'] = data['Storage (GB)'].astype('int')\ndata['Weight'] = data['Weight'].astype('float')", "_____no_output_____" ], [ "data.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1303 entries, 0 to 1302\nData columns (total 15 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Company 1303 non-null object \n 1 TypeName 1303 non-null object \n 2 Inches 1303 non-null float64\n 3 Touchscreen 1303 non-null int64 \n 4 Ips 1303 non-null int64 \n 5 Cpu_Vender 1303 non-null object \n 6 Cpu_Type 1303 non-null object \n 7 Ram 1303 non-null int32 \n 8 Storage (GB) 1303 non-null int32 \n 9 Storage Type 0 non-null float64\n 10 Gpu_Vender 1303 non-null object \n 11 Gpu_Type 1303 non-null object \n 12 Weight 1303 non-null float64\n 13 OpSys 1303 non-null object \n 14 Price_euros 1303 non-null float64\ndtypes: float64(4), int32(2), int64(2), object(7)\nmemory usage: 142.6+ KB\n" ], [ "sns.set(rc={'figure.figsize': (9,5)})", "_____no_output_____" ], [ "data['Company'].value_counts().plot(kind='bar')", "_____no_output_____" ], [ "sns.barplot(x=data['Company'], y=data['Price_euros'])", "_____no_output_____" ], [ "data['TypeName'].value_counts().plot(kind='bar')", "_____no_output_____" ], [ "sns.barplot(x=data['TypeName'], y=data['Price_euros'])", "_____no_output_____" ], [ "cpu_distr = data['Cpu_Type'].value_counts()[:10].reset_index()\ncpu_distr", "_____no_output_____" ], [ "sns.barplot(x=cpu_distr['index'], y=cpu_distr['Cpu_Type'], hue='Cpu_Vender', data=data)", "_____no_output_____" ], [ "gpu_distr = data['Gpu_Type'].value_counts()[:10].reset_index()\ngpu_distr", "_____no_output_____" ], [ "sns.barplot(x=gpu_distr['index'], y=gpu_distr['Gpu_Type'], hue='Gpu_Vender', data=data)", "_____no_output_____" ], [ "sns.barplot(x=data['OpSys'], y=data['Price_euros'])", "_____no_output_____" ], [ "corr_data = data.corr()\ncorr_data['Price_euros'].sort_values(ascending=False)", "_____no_output_____" ], [ "sns.heatmap(data.corr(), annot=True)", "_____no_output_____" ], [ "X = data.drop(columns=['Price_euros'])\ny = np.log(data['Price_euros'])", "_____no_output_____" ], [ "from sklearn.model_selection import train_test_split", "_____no_output_____" ], [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)", "_____no_output_____" ], [ "from sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.metrics import r2_score, mean_absolute_error\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor\nfrom xgboost import XGBRegressor\nfrom sklearn.ensemble import VotingRegressor, StackingRegressor", "_____no_output_____" ], [ "step1 = ColumnTransformer(transformers=[\n ('col_inf', OneHotEncoder(sparse=False, handle_unknown='ignore'), [0,1,5,6,9,10,11,13])\n],remainder='passthrough')", "_____no_output_____" ], [ "rf = RandomForestRegressor(n_estimators=350, random_state=3, max_samples=0.5, max_features=0.75, max_depth=15)\ngbdt = GradientBoostingRegressor(n_estimators=100, max_features=0.5)\nxgb = XGBRegressor(n_estimators=25, learning_rate=0.3, max_depth=5)\net = ExtraTreesRegressor(n_estimators=100, random_state=3, max_samples=0.5, max_features=0.75, max_depth=10)", "_____no_output_____" ], [ "step2 = VotingRegressor([('rf', rf), ('gbdt', gbdt), ('xgb', xgb), ('et', et)], weights=[5,1,1,1])", "_____no_output_____" ], [ "pipe = Pipeline([ \n ('step1', step1),\n ('step2', step2)])", "_____no_output_____" ], [ "pipe.fit(X_train, y_train)", "_____no_output_____" ], [ "y_pred = pipe.predict(X_test)", "_____no_output_____" ], [ "print('R2 score', r2_score(y_test, y_pred))\nprint('MAE', mean_absolute_error(y_test, y_pred))", "R2 score 0.8578750665087866\nMAE 0.16211150811805436\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import uf\n\nprint(uf.__version__)", "beta v2.8.0\n" ], [ "model = uf.ALBERTBinaryClassifier('../demo/albert_config.json', '../demo/vocab.txt')\nprint(model)", "uf.ALBERTBinaryClassifier(config_file='../demo/albert_config.json', vocab_file='../demo/vocab.txt', max_seq_length=128, label_size=None, label_weight=None, init_checkpoint=None, output_dir=None, gpu_ids=None, drop_pooler=False, do_lower_case=True, truncate_method='LIFO')\n" ], [ "X = ['天亮以前说再见', '笑着泪流满面', '去迎接应该你的', '更好的明天']\ny = [[0, 2], [1], [1], []]", "_____no_output_____" ] ], [ [ "# 训练", "_____no_output_____" ] ], [ [ "model.fit(X, y, total_steps=20)", "WARNING:tensorflow:From /Users/geyingli/Library/Python/3.8/lib/python/site-packages/tensorflow/python/util/dispatch.py:201: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n" ] ], [ [ "# 推理", "_____no_output_____" ] ], [ [ "model.predict(X)", "INFO:tensorflow:Time usage 0m-2.16s, 0.46 steps/sec, 1.85 examples/sec\n" ] ], [ [ "# 评分", "_____no_output_____" ] ], [ [ "model.score(X, y)", "INFO:tensorflow:Time usage 0m-0.98s, 1.02 steps/sec, 4.10 examples/sec\n" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code", "code", "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# <center>АНАЛИЗ ЗВУКА И ГОЛОСА</center>\n\n**Преподаватель**: Рыбин Сергей Витальевич\n\n**Группа**: 6304\n\n**Студент**: Белоусов Евгений Олегович", "_____no_output_____" ], [ "## <center>Классификация акустических шумов</center>\n\n*Необоходимый результат: неизвестно*", "_____no_output_____" ] ], [ [ "import os\nimport IPython\nimport warnings\nwarnings.filterwarnings('ignore')", "_____no_output_____" ], [ "import numpy as np\nimport pandas as pd\nimport librosa\nimport librosa.display\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\n%matplotlib inline", "_____no_output_____" ], [ "from tqdm.notebook import tqdm\n\nfrom tensorflow.keras import losses, models, optimizers\nfrom tensorflow.keras.activations import relu, softmax\nfrom tensorflow.keras.callbacks import (EarlyStopping, ModelCheckpoint, TensorBoard)\nfrom tensorflow.keras.layers import (Input, Dense, Convolution2D, BatchNormalization,\n Flatten, MaxPool2D, Activation)\nfrom tensorflow.keras.utils import Sequence\nfrom tensorflow.keras import backend as K\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import StratifiedKFold, train_test_split\nfrom sklearn.metrics import classification_report, confusion_matrix", "_____no_output_____" ], [ "# from google.colab import drive\n# drive.mount('/content/drive')", "_____no_output_____" ], [ "# Ручная часть работы - директория с набором аудиофайлов, набор меток классов, учёт разновидностей имён файлов\npredictions = \"predictions\"\ndirectory = \"./content/drive/MyDrive/Training\"\n\nlabels = [\"background\",\n \"bags\",\n \"door\",\n \"keyboard\",\n \"knocking_door\",\n \"ring\",\n \"speech\",\n \"tool\"]\n\nnum_classes = len(labels)\n\nfilename_search = {\"background\": [\"background_\"],\n \"bags\": [\"bags_\", \"bg_\", \"t_bags_\"],\n \"door\": [\"door_\", \"d_\", \"t_door_\"],\n \"keyboard\": [\"keyboard_\", \"t_keyboard_\", \"k_\"],\n \"knocking_door\": [\"knocking_door_\", \"tt_kd_\", \"t_knocking_door_\"],\n \"ring\": [\"ring_\", \"t_ring_\"],\n \"speech\": [\"speech_\"],\n \"tool\": [\"tool_\"]}", "_____no_output_____" ], [ "# Параметры конфигурации для будущей модели нейросети\nclass Config(object):\n def __init__(self,\n sampling_rate=16000, audio_duration=7, n_classes=10, use_mfcc=True,\n n_mfcc=20, n_folds=10, n_features=100, learning_rate=0.0001, max_epochs=50):\n self.sampling_rate = sampling_rate\n self.audio_duration = audio_duration\n self.n_classes = n_classes\n self.use_mfcc = use_mfcc\n self.n_mfcc = n_mfcc\n self.n_folds = n_folds\n self.learning_rate = learning_rate\n self.max_epochs = max_epochs\n self.n_features = n_features\n self.audio_length = self.sampling_rate * self.audio_duration\n if self.use_mfcc:\n self.dim = (self.n_mfcc, 1 + int(np.floor(self.audio_length / 512)), 1)\n else:\n self.dim = (self.audio_length, 1)", "_____no_output_____" ], [ "# Извлечение метки аудиошума из названия аудиофайла\ndef get_label_from_filename(filename):\n for key, value in filename_search.items():\n for val in value:\n if (filename.find(val) == 0):\n return key", "_____no_output_____" ], [ "# Подготовка датафрейма\ndef prepare_dataframe(directory):\n files = ([f.path for f in os.scandir(directory) if f.is_file()])\n # Создание датафрейма по предоставленной в условии задачи схеме\n df = pd.DataFrame(columns=[\"filename\", \"label\"])\n\n # Проход по всем аудиофайлам в наборе\n for path in tqdm(files[:]):\n filename = os.path.splitext(os.path.basename(path).strip())[0]\n label = get_label_from_filename(filename)\n \n # Добавляем обработанный аудиофайл в датафрейм\n row = pd.Series([filename, label], index = df.columns)\n df = df.append(row, ignore_index=True)\n \n return df", "_____no_output_____" ], [ "# Извлечение признаков из набора аудиофайлов\ndef prepare_data(config, directory, df):\n X = np.empty(shape=(df.shape[0], config.dim[0], config.dim[1], 1))\n files = ([f.path for f in os.scandir(directory) if f.is_file()])\n\n # Задаём длительность аудиофайла\n input_length = config.audio_length\n\n i = 0\n # Проход по всем аудиофайлам в наборе\n for path in tqdm(files[:]):\n filename = os.path.splitext(os.path.basename(path).strip())[0]\n\n data, sr = librosa.load(path, sr=config.sampling_rate)\n\n # Обрезка/приведение длительности аудиофайла к указанной в параметрах конфигурации\n if len(data) > input_length:\n max_offset = len(data) - input_length\n offset = np.random.randint(max_offset)\n data = data[offset:(input_length+offset)]\n else:\n if input_length > len(data):\n max_offset = input_length - len(data)\n offset = np.random.randint(max_offset)\n else:\n offset = 0\n data = np.pad(data, (offset, input_length - len(data) - offset), \"constant\")\n \n # Извлечение признаков MFCC с помощью библиотеки librosa\n data = librosa.feature.mfcc(data, sr=config.sampling_rate, n_mfcc=config.n_mfcc)\n data = np.expand_dims(data, axis=-1)\n X[i,] = data\n i = i + 1\n\n return X", "_____no_output_____" ], [ "# Модель свёрточной нейросети\ndef get_2d_conv_model(config):\n num_classes = config.n_classes\n \n inp = Input(shape=(config.dim[0], config.dim[1], 1))\n \n x = Convolution2D(32, (4,10), padding=\"same\")(inp)\n x = BatchNormalization()(x)\n x = Activation(\"relu\")(x)\n x = MaxPool2D()(x)\n \n x = Convolution2D(32, (4,10), padding=\"same\")(x)\n x = BatchNormalization()(x)\n x = Activation(\"relu\")(x)\n x = MaxPool2D()(x)\n \n x = Convolution2D(32, (4,10), padding=\"same\")(x)\n x = BatchNormalization()(x)\n x = Activation(\"relu\")(x)\n x = MaxPool2D()(x)\n\n x = Flatten()(x)\n x = Dense(64)(x)\n x = BatchNormalization()(x)\n x = Activation(\"relu\")(x)\n \n out = Dense(num_classes, activation=softmax)(x)\n \n model = models.Model(inputs=inp, outputs=out)\n opt = optimizers.Adam(config.learning_rate)\n model.compile(optimizer=opt, loss=losses.SparseCategoricalCrossentropy(), metrics=['acc'])\n \n return model", "_____no_output_____" ], [ "# Матрица ошибок классификации\ndef plot_confusion_matrix(predictions, y):\n max_test = y\n max_predictions = np.argmax(predictions, axis=1)\n matrix = confusion_matrix(max_test, max_predictions)\n plt.figure(figsize=(12, 8))\n sns.heatmap(matrix, xticklabels=labels, yticklabels=labels, annot=True,\n linewidths = 0.1, fmt=\"d\", cmap = 'YlGnBu');\n plt.title(\"Матрица ошибок классификации\", fontsize = 15)\n plt.ylabel(\"Настоящий класс\")\n plt.xlabel(\"Предсказанный\")\n plt.show()", "_____no_output_____" ], [ "# Подготовим датафрейм\ndf = prepare_dataframe(directory)", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ], [ "# Сериализуем датафрейм в целях дальнейшей экономии времени\ndf.to_pickle(\"./content/drive/MyDrive/SVA_lab_2_dataframe.pkl\")", "_____no_output_____" ], [ "# Десериализация ранее сохранённого датафрейма\ndf = pd.read_pickle(\"./content/drive/MyDrive/SVA_lab_2_dataframe.pkl\")", "_____no_output_____" ], [ "# Подсчёт количества аудиозаписей каждого класса\ndf[\"label\"].value_counts()", "_____no_output_____" ], [ "# Представим значения меток классов в виде целых чисел\nencode = LabelEncoder()\nencoded_labels = encode.fit_transform(df['label'].to_numpy())\ndf = df.assign(label=encoded_labels)", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ], [ "# Задаём параметры конфигурации\nconfig = Config(n_classes=num_classes, n_folds=10, n_mfcc=20)", "_____no_output_____" ], [ "X_train = prepare_data(config, directory, df)\nprint(X_train.shape)", "_____no_output_____" ], [ "# Нормализация данных\n\nmean = np.mean(X_train, axis=0)\nstd = np.std(X_train, axis=0)\n\nX_train = (X_train - mean)/std", "_____no_output_____" ], [ "X_train", "_____no_output_____" ], [ "# ПРОВЕРКА НА ТЕСТОВОМ НАБОРЕ ДАННЫХ\n\nfiles = ([f.path for f in os.scandir(\"./content/drive/MyDrive/Test\") if f.is_file()])\n# Создание датафрейма по предоставленной в условии задачи схеме\nsubmission = pd.DataFrame(columns=[\"fname\"])\n\n# Проход по всем аудиофайлам в наборе\nfor path in tqdm(files[:]):\n filename = os.path.splitext(os.path.basename(path).strip())[0]\n \n # Добавляем имя аудиофайла в датафрейм\n row = pd.Series([filename], index = submission.columns)\n submission = submission.append(row, ignore_index=True)", "_____no_output_____" ], [ "submission.head()", "_____no_output_____" ], [ "X_test = prepare_data(config, \"./content/drive/MyDrive/Test\", submission)", "_____no_output_____" ], [ "# Нормализация данных\n\nmean = np.mean(X_test, axis=0)\nstd = np.std(X_test, axis=0)\n\nX_test = (X_test - mean)/std", "_____no_output_____" ], [ "X_test", "_____no_output_____" ], [ "if not os.path.exists(predictions):\n os.mkdir(predictions)\nif os.path.exists(\"./content/drive/MyDrive/\" + predictions):\n shutil.rmtree(\"./content/drive/MyDrive/\" + predictions)", "_____no_output_____" ], [ "# Для кросс-валидации используется StratifiedKFold - разновдность KFold алгоритма, которая возвращает\n# стратифицированные папки c данными: каждый набор в папке содержит примерно такой же процент выборок каждого целевого класса,\n# что и полный набор.\nskf = StratifiedKFold(n_splits=config.n_folds)\ny_train = df[\"label\"].values\ny_train = np.stack(y_train[:])\nmodel = get_2d_conv_model(config)\ni = 0\nfor train_split, val_split in skf.split(X_train, y_train):\n K.clear_session()\n \n # Разделение имеющегося набора данных на тренировочную и валидационные выборки\n X, y, X_val, y_val = X_train[train_split], y_train[train_split], X_train[val_split], y_train[val_split]\n \n # Callback-функции для модели Keras\n # В ходе обучения сохраняем веса лучшей модели для потенциального дальнейшего использования\n checkpoint = ModelCheckpoint('best_%d.h5'%i, monitor='val_loss', verbose=1, save_best_only=True)\n early = EarlyStopping(monitor=\"val_loss\", mode=\"min\", patience=5)\n callbacks_list = [checkpoint, early]\n print(\"#\"*50)\n print(\"Fold: \", i)\n model = get_2d_conv_model(config)\n history = model.fit(X, y, validation_data=(X_val, y_val), callbacks=callbacks_list, batch_size=256, epochs=config.max_epochs)\n model.load_weights('best_%d.h5'%i)\n \n # Сохраняем предсказания модели по тренировочным данным\n print(\"TRAIN PREDICTIONS: \", i)\n predictions = model.predict(X_train, batch_size=256)\n save_train_preds_path = \"./predictions/train_predictions_{:d}.npy\".format(i)\n np.save(save_train_preds_path, predictions)\n plot_confusion_matrix(predictions, y_train)\n \n # Сохраняем предсказания модели по тестовым данным\n print(\"TEST PREDICTIONS: \", i)\n predictions = model.predict(X_test, batch_size=256)\n save_test_preds_path = \"./predictions/test_predictions_{:d}.npy\".format(i)\n np.save(save_test_preds_path, predictions)\n\n # # Создание файла с результатами (submission)\n # top_3 = np.array(labels)[np.argsort(-predictions, axis=1)[:, :3]]\n # predicted_labels = [' '.join(list(x)) for x in top_3]\n # df_test['label'] = predicted_labels\n # save_preds_path = \"./predictions/predictions_{:d}.npy\".format(i)\n # df_test[['label']].to_csv(save_preds_path)\n \n j = 0\n for prob in predictions:\n #print(prob)\n #print(np.argmax(prob))\n submission.loc[j,'score'] = max(prob)\n prob_index = list(prob).index(max(prob))\n #print(prob_index)\n submission.loc[j,'label'] = prob_index\n j += 1\n\n submission_result = submission.copy()\n submission_result['label'] = encode.inverse_transform(np.array(submission['label']).astype(int))\n submission = submission_result\n save_submission_path = \"./predictions/submission_{:d}.npy\".format(i)\n submission.to_csv(save_submission_path.format(i), index=False)\n \n i += 1", "##################################################\nFold: 0\nEpoch 1/50\n13/13 [==============================] - ETA: 0s - loss: 1.9035 - acc: 0.2924\nEpoch 00001: val_loss improved from inf to 2.05545, saving model to best_0.h5\n13/13 [==============================] - 14s 1s/step - loss: 1.9035 - acc: 0.2924 - val_loss: 2.0554 - val_acc: 0.1945\nEpoch 2/50\n13/13 [==============================] - ETA: 0s - loss: 1.3596 - acc: 0.5897\nEpoch 00002: val_loss improved from 2.05545 to 1.82722, saving model to best_0.h5\n13/13 [==============================] - 13s 1s/step - loss: 1.3596 - acc: 0.5897 - val_loss: 1.8272 - val_acc: 0.5479\nEpoch 3/50\n13/13 [==============================] - ETA: 0s - loss: 1.1423 - acc: 0.6893\nEpoch 00003: val_loss improved from 1.82722 to 1.74156, saving model to best_0.h5\n13/13 [==============================] - 13s 995ms/step - loss: 1.1423 - acc: 0.6893 - val_loss: 1.7416 - val_acc: 0.6274\nEpoch 4/50\n13/13 [==============================] - ETA: 0s - loss: 0.9896 - acc: 0.7441\nEpoch 00004: val_loss did not improve from 1.74156\n13/13 [==============================] - 13s 986ms/step - loss: 0.9896 - acc: 0.7441 - val_loss: 1.7512 - val_acc: 0.5534\nEpoch 5/50\n13/13 [==============================] - ETA: 0s - loss: 0.8792 - acc: 0.7810\nEpoch 00005: val_loss did not improve from 1.74156\n13/13 [==============================] - 13s 990ms/step - loss: 0.8792 - acc: 0.7810 - val_loss: 1.7438 - val_acc: 0.4932\nEpoch 6/50\n13/13 [==============================] - ETA: 0s - loss: 0.7968 - acc: 0.8115\nEpoch 00006: val_loss improved from 1.74156 to 1.68326, saving model to best_0.h5\n13/13 [==============================] - 13s 992ms/step - loss: 0.7968 - acc: 0.8115 - val_loss: 1.6833 - val_acc: 0.5123\nEpoch 7/50\n13/13 [==============================] - ETA: 0s - loss: 0.7258 - acc: 0.8352\nEpoch 00007: val_loss improved from 1.68326 to 1.62308, saving model to best_0.h5\n13/13 [==============================] - 13s 991ms/step - loss: 0.7258 - acc: 0.8352 - val_loss: 1.6231 - val_acc: 0.5178\nEpoch 8/50\n13/13 [==============================] - ETA: 0s - loss: 0.6704 - acc: 0.8541\nEpoch 00008: val_loss improved from 1.62308 to 1.58114, saving model to best_0.h5\n13/13 [==============================] - 13s 993ms/step - loss: 0.6704 - acc: 0.8541 - val_loss: 1.5811 - val_acc: 0.5123\nEpoch 9/50\n13/13 [==============================] - ETA: 0s - loss: 0.6143 - acc: 0.8724\nEpoch 00009: val_loss improved from 1.58114 to 1.57706, saving model to best_0.h5\n13/13 [==============================] - 13s 1s/step - loss: 0.6143 - acc: 0.8724 - val_loss: 1.5771 - val_acc: 0.5068\nEpoch 10/50\n13/13 [==============================] - ETA: 0s - loss: 0.5652 - acc: 0.8861\nEpoch 00010: val_loss improved from 1.57706 to 1.54073, saving model to best_0.h5\n13/13 [==============================] - 13s 1s/step - loss: 0.5652 - acc: 0.8861 - val_loss: 1.5407 - val_acc: 0.5096\nEpoch 11/50\n13/13 [==============================] - ETA: 0s - loss: 0.5188 - acc: 0.8967\nEpoch 00011: val_loss did not improve from 1.54073\n13/13 [==============================] - 13s 995ms/step - loss: 0.5188 - acc: 0.8967 - val_loss: 1.5598 - val_acc: 0.4877\nEpoch 12/50\n13/13 [==============================] - ETA: 0s - loss: 0.4777 - acc: 0.9108\nEpoch 00012: val_loss did not improve from 1.54073\n13/13 [==============================] - 13s 991ms/step - loss: 0.4777 - acc: 0.9108 - val_loss: 1.5500 - val_acc: 0.4959\nEpoch 13/50\n13/13 [==============================] - ETA: 0s - loss: 0.4423 - acc: 0.9205\nEpoch 00013: val_loss did not improve from 1.54073\n13/13 [==============================] - 13s 985ms/step - loss: 0.4423 - acc: 0.9205 - val_loss: 1.5495 - val_acc: 0.5205\nEpoch 14/50\n13/13 [==============================] - ETA: 0s - loss: 0.4081 - acc: 0.9287\nEpoch 00014: val_loss improved from 1.54073 to 1.51611, saving model to best_0.h5\n13/13 [==============================] - 13s 997ms/step - loss: 0.4081 - acc: 0.9287 - val_loss: 1.5161 - val_acc: 0.5260\nEpoch 15/50\n13/13 [==============================] - ETA: 0s - loss: 0.3793 - acc: 0.9440\nEpoch 00015: val_loss improved from 1.51611 to 1.44754, saving model to best_0.h5\n13/13 [==============================] - 13s 1s/step - loss: 0.3793 - acc: 0.9440 - val_loss: 1.4475 - val_acc: 0.5397\nEpoch 16/50\n13/13 [==============================] - ETA: 0s - loss: 0.3528 - acc: 0.9513\nEpoch 00016: val_loss improved from 1.44754 to 1.42411, saving model to best_0.h5\n13/13 [==============================] - 13s 998ms/step - loss: 0.3528 - acc: 0.9513 - val_loss: 1.4241 - val_acc: 0.5479\nEpoch 17/50\n13/13 [==============================] - ETA: 0s - loss: 0.3261 - acc: 0.9552\nEpoch 00017: val_loss improved from 1.42411 to 1.41346, saving model to best_0.h5\n13/13 [==============================] - 13s 1000ms/step - loss: 0.3261 - acc: 0.9552 - val_loss: 1.4135 - val_acc: 0.5507\nEpoch 18/50\n13/13 [==============================] - ETA: 0s - loss: 0.3064 - acc: 0.9580\nEpoch 00018: val_loss improved from 1.41346 to 1.32375, saving model to best_0.h5\n13/13 [==============================] - 13s 1s/step - loss: 0.3064 - acc: 0.9580 - val_loss: 1.3237 - val_acc: 0.5699\nEpoch 19/50\n13/13 [==============================] - ETA: 0s - loss: 0.2879 - acc: 0.9656\nEpoch 00019: val_loss did not improve from 1.32375\n13/13 [==============================] - 13s 993ms/step - loss: 0.2879 - acc: 0.9656 - val_loss: 1.3336 - val_acc: 0.5753\nEpoch 20/50\n13/13 [==============================] - ETA: 0s - loss: 0.2670 - acc: 0.9701\nEpoch 00020: val_loss improved from 1.32375 to 1.30981, saving model to best_0.h5\n13/13 [==============================] - 13s 1s/step - loss: 0.2670 - acc: 0.9701 - val_loss: 1.3098 - val_acc: 0.5644\nEpoch 21/50\n13/13 [==============================] - ETA: 0s - loss: 0.2514 - acc: 0.9756\nEpoch 00021: val_loss improved from 1.30981 to 1.16497, saving model to best_0.h5\n13/13 [==============================] - 13s 994ms/step - loss: 0.2514 - acc: 0.9756 - val_loss: 1.1650 - val_acc: 0.5890\nEpoch 22/50\n13/13 [==============================] - ETA: 0s - loss: 0.2358 - acc: 0.9784\nEpoch 00022: val_loss did not improve from 1.16497\n13/13 [==============================] - 13s 993ms/step - loss: 0.2358 - acc: 0.9784 - val_loss: 1.1731 - val_acc: 0.5918\nEpoch 23/50\n13/13 [==============================] - ETA: 0s - loss: 0.2233 - acc: 0.9829\nEpoch 00023: val_loss did not improve from 1.16497\n13/13 [==============================] - 13s 991ms/step - loss: 0.2233 - acc: 0.9829 - val_loss: 1.2243 - val_acc: 0.5836\nEpoch 24/50\n13/13 [==============================] - ETA: 0s - loss: 0.2116 - acc: 0.9839\nEpoch 00024: val_loss improved from 1.16497 to 1.12985, saving model to best_0.h5\n13/13 [==============================] - 13s 1s/step - loss: 0.2116 - acc: 0.9839 - val_loss: 1.1299 - val_acc: 0.5945\nEpoch 25/50\n13/13 [==============================] - ETA: 0s - loss: 0.2017 - acc: 0.9823\nEpoch 00025: val_loss improved from 1.12985 to 1.01099, saving model to best_0.h5\n13/13 [==============================] - 13s 997ms/step - loss: 0.2017 - acc: 0.9823 - val_loss: 1.0110 - val_acc: 0.6219\nEpoch 26/50\n13/13 [==============================] - ETA: 0s - loss: 0.1913 - acc: 0.9854\nEpoch 00026: val_loss improved from 1.01099 to 0.98364, saving model to best_0.h5\n13/13 [==============================] - 13s 1s/step - loss: 0.1913 - acc: 0.9854 - val_loss: 0.9836 - val_acc: 0.6137\nEpoch 27/50\n13/13 [==============================] - ETA: 0s - loss: 0.1779 - acc: 0.9903\nEpoch 00027: val_loss improved from 0.98364 to 0.89237, saving model to best_0.h5\n13/13 [==============================] - 13s 1000ms/step - loss: 0.1779 - acc: 0.9903 - val_loss: 0.8924 - val_acc: 0.6466\nEpoch 28/50\n13/13 [==============================] - ETA: 0s - loss: 0.1726 - acc: 0.9893\nEpoch 00028: val_loss improved from 0.89237 to 0.83660, saving model to best_0.h5\n13/13 [==============================] - 13s 995ms/step - loss: 0.1726 - acc: 0.9893 - val_loss: 0.8366 - val_acc: 0.6685\nEpoch 29/50\n13/13 [==============================] - ETA: 0s - loss: 0.1605 - acc: 0.9918\nEpoch 00029: val_loss did not improve from 0.83660\n13/13 [==============================] - 13s 987ms/step - loss: 0.1605 - acc: 0.9918 - val_loss: 0.8951 - val_acc: 0.6438\n" ], [ "", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# COMP5318 - Machine Learning and Data Mining: Assignment 2\n<div style=\"text-align: right\"> Group 86 </div>\n<div style=\"text-align: right\"> tlin4302 | 470322974 | Jenny Tsai-chen Lin </div>\n<div style=\"text-align: right\"> jsun4242 | 500409987 | Jiawei Sun </div>\n<div style=\"text-align: right\"> jyan2937 | 480546614 | Jinxuan Yang </div>", "_____no_output_____" ], [ "## The notebook includes sections :\n Section 0. Hardware and software specifications \n Section 1. Library and general functions \n Section 2. Data pre-processing\n Section 3. Implement algorithms\n 3.1 AdaBoost Classifier\n 3.2 Convolutional Neural Network Classifier\n 3.3 Support-Vector-Machine Classifier\n Section 4. Compare result between algorithms in train dataset \n Section 5: Best perfroming algorithms in testing data (we will submit this in seperate notebook as well)", "_____no_output_____" ], [ "## CODE RUNNING INSTRUCTIONS:\n Instruction:\n Simply change the switches in **0.Switches** blocks and run all.\n \n Dataset directory:\n Same directory as the jupyter notebook, in the format of :\n ---[current dir]\n |----[This file]\n |----[dataset]\n |----test\n |----train\n \n \n The default parameters will used the saved model to run simple tests,\n and load confusion matrices, accuracies, etc. from disk and plot them\n for display purpose.", "_____no_output_____" ], [ "### Hardware and software specifcations\nhardware:\n 1. CPU: Intel i7-8700K @ 3.70GHz\n 2. RAM: 64G DDR4 3000MHz\n 3. Graphics: NVidia GeForce GTX 1080Ti\n 4. Chipset: Z370", "_____no_output_____" ], [ "### Software specifications", "_____no_output_____" ] ], [ [ "import os, platform\nprint('OS name:', os.name, ', system:', platform.system(), ', release:', platform.release())\nimport sys\nprint(\"Anaconda version:\")\n#!conda list anaconda\nprint(\"Python version: \", sys.version)\nprint(\"Python version info: \", sys.version_info)\nimport PIL\nfrom PIL import Image\nprint(\"PIL version: \", PIL.__version__)\nimport matplotlib\nimport matplotlib.pyplot as plt\nprint(\"Matplotlib version: \", matplotlib.__version__)\n#import tensorflow as tf\n#print(\"Keras version:\", tf.keras.__version__)\nimport cv2\nprint(\"OpenCV version: \", cv2.__version__)\nimport numpy as np\nprint(\"nump version: \", np.__version__)", "OS name: nt , system: Windows , release: 10\nAnaconda version:\nPython version: 3.8.3 (default, Jul 2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]\nPython version info: sys.version_info(major=3, minor=8, micro=3, releaselevel='final', serial=0)\nPIL version: 7.2.0\nMatplotlib version: 3.2.2\nOpenCV version: 4.4.0\nnump version: 1.18.5\n" ] ], [ [ "## Section 0. Switches (Default settings is great for demo purpose)", "_____no_output_____" ], [ "#### Load saved model or run training?", "_____no_output_____" ] ], [ [ "load_saved_model = True", "_____no_output_____" ] ], [ [ "#### Run preprocessing benchmark or not?", "_____no_output_____" ] ], [ [ "run_preprocessing_benchmark = True", "_____no_output_____" ] ], [ [ "#### Run test code for 3 classifiers?", "_____no_output_____" ] ], [ [ "run_test_code_for_classifiers = True", "_____no_output_____" ] ], [ [ "#### number of threads when preprocessing images", "_____no_output_____" ] ], [ [ "g_thread_num = 6", "_____no_output_____" ] ], [ [ "#### Run hyper parameter tuning? (Slow if turned on!)", "_____no_output_____" ] ], [ [ "# Caution: Slow if turned on.\ndo_hyper_parameter_tuning = False", "_____no_output_____" ] ], [ [ "#### Run 10-fold cross validation? (Slow if turned on)", "_____no_output_____" ] ], [ [ "# Caution: Slow if turned on.\nrun_ten_fold = False", "_____no_output_____" ] ], [ [ "## Section 1. Library and general functions ", "_____no_output_____" ] ], [ [ "# Go to anaconda prompt to install package imblearn\n# anaconda: conda install -c glemaitre imbalanced-learn\n#pip install kmeans-smote\n\nfrom skimage import io, transform\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nimport cv2\n\nimport time", "_____no_output_____" ] ], [ [ "### global variables", "_____no_output_____" ] ], [ [ "# choose one of below two line depend file location******\n\ng_dataset_dir = \"./dataset/\" \n#g_dataset_dir = \"../dataset/\" \n\n\na_random_file = \"./dataset/train/1b1B1b2-2pK2q1-4p1rB-7k-8-8-3B4-3rb3.jpeg\" \n#a_random_file = \"../dataset/train/1b1B1b2-2pK2q1-4p1rB-7k-8-8-3B4-3rb3.jpeg\" \n\nsaved_model_path = \"./saved_model/\"\nabc_model_file = saved_model_path + \"abc_dump.pkl\"\nsvc_model_file = saved_model_path + \"svc_dump.pkl\"\ncnn_model_file = saved_model_path + \"cnn_weights\"\n\nten_fold_result_path = \"./ten_fold_results/\"\n\n\n# define global variable \n\ng_train_dir = g_dataset_dir + \"/train/\"\ng_test_dir = g_dataset_dir + \"/test/\"\n\ng_image_size = 400\n\ng_grid_row = 8\ng_grid_col = 8\n\ng_grid_num = g_grid_row * g_grid_col\ng_grid_size = int(g_image_size / g_grid_row)\n\n\n#Processing 1 - scale down \ng_down_sampled_size = 200\ng_down_sampled_grid_size = int(g_grid_size / (g_image_size / g_down_sampled_size))\n\n# global instance of mapping of char vs chess pieces\n# reference: Forsyth–Edwards Notation, https://en.wikipedia.org/wiki/Forsyth%E2%80%93Edwards_Notation\n# \n# pawn = \"P\", knight = \"N\", bishop = \"B\", rook = \"R\", queen = \"Q\" and king = \"K\"\n# White pieces are designated using upper-case letters (\"PNBRQK\") while black pieces use lowercase (\"pnbrqk\")\n# we use 0 to note an empty grid.\n# 13 items in total.\n\ng_piece_mapping = {\n \"P\" : \"pawn\",\n \"N\" : \"knight\",\n \"B\" : \"bishop\",\n \"R\" : \"rook\",\n \"Q\" : \"queen\",\n \"K\" : \"king\",\n\n \"p\" : \"pawn\",\n \"n\" : \"knight\",\n \"b\" : \"bishop\",\n \"r\" : \"rook\",\n \"q\" : \"queen\",\n \"k\" : \"king\",\n\n \"0\" : \"empty_grid\"\n}\n\ng_num_labels = len(g_piece_mapping)\n\ng_labels = [\"P\",\n\"N\",\n\"B\",\n\"R\",\n\"Q\",\n\"K\",\n\"p\",\n\"n\",\n\"b\",\n\"r\",\n\"q\",\n\"k\",\n\"0\"]\n", "_____no_output_____" ] ], [ [ "### Helper codes for label & board", "_____no_output_____" ] ], [ [ "#DataHelper.py\n\nimport os\n\nimport cv2\nfrom skimage import io\nimport numpy as np\n\nimport glob\nimport h5py\n\n# get clean name by a path, where in our case this gets the FEN conviniently\ndef GetCleanNameByPath(file_name):\n return os.path.splitext(os.path.basename(file_name))[0]\n\n# get full paths to the files in a directory.\ndef GetFileNamesInDir(path_name, extension=\"*\", num_return = 0):\n if num_return == 0:\n return glob.glob(path_name + \"/*.\" + extension)\n else:\n return glob.glob(path_name + \"/*.\" + extension)[:num_return]\n\n# get name list\ndef GetCleanNamesInDir(path_name, extension = \"*\", num_return = 0):\n names = GetFileNamesInDir(path_name, extension)\n offset = len(extension) + 1\n clean_names = [os.path.basename(x)[:-offset] for x in names]\n if num_return == 0:\n return clean_names\n else:\n return clean_names[:num_return]\n\n# read dataset\ndef ReadImages(file_names, path = \"\", format = cv2.IMREAD_COLOR):\n if path == \"\":\n return [cv2.imread(f, format) for f in file_names]\n else:\n return [cv2.imread(path + \"/\" + f, format) for f in file_names]\n\n# read image by name\ndef ReadImage(file_name, gray = False):\n return io.imread(file_name, as_gray = gray)\n\n\n# h5py functions\n \n# read h5py file\n# we assume the labels and \ndef ReadH5pyFile(file_name, data_name):\n h5_buffer = h5py.File(file_name)\n return h5_buffer[data_name].copy()\n\n# write h5py file\ndef WriteH5pyFile(file_name, mat, data_name = \"dataset\"):\n with h5py.File(file_name, 'w') as f:\n f.create_dataset(data_name, data = mat)\n", "_____no_output_____" ], [ "#BoardHelper.py\n\nimport re\nimport string\nfrom collections import OrderedDict \n\nimport numpy as np\nimport skimage.util\nfrom skimage.util.shape import view_as_blocks\n\n#from ChessGlobalDefs import *\n\n#FEN TO LABELS OF SQUARES\ndef FENtoL(fen): \n rules = {\n r\"-\": r\"\",\n r\"1\": r\"0\",\n r\"2\": r\"00\",\n r\"3\": r\"000\",\n r\"4\": r\"0000\",\n r\"5\": r\"00000\",\n r\"6\": r\"000000\",\n r\"7\": r\"0000000\",\n r\"8\": r\"00000000\",\n }\n\n for key in rules.keys():\n fen = re.sub(key, rules[key], fen)\n\n return list(fen)\n\n\n# Label array to char list:\ndef LabelArrayToL(arr):\n rules = {\n 0 : \"P\",\n 1 : \"N\",\n 2 : \"B\",\n 3 : \"R\",\n 4 : \"Q\",\n 5 : \"K\",\n\n 6 : \"p\",\n 7 : \"n\",\n 8 : \"b\",\n 9 : \"r\",\n 10 : \"q\",\n 11 : \"k\",\n\n 12 : \"0\"\n }\n\n flattened = arr.flatten(order = \"C\")\n\n L = []\n\n for x in flattened:\n L.append(rules[x])\n\n return L\n\n# char list to FEN\ndef LtoFEN(L):\n\n FEN = \"\"\n \n for y in range(8):\n counter = 0\n for x in range(8):\n idx = x + y * 8\n char = L[idx]\n\n if char == \"0\":\n counter += 1\n if x == 7:\n FEN += str(counter)\n else:\n if counter:\n FEN += str(counter)\n counter = 0\n\n FEN += char\n if y != 7:\n FEN += \"-\"\n \n \n return FEN\n\n\n\n# FEN to one-hot encoding, in our case, it returns an 64 by 13 array, with each row as a one-hot to a grid.\ndef FENtoOneHot(fen):\n\n # this rule is in the same format as g_piece_mapping\n #rules = {\n # \"P\" : np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n # \"N\" : np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n # \"B\" : np.array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n # \"R\" : np.array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n # \"Q\" : np.array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]),\n # \"K\" : np.array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]),\n # \n # \"p\" : np.array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]),\n # \"n\" : np.array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]),\n # \"b\" : np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]),\n # \"r\" : np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]),\n # \"q\" : np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]),\n # \"k\" : np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]),\n # \n # \"0\" : np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])\n #}\n rules = {\n \"P\" : 0,\n \"N\" : 1,\n \"B\" : 2,\n \"R\" : 3,\n \"Q\" : 4,\n \"K\" : 5,\n\n \"p\" : 6,\n \"n\" : 7,\n \"b\" : 8,\n \"r\" : 9,\n \"q\" : 10,\n \"k\" : 11,\n\n \"0\" : 12\n }\n\n L = FENtoL(fen)\n one_hot_array = np.zeros((g_grid_num, g_num_labels), dtype = np.int32) # 64 by 13\n for i, c in enumerate(L):\n one_hot_array[i, rules[c]] = 1\n\n return one_hot_array\n\n# get 8*8 char matrix\ndef LtoCharMat(l):\n if type(l) == list:\n return np.array(l).reshape((8,8))\n if type(l) == str:\n return np.array([l]).reshape((8,8))\n\ndef GetBoardCell(board_image, row = 0, col = 0, size = 50):\n return np.array(board_image)[row*size:(row+1)*size,col*size:(col+1)*size]\n\n# get grids of image\ndef ImageToGrids(image, grid_size_x, grid_size_y):\n return skimage.util.shape.view_as_blocks(image, block_shape = (grid_size_y, grid_size_x, 3)).squeeze(axis = 2)\n\n# get grids of image\ndef ImageToGrids_grey(image, grid_size_x, grid_size_y):\n return skimage.util.shape.view_as_blocks(image, block_shape = (grid_size_y, grid_size_x, 1)).squeeze(axis = 2)\n", "_____no_output_____" ] ], [ [ "## Section 2. Data pre-processing", "_____no_output_____" ], [ "### Pre-processing - generic", "_____no_output_____" ] ], [ [ "# split into 64 small square from 1 board\n# image resized to 400x 400 to 200x 200. 64 square at 25x 25 each\n\ndef PreprocessImage(image):\n image = transform.resize(image, (g_down_sampled_size, g_down_sampled_size), mode='constant')\n \n # 1st and 2nd dim is 8\n grids = ImageToGrids(image, g_down_sampled_grid_size, g_down_sampled_grid_size)\n\n return grids.reshape(g_grid_row * g_grid_col, g_down_sampled_grid_size, g_down_sampled_grid_size, 3)\n\n# split into 64 small square from 1 board -\n# output of x: number of image x 64 x 25 x 25 x 3 , y: number of image x 64 x 13\ndef func_generator(train_file_names):\n x = []\n y = []\n for image_file_name in train_file_names:\n img = ReadImage(image_file_name)\n x.append(PreprocessImage(img))\n y.append(np.array(FENtoOneHot(GetCleanNameByPath(image_file_name))))\n \n return np.array(x), np.array(y)", "_____no_output_____" ], [ "# Example output for a file - generic\nnum_train = 1\ntrain_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\",num_return = num_train)\n\nx, y = func_generator(train_file_names)\n\nprint(\"x type :\", type(x))\nprint(\"x shape:\", x.shape)\nprint(\"y type :\", type(y))\nprint(\"y shape:\", y.shape)\nprint()\nplt.imshow(x[0][1])", "x type : <class 'numpy.ndarray'>\nx shape: (1, 64, 25, 25, 3)\ny type : <class 'numpy.ndarray'>\ny shape: (1, 64, 13)\n\n" ] ], [ [ "### Pre-processing - canny", "_____no_output_____" ] ], [ [ "# Processing image with canny\nimport cv2\n\ndef PreprocessImage_canny(image):\n image = cv2.Canny(image,100,200)\n image = transform.resize(image, (g_down_sampled_size, g_down_sampled_size), mode='constant')\n # 1st and 2nd dim is 8\n image = image[..., np.newaxis]\n grids = ImageToGrids_grey(image, g_down_sampled_grid_size, g_down_sampled_grid_size)\n return grids.reshape(g_grid_row * g_grid_col, g_down_sampled_grid_size, g_down_sampled_grid_size)\n\n# atomic func:\ndef func_canny(file_name):\n img = ReadImage(file_name)\n x = PreprocessImage_canny(img)\n y = np.array(FENtoL(GetCleanNameByPath(file_name)))\n return x, y\n \n# split into 64 small square from 1 - output of x: number of image x64 x 25 x25 , y:number of image x 64\ndef func_generator_canny(image_file_names):\n xs = []\n ys = []\n for image_file_name in image_file_names:\n x, y = func_canny(image_file_name)\n xs.append(x)\n ys.append(y)\n \n return xs, ys\n", "_____no_output_____" ], [ "# Example output for a file - canny\n\nnum_train = 1\ntrain_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\",num_return = num_train)\n\nx, y = func_generator_canny(train_file_names)\n\nprint(\"x type :\", type(x))\nprint(\"x[0] type :\", type(x[0]))\nprint(\"x[0] shape:\", x[0].shape)\nprint(\"y type :\", type(y))\nprint(\"y[0] type :\", type(y[0]))\nprint(\"y[0] shape:\", y[0].shape)\n\nplt.imshow(x[0][1])", "x type : <class 'list'>\nx[0] type : <class 'numpy.ndarray'>\nx[0] shape: (64, 25, 25)\ny type : <class 'list'>\ny[0] type : <class 'numpy.ndarray'>\ny[0] shape: (64,)\n" ] ], [ [ "### Pre-processing - SIFT ", "_____no_output_____" ] ], [ [ "# Processing image with sift\nimport cv2\n\ndef ExtractSIFTForGrid(board_image, row, col, center_x = 25, center_y = 25, radius = 45):\n kps = [cv2.KeyPoint(x = center_x + 50 * col, y = center_y + 50 * row, _size = 45)]\n \n # USE THE CORRECT VERSION OF CV2\n if cv2.__version__ == \"4.4.0\":\n keypoints, descriptors = cv2.SIFT_create(edgeThreshold = 0).compute(image = board_image, keypoints = kps)\n else:\n keypoints, descriptors = cv2.xfeatures2d.SIFT_create(edgeThreshold = 0).compute(image = board_image, keypoints = kps)\n\n return keypoints[0], descriptors[0, :]\n\n\n\ndef PreprocessImage_sift(image):\n # 1st and 2nd dim is 8\n desc=[]\n for i in range(8):\n for j in range(8): \n kp, d= ExtractSIFTForGrid(image,i,j)\n desc.append(np.array(d))\n return desc\n\n# atomic func:\ndef func_sift(file_name):\n img = ReadImage(file_name)\n x = PreprocessImage_sift(img)\n y = np.array(FENtoL(GetCleanNameByPath(file_name)))\n return x, y\n \n \n# split into 64 small square from 1 - output of x: number of image x64 x128 , y:number of image x 64\ndef func_generator_sift(image_file_names):\n xs = []\n ys = []\n for image_file_name in image_file_names:\n x, y = func_sift(image_file_name)\n xs.append(np.array(x))\n ys.append(np.array(y))\n \n return xs, ys\n", "_____no_output_____" ], [ "# Example output for a file - SIFT\n\nnum_train = 1\ntrain_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\",num_return = num_train)\n\nx, y = func_generator_sift(train_file_names)\n\nprint(\"x type :\", type(x))\nprint(\"x[0] type :\", type(x[0]))\nprint(\"x[0] shape:\", x[0].shape)\nprint(\"y type :\", type(y))\nprint(\"y[0] type :\", type(y[0]))\nprint(\"y[0] shape:\", y[0].shape)\n\nplt.bar(x = range(128), height = x[0][1])\nplt.title(y[0][1])\nplt.xticks(x = range(128))\nplt.show()", "x type : <class 'list'>\nx[0] type : <class 'numpy.ndarray'>\nx[0] shape: (64, 128)\ny type : <class 'list'>\ny[0] type : <class 'numpy.ndarray'>\ny[0] shape: (64,)\n" ] ], [ [ "### Example of image input, canny, SIFT", "_____no_output_____" ] ], [ [ "import cv2\nfrom skimage.filters import sobel\n\n#print(\"Sift: decriptor size:\", cv2.SIFT_create().descriptorSize())\nimg = ReadImage(a_random_file)\nimg1 = cv2.Canny(img,100,200)\nimg2= sobel(img[:,:,0])\nprint(img.shape)\nprint(img1.shape)\nprint(img2.shape)\n\nkp, desc = ExtractSIFTForGrid(img, 0, 1)\nkp2, desc2 = ExtractSIFTForGrid(img, 0, 3)\nkp3, desc3 = ExtractSIFTForGrid(img, 0, 5)\nkp4, desc4 = ExtractSIFTForGrid(img, 1, 3)\nimg_kp = cv2.drawKeypoints(img, [kp, kp2,kp3,kp4], img, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\nimg_kp1 = cv2.drawKeypoints(img1, [kp, kp2,kp3,kp4], img1, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n\n\nprint('file name:',a_random_file)\n\n\nplt.figure(figsize=(18,6))\nplt.suptitle('Image processing output', fontsize=16)\nplt.subplot(1, 3, 1)\nplt.imshow(img_kp, aspect='auto')\nplt.title('original image with keypoint')\n\nplt.subplot(1, 3, 2)\nplt.imshow(img2, aspect='auto')\nplt.title('Sobel image')\n\nplt.subplot(1, 3, 3)\nplt.imshow(img1, aspect='auto')\nplt.title('Canny image')\nplt.show()\n\nplt.figure(figsize=(15,6))\nplt.suptitle('Sift output for original image at squares', fontsize=16)\n#plt.tight_layout()\nplt.subplot(2, 2, 1)\nplt.title('square 0,1(b)')\nplt.bar(x = range(128), height = desc)\nplt.xticks(x = range(128))\n\nplt.subplot(2,2, 2)\nplt.title('square 0,3(B)')\nplt.bar(x = range(128), height = desc2)\nplt.xticks(x = range(128))\n\nplt.subplot(2,2,3)\nplt.title('square 0,5(b)')\nplt.bar(x = range(128), height = desc3)\nplt.xticks(x = range(128))\n\n\nplt.subplot(2,2,4)\nplt.title('square 1,3(K)')\nplt.bar(x = range(128), height = desc4)\nplt.xticks(x = range(128))\nplt.show()\n\n", "(400, 400, 3)\n(400, 400)\n(400, 400)\nfile name: ./dataset/train/1b1B1b2-2pK2q1-4p1rB-7k-8-8-3B4-3rb3.jpeg\n" ] ], [ [ "### Read image - generic, canny, sift - run time", "_____no_output_____" ] ], [ [ "if run_preprocessing_benchmark:\n start_time = time.time()\n num_train = 100\n train_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\",num_return = num_train)\n\n X_org,Y_org = func_generator(train_file_names)\n print('runnning time for generic 100 images')\n print('--- {} seconds ---'.format(time.time() - start_time))\n\n X_sift,Y_sift = func_generator_sift(train_file_names)\n print('runnning time for sift 100 images')\n print('--- {} seconds ---'.format(time.time() - start_time))\n\n start_time = time.time()\n X_canny,Y_canny = func_generator_canny(train_file_names)\n print('runnning time for canny 100 images')\n print('--- {} seconds ---'.format(time.time() - start_time))", "runnning time for generic 100 images\n--- 1.9594979286193848 seconds ---\nrunnning time for sift 100 images\n--- 28.49899911880493 seconds ---\nrunnning time for canny 100 images\n--- 0.7249987125396729 seconds ---\n" ] ], [ [ "### Subset train data - high quality (image level)", "_____no_output_____" ] ], [ [ "#https://www.researchgate.net/post/How_to_use_clustering_to_reduce_data_set_samples\n\n#subset data of high quality image (at image level)\n\n#Output is \ndef file_names_highquality(image_file_names, min_piece =15):\n names = np.array(image_file_names)\n idx_sub = []\n for idx in range(len(image_file_names)):\n y = FENtoL(GetCleanNameByPath(image_file_names[idx]))\n piece_count = 64 - y.count('0')\n if piece_count >= min_piece:\n idx_sub.append(idx) \n \n return np.array(image_file_names)[idx_sub]", "_____no_output_____" ], [ "# test \nif run_preprocessing_benchmark:\n file_name_reduced = file_names_highquality(train_file_names, min_piece =15)\n print('reduced file number from',len(train_file_names),'to',len(file_name_reduced))", "reduced file number from 100 to 21\n" ] ], [ [ "### Undersempling -square (grid level)", "_____no_output_____" ] ], [ [ "# install the package if needed.\nif run_preprocessing_benchmark:\n !pip install imblearn", "Looking in indexes: https://pypi.douban.com/simple\nRequirement already satisfied: imblearn in g:\\anaconda3\\lib\\site-packages (0.0)\nRequirement already satisfied: imbalanced-learn in g:\\anaconda3\\lib\\site-packages (from imblearn) (0.7.0)\nRequirement already satisfied: scikit-learn>=0.23 in g:\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.23.2)\nRequirement already satisfied: scipy>=0.19.1 in g:\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.5.0)\nRequirement already satisfied: joblib>=0.11 in g:\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.16.0)\nRequirement already satisfied: numpy>=1.13.3 in g:\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.18.5)\nRequirement already satisfied: threadpoolctl>=2.0.0 in g:\\anaconda3\\lib\\site-packages (from scikit-learn>=0.23->imbalanced-learn->imblearn) (2.1.0)\n" ], [ "#subset data (in square level)\n#ref: #https://www.researchgate.net/post/How_to_use_clustering_to_reduce_data_set_samples\n# implement using https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.under_sampling.ClusterCentroids.html\n\nif run_preprocessing_benchmark:\n from imblearn.under_sampling import ClusterCentroids\n\n # output x: number of grid x 125, y: number of grid\n def undersampling_ClusterCentroids_canny(X,Y):\n trans = ClusterCentroids(random_state=0)\n length=len(np.array(Y))\n X= np.array(X).reshape(length*64,25*25)\n Y = np.array(Y).reshape(length*64)\n X_resampled, y_resampled = trans.fit_sample(X, Y)\n\n return X_resampled, y_resampled\n\n # output x: number of grid x 128, y: number of grid\n def undersampling_ClusterCentroids_sift(X,Y):\n trans = ClusterCentroids(random_state=0)\n length=len(np.array(Y))\n X= np.array(X).reshape(length*64,128)\n Y = np.array(Y).reshape(length*64)\n X_resampled, y_resampled = trans.fit_sample(X, Y)\n return X_resampled, y_resampled", "_____no_output_____" ], [ "if run_preprocessing_benchmark:\n # test canny -resampled\n\n start_time = time.time()\n X_resampled_canny, Y_resampled_canny = undersampling_ClusterCentroids_canny(X_canny,Y_canny)\n print('--- {} seconds ---'.format(time.time() - start_time))\n print('reduce grid number from',len(np.array(Y_canny))*64,'to',len(np.array(Y_resampled_canny)))\n\n # test sift -resampled\n\n start_time = time.time()\n X_resampled_sift, Y_resampled_sift = undersampling_ClusterCentroids_sift(X_sift,Y_sift)\n print('--- {} seconds ---'.format(time.time() - start_time))\n print('reduce grid number from',len(np.array(Y_sift))*64,'to',len(np.array(Y_resampled_sift)))", "--- 5.019449710845947 seconds ---\nreduce grid number from 6400 to 455\n--- 4.656001806259155 seconds ---\nreduce grid number from 6400 to 455\n" ] ], [ [ "### Read Image - parallel version", "_____no_output_____" ] ], [ [ "from joblib import Parallel, delayed\n\n# note: functions are first-class objects in Python. we pass it directly as parameter.\ndef Preprocess_parallel(func, file_names, job_count = 6):\n result = Parallel(n_jobs=job_count)(delayed(func)(file_name) for file_name in file_names)\n return zip(*result)", "_____no_output_____" ], [ "if run_preprocessing_benchmark:\n start_time = time.time()\n num_train = 100\n train_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\",num_return = num_train)\n\n xs, ys = Preprocess_parallel(func_canny, train_file_names)\n print('--- {} seconds ---'.format(time.time() - start_time))", "--- 1.9814999103546143 seconds ---\n" ] ], [ [ "### Read file names", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n # Using FEN to identify grid with chess\n\n num_train = 500 # small bath train\n num_test= 500 # small bath train\n\n # Reading lables\n train_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\",num_return = num_train)\n test_file_names = GetFileNamesInDir(g_test_dir, extension = \"jpeg\",num_return = num_test)", "_____no_output_____" ] ], [ [ "## Section 3. Implement algorithms", "_____no_output_____" ], [ "### Section 3.0 AdaBoostClassifier (ABC) Prototype", "_____no_output_____" ], [ "#### Import data - SIFT output ( n x 128) ", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n # import data - train\n\n start_time = time.time()\n #xs_train, ys_train = Preprocess_parallel(train_file_names) \n xs_train_sift, ys_train_sift = Preprocess_parallel(func_sift, train_file_names) #[JL - I broke Preprocess_parallel. use this instead ]\n print('xs_train_sift, ys_train_sift generated:',len(xs_train_sift))\n print(np.array(xs_train_sift).shape, np.array(ys_train_sift).shape)\n print('--- {} seconds ---'.format(time.time() - start_time))\n\n # import data - test\n\n start_time = time.time()\n #xs_train, ys_train = Preprocess_parallel(train_file_names) \n xs_test_sift, ys_test_sift = Preprocess_parallel(func_sift, test_file_names) #[JL - I broke Preprocess_parallel. use this instead ]\n print('xs_test_sift, ys_test_sift generated:',len(xs_test_sift))\n print(np.array(xs_test_sift).shape, np.array(ys_test_sift).shape)\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ] ], [ [ "#### intital prediction accuracy in sift data", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n xs_train_sift2= np.array(xs_train_sift).reshape(500*64,128)\n ys_train_sift2 = np.array(ys_train_sift).reshape(500*64)\n print('shape of train data:',np.array(xs_train_sift2).shape, np.array(ys_train_sift2).shape)\n\n # ABC classifier\n\n from sklearn.ensemble import AdaBoostClassifier\n from sklearn.metrics import accuracy_score\n from sklearn.metrics import classification_report\n start_time = time.time()\n\n ada = AdaBoostClassifier(n_estimators=50, learning_rate=0.5, random_state=42)\n ada.fit(xs_train_sift2,ys_train_sift2)\n y_pred= ada.predict(xs_train_sift2)\n\n #Evaluate its performance on the training and test set\n print(\"AdaBoost- accuracy on validation set:\", accuracy_score(ys_train_sift2 ,y_pred))\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ] ], [ [ "#### Import data - canny output ( n x 25 x 25 ) ", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n # import data - train 500\n\n start_time = time.time()\n #xs_train, ys_train = Preprocess_parallel(train_file_names) \n xs_train_canny, ys_train_canny = func_generator_canny(train_file_names) #[JL - I broke Preprocess_parallel. use this instead ]\n print('xs_train_canny, ys_train_canny generated:',len(xs_train_canny))\n print(np.array(xs_train_canny).shape, np.array(ys_train_canny).shape)\n print('--- {} seconds ---'.format(time.time() - start_time))\n\n # import data - test 500\n\n start_time = time.time()\n #xs_train, ys_train = Preprocess_parallel(train_file_names) \n xs_test_canny, ys_test_canny = func_generator_canny(test_file_names) #[JL - I broke Preprocess_parallel. use this instead ]\n print('xs_test_canny, ys_test_canny generated:',len(xs_test_canny))\n print(np.array(xs_test_canny).shape, np.array(ys_test_canny).shape)\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ] ], [ [ "#### intital prediction accuracy in canny data", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n xs_train_canny2= np.array(xs_train_canny).reshape(500*64,25*25)\n ys_train_canny2 = np.array(ys_train_canny).reshape(500*64)\n print('shape of train data:',np.array(xs_train_canny2).shape, np.array(ys_train_canny2).shape)\n\n # ABC classifier\n\n from sklearn.ensemble import AdaBoostClassifier\n from sklearn.metrics import accuracy_score\n from sklearn.metrics import classification_report\n start_time = time.time()\n\n ada = AdaBoostClassifier(n_estimators=50, learning_rate=0.5, random_state=42)\n ada.fit(xs_train_canny2,ys_train_canny2)\n y_pred= ada.predict(xs_train_canny2)\n\n #Evaluate its performance on the training and test set\n print(\"AdaBoost- accuracy on validation set:\", accuracy_score(ys_train_canny2 ,y_pred))\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ] ], [ [ "### Proceed with canny data", "_____no_output_____" ], [ "#### Split train to train and validation", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n #split 500 training data\n start_time = time.time()\n\n test_size=0.33\n\n from sklearn.model_selection import train_test_split\n X_train, X_val, Y_train, Y_val = train_test_split(\n xs_train_canny,ys_train_canny, test_size=0.33, random_state=0)\n print(np.array(X_train).shape, np.array(X_val).shape, np.array(Y_train).shape, np.array(Y_val).shape)\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ] ], [ [ "#### Undersampling traing set ", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n # test sift -resampled\n\n start_time = time.time()\n\n X_resampled, Y_resampled = undersampling_ClusterCentroids_canny(X_train,Y_train)\n print('--- {} seconds ---'.format(time.time() - start_time))\n print('reduce grid number from',len(np.array(Y_train))*64,'to',len(np.array(Y_resampled)))\n print('shape of resampled data:',np.array(X_resampled).shape, np.array(Y_resampled).shape)", "_____no_output_____" ], [ "if do_hyper_parameter_tuning:\n # reshpe train daa, validation data, and testing data as resampled\n\n X_train= np.array(X_train).reshape(335*64,25*25)\n Y_train = np.array(Y_train).reshape(335*64)\n print('shape of train data:',np.array(X_train).shape, np.array(Y_train).shape)\n\n X_val= np.array(X_val).reshape(165*64,25*25)\n Y_val = np.array(Y_val).reshape(165*64)\n print('shape of validation data:',np.array(X_val).shape, np.array(Y_val).shape)\n\n xs_test_canny= np.array(xs_test_canny).reshape(num_test*64,25*25)\n ys_test_canny = np.array(ys_test_canny).reshape(num_test*64)\n print('shape of test data:',np.array(xs_test_canny).shape, np.array(ys_test_canny).shape)\n", "_____no_output_____" ] ], [ [ "#### Base classifier- Tree (with canny & canny resampling)", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n # Tree classifier - Canny data \n from sklearn.tree import DecisionTreeClassifier\n\n start_time = time.time()\n #Apply pre-pruning by limiting the depth of the tree - max_depth=2\n tree = DecisionTreeClassifier(criterion='gini', max_depth=5)\n tree.fit(X_train, Y_train)\n #Evaluate its performance on the training and test set\n print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, Y_train)))\n print(\"Accuracy on validation set: {:.3f}\".format(tree.score(X_val, Y_val)))\n print(\"Accuracy on testing set: {:.3f}\".format(tree.score(xs_test_canny, ys_test_canny)))\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ], [ "if do_hyper_parameter_tuning:\n # Tree classifier - Canny & resampling data\n from sklearn.tree import DecisionTreeClassifier\n\n start_time = time.time()\n #Apply pre-pruning by limiting the depth of the tree \n tree = DecisionTreeClassifier(criterion='gini', max_depth=5)\n tree.fit(X_resampled, Y_resampled)\n #Evaluate its performance on the training and test set\n print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, Y_train)))\n print(\"Accuracy on validation set: {:.3f}\".format(tree.score(X_val, Y_val)))\n print(\"Accuracy on testing set: {:.3f}\".format(tree.score(xs_test_canny, ys_test_canny)))\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ] ], [ [ "#### AdaBoostClassifier- Tree (with canny & canny resampling)", "_____no_output_____" ] ], [ [ "if do_hyper_parameter_tuning:\n # ABC classifier- Canny data\n\n from sklearn.ensemble import AdaBoostClassifier\n from sklearn.metrics import accuracy_score\n from sklearn.metrics import classification_report\n start_time = time.time()\n\n ada = AdaBoostClassifier(n_estimators=50,\n base_estimator = DecisionTreeClassifier(criterion='gini', max_depth=5),\n learning_rate=0.5,\n random_state=42)\n ada.fit(X_train, Y_train)\n y_pred_val = ada.predict(X_val)\n y_pred_test = ada.predict(xs_test_canny)\n\n #Evaluate its performance on the training and test set\n print(\"AdaBoost- accuracy on validation set:\", accuracy_score(Y_val, y_pred_val))\n print(\"AdaBoost- accuracy on testing set:\", accuracy_score(ys_test_canny, y_pred_test))\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ], [ "if do_hyper_parameter_tuning:\n # ABC classifier- Canny & resampling data\n\n start_time = time.time()\n\n ada = AdaBoostClassifier(n_estimators=50,\n base_estimator = DecisionTreeClassifier(criterion='gini', max_depth=5),\n learning_rate=0.5,\n random_state=42)\n ada.fit(X_resampled, Y_resampled)\n y_pred_val = ada.predict(X_val)\n y_pred_test = ada.predict(xs_test_canny)\n\n #Evaluate its performance on the training and test set\n print(\"AdaBoost- accuracy on validation set:\", accuracy_score(Y_val, y_pred_val))\n print(\"AdaBoost- accuracy on testing set:\", accuracy_score(ys_test_canny, y_pred_test))\n print('--- {} seconds ---'.format(time.time() - start_time))", "_____no_output_____" ] ], [ [ "#### Hyper parameter tunning", "_____no_output_____" ] ], [ [ "###https://machinelearningmastery.com/adaboost-ensemble-in-python/\n\nif do_hyper_parameter_tuning:\n \n from sklearn.ensemble import AdaBoostClassifier\n from sklearn.model_selection import GridSearchCV\n\n # AdaBoost\n param_grid = [{'n_estimators': np.arange(10,50,5),\n 'learning_rate': [0.01, 0.05, 0.1, 1,5,10]\n }]\n\n start_time = time.time()\n abc = GridSearchCV(AdaBoostClassifier(random_state=42), param_grid)\n abc.fit(X_resampled, Y_resampled) \n\n print('--- {} seconds ---'.format(time.time() - start_time))\n\n # SVC\n param_grid = [{'n_estimators': np.arange(10,50,5),\n 'learning_rate': [0.01, 0.05, 0.1, 1,5,10],\n \"kernel\" : [\"linear\", \"poly\", \"rbf\", \"sigmoid\"],\n \"C\" : [0.01, 1, 10, 100]\n }]\n\n start_time = time.time()\n svc = GridSearchCV(svm.SVC(random_state=42), param_grid)\n abc.fit(X_resampled, Y_resampled) \n\n print('--- {} seconds ---'.format(time.time() - start_time))\n \n \n \nelse:\n print(\"Hyper parameter tuning skipped.\")", "Hyper parameter tuning skipped.\n" ] ], [ [ "#### GridSearchCV Result", "_____no_output_____" ] ], [ [ "\nif do_hyper_parameter_tuning:\n\n # Print grid search results\n from sklearn.metrics import classification_report\n\n means = abc.cv_results_['mean_test_score']\n stds = abc.cv_results_['std_test_score']\n params = abc.cv_results_['params']\n\n print('Grid search mean and stdev:\\n')\n\n for mean, std, p in zip(means, stds, params):\n print(\"%0.3f (+/-%0.03f) for %r\"% (mean, std * 2, p))\n\n # Print best params\n print('\\nBest parameters:', abc.best_params_)\n print(\"Detailed classification report:\")\n print()\n print(classification_report(Y_val, abc.predict(X_val)))\n print()\nelse:\n print(\"Adaboost: Hyper parameter report skipped.\")", "Adaboost: Hyper parameter report skipped.\n" ] ], [ [ "## Section 3.1 SVM Classifier (SVC)", "_____no_output_____" ], [ "#### Base class for all classifiers", "_____no_output_____" ] ], [ [ "import abc\n\n# interface of the classifiers\nclass IClassifier:\n\n # this method should accept a list of file names of the training data\n @abc.abstractmethod\n def Train(self, train_file_names):\n raise NotImplementedError()\n\n # this should accept a 400 * 400 * 3 numpy array as query data, and returns the fen notation of the board.\n @abc.abstractmethod\n def Predict(self, query_data):\n raise NotImplementedError()\n \n # this should accept a list of file names, and returns the predicted labels as 1d numpy array.\n @abc.abstractmethod\n def Predict(self, query_data):\n raise NotImplementedError() ", "_____no_output_____" ] ], [ [ "#### Class definition for SVC", "_____no_output_____" ] ], [ [ "from sklearn import svm\nimport numpy as np\n\n# image io and plotting\nfrom skimage import io, transform\nimport skimage.util\nfrom skimage.util.shape import view_as_blocks\nfrom matplotlib import pyplot as plt\n\n# parallel processing\nfrom joblib import Parallel, delayed\n# model save and load\nimport pickle\nimport os\n# profiling\nimport time\n\n# joblib needs the kernel to be a top-level function, so we defined it here.\ndef PreprocessKernel(name):\n img = ReadImage(name, gray = True)\n grids = SVCClassifier.SVCPreprocess(img)\n labels = np.array(FENtoOneHot(GetCleanNameByPath(name))).argmax(axis=1)\n return grids, labels\n\n# SVM Classifier\nclass SVCClassifier(IClassifier):\n\n def __init__(self):\n self.__svc__ = svm.SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n max_iter=-1, probability=False, random_state=None, shrinking=True,\n tol=0.001, verbose=False)\n\n # this method should accept a list of file names of the training data\n def Train(self, train_file_names):\n print(\"svc: reading image.\")\n start_time = time.time()\n xs, ys = SVCClassifier.PreprocessParallelWrapperFunc(train_file_names)\n print(\"svc: finished reading image, {} sec.\".format(time.time() - start_time))\n # train\n print(\"svc: start training.\")\n start_time = time.time()\n self.__svc__.fit(xs, ys)\n print(\"svc: finished. {} sec.\".format(time.time() - start_time))\n\n # this should accept a 400 * 400 * 3 numpy array as query data, and returns the fen notation of the board.\n def Predict(self, query_data):\n grids = SVCClassifier.SVCPreprocess(query_data)\n y_pred = self.__svc__.predict(grids)\n \n return LabelArrayToL(y_pred)\n\n # predict by file name:\n def PredictMultiple(self, file_names):\n preds = []\n truth = []\n for f in file_names:\n img = ReadImage(f, gray = True)\n y_pred = self.Predict(img)\n y_true = FENtoL(GetCleanNameByPath(f))\n preds.append(y_pred)\n truth.append(y_true)\n \n all_pred = np.vstack(preds)\n all_truth = np.vstack(truth)\n return all_pred, all_truth\n \n # parallel pre-process wrapper:\n @staticmethod\n def PreprocessParallelWrapperFunc(file_names, num_thread = g_thread_num):\n result = Parallel(n_jobs = num_thread)(delayed(PreprocessKernel)(file_name) for file_name in file_names)\n xs, ys = zip(*result)\n xs = np.concatenate(xs, axis=0)\n ys = np.concatenate(ys)\n return xs, ys\n\n @staticmethod\n def SVCPreprocess(img):\n img = transform.resize(img, (g_down_sampled_size, g_down_sampled_size), mode='constant')\n grids = skimage.util.shape.view_as_blocks(img, block_shape = (g_down_sampled_grid_size, g_down_sampled_grid_size))\n grids = grids.reshape((-1, grids.shape[3], grids.shape[3]))\n grids = grids.reshape((grids.shape[0], grids.shape[1] * grids.shape[1]))\n return grids\n\n def SaveModel(self, save_file_name):\n os.makedirs(os.path.dirname(save_file_name), exist_ok = True)\n with open(save_file_name, 'wb') as file:\n pickle.dump(self.__svc__, file)\n\n def LoadModel(self, load_file_name):\n with open(load_file_name, 'rb') as file:\n self.__svc__ = pickle.load(file)", "_____no_output_____" ] ], [ [ "#### Test code for SVC", "_____no_output_____" ] ], [ [ "if run_test_code_for_classifiers:\n svc = SVCClassifier()\n train_names = GetFileNamesInDir(g_train_dir)\n\n if load_saved_model:\n print(\"svc: loading model from \" + svc_model_file)\n svc.LoadModel(svc_model_file)\n else:\n svc.Train(train_names[:500])\n\n y_truth = FENtoL(GetCleanNameByPath(a_random_file))\n img = ReadImage(a_random_file, gray = True)\n pred = svc.Predict(img)\n print(\"truth: \", ''.join(y_truth))\n print(\"pred : \", ''.join(pred))\n\n # save model\n if not load_saved_model:\n print(\"svc: saving model to \" + svc_model_file)\n svc.SaveModel(svc_model_file)", "svc: loading model from ./saved_model/svc_dump.pkl\ntruth: 0b0B0b0000pK00q00000p0rB0000000k0000000000000000000B0000000rb000\npred : 000B0000000K00000000p0rB0000000k0000000000000000000B0000000r0000\n" ] ], [ [ "### Section 3.2 Convolutional Neural Network Classifier (CNN)", "_____no_output_____" ], [ "#### Class definition for CNN", "_____no_output_____" ] ], [ [ "import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\nimport cv2\nfrom skimage import io, transform\nimport numpy as np\nimport os\n\n#import tensorflow as tf\n#from tensorflow import keras\n#from tf.keras.models import Sequential\n#from tf.keras.layers.core import Flatten, Dense, Dropout, Activation\n#from tf.keras.layers.convolutional import Convolution2D\n\nclass CNNClassifier(IClassifier):\n\n # the file name format does not accept batch as parameter. link:\n # https://github.com/tensorflow/tensorflow/issues/38668\n s_check_point_file_name = \"./CNN_training_checkpoint/cp_{epoch:02d}-{accuracy:.2f}.ckpt\"\n s_check_point_path = os.path.dirname(s_check_point_file_name)\n s_save_frequence = 10000 # save a checkpoint every s_save_frequence batches\n\n def __init__(self):\n \n #tf.config.threading.set_inter_op_parallelism_threads(3)\n #tf.config.threading.set_intra_op_parallelism_threads(3)\n\n # define our model\n self.__model__ = keras.Sequential(\n [\n layers.Convolution2D(32, (3, 3), input_shape = (g_down_sampled_grid_size, g_down_sampled_grid_size, 3)),\n layers.Activation('relu'),\n layers.Dropout(0.1),\n layers.Convolution2D(32, (3, 3)),\n layers.Activation('relu'),\n\n layers.Convolution2D(32, (3, 3)),\n layers.Activation('relu'),\n\n layers.Flatten(),\n \n layers.Dense(128),\n layers.Activation('relu'),\n layers.Dropout(0.3),\n\n layers.Dense(13),\n layers.Activation(\"softmax\")\n ]\n )\n\n self.__model__.compile(loss = \"categorical_crossentropy\", optimizer = 'adam', metrics = [\"accuracy\"])\n \n self.__save_check_point_callback__ = tf.keras.callbacks.ModelCheckpoint(\n filepath = CNNClassifier.s_check_point_file_name,\n monitor='val_accuracy',\n save_weights_only = True,\n save_freq = CNNClassifier.s_save_frequence,\n verbose = 1\n )\n\n\n # generator\n @staticmethod\n def func_generator(train_file_names):\n for image_file_name in train_file_names:\n img = ReadImage(image_file_name)\n x = CNNClassifier.PreprocessImage(img)\n y = np.array(FENtoOneHot(GetCleanNameByPath(image_file_name)))\n yield x, y\n\n # this method should accept N * 64 * m * n numpy array as train data, and N lists of 64 chars as label.\n def Train(self, train_data_names):\n train_size = len(train_data_names)\n\n ## try load last checkpoint\n #if not self.LoadMostRecentModel():\n # os.makedirs(CNNClassifier.s_check_point_path, exist_ok = True)\n\n # train\n self.__model__.fit(CNNClassifier.func_generator(train_data_names),\n use_multiprocessing = False,\n #batch_size = 1000,\n steps_per_epoch = train_size / 20,\n epochs = 2,\n #callbacks = [self.__save_check_point_callback__],\n verbose = 1)\n\n\n # this should accept a 64 * m * n numpy array as query data, and returns the fen notation of the board.\n def Predict(self, query_data):\n grids = CNNClassifier.PreprocessImage(query_data)\n y_pred = self.__model__.predict(grids).argmax(axis=1)\n\n return y_pred\n\n \n # predict by file name:\n def PredictMultiple(self, file_names):\n preds = []\n truth = []\n for f in file_names:\n img = ReadImage(f, gray = False)\n y_pred = LabelArrayToL(self.Predict(img))\n y_true = FENtoL(GetCleanNameByPath(f))\n preds.append(y_pred)\n truth.append(y_true)\n \n all_pred = np.vstack(preds)\n all_truth = np.vstack(truth)\n return all_pred, all_truth\n \n \n def LoadModel(self, name):\n self.__model__.load_weights(name)\n \n def SaveModel(self, name):\n os.makedirs(os.path.dirname(name), exist_ok = True)\n self.__model__.save_weights(name)\n \n def PrintModel(self):\n self.__model__.summary()\n \n def LoadMostRecentModel(self):\n return self.LoadMostRecentModelFromDirectory(CNNClassifier.s_check_point_path)\n \n def LoadMostRecentModelFromDirectory(self, path):\n try:\n last_cp = tf.train.latest_checkpoint(path)\n self.__model__.load_weights(last_cp)\n print(\"Loaded checkpoint from \" + last_cp)\n return True\n except:\n print(\"No checkpoint is loaded.\")\n return False\n\n def TestAccuracy(self, test_file_names):\n num_files = len(test_file_names)\n\n predict_result = self.__model__.predict(CNNClassifier.func_generator(test_file_names)).argmax(axis=1)\n predict_result = predict_result.reshape(num_files, -1)\n predicted_fen_arr = np.array([LtoFEN(LabelArrayToL(labels)) for labels in predict_result])\n test_fens = np.array([GetCleanNameByPath(file_name) for file_name in test_file_names])\n\n final_accuracy = (predicted_fen_arr == test_fens).astype(np.float).mean()\n return final_accuracy\n\n @staticmethod\n def PreprocessImage(image):\n image = transform.resize(image, (g_down_sampled_size, g_down_sampled_size), mode='constant')\n \n # 1st and 2nd dim is 8\n grids = ImageToGrids(image, g_down_sampled_grid_size, g_down_sampled_grid_size)\n\n # debug\n #plt.imshow(grids[0][3])\n #plt.show()\n\n return grids.reshape(g_grid_row * g_grid_col, g_down_sampled_grid_size, g_down_sampled_grid_size, 3)", "_____no_output_____" ] ], [ [ "#### test code for CNN", "_____no_output_____" ] ], [ [ "if run_test_code_for_classifiers:\n if not load_saved_model:\n cnn = CNNClassifier()\n train_names = GetFileNamesInDir(g_train_dir)\n cnn.Train(train_names)\n cnn.SaveModel(cnn_model_file)\n else:\n cnn = CNNClassifier()\n cnn.PrintModel()\n print(\"cnn: loading model from \" + cnn_model_file)\n cnn.LoadModel(cnn_model_file)\n predicted_label = cnn.Predict(ReadImage(a_random_file))\n L = predicted_label\n FEN = LtoFEN(LabelArrayToL(L))\n print(\"predicted: \" + FEN)\n print(\"Original: \" + GetCleanNameByPath(a_random_file))\n\n #test_file_names = GetFileNamesInDir(g_test_dir)[:1000]\n #print(\"CNN: Testing accuracy for {} board images.\".format(len(test_file_names)))\n #accuracy = cnn.TestAccuracy(test_file_names)\n #print(\"CNN: Final accuracy: {}\".format(accuracy))\n\n labels = cnn.PredictMultiple(GetFileNamesInDir(g_test_dir)[:100])", "Model: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nconv2d (Conv2D) (None, 23, 23, 32) 896 \n_________________________________________________________________\nactivation (Activation) (None, 23, 23, 32) 0 \n_________________________________________________________________\ndropout (Dropout) (None, 23, 23, 32) 0 \n_________________________________________________________________\nconv2d_1 (Conv2D) (None, 21, 21, 32) 9248 \n_________________________________________________________________\nactivation_1 (Activation) (None, 21, 21, 32) 0 \n_________________________________________________________________\nconv2d_2 (Conv2D) (None, 19, 19, 32) 9248 \n_________________________________________________________________\nactivation_2 (Activation) (None, 19, 19, 32) 0 \n_________________________________________________________________\nflatten (Flatten) (None, 11552) 0 \n_________________________________________________________________\ndense (Dense) (None, 128) 1478784 \n_________________________________________________________________\nactivation_3 (Activation) (None, 128) 0 \n_________________________________________________________________\ndropout_1 (Dropout) (None, 128) 0 \n_________________________________________________________________\ndense_1 (Dense) (None, 13) 1677 \n_________________________________________________________________\nactivation_4 (Activation) (None, 13) 0 \n=================================================================\nTotal params: 1,499,853\nTrainable params: 1,499,853\nNon-trainable params: 0\n_________________________________________________________________\ncnn: loading model from ./saved_model/cnn_weights\npredicted: 1b1B1b2-2pK2q1-4p1rB-7k-8-8-3B4-3rb3\nOriginal: 1b1B1b2-2pK2q1-4p1rB-7k-8-8-3B4-3rb3\n" ] ], [ [ "### Section 3.3 AdaBoost Classifier", "_____no_output_____" ], [ "#### class definition", "_____no_output_____" ] ], [ [ "from sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\n\n# image io and plotting\nfrom skimage import io, transform\nimport skimage.util\nfrom skimage.util.shape import view_as_blocks\nfrom matplotlib import pyplot as plt\n# parallel processing\nfrom joblib import Parallel, delayed\n# model save and load\nimport pickle\nimport os\n# profiling\nimport time\n\n# joblib needs the kernel to be a top-level function, so we defined it here.\ndef PreprocessKernel(name):\n img = ReadImage(name, gray = True)\n grids = ABClassifier.ABCPreprocess(img)\n labels = np.array(FENtoOneHot(GetCleanNameByPath(name))).argmax(axis=1)\n return grids, labels\n\n# Adaboost Classifier\nclass ABClassifier(IClassifier):\n\n def __init__(self):\n self.__abc__ = AdaBoostClassifier(n_estimators=30,\n base_estimator = DecisionTreeClassifier(criterion='gini', max_depth=5),\n learning_rate=0.5)\n\n # this method should accept a list of file names of the training data\n def Train(self, train_file_names):\n print(\"abc: reading image.\")\n start_time = time.time()\n xs, ys = ABClassifier.PreprocessParallelWrapperFunc(train_file_names)\n print(\"abc: finished reading image, {} sec.\".format(time.time() - start_time))\n # train\n print(\"abc: start training.\")\n start_time = time.time()\n self.__abc__.fit(xs, ys)\n print(\"abc: finished. {} sec.\".format(time.time() - start_time))\n\n\n # this should accept a 400 * 400 * 3 numpy array as query data, and returns the fen notation of the board.\n def Predict(self, query_data):\n grids = ABClassifier.ABCPreprocess(query_data)\n y_pred = self.__abc__.predict(grids)\n \n return LabelArrayToL(y_pred)\n\n\n # parallel pre-process wrapper:\n @staticmethod\n def PreprocessParallelWrapperFunc(file_names, num_thread = g_thread_num):\n result = Parallel(n_jobs = num_thread)(delayed(PreprocessKernel)(file_name) for file_name in file_names)\n xs, ys = zip(*result)\n xs = np.concatenate(xs, axis=0)\n ys = np.concatenate(ys)\n return xs, ys\n\n\n @staticmethod\n def ABCPreprocess(img):\n img = transform.resize(img, (g_down_sampled_size, g_down_sampled_size), mode='constant')\n grids = skimage.util.shape.view_as_blocks(img, block_shape = (g_down_sampled_grid_size, g_down_sampled_grid_size))\n grids = grids.reshape((-1, grids.shape[3], grids.shape[3]))\n grids = grids.reshape((grids.shape[0], grids.shape[1] * grids.shape[1]))\n return grids\n\n def SaveModel(self, save_file_name):\n os.makedirs(os.path.dirname(save_file_name), exist_ok = True)\n with open(save_file_name, 'wb') as file:\n pickle.dump(self.__abc__, file)\n\n def LoadModel(self, load_file_name):\n with open(load_file_name, 'rb') as file:\n self.__abc__ = pickle.load(file)\n \n # predict by file name:\n def PredictMultiple(self, file_names):\n preds = []\n truth = []\n for f in file_names:\n img = ReadImage(f, gray = True)\n y_pred = self.Predict(img)\n y_true = FENtoL(GetCleanNameByPath(f))\n preds.append(y_pred)\n truth.append(y_true)\n \n all_pred = np.vstack(preds)\n all_truth = np.vstack(truth)\n return all_pred, all_truth", "_____no_output_____" ] ], [ [ "#### Test code for ABC", "_____no_output_____" ] ], [ [ "if run_test_code_for_classifiers:\n abc = ABClassifier()\n train_names = GetFileNamesInDir(g_train_dir)\n\n if load_saved_model:\n print(\"abc: loading model from \" + abc_model_file)\n abc.LoadModel(abc_model_file)\n else:\n abc.Train(train_names)\n\n y_truth = FENtoL(GetCleanNameByPath(a_random_file))\n img = ReadImage(a_random_file, gray = True)\n pred = abc.Predict(img)\n print(\"truth: \", ''.join(y_truth))\n print(\"pred : \", ''.join(pred))\n\n # save model\n if not load_saved_model:\n print(\"abc: saving model to \" + abc_model_file)\n abc.SaveModel(abc_model_file)", "abc: loading model from ./saved_model/abc_dump.pkl\ntruth: 0b0B0b0000pK00q00000p0rB0000000k0000000000000000000B0000000rb000\npred : 0k0B0k0000pP00q00000p0rB0000000k0000000000000000000B0000000rb000\n" ] ], [ [ "## 10-fold cross validation for 3 classifiers", "_____no_output_____" ], [ "Cv reference\nhttps://scikit-learn.org/stable/modules/cross_validation.html\n\noptions for 10 fold\n 1. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html\n 2. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html (Preferred)\n \" StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.\" \n ", "_____no_output_____" ], [ "### helper functions", "_____no_output_____" ] ], [ [ "# filters accepts a list of file names, and return the data matrix and labels\nimport random\nfrom sklearn.metrics import confusion_matrix\n\n# get balanced accuracy from confusion matrix\ndef BalancedAccuracyFromConfusionMatrix(cm):\n ret = np.empty((cm.shape[0]))\n \n for idx, row in enumerate(cm):\n ret[idx] = row[idx] / row.sum()\n \n return ret.mean()\n\n# dummy filter to return all files\ndef DefaultFilter(file_names, rate = 1):\n return file_names\n\n# filter using random_sampling:\ndef RandomFilter(file_names, rate = 1):\n # we fix the random part to assure the results are consistent\n random_seed = 4242\n random.seed(random_seed)\n return random.sample(file_names, k = int(len(file_names) * rate))\n \ndef ConfusionMatrix(classifier, test_file_names, filter = RandomFilter, sampling_rate = 0.001):\n \n confusion_matrices = []\n accuracies = []\n accuracies_balanced = []\n train_time_cost = []\n validation_time_cost = []\n \n # split name list into 10 equal parts\n division = len(test_file_names) / float(10) \n complete_name_folds = [ test_file_names[int(round(division * i)): int(round(division * (i + 1)))] for i in range(10) ]\n filtered_name_folds = complete_name_folds.copy()\n for i in range(10):\n filtered_name_folds[i] = filter(complete_name_folds[i], rate = sampling_rate)\n\n # we use filtered name folds to train, and validation.\n for iv in range(10):\n \n # merge the 9 folds:\n train_names = []\n validation_names = []\n for i in range(10):\n if i != iv:\n train_names.extend(filtered_name_folds[i])\n else:\n # validation_names = complete_name_folds[i].copy()\n validation_names = filtered_name_folds[i].copy()\n\n \n # train the classifier:\n print(\"training started: \", type(classifier).__name__, \"for fold #\", iv, \"# train files:\", len(train_names))\n t = time.time()\n classifier.Train(train_names)\n train_time_cost.append(time.time() - t)\n print(\"training finished: \", type(classifier).__name__, \"for fold #\", iv,\n \"time: {}s\".format(time.time() - t))\n \n print(\"predicting started: \", type(classifier).__name__, \"for fold #\", iv)\n t = time.time()\n ypreds, y_true = classifier.PredictMultiple(validation_names)\n validation_time_cost.append(time.time() - t)\n \n ypreds = ypreds.reshape((-1, 1))\n y_true = y_true.reshape((-1, 1))\n\n conf_mat = confusion_matrix(y_true, ypreds, labels = g_labels)\n confusion_matrices.append(conf_mat)\n accuracy = np.trace(conf_mat) / float(np.sum(conf_mat))\n accuracies.append(accuracy)\n accuracy_balanced = BalancedAccuracyFromConfusionMatrix(conf_mat)\n accuracies_balanced.append(accuracy_balanced)\n \n \n \n print(\"predicting finished: \", type(classifier).__name__, \"for fold #\", iv,\n \"time: {}s\".format(time.time() - t), \" accuracy: \", accuracy, \" balanced_accuracy:\", accuracy_balanced)\n \n return confusion_matrices, accuracies, accuracies_balanced, train_time_cost, validation_time_cost\n", "_____no_output_____" ] ], [ [ "### 10-fold routine", "_____no_output_____" ] ], [ [ "if run_ten_fold:\n # 10-fold for ABC\n train_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\")\n # random sampling rate of the each fold in 10-fold\n abc_random_sampling_rate = 0.005\n\n abc_tf = ABClassifier()\n\n confusion_matrices_abc, accuracies_abc, accuracies_balanced_abc, train_time_cost_abc, validation_time_cost_abc = \\\n ConfusionMatrix(abc_tf, train_file_names, RandomFilter, sampling_rate = abc_random_sampling_rate)\n\n", "training started: ABClassifier for fold # 0 # train files: 360\nabc: reading image.\nabc: finished reading image, 5.398272275924683 sec.\nabc: start training.\nabc: finished. 105.41199851036072 sec.\ntraining finished: ABClassifier for fold # 0 time: 110.818776845932s\npredicting started: ABClassifier for fold # 0\npredicting finished: ABClassifier for fold # 0 time: 1.8081109523773193s accuracy: 0.915234375 balanced_accuracy: 0.9887159507361434\ntraining started: ABClassifier for fold # 1 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.353529453277588 sec.\nabc: start training.\nabc: finished. 106.2824718952179 sec.\ntraining finished: ABClassifier for fold # 1 time: 107.64400005340576s\npredicting started: ABClassifier for fold # 1\npredicting finished: ABClassifier for fold # 1 time: 0.588024377822876s accuracy: 0.9953125 balanced_accuracy: 0.9773043222050123\ntraining started: ABClassifier for fold # 2 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.4164981842041016 sec.\nabc: start training.\nabc: finished. 108.95200252532959 sec.\ntraining finished: ABClassifier for fold # 2 time: 110.37699913978577s\npredicting started: ABClassifier for fold # 2\npredicting finished: ABClassifier for fold # 2 time: 0.5855247974395752s accuracy: 0.994921875 balanced_accuracy: 0.9726729328379256\ntraining started: ABClassifier for fold # 3 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.3769776821136475 sec.\nabc: start training.\nabc: finished. 105.25149726867676 sec.\ntraining finished: ABClassifier for fold # 3 time: 106.63697409629822s\npredicting started: ABClassifier for fold # 3\npredicting finished: ABClassifier for fold # 3 time: 0.5955328941345215s accuracy: 0.997265625 balanced_accuracy: 0.9829801695186311\ntraining started: ABClassifier for fold # 4 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.3645029067993164 sec.\nabc: start training.\nabc: finished. 105.66096711158752 sec.\ntraining finished: ABClassifier for fold # 4 time: 107.03396940231323s\npredicting started: ABClassifier for fold # 4\npredicting finished: ABClassifier for fold # 4 time: 0.6035027503967285s accuracy: 0.9984375 balanced_accuracy: 0.9871866850188351\ntraining started: ABClassifier for fold # 5 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.3729960918426514 sec.\nabc: start training.\nabc: finished. 106.87903761863708 sec.\ntraining finished: ABClassifier for fold # 5 time: 108.26099824905396s\npredicting started: ABClassifier for fold # 5\npredicting finished: ABClassifier for fold # 5 time: 0.5815010070800781s accuracy: 0.996484375 balanced_accuracy: 0.9763087452358707\ntraining started: ABClassifier for fold # 6 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.3584940433502197 sec.\nabc: start training.\nabc: finished. 104.68600416183472 sec.\ntraining finished: ABClassifier for fold # 6 time: 106.0530149936676s\npredicting started: ABClassifier for fold # 6\npredicting finished: ABClassifier for fold # 6 time: 0.6030011177062988s accuracy: 0.9984375 balanced_accuracy: 0.9848901098901099\ntraining started: ABClassifier for fold # 7 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.4029998779296875 sec.\nabc: start training.\nabc: finished. 106.33550238609314 sec.\ntraining finished: ABClassifier for fold # 7 time: 107.74799847602844s\npredicting started: ABClassifier for fold # 7\npredicting finished: ABClassifier for fold # 7 time: 0.5720021724700928s accuracy: 0.994140625 balanced_accuracy: 0.9539770270297656\ntraining started: ABClassifier for fold # 8 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.4405317306518555 sec.\nabc: start training.\nabc: finished. 103.98052430152893 sec.\ntraining finished: ABClassifier for fold # 8 time: 105.43050003051758s\npredicting started: ABClassifier for fold # 8\npredicting finished: ABClassifier for fold # 8 time: 0.6030175685882568s accuracy: 0.989453125 balanced_accuracy: 0.9828409905842151\ntraining started: ABClassifier for fold # 9 # train files: 360\nabc: reading image.\nabc: finished reading image, 1.3639986515045166 sec.\nabc: start training.\nabc: finished. 104.17997884750366 sec.\ntraining finished: ABClassifier for fold # 9 time: 105.55300045013428s\npredicting started: ABClassifier for fold # 9\npredicting finished: ABClassifier for fold # 9 time: 0.5954928398132324s accuracy: 0.97421875 balanced_accuracy: 0.9726182149323749\n" ], [ "if run_ten_fold:\n # 10-fold for CNN\n # random sampling rate of the each fold in 10-fold\n cnn_random_sampling_rate = 0.5\n\n train_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\")\n\n cnn_tf = CNNClassifier()\n\n confusion_matrices_cnn, accuracies_cnn, accuracies_balanced_cnn, train_time_cost_cnn, validation_time_cost_cnn = \\\n ConfusionMatrix(cnn_tf, train_file_names, RandomFilter, sampling_rate = cnn_random_sampling_rate)\n", "training started: CNNClassifier for fold # 0 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 92s 51ms/step - loss: 0.0742 - accuracy: 0.9811\nEpoch 2/2\n1800/1800 [==============================] - 92s 51ms/step - loss: 0.0129 - accuracy: 0.9967\ntraining finished: CNNClassifier for fold # 0 time: 184.50335025787354s\npredicting started: CNNClassifier for fold # 0\npredicting finished: CNNClassifier for fold # 0 time: 255.72573709487915s accuracy: 0.9953046875 balanced_accuracy: 0.972284597528154\ntraining started: CNNClassifier for fold # 1 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 88s 49ms/step - loss: 0.0044 - accuracy: 0.9987\nEpoch 2/2\n1800/1800 [==============================] - 88s 49ms/step - loss: 0.0026 - accuracy: 0.9992\ntraining finished: CNNClassifier for fold # 1 time: 176.32050108909607s\npredicting started: CNNClassifier for fold # 1\npredicting finished: CNNClassifier for fold # 1 time: 203.46679401397705s accuracy: 0.99998828125 balanced_accuracy: 0.9999432022567637\ntraining started: CNNClassifier for fold # 2 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 86s 48ms/step - loss: 0.0042 - accuracy: 0.9990\nEpoch 2/2\n1800/1800 [==============================] - 87s 49ms/step - loss: 0.0023 - accuracy: 0.9994\ntraining finished: CNNClassifier for fold # 2 time: 173.61347317695618s\npredicting started: CNNClassifier for fold # 2\npredicting finished: CNNClassifier for fold # 2 time: 258.44522190093994s accuracy: 0.999921875 balanced_accuracy: 0.9993312211503912\ntraining started: CNNClassifier for fold # 3 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 81s 45ms/step - loss: 0.0023 - accuracy: 0.9994\nEpoch 2/2\n1800/1800 [==============================] - 83s 46ms/step - loss: 0.0053 - accuracy: 0.9993\ntraining finished: CNNClassifier for fold # 3 time: 164.83446884155273s\npredicting started: CNNClassifier for fold # 3\npredicting finished: CNNClassifier for fold # 3 time: 253.1358721256256s accuracy: 1.0 balanced_accuracy: 1.0\ntraining started: CNNClassifier for fold # 4 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 81s 45ms/step - loss: 0.0039 - accuracy: 0.9993\nEpoch 2/2\n1800/1800 [==============================] - 86s 48ms/step - loss: 0.0014 - accuracy: 0.9996\ntraining finished: CNNClassifier for fold # 4 time: 167.50047063827515s\npredicting started: CNNClassifier for fold # 4\npredicting finished: CNNClassifier for fold # 4 time: 257.837361574173s accuracy: 1.0 balanced_accuracy: 1.0\ntraining started: CNNClassifier for fold # 5 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 89s 50ms/step - loss: 0.0028 - accuracy: 0.9995\nEpoch 2/2\n1800/1800 [==============================] - 88s 49ms/step - loss: 0.0025 - accuracy: 0.9995\ntraining finished: CNNClassifier for fold # 5 time: 176.9005012512207s\npredicting started: CNNClassifier for fold # 5\npredicting finished: CNNClassifier for fold # 5 time: 255.32304525375366s accuracy: 1.0 balanced_accuracy: 1.0\ntraining started: CNNClassifier for fold # 6 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 87s 48ms/step - loss: 2.2082e-04 - accuracy: 0.9999\nEpoch 2/2\n1800/1800 [==============================] - 87s 48ms/step - loss: 0.0058 - accuracy: 0.9994\ntraining finished: CNNClassifier for fold # 6 time: 174.0054988861084s\npredicting started: CNNClassifier for fold # 6\npredicting finished: CNNClassifier for fold # 6 time: 254.33951449394226s accuracy: 0.99997265625 balanced_accuracy: 0.9996995388807377\ntraining started: CNNClassifier for fold # 7 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 86s 48ms/step - loss: 6.0853e-04 - accuracy: 0.9999\nEpoch 2/2\n1800/1800 [==============================] - 91s 51ms/step - loss: 0.0046 - accuracy: 0.9994\ntraining finished: CNNClassifier for fold # 7 time: 177.45500087738037s\npredicting started: CNNClassifier for fold # 7\npredicting finished: CNNClassifier for fold # 7 time: 260.65016198158264s accuracy: 1.0 balanced_accuracy: 1.0\ntraining started: CNNClassifier for fold # 8 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 87s 48ms/step - loss: 0.0012 - accuracy: 0.9997\nEpoch 2/2\n1800/1800 [==============================] - 87s 48ms/step - loss: 0.0066 - accuracy: 0.9993\ntraining finished: CNNClassifier for fold # 8 time: 173.66347432136536s\npredicting started: CNNClassifier for fold # 8\npredicting finished: CNNClassifier for fold # 8 time: 258.1064829826355s accuracy: 0.999984375 balanced_accuracy: 0.9999181962289339\ntraining started: CNNClassifier for fold # 9 # train files: 36000\nEpoch 1/2\n1800/1800 [==============================] - 87s 48ms/step - loss: 0.0017 - accuracy: 0.9997\nEpoch 2/2\n1800/1800 [==============================] - 91s 51ms/step - loss: 6.9238e-04 - accuracy: 0.9999\ntraining finished: CNNClassifier for fold # 9 time: 178.00898122787476s\npredicting started: CNNClassifier for fold # 9\npredicting finished: CNNClassifier for fold # 9 time: 261.0290925502777s accuracy: 1.0 balanced_accuracy: 1.0\n" ], [ "if run_ten_fold:\n # 10-fold for SVM\n train_file_names = GetFileNamesInDir(g_train_dir, extension = \"jpeg\")\n # random sampling rate of the each fold in 10-fold\n svc_random_sampling_rate = 0.01\n\n svc_tf = SVCClassifier()\n\n confusion_matrices_svc, accuracies_svc, accuracies_balanced_svc, train_time_cost_svc, validation_time_cost_svc = \\\n ConfusionMatrix(svc_tf, train_file_names, RandomFilter, sampling_rate = svc_random_sampling_rate)", "training started: SVCClassifier for fold # 0 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 4.1754982471466064 sec.\nsvc: start training.\nsvc: finished. 275.877498626709 sec.\ntraining finished: SVCClassifier for fold # 0 time: 280.06999683380127s\npredicting started: SVCClassifier for fold # 0\npredicting finished: SVCClassifier for fold # 0 time: 25.094476461410522s accuracy: 0.9755859375 balanced_accuracy: 0.8390801614481257\ntraining started: SVCClassifier for fold # 1 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 4.161530256271362 sec.\nsvc: start training.\nsvc: finished. 276.26797008514404 sec.\ntraining finished: SVCClassifier for fold # 1 time: 280.44550037384033s\npredicting started: SVCClassifier for fold # 1\npredicting finished: SVCClassifier for fold # 1 time: 25.063472747802734s accuracy: 0.97578125 balanced_accuracy: 0.851003508486225\ntraining started: SVCClassifier for fold # 2 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 4.096996545791626 sec.\nsvc: start training.\nsvc: finished. 273.4409935474396 sec.\ntraining finished: SVCClassifier for fold # 2 time: 277.56246972084045s\npredicting started: SVCClassifier for fold # 2\npredicting finished: SVCClassifier for fold # 2 time: 25.160999536514282s accuracy: 0.9748046875 balanced_accuracy: 0.8442856239581942\ntraining started: SVCClassifier for fold # 3 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 2.193498373031616 sec.\nsvc: start training.\nsvc: finished. 271.1005029678345 sec.\ntraining finished: SVCClassifier for fold # 3 time: 273.30952429771423s\npredicting started: SVCClassifier for fold # 3\npredicting finished: SVCClassifier for fold # 3 time: 24.9065043926239s accuracy: 0.9771484375 balanced_accuracy: 0.8418881733972017\ntraining started: SVCClassifier for fold # 4 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 2.113495111465454 sec.\nsvc: start training.\nsvc: finished. 271.2329773902893 sec.\ntraining finished: SVCClassifier for fold # 4 time: 273.3629765510559s\npredicting started: SVCClassifier for fold # 4\npredicting finished: SVCClassifier for fold # 4 time: 24.966519832611084s accuracy: 0.9759765625 balanced_accuracy: 0.8401999249500759\ntraining started: SVCClassifier for fold # 5 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 2.094999074935913 sec.\nsvc: start training.\nsvc: finished. 270.80147099494934 sec.\ntraining finished: SVCClassifier for fold # 5 time: 272.9129693508148s\npredicting started: SVCClassifier for fold # 5\npredicting finished: SVCClassifier for fold # 5 time: 24.80652356147766s accuracy: 0.9701171875 balanced_accuracy: 0.8018837193943575\ntraining started: SVCClassifier for fold # 6 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 2.1854727268218994 sec.\nsvc: start training.\nsvc: finished. 276.1960005760193 sec.\ntraining finished: SVCClassifier for fold # 6 time: 278.39747190475464s\npredicting started: SVCClassifier for fold # 6\npredicting finished: SVCClassifier for fold # 6 time: 25.331998825073242s accuracy: 0.980078125 balanced_accuracy: 0.8347615121472585\ntraining started: SVCClassifier for fold # 7 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 3.927501916885376 sec.\nsvc: start training.\nsvc: finished. 274.04150009155273 sec.\ntraining finished: SVCClassifier for fold # 7 time: 277.98550176620483s\npredicting started: SVCClassifier for fold # 7\npredicting finished: SVCClassifier for fold # 7 time: 25.360967874526978s accuracy: 0.9787109375 balanced_accuracy: 0.8173997864853184\ntraining started: SVCClassifier for fold # 8 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 2.1504993438720703 sec.\nsvc: start training.\nsvc: finished. 273.974002122879 sec.\ntraining finished: SVCClassifier for fold # 8 time: 276.14250111579895s\npredicting started: SVCClassifier for fold # 8\npredicting finished: SVCClassifier for fold # 8 time: 25.19000005722046s accuracy: 0.97578125 balanced_accuracy: 0.8372536721897584\ntraining started: SVCClassifier for fold # 9 # train files: 720\nsvc: reading image.\nsvc: finished reading image, 2.29949951171875 sec.\nsvc: start training.\nsvc: finished. 273.2610285282135 sec.\ntraining finished: SVCClassifier for fold # 9 time: 275.57649850845337s\npredicting started: SVCClassifier for fold # 9\npredicting finished: SVCClassifier for fold # 9 time: 25.151024341583252s accuracy: 0.9740234375 balanced_accuracy: 0.8392838619821293\n" ] ], [ [ "### Serialize the results (export to hard drive)", "_____no_output_____" ] ], [ [ "if run_ten_fold:\n # dump the matrices for report.\n os.makedirs(os.path.dirname(ten_fold_result_path), exist_ok = True)\n\n np.save(ten_fold_result_path + \"confusion_matrices_abc.npy\", confusion_matrices_abc)\n np.save(ten_fold_result_path + \"accuracies_abc.npy\", accuracies_abc)\n np.save(ten_fold_result_path + \"accuracies_balanced_abc.npy\", accuracies_balanced_abc)\n np.save(ten_fold_result_path + \"train_time_cost_abc.npy\", train_time_cost_abc)\n np.save(ten_fold_result_path + \"validation_time_cost_abc.npy\", validation_time_cost_abc)\n\n np.save(ten_fold_result_path + \"confusion_matrices_cnn.npy\", confusion_matrices_cnn)\n np.save(ten_fold_result_path + \"accuracies_cnn.npy\", accuracies_cnn)\n np.save(ten_fold_result_path + \"accuracies_balanced_cnn.npy\", accuracies_balanced_cnn)\n np.save(ten_fold_result_path + \"train_time_cost_cnn.npy\", train_time_cost_cnn)\n np.save(ten_fold_result_path + \"validation_time_cost_cnn.npy\", validation_time_cost_cnn)\n\n np.save(ten_fold_result_path + \"confusion_matrices_svc.npy\", confusion_matrices_svc)\n np.save(ten_fold_result_path + \"accuracies_svc.npy\", accuracies_svc)\n np.save(ten_fold_result_path + \"accuracies_balanced_svc.npy\", accuracies_balanced_svc)\n np.save(ten_fold_result_path + \"train_time_cost_svc.npy\", train_time_cost_svc)\n np.save(ten_fold_result_path + \"validation_time_cost_svc.npy\", validation_time_cost_svc)\n\n\n svc_tf.SaveModel(svc_model_file)\n abc_tf.SaveModel(abc_model_file)\n cnn_tf.SaveModel(cnn_model_file)\n", "_____no_output_____" ] ], [ [ "### Read the results from hard drive", "_____no_output_____" ] ], [ [ "if not run_ten_fold:\n \n import numpy as np\n confusion_matrices_abc = np.load(ten_fold_result_path + \"confusion_matrices_abc.npy\")\n accuracies_abc = np.load(ten_fold_result_path + \"accuracies_abc.npy\")\n accuracies_balanced_abc = np.load(ten_fold_result_path + \"accuracies_balanced_abc.npy\")\n train_time_cost_abc = np.load(ten_fold_result_path + \"train_time_cost_abc.npy\")\n validation_time_cost_abc = np.load(ten_fold_result_path + \"validation_time_cost_abc.npy\")\n\n confusion_matrices_cnn = np.load(ten_fold_result_path + \"confusion_matrices_cnn.npy\")\n accuracies_cnn = np.load(ten_fold_result_path + \"accuracies_cnn.npy\")\n accuracies_balanced_cnn = np.load(ten_fold_result_path + \"accuracies_balanced_cnn.npy\")\n train_time_cost_cnn = np.load(ten_fold_result_path + \"train_time_cost_cnn.npy\")\n validation_time_cost_cnn = np.load(ten_fold_result_path + \"validation_time_cost_cnn.npy\")\n\n confusion_matrices_svc = np.load(ten_fold_result_path + \"confusion_matrices_svc.npy\")\n accuracies_svc = np.load(ten_fold_result_path + \"accuracies_svc.npy\")\n accuracies_balanced_svc = np.load(ten_fold_result_path + \"accuracies_balanced_svc.npy\")\n train_time_cost_svc = np.load(ten_fold_result_path + \"train_time_cost_svc.npy\")\n validation_time_cost_svc = np.load(ten_fold_result_path + \"validation_time_cost_svc.npy\")\n\n\n", "_____no_output_____" ] ], [ [ "### Plot the results", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\ndef plot_accuracy(mat_abc, mat_cnn, mat_svc, title):\n line, = plt.plot([i for i in range(len(mat_abc))],mat_abc)\n line.set_label('AdaBoost')\n line, = plt.plot([i for i in range(len(mat_cnn))],mat_cnn)\n line.set_label('CNN')\n line, = plt.plot([i for i in range(len(mat_svc))],mat_svc)\n line.set_label('SVM')\n plt.title(title)\n plt.xlabel('10 - folds')\n plt.ylabel('accuracy')\n plt.ylim(0.5, 1.1)\n plt.legend()\n plt.show()\n\n \nplot_accuracy(accuracies_abc, accuracies_cnn, accuracies_svc, \"Accuracy of 3 Classifiers\")\nplot_accuracy(accuracies_balanced_abc, accuracies_balanced_cnn, accuracies_balanced_svc, \"Balanced Accuracy of 3 Classifiers\")\n\n", "_____no_output_____" ], [ "import seaborn as sns\ndef plot_confusion_mat(conf_mat, title = \"\"):\n plt.figure(figsize=(10,7))\n ax = sns.heatmap(conf_mat, annot=True, fmt=\"d\")\n plt.ylabel('True')\n plt.xlabel('Predicted')\n plt.title(title)\n plt.show()\n \nplot_confusion_mat(sum(confusion_matrices_abc), title = \"AdaBoost Confusion Matrix\" )\nplot_confusion_mat(sum(confusion_matrices_cnn), title = \"CNN Confusion Matrix\" )\nplot_confusion_mat(sum(confusion_matrices_svc), title = \"SVM Confusion Matrix\" )\n", "_____no_output_____" ] ], [ [ "### Bonus: GUI: see GUI_with_Classifiers.ipynb", "_____no_output_____" ] ] ]

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[ [ [ "# Visualizing invasive and non-invasive EEG data\n\n[Liberty Hamilton, PhD](https://csd.utexas.edu/research/hamilton-lab)\nAssistant Professor, University of Texas at Austin\nDepartment of Speech, Language, and Hearing Sciences\nand Department of Neurology, Dell Medical School \n\nWelcome! In this notebook we will be discussing how to look at time series electrophysiological 🧠 data that is recorded noninvasively at the scalp (scalp electroencephalography or EEG), or invasively in patients who are undergoing surgical treatment for epilepsy (sometimes called intracranial EEG or iEEG, also called stereo EEG/sEEG, or electrocorticography/ECoG). \n\n### Python libraries you will be using in this tutorial:\n* MNE-python\n* matplotlib\n* numpy\n\n\nMNE-python is open source python software for exploring and analyzing human neurophysiological data (EEG/MEG/iEEG).\n\n### What you will learn to do \n* Load some sample EEG data\n* Load some sample intracranial EEG data\n* Plot the raw EEG data/iEEG data\n* Plot the power spectrum of your data\n* Epoch data according to specific task conditions (sentences)\n* Plot all epochs and averaged evoked activity\n* Plot average evoked activity in response to specific task conditions (ERPs)\n * Plot by channel as well as averaging across channels\n* Plot EEG activity at specific time points on the scalp (topomaps)\n* Customize your plots\n\n### Other Resources:\n* [MNE-python tutorials](https://mne.tools/stable/auto_tutorials/index.html) -- This has many additional resources above and beyond that also include how to preprocess your data, remove artifacts, and more!", "_____no_output_____" ], [ "<a id=\"basics1\"></a>\n# 1. The basics: loading in your data", "_____no_output_____" ] ], [ [ "!pip install matplotlib==3.2\n\nimport mne # This is the mne library\nimport numpy as np # This gives us the power of numpy, which is just generally useful for array manipulation\n%matplotlib inline\nfrom matplotlib import pyplot as plt\nfrom matplotlib import cm\n\ndatasets = {'ecog': '/home/jovyan/data/we_eeg_viz_data/ecog/sub-S0006/S0006_ecog_hg.fif',\n 'eeg': '/home/jovyan/data/we_eeg_viz_data/eeg/sub-MT0002/MT0002-eeg.fif'}\nevent_files = {'ecog': '/home/jovyan/data/we_eeg_viz_data/ecog/sub-S0006/S0006_eve.txt',\n 'eeg': '/home/jovyan/data/we_eeg_viz_data/eeg/sub-MT0002/MT0002_eve.txt'}\nstim_file = '/home/jovyan/data/we_eeg_viz_data/stimulus_list.csv'", "_____no_output_____" ], [ "# Get some information about the stimuli (here, the names of the sound files that were played)\nev_names=np.genfromtxt(stim_file, skip_header=1, delimiter=',',dtype=np.str, usecols=[1],encoding='utf-8')\nev_nums=np.genfromtxt(stim_file, skip_header=1, delimiter=',',dtype=np.int, usecols=[0], encoding='utf-8')\nevent_id = dict()\nfor i, ev_name in enumerate(ev_names):\n event_id[ev_name] = ev_nums[i]", "_____no_output_____" ] ], [ [ "## 1.1. Choose which dataset to look at (start with EEG)\n\nFor the purposes of this tutorial, we'll be looking at some scalp EEG and intracranial EEG datasets from my lab. Participants provided written informed consent for participation in our research. These data were collected from two distinct participants listening to sentences from the [TIMIT acoustic-phonetic corpus](https://catalog.ldc.upenn.edu/LDC93S1). This is a database of English sentences spoken by multiple talkers from throughout the United States, and has been used in speech recognition research, neuroscience research, and more!\n\nThe list of stimuli is in the `stimulus_list.csv` file. Each stimulus starts with either a \"f\" or a \"m\" to indicate a female or male talker. The rest of the alphanumeric string has to do with other characteristics of the talkers that we won't go into here. The stimulus timings have been provided for you in the event files (ending with the suffix `_eve.txt`. We'll talk about those more later. \n\n### EEG Data\nThe EEG data was recorded with a 64-channel [BrainVision ActiCHamp](https://www.brainproducts.com/productdetails.php?id=74) system. These data are part of an ongoing project in our lab and are unpublished. You can find similar (larger) datasets from [Broderick et al.](https://datadryad.org/stash/dataset/doi:10.5061/dryad.070jc), or Bradley Voytek's lab has a list of [Open Electrophysiology datasets](https://github.com/openlists/ElectrophysiologyData).\n\n### The ECoG Data\nThe ECoG data was recorded from 106 electrodes across multiple regions of the brain while our participant listened to TIMIT sentences. This is a smaller subset of sentences than the EEG dataset and so is a bit faster to load. The areas we recorded from are labeled according to a clinical montage. For iEEG and ECoG datasets, these names are rarely standardized, so it can be hard to know exactly what is what without additional information. Here, each channel is named according to the general location of the electrode probe to which it belongs.\n\n| Device | General location |\n|---|---|\n| RAST | Right anterior superior temporal |\n| RMST | Right middle superior temporal |\n| RPST | Right posterior superior temporal |\n| RPPST | Right posterior parietal/superior temporal |\n| RAIF | Right anterior insula |\n| RPI | Right posterior insula |\n| ROF | Right orbitofrontal |\n| RAC | Right anterior cingulate |", "_____no_output_____" ] ], [ [ "data_type = 'eeg' # Can choose from 'eeg' or 'ecog'", "_____no_output_____" ] ], [ [ "## 1.2. Load the data\n\nThis next command loads the data from our fif file of interest. The `preload=True` flag means that the data will be loaded (necessary for some operations). If `preload=False`, you can still perform some aspects of this tutorial, and this is a great option if you have a large dataset and would like to look at some of the header information and metadata before you start to analyze it.", "_____no_output_____" ] ], [ [ "raw = mne.io.read_raw_fif(datasets[data_type], preload=True) ", "_____no_output_____" ] ], [ [ "There is a lot of useful information in the info structure. For example, we can get the sampling frequency (`raw.info['sfreq']`), the channel names (`raw.info['ch_names']`), the channel types and locations (in `raw.info['chs']`), and whether any filtering operations have been performed already (`raw.info['highpass']` and `raw.info['lowpass']` show the cut-offs for the data).", "_____no_output_____" ] ], [ [ "print(raw.info)", "_____no_output_____" ], [ "sampling_freq = raw.info['sfreq'] \nnchans = raw.info['nchan']\n\nprint('The sampling frequency of our data is %d'%(sampling_freq))\nprint('Here is our list of %d channels: '%nchans)\nprint(raw.ch_names)", "_____no_output_____" ], [ "eeg_colors = {'eeg': 'k', 'eog': 'steelblue'}\nfig = raw.plot(show=False, color=eeg_colors, scalings='auto');\nfig.set_figwidth(8)\nfig.set_figheight(4)\n", "_____no_output_____" ] ], [ [ "<a id=\"plots2\"></a>\n# 2. Let's make some plots!\n\nMNE-python makes creating some plots *super easy*, which is great for data quality checking, exploration, and eventually manuscript figure generation. For example, one might wish to plot the power spectral density (PSD), which \n\n## 2.2. Power spectral density", "_____no_output_____" ] ], [ [ "raw.plot_psd();", "_____no_output_____" ] ], [ [ "## 2.3. Sensor positions (for EEG)\n\nFor EEG, MNE-python also has convenient functions for showing the location of the sensors used. Here, we have a 64-channel montage. You can also use this information to help interpret some of your plots if you're plotting a single channel or a group of channels.\n\nFor ECoG, we will not be plotting sensors in this way. If you would like read more about that process, please see [this tutorial](https://mne.tools/stable/auto_tutorials/misc/plot_ecog.html). You can also check out [Noah Benson's session](https://neurohackademy.org/course/introduction-to-the-geometry-and-structure-of-the-human-brain/) (happening in parallel with this tutorial!) for plotting 3D brains.", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n raw.plot_sensors(kind='topomap',show_names=True);\n", "_____no_output_____" ] ], [ [ "Ok, awesome! So now we know where the sensors are, how densely they tile the space, and what their names are. *Knowledge = Power!*\n\nSo what if we wanted to look at the power spectral density plot we saw above by channel? We can use `plot_psd_topo` for that! There are also customizable options for playing with the colors.", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n raw.plot_psd_topo(fig_facecolor='w', axis_facecolor='w', color='k');", "_____no_output_____" ] ], [ [ "Finally, this one works for both EEG and ECoG. Here we are looking at the power spectral density plot again, but taking the average across trials and showing +/- 1 standard deviation from the mean across channels. ", "_____no_output_____" ] ], [ [ "raw.plot_psd(area_mode='std', average=True);", "_____no_output_____" ] ], [ [ "Finally, we can plot these same figures using a narrower frequency range, and looking at a smaller set of channels using `picks`. For `plot_psd` and other functions, `picks` is a list of integer indices corresponding to your channels of interest. You can choose these by their number, or you can use the convenient `mne.pick_channels` function to choose them by name. For example, in EEG, we often see strong responses to auditory stimuli at the top of the head, so here we will restrict our EEG channels to a few at the top of the head at the midline. For ECoG, we are more likely to see responses to auditory stimuli in temporal lobe electrodes (potentially RPPST, RPST, RMST, RAST), so we'll try those.", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n picks = mne.pick_channels(raw.ch_names, include=['Pz','CPz','Cz','FCz','Fz','C1','C2','FC1','FC2','CP1','CP2'])\nelif data_type == 'ecog':\n picks = mne.pick_channels(raw.ch_names, include=['RPPST9','RPPST10','RPPST11'])\n\nraw.plot_psd(picks = picks, fmin=1, fmax=raw.info['sfreq']/2, xscale='log');", "_____no_output_____" ] ], [ [ "## Plotting responses to events\n\nOk, so this is all well and good. We can plot our raw data, the power spectrum, and the locations of the sensors. But what if we care about responses to the stimuli we described above? What if we want to look at responses to specific sentences, or the average response across all sentences, or something else? How can we determine which EEG sensors or ECoG electrodes respond to the speech stimuli?\n\nEnter.... *Epoching!* MNE-python gives you a very convenient way of rearranging your data according to events of interest. These can actually even be found automatically from a stimulus channel, if you have one (using [`mne.find_events`](https://mne.tools/stable/generated/mne.find_events.html)), which we won't use here because we already have the timings from another procedure. You can also find other types of epochs, like those based on EMG or [eye movements (EOG)](https://mne.tools/stable/generated/mne.preprocessing.find_eog_events.html). \n\nHere, we will load our event files (ending with `_eve.txt`). These contain information about the start sample, stop sample, and event ID for each stimulus. Each row in the file is one stimulus. The timings are in samples rather than in seconds, so if you are creating these on your own, pay attention to your sampling rate (in `raw.info['sfreq']`).", "_____no_output_____" ] ], [ [ "# Load some events. The format of these is start sample, end sample, and event ID.\nevents = mne.read_events(event_files[data_type])\nprint(events)\n\nnum_events = len(events)\nunique_stimuli = np.unique(np.array(events)[:,2])\nnum_unique = len(unique_stimuli)\nprint('There are %d total events, corresponding to %d unique stimuli'%(num_events, num_unique))", "_____no_output_____" ] ], [ [ "## Epochs\n\nGreat. So now that we have the events, we will \"epoch\" our data, which basically uses these timings to split up our data into trials of a given length. We will also set some parameters for data rejection to get rid of noisy trials. ", "_____no_output_____" ] ], [ [ "# Set some rejection criteria. This will be based on the peak-to-peak\n# amplitude of your data.\n\nif data_type=='eeg':\n reject = {'eeg': 60e-6} # Higher than peak to peak amplitude of 60 µV will be rejected\n scalings = None\n units = None\nelif data_type=='ecog':\n reject = {'ecog': 10} # Higher than Z-score of 10 will be rejected\n scalings = {'ecog': 1} # Don't rescale these as if they should be in µV\n units = {'ecog': 'Z-score'}\n ", "_____no_output_____" ], [ "tmin = -0.2\ntmax = 1.0\nepochs = mne.Epochs(raw, events, tmin=tmin, tmax=tmax, baseline=(None, 0), reject=reject, verbose=True)", "_____no_output_____" ] ], [ [ "So what's in this epochs data structure? If we look at it, we can see that we have an entry for each event ID, and we can see how many times that stimulus was played. You can also see whether baseline correction was done and for what time period, and whether any data was rejected.", "_____no_output_____" ] ], [ [ "epochs", "_____no_output_____" ] ], [ [ "Now, you could decide at this point that you just want to work with the data directly as a numpy array. Luckily, that's super easy to do! We can just call `get_data()` on our epochs data structure, and this will output a matrix of `[events x channels x time points]`. If you do not limit the channel type, you will get all of them (including any EOG, stimulus channels, or other non-EEG/ECoG channels).", "_____no_output_____" ] ], [ [ "ep_data = epochs.get_data()\nprint(ep_data.shape)", "_____no_output_____" ] ], [ [ "## Plotting Epoched data\n\nOk... so we are getting ahead of ourselves. MNE-python provides a lot of ways to plot our data so that we don't have to deal with writing functions to do this ourselves! For example, if we'd like to plot the EEG/ECoG for all of the single trials we just loaded, along with an average across all of these trials (and channels of interest), we can do that easily with `epochs.plot_image()`.", "_____no_output_____" ] ], [ [ "epochs.plot_image(combine='mean', scalings=scalings, units=units)", "_____no_output_____" ] ], [ [ "As before, we can choose specific channels to look at instead of looking at all of them at once. For which method do you think this would make the most difference? Why? ", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n picks = mne.pick_channels(raw.ch_names, include=['Fz','FCz','Cz','CPz','Pz'])\nelif data_type == 'ecog':\n picks = mne.pick_channels(raw.ch_names, include=['RPPST9','RPPST10','RPPST11'])\n \nepochs.plot_image(picks = picks, combine='mean', scalings=scalings, units=units)", "_____no_output_____" ] ], [ [ "We can also sort the trials, if we would like. This can be very convenient if you have reaction times or some other portion of the trial where reordering would make sense. Here, we'll just pick a channel and order by the mean activity within each trial.", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n picks = mne.pick_channels(raw.ch_names, include=['CP6'])\nelif data_type == 'ecog':\n picks = mne.pick_channels(raw.ch_names, include=['RPPST2'])\n\n# Get the data as a numpy array\neps_data = epochs.get_data()\n\n# Sort the data \nnew_order = eps_data[:,picks[0],:].mean(1).argsort(0)\n\nepochs.plot_image(picks=picks, order=new_order, scalings=scalings, units=units)", "_____no_output_____" ] ], [ [ "## Other ways to view epoched data\n\nFor EEG, another way to view these epochs by trial is using the scalp topography information. This allows us to quickly assess differences across the scalp in response to the stimuli. What do you notice about the responses?", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n epochs.plot_topo_image(vmin=-30, vmax=30, fig_facecolor='w',font_color='k');", "_____no_output_____" ] ], [ [ "## Comparing epochs of different trial types\n\nSo far we have just shown averages of activity across many different sentences. However, as mentioned above, the sentences come from multiple male and female talkers. So -- one quick split we could try is just to compare the responses to female vs. male talkers. This is relatively simple with the TIMIT stimuli because their file name starts with \"f\" or \"m\" to indicate this. ", "_____no_output_____" ] ], [ [ "# Make lists of the event ID numbers corresponding to \"f\" and \"m\" sentences\nf_evs = []\nm_evs = []\nfor k in event_id.keys():\n if k[0] == 'f':\n f_evs.append(event_id[k])\n elif k[0] == 'm':\n m_evs.append(event_id[k])\n\nprint(unique_stimuli)\nf_evs_new = [v for v in f_evs if v in unique_stimuli]\nm_evs_new = [v for v in m_evs if v in unique_stimuli]\n\n# Epoch the data separately for \"f\" and \"m\" epochs\nf_epochs = mne.Epochs(raw, events, event_id=f_evs_new, tmin=tmin, tmax=tmax, reject=reject)\nm_epochs = mne.Epochs(raw, events, event_id=m_evs_new, tmin=tmin, tmax=tmax, reject=reject)", "_____no_output_____" ] ], [ [ "Now we can plot the epochs just as we did above.", "_____no_output_____" ] ], [ [ "f_epochs.plot_image(combine='mean', show=False, scalings=scalings, units=units)\nm_epochs.plot_image(combine='mean', show=False, scalings=scalings, units=units)", "_____no_output_____" ] ], [ [ "Cool! So now we have a separate plot for the \"f\" and \"m\" talkers. However, it's not super convenient to compare the traces this way... we kind of want them on the same axis. MNE easily allows us to do this too! Instead of using the epochs, we can create `evoked` data structures, which are averaged epochs. You can [read more about evoked data structures here](https://mne.tools/dev/auto_tutorials/evoked/plot_10_evoked_overview.html).\n\n## Compare evoked data", "_____no_output_____" ] ], [ [ "evokeds = {'female': f_epochs.average(), 'male': m_epochs.average()}\nmne.viz.plot_compare_evokeds(evokeds, show_sensors='upper right',picks=picks);", "_____no_output_____" ] ], [ [ "If we actually want errorbars on this plot, we need to do this a bit differently. We can use the `iter_evoked()` method on our epochs structures to create a dictionary of conditions for which we will plot our comparisons with `plot_compare_evokeds`.", "_____no_output_____" ] ], [ [ "evokeds = {'f':list(f_epochs.iter_evoked()), 'm':list(m_epochs.iter_evoked())}\nmne.viz.plot_compare_evokeds(evokeds, picks=picks);\n", "_____no_output_____" ] ], [ [ "## Plotting scalp topography\n\nFor EEG, another common plot you may see is a topographic map showing activity (or other data like p-values, or differences between conditions). In this example, we'll show the activity at -0.2, 0, 0.1, 0.2, 0.3, and 1 second. You can also of course choose just one time to look at.", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n times=[tmin, 0, 0.1, 0.2, 0.3, tmax]\n epochs.average().plot_topomap(times, ch_type='eeg', cmap='PRGn', res=32,\n outlines='skirt', time_unit='s');", "_____no_output_____" ] ], [ [ "We can also plot arbitrary data using `mne.viz.plot_topomap`, and passing in a vector of data matching the number of EEG channels, and `raw.info` to give specifics on those channel locations.", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n chans = mne.pick_types(raw.info, eeg=True)\n data = np.random.randn(len(chans),)\n plt.figure()\n mne.viz.plot_topomap(data, raw.info, show=True)\n", "_____no_output_____" ] ], [ [ "We can even animate these topo maps! This won't work well in jupyterhub, but feel free to try on your own!", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n fig,anim=epochs.average().animate_topomap(blit=False, times=np.linspace(tmin, tmax, 100))", "_____no_output_____" ] ], [ [ "## A few more fancy EEG plots\n\nIf we want to get especially fancy, we can also use `plot_joint` with our evoked data (or averaged epoched data, as shown below). This allows us to combine the ERPs for individual channels with topographic maps at time points that we specify. Pretty awesome!", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n epochs.average().plot_joint(picks='eeg', times=[0.1, 0.2, 0.3])", "_____no_output_____" ] ], [ [ "# What if I need more control? - matplotlib alternatives\n\nIf you feel you need more specific control over your plots, it's easy to get the data into a usable format for plotting with matplotlib. You can export both the raw and epoched data using the `get_data()` function, which will allow you to save your data as a numpy array `[ntrials x nchannels x ntimepoints]`.\n\nThen, you can do whatever you want with the data! Throw it into matplotlib, use seaborn, or whatever your heart desires!", "_____no_output_____" ] ], [ [ "if data_type == 'eeg':\n picks = mne.pick_channels(raw.ch_names, include=['Fz','FCz','Cz','CPz','Pz'])\nelif data_type == 'ecog':\n picks = mne.pick_channels(raw.ch_names, include=['RPPST9','RPPST10','RPPST11'])\n \nf_data = f_epochs.get_data(picks=picks)\nm_data = m_epochs.get_data(picks=picks)\ntimes = f_epochs.times\n\nprint(f_data.shape)", "_____no_output_____" ] ], [ [ "## Plot evoked data with errorbars \n\nWe can recreate some similar plots to those in MNE-python with some of the matplotlib functions. Here we'll create something similar to what was plotted in `plot_compare_evokeds`.", "_____no_output_____" ] ], [ [ "def plot_errorbar(x, ydata, label=None, axlines=True, alpha=0.5, **kwargs):\n '''\n Plot the mean +/- standard error of ydata.\n Inputs:\n x : vector of x values\n ydata : matrix of your data (this will be averaged along the 0th dimension)\n label : A string containing the label for this plot\n axlines : [bool], whether to draw the horizontal and vertical axes\n alpha: opacity of the standard error area\n '''\n ymean = ydata.mean(0)\n ystderr = ydata.std(0)/np.sqrt(ydata.shape[0])\n plt.plot(x, ydata.mean(0), label=label, **kwargs)\n plt.fill_between(x, ymean+ystderr, ymean-ystderr, alpha=alpha, **kwargs)\n if axlines:\n plt.axvline(0, color='k', linestyle='--')\n plt.axhline(0, color='k', linestyle='--')\n plt.gca().set_xlim([x.min(), x.max()])", "_____no_output_____" ], [ "plt.figure()\nplot_errorbar(times, f_data.mean(0), label='female')\nplot_errorbar(times, m_data.mean(0), label='male')\nplt.xlabel('Time (s)')\nplt.ylabel('Z-scored high gamma')\nplt.legend()", "_____no_output_____" ] ], [ [ "## ECoG Exercise:\n\n1. If you wanted to look at each ECoG electrode individually to find which ones have responses to the speech data, how would you do this?\n2. Can you plot the comparison between \"f\" and \"m\" trials for each electrode as a subplot (try using `plt.subplot()` from `matplotlib`)", "_____no_output_____" ] ], [ [ "# Get the data for f trials\n\n# Get the data for m trials\n\n# Loop through each channel, and create a set of subplots for each", "_____no_output_____" ] ], [ [ "# Hooray, the End!\n\nYou did it! Go forth and use MNE-python in your own projects, or even contribute to the code! 🧠", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# all_no_testing\n# default_exp models.binaryClassification\n# default_cls_lvl 2", "_____no_output_____" ] ], [ [ "# Binary Horse Poo Model\n\n> Simple model to detect HorsePoo vs noHorsePoo", "_____no_output_____" ], [ "## export data", "_____no_output_____" ] ], [ [ "%load_ext autoreload\n%autoreload 2", "_____no_output_____" ], [ "#!rm -R data/tmp/horse_poo/ && rm -R data/tmp/no_horse_poo/ ", "_____no_output_____" ], [ "#!prodigy db-out binary_horse_poo ./data/tmp", "_____no_output_____" ] ], [ [ "## Description\nWith this model we will start of with a very simple binary classification. We will try to use most of the default settings from fastai. This will also be our benchmark model for further investigations. ", "_____no_output_____" ] ], [ [ "#export\nfrom fastai.vision import * \nfrom fastai.callbacks import EarlyStoppingCallback\nfrom prodigy.util import read_jsonl, write_jsonl\nfrom prodigy.components.db import connect\nfrom PooDetector.dataset_operations import extract_jsonl_to_binary_folders\nimport os\nimport shutil\nfrom fastscript import *", "_____no_output_____" ], [ "#export\ndef prepare_data(fld_input:str='data/tmp', bs=256):\n \"\"\"function to get a fastai databunch which can be used for training\"\"\"\n #tfms = get_transforms(do_flip=False, max_zoom=1, max_warp=None)\n #t_tfms = []\n #t_tfms.append(flip_lr(p=0.5))\n #t_tfms.append(symmetric_warp(magnitude=(-0.2,0.2), p=0.75))\n #t_tfms.append(rotate(degrees=(-10,10), p=0.75))\n #t_tfms.append(rand_zoom(scale=(1.,1.1), p=0.75))\n #t_tfms.append(brightness(change=(0.5*(1-0.2), 0.5*(1+0.2)), p=0.75))\n #t_tfms.append(contrast(scale=(1-0.2, 1/(1-0.2)), p=0.75))\n #tfms = (t_tfms , [])\n tfms = get_transforms()\n return (ImageList.from_folder(fld_input)\n .split_by_rand_pct(0.2) \n .label_from_folder()\n .transform(tfms, size=224)\n .databunch(bs=bs)\n .normalize(imagenet_stats))\n", "_____no_output_____" ], [ "#no_testing\ndata = prepare_data(fld_input='test_data/', bs=16)", "_____no_output_____" ], [ "#no_testing\ndata.show_batch()", "_____no_output_____" ], [ "#export \ndef get_learner(data:ImageDataBunch=None, model:Module=None):\n \"\"\"get a lerner object for training\"\"\"\n if data is None:\n data = prepare_data()\n if model is None:\n model = models.resnet50\n \n early_stopping = partial(EarlyStoppingCallback, min_delta=0.005, patience=8)\n \n return cnn_learner(data, base_arch=model, callback_fns=[early_stopping])", "_____no_output_____" ], [ "#no_testing\nlearn = get_learner(data=data)", "_____no_output_____" ], [ "#no_testing\nlearn.fit_one_cycle(2, 5e-2)\n#learn.fit_one_cycle(2, 5e-2)\nlearn.save('stage1')", "_____no_output_____" ], [ "#no_testing\nlearn.export()", "_____no_output_____" ], [ "#export \n@call_parse\ndef train_model(path_jsonl:Param(\"path to jsonl file\", str)='test_data/binary_horse_poo.jsonl',\n cycles_to_fit:Param(\"number of cycles to fit\", int)=10, \n bs:Param(\"batch size\", int)=128,\n label:Param(\"positive label for binary classification\", str)=\"horse_poo\"\n ):\n \"\"\"start training a new model with early stopping and export it\"\"\"\n path_jsonl = Path(path_jsonl)\n if path_jsonl.exists():\n path_jsonl.unlink()\n \n db = connect() # uses settings from your prodigy.json\n images = db.get_dataset(\"binary_horse_poo\")\n write_jsonl(path_jsonl, images)\n \n remove_subfolders(str(path_jsonl.parent))\n \n extract_jsonl_to_binary_folders(str(path_jsonl), label)\n \n data = prepare_data(path_jsonl.parent, bs=bs)\n \n learn = get_learner(data)\n learn.fit_one_cycle(cycles_to_fit, 5e-2)\n learn.export()\n return learn\n \n \n \n \n", "_____no_output_____" ], [ "#export \ndef remove_subfolders(path_parent:[Path, str]):\n \"\"\"reomve all subfolders\"\"\"\n path_parent = Path(path_parent)\n for root, dirs, files in os.walk(str(path_parent), topdown=False):\n for directory in dirs:\n print(f\"remove {str(Path(root) / Path(directory))}\")\n shutil.rmtree(str(Path(root) / Path(directory)))\n ", "_____no_output_____" ], [ "#no_testing\npath = Path('test_data/tmp/')\n\nif os.path.exists(str(path)) is False:\n os.mkdir(str(path))\n \nif os.path.exists(str(path / 'horse')) is False:\n os.mkdir(str(path / 'horse'))\n \nif os.path.exists(str(path / 'no_horse')) is False:\n os.mkdir(str(path / 'no_horse'))\n \nassert os.path.exists(str(path))\nassert os.path.exists(str(path / 'horse'))\nassert os.path.exists(str(path / 'no_horse'))\n\nremove_subfolders(str(path))\n\nassert not os.path.exists(str(path / 'horse'))\nassert not os.path.exists(str(path / 'no_horse'))\n\n", "_____no_output_____" ], [ "# prepare test\npath_jsonl = 'test_data/binary_horse_poo.jsonl' \npath_jsonl = Path(path_jsonl)\n\nif os.path.exists('test_data/tmp') is False:\n os.mkdir('test_data/tmp')\n\npath_fld_target = path_jsonl.parent / 'tmp'\n\nshutil.copy(str(path_jsonl), str(path_fld_target) )\npath_jsonl = path_fld_target / path_jsonl.name\nassert os.path.exists(path_jsonl)\n\n#test\n#learn = train_model(path_jsonl=path_jsonl, cycles_to_fit=2, bs=4)\n \n \n#assert os.path.exists(str(path_jsonl.parent / 'export.pkl'))\n", "_____no_output_____" ], [ "#no_testing\n#!prodigy db-out binary_horse_poo > data/tmp/binary_horse_poo.jsonl\npath_jsonl = 'data/tmp/binary_horse_poo.jsonl'\nlearn = train_model(path_jsonl=path_jsonl, cycles_to_fit=15, bs=128)", "_____no_output_____" ], [ "#no_testing\nlearn.unfreeze()", "_____no_output_____" ], [ "#no_testing\nlearn.fit_one_cycle(8)", "_____no_output_____" ], [ "#no_testing\nlearn.fit_one_cycle(8)", "_____no_output_____" ], [ "#no_testing\nlearn.export() ", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport numpy as np\nimport csv ", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# MDN-transformer with examples\n\n- What kind of data can be predicted by a mixture density network Transformer?\n - Continuous sequential data\n- Drawing data and RoboJam Touch Screem would be good examples for this, continuous values yield high resolution in 2d space.", "_____no_output_____" ], [ "# 1. Kanji Generation\n\n- Firstly, let's try modelling some drawing data for Kanji writing using an MDN-Transformer.\n\n- This work is inspired by previous work \"MDN-RNN for Kanji Generation\", hardmaru's Kanji tutorial and the original Sketch-RNN repository:\n\n - http://blog.otoro.net/2015/12/28/recurrent-net-dreams-up-fake-chinese-characters-in-vector-format-with-tensorflow/\n - https://github.com/hardmaru/sketch-rnn\n\n - The idea is to learn how to draw kanji characters from a dataset of vector representations. \n - This means learning how to move a pen in 2D space.\n - The data consists of a sequence of pen movements (loations in 2D) and whether the pen is up or down.\n - In this example, we will use one 3D MDN to model everything!\n\nWe will end up with a system that will continue writing Kanji given a short sequence, like this:\n\n", "_____no_output_____" ] ], [ [ "# Setup and modules\nimport sys\n!{sys.executable} -m pip install keras-mdn-layer \n!{sys.executable} -m pip install tensorflow\n!{sys.executable} -m pip install tensorflow-probability\n!{sys.executable} -m pip install matplotlib\n!{sys.executable} -m pip install pandas\n!{sys.executable} -m pip install svgwrite\n\nimport mdn\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D \n%matplotlib inline\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n%matplotlib notebook\n\n# Only for GPU use:\ngpus = tf.config.list_physical_devices('GPU')\nif gpus:\n try:\n # Currently, memory growth needs to be the same across GPUs\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n logical_gpus = tf.config.list_logical_devices('GPU')\n print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n except RuntimeError as e:\n # Memory growth must be set before GPUs have been initialized\n print(e)", "_____no_output_____" ] ], [ [ "### Download and process the data set", "_____no_output_____" ] ], [ [ "# Train from David Ha's Kanji dataset from Sketch-RNN: https://github.com/hardmaru/sketch-rnn-datasets\n# Other datasets in \"Sketch 3\" format should also work.\nimport urllib.request\nurl = 'https://github.com/hardmaru/sketch-rnn-datasets/raw/master/kanji/kanji.rdp25.npz' \nurllib.request.urlretrieve(url, './kanji.rdp25.npz') ", "_____no_output_____" ] ], [ [ "### Dataset\n\nIncludes about 11000 handwritten kanji characters divied into training, validation, and testing sets.", "_____no_output_____" ] ], [ [ "with np.load('./kanji.rdp25.npz', allow_pickle=True) as data:\n train_set = data['train']\n valid_set = data['valid']\n test_set = data['test']\n \nprint(\"Training kanji:\", len(train_set))\nprint(\"Validation kanji:\", len(valid_set))\nprint(\"Testing kanji:\", len(test_set))", "_____no_output_____" ], [ "# Functions for slicing up data\ndef slice_sequence_examples(sequence, num_steps):\n xs = []\n for i in range(len(sequence) - num_steps - 1):\n example = sequence[i: i + num_steps]\n xs.append(example)\n return xs\n\ndef seq_to_singleton_format(examples):\n xs = []\n ys = []\n for ex in examples:\n xs.append(ex[:SEQ_LEN])\n ys.append(ex)\n return xs, ys\n\n# Functions for making the data set\ndef format_dataset(x, y):\n return ({\n \"input\": x,\n \"target\": y[:, :-1, :],\n }, y[:, 1:, :])\n\ndef make_dataset(X, y):\n dataset = tf.data.Dataset.from_tensor_slices((X, y))\n dataset = dataset.batch(batch_size)\n dataset = dataset.map(format_dataset)\n return dataset.shuffle(2048).prefetch(16).cache()", "_____no_output_____" ], [ "# Data shapes\nNUM_FEATS = 3\nSEQ_LEN = 20\ngap_len = 1\nbatch_size = 128\n\n# Prepare training data as X and Y.\nslices = []\nfor seq in train_set:\n slices += slice_sequence_examples(seq, SEQ_LEN+gap_len)\nX, y = seq_to_singleton_format(slices)\n\nX = np.array(X)\ny = np.array(y)\ntrain_ds = make_dataset(X, y)\nprint(\"Number of training examples:\")\nprint(\"X:\", X.shape)\nprint(\"y:\", y.shape)\nprint(train_ds)", "_____no_output_____" ] ], [ [ "### Constructing the MDN Transformer\n\nOur MDN Transformer has the following settings:\n- an embedding layer with positional embedding\n- a transformer encoder\n- a transformer decoder\n- a three-dimensional mixture layer with 10 mixtures\n- train for sequence length ___\n- training for ___ epochs with a batch size of ___\n\nHere's a diagram:\n", "_____no_output_____" ] ], [ [ "class PositionalEmbedding(layers.Layer):\n def __init__(self, sequence_length, input_dim, output_dim, **kwargs):\n super().__init__(**kwargs)\n self.token_embeddings = layers.Dense(output_dim)\n self.position_embeddings = layers.Embedding(\n input_dim=sequence_length, output_dim=output_dim)\n self.sequence_length = sequence_length\n self.input_dim = input_dim\n self.output_dim = output_dim\n def call(self, inputs, padding_mask=None):\n length = inputs.shape[1]\n positions = tf.range(start=0, limit=length, delta=1)\n embedded_tokens = self.token_embeddings(inputs)\n embedded_positions = self.position_embeddings(positions)\n return embedded_tokens + embedded_positions\n def get_config(self):\n config = super().get_config()\n config.update({\n \"output_dim\": self.output_dim,\n \"sequence_length\": self.sequence_length,\n \"input_dim\": self.input_dim,\n })\n return config\n\n \nclass TransformerEncoder(layers.Layer):\n def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n super().__init__(**kwargs)\n self.embed_dim = embed_dim\n self.dense_dim = dense_dim\n self.num_heads = num_heads\n self.attention = layers.MultiHeadAttention(\n num_heads=num_heads, key_dim=embed_dim)\n self.dense_proj = keras.Sequential(\n [layers.Dense(dense_dim, activation=\"relu\"),\n layers.Dense(embed_dim),]\n )\n self.layernorm_1 = layers.LayerNormalization()\n self.layernorm_2 = layers.LayerNormalization()\n def call(self, inputs, mask=None):\n attention_output = self.attention(inputs, inputs, attention_mask=mask)\n proj_input = self.layernorm_1(inputs + attention_output)\n proj_output = self.dense_proj(proj_input)\n return self.layernorm_2(proj_input + proj_output)\n def get_config(self):\n config = super().get_config()\n config.update({\n \"embed_dim\": self.embed_dim,\n \"num_heads\": self.num_heads,\n \"dense_dim\": self.dense_dim,\n })\n return config\n\n\nclass TransformerDecoder(layers.Layer):\n def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n super().__init__(**kwargs)\n self.embed_dim = embed_dim\n self.dense_dim = dense_dim\n self.num_heads = num_heads\n self.attention_1 = layers.MultiHeadAttention(\n num_heads=num_heads, key_dim=embed_dim)\n self.attention_2 = layers.MultiHeadAttention(\n num_heads=num_heads, key_dim=embed_dim)\n self.dense_proj = keras.Sequential(\n [layers.Dense(dense_dim, activation=\"relu\"),\n layers.Dense(embed_dim),]\n )\n self.layernorm_1 = layers.LayerNormalization()\n self.layernorm_2 = layers.LayerNormalization()\n self.layernorm_3 = layers.LayerNormalization()\n self.supports_masking = True\n def get_causal_attention_mask(self, inputs):\n input_shape = tf.shape(inputs)\n batch_size, sequence_length = input_shape[0], input_shape[1]\n i = tf.range(sequence_length)[:, tf.newaxis]\n j = tf.range(sequence_length)\n mask = tf.cast(i >= j, dtype=\"int32\")\n mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))\n mult = tf.concat(\n [tf.expand_dims(batch_size, -1),\n tf.constant([1, 1], dtype=tf.int32)], axis=0)\n return tf.tile(mask, mult)\n def call(self, inputs, encoder_outputs, padding_mask=None):\n causal_mask = self.get_causal_attention_mask(inputs)\n attention_output_1 = self.attention_1(\n query=inputs,\n value=inputs,\n key=inputs,\n attention_mask=causal_mask)\n attention_output_1 = self.layernorm_1(inputs + attention_output_1)\n if padding_mask==None:\n attention_output_2 = self.attention_2(\n query=attention_output_1,\n value=encoder_outputs,\n key=encoder_outputs)\n else:\n attention_output_2 = self.attention_2(\n query=attention_output_1,\n value=encoder_outputs,\n key=encoder_outputs,\n attention_mask=padding_mask)\n attention_output_2 = self.layernorm_2(\n attention_output_1 + attention_output_2)\n proj_output = self.dense_proj(attention_output_2)\n return self.layernorm_3(attention_output_2 + proj_output)\n def get_config(self):\n config = super().get_config()\n config.update({\n \"embed_dim\": self.embed_dim,\n \"num_heads\": self.num_heads,\n \"dense_dim\": self.dense_dim,\n })\n return config", "_____no_output_____" ], [ "# Training Hyperparameters:\ninput_dim = 3\nsequence_length = 20\ntarget_length = 20\nembed_dim = 256\ndense_dim = 128\nnum_heads = 2\noutput_dim = 3\nnumber_mixtures = 10\n\nEPOCHS = 20\nSEED = 2345 # set random seed for reproducibility\nrandom.seed(SEED)\nnp.random.seed(SEED)\n\nencoder_inputs = keras.Input(shape=(sequence_length, input_dim), dtype=\"float64\", name=\"input\")\nx = PositionalEmbedding(sequence_length, input_dim, embed_dim)(encoder_inputs)\nencoder_outputs = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\nprint(encoder_outputs.shape)\n# encoder_outputs2 = TransformerEncoder(embed_dim, dense_dim, num_heads)(encoder_outputs)\n\ndecoder_inputs = keras.Input(shape=(target_length, input_dim), dtype=\"float64\", name=\"target\")\nx = PositionalEmbedding(target_length, input_dim, embed_dim)(decoder_inputs)\nx = TransformerDecoder(embed_dim, dense_dim, num_heads)(x, encoder_outputs)\n# x = TransformerDecoder(embed_dim, dense_dim, num_heads)(x, encoder_outputs2)\nx = layers.Dropout(0.2)(x)\ndecoder_outputs = layers.Dense(input_dim, activation=\"softmax\")(x)\noutputs = mdn.MDN(output_dim, number_mixtures) (decoder_outputs)\nmodel = keras.Model([encoder_inputs, decoder_inputs], outputs)\nmodel.compile(loss=mdn.get_mixture_loss_func(output_dim,number_mixtures), \n optimizer=keras.optimizers.Adam())\nmodel.summary()", "_____no_output_____" ], [ "callbacks = [\n keras.callbacks.ModelCheckpoint(\"full_transformer.keras\",\n save_best_only=True)\n]\n\nhistory=model.fit(train_ds, batch_size=batch_size, epochs=EPOCHS, callbacks=callbacks)\n\n!mkdir -p saved_model\nmodel.save('my_model_100_128_256_128_2_02')\n\n# print(f\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\")", "_____no_output_____" ], [ "plt.figure()\nplt.plot(history.history['loss'])\nplt.show()", "_____no_output_____" ], [ "ls saved_model/my_model6", "_____no_output_____" ], [ "model = keras.models.load_model('saved_model/my_model6')", "_____no_output_____" ] ], [ [ "## Generating drawings\n\nFirst need some helper functions to view the output.", "_____no_output_____" ] ], [ [ "def zero_start_position():\n \"\"\"A zeroed out start position with pen down\"\"\"\n out = np.zeros((1, 1, 3), dtype=np.float32)\n out[0, 0, 2] = 1 # set pen down.\n return out\n\ndef generate_sketch(model, start_pos, num_points=100):\n return None\n\ndef cutoff_stroke(x):\n return np.greater(x,0.5) * 1.0\n\ndef plot_sketch(sketch_array):\n \"\"\"Plot a sketch quickly to see what it looks like.\"\"\"\n sketch_df = pd.DataFrame({'x':sketch_array.T[0],'y':sketch_array.T[1],'z':sketch_array.T[2]})\n sketch_df.x = sketch_df.x.cumsum()\n sketch_df.y = -1 * sketch_df.y.cumsum()\n # Do the plot\n fig = plt.figure(figsize=(8, 8))\n ax1 = fig.add_subplot(111)\n #ax1.scatter(sketch_df.x,sketch_df.y,marker='o', c='r', alpha=1.0)\n # Need to do something with sketch_df.z\n ax1.plot(sketch_df.x,sketch_df.y,'r-')\n plt.show()", "_____no_output_____" ] ], [ [ "## SVG Drawing Function\n\nHere's Hardmaru's Drawing Functions from _write-rnn-tensorflow_. Big hat tip to Hardmaru for this!\n\nHere's the source: https://github.com/hardmaru/write-rnn-tensorflow/blob/master/utils.py", "_____no_output_____" ] ], [ [ "import svgwrite\nfrom IPython.display import SVG, display\n\ndef get_bounds(data, factor):\n min_x = 0\n max_x = 0\n min_y = 0\n max_y = 0\n\n abs_x = 0\n abs_y = 0\n for i in range(len(data)):\n x = float(data[i, 0]) / factor\n y = float(data[i, 1]) / factor\n abs_x += x\n abs_y += y\n min_x = min(min_x, abs_x)\n min_y = min(min_y, abs_y)\n max_x = max(max_x, abs_x)\n max_y = max(max_y, abs_y)\n\n return (min_x, max_x, min_y, max_y)\n\ndef draw_strokes(data, factor=1, svg_filename='sample.svg'):\n min_x, max_x, min_y, max_y = get_bounds(data, factor)\n dims = (50 + max_x - min_x, 50 + max_y - min_y)\n\n dwg = svgwrite.Drawing(svg_filename, size=dims)\n dwg.add(dwg.rect(insert=(0, 0), size=dims, fill='white'))\n\n lift_pen = 1\n\n abs_x = 25 - min_x\n abs_y = 25 - min_y\n p = \"M%s,%s \" % (abs_x, abs_y)\n\n command = \"m\"\n\n for i in range(len(data)):\n if (lift_pen == 1):\n command = \"m\"\n elif (command != \"l\"):\n command = \"l\"\n else:\n command = \"\"\n x = float(data[i, 0]) / factor\n y = float(data[i, 1]) / factor\n lift_pen = data[i, 2]\n p += command + str(x) + \",\" + str(y) + \" \"\n\n the_color = \"black\"\n stroke_width = 1\n\n dwg.add(dwg.path(p).stroke(the_color, stroke_width).fill(\"none\"))\n\n dwg.save()\n display(SVG(dwg.tostring()))", "_____no_output_____" ], [ "original = valid_set[0]\nx0 = np.array([valid_set[0][:SEQ_LEN]])\ny0 = x0\n# y0 = np.array([valid_set[0][:(SEQ_LEN+9)]])", "_____no_output_____" ], [ "# Predict a character and plot the result.\npi_temperature = 3 # seems to work well with rather high temperature (2.5)\nsigma_temp = 0.1 # seems to work well with low temp\n\n### Generation using one example from validation set as \np = x0\nsketch = p\n\nfor i in range(100):\n params = model.predict([p, p])\n out = mdn.sample_from_output(params[0][49], output_dim, number_mixtures, temp=pi_temperature, sigma_temp=sigma_temp)\n p = np.concatenate((p[:,1:],np.array([out])), axis=1)\n sketch = np.concatenate((sketch, np.array([out])), axis=1)\n\nsketch.T[2] = cutoff_stroke(sketch.T[2])\ndraw_strokes(sketch[0], factor=0.5)\ndraw_strokes(x0[0], factor=0.5)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport random\nfile = pd.read_csv('WeatherPy_vacation.csv')", "_____no_output_____" ], [ "vacation_search = file.copy().iloc[:,1:]\nvacation_search", "_____no_output_____" ], [ "cities = vacation_search.iloc[[0,1,2,4],:].reset_index().iloc[:,1:]\ncities", "_____no_output_____" ], [ "#### HOW TO ADD THIS LAYER (MARKER LAYER) INTO THE MAP BELOW WITH THE DIRECTIONS\n\n# Set up marker layer for #10 in part 3\nc_s = cities.copy()\nc_s['Location'] = [(Lat, Lng) for Lat, Lng in zip(c_s.Lat, c_s.Lng)]\nc_s = c_s.to_dict('r')\n\ncity_locations = [c['Location'] for c in c_s]\ninfo_box_template = \"\"\"\n<dl>\n<dt>City</dt><dd>{City}</dd>\n<dt>Country</dt><dd>{Country}</dd>\n<dt>Hotel</dt><dd>{Hotel}</dd>\n<dt>Max Temp</dt><dd>{Max_Temp}</dd>\n<dt>Humidity</dt><dd>{Humidity}</dd>\n<dt>Cloudiness</dt><dd>{Cloudiness}</dd>\n<dt>Rain</dt><dd>{Rain_Inches}</dd>\n<dt>Snow</dt><dd>{Snow_Inches}</dd>\n</dl>\n\"\"\"\n\ncity_info = [info_box_template.format(**c) for c in c_s]", "_____no_output_____" ], [ "## From gmaps documentation\nimport gmaps\nimport gmaps.datasets\ngmaps.configure(api_key='AIzaSyCuOo58QNcG7KZUczdwzq8p0HZWCaFg-_M')\n\n# Latitude-longitude pairs\nbalaipungut = (cities.loc[0, 'Lat'], cities.loc[0, 'Lng'])\nca_mau = (cities.loc[1, 'Lat'], cities.loc[1, 'Lng'])\ncao_bang = (cities.loc[2, 'Lat'], cities.loc[2, 'Lng'])\ndwarka = (cities.loc[3, 'Lat'], cities.loc[3, 'Lng'])\n\nfig = gmaps.figure()\ntravel_itinerary = gmaps.directions_layer(balaipungut, dwarka, waypoints=[ca_mau, cao_bang], travel_mode='DRIVING')\n\n### MARKER LAYER IS NOT ADDING TO THE GMAPS\nmarker_layer = gmaps.marker_layer(city_locations, info_box_content=city_info)\n\nfig.add_layer(marker_layer)\nfig.add_layer(travel_itinerary)\n\nfig", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code" ] ]

[ "CNRI-Python", "Naumen", "Condor-1.1", "MS-PL" ]

[ "CNRI-Python", "Naumen", "Condor-1.1", "MS-PL" ]

[ "CNRI-Python", "Naumen", "Condor-1.1", "MS-PL" ]

[ [ [ "### Imports", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\n\n#Python Standard Libs Imports\nimport json\nimport urllib2\nimport sys\nfrom datetime import datetime\nfrom os.path import isfile, join, splitext\nfrom glob import glob\n\n#Imports to enable visualizations\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline", "_____no_output_____" ] ], [ [ "### Functions", "_____no_output_____" ], [ "#### Basic Functions", "_____no_output_____" ], [ "#### OTP Functions", "_____no_output_____" ], [ "#### Analysis Functions", "_____no_output_____" ], [ "### Main Code", "_____no_output_____" ], [ "#### Reading itinerary alternatives data", "_____no_output_____" ] ], [ [ "all_itineraries = pd.read_csv('/local/tarciso/data/its/itineraries/all_itineraries.csv', parse_dates=['planned_start_time','actual_start_time','exec_start_time'])", "_____no_output_____" ], [ "all_itineraries.head()", "_____no_output_____" ], [ "all_itineraries.dtypes", "_____no_output_____" ], [ "len(all_itineraries)", "_____no_output_____" ], [ "len(all_itineraries.user_trip_id.unique())", "_____no_output_____" ] ], [ [ "#### Adding metadata for further analysis", "_____no_output_____" ] ], [ [ "def get_trip_len_bucket(trip_duration):\n if (trip_duration < 10):\n return '<10'\n elif (trip_duration < 20):\n return '10-20'\n elif (trip_duration < 30):\n return '20-30'\n elif (trip_duration < 40):\n return '30-40'\n elif (trip_duration < 50):\n return '40-50'\n elif (trip_duration >= 50):\n return '50+'\n else:\n return 'NA'\n \ndef get_day_type(trip_start_time):\n trip_weekday = trip_start_time.weekday()\n if ((trip_weekday == 0) | (trip_weekday == 4)):\n return 'MON/FRI'\n elif ((trip_weekday > 0) & (trip_weekday < 4)):\n return 'TUE/WED/THU'\n elif (trip_weekday > 4):\n return 'SAT/SUN'\n else:\n return 'NA'\n\n\nall_itineraries['trip_length_bucket'] = all_itineraries['exec_duration_mins'].apply(get_trip_len_bucket)\nall_itineraries['hour_of_day'] = all_itineraries['exec_start_time'].dt.hour\n\nperiod_of_day_list = [('hour_of_day', [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]),\n ('period_of_day', ['very_late_night','very_late_night','very_late_night','very_late_night','early_morning','early_morning','early_morning','morning','morning','morning','morning','midday','midday','midday','afternoon','afternoon','afternoon','evening','evening','evening','night','night','late_night','late_night'])]\nperiod_of_day_df = pd.DataFrame.from_items(period_of_day_list)\nperiod_of_day_df.period_of_day = period_of_day_df.period_of_day.astype('category', ordered=True)\n\nall_itineraries = all_itineraries.merge(period_of_day_df, how='inner', on='hour_of_day')\nall_itineraries['weekday'] = all_itineraries['exec_start_time'].apply(lambda x: x.weekday() < 5)\nall_itineraries['day_type'] = all_itineraries['exec_start_time'].apply(get_day_type)", "_____no_output_____" ], [ "all_itineraries", "_____no_output_____" ] ], [ [ "#### Filtering trips for whose executed itineraries there is no schedule information", "_____no_output_____" ] ], [ [ "def filter_trips_alternatives(trips_alternatives):\n min_trip_dur = 10\n max_trip_dur = 50\n max_trip_start_diff = 20\n \n return trips_alternatives[(trips_alternatives['actual_duration_mins'] >= min_trip_dur) & (trips_alternatives['actual_duration_mins'] <= max_trip_dur)] \\\n .assign(start_diff = lambda x: np.absolute(x['exec_start_time'] - x['actual_start_time'])/pd.Timedelta(minutes=1)) \\\n [lambda x: x['start_diff'] <= 20]", "_____no_output_____" ], [ "def filter_trips_with_insufficient_alternatives(trips_alternatives):\n num_trips_alternatives = trips_alternatives.groupby(['date','user_trip_id']).size().reset_index(name='num_alternatives')\n trips_with_executed_alternative = trips_alternatives[trips_alternatives['itinerary_id'] == 0][['date','user_trip_id']]\n \n return trips_alternatives.merge(trips_with_executed_alternative, on=['date','user_trip_id'], how='inner') \\\n .merge(num_trips_alternatives, on=['date','user_trip_id'], how='inner') \\\n [lambda x: x['num_alternatives'] > 1] \\\n .sort_values(['user_trip_id','itinerary_id']) ", "_____no_output_____" ], [ "clean_itineraries = filter_trips_with_insufficient_alternatives(filter_trips_alternatives(all_itineraries))", "_____no_output_____" ], [ "clean_itineraries.head()", "_____no_output_____" ], [ "sns.distplot(clean_itineraries['start_diff'])", "_____no_output_____" ], [ "len(clean_itineraries)", "_____no_output_____" ], [ "len(clean_itineraries.user_trip_id.unique())", "_____no_output_____" ], [ "exec_itineraries_with_scheduled_info = all_itineraries[(all_itineraries['itinerary_id'] == 0) & (pd.notnull(all_itineraries['planned_duration_mins']))][['date','user_trip_id']]", "_____no_output_____" ], [ "clean_itineraries2 = filter_trips_with_insufficient_alternatives(filter_trips_alternatives(all_itineraries.merge(exec_itineraries_with_scheduled_info, on=['date','user_trip_id'], how='inner')))", "_____no_output_____" ], [ "clean_itineraries2.head()", "_____no_output_____" ], [ "len(clean_itineraries2)", "_____no_output_____" ], [ "len(clean_itineraries2.user_trip_id.unique())", "_____no_output_____" ] ], [ [ "## Compute Inefficiency Metrics", "_____no_output_____" ], [ "", "_____no_output_____" ] ], [ [ "def select_best_itineraries(trips_itineraries,metric_name):\n return trips_itineraries.sort_values([metric_name]) \\\n .groupby(['date','user_trip_id']) \\\n .nth(0) \\\n .reset_index()", "_____no_output_____" ] ], [ [ "### Observed Inefficiency", "_____no_output_____" ] ], [ [ "#Choose best itinerary for each trip by selecting the ones with lower actual duration\nbest_trips_itineraries = select_best_itineraries(clean_itineraries,'actual_duration_mins')", "_____no_output_____" ], [ "best_trips_itineraries.head()", "_____no_output_____" ], [ "trips_inefficiency = best_trips_itineraries \\\n .assign(dur_diff = lambda x: x['exec_duration_mins'] - x['actual_duration_mins']) \\\n .assign(observed_inef = lambda x: x['dur_diff']/x['exec_duration_mins'])", "_____no_output_____" ], [ "trips_inefficiency.head(10)", "_____no_output_____" ], [ "sns.distplot(trips_inefficiency.observed_inef)", "_____no_output_____" ], [ "sns.violinplot(trips_inefficiency.observed_inef)", "_____no_output_____" ], [ "pos_trips_inefficiency = trips_inefficiency[trips_inefficiency['dur_diff'] > 1]", "_____no_output_____" ], [ "pos_trips_inefficiency.head()", "_____no_output_____" ] ], [ [ "#### Number of Trips with/without improvent per Trip Length Bucket", "_____no_output_____" ] ], [ [ "trips_per_length = trips_inefficiency.groupby('trip_length_bucket').size().reset_index(name='total')", "_____no_output_____" ], [ "trips_per_length", "_____no_output_____" ], [ "trips_per_length_improved = pos_trips_inefficiency.groupby('trip_length_bucket').size().reset_index(name='total')", "_____no_output_____" ], [ "trips_per_length_improved", "_____no_output_____" ], [ "sns.set_style(\"whitegrid\")\n\n#Plot 1 - background - \"total\" (top) series\nax = sns.barplot(x = trips_per_length.trip_length_bucket, y = trips_per_length.total, color = \"#66c2a5\")\n\n#Plot 2 - overlay - \"bottom\" series\nbottom_plot = sns.barplot(x = trips_per_length_improved.trip_length_bucket, y = trips_per_length_improved.total, color = \"#fc8d62\")\n\nbottom_plot.set(xlabel='Trip Length (minutes)',ylabel='Number of Trips')\n\ntopbar = plt.Rectangle((0,0),1,1,fc=\"#66c2a5\", edgecolor = 'none')\nbottombar = plt.Rectangle((0,0),1,1,fc='#fc8d62', edgecolor = 'none')\nl = plt.legend([bottombar, topbar], ['Not Optimal', 'Optimal'], bbox_to_anchor=(0, 1.2), loc=2, ncol = 2, prop={'size':10})\nl.draw_frame(False)\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/trip_length_by_optimality.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Trip Length Bucket", "_____no_output_____" ] ], [ [ "trip_len_order=['10-20','20-30','30-40','40-50']\nax = sns.boxplot(x='observed_inef',y='trip_length_bucket', orient='h', data=pos_trips_inefficiency, order=trip_len_order, color='#fc8d62')\nax.set(xlabel='Inefficiency (%)',ylabel='Trip Length')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_trip_length.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Period of Day", "_____no_output_____" ] ], [ [ "period_of_day_order = ['very_late_night','early_morning','morning','midday','afternoon','evening','night','late_night']\nax = sns.boxplot(x='observed_inef',y='period_of_day', data=pos_trips_inefficiency, order=period_of_day_order, color='#fc8d62')\nax.set(xlabel='Inefficiency (%)',ylabel='Period of Day')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Weekday/Weekend", "_____no_output_____" ] ], [ [ "ax = sns.barplot(x='trip_length_bucket',y='observed_inef', hue='weekday', data=pos_trips_inefficiency, color='#fc8d62')\nax.set(xlabel='Trip Length',ylabel='Inefficiency (%)')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Day Type", "_____no_output_____" ] ], [ [ "ax = sns.barplot(x='trip_length_bucket',y='observed_inef', hue='day_type', data=pos_trips_inefficiency, color='#fc8d62')\nax.set(xlabel='Trip Length',ylabel='Inefficiency (%)')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "### Schedule Inefficiency", "_____no_output_____" ] ], [ [ "shortest_planned_itineraries = select_best_itineraries(clean_itineraries[pd.notnull(clean_itineraries['planned_duration_mins'])],'planned_duration_mins') \\\n [['date','user_trip_id','planned_duration_mins','actual_duration_mins']] \\\n .rename(index=str,columns={'planned_duration_mins':'shortest_scheduled_planned_duration',\n 'actual_duration_mins':'shortest_scheduled_observed_duration'})\n\nshortest_planned_itineraries.head()", "_____no_output_____" ], [ "sched_inef = best_trips_itineraries \\\n .rename(index=str,columns={'actual_duration_mins':'shortest_observed_duration'}) \\\n .merge(shortest_planned_itineraries, on=['date','user_trip_id'], how='inner') \\\n .assign(sched_dur_diff = lambda x: x['shortest_scheduled_observed_duration'] - x['shortest_observed_duration']) \\\n .assign(sched_inef = lambda x: x['sched_dur_diff']/x['shortest_scheduled_observed_duration'])\n\nsched_inef.head()", "_____no_output_____" ], [ "sns.distplot(sched_inef.sched_inef)", "_____no_output_____" ], [ "sns.violinplot(sched_inef.sched_inef)", "_____no_output_____" ], [ "pos_sched_inef = sched_inef[sched_inef['sched_dur_diff'] > 1]", "_____no_output_____" ], [ "sns.distplot(pos_sched_inef.sched_inef)", "_____no_output_____" ] ], [ [ "#### Number of Trips with/without improvent per Trip Length Bucket", "_____no_output_____" ] ], [ [ "trips_per_length = sched_inef.groupby('trip_length_bucket').size().reset_index(name='total')", "_____no_output_____" ], [ "trips_per_length", "_____no_output_____" ], [ "trips_per_length_improved = pos_sched_inef.groupby('trip_length_bucket').size().reset_index(name='total')", "_____no_output_____" ], [ "trips_per_length_improved", "_____no_output_____" ], [ "sns.set_style(\"whitegrid\")\n\n#Plot 1 - background - \"total\" (top) series\nax = sns.barplot(x = trips_per_length.trip_length_bucket, y = trips_per_length.total, color = \"#66c2a5\")\n\n#Plot 2 - overlay - \"bottom\" series\nbottom_plot = sns.barplot(x = trips_per_length_improved.trip_length_bucket, y = trips_per_length_improved.total, color = \"#fc8d62\")\n\nbottom_plot.set(xlabel='Trip Length (minutes)',ylabel='Number of Trips')\n\ntopbar = plt.Rectangle((0,0),1,1,fc=\"#66c2a5\", edgecolor = 'none')\nbottombar = plt.Rectangle((0,0),1,1,fc='#fc8d62', edgecolor = 'none')\nl = plt.legend([bottombar, topbar], ['Not Optimal', 'Optimal'], bbox_to_anchor=(0, 1.2), loc=2, ncol = 2, prop={'size':10})\nl.draw_frame(False)\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/trip_length_by_optimality.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Trip Length Bucket", "_____no_output_____" ] ], [ [ "trip_len_order=['10-20','20-30','30-40','40-50']\nax = sns.boxplot(x='sched_inef',y='trip_length_bucket', orient='h', data=pos_sched_inef, order=trip_len_order, color='#fc8d62')\nax.set(xlabel='Schedule Inefficiency (%)',ylabel='Trip Length')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_trip_length.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Period of Day", "_____no_output_____" ] ], [ [ "period_of_day_order = ['very_late_night','early_morning','morning','midday','afternoon','evening','night','late_night']\nax = sns.boxplot(x='sched_inef',y='period_of_day', data=pos_sched_inef, order=period_of_day_order, color='#fc8d62')\nax.set(xlabel='Schedule Inefficiency (%)',ylabel='Period of Day')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Weekday/Weekend", "_____no_output_____" ] ], [ [ "ax = sns.barplot(x='trip_length_bucket',y='sched_inef', hue='weekday', data=pos_sched_inef, color='#fc8d62')\nax.set(xlabel='Trip Length',ylabel='Schedule Inefficiency (%)')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Day Type", "_____no_output_____" ] ], [ [ "ax = sns.barplot(x='trip_length_bucket',y='sched_inef', hue='day_type', data=pos_sched_inef, color='#fc8d62')\nax.set(xlabel='Trip Length',ylabel='Schedule Inefficiency (%)')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "### User choice plan inefficiency", "_____no_output_____" ] ], [ [ "best_scheduled_itineraries = select_best_itineraries(clean_itineraries2,'planned_duration_mins') \\\n [['date','user_trip_id','planned_duration_mins']] \\\n .rename(index=str,columns={'planned_duration_mins':'best_planned_duration_mins'})", "_____no_output_____" ], [ "best_scheduled_itineraries.head()", "_____no_output_____" ], [ "plan_inef = clean_itineraries2.merge(best_scheduled_itineraries, on=['date','user_trip_id'], how='inner') \\\n [lambda x: x['itinerary_id'] == 0] \\\n .assign(plan_dur_diff = lambda x: x['planned_duration_mins'] - x['best_planned_duration_mins']) \\\n .assign(plan_inef = lambda x: x['plan_dur_diff']/x['planned_duration_mins'])", "_____no_output_____" ], [ "sns.distplot(plan_inef.plan_inef)", "_____no_output_____" ], [ "pos_plan_inef = plan_inef[plan_inef['plan_dur_diff'] > 1]", "_____no_output_____" ], [ "sns.distplot(pos_plan_inef.plan_inef)", "_____no_output_____" ] ], [ [ "#### Number of Trips with/without improvent per Trip Length Bucket", "_____no_output_____" ] ], [ [ "trips_per_length = plan_inef.groupby('trip_length_bucket').size().reset_index(name='total')", "_____no_output_____" ], [ "trips_per_length", "_____no_output_____" ], [ "trips_per_length_improved = pos_plan_inef.groupby('trip_length_bucket').size().reset_index(name='total')", "_____no_output_____" ], [ "trips_per_length_improved", "_____no_output_____" ], [ "sns.set_style(\"whitegrid\")\n\n#Plot 1 - background - \"total\" (top) series\nax = sns.barplot(x = trips_per_length.trip_length_bucket, y = trips_per_length.total, color = \"#66c2a5\")\n\n#Plot 2 - overlay - \"bottom\" series\nbottom_plot = sns.barplot(x = trips_per_length_improved.trip_length_bucket, y = trips_per_length_improved.total, color = \"#fc8d62\")\n\nbottom_plot.set(xlabel='Trip Length (minutes)',ylabel='Number of Trips')\n\ntopbar = plt.Rectangle((0,0),1,1,fc=\"#66c2a5\", edgecolor = 'none')\nbottombar = plt.Rectangle((0,0),1,1,fc='#fc8d62', edgecolor = 'none')\nl = plt.legend([bottombar, topbar], ['Not Optimal', 'Optimal'], bbox_to_anchor=(0, 1.2), loc=2, ncol = 2, prop={'size':10})\nl.draw_frame(False)\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/trip_length_by_optimality.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Trip Length Bucket", "_____no_output_____" ] ], [ [ "trip_len_order=['10-20','20-30','30-40','40-50']\nax = sns.boxplot(x='plan_inef',y='trip_length_bucket', orient='h', data=pos_plan_inef, order=trip_len_order, color='#fc8d62')\nax.set(xlabel='Plan Inefficiency (%)',ylabel='Trip Length')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_trip_length.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Period of Day", "_____no_output_____" ] ], [ [ "period_of_day_order = ['very_late_night','early_morning','morning','midday','afternoon','evening','night','late_night']\nax = sns.boxplot(x='plan_inef',y='period_of_day', data=pos_plan_inef, order=period_of_day_order, color='#fc8d62')\nax.set(xlabel='Plan Inefficiency (%)',ylabel='Period of Day')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Weekday/Weekend", "_____no_output_____" ] ], [ [ "ax = sns.barplot(x='trip_length_bucket',y='plan_inef', hue='weekday', data=pos_plan_inef, color='#fc8d62')\nax.set(xlabel='Trip Length',ylabel='Plan Inefficiency (%)')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### Per Day Type", "_____no_output_____" ] ], [ [ "ax = sns.barplot(x='trip_length_bucket',y='plan_inef', hue='day_type', data=pos_plan_inef, color='#fc8d62')\nax.set(xlabel='Trip Length',ylabel='Plan Inefficiency (%)')\n\nfig = ax.get_figure()\nfig.set_size_inches(4.5, 3)\n#fig.savefig('/local/tarciso/masters/data/results/imp_capacity_per_day_period.pdf', bbox_inches='tight')", "_____no_output_____" ] ], [ [ "#### System Schedule Deviation\n$$\n\\begin{equation*}\n {Oe - Op}\n\\end{equation*}\n$$", "_____no_output_____" ] ], [ [ "sched_deviation = clean_itineraries[clean_itineraries['itinerary_id'] > 0] \\\n .assign(sched_dev = lambda x: x['actual_duration_mins'] - x['planned_duration_mins'])\n \nsched_deviation.head()", "_____no_output_____" ], [ "sns.distplot(sched_deviation.sched_dev)", "_____no_output_____" ] ], [ [ "#### User stop waiting time offset\n$$\n\\begin{equation*}\n {start(Oe) - start(Op)}\n\\end{equation*}\n$$", "_____no_output_____" ] ], [ [ "user_boarding_timediff = clean_itineraries[clean_itineraries['itinerary_id'] > 0] \\\n .assign(boarding_timediff = lambda x: (x['actual_start_time'] - x['planned_start_time'])/pd.Timedelta(minutes=1))\n \nuser_boarding_timediff.head()", "_____no_output_____" ], [ "sns.distplot(user_boarding_timediff.boarding_timediff)", "_____no_output_____" ] ] ]

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[ [ [ "<a href=\"https://colab.research.google.com/github/valentina-s/Oceans19-data-science-tutorial/blob/master/notebooks/1_data_loading_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "## Whale Sound Exploration\n\nIn this tutorial we will explore some data which contain right whale up-calls. The dataset was shared as part of a [2013 Kaggle competition](https://www.kaggle.com/c/whale-detection-challenge). Our goal is not to show the best winning algorithm to detect a call, but share a simple pipeline for processing oscillatory data, which possibly can be used on wide range of time series.\n\nObjectives:\n* read and extract features form audio data\n* apply dimensionality reduction techiques\n* perform supervised classification\n* learn how to evaluate machine learning models\n* train a neural network to detect whale calls", "_____no_output_____" ], [ "### Data Loading and Exploration\n---", "_____no_output_____" ] ], [ [ "# ignore warnings\nimport warnings\nwarnings.filterwarnings(\"ignore\")", "_____no_output_____" ], [ "# importing multiple visualization libraries\n%matplotlib inline\nimport matplotlib.pyplot as plt\nfrom matplotlib import mlab\nimport pylab as pl\nimport seaborn", "_____no_output_____" ], [ "# importing libraries to manipulate the data files\nimport os\nfrom glob import glob", "_____no_output_____" ], [ "# importing scientific python packages\nimport numpy as np", "_____no_output_____" ], [ "# import a library to read the .aiff format\nimport aifc", "_____no_output_____" ] ], [ [ "The `train` folder contains many `.aiff` files (2 second snippets) and we have `.csv` document which contains the corresponding labels. ", "_____no_output_____" ] ], [ [ "!wget http://oceanhackweek2018.s3.amazonaws.com/oceans_data/train.tar.gz\n!wget http://oceanhackweek2018.s3.amazonaws.com/oceans_data/labels.csv", "--2019-10-31 10:19:20-- http://oceanhackweek2018.s3.amazonaws.com/oceans_data/train.tar.gz\nResolving oceanhackweek2018.s3.amazonaws.com (oceanhackweek2018.s3.amazonaws.com)... 52.218.241.34\nConnecting to oceanhackweek2018.s3.amazonaws.com (oceanhackweek2018.s3.amazonaws.com)|52.218.241.34|:80... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 64212938 (61M) [application/x-gzip]\nSaving to: ‘train.tar.gz’\n\ntrain.tar.gz 100%[===================>] 61.24M 17.3MB/s in 3.5s \n\n2019-10-31 10:19:24 (17.3 MB/s) - ‘train.tar.gz’ saved [64212938/64212938]\n\n--2019-10-31 10:19:24-- http://oceanhackweek2018.s3.amazonaws.com/oceans_data/labels.csv\nResolving oceanhackweek2018.s3.amazonaws.com (oceanhackweek2018.s3.amazonaws.com)... 52.218.234.43\nConnecting to oceanhackweek2018.s3.amazonaws.com (oceanhackweek2018.s3.amazonaws.com)|52.218.234.43|:80... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 540016 (527K) [text/csv]\nSaving to: ‘labels.csv’\n\nlabels.csv 100%[===================>] 527.36K 786KB/s in 0.7s \n\n2019-10-31 10:19:25 (786 KB/s) - ‘labels.csv’ saved [540016/540016]\n\n" ], [ "!tar xvzf train.tar.gz", 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], [ "# read the audio filenames\nfilenames = sorted(glob(os.path.join('train','*.aiff')))\nprint('There are '+str(len(filenames))+' files.' )", "There are 9999 files.\n" ], [ "# read the labels\nimport pandas as pd\nlabels = pd.read_csv(os.path.join('labels.csv'), index_col = 0)", "_____no_output_____" ] ], [ [ "The format of the labels is:", "_____no_output_____" ] ], [ [ "labels.head(10)", "_____no_output_____" ] ], [ [ "Let's look at one of those files.", "_____no_output_____" ] ], [ [ "# reading the file info\nwhale_sample_file = 'train00006.aiff'\nwhale_aiff = aifc.open(os.path.join('train',whale_sample_file),'r')\nprint (\"Frames:\", whale_aiff.getnframes() )\nprint (\"Frame rate (frames per second):\", whale_aiff.getframerate())", "Frames: 4000\nFrame rate (frames per second): 2000\n" ], [ "# reading the data\nwhale_strSig = whale_aiff.readframes(whale_aiff.getnframes())\nwhale_array = np.fromstring(whale_strSig, np.short).byteswap()\nplt.plot(whale_array)\nplt.xlabel('Frame number')", "_____no_output_____" ], [ "signal = whale_array.astype('float64')", "_____no_output_____" ], [ "# playing a whale upcall in the notebook\nfrom IPython.display import Audio\n# Audio(signal, rate=3000, autoplay = True)# the rate is set to 3000 make the widget to run (seems the widget does not run with rate below 3000)", "_____no_output_____" ] ], [ [ "Working directly with the signals is hard (there is important frequency information). Let's calculate the spectrograms for each of the signals and use as features.", "_____no_output_____" ] ], [ [ "# a function for plotting spectrograms\ndef PlotSpecgram(P, freqs, bins):\n \"\"\"Spectrogram\"\"\"\n Z = np.flipud(P) # flip rows so that top goes to bottom, bottom to top, etc.\n xextent = 0, np.amax(bins)\n xmin, xmax = xextent\n extent = xmin, xmax, freqs[0], freqs[-1]\n im = pl.imshow(Z, extent=extent,cmap = 'plasma')\n pl.axis('auto')\n pl.xlim([0.0, bins[-1]])\n pl.ylim([0, freqs[-1]])", "_____no_output_____" ], [ "params = {'NFFT':256, 'Fs':2000, 'noverlap':192}\nP, freqs, bins = mlab.specgram(whale_array, **params)\nPlotSpecgram(P, freqs, bins)\nplt.title('Spectrogram with an Upcall')", "_____no_output_____" ] ], [ [ "### Feature Extraction\n---", "_____no_output_____" ], [ "We will go through the files and extract the spectrograms from each of them. We will do it for the first N files.", "_____no_output_____" ] ], [ [ "N = 10000 #number of files to use", "_____no_output_____" ], [ "# create a dictionary which contains all the spectrograms, labeled by the filename\nspec_dict = {}\n\n# threshold to cut higher frequencies\nm = 60\n\n# loop through all the files\nfor filename in filenames[:N]:\n # read the file\n aiff = aifc.open(filename,'r')\n whale_strSig = aiff.readframes(aiff.getnframes())\n whale_array = np.fromstring(whale_strSig, np.short).byteswap()\n # create the spectrogram\n P, freqs, bins = mlab.specgram(whale_array, **params)\n spec_dict[filename] = P[:m,:]\n\n# save the dimensions of the spectrogram\nspec_dim = P[:m,:].shape ", "_____no_output_____" ], [ "", "_____no_output_____" ] ], [ [ "Most machine learning algorithms in Python expect the data to come in a format **observations** x **features**. In order to get the data in this format we need to convert the two-dimensional spectrogram into a long vector. For that we will use the `ravel` function.", "_____no_output_____" ] ], [ [ "# We will put the data in a dictionary\nfeature_dict = {}\nfor key in filenames[:N]:\n # vectorize the spectrogram\n feature_dict[key.split('/')[-1]] = spec_dict[key].ravel()\n\n# convert to a pandas dataframe\nX = pd.DataFrame(feature_dict).T", "_____no_output_____" ], [ "X.head(5)", "_____no_output_____" ], [ "# we do not need these objects anymore so let's release them from memory\ndel feature_dict\ndel spec_dict", "_____no_output_____" ], [ "# let's save these variables for reuse\nnp.save('X.npy',X)\nnp.save('y.npy',np.array(labels['label'][X.index])[:N])", "_____no_output_____" ], [ "", "_____no_output_____" ] ], [ [ "### References:\n\nhttps://www.kaggle.com/c/whale-detection-challenge\n\nhttps://github.com/jaimeps/whale-sound-classification", "_____no_output_____" ] ], [ [ "", "_____no_output_____" ] ] ]

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[ [ [ "import os\nimport h5py \nimport numpy as np \n# -- local -- \nfrom feasibgs import util as UT\nfrom feasibgs import catalogs as Cat\nfrom feasibgs import forwardmodel as FM", "_____no_output_____" ], [ "import matplotlib as mpl \nimport matplotlib.pyplot as pl \nmpl.rcParams['text.usetex'] = True\nmpl.rcParams['font.family'] = 'serif'\nmpl.rcParams['axes.linewidth'] = 1.5\nmpl.rcParams['axes.xmargin'] = 1\nmpl.rcParams['xtick.labelsize'] = 'x-large'\nmpl.rcParams['xtick.major.size'] = 5\nmpl.rcParams['xtick.major.width'] = 1.5\nmpl.rcParams['ytick.labelsize'] = 'x-large'\nmpl.rcParams['ytick.major.size'] = 5\nmpl.rcParams['ytick.major.width'] = 1.5\nmpl.rcParams['legend.frameon'] = False\n%matplotlib inline", "_____no_output_____" ], [ "cata = Cat.GamaLegacy()\ngleg = cata.Read()", "_____no_output_____" ], [ "r_mag = UT.flux2mag(gleg['legacy-photo']['apflux_r'][:,1], method='log')", "/Users/chang/anaconda2/lib/python2.7/site-packages/feasibgs-0.0.0-py2.7.egg/feasibgs/util.py:30: RuntimeWarning: divide by zero encountered in log10\n" ], [ "float(np.sum(np.isfinite(r_mag)))/float(len(r_mag))", "_____no_output_____" ], [ "no_rmag = np.invert(np.isfinite(r_mag))", "_____no_output_____" ], [ "fig = plt.figure()\nsub = fig.add_subplot(111)\n_ = sub.hist(gleg['gama-photo']['modelmag_r'], color='C0', histtype='stepfilled', range=(16, 21), bins=40)\n_ = sub.hist(gleg['gama-photo']['modelmag_r'][no_rmag], color='C1', histtype='stepfilled', range=(16, 21), bins=40)\nsub.set_xlabel(r'$r$ band model magnitude', fontsize=20)\nsub.set_xlim([16., 21.])", "_____no_output_____" ], [ "fig = plt.figure()\nsub = fig.add_subplot(111)\n_ = sub.hist(gleg['gama-photo']['modelmag_r'], color='C0', histtype='stepfilled', range=(16, 21), bins=40, normed=True)\n_ = sub.hist(gleg['gama-photo']['modelmag_r'][no_rmag], color='C1', histtype='stepfilled', range=(16, 21), bins=40, normed=True)\nsub.set_xlabel(r'$r$ band model magnitude', fontsize=20)\nsub.set_xlim([16., 21.])", "The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\nThe 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n" ], [ "fig = plt.figure()\nsub = fig.add_subplot(111)\nsub.scatter(gleg['gama-photo']['modelmag_r'], r_mag, s=2, c='k')\nsub.scatter(gleg['gama-photo']['modelmag_r'], UT.flux2mag(gleg['legacy-photo']['apflux_r'][:,1]), s=1, c='C0')\nsub.plot([0., 25.], [0., 25.], c='C1', lw=1, ls='--')\nsub.set_xlabel('$r$-band model magnitude', fontsize=20)\nsub.set_xlim([13, 21])\nsub.set_ylabel('$r$-band apflux magnitude', fontsize=20)\nsub.set_ylim([15, 25])", "_____no_output_____" ], [ "fig = plt.figure()\nsub = fig.add_subplot(111)\nsub.scatter(gleg['gama-photo']['modelmag_r'], UT.flux2mag(gleg['legacy-photo']['flux_r']), s=2, c='C0', \n label='Legacy flux')\nsub.scatter(gleg['gama-photo']['modelmag_r'], r_mag, s=0.5, c='C1', \n label='Legacy apflux (fiber)')\nsub.plot([0., 25.], [0., 25.], c='k', lw=1, ls='--')\nsub.legend(loc='upper left', markerscale=5, handletextpad=0., prop={'size':15})\nsub.set_xlabel('$r$-band GAMA model magnitude', fontsize=20)\nsub.set_xlim([13, 20])\nsub.set_ylabel('$r$-band Legacy photometry', fontsize=20)\nsub.set_ylim([13, 23])", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Multi-Layer Perceptron, MNIST\n---\nIn this notebook, we will train an MLP to classify images from the [MNIST database](http://yann.lecun.com/exdb/mnist/) hand-written digit database.\n\nThe process will be broken down into the following steps:\n>1. Load and visualize the data\n2. Define a neural network\n3. Train the model\n4. Evaluate the performance of our trained model on a test dataset!\n\nBefore we begin, we have to import the necessary libraries for working with data and PyTorch.", "_____no_output_____" ] ], [ [ "# import libraries\nimport torch\nimport numpy as np", "_____no_output_____" ] ], [ [ "---\n## Load and Visualize the [Data](http://pytorch.org/docs/stable/torchvision/datasets.html)\n\nDownloading may take a few moments, and you should see your progress as the data is loading. You may also choose to change the `batch_size` if you want to load more data at a time.\n\nThis cell will create DataLoaders for each of our datasets.", "_____no_output_____" ] ], [ [ "# The MNIST datasets are hosted on yann.lecun.com that has moved under CloudFlare protection\n# Run this script to enable the datasets download\n# Reference: https://github.com/pytorch/vision/issues/1938\n\nfrom six.moves import urllib\nopener = urllib.request.build_opener()\nopener.addheaders = [('User-agent', 'Mozilla/5.0')]\nurllib.request.install_opener(opener)", "_____no_output_____" ], [ "from torchvision import datasets\nimport torchvision.transforms as transforms\n\n# number of subprocesses to use for data loading\nnum_workers = 0\n# how many samples per batch to load\nbatch_size = 20\n\n# convert data to torch.FloatTensor\ntransform = transforms.ToTensor()\n\n# choose the training and test datasets\ntrain_data = datasets.MNIST(root='data', train=True,\n download=True, transform=transform)\ntest_data = datasets.MNIST(root='data', train=False,\n download=True, transform=transform)\n\n# prepare data loaders\ntrain_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n num_workers=num_workers)\ntest_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, \n num_workers=num_workers)", "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\nDownloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n" ] ], [ [ "### Visualize a Batch of Training Data\n\nThe first step in a classification task is to take a look at the data, make sure it is loaded in correctly, then make any initial observations about patterns in that data.", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\n%matplotlib inline\n \n# obtain one batch of training images\ndataiter = iter(train_loader)\nimages, labels = dataiter.next()\nimages = images.numpy()\n\n# plot the images in the batch, along with the corresponding labels\nfig = plt.figure(figsize=(25, 4))\nfor idx in np.arange(20):\n ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n ax.imshow(np.squeeze(images[idx]), cmap='gray')\n # print out the correct label for each image\n # .item() gets the value contained in a Tensor\n ax.set_title(str(labels[idx].item()))", "/tmp/ipykernel_2060/611598023.py:12: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n" ] ], [ [ "### View an Image in More Detail", "_____no_output_____" ] ], [ [ "img = np.squeeze(images[1])\n\nfig = plt.figure(figsize = (12,12)) \nax = fig.add_subplot(111)\nax.imshow(img, cmap='gray')\nwidth, height = img.shape\nthresh = img.max()/2.5\nfor x in range(width):\n for y in range(height):\n val = round(img[x][y],2) if img[x][y] !=0 else 0\n ax.annotate(str(val), xy=(y,x),\n horizontalalignment='center',\n verticalalignment='center',\n color='white' if img[x][y]<thresh else 'black')", "_____no_output_____" ] ], [ [ "---\n## Define the Network [Architecture](http://pytorch.org/docs/stable/nn.html)\n\nThe architecture will be responsible for seeing as input a 784-dim Tensor of pixel values for each image, and producing a Tensor of length 10 (our number of classes) that indicates the class scores for an input image. This particular example uses two hidden layers and dropout to avoid overfitting.", "_____no_output_____" ] ], [ [ "import torch.nn as nn\nimport torch.nn.functional as F\n\n## TODO: Define the NN architecture\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n # linear layer (784 -> 1 hidden node)\n self.fc1 = nn.Linear(28 * 28, 512)\n self.fc2 = nn.Linear(512, 256)\n self.fc3 = nn.Linear(256, 128)\n self.fc4 = nn.Linear(128, 10)\n self.dropout = nn.Dropout(0.2)\n\n def forward(self, x):\n # flatten image input\n x = x.view(-1, 28 * 28)\n # add hidden layer, with relu activation function\n x = F.relu(self.fc1(x))\n x = self.dropout(x)\n x = F.relu(self.fc2(x))\n x = self.dropout(x)\n x = F.relu(self.fc3(x))\n x = self.dropout(x)\n return self.fc4(x)\n\n# initialize the NN\nmodel = Net()\nprint(model)", "Net(\n (fc1): Linear(in_features=784, out_features=512, bias=True)\n (fc2): Linear(in_features=512, out_features=256, bias=True)\n (fc3): Linear(in_features=256, out_features=128, bias=True)\n (fc4): Linear(in_features=128, out_features=10, bias=True)\n (dropout): Dropout(p=0.2, inplace=False)\n)\n" ] ], [ [ "### Specify [Loss Function](http://pytorch.org/docs/stable/nn.html#loss-functions) and [Optimizer](http://pytorch.org/docs/stable/optim.html)\n\nIt's recommended that you use cross-entropy loss for classification. If you look at the documentation (linked above), you can see that PyTorch's cross entropy function applies a softmax funtion to the output layer *and* then calculates the log loss.", "_____no_output_____" ] ], [ [ "## TODO: Specify loss and optimization functions\n\n# specify loss function\ncriterion = nn.CrossEntropyLoss()\n\n# specify optimizer\noptimizer = torch.optim.SGD(model.parameters(), lr=0.01)", "_____no_output_____" ] ], [ [ "---\n## Train the Network\n\nThe steps for training/learning from a batch of data are described in the comments below:\n1. Clear the gradients of all optimized variables\n2. Forward pass: compute predicted outputs by passing inputs to the model\n3. Calculate the loss\n4. Backward pass: compute gradient of the loss with respect to model parameters\n5. Perform a single optimization step (parameter update)\n6. Update average training loss\n\nThe following loop trains for 30 epochs; feel free to change this number. For now, we suggest somewhere between 20-50 epochs. As you train, take a look at how the values for the training loss decrease over time. We want it to decrease while also avoiding overfitting the training data. ", "_____no_output_____" ] ], [ [ "# number of epochs to train the model\nn_epochs = 30 # suggest training between 20-50 epochs\n\nmodel.train() # prep model for training\n\nfor epoch in range(n_epochs):\n # monitor training loss\n train_loss = 0.0\n \n ###################\n # train the model #\n ###################\n for data, target in train_loader:\n # clear the gradients of all optimized variables\n optimizer.zero_grad()\n # forward pass: compute predicted outputs by passing inputs to the model\n output = model(data)\n # calculate the loss\n loss = criterion(output, target)\n # backward pass: compute gradient of the loss with respect to model parameters\n loss.backward()\n # perform a single optimization step (parameter update)\n optimizer.step()\n # update running training loss\n train_loss += loss.item()*data.size(0)\n \n # print training statistics \n # calculate average loss over an epoch\n train_loss = train_loss/len(train_loader.dataset)\n\n print('Epoch: {} \\tTraining Loss: {:.6f}'.format(\n epoch+1, \n train_loss\n ))", "Epoch: 1 \tTraining Loss: 1.263758\nEpoch: 2 \tTraining Loss: 0.382051\nEpoch: 3 \tTraining Loss: 0.266715\nEpoch: 4 \tTraining Loss: 0.200021\nEpoch: 5 \tTraining Loss: 0.162390\nEpoch: 6 \tTraining Loss: 0.137244\nEpoch: 7 \tTraining Loss: 0.119641\nEpoch: 8 \tTraining Loss: 0.105442\nEpoch: 9 \tTraining Loss: 0.094517\nEpoch: 10 \tTraining Loss: 0.085037\nEpoch: 11 \tTraining Loss: 0.076285\nEpoch: 12 \tTraining Loss: 0.068212\nEpoch: 13 \tTraining Loss: 0.063617\nEpoch: 14 \tTraining Loss: 0.057780\nEpoch: 15 \tTraining Loss: 0.054958\nEpoch: 16 \tTraining Loss: 0.049273\nEpoch: 17 \tTraining Loss: 0.045710\nEpoch: 18 \tTraining Loss: 0.041795\nEpoch: 19 \tTraining Loss: 0.040301\nEpoch: 20 \tTraining Loss: 0.034952\nEpoch: 21 \tTraining Loss: 0.034301\nEpoch: 22 \tTraining Loss: 0.032995\nEpoch: 23 \tTraining Loss: 0.029734\nEpoch: 24 \tTraining Loss: 0.028685\nEpoch: 25 \tTraining Loss: 0.026271\nEpoch: 26 \tTraining Loss: 0.025489\nEpoch: 27 \tTraining Loss: 0.024299\nEpoch: 28 \tTraining Loss: 0.022422\nEpoch: 29 \tTraining Loss: 0.020229\nEpoch: 30 \tTraining Loss: 0.019866\n" ] ], [ [ "---\n## Test the Trained Network\n\nFinally, we test our best model on previously unseen **test data** and evaluate it's performance. Testing on unseen data is a good way to check that our model generalizes well. It may also be useful to be granular in this analysis and take a look at how this model performs on each class as well as looking at its overall loss and accuracy.\n\n#### `model.eval()`\n\n`model.eval(`) will set all the layers in your model to evaluation mode. This affects layers like dropout layers that turn \"off\" nodes during training with some probability, but should allow every node to be \"on\" for evaluation!", "_____no_output_____" ] ], [ [ "# initialize lists to monitor test loss and accuracy\ntest_loss = 0.0\nclass_correct = list(0. for i in range(10))\nclass_total = list(0. for i in range(10))\n\nmodel.eval() # prep model for *evaluation*\n\nfor data, target in test_loader:\n # forward pass: compute predicted outputs by passing inputs to the model\n output = model(data)\n # calculate the loss\n loss = criterion(output, target)\n # update test loss \n test_loss += loss.item()*data.size(0)\n # convert output probabilities to predicted class\n _, pred = torch.max(output, 1)\n # compare predictions to true label\n correct = np.squeeze(pred.eq(target.data.view_as(pred)))\n # calculate test accuracy for each object class\n for i in range(batch_size):\n label = target.data[i]\n class_correct[label] += correct[i].item()\n class_total[label] += 1\n\n# calculate and print avg test loss\ntest_loss = test_loss/len(test_loader.dataset)\nprint('Test Loss: {:.6f}\\n'.format(test_loss))\n\nfor i in range(10):\n if class_total[i] > 0:\n print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n str(i), 100 * class_correct[i] / class_total[i],\n class_correct[i], class_total[i]))\n else:\n print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n\nprint('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n 100. * np.sum(class_correct) / np.sum(class_total),\n np.sum(class_correct), np.sum(class_total)))", "Test Loss: 0.066156\n\nTest Accuracy of 0: 98% (970/980)\nTest Accuracy of 1: 99% (1124/1135)\nTest Accuracy of 2: 97% (1010/1032)\nTest Accuracy of 3: 98% (994/1010)\nTest Accuracy of 4: 98% (966/982)\nTest Accuracy of 5: 98% (877/892)\nTest Accuracy of 6: 97% (937/958)\nTest Accuracy of 7: 97% (1007/1028)\nTest Accuracy of 8: 97% (952/974)\nTest Accuracy of 9: 98% (993/1009)\n\nTest Accuracy (Overall): 98% (9830/10000)\n" ] ], [ [ "### Visualize Sample Test Results\n\nThis cell displays test images and their labels in this format: `predicted (ground-truth)`. The text will be green for accurately classified examples and red for incorrect predictions.", "_____no_output_____" ] ], [ [ "# obtain one batch of test images\ndataiter = iter(test_loader)\nimages, labels = dataiter.next()\n\n# get sample outputs\noutput = model(images)\n# convert output probabilities to predicted class\n_, preds = torch.max(output, 1)\n# prep images for display\nimages = images.numpy()\n\n# plot the images in the batch, along with predicted and true labels\nfig = plt.figure(figsize=(25, 4))\nfor idx in np.arange(20):\n ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n ax.imshow(np.squeeze(images[idx]), cmap='gray')\n ax.set_title(\"{} ({})\".format(str(preds[idx].item()), str(labels[idx].item())),\n color=(\"green\" if preds[idx]==labels[idx] else \"red\"))", "/tmp/ipykernel_2060/3350221300.py:15: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n" ] ] ]

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[ "MIT" ]

[ [ [ "import pandas as pd\nimport os\nimport s3fs # for reading from S3FileSystem\nimport json\n\n%matplotlib inline\nimport matplotlib.pyplot as plt\n\n\nimport torch.nn as nn\nimport torch\nimport torch.utils.model_zoo as model_zoo\nimport numpy as np\n\nimport torchvision.models as models # To get ResNet18\n\n# From - https://github.com/cfotache/pytorch_imageclassifier/blob/master/PyTorch_Image_Inference.ipynb\nimport torch\nfrom torch import nn\nfrom torch import optim\nimport torch.nn.functional as F\nfrom torchvision import datasets, transforms\nfrom PIL import Image\nfrom torch.autograd import Variable\n\nfrom torch.utils.data.sampler import SubsetRandomSampler", "_____no_output_____" ] ], [ [ "# Prepare the Model", "_____no_output_____" ] ], [ [ "SAGEMAKER_PATH = r'/home/ec2-user/SageMaker'\n\nMODEL_PATH = os.path.join(SAGEMAKER_PATH, r'sidewalk-cv-assets19/pytorch_pretrained/models/20e_slid_win_no_feats_r18.pt')", "_____no_output_____" ], [ "os.path.exists(MODEL_PATH)", "_____no_output_____" ], [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ndevice", "_____no_output_____" ], [ "# Use PyTorch's ResNet18\n# https://stackoverflow.com/questions/53612835/size-mismatch-for-fc-bias-and-fc-weight-in-pytorch\nmodel = models.resnet18(num_classes=5) ", "_____no_output_____" ], [ "model.to(device)", "_____no_output_____" ], [ "model.load_state_dict(torch.load(MODEL_PATH))\nmodel.eval()", "_____no_output_____" ] ], [ [ "# Prep Data", "_____no_output_____" ] ], [ [ "# From Galen\n\ntest_transforms = transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n\n#device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n# the dataset loads the files into pytorch vectors\n#image_dataset = TwoFileFolder(dir_containing_crops, meta_to_tensor_version=2, transform=data_transform)\n\n# the dataloader takes these vectors and batches them together for parallelization, increasing performance\n#dataloader = torch.utils.data.DataLoader(image_dataset, batch_size=4, shuffle=True, num_workers=4)\n\n# this is the number of additional features provided by the dataset\n#len_ex_feats = image_dataset.len_ex_feats\n#dataset_size = len(image_dataset)", "_____no_output_____" ], [ "# Load in the data\ndata_dir = 'images'\n\ndata = datasets.ImageFolder(data_dir, transform=test_transforms)\nclasses = data.classes", "_____no_output_____" ], [ "!ls -a images", ". 0_present 2_surface_prob 4_null\n.. 1_missing 3_obstacle .ipynb_checkpoints\n" ], [ "!rm -f -r images/.ipynb_checkpoints/", "_____no_output_____" ], [ "# Examine the classes based on folders... \n# Need to make sure that we don't get a .ipynb_checkpoints as a folder\n# Discussion here - https://forums.fast.ai/t/how-to-remove-ipynb-checkpoint/8532/19\nclasses", "_____no_output_____" ], [ "num = 10\n\nindices = list(range(len(data)))\nprint(indices)\nnp.random.shuffle(indices)\nidx = indices[:num]\n\ntest_transforms = transforms.Compose([transforms.Resize(224),\n transforms.ToTensor(),\n ])\n\n#sampler = SubsetRandomSampler(idx)\nloader = torch.utils.data.DataLoader(data, batch_size=num)\ndataiter = iter(loader)\nimages, labels = dataiter.next()", "[0, 1, 2, 3]\n" ], [ "len(images)", "_____no_output_____" ], [ "# Look at the first image\nimages[0]", "_____no_output_____" ], [ "len(labels)", "_____no_output_____" ], [ "labels", "_____no_output_____" ] ], [ [ "# Execute Inference on 2 Sample Images", "_____no_output_____" ] ], [ [ "# Note on how to make sure the model and the input tensors are both on cuda device (gpu)\n# https://discuss.pytorch.org/t/runtimeerror-input-type-torch-cuda-floattensor-and-weight-type-torch-floattensor-should-be-the-same/21782/6", "_____no_output_____" ], [ "def predict_image(image, model):\n image_tensor = test_transforms(image).float()\n image_tensor = image_tensor.unsqueeze_(0)\n input = Variable(image_tensor)\n input = input.to(device)\n output = model(input)\n index = output.data.cpu().numpy().argmax()\n return index, output ", "_____no_output_____" ], [ "to_pil = transforms.ToPILImage()\n#images, labels = get_random_images(5)\nfig=plt.figure(figsize=(10,10))\nfor ii in range(len(images)):\n image = to_pil(images[ii])\n index, output = predict_image(image, model)\n print(f'index: {index}')\n print(f'output: {output}')\n sub = fig.add_subplot(1, len(images), ii+1)\n res = int(labels[ii]) == index\n sub.set_title(str(classes[index]) + \":\" + str(res))\n plt.axis('off')\n plt.imshow(image)\nplt.show()", "index: 3\noutput: tensor([[-0.2533, 0.2435, -0.3366, 5.1900, -4.6836]], device='cuda:0',\n grad_fn=<AddmmBackward>)\nindex: 3\noutput: tensor([[-0.2533, 0.2435, -0.3366, 5.1900, -4.6836]], device='cuda:0',\n grad_fn=<AddmmBackward>)\nindex: 3\noutput: tensor([[-0.4240, 0.8602, -2.7293, 3.3282, -0.9938]], device='cuda:0',\n grad_fn=<AddmmBackward>)\nindex: 3\noutput: tensor([[-0.2533, 0.2435, -0.3366, 5.1900, -4.6836]], device='cuda:0',\n grad_fn=<AddmmBackward>)\n" ], [ "res", "_____no_output_____" ] ], [ [ "# Comments and Questions\n\nWhat's the order of the labels (and how I should order the folders for the input data?) \n\nThis file implies that there are different orders\nhttps://github.com/ProjectSidewalk/sidewalk-cv-assets19/blob/master/GSVutils/sliding_window.py\n\n```label_from_int = ('Curb Cut', 'Missing Cut', 'Obstruction', 'Sfc Problem')\npytorch_label_from_int = ('Missing Cut', \"Null\", 'Obstruction', \"Curb Cut\", \"Sfc Problem\")```", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# ENTRADA Y SALIDA DE DATOS", "_____no_output_____" ], [ "## 1. Entrada de Información por teclado (Input)", "_____no_output_____" ], [ "En python la funcion input() nos ayudará con la tarea de capturar datos del usuario", "_____no_output_____" ] ], [ [ "# input por defecto nos devuelve datos de tipo texto\n# SHIFT + TAB -> Documentacion de la palabra a ejecutar\ndecimal = input(\"Introduce un número decimal con punto: \")", "Introduce un número decimal con punto: 3.099\n" ], [ "type(decimal)", "_____no_output_____" ], [ "numero = float(decimal)", "_____no_output_____" ], [ "# tab autocompleta texto\nnumero", "_____no_output_____" ], [ "type(numero)", "_____no_output_____" ] ], [ [ "## 2. Entrada de datos por Argumentos", "_____no_output_____" ], [ "Para poder enviar información a un script y manejarla, tenemos que utilizar la librería de sistema [sys](https://www.geeksforgeeks.org/how-to-use-sys-argv-in-python/). En ella encontraremos la lista argv que almacena los argumentos enviados al script.", "_____no_output_____" ] ], [ [ "import sys\nprint(sys.argv)", "['C:\\\\Users\\\\valer\\\\anaconda3\\\\lib\\\\site-packages\\\\ipykernel_launcher.py', '-f', 'C:\\\\Users\\\\valer\\\\AppData\\\\Roaming\\\\jupyter\\\\runtime\\\\kernel-0dde64eb-5976-4dc0-aad5-83f93a56f1e0.json']\n" ] ], [ [ "## 3. Salida de información (Output)", "_____no_output_____" ], [ "La función print nos ayudará a realizar salidas de información", "_____no_output_____" ] ], [ [ "print('hola')", "hola\n" ] ], [ [ "# EJERCICIOS", "_____no_output_____" ], [ "#### 1. Realiza un programa que lea 2 números por teclado y determine los siguientes aspectos (es suficiene con mostrar True o False):\n\n- Si los dos números son iguales\n- Si los dos números son diferentes\n- Si el primero es mayor que el segundo\n- Si el segundo es mayor o igual que el primero", "_____no_output_____" ] ], [ [ "num1=float(input(\"Escribir numero 1:\"))\nnum2=float(input(\"Escribir numero 2:\"))", "Escribir numero 1: 3.7\nEscribir numero 2: 6.3\n" ], [ "print(\"Son iguales?\",num1==num2)\nprint(\"Son diferentes?\",num1!=num2)\nprint(\"Número1 mayor?\",num1>num2)\nprint(\"Número2 mayor o igual que Numero2?\",num1<=num2)", "Son iguales? False\nSon diferentes? True\nNúmero1 mayor? False\nNúmero2 mayor o igual que Numero2? True\n" ], [ "a = float( input(\"Primer número: \") )\nb = float( input(\"Segundo número: \") )", "Primer número: 5\nSegundo número: 2\n" ], [ "print(\"Los dos números son iguales: \", a == b)\nprint(\"Los dos números son diferentes: \", a != b)\nprint(\"El primero es mayor que el segundo: \", a > b)\nprint(\"El segundo es mayor o igual que el primero: \", a <= b)", "Los dos números son iguales: False\nLos dos números son diferentes: True\nEl primero es mayor que el segundo: True\nEl segundo es mayor o igual que el primero: False\n" ] ], [ [ "### 2. Escribir un programa que pregunte el nombre del usuario en la consola y después de que el usuario lo introduzca muestre por pantalla la cadena ¡Hola \"nombre\"!, donde \"nombre\" es el nombre que el usuario haya introducido.\n\n\n", "_____no_output_____" ] ], [ [ "nombre=input(\"¿Cuál es tu nombre?\")\n\"¡Hola \" + nombre+\"!\"", "¿Cuál es tu nombre? Valeria\n" ] ] ]

[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "markdown", "markdown", "markdown" ], [ "code", "code", "code", "code", "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "import pandas as pd", "_____no_output_____" ], [ "!ls '../../SpeechRecognition.EN/deepspeech.cv.i.dvd/data/'", "data_loader.py\t__init__.py __pycache__\t utils.py\r\ndistributed.py\told\t train_manifest.csv val_manifest.csv\r\n" ], [ "df = pd.read_csv('../../SpeechRecognition.EN/deepspeech.cv.i.dvd/data/train_manifest.csv', header=None)", "_____no_output_____" ], [ "train_df = df[:5000]\nval_df = df[5000:6000]\nlen(train_df), len(val_df)", "_____no_output_____" ], [ "train_df.to_csv('train_manifest.csv', header=None, index=None)\nval_df.to_csv('val_manifest.csv', header=None, index=None)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "transformedDfs = [i.transform(logDf) for i in model]\ncosts = [(i,v.stages[-1].computeCost(transformedDfs[i])) for i,v in enumerate(model)]\ncosts\n\n#transformedModels = [v.stages[-1].computeCost(transformedDfs[i]) for i,v in enumerate(model)]", "_____no_output_____" ], [ "newParamMap = ({kmeans.k: 10,kmeans.initMode:\"random\"})\nnewModel = pipeline.fit(logDf,newParamMap)\n#computedModel = pipeline.fit(logDf)\n\n#ide til næste gang beregn beste stuff for alle modeller i pipelinen, derefer tag bedste pipeline ud og byg videre på den.", "_____no_output_____" ], [ "trans = newModel.transform(logDf)\ntrans.groupBy(\"prediction\").count().show() # shows the distribution of companies \n\n\nvec = [Row(cluster=i,center=Vectors.dense(v)) for i,v in enumerate(newModel.stages[-1].clusterCenters())]\n#print(type(vec))\nSpDf = sqlContext.createDataFrame(data=vec)\n#SpDf.show(truncate=False)\n\n\n\nfeatureContributionUdf = F.udf(lambda x,y: (x-y)*(x-y),VectorUDT() )\nsqrtUdf = F.udf(lambda x,y: float(Vectors.norm(vector=x-y,p=2)),DoubleType())\nprintUdf = F.udf(lambda x: type(x),StringType())\ntoDenseUDf = F.udf(lambda x: Vectors.dense(x.toArray()),VectorUDT())\n#print(np.sum(vec[0][\"vec\"]))\njoinedDf = (trans\n .join(SpDf,on=(trans[\"prediction\"]==SpDf[\"cluster\"]),how=\"left\")\n .withColumn(colName=\"features\",col=toDenseUDf(F.col(\"features\")))\n .drop(SpDf[\"cluster\"])\n .withColumn(colName=\"contribution\",col=featureContributionUdf(F.col(\"features\"),F.col(\"center\")))\n .withColumn(colName=\"distance\",col=sqrtUdf(F.col(\"features\"),F.col(\"center\")))\n )\n", "_____no_output_____" ], [ "int_range = widgets.IntSlider()\ndisplay(int_range)\n\ndef on_value_change(change):\n print(change['new'])\n\nint_range.observe(on_value_change, names='value')", "_____no_output_____" ], [ "def printTotalAndAvgFeatContribution(df,cluster=0,toPrint=False):\n joinedRdd = (df\n .select(\"prediction\",\"contribution\")\n .rdd)\n #print(joinedRdd.take(1))\n summed = joinedRdd.reduceByKey(add)\n normedtotalContribute = summed.map(lambda x: (x[0],x[1])).collectAsMap()\n \n \n \n return normedtotalContribute", "_____no_output_____" ], [ "stuff = printTotalAndAvgFeatContribution(joinedDf)\n\ncenters = [(i,np.log(stuff[i])/np.sum(np.log(stuff[i]))) for i in range(0,10)]\ncols =joinedDf.columns[5:31]\ncenters\n\nclusters = np.array([i[1] for i in centers if i[0] in [6,9,4] ])\ntransposedCluster = np.log1p(clusters.transpose())\nN =3\n\nimport colorsys\nHSV_tuples = [(x*1.0/len(transposedCluster), 0.5, 0.5) for x in range(len(transposedCluster))]\nRGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n\nind = np.arange(N)\n#print(ind)# the x locations for the groups\nwidth = 0.5 \nplots = [plt.bar(ind, transposedCluster[1], width, color='#d62728')] \nformer = transposedCluster[1]\nfor i,v in enumerate(transposedCluster[1:]):\n plots.append(plt.bar(ind, v, width, color=RGB_tuples[i],bottom=former))\n former += v\nplt.ylabel('log Scores')\nplt.title('Component Contribution for outlier clusters')\nplt.xticks(ind+0.3, ['C_'+str(i) for i in [6,9,4]])\nplt.legend([p[0] for p in plots], cols,bbox_to_anchor=(1.05, 1.5),loc=2,borderaxespad=1)\nplt.show()", "_____no_output_____" ], [ "class DistanceTransformation(Transformer,HasInputCol,HasOutputCol):\n '''\n \n '''\n @keyword_only\n def __init__(self, inputCol=None, outputCol=None, model=None):\n super(DistanceTransformation, self).__init__()\n kwargs = self.__init__._input_kwargs\n self.setParams(**kwargs)\n\n @keyword_only\n def setParams(self, inputCol=None, outputCol=None, model=None):\n kwargs = self.setParams._input_kwargs\n return self._set(**kwargs)\n\n def _transform(self, dataset,model):\n \n \n def computeAndInsertClusterCenter(dataset,centers):\n '''\n Insert a clusterCenter as column.\n '''\n\n distanceUdf = F.udf(lambda x,y: float(np.sqrt(np.sum((x-y)*(x-y)))),DoubleType())\n\n return (dataset\n .join(F.broadcast(centers),on=(dataset[\"prediction\"]==centers[\"cluster\"]),how=\"inner\")\n .withColumn(colName=\"distance\",col=distanceUdf(F.col(\"scaledFeatures\"),F.col(\"center\")))\n .drop(\"cluster\")\n .drop(\"features\")\n .drop(\"v2\")\n )\n", "_____no_output_____" ], [ "print(getCenters(0))\n\nparamGrid = ParamGridBuilder() \\\n .addGrid(kmeans.k, [2, 4, 10]) \\\n .addGrid(kmeans.initSteps, [3,5,10]) \\\n .build()\n", "_____no_output_____" ], [ "#create an unsupervised classification evaluator\nclass ElbowEvaluation(Estimator,ValidatorParams):\n '''\n doc\n '''\n \n @keyword_only\n def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None,\n seed=None):\n super(ElbowEvaluation, self).__init__()\n kwargs = self.__init__._input_kwargs\n self._set(**kwargs)\n \n @keyword_only\n def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None):\n kwargs = self.setParams._input_kwargs\n return self._set(**kwargs)\n \n computeDistanceToCenterUdf = F.udf(lambda x,y: (x-y)*(x-y),VectorUDT())\n \n \n def _fit(self, dataset):\n est = self.getOrDefault(self.estimator)\n epm = self.getOrDefault(self.estimatorParamMaps)\n numModels = len(epm)\n eva = self.getOrDefault(self.evaluator)\n \n for j in range(numModels):\n model = est.fit(dataset, epm[j])\n model.\n \n metric = eva.evaluate(model.transform(dataset, epm[j]))\n metrics[j] += metric\n if eva.isLargerBetter():\n bestIndex = np.argmax(metrics)\n else:\n bestIndex = np.argmin(metrics)\n bestModel = est.fit(dataset, epm[bestIndex])\n return self._copyValues(TrainValidationSplitModel(bestModel, metrics))\n \n def copy(self, extra=None):\n \"\"\"\n Creates a copy of this instance with a randomly generated uid\n and some extra params. This copies creates a deep copy of\n the embedded paramMap, and copies the embedded and extra parameters over.\n\n :param extra: Extra parameters to copy to the new instance\n :return: Copy of this instance\n \"\"\"\n if extra is None:\n extra = dict()\n newTVS = Params.copy(self, extra)\n if self.isSet(self.estimator):\n newTVS.setEstimator(self.getEstimator().copy(extra))\n # estimatorParamMaps remain the same\n if self.isSet(self.evaluator):\n newTVS.setEvaluator(self.getEvaluator().copy(extra))\n return newTVS", "_____no_output_____" ], [ "\nclass ElbowEvaluationModel(Model, ValidatorParams):\n \"\"\"\n .. note:: Experimental\n\n Model from train validation split.\n\n .. versionadded:: 2.0.0\n \"\"\"\n\n def __init__(self, bestModel, validationMetrics=[]):\n super(TrainValidationSplitModel, self).__init__()\n #: best model from cross validation\n self.bestModel = bestModel\n #: evaluated validation metrics\n self.validationMetrics = validationMetrics\n\n def _transform(self, dataset):\n return self.bestModel.transform(dataset)\n\n def copy(self, extra=None):\n \"\"\"\n Creates a copy of this instance with a randomly generated uid\n and some extra params. This copies the underlying bestModel,\n creates a deep copy of the embedded paramMap, and\n copies the embedded and extra parameters over.\n And, this creates a shallow copy of the validationMetrics.\n\n :param extra: Extra parameters to copy to the new instance\n :return: Copy of this instance\n \"\"\"\n if extra is None:\n extra = dict()\n bestModel = self.bestModel.copy(extra)\n validationMetrics = list(self.validationMetrics)\n return TrainValidationSplitModel(bestModel, validationMetrics)\n\n", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# CLX Workflow\n\nThis is an introduction to the CLX Workflow and it's I/O components.", "_____no_output_____" ], [ "## What is a CLX Workflow?\n\nA CLX Workflow receives data from a particular source, performs operations on that data within a GPU dataframe, and outputs that data to a particular destination. This guide will teach you how to configure your workflow inputs and outputs around a simple workflow example.", "_____no_output_____" ], [ "## When to use a CLX Workflow\n\nA CLX Workflow provides a simple and modular way of \"plugging in\" a particular workflow to a read from different inputs and outputs. Use a CLX Workflow when you would like to deploy a workflow as part of a data pipeline.", "_____no_output_____" ], [ "#### A simple example of a custom Workflow", "_____no_output_____" ] ], [ [ "from clx.workflow.workflow import Workflow\nclass CustomWorkflow(Workflow):\n def workflow(self, dataframe):\n dataframe[\"enriched\"] = \"enriched output\"\n return dataframe", "_____no_output_____" ] ], [ [ "The Workflow relies on the Workflow class which handles the I/O and general data processing functionality. To implement a new Workflow, the developer need only implement the `workflow` function which receives an input dataframe, as shown above. \n \nA more advanced example of a Worlflow can be found [here](https://github.com/rapidsai/clx/blob/branch-0.12/clx/workflow/splunk_alert_workflow.py).\nIt is an example of a [Splunk](https://www.splunk.com/) Alert Workflow used to find anamolies in Splunk alert data.\n\n", "_____no_output_____" ], [ "## Workflow I/O Components\n\nIn order to deploy a workflow to an input an output data feed, we integrate the CLX I/O components.", "_____no_output_____" ], [ "Let's look quickly at what a workflow configuration for the source and destination might look like. You can see below we declare each of the properties within a dictionary. For more information on how to declare configuration within a configurable yaml file [go]. \n\n", "_____no_output_____" ] ], [ [ "source = {\n \"type\": \"fs\",\n \"input_format\": \"csv\",\n \"input_path\": \"/full/path/to/input/data\",\n \"schema\": [\"raw\"],\n \"delimiter\": \",\",\n \"required_cols\": [\"raw\"],\n \"dtype\": [\"str\"],\n \"header\": 0\n}\ndestination = {\n \"type\": \"fs\",\n \"output_format\": \"csv\",\n \"output_path\": \"/full/path/to/output/data\"\n}", "_____no_output_____" ] ], [ [ "\nThe first step to configuring the input and output of a workflow is to determine the source and destination type. Then to set the associated parameters for that specific type.\nAs seen above the `type` property is listed first and can be one of the following.\n", "_____no_output_____" ], [ "\nSource Types\n \n* `fs` - Read from a local filesystem\n* `dask_fs` - Increase the speed of GPU workflow operations by reading from a file using Dask\n* `kafka` - Read from [Kafka](https://kafka.apache.org/)\n\nDestination Types\n \n* `fs` - Writing to local filesystem\n* `kafka` - Write to [Kafka](https://kafka.apache.org/)\n", "_____no_output_____" ], [ "\n### Source and Destination Configurations\n\n#### Filesystem\nIf the `fs` type is used, the developer must distinguish the data format using the `input_format` attribute. Formats available are: csv, parquet, and orc.\n \nThe associated parameters available for the `fs` type and `input_format` are documented within the [cuDF I/O](https://rapidsai.github.io/projects/cudf/en/0.11.0/api.html#module-cudf.io.csv) API. For example for reading data from a csv file, reference [cudf.io.csv.read_csv](https://rapidsai.github.io/projects/cudf/en/0.11.0/api.html#cudf.io.csv.read_csv) available parameters.\n\nExample\n", "_____no_output_____" ] ], [ [ "source = {\n \"type\": \"fs\",\n \"input_format\": \"parquet\",\n \"input_path\": \"/full/path/to/input/data\",\n \"columns\": [\"x\"]\n}", "_____no_output_____" ] ], [ [ "#### Dask Filesystem\n \nIf the `dask_fs` type is used the developer must distinguish the data format using the `input_format` attribute. Formats available are: csv, parquet, and orc.\n \nThe associated parameters available for the `dask_fs` type and `input_format` are listed within the [Dask cuDF](https://rapidsai.github.io/projects/cudf/en/0.11.0/10min.html#Getting-Data-In/Out) documentation. \n \nExample", "_____no_output_____" ] ], [ [ "source = {\n \"type\": \"dask_fs\",\n \"input_format\": \"csv\",\n \"input_path\": \"/full/path/to/input/data/*.csv\"\n}", "_____no_output_____" ] ], [ [ "#### Kafka\nIf the `kafka` type is used the following parameters must be indicated \n \nSource \n \n* `kafka_brokers` - Kafka brokers\n* `group_id` - Group ID for consuming kafka messages\n* `consumer_kafka_topics` - Names of kafka topics to read from\n* `batch_size` - Indicates number of kafka messages to read before data is processed through the workflow\n* `time_window` - Maximum time window to wait for `batch_size` to be reached before workflow processing begins. \n \nDestination \n \n* `kafka_brokers` - Kafka brokers\n* `publisher_kafka_topic` - Names of kafka topic to write data to\n* `batch_size` - Indicates number of workflow-processed messages to aggregate before data is written to the kafka topic\n* `output_delimiter` - Delimiter of the data columns\n \nExample", "_____no_output_____" ] ], [ [ "source = {\n \"type\": \"kafka\",\n \"kafka_brokers\": \"kafka:9092\",\n \"group_id\": \"cyber\",\n \"batch_size\": 10,\n \"consumer_kafka_topics\": [\"topic1\", \"topic2\"],\n \"time_window\": 5\n}\ndest = {\n \"type\": \"kafka\",\n \"kafka_brokers\": \"kafka:9092\"\n \"batch_size\": 10,\n \"publisher_kafka_topic\": \"topic3\",\n \"output_delimiter\": \",\"\n}", "_____no_output_____" ] ], [ [ "## Tying it together", "_____no_output_____" ], [ "Once we have established our workflow and source and destination configurations we can now run our workflow. Let's create a workflow using the `CustomWorkflow` we created above.\n\nFirstly, we must know the parameters for instantiating a basic workflow\n \n* `name` - The name of the workflow\n* `source` - The source of input data (optional)\n* `destination` - The destination for output data (optional)", "_____no_output_____" ] ], [ [ "from clx.workflow.workflow import Workflow\nclass CustomWorkflow(Workflow):\n def workflow(self, dataframe):\n dataframe[\"enriched\"] = \"enriched output\"\n return dataframe\n \nsource = {\n \"type\": \"fs\",\n \"input_format\": \"csv\",\n \"input_path\": \"/full/path/to/input/data\",\n \"schema\": [\"raw\"],\n \"delimiter\": \",\",\n \"required_cols\": [\"raw\"],\n \"dtype\": [\"str\"],\n \"header\": 0\n}\ndestination = {\n \"type\": \"fs\",\n \"output_format\": \"csv\",\n \"output_path\": \"/full/path/to/output/data\"\n}\n\nmy_new_workflow = CustomWorkflow(source=source, destination=destination, name=\"my_new_workflow\")\nmy_new_workflow.run_workflow()", "_____no_output_____" ] ], [ [ "## Workflow configurations in an external file\n\nSometimes workflow configurations may need to change dependent upon the environment. To avoid declaring workflow configurations within sourcecode you may also declare them in an external yaml file. A workflow will look for and establish I/O connections by searching for configurations in the following order:\n \n1. /etc/clx/[workflow-name]/workflow.yaml\n1. ~/.config/clx/[workflow-name]/workflow.yaml\n1. In-line python config\n \nIf source and destination are indicated in external files, they are not required to instantiate a new workflow", "_____no_output_____" ] ], [ [ "# Workflow config located at /etc/clx/my_new_workflow/workflow.yaml\nmy_new_workflow = CustomWorkflow(name=\"my_new_workflow\")", "_____no_output_____" ] ] ]

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[ [ "markdown", "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown", "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown" ], [ "code" ], [ "markdown", "markdown" ], [ "code" ], [ "markdown" ], [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from google.colab import drive\ndrive.mount('/content/drive')", "Mounted at /content/drive\n" ], [ "path = '/content/drive/MyDrive/Research/AAAI/dataset1/second_layer_without_entropy/'", "_____no_output_____" ], [ "import numpy as np\nimport pandas as pd\n\nimport torch\nimport torchvision\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, utils\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom matplotlib import pyplot as plt\n%matplotlib inline\n\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False", "_____no_output_____" ] ], [ [ "# Generate dataset", "_____no_output_____" ] ], [ [ "np.random.seed(12)\ny = np.random.randint(0,10,5000)\nidx= []\nfor i in range(10):\n print(i,sum(y==i))\n idx.append(y==i)\nx = np.zeros((5000,2))\nnp.random.seed(12)\nx[idx[0],:] = np.random.multivariate_normal(mean = [4,6.5],cov=[[0.01,0],[0,0.01]],size=sum(idx[0]))\nx[idx[1],:] = np.random.multivariate_normal(mean = [5.5,6],cov=[[0.01,0],[0,0.01]],size=sum(idx[1]))\nx[idx[2],:] = np.random.multivariate_normal(mean = [4.5,4.5],cov=[[0.01,0],[0,0.01]],size=sum(idx[2]))\nx[idx[3],:] = np.random.multivariate_normal(mean = [3,3.5],cov=[[0.01,0],[0,0.01]],size=sum(idx[3]))\nx[idx[4],:] = np.random.multivariate_normal(mean = [2.5,5.5],cov=[[0.01,0],[0,0.01]],size=sum(idx[4]))\nx[idx[5],:] = np.random.multivariate_normal(mean = [3.5,8],cov=[[0.01,0],[0,0.01]],size=sum(idx[5]))\nx[idx[6],:] = np.random.multivariate_normal(mean = [5.5,8],cov=[[0.01,0],[0,0.01]],size=sum(idx[6]))\nx[idx[7],:] = np.random.multivariate_normal(mean = [7,6.5],cov=[[0.01,0],[0,0.01]],size=sum(idx[7]))\nx[idx[8],:] = np.random.multivariate_normal(mean = [6.5,4.5],cov=[[0.01,0],[0,0.01]],size=sum(idx[8]))\nx[idx[9],:] = np.random.multivariate_normal(mean = [5,3],cov=[[0.01,0],[0,0.01]],size=sum(idx[9]))\ncolor = ['#1F77B4','orange', 'g','brown']\nname = [1,2,3,0]\nfor i in range(10):\n if i==3:\n plt.scatter(x[idx[i],0],x[idx[i],1],c=color[3],label=\"D_\"+str(name[i]))\n elif i>=4:\n plt.scatter(x[idx[i],0],x[idx[i],1],c=color[3])\n else:\n plt.scatter(x[idx[i],0],x[idx[i],1],c=color[i],label=\"D_\"+str(name[i]))\nplt.legend()", "0 530\n1 463\n2 494\n3 517\n4 488\n5 497\n6 493\n7 507\n8 492\n9 519\n" ], [ "x[idx[0]][0], x[idx[5]][5] ", "_____no_output_____" ], [ "desired_num = 6000\nmosaic_list_of_images =[]\nmosaic_label = []\nfore_idx=[]\nfor j in range(desired_num):\n np.random.seed(j)\n fg_class = np.random.randint(0,3)\n fg_idx = np.random.randint(0,9)\n a = []\n for i in range(9):\n if i == fg_idx:\n b = np.random.choice(np.where(idx[fg_class]==True)[0],size=1)\n a.append(x[b])\n# print(\"foreground \"+str(fg_class)+\" present at \" + str(fg_idx))\n else:\n bg_class = np.random.randint(3,10)\n b = np.random.choice(np.where(idx[bg_class]==True)[0],size=1)\n a.append(x[b])\n# print(\"background \"+str(bg_class)+\" present at \" + str(i))\n a = np.concatenate(a,axis=0)\n mosaic_list_of_images.append(a)\n mosaic_label.append(fg_class)\n fore_idx.append(fg_idx)", "_____no_output_____" ], [ "len(mosaic_list_of_images), mosaic_list_of_images[0]", "_____no_output_____" ] ], [ [ "# load mosaic data", "_____no_output_____" ] ], [ [ "class MosaicDataset(Dataset):\n \"\"\"MosaicDataset dataset.\"\"\"\n\n def __init__(self, mosaic_list, mosaic_label,fore_idx):\n \"\"\"\n Args:\n csv_file (string): Path to the csv file with annotations.\n root_dir (string): Directory with all the images.\n transform (callable, optional): Optional transform to be applied\n on a sample.\n \"\"\"\n self.mosaic = mosaic_list\n self.label = mosaic_label\n self.fore_idx = fore_idx\n \n def __len__(self):\n return len(self.label)\n\n def __getitem__(self, idx):\n return self.mosaic[idx] , self.label[idx] , self.fore_idx[idx]", "_____no_output_____" ], [ "batch = 250\nmsd1 = MosaicDataset(mosaic_list_of_images[0:3000], mosaic_label[0:3000] , fore_idx[0:3000])\ntrain_loader = DataLoader( msd1 ,batch_size= batch ,shuffle=True)", "_____no_output_____" ], [ "batch = 250\nmsd2 = MosaicDataset(mosaic_list_of_images[3000:6000], mosaic_label[3000:6000] , fore_idx[3000:6000])\ntest_loader = DataLoader( msd2 ,batch_size= batch ,shuffle=True)", "_____no_output_____" ] ], [ [ "# models", "_____no_output_____" ] ], [ [ "class Focus_deep(nn.Module):\n '''\n deep focus network averaged at zeroth layer\n input : elemental data\n '''\n def __init__(self,inputs,output,K,d):\n super(Focus_deep,self).__init__()\n self.inputs = inputs\n self.output = output\n self.K = K\n self.d = d\n self.linear1 = nn.Linear(self.inputs,50, bias=False) #,self.output)\n self.linear2 = nn.Linear(50,50 , bias=False)\n self.linear3 = nn.Linear(50,self.output, bias=False) \n\n torch.nn.init.xavier_normal_(self.linear1.weight)\n torch.nn.init.xavier_normal_(self.linear2.weight)\n torch.nn.init.xavier_normal_(self.linear3.weight)\n \n def forward(self,z):\n batch = z.shape[0]\n x = torch.zeros([batch,self.K],dtype=torch.float64)\n y = torch.zeros([batch,50], dtype=torch.float64) # number of features of output\n features = torch.zeros([batch,self.K,50],dtype=torch.float64)\n x,y = x.to(\"cuda\"),y.to(\"cuda\")\n features = features.to(\"cuda\")\n for i in range(self.K):\n alp,ftrs = self.helper(z[:,i] ) # self.d*i:self.d*i+self.d\n x[:,i] = alp[:,0]\n features[:,i] = ftrs \n x = F.softmax(x,dim=1) # alphas\n for i in range(self.K):\n x1 = x[:,i] \n y = y+torch.mul(x1[:,None],features[:,i]) # self.d*i:self.d*i+self.d\n return y , x \n def helper(self,x):\n x = self.linear1(x)\n x = F.relu(x) \n x = self.linear2(x)\n x1 = F.tanh(x)\n x = F.relu(x)\n x = self.linear3(x)\n #print(x1.shape)\n return x,x1", "_____no_output_____" ], [ "class Classification_deep(nn.Module):\n '''\n input : elemental data\n deep classification module data averaged at zeroth layer\n '''\n def __init__(self,inputs,output):\n super(Classification_deep,self).__init__()\n self.inputs = inputs\n self.output = output\n self.linear1 = nn.Linear(self.inputs,50)\n #self.linear2 = nn.Linear(6,12)\n self.linear2 = nn.Linear(50,self.output)\n\n torch.nn.init.xavier_normal_(self.linear1.weight)\n torch.nn.init.zeros_(self.linear1.bias)\n torch.nn.init.xavier_normal_(self.linear2.weight)\n torch.nn.init.zeros_(self.linear2.bias)\n\n def forward(self,x):\n x = F.relu(self.linear1(x))\n #x = F.relu(self.linear2(x))\n x = self.linear2(x)\n return x ", "_____no_output_____" ], [ "# torch.manual_seed(12)\n# focus_net = Focus_deep(2,1,9,2).double()\n# focus_net = focus_net.to(\"cuda\")", "_____no_output_____" ], [ "# focus_net.linear2.weight.shape,focus_net.linear3.weight.shape", "_____no_output_____" ], [ "# focus_net.linear2.weight.data[25:,:] = focus_net.linear2.weight.data[:25,:] #torch.nn.Parameter(torch.tensor([last_layer]) )\n# (focus_net.linear2.weight[:25,:]== focus_net.linear2.weight[25:,:] )", "_____no_output_____" ], [ "# focus_net.linear3.weight.data[:,25:] = -focus_net.linear3.weight.data[:,:25] #torch.nn.Parameter(torch.tensor([last_layer]) )\n# focus_net.linear3.weight", "_____no_output_____" ], [ "# focus_net.helper( torch.randn((5,2,2)).double().to(\"cuda\") )", "_____no_output_____" ], [ "def calculate_attn_loss(dataloader,what,where,criter):\n what.eval()\n where.eval()\n r_loss = 0\n alphas = []\n lbls = []\n pred = []\n fidices = []\n with torch.no_grad():\n for i, data in enumerate(dataloader, 0):\n inputs, labels,fidx = data\n lbls.append(labels)\n fidices.append(fidx)\n inputs = inputs.double()\n inputs, labels = inputs.to(\"cuda\"),labels.to(\"cuda\")\n avg,alpha = where(inputs)\n outputs = what(avg)\n _, predicted = torch.max(outputs.data, 1)\n pred.append(predicted.cpu().numpy())\n alphas.append(alpha.cpu().numpy())\n loss = criter(outputs, labels)\n r_loss += loss.item()\n alphas = np.concatenate(alphas,axis=0)\n pred = np.concatenate(pred,axis=0)\n lbls = np.concatenate(lbls,axis=0)\n fidices = np.concatenate(fidices,axis=0)\n #print(alphas.shape,pred.shape,lbls.shape,fidices.shape) \n analysis = analyse_data(alphas,lbls,pred,fidices)\n return r_loss/i,analysis", "_____no_output_____" ], [ "def analyse_data(alphas,lbls,predicted,f_idx):\n '''\n analysis data is created here\n '''\n batch = len(predicted)\n amth,alth,ftpt,ffpt,ftpf,ffpf = 0,0,0,0,0,0\n for j in range (batch):\n focus = np.argmax(alphas[j])\n if(alphas[j][focus] >= 0.5):\n amth +=1\n else:\n alth +=1\n if(focus == f_idx[j] and predicted[j] == lbls[j]):\n ftpt += 1\n elif(focus != f_idx[j] and predicted[j] == lbls[j]):\n ffpt +=1\n elif(focus == f_idx[j] and predicted[j] != lbls[j]):\n ftpf +=1\n elif(focus != f_idx[j] and predicted[j] != lbls[j]):\n ffpf +=1\n #print(sum(predicted==lbls),ftpt+ffpt)\n return [ftpt,ffpt,ftpf,ffpf,amth,alth]", "_____no_output_____" ] ], [ [ "# training", "_____no_output_____" ] ], [ [ "number_runs = 10\nFTPT_analysis = pd.DataFrame(columns = [\"FTPT\",\"FFPT\", \"FTPF\",\"FFPF\"])\nfull_analysis= []\nfor n in range(number_runs):\n print(\"--\"*40)\n \n # instantiate focus and classification Model\n torch.manual_seed(n)\n where = Focus_deep(2,1,9,2).double()\n where.linear2.weight.data[25:,:] = where.linear2.weight.data[:25,:]\n where.linear3.weight.data[:,25:] = -where.linear3.weight.data[:,:25]\n where = where.double().to(\"cuda\")\n ex,_ = where.helper( torch.randn((5,2,2)).double().to(\"cuda\"))\n print(ex)\n\n torch.manual_seed(n)\n what = Classification_deep(50,3).double()\n where = where.to(\"cuda\")\n what = what.to(\"cuda\")\n\n\n\n # instantiate optimizer\n optimizer_where = optim.Adam(where.parameters(),lr =0.001)#,momentum=0.9)\n optimizer_what = optim.Adam(what.parameters(), lr=0.001)#,momentum=0.9)\n criterion = nn.CrossEntropyLoss()\n acti = []\n analysis_data = []\n loss_curi = []\n epochs = 2000\n\n\n # calculate zeroth epoch loss and FTPT values\n running_loss,anlys_data = calculate_attn_loss(train_loader,what,where,criterion)\n loss_curi.append(running_loss)\n analysis_data.append(anlys_data)\n\n print('epoch: [%d ] loss: %.3f' %(0,running_loss)) \n\n # training starts \n for epoch in range(epochs): # loop over the dataset multiple times\n ep_lossi = []\n running_loss = 0.0\n what.train()\n where.train()\n for i, data in enumerate(train_loader, 0):\n # get the inputs\n inputs, labels,_ = data\n inputs = inputs.double()\n inputs, labels = inputs.to(\"cuda\"),labels.to(\"cuda\")\n\n # zero the parameter gradients\n optimizer_where.zero_grad()\n optimizer_what.zero_grad()\n \n # forward + backward + optimize\n avg, alpha = where(inputs)\n outputs = what(avg)\n loss = criterion(outputs, labels)\n\n # print statistics\n running_loss += loss.item()\n loss.backward()\n optimizer_where.step()\n optimizer_what.step()\n\n running_loss,anls_data = calculate_attn_loss(train_loader,what,where,criterion)\n analysis_data.append(anls_data)\n if(epoch % 200==0):\n print('epoch: [%d] loss: %.3f' %(epoch + 1,running_loss)) \n loss_curi.append(running_loss) #loss per epoch\n if running_loss<=0.01:\n break\n print('Finished Training run ' +str(n)+' at epoch: ',epoch)\n analysis_data = np.array(analysis_data)\n FTPT_analysis.loc[n] = analysis_data[-1,:4]/30\n full_analysis.append((epoch, analysis_data))\n correct = 0\n total = 0\n with torch.no_grad():\n for data in test_loader:\n images, labels,_ = data\n images = images.double()\n images, labels = images.to(\"cuda\"), labels.to(\"cuda\")\n avg, alpha = where(images)\n outputs = what(avg)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\n print('Accuracy of the network on the 3000 test images: %f %%' % ( 100 * correct / total))\n ", "--------------------------------------------------------------------------------\n" ], [ "print(np.mean(np.array(FTPT_analysis),axis=0))", "[85.94666667 13.72666667 0.1 0.22666667]\n" ], [ "FTPT_analysis", "_____no_output_____" ], [ "FTPT_analysis[FTPT_analysis['FTPT']+FTPT_analysis['FFPT'] > 90 ]", "_____no_output_____" ], [ "print(np.mean(np.array(FTPT_analysis[FTPT_analysis['FTPT']+FTPT_analysis['FFPT'] > 90 ]),axis=0))", "[85.94666667 13.72666667 0.1 0.22666667]\n" ], [ "cnt=1\nfor epoch, analysis_data in full_analysis:\n analysis_data = np.array(analysis_data)\n # print(\"=\"*20+\"run \",cnt,\"=\"*20)\n \n plt.figure(figsize=(6,5))\n plt.plot(np.arange(0,epoch+2,1),analysis_data[:,0]/30,label=\"FTPT\")\n plt.plot(np.arange(0,epoch+2,1),analysis_data[:,1]/30,label=\"FFPT\")\n plt.plot(np.arange(0,epoch+2,1),analysis_data[:,2]/30,label=\"FTPF\")\n plt.plot(np.arange(0,epoch+2,1),analysis_data[:,3]/30,label=\"FFPF\")\n\n plt.title(\"Training trends for run \"+str(cnt))\n plt.grid()\n # plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n plt.legend()\n plt.xlabel(\"epochs\", fontsize=14, fontweight = 'bold')\n plt.ylabel(\"percentage train data\", fontsize=14, fontweight = 'bold')\n plt.savefig(path + \"run\"+str(cnt)+\".png\",bbox_inches=\"tight\")\n plt.savefig(path + \"run\"+str(cnt)+\".pdf\",bbox_inches=\"tight\")\n cnt+=1", "_____no_output_____" ], [ "FTPT_analysis.to_csv(path+\"synthetic_zeroth.csv\",index=False)", "_____no_output_____" ], [ "", "_____no_output_____" ] ] ]

[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]

[ [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code", "code" ], [ "markdown" ], [ "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "说明：\n 给定一个仅由字符a，b和c组成的字符串s。\n 返回至少包含所有这些字符a，b和c的子字符串的数目。\n\nExample 1:\n Input: s = \"abcabc\"\n Output: 10\n Explanation: \n The substrings containing at least one occurrence of the characters a, b and c are \n \"abc\", \"abca\", \"abcab\", \"abcabc\", \"bca\", \"bcab\", \"bcabc\", \"cab\", \"cabc\" and \"abc\" (again). \n \n\nExample 2:\n Input: s = \"aaacb\"\n Output: 3\n Explanation: The substrings containing at least one occurrence of the characters a, b and c are \"aaacb\", \"aacb\" and \"acb\". \n\nExample 3:\n Input: s = \"abc\"\n Output: 1\n\nConstraints:\n 1、3 <= s.length <= 5 x 10^4\n 2、s only consists of a, b or c characters.", "_____no_output_____" ] ], [ [ "class Solution:\n def numberOfSubstrings(self, s: str) -> int:\n letters = {'a', 'b', 'c'}\n N = len(s)\n count = 0\n \n for gap in range(3, N + 1):\n for start in range(N - gap + 1):\n sub_str = s[start:start + gap]\n if set(sub_str) == letters:\n count += 1\n return count", "_____no_output_____" ], [ "from collections import Counter\n\nclass Solution:\n def numberOfSubstrings(self, s: str) -> int:\n def count_letter(ct):\n if ct['a'] and ct['b'] and ct['c']:\n return True\n return False\n \n count = 0\n for i in range(len(s)):\n ctr = Counter(s[:len(s) - i])\n for j in range(i + 1):\n if j != 0:\n start_idx = j - 1\n end_idx = len(s) + start_idx - i\n ctr[s[start_idx]] -= 1\n ctr[s[end_idx]] += 1\n if count_letter(ctr):\n count += 1\n return count", "_____no_output_____" ], [ "class Solution:\n def numberOfSubstrings(self, s: str) -> int:\n a, b, c = 0, 0, 0\n ans, i, n = 0, 0, len(s)\n for j, letter in enumerate(s):\n if letter == 'a':\n a += 1\n elif letter == 'b':\n b += 1\n else:\n c += 1\n while a and b and c:\n ans += n - j\n print(n - j, n, j)\n if s[i] == 'a':\n a -= 1\n elif s[i] == 'b':\n b -= 1\n else:\n c -= 1\n i += 1\n return ans", "_____no_output_____" ], [ "solution = Solution()\nsolution.numberOfSubstrings('abcbb')", "3 5 2\n" ], [ "a = {'a', 'c', 'b'}\nb = {'a', 'b', 'c'}\nprint(a == b)", "True\n" ], [ "class Solution:\n def numberOfSubstrings(self, s: str) -> int:\n a = b = c = 0 # counter for letter a/b/c\n ans, i, n = 0, 0, len(s) # i: slow pointer\n for j, letter in enumerate(s): # j: fast pointer\n if letter == 'a': a += 1 # increment a/b/c accordingly\n elif letter == 'b': b += 1\n else: c += 1\n while a > 0 and b > 0 and c > 0: # if all of a/b/c are contained, move slow pointer\n ans += n-j # count possible substr, if a substr ends at j, then there are n-j substrs to the right that are containing all a/b/c\n if s[i] == 'a': a -= 1 # decrement counter accordingly\n elif s[i] == 'b': b -= 1\n else: c -= 1\n i += 1 # move slow pointer\n return ans ", "_____no_output_____" ] ] ]

[ "raw", "code" ]

[ [ "raw" ], [ "code", "code", "code", "code", "code", "code" ] ]

[ "ADSL" ]

[ "ADSL" ]

[ "ADSL" ]

[ [ [ "%matplotlib inline\nfrom matplotlib import style\nstyle.use('fivethirtyeight')\nimport matplotlib.pyplot as plt\nimport seaborn as sns", "_____no_output_____" ], [ "import numpy as np\nimport pandas as pd", "_____no_output_____" ], [ "import datetime as dt", "_____no_output_____" ] ], [ [ "# Reflect Tables into SQLAlchemy ORM", "_____no_output_____" ] ], [ [ "# Python SQL toolkit and Object Relational Mapper\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func", "_____no_output_____" ], [ "engine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")", "_____no_output_____" ], [ "# reflect an existing database into a new model\nBase = automap_base()\n# reflect the tables\nBase.prepare(engine, reflect=True)", "_____no_output_____" ], [ "# We can view all of the classes that automap found\nBase.classes.keys()", "_____no_output_____" ], [ "# Save references to each table\nMeasurement = Base.classes.measurement\nStation = Base.classes.station", "_____no_output_____" ], [ "# Create our session (link) from Python to the DB\nsession = Session(engine)", "_____no_output_____" ] ], [ [ "# Exploratory Climate Analysis", "_____no_output_____" ] ], [ [ "# Design a query to retrieve the last 12 months of precipitation data and plot the results\nlast_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()[0]\nlast_date\n# Calculate the date 1 year ago from the last data point in the database\nlast_year = dt.datetime.strptime(last_date, \"%Y-%m-%d\") - dt.timedelta(days=365)\nlast_year\n", "_____no_output_____" ], [ "# Perform a query to retrieve the data and precipitation scores\nquery = session.query(Measurement.date,Measurement.prcp).filter(Measurement.date>=last_year).all()\n \nquery", "_____no_output_____" ], [ "# Save the query results as a Pandas DataFrame and set the index to the date column\nprec_df = pd.DataFrame(query,columns=['date','precipitation'])\nprec_df\nprec_df['date']= pd.to_datetime(prec_df['date']) #format= '%y-%m-%d')\n# set the index\nprec_df.set_index(\"date\",inplace=True)\n# Sort the dataframe by date\nprec_df = prec_df.sort_values(by= \"date\",ascending=True)\nprec_df.head(20)", "_____no_output_____" ], [ "# Use Pandas Plotting with Matplotlib to plot the data\nprec_df.plot(title = \"Precipitation (12 months)\", color ='blue', alpha = 0.8 , figsize =(10,6))\nplt.legend(loc='upper center',prop={'size':10})\nplt.savefig(\"Images/Precipitation.png\")\nplt.show()", "_____no_output_____" ] ], [ [ "", "_____no_output_____" ] ], [ [ "# Use Pandas to calcualte the summary statistics for the precipitation data\nprec_df.describe()", "_____no_output_____" ] ], [ [ "", "_____no_output_____" ] ], [ [ "# Design a query to show how many stations are available in this dataset?\nstation_count = session.query(Station.station).count()\nstation_count", "_____no_output_____" ], [ "# What are the most active stations? (i.e. what stations have the most rows)?\n# List the stations and the counts in descending order.\nactive_station = session.query(Measurement.station, func.count(Measurement.station)).\\\n group_by(Measurement.station).\\\n order_by(func.count(Measurement.station).desc()).all()\nactive_station", "_____no_output_____" ], [ "# Using the station id from the previous query, calculate the lowest temperature recorded, \n# highest temperature recorded, and average temperature of the most active station?\ntemp = [Measurement.station,\n func.min(Measurement.tobs),\n func.max(Measurement.tobs),\n func.avg(Measurement.tobs),]\nall_temp = session.query(*temp).group_by(Measurement.station).\\\n order_by(func.count(Measurement.station).desc()).all()\nall_temp", "_____no_output_____" ], [ "# Choose the station with the highest number of temperature observations.\nhighest_temp = session.query(Measurement.station,func.count(Measurement.tobs)).\\\n group_by(Measurement.tobs).\\\n order_by(func.count(Measurement.tobs).desc()).all()\nhighest_temp", "_____no_output_____" ], [ "# Query the last 12 months of temperature observation data for this station and plot the results as a histogram\nlast_year_data = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date>=last_year).all()\nlast_year_data\n\nlast_year_data_df = pd.DataFrame(last_year_data)\nlast_year_data_df\nlast_year_data_df.hist()\nplt.title(\"Temperature over 12 months\")\nplt.ylabel(\"Frequency\")\nplt.legend(\"tobs\",loc='upper right')\nplt.savefig(\"Images/Temperature over 12 months\")\nplt.tight_layout\nplt.show()", "_____no_output_____" ] ], [ [ "", "_____no_output_____" ] ], [ [ "# This function called `calc_temps` will accept start date and end date in the format '%Y-%m-%d' \n# and return the minimum, average, and maximum temperatures for that range of dates\ndef calc_temps(start_date, end_date):\n \"\"\"TMIN, TAVG, and TMAX for a list of dates.\n \n Args:\n start_date (string): A date string in the format %Y-%m-%d\n end_date (string): A date string in the format %Y-%m-%d\n \n Returns:\n TMIN, TAVE, and TMAX\n \"\"\"\n \n return session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\\\n filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all()\n\n# function usage example\nprint(calc_temps('2012-02-28', '2012-03-05'))", "[(62.0, 69.57142857142857, 74.0)]\n" ], [ "# Use your previous function `calc_temps` to calculate the tmin, tavg, and tmax \n\n# for your trip using the previous year's data for those same dates.\nTemp = calc_temps('2016-08-23','2017-08-23')\nTemp", "_____no_output_____" ], [ "# Plot the results from your previous query as a bar chart. \n# Use \"Trip Avg Temp\" as your Title\n# Use the average temperature for the y value\n# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)\nplt.figure(figsize=(3,6))\nsns.barplot(data=Temp,color = \"lightsalmon\")\nplt.ylabel('Temp(F)')\nplt.title(\"Trip Avg Temp\")\nplt.tight_layout\nplt.savefig(\"Images/Trip Avg Temp.png\")\nplt.show()", "_____no_output_____" ], [ "def precipitation(start_date, end_date):\n select_column = [Measurement.station, \n Station.name, \n Station.latitude, \n Station.longitude, \n Station.elevation, \n Measurement.prcp]\n\n return session.query(*select_column).\\\n filter(Measurement.station == Station.station).filter(Measurement.date >= start_date).\\\nfilter(Measurement.date <= end_date).group_by(Measurement.station).order_by(Measurement.prcp.desc()).all()\nprint(precipitation('2016-02-26','2016-03-02'))", "[('USC00513117', 'KANEOHE 838.1, HI US', 21.4234, -157.8015, 14.6, 0.38), ('USC00514830', 'KUALOA RANCH HEADQUARTERS 886.9, HI US', 21.5213, -157.8374, 7.0, 0.36), ('USC00519281', 'WAIHEE 837.5, HI US', 21.45167, -157.84888999999998, 32.9, 0.3), ('USC00516128', 'MANOA LYON ARBO 785.2, HI US', 21.3331, -157.8025, 152.4, 0.04), ('USC00519523', 'WAIMANALO EXPERIMENTAL FARM, HI US', 21.33556, -157.71139, 19.5, 0.0), ('USC00519397', 'WAIKIKI 717.2, HI US', 21.2716, -157.8168, 3.0, 0.0), ('USC00517948', 'PEARL CITY, HI US', 21.3934, -157.9751, 11.9, None)]\n" ] ], [ [ "## Optional Challenge Assignment", "_____no_output_____" ] ], [ [ "# Create a query that will calculate the daily normals \n# (i.e. the averages for tmin, tmax, and tavg for all historic data matching a specific month and day)\n\ndef daily_normals(date):\n \"\"\"Daily Normals.\n \n Args:\n date (str): A date string in the format '%m-%d'\n \n Returns:\n A list of tuples containing the daily normals, tmin, tavg, and tmax\n \n \"\"\"\n \n sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]\n return session.query(*sel).filter(func.strftime(\"%m-%d\", Measurement.date) == date).all()\n \ndaily_normals(\"01-01\")", "_____no_output_____" ], [ "# calculate the daily normals for your trip\n# push each tuple of calculations into a list called `normals`\n\n# Set the start and end date of the trip\n\n# Use the start and end date to create a range of dates\n\n# Stip off the year and save a list of %m-%d strings\n\n# Loop through the list of %m-%d strings and calculate the normals for each date\n", "_____no_output_____" ], [ "# Load the previous query results into a Pandas DataFrame and add the `trip_dates` range as the `date` index\n", "_____no_output_____" ], [ "# Plot the daily normals as an area plot with `stacked=False`\n", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<font size=\"+5\">#02 | Decision Tree. A Supervised Classification Model</font>", "_____no_output_____" ], [ "- Subscribe to my [Blog ↗](https://blog.pythonassembly.com/)\n- Let's keep in touch on [LinkedIn ↗](www.linkedin.com/in/jsulopz) 😄", "_____no_output_____" ], [ "# Discipline to Search Solutions in Google", "_____no_output_____" ], [ "> Apply the following steps when **looking for solutions in Google**:\n>\n> 1. **Necesity**: How to load an Excel in Python?\n> 2. **Search in Google**: by keywords\n> - `load excel python`\n> - ~~how to load excel in python~~\n> 3. **Solution**: What's the `function()` that loads an Excel in Python?\n> - A Function to Programming is what the Atom to Phisics.\n> - Every time you want to do something in programming\n> - **You will need a `function()`** to make it\n> - Theferore, you must **detect parenthesis `()`**\n> - Out of all the words that you see in a website\n> - Because they indicate the presence of a `function()`.", "_____no_output_____" ], [ "# Load the Data", "_____no_output_____" ], [ "> Load the Titanic dataset with the below commands\n> - This dataset **people** (rows) aboard the Titanic\n> - And their **sociological characteristics** (columns)\n> - The aim of this dataset is to predict the probability to `survive`\n> - Based on the social demographic characteristics.", "_____no_output_____" ] ], [ [ "import seaborn as sns\n\ndf = sns.load_dataset(name='titanic').iloc[:, :4]", "_____no_output_____" ], [ "df.head()", "_____no_output_____" ] ], [ [ "# `DecisionTreeClassifier()` Model in Python", "_____no_output_____" ], [ "## Build the Model", "_____no_output_____" ], [ "> 1. **Necesity**: Build Model\n> 2. **Google**: How do you search for the solution?\n> 3. **Solution**: Find the `function()` that makes it happen", "_____no_output_____" ], [ "## Code Thinking\n\n> Which function computes the Model?\n> - `fit()`\n>\n> How could can you **import the function in Python**?", "_____no_output_____" ] ], [ [ "fit()", "_____no_output_____" ], [ "model.fit()", "_____no_output_____" ] ], [ [ "`model = ?`", "_____no_output_____" ] ], [ [ "from sklearn.tree import DecisionTreeClassifier", "_____no_output_____" ], [ "model = DecisionTreeClassifier()", "_____no_output_____" ], [ "model.__dict__", "_____no_output_____" ], [ "model.fit()", "_____no_output_____" ] ], [ [ "### Separate Variables for the Model\n\n> Regarding their role:\n> 1. **Target Variable `y`**\n>\n> - [ ] What would you like **to predict**?\n>\n> 2. **Explanatory Variable `X`**\n>\n> - [ ] Which variable will you use **to explain** the target?", "_____no_output_____" ] ], [ [ "explanatory = df.drop(columns='survived')\ntarget = df.survived", "_____no_output_____" ] ], [ [ "### Finally `fit()` the Model", "_____no_output_____" ] ], [ [ "model.__dict__", "_____no_output_____" ], [ "model.fit(X=explanatory, y=target)", "_____no_output_____" ], [ "import pandas as pd", "_____no_output_____" ], [ "pd.get_dummies(data=df)", "_____no_output_____" ], [ "df = pd.get_dummies(data=df, drop_first=True)", "_____no_output_____" ], [ "df", "_____no_output_____" ], [ "explanatory = df.drop(columns='survived')", "_____no_output_____" ], [ "target = df.survived", "_____no_output_____" ], [ "model.fit(X=explanatory, y=target)", "_____no_output_____" ], [ "df", "_____no_output_____" ], [ "df.isna().sum()", "_____no_output_____" ], [ "df.fillna('hola')", "_____no_output_____" ], [ "df.dropna(inplace=True) # df = df.dropna()", "_____no_output_____" ], [ "df", "_____no_output_____" ], [ "df.dropna(inplace=True) # df = df.dropna()", "_____no_output_____" ], [ "df", "_____no_output_____" ], [ "explanatory = df.drop(columns='survived')\ntarget = df.survived", "_____no_output_____" ], [ "model.fit(X=explanatory, y=target)", "_____no_output_____" ] ], [ [ "## Calculate a Prediction with the Model", "_____no_output_____" ], [ "> - `model.predict_proba()`", "_____no_output_____" ] ], [ [ "model.predict_proba()", "_____no_output_____" ] ], [ [ "## Model Visualization", "_____no_output_____" ], [ "> - `tree.plot_tree()`", "_____no_output_____" ], [ "## Model Interpretation", "_____no_output_____" ], [ "> Why `sex` is the most important column? What has to do with **EDA** (Exploratory Data Analysis)?", "_____no_output_____" ] ], [ [ "%%HTML\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/7VeUPuFGJHk\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>", "_____no_output_____" ] ], [ [ "# Prediction vs Reality", "_____no_output_____" ], [ "> How good is our model?", "_____no_output_____" ] ], [ [ "dfsel = df[['survived']].copy()", "_____no_output_____" ], [ "dfsel['pred'] = model.predict(X=explanatory)", "_____no_output_____" ], [ "dfsel.sample(10)", "_____no_output_____" ], [ "comp = dfsel.survived == dfsel.pred", "_____no_output_____" ], [ "comp.sum()", "_____no_output_____" ], [ "comp.sum()/714", "_____no_output_____" ], [ "comp.mean()", "_____no_output_____" ] ], [ [ "## Precision", "_____no_output_____" ], [ "> - `model.score()`", "_____no_output_____" ] ], [ [ "model.score(X=explanatory, y=target)", "_____no_output_____" ] ], [ [ "## Confusion Matrix", "_____no_output_____" ], [ "> 1. **Sensitivity** (correct prediction on positive value, $y=1$)\n> 2. **Specificity** (correct prediction on negative value $y=0$).", "_____no_output_____" ], [ "## ROC Curve", "_____no_output_____" ], [ "> A way to summarise all the metrics (score, sensitivity & specificity)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Parse Java Methods\n----\n(C) Maxim Gansert, 2020, Mindscan Engineering", "_____no_output_____" ] ], [ [ "import sys\nsys.path.insert(0,'../src')\n\nimport os\nimport datetime", "_____no_output_____" ], [ "from com.github.c2nes.javalang import tokenizer, parser, ast\nfrom de.mindscan.fluentgenesis.dataprocessing.method_extractor import tokenize_file, extract_allmethods_from_compilation_unit\n", "_____no_output_____" ], [ "from de.mindscan.fluentgenesis.bpe.bpe_model import BPEModel\nfrom de.mindscan.fluentgenesis.bpe.bpe_encoder_decoder import SimpleBPEEncoder\nfrom de.mindscan.fluentgenesis.dataprocessing.method_dataset import MethodDataset", "_____no_output_____" ], [ "def split_methodbody_into_multiple_lines(method_body):\n result = []\n current_line_number = -1\n current_line_tokens = []\n for token in method_body:\n token_line = token.position[0]\n \n if token_line != current_line_number:\n current_line_number = token_line\n if len(current_line_tokens) != 0:\n result.append(current_line_tokens)\n current_line_tokens = []\n current_line_tokens.append(token.value)\n pass\n if len(current_line_tokens) !=0:\n result.append(current_line_tokens)\n pass\n return result\n", "_____no_output_____" ], [ "def process_source_file(dataset_directory, source_file_path, encoder, dataset):\n # derive the full source file path\n full_source_file_path = os.path.join( dataset_directory, source_file_path);\n \n # Work on the source file\n java_tokenlist = tokenize_file(full_source_file_path)\n parsed_compilation_unit = parser.parse(java_tokenlist)\n \n # collect file names, line numbers, method names, class names etc \n all_methods_per_source = extract_allmethods_from_compilation_unit(parsed_compilation_unit, java_tokenlist)\n \n for single_method in all_methods_per_source:\n try:\n method_name = single_method['method_name']\n method_class_name = single_method['class_name']\n method_body = single_method['method_body']\n \n multi_line_body = split_methodbody_into_multiple_lines(method_body)\n one_line = [item for sublist in multi_line_body for item in sublist]\n print(one_line)\n \n # encode body code and methodnames using the bpe-vocabulary\n bpe_encoded_methodname = encoder.encode( [ method_name ] )\n bpe_encoded_methodbody_ml = encoder.encode_multi_line( multi_line_body )\n \n # do some calculations on the tokens and on the java code, so selection of smaller datasets is possible\n bpe_encoded_method_name_length = len(bpe_encoded_methodname)\n bpe_encoded_method_body_length = sum([len(line) for line in bpe_encoded_methodbody_ml])\n \n # save this into dataset\n method_data = { \n \"source_file_path\": source_file_path,\n \"method_class_name\": method_class_name,\n \"method_name\": method_name,\n \"encoded_method_name_length\": bpe_encoded_method_name_length,\n \"encoded_method_name\": bpe_encoded_methodname,\n \"encoded_method_body_length\": bpe_encoded_method_body_length,\n \"encoded_method_body\": bpe_encoded_methodbody_ml,\n \"method_body\": method_body \n }\n dataset.add_method_data( method_data )\n except:\n # ignore problematic method\n pass\n", "_____no_output_____" ], [ "\nmodel = BPEModel(\"16K-full\", \"../src/de/mindscan/fluentgenesis/bpe/\")\nmodel.load_hparams()\n\ndataset_directory = 'D:\\\\Downloads\\\\Big-Code-excerpt\\\\'\n\nmodel_vocabulary = model.load_tokens()\nmodel_bpe_data = model.load_bpe_pairs()\n\nencoder = SimpleBPEEncoder(model_vocabulary, model_bpe_data)\n\nmethod_dataset = MethodDataset(dataset_name='parseMethodPythonNotebook1.jsonl')\nmethod_dataset.prepareNewDataset(dataset_directory)\n \nprocess_source_file(dataset_directory,'wordhash/WordMap.java' ,encoder, method_dataset )\n\nmethod_dataset.finish()\n", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Simple Perceptron", "_____no_output_____" ] ], [ [ "import tensorflow as tf\nimport pandas as pd\nimport numpy as np", "_____no_output_____" ], [ "%load_ext tensorboard", "_____no_output_____" ], [ "# Import the dataset\n\n(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()", "_____no_output_____" ], [ "# For now, we'll focus on digit images of fives\n\nis_five_train = y_train == 5\nis_five_test = y_test == 5\n\nlabels = [\"Not five\", \"five\"]", "_____no_output_____" ], [ "# Specifying the input shape for the perceptron (here, img_width x img_height)\nimg_width = x_train.shape[1]\nimg_height = x_train.shape[2]", "_____no_output_____" ] ], [ [ "# Creating a model\n\n**Step 1: Design your model**\n\nModel 1:\n\n```\n(28, 28) ---> Flatten (784) ---> Dense (1)\n\nloss : mse (default attr.)\noptimizer : adam (default attr.)\n```", "_____no_output_____" ] ], [ [ "# Imports required for building model\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Flatten, Dense\n\n# Create a simple perceptron model\nmodel = Sequential(name=\"model_1\")\nmodel.add(Flatten(input_shape=(img_width, img_height)))\nmodel.add(Dense(1))\n\n# Shows how your model was built\nmodel.summary()", "Model: \"model_1\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nflatten (Flatten) (None, 784) 0 \n_________________________________________________________________\ndense (Dense) (None, 1) 785 \n=================================================================\nTotal params: 785\nTrainable params: 785\nNon-trainable params: 0\n_________________________________________________________________\n" ] ], [ [ "**Step 2: Specify your model parameters**", "_____no_output_____" ] ], [ [ "model_params = {\n \"epochs\": 10,\n \"batch_size\": 32,\n \"loss\": \"mse\",\n \"optimizer\": \"adam\",\n}", "_____no_output_____" ] ], [ [ "**Step 3: Compile the model with the parameters**", "_____no_output_____" ] ], [ [ "model.compile(loss=model_params[\"loss\"],\n optimizer=model_params[\"optimizer\"],\n metrics=['accuracy'])", "_____no_output_____" ] ], [ [ "**Step 4: Fit / Train the model**\n\n***(Optional)*** Specify tensorboard callbacks. \nRun tensorboard command first (after below cell) and then run the below cell. Then click on refersh icon on top right of tensorboard after every epoch to live track the accuracy and loss", "_____no_output_____" ] ], [ [ "from tensorflow.keras.callbacks import TensorBoard\n\nlog_dir = \"logs/fit/\" + model.name\ntb_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n\n\nmodel.fit(x_train, is_five_train, \n epochs=model_params[\"epochs\"], \n batch_size=model_params[\"batch_size\"],\n validation_data=(x_test, is_five_test), \n callbacks=[tb_callback])\n\n", "Epoch 1/10\n1875/1875 [==============================] - 5s 2ms/step - loss: 846.0928 - accuracy: 0.5121 - val_loss: 18.9043 - val_accuracy: 0.5069\nEpoch 2/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 12.5497 - accuracy: 0.5668 - val_loss: 1.0088 - val_accuracy: 0.6655\nEpoch 3/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 2.6123 - accuracy: 0.6344 - val_loss: 8.5548 - val_accuracy: 0.7142\nEpoch 4/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 4.5394 - accuracy: 0.6092 - val_loss: 1.2580 - val_accuracy: 0.5962\nEpoch 5/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 8.3724 - accuracy: 0.5965 - val_loss: 11.7147 - val_accuracy: 0.2687\nEpoch 6/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 3.3527 - accuracy: 0.6406 - val_loss: 2.6416 - val_accuracy: 0.5752\nEpoch 7/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.6351 - accuracy: 0.5947 - val_loss: 3.1100 - val_accuracy: 0.8704\nEpoch 8/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.7725 - accuracy: 0.5806 - val_loss: 14.9855 - val_accuracy: 0.8948\nEpoch 9/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 3.5270 - accuracy: 0.6323 - val_loss: 1.4845 - val_accuracy: 0.4890\nEpoch 10/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.1996 - accuracy: 0.5977 - val_loss: 3.4266 - val_accuracy: 0.7215\n" ], [ "%tensorboard --logdir logs/fit", "_____no_output_____" ] ], [ [ "**Step 5: Evaluate the model**\n\nPredict the test dataset and analyze them with corresponding test labels to check whether they make sense.", "_____no_output_____" ] ], [ [ "# Predict first 10 test samples\nn_samples = 10000\npredictions = model.predict(x_test[:n_samples, :, :])\ntrue_labels = y_test[:n_samples]\nis_five = is_five_test[:n_samples]\n\npd.DataFrame(data={\n \"predicted_label\": predictions.flatten(),\n \"true_label\": true_labels,\n \"is_five\": is_five\n})", "_____no_output_____" ] ], [ [ "**Step 6: Debug, rebuild, retrain and re-evaluate your model**", "_____no_output_____" ] ], [ [ "# Let's try more epochs, say 100\nmodel_params[\"epochs\"] = 100", "_____no_output_____" ], [ "# create a new model with same architecture\nnew_model = tf.keras.models.clone_model(model)\nnew_model._name = \"model_2\"\nnew_model.summary()", "Model: \"model_2\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nflatten (Flatten) (None, 784) 0 \n_________________________________________________________________\ndense (Dense) (None, 1) 785 \n=================================================================\nTotal params: 785\nTrainable params: 785\nNon-trainable params: 0\n_________________________________________________________________\n" ], [ "new_model.compile(loss=model_params[\"loss\"],\n optimizer=model_params[\"optimizer\"],\n metrics=['accuracy'])\n\nlog_dir = \"logs/fit/\" + new_model.name\ntb_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n\n\nnew_model.fit(x_train, is_five_train, \n epochs=model_params[\"epochs\"], \n batch_size=model_params[\"batch_size\"],\n validation_data=(x_test, is_five_test), \n callbacks=[tb_callback])\n", "Epoch 1/100\n1875/1875 [==============================] - 5s 2ms/step - loss: 1075.7101 - accuracy: 0.5077 - val_loss: 22.3630 - val_accuracy: 0.5880\nEpoch 2/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 10.1214 - accuracy: 0.5674 - val_loss: 0.8949 - val_accuracy: 0.6680\nEpoch 3/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 3.1649 - accuracy: 0.6398 - val_loss: 6.1561 - val_accuracy: 0.2850\nEpoch 4/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 9.0277 - accuracy: 0.5779 - val_loss: 7.5212 - val_accuracy: 0.5772\nEpoch 5/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 4.9109 - accuracy: 0.6067 - val_loss: 7.3839 - val_accuracy: 0.8926\nEpoch 6/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.4243 - accuracy: 0.5946 - val_loss: 1.3850 - val_accuracy: 0.5076\nEpoch 7/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 5.9954 - accuracy: 0.5996 - val_loss: 7.5086 - val_accuracy: 0.4802\nEpoch 8/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.3299 - accuracy: 0.5772 - val_loss: 0.8847 - val_accuracy: 0.8289\nEpoch 9/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 5.3561 - accuracy: 0.5939 - val_loss: 4.4207 - val_accuracy: 0.7306\nEpoch 10/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.5013 - accuracy: 0.5843 - val_loss: 1.6028 - val_accuracy: 0.7339\nEpoch 11/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 5.3170 - accuracy: 0.6171 - val_loss: 1.6479 - val_accuracy: 0.6641\nEpoch 12/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.8225 - accuracy: 0.5762 - val_loss: 8.8483 - val_accuracy: 0.7039\nEpoch 13/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.8521 - accuracy: 0.5827 - val_loss: 3.4935 - val_accuracy: 0.8158\nEpoch 14/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.8270 - accuracy: 0.5957 - val_loss: 6.8380 - val_accuracy: 0.3335\nEpoch 15/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.4007 - accuracy: 0.5757 - val_loss: 10.9967 - val_accuracy: 0.3341\nEpoch 16/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.1901 - accuracy: 0.5791 - val_loss: 3.5838 - val_accuracy: 0.7660\nEpoch 17/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.1940 - accuracy: 0.5978 - val_loss: 2.6676 - val_accuracy: 0.7571\nEpoch 18/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.4512 - accuracy: 0.5899 - val_loss: 4.1473 - val_accuracy: 0.5156\nEpoch 19/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.9355 - accuracy: 0.5803 - val_loss: 1.1060 - val_accuracy: 0.5152\nEpoch 20/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.6911 - accuracy: 0.6258 - val_loss: 3.8794 - val_accuracy: 0.5651\nEpoch 21/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.9757 - accuracy: 0.5804 - val_loss: 6.8071 - val_accuracy: 0.3804\nEpoch 22/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.1952 - accuracy: 0.5839 - val_loss: 1.3648 - val_accuracy: 0.6957\nEpoch 23/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 11.3287 - accuracy: 0.5919 - val_loss: 11.6835 - val_accuracy: 0.2638\nEpoch 24/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.6961 - accuracy: 0.5989 - val_loss: 3.5491 - val_accuracy: 0.7919\nEpoch 25/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 8.1636 - accuracy: 0.5791 - val_loss: 2.4758 - val_accuracy: 0.4574\nEpoch 26/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 5.3816 - accuracy: 0.5808 - val_loss: 4.7198 - val_accuracy: 0.5978\nEpoch 27/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.2695 - accuracy: 0.5779 - val_loss: 2.3531 - val_accuracy: 0.7773\nEpoch 28/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 10.1839 - accuracy: 0.5726 - val_loss: 2.1647 - val_accuracy: 0.8572\nEpoch 29/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.2325 - accuracy: 0.5874 - val_loss: 15.6673 - val_accuracy: 0.2325\nEpoch 30/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.2225 - accuracy: 0.5795 - val_loss: 4.7152 - val_accuracy: 0.2358\nEpoch 31/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 4.9727 - accuracy: 0.6087 - val_loss: 1.7701 - val_accuracy: 0.5184\nEpoch 32/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.2206 - accuracy: 0.5982 - val_loss: 2.7474 - val_accuracy: 0.2190\nEpoch 33/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 4.9630 - accuracy: 0.6188 - val_loss: 1.1045 - val_accuracy: 0.4047\nEpoch 34/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.0232 - accuracy: 0.5965 - val_loss: 1.9290 - val_accuracy: 0.3890\nEpoch 35/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 8.1054 - accuracy: 0.5820 - val_loss: 4.1276 - val_accuracy: 0.6441\nEpoch 36/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 4.8139 - accuracy: 0.5983 - val_loss: 15.3575 - val_accuracy: 0.5655\nEpoch 37/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.6663 - accuracy: 0.6097 - val_loss: 2.9330 - val_accuracy: 0.8461\nEpoch 38/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.3861 - accuracy: 0.5926 - val_loss: 0.8530 - val_accuracy: 0.8511\nEpoch 39/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 4.1475 - accuracy: 0.6318 - val_loss: 15.1410 - val_accuracy: 0.2268\nEpoch 40/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.8630 - accuracy: 0.5900 - val_loss: 0.9914 - val_accuracy: 0.8374\nEpoch 41/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.4843 - accuracy: 0.5995 - val_loss: 3.2436 - val_accuracy: 0.5349\nEpoch 42/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 3.1219 - accuracy: 0.6244 - val_loss: 4.3599 - val_accuracy: 0.2940\nEpoch 43/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 5.7674 - accuracy: 0.5805 - val_loss: 9.4905 - val_accuracy: 0.7620\nEpoch 44/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.2947 - accuracy: 0.5976 - val_loss: 10.6703 - val_accuracy: 0.3834\nEpoch 45/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 11.1182 - accuracy: 0.6002 - val_loss: 3.4123 - val_accuracy: 0.7699\nEpoch 46/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.0375 - accuracy: 0.5855 - val_loss: 7.9611 - val_accuracy: 0.6393\nEpoch 47/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 4.3514 - accuracy: 0.6082 - val_loss: 1.4015 - val_accuracy: 0.5497\nEpoch 48/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 5.3363 - accuracy: 0.6073 - val_loss: 8.3178 - val_accuracy: 0.2459\nEpoch 49/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 3.9161 - accuracy: 0.6015 - val_loss: 3.8096 - val_accuracy: 0.8481\nEpoch 50/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.7622 - accuracy: 0.5849 - val_loss: 6.1672 - val_accuracy: 0.8853\nEpoch 51/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.1732 - accuracy: 0.5929 - val_loss: 1.4365 - val_accuracy: 0.9011\nEpoch 52/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 6.2126 - accuracy: 0.6012 - val_loss: 6.4458 - val_accuracy: 0.4286\nEpoch 53/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.3233 - accuracy: 0.5842 - val_loss: 1.0799 - val_accuracy: 0.8157\nEpoch 54/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 5.5092 - accuracy: 0.5994 - val_loss: 16.7491 - val_accuracy: 0.8223\nEpoch 55/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.6637 - accuracy: 0.5868 - val_loss: 4.3699 - val_accuracy: 0.6262\nEpoch 56/100\n1875/1875 [==============================] - 4s 2ms/step - loss: 7.4689 - accuracy: 0.5770 - val_loss: 33.9254 - val_accuracy: 0.8629\nEpoch 57/100\n" ], [ "%tensorboard --logdir logs/fit", "_____no_output_____" ], [ "n_samples = 10000\npredictions = new_model.predict(x_test[:n_samples, :, :])\ntrue_labels = y_test[:n_samples]\nis_five = is_five_test[:n_samples]\n\npd.DataFrame(data={\n \"predicted_label\": predictions.flatten(),\n \"true_label\": true_labels,\n \"is_five\": is_five\n})", "_____no_output_____" ] ], [ [ "### We haven't tried an activation function. That's why our model is broken. Let's add a sigmoid activation to our perceptron", "_____no_output_____" ] ], [ [ "model_params[\"epochs\"] = 10\nmodel_params", "_____no_output_____" ], [ "new_model = Sequential(name=\"model_3\")\nnew_model.add(Flatten(input_shape=(img_width, img_height)))\nnew_model.add(Dense(1, activation='relu'))\n\nnew_model.summary()", "Model: \"model_3\"\n_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\nflatten_2 (Flatten) (None, 784) 0 \n_________________________________________________________________\ndense_2 (Dense) (None, 1) 785 \n=================================================================\nTotal params: 785\nTrainable params: 785\nNon-trainable params: 0\n_________________________________________________________________\n" ], [ "new_model.compile(loss=model_params[\"loss\"],\n optimizer=model_params[\"optimizer\"],\n metrics=['accuracy'])\n\nlog_dir = \"logs/fit/\" + new_model.name\ntb_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n\n\nnew_model.fit(x_train, is_five_train, \n epochs=model_params[\"epochs\"], \n batch_size=model_params[\"batch_size\"],\n validation_data=(x_test, is_five_test), \n callbacks=[tb_callback])", "Epoch 1/10\n1875/1875 [==============================] - 5s 3ms/step - loss: 1.1484 - accuracy: 0.9082 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 2/10\n1875/1875 [==============================] - 5s 2ms/step - loss: 0.0908 - accuracy: 0.9092 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 3/10\n1875/1875 [==============================] - 5s 2ms/step - loss: 0.0887 - accuracy: 0.9113 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 4/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.0900 - accuracy: 0.9100 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 5/10\n1875/1875 [==============================] - 5s 2ms/step - loss: 0.0897 - accuracy: 0.9103 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 6/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.0904 - accuracy: 0.9096 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 7/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.0901 - accuracy: 0.9099 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 8/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.0925 - accuracy: 0.9075 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 9/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.0907 - accuracy: 0.9093 - val_loss: 0.0892 - val_accuracy: 0.9108\nEpoch 10/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.0897 - accuracy: 0.9103 - val_loss: 0.0892 - val_accuracy: 0.9108\n" ], [ "%tensorboard --logdir logs/fit", "_____no_output_____" ], [ "n_samples = 10000\npredictions = new_model.predict(x_test[:n_samples, :, :])\ntrue_labels = y_test[:n_samples]\nis_five = is_five_test[:n_samples]\n\npd.DataFrame(data={\n \"predicted_label\": predictions.flatten(),\n \"true_label\": true_labels,\n \"is_five\": is_five\n})", "_____no_output_____" ] ] ]

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[ [ [ "from bs4 import BeautifulSoup\nimport requests\nimport pymongo\nfrom splinter import Browser\nimport pandas as pd\nimport numpy as np\nimport re\n\n###twitter\nimport json\nimport tweepy \nimport sys\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nimport os\nGOOGLE_CHROME_BIN=os.environ.get(\"GOOGLE_CHROME_BIN\")\nCHROMEDRIVER_PATH=os.environ.get(\"CHROMEDRIVER_PATH\")", "_____no_output_____" ], [ "GOOGLE_CHROME_BIN", "_____no_output_____" ], [ "CHROMEDRIVER_PATH", "_____no_output_____" ], [ "chrome_options = Options()\n# chrome_options.binary_location = GOOGLE_CHROME_BIN\n# chrome_options.add_argument('--headless')\n# chrome_options.add_argument('--disable-gpu')\n# chrome_options.add_argument('--no-sandbox')\n# chrome_options.add_argument('--disable-dev-shm-usage')\ndriver = webdriver.Chrome(chrome_options=chrome_options)\n# executable_path=CHROMEDRIVER_PATH,", "/Users/jyj/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel/__main__.py:7: DeprecationWarning: use options instead of chrome_options\n" ], [ "driver = webdriver.Chrome(chrome_options=chrome_options)\ndriver.get(\"https://www.wenzhao.ca/sign-in/\")\n\n# driver.get (\"https://www.facebook.com\")\n \ndriver.find_element_by_id(\"rcp_user_login\").send_keys(\"[email protected]\")\ndriver.find_element_by_id(\"rcp_user_pass\").send_keys(\"wenZhao#edc@wsx\")\ndriver.find_element_by_id(\"rcp_login_submit\").click()", "/Users/jyj/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel/__main__.py:1: DeprecationWarning: use options instead of chrome_options\n if __name__ == '__main__':\n" ] ] ]

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[ [ [ "# Transfer Learning\n\nIn this notebook, you'll learn how to use pre-trained networks to solved challenging problems in computer vision. Specifically, you'll use networks trained on [ImageNet](http://www.image-net.org/) [available from torchvision](http://pytorch.org/docs/0.3.0/torchvision/models.html). \n\nImageNet is a massive dataset with over 1 million labeled images in 1000 categories. It's used to train deep neural networks using an architecture called convolutional layers. I'm not going to get into the details of convolutional networks here, but if you want to learn more about them, please [watch this](https://www.youtube.com/watch?v=2-Ol7ZB0MmU).\n\nOnce trained, these models work astonishingly well as feature detectors for images they weren't trained on. Using a pre-trained network on images not in the training set is called transfer learning. Here we'll use transfer learning to train a network that can classify our cat and dog photos with near perfect accuracy.\n\nWith `torchvision.models` you can download these pre-trained networks and use them in your applications. We'll include `models` in our imports now.", "_____no_output_____" ] ], [ [ "%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\n\nimport matplotlib.pyplot as plt\n\nimport torch\nfrom torch import nn\nfrom torch import optim\nimport torch.nn.functional as F\nfrom torchvision import datasets, transforms, models", "_____no_output_____" ] ], [ [ "Most of the pretrained models require the input to be 224x224 images. Also, we'll need to match the normalization used when the models were trained. Each color channel was normalized separately, the means are `[0.485, 0.456, 0.406]` and the standard deviations are `[0.229, 0.224, 0.225]`.", "_____no_output_____" ] ], [ [ "data_dir = 'Cat_Dog_data/Cat_Dog_data'\n\n# TODO: Define transforms for the training data and testing data\ntrain_transforms = transforms.Compose([transforms.RandomRotation(30),\n transforms.RandomResizedCrop(224),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],\n [.229, 0.224, 0.225])])\n\ntest_transforms = transforms.Compose([transforms.Resize(255),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406],\n [.229, 0.224, 0.225])]) \n\n# Pass transforms in here, then run the next cell to see how the transforms look\ntrain_data = datasets.ImageFolder(data_dir + '/train', transform=train_transforms)\ntest_data = datasets.ImageFolder(data_dir + '/test', transform=test_transforms)\n\ntrainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\ntestloader = torch.utils.data.DataLoader(test_data, batch_size=64)", "_____no_output_____" ] ], [ [ "We can load in a model such as [DenseNet](http://pytorch.org/docs/0.3.0/torchvision/models.html#id5). Let's print out the model architecture so we can see what's going on.", "_____no_output_____" ] ], [ [ "model = models.densenet121(pretrained=True)\nmodel", "/home/user/miniconda/envs/py36/lib/python3.6/site-packages/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n nn.init.kaiming_normal(m.weight.data)\n" ] ], [ [ "This model is built out of two main parts, the features and the classifier. The features part is a stack of convolutional layers and overall works as a feature detector that can be fed into a classifier. The classifier part is a single fully-connected layer `(classifier): Linear(in_features=1024, out_features=1000)`. This layer was trained on the ImageNet dataset, so it won't work for our specific problem. That means we need to replace the classifier, but the features will work perfectly on their own. In general, I think about pre-trained networks as amazingly good feature detectors that can be used as the input for simple feed-forward classifiers.", "_____no_output_____" ] ], [ [ "# Freeze parameters so we don't backprop through them\nfor param in model.parameters():\n param.requires_grad = False\n\nfrom collections import OrderedDict\nclassifier = nn.Sequential(OrderedDict([\n ('fc1', nn.Linear(1024, 500)),\n ('relu', nn.ReLU()),\n ('fc2', nn.Linear(500, 2)),\n ('output', nn.LogSoftmax(dim=1))\n ]))\n \nmodel.classifier = classifier", "_____no_output_____" ] ], [ [ "With our model built, we need to train the classifier. However, now we're using a **really deep** neural network. If you try to train this on a CPU like normal, it will take a long, long time. Instead, we're going to use the GPU to do the calculations. The linear algebra computations are done in parallel on the GPU leading to 100x increased training speeds. It's also possible to train on multiple GPUs, further decreasing training time.\n\nPyTorch, along with pretty much every other deep learning framework, uses [CUDA](https://developer.nvidia.com/cuda-zone) to efficiently compute the forward and backwards passes on the GPU. In PyTorch, you move your model parameters and other tensors to the GPU memory using `model.to('cuda')`. You can move them back from the GPU with `model.to('cpu')` which you'll commonly do when you need to operate on the network output outside of PyTorch. As a demonstration of the increased speed, I'll compare how long it takes to perform a forward and backward pass with and without a GPU.", "_____no_output_____" ] ], [ [ "import time", "_____no_output_____" ], [ "for device in ['cpu', 'cuda']:\n\n criterion = nn.NLLLoss()\n # Only train the classifier parameters, feature parameters are frozen\n optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n\n model.to(device)\n\n for ii, (inputs, labels) in enumerate(trainloader):\n\n # Move input and label tensors to the GPU\n inputs, labels = inputs.to(device), labels.to(device)\n\n start = time.time()\n\n outputs = model.forward(inputs)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n\n if ii==3:\n break\n \n print(f\"Device = {device}; Time per batch: {(time.time() - start)/3:.3f} seconds\")", "Device = cpu; Time per batch: 11.147 seconds\nDevice = cuda; Time per batch: 0.014 seconds\n" ] ], [ [ "You can write device agnostic code which will automatically use CUDA if it's enabled like so:\n```python\n# at beginning of the script\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n...\n\n# then whenever you get a new Tensor or Module\n# this won't copy if they are already on the desired device\ninput = data.to(device)\nmodel = MyModule(...).to(device)\n```\n\nFrom here, I'll let you finish training the model. The process is the same as before except now your model is much more powerful. You should get better than 95% accuracy easily.\n\n>**Exercise:** Train a pretrained models to classify the cat and dog images. Continue with the DenseNet model, or try ResNet, it's also a good model to try out first. Make sure you are only training the classifier and the parameters for the features part are frozen.", "_____no_output_____" ], [ "#### Resnet101", "_____no_output_____" ], [ "Load the ResNet101 model.", "_____no_output_____" ] ], [ [ "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Device {device}\")\n\nmodel = models.resnet101(pretrained=True)\nmodel", "Device cuda:0\n" ] ], [ [ "Freeze parameters.", "_____no_output_____" ] ], [ [ "for param in model.parameters():\n param.requires_grad = False", "_____no_output_____" ] ], [ [ "Replace model head.", "_____no_output_____" ] ], [ [ "model.fc = nn.Sequential(nn.Linear(2048, 256),\n nn.ReLU(),\n nn.Dropout(0.2),\n nn.Linear(256, 2),\n nn.LogSoftmax(dim=1))", "_____no_output_____" ], [ "model.to(device)", "_____no_output_____" ] ], [ [ "Define the loss function and the optimizer.", "_____no_output_____" ] ], [ [ "criterion = nn.NLLLoss()\noptimizer = optim.Adam(model.fc.parameters())", "_____no_output_____" ] ], [ [ "Training loop.", "_____no_output_____" ] ], [ [ "n_epochs = 1\nprint_every = 5\nstep, train_loss, train_acc, test_loss, test_acc = 0, 0, 0, 0, 0\n\ndef get_batch_acc(preds, labels):\n _, pred_class = preds.topk(1, dim=1)\n correct_class = (pred_class == labels.view(*pred_class.shape))\n batch_acc = torch.mean(correct_class.type(torch.FloatTensor))\n return batch_acc\n\nfor epoch in range(n_epochs):\n for images, labels in trainloader:\n images = images.to(device)\n labels = labels.to(device)\n optimizer.zero_grad()\n # forward pass\n preds = model(images)\n # compute loss & accuracy\n batch_loss = criterion(preds, labels)\n batch_acc = get_batch_acc(preds, labels)\n # backward pass\n batch_loss.backward()\n # gradient descent step\n optimizer.step()\n # update running loss and accuracy\n train_loss += batch_loss.item() / print_every\n train_acc += batch_acc.item() / print_every\n \n if step % print_every == 0:\n # Evaluate on test data\n with torch.no_grad():\n model.eval()\n for test_images, test_labels in testloader:\n test_images, test_labels = test_images.to(device), test_labels.to(device)\n test_preds = model(test_images)\n batch_loss = criterion(test_preds, test_labels)\n batch_acc = get_batch_acc(test_preds, test_labels)\n test_loss += batch_loss.item() / len(testloader)\n test_acc += batch_acc.item() / len(testloader)\n print(f\"Epoch: {epoch} | Step: {step}/{int(len(trainloader) / print_every)} | Train loss: {train_loss} | Train acc: {train_acc} | Test loss: {test_loss} | Test acc: {test_acc}\")\n model.train()\n train_loss, train_acc, test_loss, test_acc = 0, 0, 0, 0\n \n step += 1", "Epoch: 0 | Step: 0/70 | Train loss: 0.1420997977256775 | Train acc: 0.09375 | Test loss: 0.5888391725718976 | Test acc: 0.5136718750000002\nEpoch: 0 | Step: 5/70 | Train loss: 0.6307643175125123 | Train acc: 0.628125 | Test loss: 0.23891626708209512 | Test acc: 0.9695312500000004\nEpoch: 0 | Step: 10/70 | Train loss: 0.28028063476085663 | Train acc: 0.9125 | Test loss: 0.12141264081001284 | Test acc: 0.9765625000000002\nEpoch: 0 | Step: 15/70 | Train loss: 0.24202772676944734 | Train acc: 0.909375 | Test loss: 0.17700428750831634 | Test acc: 0.9289062500000004\nEpoch: 0 | Step: 20/70 | Train loss: 0.24063588082790371 | Train acc: 0.8937499999999999 | Test loss: 0.12248691681306804 | Test acc: 0.9554687500000005\nEpoch: 0 | Step: 25/70 | Train loss: 0.17640648782253265 | Train acc: 0.934375 | Test loss: 0.060022399388253704 | Test acc: 0.9773437500000003\nEpoch: 0 | Step: 30/70 | Train loss: 0.18905198574066162 | Train acc: 0.9124999999999999 | Test loss: 0.05537798898294568 | Test acc: 0.9785156250000003\nEpoch: 0 | Step: 35/70 | Train loss: 0.1329597905278206 | Train acc: 0.9375 | Test loss: 0.06576174749061464 | Test acc: 0.9738281250000004\nEpoch: 0 | Step: 40/70 | Train loss: 0.14060658365488055 | Train acc: 0.94375 | Test loss: 0.05532630463130773 | Test acc: 0.9777343750000004\nEpoch: 0 | Step: 45/70 | Train loss: 0.20088366121053694 | Train acc: 0.9031250000000001 | Test loss: 0.0554607379483059 | Test acc: 0.9816406250000005\nEpoch: 0 | Step: 50/70 | Train loss: 0.1727498099207878 | Train acc: 0.928125 | Test loss: 0.06095148560125382 | Test acc: 0.9765625000000004\nEpoch: 0 | Step: 55/70 | Train loss: 0.09459403157234192 | Train acc: 0.9562499999999999 | Test loss: 0.04441865370608867 | Test acc: 0.9839843750000005\nEpoch: 0 | Step: 60/70 | Train loss: 0.10896287113428114 | Train acc: 0.9562499999999999 | Test loss: 0.06307774395681919 | Test acc: 0.9757812500000005\nEpoch: 0 | Step: 65/70 | Train loss: 0.16338396668434144 | Train acc: 0.928125 | Test loss: 0.04214015202596784 | Test acc: 0.9851562500000004\nEpoch: 0 | Step: 70/70 | Train loss: 0.14495114535093306 | Train acc: 0.9500000000000001 | Test loss: 0.0413937923964113 | Test acc: 0.9855468750000004\nEpoch: 0 | Step: 75/70 | Train loss: 0.13258390128612518 | Train acc: 0.9437500000000001 | Test loss: 0.07264044333714992 | Test acc: 0.9746093750000003\nEpoch: 0 | Step: 80/70 | Train loss: 0.1904216706752777 | Train acc: 0.925 | Test loss: 0.08890908064786346 | Test acc: 0.9605468750000005\nEpoch: 0 | Step: 85/70 | Train loss: 0.16921156644821167 | Train acc: 0.93125 | Test loss: 0.04083143188618124 | Test acc: 0.9867187500000004\nEpoch: 0 | Step: 90/70 | Train loss: 0.14072314500808714 | Train acc: 0.9374999999999999 | Test loss: 0.05287259081378579 | Test acc: 0.9812500000000003\nEpoch: 0 | Step: 95/70 | Train loss: 0.13679716885089876 | Train acc: 0.9312500000000001 | Test loss: 0.04134784317575395 | Test acc: 0.9851562500000005\nEpoch: 0 | Step: 100/70 | Train loss: 0.08950164318084716 | Train acc: 0.96875 | Test loss: 0.041062067332677546 | Test acc: 0.9863281250000002\nEpoch: 0 | Step: 105/70 | Train loss: 0.16072152853012084 | Train acc: 0.925 | Test loss: 0.044542220793664454 | Test acc: 0.9832031250000005\nEpoch: 0 | Step: 110/70 | Train loss: 0.130016753077507 | Train acc: 0.959375 | Test loss: 0.038838218268938365 | Test acc: 0.9855468750000005\nEpoch: 0 | Step: 115/70 | Train loss: 0.1426736406981945 | Train acc: 0.94375 | Test loss: 0.04202921178657562 | Test acc: 0.9855468750000006\nEpoch: 0 | Step: 120/70 | Train loss: 0.18757236003875732 | Train acc: 0.903125 | Test loss: 0.041919583408162 | Test acc: 0.9871093750000004\nEpoch: 0 | Step: 125/70 | Train loss: 0.10435906201601029 | Train acc: 0.9499999999999998 | Test loss: 0.05947930146940051 | Test acc: 0.9773437500000005\nEpoch: 0 | Step: 130/70 | Train loss: 0.14933517575263977 | Train acc: 0.9312499999999999 | Test loss: 0.037968704686500133 | Test acc: 0.9863281250000006\nEpoch: 0 | Step: 135/70 | Train loss: 0.17588062286376954 | Train acc: 0.9437500000000001 | Test loss: 0.05181127830874175 | Test acc: 0.9792968750000004\nEpoch: 0 | Step: 140/70 | Train loss: 0.15228946805000304 | Train acc: 0.9249999999999999 | Test loss: 0.054136648681014775 | Test acc: 0.9808593750000005\nEpoch: 0 | Step: 145/70 | Train loss: 0.17649099677801133 | Train acc: 0.928125 | Test loss: 0.06559804994612933 | Test acc: 0.9761718750000002\nEpoch: 0 | Step: 150/70 | Train loss: 0.18984051644802094 | Train acc: 0.925 | Test loss: 0.060335627128370116 | Test acc: 0.9757812500000005\nEpoch: 0 | Step: 155/70 | Train loss: 0.11635222733020784 | Train acc: 0.9468749999999999 | Test loss: 0.056617066054604946 | Test acc: 0.9796875000000004\nEpoch: 0 | Step: 160/70 | Train loss: 0.14122935086488722 | Train acc: 0.940625 | Test loss: 0.0529679870000109 | Test acc: 0.9792968750000005\nEpoch: 0 | Step: 165/70 | Train loss: 0.10575526505708696 | Train acc: 0.959375 | Test loss: 0.03797864471562206 | Test acc: 0.9855468750000006\nEpoch: 0 | Step: 170/70 | Train loss: 0.0790023796260357 | Train acc: 0.965625 | Test loss: 0.041275303298607464 | Test acc: 0.9847656250000003\nEpoch: 0 | Step: 175/70 | Train loss: 0.14691830575466155 | Train acc: 0.946875 | Test loss: 0.041710160952061405 | Test acc: 0.9843750000000004\nEpoch: 0 | Step: 180/70 | Train loss: 0.127793987095356 | Train acc: 0.9593749999999999 | Test loss: 0.0420147635275498 | Test acc: 0.9847656250000003\nEpoch: 0 | Step: 185/70 | Train loss: 0.150615394115448 | Train acc: 0.9375 | Test loss: 0.03997119485866279 | Test acc: 0.9843750000000006\nEpoch: 0 | Step: 190/70 | Train loss: 0.10459670722484589 | Train acc: 0.9562499999999999 | Test loss: 0.038834996591322134 | Test acc: 0.9855468750000006\nEpoch: 0 | Step: 195/70 | Train loss: 0.10772354453802109 | Train acc: 0.95 | Test loss: 0.039422417338937504 | Test acc: 0.9855468750000005\nEpoch: 0 | Step: 200/70 | Train loss: 0.11106140911579132 | Train acc: 0.9531249999999999 | Test loss: 0.042719195154495536 | Test acc: 0.9832031250000004\nEpoch: 0 | Step: 205/70 | Train loss: 0.14702724367380143 | Train acc: 0.9281250000000001 | Test loss: 0.04222033789847046 | Test acc: 0.9843750000000004\nEpoch: 0 | Step: 210/70 | Train loss: 0.15944554209709166 | Train acc: 0.9312499999999999 | Test loss: 0.04301619925536217 | Test acc: 0.9843750000000004\nEpoch: 0 | Step: 215/70 | Train loss: 0.14402222633361816 | Train acc: 0.9500000000000001 | Test loss: 0.04359726167749615 | Test acc: 0.9847656250000004\nEpoch: 0 | Step: 220/70 | Train loss: 0.16783702224493027 | Train acc: 0.9374999999999999 | Test loss: 0.04309091055765748 | Test acc: 0.9839843750000002\nEpoch: 0 | Step: 225/70 | Train loss: 0.1488300547003746 | Train acc: 0.95 | Test loss: 0.06570144428405911 | Test acc: 0.9738281250000005\nEpoch: 0 | Step: 230/70 | Train loss: 0.13624729514122008 | Train acc: 0.9468749999999999 | Test loss: 0.04075988531112671 | Test acc: 0.9871093750000005\nEpoch: 0 | Step: 235/70 | Train loss: 0.13050326555967331 | Train acc: 0.9312499999999999 | Test loss: 0.04222942637279629 | Test acc: 0.9859375000000006\nEpoch: 0 | Step: 240/70 | Train loss: 0.13238141909241674 | Train acc: 0.953125 | Test loss: 0.046418800204992304 | Test acc: 0.9835937500000006\nEpoch: 0 | Step: 245/70 | Train loss: 0.05954338535666465 | Train acc: 0.9843749999999999 | Test loss: 0.04885823905933648 | Test acc: 0.9812500000000003\nEpoch: 0 | Step: 250/70 | Train loss: 0.16078078895807266 | Train acc: 0.934375 | Test loss: 0.03736194064840675 | Test acc: 0.9863281250000004\nEpoch: 0 | Step: 255/70 | Train loss: 0.15792878568172455 | Train acc: 0.940625 | Test loss: 0.04166025612503291 | Test acc: 0.9847656250000004\nEpoch: 0 | Step: 260/70 | Train loss: 0.14938536137342454 | Train acc: 0.946875 | Test loss: 0.045122843678109356 | Test acc: 0.9843750000000003\nEpoch: 0 | Step: 265/70 | Train loss: 0.11566838473081589 | Train acc: 0.959375 | Test loss: 0.03719602164346725 | Test acc: 0.9863281250000004\nEpoch: 0 | Step: 270/70 | Train loss: 0.1031675636768341 | Train acc: 0.959375 | Test loss: 0.03917242186143994 | Test acc: 0.9847656250000004\nEpoch: 0 | Step: 275/70 | Train loss: 0.1409957207739353 | Train acc: 0.93125 | Test loss: 0.0431375071639195 | Test acc: 0.9839843750000006\n" ] ], [ [ "#### Densenet121", "_____no_output_____" ] ], [ [ "# Use GPU if it's available\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nmodel = models.densenet121(pretrained=True)\n\n# Freeze parameters so we don't backprop through them\nfor param in model.parameters():\n param.requires_grad = False\n \nmodel.classifier = nn.Sequential(nn.Linear(1024, 256),\n nn.ReLU(),\n nn.Dropout(0.2),\n nn.Linear(256, 2),\n nn.LogSoftmax(dim=1))\n\ncriterion = nn.NLLLoss()\n\n# Only train the classifier parameters, feature parameters are frozen\noptimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n\nmodel.to(device);", "_____no_output_____" ], [ "device", "_____no_output_____" ], [ "## TODO: Use a pretrained model to classify the cat and dog images\n\nepochs = 1\nsteps = 0\nrunning_loss = 0\nprint_every = 5\nfor epoch in range(epochs):\n for inputs, labels in trainloader:\n steps += 1\n # Move input and label tensors to the default device\n inputs, labels = inputs.to(device), labels.to(device)\n \n optimizer.zero_grad()\n \n logps = model.forward(inputs)\n loss = criterion(logps, labels)\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item()\n \n if steps % print_every == 0:\n test_loss = 0\n accuracy = 0\n model.eval()\n with torch.no_grad():\n for inputs, labels in testloader:\n inputs, labels = inputs.to(device), labels.to(device)\n logps = model.forward(inputs)\n batch_loss = criterion(logps, labels)\n \n test_loss += batch_loss.item()\n \n # Calculate accuracy\n ps = torch.exp(logps)\n top_p, top_class = ps.topk(1, dim=1)\n equals = top_class == labels.view(*top_class.shape)\n accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n \n print(f\"Epoch {epoch+1}/{epochs}.. \"\n f\"Train loss: {running_loss/print_every:.3f}.. \"\n f\"Test loss: {test_loss/len(testloader):.3f}.. \"\n f\"Test accuracy: {accuracy/len(testloader):.3f}\")\n running_loss = 0\n model.train()", "Epoch 1/1.. Train loss: 1.046.. Test loss: 0.468.. Test accuracy: 0.691\nEpoch 1/1.. Train loss: 0.476.. Test loss: 0.286.. Test accuracy: 0.880\nEpoch 1/1.. Train loss: 0.347.. Test loss: 0.218.. Test accuracy: 0.920\nEpoch 1/1.. Train loss: 0.316.. Test loss: 0.100.. Test accuracy: 0.976\nEpoch 1/1.. Train loss: 0.262.. Test loss: 0.088.. Test accuracy: 0.977\nEpoch 1/1.. Train loss: 0.287.. Test loss: 0.144.. Test accuracy: 0.942\nEpoch 1/1.. Train loss: 0.187.. Test loss: 0.094.. Test accuracy: 0.970\nEpoch 1/1.. Train loss: 0.263.. Test loss: 0.063.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.189.. Test loss: 0.100.. Test accuracy: 0.963\nEpoch 1/1.. Train loss: 0.176.. Test loss: 0.061.. Test accuracy: 0.979\nEpoch 1/1.. Train loss: 0.186.. Test loss: 0.056.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.142.. Test loss: 0.061.. Test accuracy: 0.978\nEpoch 1/1.. Train loss: 0.131.. Test loss: 0.050.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.178.. Test loss: 0.049.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.198.. Test loss: 0.055.. Test accuracy: 0.981\nEpoch 1/1.. Train loss: 0.135.. Test loss: 0.060.. Test accuracy: 0.977\nEpoch 1/1.. Train loss: 0.131.. Test loss: 0.048.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.163.. Test loss: 0.055.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.121.. Test loss: 0.046.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.147.. Test loss: 0.048.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.156.. Test loss: 0.061.. Test accuracy: 0.978\nEpoch 1/1.. Train loss: 0.208.. Test loss: 0.085.. Test accuracy: 0.965\nEpoch 1/1.. Train loss: 0.310.. Test loss: 0.086.. Test accuracy: 0.974\nEpoch 1/1.. Train loss: 0.184.. Test loss: 0.058.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.184.. Test loss: 0.052.. Test accuracy: 0.981\nEpoch 1/1.. Train loss: 0.127.. Test loss: 0.052.. Test accuracy: 0.984\nEpoch 1/1.. Train loss: 0.137.. Test loss: 0.045.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.142.. Test loss: 0.043.. Test accuracy: 0.983\nEpoch 1/1.. Train loss: 0.144.. Test loss: 0.069.. Test accuracy: 0.973\nEpoch 1/1.. Train loss: 0.158.. Test loss: 0.058.. Test accuracy: 0.981\nEpoch 1/1.. Train loss: 0.168.. Test loss: 0.046.. Test accuracy: 0.983\nEpoch 1/1.. Train loss: 0.226.. Test loss: 0.046.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.196.. Test loss: 0.046.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.205.. Test loss: 0.051.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.181.. Test loss: 0.074.. Test accuracy: 0.977\nEpoch 1/1.. Train loss: 0.178.. Test loss: 0.053.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.190.. Test loss: 0.049.. Test accuracy: 0.983\nEpoch 1/1.. Train loss: 0.182.. Test loss: 0.063.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.180.. Test loss: 0.054.. Test accuracy: 0.978\nEpoch 1/1.. Train loss: 0.163.. Test loss: 0.047.. Test accuracy: 0.981\nEpoch 1/1.. Train loss: 0.146.. Test loss: 0.043.. Test accuracy: 0.984\nEpoch 1/1.. Train loss: 0.135.. Test loss: 0.067.. Test accuracy: 0.975\nEpoch 1/1.. Train loss: 0.166.. Test loss: 0.042.. Test accuracy: 0.984\nEpoch 1/1.. Train loss: 0.237.. Test loss: 0.065.. Test accuracy: 0.979\nEpoch 1/1.. Train loss: 0.172.. Test loss: 0.067.. Test accuracy: 0.974\nEpoch 1/1.. Train loss: 0.139.. Test loss: 0.041.. Test accuracy: 0.985\nEpoch 1/1.. Train loss: 0.131.. Test loss: 0.041.. Test accuracy: 0.986\nEpoch 1/1.. Train loss: 0.120.. Test loss: 0.046.. Test accuracy: 0.981\nEpoch 1/1.. Train loss: 0.146.. Test loss: 0.042.. Test accuracy: 0.984\nEpoch 1/1.. Train loss: 0.152.. Test loss: 0.040.. Test accuracy: 0.986\nEpoch 1/1.. Train loss: 0.151.. Test loss: 0.042.. Test accuracy: 0.984\nEpoch 1/1.. Train loss: 0.100.. Test loss: 0.042.. Test accuracy: 0.983\nEpoch 1/1.. Train loss: 0.148.. Test loss: 0.048.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.111.. Test loss: 0.043.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.137.. Test loss: 0.043.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.106.. Test loss: 0.045.. Test accuracy: 0.981\nEpoch 1/1.. Train loss: 0.204.. Test loss: 0.043.. Test accuracy: 0.983\nEpoch 1/1.. Train loss: 0.209.. Test loss: 0.043.. Test accuracy: 0.983\nEpoch 1/1.. Train loss: 0.170.. Test loss: 0.043.. Test accuracy: 0.985\nEpoch 1/1.. Train loss: 0.141.. Test loss: 0.054.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.133.. Test loss: 0.042.. Test accuracy: 0.983\nEpoch 1/1.. Train loss: 0.106.. Test loss: 0.040.. Test accuracy: 0.984\nEpoch 1/1.. Train loss: 0.141.. Test loss: 0.042.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.152.. Test loss: 0.044.. Test accuracy: 0.983\nEpoch 1/1.. Train loss: 0.149.. Test loss: 0.039.. Test accuracy: 0.984\nEpoch 1/1.. Train loss: 0.189.. Test loss: 0.040.. Test accuracy: 0.986\nEpoch 1/1.. Train loss: 0.109.. Test loss: 0.053.. Test accuracy: 0.982\nEpoch 1/1.. Train loss: 0.184.. Test loss: 0.054.. Test accuracy: 0.980\nEpoch 1/1.. Train loss: 0.218.. Test loss: 0.079.. Test accuracy: 0.971\nEpoch 1/1.. Train loss: 0.174.. Test loss: 0.044.. Test accuracy: 0.983\n" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "%matplotlib inline\nfrom matplotlib import pyplot as plt\nplt.rcParams['figure.figsize'] = (10, 8)\nimport seaborn as sns\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nimport collections\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn import preprocessing\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score", "_____no_output_____" ], [ "import pickle", "_____no_output_____" ], [ "cmp_df = pd.DataFrame(columns=['ID', 'TITLE', 'CMP_TYPE_CUSTOMER', 'CMP_TYPE_PARTNER'])", "_____no_output_____" ], [ "cmp_df", "_____no_output_____" ], [ "data_train = pd.read_csv('data/dataframe.csv', sep=';', index_col='ID')", "_____no_output_____" ], [ "data_test = pd.read_csv('data/dataframe.csv', sep=';', index_col='ID')", "_____no_output_____" ], [ "data_train", "_____no_output_____" ], [ "title_df = pd.DataFrame(data_train['TITLE'])", "_____no_output_____" ], [ "title_df.head()", "_____no_output_____" ], [ "data_train = data_train.drop(columns=['TITLE'], axis=1)\ndata_test = data_test.drop(columns=['TITLE'], axis=1)", "_____no_output_____" ], [ "data_test.describe(include='all').T", "_____no_output_____" ], [ "fig = plt.figure(figsize=(25, 15))\ncols = 5\nrows = np.ceil(float(data_train.shape[1]) / cols)\nfor i, column in enumerate(data_train.columns):\n ax = fig.add_subplot(rows, cols, i + 1)\n ax.set_title(column)\n if data_train.dtypes[column] == np.object:\n data_train[column].value_counts().plot(kind=\"bar\", axes=ax)\n else:\n data_train[column].hist(axes=ax)\n plt.xticks(rotation=\"vertical\")\nplt.subplots_adjust(hspace=0.7, wspace=0.2)", "_____no_output_____" ], [ "X_train=data_train.drop(['target'], axis=1)\ny_train = data_train['target']\n\nX_test=data_test.drop(['target'], axis=1)\ny_test = data_test['target']", "_____no_output_____" ], [ "tree = DecisionTreeClassifier(criterion = \"entropy\", max_depth = 3, random_state = 17)\ntree.fit(X = X_train, y = y_train)", "_____no_output_____" ], [ "tree_predictions = tree.predict(X = X_test) # Ваш код здесь", "_____no_output_____" ], [ "tree_predictions", "_____no_output_____" ], [ "accuracy_score = tree.score(X = X_test, y = y_test)\nprint(accuracy_score)", "0.9065743944636678\n" ], [ "rf = RandomForestClassifier(criterion=\"entropy\", random_state = 17, n_estimators=200,\n max_depth=4, max_features=0.15 ,n_jobs=-1)\nrf.fit(X = X_train, y = y_train) # Ваш код здесь", "_____no_output_____" ], [ "forest_predictions = rf.predict(X = X_test)\nprint(forest_predictions)", "[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0\n 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0\n 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0]\n" ], [ "accuracy_score = rf.score(X = X_test, y = y_test)\nprint(accuracy_score)", "0.9480968858131488\n" ], [ "pickle.dump(rf, open('random_forest.sav', 'wb'))", "_____no_output_____" ], [ "model = pickle.load(open('random_forest.sav', 'rb'))", "_____no_output_____" ], [ "predict = model.predict(X=X_test)", "_____no_output_____" ], [ "predict", "_____no_output_____" ], [ "title_df = title_df.reset_index(drop=True)", "_____no_output_____" ], [ "predict = pd.Series(predict).rename('PREDICT')", "_____no_output_____" ], [ "predict = predict.rename('PREDICT')", "_____no_output_____" ], [ "result_df = pd.concat([title_df, predict], axis=1)", "_____no_output_____" ], [ "result_df", "_____no_output_____" ] ], [ [ "--------------------------------------------------------------------------------------------------------------------------------", "_____no_output_____" ] ], [ [ "forest_params = {'max_depth': range(4, 21),\n 'max_features': range(7, 45),\n 'random_state': range(1, 100)}", "_____no_output_____" ], [ "locally_best_forest = GridSearchCV(rf, forest_params, n_jobs=-1)", "_____no_output_____" ], [ "locally_best_forest.fit(X = X_train, y = y_train)", "_____no_output_____" ], [ "print(\"Best params:\", locally_best_forest.best_params_)\nprint(\"Best cross validaton score\", locally_best_forest.best_score_)", "Best params: {'max_depth': 4, 'max_features': 7, 'random_state': 40}\nBest cross validaton score 0.8650519031141869\n" ], [ "tuned_forest_predictions = locally_best_forest.predict(X = X_test) # Ваш код здесь\naccuracy_score = locally_best_forest.score(X = X_test, y = y_test)\nprint(accuracy_score)", "0.9342560553633218\n" ], [ "tuned_forest_predictions", "_____no_output_____" ] ] ]

[ "code", "markdown", "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%matplotlib inline\n\nimport matplotlib.pyplot as plt\n\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import Normalizer, MinMaxScaler\nfrom keras.layers import Dense, Conv2D, MaxPool2D, Dropout, Flatten, Input\nfrom keras.models import Sequential, Model\nfrom keras.utils import to_categorical\nfrom keras.datasets import boston_housing, mnist", "_____no_output_____" ], [ "(x_train, y_train), (x_test, y_test) = mnist.load_data()\nx_train = x_train.reshape((-1, 28*28))\nx_test = x_test.reshape((-1, 28*28))\ny_train = to_categorical(y_train, num_classes=10)\ny_test = to_categorical(y_test, num_classes=10)\nprint(x_train.shape)\nprint(y_train.shape)", "(60000, 784)\n(60000, 10)\n" ], [ "# model = Sequential()\n# model.add(Dense(400, input_shape=(784, ), activation='relu'))\n# model.add(Dense(200, activation='relu'))\n# model.add(Dense(100, activation='relu'))\n# model.add(Dense(2, activation='relu'))\n# model.add(Dense(100, activation='relu'))\n# model.add(Dense(200, activation='relu'))\n# model.add(Dense(400, activation='relu'))\n# model.add(Dense(784, activation='relu'))\n\ninpt = Input(shape=(784, ))\ninner = Dense(300, activation='sigmoid')(inpt)\ninner = Dense(150, activation='sigmoid')(inner)\ncode = Dense(2, activation='sigmoid')(inner)\ninner = Dense(150, activation='sigmoid')(code)\ninner = Dense(300, activation='sigmoid')(inner)\ndecode = Dense(784, activation='sigmoid')(inner)\n\nautoencoder = Model(inpt, decode)\nencoder = Model(inpt, code)", "_____no_output_____" ], [ "autoencoder.summary()", "_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\ninput_7 (InputLayer) (None, 784) 0 \n_________________________________________________________________\ndense_34 (Dense) (None, 300) 235500 \n_________________________________________________________________\ndense_35 (Dense) (None, 150) 45150 \n_________________________________________________________________\ndense_36 (Dense) (None, 2) 302 \n_________________________________________________________________\ndense_37 (Dense) (None, 150) 450 \n_________________________________________________________________\ndense_38 (Dense) (None, 300) 45300 \n_________________________________________________________________\ndense_39 (Dense) (None, 784) 235984 \n=================================================================\nTotal params: 562,686\nTrainable params: 562,686\nNon-trainable params: 0\n_________________________________________________________________\n" ], [ "encoder.summary()", "_________________________________________________________________\nLayer (type) Output Shape Param # \n=================================================================\ninput_7 (InputLayer) (None, 784) 0 \n_________________________________________________________________\ndense_34 (Dense) (None, 300) 235500 \n_________________________________________________________________\ndense_35 (Dense) (None, 150) 45150 \n_________________________________________________________________\ndense_36 (Dense) (None, 2) 302 \n=================================================================\nTotal params: 280,952\nTrainable params: 280,952\nNon-trainable params: 0\n_________________________________________________________________\n" ], [ "autoencoder.compile(loss=\"binary_crossentropy\", \n optimizer='adam')", "_____no_output_____" ], [ "model.fit(x_train, x_train, epochs=10)", "Epoch 1/10\n60000/60000 [==============================] - 14s 231us/step - loss: 2874.0705\nEpoch 2/10\n60000/60000 [==============================] - 13s 223us/step - loss: 2949.9649\nEpoch 3/10\n60000/60000 [==============================] - 14s 227us/step - loss: 2958.6945\nEpoch 4/10\n60000/60000 [==============================] - 13s 223us/step - loss: 3009.1767\nEpoch 5/10\n60000/60000 [==============================] - 13s 225us/step - loss: 2956.3930\nEpoch 6/10\n60000/60000 [==============================] - 13s 225us/step - loss: 2882.7399\nEpoch 7/10\n60000/60000 [==============================] - 14s 225us/step - loss: 2874.1891\nEpoch 8/10\n60000/60000 [==============================] - 14s 226us/step - loss: 2843.9117\nEpoch 9/10\n60000/60000 [==============================] - 14s 226us/step - loss: 2826.2436\nEpoch 10/10\n60000/60000 [==============================] - 14s 227us/step - loss: 2811.0035\n" ], [ "for x, y in list(zip(x_test, y_test))[:10]:\n img = x.reshape(28, 28)\n pred = autoencoder.predict(x.reshape(1, -1))\n pred = pred.reshape((28, 28))\n plt.imshow(img)\n plt.show()\n \n plt.imshow(pred)\n plt.show()\n print()", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code", "code", "code", "code", "code", "code", "code", "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# TEXT\n\nThis notebook serves as supporting material for topics covered in **Chapter 22 - Natural Language Processing** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [text.py](https://github.com/aimacode/aima-python/blob/master/text.py).", "_____no_output_____" ] ], [ [ "from text import *\nfrom utils import open_data\nfrom notebook import psource", "_____no_output_____" ] ], [ [ "## CONTENTS\n\n* Text Models\n* Viterbi Text Segmentation\n* Information Retrieval\n* Information Extraction\n* Decoders", "_____no_output_____" ], [ "## TEXT MODELS\n\nBefore we start analyzing text processing algorithms, we will need to build some language models. Those models serve as a look-up table for character or word probabilities (depending on the type of model). These models can give us the probabilities of words or character sequences appearing in text. Take as example \"the\". Text models can give us the probability of \"the\", *P(\"the\")*, either as a word or as a sequence of characters (\"t\" followed by \"h\" followed by \"e\"). The first representation is called \"word model\" and deals with words as distinct objects, while the second is a \"character model\" and deals with sequences of characters as objects. Note that we can specify the number of words or the length of the char sequences to better suit our needs. So, given that number of words equals 2, we have probabilities in the form *P(word1, word2)*. For example, *P(\"of\", \"the\")*. For char models, we do the same but for chars.\n\nIt is also useful to store the conditional probabilities of words given preceding words. That means, given we found the words \"of\" and \"the\", what is the chance the next word will be \"world\"? More formally, *P(\"world\"|\"of\", \"the\")*. Generalizing, *P(Wi|Wi-1, Wi-2, ... , Wi-n)*.\n\nWe call the word model *N-Gram Word Model* (from the Greek \"gram\", the root of \"write\", or the word for \"letter\") and the char model *N-Gram Character Model*. In the special case where *N* is 1, we call the models *Unigram Word Model* and *Unigram Character Model* respectively.\n\nIn the `text` module we implement the two models (both their unigram and n-gram variants) by inheriting from the `CountingProbDist` from `learning.py`. Note that `CountingProbDist` does not return the actual probability of each object, but the number of times it appears in our test data.\n\nFor word models we have `UnigramWordModel` and `NgramWordModel`. We supply them with a text file and they show the frequency of the different words. We have `UnigramCharModel` and `NgramCharModel` for the character models.\n\nExecute the cells below to take a look at the code.", "_____no_output_____" ] ], [ [ "psource(UnigramWordModel, NgramWordModel, UnigramCharModel, NgramCharModel)", "_____no_output_____" ] ], [ [ "Next we build our models. The text file we will use to build them is *Flatland*, by Edwin A. Abbott. We will load it from [here](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/EN-text/flatland.txt). In that directory you can find other text files we might get to use here.", "_____no_output_____" ], [ "### Getting Probabilities\n\nHere we will take a look at how to read text and find the probabilities for each model, and how to retrieve them.\n\nFirst the word models:", "_____no_output_____" ] ], [ [ "flatland = open_data(\"EN-text/flatland.txt\").read()\nwordseq = words(flatland)\n\nP1 = UnigramWordModel(wordseq)\nP2 = NgramWordModel(2, wordseq)\n\nprint(P1.top(5))\nprint(P2.top(5))\n\nprint(P1['an'])\nprint(P2[('i', 'was')])", "[(2081, 'the'), (1479, 'of'), (1021, 'and'), (1008, 'to'), (850, 'a')]\n[(368, ('of', 'the')), (152, ('to', 'the')), (152, ('in', 'the')), (86, ('of', 'a')), (80, ('it', 'is'))]\n0.0036724740723330495\n0.00114584557527324\n" ] ], [ [ "We see that the most used word in *Flatland* is 'the', with 2081 occurrences, while the most used sequence is 'of the' with 368 occurrences. Also, the probability of 'an' is approximately 0.003, while for 'i was' it is close to 0.001. Note that the strings used as keys are all lowercase. For the unigram model, the keys are single strings, while for n-gram models we have n-tuples of strings.\n\nBelow we take a look at how we can get information from the conditional probabilities of the model, and how we can generate the next word in a sequence.", "_____no_output_____" ] ], [ [ "flatland = open_data(\"EN-text/flatland.txt\").read()\nwordseq = words(flatland)\n\nP3 = NgramWordModel(3, wordseq)\n\nprint(\"Conditional Probabilities Table:\", P3.cond_prob[('i', 'was')].dictionary, '\\n')\nprint(\"Conditional Probability of 'once' give 'i was':\", P3.cond_prob[('i', 'was')]['once'], '\\n')\nprint(\"Next word after 'i was':\", P3.cond_prob[('i', 'was')].sample())", "Conditional Probabilities Table: {'now': 2, 'glad': 1, 'keenly': 1, 'considered': 1, 'once': 2, 'not': 4, 'in': 2, 'by': 1, 'simulating': 1, 'intoxicated': 1, 'wearied': 1, 'quite': 1, 'certain': 2, 'sitting': 1, 'to': 2, 'rapidly': 1, 'will': 1, 'describing': 1, 'allowed': 1, 'at': 2, 'afraid': 1, 'covered': 1, 'approaching': 1, 'standing': 1, 'myself': 1, 'surprised': 1, 'unusually': 1, 'rapt': 1, 'pleased': 1, 'crushed': 1} \n\nConditional Probability of 'once' give 'i was': 0.05128205128205128 \n\nNext word after 'i was': wearied\n" ] ], [ [ "First we print all the possible words that come after 'i was' and the times they have appeared in the model. Next we print the probability of 'once' appearing after 'i was', and finally we pick a word to proceed after 'i was'. Note that the word is picked according to its probability of appearing (high appearance count means higher chance to get picked).", "_____no_output_____" ], [ "Let's take a look at the two character models:", "_____no_output_____" ] ], [ [ "flatland = open_data(\"EN-text/flatland.txt\").read()\nwordseq = words(flatland)\n\nP1 = UnigramCharModel(wordseq)\nP2 = NgramCharModel(2, wordseq)\n\nprint(P1.top(5))\nprint(P2.top(5))\n\nprint(P1['z'])\nprint(P2[('g', 'h')])", "[(19208, 'e'), (13965, 't'), (12069, 'o'), (11702, 'a'), (11440, 'i')]\n[(5364, (' ', 't')), (4573, ('t', 'h')), (4063, (' ', 'a')), (3654, ('h', 'e')), (2967, (' ', 'i'))]\n0.0006028715031814578\n0.0032371578540395666\n" ] ], [ [ "The most common letter is 'e', appearing more than 19000 times, and the most common sequence is \"\\_t\". That is, a space followed by a 't'. Note that even though we do not count spaces for word models or unigram character models, we do count them for n-gram char models.\n\nAlso, the probability of the letter 'z' appearing is close to 0.0006, while for the bigram 'gh' it is 0.003.", "_____no_output_____" ], [ "### Generating Samples\n\nApart from reading the probabilities for n-grams, we can also use our model to generate word sequences, using the `samples` function in the word models.", "_____no_output_____" ] ], [ [ "flatland = open_data(\"EN-text/flatland.txt\").read()\nwordseq = words(flatland)\n\nP1 = UnigramWordModel(wordseq)\nP2 = NgramWordModel(2, wordseq)\nP3 = NgramWordModel(3, wordseq)\n\nprint(P1.samples(10))\nprint(P2.samples(10))\nprint(P3.samples(10))", "hearing as inside is confined to conduct by the duties\nall and of voice being in a day of the\nparty they are stirred to mutual warfare and perish by\n" ] ], [ [ "For the unigram model, we mostly get gibberish, since each word is picked according to its frequency of appearance in the text, without taking into consideration preceding words. As we increase *n* though, we start to get samples that do have some semblance of conherency and do remind a little bit of normal English. As we increase our data, these samples will get better.\n\nLet's try it. We will add to the model more data to work with and let's see what comes out.", "_____no_output_____" ] ], [ [ "data = open_data(\"EN-text/flatland.txt\").read()\ndata += open_data(\"EN-text/sense.txt\").read()\n\nwordseq = words(data)\n\nP3 = NgramWordModel(3, wordseq)\nP4 = NgramWordModel(4, wordseq)\nP5 = NgramWordModel(5, wordseq)\nP7 = NgramWordModel(7, wordseq)\n\nprint(P3.samples(15))\nprint(P4.samples(15))\nprint(P5.samples(15))\nprint(P7.samples(15))", "leave them at cleveland this christmas now pray do not ask you to relate or\nmeaning and both of us sprang forward in the direction and no sooner had they\npalmer though very unwilling to go as well from real humanity and good nature as\ntime about what they should do and they agreed he should take orders directly and\n" ] ], [ [ "Notice how the samples start to become more and more reasonable as we add more data and increase the *n* parameter. We are still a long way to go though from realistic text generation, but at the same time we can see that with enough data even rudimentary algorithms can output something almost passable.", "_____no_output_____" ], [ "## VITERBI TEXT SEGMENTATION\n\n### Overview\n\nWe are given a string containing words of a sentence, but all the spaces are gone! It is very hard to read and we would like to separate the words in the string. We can accomplish this by employing the `Viterbi Segmentation` algorithm. It takes as input the string to segment and a text model, and it returns a list of the separate words.\n\nThe algorithm operates in a dynamic programming approach. It starts from the beginning of the string and iteratively builds the best solution using previous solutions. It accomplishes that by segmentating the string into \"windows\", each window representing a word (real or gibberish). It then calculates the probability of the sequence up that window/word occurring and updates its solution. When it is done, it traces back from the final word and finds the complete sequence of words.", "_____no_output_____" ], [ "### Implementation", "_____no_output_____" ] ], [ [ "psource(viterbi_segment)", "_____no_output_____" ] ], [ [ "The function takes as input a string and a text model, and returns the most probable sequence of words, together with the probability of that sequence.\n\nThe \"window\" is `w` and it includes the characters from *j* to *i*. We use it to \"build\" the following sequence: from the start to *j* and then `w`. We have previously calculated the probability from the start to *j*, so now we multiply that probability by `P[w]` to get the probability of the whole sequence. If that probability is greater than the probability we have calculated so far for the sequence from the start to *i* (`best[i]`), we update it.", "_____no_output_____" ], [ "### Example\n\nThe model the algorithm uses is the `UnigramTextModel`. First we will build the model using the *Flatland* text and then we will try and separate a space-devoid sentence.", "_____no_output_____" ] ], [ [ "flatland = open_data(\"EN-text/flatland.txt\").read()\nwordseq = words(flatland)\nP = UnigramWordModel(wordseq)\ntext = \"itiseasytoreadwordswithoutspaces\"\n\ns, p = viterbi_segment(text,P)\nprint(\"Sequence of words is:\",s)\nprint(\"Probability of sequence is:\",p)", "Sequence of words is: ['it', 'is', 'easy', 'to', 'read', 'words', 'without', 'spaces']\nProbability of sequence is: 2.273672843573388e-24\n" ] ], [ [ "The algorithm correctly retrieved the words from the string. It also gave us the probability of this sequence, which is small, but still the most probable segmentation of the string.", "_____no_output_____" ], [ "## INFORMATION RETRIEVAL\n\n### Overview\n\nWith **Information Retrieval (IR)** we find documents that are relevant to a user's needs for information. A popular example is a web search engine, which finds and presents to a user pages relevant to a query. Information retrieval is not limited only to returning documents though, but can also be used for other type of queries. For example, answering questions when the query is a question, returning information when the query is a concept, and many other applications. An IR system is comprised of the following:\n\n* A body (called corpus) of documents: A collection of documents, where the IR will work on.\n\n* A query language: A query represents what the user wants.\n\n* Results: The documents the system grades as relevant to a user's query and needs.\n\n* Presententation of the results: How the results are presented to the user.\n\nHow does an IR system determine which documents are relevant though? We can sign a document as relevant if all the words in the query appear in it, and sign it as irrelevant otherwise. We can even extend the query language to support boolean operations (for example, \"paint AND brush\") and then sign as relevant the outcome of the query for the document. This technique though does not give a level of relevancy. All the documents are either relevant or irrelevant, but in reality some documents are more relevant than others.\n\nSo, instead of a boolean relevancy system, we use a *scoring function*. There are many scoring functions around for many different situations. One of the most used takes into account the frequency of the words appearing in a document, the frequency of a word appearing across documents (for example, the word \"a\" appears a lot, so it is not very important) and the length of a document (since large documents will have higher occurrences for the query terms, but a short document with a lot of occurrences seems very relevant). We combine these properties in a formula and we get a numeric score for each document, so we can then quantify relevancy and pick the best documents.\n\nThese scoring functions are not perfect though and there is room for improvement. For instance, for the above scoring function we assume each word is independent. That is not the case though, since words can share meaning. For example, the words \"painter\" and \"painters\" are closely related. If in a query we have the word \"painter\" and in a document the word \"painters\" appears a lot, this might be an indication that the document is relevant but we are missing out since we are only looking for \"painter\". There are a lot of ways to combat this. One of them is to reduce the query and document words into their stems. For example, both \"painter\" and \"painters\" have \"paint\" as their stem form. This can improve slightly the performance of algorithms.\n\nTo determine how good an IR system is, we give the system a set of queries (for which we know the relevant pages beforehand) and record the results. The two measures for performance are *precision* and *recall*. Precision measures the proportion of result documents that actually are relevant. Recall measures the proportion of relevant documents (which, as mentioned before, we know in advance) appearing in the result documents.", "_____no_output_____" ], [ "### Implementation\n\nYou can read the source code by running the command below:", "_____no_output_____" ] ], [ [ "psource(IRSystem)", "_____no_output_____" ] ], [ [ "The `stopwords` argument signifies words in the queries that should not be accounted for in documents. Usually they are very common words that do not add any significant information for a document's relevancy.\n\nA quick guide for the functions in the `IRSystem` class:\n\n* `index_document`: Add document to the collection of documents (named `documents`), which is a list of tuples. Also, count how many times each word in the query appears in each document.\n\n* `index_collection`: Index a collection of documents given by `filenames`.\n\n* `query`: Returns a list of `n` pairs of `(score, docid)` sorted on the score of each document. Also takes care of the special query \"learn: X\", where instead of the normal functionality we present the output of the terminal command \"X\".\n\n* `score`: Scores a given document for the given word using `log(1+k)/log(1+n)`, where `k` is the number of query words in a document and `k` is the total number of words in the document. Other scoring functions can be used and you can overwrite this function to better suit your needs.\n\n* `total_score`: Calculate the sum of all the query words in given document.\n\n* `present`/`present_results`: Presents the results as a list.\n\nWe also have the class `Document` that holds metadata of documents, like their title, url and number of words. An additional class, `UnixConsultant`, can be used to initialize an IR System for Unix command manuals. This is the example we will use to showcase the implementation.", "_____no_output_____" ], [ "### Example\n\nFirst let's take a look at the source code of `UnixConsultant`.", "_____no_output_____" ] ], [ [ "psource(UnixConsultant)", "_____no_output_____" ] ], [ [ "The class creates an IR System with the stopwords \"how do i the a of\". We could add more words to exclude, but the queries we will test will generally be in that format, so it is convenient. After the initialization of the system, we get the manual files and start indexing them.\n\nLet's build our Unix consultant and run a query:", "_____no_output_____" ] ], [ [ "uc = UnixConsultant()\n\nq = uc.query(\"how do I remove a file\")\n\ntop_score, top_doc = q[0][0], q[0][1]\nprint(top_score, uc.documents[top_doc].url)", "0.7682667868462166 aima-data/MAN/rm.txt\n" ] ], [ [ "We asked how to remove a file and the top result was the `rm` (the Unix command for remove) manual. This is exactly what we wanted! Let's try another query:", "_____no_output_____" ] ], [ [ "q = uc.query(\"how do I delete a file\")\n\ntop_score, top_doc = q[0][0], q[0][1]\nprint(top_score, uc.documents[top_doc].url)", "0.7546722691607105 aima-data/MAN/diff.txt\n" ] ], [ [ "Even though we are basically asking for the same thing, we got a different top result. The `diff` command shows the differences between two files. So the system failed us and presented us an irrelevant document. Why is that? Unfortunately our IR system considers each word independent. \"Remove\" and \"delete\" have similar meanings, but since they are different words our system will not make the connection. So, the `diff` manual which mentions a lot the word `delete` gets the nod ahead of other manuals, while the `rm` one isn't in the result set since it doesn't use the word at all.", "_____no_output_____" ], [ "## INFORMATION EXTRACTION\n\n**Information Extraction (IE)** is a method for finding occurrences of object classes and relationships in text. Unlike IR systems, an IE system includes (limited) notions of syntax and semantics. While it is difficult to extract object information in a general setting, for more specific domains the system is very useful. One model of an IE system makes use of templates that match with strings in a text.\n\nA typical example of such a model is reading prices from web pages. Prices usually appear after a dollar and consist of numbers, maybe followed by two decimal points. Before the price, usually there will appear a string like \"price:\". Let's build a sample template.\n\nWith the following regular expression (*regex*) we can extract prices from text:\n\n`[$][0-9]+([.][0-9][0-9])?`\n\nWhere `+` means 1 or more occurrences and `?` means atmost 1 occurrence. Usually a template consists of a prefix, a target and a postfix regex. In this template, the prefix regex can be \"price:\", the target regex can be the above regex and the postfix regex can be empty.\n\nA template can match with multiple strings. If this is the case, we need a way to resolve the multiple matches. Instead of having just one template, we can use multiple templates (ordered by priority) and pick the match from the highest-priority template. We can also use other ways to pick. For the dollar example, we can pick the match closer to the numerical half of the highest match. For the text \"Price $90, special offer $70, shipping $5\" we would pick \"$70\" since it is closer to the half of the highest match (\"$90\").", "_____no_output_____" ], [ "The above is called *attribute-based* extraction, where we want to find attributes in the text (in the example, the price). A more sophisticated extraction system aims at dealing with multiple objects and the relations between them. When such a system reads the text \"$100\", it should determine not only the price but also which object has that price.\n\nRelation extraction systems can be built as a series of finite state automata. Each automaton receives as input text, performs transformations on the text and passes it on to the next automaton as input. An automata setup can consist of the following stages:\n\n1. **Tokenization**: Segments text into tokens (words, numbers and punctuation).\n\n2. **Complex-word Handling**: Handles complex words such as \"give up\", or even names like \"Smile Inc.\".\n\n3. **Basic-group Handling**: Handles noun and verb groups, segmenting the text into strings of verbs or nouns (for example, \"had to give up\").\n\n4. **Complex Phrase Handling**: Handles complex phrases using finite-state grammar rules. For example, \"Human+PlayedChess(\"with\" Human+)?\" can be one template/rule for capturing a relation of someone playing chess with others.\n\n5. **Structure Merging**: Merges the structures built in the previous steps.", "_____no_output_____" ], [ "Finite-state, template based information extraction models work well for restricted domains, but perform poorly as the domain becomes more and more general. There are many models though to choose from, each with its own strengths and weaknesses. Some of the models are the following:\n\n* **Probabilistic**: Using Hidden Markov Models, we can extract information in the form of prefix, target and postfix from a given text. Two advantages of using HMMs over templates is that we can train HMMs from data and don't need to design elaborate templates, and that a probabilistic approach behaves well even with noise. In a regex, if one character is off, we do not have a match, while with a probabilistic approach we have a smoother process.\n\n* **Conditional Random Fields**: One problem with HMMs is the assumption of state independence. CRFs are very similar to HMMs, but they don't have the latter's constraint. In addition, CRFs make use of *feature functions*, which act as transition weights. For example, if for observation $e_{i}$ and state $x_{i}$ we have $e_{i}$ is \"run\" and $x_{i}$ is the state ATHLETE, we can have $f(x_{i}, e_{i}) = 1$ and equal to 0 otherwise. We can use multiple, overlapping features, and we can even use features for state transitions. Feature functions don't have to be binary (like the above example) but they can be real-valued as well. Also, we can use any $e$ for the function, not just the current observation. To bring it all together, we weigh a transition by the sum of features.\n\n* **Ontology Extraction**: This is a method for compiling information and facts in a general domain. A fact can be in the form of `NP is NP`, where `NP` denotes a noun-phrase. For example, \"Rabbit is a mammal\".", "_____no_output_____" ], [ "## DECODERS\n\n### Introduction\n\nIn this section we will try to decode ciphertext using probabilistic text models. A ciphertext is obtained by performing encryption on a text message. This encryption lets us communicate safely, as anyone who has access to the ciphertext but doesn't know how to decode it cannot read the message. We will restrict our study to <b>Monoalphabetic Substitution Ciphers</b>. These are primitive forms of cipher where each letter in the message text (also known as plaintext) is replaced by another another letter of the alphabet.\n\n### Shift Decoder\n\n#### The Caesar cipher\n\nThe Caesar cipher, also known as shift cipher is a form of monoalphabetic substitution ciphers where each letter is <i>shifted</i> by a fixed value. A shift by <b>`n`</b> in this context means that each letter in the plaintext is replaced with a letter corresponding to `n` letters down in the alphabet. For example the plaintext `\"ABCDWXYZ\"` shifted by `3` yields `\"DEFGZABC\"`. Note how `X` became `A`. This is because the alphabet is cyclic, i.e. the letter after the last letter in the alphabet, `Z`, is the first letter of the alphabet - `A`.", "_____no_output_____" ] ], [ [ "plaintext = \"ABCDWXYZ\"\nciphertext = shift_encode(plaintext, 3)\nprint(ciphertext)", "DEFGZABC\n" ] ], [ [ "#### Decoding a Caesar cipher\n\nTo decode a Caesar cipher we exploit the fact that not all letters in the alphabet are used equally. Some letters are used more than others and some pairs of letters are more probable to occur together. We call a pair of consecutive letters a <b>bigram</b>.", "_____no_output_____" ] ], [ [ "print(bigrams('this is a sentence'))", "['th', 'hi', 'is', 's ', ' i', 'is', 's ', ' a', 'a ', ' s', 'se', 'en', 'nt', 'te', 'en', 'nc', 'ce']\n" ] ], [ [ "We use `CountingProbDist` to get the probability distribution of bigrams. In the latin alphabet consists of only only `26` letters. This limits the total number of possible substitutions to `26`. We reverse the shift encoding for a given `n` and check how probable it is using the bigram distribution. We try all `26` values of `n`, i.e. from `n = 0` to `n = 26` and use the value of `n` which gives the most probable plaintext.", "_____no_output_____" ] ], [ [ "%psource ShiftDecoder", "_____no_output_____" ] ], [ [ "#### Example\n\nLet us encode a secret message using Caeasar cipher and then try decoding it using `ShiftDecoder`. We will again use `flatland.txt` to build the text model", "_____no_output_____" ] ], [ [ "plaintext = \"This is a secret message\"\nciphertext = shift_encode(plaintext, 13)\nprint('The code is', '\"' + ciphertext + '\"')", "The code is \"Guvf vf n frperg zrffntr\"\n" ], [ "flatland = open_data(\"EN-text/flatland.txt\").read()\ndecoder = ShiftDecoder(flatland)\n\ndecoded_message = decoder.decode(ciphertext)\nprint('The decoded message is', '\"' + decoded_message + '\"')", "The decoded message is \"This is a secret message\"\n" ] ], [ [ "### Permutation Decoder\nNow let us try to decode messages encrypted by a general mono-alphabetic substitution cipher. The letters in the alphabet can be replaced by any permutation of letters. For example, if the alphabet consisted of `{A B C}` then it can be replaced by `{A C B}`, `{B A C}`, `{B C A}`, `{C A B}`, `{C B A}` or even `{A B C}` itself. Suppose we choose the permutation `{C B A}`, then the plain text `\"CAB BA AAC\"` would become `\"ACB BC CCA\"`. We can see that Caesar cipher is also a form of permutation cipher where the permutation is a cyclic permutation. Unlike the Caesar cipher, it is infeasible to try all possible permutations. The number of possible permutations in Latin alphabet is `26!` which is of the order $10^{26}$. We use graph search algorithms to search for a 'good' permutation.", "_____no_output_____" ] ], [ [ "psource(PermutationDecoder)", "_____no_output_____" ] ], [ [ "Each state/node in the graph is represented as a letter-to-letter map. If there is no mapping for a letter, it means the letter is unchanged in the permutation. These maps are stored as dictionaries. Each dictionary is a 'potential' permutation. We use the word 'potential' because every dictionary doesn't necessarily represent a valid permutation since a permutation cannot have repeating elements. For example the dictionary `{'A': 'B', 'C': 'X'}` is invalid because `'A'` is replaced by `'B'`, but so is `'B'` because the dictionary doesn't have a mapping for `'B'`. Two dictionaries can also represent the same permutation e.g. `{'A': 'C', 'C': 'A'}` and `{'A': 'C', 'B': 'B', 'C': 'A'}` represent the same permutation where `'A'` and `'C'` are interchanged and all other letters remain unaltered. To ensure that we get a valid permutation, a goal state must map all letters in the alphabet. We also prevent repetitions in the permutation by allowing only those actions which go to a new state/node in which the newly added letter to the dictionary maps to previously unmapped letter. These two rules together ensure that the dictionary of a goal state will represent a valid permutation.\nThe score of a state is determined using word scores, unigram scores, and bigram scores. Experiment with different weightages for word, unigram and bigram scores and see how they affect the decoding.", "_____no_output_____" ] ], [ [ "ciphertexts = ['ahed world', 'ahed woxld']\n\npd = PermutationDecoder(canonicalize(flatland))\nfor ctext in ciphertexts:\n print('\"{}\" decodes to \"{}\"'.format(ctext, pd.decode(ctext)))", "\"ahed world\" decodes to \"shed could\"\n\"ahed woxld\" decodes to \"shew atiow\"\n" ] ], [ [ "As evident from the above example, permutation decoding using best first search is sensitive to initial text. This is because not only the final dictionary, with substitutions for all letters, must have good score but so must the intermediate dictionaries. You could think of it as performing a local search by finding substitutions for each letter one by one. We could get very different results by changing even a single letter because that letter could be a deciding factor for selecting substitution in early stages which snowballs and affects the later stages. To make the search better we can use different definitions of score in different stages and optimize on which letter to substitute first.", "_____no_output_____" ] ] ]

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[ [ [ "# VacationPy\n----\n\n#### Note\n* Keep an eye on your API usage. Use https://developers.google.com/maps/reporting/gmp-reporting as reference for how to monitor your usage and billing.\n\n* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.", "_____no_output_____" ], [ "### Store Part I results into DataFrame\n* Load the csv exported in Part I to a DataFrame", "_____no_output_____" ] ], [ [ "# Dependencies and Setup\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport requests\nimport gmaps\nimport os\n\n# Import API key\nfrom api_keys import g_key\nprint(g_key)", "AIzaSyBaFJ4YJWuNolXhaMX3S48Oxw7K44uUK6w\n" ], [ "csvfile = \"../output_data/weather_data.csv\"\nheatmap_df = pd.read_csv(csvfile)\nheatmap_df", "_____no_output_____" ] ], [ [ "### Humidity Heatmap\n* Configure gmaps.\n* Use the Lat and Lng as locations and Humidity as the weight.\n* Add Heatmap layer to map.", "_____no_output_____" ] ], [ [ "gmaps.configure(api_key=g_key)\nlocations = heatmap_df[[\"Lat\", \"Lng\"]].astype(float)\nhumidity = heatmap_df[\"Humidity\"].astype(float)\nhumidity.max()", "_____no_output_____" ], [ "fig = gmaps.figure()\nheatmap_layer = gmaps.heatmap_layer(locations, weights=humidity, \n dissipating=False, max_intensity=100,\n point_radius = 1)\nfig.add_layer(heatmap_layer)\nfig", "_____no_output_____" ] ], [ [ "### Create new DataFrame fitting weather criteria\n* Narrow down the cities to fit weather conditions.\n* Drop any rows will null values.", "_____no_output_____" ] ], [ [ "heatmap_data = heatmap_df.loc[(heatmap_df[\"Max Temp\"] < 100) & \n (heatmap_df[\"Max Temp\"] >=70) &\n (heatmap_df[\"Cloudiness\"] == 0) &\n (heatmap_df[\"Wind Speed\"] < 10)].dropna()\nheatmap_data", "_____no_output_____" ] ], [ [ "### Hotel Map\n* Store into variable named `hotel_df`.\n* Add a \"Hotel Name\" column to the DataFrame.\n* Set parameters to search for hotels with 5000 meters.\n* Hit the Google Places API for each city's coordinates.\n* Store the first Hotel result into the DataFrame.\n* Plot markers on top of the heatmap.", "_____no_output_____" ] ], [ [ "hotel_df = heatmap_data.loc[:,[\"City\",\"Country\",\"Lat\",\"Lng\"]]\nhotel_df[\"Hotel Name\"] = \"\"\nhotel_df", "_____no_output_____" ], [ "import json", "_____no_output_____" ], [ "url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json?\"\nparams = {\n \"radius\": 5000,\n \"type\": \"hotel\",\n \"keyword\": \"hotel\",\n \"key\": g_key}\n\nfor index, row in hotel_df.iterrows():\n lat = row['Lat']\n lon = row['Lng']\n city_name = row['City']\n\n params['location'] = f\"{lat},{lon}\"\n response = requests.get(url, params=params).json()\n results = response['results']\n \n try:\n print(f\"Closest hotel in {city_name} is {results[0]['name']}.\")\n hotel_df.loc[index, \"Hotel Name\"] = results[0]['name']\n\n # if there is no hotel available, show missing field\n except (KeyError, IndexError):\n print(\"No hotel\")", "No hotel\nNo hotel\nNo hotel\nClosest hotel in Sakakah is Raoum Inn Hotel.\nNo hotel\nNo hotel\nClosest hotel in Yulara is Sails in the Desert.\nClosest hotel in Tupã is Grande Hotel Tamoios.\nClosest hotel in Erzin is Hattusa Vacation Thermal Club Erzin.\nNo hotel\nClosest hotel in Ibrā’ is Ibra Hotel.\nClosest hotel in Atar is Odar kanawal.\nClosest hotel in Cabo San Lucas is Welk Resorts Cabo San Lucas - Sirena del Mar.\nClosest hotel in Bani Walid is فندق الزيتونة.\n" ], [ "hotel_df", "_____no_output_____" ], [ "hotel_drop = hotel_df.loc[hotel_df[\"Hotel Name\"] != \"\"]\nhotel_drop", "_____no_output_____" ], [ "# NOTE: Do not change any of the code in this cell\n\n# Using the template add the hotel marks to the heatmap\ninfo_box_template = \"\"\"\n<dl>\n<dt>Name</dt><dd>{Hotel Name}</dd>\n<dt>City</dt><dd>{City}</dd>\n<dt>Country</dt><dd>{Country}</dd>\n</dl>\n\"\"\"\n# Store the DataFrame Row\n\nfor index, row in hotel_drop.iterrows():\n city = row['City']\n country = row['Country']\n lat = row['Lat']\n lon = row['Lng']\n hotel_name = row['Hotel Name']\n\n# NOTE: be sure to update with your DataFrame name\nhotel_info = [info_box_template.format(**row) for index, row in hotel_drop.iterrows()]\nlocations = hotel_drop[[\"Lat\", \"Lng\"]]\n\nhotel_info", "_____no_output_____" ], [ "# Add marker layer ontop of heat map\n\nmarkers = gmaps.marker_layer(locations, info_box_content = hotel_info)\nfig.add_layer(markers)\n\n# Display figure\n\nfig", "_____no_output_____" ] ], [ [ "- It found only 2 locations which has a max temperature lower than 80 degrees but higher than 70. Thus, tried to find lower then 100 degrees but higher than 70 and found 8 locations.\n- We can find more locations in Northern Hemisphere then southern.\n- In addition, most hotels are located near the Mediterranean area.\n", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# Cálculo de la factura eléctrica\n\n- Recuperación de datos de consumo eléctrico horario del medidor de consumo ([enerPI]()) vía JSON.\nPara otros casos, formar un `pandas.Series` de índice horario con datos de consumo en kWh.\n- Generación de factura eléctrica mediante `esiosdata.FacturaElec`\n- Simulación de cambio de tarifa eléctrica para el mismo consumo\n- Alguna gráfica de los patrones de consumo diario", "_____no_output_____" ] ], [ [ "%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\nfrom glob import glob\nimport matplotlib.pyplot as plt\nimport os\nimport pandas as pd\nimport requests\nfrom esiosdata import FacturaElec\nfrom esiosdata.prettyprinting import *\n\n\n# enerPI JSON API\nip_enerpi = '192.168.1.44'\nt0, tf = '2016-11-01', '2016-12-24'\nurl = 'http://{}/enerpi/api/consumption/from/{}/to/{}'.format(ip_enerpi, t0, tf)\nprint(url)\nr = requests.get(url)\nif r.ok:\n data = r.json()\n data_consumo = pd.DataFrame(pd.Series(data, name='kWh')).sort_index().reset_index()\n data_consumo.index = data_consumo['index'].apply(lambda x: pd.Timestamp.fromtimestamp(float(x) / 1000.))\n data_consumo.drop('index', axis=1, inplace=True)\n print_ok(data_consumo.head())\n print_ok(data_consumo.tail())\nelse:\n print_err(r)", "http://192.168.1.44/enerpi/api/consumption/from/2016-11-01/to/2016-12-24\n\u001b[1m\u001b[32m kWh\nindex \n2016-11-01 00:00:00 0.3179\n2016-11-01 01:00:00 0.2329\n2016-11-01 02:00:00 0.2317\n2016-11-01 03:00:00 0.2343\n2016-11-01 04:00:00 0.2242\u001b[0m\n\u001b[1m\u001b[32m kWh\nindex \n2016-12-24 19:00:00 0.3294\n2016-12-24 20:00:00 0.5632\n2016-12-24 21:00:00 0.4405\n2016-12-24 22:00:00 0.2757\n2016-12-24 23:00:00 0.2938\u001b[0m\n" ], [ "# Consumo Total en intervalo:\nc_tot = round(data_consumo.kWh.round(3).sum(), 3)\nprint_ok(c_tot)\n\n# Plot consumo diario en kWh\ndata_consumo.kWh.resample('D').sum().plot(figsize=(16, 9));", "\u001b[1m\u001b[32m517.615\u001b[0m\n" ] ], [ [ "## Factura eléctrica con datos horarios", "_____no_output_____" ] ], [ [ "factura = FacturaElec(consumo=data_consumo.kWh)\nprint_info(factura)", "Asignando timezone a consumo horario: Europe/Madrid\n\u001b[34mFACTURA ELÉCTRICA:\n--------------------------------------------------------------------------------\n* Fecha inicio \t31/10/2016\n* Fecha final \t24/12/2016\n* Peaje de acceso \t2.0A (General)\n* Potencia contratada \t3.45 kW\n* Consumo periodo \t517.61 kWh\n* ¿Bono Social? \tNo\n* Equipo de medida \t1.43 €\n* Impuestos \tPenínsula y Baleares (IVA)\n* Días facturables \t54\n--------------------------------------------------------------------------------\n\n- CÁLCULO DEL TÉRMINO FIJO POR POTENCIA CONTRATADA:\n 3.45 kW * 42.043426 €/kW/año * 54 días (2016) / 366 = 21.40 €\n -> Término fijo 21.40 €\n\n- CÁLCULO DEL TÉRMINO VARIABLE POR ENERGÍA CONSUMIDA (TARIFA 2.0A):\n Periodo 1: 0.125777 €/kWh -> 65.10€(P1)\n - Peaje de acceso: 518kWh * 0.044027€/kWh = 22.79€\n - Coste de la energía: 518kWh * 0.081750€/kWh = 42.31€\n -> Término de consumo 65.10 €\n\n\n\n- IMPUESTO ELÉCTRICO:\n 5.11269632% x (21.40€ + 65.10€) 4.42 €\n\n- EQUIPO DE MEDIDA: 1.43 €\n\n- IVA O EQUIVALENTE:\n 21% de 92.35€ 19.39 €\n\n################################################################################\n# TOTAL FACTURA 111.74 €\n################################################################################\n\u001b[0m\n" ], [ "factura.tipo_peaje = 2\nprint_cyan(factura)", "\u001b[36mFACTURA ELÉCTRICA:\n--------------------------------------------------------------------------------\n* Fecha inicio \t31/10/2016\n* Fecha final \t24/12/2016\n* Peaje de acceso \t2.0DHA (Nocturna)\n* Potencia contratada \t3.45 kW\n* Consumo periodo \t517.61 kWh\n* ¿Bono Social? \tNo\n* Equipo de medida \t1.43 €\n* Impuestos \tPenínsula y Baleares (IVA)\n* Días facturables \t54\n--------------------------------------------------------------------------------\n\n- CÁLCULO DEL TÉRMINO FIJO POR POTENCIA CONTRATADA:\n 3.45 kW * 42.043426 €/kW/año * 54 días (2016) / 366 = 21.40 €\n -> Término fijo 21.40 €\n\n- CÁLCULO DEL TÉRMINO VARIABLE POR ENERGÍA CONSUMIDA (TARIFA 2.0DHA):\n Periodo 1: 0.147546 €/kWh -> 38.45€(P1)\n - Peaje de acceso: 261kWh * 0.062012€/kWh = 16.16€\n - Coste de la energía: 261kWh * 0.085534€/kWh = 22.29€\n Periodo 2: 0.073607 €/kWh -> 18.92€(P2)\n - Peaje de acceso: 257kWh * 0.002215€/kWh = 0.57€\n - Coste de la energía: 257kWh * 0.071392€/kWh = 18.35€\n -> Término de consumo 57.37 €\n\n\n\n- IMPUESTO ELÉCTRICO:\n 5.11269632% x (21.40€ + 57.37€) 4.03 €\n\n- EQUIPO DE MEDIDA: 1.43 €\n\n- IVA O EQUIVALENTE:\n 21% de 84.23€ 17.69 €\n\n################################################################################\n# TOTAL FACTURA 101.92 €\n################################################################################\n\u001b[0m\n" ], [ "factura.tipo_peaje = 3\nprint_magenta(factura)", "\u001b[35mFACTURA ELÉCTRICA:\n--------------------------------------------------------------------------------\n* Fecha inicio \t31/10/2016\n* Fecha final \t24/12/2016\n* Peaje de acceso \t2.0DHS (Vehículo eléctrico)\n* Potencia contratada \t3.45 kW\n* Consumo periodo \t517.61 kWh\n* ¿Bono Social? \tNo\n* Equipo de medida \t1.43 €\n* Impuestos \tPenínsula y Baleares (IVA)\n* Días facturables \t54\n--------------------------------------------------------------------------------\n\n- CÁLCULO DEL TÉRMINO FIJO POR POTENCIA CONTRATADA:\n 3.45 kW * 42.043426 €/kW/año * 54 días (2016) / 366 = 21.40 €\n -> Término fijo 21.40 €\n\n- CÁLCULO DEL TÉRMINO VARIABLE POR ENERGÍA CONSUMIDA (TARIFA 2.0DHS):\n Periodo 1: 0.148466 €/kWh -> 39.89€(P1)\n - Peaje de acceso: 269kWh * 0.062012€/kWh = 16.66€\n - Coste de la energía: 269kWh * 0.086454€/kWh = 23.23€\n Periodo 2: 0.079779 €/kWh -> 13.46€(P2)\n - Peaje de acceso: 169kWh * 0.002879€/kWh = 0.49€\n - Coste de la energía: 169kWh * 0.076900€/kWh = 12.97€\n Periodo 3: 0.062719 €/kWh -> 5.03€(P3)\n - Peaje de acceso: 80kWh * 0.000886€/kWh = 0.07€\n - Coste de la energía: 80kWh * 0.061833€/kWh = 4.96€\n -> Término de consumo 58.38 €\n\n\n\n- IMPUESTO ELÉCTRICO:\n 5.11269632% x (21.40€ + 58.38€) 4.08 €\n\n- EQUIPO DE MEDIDA: 1.43 €\n\n- IVA O EQUIVALENTE:\n 21% de 85.29€ 17.91 €\n\n################################################################################\n# TOTAL FACTURA 103.20 €\n################################################################################\n\u001b[0m\n" ] ], [ [ "### EXPORT TO CSV\n\n* Para importar en https://facturaluz2.cnmc.es/facturaluz2.html", "_____no_output_____" ] ], [ [ "path_csv = os.path.expanduser('~/Desktop/')\ndf_csv = factura.generacion_csv_oficial_consumo_horario(path_csv)\nprint_ok(df_csv.tail())\nfile_path = glob(path_csv + '*.csv')[0]\nwith open(file_path, 'r') as f:\n print_magenta(f.read()[:500])", "\u001b[1m\u001b[32m CUPS Fecha Hora Consumo_kWh Metodo_obtencion\nindex \n2016-12-24 19:00:00+01:00 ES00XXXXXXXXXXXXXXDB 24/12/2016 20 0.329 R\n2016-12-24 20:00:00+01:00 ES00XXXXXXXXXXXXXXDB 24/12/2016 21 0.563 R\n2016-12-24 21:00:00+01:00 ES00XXXXXXXXXXXXXXDB 24/12/2016 22 0.440 R\n2016-12-24 22:00:00+01:00 ES00XXXXXXXXXXXXXXDB 24/12/2016 23 0.276 R\n2016-12-24 23:00:00+01:00 ES00XXXXXXXXXXXXXXDB 24/12/2016 24 0.294 R\u001b[0m\n\u001b[35mCUPS;Fecha;Hora;Consumo_kWh;Metodo_obtencion\nES00XXXXXXXXXXXXXXDB;01/11/2016;1;0,318;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;2;0,233;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;3;0,232;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;4;0,234;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;5;0,224;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;6;0,235;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;7;0,226;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;8;0,236;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;9;0,353;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;10;1,106;R\nES00XXXXXXXXXXXXXXDB;01/11/2016;11\u001b[0m\n" ] ], [ [ "### Plots del periodo facturado", "_____no_output_____" ] ], [ [ "print_ok('Consumo diario')\nfactura.plot_consumo_diario()\nplt.show()", "\u001b[1m\u001b[32mConsumo diario\u001b[0m\n" ], [ "print_ok('Patrón de consumo semanal')\nfactura.plot_patron_semanal_consumo()\nplt.show()", "\u001b[1m\u001b[32mPatrón de consumo semanal\u001b[0m\n" ] ] ]

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