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[ "MIT" ]

[ "MIT" ]

[ [ [ "# DAPA Tutorial #3: Timeseries - Sentinel-2", "_____no_output_____" ], [ "## Load environment variables\nPlease make sure that the environment variable \"DAPA_URL\" is set in the `custom.env` file. You can check this by executing the following block. \n\nIf DAPA_URL is not set, please create a text file named `custom.env` in your home directory with the following input: \n>DAPA_URL=YOUR-PERSONAL-DAPA-APP-URL", "_____no_output_____" ] ], [ [ "from edc import setup_environment_variables\nsetup_environment_variables()", "_____no_output_____" ] ], [ [ "## Check notebook compabtibility\n**Please note:** If you conduct this notebook again at a later time, the base image of this Jupyter Hub service can include newer versions of the libraries installed. Thus, the notebook execution can fail. This compatibility check is only necessary when something is broken. ", "_____no_output_____" ] ], [ [ "from edc import check_compatibility\ncheck_compatibility(\"user-0.24.5\", dependencies=[])", "_____no_output_____" ] ], [ [ "## Load libraries\nPython libraries used in this tutorial will be loaded.", "_____no_output_____" ] ], [ [ "import os\nimport xarray as xr\nimport pandas as pd\nimport requests\nimport matplotlib\nfrom ipyleaflet import Map, Rectangle, Marker, DrawControl, basemaps, basemap_to_tiles\n%matplotlib inline", "_____no_output_____" ] ], [ [ "## Set DAPA endpoint\nExecute the following code to check if the DAPA_URL is available in the environment variable and to set the `/dapa` endpoint. ", "_____no_output_____" ] ], [ [ "service_url = None\ndapa_url = None\n\nif 'DAPA_URL' not in os.environ:\n print('!! DAPA_URL does not exist as environment variable. Please make sure this is the case - see first block of this notebook! !!')\nelse: \n service_url = os.environ['DAPA_URL']\n dapa_url = '{}/{}'.format(service_url, 'oapi')\n print('DAPA path: {}'.format(dapa_url.replace(service_url, '')))", "DAPA path: /oapi\n" ] ], [ [ "## Get collections supported by this endpoint\nThis request provides a list of collections. The path of each collection is used as starting path of this service.", "_____no_output_____" ] ], [ [ "collections_url = '{}/{}'.format(dapa_url, 'collections')\ncollections = requests.get(collections_url, headers={'Accept': 'application/json'})\n\nprint('DAPA path: {}'.format(collections.url.replace(service_url, '')))\ncollections.json()", "DAPA path: /oapi/collections\n" ] ], [ [ "## Get fields of collection Sentinel-2 L2A\nThe fields (or variables in other DAPA endpoints - these are the bands of the raster data) can be retrieved in all requests to the DAPA endpoint. In addition to the fixed set of fields, \"virtual\" fields can be used to conduct math operations (e.g., the calculation of indices). ", "_____no_output_____" ] ], [ [ "collection = 'S2L2A'\n\nfields_url = '{}/{}/{}/{}'.format(dapa_url, 'collections', collection, 'dapa/fields')\nfields = requests.get(fields_url, headers={'Accept': 'application/json'})\n\nprint('DAPA path: {}'.format(fields.url.replace(service_url, '')))\nfields.json()", "DAPA path: /oapi/collections/S2L2A/dapa/fields\n" ] ], [ [ "## Retrieve NDVI as 1d time-series extraced for a single point", "_____no_output_____" ], [ "### Set DAPA URL and parameters\nThe output of this request is a time-series requested from a point of interest (`timeseries/position` endpoint). As the input collection (S2L2A) is a multi-temporal raster and the requested geometry is a point, no aggregation is conducted.\n\nTo retrieve a time-series of a point, the parameter `point` needs to be provided. The `time` parameter allows to extract data only within a specific time span. The band (`field`) from which the point is being extracted needs to be specified as well.", "_____no_output_____" ] ], [ [ "# DAPA URL\nurl = '{}/{}/{}/{}'.format(dapa_url, 'collections', collection, 'dapa/timeseries/position')\n\n# Parameters for this request\nparams = {\n 'point': '11.49,48.05',\n 'time': '2018-04-01T00:00:00Z/2018-05-01T00:00:00Z',\n 'fields': 'NDVI=(B08-B04)/(B08%2BB04)' # Please note: + signs need to be URL encoded -> %2B\n}\n\n# show point in the map\nlocation = list(reversed([float(coord) for coord in params['point'].split(',')]))\nm = Map(\n basemap=basemap_to_tiles(basemaps.OpenStreetMap.Mapnik),\n center=location,\n zoom=10\n)\n\nmarker = Marker(location=location, draggable=False)\nm.add_layer(marker)\n\nm", "_____no_output_____" ] ], [ [ "### Build request URL and conduct request", "_____no_output_____" ] ], [ [ "params_str = \"&\".join(\"%s=%s\" % (k, v) for k,v in params.items())\nr = requests.get(url, params=params_str)\n\nprint('DAPA path: {}'.format(r.url.replace(service_url, '')))\nprint('Status code: {}'.format(r.status_code))", "DAPA path: /oapi/collections/S2L2A/dapa/timeseries/position?point=11.49,48.05&time=2018-04-01T00:00:00Z/2018-05-01T00:00:00Z&fields=NDVI=(B08-B04)/(B08%2BB04)\nStatus code: 200\n" ] ], [ [ "### Write timeseries dataset to CSV file\nThe response of this request returns data as CSV including headers splitted by comma. Additional output formats (e.g., CSV with headers included) will be integrated within the testbed activtiy. \n\nYou can either write the response to file or use it as string (`r.content` variable). ", "_____no_output_____" ] ], [ [ "# write time-series data to CSV file\nwith open('timeseries_s2.csv', 'wb') as filew:\n filew.write(r.content)", "_____no_output_____" ] ], [ [ "### Open timeseries dataset with pandas\nTime-series data can be opened, processed, and plotted easily with the `Pandas` library. You only need to specify the `datetime` column to automatically convert dates from string to a datetime object. ", "_____no_output_____" ] ], [ [ "# read data into Pandas dataframe\nds = pd.read_csv('timeseries_s2.csv', parse_dates=['datetime'])\n\n# set index to datetime column\nds.set_index('datetime', inplace=True)\n\n# show dataframe\nds", "_____no_output_____" ] ], [ [ "### Plot NDVI data", "_____no_output_____" ] ], [ [ "ds.plot()", "_____no_output_____" ] ], [ [ "### Output CSV file", "_____no_output_____" ] ], [ [ "!cat timeseries_s2.csv", "datetime,NDVI\r\r\n2018-04-02T10:24:35Z,0.7012165\r\r\n2018-04-04T10:10:21Z,0.22945666\r\r\n2018-04-07T10:20:20Z,0.8333333\r\r\n2018-04-09T10:13:43Z,0.63461536\r\r\n2018-04-12T10:20:24Z,0.10066674\r\r\n2018-04-14T10:15:36Z,0.5091714\r\r\n2018-04-17T10:20:21Z,0.49720672\r\r\n2018-04-19T10:14:57Z,0.68678766\r\r\n2018-04-22T10:21:15Z,0.67164177\r\r\n2018-04-24T10:15:26Z,0.0168028\r\r\n2018-04-27T10:20:22Z,0.77434456\r\r\n2018-04-29T10:12:58Z,0.2144858\r\r\n" ] ], [ [ "## Time-series aggregated over area", "_____no_output_____" ] ], [ [ "# DAPA URL\nurl = '{}/{}/{}/{}'.format(dapa_url, 'collections', collection, 'dapa/timeseries/area')\n\n# Parameters for this request\nparams = {\n #'point': '11.49,48.05',\n 'bbox': '11.49,48.05,11.66,48.22',\n 'aggregate': 'min,max,avg',\n 'time': '2018-04-01T00:00:00Z/2018-05-01T00:00:00Z',\n 'fields': 'NDVI=(B08-B04)/(B08%2BB04)' # Please note: + signs need to be URL encoded -> %2B\n}\n\nparams_str = \"&\".join(\"%s=%s\" % (k, v) for k,v in params.items())\nr = requests.get(url, params=params_str)\n\nprint('DAPA path: {}'.format(r.url.replace(service_url, '')))\nprint('Status code: {}'.format(r.status_code))", "DAPA path: /oapi/collections/S2L2A/dapa/timeseries/area?bbox=11.49,48.05,11.66,48.22&aggregate=min,max,avg&time=2018-04-01T00:00:00Z/2018-05-01T00:00:00Z&fields=NDVI=(B08-B04)/(B08%2BB04)\nStatus code: 200\n" ], [ "# read data into Pandas dataframe\nfrom io import StringIO\nds = pd.read_csv(StringIO(r.text), parse_dates=['datetime'])\n\n# set index to datetime column\nds.set_index('datetime', inplace=True)\n\n# show dataframe\nds", "_____no_output_____" ], [ "ds.plot()", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport gc\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline\n\npal = sns.color_palette()\n", "_____no_output_____" ], [ "df_train = pd.read_csv('train.csv')\ndf_train.head()", "_____no_output_____" ], [ "print('Total number of question pairs for training: {}'.format(len(df_train)))\nprint('Duplicate pairs: {}%'.format(round(df_train['is_duplicate'].mean()*100, 2)))\nqids = pd.Series(df_train['qid1'].tolist() + df_train['qid2'].tolist())\nprint('Total number of questions in the training data: {}'.format(len(\n np.unique(qids))))\nprint('Number of questions that appear multiple times: {}'.format(np.sum(qids.value_counts() > 1)))\n\nplt.figure(figsize=(12, 5))\nplt.hist(qids.value_counts(), bins=50)\nplt.yscale('log', nonposy='clip')\nplt.title('Log-Histogram of question appearance counts')\nplt.xlabel('Number of occurences of question')\nplt.ylabel('Number of questions')\nprint()", "Total number of question pairs for training: 404290\nDuplicate pairs: 36.92%\nTotal number of questions in the training data: 537933\nNumber of questions that appear multiple times: 111780\n\n" ], [ "df_test = pd.read_csv('test.csv')\ndf_test.head()", "_____no_output_____" ], [ "print('Total number of question pairs for testing: {}'.format(len(df_test)))", "Total number of question pairs for testing: 200000\n" ], [ "train_qs = pd.Series(df_train['question1'].tolist() + df_train['question2'].tolist()).astype(str)\ntest_qs = pd.Series(df_test['question1'].tolist() + df_test['question2'].tolist()).astype(str)\n\ndist_train = train_qs.apply(len)\ndist_test = test_qs.apply(len)\nplt.figure(figsize=(15, 10))\nplt.hist(dist_train, bins=200, range=[0, 200], color=pal[2], normed=True, label='train')\nplt.hist(dist_test, bins=200, range=[0, 200], color=pal[1], normed=True, alpha=0.5, label='test')\nplt.title('Normalised histogram of character count in questions', fontsize=15)\nplt.legend()\nplt.xlabel('Number of characters', fontsize=15)\nplt.ylabel('Probability', fontsize=15)\n\nprint('mean-train {:.2f} std-train {:.2f} mean-test {:.2f} std-test {:.2f} max-train {:.2f} max-test {:.2f}'.format(dist_train.mean(), \n dist_train.std(), dist_test.mean(), dist_test.std(), dist_train.max(), dist_test.max()))", "/home/manjunath/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n alternative=\"'density'\", removal=\"3.1\")\n/home/manjunath/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n alternative=\"'density'\", removal=\"3.1\")\n" ] ], [ [ "We can see that most questions have anywhere from 15 to 150 characters in them. It seems that the test distribution is a little different from the train one, but not too much so.", "_____no_output_____" ] ], [ [ "dist_train = train_qs.apply(lambda x: len(x.split(' ')))\ndist_test = test_qs.apply(lambda x: len(x.split(' ')))\n\nplt.figure(figsize=(15, 10))\nplt.hist(dist_train, bins=50, range=[0, 50], color=pal[2], normed=True, label='train')\nplt.hist(dist_test, bins=50, range=[0, 50], color=pal[1], normed=True, alpha=0.5, label='test')\nplt.title('Normalised histogram of word count in questions', fontsize=15)\nplt.legend()\nplt.xlabel('Number of words', fontsize=15)\nplt.ylabel('Probability', fontsize=15)\n\nprint('mean-train {:.2f} std-train {:.2f} mean-test {:.2f} std-test {:.2f} max-train {:.2f} max-test {:.2f}'.format(dist_train.mean(), \n dist_train.std(), dist_test.mean(), dist_test.std(), dist_train.max(), dist_test.max()))", "/home/manjunath/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n alternative=\"'density'\", removal=\"3.1\")\n/home/manjunath/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n alternative=\"'density'\", removal=\"3.1\")\n" ] ], [ [ "### WordCloud", "_____no_output_____" ] ], [ [ "from wordcloud import WordCloud\ncloud = WordCloud(width=1440, height=1080).generate(\" \".join(train_qs.astype(str)))\nplt.figure(figsize=(20, 15))\nplt.imshow(cloud)\nplt.axis('off')", "_____no_output_____" ] ], [ [ "## Semantic Analysis", "_____no_output_____" ] ], [ [ "qmarks = np.mean(train_qs.apply(lambda x: '?' in x))\nmath = np.mean(train_qs.apply(lambda x: '[math]' in x))\nfullstop = np.mean(train_qs.apply(lambda x: '.' in x))\ncapital_first = np.mean(train_qs.apply(lambda x: x[0].isupper()))\ncapitals = np.mean(train_qs.apply(lambda x: max([y.isupper() for y in x])))\nnumbers = np.mean(train_qs.apply(lambda x: max([y.isdigit() for y in x])))\n\nprint('Questions with question marks: {:.2f}%'.format(qmarks * 100))\nprint('Questions with [math] tags: {:.2f}%'.format(math * 100))\nprint('Questions with full stops: {:.2f}%'.format(fullstop * 100))\nprint('Questions with capitalised first letters: {:.2f}%'.format(capital_first * 100))\nprint('Questions with capital letters: {:.2f}%'.format(capitals * 100))\nprint('Questions with numbers: {:.2f}%'.format(numbers * 100))", "Questions with question marks: 99.87%\nQuestions with [math] tags: 0.12%\nQuestions with full stops: 6.31%\nQuestions with capitalised first letters: 99.81%\nQuestions with capital letters: 99.95%\nQuestions with numbers: 11.83%\n" ] ], [ [ "# Initial Feature Analysis\n\nBefore we create a model, we should take a look at how powerful some features are. I will start off with the word share feature from the benchmark model.", "_____no_output_____" ] ], [ [ "from nltk.corpus import stopwords\n\nstops = set(stopwords.words(\"english\"))\n\ndef word_match_share(row):\n q1words = {}\n q2words = {}\n for word in str(row['question1']).lower().split():\n if word not in stops:\n q1words[word] = 1\n for word in str(row['question2']).lower().split():\n if word not in stops:\n q2words[word] = 1\n if len(q1words) == 0 or len(q2words) == 0:\n # The computer-generated chaff includes a few questions that are nothing but stopwords\n return 0\n shared_words_in_q1 = [w for w in q1words.keys() if w in q2words]\n shared_words_in_q2 = [w for w in q2words.keys() if w in q1words]\n R = (len(shared_words_in_q1) + len(shared_words_in_q2))/(len(q1words) + len(q2words))\n return R\n\nplt.figure(figsize=(15, 5))\ntrain_word_match = df_train.apply(word_match_share, axis=1, raw=True)\nplt.hist(train_word_match[df_train['is_duplicate'] == 0], bins=20, normed=True, label='Not Duplicate')\nplt.hist(train_word_match[df_train['is_duplicate'] == 1], bins=20, normed=True, alpha=0.7, label='Duplicate')\nplt.legend()\nplt.title('Label distribution over word_match_share', fontsize=15)\nplt.xlabel('word_match_share', fontsize=15)", "/home/manjunath/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n alternative=\"'density'\", removal=\"3.1\")\n/home/manjunath/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n alternative=\"'density'\", removal=\"3.1\")\n" ], [ "from collections import Counter\n\n# If a word appears only once, we ignore it completely (likely a typo)\n# Epsilon defines a smoothing constant, which makes the effect of extremely rare words smaller\ndef get_weight(count, eps=10000, min_count=2):\n if count < min_count:\n return 0\n else:\n return 1 / (count + eps)\n\neps = 5000 \nwords = (\" \".join(train_qs)).lower().split()\ncounts = Counter(words)\nweights = {word: get_weight(count) for word, count in counts.items()}", "_____no_output_____" ], [ "print('Most common words and weights: \\n')\nprint(sorted(weights.items(), key=lambda x: x[1] if x[1] > 0 else 9999)[:10])\nprint('\\nLeast common words and weights: ')\n(sorted(weights.items(), key=lambda x: x[1], reverse=True)[:10])", "Most common words and weights: \n\n[('the', 2.5891040146646852e-06), ('what', 3.115623919267953e-06), ('is', 3.5861702928825277e-06), ('how', 4.366449945201053e-06), ('i', 4.4805878531263305e-06), ('a', 4.540645588989843e-06), ('to', 4.671434644293609e-06), ('in', 4.884625153865692e-06), ('of', 5.920242493132519e-06), ('do', 6.070908207867897e-06)]\n\nLeast common words and weights: \n" ], [ "def tfidf_word_match_share(row):\n q1words = {}\n q2words = {}\n for word in str(row['question1']).lower().split():\n if word not in stops:\n q1words[word] = 1\n for word in str(row['question2']).lower().split():\n if word not in stops:\n q2words[word] = 1\n if len(q1words) == 0 or len(q2words) == 0:\n # The computer-generated chaff includes a few questions that are nothing but stopwords\n return 0\n \n shared_weights = [weights.get(w, 0) for w in q1words.keys() if w in q2words] + [weights.get(w, 0) for w in q2words.keys() if w in q1words]\n total_weights = [weights.get(w, 0) for w in q1words] + [weights.get(w, 0) for w in q2words]\n \n R = np.sum(shared_weights) / np.sum(total_weights)\n return R", "_____no_output_____" ], [ "plt.figure(figsize=(15, 5))\ntfidf_train_word_match = df_train.apply(tfidf_word_match_share, axis=1, raw=True)\nplt.hist(tfidf_train_word_match[df_train['is_duplicate'] == 0].fillna(0), bins=20, normed=True, label='Not Duplicate')\nplt.hist(tfidf_train_word_match[df_train['is_duplicate'] == 1].fillna(0), bins=20, normed=True, alpha=0.7, label='Duplicate')\nplt.legend()\nplt.title('Label distribution over tfidf_word_match_share', fontsize=15)\nplt.xlabel('word_match_share', fontsize=15)", "/home/manjunath/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: RuntimeWarning: invalid value encountered in double_scalars\n/home/manjunath/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n alternative=\"'density'\", removal=\"3.1\")\n/home/manjunath/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n alternative=\"'density'\", removal=\"3.1\")\n" ], [ "from sklearn.metrics import roc_auc_score\nprint('Original AUC:', roc_auc_score(df_train['is_duplicate'], train_word_match))\nprint(' TFIDF AUC:', roc_auc_score(df_train['is_duplicate'], tfidf_train_word_match.fillna(0)))", "Original AUC: 0.7804327049353577\n TFIDF AUC: 0.7704802292218704\n" ] ], [ [ "## Rebalancing the Data\nHowever, before I do this, I would like to rebalance the data that XGBoost receives, since we have 37% positive class in our training data, and only 17% in the test data. By re-balancing the data so our training set has 17% positives, we can ensure that XGBoost outputs probabilities that will better match the data, and should get a better score (since LogLoss looks at the probabilities themselves and not just the order of the predictions like AUC)", "_____no_output_____" ] ], [ [ "# First we create our training and testing data\nx_train = pd.DataFrame()\nx_test = pd.DataFrame()\nx_train['word_match'] = train_word_match\nx_train['tfidf_word_match'] = tfidf_train_word_match\nx_test['word_match'] = df_test.apply(word_match_share, axis=1, raw=True)\nx_test['tfidf_word_match'] = df_test.apply(tfidf_word_match_share, axis=1, raw=True)\n\ny_train = df_train['is_duplicate'].values", "/home/manjunath/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: RuntimeWarning: invalid value encountered in double_scalars\n/home/manjunath/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: RuntimeWarning: invalid value encountered in long_scalars\n" ], [ "pos_train = x_train[y_train == 1]\nneg_train = x_train[y_train == 0]\n\n# Now we oversample the negative class\n# There is likely a much more elegant way to do this...\np = 0.165\nscale = ((len(pos_train) / (len(pos_train) + len(neg_train))) / p) - 1\nwhile scale > 1:\n neg_train = pd.concat([neg_train, neg_train])\n scale -=1\nneg_train = pd.concat([neg_train, neg_train[:int(scale * len(neg_train))]])\nprint(len(pos_train) / (len(pos_train) + len(neg_train)))\n\nx_train = pd.concat([pos_train, neg_train])\ny_train = (np.zeros(len(pos_train)) + 1).tolist() + np.zeros(len(neg_train)).tolist()\ndel pos_train, neg_train", "0.19124366100096607\n" ], [ "# Finally, we split some of the data off for validation\nfrom sklearn.model_selection import train_test_split\n\nx_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.2, random_state=4242)", "_____no_output_____" ] ], [ [ "## XGBoost", "_____no_output_____" ] ], [ [ "import xgboost as xgb\n\n# Set our parameters for xgboost\nparams = {}\nparams['objective'] = 'binary:logistic'\nparams['eval_metric'] = 'logloss'\nparams['eta'] = 0.02\nparams['max_depth'] = 4\n\nd_train = xgb.DMatrix(x_train, label=y_train)\nd_valid = xgb.DMatrix(x_valid, label=y_valid)\n\nwatchlist = [(d_train, 'train'), (d_valid, 'valid')]\n\nbst = xgb.train(params, d_train, 400, watchlist, early_stopping_rounds=50, verbose_eval=10)", "[0]\ttrain-logloss:0.68269\tvalid-logloss:0.683374\nMultiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.\n\nWill train until valid-logloss hasn't improved in 50 rounds.\n[10]\ttrain-logloss:0.602603\tvalid-logloss:0.602534\n[20]\ttrain-logloss:0.545461\tvalid-logloss:0.545854\n[30]\ttrain-logloss:0.503691\tvalid-logloss:0.504447\n[40]\ttrain-logloss:0.471857\tvalid-logloss:0.473559\n[50]\ttrain-logloss:0.448647\tvalid-logloss:0.450074\n[60]\ttrain-logloss:0.430427\tvalid-logloss:0.431971\n[70]\ttrain-logloss:0.416376\tvalid-logloss:0.418081\n[80]\ttrain-logloss:0.405567\tvalid-logloss:0.407152\n[90]\ttrain-logloss:0.396786\tvalid-logloss:0.398582\n[100]\ttrain-logloss:0.3901\tvalid-logloss:0.391865\n[110]\ttrain-logloss:0.384277\tvalid-logloss:0.386504\n[120]\ttrain-logloss:0.380254\tvalid-logloss:0.382223\n[130]\ttrain-logloss:0.376898\tvalid-logloss:0.378791\n[140]\ttrain-logloss:0.374257\tvalid-logloss:0.376091\n[150]\ttrain-logloss:0.371744\tvalid-logloss:0.373982\n[160]\ttrain-logloss:0.370124\tvalid-logloss:0.372241\n[170]\ttrain-logloss:0.368652\tvalid-logloss:0.37084\n[180]\ttrain-logloss:0.367913\tvalid-logloss:0.369715\n[190]\ttrain-logloss:0.366871\tvalid-logloss:0.368881\n[200]\ttrain-logloss:0.36575\tvalid-logloss:0.368148\n[210]\ttrain-logloss:0.365308\tvalid-logloss:0.367528\n[220]\ttrain-logloss:0.365011\tvalid-logloss:0.367085\n[230]\ttrain-logloss:0.364313\tvalid-logloss:0.366691\n[240]\ttrain-logloss:0.363959\tvalid-logloss:0.366344\n[250]\ttrain-logloss:0.363817\tvalid-logloss:0.366055\n[260]\ttrain-logloss:0.363658\tvalid-logloss:0.365773\n[270]\ttrain-logloss:0.363366\tvalid-logloss:0.365585\n[280]\ttrain-logloss:0.363265\tvalid-logloss:0.365279\n[290]\ttrain-logloss:0.363065\tvalid-logloss:0.365113\n[300]\ttrain-logloss:0.362607\tvalid-logloss:0.365007\n[310]\ttrain-logloss:0.362527\tvalid-logloss:0.364859\n[320]\ttrain-logloss:0.362374\tvalid-logloss:0.364667\n[330]\ttrain-logloss:0.362283\tvalid-logloss:0.36456\n[340]\ttrain-logloss:0.362097\tvalid-logloss:0.364431\n[350]\ttrain-logloss:0.361984\tvalid-logloss:0.364321\n[360]\ttrain-logloss:0.361897\tvalid-logloss:0.364218\n[370]\ttrain-logloss:0.361728\tvalid-logloss:0.36413\n[380]\ttrain-logloss:0.361598\tvalid-logloss:0.364054\n[390]\ttrain-logloss:0.361526\tvalid-logloss:0.36398\n[399]\ttrain-logloss:0.36143\tvalid-logloss:0.36393\n" ], [ "d_test = xgb.DMatrix(x_test)\np_test = bst.predict(d_test)\n\nsub = pd.DataFrame()\nsub['test_id'] = df_test['test_id']\nsub['is_duplicate'] = p_test\nsub.to_csv('simple_xgb.csv', index=False)", "_____no_output_____" ], [ "sub.head()", "_____no_output_____" ], [ "def logloss(ptest):\n s = 0\n for res in ptest:\n s+=np.log(res)\n return -s\n\nprint(logloss(p_test)/len(p_test))", "3.9213494079892337\n" ] ] ]

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

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# default_exp models.OmniScaleCNN", "_____no_output_____" ] ], [ [ "# OmniScaleCNN\n\n> This is an unofficial PyTorch implementation by Ignacio Oguiza - [email protected] based on:\n\n* Rußwurm, M., & Körner, M. (2019). Self-attention for raw optical satellite time series classification. arXiv preprint arXiv:1910.10536.\n* Official implementation: https://github.com/dl4sits/BreizhCrops/blob/master/breizhcrops/models/OmniScaleCNN.py", "_____no_output_____" ] ], [ [ "#export\nfrom tsai.imports import *\nfrom tsai.models.layers import *\nfrom tsai.models.utils import *", "_____no_output_____" ], [ "#export\n#This is an unofficial PyTorch implementation by Ignacio Oguiza - [email protected] based on:\n# Rußwurm, M., & Körner, M. (2019). Self-attention for raw optical satellite time series classification. arXiv preprint arXiv:1910.10536.\n# Official implementation: https://github.com/dl4sits/BreizhCrops/blob/master/breizhcrops/models/OmniScaleCNN.py\n\nclass SampaddingConv1D_BN(Module):\n def __init__(self, in_channels, out_channels, kernel_size):\n self.padding = nn.ConstantPad1d((int((kernel_size - 1) / 2), int(kernel_size / 2)), 0)\n self.conv1d = torch.nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size)\n self.bn = nn.BatchNorm1d(num_features=out_channels)\n\n def forward(self, x):\n x = self.padding(x)\n x = self.conv1d(x)\n x = self.bn(x)\n return x\n\n\nclass build_layer_with_layer_parameter(Module):\n \"\"\"\n formerly build_layer_with_layer_parameter\n \"\"\"\n def __init__(self, layer_parameters):\n \"\"\"\n layer_parameters format\n [in_channels, out_channels, kernel_size,\n in_channels, out_channels, kernel_size,\n ..., nlayers\n ]\n \"\"\"\n self.conv_list = nn.ModuleList()\n\n for i in layer_parameters:\n # in_channels, out_channels, kernel_size\n conv = SampaddingConv1D_BN(i[0], i[1], i[2])\n self.conv_list.append(conv)\n\n def forward(self, x):\n\n conv_result_list = []\n for conv in self.conv_list:\n conv_result = conv(x)\n conv_result_list.append(conv_result)\n\n result = F.relu(torch.cat(tuple(conv_result_list), 1))\n return result\n\n\nclass OmniScaleCNN(Module):\n def __init__(self, c_in, c_out, seq_len, layers=[8 * 128, 5 * 128 * 256 + 2 * 256 * 128], few_shot=False):\n\n receptive_field_shape = seq_len//4\n layer_parameter_list = generate_layer_parameter_list(1,receptive_field_shape, layers, in_channel=c_in)\n self.few_shot = few_shot\n self.layer_parameter_list = layer_parameter_list\n self.layer_list = []\n for i in range(len(layer_parameter_list)):\n layer = build_layer_with_layer_parameter(layer_parameter_list[i])\n self.layer_list.append(layer)\n self.net = nn.Sequential(*self.layer_list)\n self.gap = GAP1d(1)\n out_put_channel_number = 0\n for final_layer_parameters in layer_parameter_list[-1]:\n out_put_channel_number = out_put_channel_number + final_layer_parameters[1]\n self.hidden = nn.Linear(out_put_channel_number, c_out)\n\n def forward(self, x):\n x = self.net(x)\n x = self.gap(x)\n if not self.few_shot: x = self.hidden(x)\n return x\n\ndef get_Prime_number_in_a_range(start, end):\n Prime_list = []\n for val in range(start, end + 1):\n prime_or_not = True\n for n in range(2, val):\n if (val % n) == 0:\n prime_or_not = False\n break\n if prime_or_not:\n Prime_list.append(val)\n return Prime_list\n\n\ndef get_out_channel_number(paramenter_layer, in_channel, prime_list):\n out_channel_expect = max(1, int(paramenter_layer / (in_channel * sum(prime_list))))\n return out_channel_expect\n\n\ndef generate_layer_parameter_list(start, end, layers, in_channel=1):\n prime_list = get_Prime_number_in_a_range(start, end)\n\n layer_parameter_list = []\n for paramenter_number_of_layer in layers:\n out_channel = get_out_channel_number(paramenter_number_of_layer, in_channel, prime_list)\n\n tuples_in_layer = []\n for prime in prime_list:\n tuples_in_layer.append((in_channel, out_channel, prime))\n in_channel = len(prime_list) * out_channel\n\n layer_parameter_list.append(tuples_in_layer)\n\n tuples_in_layer_last = []\n first_out_channel = len(prime_list) * get_out_channel_number(layers[0], 1, prime_list)\n tuples_in_layer_last.append((in_channel, first_out_channel, 1))\n tuples_in_layer_last.append((in_channel, first_out_channel, 2))\n layer_parameter_list.append(tuples_in_layer_last)\n return layer_parameter_list", "_____no_output_____" ], [ "bs = 16\nc_in = 3\nseq_len = 12\nc_out = 2\nxb = torch.rand(bs, c_in, seq_len)\nm = create_model(OmniScaleCNN, c_in, c_out, seq_len)\ntest_eq(OmniScaleCNN(c_in, c_out, seq_len)(xb).shape, [bs, c_out])\nm", "_____no_output_____" ], [ "#hide\nfrom tsai.imports import *\nfrom tsai.export import *\nnb_name = get_nb_name()\n# nb_name = \"109_models.OmniScaleCNN.ipynb\"\ncreate_scripts(nb_name);", "_____no_output_____" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Natural Language Processing - Unsupervised Topic Modeling with Reddit Posts\n\n###### This project dives into multiple techniques used for NLP and subtopics such as dimensionality reduction, topic modeling, and clustering.\n\n1. [Google BigQuery](#Google-BigQuery)\n1. [Exploratory Data Analysis (EDA) & Preprocessing](#Exploratory-Data-Analysis-&-Preprocessing)\n1. [Singular Value Decomposition (SVD)](#Singular-Value-Decomposition-(SVD))\n1. [Latent Semantic Analysis (LSA - applied SVD)](#Latent-Semantic-Analysis-(LSA))\n1. [Similarity Scoring Metrics](#sim)\n1. [KMeans Clustering](#km)\n1. [Latent Dirichlet Allocation (LDA)](#lda)\n1. [pyLDAvis - interactive d3 for LDA](#py)\n - This was separated out in a new notebook to quickly view visual (load files and see visualization)", "_____no_output_____" ] ], [ [ "# Easter Egg to start your imports\n#import this \nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\nimport logging\nimport pickle\nimport sys\nimport os\n\nfrom google.cloud import bigquery", "_____no_output_____" ], [ "import warnings\ndef warn(*args, **kwargs):\n pass\nwarnings.warn = warn\n\n# Logging is the verbose for Gensim\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n\n#plt.style.available # Style options\nplt.style.use('fivethirtyeight')\nsns.set_context(\"talk\")\n%matplotlib inline\n\npd.options.display.max_rows = 99\npd.options.display.max_columns = 99\npd.options.display.max_colwidth = 99\n#pd.describe_option('display') # Option settings\n\nfloat_formatter = lambda x: \"%.3f\" % x if x>0 else \"%.0f\" % x\nnp.set_printoptions(formatter={'float_kind':float_formatter})\npd.set_option('display.float_format', float_formatter)", "_____no_output_____" ] ], [ [ "## Google BigQuery", "_____no_output_____" ] ], [ [ "%%time\n\npath = \"data/posts.pkl\"\nkey = 'fakeKey38i7-4259.json'\n\nif not os.path.isdir('data/'):\n os.makedirs('data/')\n\n# Set GOOGLE_APPLICATION_CREDENTIALS before querying\ndef bigQuery(QUERY, key=key):\n \"\"\" \n Instantiates a client using a key, \n Requests a SQL query from the Big Query API,\n Returns the queried table as a DataFrame\n \"\"\"\n client = bigquery.Client.from_service_account_json(key)\n job_config = bigquery.QueryJobConfig()\n job_config.use_legacy_sql = False\n query_job = client.query(QUERY, job_config=job_config)\n return query_job.result().to_dataframe()\n\n# SQL query for Google BigQuery\nQUERY = (\n \"\"\"\n SELECT created_utc, subreddit, author, domain, url, num_comments, \n score, title, selftext, id, gilded, retrieved_on, over_18 \n FROM `fh-bigquery.reddit_posts.*` \n WHERE _table_suffix IN ( '2016_06' ) \n AND LENGTH(selftext) > 550\n AND LENGTH(title) > 15\n AND LENGTH(title) < 345\n AND score > 8\n AND is_self = true \n AND NOT subreddit IS NULL \n AND NOT subreddit = 'de' \n AND NOT subreddit = 'test' \n AND NOT subreddit = 'tr' \n AND NOT subreddit = 'A6XHE' \n AND NOT subreddit = 'es' \n AND NOT subreddit = 'removalbot'\n AND NOT subreddit = 'tldr'\n AND NOT selftext LIKE '[removed]' \n AND NOT selftext LIKE '[deleted]' \n ;\"\"\")\n \n#df = bigQuery(QUERY)\n#df.to_pickle(path)\n\ndf = pd.read_pickle(path)\ndf.info(memory_usage='Deep')", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 146996 entries, 0 to 146995\nData columns (total 13 columns):\ncreated_utc 146996 non-null int64\nsubreddit 146996 non-null object\nauthor 146996 non-null object\ndomain 146996 non-null object\nurl 146996 non-null object\nnum_comments 146996 non-null int64\nscore 146996 non-null int64\ntitle 146996 non-null object\nselftext 146996 non-null object\nid 146996 non-null object\ngilded 146996 non-null int64\nretrieved_on 146996 non-null int64\nover_18 146996 non-null bool\ndtypes: bool(1), int64(5), object(7)\nmemory usage: 13.6+ MB\nCPU times: user 443 ms, sys: 265 ms, total: 709 ms\nWall time: 713 ms\n" ] ], [ [ "## Exploratory Data Analysis & Preprocessing", "_____no_output_____" ] ], [ [ "# Exploring data by length of .title or .selftext\ndf[[ True if 500 < len(x) < 800 else False for x in df.selftext ]].sample(1, replace=False)", "_____no_output_____" ], [ "%%time\nrun = False\npath = '/home/User/data/gif'\n\n# Run through various selftext lengths and save the plots of the distribution of the metric\n# Gif visual after piecing all the frames together\nwhile run==True:\n for i in range(500,20000,769):\n tempath = os.path.join(path, f\"textlen{i}.png\") # PEP498 requires python 3.6\n print(tempath)\n\n # Look at histogram of posts with len<i\n cuts = [len(x) for x in df.selftext if len(x)<i]\n\n # Save plot\n plt.figure()\n plt.hist(cuts, bins=30) #can change bins based on function of i\n plt.savefig(tempath, dpi=120, format='png', bbox_inches='tight', pad_inches=0.1)\n plt.close()", "_____no_output_____" ], [ "# Bin Settings\ndef binSize(lower, upper, buffer=.05):\n bins = upper - lower\n buffer = int(buffer*bins)\n bins -= buffer\n print('Lower Bound:', lower)\n print('Upper Bound:', upper)\n return bins, lower, upper\n\n# Plotting \ndef plotHist(tmp, bins, title, xlabel, ylabel, l, u):\n plt.figure(figsize=(10,6))\n plt.hist(tmp, bins=bins)\n plt.title(title)\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n plt.xlim(lower + l, upper + u)\n print('\\nLocal Max %s:' % xlabel, max(tmp))\n print('Local Average %s:' % xlabel, int(np.mean(tmp)))\n print('Local Median %s:' % xlabel, int(np.median(tmp)))", "_____no_output_____" ], [ "# Create the correct bin size\nbins, lower, upper = binSize(lower=0, upper=175)\n\n# Plot distribution of lower scores\ntmp = df[[ True if lower <= x <= upper else False for x in df['score'] ]]['score']\nplotHist(tmp=tmp, bins=bins, title='Lower Post Scores', xlabel='Scoring', ylabel='Frequency', l=5, u=5);", "Lower Bound: 0\nUpper Bound: 175\n\nLocal Max Scoring: 175\nLocal Average Scoring: 31\nLocal Median Scoring: 19\n" ], [ "# Titles should be less than 300 charcters \n# Outliers are due to unicode translation\n# Plot lengths of titles\ntmp = [ len(x) for x in df.title ]\nbins, lower, upper = binSize(lower=0, upper=300, buffer=-.09)\n\nplotHist(tmp=tmp, bins=bins, title='Lengths of Titles', xlabel='Length', ylabel='Frequency', l=10, u=0);", "Lower Bound: 0\nUpper Bound: 300\n\nLocal Max Lengths: 343\nLocal Average Lengths: 58\nLocal Median Lengths: 49\n" ], [ "# Slice lengths of texts and plot histogram\nbins, lower, upper = binSize(lower=500, upper=5000, buffer=.011)\ntmp = [len(x) for x in df.selftext if lower <= len(x) <= upper]\n\nplotHist(tmp=tmp, bins=bins, title='Length of Self Posts Under 5k', xlabel='Length', ylabel='Frequency', l=10, u=0)\nplt.ylim(0, 200);\n\n# Anomalies could be attributed to bots or duplicate reposts", "Lower Bound: 500\nUpper Bound: 5000\n\nLocal Max Lengths: 5000\nLocal Average Lengths: 1479\nLocal Median Lengths: 1128\n" ], [ "# Posts per Subreddit\ntmp = df.groupby('subreddit')['id'].nunique().sort_values(ascending=False)\ntop = 100\ns = sum(tmp)\nprint('Subreddits:', len(tmp))\nprint('Total Posts:', s)\nprint('Total Posts from Top %s:' % top, sum(tmp[:top]), ', %.3f of Total' % (sum(tmp[:top])/s))\nprint('Total Posts from Top 10:', sum(tmp[:10]), ', %.3f of Total' % (sum(tmp[:10])/s))\nprint('\\nTop 10 Contributors:', tmp[:10])\n\n\n\nplt.figure(figsize=(10,6))\nplt.plot(tmp, 'go')\nplt.xticks('')\nplt.title('Top %s Subreddit Post Counts' % top)\nplt.xlabel('Subreddits, Ranked')\nplt.ylabel('Post Count') \nplt.xlim(-2, top+1)\nplt.ylim(0, 2650);", "Subreddits: 8497\nTotal Posts: 146996\nTotal Posts from Top 100: 47498 , 0.323 of Total\nTotal Posts from Top 10: 13683 , 0.093 of Total\n\nTop 10 Contributors: subreddit\nnoveltranslations 2583\nThe_Donald 2241\nrelationships 2129\nJUSTNOMIL 1136\nexmormon 1065\nraisedbynarcissists 1015\nasoiaf 924\nOverwatch 870\nlegaladvice 869\nnosleep 851\nName: id, dtype: int64\n" ], [ "path1 = 'data/origin.pkl'\n#path2 = 'data/grouped.pkl'\n\n# Save important data\norigin_df = df.loc[:,['created_utc', 'subreddit', 'author', 'title', 'selftext', 'id']] \\\n .copy().reset_index().rename(columns={\"index\": \"position\"})\nprint(origin_df.info())\norigin_df.to_pickle(path1)\n\nposts_df = origin_df.loc[:,['title', 'selftext']]\nposts_df['text'] = posts_df.title + ' ' + df.selftext\n#del origin_df\n\n# To group the results later\ndef groupUserPosts(x):\n ''' Group users' id's by post '''\n return pd.Series(dict(ids = \", \".join(x['id']), \n text = \", \".join(x['text'])))\n\n###df = posts_df.groupby('author').apply(groupUserPosts) \n#df.to_pickle(path2)\n\ndf = posts_df.text.to_frame()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 146996 entries, 0 to 146995\nData columns (total 7 columns):\nposition 146996 non-null int64\ncreated_utc 146996 non-null int64\nsubreddit 146996 non-null object\nauthor 146996 non-null object\ntitle 146996 non-null object\nselftext 146996 non-null object\nid 146996 non-null object\ndtypes: int64(2), object(5)\nmemory usage: 7.9+ MB\nNone\n" ], [ "origin_df.sample(2).drop('author', axis=1)", "_____no_output_____" ], [ "%%time\ndef clean_text(df, text_field):\n '''\n Clean all the text data within a certain text column of the dataFrame.\n '''\n df[text_field] = df[text_field].str.replace(r\"http\\S+\", \" \")\n df[text_field] = df[text_field].str.replace(r\"&[a-z]{2,4};\", \"\")\n df[text_field] = df[text_field].str.replace(\"\\\\n\", \" \")\n df[text_field] = df[text_field].str.replace(r\"#f\", \"\")\n df[text_field] = df[text_field].str.replace(r\"[\\’\\'\\`\\\":]\", \"\")\n df[text_field] = df[text_field].str.replace(r\"[^A-Za-z0-9]\", \" \")\n df[text_field] = df[text_field].str.replace(r\" +\", \" \")\n df[text_field] = df[text_field].str.lower()\n \nclean_text(df, 'text')", "CPU times: user 42.8 s, sys: 564 ms, total: 43.3 s\nWall time: 43.4 s\n" ], [ "df.sample(3)", "_____no_output_____" ], [ "# For exploration of users\ndf[origin_df.author == '<Redacted>'][:3]\n\n# User is a post summarizer and aggregator, added /r/tldr to the blocked list!", "_____no_output_____" ], [ "# Slice lengths of texts and plot histogram\nbins, lower, upper = binSize(lower=500, upper=5000, buffer=.015)\ntmp = [len(x) for x in df.text if lower <= len(x) <= upper]\n\nplotHist(tmp=tmp, bins=bins, title='Cleaned - Length of Self Posts Under 5k', \n xlabel='Lengths', ylabel='Frequency', l=0, u=0)\nplt.ylim(0, 185);", "Lower Bound: 500\nUpper Bound: 5000\n\nLocal Max Lengths: 5000\nLocal Average Lengths: 1531\nLocal Median Lengths: 1185\n" ], [ "# Download everything for nltk! ('all')\nimport nltk\nnltk.download() # (Change config save path)\nnltk.data.path.append('/home/User/data/')", "NLTK Downloader\n---------------------------------------------------------------------------\n d) Download l) List u) Update c) Config h) Help q) Quit\n---------------------------------------------------------------------------\nDownloader> c\n\nData Server:\n - URL: <https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml>\n - 7 Package Collections Available\n - 106 Individual Packages Available\n\nLocal Machine:\n - Data directory: /home/Dave/nltk_data\n\n---------------------------------------------------------------------------\n s) Show Config u) Set Server URL d) Set Data Dir m) Main Menu\n---------------------------------------------------------------------------\nConfig> d\n New Directory> /home/Dave/data\n\n---------------------------------------------------------------------------\n s) Show Config u) Set Server URL d) Set Data Dir m) Main Menu\n---------------------------------------------------------------------------\nConfig> m\n\n---------------------------------------------------------------------------\n d) Download l) List u) Update c) Config h) Help q) Quit\n---------------------------------------------------------------------------\nDownloader> q\n" ], [ "from nltk.corpus import stopwords\n\n# \"stopeng\" is our extended list of stopwords for use in the CountVectorizer\n# I could spend days extending this list for fine tuning results\nstopeng = stopwords.words('english')\nstopeng.extend([x.replace(\"\\'\", \"\") for x in stopeng])\nstopeng.extend(['nbsp', 'also', 'really', 'ive', 'even', 'jon', 'lot', 'could', 'many'])\nstopeng = list(set(stopeng))", "_____no_output_____" ], [ "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\n# Count vectorization for LDA\ncv = CountVectorizer(token_pattern='\\\\w{3,}', max_df=.30, min_df=.0001, \n stop_words=stopeng, ngram_range=(1,1), lowercase=False,\n dtype='uint8')\n\n# Vectorizer object to generate term frequency-inverse document frequency matrix\ntfidf = TfidfVectorizer(token_pattern='\\\\w{3,}', max_df=.30, min_df=.0001, \n stop_words=stopeng, ngram_range=(1,1), lowercase=False,\n sublinear_tf=True, smooth_idf=False, dtype='float32')", "_____no_output_____" ] ], [ [ "###### Tokenization is one of the most important steps in NLP, I will explain some of my parameter choices in the README. CountVectorizer was my preferred choice. I used these definitions to help me in the iterative process of building an unsupervised model.\n\n###### The goal of using tf-idf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus.\n\n###### Smooth = False: The effect of adding “1” to the idf in the equation above is that terms with zero idf, i.e., terms that occur in all documents in a training set, will not be entirely ignored.\n\n###### sublinear_tf = True: “l” (logarithmic), replaces tf with 1 + log(tf)", "_____no_output_____" ] ], [ [ "%%time\n# Count & tf-idf vectorizer fits the tokenizer and transforms data into new matrix\ncv_vecs = cv.fit_transform(df.text).transpose()\ntf_vecs = tfidf.fit_transform(df.text).transpose()\npickle.dump(cv_vecs, open('data/cv_vecs.pkl', 'wb'))\n\n# Checking the shape and size of the count vectorizer transformed matrix\n# 47,317 terms\n# 146996 documents\nprint(\"Sparse Shape:\", cv_vecs.shape) \nprint('CV:', sys.getsizeof(cv_vecs))\nprint('Tf-Idf:', sys.getsizeof(tf_vecs))", "Sparse Shape: (47317, 146996)\nCV: 56\nTf-Idf: 56\nCPU times: user 1min 22s, sys: 1.79 s, total: 1min 24s\nWall time: 1min 24s\n" ], [ "# IFF using a subset can you store these in a Pandas DataFrame/\n\n#tfidf_df = pd.DataFrame(tf_vecs.transpose().todense(), columns=[tfidf.get_feature_names()]).astype('float32')\n#cv_df = pd.DataFrame(cv_vecs.transpose().todense(), columns=[cv.get_feature_names()]).astype('uint8')\n\n#print(cv_df.info())\n#print(tfidf_df.info())", "_____no_output_____" ], [ "#cv_description = cv_df.describe().T\n#tfidf_description = tfidf_df.describe().T\n\n#tfidf_df.sum().sort_values(ascending=False)", "_____no_output_____" ], [ "# Explore the document-term vectors\n#cv_description.sort_values(by='max', ascending=False)\n#tfidf_description.sort_values(by='mean', ascending=False)", "_____no_output_____" ] ], [ [ "## Singular Value Decomposition (SVD)", "_____no_output_____" ] ], [ [ "#from sklearn.utils.extmath import randomized_svd\n\n# Randomized SVD for extracting the full decomposition\n#U, Sigma, VT = randomized_svd(tf_vecs, n_components=8, random_state=42)", "_____no_output_____" ], [ "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import Normalizer\n\ndef Trunc_SVD(vectorized, n_components=300, iterations=1, normalize=False, random_state=42):\n \"\"\"\n Performs LSA/LSI on a sparse document term matrix, returns a fitted, transformed, (normalized) LSA object\n \"\"\"\n # Already own the vectorized data for LSA, just transpose it back to normal\n vecs_lsa = vectorized.T\n\n # Initialize SVD object as LSA\n lsa = TruncatedSVD(n_components=n_components, n_iter=iterations, algorithm='randomized', random_state=random_state)\n dtm_lsa = lsa.fit(vecs_lsa)\n print(\"Explained Variance - LSA {}:\".format(n_components), dtm_lsa.explained_variance_ratio_.sum())\n if normalize:\n dtm_lsa_t = lsa.fit_transform(vecs_lsa)\n dtm_lsa_t = Normalizer(copy=False).fit_transform(dtm_lsa_t)\n return dtm_lsa, dtm_lsa_t\n return dtm_lsa\n\n\ndef plot_SVD(lsa, title, level=None):\n \"\"\"\n Plots the singular values of an LSA object\n \"\"\"\n plt.figure(num=1, figsize=(15,10))\n plt.suptitle(title, fontsize=22, x=.55, y=.45, horizontalalignment='left')\n plt.subplot(221)\n plt.title('Explained Variance by each Singular Value')\n plt.plot(lsa.explained_variance_[:level])\n \n plt.subplot(222)\n plt.title('Explained Variance Ratio by each Singular Value')\n plt.plot(lsa.explained_variance_ratio_[:level])\n \n plt.subplot(223)\n plt.title(\"Singular Values ('Components')\")\n plt.plot(lsa.singular_values_[:level])\n plt.show()", "_____no_output_____" ], [ "%%time\ncomponents = 350\ncv_dtm_lsa = Trunc_SVD(cv_vecs, n_components=components, iterations=5, normalize=False)\nplot_SVD(cv_dtm_lsa, title='Count Vectorizer', level=25)\n\ntf_dtm_lsa = Trunc_SVD(tf_vecs, n_components=components, iterations=5, normalize=False)\nplot_SVD(tf_dtm_lsa, title='Term Frequency - \\nInverse Document Frequency', level=25)", "Explained Variance - LSA 350: 0.4865536129677702\n" ], [ "# Numerically confirming the elbow in the above plot\nprint('SVD Value| CV | TFIDF')\nprint('Top 2: ',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:2])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:2])),3))\nprint('Top 3: ',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:3])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:3])),3))\nprint('Top 4: ',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:4])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:4])),3))\nprint('Top 5: ',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:5])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:5])),3))\nprint('Top 6: ',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:6])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:6])),3))\nprint('Top 7: ',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:7])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:7])),3))\nprint('Top 8: ',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:8])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:8])),3))\nprint('Top 16:\\t',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:16])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:16])),3))\nprint('Top 32:\\t',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:32])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:32])),3))\nprint('Top 64:\\t',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:64])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:64])),3))\nprint('Top 128:',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:128])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:128])),3))\nprint('Top 256:',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:256])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:256])),3))\nprint('Top 350:',round(sum(list(cv_dtm_lsa.explained_variance_ratio_[:350])),3),round(sum(list(tf_dtm_lsa.explained_variance_ratio_[:350])),3))", "SVD Value| CV | TFIDF\nTop 2: 0.065 0.008\nTop 3: 0.079 0.01\nTop 4: 0.091 0.012\nTop 5: 0.101 0.014\nTop 6: 0.107 0.015\nTop 7: 0.114 0.017\nTop 8: 0.12 0.019\nTop 16:\t 0.157 0.028\nTop 32:\t 0.205 0.041\nTop 64:\t 0.265 0.059\nTop 128: 0.342 0.088\nTop 256: 0.439 0.134\nTop 350: 0.487 0.161\n" ], [ "# Close look at the elbow plots\ndef elbow(dtm_lsa):\n evr = dtm_lsa.explained_variance_ratio_[:20]\n print(\"Explained Variance Ratio (EVR):\\n\", evr)\n print(\"Difference in EVR (start 3):\\n\", np.diff(evr[2:]))\n plt.figure()\n plt.plot(-np.diff(evr[2:]))\n plt.xticks(range(-1,22), range(2,20))\n plt.suptitle('Difference in Explained Variance Ratio', fontsize=15);\n plt.title('Start from 3, moves up to 20');\n\n# Count Vectorizer\nelbow(cv_dtm_lsa)", "Explained Variance Ratio (EVR):\n [0.04145 0.02402 0.01375 0.01160 0.00975 0.00689 0.00656 0.00591 0.00551\n 0.00509 0.00508 0.00479 0.00452 0.00442 0.00407 0.00397 0.00381 0.00355\n 0.00350 0.00337]\nDifference in EVR (start 3):\n [-0.00216 -0.00185 -0.00286 -0.00033 -0.00066 -0.00040 -0.00042 -0.00001\n -0.00030 -0.00027 -0.00009 -0.00035 -0.00010 -0.00016 -0.00026 -0.00005\n -0.00012]\n" ], [ "# Tf-Idf\nelbow(tf_dtm_lsa)", "Explained Variance Ratio (EVR):\n [0.00400 0.00357 0.00222 0.00220 0.00184 0.00166 0.00158 0.00145 0.00128\n 0.00123 0.00122 0.00113 0.00111 0.00106 0.00103 0.00098 0.00097 0.00095\n 0.00092 0.00088]\nDifference in EVR (start 3):\n [-0.00002 -0.00036 -0.00017 -0.00008 -0.00013 -0.00017 -0.00005 -0.00001\n -0.00009 -0.00003 -0.00004 -0.00004 -0.00005 -0.00001 -0.00002 -0.00003\n -0.00004]\n" ] ], [ [ "###### The count vectorizer seems like it will be more fool proof, so I will use cv for my study. 8 might be a good cutoff value for the number of components kept in dimensionality reduction, I will try to confirm this later with KMeans clustering. The intuition behind this is that the slope after the 8th element is significantly different from the first elements. Keeping just 2 components would not be sufficient enough for clustering because we want to retain as much information as we can while still cutting down the dimensions to find some kind of human readable latent concept space.\n\n###### I am going to try out 2 quick methods before clustering and moving onto my main goal of topic modeling with LDA.", "_____no_output_____" ], [ "## Latent Semantic Analysis (LSA)", "_____no_output_____" ] ], [ [ "%%time\nfrom gensim import corpora, matutils, models\n\n# Convert sparse matrix of term-doc counts to a gensim corpus\ncv_corpus = matutils.Sparse2Corpus(cv_vecs)\npickle.dump(cv_corpus, open('data/cv_corpus.pkl', 'wb'))\n\n# Maps index to term\nid2word = dict((v, k) for k, v in cv.vocabulary_.items())\n\n# This is for Python 3, Need this for something at the end\nid2word = corpora.Dictionary.from_corpus(cv_corpus, id2word=id2word)\npickle.dump(lda, open('data/id2word.pkl', 'wb'))\n\n# Fitting an LSI model\nlsi = models.LsiModel(corpus=cv_corpus, id2word=id2word, num_topics=10)", "2018-03-11 18:22:19,137 : INFO : 'pattern' package not found; tag filters are not available for English\n2018-03-11 18:22:19,167 : INFO : adding document #0 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:20,327 : INFO : adding document #10000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:21,548 : INFO : adding document #20000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:22,807 : INFO : adding document #30000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:24,019 : INFO : adding document #40000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:25,235 : INFO : adding document #50000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:26,512 : INFO : adding document #60000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:27,745 : INFO : adding document #70000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:29,009 : INFO : adding document #80000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:30,274 : INFO : adding document #90000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:31,516 : INFO : adding document #100000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:32,809 : INFO : adding document #110000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:34,065 : INFO : adding document #120000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:35,326 : INFO : adding document #130000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:36,574 : INFO : adding document #140000 to Dictionary(0 unique tokens: [])\n2018-03-11 18:22:37,703 : INFO : built Dictionary(47317 unique tokens: ['widen', 'breadth', 'circular', 'perkins', 'resiliency']...) from 146996 documents (total 23815900 corpus positions)\n2018-03-11 18:22:37,711 : INFO : using serial LSI version on this node\n2018-03-11 18:22:37,712 : INFO : updating model with new documents\n2018-03-11 18:22:38,321 : INFO : preparing a new chunk of documents\n2018-03-11 18:22:39,089 : INFO : using 100 extra samples and 2 power iterations\n2018-03-11 18:22:39,090 : INFO : 1st phase: constructing (47317, 110) action matrix\n2018-03-11 18:22:39,755 : INFO : orthonormalizing (47317, 110) action matrix\n2018-03-11 18:22:43,037 : INFO : 2nd phase: running dense svd on (110, 20000) matrix\n2018-03-11 18:22:43,512 : INFO : computing the final decomposition\n2018-03-11 18:22:43,516 : INFO : keeping 10 factors (discarding 56.637% of energy spectrum)\n" ], [ "%%time\n# Retrieve vectors for the original cv corpus in the LS space (\"transform\" in sklearn)\nlsi_corpus = lsi[cv_corpus]\n\n# Dump the resulting document vectors into a list\ndoc_vecs = [doc for doc in lsi_corpus]", "CPU times: user 16.1 s, sys: 1.35 s, total: 17.4 s\nWall time: 17.4 s\n" ], [ "doc_vecs[0][:5]", "_____no_output_____" ], [ "# Sum of a documents' topics (?)\nfor i in range(5):\n print(sum(doc_vecs[i][1]))", "-1.4106649569930907\n-0.031444038368904303\n-1.2723604749491777\n0.15042521472269477\n0.690384834127101\n" ] ], [ [ "## <a id='sim'></a> Similarity Scoring", "_____no_output_____" ] ], [ [ "from gensim import similarities\n\n# Create an index transformer that calculates similarity based on our space\nindex = similarities.MatrixSimilarity(doc_vecs, num_features=300)\n\n# Return the sorted list of cosine similarities to the docu document\ndocu = 5 # Change docu as needed\nsims = sorted(enumerate(index[doc_vecs[docu]]), key=lambda item: -item[1])\nnp.r_[sims[:10] , sims[-10:]]", "2018-03-11 06:05:35,806 : INFO : creating matrix with 146996 documents and 300 features\n" ], [ "# Viewing similarity of top documents\ntop = 1\nfor sim_doc_id, sim_score in sims[:top + 1]: \n print(\"\\nScore:\", sim_score)\n print(\"Document Text:\\n\", df.text[sim_doc_id])", "\nScore: 0.99999994\nDocument Text:\n I just recharged the A/C on my 2008 328xi Just wanted to share my success story.\n\nThis was an extremely easy project, after about 10 minutes of research.\n\nThe most difficult part was knowing which knob was the low-pressure service knob. After I convinced myself I had it right, the entire thing was smooth sailing.\n\nI was a little disappointed about the lack of specific directions on the interwebs, but there was enough for me to complete this successfully.\n\nI had NO cold air at all. The entire thing just blew hot air with all the recommended settings. Since it's now 103 degrees here in Boulder, CO, I figured it was time.\n\nI bought the hose/gauge and can of refrigerant at an o'reilly's auto parts store. \n\nHere are the steps I took to check the pressure of the system:\n\nDISCLAIMER: I know very little about engine systems. However, I do have a little bit of gumption and a can-do attitude.\n\n1. Let car run for 3 minutes with A/C at max, with recycled air setting\n2. unscrewed the dust cap\n3. attached the hose/gauge\n4. read the gauge\n\nIt's really that easy. The hose/gauge has a clicking mechanism that actually connects securely to the service port, so it was kind of a no-brainer to be confident it was attached. The gauge read less than 10. The gauge was divided up into three ranges: low, normal and warning. 10 was in the low range, and probably refers to PSI or pounds per square inch. Correct me if I'm wrong.\n\nHere's the steps I took to recharging the system. (with gloves and glasses on!)\n\n1. Engine running for 5 minutes with A/C going full blast\n2. Shook can of refrigerant (freon, presumably) while 5 minutes passes\n3. Attached hose/gauge to refrigerant\n4. Pierced refrigerant with the provided attachment\n5. Opened the airflow between can of refrigerant and my car's A/C system\n6. Gauge spiked to \"warning\" range, but then settled back down to the normal range\n7. placed can in warm water, to facilitate emptying of gas into system\n8. Waited for 5-8 minutes\n\nThat's about it. The can of refrigerant got cold while it was emptying into the car, but I knew it was empty when the can was no longer cold. I then pretty much reversed my steps to remove the hose/gauge and replace the dust cap securely. I test drove the car, and the cool air was heavenly while I glanced down at the dashboard thermometer and read the highest number I've ever seen on it, 103. \n\nFuckin' A.\n\nLet me know if you have any questions, or need pictures. I'll do what I can to help.\n\nCheers.\n\nScore: 0.9876238\nDocument Text:\n Water softener ran out of salt, and this was my experience After hosting several visitors to my house, plus forgetting to fill the salt bucket, I experience hard water lather and the delightfult return to soft water lather.\n\nI've been using Mikes Natural Soap for about 3 weeks with a SOC boar. I've used this same brush every day since mid January and it is well broken in. Before using MNS I used a puck of Strop Shoppe and an entire puck of Haslinger. For MNS I load generously at about 1.5 gm/load. I have a base weight, so after 2 weeks I know how much I am loading. I face later for 1-2 minutes and then finish the brush in a scuttle to thicken the lather a bit more and to have enough for a second pass and touch up. That last step in the scuttle to create very thick and warm lather is a wonderful step.\n\nBaseline, with soft water, the lather is as described by other users, rich, thick, holds water well, and very slick. I find it very similar to MWS, slightly better than Strop Shoppe and not quite as good as Haslinger. \n\nWhen the water softener needed recharging, and the water was hard, the brush was much harder to load, the lather took much longer to develop--2 to 3 minutes, and broke down very quickly--before finishing the first pass. It was still a fine shave but very thin and slick lather. The scuttle made the lather much worse. Warm and pasty instead of warm and thick. So as the micelles form to trap water and air to make a lather emusion, they break down from the fatty acids binding to Mg++ and Ca++ cations. The heat from the scuttle accelerates the reaction, breaking the emulsion down even faster. Further, tallow soaps have longer chain fatty acids (C16, C18) than soaps with more coconut oils (C12), which to me seems to explain part of the problem with diffiuclty lather tallow based soaps for some users.\n\nThus, I knew I had about 3 days before recharging the hot water heater to try a few things.\n\nFirst, citric acid. I already had some. I sprinkled it like salt. It softened the water (that is, no scum in the sink when I drained), but only modestly better lather. In retrospect, I should have measured our water hardness in ppm (same as mg/L) and done the chemistry to add the correct molar concentration of citric acid, and converted back to weight. However, that takes me back to college chemistry and not an exercise I wanted to do. \n\nSecond distilled water. We had a bit already in the house for the steam iron. Easy to load but very airy and fluffy. Not the experience I wanted.\n\nThird, return to soft water. So after 3 days the hot water heater has recharged with soft water and the lather is back to where I started--thick like yougurt, slick, protective, and nicely warmed from the scuttle.\n\nSo, for W-E's who remember more chemistry that I do, what is happening?\n\nWhen water is softened via an ion exhange process, Ca++ and Mg++ are exchanged for Na+, so the water has sodium carbonate (Na2C03--washing soda) instead of magnesium carbonate or calcium carbonate (CaC03 or lime) \n\nSo, does the sodium carbonate that has replaced calcium carbonate and magnesium carbonate at equal molar concentration produce a better and more stable lather? \n\nIs calcium carbonate a better solution than citric acid? If so how much? Arm and Hammer recommends 2 tablespoons per gallon (0.75% solutiion). Or is citirc acid still okay but I simply should have measured my hard water with a strip from the hardware store and calculated the molar concentration to soften the water rather than guessed? \n\nAny insight would be appreciated. I know hard water is a common \"lather killer\" and this certainly confirms that observation.\n\nI think W-E's would like to know a number of solutions, so they can continue to enjoy a number of products.\n\n\n" ] ], [ [ "###### The metrics look artifically high, and do not match well for each document. The similarity method could be used to optimize keyword search if we were trying to expand the reach of a certain demographic using these rankings. The next step would be to improve on this method with word2vec or a better LSI model.", "_____no_output_____" ], [ "# <a id='km'></a>KMeans Clustering", "_____no_output_____" ] ], [ [ "lsi_red = matutils.corpus2dense(lsi_corpus, num_terms=300).transpose()\nprint('Reduced LS space shape:', lsi_red.shape)\nprint('Reduced LS space size in bytes:', sys.getsizeof(lsi_red))\n\n# Taking a subset for Kmeans due to memory dropout\nlsi_red_sub = lsi_red.copy()\nnp.random.shuffle(lsi_red_sub)\nlsi_red_sub = lsi_red_sub[:30000]\nlsi_red_sub = Normalizer(copy=False).fit_transform(lsi_red_sub) # Normalized for the Euclidean metric\nprint('Reduced LS space subset shape:', lsi_red_sub.shape)\nprint('Reduced LS space subset size in bytes:', sys.getsizeof(lsi_red_sub))", "Reduced LS space shape: (146996, 300)\nReduced LS space size in bytes: 112\nReduced LS space subset shape: (30000, 300)\nReduced LS space subset size in bytes: 112\n" ], [ "from sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\n\n# Calculating Silhouette coefficients and Sum of Squared Errors\ndef silhouette_co(start, stop, lsi_red_sub, random_state=42, n_jobs=-2, verbose=4):\n \"\"\" \n Input a normalized subset of a reduced dense latent semantic matrix\n Returns list of scores for plotting\n \"\"\"\n SSEs = []\n Sil_coefs = []\n try_clusters = range(start, stop)\n for k in try_clusters:\n km = KMeans(n_clusters=k, random_state=random_state, n_jobs=n_jobs)\n km.fit(lsi_red_sub)\n labels = km.labels_\n Sil_coefs.append(silhouette_score(lsi_red_sub, labels, metric='euclidean'))\n SSEs.append(km.inertia_)\n if k%verbose==0:\n print(k)\n return SSEs, Sil_coefs, try_clusters", "_____no_output_____" ], [ "%%time\nSSEs, Sil_coefs, try_clusters = silhouette_co(start=2, stop=40, lsi_red_sub=lsi_red_sub)", "_____no_output_____" ], [ "def plot_sil(try_clusters, Sil_coefs, SSEs):\n \"\"\" Function for visualizing/ finding the best clustering point \"\"\"\n # Plot Silhouette scores\n fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,5), sharex=True, dpi=200)\n ax1.plot(try_clusters, Sil_coefs)\n ax1.title('Silhouette of Clusters')\n ax1.set_xlabel('Number of Clusters')\n ax1.set_ylabel('Silhouette Coefficient')\n\n # Plot errors\n ax2.plot(try_clusters, SSEs)\n ax2.title(\"Cluster's Error\")\n ax2.set_xlabel('Number of Clusters')\n ax2.set_ylabel('SSE');\n \nplot_sil(try_clusters=try_clusters, Sil_coefs=Sil_coefs, SSEs=SSes)", "_____no_output_____" ] ], [ [ "###### This suggests that there arent meaningful clusters in the normalized LS300 space. To test if 300 dimensions is too large, I will try clustering again with a reduced input.", "_____no_output_____" ] ], [ [ "# Fix IndexError: index 10\nlsi_red5 = matutils.corpus2dense(lsi_corpus, num_terms=10).transpose()\nprint('Reduced LSI space shape:', lsi_red5.shape)\nprint('Reduced LS space subset size in bytes:', sys.getsizeof(lsi_red5))\n\n# Taking a subset for Kmeans due to memory dropout\nlsi_red_sub5 = lsi_red5.copy()\nnp.random.shuffle(lsi_red_sub5)\nlsi_red_sub5 = lsi_red_sub5[:5000]\nlsi_red_sub5 = Normalizer(copy=False).fit_transform(lsi_red_sub5) # Normalized for the Euclidean metric\nprint('Reduced LSI space subset shape:', lsi_red_sub5.shape)\nprint('Reduced LS space subset size in bytes:', sys.getsizeof(lsi_red_sub5))", "_____no_output_____" ], [ "%%time\nSSEs, Sil_coefs, try_clusters = silhouette_co(start=2, stop=40, lsi_red_sub=lsi_red_sub)\nplot_sil(try_clusters=try_clusters, Sil_coefs=Sil_coefs, SSEs=SSes)", "_____no_output_____" ] ], [ [ "###### Due to project deadlines, I was not able to complete this method but I wanted to preserve the effort and document the process for later use. I will move on to LDA.", "_____no_output_____" ] ], [ [ "# Cluster with the best results\n#kmeans = KMeans(n_clusters=20, n_jobs=-2)\n#lsi_clusters = kmeans.fit_predict(lsi_red)\n\n# Take a look at the \nprint(lsi_clusters[0:15])\ndf.text[0:2]", "[0 0 0 0 0 0 0 0 0 0 7 0 0 0 0]\n" ], [ "from sklearn.metrics import silhouette_samples, silhouette_score\n\n# Validating cluster performance\n# Select range around best result, plot the silhouette distributions for each cluster\nfor k in range(14,17):\n plt.figure(dpi=120, figsize=(8,6))\n ax1 = plt.gca()\n km = KMeans(n_clusters=k, random_state=1)\n km.fit(X)\n labels = km.labels_\n silhouette_avg = silhouette_score(X, labels)\n print(\"For n_clusters =\", k,\n \"The average silhouette_score is :\", silhouette_avg)\n\n # Compute the silhouette scores for each sample\n sample_silhouette_values = silhouette_samples(X, labels)\n y_lower = 100\n for i in range(k):\n # Aggregate the silhouette scores for samples belonging to cluster i\n ith_cluster_silhouette_values = sample_silhouette_values[labels == i]\n \n #Sort\n ith_cluster_silhouette_values.sort()\n \n size_cluster_i = ith_cluster_silhouette_values.shape[0]\n y_upper = y_lower + size_cluster_i\n\n color = plt.cm.spectral(float(i) / k)\n ax1.fill_betweenx(np.arange(y_lower, y_upper),\n 0, ith_cluster_silhouette_values,\n facecolor=color, edgecolor=color, alpha=0.7)\n\n # Label the silhouette plots with their cluster numbers at the middle\n ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n\n # Compute the new y_lower for next plot\n y_lower = y_upper + 10 # 10 for the 0 samples\n \n ax1.set_title(\"The silhouette plot for the various clusters.\")\n ax1.set_xlabel(\"The silhouette coefficient values\")\n ax1.set_ylabel(\"Cluster label\")\n\n # The vertical line for average silhouette score of all the values\n ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n\n ax1.set_yticks([]) # Clear the yaxis labels / ticks\n ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])", "_____no_output_____" ] ], [ [ "## <a id='lda'></a> Latent Dirichlet Allocation (LDA)", "_____no_output_____" ] ], [ [ "%%time\nrun = False\n\npasses = 85\nif run==True:\n lda = models.LdaMulticore(corpus=cv_corpus, num_topics=15, id2word=id2word, passes=passes, \n workers=13, random_state=42, eval_every=None, chunksize=6000)", "2018-03-08 22:48:40,670 : INFO : using symmetric alpha at 0.06666666666666667\n2018-03-08 22:48:40,673 : INFO : using symmetric eta at 0.06666666666666667\n2018-03-08 22:48:40,689 : INFO : using serial LDA version on this node\n2018-03-08 22:48:40,811 : INFO : running online LDA training, 15 topics, 100 passes over the supplied corpus of 146996 documents, updating every 78000 documents, evaluating every ~0 documents, iterating 50x with a convergence threshold of 0.001000\n2018-03-08 22:48:40,815 : INFO : training LDA model using 13 processes\n2018-03-08 22:48:43,307 : INFO : PROGRESS: pass 0, dispatched chunk #0 = documents up to #6000/146996, outstanding queue size 1\n2018-03-08 22:48:52,172 : INFO : PROGRESS: pass 0, dispatched chunk #1 = documents up to #12000/146996, outstanding queue size 2\n2018-03-08 22:48:58,812 : INFO : PROGRESS: pass 0, dispatched chunk #2 = documents up to #18000/146996, outstanding queue size 3\n2018-03-08 22:49:05,247 : INFO : PROGRESS: pass 0, dispatched chunk #3 = documents up to #24000/146996, outstanding queue size 3\n2018-03-08 22:49:12,401 : INFO : PROGRESS: pass 0, dispatched chunk #4 = documents up to #30000/146996, outstanding queue size 3\n2018-03-08 22:49:19,633 : INFO : PROGRESS: pass 0, dispatched chunk #5 = documents up to #36000/146996, outstanding queue size 3\n2018-03-08 22:49:26,781 : INFO : PROGRESS: pass 0, dispatched chunk #6 = documents up to #42000/146996, outstanding queue size 3\n2018-03-08 22:49:33,834 : INFO : PROGRESS: pass 0, dispatched chunk #7 = documents up to #48000/146996, outstanding queue size 3\n2018-03-08 22:49:41,172 : INFO : PROGRESS: pass 0, dispatched chunk #8 = documents up to #54000/146996, outstanding queue size 3\n2018-03-08 22:49:48,312 : INFO : PROGRESS: pass 0, dispatched chunk #9 = documents up to #60000/146996, outstanding queue size 3\n2018-03-08 22:49:55,930 : INFO : PROGRESS: pass 0, dispatched chunk #10 = documents up to #66000/146996, outstanding queue size 3\n2018-03-08 22:50:02,804 : INFO : PROGRESS: pass 0, dispatched chunk #11 = documents up to #72000/146996, outstanding queue size 3\n2018-03-08 22:50:10,214 : INFO : PROGRESS: pass 0, dispatched chunk #12 = documents up to #78000/146996, outstanding queue size 3\n2018-03-08 22:50:19,620 : INFO : PROGRESS: pass 0, dispatched chunk #13 = documents up to #84000/146996, outstanding queue size 3\n2018-03-08 22:50:26,716 : INFO : PROGRESS: pass 0, dispatched chunk #14 = documents up to #90000/146996, outstanding queue size 3\n2018-03-08 22:50:32,725 : INFO : merging changes from 78000 documents into a model of 146996 documents\n2018-03-08 22:50:32,835 : INFO : topic #7 (0.067): 0.005*\"well\" + 0.004*\"game\" + 0.004*\"want\" + 0.004*\"think\" + 0.003*\"see\" + 0.003*\"good\" + 0.003*\"first\" + 0.003*\"much\" + 0.003*\"team\" + 0.003*\"something\"\n2018-03-08 22:50:32,840 : INFO : topic #10 (0.067): 0.005*\"think\" + 0.004*\"make\" + 0.004*\"well\" + 0.004*\"much\" + 0.003*\"want\" + 0.003*\"back\" + 0.003*\"work\" + 0.003*\"since\" + 0.003*\"right\" + 0.003*\"game\"\n2018-03-08 22:50:32,845 : INFO : topic #2 (0.067): 0.005*\"game\" + 0.004*\"good\" + 0.004*\"back\" + 0.004*\"new\" + 0.003*\"think\" + 0.003*\"make\" + 0.003*\"need\" + 0.003*\"two\" + 0.003*\"much\" + 0.003*\"feel\"\n2018-03-08 22:50:32,850 : INFO : topic #0 (0.067): 0.004*\"game\" + 0.004*\"first\" + 0.004*\"want\" + 0.003*\"think\" + 0.003*\"well\" + 0.003*\"still\" + 0.003*\"make\" + 0.003*\"way\" + 0.003*\"last\" + 0.003*\"see\"\n2018-03-08 22:50:32,854 : INFO : topic #12 (0.067): 0.003*\"back\" + 0.003*\"think\" + 0.003*\"game\" + 0.003*\"make\" + 0.003*\"want\" + 0.003*\"see\" + 0.003*\"still\" + 0.003*\"going\" + 0.003*\"first\" + 0.003*\"two\"\n" ], [ "# Save model after your last run, or continue to update LDA\n#pickle.dump(lda, open('data/lda_gensim.pkl', 'wb'))\n\n# Gensim save\n#lda.save('data/gensim_lda.model')\nlda = models.LdaModel.load('data/gensim_lda.model')", "2018-03-11 18:37:34,480 : INFO : loading LdaModel object from data/gensim_lda.model\n2018-03-11 18:37:34,484 : INFO : loading expElogbeta from data/gensim_lda.model.expElogbeta.npy with mmap=None\n2018-03-11 18:37:34,489 : INFO : setting ignored attribute state to None\n2018-03-11 18:37:34,490 : INFO : setting ignored attribute id2word to None\n2018-03-11 18:37:34,491 : INFO : setting ignored attribute dispatcher to None\n2018-03-11 18:37:34,492 : INFO : loaded data/gensim_lda.model\n2018-03-11 18:37:34,493 : INFO : loading LdaState object from data/gensim_lda.model.state\n2018-03-11 18:37:34,517 : INFO : loaded data/gensim_lda.model.state\n" ], [ "%%time\n# Transform the docs from the word space to the topic space (like \"transform\" in sklearn)\nlda_corpus = lda[cv_corpus]\n\n# Store the documents' topic vectors in a list so we can take a peak\nlda_docs = [doc for doc in lda_corpus]", "CPU times: user 7min 36s, sys: 12min 58s, total: 20min 34s\nWall time: 2min 20s\n" ], [ "# Review Dirichlet distribution for documents\nlda_docs[25000]", "_____no_output_____" ], [ "# Manually review the document to see if it makes sense! \n# Look back at the topics that it matches with to confirm the result!\ndf.iloc[25000]", "_____no_output_____" ], [ "#bow = df.iloc[1,0].split()\n\n# Print topic probability distribution for a document\n#print(lda[bow]) #Values unpack error\n\n# Given a chunk of sparse document vectors, estimate gamma:\n# (parameters controlling the topic weights) for each document in the chunk.\n#lda.inference(bow) #Not enough values\n\n# Makeup of each topic! Interpretable! \n# The d3 visualization below is far better for looking at the interpretations.\nlda.print_topics(num_words=10, num_topics=1)", "2018-03-11 07:47:06,762 : INFO : topic #1 (0.067): 0.005*\"water\" + 0.005*\"food\" + 0.004*\"use\" + 0.003*\"make\" + 0.003*\"much\" + 0.003*\"used\" + 0.003*\"well\" + 0.003*\"skin\" + 0.003*\"first\" + 0.003*\"good\"\n" ] ], [ [ "## <a id='py'></a> pyLDAvis", "_____no_output_____" ] ], [ [ "# For quickstart, we can just jump straight to results\nimport pickle\nfrom gensim import models\ndef loadingPickles():\n id2word = pickle.load(open('data/id2word.pkl','rb'))\n cv_vecs = pickle.load(open('data/cv_vecs.pkl','rb'))\n cv_corpus = pickle.load(open('data/cv_corpus.pkl','rb'))\n lda = models.LdaModel.load('data/gensim_lda.model')\n return id2word, cv_vecs, cv_corpus, lda", "_____no_output_____" ], [ "import pyLDAvis.gensim\nimport gensim\n\n# Enables visualization in jupyter notebook\npyLDAvis.enable_notebook()\n\n# Prepare the visualization\n# Change multidimensional scaling function via mds parameter\n# Options are tsne, mmds, pcoa \n# cv_corpus or cv_vecs work equally\nid2word, _, cv_corpus, lda = loadingPickles()\nviz = pyLDAvis.gensim.prepare(topic_model=lda, corpus=cv_corpus, dictionary=id2word, mds='mmds')\n\n# Save the html for sharing!\npyLDAvis.save_html(viz,'data/viz.html')\n\n# Interact! Saliency is the most important metric that changes the story of each topic.\npyLDAvis.display(viz)", "/usr/local/lib/python3.5/dist-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n.ix is deprecated. Please use\n.loc for label based indexing or\n.iloc for positional indexing\n\nSee the documentation here:\nhttp://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n topic_term_dists = topic_term_dists.ix[topic_order]\n" ] ], [ [ "# There you have it. There is a ton of great information right here that I will conclude upon in the README and the slides on my github. \n\n###### In it I will discuss what I could do with this information. I did not end up using groupUserPosts but I could create user profiles based on the aggregate of their document topic distributions. I believe this is a great start to understanding NLP and how it can be used. I would consider working on this again but with more technologies needed for big data.", "_____no_output_____" ] ] ]

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[ [ [ "# Forecasting in Statsmodels\n\nThis notebook describes forecasting using time series models in Statsmodels.\n\n**Note**: this notebook applies only to the state space model classes, which are:\n\n- `sm.tsa.SARIMAX`\n- `sm.tsa.UnobservedComponents`\n- `sm.tsa.VARMAX`\n- `sm.tsa.DynamicFactor`", "_____no_output_____" ] ], [ [ "%matplotlib inline\n\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\n\nmacrodata = sm.datasets.macrodata.load_pandas().data\nmacrodata.index = pd.period_range('1959Q1', '2009Q3', freq='Q')", "_____no_output_____" ] ], [ [ "## Basic example\n\nA simple example is to use an AR(1) model to forecast inflation. Before forecasting, let's take a look at the series:", "_____no_output_____" ] ], [ [ "endog = macrodata['infl']\nendog.plot(figsize=(15, 5))", "_____no_output_____" ] ], [ [ "### Constructing and estimating the model", "_____no_output_____" ], [ "The next step is to formulate the econometric model that we want to use for forecasting. In this case, we will use an AR(1) model via the `SARIMAX` class in Statsmodels.\n\nAfter constructing the model, we need to estimate its parameters. This is done using the `fit` method. The `summary` method produces several convinient tables showing the results.", "_____no_output_____" ] ], [ [ "# Construct the model\nmod = sm.tsa.SARIMAX(endog, order=(1, 0, 0), trend='c')\n# Estimate the parameters\nres = mod.fit()\n\nprint(res.summary())", "_____no_output_____" ] ], [ [ "### Forecasting", "_____no_output_____" ], [ "Out-of-sample forecasts are produced using the `forecast` or `get_forecast` methods from the results object.\n\nThe `forecast` method gives only point forecasts.", "_____no_output_____" ] ], [ [ "# The default is to get a one-step-ahead forecast:\nprint(res.forecast())", "_____no_output_____" ] ], [ [ "The `get_forecast` method is more general, and also allows constructing confidence intervals.", "_____no_output_____" ] ], [ [ "# Here we construct a more complete results object.\nfcast_res1 = res.get_forecast()\n\n# Most results are collected in the `summary_frame` attribute.\n# Here we specify that we want a confidence level of 90%\nprint(fcast_res1.summary_frame(alpha=0.10))", "_____no_output_____" ] ], [ [ "The default confidence level is 95%, but this can be controlled by setting the `alpha` parameter, where the confidence level is defined as $(1 - \\alpha) \\times 100\\%$. In the example above, we specified a confidence level of 90%, using `alpha=0.10`.", "_____no_output_____" ], [ "### Specifying the number of forecasts\n\nBoth of the functions `forecast` and `get_forecast` accept a single argument indicating how many forecasting steps are desired. One option for this argument is always to provide an integer describing the number of steps ahead you want.", "_____no_output_____" ] ], [ [ "print(res.forecast(steps=2))", "_____no_output_____" ], [ "fcast_res2 = res.get_forecast(steps=2)\n# Note: since we did not specify the alpha parameter, the\n# confidence level is at the default, 95%\nprint(fcast_res2.summary_frame())", "_____no_output_____" ] ], [ [ "However, **if your data included a Pandas index with a defined frequency** (see the section at the end on Indexes for more information), then you can alternatively specify the date through which you want forecasts to be produced:", "_____no_output_____" ] ], [ [ "print(res.forecast('2010Q2'))", "_____no_output_____" ], [ "fcast_res3 = res.get_forecast('2010Q2')\nprint(fcast_res3.summary_frame())", "_____no_output_____" ] ], [ [ "### Plotting the data, forecasts, and confidence intervals\n\nOften it is useful to plot the data, the forecasts, and the confidence intervals. There are many ways to do this, but here's one example", "_____no_output_____" ] ], [ [ "fig, ax = plt.subplots(figsize=(15, 5))\n\n# Plot the data (here we are subsetting it to get a better look at the forecasts)\nendog.loc['1999':].plot(ax=ax)\n\n# Construct the forecasts\nfcast = res.get_forecast('2011Q4').summary_frame()\nfcast['mean'].plot(ax=ax, style='k--')\nax.fill_between(fcast.index, fcast['mean_ci_lower'], fcast['mean_ci_upper'], color='k', alpha=0.1);", "_____no_output_____" ] ], [ [ "### Note on what to expect from forecasts\n\nThe forecast above may not look very impressive, as it is almost a straight line. This is because this is a very simple, univariate forecasting model. Nonetheless, keep in mind that these simple forecasting models can be extremely competitive.", "_____no_output_____" ], [ "## Prediction vs Forecasting\n\nThe results objects also contain two methods that all for both in-sample fitted values and out-of-sample forecasting. They are `predict` and `get_prediction`. The `predict` method only returns point predictions (similar to `forecast`), while the `get_prediction` method also returns additional results (similar to `get_forecast`).\n\nIn general, if your interest is out-of-sample forecasting, it is easier to stick to the `forecast` and `get_forecast` methods.", "_____no_output_____" ], [ "## Cross validation\n\n**Note**: some of the functions used in this section were first introduced in Statsmodels v0.11.0.\n\nA common use case is to cross-validate forecasting methods by performing h-step-ahead forecasts recursively using the following process:\n\n1. Fit model parameters on a training sample\n2. Produce h-step-ahead forecasts from the end of that sample\n3. Compare forecasts against test dataset to compute error rate\n4. Expand the sample to include the next observation, and repeat\n\nEconomists sometimes call this a pseudo-out-of-sample forecast evaluation exercise, or time-series cross-validation.", "_____no_output_____" ], [ "### Basic example", "_____no_output_____" ], [ "We will conduct a very simple exercise of this sort using the inflation dataset above. The full dataset contains 203 observations, and for expositional purposes we'll use the first 80% as our training sample and only consider one-step-ahead forecasts.", "_____no_output_____" ], [ "A single iteration of the above procedure looks like the following:", "_____no_output_____" ] ], [ [ "# Step 1: fit model parameters w/ training sample\ntraining_obs = int(len(endog) * 0.8)\n\ntraining_endog = endog[:training_obs]\ntraining_mod = sm.tsa.SARIMAX(\n training_endog, order=(1, 0, 0), trend='c')\ntraining_res = training_mod.fit()\n\n# Print the estimated parameters\nprint(training_res.params)", "_____no_output_____" ], [ "# Step 2: produce one-step-ahead forecasts\nfcast = training_res.forecast()\n\n# Step 3: compute root mean square forecasting error\ntrue = endog.reindex(fcast.index)\nerror = true - fcast\n\n# Print out the results\nprint(pd.concat([true.rename('true'),\n fcast.rename('forecast'),\n error.rename('error')], axis=1))", "_____no_output_____" ] ], [ [ "To add on another observation, we can use the `append` or `extend` results methods. Either method can produce the same forecasts, but they differ in the other results that are available:\n\n- `append` is the more complete method. It always stores results for all training observations, and it optionally allows refitting the model parameters given the new observations (note that the default is *not* to refit the parameters).\n- `extend` is a faster method that may be useful if the training sample is very large. It *only* stores results for the new observations, and it does not allow refitting the model parameters (i.e. you have to use the parameters estimated on the previous sample).\n\nIf your training sample is relatively small (less than a few thousand observations, for example) or if you want to compute the best possible forecasts, then you should use the `append` method. However, if that method is infeasible (for example, becuase you have a very large training sample) or if you are okay with slightly suboptimal forecasts (because the parameter estimates will be slightly stale), then you can consider the `extend` method.", "_____no_output_____" ], [ "A second iteration, using the `append` method and refitting the parameters, would go as follows (note again that the default for `append` does not refit the parameters, but we have overridden that with the `refit=True` argument):", "_____no_output_____" ] ], [ [ "# Step 1: append a new observation to the sample and refit the parameters\nappend_res = training_res.append(endog[training_obs:training_obs + 1], refit=True)\n\n# Print the re-estimated parameters\nprint(append_res.params)", "_____no_output_____" ] ], [ [ "Notice that these estimated parameters are slightly different than those we originally estimated. With the new results object, `append_res`, we can compute forecasts starting from one observation further than the previous call:", "_____no_output_____" ] ], [ [ "# Step 2: produce one-step-ahead forecasts\nfcast = append_res.forecast()\n\n# Step 3: compute root mean square forecasting error\ntrue = endog.reindex(fcast.index)\nerror = true - fcast\n\n# Print out the results\nprint(pd.concat([true.rename('true'),\n fcast.rename('forecast'),\n error.rename('error')], axis=1))", "_____no_output_____" ] ], [ [ "Putting it altogether, we can perform the recursive forecast evaluation exercise as follows:", "_____no_output_____" ] ], [ [ "# Setup forecasts\nnforecasts = 3\nforecasts = {}\n\n# Get the number of initial training observations\nnobs = len(endog)\nn_init_training = int(nobs * 0.8)\n\n# Create model for initial training sample, fit parameters\ninit_training_endog = endog.iloc[:n_init_training]\nmod = sm.tsa.SARIMAX(training_endog, order=(1, 0, 0), trend='c')\nres = mod.fit()\n\n# Save initial forecast\nforecasts[training_endog.index[-1]] = res.forecast(steps=nforecasts)\n\n# Step through the rest of the sample\nfor t in range(n_init_training, nobs):\n # Update the results by appending the next observation\n updated_endog = endog.iloc[t:t+1]\n res = res.append(updated_endog, refit=False)\n \n # Save the new set of forecasts\n forecasts[updated_endog.index[0]] = res.forecast(steps=nforecasts)\n\n# Combine all forecasts into a dataframe\nforecasts = pd.concat(forecasts, axis=1)\n\nprint(forecasts.iloc[:5, :5])", "_____no_output_____" ] ], [ [ "We now have a set of three forecasts made at each point in time from 1999Q2 through 2009Q3. We can construct the forecast errors by subtracting each forecast from the actual value of `endog` at that point.", "_____no_output_____" ] ], [ [ "# Construct the forecast errors\nforecast_errors = forecasts.apply(lambda column: endog - column).reindex(forecasts.index)\n\nprint(forecast_errors.iloc[:5, :5])", "_____no_output_____" ] ], [ [ "To evaluate our forecasts, we often want to look at a summary value like the root mean square error. Here we can compute that for each horizon by first flattening the forecast errors so that they are indexed by horizon and then computing the root mean square error fore each horizon.", "_____no_output_____" ] ], [ [ "# Reindex the forecasts by horizon rather than by date\ndef flatten(column):\n return column.dropna().reset_index(drop=True)\n\nflattened = forecast_errors.apply(flatten)\nflattened.index = (flattened.index + 1).rename('horizon')\n\nprint(flattened.iloc[:3, :5])", "_____no_output_____" ], [ "# Compute the root mean square error\nrmse = (flattened**2).mean(axis=1)**0.5\n\nprint(rmse)", "_____no_output_____" ] ], [ [ "#### Using `extend`\n\nWe can check that we get similar forecasts if we instead use the `extend` method, but that they are not exactly the same as when we use `append` with the `refit=True` argument. This is because `extend` does not re-estimate the parameters given the new observation.", "_____no_output_____" ] ], [ [ "# Setup forecasts\nnforecasts = 3\nforecasts = {}\n\n# Get the number of initial training observations\nnobs = len(endog)\nn_init_training = int(nobs * 0.8)\n\n# Create model for initial training sample, fit parameters\ninit_training_endog = endog.iloc[:n_init_training]\nmod = sm.tsa.SARIMAX(training_endog, order=(1, 0, 0), trend='c')\nres = mod.fit()\n\n# Save initial forecast\nforecasts[training_endog.index[-1]] = res.forecast(steps=nforecasts)\n\n# Step through the rest of the sample\nfor t in range(n_init_training, nobs):\n # Update the results by appending the next observation\n updated_endog = endog.iloc[t:t+1]\n res = res.extend(updated_endog)\n \n # Save the new set of forecasts\n forecasts[updated_endog.index[0]] = res.forecast(steps=nforecasts)\n\n# Combine all forecasts into a dataframe\nforecasts = pd.concat(forecasts, axis=1)\n\nprint(forecasts.iloc[:5, :5])", "_____no_output_____" ], [ "# Construct the forecast errors\nforecast_errors = forecasts.apply(lambda column: endog - column).reindex(forecasts.index)\n\nprint(forecast_errors.iloc[:5, :5])", "_____no_output_____" ], [ "# Reindex the forecasts by horizon rather than by date\ndef flatten(column):\n return column.dropna().reset_index(drop=True)\n\nflattened = forecast_errors.apply(flatten)\nflattened.index = (flattened.index + 1).rename('horizon')\n\nprint(flattened.iloc[:3, :5])", "_____no_output_____" ], [ "# Compute the root mean square error\nrmse = (flattened**2).mean(axis=1)**0.5\n\nprint(rmse)", "_____no_output_____" ] ], [ [ "By not re-estimating the parameters, our forecasts are slightly worse (the root mean square error is higher at each horizon). However, the process is faster, even with only 200 datapoints. Using the `%%timeit` cell magic on the cells above, we found a runtime of 570ms using `extend` versus 1.7s using `append` with `refit=True`. (Note that using `extend` is also faster than using `append` with `refit=False`).", "_____no_output_____" ], [ "## Indexes\n\nThroughout this notebook, we have been making use of Pandas date indexes with an associated frequency. As you can see, this index marks our data as at a quarterly frequency, between 1959Q1 and 2009Q3.", "_____no_output_____" ] ], [ [ "print(endog.index)", "_____no_output_____" ] ], [ [ "In most cases, if your data has an associated data/time index with a defined frequency (like quarterly, monthly, etc.), then it is best to make sure your data is a Pandas series with the appropriate index. Here are three examples of this:", "_____no_output_____" ] ], [ [ "# Annual frequency, using a PeriodIndex\nindex = pd.period_range(start='2000', periods=4, freq='A')\nendog1 = pd.Series([1, 2, 3, 4], index=index)\nprint(endog1.index)", "_____no_output_____" ], [ "# Quarterly frequency, using a DatetimeIndex\nindex = pd.date_range(start='2000', periods=4, freq='QS')\nendog2 = pd.Series([1, 2, 3, 4], index=index)\nprint(endog2.index)", "_____no_output_____" ], [ "# Monthly frequency, using a DatetimeIndex\nindex = pd.date_range(start='2000', periods=4, freq='M')\nendog3 = pd.Series([1, 2, 3, 4], index=index)\nprint(endog3.index)", "_____no_output_____" ] ], [ [ "In fact, if your data has an associated date/time index, it is best to use that even if does not have a defined frequency. An example of that kind of index is as follows - notice that it has `freq=None`:", "_____no_output_____" ] ], [ [ "index = pd.DatetimeIndex([\n '2000-01-01 10:08am', '2000-01-01 11:32am',\n '2000-01-01 5:32pm', '2000-01-02 6:15am'])\nendog4 = pd.Series([0.2, 0.5, -0.1, 0.1], index=index)\nprint(endog4.index)", "_____no_output_____" ] ], [ [ "You can still pass this data to Statsmodels' model classes, but you will get the following warning, that no frequency data was found:", "_____no_output_____" ] ], [ [ "mod = sm.tsa.SARIMAX(endog4)\nres = mod.fit()", "_____no_output_____" ] ], [ [ "What this means is that you cannot specify forecasting steps by dates, and the output of the `forecast` and `get_forecast` methods will not have associated dates. The reason is that without a given frequency, there is no way to determine what date each forecast should be assigned to. In the example above, there is no pattern to the date/time stamps of the index, so there is no way to determine what the next date/time should be (should it be in the morning of 2000-01-02? the afternoon? or maybe not until 2000-01-03?).\n\nFor example, if we forecast one-step-ahead:", "_____no_output_____" ] ], [ [ "res.forecast(1)", "_____no_output_____" ] ], [ [ "The index associated with the new forecast is `4`, because if the given data had an integer index, that would be the next value. A warning is given letting the user know that the index is not a date/time index.\n\nIf we try to specify the steps of the forecast using a date, we will get the following exception:\n\n KeyError: 'The `end` argument could not be matched to a location related to the index of the data.'\n", "_____no_output_____" ] ], [ [ "# Here we'll catch the exception to prevent printing too much of\n# the exception trace output in this notebook\ntry:\n res.forecast('2000-01-03')\nexcept KeyError as e:\n print(e)", "_____no_output_____" ] ], [ [ "Ultimately there is nothing wrong with using data that does not have an associated date/time frequency, or even using data that has no index at all, like a Numpy array. However, if you can use a Pandas series with an associated frequency, you'll have more options for specifying your forecasts and get back results with a more useful index.", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport numpy as np\nfrom bayes_opt import BayesianOptimization\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import cross_val_score\nfrom Data_Processing import DataProcessing\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.decomposition import PCA", "_____no_output_____" ], [ "pop = pd.read_csv('../Data/population.csv')\ntrain = pd.read_csv('../Data/train.csv')\ntest = pd.read_csv('../Data/test.csv')", "_____no_output_____" ], [ "X, y, test = DataProcessing(train, test, pop)\npca = PCA(n_components=20)\nX = pca.fit_transform(X)\ny = y.ravel()", "_____no_output_____" ], [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)", "_____no_output_____" ], [ "#Bayesian optimization\ndef bayesian_optimization(dataset, function, parameters):\n X_train, y_train, X_test, y_test = dataset\n n_iterations = 5\n gp_params = {\"alpha\": 1e-4}\n\n BO = BayesianOptimization(function, parameters)\n BO.maximize(n_iter=n_iterations, **gp_params)\n\n return BO.max", "_____no_output_____" ], [ "def rfc_optimization(cv_splits):\n def function(n_estimators, max_depth, min_samples_split):\n return cross_val_score(\n RandomForestRegressor(\n n_estimators=int(max(n_estimators,0)), \n max_depth=int(max(max_depth,1)),\n min_samples_split=int(max(min_samples_split,2)), \n n_jobs=-1, \n random_state=42), \n X=X_train, \n y=y_train, \n cv=cv_splits,\n scoring=\"neg_mean_squared_error\",\n n_jobs=-1).mean()\n\n parameters = {\"n_estimators\": (10, 1000),\n \"max_depth\": (1, 150),\n \"min_samples_split\": (2, 10)\n# \"criterion\": ('squared_error', 'absolute_error', 'poisson'),\n# 'bootstrap': (True, False)\n }\n \n return function, parameters", "_____no_output_____" ], [ "def xgb_optimization(cv_splits, eval_set):\n def function(eta, gamma, max_depth):\n return cross_val_score(\n xgb.XGBClassifier(\n objective=\"binary:logistic\",\n learning_rate=max(eta, 0),\n gamma=max(gamma, 0),\n max_depth=int(max_depth), \n seed=42,\n nthread=-1,\n scale_pos_weight = len(y_train[y_train == 0])/\n len(y_train[y_train == 1])), \n X=X_train, \n y=y_train, \n cv=cv_splits,\n scoring=\"roc_auc\",\n fit_params={\n \"early_stopping_rounds\": 10, \n \"eval_metric\": \"auc\", \n \"eval_set\": eval_set},\n n_jobs=-1).mean()\n\n parameters = {\"eta\": (0.001, 0.4),\n \"gamma\": (0, 20),\n \"max_depth\": (1, 2000),\n \"criterion\": ('squared_error', 'absolute_error', 'poisson'),\n 'bootstrap': (True, False),\n }\n \n return function, parameters", "_____no_output_____" ], [ "#Train model\ndef train(X_train, y_train, X_test, y_test, function, parameters):\n dataset = (X_train, y_train, X_test, y_test)\n cv_splits = 4\n \n best_solution = bayesian_optimization(dataset, function, parameters) \n params = best_solution[\"params\"]\n\n model = RandomForestRegressor(\n n_estimators=int(max(params[\"n_estimators\"], 0)),\n max_depth=int(max(params[\"max_depth\"], 1)),\n min_samples_split=int(max(params[\"min_samples_split\"], 2)), \n n_jobs=-1, \n random_state=42)\n\n model.fit(X_train, y_train)\n \n return model", "_____no_output_____" ], [ "rfc, params = rfc_optimization(4)", "_____no_output_____" ], [ "model = train(X_train, y_train, X_test, y_test, rfc, params)", "| iter | target | max_depth | min_sa... | n_esti... |\n-------------------------------------------------------------\n" ], [ "y_true = y_test\ny_pred = model.predict(X_test)\n\nmean_squared_error(y_true, y_pred)", "_____no_output_____" ] ] ]

[ "code" ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "<img width=\"10%\" alt=\"Naas\" src=\"https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160\"/>", "_____no_output_____" ], [ "# Naas - Get help\n<a href=\"https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/Naas/Naas_Get_help.ipynb\" target=\"_parent\"><img src=\"https://img.shields.io/badge/-Open%20in%20Naas-success?labelColor=000000&logo=data:image/svg+xml;base64,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\"/></a>", "_____no_output_____" ], [ "**Tags:** #naas #help", "_____no_output_____" ], [ "## Input", "_____no_output_____" ], [ "### Import needed library", "_____no_output_____" ] ], [ [ "import naas", "_____no_output_____" ] ], [ [ "## Output", "_____no_output_____" ], [ "### Open help chatbox", "_____no_output_____" ] ], [ [ "naas.open_help()", "_____no_output_____" ] ], [ [ "### Close help chatbox", "_____no_output_____" ] ], [ [ "naas.close_help()", "_____no_output_____" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<h3>Les commentaires</h3>\r\n<p>Lorsque vous écrivez un programme, vous serez le plus souvent appelé à commenter vos lignes de code afin de vous les expliquer à vous ou aux autres programmeurs qui vous liront; c'est une bonne pratique.</p>\r\n<p>Les commentaires entre codes facilitent aux humains la compréhension du code et par conséquent, ils ne sont pas ignorés par Python. Voici notre programme de tout à l'heure, commenté.</p>", "_____no_output_____" ] ], [ [ "'''\r\nFonction pour dire bonjour à l'utilisateur :\r\ncette fonction va afficher un message sympa à l'écran\r\n'''\r\ndef direBonjour():\r\n print('Bonjour cher utilisateur')\r\n\r\n#ici j'appelle ma fonction\r\ndireBonjour()", "Bonjour cher utilisateur\n" ] ], [ [ "<p>Lorsque le programme rencontre des lignes des textes entre les trois guillemets simples <code>'''</code> et <code>'''</code> il les ignore à l'exécution: il l'utilise pour la documentation du code.</p>\r\n<p>Une autre façon de commenter le code sur une seule ligne est d'utiliser le caractère <code>#</code> suivi du commentaire comme nous l'avons fait sur la ligne </p>", "_____no_output_____" ] ] ]

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

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

[ "CC-BY-3.0" ]

[ "CC-BY-3.0" ]

[ "CC-BY-3.0" ]

[ [ [ "# default_exp visualise", "_____no_output_____" ] ], [ [ "# Visualise\n\n> Visualise the datacube interactively. ", "_____no_output_____" ] ], [ [ "#hide\nfrom nbdev.showdoc import *", "_____no_output_____" ] ] ]

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

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

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "from keras.layers import Input, Dense, merge\nfrom keras.models import Model\nfrom keras.layers import Convolution2D, MaxPooling2D, Reshape, BatchNormalization\nfrom keras.layers import Activation, Dropout, Flatten, Dense", "_____no_output_____" ], [ "def default_categorical():\n img_in = Input(shape=(120, 160, 3), name='img_in') # First layer, input layer, Shape comes from camera.py resolution, RGB\n x = img_in\n x = Convolution2D(24, (5,5), strides=(2,2), activation='relu', name = 'conv1')(x) # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation\n x = Convolution2D(32, (5,5), strides=(2,2), activation='relu', name = 'conv2')(x) # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion\n x = Convolution2D(64, (5,5), strides=(2,2), activation='relu', name = 'conv3')(x) # 64 features, 5px5p kernal window, 2wx2h stride, relu\n x = Convolution2D(64, (3,3), strides=(2,2), activation='relu', name = 'conv4')(x) # 64 features, 3px3p kernal window, 2wx2h stride, relu\n x = Convolution2D(64, (3,3), strides=(1,1), activation='relu', name = 'conv5')(x) # 64 features, 3px3p kernal window, 1wx1h stride, relu\n\n # Possibly add MaxPooling (will make it less sensitive to position in image). Camera angle fixed, so may not to be needed\n\n x = Flatten(name='flattened')(x) # Flatten to 1D (Fully connected)\n x = Dense(100, activation='relu', name = 'dense1')(x) # Classify the data into 100 features, make all negatives 0\n x = Dropout(.1)(x) # Randomly drop out (turn off) 10% of the neurons (Prevent overfitting)\n x = Dense(50, activation='relu', name = 'dense2')(x) # Classify the data into 50 features, make all negatives 0\n x = Dropout(.1)(x) # Randomly drop out 10% of the neurons (Prevent overfitting)\n #categorical output of the angle\n angle_out = Dense(15, activation='softmax', name='angle_out')(x) # Connect every input with every output and output 15 hidden units. Use Softmax to give percentage. 15 categories and find best one based off percentage 0.0-1.0\n \n #continous output of throttle\n throttle_out = Dense(1, activation='relu', name='throttle_out')(x) # Reduce to 1 number, Positive number only\n \n model = Model(inputs=[img_in], outputs=[angle_out, throttle_out])\n \n return model", "_____no_output_____" ], [ "model = default_categorical()\nmodel.load_weights('weights.h5')", "_____no_output_____" ], [ "img_in = Input(shape=(120, 160, 3), name='img_in')\nx = img_in\nx = Convolution2D(24, (5,5), strides=(2,2), activation='relu', name='conv1')(x)\nx = Convolution2D(32, (5,5), strides=(2,2), activation='relu', name='conv2')(x)\nx = Convolution2D(64, (5,5), strides=(2,2), activation='relu', name='conv3')(x)\nx = Convolution2D(64, (3,3), strides=(2,2), activation='relu', name='conv4')(x)\nconv_5 = Convolution2D(64, (3,3), strides=(1,1), activation='relu', name='conv5')(x)\nconvolution_part = Model(inputs=[img_in], outputs=[conv_5])", "_____no_output_____" ], [ "for layer_num in ('1', '2', '3', '4', '5'):\n convolution_part.get_layer('conv' + layer_num).set_weights(model.get_layer('conv' + layer_num).get_weights())", "_____no_output_____" ], [ "from keras import backend as K\n\ninp = convolution_part.input # input placeholder\noutputs = [layer.output for layer in convolution_part.layers[1:]] # all layer outputs\nfunctor = K.function([inp], outputs)", "_____no_output_____" ], [ "import tensorflow as tf", "_____no_output_____" ], [ "import numpy as np", "_____no_output_____" ], [ "import pdb", "_____no_output_____" ], [ "kernel_3x3 = tf.constant(np.array([\n [[[1]], [[1]], [[1]]], \n [[[1]], [[1]], [[1]]], \n [[[1]], [[1]], [[1]]]\n]), tf.float32)\n\nkernel_5x5 = tf.constant(np.array([\n [[[1]], [[1]], [[1]], [[1]], [[1]]], \n [[[1]], [[1]], [[1]], [[1]], [[1]]], \n [[[1]], [[1]], [[1]], [[1]], [[1]]],\n [[[1]], [[1]], [[1]], [[1]], [[1]]],\n [[[1]], [[1]], [[1]], [[1]], [[1]]]\n]), tf.float32)\n\nlayers_kernels = {5: kernel_3x3, 4: kernel_3x3, 3: kernel_5x5, 2: kernel_5x5, 1: kernel_5x5}\n\nlayers_strides = {5: [1, 1, 1, 1], 4: [1, 2, 2, 1], 3: [1, 2, 2, 1], 2: [1, 2, 2, 1], 1: [1, 2, 2, 1]}\n\ndef compute_visualisation_mask(img):\n# pdb.set_trace()\n activations = functor([np.array([img])])\n activations = [np.reshape(img, (1, img.shape[0], img.shape[1], img.shape[2]))] + activations\n upscaled_activation = np.ones((3, 6))\n for layer in [5, 4, 3, 2, 1]:\n averaged_activation = np.mean(activations[layer], axis=3).squeeze(axis=0) * upscaled_activation\n output_shape = (activations[layer - 1].shape[1], activations[layer - 1].shape[2])\n x = tf.constant(\n np.reshape(averaged_activation, (1,averaged_activation.shape[0],averaged_activation.shape[1],1)),\n tf.float32\n )\n conv = tf.nn.conv2d_transpose(\n x, layers_kernels[layer],\n output_shape=(1,output_shape[0],output_shape[1], 1), \n strides=layers_strides[layer], \n padding='VALID'\n )\n with tf.Session() as session:\n result = session.run(conv)\n upscaled_activation = np.reshape(result, output_shape)\n final_visualisation_mask = upscaled_activation\n return (final_visualisation_mask - np.min(final_visualisation_mask))/(np.max(final_visualisation_mask) - np.min(final_visualisation_mask))", "_____no_output_____" ], [ "import cv2\nimport numpy as np", "_____no_output_____" ], [ "from matplotlib import pyplot as plt\n%matplotlib inline", "_____no_output_____" ], [ "import matplotlib.pyplot as plt\nfrom matplotlib import animation\nfrom IPython.display import display, HTML\n\ndef plot_movie_mp4(image_array):\n dpi = 72.0\n xpixels, ypixels = image_array[0].shape[0], image_array[0].shape[1]\n fig = plt.figure(figsize=(ypixels/dpi, xpixels/dpi), dpi=dpi)\n im = plt.figimage(image_array[0])\n\n def animate(i):\n im.set_array(image_array[i])\n return (im,)\n\n anim = animation.FuncAnimation(fig, animate, frames=len(image_array))\n display(HTML(anim.to_html5_video()))", "_____no_output_____" ], [ "from glob import iglob", "_____no_output_____" ], [ "imgs = []\nalpha = 0.004\nbeta = 1.0 - alpha\ncounter = 0\nfor path in sorted(iglob('imgs/*.jpg')):\n img = cv2.imread(path)\n salient_mask = compute_visualisation_mask(img)\n salient_mask_stacked = np.dstack((salient_mask,salient_mask))\n salient_mask_stacked = np.dstack((salient_mask_stacked,salient_mask))\n blend = cv2.addWeighted(img.astype('float32'), alpha, salient_mask_stacked, beta, 0.0)\n imgs.append(blend)\n counter += 1\n if counter >= 400:\n break", "_____no_output_____" ], [ "plot_movie_mp4(imgs)", "_____no_output_____" ] ] ]

[ "code" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Lendo/Tratando dados", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline", "_____no_output_____" ], [ "df = pd.read_csv('emotions.csv')", "_____no_output_____" ], [ "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')", "_____no_output_____" ], [ "df.columns[2548]", "_____no_output_____" ] ], [ [ "# Treinando modelo", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import train_test_split\n\nx_train, x_test, y_train, y_test = train_test_split(df.drop('label',axis=1), \n df['label'], test_size=0.30)", "_____no_output_____" ], [ "from sklearn.hmm import MultinomialHMM", "_____no_output_____" ], [ "dtree = RandomForestClassifier(n_estimators = 200)\ndtree.fit(x_train, y_train)\npredict = dtree.predict(x_test)", "_____no_output_____" ], [ "predict", "_____no_output_____" ], [ "from sklearn.metrics import classification_report", "_____no_output_____" ], [ "print(classification_report(y_test, predict))", " precision recall f1-score support\n\n NEGATIVE 0.99 0.98 0.98 203\n NEUTRAL 1.00 1.00 1.00 206\n POSITIVE 0.98 0.98 0.98 231\n\n accuracy 0.99 640\n macro avg 0.99 0.99 0.99 640\nweighted avg 0.99 0.99 0.99 640\n\n" ], [ "import pickle\npickle.dump(dtree, open('DEUS VULT.sat', 'wb'))\n#modelo = pickle.load(open('DEUS VULT.sat', 'rb'))", "_____no_output_____" ] ], [ [ "# Teste para Erro", "_____no_output_____" ] ], [ [ "x_train, x_test, y_train, y_test = train_test_split(df.drop('label',axis=1), \n df['label'], test_size=0.30)\n\ny_train_forjado = []\n\nfor i in range(0, 1492):\n y_train_forjado.append('POSITIVE')", "_____no_output_____" ], [ "type(y_train)", "_____no_output_____" ], [ "dtree = DecisionTreeClassifier()\ndtree.fit(x_train, pd.Series(y_train_forjado))\npredict = dtree.predict(x_test)", "_____no_output_____" ], [ "print(classification_report(y_test, predict))", " precision recall f1-score support\n\n NEGATIVE 0.00 0.00 0.00 228\n NEUTRAL 0.00 0.00 0.00 221\n POSITIVE 0.30 1.00 0.46 191\n\n accuracy 0.30 640\n macro avg 0.10 0.33 0.15 640\nweighted avg 0.09 0.30 0.14 640\n\n" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Notebook to visualize location data", "_____no_output_____" ] ], [ [ "import csv", "_____no_output_____" ], [ "# count the number of Starbucks in DC\nwith open('starbucks.csv') as file:\n csvinput = csv.reader(file)\n\n acc = 0\n for record in csvinput:\n if 'DC' in record[3]:\n acc += 1\n \nprint( acc )", "75\n" ], [ "def parse_locations(csv_iterator,state=''):\n \"\"\" strip out long/lat and convert to a list of floating point 2-tuples --\n optionally, filter by a specified state \"\"\"\n return [ ( float(row[0]), float(row[1])) for row in csv_iterator \n if state in row[3]]\n\ndef get_locations(filename, state=''):\n \"\"\" read a list of longitude/latitude pairs from a csv file, \n optionally, filter by a specified state \"\"\"\n with open(filename, 'r') as input_file:\n csvinput = csv.reader(input_file)\n location_data = parse_locations(csvinput,state)\n return location_data", "_____no_output_____" ], [ "# get the data from all starbucks locations\nstarbucks_locations = get_locations('starbucks.csv')\n# get the data from burger locations\"\nburger_locations = get_locations('burgerking.csv') + \\\n get_locations('mcdonalds.csv') + \\\n get_locations('wendys.csv')", "_____no_output_____" ], [ "# look at the first few (10) data points of each\nfor n in range(10):\n print( starbucks_locations[n] )\n \nprint() \n \nfor n in range(10):\n print( burger_locations[n] ) ", "(-159.459214, 21.879285)\n(-159.380923, 21.97116)\n(-159.375636, 21.971295)\n(-159.34927, 21.979465)\n(-159.315957, 22.078248)\n(-158.18458, 21.434788)\n(-158.116013, 21.343991)\n(-158.08179, 21.3341)\n(-158.061706, 21.648414)\n(-158.058557, 21.483994)\n\n(-149.95032, 61.13782)\n(-149.909, 61.19542)\n(-149.88789, 61.21743)\n(-149.86801, 61.18736)\n(-149.86568, 61.14431)\n(-149.83383, 61.18072)\n(-149.81281, 61.21526)\n(-149.77812, 61.19598)\n(-149.57101, 61.32533)\n(-149.8196, 61.27584)\n" ], [ "# a common, powerful plotting library\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "# set figure size\nplt.figure(figsize=(12, 9))\n\n# get the axes of the plot and set them to be equal-aspect and limited (specify bounds) by data\nax = plt.axes()\nax.set_aspect('equal', 'datalim')\n\n# plot the data\nplt.scatter(*zip(*starbucks_locations), s=1)\n\nplt.legend([\"Starbucks\"])\n\n# jupyter automatically plots this inline. On the console, you need to invoke plt.show()\n# FYI: In that case, execution halts until you close the window it opens.", "_____no_output_____" ], [ "# set figure size\nplt.figure(figsize=(12, 9))\n\n# get the axes of the plot and set them to be equal-aspect and limited (specify bounds) by data\nax = plt.axes()\nax.set_aspect('equal', 'datalim')\n\n# plot the data\nplt.scatter(*zip(*burger_locations), color='green', s=1)\n\nplt.legend([\"Burgers\"])", "_____no_output_____" ], [ "lat, lon = zip(*get_locations('burgerking.csv'))\n\nmin_lat = min(lat)\nmax_lat = max(lat)\nmin_lon = min(lon)\nmax_lon = max(lon)\n\nlat, lon = zip(*get_locations('mcdonalds.csv'))\n\nmin_lat = min(min_lat,min(lat))\nmax_lat = max(max_lat,max(lat))\nmin_lon = min(min_lon,min(lon))\nmax_lon = max(max_lon,max(lon))\n\nlat, lon = zip(*get_locations('wendys.csv'))\n\nmin_lat = min(min_lat,min(lat))\nmax_lat = max(max_lat,max(lat))\nmin_lon = min(min_lon,min(lon))\nmax_lon = max(max_lon,max(lon))\n\nlat, lon = zip(*get_locations('pizzahut.csv'))\n\nmin_lat = min(min_lat,min(lat))\nmax_lat = max(max_lat,max(lat))\nmin_lon = min(min_lon,min(lon))\nmax_lon = max(max_lon,max(lon))\n", "_____no_output_____" ], [ "# set figure size\nfig = plt.figure(figsize=(12, 9))\n#fig = plt.figure()\n\n\n\n\nplt.subplot(2,2,1)\nplt.scatter(*zip(*get_locations('burgerking.csv')), color='black', s=1, alpha=0.2)\nplt.xlim(min_lat-5,max_lat+5)\nplt.ylim(min_lon-5,max_lon+5)\nplt.gca().set_aspect('equal')\nplt.subplot(2,2,2)\nplt.scatter(*zip(*get_locations('mcdonalds.csv')), color='black', s=1, alpha=0.2)\nplt.xlim(min_lat-5,max_lat+5)\nplt.ylim(min_lon-5,max_lon+5)\nplt.gca().set_aspect('equal')\nplt.subplot(2,2,3)\nplt.scatter(*zip(*get_locations('wendys.csv')), color='black', s=1, alpha=0.2)\nplt.xlim(min_lat-5,max_lat+5)\nplt.ylim(min_lon-5,max_lon+5)\nplt.gca().set_aspect('equal')\nplt.subplot(2,2,4)\nplt.scatter(*zip(*get_locations('pizzahut.csv')), color='black', s=1, alpha=0.2)\nplt.xlim(min_lat-5,max_lat+5)\nplt.ylim(min_lon-5,max_lon+5)\nplt.gca().set_aspect('equal')\n\n#plt.scatter(*zip(*get_locations('dollar-tree.csv')), color='black', s=1, alpha=0.2)", "_____no_output_____" ], [ "# get the starbucks in DC\nstarbucks_dc_locations = get_locations('starbucks.csv', state='DC')\n\nburger_dc_locations = get_locations('burgerking.csv', state='DC') + \\\n get_locations('mcdonalds.csv', state='DC') + \\\n get_locations('wendys.csv', state='DC')", "_____no_output_____" ], [ "# show the first 10 locations of each:\nfor n in range(10):\n print( starbucks_dc_locations[n] )\n \nprint() \n \nfor n in range(min(10,len(burger_dc_locations))):\n print( burger_dc_locations[n] )\n ", "(-77.102842, 38.926656)\n(-77.095791, 38.91756)\n(-77.095684, 38.944565)\n(-77.085464, 38.960783)\n(-77.084843, 38.933583)\n(-77.079661, 38.948279)\n(-77.07613, 38.912007)\n(-77.074559, 38.963113)\n(-77.073222, 38.935104)\n(-77.071562, 38.920283)\n\n(-77.06535, 38.947)\n(-77.04546, 38.90951)\n(-77.04172, 38.92349)\n(-77.03727, 38.90221)\n(-77.03613, 38.97252)\n(-77.02283, 38.92583)\n(-77.01903, 38.89839)\n(-77.01655, 38.84225)\n(-77.00158, 38.90777)\n(-77.08684, 38.9384)\n" ], [ "# set figure size\nplt.figure(figsize=(12, 9))\n\n# get the axes of the plot and set them to be equal-aspect and limited by data\nax = plt.axes()\nax.set_aspect('equal', 'datalim')\n\n# plot the data\nplt.scatter(*zip(*starbucks_dc_locations))\nplt.scatter(*zip(*burger_dc_locations), color='green')", "_____no_output_____" ], [ "# We also want to plot the DC boundaries, so we have a better idea where these things are\n# the data is contained in DC.txt\n\n# let's inspect it. Observe the format\nwith open('DC.txt') as file:\n for line in file:\n print(line,end='') # lines already end with a newline so don't print another", " -77.120201 38.791401\n -76.909706 38.994400\n1\n\nDistrict of Columbia\nDC\n25\n -77.120201 38.934200\n -77.042305 38.994400\n -77.036400 38.991402\n -77.008301 38.969601\n -76.909706 38.892700\n -77.038902 38.791401\n -77.036102 38.814800\n -77.040703 38.821602\n -77.039505 38.832100\n -77.045197 38.834599\n -77.046303 38.841202\n -77.033104 38.841599\n -77.031998 38.850399\n -77.038101 38.861801\n -77.042908 38.863400\n -77.039200 38.865700\n -77.040901 38.871101\n -77.045708 38.875099\n -77.046600 38.871201\n -77.049400 38.870602\n -77.054398 38.879002\n -77.058556 38.879955\n -77.068504 38.899700\n -77.090508 38.904099\n -77.101501 38.910999\n\n" ], [ "with open('DC.txt') as file:\n # get the lower left and upper right coords for the bounding box\n ll_long, ll_lat = map(float, next(file).split())\n ur_long, ur_lat = map(float, next(file).split())\n # get the number of regions \n num_records = int(next(file))\n # there better just be one\n assert num_records == 1\n # then a blank line\n next(file)\n # Title of \"county\"\n county_name = next(file).rstrip() # removes newline at end\n # \"State\" county resides in\n state_name = next(file).rstrip()\n # this is supposed to be DC\n assert state_name == \"DC\"\n # number of points to expect\n num_pairs = int(next(file))\n dc_boundary = [ tuple(map(float,next(file).split())) for n in range(num_pairs)]\n \n", "_____no_output_____" ], [ "dc_boundary", "_____no_output_____" ], [ "# add the beginning to the end so that it closes up\ndc_boundary.append(dc_boundary[0])", "_____no_output_____" ], [ "# draw it!\n\nax = plt.axes()\nax.set_aspect('equal', 'datalim')\n\nplt.plot(*zip(*dc_boundary))", "_____no_output_____" ], [ "# draw both the starbucks location and DC boundary together\n\nplt.figure(figsize=(12, 9))\n\nax = plt.axes()\nax.set_aspect('equal', 'datalim')\n\nplt.scatter(*zip(*starbucks_dc_locations))\nplt.scatter(*zip(*burger_dc_locations), color='green')\nplt.plot(*zip(*dc_boundary))", "_____no_output_____" ], [ "# draw both the starbucks location and DC boundary together\n\nplt.figure(figsize=(12, 9))\n\nax = plt.axes()\nax.set_aspect('equal', 'datalim')\n\nplt.scatter(*zip(*get_locations('burgerking.csv', state='DC')), color='red')\nplt.scatter(*zip(*get_locations('mcdonalds.csv', state='DC')), color='green')\nplt.scatter(*zip(*get_locations('wendys.csv', state='DC')), color='blue')\nplt.scatter(*zip(*get_locations('pizzahut.csv', state='DC')), color='yellow')\n\nplt.scatter(*zip(*get_locations('dollar-tree.csv', state='DC')), color='black')\nplt.plot(*zip(*dc_boundary))", "_____no_output_____" ] ], [ [ "### But where's AU?", "_____no_output_____" ] ], [ [ "# draw both the starbucks location and DC boundary together\n\nplt.figure(figsize=(12, 9))\n\nax = plt.axes()\nax.set_aspect('equal', 'datalim')\n\nplt.scatter(*zip(*starbucks_dc_locations))\nplt.scatter(*zip(*burger_dc_locations), color='green')\nplt.plot(*zip(*dc_boundary))\n\n# add a red dot right over Anderson\nplt.scatter([-77.0897511],[38.9363019],color='red')", "_____no_output_____" ], [ "from ipyleaflet import Map, basemaps, basemap_to_tiles, Marker, CircleMarker\n\nm = Map(layers=(basemap_to_tiles(basemaps.OpenStreetMap.HOT), ),\n center=(38.898082, -77.036696),\n zoom=11)\n\n# marker for AU\nmarker = Marker(location=(38.937831, -77.088852), radius=2, color='green')\nm.add_layer(marker)\n\nfor (long,lat) in starbucks_dc_locations:\n marker = CircleMarker(location=(lat,long), radius=1, color='steelblue')\n m.add_layer(marker);\n\nfor (long,lat) in burger_dc_locations:\n marker = CircleMarker(location=(lat,long), radius=1, color='green')\n m.add_layer(marker);\n \n \n\nm", "_____no_output_____" ], [ "\n", "_____no_output_____" ] ] ]

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

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "li = ['hey this is sai','i am in mumbai']\nprint(list(map(lambda x:x.title(),li)))", "['Hey This Is Sai', 'I Am In Mumbai']\n" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "https://stackoverflow.com/questions/64918649/streamlit-with-colab-and-pyngrok-failed-to-complete-tunnel-connection-versio\n\nhttps://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Create_streamlit_app.ipynb#scrollTo=vWmc_s2ezvU0\n\nhttps://medium.com/@jcharistech/how-to-run-streamlit-apps-from-colab-29b969a1bdfc\n\n", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Dense DAISY feature description\nhttps://scikit-image.org/docs/dev/auto_examples/features_detection/plot_daisy.htmlfrom", "_____no_output_____" ] ], [ [ "from skimage.feature import daisy\nfrom skimage import data\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "%%time\nimg = data.camera()\ndescs = daisy(img, step=5, radius=8, visualize=True)", "CPU times: user 862 ms, sys: 262 ms, total: 1.12 s\nWall time: 1.13 s\n" ], [ "descs.shape", "_____no_output_____" ] ] ]

[ "markdown", "code" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nfrom pydotplus import graph_from_dot_file\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz", "_____no_output_____" ], [ "fn = 'data.csv'\ndata = pd.read_csv(fn)\n\nprint(data.shape)\ndata.head()", "(476, 32)\n" ], [ "class_names = [\n 'Ostre zapalenie wyrostka robaczkowego',\n 'Zapalenie uchyłków jelit',\n 'Niedrożność mechaniczna jelit',\n 'Perforowany wrzód trawienny',\n 'Zapalenie woreczka żółciowego',\n 'Ostre zapalenie trzustki',\n 'Niecharakterystyczny ból brzucha',\n 'Inne przyczyny ostrego bólu brzucha',\n]", "_____no_output_____" ], [ "train_x, test_x = train_test_split(data)\n\ntrain_y = train_x.pop('Choroba')\ntest_y = test_x.pop('Choroba')", "_____no_output_____" ], [ "tree = DecisionTreeClassifier(random_state=42,\n min_samples_leaf=1,\n )\ntree = tree.fit(train_x, train_y)\ntree", "_____no_output_____" ], [ "dot_file = 'tree.dot'\nexport_graphviz(tree,\n out_file=dot_file,\n feature_names=train_x.columns,\n class_names=class_names,\n filled=True,\n impurity=False,\n rounded=True,\n )", "_____no_output_____" ], [ "graph = graph_from_dot_file(dot_file)\ngraph.write_pdf('tree.pdf')\ngraph.write_png('tree.png')", "_____no_output_____" ], [ "tree.score(test_x, test_y)", "_____no_output_____" ] ] ]

[ "code" ]

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

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "from gs_quant.session import Environment, GsSession\nfrom gs_quant.common import PayReceive, Currency\nfrom gs_quant.instrument import IRSwap\nfrom gs_quant.risk import Cashflows", "_____no_output_____" ], [ "# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\nGsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))", "_____no_output_____" ], [ "swap = IRSwap(PayReceive.Receive, '5y', Currency.EUR, fixed_rate='atm+10')", "_____no_output_____" ], [ "# returns a dataframe of future cashflows \n# note this feature will be expanded to cover portfolios in future releases\nswap.calc(Cashflows)", "_____no_output_____" ] ] ]

[ "code" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# 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'", "_____no_output_____" ] ], [ [ "**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} $.", "_____no_output_____" ] ], [ [ "# GRADED FUNCTION: compute_cost_with_regularization\n\ndef compute_cost_with_regularization(A3, Y, parameters, lambd):\n \"\"\"\n Implement the cost function with L2 regularization. See formula (2) above.\n \n Arguments:\n A3 -- post-activation, output of forward propagation, of shape (output size, number of examples)\n Y -- \"true\" labels vector, of shape (output size, number of examples)\n parameters -- python dictionary containing parameters of the model\n \n Returns:\n cost - value of the regularized loss function (formula (2))\n \"\"\"\n m = Y.shape[1]\n W1 = parameters[\"W1\"]\n W2 = parameters[\"W2\"]\n W3 = parameters[\"W3\"]\n \n cross_entropy_cost = compute_cost(A3, Y) # This gives you the cross-entropy part of the cost\n \n ### START CODE HERE ### (approx. 1 line)\n L2_regularization_cost = 1/m*lambd/2*(np.sum(np.square(W1))+np.sum(np.square(W2)) + np.sum(np.square(W3)))\n ### END CODER HERE ###\n \n cost = cross_entropy_cost + L2_regularization_cost\n \n return cost", "_____no_output_____" ], [ "A3, Y_assess, parameters = compute_cost_with_regularization_test_case()\n\nprint(\"cost = \" + str(compute_cost_with_regularization(A3, Y_assess, parameters, lambd = 0.1)))", "cost = 1.78648594516\n" ] ], [ [ "**Expected Output**: \n\n<table> \n <tr>\n <td>\n **cost**\n </td>\n <td>\n 1.78648594516\n </td>\n \n </tr>\n\n</table> ", "_____no_output_____" ], [ "Of course, because you changed the cost, you have to change backward propagation as well! All the gradients have to be computed with respect to this new cost. \n\n**Exercise**: Implement the changes needed in backward propagation to take into account regularization. The changes only concern dW1, dW2 and dW3. For each, you have to add the regularization term's gradient ($\\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) # 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) # Step 1: initialize matrix D2 = np.random.rand(..., ...)\n D2 = (D2 > keep_prob) # 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 = [[ 0.49683389 0.49683389 0.49683389 0.36974721 0.49683389]]\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 14% 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)", "_____no_output_____" ] ], [ [ "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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[ [ [ "### Calculating intensity in an inclined direction $\\cos\\theta \\neq 1$ \n\nFor this first part we are going to use our good old FALC model and calculate intensity in direction other than $\\mu = 1$. This is also an essential part of scattering problems! \n\n### We will assume that we are dealing with continuum everywhere!", "_____no_output_____" ] ], [ [ "import numpy as np\nimport matplotlib.pyplot as plt \nimport matplotlib\nmatplotlib.rcParams.update({\n \"text.usetex\": False,\n \"font.size\" : 16,\n \"font.family\": \"sans-serif\",\n \"font.sans-serif\": [\"Helvetica\"]})\n", "_____no_output_____" ], [ "# Let's load the data:\natmos = np.loadtxt(\"falc_71.dat\",unpack=True,skiprows=1)\natmos.shape", "_____no_output_____" ], [ "# 0th parameter - log optical depth in continuum (lambda = 500 nm)\n# 2nd parameter - Temperature\n\nplt.figure(figsize=[9,5])\nplt.plot(atmos[0],atmos[2])", "_____no_output_____" ], [ "llambda = 500E-9 # in nm\nk = 1.38E-23 # Boltzmann constant \nc = 3E8 # speed of light\nh_p = 6.626E-34\n\nT = np.copy(atmos[2])\nlogtau = np.copy(atmos[0])\ntau = 10.** logtau\n\n# We will separate now Planck function and the source function:\nB = 2*h_p*c**2.0 / llambda**5.0 / (np.exp(h_p*c/(llambda*k*T))-1)\nS = np.copy(B)\n\n", "_____no_output_____" ], [ "plt.figure(figsize=[9,5])\nplt.plot(logtau,S)", "_____no_output_____" ] ], [ [ "#### We want to use a very simple formal solution:\n\n### $$I = I_{inc} e^{-\\Delta \\tau} + S(1-e^{-\\Delta \\tau}) $$\n\n", "_____no_output_____" ] ], [ [ "# Let's define a function that is doing that:\n\ndef synthesis_whole(S,tau):\n \n ND = len(S)\n intensity = np.zeros(ND)\n \n # At the bottom, intensity is equal to the source function (similar to Blackbody)\n intensity[ND-1] = S[ND-1]\n \n for d in range(ND-2,-1,-1):\n # optical thickness of the layer:\n delta_tau = tau[d+1] - tau[d]\n # Mean source function:\n S_mean = (S[d+1] + S[d])*0.5\n \n intensity[d] = intensity[d+1] * np.exp(-delta_tau) + S_mean *(1.-np.exp(-delta_tau))\n \n return intensity", "_____no_output_____" ], [ "intensity = synthesis_whole(S,tau)", "_____no_output_____" ], [ "plt.figure(figsize=[9,5])\nplt.plot(logtau,S,label='Source Function')\nplt.plot(logtau,intensity,label='Intensity in the direction theta =0 ')\nplt.legend()\nplt.xlabel(\"$\\\\log\\\\tau$\")", "_____no_output_____" ] ], [ [ "### Discuss this 3-4 mins. \nWrite the conclusions here:\n\nIntensity is not the same as the source function. It is \"nonlocal\" (ok Ivan, we get it, you said million times!)\n\n\n### Now how about using a grid of $mu$ angles?\n\n### We are solving \n### $$\\mu \\frac{dI}{d\\tau} = I-S $$\n\n### $$\\frac{dI}{d\\tau / \\mu} = I-S $$\n\n### $\\mu$ goes from 0 to 1 \n\n", "_____no_output_____" ] ], [ [ "NM = 10\nND = len(S)\n\n\n# create mu grid\nmu = np.linspace(0.1,1.0,NM)\n\nintensity_grid = np.zeros([NM,ND])\n\n# Calculate the intensity in each direction:\nfor m in range(0,10):\n intensity_grid[m] = synthesis_whole(S,tau/mu[m])", "_____no_output_____" ], [ "plt.figure(figsize=[9,5])\nplt.plot(mu,intensity_grid[:,0])\nplt.ylabel(\"$I^+(\\mu)$\")\nplt.xlabel(\"$\\mu = \\cos\\\\theta$\")", "_____no_output_____" ] ], [ [ "### Limb darkening!\n\n", "_____no_output_____" ], [ "### Limb darkening explanation:\n\n", "_____no_output_____" ], [ "# Now the scattering problem! \n\n### Solve, iteratively:\n\n### $$S = \\epsilon B + (1-\\epsilon) J $$\n### $$ J = \\frac{1}{4\\pi} \\int_0^{\\pi} \\int_0^{2\\pi} I(\\theta,\\phi) \\sin \\theta d\\theta d\\phi = \\frac{1}{2} \\int_{-1}^{1} I(\\mu) d\\mu $$\nand for this second equation we need to formally solve: \n### $$ \\frac{dI}{d\\tau/\\mu} = I-S $$", "_____no_output_____" ], [ "### How to calculate J at the top of the atmosphere from the intensity we just calculated?", "_____no_output_____" ], [ "Would you all agree that, at the top:\n\n$$J = \\frac{1}{2} \\int_{-1}^{1} I(\\mu) d\\mu = \\frac{1}{2} \\int_{0}^{1} I(\\mu) d\\mu = \\frac{1}{2}\\sum_m (I_m + I_{m+1}) \\frac{\\mu_{m+1}-\\mu_{m}}{2}$$", "_____no_output_____" ] ], [ [ "# simplified trapezoidal rule:\n# Starting value:\nJ = 0.\nfor m in range(0,NM-1):\n J += (intensity_grid[m,0] + intensity_grid[m+1,0]) * (mu[m+1] - mu[m])/2.0\nJ *= 0.5", "_____no_output_____" ], [ "print (\"ratio of the mean intensity at the surface to the Planck function at the surface is: \", J/B[0])", "ratio of the mean intensity at the surface to the Planck function at the surface is: 0.11910542541966643\n" ] ], [ [ "### Now the next step would be, to say: \n\n### $$S = \\epsilon B + \\epsilon J $$\n\nThere are, however, few hurdles before that: \n\n- We don't know $\\epsilon$. Let's assume there is a lot of scattering and set $\\epsilon=10^{-2}$\n- We don't know J everywhere, we only calculate at the top. This one is harder to fix. \n\nTo really calculate J everywhere properly we need to solve radiative transfer equation going inward. \n\n### Convince yourself that that is really what we have to do. \n\nHow do we solve the RTE inward? Identically to upward except we start from the top and go down. Let's sketch a scheme for that. ", "_____no_output_____" ] ], [ [ "# Old RTE solver looks like this. Renamed it to out so that we know it is the outgoing intensity\n\ndef synthesis_out(S,tau):\n \n ND = len(S)\n intensity = np.zeros(ND)\n \n # At the bottom, intensity is equal to the source function (similar to Blackbody)\n intensity[ND-1] = S[ND-1]\n \n for d in range(ND-2,-1,-1):\n # optical thickness of the layer:\n delta_tau = tau[d+1] - tau[d]\n # Mean source function:\n S_mean = (S[d+1] + S[d])*0.5\n \n intensity[d] = intensity[d+1] * np.exp(-delta_tau) + S_mean *(1.-np.exp(-delta_tau))\n \n return intensity\n\ndef synthesis_in(S,tau):\n \n ND = len(S)\n intensity = np.zeros(ND)\n \n # At the top, intensity is equal to ZERO\n intensity[0] = 0.0\n \n # The main difference now is that this \"previous\" or \"upwind\" point is the point before (d-1)\n \n for d in range(1,ND,1):\n # optical thickness of the layer:\n delta_tau = tau[d] - tau[d-1] # Note that I am using previous point now\n # Mean source function:\n S_mean = (S[d] + S[d-1])*0.5\n \n intensity[d] = intensity[d-1] * np.exp(-delta_tau) + S_mean *(1.-np.exp(-delta_tau))\n \n return intensity\n", "_____no_output_____" ], [ "# Now we can solve and we will have two different intensities, in and out one \n\nint_out = np.zeros([NM,ND])\nint_in = np.zeros([NM,ND])\n\n# We solve in multiple directions:\nfor m in range(0,10):\n int_out[m] = synthesis_out(S,tau/mu[m])\n int_in[m] = synthesis_in (S,tau/mu[m])", "_____no_output_____" ], [ "# It is interesting now to visualize the outgoing intensity, and, \n# for example, the intensity at the bottom. How about that:\n\nplt.figure(figsize=[9,5])\nplt.plot(mu,int_out[:,0],label='Outgoing intensity')\nplt.plot(mu,int_in[:,0], label='Ingoing intensity')\nplt.legend()\nplt.xlabel(\"$\\\\mu$\")\nplt.ylabel(\"Intensity\")\nplt.title(\"Intensity distribution at the top of the atmosphere\")", "_____no_output_____" ], [ "# Here is the bottom intensity\n# Don't be confused by the expression outgoing / ingoing. We mean \"at that point\"\n\nplt.figure(figsize=[9,5])\nplt.plot(mu,int_out[:,ND-1],label='Outgoing intensity')\nplt.plot(mu,int_in[:,ND-1], label='Ingoing intensity')\nplt.legend()\nplt.xlabel(\"$\\\\mu$\")\nplt.ylabel(\"Intensity\")\nplt.ylim([0,1.5E14])\nplt.title(\"Intensity distribution at the bottom of the atmosphere\")", "_____no_output_____" ] ], [ [ "### Take a moment here and try to think about these two things: \n\n- The intensity at the bottom is much more close to each other and much less strongly depends on $\\mu$. Would you call that situation \"isotropic\"?\n- Why is this so?", "_____no_output_____" ] ], [ [ "# Now we can calculate mean intensity similarly to above. We don't even have to use super strict \n# trapezoidal integration. Just add all the intensities and divide by 20\n\nJ = np.sum(int_in+int_out,axis=0)/20.", "_____no_output_____" ], [ "# Then define the epsilon\n\nepsilon = 1E-2\n\n# And then we can update the source function:\nS = epsilon * B + (1-epsilon) * J\n\n# And we can now plot B (Planck function, our \"old value for the source function\") vs our \"new\" \n# source function\n\nplt.figure(figsize=[9,5])\nplt.plot(logtau,B,label=\"Planck Function\")\nplt.plot(logtau,S,label=\"Source Function\")\nplt.xlabel(\"$\\\\log \\\\tau$\")\nplt.legend()", "_____no_output_____" ] ], [ [ "Now: This is new value of the source function! We should now recalculate the new intensity. \n\nBut wait, this new intensity now results in a new Source function. \n\nWhich then results in the new intensity....\n\nThis leads to an iterative process, known as \"Lambda\" iteration. We repeat the process until (a very slow) convergence. Let's try!", "_____no_output_____" ] ], [ [ "# I will define an additional function to make things clearer:\n\ndef update_S(int_in, int_out, epsilon, B):\n J = np.sum(int_in+int_out,axis=0)/20.\n S = epsilon * B + (1-epsilon) * J\n return S", "_____no_output_____" ], [ "# And then we devise an iterative scheme:\n\nmax_iter = 100\n\n# Start from the source function equal to the Planck function:\nS = np.copy(B)\n\nfor i in range(0,max_iter):\n \n # solve RTE:\n for m in range(0,10):\n int_out[m] = synthesis_out(S,tau/mu[m])\n int_in[m] = synthesis_in (S,tau/mu[m])\n \n # update the source function:\n \n S_new = update_S(int_in,int_out,epsilon,B)\n \n # find where did the source function change the most \n \n relative_change = np.amax(np.abs((S_new-S)/S))\n print(\"Relative change in this iteration is: \",relative_change)\n \n # And assign new source function to the old one:\n S = np.copy(S_new)", "Relative change in this iteration is: 1.3314924320494517\nRelative change in this iteration is: 0.04805746551383416\nRelative change in this iteration is: 0.022455016014927415\nRelative change in this iteration is: 0.019691776769293193\nRelative change in this iteration is: 0.01776157724309405\nRelative change in this iteration is: 0.01652685527990853\nRelative change in this iteration is: 0.015619266117131922\nRelative change in this iteration is: 0.01503514328248859\nRelative change in this iteration is: 0.014481956406480876\nRelative change in this iteration is: 0.013891749317768114\nRelative change in this iteration is: 0.01331940145864249\nRelative change in this iteration is: 0.012787999559594275\nRelative change in this iteration is: 0.01230499264377662\nRelative change in this iteration is: 0.011870217784521643\nRelative change in this iteration is: 0.01148002673728799\nRelative change in this iteration is: 0.011129430889475208\nRelative change in this iteration is: 0.010813199708225122\nRelative change in this iteration is: 0.01052639525000082\nRelative change in this iteration is: 0.010264601941141491\nRelative change in this iteration is: 0.010023994398619377\nRelative change in this iteration is: 0.00980132281604571\nRelative change in this iteration is: 0.009593860119202186\nRelative change in this iteration is: 0.009399335051463401\nRelative change in this iteration is: 0.00921586391391034\nRelative change in this iteration is: 0.009041887192909583\nRelative change in this iteration is: 0.00887611368209911\nRelative change in this iteration is: 0.008717472744430008\nRelative change in this iteration is: 0.008565074358955678\nRelative change in this iteration is: 0.008418176141613611\nRelative change in this iteration is: 0.008276156375340717\nRelative change in this iteration is: 0.008138492089803452\nRelative change in this iteration is: 0.008004741312654376\nRelative change in this iteration is: 0.00787452872644683\nRelative change in this iteration is: 0.007747534083037639\nRelative change in this iteration is: 0.007623482838041036\nRelative change in this iteration is: 0.007502138565932556\nRelative change in this iteration is: 0.007383296800261419\nRelative change in this iteration is: 0.007266780013345936\nRelative change in this iteration is: 0.007152433507201133\nRelative change in this iteration is: 0.007040122033960388\nRelative change in this iteration is: 0.00692972700144494\nRelative change in this iteration is: 0.0068211441493597315\nRelative change in this iteration is: 0.006714281605311472\nRelative change in this iteration is: 0.00660905824862608\nRelative change in this iteration is: 0.006505402324770845\nRelative change in this iteration is: 0.006403250264879413\nRelative change in this iteration is: 0.006302545674097195\nRelative change in this iteration is: 0.0062032384597361856\nRelative change in this iteration is: 0.006105284075931407\nRelative change in this iteration is: 0.00600864286603329\nRelative change in this iteration is: 0.005913279487516914\nRelative change in this iteration is: 0.005819162407019697\nRelative change in this iteration is: 0.005726263455373415\nRelative change in this iteration is: 0.005634557434262397\nRelative change in this iteration is: 0.005544021767576933\nRelative change in this iteration is: 0.005454636191684033\nRelative change in this iteration is: 0.005366382479733791\nRelative change in this iteration is: 0.005279244195900238\nRelative change in this iteration is: 0.00519320647604306\nRelative change in this iteration is: 0.005108255831772064\nRelative change in this iteration is: 0.005024492860981598\nRelative change in this iteration is: 0.004942873327425288\nRelative change in this iteration is: 0.004862175341229857\nRelative change in this iteration is: 0.004782402081509607\nRelative change in this iteration is: 0.004703556179800586\nRelative change in this iteration is: 0.004625639736273327\nRelative change in this iteration is: 0.0045486543355527185\nRelative change in this iteration is: 0.004472601062211104\nRelative change in this iteration is: 0.004397480515958641\nRelative change in this iteration is: 0.004323292826578827\nRelative change in this iteration is: 0.00425003766862223\nRelative change in this iteration is: 0.004177714275888769\nRelative change in this iteration is: 0.004106321455710899\nRelative change in this iteration is: 0.004035857603046488\nRelative change in this iteration is: 0.003966320714399837\nRelative change in this iteration is: 0.0038977084015627103\nRelative change in this iteration is: 0.0038300179051948863\nRelative change in this iteration is: 0.0037632461082352433\nRelative change in this iteration is: 0.0036973895491447376\nRelative change in this iteration is: 0.003632444434989971\nRelative change in this iteration is: 0.0035684066543515917\nRelative change in this iteration is: 0.00350527179006848\nRelative change in this iteration is: 0.003443035131802984\nRelative change in this iteration is: 0.0033816916884323324\nRelative change in this iteration is: 0.0033212362002559273\nRelative change in this iteration is: 0.0032616631510193872\nRelative change in this iteration is: 0.003202966779748377\nRelative change in this iteration is: 0.0031451410923833438\nRelative change in this iteration is: 0.0030881798732229665\nRelative change in this iteration is: 0.003032076696157362\nRelative change in this iteration is: 0.0029768249356989457\nRelative change in this iteration is: 0.0029224177777978856\nRelative change in this iteration is: 0.0028688482304447885\nRelative change in this iteration is: 0.0028161091340556743\nRelative change in this iteration is: 0.002764193171632812\nRelative change in this iteration is: 0.0027130928787004144\nRelative change in this iteration is: 0.0026628006530184352\nRelative change in this iteration is: 0.0026133087640589857\nRelative change in this iteration is: 0.002564609362258417\nRelative change in this iteration is: 0.0025166944880325817\n" ] ], [ [ "### Spend some time figuring this out. This is a common technique for solving various kinds of coupled equations in astrophysics! ", "_____no_output_____" ], [ "To finish the story let's visualize the final solution of this, and then do one more example - constant temperature!", "_____no_output_____" ] ], [ [ "plt.figure(figsize=[9,5])\nplt.plot(logtau,B,label=\"Planck Function\")\nplt.plot(logtau,S,label=\"Source Function\")\nplt.xlabel(\"$\\\\log \\\\tau$\")\nplt.title(\"Scattering Source function\")\nplt.legend()", "_____no_output_____" ] ], [ [ "### It drops very very very low, even in great depths! This is exactly not the greatest example as in the great depths the epsilon will never be so small. Epsilon is generally depth dependent.", "_____no_output_____" ], [ "## Example - Scattering in an isothermal atmosphere", "_____no_output_____" ] ], [ [ "## We will do all the same as before except we will now use a different tau grid, to make atmosphere\n# much deeper. And we will use B = const, to reproduce some plots from the literature:\n\nlogtau = np.linspace(-4,4,81)\ntau = 10.**logtau\nND = len(logtau)\nB = np.zeros(ND)\nB[:] = 1.0 # Units do not matter, we can say everything is in units of B\nS = np.copy(B)\n\nmax_iter = 100\n\n# Start from the source function equal to the Planck function:\nint_out = np.zeros([NM,ND])\nint_in = np.zeros([NM,ND])\n\nfor i in range(0,max_iter):\n \n # solve RTE:\n for m in range(0,10):\n int_out[m] = synthesis_out(S,tau/mu[m])\n int_in[m] = synthesis_in (S,tau/mu[m])\n \n # update the source function:\n \n S_new = update_S(int_in,int_out,epsilon,B)\n \n # find where did the source function change the most \n \n relative_change = np.amax(np.abs((S_new-S)/S))\n print(\"Relative change in this iteration is: \",relative_change)\n \n # And assign new source function to the old one:\n S = np.copy(S_new)", "Relative change in this iteration is: 0.495\nRelative change in this iteration is: 0.24554435193688087\nRelative change in this iteration is: 0.16165180317576575\nRelative change in this iteration is: 0.11907785184516301\nRelative change in this iteration is: 0.0934605504492721\nRelative change in this iteration is: 0.07639398818170434\nRelative change in this iteration is: 0.06426231766991904\nRelative change in this iteration is: 0.055184567422810145\nRelative change in this iteration is: 0.04816100504989594\nRelative change in this iteration is: 0.042561204227476675\nRelative change in this iteration is: 0.03800930941085114\nRelative change in this iteration is: 0.03422423011216586\nRelative change in this iteration is: 0.03104905444678713\nRelative change in this iteration is: 0.02833596506538578\nRelative change in this iteration is: 0.02599331859923862\nRelative change in this iteration is: 0.023963907384147783\nRelative change in this iteration is: 0.022177874061694313\nRelative change in this iteration is: 0.020595244174362176\nRelative change in this iteration is: 0.019196061582771876\nRelative change in this iteration is: 0.017941215852293142\nRelative change in this iteration is: 0.016809536981195885\nRelative change in this iteration is: 0.015786824465279008\nRelative change in this iteration is: 0.01486429969338762\nRelative change in this iteration is: 0.014021310076619498\nRelative change in this iteration is: 0.013248506341107262\nRelative change in this iteration is: 0.012537929485077428\nRelative change in this iteration is: 0.011889703114935922\nRelative change in this iteration is: 0.01129098840247929\nRelative change in this iteration is: 0.010735588499547501\nRelative change in this iteration is: 0.01021926561681866\nRelative change in this iteration is: 0.009738307223026725\nRelative change in this iteration is: 0.009293802209125204\nRelative change in this iteration is: 0.008879896632323993\nRelative change in this iteration is: 0.00849182762994324\nRelative change in this iteration is: 0.008127436753987761\nRelative change in this iteration is: 0.007784793644885518\nRelative change in this iteration is: 0.007462166986852504\nRelative change in this iteration is: 0.007160330312042671\nRelative change in this iteration is: 0.0068778630567019215\nRelative change in this iteration is: 0.006610602980846975\nRelative change in this iteration is: 0.0063574691294311335\nRelative change in this iteration is: 0.006117477330138822\nRelative change in this iteration is: 0.005889729748688644\nRelative change in this iteration is: 0.005673405755313838\nRelative change in this iteration is: 0.005467753916149217\nRelative change in this iteration is: 0.005275659026641683\nRelative change in this iteration is: 0.0050926849261230785\nRelative change in this iteration is: 0.004918112588820311\nRelative change in this iteration is: 0.004751441738192838\nRelative change in this iteration is: 0.004592209332153507\nRelative change in this iteration is: 0.00443998622898464\nRelative change in this iteration is: 0.004294374200710125\nRelative change in this iteration is: 0.004155003252898637\nRelative change in this iteration is: 0.00402152921526048\nRelative change in this iteration is: 0.0038959803506103366\nRelative change in this iteration is: 0.003775872549660567\nRelative change in this iteration is: 0.00366052438072716\nRelative change in this iteration is: 0.0035496961347309857\nRelative change in this iteration is: 0.0034431630263746407\nRelative change in this iteration is: 0.003340714082175376\nRelative change in this iteration is: 0.0032421511246688273\nRelative change in this iteration is: 0.003147287843391393\nRelative change in this iteration is: 0.0030559489442496994\nRelative change in this iteration is: 0.0029679693697882105\nRelative change in this iteration is: 0.0028831935836545496\nRelative change in this iteration is: 0.0028030048234133666\nRelative change in this iteration is: 0.002726003900987511\nRelative change in this iteration is: 0.002651641287642232\nRelative change in this iteration is: 0.0025798047075349193\nRelative change in this iteration is: 0.002510387703436588\nRelative change in this iteration is: 0.002443289279160177\nRelative change in this iteration is: 0.0023784135673349653\nRelative change in this iteration is: 0.0023156695205066883\nRelative change in this iteration is: 0.0022549706237164936\nRelative change in this iteration is: 0.002196234626894639\nRelative change in this iteration is: 0.002139383295506079\nRelative change in this iteration is: 0.0020843421780647802\nRelative change in this iteration is: 0.002031040389218473\nRelative change in this iteration is: 0.0019798898999426476\nRelative change in this iteration is: 0.0019311271517398524\nRelative change in this iteration is: 0.0018838188385035053\nRelative change in this iteration is: 0.0018379123965208055\nRelative change in this iteration is: 0.0017933575326783595\nRelative change in this iteration is: 0.00175010610978349\nRelative change in this iteration is: 0.0017081120384859977\nRelative change in this iteration is: 0.001667331175375418\nRelative change in this iteration is: 0.0016277212268620467\nRelative change in this iteration is: 0.0015892416584840187\nRelative change in this iteration is: 0.0015518536092905034\nRelative change in this iteration is: 0.0015155198109928576\nRelative change in this iteration is: 0.0014802045115813564\nRelative change in this iteration is: 0.0014458734031368478\nRelative change in this iteration is: 0.0014124935535775338\nRelative change in this iteration is: 0.0013800333421006821\nRelative change in this iteration is: 0.0013486081163542522\nRelative change in this iteration is: 0.0013187139817326114\nRelative change in this iteration is: 0.0012895995643111046\nRelative change in this iteration is: 0.0012612408560099988\nRelative change in this iteration is: 0.0012336147131749675\nRelative change in this iteration is: 0.001206698820871928\n" ] ], [ [ "### We have reached some sort of convergence. (Relative change of roughly 1E-3).\n\nLet's have a look at our source function:", "_____no_output_____" ] ], [ [ "plt.figure(figsize=[9,5])\nplt.plot(logtau,B,label=\"Planck Function\")\nplt.plot(logtau,S,label=\"Source Function\")\nplt.xlabel(\"$\\\\log \\\\tau$\")\nplt.title(\"Scattering (NLTE) Source function\")\nplt.legend()", "_____no_output_____" ] ], [ [ "### This is a famous plot! What this tells us that even in the isothermal atmosphere the source function, in presence of scattering, drops below the Planck function. Extremely important effect for formation of strong chromospheric spectral lines. \n\n#### Note that this was a continuum example. The line situation is a tad more complicated and will involve an additional integration over the wavelengths. Numerically, results are different, but the spirit is the same! ", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Working with code cells\n\nIn this notebook you'll get some experience working with code cells.\n\nFirst, run the cell below. As I mentioned before, you can run the cell by selecting it the click the \"run cell\" button above. However, it's easier to run it by pressing **Shift + Enter** so you don't have to take your hands away from the keyboard.", "_____no_output_____" ] ], [ [ "# Select the cell, then press Shift + Enter\n3**2", "_____no_output_____" ] ], [ [ "Shift + Enter runs the cell then selects the next cell or creates a new one if necessary. You can run a cell without changing the selected cell by pressing **Control + Enter**.\n\nThe output shows up below the cell. It's printing out the result just like in a normal Python shell. Only the very last result in a cell will be printed though. Otherwise, you'll need to use `print()` print out any variables. \n\n> **Exercise:** Run the next two cells to test this out. Think about what you expect to happen, then try it.", "_____no_output_____" ] ], [ [ "3**2\n4**2", "_____no_output_____" ], [ "print(3**2)\n4**2", "9\n" ] ], [ [ "Now try assigning a value to a variable.", "_____no_output_____" ] ], [ [ "import string\n\nmindset = 'growth'\ncodes = [string.ascii_letters.index(char) for char in mindset]", "_____no_output_____" ] ], [ [ "There is no output, `'growth'` has been assigned to the variable `mindset`. All variables, functions, and classes created in a cell are available in every other cell in the notebook.\n\nWhat do you think the output will be when you run the next cell? Feel free to play around with this a bit to get used to how it works.", "_____no_output_____" ] ], [ [ "print(mindset[:4])\nprint(codes)", "grow\n[6, 17, 14, 22, 19, 7]\n" ] ], [ [ "## Code completion\n\nWhen you're writing code, you'll often be using a variable or function often and can save time by using code completion. That is, you only need to type part of the name, then press **tab**.\n\n> **Exercise:** Place the cursor at the end of `mind` in the next cell and press **tab**", "_____no_output_____" ] ], [ [ "mindset", "_____no_output_____" ] ], [ [ "Here, completing `mind` writes out the full variable name `mindset`. If there are multiple names that start the same, you'll get a menu, see below.", "_____no_output_____" ] ], [ [ "# Run this cell\nmindful = True", "_____no_output_____" ], [ "# Complete the name here again, choose one from the menu\nmindful\n", "_____no_output_____" ] ], [ [ "Remember that variables assigned in one cell are available in all cells. This includes cells that you've previously run and cells that are above where the variable was assigned. Try doing the code completion on the cell third up from here.\n\nCode completion also comes in handy if you're using a module but don't quite remember which function you're looking for or what the available functions are. I'll show you how this works with the [random](https://docs.python.org/3/library/random.html) module. This module provides functions for generating random numbers, often useful for making fake data or picking random items from lists.", "_____no_output_____" ] ], [ [ "# Run this\nimport random", "_____no_output_____" ] ], [ [ "> **Exercise:** In the cell below, place the cursor after `random.` then press **tab** to bring up the code completion menu for the module. Choose `random.randint` from the list, you can move through the menu with the up and down arrow keys.", "_____no_output_____" ] ], [ [ "random.randint", "_____no_output_____" ] ], [ [ "Above you should have seen all the functions available from the random module. Maybe you're looking to draw random numbers from a [Gaussian distribution](https://en.wikipedia.org/wiki/Normal_distribution), also known as the normal distribution or the \"bell curve\". \n\n## Tooltips\n\nYou see there is the function `random.gauss` but how do you use it? You could check out the [documentation](https://docs.python.org/3/library/random.html), or just look up the documentation in the notebook itself.\n\n> **Exercise:** In the cell below, place the cursor after `random.gauss` the press **shift + tab** to bring up the tooltip.", "_____no_output_____" ] ], [ [ "random.gauss", "_____no_output_____" ] ], [ [ "You should have seen some simple documentation like this:\n\n Signature: random.gauss(mu, sigma)\n Docstring:\n Gaussian distribution.\n \nThe function takes two arguments, `mu` and `sigma`. These are the standard symbols for the mean and the standard deviation, respectively, of the Gaussian distribution. Maybe you're not familiar with this though, and you need to know what the parameters actually mean. This will happen often, you'll find some function, but you need more information. You can show more information by pressing **shift + tab** twice.\n\n> **Exercise:** In the cell below, show the full help documentation by pressing **shift + tab** twice.", "_____no_output_____" ] ], [ [ "random.gauss", "_____no_output_____" ] ], [ [ "You should see more help text like this:\n\n mu is the mean, and sigma is the standard deviation. This is\n slightly faster than the normalvariate() function.", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# 基于Tensorflow的softmax回归", "_____no_output_____" ], [ "Tensorflow是近年来非常非常流行的一个分布式的机器学习框架，之前一直想学习但是一直被各种各样的事情耽搁着。这学期恰好选了“人工神经网络”这门课，不得不接触这个框架了。最开始依照书上的教程通过Anaconda来配置环境，安装tensorflow。结果tensorflow是安装好了但是用起来是真麻烦。最后卸载了Anaconda在裸机上用`pip install tensorflow`来安装，可是裸机上的python是3.6.3版本的，似乎不支持tensorflow，于是在电脑上安装了另一个版本的python才算解决了这个问题，哎！说多了都是泪。言归正传，现在通过一个softmax实现手写字母识别的例子来正式进入tensorflow学习之旅。\n\nsoftmax是一个非常常见的函数处理方式，它允许我们将模型的输出归一化并且以概率的形式输出，是非常有用的一种处理方式。具体的内容可以参见这个知乎问题[Softmax 函数的特点和作用是什么？](https://www.zhihu.com/question/23765351)\n\n## 数据集\n\n本例采用的是`mnist`数据集，它在机器学习领域非常有名。首先我们来认识一下这个数据集，tensorflow能够自动下载并使用这个数据集。获取到数据集之后首先查看一下训练集的大小，由于这次softmax回归使用的是mnist中的手写图片作为训练集，因此为了直观地了解一下数据集还需要查看其中的一些手写图片，在这里就用到了matplotlib这个框架来绘图。", "_____no_output_____" ] ], [ [ "from tensorflow.examples.tutorials.mnist import input_data\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"]='3'#禁止输出警告信息\n#加载mnist数据集，one_hot设定为True是使用向量的形式编码数据类别，这主要是考虑到使用softmax\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\nprint(\"训练集数据的大小为：{0}\".format(mnist.train.images.shape))\nfig = plt.figure(\"数据展示\")\nfor k in range(3):\n result = []\n temp = []\n img = mnist.train.images[k]#获得第一幅图片，是一个28*28的图片展开成的784维的向量，\n for i in range(img.shape[0]):\n temp.append(img[i])\n if (i + 1) % 28 == 0:\n result.append(temp)\n temp = []\n img = np.matrix(result, dtype=np.float)#获取第一幅图片的矩阵形式\n ax = fig.add_subplot(130 + k + 1)\n ax.imshow(img)\nplt.show()", "Extracting MNIST_data/train-images-idx3-ubyte.gz\nExtracting MNIST_data/train-labels-idx1-ubyte.gz\nExtracting MNIST_data/t10k-images-idx3-ubyte.gz\nExtracting MNIST_data/t10k-labels-idx1-ubyte.gz\n训练集数据的大小为：(55000, 784)\n" ] ], [ [ "从上面代码的输出可以看到：该数据集的训练集的规模是55000x784。数据集中每一个行向量代表着一个28x28图片的一维展开，虽然说在图片识别中像素点的位置页蕴含着非常大的信息，但是在这里就不在意那么多了，仅仅将其一维展开就可以。笔者用mat将数据集中的前三个图像画了出来展示在上面，接下来就要用到softmax回归的方法实现一个基本的首写字母识别。\n\n## softmax回归\n\n在这里简要介绍一下softmax回归的相关知识。softmax回归也是线性回归的一种，只不过是在输出层使用了softmax函数进行处理。具体的训练方法上也是使用了经典的随机梯度下降法训练。其判别函数有如下形式：\n\n$$f(x)=wx+b$$\n\n**注意：softmax回归可以用于处理多分类问题，因此上式中的所有变量都是矩阵或向量形式。**\n\n模型的输出f(x)还并不是最终的输出，还需要用softmax函数进行处理，softmax函数的形式如下所示:\n\n$$softmax(x)=\\frac{exp(x_{i})}{\\sum_{j}^{n}exp(x_{j})}$$\n\n这样处理后的模型输出可以表达输入数据在各个标签分类上的概率表达，此外使用softmax函数还有着其他很多的好处，最主要的还是在损失函数上的便利。此处不一一列举。\n\nsoftmax回归的损失函数采用信息熵的形式给出：\n\n$$H_{y^{'}}(y)=-\\sum y_{i}^{'}ln(y_{i})$$\n\n最后，笔者想在softmax函数下推导上述损失函数的随机梯度下降法的迭代公式，虽然tensorflow为我们做了这件事，但是作为算法编写者的我们依然有必要了解这其中的细节。首先，我们需要得到损失函数关于`w`的梯度：\n\n$$\\frac{\\partial H_{y^{'}}(y)}{\\partial w}=\\frac{\\partial -\\sum y_{i}^{'}ln(y_{i})}{\\partial w}=\\frac{\\partial -yln(softmax(f(xw + b))}{\\partial w}$$\n\n该求导比较复杂，采用链式求导法：\n\n$$\\frac{\\partial Loss}{\\partial w}=\\frac{\\partial Loss}{\\partial softmax(xw + b)}\\frac{\\partial softmax(xw + b)}{\\partial xw + b}\\frac{\\partial xw + b}{\\partial w}$$\n\n上述链式求导就比较简单，第一项和最后一项的求导都很容易得到，关键是第二项的求导。在这里我们直接给出softmax函数的求导公式。\n\n$$\\frac{\\partial softmax(xw + b)}{\\partial xw + b}=softmax(xw + b)(1 - softmax(xw + b))$$\n\n又由于上述第一项和第二项的求导为：\n\n$$\\frac{\\partial Loss}{\\partial softmax(xw + b)}=\\frac{\\partial -yln(softmax(f(xw + b))}{\\partial softmax(xw + b)}=\\frac{-y}{softmax(xw + b)}$$\n\n$$\\frac{\\partial xw + b}{\\partial w}=x$$\n\n因此：\n\n$$\\frac{\\partial -yln(softmax(f(xw + b))}{\\partial w}=y(softmax(xw + b) - 1)x$$\n\n接下来就可以使用随机梯度下降法的迭代公式来迭代求解`w`：\n\n$$w=w+\\alpha \\frac{\\partial -yln(softmax(f(xw + b))}{\\partial w}$$\n\n## tensroflow实现softmax回归\n\n首先我们定义模型中的各个参数，其中x和真实的标签值y_不设定死，使用`placeholder`占据计算图的一个节点。代码如下：", "_____no_output_____" ] ], [ [ "import tensorflow as tf\n\nsession = tf.InteractiveSession()#定义一个交互式会话\nx = tf.placeholder(tf.float32, [None, 784])\nw = tf.Variable(tf.zeros([784, 10]))#权重w初始化为0\nb = tf.Variable(tf.zeros([1, 10]))#bias初始化为0\ny = tf.nn.softmax(tf.matmul(x, w) + b)\ny_ = tf.placeholder(tf.float32, [None, 10])", "_____no_output_____" ] ], [ [ "设定其损失函数`cross_entry`，规定优化目标，初始化全局参数：", "_____no_output_____" ] ], [ [ "cross_entry = -tf.reduce_mean(tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\ntrain_set = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entry)\ntf.global_variables_initializer().run()", "_____no_output_____" ] ], [ [ "准备工作都差不多做完了，接下来应该进行模型的训练。在本例中迭代一千次，每次从训练集中随机选取100组数据训练模型。", "_____no_output_____" ] ], [ [ "for i in range(1000):\n trainSet, trainLabel = mnist.train.next_batch(100)\n train_set.run(feed_dict={x: trainSet, y_: trainLabel})", "_____no_output_____" ] ], [ [ "以上，我们已经完成了模型的训练，现在应该检测一下模型的效果。我们使用mnist的测试数据来测试模型的分类效果：", "_____no_output_____" ] ], [ [ "accuracy = tf.cast(tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)), dtype=tf.float32)\naccuracy = tf.reduce_mean(accuracy)\nprint(\"模型的分类正确率为{0:.3f}\".format(session.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})))", "模型的分类正确率为0.917\n" ] ], [ [ "由上面代码的输出可以看到，该例程的分类准确率还是非常高的，达到了接近92%。这篇入门介绍中用到的softmax回归其实本质上可以看作是一个无隐层的神经网络模型，只拥有一个输入层和一个输出层，二者中间使用了简单的线性方法连接。在实际的手写图片识别中可能并不会用到这样的线性模型，更多的是使用卷积神经网络（CNN）。\n\n## 后记\n\n这篇文章算是笔者tensorflow入门的一个小应用。说一下我对神经网络，机器学习和tensorflow的看法吧。最近这几年最热的名词可能就是人工智能，深度学习了。笔者也未能免俗，随着这股洪流加入了浩浩荡荡的机器学习大军。从最开始最简单的随机梯度下降法，线性回归到后来有些难的序列最小优化算法，支持向量机。一步步走来发现这个计算机学科的分枝还是非常有意思的，看似非常严谨，枯燥却又十分优美的数学模型竟然能够表现出一丝丝“智能”，有时候真的会惊艳到我。\n\n从2006年起，随着计算机计算能力的快速提升，曾经被冷落的神经网络又开始热了起来。对于神经网络，个人对它的未来不是很确定，一则是因为神经网络在历史上的命运可谓是大起大落，曾经的感知机，BP都是炙手可热，却又都草草收场，谁知道这一次的AI火热是不是能持续下去，也许碰到某一个天花板就又归于沉积了呢？说到底目前的AI行业所研究的“弱人工智能”距离真正的AI还是相差甚远。都已经说不清这到底是人类技术的问题，还是哲学的问题。二则是，目前神经网络模型的可解释性太差，相较于SVM这样的模型，人们似乎说不出为什么神经网络能够表现出如此强大的分类能力。三则是，人类在AI行业的探索上总是在刻意模仿自己的大脑，在神经网络的设计中融入了很多人脑中的机制，但这真的是一条正确的路吗？人类飞上蓝天不是靠着挥舞的翅膀而是飞机的机翼。\n\nTensorflow是Google公司推出的一款非常流行的机器学习框架，目前看来已经占据了机器学习框架的绝对霸主地位。对于这些机器学习框架我个人的感觉是不能脱离它们自己闭门造车，但也不能过度依赖。之前笔者是排斥一切框架的，很多经典的机器学习算法都是自己编写。然而到了神经网络这一块，自己再手动编程的代价太大了，于是就不得不入了框架的坑。我的观点是：对于一个算法一定要自己将其弄懂，其中的数学推导搞清楚再看代码，用框架。切忌将模型作为一个黑箱工具来使用，虽然这在短期来看确实效率很高，但长期来看绝对是百害无一利的。学习的过程中还可以自己动手做一些小demo，提升一下编程的乐趣，还是一件非常有益的事情。", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# Functional Python\n\n## BProf Python course\n\n### June 25-29, 2018\n\n#### Judit Ács", "_____no_output_____" ], [ "Python has 3 built-in functions that originate from functional programming.\n\n## Map\n\n- `map` applies a function on each element of a sequence", "_____no_output_____" ] ], [ [ "def double(e):\n return e * 2\n\nl = [2, 3, \"abc\"]\n\nlist(map(double, l))", "_____no_output_____" ], [ "map(double, l)", "_____no_output_____" ], [ "%%python2\n\ndef double(e):\n return e * 2\n\nl = [2, 3, \"abc\"]\n\nprint(map(double, l))", "[4, 6, 'abcabc']\n" ], [ "list(map(lambda x: x * 2, [2, 3, \"abc\"]))", "_____no_output_____" ], [ "class Doubler:\n def __call__(self, arg):\n return arg * 2\n \nlist(map(Doubler(), l))", "_____no_output_____" ], [ "[x * 2 for x in l]", "_____no_output_____" ] ], [ [ "## Filter\n\n- filter creates a list of elements for which a function returns true", "_____no_output_____" ] ], [ [ "def is_even(n):\n return n % 2 == 0\n\nl = [2, 3, -1, 0, 2]\n\nlist(filter(is_even, l))", "_____no_output_____" ], [ "list(filter(lambda x: x % 2 == 0, range(8)))", "_____no_output_____" ], [ "[e for e in l if e % 2 == 0]", "_____no_output_____" ] ], [ [ "### Most comprehensions can be rewritten using map and filter", "_____no_output_____" ] ], [ [ "l = [2, 3, 0, -1, 2, 0, 1]\n\nsignum = [x / abs(x) if x != 0 else x for x in l]\nprint(signum)", "[1.0, 1.0, 0, -1.0, 1.0, 0, 1.0]\n" ], [ "list(map(lambda x: x / abs(x) if x != 0 else 0, l))", "_____no_output_____" ], [ "even = [x for x in l if x % 2 == 0]\nprint(even)", "[2, 0, 2, 0]\n" ], [ "print(list(filter(lambda x: x % 2 == 0, l)))", "[2, 0, 2, 0]\n" ] ], [ [ "## Reduce\n\n- reduce applies a rolling computation on a sequence\n- the first argument of `reduce` is two-argument function\n- the second argument is the sequence\n- the result is accumulated in an accumulator", "_____no_output_____" ] ], [ [ "from functools import reduce\n\nl = [1, 2, -1, 4]\nreduce(lambda x, y: x*y, l)", "_____no_output_____" ] ], [ [ "an initial value for the accumulator may be supplied", "_____no_output_____" ] ], [ [ "reduce(lambda x, y: x*y, l, 10)", "_____no_output_____" ], [ "reduce(lambda x, y: max(x, y), l)\nreduce(max, l)", "_____no_output_____" ], [ "reduce(max, map(lambda n: n*n, l))", "_____no_output_____" ], [ "reduce(lambda x, y: x + int(y % 2 == 0), l, 0)", "_____no_output_____" ] ], [ [ "# `any` and `all`\n\nChecks if any or every element of an iterable evaluates to `False` in a boolean context.", "_____no_output_____" ] ], [ [ "def is_even(num):\n if num % 2 == 0:\n print(\"{} is even\".format(num))\n return True\n print(\"{} is odd\".format(num))\n return False\n\nl = [2, 4, 0, -1, 6, 8, 1]\n\n# all(map(is_even, l))\nall(is_even(i) for i in l)", "2 is even\n4 is even\n0 is even\n-1 is odd\n" ], [ "l = [3, 1, 5, 0, 7, 0, 0]\n# any(map(is_even, l))\nany(is_even(i) for i in l)", "3 is odd\n1 is odd\n5 is odd\n0 is even\n" ] ], [ [ "## `zip`", "_____no_output_____" ] ], [ [ "x = [1, 2, 0]\ny = [-2, 6, 0, 2]\n\nfor pair in zip(x, y):\n print(type(pair), pair)", "<class 'tuple'> (1, -2)\n<class 'tuple'> (2, 6)\n<class 'tuple'> (0, 0)\n" ], [ "for pair in zip(x, y, x, y):\n print(type(pair), pair)", "<class 'tuple'> (1, -2, 1, -2)\n<class 'tuple'> (2, 6, 2, 6)\n<class 'tuple'> (0, 0, 0, 0)\n" ] ], [ [ "## for and while loops do not create a new scope but functions do", "_____no_output_____" ] ], [ [ "y = \"outside foo\"\n\ndef foo():\n i = 2\n for _ in range(4):\n y = 3\n print(y)\n \nprint(\"Calling foo\")\nfoo()\nprint(\"Global y unchanged: {}\".format(y))", "Calling foo\n3\nGlobal y unchanged: outside foo\n" ] ], [ [ "# Global Interpreter Lock (GIL)\n\n- CPython, the reference implementation has a reference counting garbage collector\n- reference counting GC is **not** thread-safe :(\n- \"GIL, is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once\"\n- IO, image processing and Numpy (numerical computation and matrix library) heavy lifting happens outside the GIL\n- other computations cannot fully take advantage of multithreading :(\n- Jython and IronPython do not have a GIL\n\n## See also\n\n[Python wiki page on the GIL](https://wiki.python.org/moin/GlobalInterpreterLock)\n\n[Live GIL removal (advanced)](https://www.youtube.com/watch?v=pLqv11ScGsQ)", "_____no_output_____" ] ] ]

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[ "MIT" ]

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[ [ [ "#r \"nuget:Microsoft.ML,1.4.0\"\n#r \"nuget:Microsoft.ML.AutoML,0.16.0\"\n#r \"nuget:Microsoft.Data.Analysis,0.1.0\"", "_____no_output_____" ], [ "using Microsoft.Data.Analysis;\nusing XPlot.Plotly;", "_____no_output_____" ], [ "using Microsoft.AspNetCore.Html;\nFormatter<DataFrame>.Register((df, writer) =>\n{\n var headers = new List<IHtmlContent>();\n headers.Add(th(i(\"index\")));\n headers.AddRange(df.Columns.Select(c => (IHtmlContent) th(c.Name)));\n var rows = new List<List<IHtmlContent>>();\n var take = 20;\n for (var i = 0; i < Math.Min(take, df.RowCount); i++)\n {\n var cells = new List<IHtmlContent>();\n cells.Add(td(i));\n foreach (var obj in df[i])\n {\n cells.Add(td(obj));\n }\n rows.Add(cells);\n }\n \n var t = table(\n thead(\n headers),\n tbody(\n rows.Select(\n r => tr(r))));\n \n writer.Write(t);\n}, \"text/html\");", "_____no_output_____" ], [ "using System.IO;\nusing System.Net.Http;\nstring housingPath = \"housing.csv\";\nif (!File.Exists(housingPath))\n{\n var contents = new HttpClient()\n .GetStringAsync(\"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\").Result;\n \n File.WriteAllText(\"housing.csv\", contents);\n}", "_____no_output_____" ], [ "var housingData = DataFrame.LoadCsv(housingPath);\nhousingData", "_____no_output_____" ], [ "housingData.Description()", "_____no_output_____" ], [ "Chart.Plot(\n new Graph.Histogram()\n {\n x = housingData[\"median_house_value\"],\n nbinsx = 20\n }\n)", "_____no_output_____" ], [ "var chart = Chart.Plot(\n new Graph.Scattergl()\n {\n x = housingData[\"longitude\"],\n y = housingData[\"latitude\"],\n mode = \"markers\",\n marker = new Graph.Marker()\n {\n color = housingData[\"median_house_value\"],\n colorscale = \"Jet\"\n }\n }\n);\n\nchart.Width = 600;\nchart.Height = 600;\ndisplay(chart);", "_____no_output_____" ], [ "static T[] Shuffle<T>(T[] array)\n{\n Random rand = new Random();\n for (int i = 0; i < array.Length; i++)\n {\n int r = i + rand.Next(array.Length - i);\n T temp = array[r];\n array[r] = array[i];\n array[i] = temp;\n }\n return array;\n}\n\nint[] randomIndices = Shuffle(Enumerable.Range(0, (int)housingData.RowCount).ToArray());\nint testSize = (int)(housingData.RowCount * .1);\nint[] trainRows = randomIndices[testSize..];\nint[] testRows = randomIndices[..testSize];\n\nDataFrame housing_train = housingData[trainRows];\nDataFrame housing_test = housingData[testRows];\n\ndisplay(housing_train.RowCount);\ndisplay(housing_test.RowCount);", "_____no_output_____" ], [ "using Microsoft.ML;\nusing Microsoft.ML.Data;\nusing Microsoft.ML.AutoML;", "_____no_output_____" ], [ "#!time\n\nvar mlContext = new MLContext();\n\nvar experiment = mlContext.Auto().CreateRegressionExperiment(maxExperimentTimeInSeconds: 15);\nvar result = experiment.Execute(housing_train, labelColumnName:\"median_house_value\");", "_____no_output_____" ], [ "var scatters = result.RunDetails.Where(d => d.ValidationMetrics != null).GroupBy( \n r => r.TrainerName,\n (name, details) => new Graph.Scattergl()\n {\n name = name,\n x = details.Select(r => r.RuntimeInSeconds),\n y = details.Select(r => r.ValidationMetrics.MeanAbsoluteError),\n mode = \"markers\",\n marker = new Graph.Marker() { size = 12 }\n });\n\nvar chart = Chart.Plot(scatters);\nchart.WithXTitle(\"Training Time\");\nchart.WithYTitle(\"Error\");\ndisplay(chart);\n\nConsole.WriteLine($\"Best Trainer:{result.BestRun.TrainerName}\");", "_____no_output_____" ], [ "var testResults = result.BestRun.Model.Transform(housing_test);\n\nvar trueValues = testResults.GetColumn<float>(\"median_house_value\");\nvar predictedValues = testResults.GetColumn<float>(\"Score\");\n\nvar predictedVsTrue = new Graph.Scattergl()\n{\n x = trueValues,\n y = predictedValues,\n mode = \"markers\",\n};\n\nvar maximumValue = Math.Max(trueValues.Max(), predictedValues.Max());\n\nvar perfectLine = new Graph.Scattergl()\n{\n x = new[] {0, maximumValue},\n y = new[] {0, maximumValue},\n mode = \"lines\",\n};\n\nvar chart = Chart.Plot(new[] {predictedVsTrue, perfectLine });\nchart.WithXTitle(\"True Values\");\nchart.WithYTitle(\"Predicted Values\");\nchart.WithLegend(false);\nchart.Width = 600;\nchart.Height = 600;\ndisplay(chart);", "_____no_output_____" ], [ "#!lsmagic", "_____no_output_____" ], [ "new [] { 1,2,3 } ", "_____no_output_____" ], [ "new { foo =\"123\" }", "_____no_output_____" ], [ "#!fsharp\n[1;2;3]", "_____no_output_____" ], [ "b(\"hello\").ToString()", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ [ [ "# Matching Registry and PA State Business License Data", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport mwdsbe\nimport mwdsbe.datasets.licenses as licenses\nimport schuylkill as skool\nimport time", "_____no_output_____" ], [ "def drop_duplicates_by_date(df, date_column):\n df.sort_values(by=date_column, ascending=False, inplace=True)\n df = df.loc[~df.index.duplicated(keep=\"first\")]\n df.sort_index(inplace=True)\n return df", "_____no_output_____" ] ], [ [ "## Data", "_____no_output_____" ] ], [ [ "registry = mwdsbe.load_registry() # geopandas df\nlicense = licenses.CommercialActivityLicenses().get()", "_____no_output_____" ], [ "registry.head()", "_____no_output_____" ], [ "state_license = pd.read_csv('./data/PAStateBusinessLicense/Sales_Tax_Licenses_and_Certificates_Current_Monthly_County_Revenue.csv')", "_____no_output_____" ], [ "print('Size of state_license data:', len(state_license))", "Size of state_license data: 347532\n" ], [ "# convert state_license column names from titlecase to snakecase\ndef to_snake_case(aList):\n res = []\n for item in aList:\n words = item.strip().lower().split(' ')\n item = '_'.join(words)\n res.append(item)\n return res", "_____no_output_____" ], [ "state_license.columns = to_snake_case(state_license.columns.tolist())", "_____no_output_____" ], [ "# clean data\nignore_words = ['inc', 'group', 'llc', 'corp', 'pc', 'incorporated', 'ltd', 'co', 'associates', 'services', 'company', 'enterprises', 'enterprise', 'service', 'corporation']\ncleaned_registry = skool.clean_strings(registry, ['company_name', 'dba_name'], True, ignore_words)\ncleaned_license = skool.clean_strings(license, ['company_name'], True, ignore_words)\ncleaned_state_license = skool.clean_strings(state_license, ['legal_name', 'trade_name'], True, ignore_words)\n\ncleaned_registry = cleaned_registry.dropna(subset=['company_name'])\ncleaned_license = cleaned_license.dropna(subset=['company_name'])\ncleaned_state_license = cleaned_state_license.dropna(subset=['legal_name'])", "_____no_output_____" ], [ "len(cleaned_license)", "_____no_output_____" ], [ "cleaned_state_license.head()", "_____no_output_____" ], [ "# just getting PA state in registry\npa_registry = cleaned_registry[cleaned_registry.location_state == 'PA']", "_____no_output_____" ], [ "len(pa_registry)", "_____no_output_____" ] ], [ [ "## Merge registry and state_license by company_name and legal_name / trade name", "_____no_output_____" ] ], [ [ "# t1 = time.time()\n# merged = (\n# skool.tf_idf_merge(pa_registry, cleaned_state_license, left_on=\"company_name\", right_on=\"legal_name\", score_cutoff=85)\n# .pipe(skool.tf_idf_merge, pa_registry, cleaned_state_license, left_on=\"company_name\", right_on=\"trade_name\", score_cutoff=85)\n# .pipe(skool.tf_idf_merge, pa_registry, cleaned_state_license, left_on=\"dba_name\", right_on=\"legal_name\", score_cutoff=85)\n# .pipe(skool.tf_idf_merge, pa_registry, cleaned_state_license, left_on=\"dba_name\", right_on=\"trade_name\", score_cutoff=85)\n# )\n# t = time.time() - t1", "_____no_output_____" ], [ "# print('Execution time:', t/60, 'min')", "Execution time: 22.08829193909963 min\n" ], [ "# matched = merged.dropna(subset=['legal_name'])", "_____no_output_____" ], [ "# matched.to_excel (r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\state_license\\pa-registry-full-state-license\\tf-idf-85.xlsx', header=True)", "_____no_output_____" ], [ "matched_state = pd.read_excel(r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\state_license\\pa-registry-full-state-license\\tf-idf-85.xlsx')", "_____no_output_____" ], [ "len(matched_state)", "_____no_output_____" ], [ "exact_matches = matched_state[matched_state.match_probability == 1]", "_____no_output_____" ], [ "len(exact_matches)", "_____no_output_____" ] ], [ [ "##### Eliminate companies with different zip code", "_____no_output_____" ] ], [ [ "matched_state['postal_code_clean'] = matched_state.postal_code.astype(str).apply(lambda x : x.split(\"-\")[0]).astype(float)", "_____no_output_____" ], [ "matched_state = matched_state.set_index('left_index')", "_____no_output_____" ], [ "matched_state_zip = matched_state[matched_state.zip_code == matched_state.postal_code_clean]", "_____no_output_____" ], [ "len(matched_state_zip)", "_____no_output_____" ], [ "matched_state_zip['expiration_date'] = pd.to_datetime(matched_state_zip['expiration_date'], errors='coerce')", "_____no_output_____" ], [ "matched_state_zip = drop_duplicates_by_date(matched_state_zip, 'expiration_date')", "_____no_output_____" ], [ "len(matched_state_zip) # state_license, same zip code, without duplicates", "_____no_output_____" ], [ "matched_state_zip", "_____no_output_____" ] ], [ [ "## Comparing between match between \"registry-opendata license\" and \"registry-state license\"\nHow many more new companies do we get from registry-opendata license matching?", "_____no_output_____" ] ], [ [ "# t1 = time.time()\n# merged = (\n# skool.tf_idf_merge(cleaned_registry, cleaned_license, on=\"company_name\", score_cutoff=85)\n# .pipe(skool.tf_idf_merge, cleaned_registry, cleaned_license, left_on=\"dba_name\", right_on=\"company_name\", score_cutoff=85)\n# )\n# t = time.time() - t1", "_____no_output_____" ], [ "# print('Execution time:', t/60, 'min')", "Execution time: 3.3725738008817037 min\n" ], [ "# matched_openphilly_license = merged.dropna(subset=['company_name_y'])", "_____no_output_____" ], [ "# len(matched_openphilly_license)", "_____no_output_____" ], [ "# matched_openphilly_license.issue_date = matched_openphilly_license.issue_date.astype(str)", "C:\\Users\\dabinlee\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5208: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n self[name] = value\n" ], [ "# matched_openphilly_license.to_excel (r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\license-opendataphilly\\tf-idf\\tf-idf-85.xlsx', header=True)", "_____no_output_____" ] ], [ [ "##### Loading matched of registry and opendataphilly_license data", "_____no_output_____" ] ], [ [ "matched_opendataphilly_license = pd.read_excel(r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\license-opendataphilly\\tf-idf\\tf-idf-85.xlsx')", "_____no_output_____" ], [ "matched_opendataphilly_license = matched_opendataphilly_license.set_index('left_index')", "_____no_output_____" ], [ "len(matched_opendataphilly_license)", "_____no_output_____" ], [ "matched_opendataphilly_license = drop_duplicates_by_date(matched_opendataphilly_license, \"issue_date\") # without duplicates", "C:\\Users\\dabinlee\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n after removing the cwd from sys.path.\n" ], [ "len(matched_opendataphilly_license)", "_____no_output_____" ], [ "matched_opendataphilly_license.tail()", "_____no_output_____" ], [ "# unique company?\nlen(matched_opendataphilly_license.index.unique()) # yes", "_____no_output_____" ], [ "diff = matched_state_zip.index.difference(matched_opendataphilly_license.index).tolist()", "_____no_output_____" ], [ "len(diff)", "_____no_output_____" ], [ "matched_state_zip.loc[diff][['company_name', 'dba_name', 'legal_name', 'trade_name']]", "_____no_output_____" ], [ "# newly matched: matching with state_license data\ndifference = matched_state_zip.loc[diff]", "_____no_output_____" ], [ "difference", "_____no_output_____" ], [ "# difference.to_excel (r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\difference.xlsx', header=True)", "_____no_output_____" ] ], [ [ "## Investigate missing companies in opendataphilly license data\nWe found 94 newly matched companies from matching between registry and state_license, why these are not appeared in matching between registry and opendataphilly license data?", "_____no_output_____" ] ], [ [ "t1 = time.time()\nmerged = (\n skool.tf_idf_merge(cleaned_registry, cleaned_license, on=\"company_name\", score_cutoff=0)\n .pipe(skool.tf_idf_merge, cleaned_registry, cleaned_license, left_on=\"dba_name\", right_on=\"company_name\", score_cutoff=0)\n)\nt = time.time() - t1", "_____no_output_____" ], [ "print('Execution time:', t/60, 'min')", "Execution time: 4.075732707977295 min\n" ], [ "matched = merged.dropna(subset=['company_name_y'])", "_____no_output_____" ], [ "matched = drop_duplicates_by_date(matched, 'issue_date')", "C:\\Users\\dabinlee\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n \nC:\\Users\\dabinlee\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n after removing the cwd from sys.path.\n" ], [ "matched", "_____no_output_____" ], [ "cleaned_registry.loc[cleaned_registry.index.difference(matched.index)]", "_____no_output_____" ], [ "matched = matched.loc[difference.index]", "C:\\Users\\dabinlee\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: \nPassing list-likes to .loc or [] with any missing label will raise\nKeyError in the future, you can use .reindex() as an alternative.\n\nSee the documentation here:\nhttps://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n \"\"\"Entry point for launching an IPython kernel.\n" ], [ "matched.issue_date = matched.issue_date.astype(str)\nmatched.to_excel (r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\missing94.xlsx', header=True)", "_____no_output_____" ], [ "matched.match_probability.median()", "_____no_output_____" ], [ "difference.match_probability.median()", "_____no_output_____" ] ], [ [ "### Compare intersection between matched_opendataphilly_license and matched_state_zip\n* matched_opendataphilly_license: matched data between pa_registry and license data from opendataphilly\n* matched_state_zip: matched data between pa_registry and license data from state_registry and filter matches which do not match zipcodes", "_____no_output_____" ] ], [ [ "intersection = matched_state_zip.index.intersection(matched_opendataphilly_license.index).tolist()", "_____no_output_____" ], [ "len(intersection) # 246 - 94", "_____no_output_____" ], [ "intersection1 = matched_opendataphilly_license.loc[intersection]", "_____no_output_____" ], [ "len(intersection1)", "_____no_output_____" ], [ "intersection2 = matched_state_zip.loc[intersection]", "_____no_output_____" ], [ "len(intersection2)", "_____no_output_____" ], [ "intersection1 = intersection1[['company_name_x', 'dba_name', 'match_probability', 'company_name_y']]", "_____no_output_____" ], [ "intersection2 = intersection2[['match_probability', 'legal_name', 'trade_name']]", "_____no_output_____" ], [ "intersection = intersection1.merge(intersection2, left_index=True, right_index=True)", "_____no_output_____" ], [ "intersection", "_____no_output_____" ], [ "# intersection.to_excel (r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\intersection.xlsx', header=True)", "_____no_output_____" ] ], [ [ "## Merge by address - Not matching well", "_____no_output_____" ] ], [ [ "cleaned_state_license.head()", "_____no_output_____" ], [ "# split street address from address_with_lat/long\ncleaned_state_license['street_address'] = cleaned_state_license['address_with_lat/long'].astype(str).apply(lambda x : x.split(\"\\n\")[0])", "_____no_output_____" ], [ "cleaned_state_license.head()", "_____no_output_____" ], [ "pa_registry.head()", "_____no_output_____" ], [ "# clean street information\ncleaned_pa_registry = skool.clean_strings(pa_registry, ['location'], True)\ncleaned_state_license = skool.clean_strings(cleaned_state_license, ['street_address'], True)\n\ncleaned_pa_registry = cleaned_pa_registry.dropna(subset=['location'])\ncleaned_state_license = cleaned_state_license.dropna(subset=['street_address'])", "_____no_output_____" ], [ "t1 = time.time()\nmerged_by_street = skool.tf_idf_merge(cleaned_pa_registry, cleaned_state_license, left_on='location', right_on='street_address', score_cutoff=95)\nt = time.time() - t1", "_____no_output_____" ], [ "print('Execution time:', t/60, 'min')", "Execution time: 7.25786319176356 min\n" ], [ "matched_by_street = merged_by_street.dropna(subset=['street_address'])", "_____no_output_____" ], [ "len(matched_by_street)", "_____no_output_____" ], [ "len(matched_by_street.index.unique()) # bug in tf-idf merge: not doing best match", "_____no_output_____" ], [ "matched_by_street[['company_name', 'location', 'match_probability', 'legal_name', 'trade_name', 'street_address']]", "_____no_output_____" ], [ "# matched_by_street.to_excel (r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\state_license\\by_street\\tf-idf-95.xlsx', header=True)", "_____no_output_____" ], [ "diff = matched_by_street.index.difference(matched_opendataphilly_license.index) # newly catched matches", "_____no_output_____" ], [ "len(diff)", "_____no_output_____" ], [ "newly_matched_by_street = matched_by_street.loc[diff][['company_name', 'dba_name', 'location', 'legal_name', 'trade_name', 'street_address']]", "_____no_output_____" ], [ "# newly_matched_by_street.to_excel (r'C:\\Users\\dabinlee\\Desktop\\mwdsbe\\data\\state_license\\by_street\\tf-idf-95-diff.xlsx', header=True)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nfrom datetime import datetime, timedelta\nimport pickle", "_____no_output_____" ], [ "start_date = datetime(2018, 10, 16, 0, 0, 0)\nend_date = datetime(2018, 12, 31, 0, 0, 0)\nnum_days = (end_date - start_date).days + 1\ndfs = pd.DataFrame(index=range(num_days))\nentries = []\n\n\nfor d in range(num_days):\n day = start_date + timedelta(days=d)\n dstr = day.strftime('%Y%m%d')\n url = 'http://www.espn.com/nba/schedule/_/date/{0}'.format(dstr)\n x = pd.read_html(url)\n df = x[0]\n if (len(df) > 1):\n for j in range(len(df)):\n t1 = df['matchup'].iloc[j]\n t2 = df['Unnamed: 1'].iloc[j]\n t1s = t1.split(' ')\n home = t1s[-1:][0]\n t2s = t2.split(' ')\n away = t2s[-1:][0]\n entries.append((day, home, away))\n print(dstr, home, away)\n\ndfs = pd.DataFrame(entries, columns=['day', 'home', 'away'])", "20181016 PHI BOS\n20181016 OKC GS\n20181017 MIL CHA\n20181017 BKN DET\n20181017 MEM IND\n20181017 MIA ORL\n20181017 ATL NY\n20181017 CLE TOR\n20181017 NO HOU\n20181017 MIN SA\n20181017 UTAH SAC\n20181017 DAL PHX\n20181017 DEN LAC\n20181018 CHI PHI\n20181018 MIA WSH\n20181018 LAL POR\n20181019 CHA ORL\n20181019 NY BKN\n20181019 BOS TOR\n20181019 ATL MEM\n20181019 CLE MIN\n20181019 SAC NO\n20181019 IND MIL\n20181019 GS UTAH\n20181019 OKC LAC\n20181020 TOR WSH\n20181020 BKN IND\n20181020 BOS NY\n20181020 ORL PHI\n20181020 CHA MIA\n20181020 DET CHI\n20181020 MIN DAL\n20181020 PHX DEN\n20181020 SA POR\n20181020 HOU LAL\n20181021 ATL CLE\n20181021 SAC OKC\n20181021 GS DEN\n20181021 HOU LAC\n20181022 ORL BOS\n20181022 CHA TOR\n20181022 NY MIL\n20181022 IND MIN\n20181022 CHI DAL\n20181022 MEM UTAH\n20181022 WSH POR\n20181022 PHX GS\n20181022 SA LAL\n20181023 PHI DET\n20181023 LAC NO\n20181023 SAC DEN\n20181024 DAL ATL\n20181024 BKN CLE\n20181024 NY MIA\n20181024 MIN TOR\n20181024 CHA CHI\n20181024 UTAH HOU\n20181024 IND SA\n20181024 PHI MIL\n20181024 LAL PHX\n20181024 MEM SAC\n20181024 WSH GS\n20181025 CLE DET\n20181025 POR ORL\n20181025 BOS OKC\n20181025 DEN LAL\n20181026 CHI CHA\n20181026 GS NY\n20181026 DAL TOR\n20181026 LAC HOU\n20181026 MIL MIN\n20181026 BKN NO\n20181026 WSH SAC\n20181027 BOS DET\n20181027 UTAH NO\n20181027 CHI ATL\n20181027 IND CLE\n20181027 CHA PHI\n20181027 POR MIA\n20181027 PHX MEM\n20181027 ORL MIL\n20181027 LAL SA\n20181028 GS BKN\n20181028 UTAH DAL\n20181028 PHX OKC\n20181028 WSH LAC\n20181029 POR IND\n20181029 ATL PHI\n20181029 SAC MIA\n20181029 BKN NY\n20181029 GS CHI\n20181029 TOR MIL\n20181029 LAL MIN\n20181029 DAL SA\n20181029 NO DEN\n20181030 MIA CHA\n20181030 ATL CLE\n20181030 SAC ORL\n20181030 DET BOS\n20181030 PHI TOR\n20181030 POR HOU\n20181030 WSH MEM\n20181030 LAC OKC\n20181031 DET BKN\n20181031 IND NY\n20181031 DEN CHI\n20181031 UTAH MIN\n20181031 NO GS\n20181031 DAL LAL\n20181031 SA PHX\n20181101 OKC CHA\n20181101 DEN CLE\n20181101 LAC PHI\n20181101 SAC ATL\n20181101 MIL BOS\n20181101 NO POR\n20181102 LAC ORL\n20181102 HOU BKN\n20181102 OKC WSH\n20181102 IND CHI\n20181102 NY DAL\n20181102 MEM UTAH\n20181102 TOR PHX\n20181102 MIN GS\n20181103 DET PHI\n20181103 CLE CHA\n20181103 BOS IND\n20181103 MIA ATL\n20181103 HOU CHI\n20181103 NO SA\n20181103 UTAH DEN\n20181103 LAL POR\n20181104 SAC MIL\n20181104 PHI BKN\n20181104 NY WSH\n20181104 ORL SA\n20181104 MEM PHX\n20181104 MIN POR\n20181104 TOR LAL\n20181105 MIA DET\n20181105 HOU IND\n20181105 CLE ORL\n20181105 CHI NY\n20181105 NO OKC\n20181105 BOS DEN\n20181105 TOR UTAH\n20181105 MEM GS\n20181105 MIN LAC\n20181106 ATL CHA\n20181106 WSH DAL\n20181106 BKN PHX\n20181106 MIL POR\n20181107 OKC CLE\n20181107 DET ORL\n20181107 NY ATL\n20181107 SA MIA\n20181107 PHI IND\n20181107 DEN MEM\n20181107 CHI NO\n20181107 DAL UTAH\n20181107 TOR SAC\n20181107 MIN LAL\n20181108 HOU OKC\n20181108 BOS PHX\n20181108 LAC POR\n20181108 MIL GS\n20181109 WSH ORL\n20181109 CHA PHI\n20181109 DET ATL\n20181109 IND MIA\n20181109 BKN DEN\n20181109 BOS UTAH\n20181109 MIN SAC\n20181110 NY TOR\n20181110 MIL LAC\n20181110 PHX NO\n20181110 WSH MIA\n20181110 CLE CHI\n20181110 PHI MEM\n20181110 HOU SA\n20181110 BKN GS\n20181110 OKC DAL\n20181110 LAL SAC\n20181111 CHA DET\n20181111 IND HOU\n20181111 ORL NY\n20181111 MIL DEN\n20181111 BOS POR\n20181111 ATL LAL\n20181112 ORL WSH\n20181112 PHI MIA\n20181112 NO TOR\n20181112 DAL CHI\n20181112 UTAH MEM\n20181112 BKN MIN\n20181112 PHX OKC\n20181112 SA SAC\n20181112 GS LAC\n20181113 CHA CLE\n20181113 HOU DEN\n20181113 ATL GS\n20181114 PHI ORL\n20181114 CLE WSH\n20181114 CHI BOS\n20181114 MIA BKN\n20181114 DET TOR\n20181114 MEM MIL\n20181114 NO MIN\n20181114 NY OKC\n20181114 UTAH DAL\n20181114 SA PHX\n20181114 POR LAL\n20181115 GS HOU\n20181115 ATL DEN\n20181115 SA LAC\n20181116 TOR BOS\n20181116 MIA IND\n20181116 UTAH PHI\n20181116 BKN WSH\n20181116 SAC MEM\n20181116 POR MIN\n20181116 NY NO\n20181116 CHI MIL\n20181117 LAC BKN\n20181117 PHI CHA\n20181117 ATL IND\n20181117 LAL ORL\n20181117 DEN NO\n20181117 UTAH BOS\n20181117 TOR CHI\n20181117 SAC HOU\n20181117 GS DAL\n20181117 OKC PHX\n20181118 MEM MIN\n20181118 LAL MIA\n20181118 NY ORL\n20181118 POR WSH\n20181118 GS SA\n20181119 BOS CHA\n20181119 CLE DET\n20181119 UTAH IND\n20181119 PHX PHI\n20181119 LAC ATL\n20181119 DAL MEM\n20181119 DEN MIL\n20181119 SA NO\n20181119 OKC SAC\n20181120 TOR ORL\n20181120 LAC WSH\n20181120 BKN MIA\n20181120 POR NY\n20181121 IND CHA\n20181121 NO PHI\n20181121 TOR ATL\n20181121 NY BOS\n20181121 LAL CLE\n20181121 PHX CHI\n20181121 DET HOU\n20181121 POR MIL\n20181121 DEN MIN\n20181121 BKN DAL\n20181121 MEM SA\n20181121 SAC UTAH\n20181121 OKC GS\n20181123 MIN BKN\n20181123 MEM LAC\n20181123 HOU DET\n20181123 BOS ATL\n20181123 NO NY\n20181123 CLE PHI\n20181123 WSH TOR\n20181123 SA IND\n20181123 MIA CHI\n20181123 CHA OKC\n20181123 PHX MIL\n20181123 ORL DEN\n20181123 POR GS\n20181123 UTAH LAL\n20181124 HOU CLE\n20181124 NO WSH\n20181124 CHI MIN\n20181124 DEN OKC\n20181124 BOS DAL\n20181124 SA MIL\n20181124 SAC GS\n20181125 ORL LAL\n20181125 PHX DET\n20181125 CHA ATL\n20181125 PHI BKN\n20181125 MIA TOR\n20181125 NY MEM\n20181125 UTAH SAC\n20181125 LAC POR\n20181126 MIL CHA\n20181126 MIN CLE\n20181126 HOU WSH\n20181126 SA CHI\n20181126 BOS NO\n20181126 IND UTAH\n20181126 ORL GS\n20181127 NY DET\n20181127 ATL MIA\n20181127 TOR MEM\n20181127 LAL DEN\n20181127 IND PHX\n20181128 ATL CHA\n20181128 NY PHI\n20181128 UTAH BKN\n20181128 DAL HOU\n20181128 CHI MIL\n20181128 SA MIN\n20181128 WSH NO\n20181128 CLE OKC\n20181128 ORL POR\n20181128 PHX LAC\n20181129 GS TOR\n20181129 IND LAL\n20181129 LAC SAC\n20181130 CLE BOS\n20181130 UTAH CHA\n20181130 CHI DET\n20181130 WSH PHI\n20181130 MEM BKN\n20181130 NO MIA\n20181130 ATL OKC\n20181130 HOU SA\n20181130 ORL PHX\n20181130 DAL LAL\n20181130 DEN POR\n20181201 MIL NY\n20181201 GS DET\n20181201 BKN WSH\n20181201 TOR CLE\n20181201 CHI HOU\n20181201 BOS MIN\n20181201 IND SAC\n20181202 PHX LAL\n20181202 NO CHA\n20181202 UTAH MIA\n20181202 MEM PHI\n20181202 LAC DAL\n20181202 POR SA\n20181203 OKC DET\n20181203 GS ATL\n20181203 CLE BKN\n20181203 WSH NY\n20181203 DEN TOR\n20181203 HOU MIN\n20181203 LAC NO\n20181204 CHI IND\n20181204 ORL MIA\n20181204 POR DAL\n20181204 SAC PHX\n20181204 SA UTAH\n20181205 GS CLE\n20181205 DEN ORL\n20181205 WSH ATL\n20181205 OKC BKN\n20181205 PHI TOR\n20181205 LAC MEM\n20181205 DET MIL\n20181205 CHA MIN\n20181205 DAL NO\n20181205 SA LAL\n20181206 NY BOS\n20181206 PHX POR\n20181206 HOU UTAH\n20181207 DEN CHA\n20181207 PHI DET\n20181207 IND ORL\n20181207 TOR BKN\n20181207 SAC CLE\n20181207 OKC CHI\n20181207 MEM NO\n20181207 LAL SA\n20181207 MIA PHX\n20181207 GS MIL\n20181208 HOU DAL\n20181208 SAC IND\n20181208 DEN ATL\n20181208 WSH CLE\n20181208 BKN NY\n20181208 BOS CHI\n20181208 LAL MEM\n20181208 MIN POR\n20181208 MIA LAC\n20181209 NO DET\n20181209 MIL TOR\n20181209 UTAH SA\n20181209 CHA NY\n20181210 WSH IND\n20181210 DET PHI\n20181210 NO BOS\n20181210 SAC CHI\n20181210 CLE MIL\n20181210 UTAH OKC\n20181210 ORL DAL\n20181210 MEM DEN\n20181210 LAC PHX\n20181210 MIN GS\n20181210 MIA LAL\n20181211 POR HOU\n20181211 PHX SA\n20181211 TOR LAC\n20181212 DET CHA\n20181212 NY CLE\n20181212 MIL IND\n20181212 BKN PHI\n20181212 BOS WSH\n20181212 POR MEM\n20181212 OKC NO\n20181212 ATL DAL\n20181212 MIA UTAH\n20181212 MIN SAC\n20181212 TOR GS\n20181213 LAL HOU\n20181213 LAC SA\n20181213 CHI ORL\n20181213 DAL PHX\n20181214 ATL BOS\n20181214 NY CHA\n20181214 WSH BKN\n20181214 MIL CLE\n20181214 IND PHI\n20181214 MIA MEM\n20181214 OKC DEN\n20181214 TOR POR\n20181214 GS SAC\n20181215 UTAH ORL\n20181215 LAL CHA\n20181215 BOS DET\n20181215 HOU MEM\n20181215 CHI SA\n20181215 LAC OKC\n20181215 MIN PHX\n20181216 ATL BKN\n20181216 PHI CLE\n20181216 NY IND\n20181216 LAL WSH\n20181216 SAC DAL\n20181216 MIA NO\n20181216 TOR DEN\n20181217 MIL DET\n20181217 PHX NY\n20181217 UTAH HOU\n20181217 SAC MIN\n20181217 CHI OKC\n20181217 PHI SA\n20181217 MEM GS\n20181217 POR LAC\n20181218 CLE IND\n20181218 WSH ATL\n20181218 LAL BKN\n20181218 DAL DEN\n20181219 CLE CHA\n20181219 SA ORL\n20181219 NY PHI\n20181219 PHX BOS\n20181219 IND TOR\n20181219 BKN CHI\n20181219 WSH HOU\n20181219 NO MIL\n20181219 DET MIN\n20181219 GS UTAH\n20181219 MEM POR\n20181219 OKC SAC\n20181220 HOU MIA\n20181220 DAL LAC\n20181221 DET CHA\n20181221 CLE TOR\n20181221 IND BKN\n20181221 ATL NY\n20181221 MIL BOS\n20181221 ORL CHI\n20181221 MIN SA\n20181221 UTAH POR\n20181221 MEM SAC\n20181221 NO LAL\n20181222 DEN LAC\n20181222 PHX WSH\n20181222 TOR PHI\n20181222 MIL MIA\n20181222 SA HOU\n20181222 DAL GS\n20181222 OKC UTAH\n20181223 ATL DET\n20181223 WSH IND\n20181223 CHA BOS\n20181223 PHX BKN\n20181223 CHI CLE\n20181223 MIA ORL\n20181223 NO SAC\n20181223 MIN OKC\n20181223 LAC GS\n20181223 DAL POR\n20181223 MEM LAL\n" ], [ "dfs.home.unique()", "_____no_output_____" ], [ "mydf = dfs.copy()", "_____no_output_____" ], [ "atlantic = ['BOS', 'BRK', 'NYK', 'PHI', 'TOR']\ncentral = ['CHI', 'CLE', 'DET', 'IND', 'MIL']\nsoutheast = ['ATL', 'CHA', 'MIA', 'ORL', 'WAS']\n\nsouthwest = ['DAL', 'HOU', 'MEM', 'NOP', 'SAS']\nnorthwest = ['DEN', 'MIN', 'OKC', 'POR', 'UTA']\npacific = ['GSW', 'LAC', 'LAL', 'PHX', 'SAC']", "_____no_output_____" ], [ "mydf.home.replace({'NO': 'NOP', 'BKN': 'BRK', 'NY': 'NYK', 'UTAH': 'UTA', 'GS': 'GSW', 'SA': 'SAS', \n 'WSH': 'WAS'}, inplace=True) \nmydf.away.replace({'NO': 'NOP', 'BKN': 'BRK', 'NY': 'NYK', 'UTAH': 'UTA', 'GS': 'GSW', 'SA': 'SAS', \n 'WSH': 'WAS'}, inplace=True)", "_____no_output_____" ], [ "mydf.head()", "_____no_output_____" ], [ "mydf['month'] = mydf['day'].dt.month", "_____no_output_____" ], [ "mydf.head()", "_____no_output_____" ], [ "mydf.shape", "_____no_output_____" ], [ "nbs_df = pd.read_csv('social_nba_2.csv')", "_____no_output_____" ], [ "nbs_df2 = nbs_df[(nbs_df.year==2018) & (nbs_df.month==8)]\nnbs_df2.team.unique()", "_____no_output_____" ], [ "dx = nbs_df2.copy()", "_____no_output_____" ], [ "dx.columns", "_____no_output_____" ], [ "dx[(dx.team==\"MIA\") | (dx.team==\"BOS\") | (dx.team==\"OKC\") | (dx.team==\"NYK\") | (dx.team==\"CHI\") ]", "_____no_output_____" ], [ "dx[(dx.team==\"SAS\")]", "_____no_output_____" ], [ "for j in range(len(mydf)):\n mydf.loc[mydf.index==j, 'gts'] = dx[dx.team==mydf.iloc[j].home]['gts'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['gts'].iloc[0]\n mydf.loc[mydf.index==j, 'wp'] = dx[dx.team==mydf.iloc[j].home]['wp_pageviews'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['wp_pageviews'].iloc[0]\n mydf.loc[mydf.index==j, 'tts'] = dx[dx.team==mydf.iloc[j].home]['TTS'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['TTS'].iloc[0]\n mydf.loc[mydf.index==j, 'unq'] = dx[dx.team==mydf.iloc[j].home]['UNQ'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['UNQ'].iloc[0]\n mydf.loc[mydf.index==j, 'fb_foll'] = dx[dx.team==mydf.iloc[j].home]['Followers_Facebook'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Followers_Facebook'].iloc[0]\n mydf.loc[mydf.index==j, 'tw_foll'] = dx[dx.team==mydf.iloc[j].home]['Followers_Twitter'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Followers_Twitter'].iloc[0]\n mydf.loc[mydf.index==j, 'inst_foll'] = dx[dx.team==mydf.iloc[j].home]['Followers_Instagram'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Followers_Instagram'].iloc[0]\n mydf.loc[mydf.index==j, 'snap_foll'] = dx[dx.team==mydf.iloc[j].home]['Followers_Snapchat'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Followers_Snapchat'].iloc[0]\n mydf.loc[mydf.index==j, 'wb_foll'] = dx[dx.team==mydf.iloc[j].home]['Followers_Weibo'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Followers_Weibo'].iloc[0]\n mydf.loc[mydf.index==j, 'fb_eng'] = dx[dx.team==mydf.iloc[j].home]['Engagements_Facebook'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Engagements_Facebook'].iloc[0]\n mydf.loc[mydf.index==j, 'tw_eng'] = dx[dx.team==mydf.iloc[j].home]['Engagements_Twitter'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Engagements_Twitter'].iloc[0]\n mydf.loc[mydf.index==j, 'inst_eng'] = dx[dx.team==mydf.iloc[j].home]['Engagements_Instagram'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Engagements_Instagram'].iloc[0]\n mydf.loc[mydf.index==j, 'fb_imps'] = dx[dx.team==mydf.iloc[j].home]['Impressions_Facebook'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Impressions_Facebook'].iloc[0]\n mydf.loc[mydf.index==j, 'tw_imps'] = dx[dx.team==mydf.iloc[j].home]['Impressions_Twitter'].iloc[0] + dx[dx.team==mydf.iloc[j].away]['Impressions_Twitter'].iloc[0]", "_____no_output_____" ], [ "mydf.head()", "_____no_output_____" ], [ "mydf.shape", "_____no_output_____" ], [ "social_feats = [None]*4\n\nfor i in range(4):\n \n j = str(i+1)\n social_feats[i] = [None]*14\n \n mydf['gts_'+j] = mydf['gts']\n mydf['wp_'+j] = mydf['wp']\n mydf['tts_'+j] = mydf['tts']\n mydf['unq_'+j] = mydf['unq']\n\n mydf['fb_foll_'+j] = mydf['fb_foll']\n mydf['inst_foll_'+j] = mydf['inst_foll']\n mydf['tw_foll_'+j] = mydf['tw_foll']\n mydf['snap_foll_'+j] = mydf['snap_foll']\n mydf['wb_foll_'+j] = mydf['wb_foll']\n\n mydf['fb_eng_'+j] = mydf['fb_eng']\n mydf['inst_eng_'+j] = mydf['inst_eng']\n mydf['tw_eng_'+j] = mydf['tw_eng']\n mydf['fb_imps_'+j] = mydf['fb_imps']\n mydf['tw_imps_'+j] = mydf['tw_imps']\n \n social_feats[i] = ['gts_'+j, 'wp_'+j, 'tts_'+j, 'unq_'+j,\n 'fb_foll_'+j, 'inst_foll_'+j, 'tw_foll_'+j, 'snap_foll_'+j, 'wb_foll_'+j, \n 'fb_eng_'+j, 'inst_eng_'+j, 'tw_eng_'+j, 'fb_imps_'+j, 'tw_imps_'+j]\n ", "_____no_output_____" ], [ "mydf.shape", "_____no_output_____" ], [ "mydf.head()", "_____no_output_____" ], [ "!ls *.pkl", "LR_model_avg_markup_norm_1.pkl\tRF_model_avg_markup_norm_2.pkl\r\nLR_model_avg_markup_norm_2.pkl\tRF_model_avg_markup_norm_3.pkl\r\nLR_model_avg_markup_norm_3.pkl\tRF_model_avg_markup_norm_4.pkl\r\nLR_model_avg_markup_norm_4.pkl\tRF_model_norm_minutes_1.pkl\r\nLR_model_norm_minutes_1.pkl\tRF_model_norm_minutes_2.pkl\r\nLR_model_norm_minutes_2.pkl\tRF_model_norm_minutes_3.pkl\r\nLR_model_norm_minutes_3.pkl\tRF_model_norm_minutes_4.pkl\r\nLR_model_norm_minutes_4.pkl\tRF_model_Unique_Viewers_1.pkl\r\nLR_model_Unique_Viewers_1.pkl\tRF_model_Unique_Viewers_2.pkl\r\nLR_model_Unique_Viewers_2.pkl\tRF_model_Unique_Viewers_3.pkl\r\nLR_model_Unique_Viewers_3.pkl\tRF_model_Unique_Viewers_4.pkl\r\nLR_model_Unique_Viewers_4.pkl\ttesting_gts.pkl\r\nRF_model_avg_markup_norm_1.pkl\ttraining_gts.pkl\r\n" ], [ "modelfile = 'RF_model_Unique_Viewers_2.pkl'\nwith open(modelfile, 'rb') as fd:\n model = pickle.load(fd)", "_____no_output_____" ], [ "mydf1 = mydf[mydf.month==10]\nmydf2 = mydf[mydf.month==11]\nmydf3 = mydf[mydf.month==12]", "_____no_output_____" ], [ "j=1\ntgt_features = ['Unique_Viewers', 'norm_minutes', 'avg_markup_norm']\nfor f in tgt_features:\n modelfile = 'RF_model_'+f+'_'+str(j+1)+'.pkl'\n with open(modelfile, 'rb') as fd:\n model = pickle.load(fd)\n mydf1.loc[:, f] = model.predict(mydf1[social_feats[j]])", "/home/asanzgiri/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n self.obj[key] = _infer_fill_value(value)\n/home/asanzgiri/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n self.obj[item] = s\n" ], [ "j=2\ntgt_features = ['Unique_Viewers', 'norm_minutes', 'avg_markup_norm']\nfor f in tgt_features:\n modelfile = 'RF_model_'+f+'_'+str(j+1)+'.pkl'\n with open(modelfile, 'rb') as fd:\n model = pickle.load(fd)\n mydf2.loc[:, f] = model.predict(mydf2[social_feats[j]])", "/home/asanzgiri/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n self.obj[item] = s\n/home/asanzgiri/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n self.obj[key] = _infer_fill_value(value)\n" ], [ "j=3\ntgt_features = ['Unique_Viewers', 'norm_minutes', 'avg_markup_norm']\nfor f in tgt_features:\n modelfile = 'RF_model_'+f+'_'+str(j+1)+'.pkl'\n with open(modelfile, 'rb') as fd:\n model = pickle.load(fd)\n mydf3.loc[:, f] = model.predict(mydf3[social_feats[j]])", "/home/asanzgiri/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n self.obj[key] = _infer_fill_value(value)\n/home/asanzgiri/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n self.obj[item] = s\n" ], [ "mydf_final = pd.concat([mydf1, mydf2, mydf3])", "_____no_output_____" ], [ "mydf_final.shape", "_____no_output_____" ], [ "mydf_final.columns", "_____no_output_____" ], [ "mydf_save = mydf_final[['day', 'home', 'away', 'gts', 'wp', 'tts', 'unq', 'fb_foll',\n 'tw_foll', 'inst_foll', 'snap_foll', 'wb_foll', 'fb_eng', 'tw_eng',\n 'inst_eng', 'fb_imps', 'tw_imps', 'Unique_Viewers', 'norm_minutes',\n 'avg_markup_norm']]", "_____no_output_____" ], [ "mydf_save.to_csv('final_predictions.csv', index=None)", "_____no_output_____" ], [ "def get_games_for_day(df, day):\n \n dfg = df[df.day==day][['home', 'away', 'Unique_Viewers', 'norm_minutes', 'avg_markup_norm']]\n print(dfg.head())", "_____no_output_____" ], [ "get_games_for_day(mydf_save, '2018-10-16')", " home away Unique_Viewers norm_minutes avg_markup_norm\n0 PHI BOS 32430.807999 37.403667 1.323228\n1 OKC GSW 51098.976960 55.213687 2.313532\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import sys\nimport gym\nimport numpy as np\nimport random\nimport math\nfrom collections import defaultdict, deque\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport check_test\nfrom plot_utils import plot_values", "_____no_output_____" ], [ "#create an instance of CliffWalking environment\nenv = gym.make('CliffWalking-v0')", "_____no_output_____" ], [ "print(\"Actions_space: {0}\".format(env.action_space))\nprint(\"State Space: {0}\".format(env.observation_space))\nprint(\"Action Space (env.action_space.n) {0}: \".format(env.action_space.n))", "Actions_space: Discrete(4)\nState Space: Discrete(48)\nAction Space (env.action_space.n) 4: \n" ], [ "def epsilon_greedy(Q, state, nA, eps):\n if random.random() > eps:\n return np.argmax(Q[state])\n else:\n return random.choice(np.arange(nA))\n\n\"\"\" \ndef update_Q_expected_sarsa(alpha, gamma, Q, \\\n eps, nA,\\\n state, action, reward, next_state=None):\n \n current = Q[state][action]\n \n #construct an epsilon-greedy policy\n policy_s = np.ones(nA) * ( eps / nA) #epsilon-greedy strategy for equiprobable selection\n policy_s[np.argmax(Q[state])] = 1 - eps + ( eps /nA) # epsilon-greedy strategy for greedy selection\n \n #In case of Expected SARSA , each state_action is multiplied with the probability\n next_reward = np.dot(Q[next_state] , policy_s)\n \n target = reward + gamma * next_reward\n \n new_current_reward = current + (alpha * ( target - current))\n \n return new_current_reward\n\n\"\"\"\n\ndef update_Q_expected_sarsa(alpha, gamma, Q, nA, eps, state,action, reward, next_state=None):\n #print(\"The state is : {0}\".format(state))\n #print(\"The action is : {0}\".format(action))\n current = Q[state][action]\n\n policy_s = get_probs(Q, eps, nA)\n Qsa_next = np.dot(Q[next_state] , policy_s)\n \n target = reward + gamma * Qsa_next\n \n new_value = current + (alpha * (target - current))\n \n return new_value\n \n \n\ndef get_probs(Q, epsilon, nA):\n \"\"\" obtains the action probabilities corresponding to epsilon-greedy policy \"\"\"\n policy_s = np.ones(nA) * (epsilon / nA)\n greedy_action = np.argmax(Q)\n policy_s[greedy_action] = 1 - epsilon + (epsilon / nA)\n return policy_s", "_____no_output_____" ], [ "def expected_sarsa(env, num_episodes, alpha, gamma=1.0,plot_every=100):\n # initialize empty dictionary of arrays\n Q = defaultdict(lambda: np.zeros(env.nA))\n \n tmp_scores = deque(maxlen=plot_every)\n avg_scores = deque(maxlen=num_episodes)\n \n \n # loop over episodes\n for i_episode in range(1, num_episodes+1):\n \n # monitor progress\n if i_episode % 100 == 0:\n print(\"\\rEpisode {}/{}\".format(i_episode, num_episodes), end=\"\")\n sys.stdout.flush()\n \n eps = 0.05\n state = env.reset()\n score = 0 \n\n \n while True:\n action = epsilon_greedy(Q, state, env.nA, eps )\n \n next_state, reward, done, info = env.step(action)\n score += reward\n Q[state][action] = update_Q_expected_sarsa(alpha, gamma, Q, env.nA, eps, state,action, reward, next_state)\n state = next_state\n \n if done:\n tmp_scores.append(score) # append score\n break\n \n if (i_episode % plot_every == 0):\n avg_scores.append(np.mean(tmp_scores))\n \n # plot performance\n plt.plot(np.linspace(0,num_episodes,len(avg_scores),endpoint=False), np.asarray(avg_scores))\n plt.xlabel('Episode Number')\n plt.ylabel('Average Reward (Over Next %d Episodes)' % plot_every)\n plt.show()\n # print best 100-episode performance\n print(('Best Average Reward over %d Episodes: ' % plot_every), np.max(avg_scores))\n return Q", "_____no_output_____" ], [ "def expected_sarsa(env, num_episodes, alpha, gamma=1.0,max_steps_per_episode=100):\n # initialize empty dictionary of arrays\n Q = defaultdict(lambda: np.zeros(env.nA))\n \n tmp_scores = deque(maxlen=max_steps_per_episode)\n avg_scores = deque(maxlen=num_episodes)\n \n \n # loop over episodes\n for i_episode in range(1, num_episodes+1):\n \n # monitor progress\n if i_episode % 100 == 0:\n print(\"\\rEpisode {}/{}\".format(i_episode, num_episodes), end=\"\")\n sys.stdout.flush()\n \n eps = 0.05\n state = env.reset()\n score = 0 \n \n for step in range(max_steps_per_episode): \n action = epsilon_greedy(Q, state, env.nA, eps )\n \n next_state, reward, done, info = env.step(action)\n score += reward\n Q[state][action] = update_Q_expected_sarsa(alpha, gamma, Q, env.nA, eps, state,action, reward, next_state)\n state = next_state\n \n if done:\n tmp_scores.append(score) # append score\n break\n \n if (i_episode % max_steps_per_episode == 0):\n avg_scores.append(np.mean(tmp_scores))\n \n # plot performance\n plt.plot(np.linspace(0,num_episodes,len(avg_scores),endpoint=False), np.asarray(avg_scores))\n plt.xlabel('Episode Number')\n plt.ylabel('Average Reward (Over Next %d Episodes)' % max_steps_per_episode)\n plt.show()\n # print best 100-episode performance\n print(('Best Average Reward over %d Episodes: ' % max_steps_per_episode), np.max(avg_scores))\n return Q", "_____no_output_____" ], [ "# obtain the estimated optimal policy and corresponding action-value function\nQ_expsarsa = expected_sarsa(env, 10000, 1)\n\n# print the estimated optimal policy\npolicy_expsarsa = np.array([np.argmax(Q_expsarsa[key]) if key in Q_expsarsa else -1 for key in np.arange(48)]).reshape(4,12)\ncheck_test.run_check('td_control_check', policy_expsarsa)\nprint(\"\\nEstimated Optimal Policy (UP = 0, RIGHT = 1, DOWN = 2, LEFT = 3, N/A = -1):\")\nprint(policy_expsarsa)\n\n# plot the estimated optimal state-value function\nplot_values([np.max(Q_expsarsa[key]) if key in Q_expsarsa else 0 for key in np.arange(48)])", "Episode 10000/10000" ] ] ]

[ "code" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Secara Visual", "_____no_output_____" ] ], [ [ "import pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline\nplt.style.use('seaborn')", "_____no_output_____" ], [ "iris = pd.read_csv('data/iris.csv')", "_____no_output_____" ], [ "iris.shape", "_____no_output_____" ], [ "iris.head()", "_____no_output_____" ], [ "iris.plot(x='sepal_length', y='sepal_width')", "_____no_output_____" ], [ "iris.plot(x='sepal_length', y='sepal_width', kind='scatter')\nplt.xlabel('sepal length (cm)')\nplt.ylabel('sepal width (cm)')", "_____no_output_____" ], [ "iris.plot(y='sepal_length', kind='box')\nplt.ylabel('sepal width (cm)')", "_____no_output_____" ], [ "iris.plot(y='sepal_length', kind='hist')\nplt.xlabel('sepal length (cm)')", "_____no_output_____" ], [ "iris.plot(y='sepal_length', kind='hist', bins=30, range=(4,8), normed=True)\nplt.xlabel('sepal length (cm)')\nplt.show()", "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\hist.py:59: MatplotlibDeprecationWarning: \nThe 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)\n" ], [ "iris.plot(y='sepal_length', kind='hist', bins=30, range=(4,8), cumulative=True, normed=True)\nplt.xlabel('sepal length (cm)')\nplt.title('Cumulative distribution function (CDF)')", "_____no_output_____" ] ], [ [ "# Secara Statistik", "_____no_output_____" ] ], [ [ "iris.describe()", "_____no_output_____" ], [ "iris.count()", "_____no_output_____" ], [ "iris['sepal_length'].count()", "_____no_output_____" ], [ "iris.mean()", "_____no_output_____" ], [ "iris.std()", "_____no_output_____" ], [ "iris.median()", "_____no_output_____" ], [ "q = 0.5\niris.quantile(q)", "_____no_output_____" ], [ "q = [0.25, 0.75]\niris.quantile(q)", "_____no_output_____" ], [ "iris.min()", "_____no_output_____" ], [ "iris.max()", "_____no_output_____" ], [ "iris.plot(kind= 'box')\nplt.ylabel('[cm]')", "_____no_output_____" ] ], [ [ "# Filtering", "_____no_output_____" ] ], [ [ "iris.head()", "_____no_output_____" ], [ "iris['species'].describe()", "_____no_output_____" ], [ "iris['species'].value_counts()", "_____no_output_____" ], [ "iris['species'].unique()", "_____no_output_____" ], [ "index_setosa = iris['species'] == 'setosa'\nindex_versicolor = iris['species'] == 'versicolor'\nindex_virginica = iris['species'] == 'virginica'", "_____no_output_____" ], [ "setosa = iris[index_setosa]\nversicolor = iris[index_versicolor]\nvirginica = iris[index_virginica]", "_____no_output_____" ], [ "setosa['species'].unique()", "_____no_output_____" ], [ "versicolor['species'].unique()", "_____no_output_____" ], [ "virginica['species'].unique()", "_____no_output_____" ], [ "setosa.head(2)", "_____no_output_____" ], [ "versicolor.head(2)", "_____no_output_____" ], [ "virginica.head(2)", "_____no_output_____" ], [ "iris.plot(kind= 'hist', bins=50, range=(0,8), alpha=0.4)\nplt.title('Entire iris data set')\nplt.xlabel('[cm]')", "_____no_output_____" ], [ "setosa.plot(kind='hist', bins=50, range=(0,8), alpha=0.5)\nplt.title('Setosa data set')\nplt.xlabel('[cm]')", "_____no_output_____" ], [ "versicolor.plot(kind='hist', bins=50, range=(0,8), alpha=0.5)\nplt.title('Versicolor data set')\nplt.xlabel('[cm]')", "_____no_output_____" ], [ "virginica.plot(kind='hist', bins=50, range=(0,8), alpha=0.3)\nplt.title('Virginica data set')\nplt.xlabel('[cm]')", "_____no_output_____" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%matplotlib inline\nfrom pyvista import set_plot_theme\nset_plot_theme('document')", "_____no_output_____" ] ], [ [ "Geometric Objects {#ref_geometric_example}\n=================\n\nThe \\\"Hello, world!\\\" of VTK\n", "_____no_output_____" ] ], [ [ "import pyvista as pv", "_____no_output_____" ] ], [ [ "This runs through several of the available geometric objects available\nin VTK which PyVista provides simple convenience methods for generating.\n\nLet\\'s run through creating a few geometric objects!\n", "_____no_output_____" ] ], [ [ "cyl = pv.Cylinder()\narrow = pv.Arrow()\nsphere = pv.Sphere()\nplane = pv.Plane()\nline = pv.Line()\nbox = pv.Box()\ncone = pv.Cone()\npoly = pv.Polygon()\ndisc = pv.Disc()", "_____no_output_____" ] ], [ [ "Now let\\'s plot them all in one window\n", "_____no_output_____" ] ], [ [ "p = pv.Plotter(shape=(3, 3))\n# Top row\np.subplot(0, 0)\np.add_mesh(cyl, color=\"tan\", show_edges=True)\np.subplot(0, 1)\np.add_mesh(arrow, color=\"tan\", show_edges=True)\np.subplot(0, 2)\np.add_mesh(sphere, color=\"tan\", show_edges=True)\n# Middle row\np.subplot(1, 0)\np.add_mesh(plane, color=\"tan\", show_edges=True)\np.subplot(1, 1)\np.add_mesh(line, color=\"tan\", line_width=3)\np.subplot(1, 2)\np.add_mesh(box, color=\"tan\", show_edges=True)\n# Bottom row\np.subplot(2, 0)\np.add_mesh(cone, color=\"tan\", show_edges=True)\np.subplot(2, 1)\np.add_mesh(poly, color=\"tan\", show_edges=True)\np.subplot(2, 2)\np.add_mesh(disc, color=\"tan\", show_edges=True)\n# Render all of them\np.show()", "_____no_output_____" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# *bienvenue sur notre page qui presentera la fonction traiter des differents suports robotiques mindstorm*", "_____no_output_____" ], [ "![lego1](img\\lego1.jpg \"lego mindstorm\")", "_____no_output_____" ], [ "### Ici vous trouverez toute les information sur les briques de controle des lego Mindstorm EV3.\n\n", "_____no_output_____" ], [ "la brique de controlle du EV3 est un mini aurdinateur sous linux avec un ecran de petite taille qui permet de faire de faire de petite action avec une configuration minimale: \nSystème d'exploitation – LINUX \nProcesseur ARM9 300 MHz \nMémoire flash – 16 Mo \nMémoire vive – 64 Mo \nRésolution de l'écran – 178x128/noir & blanc \nCommunication USB 2.0 vers PC – Jusqu'à 480 Mbit/s \nCommunication USB 1.1 – Jusqu'à 12 Mbit/s \nCarte MicroSD – Compatible SDHC, version 2.0, \nmax. 32 Go \nPorts pour moteurs et capteurs \nConnecteurs – RJ12 \nCompatible Auto ID \nAlimentation – 6 piles AA \n(rechargeables) \n![fdfsdf](img/lego2.jpg \"brique de controlle\")\n### Le fontionnement de la brique de contrôle:\n\nla brique de controlle est programmer par ordinateur et gere toute les info elle même grace a son processeur \nil y a plusieur capteurs qui peuvent etre mis sur le robot ce qui permet un nombre infinit de possibilité de creation \nAvec tout ces capteur le robot peut effectue avec la bonne programmation plusieur action en autonomie\n# la programmation de la brique\n\n### le logiciel :\n\nle lego mindstorm est programmable sur un logiciel spécial fournit gratuitement.\n![fdfsdf](img/lego5.png \"brique de controlle\")\nle logiciel est un logiciel de programmation graphique \nil existe un lçogiciel tiers qui s'appele [enchanting](http://enchanting.robotclub.ab.ca/tiki-index.php \"site officiel\")\n![fdfsdf](img/System-1.png \"brique de controlle\")", "_____no_output_____" ], [ "## la brique de version anterieur la NXT\n![fdfsdf](img/lego6.jpg \"brique de controlle\")\nelle est la petite soeur de la brique EV3 et embarque moins de possibilité tel que le bluetooth pour la programmer avec une tablet ou un smartphone \n![fdfsdf](img/lego7.jpg \"brique de controlle\")\non voit sont anteriorité sur sont logiciel de programmation qui est moins disigne et plus technique. \nles deux logiciel on un points commun c'est que la programmation s'effectue equipement après equipement avec des block comme dans scratch la brique NXT peut aussi être programmé avec le logiciel [enchanting](http://enchanting.robotclub.ab.ca/tiki-index.php \"site officiel\")", "_____no_output_____" ], [ "## _le but du projet_ \n \n le but de notre projet est de participer a robofesta qui est une conpetition en 2 épreuve, une epreuve de sauvetage et une épreuve de chorégraphie.", "_____no_output_____" ] ] ]

[ "markdown" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import cv2\nimport numpy as np\nimport os\nimport math\nfrom scipy.spatial import distance as dist\nfrom collections import OrderedDict\nfrom scipy.optimize import linear_sum_assignment\nfrom kalman_utils.KFilter import *", "_____no_output_____" ], [ "from filterpy.kalman import KalmanFilter, UnscentedKalmanFilter, MerweScaledSigmaPoints\nfrom filterpy.common import Q_discrete_white_noise", "_____no_output_____" ], [ "a = [[170, 175, 196, 209], [150, 557, 174, 577], [625, 194, 640, 209], [170, 175, 196, 209], [173, 225, 202, 253], [435, 526, 476, 568], [435, 576, 476, 603]]\n\nb = np.array([[170, 175, 196, 209], [150, 557, 174, 577], [625, 194, 640, 209], [170, 175, 196, 209], [173, 225, 202, 253], [435, 526, 476, 568], [435, 576, 476, 603]])\n\nno_ball_box = list(set([tuple(set(i)) for i in a]))", "_____no_output_____" ], [ "no_ball_box", "_____no_output_____" ], [ "for i in a:\n print(set(i))", "[170, 175, 196, 209]\n[150, 557, 174, 577]\n[625, 194, 640, 209]\n[170, 175, 196, 209]\n[173, 225, 202, 253]\n[435, 526, 476, 568]\n[435, 576, 476, 603]\n" ], [ "for i in a:\n print(set(i))", "{209, 170, 196, 175}\n{577, 174, 557, 150}\n{640, 625, 194, 209}\n{209, 170, 196, 175}\n{225, 202, 173, 253}\n{568, 435, 476, 526}\n{576, 603, 435, 476}\n" ], [ "new_array = [tuple(row) for row in b]", "_____no_output_____" ], [ "c = np.unique(new_array)", "_____no_output_____" ], [ "c", "_____no_output_____" ], [ "new_array", "_____no_output_____" ], [ "['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',\n 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',\n 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',\n 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',\n 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',\n 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',\n 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',\n 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',\n 'hair drier', 'toothbrush'].index(\"tennis racket\")", "_____no_output_____" ], [ "for i in range(640, 1080):\n a = i\n\n \n if ((9 * a)/16).is_integer():\n print(\"x : \",i)\n print(\"y : \",(9 * a)/16)\n print(\"total_pixel : \", a * (9 * a)/16 *2 )\n ", "x : 640\ny : 360.0\ntotal_pixel : 460800.0\nx : 656\ny : 369.0\ntotal_pixel : 484128.0\nx : 672\ny : 378.0\ntotal_pixel : 508032.0\nx : 688\ny : 387.0\ntotal_pixel : 532512.0\nx : 704\ny : 396.0\ntotal_pixel : 557568.0\nx : 720\ny : 405.0\ntotal_pixel : 583200.0\nx : 736\ny : 414.0\ntotal_pixel : 609408.0\nx : 752\ny : 423.0\ntotal_pixel : 636192.0\nx : 768\ny : 432.0\ntotal_pixel : 663552.0\nx : 784\ny : 441.0\ntotal_pixel : 691488.0\nx : 800\ny : 450.0\ntotal_pixel : 720000.0\nx : 816\ny : 459.0\ntotal_pixel : 749088.0\nx : 832\ny : 468.0\ntotal_pixel : 778752.0\nx : 848\ny : 477.0\ntotal_pixel : 808992.0\nx : 864\ny : 486.0\ntotal_pixel : 839808.0\nx : 880\ny : 495.0\ntotal_pixel : 871200.0\nx : 896\ny : 504.0\ntotal_pixel : 903168.0\nx : 912\ny : 513.0\ntotal_pixel : 935712.0\nx : 928\ny : 522.0\ntotal_pixel : 968832.0\nx : 944\ny : 531.0\ntotal_pixel : 1002528.0\nx : 960\ny : 540.0\ntotal_pixel : 1036800.0\nx : 976\ny : 549.0\ntotal_pixel : 1071648.0\nx : 992\ny : 558.0\ntotal_pixel : 1107072.0\nx : 1008\ny : 567.0\ntotal_pixel : 1143072.0\nx : 1024\ny : 576.0\ntotal_pixel : 1179648.0\nx : 1040\ny : 585.0\ntotal_pixel : 1216800.0\nx : 1056\ny : 594.0\ntotal_pixel : 1254528.0\nx : 1072\ny : 603.0\ntotal_pixel : 1292832.0\n" ], [ "1276,600", "_____no_output_____" ], [ "x_meter2pix = 23.77 / x_pix_length\ny_meter2pix = 10.97 / y_pix_length", "_____no_output_____" ], [ "y_pix_length, x_pix_length = 600, 1276", "_____no_output_____" ], [ "x_meter2pix * 1500\n810 * y_meter2pix", "_____no_output_____" ], [ "(1500 - 1276)/3", "_____no_output_____" ], [ "def trans_xy(img_ori, point_list):\n\n for i in range(len(point_list[1])):\n x_cen, y_cen = point_list[1][i]\n\n point_list[1][i][1] = y_cen - (img_ori.shape[0] / 2)\n\n return point_list", "_____no_output_____" ], [ "img = np.zeros([752, 423 * 2, 3])\n \ntrans_xy(img,a)", "_____no_output_____" ], [ "def cal_ball_position(ball_stats_list):\n \n net_length = 13.11\n post_hegith_avg = 1.125\n \n for i in range(len(ball_stats_list)):\n \n ball_distance_list = ball_stats_list[i][0]\n ball_height_list = ball_stats_list[i][1]\n\n height = sum(ball_height_list) / 2 - post_hegith_avg\n\n if sum(ball_distance_list) < 13:\n return [np.nan, np.nan, np.nan]\n\n ball2net_length_x_L = ball_distance_list[0] * np.sin(theta_L)\n ball_position_y_L = ball_distance_list[0] * np.cos(theta_L)\n\n ball_plate_angle_L = np.arcsin(height / ball2net_length_x_L)\n\n ball_position_x_L = ball2net_length_x_L * np.cos(ball_plate_angle_L)\n\n ball2net_length_x_R = ball_distance_list[1] * np.sin(theta_R)\n ball_position_y_R = ball_distance_list[1] * np.cos(theta_R)\n\n ball_plate_angle_R = np.arcsin(height / ball2net_length_x_R)\n\n ball_position_x_R = ball2net_length_x_R * np.cos(ball_plate_angle_R)\n\n\n \"\"\"print(\"theta_L, theta_R : \", np.rad2deg(self.theta_L), np.rad2deg(self.theta_R))\n print(\"ball_plate_angle_L, ball_plate_angle_R : \", np.rad2deg(ball_plate_angle_L), np.rad2deg(ball_plate_angle_R))\n print([-ball_position_x_L, ball_position_y_L - 6.4, height + 1])\n print([-ball_position_x_R, 6.4 - ball_position_y_R, height + 1])\"\"\"\n\n if theta_L > theta_R:\n ball_position_y = ball_position_y_L - (net_length / 2)\n\n else :\n ball_position_y = (net_length / 2) - ball_position_y_R\n\n return [-ball_position_x_L, ball_position_y, height + post_hegith_avg]\n", "_____no_output_____" ], [ "def get_depth_height(L_pos, R_pos):\n \n depth_height = []\n\n cx = 360\n cy = 204\n focal_length = 320.754\n\n net_length = 13.11\n\n post_hegith_left = 1.13\n post_hegith_right = 1.12 \n\n for i in range(len(L_pos)):\n x_L, y_L = L_pos[i][0] - cx, L_pos[i][1] - cy\n\n for j in range(len(R_pos)):\n x_R, y_R = R_pos[j][0] - cx, R_pos[j][1] - cy\n\n\n c_L = np.sqrt(focal_length ** 2 + x_L ** 2 + y_L ** 2)\n a_L = np.sqrt(focal_length ** 2 + x_L ** 2)\n\n if x_L < 0:\n th_L = 0.785398 + np.arccos(focal_length / a_L)\n\n else :\n th_L = 0.785398 - np.arccos(focal_length / a_L)\n\n\n b_L = a_L * np.cos(th_L)\n\n c_R = np.sqrt(focal_length ** 2 + x_R ** 2 + y_R ** 2)\n a_R = np.sqrt(focal_length ** 2 + x_R ** 2)\n\n if x_R > 0:\n th_R = 0.785398 + np.arccos(focal_length / a_R)\n\n else :\n th_R = 0.785398 - np.arccos(focal_length / a_R)\n\n b_R = a_R * np.cos(th_R)\n\n theta_L = np.arccos(b_L/c_L)\n theta_R = np.arccos(b_R/c_R)\n\n\n D_L = net_length * np.sin(theta_R) / np.sin(3.14 - (theta_L + theta_R))\n D_R = net_length * np.sin(theta_L) / np.sin(3.14 - (theta_L + theta_R))\n\n height_L = abs(D_L * np.sin(np.arcsin(y_L/c_L)))\n height_R = abs(D_R * np.sin(np.arcsin(y_R/c_R)))\n\n #height_L = abs(D_L * np.sin(np.arctan(y_L/a_L)))\n #height_R = abs(D_R * np.sin(np.arctan(y_R/a_R)))\n\n if y_L < 0:\n height_L += post_hegith_left\n\n else:\n height_L -= post_hegith_left \n\n\n if y_R < 0:\n height_R += post_hegith_right\n\n else:\n height_R -= post_hegith_right \n\n\n print(L_pos[i],R_pos[j])\n print([D_L, D_R, height_L, height_R])\n depth_height.append([[D_L, D_R], [height_L, height_R]])\n\n return depth_height", "_____no_output_____" ], [ "ball_cen_left = [[260, 162]]\nball_cen_right = [[351, 167]]\n\nball_stats_list = get_depth_height(ball_cen_left,ball_cen_right)", "[260, 162] [351, 167]\n[9.458568396152303, 12.128951367682397, 2.3032568761005505, 2.509357032389613]\n" ], [ "cal_ball_position(ball_stats_list)", "_____no_output_____" ], [ "def get_ball_pos(L_pos, R_pos):\n \n depth_height = []\n\n cx = 360\n cy = 204\n focal_length = 320.754\n\n net_length = 13.11\n\n post_hegith_left = 1.13\n post_hegith_right = 1.12 \n\n post_hegith_avg = (post_hegith_left + post_hegith_right) / 2\n\n for i in range(len(L_pos)):\n x_L, y_L = L_pos[i][0] - cx, L_pos[i][1] - cy\n\n for j in range(len(R_pos)):\n x_R, y_R = R_pos[j][0] - cx, R_pos[j][1] - cy\n\n\n c_L = np.sqrt(focal_length ** 2 + x_L ** 2 + y_L ** 2)\n a_L = np.sqrt(focal_length ** 2 + x_L ** 2)\n\n if x_L < 0:\n th_L = 0.785398 + np.arccos(focal_length / a_L)\n\n else :\n th_L = 0.785398 - np.arccos(focal_length / a_L)\n\n\n b_L = a_L * np.cos(th_L)\n\n c_R = np.sqrt(focal_length ** 2 + x_R ** 2 + y_R ** 2)\n a_R = np.sqrt(focal_length ** 2 + x_R ** 2)\n\n if x_R > 0:\n th_R = 0.785398 + np.arccos(focal_length / a_R)\n\n else :\n th_R = 0.785398 - np.arccos(focal_length / a_R)\n\n b_R = a_R * np.cos(th_R)\n\n theta_L = np.arccos(b_L/c_L)\n theta_R = np.arccos(b_R/c_R)\n\n\n D_L = net_length * np.sin(theta_R) / np.sin(3.14 - (theta_L + theta_R))\n D_R = net_length * np.sin(theta_L) / np.sin(3.14 - (theta_L + theta_R))\n\n height_L = abs(D_L * np.sin(np.arcsin(y_L/c_L)))\n height_R = abs(D_R * np.sin(np.arcsin(y_R/c_R)))\n\n #height_L = abs(D_L * np.sin(np.arctan(y_L/a_L)))\n #height_R = abs(D_R * np.sin(np.arctan(y_R/a_R)))\n\n if y_L < 0:\n height_L += post_hegith_left\n\n else:\n height_L -= post_hegith_left \n\n\n if y_R < 0:\n height_R += post_hegith_right\n\n else:\n height_R -= post_hegith_right \n\n ball_height_list = [height_L, height_R]\n ball_distance_list = [D_L, D_R]\n\n height = sum(ball_height_list) / 2 - post_hegith_avg\n\n ball2net_length_x_L = ball_distance_list[0] * np.sin(theta_L)\n ball_position_y_L = ball_distance_list[0] * np.cos(theta_L)\n\n ball_plate_angle_L = np.arcsin(height / ball2net_length_x_L)\n\n ball_position_x_L = ball2net_length_x_L * np.cos(ball_plate_angle_L)\n\n ball2net_length_x_R = ball_distance_list[1] * np.sin(theta_R)\n ball_position_y_R = ball_distance_list[1] * np.cos(theta_R)\n\n ball_plate_angle_R = np.arcsin(height / ball2net_length_x_R)\n\n ball_position_x_R = ball2net_length_x_R * np.cos(ball_plate_angle_R)\n\n if theta_L > theta_R:\n ball_position_y = ball_position_y_L - (net_length / 2)\n\n else :\n ball_position_y = (net_length / 2) - ball_position_y_R\n\n\n print(L_pos[i],R_pos[j])\n #print([D_L, D_R, height_L, height_R])\n print([-ball_position_x_L, ball_position_y, height + post_hegith_avg])\n\n depth_height.append([[D_L, D_R], [height_L, height_R]])\n\n return [-ball_position_x_L, ball_position_y, height + post_hegith_avg]", "_____no_output_____" ], [ "ball_cen_left = [[298, 153]]\nball_cen_right = [[319, 160]]\n\nball_stats_list = get_ball_pos(ball_cen_left,ball_cen_right)", "[298, 153] [319, 160]\n[-6.66652032789926, -2.0337485082041438, 2.493953152765253]\n" ], [ "def check_vel_noise():\n\n y_vel_list = np.array(esti_ball_val_list)[:,1]\n\n\n if len(y_vel_list) > 3 :\n\n vel_mean = np.mean(y_vel_list)\n\n if abs(abs(vel_mean) - abs(y_vel_list[-1])) > 2:\n\n vel_mean = np.mean(y_vel_list[:-1])\n esti_ball_val_list[-1][1] = vel_mean\n\n return esti_ball_val_list[-1]\n\n else:\n return esti_ball_val_list[-1]\n", "_____no_output_____" ], [ "def cal_landing_point(pos):\n\n t_list = []\n\n #vel = self.check_vel_noise()\n\n x0, y0, z0 = pos[0], pos[1], pos[2]\n vx, vy, vz = vel[0], vel[1], vel[2]\n\n a = -((0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vz ** 2 ) / 0.057 + 9.8 / 2 )\n b = vz\n c = z0\n\n t_list.append((-b + np.sqrt(b ** 2 - 4 * a * c))/(2 * a))\n t_list.append((-b - np.sqrt(b ** 2 - 4 * a * c))/(2 * a))\n\n t = max(t_list)\n\n x = np.array(x0 + vx * t - (0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vx ** 2 ) * (t ** 2) / 0.057,float)\n y = np.array(y0 + vy * t - (0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vy ** 2 ) * (t ** 2) / 0.057,float)\n z = np.array(z0 + vz * t - ((0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vz ** 2 ) / 0.057 + 9.8 / 2) * (t ** 2),float)\n\n return [np.round(x,3), np.round(y,3), np.round(z,3)]", "_____no_output_____" ], [ "cal_landing_point(cal_landing_point)", "_____no_output_____" ], [ "for i in range(3):\n for j in range(4):\n print(j)\n if j == 3:\n break\n \n ", "0\n1\n2\n3\n0\n1\n2\n3\n0\n1\n2\n3\n" ], [ "\nclass Ball_Pos_Estimation():\n\n def __init__(self):\n self.pre_ball_cen_left_list = []\n self.pre_ball_cen_right_list = []\n\n\n\n def check_ball_move_update(self, ball_cen_left_list, ball_cen_right_list):\n\n self.swing_check = True\n\n left_flag = False\n right_flag = False\n\n self.ball_cen_left_list = ball_cen_left_list\n self.ball_cen_right_list = ball_cen_right_list\n\n if len(self.pre_ball_cen_left_list) or len(self.pre_ball_cen_right_list):\n \n for i in range(len(self.ball_cen_left_list)):\n\n if left_flag:\n break \n\n x_cen = self.ball_cen_left_list[i][0]\n \n for j in range(len(self.pre_ball_cen_left_list)):\n\n pre_x_cen = self.pre_ball_cen_left_list[j][0]\n \n if x_cen > pre_x_cen:\n\n self.pre_ball_cen_left_list = self.ball_cen_left_list\n left_flag = True\n break\n \n for i in range(len(self.ball_cen_right_list)):\n\n if right_flag:\n break \n\n x_cen = self.ball_cen_right_list[i][0]\n \n for j in range(len(self.pre_ball_cen_right_list)):\n\n pre_x_cen = self.pre_ball_cen_right_list[j][0]\n\n if x_cen < pre_x_cen:\n\n self.pre_ball_cen_right_list = self.ball_cen_right_list\n right_flag = True\n \n break\n \n if left_flag == False and right_flag == False : \n self.pre_ball_cen_left_list = []\n self.pre_ball_cen_right_list = []\n self.swing_check = False\n return False\n\n return True\n\n else:\n self.pre_ball_cen_left_list = self.ball_cen_left_list\n self.pre_ball_cen_right_list = self.ball_cen_right_list\n\n self.swing_check = False\n\n return True", "_____no_output_____" ], [ "estimation_ball = Ball_Pos_Estimation()", "_____no_output_____" ], [ "ball_cen_left = [[419, 151]]\nball_cen_right = [[201, 153]]\n\nif estimation_ball.check_ball_move_update(ball_cen_left, ball_cen_right):\n pass\n\nprint(estimation_ball.swing_check)", "False\n" ], [ "ball_cen_left = [[392, 160]]\nball_cen_right = [[223, 160]]\n\nif estimation_ball.check_ball_move_update(ball_cen_left, ball_cen_right):\n pass\n\nprint(estimation_ball.swing_check)", "False\n" ], [ "ball_cen_left = [[281, 194], [716, 230]]\nball_cen_right = []\n\nestimation_ball.check_ball_move_update(ball_cen_left, ball_cen_right)", "_____no_output_____" ], [ "ball_pos_list = [np.nan,np.nan,np.nan]", "_____no_output_____" ], [ "np.isnan(ball_pos_list[0])", "_____no_output_____" ], [ "a = 2\n\nif a == 1:\n print(1)\n \nelif a == 2:\n print(2)", "2\n" ], [ "\n\ndef fx(x, dt):\n # state transition function - predict next state based\n # on constant velocity model x = vt + x_0\n\n F = np.matrix([[1.0, 0.0, 0.0, dt, 0.0, 0.0, 1/2.0*dt**2, 0.0, 0.0],\n [0.0, 1.0, 0.0, 0.0, dt, 0.0, 0.0, 1/2.0*dt**2, 0.0],\n [0.0, 0.0, 1.0, 0.0, 0.0, dt, 0.0, 0.0, 1/2.0*dt**2],\n [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, dt, 0.0, 0.0],\n [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, dt, 0.0],\n [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, dt],\n [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],\n [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],\n [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])\n\n return np.dot(F,x)\n\ndef hx(x):\n # measurement function - convert state into a measurement\n # where measurements are [x_pos, y_pos]\n\n \n H = np.matrix([[1.0, 0.0, 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, 0.0],\n [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])\n \n return np.array([x[0], x[3], x[6]])\n\nclass UK_filter():\n\n def __init__(self, dt, std_acc, x_std_meas, y_std_meas,z_std_meas, init_x, init_y, init_z): \n \n self.init_x = init_x \n self.init_y = init_y\n self.init_z = init_z\n \n self.dt = dt\n self.z_std = 0.1\n\n self.points = MerweScaledSigmaPoints(9, alpha=.1, beta=2., kappa=-1)\n self.f = UnscentedKalmanFilter(dim_x=9, dim_z=3, dt=self.dt, fx=fx, hx=hx, points=self.points)\n\n self.f.x = np.array([self.init_x,0,0,self.init_y,0,0, self.init_z, 0,0])\n\n\n self.f.P = np.eye(9)\n\n self.f.Q = np.matrix([[(dt**6)/36, 0, 0, (dt**5)/12, 0, 0, (dt**4)/6, 0, 0],\n [0, (dt**6)/36, 0, 0, (dt**5)/12, 0, 0, (dt**4)/6, 0],\n [0, 0, (dt**6)/36, 0, 0, (dt**5)/12, 0, 0, (dt**4)/6],\n [(dt**5)/12, 0, 0, (dt**4)/4, 0, 0, (dt**3)/2, 0, 0],\n [0, (dt**5)/12, 0, 0, (dt**4)/4, 0, 0, (dt**3)/2, 0],\n [0, 0, (dt**5)/12, 0, 0, (dt**4)/4, 0, 0, (dt**3)/2],\n [(dt**4)/6, 0, 0, (dt**3)/2, 0, 0, (dt**2), 0, 0],\n [0, (dt**4)/6, 0, 0, (dt**3)/2, 0, 0, (dt**2), 0],\n [0, 0, (dt**4)/6, 0, 0, (dt**3)/2, 0, 0, (dt**2)]]) *std_acc**2\n\n\n #self.f.Q = Q_discrete_white_noise(2, dt = self.dt, var = 0.01**2, block_size = 2)\n\n\n self.f.R = np.array([[x_std_meas**2, 0, 0],\n [0, y_std_meas**2, 0],\n [0, 0, z_std_meas**2]])\n\n #self.f.predict()", "_____no_output_____" ], [ "a = UK_filter(dt = 0.1, std_acc = 10, x_std_meas = 1, y_std_meas = 1,z_std_meas = 1,\n init_x = 0, init_y = 0, init_z = 0)", "_____no_output_____" ], [ "a.f.predict()", "_____no_output_____" ], [ "a.f.update([1,1,1])", "_____no_output_____" ], [ "a.f.x.reshape([3,3])", "_____no_output_____" ], [ "a.f.update([2,2,2])", "_____no_output_____" ], [ "a.f.update([10,10,10])", "_____no_output_____" ], [ "a.f.update([20,20,20])", "_____no_output_____" ], [ "a.f.update([30,30,30])", "_____no_output_____" ], [ "ball_cand_trajectory = [[], [[[-8.499031225886979, 0.311750401425118, 1.404671677765355]]], [[[-7.654383459951477, 0.37339492038427213, 1.528884790477008]]], [[[-6.812550514254101, 0.4358689710714039, 1.6515142171759307]]], [[[-6.005829995239098, 0.5240465335704965, 1.7282085158171043]]], [[[-5.174512338451282, 0.5984737933774342, 1.802634978706663]]], [[[-4.387167345972433, 0.6819632256971389, 1.8761140620728325]]], [[[-3.625711866264724, 0.7480947422039854, 1.9102820296223895]]], [[[-2.8421647085007566, 0.8618655406628255, 1.950691295218776]]], [[[-2.058537458662639, 1.064584863759384, 1.9592570246492662]]], [[]], [[]], [[]]]", "_____no_output_____" ], [ "len(ball_cand_trajectory)", "_____no_output_____" ], [ "ball_cand_trajectory[3][0][0]", "_____no_output_____" ], [ "ball_pos_list = [[-1,0,0], [1,3,4],[2,4,5]]", "_____no_output_____" ], [ "b = np.array(ball_pos_list)", "_____no_output_____" ], [ "b[:,0].argmin()", "_____no_output_____" ], [ "b[:][0]", "_____no_output_____" ], [ "a = [0, 0, 0]\nb = [1, 2, 3]\n\n\n\ndef get_distance(point_1, point_2):\n\n return (np.sqrt((point_2[0]-point_1[0])**2 + (point_2[1]-point_1[1])**2 + (point_2[2]-point_1[2])**2))\n", "_____no_output_____" ], [ "get_distance(a,b)", "_____no_output_____" ], [ "ball_pos_list = [[-7.923465928004007, -0.6755867599611189, 2.580941671512611]]", "_____no_output_____" ], [ "x_pos, y_pos, z_pos = ball_pos_list[np.array(ball_pos_list)[:,0].argmin()]\n", "_____no_output_____" ], [ "np.array(ball_pos_list)[:,0].argmin()\n", "_____no_output_____" ], [ "x_pos, y_pos, z_pos", "_____no_output_____" ], [ "a = np.array([[1,2,3],[0,-5,0],[-8,0,0]])\n", "_____no_output_____" ], [ "a.append([[1,2],[3,4]])", "_____no_output_____" ], [ "a", "_____no_output_____" ], [ "from kalman_utils.KFilter import *", "_____no_output_____" ], [ "dT = 1 / 25", "_____no_output_____" ], [ "ball_pos = [-9.285799665284836, -1.5959832449913565, 2.874695965876609]", "_____no_output_____" ], [ "kf = Kalman_filiter(ball_pos[0], ball_pos[1], ball_pos[2], dT)", "nstates 7\ntransitionMatrix: shape:(7, 7)\n[[1. 0. 0. 0. 0. 0. 0.]\n [0. 1. 0. 0. 0. 0. 0.]\n [0. 0. 1. 0. 0. 0. 0.]\n [0. 0. 0. 1. 0. 0. 0.]\n [0. 0. 0. 0. 1. 0. 0.]\n [0. 0. 0. 0. 0. 1. 0.]\n [0. 0. 0. 0. 0. 0. 1.]]\nmeasurementMatrix: shape:(3, 7)\n[[1. 0. 0. 0. 0. 0. 0.]\n [0. 1. 0. 0. 0. 0. 0.]\n [0. 0. 1. 0. 0. 0. 0.]]\nprocessNoiseCov: shape:(7, 7)\n[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n [0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]\n [0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]\n [0.e+00 0.e+00 0.e+00 8.e+00 0.e+00 0.e+00 0.e+00]\n [0.e+00 0.e+00 0.e+00 0.e+00 8.e+00 0.e+00 0.e+00]\n [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 8.e+00 0.e+00]\n [0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]\nmeasurementNoiseCov: shape:(3, 3)\n[[1.e-06 0.e+00 0.e+00]\n [0.e+00 1.e-06 0.e+00]\n [0.e+00 0.e+00 1.e-06]]\nstatePost: shape:(7, 1)\n[[-9.2858 ]\n [-1.5959833]\n [ 2.874696 ]\n [ 0.1 ]\n [ 0.1 ]\n [ 0.1 ]\n [ 9.801 ]]\n1111\n" ], [ "kf.get_predict()", "_____no_output_____" ], [ "ball_pos_list = [[-8.3324296128703, -1.426689529754115, 2.8019436403665923],[-7.459506668999277, -1.286501720742379, 2.720148095378055],[-6.540641694266555, -1.135716227096026, 2.6019241593327513],[-5.68329300302514, -0.9730271651650142, 2.519130845990981]]", "_____no_output_____" ], [ "kf.update(ball_pos[0], ball_pos[1], ball_pos[2], dT)", "dT: 0.4000\n---------------------------------------------------\nmeas current : [-8.33243 -1.4266895 2.8019435]\npred predicted : [-8.789115 -1.4913363 2.8583198]\n\n" ], [ "ball_pos = ball_pos_list.pop(0)\nkf.update(ball_pos[0], ball_pos[1], ball_pos[2], dT)", "dT: 0.4000\n---------------------------------------------------\nmeas current : [-7.4595065 -1.2865018 2.720148 ]\npred predicted : [-7.4595075 -1.2865019 2.7201471]\n\n" ], [ "ball_pos = ball_pos_list.pop(0)\nkf.update(ball_pos[0], ball_pos[1], ball_pos[2], dT)", "dT: 0.4000\n---------------------------------------------------\nmeas current : [-6.540642 -1.1357162 2.6019242]\npred predicted : [-6.5406413 -1.1357162 2.601923 ]\n\n" ], [ "ball_pos = ball_pos_list.pop(0)\nkf.update(ball_pos[0], ball_pos[1], ball_pos[2], dT)", "dT: 0.4000\n---------------------------------------------------\nmeas current : [-5.683293 -0.97302717 2.519131 ]\npred predicted : [-5.683293 -0.97302717 2.5191298 ]\n\n" ], [ "kf.predict(dT)", "dT: 0.4000\n---------------------------------------------------\nmeas current : None (only predicting)\npred predicted without meas: [-4.8259444 -0.8103382 0.8681732]\n\n" ], [ "kf.predict(dT)", "dT: 0.4000\n---------------------------------------------------\nmeas current : None (only predicting)\npred predicted without meas: [-3.968596 -0.6476492 -2.3509436]\n\n" ], [ "kf.get_predict()", "_____no_output_____" ], [ "kf.KF.getPostState()", "_____no_output_____" ], [ "np .array(ball_pos_list)[:,1][:-1]", "_____no_output_____" ], [ "np.mean(np.array(ball_pos_list)[:,1][:-1])", "_____no_output_____" ], [ "a = [[[-9.039131345617234, -0.8137119203553631, 2.7444703070749017]], [[-7.900829857194978, -0.654493068471937, 2.6104207999239812]], [[-6.849655790366586, -0.5241522117656086, 2.4875451129360613]], [[-5.793390039377407, -0.39658750289812605, 2.3529254544909817]]]\n", "_____no_output_____" ], [ "b = np.array(a).reshape([-1,3])\nb", "_____no_output_____" ], [ "sum(np.diff(b[:,1])) / 0.04", "_____no_output_____" ], [ "for i in []:\n print(1)", "_____no_output_____" ], [ "ball_cand_pos = [[-9.03913135, -0.81371192, 2.74447031],\n [-7.90082986, -0.65449307, 2.6104208 ],\n [-6.84965579, -0.52415221, 2.48754511],\n [-5.79339004, -0.3965875 , 2.35292545]]\n\ndel_list = [1,2,3]\n\na= np.array(ball_cand_pos)\n\nnp.delete(a,del_list,axis = 0)", "_____no_output_____" ], [ "ball_cand_pos.pop(tuple(del_list))", "_____no_output_____" ], [ "tuple(del_list)", "_____no_output_____" ], [ "if 3 == 3:\n print(1)", "1\n" ], [ "estimation_ball_trajectory_list = np.array([[-8.075992233062022, -2.0712591029119727, 2.0143476038492176], [-7.041614424760605, -1.9654479963268496, 1.998508488845969], [-6.065044696824322, -1.879832764271466, 1.9635872373067076], [-5.107183755941063, -1.7968180783990304, 1.925614986159784], [-4.169832727705863, -1.6838107370497974, 1.884759164010521], [-3.2611958072772738, -1.5644043096331428, 1.81101188769961], [-2.368649316849956, -1.4805807124989778, 1.737028408823873], [-1.4834200661316506, -1.378965700223568, 1.6496367839315553], [-0.7006541039923906, -1.2862463955187806, 1.5580097756344515]])\nestimation_ball_trajectory_list", "_____no_output_____" ], [ "x_pos_list = estimation_ball_trajectory_list[:,0]\ny_pos_list = estimation_ball_trajectory_list[:,1]\nz_pos_list = estimation_ball_trajectory_list[:,2]\n", "_____no_output_____" ], [ "np.diff(estimation_ball_trajectory_list)", "_____no_output_____" ], [ "x_pos_list\nnp.diff(x_pos_list)[-1]", "_____no_output_____" ], [ "def cal_landing_point(pos_list):\n\n t_list = []\n\n if len(pos_list) < 4 : return [np.nan, np.nan, np.nan]\n\n pos = pos_list[-1]\n\n x0, y0, z0 = pos[0], pos[1], pos[2]\n\n vx, vy, vz = get_velocity(pos_list)\n\n a = -((0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vz ** 2 ) / 0.057 + 9.8 / 2 )\n b = vz\n c = z0\n\n t_list.append((-b + np.sqrt(b ** 2 - 4 * a * c))/(2 * a))\n t_list.append((-b - np.sqrt(b ** 2 - 4 * a * c))/(2 * a))\n\n t = max(t_list)\n \n x = np.array(x0 + vx * t - (0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vx ** 2 ) * (t ** 2) / 0.057,float)\n y = np.array(y0 + vy * t - (0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vy ** 2 ) * (t ** 2) / 0.057,float)\n z = np.array(z0 + vz * t - ((0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vz ** 2 ) / 0.057 + 9.8 / 2) * (t ** 2),float)\n \n return [np.round(x,3), np.round(y,3), np.round(z,3)]\n\n\ndef get_velocity(pos_list):\n \n t = 1 / 30\n \n np_pos_list = np.array(pos_list)\n\n x_pos_list = np_pos_list[:,0]\n y_pos_list = np_pos_list[:,1]\n z_pos_list = np_pos_list[:,2] \n\n vel_x_list = np.diff(x_pos_list) / t\n vel_y_list = np.diff(y_pos_list) / t\n vel_z_list = np.diff(z_pos_list) / t\n\n return vel_x_list[-1], vel_y_list[-1], vel_z_list[-1]\n", "_____no_output_____" ], [ "ball_pos_jrajectory = [[-7.654350032583985, 0.37375046201544926, 1.5602039272816657], [-6.812516855329211, 0.4362023210314696, 1.6809758292885233], [-6.005632695613204, 0.5230876456730043, 1.7703496818844444], [-5.17434312120148, 0.5969135946437563, 1.842121410014573], [-4.40319049584631, 0.6610575247176333, 1.90162399321874], [-3.6256202508366666, 0.7485738124651196, 1.9551274411299535], [-2.8420151676001075, 0.8677493333923483, 1.980619130934988], [-2.058931199304543, 0.9710952377297057, 1.9892246369063118], [-1.3541772365570068, 1.0641107559204102, 1.9812870025634766], [-0.7198931574821472, 1.1478245258331299, 1.9584616422653198], [-0.14903749525547028, 1.223166823387146, 1.9222372770309448]]\nball_pos_jrajectory", "_____no_output_____" ], [ "cal_landing_point(ball_pos_jrajectory)", "_____no_output_____" ], [ "a = np.array([ -3.1019, -2.3294, -1.513])", "_____no_output_____" ], [ "np.mean(np.diff(a))/(1/25)", "_____no_output_____" ], [ "np.diff(a)/(1/25)", "_____no_output_____" ], [ "x0, y0, z0 = -0.14778512716293335, -3.731870412826538, 2.6436607837677\nvx, vy, vz = 13.437911868095398, 0.15355348587036133, 0.08598566055297852\n\n", "_____no_output_____" ], [ "t_list = []\na = -((0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vz ** 2 ) / 0.057 + 9.8 / 2 )\nb = vz\nc = z0\n\nt_list.append((-b + np.sqrt(b ** 2 - 4 * a * c))/(2 * a))\nt_list.append((-b - np.sqrt(b ** 2 - 4 * a * c))/(2 * a))\n\nt = max(t_list)\n\ndrag_x = (0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vx ** 2 )\ndrag_y = (0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vy ** 2 )\ndrag_z = (0.5 * 0.507 * 1.2041 * np.pi * (0.033 ** 2) * vz ** 2 )\n\ndrag_x = 0\ndrag_y = 0\ndrag_z = 0\n\nx = np.array(x0 + vx * t - drag_x * (t ** 2) / 0.057,float)\ny = np.array(y0 + vy * t - drag_y * (t ** 2) / 0.057,float)\nz = np.array(z0 + vz * t - (drag_z / 0.057 + 9.8 / 2) * (t ** 2),float)\n\n[np.round(x,3), np.round(y,3), np.round(z,3)]\n", "_____no_output_____" ], [ "t_list", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import geopandas as gpd\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport swifter\nimport matplotlib.pyplot as plt\nimport rasterio\nimport datetime\nimport os, shutil\nfrom joblib import delayed, Parallel\nimport tqdm", "_____no_output_____" ] ], [ [ "### Settings ", "_____no_output_____" ] ], [ [ "# Slumps\nINFERENCE_DIR = Path(r'Q:\\p_aicore_pf\\initze\\processed\\inference\\RTS_6Regions_V01_UnetPlusPlus_resnet34_FocalLoss_sh6_50_bs100_2021-12-04_22-27-53')\n# Pingo\n#INFERENCE_DIR = Path(r'Q:\\p_aicore_pf\\initze\\processed\\inference\\pingo_UnetPP_v1_2021-12-12_09-56-50')\n\nOUTPUT_DIR = Path(r'C:\\Users\\initze\\OneDrive\\100_AI-CORE\\16_inference_statistics')\nout_file = OUTPUT_DIR / f'{INFERENCE_DIR.stem}_merged_datasets.shp'", "_____no_output_____" ], [ "print(out_file)", "_____no_output_____" ], [ "def get_vector(f):\n gdf = gpd.read_file(f).to_crs(epsg=4326)\n gdf['id_local'] = gdf.index\n gdf['dataset'] = f.stem\n gdf['model'] = f.parts[-2]\n split = f.stem.split('_')\n if len(split)==4:\n gdf[['scene', 'tile_id', 'date', 'sensor']] = split\n else:\n gdf[['date', 'scene', 'sensor']] = split\n return gdf\n\ndef load_dataset(f):\n try:\n return get_vector(f)\n except:\n print(f'Error on {f.stem}')", "_____no_output_____" ] ], [ [ "### create filelist", "_____no_output_____" ] ], [ [ "flist = list(INFERENCE_DIR.glob('*'))", "_____no_output_____" ] ], [ [ "#### Load files and add to list ", "_____no_output_____" ] ], [ [ "%time ds_list = Parallel(n_jobs=10)(delayed(load_dataset)(f) for f in tqdm.tqdm_notebook(flist[:]))", "_____no_output_____" ], [ "ds_list = []\nfor f in flist[:]:\n try:\n ds_list.append(get_vector(f))\n except:\n print(f'Error on {f.stem}')", "_____no_output_____" ] ], [ [ "#### Merge all GDF to one ", "_____no_output_____" ] ], [ [ "rdf = gpd.GeoDataFrame( pd.concat( ds_list, ignore_index=True) )", "_____no_output_____" ] ], [ [ "#### Set projection (got lost during merge with pandas) ", "_____no_output_____" ] ], [ [ "rdf = rdf.set_crs(epsg=4326)", "_____no_output_____" ] ], [ [ "#### Calculate time variables for later analysis ", "_____no_output_____" ] ], [ [ "rdf['year'] = pd.to_datetime(rdf.iloc[:]['date'], infer_datetime_format=True).dt.year\nrdf['month'] = pd.to_datetime(rdf.iloc[:]['date'], infer_datetime_format=True).dt.month\nrdf['doy'] = pd.to_datetime(rdf.iloc[:]['date'], infer_datetime_format=True).dt.day_of_year", "_____no_output_____" ] ], [ [ "#### Write to file", "_____no_output_____" ] ], [ [ "rdf.to_file(out_file)", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "Copyright (c) Microsoft Corporation. All rights reserved. \nLicensed under the MIT License.", "_____no_output_____" ], [ "", "_____no_output_____" ], [ "# How to Publish a Pipeline and Invoke the REST endpoint\nIn this notebook, we will see how we can publish a pipeline and then invoke the REST endpoint.", "_____no_output_____" ], [ "## Prerequisites and Azure Machine Learning Basics\nIf you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration Notebook](https://aka.ms/pl-config) first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n\n### Initialization Steps", "_____no_output_____" ] ], [ [ "import azureml.core\nfrom azureml.core import Workspace, Datastore, Experiment, Dataset\nfrom azureml.core.compute import AmlCompute\nfrom azureml.core.compute import ComputeTarget\n\n# Check core SDK version number\nprint(\"SDK version:\", azureml.core.VERSION)\n\nfrom azureml.data.data_reference import DataReference\nfrom azureml.pipeline.core import Pipeline, PipelineData\nfrom azureml.pipeline.steps import PythonScriptStep\nfrom azureml.pipeline.core.graph import PipelineParameter\n\nprint(\"Pipeline SDK-specific imports completed\")\n\nws = Workspace.from_config()\nprint(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n\n# Default datastore (Azure blob storage)\n# def_blob_store = ws.get_default_datastore()\ndef_blob_store = Datastore(ws, \"workspaceblobstore\")\nprint(\"Blobstore's name: {}\".format(def_blob_store.name))", "_____no_output_____" ] ], [ [ "### Compute Targets\n#### Retrieve an already attached Azure Machine Learning Compute", "_____no_output_____" ] ], [ [ "from azureml.core.compute_target import ComputeTargetException\n\naml_compute_target = \"cpu-cluster\"\ntry:\n aml_compute = AmlCompute(ws, aml_compute_target)\n print(\"found existing compute target.\")\nexcept ComputeTargetException:\n print(\"creating new compute target\")\n \n provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n min_nodes = 1, \n max_nodes = 4) \n aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n", "_____no_output_____" ], [ "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n# example: un-comment the following line.\n# print(aml_compute.get_status().serialize())", "_____no_output_____" ] ], [ [ "## Building Pipeline Steps with Inputs and Outputs\nA step in the pipeline can take [dataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) as input. This dataset can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline.", "_____no_output_____" ] ], [ [ "# Uploading data to the datastore\ndata_path = def_blob_store.upload_files([\"./20news.pkl\"], target_path=\"20newsgroups\", overwrite=True)", "_____no_output_____" ], [ "# Reference the data uploaded to blob storage using file dataset\n# Assign the datasource to blob_input_data variable\nblob_input_data = Dataset.File.from_files(data_path).as_named_input(\"test_data\")\nprint(\"Dataset created\")", "_____no_output_____" ], [ "# Define intermediate data using PipelineData\nprocessed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\nprint(\"PipelineData object created\")", "_____no_output_____" ] ], [ [ "#### Define a Step that consumes a dataset and produces intermediate data.\nIn this step, we define a step that consumes a dataset and produces intermediate data.\n\n**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** \n\nThe best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.", "_____no_output_____" ] ], [ [ "# trainStep consumes the datasource (Datareference) in the previous step\n# and produces processed_data1\n\nsource_directory = \"publish_run_train\"\n\ntrainStep = PythonScriptStep(\n script_name=\"train.py\", \n arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n inputs=[blob_input_data],\n outputs=[processed_data1],\n compute_target=aml_compute, \n source_directory=source_directory\n)\nprint(\"trainStep created\")", "_____no_output_____" ] ], [ [ "#### Define a Step that consumes intermediate data and produces intermediate data\nIn this step, we define a step that consumes an intermediate data and produces intermediate data.\n\n**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** ", "_____no_output_____" ] ], [ [ "# extractStep to use the intermediate data produced by step4\n# This step also produces an output processed_data2\nprocessed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\nsource_directory = \"publish_run_extract\"\n\nextractStep = PythonScriptStep(\n script_name=\"extract.py\",\n arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n inputs=[processed_data1],\n outputs=[processed_data2],\n compute_target=aml_compute, \n source_directory=source_directory)\nprint(\"extractStep created\")", "_____no_output_____" ] ], [ [ "#### Define a Step that consumes multiple intermediate data and produces intermediate data\nIn this step, we define a step that consumes multiple intermediate data and produces intermediate data.", "_____no_output_____" ], [ "### PipelineParameter", "_____no_output_____" ], [ "This step also has a [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.pipelineparameter?view=azure-ml-py) argument that help with calling the REST endpoint of the published pipeline.", "_____no_output_____" ] ], [ [ "# We will use this later in publishing pipeline\npipeline_param = PipelineParameter(name=\"pipeline_arg\", default_value=10)\nprint(\"pipeline parameter created\")", "_____no_output_____" ] ], [ [ "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**", "_____no_output_____" ] ], [ [ "# Now define step6 that takes two inputs (both intermediate data), and produce an output\nprocessed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\nsource_directory = \"publish_run_compare\"\n\ncompareStep = PythonScriptStep(\n script_name=\"compare.py\",\n arguments=[\"--compare_data1\", processed_data1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param],\n inputs=[processed_data1, processed_data2],\n outputs=[processed_data3], \n compute_target=aml_compute, \n source_directory=source_directory)\nprint(\"compareStep created\")", "_____no_output_____" ] ], [ [ "#### Build the pipeline", "_____no_output_____" ] ], [ [ "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\nprint (\"Pipeline is built\")", "_____no_output_____" ] ], [ [ "## Run published pipeline\n### Publish the pipeline", "_____no_output_____" ] ], [ [ "published_pipeline1 = pipeline1.publish(name=\"My_New_Pipeline\", description=\"My Published Pipeline Description\", continue_on_step_failure=True)\npublished_pipeline1", "_____no_output_____" ] ], [ [ "Note: the continue_on_step_failure parameter specifies whether the execution of steps in the Pipeline will continue if one step fails. The default value is False, meaning when one step fails, the Pipeline execution will stop, canceling any running steps.", "_____no_output_____" ], [ "### Publish the pipeline from a submitted PipelineRun\nIt is also possible to publish a pipeline from a submitted PipelineRun", "_____no_output_____" ] ], [ [ "# submit a pipeline run\npipeline_run1 = Experiment(ws, 'Pipeline_experiment').submit(pipeline1)\n# publish a pipeline from the submitted pipeline run\npublished_pipeline2 = pipeline_run1.publish_pipeline(name=\"My_New_Pipeline2\", description=\"My Published Pipeline Description\", version=\"0.1\", continue_on_step_failure=True)\npublished_pipeline2", "_____no_output_____" ] ], [ [ "### Get published pipeline\n\nYou can get the published pipeline using **pipeline id**.\n\nTo get all the published pipelines for a given workspace(ws): \n```css\nall_pub_pipelines = PublishedPipeline.get_all(ws)\n```", "_____no_output_____" ] ], [ [ "from azureml.pipeline.core import PublishedPipeline\n\npipeline_id = published_pipeline1.id # use your published pipeline id\npublished_pipeline = PublishedPipeline.get(ws, pipeline_id)\npublished_pipeline", "_____no_output_____" ] ], [ [ "### Run published pipeline using its REST endpoint\n[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace.", "_____no_output_____" ] ], [ [ "from azureml.core.authentication import InteractiveLoginAuthentication\nimport requests\n\nauth = InteractiveLoginAuthentication()\naad_token = auth.get_authentication_header()\n\nrest_endpoint1 = published_pipeline.endpoint\n\nprint(\"You can perform HTTP POST on URL {} to trigger this pipeline\".format(rest_endpoint1))\n\n# specify the param when running the pipeline\nresponse = requests.post(rest_endpoint1, \n headers=aad_token, \n json={\"ExperimentName\": \"My_Pipeline1\",\n \"RunSource\": \"SDK\",\n \"ParameterAssignments\": {\"pipeline_arg\": 45}})", "_____no_output_____" ], [ "try:\n response.raise_for_status()\nexcept Exception: \n raise Exception('Received bad response from the endpoint: {}\\n'\n 'Response Code: {}\\n'\n 'Headers: {}\\n'\n 'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n\nrun_id = response.json().get('Id')\nprint('Submitted pipeline run: ', run_id)", "_____no_output_____" ] ], [ [ "# Next: Data Transfer\nThe next [notebook](https://aka.ms/pl-data-trans) will showcase data transfer steps between different types of data stores.", "_____no_output_____" ] ] ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "## LIDAR to 2D grid map example\n\nThis simple tutorial shows how to read LIDAR (range) measurements from a file and convert it to occupancy grid.\n\nOccupancy grid maps (_Hans Moravec, A.E. Elfes: High resolution maps from wide angle sonar, Proc. IEEE Int. Conf. Robotics Autom. (1985)_) are a popular, probabilistic approach to represent the environment. The grid is basically discrete representation of the environment, which shows if a grid cell is occupied or not. Here the map is represented as a `numpy array`, and numbers close to 1 means the cell is occupied (_marked with red on the next image_), numbers close to 0 means they are free (_marked with green_). The grid has the ability to represent unknown (unobserved) areas, which are close to 0.5.\n\n\n\n\nIn order to construct the grid map from the measurement we need to discretise the values. But, first let's need to `import` some necessary packages.", "_____no_output_____" ] ], [ [ "import math\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom math import cos, sin, radians, pi", "_____no_output_____" ] ], [ [ "The measurement file contains the distances and the corresponding angles in a `csv` (comma separated values) format. Let's write the `file_read` method:", "_____no_output_____" ] ], [ [ "def file_read(f):\n \"\"\"\n Reading LIDAR laser beams (angles and corresponding distance data)\n \"\"\"\n measures = [line.split(\",\") for line in open(f)]\n angles = []\n distances = []\n for measure in measures:\n angles.append(float(measure[0]))\n distances.append(float(measure[1]))\n angles = np.array(angles)\n distances = np.array(distances)\n return angles, distances", "_____no_output_____" ] ], [ [ "From the distances and the angles it is easy to determine the `x` and `y` coordinates with `sin` and `cos`. \nIn order to display it `matplotlib.pyplot` (`plt`) is used.", "_____no_output_____" ] ], [ [ "ang, dist = file_read(\"lidar01.csv\")\nox = np.sin(ang) * dist\noy = np.cos(ang) * dist\nplt.figure(figsize=(6,10))\nplt.plot([oy, np.zeros(np.size(oy))], [ox, np.zeros(np.size(oy))], \"ro-\") # lines from 0,0 to the \nplt.axis(\"equal\")\nbottom, top = plt.ylim() # return the current ylim\nplt.ylim((top, bottom)) # rescale y axis, to match the grid orientation\nplt.grid(True)\nplt.show()", "_____no_output_____" ] ], [ [ "The `lidar_to_grid_map.py` contains handy functions which can used to convert a 2D range measurement to a grid map. For example the `bresenham` gives the a straight line between two points in a grid map. Let's see how this works.", "_____no_output_____" ] ], [ [ "import lidar_to_grid_map as lg\nmap1 = np.ones((50, 50)) * 0.5\nline = lg.bresenham((2, 2), (40, 30))\nfor l in line:\n map1[l[0]][l[1]] = 1\nplt.imshow(map1)\nplt.colorbar()\nplt.show()", "_____no_output_____" ], [ "line = lg.bresenham((2, 30), (40, 30))\nfor l in line:\n map1[l[0]][l[1]] = 1\nline = lg.bresenham((2, 30), (2, 2))\nfor l in line:\n map1[l[0]][l[1]] = 1\nplt.imshow(map1)\nplt.colorbar()\nplt.show()", "_____no_output_____" ] ], [ [ "To fill empty areas, a queue-based algorithm can be used that can be used on an initialized occupancy map. The center point is given: the algorithm checks for neighbour elements in each iteration, and stops expansion on obstacles and free boundaries.", "_____no_output_____" ] ], [ [ "from collections import deque\ndef flood_fill(cpoint, pmap):\n \"\"\"\n cpoint: starting point (x,y) of fill\n pmap: occupancy map generated from Bresenham ray-tracing\n \"\"\"\n # Fill empty areas with queue method\n sx, sy = pmap.shape\n fringe = deque()\n fringe.appendleft(cpoint)\n while fringe:\n n = fringe.pop()\n nx, ny = n\n # West\n if nx > 0:\n if pmap[nx - 1, ny] == 0.5:\n pmap[nx - 1, ny] = 0.0\n fringe.appendleft((nx - 1, ny))\n # East\n if nx < sx - 1:\n if pmap[nx + 1, ny] == 0.5:\n pmap[nx + 1, ny] = 0.0\n fringe.appendleft((nx + 1, ny))\n # North\n if ny > 0:\n if pmap[nx, ny - 1] == 0.5:\n pmap[nx, ny - 1] = 0.0\n fringe.appendleft((nx, ny - 1))\n # South\n if ny < sy - 1:\n if pmap[nx, ny + 1] == 0.5:\n pmap[nx, ny + 1] = 0.0\n fringe.appendleft((nx, ny + 1))", "_____no_output_____" ] ], [ [ "This algotihm will fill the area bounded by the yellow lines starting from a center point (e.g. (10, 20)) with zeros:", "_____no_output_____" ] ], [ [ "flood_fill((10, 20), map1)\nmap_float = np.array(map1)/10.0\nplt.imshow(map1)\nplt.colorbar()\nplt.show()", "_____no_output_____" ] ], [ [ "Let's use this flood fill on real data:", "_____no_output_____" ] ], [ [ "xyreso = 0.02 # x-y grid resolution\nyawreso = math.radians(3.1) # yaw angle resolution [rad]\nang, dist = file_read(\"lidar01.csv\")\nox = np.sin(ang) * dist\noy = np.cos(ang) * dist\npmap, minx, maxx, miny, maxy, xyreso = lg.generate_ray_casting_grid_map(ox, oy, xyreso, False)\nxyres = np.array(pmap).shape\nplt.figure(figsize=(20,8))\nplt.subplot(122)\nplt.imshow(pmap, cmap = \"PiYG_r\") \nplt.clim(-0.4, 1.4)\nplt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor = True)\nplt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor = True)\nplt.grid(True, which=\"minor\", color=\"w\", linewidth = .6, alpha = 0.5)\nplt.colorbar()\nplt.show()", "The grid map is 150 x 100 .\n" ] ] ]

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

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/DebjitHore/Complete-Data-Structures-and-Algorithms-in-Python/blob/main/Recursion_Udemy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "Recursion- A way of solving a problem by having a function call itself. Performing the same operation with different inputs. Usually smaller inputs for convergence, with a base condition to prevent infinite loop. \nIRL example- Russian Doll.", "_____no_output_____" ], [ "#Pseudocode", "_____no_output_____" ] ], [ [ "def openRussianDoll(doll):\n if doll==1: #Smallest Doll\n print('All Dolls opened')\n else:\n openRussianDoll(doll-1)\n", "_____no_output_____" ] ], [ [ "#Recursion logic.\n\nStack Memory is used and last in, first out is followed. \nRecursion is less time and space efficient as against iteration. But it is easier to code.", "_____no_output_____" ], [ "#How to write Recursion", "_____no_output_____" ], [ "##Factorial", "_____no_output_____" ] ], [ [ "def factorial(n):\n assert n>=0 and int (n)==n, 'The number must be positive integer only'\n if n in [0,1]:\n return 1\n else:\n return n*factorial(n-1)", "_____no_output_____" ], [ "factorial(5)", "_____no_output_____" ] ], [ [ "##Fibonnaci", "_____no_output_____" ], [ "n-th term of a Fibonnaci series", "_____no_output_____" ] ], [ [ "fib_list=[]\ndef fibonacci(n):\n assert n>=0 and int (n) ==n, 'The number must be integer and positive'\n if n in [0,1]:\n return n\n else:\n return fibonacci(n-1)+fibonacci(n-2)", "_____no_output_____" ], [ "fibonacci(5)", "_____no_output_____" ] ], [ [ "Fibonacci series containing of n terms", "_____no_output_____" ] ], [ [ "a=0 \nb=1\nfib_list=[0,1]\nn= int(input('No of terms you want'))\nassert int (n)==n and n>0\ni=0\nif n==1:\n print(0)\nelse:\n while i<n-2:\n temp=a+b\n fib_list.append(temp)\n a=b\n b=temp\n i+=1\n print(fib_list)", "No of terms you want15\n[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377]\n" ] ], [ [ "##Sum of digits of an integer number\n", "_____no_output_____" ] ], [ [ "def sumOfDigits(n):\n sum=0\n assert int (n) == n and n>=0, 'Must be a positive integer'\n while n>0:\n return n%10 +sumOfDigits(int (n/10))\n else:\n return 0", "_____no_output_____" ], [ "sumOfDigits(546)", "_____no_output_____" ], [ "#sumOfDigits(-87)", "_____no_output_____" ] ], [ [ "##Power of a number", "_____no_output_____" ] ], [ [ "def powerOfNumber(x,n):\n assert int (n) ==n and n >=0\n if n==0:\n return 1\n if n==1:\n return x\n else:\n return x*powerOfNumber(x, n-1)\n ", "_____no_output_____" ], [ "powerOfNumber( -2, 5)", "_____no_output_____" ], [ "def gcd(a, b):\n assert int (a) == a and int (b) ==b, 'Integer numbers only'\n if a%b==0:\n return b\n else:\n return gcd(b, a%b)", "_____no_output_____" ], [ "print(gcd(48,18))", "6\n" ] ], [ [ "## Decimal to Binary", "_____no_output_____" ] ], [ [ "def decToBinary(n):\n assert int (n) ==n\n if n is 0: \n return 0\n if n is 1:\n return 1\n else:\n return (int (n%2))+10*decToBinary(int (n/2))\n", "_____no_output_____" ], [ "decToBinary(13)", "_____no_output_____" ] ], [ [ "##Reverse of a number using Recursion", "_____no_output_____" ] ], [ [ "def reverseNumber(n, r):\n #assert int (n) == n and n >0\n if n ==0:\n return r\n else:\n return reverseNumber( int ( n/10), r*10+n%10)\n\n", "_____no_output_____" ], [ "reverseNumber(647, 0)", "_____no_output_____" ] ], [ [ "#Largest number in an Array.", "_____no_output_____" ] ], [ [ "def findMaxNumRec( sampleArray, n): #n is the length of array\n if n==1:\n return sampleArray[0]\n else:\n return max(sampleArray[n-1], findMaxNumRec(sampleArray, n-1))\n", "_____no_output_____" ], [ "findMaxNumRec([11, 14, 7, 9, 3, 12], 6)", "_____no_output_____" ], [ "", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "print(\"Hola mundo\")", "Hola mundo\n" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Settings/scenario management\n\nCapacity expansion modeling is often an exercise in exploring the difference between different technical, cost, or policy scenarios across a range of planning years, so PowerGenome has a built-in method for creating modified versions of a single baseline scenario. Within the settings file this shows up in how planning periods are defined and a nested dictionary that allows any \"normal\" parameter to be modified for different scenarios.\n\n## Scenario management files\n\nScenario management is deeply build into the input file structure. So much so, in fact, that it might be difficult to create inputs for a single scenario without following the layout designed for multiple scenarios.\n\n### Scenario names\n\nEach scenario has a long name and a short identifier, defined in the `case_id_description_fn` file (`test_case_id_description.csv` in the example). These cases are assumed to be the same across planning periods. When using the command line interface, case folders are created using the format `<case_id>_<model_year>_<case_description>`, so they look something like `p1_2030_Tech_CES_with_RPS`. Case IDs are used in the `scenario_definitions_fn` file (it's `test_scenario_inputs.csv` or `test_scenario_inputs_short.csv` in the example), and the `emission_policies_fn` (`test_rps_ces_emission_limits.csv`).\n\n## Planning periods\n\nWhen running a single planning period, many functions expect the parameters `model_year` and `model_first_planning_year` to be integers (a single year). In a multi-planning period settings file, each of these parameters should be a list of integers and they should be the same length. They now represent a paired series of the first and last years in each of the planning periods to be investigated.\n\n```\nmodel_year: [2030, 2045]\nmodel_first_planning_year: [2020, 2031]\n```\n\nIn this case, planning years of 2030 and 2045 will be investigated. Hourly demand is calculated for planning years. The first year in a planning period is needed because technology costs are calculated as the average of all costs over a planning period. So for the first planning period of 2020-2030, load/demand will be calculated for 2030 and the cost of building a new generator will be the average of all values from 2020-2030.\n\n## Settings management\n\nThe parameter `settings_management` is a nested dictionary with alternative values for any parameters that will be modified as part of a sensitivity, or that might have different values across planning periods. The structure of this dictionary is:\n\n```\nsettings_management:\n <model year>:\n <sensitivity column name>:\n <sensitivity value name>:\n <settings parameter name>:\n <settings parameter value>\n```\n\n`<sensitivity column name>` is the name of a column in the `scenario_definitions_fn` parameter (it's `test_scenario_inputs.csv` in the example). The first columns of this file have a `case_id` and `year` that uniquely define each model run. Model runs might test the effect of different natural gas prices (`ng_price` in the example file), with values of `reference` and `low`. The corresponding section of the `settings_management` parameter for the planning year 2030 will look like:\n\n```\nsettings_management:\n 2030:\n ng_price: # <sensitivity column name>\n reference: # <sensitivity value name>\n aeo_fuel_scenarios: # <settings parameter name>\n naturalgas: reference # <settings parameter value>\n low:\n aeo_fuel_scenarios:\n naturalgas: high_resource\n```\nSo in this case we're modifying the settings parameter `aeo_fuel_scenarios` by defining different AEO scenario names for the `naturalgas` fuel type. By default, this section of the settings file looks like:\n\n```\neia_series_scenario_names:\n reference: REF2020\n low_price: LOWPRICE\n high_price: HIGHPRICE\n high_resource: HIGHOGS\n low_resource: LOWOGS\n\naeo_fuel_scenarios:\n coal: reference\n naturalgas: reference\n distillate: reference\n uranium: reference\n```\n\nSo we're changing the AEO case from `reference` to `high_resource` (which correspond to `REF2020` and `HIGHOGS` in the EIA open data API).\n\nIt's important to understand that parameter values are updated by searching for `key:value` pairs in a dictionary and updating them. This means that in the example above I was able to change the AEO scenario for just natural gas prices, and I didn't have to list the other fuel types. But if the `value` is a list and only one item should be changed, then the entire list must be included in `settings_management`. As an example, cost scenarios for new-build generators are usually defined like:\n\n```\n# Format for each list item is <technology>, <tech_detail>, <cost_case>, <size>\natb_new_gen:\n - [NaturalGas, CCCCSAvgCF, Mid, 500]\n - [NaturalGas, CCAvgCF, Mid, 500]\n - [NaturalGas, CTAvgCF, Mid, 100]\n - [LandbasedWind, LTRG1, Mid, 1]\n - [OffShoreWind, OTRG10, Mid, 1]\n - [UtilityPV, LosAngeles, Mid, 1]\n - [Battery, \"*\", Mid, 1]\n```\n\nIf I want to have low cost renewables capex in a scenario, the corresponding section of `settings_management` should include all technologies, even if they don't change. This is because the ATB technologies are defined in a list of lists.\n\n```\nsettings_management:\n 2030:\n renewable_capex:\n low:\n atb_new_gen:\n - [NaturalGas, CCCCSAvgCF, Mid, 500]\n - [NaturalGas, CCAvgCF, Mid, 500]\n - [NaturalGas, CTAvgCF, Mid, 100]\n - [LandbasedWind, LTRG1, Low, 1]\n - [OffShoreWind, OTRG10, Low, 1]\n - [UtilityPV, LosAngeles, Low, 1]\n - [Battery, \"*\", Low, 1]\n````", "_____no_output_____" ] ], [ [ "%load_ext autoreload\n%autoreload 2", "_____no_output_____" ], [ "from pathlib import Path\n\nimport pandas as pd\nfrom powergenome.util import (\n build_scenario_settings,\n init_pudl_connection,\n load_settings,\n check_settings\n)", "_____no_output_____" ] ], [ [ "## Import settings\n\nSettings are imported by reading the YAML file and converting it to a Python dictionary. In the code below I'm loading the settings and creating a nested dictionary `scenario_settings` that has all of the modified parameters for each case.\n\nSettings can also be checked for some common errors using the `check_settings` function.", "_____no_output_____" ] ], [ [ "cwd = Path.cwd()\n\nsettings_path = (\n cwd.parent / \"example_systems\" / \"CA_AZ\" / \"test_settings.yml\"\n)\nsettings = load_settings(settings_path)\nsettings[\"input_folder\"] = settings_path.parent / settings[\"input_folder\"]\nscenario_definitions = pd.read_csv(\n settings[\"input_folder\"] / settings[\"scenario_definitions_fn\"]\n)\nscenario_settings = build_scenario_settings(settings, scenario_definitions)\n\npudl_engine, pudl_out, pg_engine = init_pudl_connection(\n freq=\"AS\",\n start_year=min(settings.get(\"data_years\")),\n end_year=max(settings.get(\"data_years\")),\n)\n\ncheck_settings(settings, pg_engine)", "_____no_output_____" ] ], [ [ "We can check to see if the natural gas price has changed from case `p1` to `s1`, and confirm that they are different.", "_____no_output_____" ] ], [ [ "scenario_settings[2030][\"p1\"][\"aeo_fuel_scenarios\"]", "_____no_output_____" ], [ "scenario_settings[2030][\"s1\"][\"aeo_fuel_scenarios\"]", "_____no_output_____" ] ], [ [ "The values of `model_year` and `model_first_planning_year` have also changed from lists to integers.", "_____no_output_____" ] ], [ [ "settings[\"model_year\"], settings[\"model_first_planning_year\"]", "_____no_output_____" ], [ "scenario_settings[2030][\"p1\"][\"model_year\"], scenario_settings[2030][\"p1\"][\"model_first_planning_year\"]", "_____no_output_____" ], [ "scenario_settings[2045][\"p1\"][\"model_year\"], scenario_settings[2045][\"p1\"][\"model_first_planning_year\"]", "_____no_output_____" ] ], [ [ "## Scenario data not defined in the settings file\n\nSome case/scenario data is defined in input CSV files rather that the settings YAML file. This is true for demand response (`demand_response_fn`, or the example file `test_ev_load_shifting.csv`). If you are supplying your own hourly demand profiles, it is also true for `regional_load_fn` (`test_regional_load_profiles.csv`). \n\n### Demand response\n\nThe demand response CSV file has 4 header rows, which correspond to the resource type, the model planning year, the scenario name, and the model region. The resource type should match a resource defined in the settings parameter `demand_response_resources`. \n\n```\n# Name of the DSM resource, fraction of load that can be shifted, and number of hours\n# that it can be shifted\ndemand_response_resources:\n 2030:\n ev_load_shifting:\n fraction_shiftable: 0.8\n parameter_values:\n Max_DSM_delay: 5\n DR: 2\n 2045:\n ev_load_shifting:\n fraction_shiftable: 0.8\n parameter_values:\n Max_DSM_delay: 5\n DR: 2\ndemand_response: 'moderate'\n```\n\nThe settings parameter `demand_response` - which can be changed via `settings_management` - is used to select the DR scenario in the CSV file.\n\n\n### User-supplied load\n\nIf you want to use your own load projections, define an input file with the parameter `regional_load_fn`. The first three rows are headers corresponding to the model year, electrification scenario, and model region. The electrification scenario names should match values in the column `electrification` of `scenario_definitions_fn`. This doesn't match with how demand response is handled and may be changed in the future.", "_____no_output_____" ] ] ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport statistics\nimport math\n\nfrom sklearn.linear_model import LinearRegression\nfrom scipy.optimize import curve_fit", "_____no_output_____" ], [ "er_cas_100_data = pd.read_csv('proc_er_cas_100.csv')\n\ndel er_cas_100_data['Unnamed: 0']", "_____no_output_____" ], [ "er_500_50_0012 = pd.read_csv('proc_er_500_50_0012.csv')\n\ndel er_500_50_0012['Unnamed: 0']", "_____no_output_____" ], [ "er_1000_50_0006 = pd.read_csv('proc_er_1000_50_0006.csv')\n\ndel er_1000_50_0006['Unnamed: 0']", "_____no_output_____" ], [ "er_1500_50_0004 = pd.read_csv('proc_er_1500_50_0004.csv')\n\ndel er_1500_50_0004['Unnamed: 0']", "_____no_output_____" ], [ "er_cas_100_data", "_____no_output_____" ], [ "er_500_50_0012", "_____no_output_____" ], [ "er_1000_50_0006", "_____no_output_____" ], [ "er_1500_50_0004", "_____no_output_____" ], [ "er_cas_100_dict = {}\n\nfor i in range(100):\n target = list(range(i*30, (i+1)*30))\n \n temp_er_cas_100 = er_cas_100_data[i*30 + 0 : (i+1)*30]\n \n alive = 0\n for index in target:\n if (temp_er_cas_100['alive_nodes'][index] != 0) and (temp_er_cas_100['fin_larg_comp_a'][index] != 0):\n alive += 1\n p_k = 0.8 * 499 * temp_er_cas_100['t'][index]\n \n if i == 0:\n er_cas_100_dict['attack_size'] = [statistics.mean(temp_er_cas_100['attack_size'].values.tolist())]\n er_cas_100_dict['t'] = [statistics.mean(temp_er_cas_100['t'].values.tolist())]\n er_cas_100_dict['init_intra_edge_a'] = [statistics.mean(temp_er_cas_100['init_intra_edge_a'].values.tolist())]\n er_cas_100_dict['alive ratio'] = [alive / 30]\n er_cas_100_dict['p<k>'] = [p_k]\n else:\n er_cas_100_dict['attack_size'].append(statistics.mean(temp_er_cas_100['attack_size'].values.tolist()))\n er_cas_100_dict['t'].append(statistics.mean(temp_er_cas_100['t'].values.tolist()))\n er_cas_100_dict['init_intra_edge_a'].append(statistics.mean(temp_er_cas_100['init_intra_edge_a'].values.tolist()))\n er_cas_100_dict['alive ratio'].append(alive / 30)\n er_cas_100_dict['p<k>'].append(p_k)", "_____no_output_____" ], [ "plt.plot(er_cas_100_dict['p<k>'], er_cas_100_dict['alive ratio'])\nplt.title('The ratio that shows whether largest component is alive or not')\nplt.show()", "_____no_output_____" ], [ "er_500_50_0012_dict = {}\n\nfor i in range(100):\n target = list(range(i*50, (i+1)*50))\n \n temp_er_500_50_0012 = er_500_50_0012[i*50 + 0 : (i+1)*50]\n \n alive = 0\n for index in target:\n if (temp_er_500_50_0012['alive_nodes'][index] != 0) and (temp_er_500_50_0012['fin_larg_comp_a'][index] != 0):\n alive += 1\n p_k = 0.8 * 499 * temp_er_500_50_0012['t'][index]\n \n if i == 0:\n er_500_50_0012_dict['attack_size'] = [statistics.mean(temp_er_500_50_0012['attack_size'].values.tolist())]\n er_500_50_0012_dict['t'] = [statistics.mean(temp_er_500_50_0012['t'].values.tolist())]\n er_500_50_0012_dict['init_intra_edge_a'] = [statistics.mean(temp_er_500_50_0012['init_intra_edge_a'].values.tolist())]\n er_500_50_0012_dict['alive ratio'] = [alive / 50]\n er_500_50_0012_dict['p<k>'] = [p_k]\n er_500_50_0012_dict['alive_nodes'] = [statistics.mean(temp_er_cas_100['alive_nodes'].values.tolist())]\n else:\n er_500_50_0012_dict['attack_size'].append(statistics.mean(temp_er_500_50_0012['attack_size'].values.tolist()))\n er_500_50_0012_dict['t'].append(statistics.mean(temp_er_500_50_0012['t'].values.tolist()))\n er_500_50_0012_dict['init_intra_edge_a'].append(statistics.mean(temp_er_500_50_0012['init_intra_edge_a'].values.tolist()))\n er_500_50_0012_dict['alive ratio'].append(alive / 50)\n er_500_50_0012_dict['p<k>'].append(p_k)\n er_500_50_0012_dict['alive_nodes'].append(statistics.mean(temp_er_cas_100['alive_nodes'].values.tolist()))", "_____no_output_____" ], [ "plt.plot(er_500_50_0012_dict['p<k>'], er_500_50_0012_dict['alive ratio'])\nplt.axvline(x=2.4554, color='r', linestyle='--')\nplt.title('N=500, K=100')\nplt.xlabel(\"p<k>\")\nplt.ylabel(\"proportion of survived largest component\")\nplt.savefig(\"er_n500_k100\")\nplt.show()", "_____no_output_____" ], [ "X = er_500_50_0012_dict['p<k>']\nY = er_500_50_0012_dict['log_reg_p<k>']", "_____no_output_____" ], [ "def sigmoid(x, L ,x0, k, b):\n y = L / (1 + np.exp(-k*(x-x0)))+b\n return (y)\n\np0 = [max(Y), np.median(X),1,min(Y)] # this is an mandatory initial guess\n\npopt, pcov = curve_fit(sigmoid, X, Y,p0, method='dogbox')", "_____no_output_____" ], [ "plt.scatter(X, Y, marker='.')\nplt.plot(X, Y, linewidth=2)\nplt.plot(X, sigmoid(X, *popt), color='red', linewidth=2)\nplt.show()", "_____no_output_____" ], [ "plt.plot(er_500_50_0012_dict['p<k>'], er_500_50_0012_dict['log_reg_p<k>'])\nplt.axvline(x=2.4554, color='r', linestyle='--')\nplt.title('N=500, K=100')\nplt.xlabel(\"p<k>\")\nplt.ylabel(\"percentage of survived largest component\")\nplt.savefig(\"er_n500_k100\")\nplt.show()", "_____no_output_____" ], [ "er_1000_50_0006_dict = {}\n\nfor i in range(100):\n target = list(range(i*50, (i+1)*50))\n \n temp_er_1000_50_0006 = er_1000_50_0006[i*50 + 0 : (i+1)*50]\n \n alive = 0\n for index in target:\n if (temp_er_1000_50_0006['alive_nodes'][index] != 0) and (temp_er_1000_50_0006['fin_larg_comp_a'][index] != 0):\n alive += 1\n p_k = 0.8 * 999 * temp_er_1000_50_0006['t'][index]\n \n if i == 0:\n er_1000_50_0006_dict['attack_size'] = [statistics.mean(temp_er_1000_50_0006['attack_size'].values.tolist())]\n er_1000_50_0006_dict['t'] = [statistics.mean(temp_er_1000_50_0006['t'].values.tolist())]\n er_1000_50_0006_dict['init_intra_edge_a'] = [statistics.mean(temp_er_1000_50_0006['init_intra_edge_a'].values.tolist())]\n er_1000_50_0006_dict['alive ratio'] = [alive / 50]\n er_1000_50_0006_dict['p<k>'] = [p_k]\n else:\n er_1000_50_0006_dict['attack_size'].append(statistics.mean(temp_er_1000_50_0006['attack_size'].values.tolist()))\n er_1000_50_0006_dict['t'].append(statistics.mean(temp_er_1000_50_0006['t'].values.tolist()))\n er_1000_50_0006_dict['init_intra_edge_a'].append(statistics.mean(temp_er_1000_50_0006['init_intra_edge_a'].values.tolist()))\n er_1000_50_0006_dict['alive ratio'].append(alive / 50)\n er_1000_50_0006_dict['p<k>'].append(p_k)", "_____no_output_____" ], [ "plt.plot(er_1000_50_0006_dict['p<k>'], er_1000_50_0006_dict['alive ratio'])\nplt.axvline(x=2.4554, color='r', linestyle='--')\nplt.title('N=1000, K=200')\nplt.xlabel(\"p<k>\")\nplt.ylabel(\"proportion of survived largest component\")\nplt.savefig(\"er_n1000_k200\")\nplt.show()", "_____no_output_____" ], [ "er_1500_50_0004_dict = {}\n\nfor i in range(100):\n target = list(range(i*50, (i+1)*50))\n \n temp_er_1500_50_0004 = er_1500_50_0004[i*50 + 0 : (i+1)*50]\n \n alive = 0\n for index in target:\n if (temp_er_1500_50_0004['alive_nodes'][index] != 0) and (temp_er_1500_50_0004['fin_larg_comp_a'][index] != 0):\n alive += 1\n p_k = 0.8 * 1499 * temp_er_1500_50_0004['t'][index]\n \n if i == 0:\n er_1500_50_0004_dict['attack_size'] = [statistics.mean(temp_er_1500_50_0004['attack_size'].values.tolist())]\n er_1500_50_0004_dict['t'] = [statistics.mean(temp_er_1500_50_0004['t'].values.tolist())]\n er_1500_50_0004_dict['init_intra_edge_a'] = [statistics.mean(temp_er_1500_50_0004['init_intra_edge_a'].values.tolist())]\n er_1500_50_0004_dict['alive ratio'] = [alive / 50]\n er_1500_50_0004_dict['p<k>'] = [p_k]\n else:\n er_1500_50_0004_dict['attack_size'].append(statistics.mean(temp_er_1500_50_0004['attack_size'].values.tolist()))\n er_1500_50_0004_dict['t'].append(statistics.mean(temp_er_1500_50_0004['t'].values.tolist()))\n er_1500_50_0004_dict['init_intra_edge_a'].append(statistics.mean(temp_er_1500_50_0004['init_intra_edge_a'].values.tolist()))\n er_1500_50_0004_dict['alive ratio'].append(alive / 50)\n er_1500_50_0004_dict['p<k>'].append(p_k)", "_____no_output_____" ], [ "plt.plot(er_1500_50_0004_dict['p<k>'], er_1500_50_0004_dict['alive ratio'])\nplt.axvline(x=2.4554, color='r', linestyle='--')\nplt.title('N=1500, K=300')\nplt.xlabel(\"p<k>\")\nplt.ylabel(\"proportion of survived largest component\")\nplt.savefig(\"er_n1500_k300\")\nplt.show()", "_____no_output_____" ], [ "plt.plot(er_500_50_0012_dict['p<k>'], er_500_50_0012_dict['alive ratio'])\nplt.plot(er_1000_50_0006_dict['p<k>'], er_1000_50_0006_dict['alive ratio'])\nplt.plot(er_1500_50_0004_dict['p<k>'], er_1500_50_0004_dict['alive ratio'])\nplt.axvline(x=2.4554, color='r', linestyle='--')\nplt.title('Total Graph (Expanded)')\nplt.xlabel(\"p<k>\")\nplt.ylabel(\"proportion of survived largest component\")\nplt.legend(['N=500', 'N=1000', 'N=1500'])\nplt.savefig(\"er_total_expanded\")\nplt.show()", "_____no_output_____" ], [ "plt.plot(er_500_50_0012_dict['p<k>'], er_500_50_0012_dict['alive ratio'])\nplt.plot(er_1000_50_0006_dict['p<k>'], er_1000_50_0006_dict['alive ratio'])\nplt.plot(er_1500_50_0004_dict['p<k>'], er_1500_50_0004_dict['alive ratio'])\nplt.axvline(x=2.4554, color='r', linestyle='--')\nplt.title('Total Graph')\nplt.xlabel(\"p<k>\")\nplt.ylabel(\"proportion of survived largest component\")\nplt.legend(['N=500', 'N=1000', 'N=1500'])\nplt.xlim([2.36, 2.5])\nplt.savefig(\"er_total\")\nplt.show()", "_____no_output_____" ] ] ]

[ "code" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from pymongo import MongoClient\nimport pandas as pd\nimport datetime", "_____no_output_____" ], [ "client = MongoClient()\ncharacters = client.ck2.characters", "_____no_output_____" ] ], [ [ "This notebook tries to build a world tree by drawing and edge between every character in the save file with their father and mother. Running this code will generate a network with over 270,000 nodes out of a total of almost 400,000. This was taking far too long for Gephi to graph so I did not continue.\n\nThe next 3 notebooks contain the first code I wrote for extracting data from the save file. I wild manually copy and paste out the dynasty data from both files, the character data and the title data and save them in seperate files.", "_____no_output_____" ], [ "## Get Parent/Child Edges", "_____no_output_____" ] ], [ [ "pipeline = [\n {\n \"$unwind\" : \"$parents\" \n },\n {\n \"$lookup\" :\n {\n \"from\" : \"dynasties\",\n \"localField\" : \"dnt\",\n \"foreignField\" : \"_id\",\n \"as\" : \"dynasty\"\n }\n },\n {\n \"$unwind\" : \"$dynasty\"\n },\n {\n \"$match\" : {\"parents\" : {\"$nin\" : [None]}, \"$or\" : [{\"cul\" : \"irish\"}, {\"dynasty.culture\" : \"irish\"}]}\n },\n {\n \"$project\" : {\"_id\" : 1, \"parents\" : 1}\n }\n]", "_____no_output_____" ], [ "relation_df = pd.DataFrame(list(characters.aggregate(pipeline)))", "_____no_output_____" ] ], [ [ "## Get all Characters", "_____no_output_____" ] ], [ [ "pipeline = [\n {\n \"$lookup\" :\n {\n \"from\" : \"dynasties\",\n \"localField\" : \"dnt\",\n \"foreignField\" : \"_id\",\n \"as\" : \"dynasty\"\n }\n },\n {\n \"$unwind\" : \"$dynasty\"\n },\n {\n \"$project\" : {\"_id\" : 1, \"name\" : {\"$concat\" : [\"$bn\", \" \", \"$dynasty.name\"]}, \n \"culture\" : {\"$ifNull\" : [\"$cul\", \"$dynasty.culture\"]}, \n \"religion\" : {\"$ifNull\" : [\"$rel\", \"$dynasty.religion\"]} }\n }\n]", "_____no_output_____" ] ], [ [ "## Build Network", "_____no_output_____" ] ], [ [ "import networkx as nx\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "chars = list(characters.aggregate(pipeline))", "_____no_output_____" ], [ "for char in chars:\n for key in list(char.keys()):\n val = char[key]\n if isinstance(val, type(None)):\n del char[key]", "_____no_output_____" ], [ "G = nx.Graph()\n\nfor char in chars: #characters.aggregate(pipeline):\n if \"culture\" in char and \"religion\" in char and \"name\" in char:\n G.add_node(char[\"_id\"], name = char['name'], culture = char['culture'], religion = char['religion'])", "_____no_output_____" ], [ "relation_df = relation_df.dropna(axis=0, how='any')\n\nfor i in range(len(relation_df)):\n G.add_edge(relation_df.loc[i, \"_id\"], relation_df.loc[i, \"parents\"])\n\nG.remove_nodes_from(nx.isolates(G)) #drop unconnected nodes ", "_____no_output_____" ], [ "#nx.draw(G)\n#plt.show()", "_____no_output_____" ], [ "nx.write_graphml(max(nx.connected_component_subgraphs(G), key=len), \"ck2-World-Tree-2.graphml\")", "_____no_output_____" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import json\n%run ../Python_files/util.py\n\n# tmc_ref_speed_dict.keys\n\n# AM: 7:00 am - 9:00 am\n# MD: 11:00 am - 13:00 pm\n# PM: 17:00 pm - 19:00 pm\n# NT: 21:00 pm - 23:00 pm\n\ndata_folder = '/home/jzh/INRIX/All_INRIX_2012_filtered_journal/'", "No dicts found; please check load_dicts...\n" ], [ "month = 4\n\n# Load JSON data\ninput_file = data_folder + 'filtered_month_%s_%s_MD_dict_journal.json' %(month, 1)\nwith open(input_file, 'r') as json_file:\n temp_dict_1 = json.load(json_file)\n \ninput_file = data_folder + 'filtered_month_%s_%s_MD_dict_journal.json' %(month, 2)\nwith open(input_file, 'r') as json_file:\n temp_dict_2 = json.load(json_file)\n \ninput_file = data_folder + 'filtered_month_%s_%s_MD_dict_journal.json' %(month, 3)\nwith open(input_file, 'r') as json_file:\n temp_dict_3 = json.load(json_file)\n \ninput_file = data_folder + 'filtered_month_%s_%s_MD_dict_journal.json' %(month, 4)\nwith open(input_file, 'r') as json_file:\n temp_dict_4 = json.load(json_file)\n \ninput_file = data_folder + 'filtered_month_%s_%s_MD_dict_journal.json' %(month, 5)\nwith open(input_file, 'r') as json_file:\n temp_dict_5 = json.load(json_file)\n \ninput_file = data_folder + 'filtered_month_%s_%s_MD_dict_journal.json' %(month, 6)\nwith open(input_file, 'r') as json_file:\n temp_dict_6 = json.load(json_file)\n \ninput_file = data_folder + 'filtered_month_%s_%s_MD_dict_journal.json' %(month, 7)\nwith open(input_file, 'r') as json_file:\n temp_dict_7 = json.load(json_file)\n \ninput_file = data_folder + 'filtered_month_%s_%s_MD_dict_journal.json' %(month, 8)\nwith open(input_file, 'r') as json_file:\n temp_dict_8 = json.load(json_file)", "_____no_output_____" ], [ "temp_dict_1.update(temp_dict_2)\ntemp_dict_1.update(temp_dict_3)\ntemp_dict_1.update(temp_dict_4)\ntemp_dict_1.update(temp_dict_5)\ntemp_dict_1.update(temp_dict_6)\ntemp_dict_1.update(temp_dict_7)\ntemp_dict_1.update(temp_dict_8)", "_____no_output_____" ], [ "# Writing JSON data\ninput_file_MD = data_folder + 'filtered_month_%s_MD_dict_journal.json' %(month)\nwith open(input_file_MD, 'w') as json_file_MD:\n json.dump(temp_dict_1, json_file_MD)", "_____no_output_____" ] ] ]

[ "code" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "% 利用yamlip求解TSP问题\nclear;clc;close all;\nd = load('tsp_dist_matrix.txt')'; %导入邻接矩阵\nn = size(d,1);", "_____no_output_____" ], [ "% 决策变量\nx = binvar(n,n,'full');\nu = sdpvar(1,n);", "_____no_output_____" ] ], [ [ "* [.*](https://zhidao.baidu.com/question/318809970.html)\n 数组加法：A+B，数组加法和矩阵加法相同。\n\n 数组减法：A-­B ，数组减法和矩阵减法相同。\n\n 数组乘法：A.*B，A 和 B 的元素逐个对应相乘，两数组之间必须有相同的形，或其中一个是标量。\n\n 矩阵乘法：A*B，A 和 B 的矩阵乘法，A 的列数必须和 B 的行数相同。\n\n 数组右除法：A./B，A 和 B 的元素逐个对应相除：A(i,j)/B(i,j)两数组之间必须有相同的形，或其中一个是标量。\n\n 数组左除法：A.\\B，A 和 B 的元素逐个对应相除：B(i,j)/A(i,j)两数组之间必须有相同的形，或其中一个是标量。\n\n 矩阵右除法：A/B 矩阵除法，等价于 A*inv(B)， inv(B)是 B 的逆阵。\n\n 矩阵左除法：A\\B 矩阵除法，等价于 inv(B)*A， inv(A)是 A 的逆阵。\n\n 数组指数运算：A.^B，AB中的元素逐个进行如下运算：A(i,j)^B(i,j)，A(i,j)/B(i,j)两数组之间必须有相同的形，或其中一个是标量。", "_____no_output_____" ] ], [ [ "% 目标 \nz = sum(sum(d.*x));", "_____no_output_____" ] ], [ [ "\\begin{aligned} \\min Z=& \\sum_{i=1}^{n} \\sum_{j=1}^{n} d_{i j} x_{i j} \\\\ \n&\\left(\\begin{array}{cc}{\\sum_{i=1, i \\neq j}^{n} x_{i j}=1,} & {j=1, \\cdots, n} \\\\ \n{\\sum_{j=1, j \\neq i}^{n} x_{i j}=1,} & {i=1, \\cdots, n} \\\\ \n{u_{i}-u_{j}+n x_{i j} \\leq n-1,} & { \\quad 1<i \\neq j \\leq n}\\\\\n{x_{i j}=\\{0 , 1\\},} & {i, j=1, \\cdots, n} \\\\ \n{u_{i}\\in\\mathbb{R},} & {i=1, \\cdots, n}\\end{array}\\right.\\end{aligned}", "_____no_output_____" ], [ "- '>'（大于），>=（大于等于），<（小于），<=（小于等于）， ==（等于）~=（不等于）\n* matlab逻辑符号：\n &（与），|（或），~（非）， xor（异或）", "_____no_output_____" ] ], [ [ "% 约束添加\nC = [];\nfor j = 1:n\n s = sum(x(:,j))-x(j,j);\n C = [C, s == 1];\nend\nfor i = 1:n\n s = sum(x(i,:)) - x(i,i);\n C = [C, s == 1];\nend\nfor i = 2:n\n for j = 2:n\n if i~=j\n C = [C,u(i)-u(j) + n*x(i,j)<=n-1];\n end\n end\nend", "\n" ], [ "% 求解 假如不把z加进去，则能得到一个可行解\nresult= optimize(C,z);", "警告: 文件: C:\\Program Files\\IBM\\ILOG\\CPLEX_Studio_Community128\\cplex\\matlab\\x64_win64\\@Cplex\\Cplex.p 行: 965 列: 0\n在嵌套函数中定义 \"changedParam\" 会将其与父函数共享。在以后的版本中，要在父函数和嵌套函数之间共享 \"changedParam\"，请在父函数中显式定义它。\n> In cplexoptimset\n In sdpsettings>setup_cplex_options (line 617)\n In sdpsettings (line 145)\n In solvesdp (line 131)\n In optimize (line 31)\nOptimize a model with 92 rows, 99 columns and 396 nonzeros\nVariable types: 9 continuous, 90 integer (90 binary)\nCoefficient statistics:\n Matrix range [1e+00, 1e+01]\n Objective range [3e+00, 3e+01]\n Bounds range [1e+00, 1e+00]\n RHS range [1e+00, 9e+00]\nFound heuristic solution: objective 117.0000000\nPresolve removed 0 rows and 1 columns\nPresolve time: 0.00s\nPresolved: 92 rows, 98 columns, 548 nonzeros\nVariable types: 8 continuous, 90 integer (90 binary)\n\nRoot relaxation: objective 7.460000e+01, 40 iterations, 0.00 seconds\n\n Nodes | Current Node | Objective Bounds | Work\n Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n\n 0 0 74.60000 0 10 117.00000 74.60000 36.2% - 0s\nH 0 0 77.0000000 74.60000 3.12% - 0s\n 0 0 cutoff 0 77.00000 77.00000 0.00% - 0s\n\nCutting planes:\n Learned: 3\n MIR: 3\n\nExplored 1 nodes (47 simplex iterations) in 0.04 seconds\nThread count was 8 (of 8 available processors)\n\nSolution count 2: 77 117 \n\nOptimal solution found (tolerance 1.00e-04)\nBest objective 7.700000000000e+01, best bound 7.700000000000e+01, gap 0.0000%\n\n" ], [ "% 求解\nif result.problem == 0\n value(x)\n value(z)\nelse\n disp('求解过程中出错');\nend", "\nans =\n\n NaN 0 0 1 0 0 0 0 0 0\n 0 NaN 0 0 0 0 1 0 0 0\n 0 1 NaN 0 0 0 0 0 0 0\n 0 0 1 NaN 0 0 0 0 0 0\n 0 0 0 0 NaN 1 0 0 0 0\n 0 0 0 0 0 NaN 0 1 0 0\n 0 0 0 0 1 0 NaN 0 0 0\n 0 0 0 0 0 0 0 NaN 0 1\n 1 0 0 0 0 0 0 0 NaN 0\n 0 0 0 0 0 0 0 0 1 NaN\n\n\nans =\n\n 77\n\n\n" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Pythonの基礎", "_____no_output_____" ], [ "## データ型", "_____no_output_____" ] ], [ [ "# [変数名] = [値] と書くと，その変数名を持った変数に値を代入できる．代入された値は後で利用したりすることができる．\n\nx = 1 # 整数\ny = 2.1 # 実数\nz = 2 * x + y # 四則演算 (×→*, ÷→/)\nprint(z) # print(x)でxを表示．print(x, y, ...)ともかけて，xとyにスペースが入って出力される", "4.1\n" ], [ "str1 = \"これは文字列\"\nstr2 = 'これも文字列'\nstr3 = \"\"\"\nこのように書くと\n複数行に渡る文字列\nを表現できる\n\"\"\"\nstr4 = '''\nこちらでも\n良い\n'''\nstr5 = str1 + str2 # 文字列の+は文字列の結合\nprint(str5)", "これは文字列これも文字列\n" ], [ "arr1 = [1, 2, 3] # リスト\narr2 = [3, 4, 5]\narr3 = arr1 + arr2 # リストの+はリストの結合\nprint(arr3)\narr4 = [\"文字列\", \"文字列\"] # 何のリストであっても良い\narr5 = [1, \"文字列\"] # 文字と数字が入り乱れても良い\n\n# arr[i] でarrリストのi+1番目の値を返す．iがマイナスの場合，末尾から数えて-i番目の値を返す\nfirst = arr1[0] # 1番目の値を返す\nprint(first) \nsecond = arr1[1] # 2番目の値を返す\nprint(second) \nlast = arr1[-1] # 後ろから1番目の値を返す\nprint(last)\n\n# arr[i:j]でi+1番目以降，j+1番目より前の値をリストで返す．iとjは省略可能で，省略されるとそれぞれ「最初から」，「最後まで」と解釈される．\nprint(arr1[1:]) # 2番目以降の値をリストで返す\nprint(arr1[:2]) # 3番目より前の値をリストで返す\nprint(arr3[1:3]) # 1番目以降，4番目より前の値をリストで返す\n\n# 文字列も配列のようにアクセス可能\ns = \"あいうえお\"\nprint(s[1])\nprint(s[1:3])\n\n# arr.append(x) で 配列arrの末尾に要素xを追加する\narr = [1, 2]\narr.append(3)\nprint(arr)", "[1, 2, 3, 3, 4, 5]\n1\n2\n3\n[2, 3]\n[1, 2]\n[2, 3]\nい\nいう\n[1, 2, 3]\n" ], [ "dict1 = {\"a\": 1, \"b\": 3, \"c\": 4} # 辞書．{key1: val1, key2: val2, ...}と書くと辞書を作れる．\n# 辞書はキーと値からなる順序付けされていないデータ\n# 1つのキーに対応する値は1つのみ\nprint(dict1[\"a\"]) # dict[key]とすれば，dict辞書のkeyに対応した値を返す\ndict2 = {} # 空の辞書も作れる\ndict2[\"a\"] = 2 # dict[key] = valueとすれば，dict辞書のkeyに値valueを対応させられる\ndict2[\"b\"] = 10\nprint(dict2)\ndict3 = {1: \"b\", 3: \"d\"} # keyは数値，文字列などを用いることが可能，リストや辞書は不可．valueは何でも良い．", "1\n{'b': 10, 'a': 2}\n" ], [ "# ブール型．True or False\nbool1 = True\nbool2 = False \nbool3 = 1 == 2 # 1 == 2はFalseとなる\nbool4 = 5 > 2 # 5 > 2はTrueとなる\nbool5 = 4 in [1, 2, 3] # x in AはAがリストの場合Aにxが含まれればTrue\nprint(bool5)\nbool6 = 4 in {1: \"a\", 3: \"b\", 4: \"c\"} # Aが辞書の場合，Aのキーにxが含まれればTrue\nprint(bool6)\nbool7 = \"きく\" in \"かきくけこ\" # Aが文字列の場合，xがAの部分文字列ならTrue\nprint(bool7)\n\n# ブール型は特にif文で利用される（詳細は後述）\nd = {\"a\": 1, \"b\": 2}\nif \"a\" in d:\n print(\"The condition was True\")\nelse:\n print(\"The condition was False\")", "False\nTrue\nTrue\nThe condition was True\n" ] ], [ [ "## 制御構文", "_____no_output_____" ] ], [ [ "\"\"\"\nif condition:\n # conditionがTrueのときにここが実行される\n else:\n # conditionがFalseのときにここが実行される（else:以降は省略可能）\n\"\"\"\n# 長いコメントは\"\"\"や'''で書ける\n\nx = 3\nif x > 2:\n print(\"x > 2\")\nelse:\n print(\"x <= 2\")\n\nif x == 3:\n print(\"x == 3\")\n\n# Pythonでインデントは構文の一部!! \n# インデントが増えた箇所から元のインデント数に戻るまで，インデント数が同じ箇所は同じコードブロックと見なされる．\n# コードブロック: if文やfor文，関数定義の影響が及ぼされる範囲\n\nif x < 2: # インデント0\n print(\"x < 2\") # インデント1\nelse: # インデント0\n print(\"x >= 2\") # インデント1\n print(\"This line was also executed\") # インデント1 この行もx < 2でない場合にのみ実行される\n \nif x / 3 == 1: # インデント0\n print(\"x / 3 == 1\") # インデント1\nelse: # インデント0\n print(\"x / 3 != 1\") # インデント1\nprint(\"This line is executed whatever the condition is\") # インデント0 この行は直前の行と同じコードブロックではない", "x > 2\nx == 3\nx >= 2\nThis line was also executed\nx / 3 == 1\nThis line is executed whatever the condition is\n" ], [ "\"\"\"\nfor x in arr:\n # リストarrの各要素xに対して，この箇所が実行される\n\"\"\"\n\nfor x in [0, 1, 2]:\n print(x * 2)\n\nfor x in range(4): # range(x)で（厳密には違うが）xより小さい整数までの配列が得られる\n print(x * 2 + 2)\n\nfor x in range(1, 4): # range(x, y)で（厳密には違うが）xからyより小さい整数までの配列が得られる\n print(x * 3)\n\nfor i, x in enumerate([3, 4, 5]): # for i, x in enumerate(arr)とすれば，iに0, 1, ...というように何番目の繰り返しかを表す整数が代入される．\n print(i, x)", "0\n2\n4\n2\n4\n6\n8\n3\n6\n9\n0 3\n1 4\n2 5\n" ], [ "\"\"\"\nwhile condition:\n # conditionがTureである間，この箇所が実行される\n\"\"\"\n\ni = 0\nwhile i < 3:\n print(i)\n i = i + 1 # i += 1とも書ける", "0\n1\n2\n" ], [ "\"\"\"\ntry:\n # 例外が起こりそうな操作\nexcept:\n # 例外が起こったときに実行される\n\"\"\"\n\ntry:\n z = \"文字\" + 1 # 文字列と数値は足せない\nexcept:\n print(\"例外が発生しました\")\n\ntry:\n z = \"文字\" + 1 # 文字列と数値は足せない\nexcept Exception as e: # 変数eに例外が代入される\n print(e)", "例外が発生しました\nCan't convert 'int' object to str implicitly\n" ] ], [ [ "## 関数", "_____no_output_____" ] ], [ [ "\"\"\"\ndef function_name(var1, var2, ...):\n # 関数の中身. e.g. x = var1 + var2\n return x\n# 引数var1, var2, ...を与えて，xを返す関数function_nameを定義できる\n\"\"\"\n\ndef square(x):\n result = x * x\n return result\n\ny = square(2)\nprint(y)", "4\n" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\nimport itertools\nimport math\nimport scipy\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport matplotlib.patches as patches\nfrom matplotlib import animation\nfrom matplotlib import transforms\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport xarray as xr\nimport dask\nfrom sklearn.cluster import KMeans\nfrom sklearn.cluster import AgglomerativeClustering\nimport pandas as pd\nimport netCDF4", "_____no_output_____" ], [ "def latent_space_analysis(Images, title, iden):\n mean_image = np.mean(Images, axis=0)\n var_image = np.std(Images, axis=0)\n cmap=\"RdBu_r\"\n fig, ax = plt.subplots(1,2, figsize=(16,2))\n cs0 = ax[0].imshow(var_image, cmap=cmap)\n ax[0].set_title(\"Image Standard Deviation\")\n cs1 = ax[1].imshow(mean_image, cmap=cmap)\n ax[1].set_title(\"Image Mean\")\n ax[0].set_ylim(ax[0].get_ylim()[::-1])\n ax[1].set_ylim(ax[1].get_ylim()[::-1])\n ax[1].set_xlabel(\"CRMs\")\n ax[0].set_xlabel(\"CRMs\")\n ax[0].set_ylabel(\"Pressure\")\n ax[1].set_yticks([])\n y_ticks = np.arange(1300, 0, -300)\n ax[0].set_yticklabels(y_ticks)\n ax[1].set_yticklabels(y_ticks)\n divider = make_axes_locatable(ax[0])\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n fig.colorbar(cs0, cax=cax)\n divider = make_axes_locatable(ax[1])\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n fig.colorbar(cs1, cax=cax)\n plt.suptitle(title)\n #plt.savefig(\"/fast/gmooers/gmooers_git/CBRAIN-CAM/MAPS/model_graphs/latent_space_components/\"+iden+'_'+title+'.png')\n ", "_____no_output_____" ], [ "z_test_tsne = np.load(\"/fast/gmooers/gmooers_git/CBRAIN-CAM/MAPS/Synoptic_Latent_Spaces/2D_PCA_Latent_Space__31.npy\")\nTest_Images = np.load(\"/fast/gmooers/Preprocessed_Data/Centered_50_50/Space_Time_W_Test.npy\")\nMax_Scalar = np.load(\"/fast/gmooers/Preprocessed_Data/Centered_50_50/Space_Time_Max_Scalar.npy\")\nMin_Scalar = np.load(\"/fast/gmooers/Preprocessed_Data/Centered_50_50/Space_Time_Min_Scalar.npy\")\nTest_Images = np.interp(Test_Images, (0, 1), (Min_Scalar, Max_Scalar))", "_____no_output_____" ], [ "plt.scatter(x=z_test_tsne[:, 0], y=z_test_tsne[:, 1], c=\"#3D9AD1\", s=0.1)\nplt.show()", "_____no_output_____" ], [ "horz_line = np.squeeze(np.argwhere(np.logical_and(z_test_tsne[:,1] > -8.1, z_test_tsne[:,1] < -7.90)))\nvert_line = np.squeeze(np.argwhere(np.logical_and(z_test_tsne[:,0] > -12.30, z_test_tsne[:,0] < -11.70)))\n#horz_line = np.squeeze(np.argwhere(np.logical_and(z_test_tsne[:,1] > -8.005, z_test_tsne[:,1] < -7.995)))\n#vert_line = np.squeeze(np.argwhere(np.logical_and(z_test_tsne[:,0] > -12.025, z_test_tsne[:,0] < -11.975)))", "_____no_output_____" ], [ "horz_line_images = Test_Images[horz_line,:,:]\nhorz_line_latent = z_test_tsne[horz_line,:]\n\nvert_line_images = Test_Images[vert_line,:,:]\nvert_line_latent = z_test_tsne[vert_line,:]\n\nhorz_line_images_sorted = np.empty(horz_line_images.shape)\nhorz_line_latent_sorted = np.empty(horz_line_latent.shape)\nvert_line_images_sorted = np.empty(vert_line_images.shape)\nvert_line_latent_sorted = np.empty(vert_line_latent.shape)", "_____no_output_____" ], [ "count = 0\nfor i in range(len(horz_line_images_sorted)):\n ind = np.nanargmin(horz_line_latent[:,0])\n horz_line_images_sorted[count,:] = horz_line_images[ind,:]\n horz_line_latent_sorted[count,:] = horz_line_latent[ind,:]\n horz_line_latent[ind,:] = np.array([1000.0,1000.0])\n #horz_line_images[ind,:] = np.array([1000.0,1000.0])\n count = count+1\n \ncount = 0\nfor i in range(len(vert_line_images_sorted)):\n ind = np.nanargmin(vert_line_latent[:,1])\n vert_line_images_sorted[count,:] = vert_line_images[ind,:]\n vert_line_latent_sorted[count,:] = vert_line_latent[ind,:]\n vert_line_latent[ind,:] = np.array([10000.0,10000.0])\n #vert_line_image[ind,:] = np.array([1000.0,1000.0])\n count = count+1\n ", "_____no_output_____" ], [ "print(np.where(z_test_tsne == horz_line_latent_sorted[0]))\nprint(np.where(z_test_tsne == horz_line_latent_sorted[-1]))\nprint(np.where(z_test_tsne == vert_line_latent_sorted[0]))\nprint(np.where(z_test_tsne == vert_line_latent_sorted[-1]))", "(array([4781, 4781]), array([0, 1]))\n(array([536, 536]), array([0, 1]))\n(array([20826, 20826]), array([0, 1]))\n(array([18929, 18929]), array([0, 1]))\n" ], [ "plt.scatter(x=z_test_tsne[:, 0], y=z_test_tsne[:, 1], c=\"#3D9AD1\", s=2.0)\nplt.scatter(x=horz_line_latent_sorted[:, 0], y=horz_line_latent_sorted[:, 1], c=\"Red\", s=2.0)\nplt.scatter(x=vert_line_latent_sorted[:, 0], y=vert_line_latent_sorted[:, 1], c=\"Purple\", s=2.0)\nplt.show()", "_____no_output_____" ], [ "print(horz_line_latent_sorted.shape)\nprint(vert_line_latent_sorted.shape)", "(23, 2)\n(36, 2)\n" ], [ "path = \"/DFS-L/DATA/pritchard/gmooers/Workflow/MAPS/SPCAM/100_Days/New_SPCAM5/archive/TimestepOutput_Neuralnet_SPCAM_216/atm/hist/TimestepOutput_Neuralnet_SPCAM_216.cam.h1.2009-01-20-00000.nc\"\nextra_variables = xr.open_dataset(path)\nha = extra_variables.hyai.values\nhb = extra_variables.hybi.values\nPS = 1e5\nPressures_real = PS*ha+PS*hb\n\nfz = 15\nlw = 4\nsiz = 100\nXNNA = 1.25 # Abscissa where architecture-constrained network will be placed\nXTEXT = 0.25 # Text placement\nYTEXT = 0.3 # Text placement\n\nplt.rc('text', usetex=False)\nmatplotlib.rcParams['mathtext.fontset'] = 'stix'\nmatplotlib.rcParams['font.family'] = 'STIXGeneral'\n#mpl.rcParams[\"font.serif\"] = \"STIX\"\nplt.rc('font', family='serif', size=fz)\nmatplotlib.rcParams['lines.linewidth'] = lw\n\nothers = netCDF4.Dataset(\"/fast/gmooers/Raw_Data/extras/TimestepOutput_Neuralnet_SPCAM_216.cam.h1.2009-01-01-72000.nc\")\nlevs = np.array(others.variables['lev'])\nnew = np.flip(levs)\ncrms = np.arange(1,129,1)\nXs, Zs = np.meshgrid(crms, new)", "_____no_output_____" ], [ "horz_line_latent_sorted = np.flip(horz_line_latent_sorted, axis=0)\nvert_line_latent_sorted = np.flip(vert_line_latent_sorted, axis=0)\nhorz_line_images_sorted = np.flip(horz_line_images_sorted, axis=0)\nvert_line_images_sorted = np.flip(vert_line_images_sorted, axis=0)", "_____no_output_____" ], [ "# change vx/vy to location on sorted images\ndef mikes_latent_animation(h_coords, v_coords, h_const, v_const, latent_space, xdist, ydist, X, Z, hline, vline, h_images, v_images):\n fig, ax = plt.subplots(2,2, figsize=(36,16))\n feat_list = []\n #the real total you need\n num_steps = len(h_coords)\n #num_steps = 20\n cmap= \"RdBu_r\"\n \n dummy_horz = np.zeros(shape=(30,128))\n dummy_horz[:,:] = np.nan\n dummy_vert = np.zeros(shape=(30,128))\n dummy_vert[:,:] = np.nan\n count = 29\n for i in range(num_steps):\n \n for j in range(len(dummy_horz)):\n dummy_horz[count,:] = h_images[i,j,:]\n if i <= len(v_coords) -1:\n dummy_vert[count,:] = v_images[i,j,:]\n else:\n dummy_vert[count,:] = v_images[-1,j,:]\n count = count-1\n \n h_rect = patches.Rectangle((h_coords[i],h_const),xdist,ydist,linewidth=4,edgecolor='black',facecolor='none')\n if i <= len(v_coords) -1:\n v_rect = patches.Rectangle((v_const,v_coords[i]),xdist,ydist,linewidth=4,edgecolor='black',facecolor='none')\n else:\n v_rect = patches.Rectangle((v_const,v_coords[-1]),xdist,ydist,linewidth=4,edgecolor='black',facecolor='none')\n \n \n \n ax[0,0].scatter(latent_space[:, 0], latent_space[:, 1], c=\"#3D9AD1\", s=0.4, animated=True)\n ax[0,0].scatter(x=hline[:, 0], y=hline[:, 1], c=\"Red\", s=2.0, animated=True)\n cs0 = ax[0,0].add_patch(h_rect)\n \n cs2 = ax[1,0].scatter(latent_space[:, 0], latent_space[:, 1], c=\"#3D9AD1\", s=0.4, animated=True)\n ax[1,0].scatter(x=vline[:, 0], y=vline[:, 1], c=\"Green\", s=2.0, animated=True)\n cs2 = ax[1,0].add_patch(v_rect)\n \n \n cs3 = ax[1,1].pcolor(X, Z, dummy_vert, cmap=cmap, animated=True, vmin = -1.0, vmax = 1.0)\n ax[1,1].set_title(\"(y) Vertical Velocity\", fontsize=fz*2.0)\n cs1 = ax[0,1].pcolor(X, Z, dummy_horz, cmap=cmap, animated=True, vmin = -1.0, vmax = 1.0)\n ax[0,1].set_title(\"(x) Vertical Velocity\", fontsize=fz*2.0)\n \n ax[0,1].set_xlabel(\"CRMs\", fontsize=fz*1.5)\n ax[1,1].set_xlabel(\"CRMs\", fontsize=fz*1.5)\n ax[0,1].set_ylabel(\"Pressure (hpa)\", fontsize=fz*1.5)\n ax[1,1].set_ylabel(\"Pressure (hpa)\", fontsize=fz*1.5)\n \n y_ticks = np.array([1000, 800, 600, 400, 200])\n ax[1,1].set_yticklabels(y_ticks)\n ax[0,1].set_yticklabels(y_ticks)\n \n divider = make_axes_locatable(ax[1,1])\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n fig.colorbar(cs1, cax=cax)\n divider = make_axes_locatable(ax[0,1])\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n fig.colorbar(cs1, cax=cax)\n \n feat_list.append([cs2, cs3, cs1, cs0])\n \n\n count = 29 \n \n ani = animation.ArtistAnimation(fig, feat_list, interval = 125, blit = False, repeat = True)\n ani.save('/fast/gmooers/gmooers_git/CBRAIN-CAM/MAPS/Animations/Figures/31_W_Axis_Test_Horz_Vert_500.mp4')\n plt.show()\n \n \nmikes_latent_animation(horz_line_latent_sorted[:,0], vert_line_latent_sorted[:,1], -8.0, -12.0, z_test_tsne, 0.2, 1, Xs, Zs, horz_line_latent_sorted, vert_line_latent_sorted, horz_line_images_sorted, vert_line_images_sorted)", "_____no_output_____" ], [ "x0, y0 = -30, -14 # These are in _pixel_ coordinates!!\nx1, y1 = 10, 32\nlength = int(np.hypot(x1-x0, y1-y0))\nx, y = np.linspace(x0, x1, 8*length), np.linspace(y0, y1, 8*length)\n\nx2, y2 = -38, -12 # These are in _pixel_ coordinates!!\nx3, y3 = 115, 5\nlength = int(np.hypot(x3-x2, y3-y2))\nx3, y3 = np.linspace(x2, x3, 3*length), np.linspace(y2, y3, 3*length)", "_____no_output_____" ], [ "top_line = np.zeros(shape=(len(x3),2))\nshallow_line = np.zeros(shape=(len(x),2))\ntop_line[:,0] = x3\ntop_line[:,1] = y3\nshallow_line[:,0] = x\nshallow_line[:,1] = y", "_____no_output_____" ], [ "plt.scatter(x=z_test_tsne[:, 0], y=z_test_tsne[:, 1], c=\"#3D9AD1\", s=0.1)\nplt.scatter(x=top_line[:, 0], y=top_line[:, 1], c=\"red\", s=0.1)\nplt.scatter(x=shallow_line[:, 0], y=shallow_line[:, 1], c=\"green\", s=0.1)\nplt.show()", "_____no_output_____" ], [ "shallow_line.shape", "_____no_output_____" ], [ "z_test_tsne_saved = np.zeros(z_test_tsne.shape)\nfor i in range(len(z_test_tsne)):\n z_test_tsne_saved[i,:] = z_test_tsne[i,:]", "_____no_output_____" ], [ "def list_maker(original_array, latant_space, image_dataset):\n new_list = np.empty(original_array.shape)\n value_list =np.empty(latant_space[:,0].shape)\n new_images = np.empty(shape=(len(original_array),30,128))\n for i in range(len(original_array)):\n temp_x = original_array[i,0]\n temp_y = original_array[i,1]\n for j in range(len(latant_space)):\n #value_list[j] = np.abs(temp_x-latant_space[j,0])+np.abs(temp_y-latant_space[j,1])\n value_list[j] = np.sqrt((temp_x-latant_space[j,0])**2+(temp_y-latant_space[j,1])**2)\n point = np.argmin(value_list)\n new_list[i,:] = latant_space[point]\n new_images[i,:,:] = image_dataset[point,:,:]\n latant_space[point] = np.array([100000,100000])\n value_list[:] = np.nan\n \n \n return new_list, new_images\n \n \n \naxis_list, axis_images = list_maker(top_line, z_test_tsne, Test_Images)\nshallow_list, shallow_images = list_maker(shallow_line, z_test_tsne, Test_Images)", "_____no_output_____" ], [ "plt.scatter(x=z_test_tsne_saved[:, 0], y=z_test_tsne_saved[:, 1], c=\"#3D9AD1\", s=0.1)\nplt.scatter(x=top_line[:, 0], y=top_line[:, 1], c=\"red\", s=0.5)\nplt.scatter(x=axis_list[:, 0], y=axis_list[:, 1], c=\"green\", s=0.5)\nplt.scatter(x=shallow_line[:, 0], y=shallow_line[:, 1], c=\"red\", s=0.5)\nplt.scatter(x=shallow_list[:, 0], y=shallow_list[:, 1], c=\"green\", s=0.5)", "_____no_output_____" ], [ "print(shallow_line.shape)\nprint(axis_list.shape)\nprint(shallow_list.shape)\nprint(top_line.shape)", "(60, 2)\n(153, 2)\n(60, 2)\n(153, 2)\n" ], [ "shallow_line = np.flip(shallow_line, axis=0)\naxis_list = np.flip(axis_list, axis=0)\nshallow_list = np.flip(shallow_list, axis=0)\ntop_line = np.flip(top_line, axis=0)\naxis_images = np.flip(axis_images, axis=0)\nshallow_images = np.flip(shallow_images, axis=0)", "_____no_output_____" ], [ "# change vx/vy to location on sorted images\ndef rotated_latent_animation(h_coords, v_coords, latent_space, xdist, ydist, X, Z, h_images, v_images, hline, vline):\n fig, ax = plt.subplots(2,2, figsize=(36,16))\n feat_list = []\n #the real total you need\n num_steps = len(h_coords)\n #num_steps = 10\n cmap= \"RdBu_r\"\n \n dummy_horz = np.zeros(shape=(30,128))\n dummy_horz[:,:] = np.nan\n dummy_vert = np.zeros(shape=(30,128))\n dummy_vert[:,:] = np.nan\n count = 29\n for i in range(num_steps):\n \n for j in range(len(dummy_horz)):\n dummy_horz[count,:] = h_images[i,j,:]\n if i <= len(v_coords) -1:\n dummy_vert[count,:] = v_images[i,j,:]\n else:\n dummy_vert[count,:] = v_images[-1,j,:]\n count = count-1\n \n h_rect = patches.Rectangle((h_coords[i,0],h_coords[i,1]),xdist,ydist,linewidth=4,edgecolor='black',facecolor='none')\n if i <= len(v_coords) -1:\n v_rect = patches.Rectangle((v_coords[i,0],v_coords[i,1]),xdist,ydist,linewidth=4,edgecolor='black',facecolor='none')\n else:\n v_rect = patches.Rectangle((v_coords[-1,0],v_coords[-1,1]),xdist,ydist,linewidth=4,edgecolor='black',facecolor='none')\n \n \n \n ax[0,0].scatter(latent_space[:, 0], latent_space[:, 1], c=\"#3D9AD1\", s=0.4, animated=True)\n ax[0,0].scatter(x=hline[:, 0], y=hline[:, 1], c=\"Red\", s=2.0, animated=True)\n cs0 = ax[0,0].add_patch(h_rect)\n \n cs2 = ax[1,0].scatter(latent_space[:, 0], latent_space[:, 1], c=\"#3D9AD1\", s=0.4, animated=True)\n ax[1,0].scatter(x=vline[:, 0], y=vline[:, 1], c=\"Green\", s=2.0, animated=True)\n cs2 = ax[1,0].add_patch(v_rect)\n \n \n cs3 = ax[1,1].pcolor(X, Z, dummy_vert, cmap=cmap, animated=True, vmin = -1.0, vmax = 1.0)\n #ax[1,1].set_title(\"(y) Shallow convection\", fontsize=fz*2.0)\n cs1 = ax[0,1].pcolor(X, Z, dummy_horz, cmap=cmap, animated=True, vmin = -1.0, vmax = 1.0)\n #ax[0,1].set_title(\"(x) Deep Convection\", fontsize=fz*2.0)\n \n ax[0,1].set_xlabel(\"CRMs\", fontsize=fz*1.5)\n ax[1,1].set_xlabel(\"CRMs\", fontsize=fz*1.5)\n ax[0,1].set_ylabel(\"Pressure (hpa)\", fontsize=fz*1.5)\n ax[1,1].set_ylabel(\"Pressure (hpa)\", fontsize=fz*1.5)\n \n y_ticks = np.array([1000, 800, 600, 400, 200])\n ax[1,1].set_yticklabels(y_ticks)\n ax[0,1].set_yticklabels(y_ticks)\n \n divider = make_axes_locatable(ax[1,1])\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n fig.colorbar(cs1, cax=cax)\n divider = make_axes_locatable(ax[0,1])\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n fig.colorbar(cs1, cax=cax)\n \n feat_list.append([cs2, cs3, cs1, cs0])\n \n\n count = 29 \n \n ani = animation.ArtistAnimation(fig, feat_list, interval = 125, blit = False, repeat = True)\n ani.save('/fast/gmooers/gmooers_git/CBRAIN-CAM/MAPS/Animations/Figures/31_W_Diagonals_500.mp4')\n plt.show()\n \n \nrotated_latent_animation(top_line, shallow_line, z_test_tsne_saved, 0.4, 1.5, Xs, Zs, axis_images, shallow_images, axis_list, shallow_list)", "_____no_output_____" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# General\nfrom os import path\nfrom random import randrange\n\nfrom sklearn.model_selection import train_test_split, GridSearchCV #cross validation\nfrom sklearn.metrics import confusion_matrix, plot_confusion_matrix, make_scorer\nfrom sklearn.metrics import accuracy_score, roc_auc_score, balanced_accuracy_score\n\nfrom sklearn.preprocessing import LabelEncoder\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport xgboost as xgb\nfrom sklearn.ensemble import RandomForestClassifier\n\nimport pickle\nimport joblib \n", "_____no_output_____" ] ], [ [ "## TRAIN SET", "_____no_output_____" ] ], [ [ "trainDataFull = pd.read_csv(\"trainData.csv\")\ntrainDataFull.head(3)", "_____no_output_____" ], [ "trainDataFull.info()", "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 61878 entries, 0 to 61877\nColumns: 104 entries, v1 to target\ndtypes: float64(103), int64(1)\nmemory usage: 49.1 MB\n" ], [ "trainDataFull.describe()", "_____no_output_____" ], [ "trainData = trainDataFull.loc[:,'v1':'v99']\ntrainData.head(3)", "_____no_output_____" ], [ "trainLabels = trainDataFull.loc[:,'target']\ntrainLabels.unique()", "_____no_output_____" ], [ "# encode string class values as integers\nlabel_encoder = LabelEncoder()\nlabel_encoder = label_encoder.fit(trainLabels)\nlabel_encoded_y = label_encoder.transform(trainLabels)\nlabel_encoded_y", "_____no_output_____" ], [ "X_train, X_test, y_train, y_test = train_test_split(trainData.values, \n label_encoded_y, \n test_size = 0.3, \n random_state = 33,\n shuffle = True,\n stratify = label_encoded_y)", "_____no_output_____" ] ], [ [ "## MODEL-2 (Random Forest Classifier)", "_____no_output_____" ] ], [ [ "RFC_model = RandomForestClassifier(n_estimators=800,\n verbose=2,\n random_state=0,\n criterion='gini')\nRFC_model", "_____no_output_____" ], [ "RFC_model.fit(X_train, y_train)", "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n" ], [ "# make predictions for test data\ny_pred = RFC_model.predict(X_test)\ny_pred", "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n[Parallel(n_jobs=1)]: Done 800 out of 800 | elapsed: 2.8s finished\n" ], [ "predictions = [round(value) for value in y_pred]", "_____no_output_____" ], [ "# evaluate predictions\naccuracy = accuracy_score(y_test, predictions)\nprint(\"Accuracy: %.2f%%\" % (accuracy * 100.0))", "Accuracy: 80.88%\n" ], [ "#fig = plt.figure(figsize=(10,10))\nplot_confusion_matrix(RFC_model,\n X_test,\n y_test,\n values_format='d')", "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n[Parallel(n_jobs=1)]: Done 800 out of 800 | elapsed: 3.1s finished\n" ] ], [ [ "## Save Valid Score", "_____no_output_____" ] ], [ [ "y_score = RFC_model.predict_proba(X_test)\ny_score[0]", "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n[Parallel(n_jobs=1)]: Done 800 out of 800 | elapsed: 3.2s finished\n" ], [ "valid_score = pd.DataFrame(y_score, columns=['c1','c2','c3','c4','c5','c6','c7','c8','c9'])\nvalid_score", "_____no_output_____" ], [ "valid_score.to_csv('./results/valid-submission-RFC.csv', index = False)", "_____no_output_____" ] ], [ [ "## Save & Load Model", "_____no_output_____" ], [ "## joblib", "_____no_output_____" ] ], [ [ "# Save the model as a pickle in a file \njoblib.dump(RFC_model, './model/model_RFC.pkl') \n \n# Load the model from the file \nRFC_model_from_joblib = joblib.load('./model/model_RFC.pkl') \n \n# Use the loaded model to make predictions \nRFC_model_predictions = RFC_model_from_joblib.predict(X_test) \n\n# evaluate predictions\naccuracy = accuracy_score(y_test, RFC_model_predictions)\nprint(\"Accuracy: %.2f%%\" % (accuracy * 100.0))", "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n" ] ], [ [ "## GridSearchCV ", "_____no_output_____" ] ], [ [ "clf = GridSearchCV(RFC_model_model,\n {'max_depth': [4, 6],\n 'n_estimators': [100, 200]}, \n verbose=1,\n cv=2)\nclf.fit(X_train, \n y_train, \n early_stopping_rounds=10,\n eval_metric='mlogloss',\n eval_set=[(X_train, y_train), (X_test, y_test)], \n verbose=True)\nprint(clf.best_score_)\nprint(clf.best_params_)", "_____no_output_____" ], [ "# Save the model as a pickle in a file \njoblib.dump(clf.best_estimator_, './model/clf.pkl')\n\n# Load the model from the file \nclf_from_joblib = joblib.load('./model/clf.pkl') \n\n# Use the loaded model to make predictions \nclf_predictions = clf_from_joblib.predict(X_test) \n\n# evaluate predictions\naccuracy = accuracy_score(y_test, clf_predictions)\nprint(\"Accuracy: %.2f%%\" % (accuracy * 100.0))", "_____no_output_____" ] ], [ [ "# TEST", "_____no_output_____" ] ], [ [ "testData = pd.read_csv(\"testData.csv\")\ntestData", "_____no_output_____" ], [ "# Use the loaded model to make predictions \ntest_predictions = RFC_model.predict(testData.values)\ntest_predictions", "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n[Parallel(n_jobs=1)]: Done 800 out of 800 | elapsed: 26.3s finished\n" ], [ "# Use the loaded model to make predictions probability\ntest_predictions = RFC_model.predict_proba(testData.values)\ntest_predictions", "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n[Parallel(n_jobs=1)]: Done 800 out of 800 | elapsed: 25.9s finished\n" ], [ "result = pd.DataFrame(test_predictions, columns=['c1','c2','c3','c4','c5','c6','c7','c8','c9'])\nresult", "_____no_output_____" ], [ "result.to_csv('./results/test-submission-RFC.csv', index = False)", "_____no_output_____" ] ], [ [ "## REFERENCES", "_____no_output_____" ], [ "1- https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn\n\n2- https://github.com/dmlc/xgboost/blob/master/demo/guide-python/sklearn_examples.py\n\n3- https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n\n4- https://www.datacamp.com/community/tutorials/xgboost-in-python\n\n5- https://scikit-learn.org/stable/modules/ensemble.html#voting-classifier\n\n6- https://www.datacamp.com/community/tutorials/random-forests-classifier-python?utm_source=adwords_ppc&utm_campaignid=1455363063&utm_adgroupid=65083631748&utm_device=c&utm_keyword=&utm_matchtype=b&utm_network=g&utm_adpostion=&utm_creative=332602034364&utm_targetid=aud-392016246653:dsa-429603003980&utm_loc_interest_ms=&utm_loc_physical_ms=1012782&gclid=EAIaIQobChMI49HTjNO06wIVB-ztCh23nwMLEAAYASAAEgKKEvD_BwE", "_____no_output_____" ] ] ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "<br>\n\n# Layers\n\nUma vez criada uma sequencia de códigos, foi possível definir uma funão que integra todos eles, apresentada abaixo:", "_____no_output_____" ] ], [ [ "from get_data_datageo import *", "_____no_output_____" ] ], [ [ "<br>\n\n## Sedes Municipais", "_____no_output_____" ] ], [ [ "# Input dos caminhos para os metadados\nurl = 'http://datageo.ambiente.sp.gov.br/geoportal/catalog/search/resource/details.page?uuid='\nid_metadados = '{64BF344A-3AD0-410A-A3AA-DFE01C4E9BBB}'\n\n# URL\nurl_meta = '{}{}'.format(url, id_metadados)\n\n# Download\ngdf = download_datageo_shp(url_meta)\n\n# Renomeia Colunas\ngdf = gdf.rename(\n columns={\n 'Nome': 'nome_municipio'\n }\n)\n\n# Deleta Colunas\ngdf = gdf.drop(['Codigo_CET'], axis=1)\n\n# Results\nprint(gdf.dtypes)\ndisplay(gdf.head(5))\n\n# Salva\ngdf.to_file(os.path.join('data', 'sedes_municipais.geojson'), driver='GeoJSON', encoding='utf-8')\ngdf.to_file(os.path.join('data', 'sedes_municipais.gpkg'), layer='Sedes', driver='GPKG')", "Página com metadados: http://datageo.ambiente.sp.gov.br/geoportal/catalog/search/resource/details.page?uuid={64BF344A-3AD0-410A-A3AA-DFE01C4E9BBB}\nResposta da página foi <Response [200]>\n> Encontrei o shapefile\nLink: http://datageo.ambiente.sp.gov.br/geoserver/datageo/SedesMunicipais/wfs?version=1.0.0&request=GetFeature&outputFormat=SHAPE-ZIP&typeName=SedesMunicipais\nEncontrei 1 arquivos \".shp\", sendo que o primeiro deles é o \"SedesMunicipaisPoint.shp\"\n Codigo_CET Nome geometry\n0 730 Rosana POINT (-53.05578 -22.57789)\n1 725 Euclides da Cunha Paulista POINT (-52.59680 -22.55792)\n2 690 Teodoro Sampaio POINT (-52.16922 -22.53031)\n3 561 Presidente Epitácio POINT (-52.10945 -21.76577)\n4 435 Marabá Paulista POINT (-51.96234 -22.10382)\nepsg:4674\nepsg:4326\nnome_municipio object\ngeometry geometry\ndtype: object\n" ] ], [ [ "<br>\n\n## Limite Municipal", "_____no_output_____" ] ], [ [ "# Input dos caminhos para os metadados\nurl = 'http://datageo.ambiente.sp.gov.br/geoportal/catalog/search/resource/details.page?uuid='\nid_metadados = '{74040682-561A-40B8-BB2F-E188B58088C1}'\n\n# URL\nurl_meta = '{}{}'.format(url, id_metadados)\n\n# Download\ngdf = download_datageo_shp(url_meta)\n\n# Renomeia Colunas\ngdf = gdf.rename(\n columns={\n 'Cod_ibge':'id_ibge',\n 'Nome':'nome_municipio',\n 'Rotulo':'rotulo_municipio'\n }\n)\n\n# Deleta Colunas\ngdf = gdf.drop(['Cod_Cetesb', 'UGRHI', 'Nome_ugrhi'], axis=1)\n\n# Results\nprint(gdf.dtypes)\ndisplay(gdf.head(5))\n\n# Salva\ngdf.to_file(os.path.join('data', 'limite_municipal.geojson'), driver='GeoJSON', encoding='utf-8')\ngdf.to_file(os.path.join('data', 'limite_municipal.gpkg'), layer='Limite', driver='GPKG')", "Página com metadados: http://datageo.ambiente.sp.gov.br/geoportal/catalog/search/resource/details.page?uuid={74040682-561A-40B8-BB2F-E188B58088C1}\nResposta da página foi <Response [200]>\n> Encontrei o shapefile\nLink: http://datageo.ambiente.sp.gov.br/geoserver/datageo/LimiteMunicipal/wfs?version=1.0.0&request=GetFeature&outputFormat=SHAPE-ZIP&typeName=LimiteMunicipal\nEncontrei 1 arquivos \".shp\", sendo que o primeiro deles é o \"LimiteMunicipalPolygon.shp\"\n Cod_Cetesb Cod_ibge Nome Rotulo UGRHI \\\n0 150 3500105 Adamantina Adamantina 21 \n1 151 3500204 Adolfo Adolfo 16 \n2 152 3500303 Aguai Aguaí 9 \n3 154 3500402 Aguas da Prata Águas da Prata 9 \n4 153 3500501 Aguas de Lindoia Águas de Lindóia 9 \n\n Nome_ugrhi geometry \n0 PEIXE POLYGON ((-51.17735 -21.69213, -51.17716 -21.6... \n1 TIETÊ/BATALHA POLYGON ((-49.74715 -21.29637, -49.74682 -21.2... \n2 MOGI-GUAÇU POLYGON ((-47.23298 -22.05409, -47.23289 -22.0... \n3 MOGI-GUAÇU POLYGON ((-46.75758 -21.84763, -46.75750 -21.8... \n4 MOGI-GUAÇU POLYGON ((-46.66020 -22.47948, -46.66018 -22.4... \nepsg:4674\nepsg:4326\nid_ibge int64\nnome_municipio object\nrotulo_municipio object\ngeometry geometry\ndtype: object\n" ] ] ]

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

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "%reload_ext autoreload\n%autoreload 2\n\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append(\"data_resolution\")\nfrom resolution_helpers import invalid_fips, remove_cols", "_____no_output_____" ], [ "asian_data_path = \"~/ASR-PM2.5/datasets/input_datasets/Asian_subgroup/2015Asiansubgroupdataset.csv\"\n\nasian_data = pd.read_csv(asian_data_path, dtype = {\"FIPS\": str}).dropna()", "_____no_output_____" ], [ "asian_data_updated = asian_data.rename(columns = {\"B01003_001E\": \"estimated_pop\"})", "_____no_output_____" ], [ "asian_data_updated", "_____no_output_____" ], [ "asian_fips = asian_data_updated[\"FIPS\"]\nasian_fips", "_____no_output_____" ], [ "invalid_asian_fips = invalid_fips(asian_fips)\nassert len(invalid_asian_fips) == 0", "_____no_output_____" ], [ "asian_name = asian_data_updated[\"NAME\"]", "_____no_output_____" ], [ "asian_name", "_____no_output_____" ], [ "zip(asian_data_updated.POPGROUP, asian_data_updated.POPGROUP_LABEL)", "_____no_output_____" ], [ "dict(zip(asian_data_updated.POPGROUP, asian_data_updated.POPGROUP_LABEL))", "_____no_output_____" ], [ "asian_data_updated.columns", "_____no_output_____" ], [ "keep_cols = [\"FIPS\", \"POPGROUP\", \"estimated_pop\", \"POPGROUP_LABEL\"]", "_____no_output_____" ], [ "remove_cols(asian_data_updated, keep_cols)\nasian_data_updated.columns", "_____no_output_____" ], [ "asian_data_output_path = \"~/ASR-PM2.5/datasets/intermediate_datasets/resultforasiandataset.csv\"", "_____no_output_____" ], [ "asian_data_updated.to_csv(asian_data_output_path)", "_____no_output_____" ] ] ]

[ "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" ]

[ [ [ "<img src=\"../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">", "_____no_output_____" ], [ "# Accreditation protocol", "_____no_output_____" ], [ "Accreditation Protocol (AP) is a protocol devised to characterize the reliability of noisy quantum devices.<br>\n\nGiven a noisy quantum device implementing a \"target\" quantum circuit, AP certifies an upper-bound on the variation distance between the probability distribution of the outputs returned by the device and the ideal probability distribution.\nThis method is based on Ferracin et al, \"Accrediting outputs of noisy intermediate-scale quantum devices\", https://arxiv.org/abs/1811.09709.\n\nThis notebook gives an example for how to use the ignis.characterization.accreditation module. This particular example shows how to accredit the outputs of a 4-qubit quantum circuit of depth 5. All the circuits are run using the noisy Aer simulator.", "_____no_output_____" ] ], [ [ "#Import general libraries (needed for functions)\nimport numpy as np\nfrom numpy import random\nimport qiskit\n\n#Import Qiskit classes\nfrom qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, Aer, execute\nfrom qiskit.providers.aer.noise import NoiseModel\nfrom qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error\n\n#Import the accreditation functions.\nfrom qiskit.ignis.verification.accreditation import accreditationFitter\nfrom qiskit.ignis.verification.accreditation import accreditation_circuits", "_____no_output_____" ] ], [ [ "# Input to the protocol", "_____no_output_____" ], [ "AP can accredit the outputs of a __target circuit__ that<br>\n1) Takes as input $n$ qubits in the state $|{0}>$<br>\n2) Ends with single-qubit measurements in the Pauli-$Z$ basis<br>\n3) Is made of $m$ \"bands\", each band containing a round of single-qubit gates and a round of controlled-$Z$ gates.<br>\nThe accreditation is made by employing __trap circuits__, circuits that can be efficiently simulated on a classical computer and that whose outputs are used to witness the correct functionality of the device.<br>\n\nLet's now draw a target quantum circuit!\nWe start with a simple circuit to generate and measure 4-qubits GHZ states.", "_____no_output_____" ] ], [ [ "# Create a Quantum Register with n_qb qubits.\nq_reg = QuantumRegister(4, 'q')\n# Create a Classical Register with n_qb bits.\nc_reg = ClassicalRegister(4, 's')\n# Create a Quantum Circuit acting on the q register\ntarget_circuit = QuantumCircuit(q_reg, c_reg)\n\ntarget_circuit.h(0)\ntarget_circuit.h(1)\ntarget_circuit.h(2)\ntarget_circuit.h(3)\ntarget_circuit.cz(0,1)\ntarget_circuit.cz(0,2)\ntarget_circuit.cz(0,3)\ntarget_circuit.h(1)\ntarget_circuit.h(2)\ntarget_circuit.h(3)\n\ntarget_circuit.measure(q_reg, c_reg)\n\ntarget_circuit.draw(output = 'mpl')", "_____no_output_____" ] ], [ [ "# Generating accreditation circuits", "_____no_output_____" ], [ "The function $accreditation\\_circuits$ generates all the circuits required by AP, target and traps. It automatically appends random Pauli gates to the circuits (if the implementation is noisy, these random Pauli gates reduce the noise to Pauli errors ! ) <br>\n\nIt also returns the list $postp\\_list$ of strings required to post-process the outputs, as well as the number $v\\_zero$ indicating the circuit implementing the target.\n\nThis is the target circuit with randomly chosen Pauli gates:", "_____no_output_____" ] ], [ [ "v = 10\n\ncirc_list, postp_list, v_zero = accreditation_circuits(target_circuit, v)\ncirc_list[(v_zero)%(v+1)][0].draw(output = 'mpl')", "_____no_output_____" ] ], [ [ "This is how a trap looks like:", "_____no_output_____" ] ], [ [ "circ_list[(v_zero+1)%(v+1)][0].draw(output = 'mpl')", "_____no_output_____" ] ], [ [ "# Simulate the ideal circuits", "_____no_output_____" ], [ "Let's implement AP.\n\nWe use $accreditation\\_circuits$ to generate target and trap circuits.\nThen, we use the function $single\\_protocol\\_run$ to implement all these circuits, keeping the output of the target only if all of the traps return the correct output.", "_____no_output_____" ] ], [ [ "simulator = qiskit.Aer.get_backend('qasm_simulator')\n\ntest_1 = accreditationFitter()\n\n# Create target and trap circuits with random Pauli gates\ncircuit_list, postp_list, v_zero = accreditation_circuits(target_circuit, v)\n\noutputs_list = []\nfor circuit_k in range(v+1):\n job = execute(circuit_list[circuit_k], simulator,\n shots=1, memory=True)\n outputs_list.append([job.result().get_memory()[0]])\n\n# Post-process the outputs and see if the protocol accepts\ntest_1.single_protocol_run(outputs_list, postp_list, v_zero)\n\nprint(\"Outputs of the target: \",test_1.outputs,\" , AP\",test_1.flag,\"these outputs!\")", "Outputs of the target: [0, 0, 0, 0] , AP accepted these outputs!\n" ] ], [ [ "In the absence of noise, all traps return the expected output, therefore we always accept the output of the target.<br>\n\nTo obtain an upper-bound on the variation distance on the outputs of the target circuit, we need to implement AP $d$ times, each time with ___v___ different trap circuits.", "_____no_output_____" ] ], [ [ "# Number of runs\nd = 20\n\ntest_2 = accreditationFitter()\n\nfor run in range(d):\n \n # Create target and trap circuits with random Pauli gates\n circuit_list, postp_list, v_zero = accreditation_circuits(target_circuit, v)\n \n outputs_list = []\n # Implement all these circuits\n for circuit_k in range(v+1):\n job = execute(circuit_list[circuit_k], simulator,\n shots=1, memory=True)\n outputs_list.append([job.result().get_memory()[0]])\n\n # Post-process the outputs and see if the protocol accepts\n test_2.single_protocol_run(outputs_list, postp_list, v_zero)\n print(\"Protocol run number\",run+1,\", outputs of the target\",test_2.flag)\n \nprint('\\nAfter',test_2.num_runs,'runs, AP has accepted',test_2.N_acc,'outputs!')\n\nprint('\\nList of accepted outputs:\\n', test_2.outputs)", "Protocol run number 1 , outputs of the target accepted\nProtocol run number 2 , outputs of the target accepted\nProtocol run number 3 , outputs of the target accepted\nProtocol run number 4 , outputs of the target accepted\nProtocol run number 5 , outputs of the target accepted\nProtocol run number 6 , outputs of the target accepted\nProtocol run number 7 , outputs of the target accepted\nProtocol run number 8 , outputs of the target accepted\nProtocol run number 9 , outputs of the target accepted\nProtocol run number 10 , outputs of the target accepted\nProtocol run number 11 , outputs of the target accepted\nProtocol run number 12 , outputs of the target accepted\nProtocol run number 13 , outputs of the target accepted\nProtocol run number 14 , outputs of the target accepted\nProtocol run number 15 , outputs of the target accepted\nProtocol run number 16 , outputs of the target accepted\nProtocol run number 17 , outputs of the target accepted\nProtocol run number 18 , outputs of the target accepted\nProtocol run number 19 , outputs of the target accepted\nProtocol run number 20 , outputs of the target accepted\n\nAfter 20 runs, AP has accepted 20 outputs!\n\nList of accepted outputs:\n [[1 1 1 1]\n [0 0 0 0]\n [1 1 1 1]\n [1 1 1 1]\n [1 1 1 1]\n [0 0 0 0]\n [1 1 1 1]\n [1 1 1 1]\n [1 1 1 1]\n [1 1 1 1]\n [1 1 1 1]\n [0 0 0 0]\n [0 0 0 0]\n [1 1 1 1]\n [1 1 1 1]\n [1 1 1 1]\n [0 0 0 0]\n [1 1 1 1]\n [0 0 0 0]\n [0 0 0 0]]\n" ] ], [ [ "The function $bound\\_variation\\_distance$ calculates the upper-bound on the variation distance (VD) using\n\n$$VD\\leq \\frac{\\varepsilon}{N_{\\textrm{acc}}/d-\\theta}\\textrm{ ,}$$\n\nwhere $\\theta\\in[0,1]$ is a positive number and<br>\n\n$$\\varepsilon= \\frac{1.7}{v+1}$$\n\nis the maximum probability of accepting an incorrect state for the target.<br>\nThe function $bound\\_variation\\_distance$ also calculates the confidence in the bound as \n\n$$1-2\\textrm{exp}\\big(-2\\theta d^2\\big)$$", "_____no_output_____" ] ], [ [ "theta = 5/100\n\ntest_2.bound_variation_distance(theta)\n\nprint(\"AP accepted\",test_2.N_acc,\"out of\",test_2.num_runs,\"times.\")\nprint(\"With confidence\",test_2.confidence,\"AP certifies that VD is upper-bounded by\",test_2.bound)", "AP accepted 20 out of 20 times.\nWith confidence 1.0 AP certifies that VD is upper-bounded by 0.16267942583732053\n" ] ], [ [ "# Defining the noise model", "_____no_output_____" ], [ "We define a noise model for the simulator. We add depolarizing error probabilities to the cotrolled-$Z$ and single-qubit gates.", "_____no_output_____" ] ], [ [ "noise_model = NoiseModel()\n\np1q = 0.002\nnoise_model.add_all_qubit_quantum_error(depolarizing_error(p1q, 1), 'u1')\nnoise_model.add_all_qubit_quantum_error(depolarizing_error(p1q, 1), 'u2')\nnoise_model.add_all_qubit_quantum_error(depolarizing_error(p1q, 1), 'u3')\np2q = 0.02\nnoise_model.add_all_qubit_quantum_error(depolarizing_error(p2q, 2), 'cz')\n\nbasis_gates = ['u1','u2','u3','cz']", "_____no_output_____" ] ], [ [ "We then implement noisy circuits and pass their outputs to $single\\_protocol\\_run$.", "_____no_output_____" ] ], [ [ "test_3 = accreditationFitter()\n\nfor run in range(d):\n \n # Create target and trap circuits with random Pauli gates\n circuit_list, postp_list, v_zero = accreditation_circuits(target_circuit, v)\n \n outputs_list = []\n # Implement all these circuits with noise\n for circuit_k in range(v+1):\n job = execute(circuit_list[circuit_k], simulator,\n noise_model=noise_model, basis_gates=basis_gates,\n shots=1, memory=True)\n outputs_list.append([job.result().get_memory()[0]])\n\n # Post-process the outputs and see if the protocol accepts\n test_3.single_protocol_run(outputs_list, postp_list, v_zero)\n print(\"Protocol run number\",run+1,\", outputs of the target\",test_3.flag)\n \nprint(\"\\nAP accepted\",test_3.N_acc,\"out of\",test_3.num_runs,\"times.\")\nprint('\\nList of accepted outputs:\\n', test_3.outputs)\n\ntheta = 5/100\n\ntest_3.bound_variation_distance(theta)\nprint(\"\\nWith confidence\",test_3.confidence,\"AP certifies that VD is upper-bounded by\",test_3.bound)", "Protocol run number 1 , outputs of the target rejected\nProtocol run number 2 , outputs of the target rejected\nProtocol run number 3 , outputs of the target rejected\nProtocol run number 4 , outputs of the target accepted\nProtocol run number 5 , outputs of the target accepted\nProtocol run number 6 , outputs of the target accepted\nProtocol run number 7 , outputs of the target accepted\nProtocol run number 8 , outputs of the target rejected\nProtocol run number 9 , outputs of the target accepted\nProtocol run number 10 , outputs of the target accepted\nProtocol run number 11 , outputs of the target accepted\nProtocol run number 12 , outputs of the target rejected\nProtocol run number 13 , outputs of the target rejected\nProtocol run number 14 , outputs of the target accepted\nProtocol run number 15 , outputs of the target rejected\nProtocol run number 16 , outputs of the target accepted\nProtocol run number 17 , outputs of the target accepted\nProtocol run number 18 , outputs of the target accepted\nProtocol run number 19 , outputs of the target rejected\nProtocol run number 20 , outputs of the target accepted\n\nAP accepted 12 out of 20 times.\n\nList of accepted outputs:\n [[0 0 0 0]\n [1 1 0 1]\n [1 1 1 1]\n [1 1 1 1]\n [0 0 0 0]\n [0 0 0 0]\n [1 1 1 1]\n [0 0 0 0]\n [0 0 0 0]\n [0 0 0 0]\n [0 0 0 0]\n [1 1 1 1]]\n\nWith confidence 1.0 AP certifies that VD is upper-bounded by 0.28099173553719003\n" ] ], [ [ "Changing the number of trap circuits per protocol run changes the upper-bound on the VD, but not the confidence.<br>\n\nWhat number of trap circuits will ensure the minimal upper-bound for your target circuit?", "_____no_output_____" ] ], [ [ "min_traps = 4\nmax_traps = 10\n\n\nfor num_trap_circs in range(0,max_traps-min_traps): \n \n test_4 = accreditationFitter()\n for run in range(d):\n\n # Create target and trap circuits with random Pauli gates\n circuit_list, postp_list, v_zero = accreditation_circuits(target_circuit, num_trap_circs+min_traps)\n\n outputs_list = []\n # Implement all these circuits with noise\n for circuit_k in range(num_trap_circs+min_traps+1):\n job = execute(circuit_list[circuit_k], simulator,\n noise_model=noise_model, basis_gates=basis_gates,\n shots=1, memory=True)\n outputs_list.append([job.result().get_memory()[0]])\n\n # Post-process the outputs and see if the protocol accepts\n test_4.single_protocol_run(outputs_list, postp_list, v_zero)\n\n print(\"\\nWith\", num_trap_circs+min_traps,\n \"traps, AP accepted\", test_4.N_acc, \n \"out of\", test_4.num_runs, \"times.\")\n test_4.bound_variation_distance(theta)\n print(\"With confidence\", test_4.confidence,\n \"AP with\", num_trap_circs+min_traps,\n \"traps certifies that VD is upper-bounded by\", test_4.bound)", "\nWith 4 traps, AP accepted 15 out of 20 times.\nWith confidence 1.0 AP with 4 traps certifies that VD is upper-bounded by 0.48571428571428577\n\nWith 5 traps, AP accepted 14 out of 20 times.\nWith confidence 1.0 AP with 5 traps certifies that VD is upper-bounded by 0.4358974358974359\n\nWith 6 traps, AP accepted 9 out of 20 times.\nWith confidence 1.0 AP with 6 traps certifies that VD is upper-bounded by 0.6071428571428572\n\nWith 7 traps, AP accepted 13 out of 20 times.\nWith confidence 1.0 AP with 7 traps certifies that VD is upper-bounded by 0.35416666666666674\n\nWith 8 traps, AP accepted 12 out of 20 times.\nWith confidence 1.0 AP with 8 traps certifies that VD is upper-bounded by 0.3434343434343434\n\nWith 9 traps, AP accepted 12 out of 20 times.\nWith confidence 1.0 AP with 9 traps certifies that VD is upper-bounded by 0.3090909090909091\n" ] ] ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# **Behavioral Cloning** \n---", "_____no_output_____" ], [ "**Behavioral Cloning Project**\n\nThe goals / steps of this project are the following:\n* Use the simulator to collect data of good driving behavior\n* Build, a convolution neural network in Keras that predicts steering angles from images\n* Train and validate the model with a training and validation set\n* Test that the model successfully drives around track one without leaving the road\n* Summarize the results with a written report\n\n", "_____no_output_____" ], [ "## Rubric Points\n### Here I will consider the [rubric points](https://review.udacity.com/#!/rubrics/432/view) individually and describe how I addressed each point in my implementation. \n\n---", "_____no_output_____" ], [ "### Files Submitted & Code Quality\n\n#### 1. Submission includes all required files and can be used to run the simulator in autonomous mode\nMy project includes the following files:\n* `model.ipynb` containing the script to create and train the model\n* `drive.py` for driving the car in autonomous mode\n* `model.h5` containing a trained convolution neural network \n* `writeup_report.md` summarizing the results", "_____no_output_____" ], [ "#### 2. Submission includes functional code\nUsing the Udacity provided simulator and my drive.py file, the car can be driven autonomously around the track by executing \n```sh\npython drive.py model.h5\n```", "_____no_output_____" ], [ "#### 3. Submission code is usable and readable\n\nThe `model.ipynb` file contains the code for training and saving the convolution neural network. The file shows the pipeline I used for training and validating the model, and it contains comments to explain how the code works.", "_____no_output_____" ], [ "### Model Architecture and Training Strategy", "_____no_output_____" ], [ "#### 1. An appropriate model architecture has been employed\n\nMy model consists of a convolution neural network with 5x5 filter sizes and depths between 6 and 120 (`model.ipynb` file, cell 12) \n\nThe model includes LeakyReLU layers to introduce nonlinearity, and the data is normalized in the model using a Keras lambda layer. \n\n#### 2. Attempts to reduce overfitting in the model\n\nOverfitting is controlled by following steps.\n* In each convolution layer, I have used *Max Pooling*. It helps in reducing the dimension as well as makes neurans to perform better. \n* Using data augumentation techniques, I have distributed the training data across all the output class.\n* In addition to that, the model was trained and validated on different data sets to ensure that the model was not overfitting (code line 10-16). The model was tested by running it through the simulator and ensuring that the vehicle could stay on the track.\n\n#### 3. Model parameter tuning\n\n* `Learning rate` : The model used an adam optimizer, so the learning rate was not tuned manually (`model.ipynb` file, cell 12, line 40).\n\n#### 4. Appropriate training data\n\nI have used Udacity training data. There were **Three** images(Center,Left,Right) for every frame and steering angle for center image. We have *8036* frame details. So, totally there were *24108* images given as input.\n\n\n***Data Distribution Of Given Input Data***\n\n\n\nFrom the above graph, it is observed that we didn't have same amount of data in each output classes. we can achieve equal distribution by two ways.\n1. We can improve the samples for output classes which are lower\n2. Reducing the samples which has large amount of data\n\nI chose the second way. As most of the data has roughly 300 images in an average, increaing these output classes is not the good choice. For the given problem, we don't require these much data also. So, I have selected only maximum of 200 images per output class. Additionaly, I have skipped output classes which has less then 10 images.\n\n\n***Data Distribution Of Selected Data***\n\n\n\nThe above data is comparatively well distributed. Agin, this is not evenly distributed in all output classes. As we don't take large turn everytime. Mostly we drive straightly with slight turn. So, these selected data will work without any issue.\n\nI have used a combination of central, left and right images for training my model. This will help in recovering from the left and right sides of the road. \n\nFor details about how I created the training data, see the next section. ", "_____no_output_____" ], [ "### Model Architecture and Training Strategy", "_____no_output_____" ], [ "#### 1. Solution Design Approach\nI have divided the problem into Data Augumentation & Building the neural network. For each change in the data set, I will check my model on different model architecture. From each set, minimum one model will be selected for further improvement.\n\n***SET 1 :***\n\n\n***SET 2***\n\n\n***SET 3***\n\n\n***SET 4***\n\n> **Note :** For each SET seperate python notebook is used. These notebooks are also uploaded with results for reference.(For example : `SET 1` uses `1_Mode_Training.ipynb` respectively)", "_____no_output_____" ], [ "#### 2. Final Model Architecture\nMy final model consists of **Four** hidden layers.( 3 Convolution layer followed by one dense layer)\n\n| Layer \t\t| Description\t \t\t\t\t\t| \n|:---------------------:|:---------------------------------------------:| \n| Input \t\t| 160x320x3 \t\t\t\t\t\t | \n| Resizing Image | 85x320x3 |\n| Convolution 5x5 \t| 1x1 stride, valid padding, outputs 81x316x6 \t|\n| Leaky ReLU Activation\t\t\t\t\t\t\t\t\t\t\t\t\t|\n| Max pooling\t \t| 2x2 stride, 2x2 filter, outputs 40x158x6\t\t|\n| Convolution 5x5 \t| 1x1 stride, valid padding, outputs 36x154x36 \t|\n| Leaky ReLU Activation\t\t\t\t\t\t\t\t\t\t\t\t\t|\n| Max pooling\t \t| 2x2 stride, 2x2 filter, outputs 18x77x3\t |\n| Convolution 5x5 \t| 1x1 stride, valid padding, outputs 14x73x120 \t|\n| Leaky ReLU Activation\t\t\t\t\t\t\t\t\t\t\t\t\t|\n| Max pooling\t \t| 2x2 stride, 2x2 filter, outputs 7x36x120\t |\n| Fully connected#1\t\t| 30240 input, 256 output\t\t\t\t |\n| Fully connected#2\t\t| 256 input, 1 output \t\t\t\t |\n", "_____no_output_____" ], [ "#### 3. Creation of the Training Set & Training Process\n**Training Set Selection :**\n\nAs discussed in the previous section, apart from 24108 training images 10818 images selected. Among them 20 percent of the images are used for validation.\nThe input data is distributed among all output classes to avoid biased output. The whole data set is shuffled to get random classes in each batch.\n\n\n**Data Augumentation :**\n\nThe upper portion of the image not required for detecting the lanes. so, we are slicing the images in the following way. This will reduce the computation cost as well as increase the accuracy.\n> Input Image :\n>> \n\n> Output Image :\n>> \n\nThe cropped image is normalized using the below formulae:\n```python\n>> x=(x/255.0)-0.5\n```\n**Training Process :**\n\n* Among 80% of input data is taken for training and remaining 20% for validation.\n* A batch of 32 augumented image is evaluated by my model\n* The loss will be calculated using `Mean Square Error` function.\n* Depending upon the loss, `Adam optimizer` will update the weights by back propogation algorithm\n* This process is continued for all the batches in our training data. Then, the model is evaluated against the validation data\n\nThe whole training process is repeated for 20 cycle (Epochs). I am plotting the Epochs Vs Loss function to understand the behaviour of my model\n\n>  \n>>Red line : Validation loss\n\n>>Blue line : Training loss", "_____no_output_____" ] ] ]

[ "markdown" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ngroup_names = ['Dutch', 'English', 'Other', 'French']\ncounts = pd.Series([1973, 882, 33, 25], \n index=['Dutch (67.7%)', 'English (30.3%)', 'Other (1.1%)', 'French (0.9%)'])\nexplode = (0, 0.05, 0.1, 0.3)\nfig1 = plt.gcf()\ncounts.plot(kind='pie', colors=['#33a02c','#1f78b4','#b2df8a','#a6cee3'], fontsize=11, explode=explode, labeldistance = 1.1, startangle=70)\nplt.axis('equal')\nplt.ylabel('')\n#plt.legend(labels=counts.index, loc=4, bbox_to_anchor=(0.2, 0.7))\nplt.show()\nfig1.savefig('language.png', dpi=200)", "_____no_output_____" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "<a href=\"https://colab.research.google.com/github/ashraj98/rbf-sin-approx/blob/main/Lab2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>", "_____no_output_____" ], [ "# Lab 2\n\n### Ashwin Rajgopal", "_____no_output_____" ], [ "Start off by importing numpy for matrix math, random for random ordering of samples and pyplot for plotting results.", "_____no_output_____" ] ], [ [ "import matplotlib.pyplot as plt\nimport numpy as np\nimport random", "_____no_output_____" ] ], [ [ "#### Creating the samples\n\nX variables can be generated by using `np.random.rand` to generate a array of random numbers between 0 and 1, which is what is required. The same can be done to generate the noise, but then it needs to be divided by 5 and subtracted by .1 to fit the interval [-0.1, 0.1]. The expected values can then by generated applying the function to the inputs and adding the noise.\n\nFor plotting the original function that will be approximated by the RBF network, `linspace` can be used to generate equally spaced inputs to make a smooth plot of the function.", "_____no_output_____" ] ], [ [ "X = np.random.rand(1, 75).flatten()\nnoise = np.random.rand(1, 75).flatten() / 5 - 0.1\nD = 0.5 + 0.4 * np.sin(2 * np.pi * X) + noise\n\nfunc_X = np.linspace(0, 1, 100)\nfunc_Y = 0.5 + 0.4 * np.sin(2 * np.pi * func_X)", "_____no_output_____" ] ], [ [ "#### K-means algorithm\n\nThis function finds the centers and variances given uncategorized inputs and number of clusters. It also takes in a flag to determined whether to output an averaged variance for all clusters or use specialized variances for each cluster.\n\nThe algorithm begins by choosing random points from the inputs as the center of the clusters, so that every cluster will have at least point assigned to it. Then the algorithm repetitively assigns points to each cluster using Euclidean distance and averages the assigned points for each cluster to find the new centers. The new centers are compared with the old centers, and if they are the same, the algorithm is stopped.\n\nThen using the last assignment of the points, the variance for each cluster is calculated. If a cluster does not have more than one point assigned to it, it is skipped.\n\nIf `use_same_width=True`, then an normalized variance is used for all clusters. The maximum distance is used by using an outer subtraction between the centers array and itself, and then it is divided by `sqrt(2 * # of clusters)`.\n\nIf `use_same_width=False`, then for all clusters that had only one point assigned to it, the average of all the other variances is used as the variance for these clusters.", "_____no_output_____" ] ], [ [ "def kmeans(clusters=2, X=X, use_same_width=False):\n centers = np.random.choice(X, clusters, replace=False)\n diff = 1\n while diff != 0:\n assigned = [[] for i in range(clusters)]\n for x in X:\n assigned_center = np.argmin(np.abs(centers - x))\n assigned[assigned_center].append(x.item())\n new_centers = np.array([np.average(points) for points in assigned])\n diff = np.sum(np.abs(new_centers - centers))\n centers = new_centers\n variances = []\n no_var = []\n for i in range(clusters):\n if len(assigned[i]) < 2:\n no_var.append(i)\n else:\n variances.append(np.var(assigned[i]))\n if use_same_width:\n d_max = np.max(np.abs(np.subtract.outer(centers, centers)))\n avg_var = d_max / np.sqrt(2 * clusters)\n variances = [avg_var for i in range(clusters)]\n else:\n if len(no_var) > 0:\n avg_var = np.average(variances)\n for i in no_var:\n variances.insert(i, avg_var)\n return (centers, np.array(variances))", "_____no_output_____" ] ], [ [ "The function below defines the gaussian function. Given the centers and variances for all clusters, it calculates the output for all gaussians at once for a single input.", "_____no_output_____" ] ], [ [ "def gaussian(centers, variances, x):\n return np.exp((-1 / (2 * variances)) * ((centers - x) ** 2))", "_____no_output_____" ] ], [ [ "#### Training the RBF Network\n\nFor each gaussian, a random weight is generated in the interval [-1, 1]. The same happens for a bias term as well.\n\nThen, for the number of epochs specified, the algorithm calculates the gaussian outputs for each input, and then takes the weighted sum and adds the bias to get the output of the network. Then the LMS algorithm is applied.\n\nAfterwards, the `linspace`d inputs are used to generate the outputs, which allows for plotting the approximating function. Then both the approximated function (red) and the approximating function (blue) are plot, as well as the training data with the noise.", "_____no_output_____" ] ], [ [ "def train(centers, variances, lr, epochs=100):\n num_centers = len(centers)\n W = np.random.rand(1, num_centers) * 2 - 1\n b = np.random.rand(1, 1) * 2 - 1\n order = list(range(len(X)))\n for i in range(epochs):\n random.shuffle(order)\n for j in order:\n x = X[j]\n d = D[j]\n G = gaussian(centers, variances, x)\n y = W.dot(G) + b\n e = d - y\n W += lr * e * G.reshape(1, num_centers)\n b += lr * e\n est_Y = []\n for x in func_X:\n G = gaussian(centers, variances, x)\n y = W.dot(G) + b\n est_Y.append(y.item())\n est_Y = np.array(est_Y)\n fig = plt.figure()\n ax = plt.axes()\n ax.scatter(X, D, label='Sampled')\n ax.plot(func_X, est_Y, '-b', label='Approximate')\n ax.plot(func_X, func_Y, '-r', label='Original')\n plt.title(f'Bases = ${num_centers}, Learning Rate = ${lr}')\n plt.xlabel('x')\n plt.ylabel('y')\n plt.legend(loc=\"upper right\")", "_____no_output_____" ] ], [ [ "The learning rates and number of bases that needed to be tested are defined, and then K-means is run for each combination of base and learning rate. The output of the K-means is used as the input for the RBF training algorithm, and the results are plotted.", "_____no_output_____" ] ], [ [ "bases = [2, 4, 7, 11, 16]\nlearning_rates = [.01, .02]\nfor base in bases:\n for lr in learning_rates:\n centers, variances = kmeans(base, X)\n train(centers=centers, variances=variances, lr=lr)", "_____no_output_____" ] ], [ [ "The best function approximates seem to be with 2 bases. As soon as the bases are increased to 4, overfitting starts to occur, with 16 bases having extreme overfitting.\n\nIncreasing the learning rate seems to decrease the training error but in some cases increases the overfitting of the data.", "_____no_output_____" ], [ "Run the same combinations or number of bases and learning rate again, but this time using the same Gaussian width for all bases.", "_____no_output_____" ] ], [ [ "for base in bases:\n for lr in learning_rates:\n centers, variances = kmeans(base, X, use_same_width=True)\n train(centers=centers, variances=variances, lr=lr, epochs=100)", "_____no_output_____" ] ], [ [ "Using the same width for each base seems to drastically decrease overfitting. Even with 16 bases, the approximating function is very smooth. However, after 100 epochs, the training error is still very high, and the original function is not well approximated.\n\nAfter running the training with significantly more epochs (10,000 to 100,000), the function becomes well approximated for large number of bases. But for smaller number of bases like 2, the approximating function is still not close to the approximated function, whereas when using different Gaussian widths, 2 bases was the best approximator of the original function.\n\nSo, using the same widths, the training takes significantly longer and requires many bases to be used to approximate the original function well.", "_____no_output_____" ] ] ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "from pynq import Overlay\nimport numpy as np\n\n\noverlay = Overlay('/home/xilinx/pynq/overlays/Manuel/Lab_02/lab_02_design_ver_1d.bit')", "_____no_output_____" ], [ "#overlay?", "_____no_output_____" ], [ "# AXI lite interface object instance\n# Used for data signals\n#\n# Address | Address | Signal\n# (HEX) | (DEC) |\n# 0x0000 | 0000 | input pointer_A\n# 0x0004 | 0004 | input pointer_B\n# 0x0008 | 0008 | input pointer_code\n# 0x000C | 0012 | output pointer_Y\n# 0x0010 | 0016 | output pointer_Cout\n\n# Data Signals\npointer_code = 0x0000\npointer_A = 0x0004\npointer_B = 0x0008\npointer_Y = 0x000C\npointer_Cout =0x0010\n\naxi_interface = overlay.axi_alu_data_0", "_____no_output_____" ], [ "# Load A and B into memory\nA = 31\nB = 32\n\naxi_interface.write(pointer_A, A)\naxi_interface.write(pointer_B, B)", "_____no_output_____" ], [ "# code | function\n# 000 | A & B\n# 001 | A | B\n# 010 | A + B\n# 011 | Not defined\n# 100 | A & (~B)\n# 101 | A | (~B)\n# 110 | A - B\n# 111 | SLT\n\n# codes\nand_ = 0\nor_ = 1\nadd_ = 2\nand_not_ = 4\nor_not_ = 5\ndif_ = 6\nslt_ = 7\n\n#Load code\ncode = dif_\naxi_interface.write(pointer_code, code) ", "_____no_output_____" ], [ "# Load data to register\naxi_interface.write(pointer_Y, 1)", "_____no_output_____" ], [ "# Read result\nprint((np.array(axi_interface.read(pointer_Y))).astype('int16'))", "-1\n" ], [ "# Load data to register\naxi_interface.write(pointer_Cout,0)", "_____no_output_____" ], [ "# Read result\nprint((np.array(axi_interface.read(pointer_Cout))).astype('int16'))", "0\n" ] ] ]

[ "code" ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import csv\nimport numpy as np \n\nposts = []\npath = 'data/Constraint_Train.csv'\nnum_long_posts = 0\nnum_real = 0\n\nwith open(path, newline='', encoding='utf-8') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',', quotechar='\\\"')\n spamreader.__next__() # Skip header row\n for row in spamreader:\n if len(row[1]) > 280:\n num_long_posts += 1\n else: \n row[1] = row[1].replace(',', ' , ')\\\n .replace(\"'\", \" ' \")\\\n .replace('.', ' . ')\\\n .replace('!', ' ! ')\\\n .replace('?', ' ? ')\\\n .replace(';', ' ; ')\n words = row[1].split()\n num_real = num_real + 1 if row[2] == 'real' else num_real\n sentence = [word.lower() for word in words]\n posts.append(sentence)\n\nvocab = set([w for s in posts for w in s])\n\ntrain = posts[:]\n\nprint(\"First 10 posts in training set: \\n\", train[:10])\n\nfrom collections import Counter\n\nprint(\"- Number of datapoints in training set: \", len(posts))\nreal_percentage = num_real * 100 / len(posts)\nprint(\"- Split of training data between real and fake: \", real_percentage, \\\n \"% real, \", 100 - real_percentage, \"% fake\")\nlengths = [len(post) for post in train]\nprint(\"- Average post length in train:\", np.mean(lengths))\nchars = []\nfor post in train:\n length = 0\n for word in post:\n length += len(word)\n chars.append(length) \nprint(\"- Average num charachters in post in train: \", np.mean(chars))\nprint(\"- Num posts removed because they were longer than 280 charachters: \", num_long_posts)\nwords = [word for post in train for word in post]\ncnt = Counter(words)\nprint(\"- Number of unique words in train: \", len(cnt.keys()))\nprint(\"- 10 most common words in train: \", [(i, round(cnt[i] / len(words) * 100.0, 2)) for i, ntount in cnt.most_common(10)])\ntot = np.sum(list(cnt.values()))\nprint(\"- Total words in train:\", tot)", "First 10 posts in training set: \n [['the', 'cdc', 'currently', 'reports', '99031', 'deaths', '.', 'in', 'general', 'the', 'discrepancies', 'in', 'death', 'counts', 'between', 'different', 'sources', 'are', 'small', 'and', 'explicable', '.', 'the', 'death', 'toll', 'stands', 'at', 'roughly', '100000', 'people', 'today', '.'], ['states', 'reported', '1121', 'deaths', 'a', 'small', 'rise', 'from', 'last', 'tuesday', '.', 'southern', 'states', 'reported', '640', 'of', 'those', 'deaths', '.', 'https://t', '.', 'co/yasgrtt4ux'], ['politically', 'correct', 'woman', '(almost)', 'uses', 'pandemic', 'as', 'excuse', 'not', 'to', 'reuse', 'plastic', 'bag', 'https://t', '.', 'co/thf8gunfpe', '#coronavirus', '#nashville'], ['#indiafightscorona:', 'we', 'have', '1524', '#covid', 'testing', 'laboratories', 'in', 'india', 'and', 'as', 'on', '25th', 'august', '2020', '36827520', 'tests', 'have', 'been', 'done', ':', '@profbhargava', 'dg', '@icmrdelhi', '#staysafe', '#indiawillwin', 'https://t', '.', 'co/yh3zxknnhz'], ['populous', 'states', 'can', 'generate', 'large', 'case', 'counts', 'but', 'if', 'you', 'look', 'at', 'the', 'new', 'cases', 'per', 'million', 'today', '9', 'smaller', 'states', 'are', 'showing', 'more', 'cases', 'per', 'million', 'than', 'california', 'or', 'texas:', 'al', 'ar', 'id', 'ks', 'ky', 'la', 'ms', 'nv', 'and', 'sc', '.', 'https://t', '.', 'co/1pyw6cwras'], ['covid', 'act', 'now', 'found', '\"on', 'average', 'each', 'person', 'in', 'illinois', 'with', 'covid-19', 'is', 'infecting', '1', '.', '11', 'other', 'people', '.', 'data', 'shows', 'that', 'the', 'infection', 'growth', 'rate', 'has', 'declined', 'over', 'time', 'this', 'factors', 'in', 'the', 'stay-at-home', 'order', 'and', 'other', 'restrictions', 'put', 'in', 'place', '.', '\"', 'https://t', '.', 'co/hhigdd24fe'], ['if', 'you', 'tested', 'positive', 'for', '#covid19', 'and', 'have', 'no', 'symptoms', 'stay', 'home', 'and', 'away', 'from', 'other', 'people', '.', 'learn', 'more', 'about', 'cdc’s', 'recommendations', 'about', 'when', 'you', 'can', 'be', 'around', 'others', 'after', 'covid-19', 'infection:', 'https://t', '.', 'co/z5kkxpqkyb', '.', 'https://t', '.', 'co/9pamy0rxaf'], ['obama', 'calls', 'trump’s', 'coronavirus', 'response', 'a', 'chaotic', 'disaster', 'https://t', '.', 'co/dedqzehasb'], ['?', '?', '?', 'clearly', ',', 'the', 'obama', 'administration', 'did', 'not', 'leave', 'any', 'kind', 'of', 'game', 'plan', 'for', 'something', 'like', 'this', '.', '?', '?', '�'], ['retraction—hydroxychloroquine', 'or', 'chloroquine', 'with', 'or', 'without', 'a', 'macrolide', 'for', 'treatment', 'of', 'covid-19:', 'a', 'multinational', 'registry', 'analysis', '-', 'the', 'lancet', 'https://t', '.', 'co/l5v2x6g9or']]\n- Number of datapoints in training set: 5604\n- Split of training data between real and fake: 49.10778015703069 % real, 50.89221984296931 % fake\n- Average post length in train: 27.63508208422555\n- Average num charachters in post in train: 138.05442541042112\n- Num posts removed because they were longer than 280 charachters: 816\n- Number of unique words in train: 19395\n- 10 most common words in train: [('.', 7.03), ('the', 3.49), ('of', 2.24), ('https://t', 2.18), ('to', 2.07), ('in', 1.94), ('a', 1.57), ('and', 1.41), ('is', 1.07), ('for', 0.92)]\n- Total words in train: 154867\n" ], [ "import csv\nimport numpy as np \n\nposts = []\npath = 'data/Constraint_Val.csv'\nnum_long_posts = 0\nnum_real = 0\n\nwith open(path, newline='', encoding='utf-8') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',', quotechar='\\\"')\n spamreader.__next__() # Skip header row\n for row in spamreader:\n if len(row[1]) > 280:\n num_long_posts += 1\n else: \n row[1] = row[1].replace(',', ' , ')\\\n .replace(\"'\", \" ' \")\\\n .replace('.', ' . ')\\\n .replace('!', ' ! ')\\\n .replace('?', ' ? ')\\\n .replace(';', ' ; ')\n words = row[1].split()\n num_real = num_real + 1 if row[2] == 'real' else num_real\n sentence = [word.lower() for word in words]\n posts.append(sentence)\n\nvocab = set([w for s in posts for w in s])\n\nval = posts[:]\n\nprint(\"First 10 posts in validation set: \\n\", val[:10])\n\nfrom collections import Counter\n\nprint(\"- Number of datapoints in validation set: \", len(posts))\nreal_percentage = num_real * 100 / len(posts)\nprint(\"- Split of training data between real and fake: \", real_percentage, \\\n \"% real, \", 100 - real_percentage, \"% fake\")\nlengths = [len(post) for post in val]\nprint(\"- Average post length in val:\", np.mean(lengths))\nchars = []\nfor post in val:\n length = 0\n for word in post:\n length += len(word)\n chars.append(length) \nprint(\"- Average num charachters in post in val: \", np.mean(chars))\nprint(\"- Num posts removed because they were longer than 280 charachters: \", num_long_posts)\nwords = [word for post in val for word in post]\ncnt = Counter(words)\nprint(\"- Number of unique words in val: \", len(cnt.keys()))\nprint(\"- 10 most common words in val: \", [(i, round(cnt[i] / len(words) * 100.0, 2)) for i, ntount in cnt.most_common(10)])\ntot = np.sum(list(cnt.values()))\nprint(\"- Total words in val:\", tot)", "First 10 posts in validation set: \n [['chinese', 'converting', 'to', 'islam', 'after', 'realising', 'that', 'no', 'muslim', 'was', 'affected', 'by', '#coronavirus', '#covd19', 'in', 'the', 'country'], ['11', 'out', 'of', '13', 'people', '(from', 'the', 'diamond', 'princess', 'cruise', 'ship)', 'who', 'had', 'intially', 'tested', 'negative', 'in', 'tests', 'in', 'japan', 'were', 'later', 'confirmed', 'to', 'be', 'positive', 'in', 'the', 'united', 'states', '.'], ['covid-19', 'is', 'caused', 'by', 'a', 'bacterium', ',', 'not', 'virus', 'and', 'can', 'be', 'treated', 'with', 'aspirin'], ['mike', 'pence', 'in', 'rnc', 'speech', 'praises', 'donald', 'trump’s', 'covid-19', '“seamless”', 'partnership', 'with', 'governors', 'and', 'leaves', 'out', 'the', 'president', \"'\", 's', 'state', 'feuds:', 'https://t', '.', 'co/qj6hsewtgb', '#rnc2020', 'https://t', '.', 'co/ofoerzdfyy'], ['6/10', 'sky', \"'\", 's', '@edconwaysky', 'explains', 'the', 'latest', '#covid19', 'data', 'and', 'government', 'announcement', '.', 'get', 'more', 'on', 'the', '#coronavirus', 'data', 'here👇', 'https://t', '.', 'co/jvgzlsbfjh', 'https://t', '.', 'co/pygskxesbg'], ['no', 'one', 'can', 'leave', 'managed', 'isolation', 'for', 'any', 'reason', 'without', 'returning', 'a', 'negative', 'test', '.', 'if', 'they', 'refuse', 'a', 'test', 'they', 'can', 'then', 'be', 'held', 'for', 'a', 'period', 'of', 'up', 'to', '28', 'days', '.', '\\u2063', '\\u2063', 'on', 'june', 'the', '16th', 'exemptions', 'on', 'compassionate', 'grounds', 'have', 'been', 'suspended', '.', '\\u2063', '\\u2063'], ['#indiafightscorona', 'india', 'has', 'one', 'of', 'the', 'lowest', '#covid19', 'mortality', 'globally', 'with', 'less', 'than', '2%', 'case', 'fatality', 'rate', '.', 'as', 'a', 'result', 'of', 'supervised', 'home', 'isolation', '&amp', ';', 'effective', 'clinical', 'treatment', 'many', 'states/uts', 'have', 'cfr', 'lower', 'than', 'the', 'national', 'average', '.', 'https://t', '.', 'co/qlik8ypp7e'], ['rt', '@who:', '#covid19', 'transmission', 'occurs', 'primarily', 'through', 'direct', 'indirect', 'or', 'close', 'contact', 'with', 'infected', 'people', 'through', 'their', 'saliva', 'and', 'res…'], ['news', 'and', 'media', 'outlet', 'abp', 'majha', 'on', 'the', 'basis', 'of', 'an', 'internal', 'memo', 'of', 'south', 'central', 'railway', 'reported', 'that', 'a', 'special', 'train', 'has', 'been', 'announced', 'to', 'take', 'the', 'stranded', 'migrant', 'workers', 'home', '.'], ['?', '?', '?', 'church', 'services', 'can', '?', '?', '?', 't', 'resume', 'until', 'we', '?', '?', '?', 're', 'all', 'vaccinated', ',', 'says', 'bill', 'gates', '.', '?', '?', '�']]\n- Number of datapoints in validation set: 1873\n- Split of training data between real and fake: 49.386011745862255 % real, 50.613988254137745 % fake\n- Average post length in val: 27.6903363587827\n- Average num charachters in post in val: 137.95835557928456\n- Num posts removed because they were longer than 280 charachters: 267\n- Number of unique words in val: 9225\n- 10 most common words in val: [('.', 7.04), ('the', 3.5), ('of', 2.24), ('https://t', 2.15), ('to', 2.02), ('in', 2.01), ('a', 1.56), ('and', 1.34), ('is', 1.11), ('for', 0.92)]\n- Total words in val: 51864\n" ], [ "import csv\nimport numpy as np \n\nposts = []\npath = 'data/english_test_with_labels.csv'\nnum_long_posts = 0\nnum_real = 0\n\nwith open(path, newline='', encoding='utf-8') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',', quotechar='\\\"')\n spamreader.__next__() # Skip header row\n for row in spamreader:\n if len(row[1]) > 280:\n num_long_posts += 1\n else: \n row[1] = row[1].replace(',', ' , ')\\\n .replace(\"'\", \" ' \")\\\n .replace('.', ' . ')\\\n .replace('!', ' ! ')\\\n .replace('?', ' ? ')\\\n .replace(';', ' ; ')\n words = row[1].split()\n num_real = num_real + 1 if row[2] == 'real' else num_real\n sentence = [word.lower() for word in words]\n posts.append(sentence)\n\nvocab = set([w for s in posts for w in s])\n\ntest = posts[:]\n\nprint(\"First 10 posts in test set: \\n\", test[:10])\n\nfrom collections import Counter\n\nprint(\"- Number of datapoints in test set: \", len(posts))\nreal_percentage = num_real * 100 / len(posts)\nprint(\"- Split of training data between real and fake: \", real_percentage, \\\n \"% real, \", 100 - real_percentage, \"% fake\")\nlengths = [len(post) for post in test]\nprint(\"- Average post length in test:\", np.mean(lengths))\nchars = []\nfor post in test:\n length = 0\n for word in post:\n length += len(word)\n chars.append(length) \nprint(\"- Average num charachters in post in test: \", np.mean(chars))\nprint(\"- Num posts removed because they were longer than 280 charachters: \", num_long_posts)\nwords = [word for post in test for word in post]\ncnt = Counter(words)\nprint(\"- Number of unique words in test: \", len(cnt.keys()))\nprint(\"- 10 most common words in test: \", [(i, round(cnt[i] / len(words) * 100.0, 2)) for i, ntount in cnt.most_common(10)])\ntot = np.sum(list(cnt.values()))\nprint(\"- Total words in test:\", tot)", "First 10 posts in test set: \n [['our', 'daily', 'update', 'is', 'published', '.', 'states', 'reported', '734k', 'tests', '39k', 'new', 'cases', 'and', '532', 'deaths', '.', 'current', 'hospitalizations', 'fell', 'below', '30k', 'for', 'the', 'first', 'time', 'since', 'june', '22', '.', 'https://t', '.', 'co/wzsyme0sht'], ['alfalfa', 'is', 'the', 'only', 'cure', 'for', 'covid-19', '.'], ['president', 'trump', 'asked', 'what', 'he', 'would', 'do', 'if', 'he', 'were', 'to', 'catch', 'the', 'coronavirus', 'https://t', '.', 'co/3mewhusrzi', '#donaldtrump', '#coronavirus'], ['states', 'reported', '630', 'deaths', '.', 'we', 'are', 'still', 'seeing', 'a', 'solid', 'national', 'decline', '.', 'death', 'reporting', 'lags', 'approximately', '28', 'days', 'from', 'symptom', 'onset', 'according', 'to', 'cdc', 'models', 'that', 'consider', 'lags', 'in', 'symptoms', 'time', 'in', 'hospital', 'and', 'the', 'death', 'reporting', 'process', '.', 'https://t', '.', 'co/lbmcot3h9a'], ['this', 'is', 'the', 'sixth', 'time', 'a', 'global', 'health', 'emergency', 'has', 'been', 'declared', 'under', 'the', 'international', 'health', 'regulations', 'but', 'it', 'is', 'easily', 'the', 'most', 'severe-@drtedros', 'https://t', '.', 'co/jvkc0ptett'], ['low', '#vitamind', 'was', 'an', 'independent', 'predictor', 'of', 'worse', 'prognosis', 'in', 'patients', 'with', 'covid-19', '.', 'https://t', '.', 'co/cgd6kphn31', 'https://t', '.', 'co/chtni8k4jd'], ['a', 'common', 'question:', 'why', 'are', 'the', 'cumulative', 'outcome', 'numbers', 'smaller', 'than', 'the', 'current', 'outcome', 'numbers', '?', 'a:', 'most', 'states', 'report', 'current', 'but', 'a', 'few', 'states', 'report', 'cumulative', '.', 'they', 'are', 'apples', 'and', 'oranges', 'and', 'we', 'don', \"'\", 't', 'feel', 'comfortable', 'filling', 'in', 'state', 'cumulative', 'boxes', 'with', 'current', '#s', '.'], ['the', 'government', 'should', 'consider', 'bringing', 'in', 'any', 'new', 'national', 'lockdown', 'rules', 'over', 'christmas', 'rather', 'than', 'now', 'says', 'an', 'oxford', 'university', 'professor', 'https://t', '.', 'co/pdols6cqon'], ['two', 'interesting', 'correlations:', '1)', 'children', 'tend', 'to', 'weather', 'covid-19', 'pretty', 'well', ';', 'they', 'also', 'get', 'a', 'ton', 'of', 'vitamin', 'd', '.', '2)', 'black', 'people', 'are', 'getting', 'slammed', 'by', 'covid-19', ';', 'black', 'people', 'also', 'have', 'much', 'higher', 'instances', 'of', 'vitamin', 'd', 'deficiency', '(76%', 'vs', '40%', 'in', 'the', 'general', 'population)', '.'], ['a', 'photo', 'shows', 'a', '19-year-old', 'vaccine', 'for', 'canine', 'coronavirus', 'that', 'could', 'be', 'used', 'to', 'prevent', 'the', 'new', 'coronavirus', 'causing', 'covid-19', '.']]\n- Number of datapoints in test set: 1848\n- Split of training data between real and fake: 48.917748917748916 % real, 51.082251082251084 % fake\n- Average post length in test: 27.34469696969697\n- Average num charachters in post in test: 136.79978354978354\n- Num posts removed because they were longer than 280 charachters: 292\n- Number of unique words in test: 9101\n- 10 most common words in test: [('.', 6.97), ('the', 3.53), ('of', 2.21), ('https://t', 2.19), ('to', 1.99), ('in', 1.92), ('a', 1.65), ('and', 1.35), ('?', 1.06), ('for', 1.03)]\n- Total words in test: 50533\n" ] ] ]

[ "code" ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "To finish, check out: http://articles.adsabs.harvard.edu/cgi-bin/nph-iarticle_query?1992AJ....104.2213L&amp;data_type=PDF_HIGH&amp;whole_paper=YES&amp;type=PRINTER&amp;filetype=.pdf", "_____no_output_____" ] ], [ [ "# Third-party\nfrom astropy.io import ascii, fits\nimport astropy.coordinates as coord\nimport astropy.units as u\nfrom astropy.constants import c\nimport matplotlib as mpl\nimport matplotlib.pyplot as pl\nimport numpy as np\nfrom scipy.interpolate import interp1d\n\npl.style.use('apw-notebook')\n%matplotlib inline\n# pl.style.use('classic')\n# %matplotlib notebook", "_____no_output_____" ], [ "data_files = [\"../data/apVisit-r5-6994-56770-261.fits\", \"../data/apVisit-r5-6994-56794-177.fits\"]\nmodel_file = \"../data/apStar-r5-2M00004994+1621552.fits\"", "_____no_output_____" ], [ "min_wvln = 15329\nmax_wvln = 15359", "_____no_output_____" ], [ "def load_file(filename, chip):\n hdulist1 = fits.open(filename)\n wvln = hdulist1[4].data[chip]\n ix = (wvln >= min_wvln) & (wvln <= max_wvln)\n \n wvln = wvln[ix]\n flux = hdulist1[1].data[chip,ix]\n flux_err = hdulist1[2].data[chip,ix]\n \n return {'wvln': wvln, 'flux': flux, 'flux_err': flux_err}\n \ndef load_model_file(filename):\n hdulist1 = fits.open(filename)\n flux = hdulist1[1].data[0]\n flux_err = hdulist1[2].data[0]\n wvln = 10**(hdulist1[0].header['CRVAL1'] + np.arange(flux.size) * hdulist1[0].header['CDELT1'])\n \n# ix = (wvln >= min_wvln) & (wvln <= max_wvln)\n ix = (wvln < 15750) & (wvln > 15150) # HACK: magic numbers\n return {'wvln': wvln[ix], 'flux': flux[ix], 'flux_err': flux_err[ix]}", "_____no_output_____" ], [ "d = load_file(fn, chip=2)\nd['wvln'].shape", "_____no_output_____" ], [ "chip = 2\n\nfig,ax = pl.subplots(1,1,figsize=(12,6))\n\nfor fn in data_files:\n d = load_file(fn, chip=chip)\n ax.plot(d['wvln'], d['flux'], drawstyle='steps', marker=None)\n \nref_spec = load_model_file(model_file)\nax.plot(ref_spec['wvln'], 3.2*ref_spec['flux'], drawstyle='steps', marker=None, lw=2.) # HACK: scale up\n\n# _d = 175\n# ax.set_xlim(15150.+_d, 15175.+_d)\n# ax.set_ylim(10000, 20000)", "_____no_output_____" ], [ "all_spectra = [load_file(f, chip=2) for f in files]", "_____no_output_____" ], [ "ref_spec['interp'] = interp1d(ref_spec['wvln'], ref_spec['flux'], kind='cubic', bounds_error=False)", "_____no_output_____" ], [ "def get_design_matrix(data, ref_spec, v1, v2):\n \"\"\"\n Note: Positive velocity is a redshift.\n \"\"\"\n X = np.ones((3, data['wvln'].shape[0]))\n X[1] = ref_spec['interp'](data['wvln'] * (1 + v1/c)) # this is only good to first order in (v/c)\n X[2] = ref_spec['interp'](data['wvln'] * (1 + v2/c))\n return X", "_____no_output_____" ], [ "def get_optimal_chisq(data, ref_spec, v1, v2):\n X = get_design_matrix(data, ref_spec, v1, v2)\n return np.linalg.solve( X.dot(X.T), X.dot(data['flux']) )", "_____no_output_____" ], [ "spec_i = 1\nv1 = 35 * u.km/u.s\nv2 = -5 * u.km/u.s\nX = get_design_matrix(all_spectra[spec_i], ref_spec, v1, v2)\nopt_pars = get_optimal_chisq(all_spectra[spec_i], ref_spec, v1, v2)\nopt_pars", "_____no_output_____" ], [ "def make_synthetic_spectrum(X, pars):\n return X.T.dot(pars)\n\ndef compute_chisq(data, X, opt_pars):\n synth_spec = make_synthetic_spectrum(X, opt_pars)\n return -np.sum((synth_spec - data['flux'])**2)", "_____no_output_____" ], [ "# opt_pars = np.array([1.1E+4, 0.5, 0.5])\nsynth_spec = make_synthetic_spectrum(X, opt_pars)", "_____no_output_____" ], [ "pl.plot(all_spectra[spec_i]['wvln'], all_spectra[spec_i]['flux'], marker=None, drawstyle='steps')\npl.plot(all_spectra[spec_i]['wvln'], synth_spec, marker=None, drawstyle='steps')", "_____no_output_____" ], [ "_v1_grid = np.linspace(25, 45, 32)\n_v2_grid = np.linspace(-15, 5, 32)\nshp = (_v1_grid.size, _v2_grid.size)\nv_grid = np.vstack(map(np.ravel, np.meshgrid(_v1_grid, _v2_grid))).T\nv_grid.shape", "_____no_output_____" ], [ "chisq = np.zeros(v_grid.shape[0])\nfor i in range(v_grid.shape[0]):\n v1,v2 = v_grid[i]\n opt_pars = get_optimal_chisq(all_spectra[spec_i], ref_spec, \n v1*u.km/u.s, v2*u.km/u.s)\n chisq[i] = compute_chisq(all_spectra[spec_i], X, opt_pars)", "_____no_output_____" ], [ "fig,ax = pl.subplots(1,1,figsize=(9,8))\n\ncb = ax.pcolormesh(v_grid[:,0].reshape(shp), v_grid[:,1].reshape(shp), \n chisq.reshape(shp), cmap='magma')\n\nfig.colorbar(cb)", "_____no_output_____" ], [ "fig,ax = pl.subplots(1,1,figsize=(9,8))\n\ncb = ax.pcolormesh(v_grid[:,0].reshape(shp), v_grid[:,1].reshape(shp), \n np.exp(chisq-chisq.max()).reshape(shp), cmap='magma')\n\nfig.colorbar(cb)", "_____no_output_____" ], [ "fig,ax = pl.subplots(1,1,figsize=(9,8))\n\ncb = ax.pcolormesh(v_grid[:,0].reshape(shp), v_grid[:,1].reshape(shp), \n chisq.reshape(shp), cmap='magma')\n\nfig.colorbar(cb)", "_____no_output_____" ], [ "fig,ax = pl.subplots(1,1,figsize=(9,8))\n\ncb = ax.pcolormesh(v_grid[:,0].reshape(shp), v_grid[:,1].reshape(shp), \n np.exp(chisq-chisq.max()).reshape(shp), cmap='magma')\n\nfig.colorbar(cb)", "_____no_output_____" ] ], [ [ "---\n\ntry using levmar to optimize", "_____no_output_____" ] ], [ [ "from scipy.optimize import leastsq", "_____no_output_____" ], [ "def errfunc(pars, data_spec, ref_spec):\n v1,v2,a,b,c = pars\n X = get_design_matrix(data_spec, ref_spec, v1*u.km/u.s, v2*u.km/u.s)\n synth_spec = make_synthetic_spectrum(X, [a,b,c])\n return (synth_spec - data_spec['flux'])", "_____no_output_____" ], [ "levmar_opt_pars,ier = leastsq(errfunc, x0=[35,-5]+opt_pars.tolist(), args=(all_spectra[0], ref_spec))", "_____no_output_____" ], [ "levmar_opt_pars", "_____no_output_____" ], [ "data_spec = all_spectra[0]\nX = get_design_matrix(data_spec, ref_spec, levmar_opt_pars[0]*u.km/u.s, levmar_opt_pars[1]*u.km/u.s)\nsynth_spec = make_synthetic_spectrum(X, levmar_opt_pars[2:])\npl.plot(data_spec['wvln'], data_spec['flux'], marker=None, drawstyle='steps')\npl.plot(data_spec['wvln'], synth_spec, marker=None, drawstyle='steps')", "_____no_output_____" ] ] ]

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

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "# Final Checks for model", "_____no_output_____" ] ], [ [ "import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt", "_____no_output_____" ], [ "df = pd.read_csv(f\"D:/Docs/train_1.csv\", encoding='mac_roman')", "_____no_output_____" ] ], [ [ "## 1. Use ONLY compliance available columns", "_____no_output_____" ] ], [ [ "df = df[df['compliance'].notna()]\ndf.shape", "_____no_output_____" ], [ "df['fine_amount'] = df['fine_amount'].fillna(0)\ndf.shape", "_____no_output_____" ], [ "df['compliance'].value_counts()", "_____no_output_____" ] ], [ [ "## 2. Build the actual model", "_____no_output_____" ] ], [ [ "from sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfeature_names_tickets = ['ticket_id', 'fine_amount']\nX_tickets = df[feature_names_tickets]\ny_tickets = df['compliance']\n\n#Test size is chosen to get X_test value of 61,001 as the same is provided test data\nX_train, X_test, y_train, y_test = train_test_split(X_tickets, y_tickets, test_size = 0.38153900, random_state = 0)\nclf = LogisticRegression(C=100).fit(X_train, y_train)\nprint(X_train.shape)\nprint(X_test.shape)", "(98879, 2)\n(61001, 2)\n" ] ], [ [ "## 3. Apply GridSearchCV", "_____no_output_____" ] ], [ [ "from sklearn.model_selection import GridSearchCV\nparam_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000] }\ngrid_search = GridSearchCV(estimator = clf, param_grid = param_grid,\n scoring = 'accuracy', cv = 5, verbose=0)\ngrid_search.fit(X_train, y_train)\nprint('Best coring:\\n Best C: {}'.format(grid_search.best_score_))", "Best coring:\n Best C: 0.9269713488926803\n" ], [ "#Fit based on new model now\nclf_best = LogisticRegression(C = 0.92).fit(X_train, y_train)", "_____no_output_____" ] ], [ [ "## 7. Check ROC / AUC", "_____no_output_____" ] ], [ [ "# First we need to load our test dataset\ndf1 = pd.read_csv(f\"D:/Docs/test_1.csv\", encoding='mac_roman')\ndf1['fine_amount'] = df1['fine_amount'].fillna(0)\ndf1.shape", "_____no_output_____" ], [ "feature_names_test = ['ticket_id', 'fine_amount']\nX_test_new = df1[feature_names_test]\nprint(X_test.shape)\nprint(X_test_new.shape)", "(61001, 2)\n(61001, 2)\n" ], [ "from sklearn.metrics import roc_curve, auc\ny_score_lr = clf_best.decision_function(X_test_new)\nfpr_lr, tpr_lr, _ = roc_curve(y_test, y_score_lr)\nroc_auc_lr = auc(fpr_lr, tpr_lr)\nplt.figure()\nplt.xlim([-0.01, 1.00])\nplt.ylim([-0.01, 1.01])\nplt.plot(fpr_lr, tpr_lr, lw=3, label='LogRegr ROC curve (area = {:0.2f})'.format(roc_auc_lr))\nplt.xlabel('False Positive Rate', fontsize=16)\nplt.ylabel('True Positive Rate', fontsize=16)\nplt.title('ROC curve', fontsize=16)\nplt.legend(loc='lower right', fontsize=13)\nplt.plot([0, 1], [0, 1], color='red', lw=3, linestyle='--')\nplt.axes().set_aspect('equal')\nplt.show()", "<ipython-input-11-9dbb75066776>:14: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n plt.axes().set_aspect('equal')\n" ], [ "score = clf.score(X_test_new, y_test)\nprint(score)", "0.9282634710906379\n" ], [ "predictions = clf.predict(X_test_new)\npredictions.shape", "_____no_output_____" ], [ "print(predictions.sum())", "0.0\n" ], [ "pred_values = pd.DataFrame(predictions, columns='Pred') \npred_values.to_csv('result_pred.csv')", "_____no_output_____" ] ] ]

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

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

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "BRANCH = 'main'", "_____no_output_____" ], [ "\"\"\"\nYou can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n\nInstructions for setting up Colab are as follows:\n1. Open a new Python 3 notebook.\n2. Import this notebook from GitHub (File -> Upload Notebook -> \"GITHUB\" tab -> copy/paste GitHub URL)\n3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select \"GPU\" for hardware accelerator)\n4. Run this cell to set up dependencies.\n\"\"\"\n# If you're using Google Colab and not running locally, run this cell\n\n# install NeMo\nBRANCH = 'main'\n!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]", "_____no_output_____" ], [ "import os\nimport wget\nfrom nemo.collections import nlp as nemo_nlp\nfrom nemo.collections import common as nemo_common\nfrom omegaconf import OmegaConf", "_____no_output_____" ] ], [ [ "# Tokenizers Background\n\nFor Natural Language Processing, tokenization is an essential part of data preprocessing. It is the process of splitting a string into a list of tokens. One can think of token as parts like a word is a token in a sentence.\nDepending on the application, different tokenizers are more suitable than others. \n\n\nFor example, a WordTokenizer that splits the string on any whitespace, would tokenize the following string \n\n\"My first program, Hello World.\" -> [\"My\", \"first\", \"program,\", \"Hello\", \"World.\"]\n\nTo turn the tokens into numerical model input, the standard method is to use a vocabulary and one-hot vectors for [word embeddings](https://en.wikipedia.org/wiki/Word_embedding). If a token appears in the vocabulary, its index is returned, if not the index of the unknown token is returned to mitigate out-of-vocabulary (OOV).\n\n\n", "_____no_output_____" ], [ "# Tokenizers in NeMo\n\nIn NeMo, we support the most used tokenization algorithms. We offer a wrapper around [Hugging Faces's AutoTokenizer](https://huggingface.co/transformers/model_doc/auto.html#autotokenizer) - a factory class that gives access to all Hugging Face tokenizers. This includes particularly all BERT-like model tokenizers, such as BertTokenizer, AlbertTokenizer, RobertaTokenizer, GPT2Tokenizer. Apart from that, we also support other tokenizers such as WordTokenizer, CharTokenizer, and [Google's SentencePieceTokenizer](https://github.com/google/sentencepiece). \n\n\nWe make sure that all tokenizers are compatible with BERT-like models, e.g. BERT, Roberta, Albert, and Megatron. For that, we provide a high-level user API `get_tokenizer()`, which allows the user to instantiate a tokenizer model with only four input arguments: \n* `tokenizer_name: str`\n* `tokenizer_model: Optional[str] = None`\n* `vocab_file: Optional[str] = None`\n* `special_tokens: Optional[Dict[str, str]] = None`\n\nHugging Face and Megatron tokenizers (which uses Hugging Face underneath) can be automatically instantiated by only `tokenizer_name`, which downloads the corresponding `vocab_file` from the internet. \n\nFor SentencePieceTokenizer, WordTokenizer, and CharTokenizers `tokenizer_model` or/and `vocab_file` can be generated offline in advance using [`scripts/tokenizers/process_asr_text_tokenizer.py`](https://github.com/NVIDIA/NeMo/blob/stable/scripts/tokenizers/process_asr_text_tokenizer.py)\n\nThe tokenizers in NeMo are designed to be used interchangeably, especially when\nused in combination with a BERT-based model.\n\nLet's take a look at the list of available tokenizers:", "_____no_output_____" ] ], [ [ "nemo_nlp.modules.get_tokenizer_list()", "_____no_output_____" ] ], [ [ "# Hugging Face AutoTokenizer", "_____no_output_____" ] ], [ [ "# instantiate tokenizer wrapper using pretrained model name only\ntokenizer1 = nemo_nlp.modules.get_tokenizer(tokenizer_name=\"bert-base-cased\")\n\n# the wrapper has a reference to the original HuggingFace tokenizer\nprint(tokenizer1.tokenizer)", "_____no_output_____" ], [ "# check vocabulary (this can be very long)\nprint(tokenizer1.tokenizer.vocab)", "_____no_output_____" ], [ "# show all special tokens if it has any\nprint(tokenizer1.tokenizer.all_special_tokens)", "_____no_output_____" ], [ "# instantiate tokenizer using custom vocabulary\nvocab_file = \"myvocab.txt\"\nvocab = [\"he\", \"llo\", \"world\"]\nwith open(vocab_file, 'w', encoding='utf-8') as vocab_fp:\n vocab_fp.write(\"\\n\".join(vocab))", "_____no_output_____" ], [ "tokenizer2 = nemo_nlp.modules.get_tokenizer(tokenizer_name=\"bert-base-cased\", vocab_file=vocab_file)", "_____no_output_____" ], [ "# Since we did not overwrite special tokens they should be the same as before\nprint(tokenizer1.tokenizer.all_special_tokens == tokenizer2.tokenizer.all_special_tokens )", "_____no_output_____" ] ], [ [ "## Adding Special tokens\n\nWe do not recommend overwriting special tokens for Hugging Face pretrained models, \nsince these are the commonly used default values. \n\nIf a user still wants to overwrite the special tokens, specify some of the following keys:", "_____no_output_____" ] ], [ [ "special_tokens_dict = {\"unk_token\": \"<UNK>\", \n \"sep_token\": \"<SEP>\", \n \"pad_token\": \"<PAD>\", \n \"bos_token\": \"<CLS>\", \n \"mask_token\": \"<MASK>\",\n \"eos_token\": \"<SEP>\",\n \"cls_token\": \"<CLS>\"}\ntokenizer3 = nemo_nlp.modules.get_tokenizer(tokenizer_name=\"bert-base-cased\",\n vocab_file=vocab_file,\n special_tokens=special_tokens_dict)\n\n# print newly set special tokens\nprint(tokenizer3.tokenizer.all_special_tokens)\n# the special tokens should be different from the previous special tokens\nprint(tokenizer3.tokenizer.all_special_tokens != tokenizer1.tokenizer.all_special_tokens )", "_____no_output_____" ] ], [ [ "Notice, that if you specify tokens that were not previously included in the tokenizer's vocabulary file, new tokens will be added to the vocabulary file. You will see a message like this:", "_____no_output_____" ], [ "`['<MASK>', '<CLS>', '<SEP>', '<PAD>', '<SEP>', '<CLS>', '<UNK>'] \n will be added to the vocabulary.\n Please resize your model accordingly`", "_____no_output_____" ] ], [ [ "# A safer way to add special tokens is the following:\n\n# define your model\npretrained_model_name = 'bert-base-uncased'\nconfig = {\"language_model\": {\"pretrained_model_name\": pretrained_model_name}, \"tokenizer\": {}}\nomega_conf = OmegaConf.create(config)\nmodel = nemo_nlp.modules.get_lm_model(cfg=omega_conf)\n\n# define pretrained tokenizer\ntokenizer_default = nemo_nlp.modules.get_tokenizer(tokenizer_name=pretrained_model_name)", "_____no_output_____" ], [ "tokenizer_default.text_to_tokens('<MY_NEW_TOKEN> and another word')", "_____no_output_____" ] ], [ [ "As you can see in the above, the tokenizer splits `<MY_NEW_TOKEN>` into subtokens. Let's add this to the special tokens to make sure the tokenizer does not split this into subtokens.", "_____no_output_____" ] ], [ [ "special_tokens = {'bos_token': '<BOS>',\n 'cls_token': '<CSL>',\n 'additional_special_tokens': ['<MY_NEW_TOKEN>', '<ANOTHER_TOKEN>']}\ntokenizer_default.add_special_tokens(special_tokens_dict=special_tokens)\n\n# resize your model so that the embeddings for newly added tokens are updated during training/finetuning\nmodel.resize_token_embeddings(tokenizer_default.vocab_size)\n\n# let's make sure the tokenizer doesn't split our special tokens into subtokens\ntokenizer_default.text_to_tokens('<MY_NEW_TOKEN> and another word')", "_____no_output_____" ] ], [ [ "Now, the model doesn't break down our special token into the subtokens.", "_____no_output_____" ], [ "## Megatron model tokenizer", "_____no_output_____" ] ], [ [ "# Megatron tokenizers are instances of the Hugging Face BertTokenizer. \ntokenizer4 = nemo_nlp.modules.get_tokenizer(tokenizer_name=\"megatron-bert-cased\")", "_____no_output_____" ] ], [ [ "# Train custom tokenizer model and vocabulary from text file ", "_____no_output_____" ], [ "We use the [`scripts/tokenizers/process_asr_text_tokenizer.py`](https://github.com/NVIDIA/NeMo/blob/stable/scripts/tokenizers/process_asr_text_tokenizer.py) script to create a custom tokenizer model with its own vocabulary from an input file", "_____no_output_____" ] ], [ [ "# download tokenizer script\nscript_file = \"process_asr_text_tokenizer.py\"\n\nif not os.path.exists(script_file):\n print('Downloading script file...')\n wget.download(f'https://raw.githubusercontent.com/NVIDIA/NeMo/{BRANCH}/scripts/tokenizers/process_asr_text_tokenizer.py')\nelse:\n print ('Script already exists')", "_____no_output_____" ], [ "# Let's prepare some small text data for the tokenizer\ndata_text = \"NeMo is a toolkit for creating Conversational AI applications. \\\nNeMo toolkit makes it possible for researchers to easily compose complex neural network architectures \\\nfor conversational AI using reusable components - Neural Modules. \\\nNeural Modules are conceptual blocks of neural networks that take typed inputs and produce typed outputs. \\\nSuch modules typically represent data layers, encoders, decoders, language models, loss functions, or methods of combining activations. \\\nThe toolkit comes with extendable collections of pre-built modules and ready-to-use models for automatic speech recognition (ASR), \\\nnatural language processing (NLP) and text synthesis (TTS). \\\nBuilt for speed, NeMo can utilize NVIDIA's Tensor Cores and scale out training to multiple GPUs and multiple nodes.\"", "_____no_output_____" ], [ "# Write the text data into a file\ndata_file=\"data.txt\"\n\nwith open(data_file, 'w') as data_fp:\n data_fp.write(data_text)", "_____no_output_____" ], [ "# Some additional parameters for the tokenizer\n# To tokenize at unigram, char or word boundary instead of using bpe, change --spe_type accordingly. \n# More details see https://github.com/google/sentencepiece#train-sentencepiece-model\n\ntokenizer_spe_type = \"bpe\" # <-- Can be `bpe`, `unigram`, `word` or `char`\nvocab_size = 32", "_____no_output_____" ], [ "! python process_asr_text_tokenizer.py --data_file=$data_file --data_root=. --vocab_size=$vocab_size --tokenizer=spe --spe_type=$tokenizer_spe_type", "_____no_output_____" ], [ "# See created tokenizer model and vocabulary\nspe_model_dir=f\"tokenizer_spe_{tokenizer_spe_type}_v{vocab_size}\"\n! ls $spe_model_dir", "_____no_output_____" ] ], [ [ "# Use custom tokenizer for data preprocessing\n## Example: SentencePiece for BPE", "_____no_output_____" ] ], [ [ "# initialize tokenizer with created tokenizer model, which inherently includes the vocabulary and specify optional special tokens\ntokenizer_spe = nemo_nlp.modules.get_tokenizer(tokenizer_name=\"sentencepiece\", tokenizer_model=spe_model_dir+\"/tokenizer.model\", special_tokens=special_tokens_dict)\n\n# specified special tokens are added to the vocabuary\nprint(tokenizer_spe.vocab_size)", "_____no_output_____" ] ], [ [ "# Using any tokenizer to tokenize text into BERT compatible input\n", "_____no_output_____" ] ], [ [ "text=\"hello world\"\n\n# create tokens\ntokenized = [tokenizer_spe.bos_token] + tokenizer_spe.text_to_tokens(text) + [tokenizer_spe.eos_token]\nprint(tokenized)\n\n# turn token into input_ids for a neural model, such as BERTModule\n\nprint(tokenizer_spe.tokens_to_ids(tokenized))", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ "Apache-2.0" ]

[ [ [ "# T₂ Ramsey Experiment", "_____no_output_____" ], [ "This experiment serves as one of the series of experiments used to characterize a single qubit. Its purpose is to determine two of the qubit's properties: *Ramsey* or *detuning frequency* and $T_2\\ast$. The rough frequency of the qubit was already determined previously. Here, we would like to measure the *detuning*, that is the difference between the qubit's precise frequency and the frequency of the rotation pulses (based on the rough frequency). This part of the experiment is called a *Ramsey Experiment*. $T_2\\ast$ represents the rate of decay toward a mixed state, when the qubit is initialized to the |+⟩ state.", "_____no_output_____" ] ], [ [ "import qiskit\nfrom qiskit_experiments.library import T2Ramsey", "_____no_output_____" ] ], [ [ "The circuit used for the experiment comprises the following:\n\n 1. Hadamard gate\n 2. delay\n 3. p (phase) gate that rotates the qubit in the x-y plane \n 4. Hadamard gate\n 5. measurement\n\nDuring the delay time, we expect the qubit to precess about the z-axis. If the p gate and the precession offset each other perfectly, then the qubit will arrive at the |0⟩ state (after the second Hadamard gate). By varying the extension of the delays, we get a series of oscillations of the qubit state between the |0⟩ and |1⟩ states. We can draw the graph of the resulting function, and can analytically extract the desired values.", "_____no_output_____" ] ], [ [ "# set the computation units to microseconds\nunit = 'us' #microseconds\nqubit = 0\n# set the desired delays\ndelays = list(range(1, 150, 2))", "_____no_output_____" ], [ "# Create a T2Ramsey experiment. Print the first circuit as an example\nexp1 = T2Ramsey(qubit, delays, unit=unit)\nprint(exp1.circuits()[0])", " ┌───┐┌──────────────┐┌──────┐ ░ ┌───┐ ░ ┌─┐\nq_0: ┤ H ├┤ Delay(1[us]) ├┤ P(0) ├─░─┤ H ├─░─┤M├\n └───┘└──────────────┘└──────┘ ░ └───┘ ░ └╥┘\nc: 1/═════════════════════════════════════════╩═\n 0 \n" ] ], [ [ "We run the experiment on a simple, simulated backend, created specifically for this experiment's tutorial.", "_____no_output_____" ] ], [ [ "from qiskit_experiments.test.t2ramsey_backend import T2RamseyBackend\n# FakeJob is a wrapper for the backend, to give it the form of a job\nfrom qiskit_experiments.test.utils import FakeJob\nimport qiskit_experiments.matplotlib\nfrom qiskit_experiments.matplotlib import pyplot, requires_matplotlib\nfrom qiskit_experiments.matplotlib import HAS_MATPLOTLIB\n\nconversion_factor = 1E-6\n# The behavior of the backend is determined by the following parameters\nbackend = T2RamseyBackend(\n p0={\"a_guess\":[0.5], \"t2ramsey\":[80.0], \"f_guess\":[0.02], \"phi_guess\":[0.0],\n \"b_guess\": [0.5]},\n initial_prob_plus=[0.0],\n readout0to1=[0.02],\n readout1to0=[0.02],\n conversion_factor=conversion_factor,\n )\n", "_____no_output_____" ] ], [ [ "The resulting graph will have the form:\n$ f(t) = a^{-t/T_2*} \\cdot cos(2 \\pi f t + \\phi) + b $\nwhere *t* is the delay, $T_2*$ is the decay factor, and *f* is the detuning frequency.\n`conversion_factor` is a scaling factor that depends on the measurement units used. It is 1E-6 here, because the unit is microseconds.", "_____no_output_____" ] ], [ [ "exp1.set_analysis_options(user_p0=None, plot=True)\nexpdata1 = exp1.run(backend=backend, shots=2000)\nexpdata1.block_for_results() # Wait for job/analysis to finish.\n# Display the figure\ndisplay(expdata1.figure(0))", "_____no_output_____" ], [ "# T2* results:\nt2ramsey = expdata1.analysis_results(0).data()\nt2ramsey", "_____no_output_____" ], [ "# Frequency result:\nfrequency = expdata1.analysis_results(1).data()\nfrequency", "_____no_output_____" ] ], [ [ "### Providing initial user estimates\nThe user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameters $A$ and $B$ is $0.5$. Varying the value of $\\phi$ will shift the graph along the x-axis. Since this is not of interest to us, we can safely initialize $\\phi$ to 0. In this experiment, `t2ramsey` and `f` are the parameters of interest. Good estimates for them are values computed in previous experiments on this qubit or a similar values computed for other qubits.", "_____no_output_____" ] ], [ [ "from qiskit_experiments.library.characterization import T2RamseyAnalysis\nuser_p0={\n \"A\": 0.5,\n \"t2ramsey\": 85.0,\n \"f\": 0.021,\n \"phi\": 0,\n \"B\": 0.5\n }\nexp_with_p0 = T2Ramsey(qubit, delays, unit=unit)\nexp_with_p0.set_analysis_options(user_p0=user_p0, plot=True)\nexpdata_with_p0 = exp_with_p0.run(backend=backend, shots=2000)\nexpdata_with_p0.block_for_results()\ndisplay(expdata_with_p0.figure(0))\nt2ramsey = expdata_with_p0.analysis_results(0).data()[\"value\"]\nfrequency = expdata_with_p0.analysis_results(1).data()[\"value\"]\nprint(\"T2Ramsey:\", t2ramsey)\nprint(\"Fitted frequency:\", frequency)", "_____no_output_____" ] ], [ [ "The units can be changed, but the output in the result is always given in seconds. The units in the backend must be adjusted accordingly.", "_____no_output_____" ] ], [ [ "from qiskit.utils import apply_prefix\nunit = 'ns'\ndelays = list(range(1000, 150000, 2000))\nconversion_factor = apply_prefix(1, unit)\nprint(conversion_factor)", "1e-09\n" ], [ "p0={\"a_guess\":[0.5], \"t2ramsey\":[80000], \"f_guess\":[0.00002], \"phi_guess\":[0.0],\n \"b_guess\": [0.5]}\nbackend_in_ns = T2RamseyBackend(\n p0=p0,\n initial_prob_plus=[0.0],\n readout0to1=[0.02],\n readout1to0=[0.02],\n conversion_factor=conversion_factor\n )\nexp_in_ns = T2Ramsey(qubit, delays, unit=unit)\nexp_in_ns.set_analysis_options(user_p0=None, plot=True)\nexpdata_in_ns = exp_in_ns.run(backend=backend_in_ns, shots=2000)\nexpdata_in_ns.block_for_results()\ndisplay(expdata_in_ns.figure(0))\nt2ramsey = expdata_in_ns.analysis_results(0).data()[\"value\"]\nfrequency = expdata_in_ns.analysis_results(1).data()[\"value\"]\nprint(\"T2Ramsey:\", t2ramsey)\nprint(\"Fitted frequency:\", frequency)", "_____no_output_____" ] ], [ [ "### Adding data to an existing experiment\nIt is possible to add data to an experiment, after the analysis of the first set of data. In the next example we add exp2 to `exp_in_ns` that we showed above.", "_____no_output_____" ] ], [ [ "more_delays = list(range(2000, 150000, 2000)) \nexp_new = T2Ramsey(qubit, more_delays, unit=unit)\nexp_new.set_analysis_options(user_p0=None, plot=True)\nexpdata_new = exp_new.run(\n backend=backend_in_ns,\n experiment_data=expdata_in_ns,\n shots=2000\n )\nexpdata_new.block_for_results()\ndisplay(expdata_new.figure(1))", "_____no_output_____" ], [ "# The results of the second execution are indices 2 and 3 of the analysis result\nt2ramsey = expdata_new.analysis_results(2).data()[\"value\"]\nfrequency = expdata_new.analysis_results(3).data()[\"value\"]\nprint(\"T2Ramsey:\", t2ramsey)\nprint(\"Fitted frequency:\", frequency)", "T2Ramsey: 8.006074688405924e-05\nFitted frequency: 98980008.19000898\n" ] ] ]

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[ "ADSL" ]

[ [ [ "### Note\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_____" ] ], [ [ "# Dependencies and Setup\nimport pandas as pd\n\n# File to Load (Remember to Change These)\nfile_to_load = \"Resources/purchase_data.csv\"\n\n# Read Purchasing File and store into Pandas data frame\npurchase_data = pd.read_csv(file_to_load)\npurchase_data", "_____no_output_____" ] ], [ [ "## Player Count", "_____no_output_____" ], [ "* Display the total number of players\n", "_____no_output_____" ] ], [ [ "players = purchase_data[\"SN\"].unique()\n\nplayer_count = pd.DataFrame([{\"Unique User Count\":len(players)}])\nplayer_count\n\n", "_____no_output_____" ] ], [ [ "## Purchasing Analysis (Total)", "_____no_output_____" ], [ "* Run basic calculations to obtain number of unique items, average price, etc.\n\n\n* Create a summary data frame to hold the results\n\n\n* Optional: give the displayed data cleaner formatting\n\n\n* Display the summary data frame\n", "_____no_output_____" ] ], [ [ "unique_items =len(purchase_data[\"Item Name\"].unique())\ntotal_revenue = purchase_data[\"Price\"].sum()\npurchases_count = len(purchase_data[\"Price\"])\naverage_price = total_revenue / purchases_count\n#the data frame for Analysis of the purchases\npurchasing_analysis = pd.DataFrame([{\"Number of Unique Items\":unique_items,\n \"Average Price\":average_price,\n \"Number of Purchases\":purchases_count,\n \"Total Revenue\":total_revenue}])\n\n#change the currency of the prices and the total revenue\npurchasing_analysis[\"Average Price\"] = purchasing_analysis[\"Average Price\"].map(\"${0:,.2f}\".format)\npurchasing_analysis[\"Total Revenue\"] = purchasing_analysis[\"Total Revenue\"].map(\"${0:,.2f}\".format)\n\npurchasing_analysis", "_____no_output_____" ] ], [ [ "## Gender Demographics", "_____no_output_____" ], [ "* Percentage and Count of Male Players\n\n\n* Percentage and Count of Female Players\n\n\n* Percentage and Count of Other / Non-Disclosed\n\n\n", "_____no_output_____" ] ], [ [ "#graphics summary\n\n\n#Lets create a df so we can store the screen names/gender and purchase data from the script\npurchaser_genders = purchase_data[[\"SN\",\"Gender\",\"Price\"]].copy()\n\n#seperate the differet genders for the players\ndif_player_gen = purchaser_genders.drop_duplicates(subset=[\"SN\",\"Gender\"], keep=\"first\")\ndif_player_count = len(dif_player_gen)\n\n#save the values to the summary of the Demographics summary\ngender_counts = dif_player_gen[\"Gender\"].value_counts()\n\ngender_demog = pd.DataFrame({\"Unique Player by Gender\":gender_counts,\n \"% of Total\":gender_counts/dif_player_count})\n \n#change the format of the % to a percentile format\ngender_demog[\"% of Total\"] = gender_demog[\"% of Total\"].map(\"{0:.1%}\".format) ", "_____no_output_____" ] ], [ [ "\n## Purchasing Analysis (Gender)", "_____no_output_____" ], [ "* Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. by gender\n\n\n\n\n* Create a summary data frame to hold the results\n\n\n* Optional: give the displayed data cleaner formatting\n\n\n* Display the summary data frame", "_____no_output_____" ] ], [ [ "# THe Analysis for the purchase by gender\ngroup_purchaser_gen = purchaser_genders.groupby([\"Gender\"])\n\npurchase_cby_gen = group_purchaser_gen[\"Price\"].count()\naverage_purchase_pby_gen = group_purchaser_gen[\"Price\"].mean()\npurchase_sumby_gen = group_purchaser_gen[\"Price\"].sum()\n\n#create the summury for purchase by genders\npurchasing_analysis_gen = pd.DataFrame({\"Purchase Count\":purchase_cby_gen,\n \"Avg Purchase Price\":average_purchase_pby_gen,\n \"Total Purchase Value\":purchase_sumby_gen,\n \"% of Total Revenue\":purchase_sumby_gen/total_revenue,\n \"Avg Purchase / Person\":purchase_cby_gen/gender_counts})\n# the format must be set for the summary\npurchasing_analysis_gen[\"Avg Purchase Price\"] = purchasing_analysis_gen[\"Avg Purchase Price\"].map(\"${0:,.2f}\".format)\npurchasing_analysis_gen[\"Total Purchase Value\"] = purchasing_analysis_gen[\"Total Purchase Value\"].map(\"${0:,.2f}\".format)\npurchasing_analysis_gen[\"% of Total Revenue\"] = purchasing_analysis_gen[\"% of Total Revenue\"].map(\"{0:.1%}\".format)\npurchasing_analysis_gen[\"Avg Purchase / Person\"] = purchasing_analysis_gen[\"Avg Purchase / Person\"].map(\"{0:.1f}\".format)\n\npurchasing_analysis_gen\n", "_____no_output_____" ] ], [ [ "## Age Demographics", "_____no_output_____" ], [ "* Establish bins for ages\n\n\n* Categorize the existing players using the age bins. Hint: use pd.cut()\n\n\n* Calculate the numbers and percentages by age group\n\n\n* Create a summary data frame to hold the results\n\n\n* Optional: round the percentage column to two decimal points\n\n\n* Display Age Demographics Table\n", "_____no_output_____" ] ], [ [ "##AGE DEMOGRAPGHICS####\n#Create data frame to stor names, age, and purchase data\npurchaser_ages = purchase_data[[\"SN\",\"Age\",\"Price\"]].copy()\n#eliminate the duplicates and create a for them\ndif_player_ages = purchaser_ages.drop_duplicates(subset=[\"SN\",\"Age\"],keep=\"first\")\n\n#create a bukey for storage for different ages and move them to a new list \nage_stg = (0,10,15,20,25,30,35,100)\nage_lb = (\"<10\",\"10-14\",\"15-19\",\"20-24\",\"25-29\",\"30-34\",\"50+\")\nage_list = pd.cut(dif_player_ages[\"Age\"], bins=age_stg, right=False, labels=age_lb)\n\ndif_age_counts = pd.DataFrame({\"Unique User Count\":dif_player_ages[\"SN\"],\n \"Age Bin\":age_list})\ndif_gage_counts = dif_age_counts.groupby([\"Age Bin\"]).count()\n\ndif_gage_counts[\"% of Total\"] = dif_gage_counts[\"Unique User Count\"] / dif_player_count\n\n#Format the % of the Total\ndif_gage_counts[\"% of Total\"] = dif_gage_counts[\"% of Total\"].map(\"{0:.2%}\".format)\n\n\ndif_gage_counts\n", "_____no_output_____" ] ], [ [ "## Purchasing Analysis (Age)", "_____no_output_____" ], [ "* Bin the purchase_data data frame by age\n\n\n* Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. in the table below\n\n\n* Create a summary data frame to hold the results\n\n\n* Optional: give the displayed data cleaner formatting\n\n\n* Display the summary data frame", "_____no_output_____" ] ], [ [ "#Purchase Analysis\n#make rows and colums labeled withthe data pulled and age from the lists\nall_purchase_ab = pd.cut(purchase_data[\"Age\"], bins=age_stg,\n right=False, labels=age_lb)\npurchaser_ages[\"Age Bin\"] = all_purchase_ab\n\ngpurchaser_acounts = purchaser_ages.groupby([\"Age Bin\"]).count()\ngpurchaser_asum = purchaser_ages.groupby([\"Age Bin\"]).sum()\ngpurchaser_amean = purchaser_ages.groupby([\"Age Bin\"]).mean()\n\npurchase_cby_age = gpurchaser_acounts[\"SN\"]\npurchase_sby_age = gpurchaser_asum[\"Price\"]\npurchase_aby_age = gpurchaser_amean[\"Price\"]\navg_purchase_by_person = purchase_sby_age / dif_gage_counts[\"Unique User Count\"]\n\npurchasing_analysis_age = pd.DataFrame({\"Purchase Count\":purchase_cby_age,\n \"Average Purchase Price\":purchase_aby_age,\n \"Total Purchase Value\":purchase_sby_age,\n \"% of Total Revenue\":purchase_sby_age / total_revenue,\n \"Avg Total Purchase / Person\":avg_purchase_by_person})\n\n\n\n\n\n\n#create dataframe for age of the purchasing analysis\npurchasing_analysis_age[\"Average Purchase Price\"] = purchasing_analysis_age[\"Average Purchase Price\"].map(\"${0:,.2f}\".format)\npurchasing_analysis_age[\"Total Purchase Value\"] = purchasing_analysis_age[\"Total Purchase Value\"].map(\"${0:,.2f}\".format)\npurchasing_analysis_age[\"% of Total Revenue\"] = purchasing_analysis_age[\"% of Total Revenue\"].map(\"{0:.1%}\".format)\npurchasing_analysis_age[\"Avg Total Purchase / Person\"] = purchasing_analysis_age[\"Avg Total Purchase / Person\"].map(\"${0:,.2f}\".format)\n\n\npurchasing_analysis_age", "_____no_output_____" ] ], [ [ "## Top Spenders", "_____no_output_____" ], [ "* Run basic calculations to obtain the results in the table below\n\n\n* Create a summary data frame to hold the results\n\n\n* Sort the total purchase value column in descending order\n\n\n* Optional: give the displayed data cleaner formatting\n\n\n* Display a preview of the summary data frame\n\n", "_____no_output_____" ] ], [ [ "#find the top 5 spenders\n\npurchase_total_sum_SN = purchase_data.groupby([\"SN\"]).sum()\nsort_purchase_total_sum_SN = purchase_total_sum_SN.sort_values(\"Price\", ascending=False)\n\n\npurchase_cby_sn = purchase_data[\"SN\"].value_counts()\n\npurchase_sby_sn = sort_purchase_total_sum_SN[\"Price\"]\n\ntop_spenders = pd.DataFrame({\"Purchase Count\":purchase_cby_sn,\n \"Avg Purchase Price\":purchase_sby_sn / purchase_cby_sn,\n \"Total Purchase Value\":purchase_sby_sn})\n\n\n#create the summary of the spenders from highest to lowest\ntop_spenders = top_spenders.sort_values(\"Total Purchase Value\", ascending=False)\n\n# the Formatting for the data summary\ntop_spenders[\"Avg Purchase Price\"] = top_spenders[\"Avg Purchase Price\"].map(\"${0:,.2f}\".format)\ntop_spenders[\"Total Purchase Value\"] = top_spenders[\"Total Purchase Value\"].map(\"${0:,.2f}\".format)\n\n\n\ntop_spenders.head(5)\n", "_____no_output_____" ] ], [ [ "## Most Popular Items", "_____no_output_____" ], [ "* Retrieve the Item ID, Item Name, and Item Price columns\n\n\n* Group by Item ID and Item Name. Perform calculations to obtain purchase count, item price, and total purchase value\n\n\n* Create a summary data frame to hold the results\n\n\n* Sort the purchase count column in descending order\n\n\n* Optional: give the displayed data cleaner formatting\n\n\n* Display a preview of the summary data frame\n\n", "_____no_output_____" ] ], [ [ "#Iteams that are the most popular\nitems= purchase_data[[\"Item ID\",\"Item Name\",\"Price\"]]\ngroup_items = items.groupby([\"Item ID\",\"Item Name\"])\n\npurchase_cby_item = group_items[\"Item ID\"].count()\ntotal_purchase_vby_item = group_items[\"Price\"].sum()\nitem_price = total_purchase_vby_item / purchase_cby_item\n\n#create a df for most popular items in the shop\nmost_pop_items = pd.DataFrame({\"Purchase Count\": purchase_cby_item,\n \"Item Price\": item_price,\n \"Total Purchase Value\": total_purchase_vby_item})\n\n\nmost_pop_items = most_pop_items.sort_values(\"Purchase Count\", ascending=False)\n\n\n#Formating for the summary of pupular items\nmost_pop_items[\"Item Price\"] =most_pop_items[\"Item Price\"].map(\"${0:,.2f}\".format)\nmost_pop_items[\"Total Purchase Value\"] =most_pop_items[\"Total Purchase Value\"].map(\"${0:,.2f}\".format)\n\n\n\n\n\nmost_pop_items.head(5)", "_____no_output_____" ] ], [ [ "## Most Profitable Items", "_____no_output_____" ], [ "* Sort the above table by total purchase value in descending order\n\n\n* Optional: give the displayed data cleaner formatting\n\n\n* Display a preview of the data frame\n\n", "_____no_output_____" ] ], [ [ "###Profitable items####\n#make a copy of the most popular items table and sort by the purchase value\nmost_profit_items = pd.DataFrame({\"Purchase Count\": purchase_cby_item,\n \"Item Price\": item_price,\n \"Total Purchase Value\": total_purchase_vby_item})\n\nmost_profit_items = most_profit_items.sort_values(\"Total Purchase Value\", ascending=False)\n\n\nmost_profit_items[\"Item Price\"] =most_profit_items[\"Item Price\"].map(\"${0:,.2f}\".format)\nmost_profit_items[\"Total Purchase Value\"] =most_profit_items[\"Total Purchase Value\"].map(\"${0:,.2f}\".format)\n\n\nmost_profit_items", "_____no_output_____" ] ] ]

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[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import pytest\nfrom datetime import datetime\nimport pytz\nimport matplotlib.pyplot as plt\n\nfrom nyisotoolkit import NYISOVis\nfrom nyisotoolkit.nyisovis.nyisovis import basic_plots, statistical_plots\nfrom nyisotoolkit.nyisodata.utils import current_year\n\nbasic_plots({\"redownload\":True, 'year': 2021})", "Downloading 2021 fuel_mix_5m...Completed!\n" ] ] ]

[ "code" ]

[ [ "code" ] ]

[ "MIT" ]

[ "MIT" ]

[ "MIT" ]

[ [ [ "import os\nimport pandas as pd\nfrom sklearn.model_selection import StratifiedShuffleSplit\n\nfrom dimensionality_reduction import reduce_dimension\nimport load_database\nfrom algorithms import *", "_____no_output_____" ], [ "import warnings\nwarnings.filterwarnings('ignore')", "_____no_output_____" ], [ "database_name = os.environ['DATABASE']\nn_components = int(os.environ['N_COMPONENTS'])\ndimensionality_algorithm = os.environ['DIMENSIONALITY_ALGORITHM']", "_____no_output_____" ], [ "result_path = 'results/%s_%s_%s.csv' %(database_name, n_components, dimensionality_algorithm)", "_____no_output_____" ], [ "X, y = load_database.load(database_name)\nX = reduce_dimension(dimensionality_algorithm, X, n_components) if n_components else X", "_____no_output_____" ], [ "X.shape", "_____no_output_____" ], [ "results = {}", "_____no_output_____" ], [ "sss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)\nfor train_index, test_index in sss.split(X, y):\n X_train, X_test = X[train_index], X[test_index]\n y_train, y_test = y[train_index], y[test_index]", "_____no_output_____" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'ada_boost')\nresults.update(result)", "42.233148999999685\n{'algorithm': 'SAMME.R', 'learning_rate': 0.7, 'n_estimators': 90}\n0.7327142857142858\n0.736 0.7313571428571428 0.7375798240707979 0.7326600256298489\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'bagging')\nresults.update(result)", "176.19572799999878\n{'bootstrap_features': 1, 'n_estimators': 45}\n0.941375\n1.0 0.9496428571428571 1.0 0.9495383409311723\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'extra_trees')\nresults.update(result)", "3.843316999998933\n{'criterion': 'gini', 'n_estimators': 45, 'warm_start': 1}\n0.9479464285714285\n1.0 0.9527142857142857 1.0 0.9526039398542944\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'random_forest')\nresults.update(result)", "19.513483999995515\n{'criterion': 'gini', 'n_estimators': 45, 'oob_score': 1, 'warm_start': 1}\n0.943125\n1.0 0.9510714285714286 1.0 0.9509864700085621\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'logistic_regression')\nresults.update(result)", "90.69816300000093\n{'C': 1.4, 'solver': 'newton-cg', 'tol': 0.0001}\n0.8970892857142857\n0.902125 0.8981428571428571 0.9015956335472866 0.897764044362089\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'passive_aggressive')\nresults.update(result)", "2.882740000000922\n{'early_stopping': False, 'loss': 'squared_hinge', 'tol': 2.5e-05, 'warm_start': 0}\n0.881125\n0.8405 0.8385 0.8438246136342006 0.8409161984961777\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'ridge')\nresults.update(result)", "0.9156469999943511\n{'alpha': 1.1, 'tol': 0.0001}\n0.8463571428571428\n0.8481428571428572 0.845 0.8460823674045024 0.8429885992486512\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'sgd')\nresults.update(result)", "26.65538199999719\n{'alpha': 0.0008, 'loss': 'hinge', 'penalty': 'none', 'tol': 1.4285714285714285e-05}\n0.8955714285714286\n0.8986964285714286 0.8958571428571429 0.8984431924595215 0.8956926757062251\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'bernoulli')\nresults.update(result)", "0.388264999994135\n{'alpha': 0.1}\n0.11253571428571428\n0.11348214285714285 0.1125 0.024675544278649344 0.022752808988764042\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'gaussian')\nresults.update(result)", "0.3508640000000014\n{'var_smoothing': 1e-10}\n0.8674285714285714\n0.8691607142857143 0.8647142857142858 0.8698778976207744 0.8654317751133429\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'k_neighbors')\nresults.update(result)", "2.91838600000483\n{'algorithm': 'ball_tree', 'n_neighbors': 4, 'p': 1, 'weights': 'distance'}\n0.9627678571428572\n1.0 0.9689285714285715 1.0 0.968860965105337\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'nearest_centroid')\nresults.update(result)", "0.23393499999656342\n{'metric': 'euclidean'}\n0.8398214285714286\n0.8411964285714286 0.839 0.8412242565990308 0.8390338703940627\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'mlp')\nresults.update(result)", "338.5127929999944\n{'activation': 'tanh', 'alpha': 3.3333333333333333e-06, 'early_stopping': True, 'learning_rate': 'constant', 'solver': 'lbfgs'}\n0.94825\n0.9532321428571429 0.9481428571428572 0.9532164172741119 0.9481439841265297\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'linear_svc')\nresults.update(result)", "27.007058999995934\n{'C': 1.4, 'multi_class': 'crammer_singer', 'penalty': 'l2', 'tol': 0.0001}\n0.9112857142857143\n0.9166071428571428 0.9128571428571428 0.9163419270321945 0.9126324414696202\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'decision_tree')\nresults.update(result)", "14.737384999993083\n{'criterion': 'entropy', 'splitter': 'best'}\n0.8263928571428572\n1.0 0.8470714285714286 1.0 0.8470532342817909\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'extra_tree')\nresults.update(result)", "2.188074999998207\n{'criterion': 'entropy', 'splitter': 'best'}\n0.7529107142857143\n1.0 0.7637857142857143 1.0 0.7639222694989904\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'gradient_boosting')\nresults.update(result)", "331.9900750000015\n{'criterion': 'friedman_mse', 'learning_rate': 0.3, 'loss': 'deviance', 'tol': 1e-05}\n0.9321071428571429\n0.9848571428571429 0.9386428571428571 0.9848517392629834 0.9386072061126688\n" ], [ "result = train_test(X_train, y_train, X_test, y_test, 'hist_gradient_boosting')\nresults.update(result)", "62.312362999997276\n{'l2_regularization': 0, 'tol': 1e-08}\n0.9595714285714285\n1.0 0.9657142857142857 1.0 0.9657132879641288\n" ], [ "df = pd.DataFrame.from_records(results)", "_____no_output_____" ], [ "df", "_____no_output_____" ], [ "df.to_csv(result_path)", "_____no_output_____" ] ] ]

[ "code" ]

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[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ "BSD-3-Clause" ]

[ [ [ "# Experimental data analysis on foil open area\n## Brian Larsen, ISR-1\n## Data provided by Phil Fernandes, ISR-1 2016-9-14", "_____no_output_____" ], [ "The setup is a foil in its holder mounted to a foil holder meant to bock incident ions. The foil has a ~0.6mm hole in it to provide a baseline. The goal is to use the relative intensity of the witness hole to determine the intensity of holes in the foil.\n\nA quick summary:\n* Foil is placed 0.66” from front of MCP surface\n* Beam is rastered to cover full foil and “witness” aperture\n* Beam is 1.0 keV Ar+, slightly underfocused\n* Accumulate data for set period of time (either 60s or 180s, identified in spreadsheet)\n* Total_cts is the # of counts through the foil and the witness aperture\n* Witness_cts is the # of counts in the witness aperture only\n* Foil_cts = total_cts – witness_cts\n* Open area OA = (foil_cts/witness_cts) * (witness_area/foil_area)", "_____no_output_____" ] ], [ [ "import itertools\nfrom pprint import pprint\nfrom operator import getitem\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LogNorm\nimport numpy as np\nimport spacepy.plot as spp\nimport pymc as mc\nimport tqdm\n\nfrom MCA_file_viewer_v001 import GetMCAfile", "/Users/balarsen/miniconda3/envs/python3/lib/python3.6/site-packages/matplotlib/__init__.py:913: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n warnings.warn(self.msg_depr % (key, alt_key))\n" ], [ "def plot_box(x, y, c='r', lw=0.6, ax=None):\n if ax is None:\n plt.plot((xind[0], xind[0]), (yind[0], yind[1]), lw=lw, c=c)\n plt.plot((xind[1], xind[1]), (yind[0], yind[1]), lw=lw, c=c)\n plt.plot((xind[0], xind[1]), (yind[0], yind[0]), lw=lw, c=c)\n plt.plot((xind[0], xind[1]), (yind[1], yind[1]), lw=lw, c=c)\n else:\n ax.plot((xind[0], xind[0]), (yind[0], yind[1]), lw=lw, c=c)\n ax.plot((xind[1], xind[1]), (yind[0], yind[1]), lw=lw, c=c)\n ax.plot((xind[0], xind[1]), (yind[0], yind[0]), lw=lw, c=c)\n ax.plot((xind[0], xind[1]), (yind[1], yind[1]), lw=lw, c=c)\n ", "_____no_output_____" ], [ "ZZ, XX, YY = GetMCAfile('16090203.mca')\n# It is believed as of 2016-09-19 that the MCA records 2 counts for each count. \n# This means all data are even and all the data can be divided by 2 to give the\n# right number of counts. Per emails Larsen-Fernandes 2016-09-17\n# These data are integres and care muct be taken to assure that /2 does not\n# lead to number that are not representable in float\nZZ = ZZ.astype(float)\nZZ /= 2\nXX = XX.astype(np.uint16) # as they all should be integers anyway\n", "_____no_output_____" ], [ "xind = (986, 1003)\nyind = (492, 506)\n\nfig = plt.figure(figsize=(20,8))\nax1 = fig.add_subplot(131)\nax2 = fig.add_subplot(132)\nax3 = fig.add_subplot(133)\n\npc = ax1.pcolormesh(XX, YY, ZZ, norm=LogNorm())\nplt.colorbar(pc, ax=ax1)\nplot_box(xind, yind, ax=ax1)\n\nax2.hist(ZZ.flatten(), 20)\nax2.set_yscale('log')\n\nax3.hist(ZZ.flatten(), 20, normed=True)\nax3.set_yscale('log')", "_____no_output_____" ] ], [ [ "## Do some calculations to try and match Phil's analysis", "_____no_output_____" ], [ "Phil's data:\n\nFile name\tWitness cts\tTotal cts\tFoil cts\tOpen area\n\n16090203\t658\t4570\t3912\t0.00102", "_____no_output_____" ] ], [ [ "total_cnts = ZZ.sum()\nprint('Total counts:{0} -- Phil got {1} -- remember /2'.format(total_cnts, 4570/2)) # remember we did a /2", "_____no_output_____" ], [ "# Is the whitness hole at x=1000, y=500?\nXX.shape, YY.shape, ZZ.shape", "_____no_output_____" ], [ "\nprint(ZZ[yind[0]:yind[1], xind[0]:xind[1]])\nplt.figure()\nplt.pcolormesh(XX[xind[0]:xind[1]], YY[yind[0]:yind[1]], ZZ[yind[0]:yind[1], xind[0]:xind[1]] , norm=LogNorm())\nplt.colorbar()\n\nwitness_counts = ZZ[yind[0]:yind[1], xind[0]:xind[1]].sum()\n\nprint('Witness counts: {0}, Phil got {1}/2={2}'.format(witness_counts, 658, 658/2))\nwit_pixels = 46\nprint('There {0} pixels in the witness peak'.format(wit_pixels))\n\ntotal_counts = ZZ.sum()\nprint(\"There are a total of {0} counts\".format(total_counts))\n", "_____no_output_____" ] ], [ [ "## Can we get a noise estimate? \n1) Try all pixels with a value where a neighbor does not. This assumes that real holes are large enough to have a point spread function and therefore cannot be in a single pixel.", "_____no_output_____" ] ], [ [ "def neighbor_inds(x, y, xlim=(0,1023), ylim=(0,1023), center=False, mask=False):\n \"\"\"\n given an x and y index return the 8 neighbor indices\n \n if center also return the center index\n if mask return a boolean mask over the whole 2d array\n \"\"\"\n xi = np.clip([x + v for v in [-1, 0, 1]], xlim[0], xlim[1])\n yi = np.clip([y + v for v in [-1, 0, 1]], ylim[0], ylim[1])\n ans = [(i, j) for i, j in itertools.product(xi, yi)]\n if not center:\n ans.remove((x,y))\n if mask:\n out = np.zeros((np.diff(xlim)+1, np.diff(ylim)+1), dtype=np.bool)\n for c in ans:\n out[c] = True\n else:\n out = ans\n return np.asarray(out)\n\nprint(neighbor_inds(2,2))\nprint(neighbor_inds(2,2, mask=True))\nprint(ZZ[neighbor_inds(500, 992, mask=True)])\n\n ", "_____no_output_____" ], [ "def get_alone_pixels(dat):\n \"\"\"\n loop over all the data and store the value of all lone pixels\n \"\"\"\n ans = []\n for index, x in tqdm.tqdm_notebook(np.ndenumerate(dat)):\n if (np.sum([ZZ[i, j] for i, j in neighbor_inds(index[0], index[1])]) == 0) and x != 0:\n ans.append((index, x))\n return ans\n# print((neighbor_inds(5, 4)))\nalone = get_alone_pixels(ZZ)\npprint(alone)\n# ZZ[neighbor_inds(5, 4)[0]].shape\n# print((neighbor_inds(5, 4))[0])\n# print(ZZ[(neighbor_inds(5, 4))[0]].shape)\n# ZZ[4,3]", "_____no_output_____" ], [ "ZZ[(965, 485)]", "_____no_output_____" ], [ "print(neighbor_inds(4,3)[0])\nprint(ZZ[neighbor_inds(4,3)[0]])\nprint(ZZ[3,2])\n\nni = neighbor_inds(4,3)[0]\nprint(ZZ[ni[0], ni[1]])", "_____no_output_____" ], [ "(ZZ % 2).any() # not all even any longer", "_____no_output_____" ] ], [ [ "### Noise estimates\nNot we assume that all lone counts are noise that can be considered random and uniform over the MCP. \nThis then provides a number of counts per MCA pixel that we can use. ", "_____no_output_____" ] ], [ [ "n_noise = np.sum([v[1] for v in alone])\nn_pixels = 1024*1024\nnoise_pixel = n_noise/n_pixels\nprint(\"There were a total of {0} random counts over {1} pixels, {2} cts/pixel\".format(n_noise, n_pixels, noise_pixel))", "_____no_output_____" ] ], [ [ "Maybe we should consider just part of the MCP, lets get the min,max X and min,max Y where there are counts and just use that area. This will increase the cts/pixel.", "_____no_output_____" ] ], [ [ "minx_tmp = ZZ.sum(axis=0)\nminx_tmp.shape\nprint(minx_tmp)\n\nminy_tmp = ZZ.sum(axis=1)\nminy_tmp.shape\nprint(miny_tmp)\n\n", "_____no_output_____" ] ], [ [ "Looks to go all the way to all sides in X-Y.", "_____no_output_____" ], [ "## Work to total open area calculations\nNow we can model the total open area of the foil given the noise estimate per pixel and the pixels that are a part of the witness sample and the total area.\n\nWe model the observed background as Poisson with center at the real background:\n\n$obsnbkg \\sim Pois(nbkg)$\n\nWe model the observed witness sample, $obswit$, as Poisson with center of background per pixel times number of pixels in peak plus the number of real counts:\n\n$obswit \\sim Pois(nbkg/C + witc)$, $C = \\frac{A_w}{A_t}$\n\nThis then leaves the number of counts in open areas of the system (excluding witness) as a Poisson with center of background per pixel times number of pixels in the system (less witness) plus the real number of counts.\n\n$obsopen \\sim Pois(nbkg/D + realc)$, $D=\\frac{A_t - A_w}{A_t}$\n\nThen then the open area is given by the ratio number of counts, $realc$, over an unknown area, $A_o$, as related to witness counts, $witc$, to the witness area, $A_w$, which is assumed perfect as as 0.6mm hole.\n\n$\\frac{A_o}{realc}=\\frac{A_w}{witc} => A_o = \\frac{A_w}{witc}realc $\n", "_____no_output_____" ] ], [ [ "Aw = np.pi*(0.2/2)**2 # mm**2\nAf = 182.75 # mm**2 this is the area of the foil\nW_F_ratio = Aw/Af\n\nprint(Aw, Af, W_F_ratio)\n\nC = wit_pixels/n_pixels\nD = (n_pixels-wit_pixels)/n_pixels\nprint('C', C, 'D', D)\n\n\nnbkg = mc.Uniform('nbkg', 1, n_noise*5) # just 1 to some large number\nobsnbkg = mc.Poisson('obsnbkg', nbkg, observed=True, value=n_noise)\n\nwitc = mc.Uniform('witc', 0, witness_counts*5) # just 0 to some large number\nobswit = mc.Poisson('obswit', nbkg*C + witc, observed=True, value=witness_counts)\n\nrealc = mc.Uniform('realc', 0, total_counts*5) # just 0 to some large number\nobsopen = mc.Poisson('obsopen', nbkg*D + realc, observed=True, value=total_counts-witness_counts)\n\[email protected](plot=True)\ndef open_area(realc=realc, witc=witc):\n return realc*Aw/witc/Af\n\nmodel = mc.MCMC([nbkg, obsnbkg, witc, obswit, realc, obsopen, open_area])", "_____no_output_____" ], [ "model.sample(200000, burn=100, thin=30, burn_till_tuned=True)\nmc.Matplot.plot(model)\n\n# 1000, burn=100, thin=30 0.000985 +/- 0.000058\n# 10000, burn=100, thin=30 0.000982 +/- 0.000061\n# 100000, burn=100, thin=30 0.000984 +/- 0.000059\n# 200000, burn=100, thin=30 0.000986 +/- 0.000059\n# 1000000, burn=100, thin=30 0.000985 +/- 0.000059", "_____no_output_____" ], [ "print(\"Foil 1 \\n\")\n\nwitc_mean = np.mean(witc.trace()[...])\nwitc_std = np.std(witc.trace()[...])\n\nprint(\"Found witness counts of {0} turn into {1} +/- {2} ({3:.2f}%)\\n\".format(witness_counts, witc_mean, witc_std, witc_std/witc_mean*100))\n\nrealc_mean = np.mean(realc.trace()[...])\nrealc_std = np.std(realc.trace()[...])\n\nprint(\"Found non-witness counts of {0} turn into {1} +/- {2} ({3:.2f}%)\\n\".format(total_counts-witness_counts, realc_mean, realc_std, realc_std/realc_mean*100))\n\nnbkg_mean = np.mean(nbkg.trace()[...])\nnbkg_std = np.std(nbkg.trace()[...])\n\nprint(\"Found noise counts of {0} turn into {1} +/- {2} ({3:.2f}%)\\n\".format(0, nbkg_mean, nbkg_std, nbkg_std/nbkg_mean*100))\n\nOA_median = np.median(open_area.trace()[...])\nOA_mean = np.mean(open_area.trace()[...])\nOA_std = np.std(open_area.trace()[...])\nprint(\"The open area fraction is {0:.6f} +/- {1:.6f} ({2:.2f}%) at the 1 stddev level from 1 measurement\\n\".format(OA_mean, OA_std,OA_std/OA_mean*100 ))\nprint(\"Phil got {0} for 1 measurement\\n\".format(0.00139))\nprint(\"The ratio Brian/Phil is: {0:.6f} or {1:.6f}\".format(OA_mean/0.00139, 0.00139/OA_mean))\n", "_____no_output_____" ] ], [ [ "## Run again allowing some uncertainity on witness and foil areas", "_____no_output_____" ] ], [ [ "_Aw = np.pi*(0.2/2)**2 # mm**2\n_Af = 182.75 # mm**2 this is the area of the foil\n\nAw = mc.Normal('Aw', _Aw, (_Aw*0.2)**-2) # 20%\nAf = mc.Normal('Af', _Af, (_Af*0.1)**-2) # 10%\n\nprint(_Aw, _Af)\n\nC = wit_pixels/n_pixels\nD = (n_pixels-wit_pixels)/n_pixels\nprint('C', C, 'D', D)\n\n\nnbkg = mc.Uniform('nbkg', 1, n_noise*5) # just 1 to some large number\nobsnbkg = mc.Poisson('obsnbkg', nbkg, observed=True, value=n_noise)\n\nwitc = mc.Uniform('witc', 0, witness_counts*5) # just 0 to some large number\nobswit = mc.Poisson('obswit', nbkg*C + witc, observed=True, value=witness_counts)\n\nrealc = mc.Uniform('realc', 0, total_counts*5) # just 0 to some large number\nobsopen = mc.Poisson('obsopen', nbkg*D + realc, observed=True, value=total_counts-witness_counts)\n\[email protected](plot=True)\ndef open_area(realc=realc, witc=witc, Aw=Aw, Af=Af):\n return realc*Aw/witc/Af\n\nmodel = mc.MCMC([nbkg, obsnbkg, witc, obswit, realc, obsopen, open_area, Af, Aw])", "_____no_output_____" ], [ "model.sample(200000, burn=100, thin=30, burn_till_tuned=True)\n", "_____no_output_____" ], [ "mc.Matplot.plot(nbkg)\nmc.Matplot.plot(witc)\nmc.Matplot.plot(realc)\n# mc.Matplot.plot(open_area)\nmc.Matplot.plot(Aw)\n\n_ = spp.plt.hist(open_area.trace(), 20)\n\n", "_____no_output_____" ], [ "print(\"Foil 1 \\n\")\n\nwitc_mean = np.mean(witc.trace()[...])\nwitc_std = np.std(witc.trace()[...])\n\nprint(\"Found witness counts of {0} turn into {1} +/- {2} ({3:.2f}%)\\n\".format(witness_counts, witc_mean, witc_std, witc_std/witc_mean*100))\n\nrealc_mean = np.mean(realc.trace()[...])\nrealc_std = np.std(realc.trace()[...])\n\nprint(\"Found non-witness counts of {0} turn into {1} +/- {2} ({3:.2f}%)\\n\".format(total_counts-witness_counts, realc_mean, realc_std, realc_std/realc_mean*100))\n\nnbkg_mean = np.mean(nbkg.trace()[...])\nnbkg_std = np.std(nbkg.trace()[...])\n\nprint(\"Found noise counts of {0} turn into {1} +/- {2} ({3:.2f}%)\\n\".format(0, nbkg_mean, nbkg_std, nbkg_std/nbkg_mean*100))\n\nOA_median = np.median(open_area.trace()[...])\nOA_mean = np.mean(open_area.trace()[...])\nOA_std = np.std(open_area.trace()[...])\nprint(\"The open area fraction is {0:.6f} +/- {1:.6f} ({2:.2f}%) at the 1 stddev level from 1 measurement\\n\".format(OA_mean, OA_std,OA_std/OA_mean*100 ))\nprint(\"Phil got {0} for 1 measurement\\n\".format(0.00139))\nprint(\"The ratio Brian/Phil is: {0:.6f} or {1:.6f}\".format(OA_mean/0.00139, 0.00139/OA_mean))\n", "_____no_output_____" ], [ "mc.Matplot.plot(Aw)\n", "_____no_output_____" ] ] ]

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[ "Apache-2.0" ]

[ "Apache-2.0" ]