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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
72083691/cell_35 | [
"text_html_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.preprocessing import TransactionEncoder
import pandas as pd
import time
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.shape
all_transactions = [transaction[1]['itemDescription'].tolist() for transaction in list(data.groupby(['Member_number', 'Date']))]
trans_encoder = TransactionEncoder()
trans_encoder_matrix = trans_encoder.fit(all_transactions).transform(all_transactions)
trans_encoder_matrix = pd.DataFrame(trans_encoder_matrix, columns=trans_encoder.columns_)
def perform_rule_calculation(transact_items_matrix, rule_type, min_support=0.001):
"""
excution time for the corresponding algorithm
"""
start_time = 0
total_execution = 0
if rule_type == 'fpmax':
start_time = time.time()
rule_items = fpmax(transact_items_matrix, min_support=min_support, use_colnames=True)
total_execution = time.time() - start_time
if rule_type == 'apriori':
start_time = time.time()
rule_items = apriori(transact_items_matrix, min_support=min_support, use_colnames=True)
total_execution = time.time() - start_time
if rule_type == 'Fpgrowth':
start_time = time.time()
rule_items = fpgrowth(transact_items_matrix, min_support=min_support, use_colnames=True)
total_execution = time.time() - start_time
rule_items['number_of_items'] = rule_items['itemsets'].apply(lambda x: len(x))
return (rule_items, total_execution)
apriori_matrix, apriori_exec_time = perform_rule_calculation(trans_encoder_matrix, rule_type='apriori')
print('Apriori Execution took: {} seconds'.format(apriori_exec_time)) | code |
72083691/cell_43 | [
"image_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
def compute_association_rule(rule_matrix, metric='lift', min_thresh=1):
"""
Compute the final association rule
rule_matrix: the corresponding algorithms matrix
metric: the metric to be used (default is lift)
min_thresh: the minimum threshold (default is 1)
Returns
Rules:: Information for each transaction satisfying the given metric & threshold
"""
rules = association_rules(rule_matrix, metric=metric, min_threshold=min_thresh)
return rules
apripri_rule = compute_association_rule(apriori_matrix, metric='confidence', min_thresh=0.2)
apripri_rule.head() | code |
72083691/cell_31 | [
"text_html_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
def compute_association_rule(rule_matrix, metric='lift', min_thresh=1):
"""
Compute the final association rule
rule_matrix: the corresponding algorithms matrix
metric: the metric to be used (default is lift)
min_thresh: the minimum threshold (default is 1)
Returns
Rules:: Information for each transaction satisfying the given metric & threshold
"""
rules = association_rules(rule_matrix, metric=metric, min_threshold=min_thresh)
return rules
fp_growth_rule_lift = compute_association_rule(fpgrowth_matrix)
fp_growth_rule_lift.head() | code |
72083691/cell_46 | [
"text_plain_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.preprocessing import TransactionEncoder
import pandas as pd
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.shape
all_transactions = [transaction[1]['itemDescription'].tolist() for transaction in list(data.groupby(['Member_number', 'Date']))]
trans_encoder = TransactionEncoder()
trans_encoder_matrix = trans_encoder.fit(all_transactions).transform(all_transactions)
trans_encoder_matrix = pd.DataFrame(trans_encoder_matrix, columns=trans_encoder.columns_)
fmax = fpmax(trans_encoder_matrix, min_support=0.01, use_colnames=True)
fmax.head() | code |
72083691/cell_24 | [
"text_html_output_1.png"
] | val = {'name': 12}
value = list(val.items())[0]
value | code |
72083691/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.shape
all_transactions = [transaction[1]['itemDescription'].tolist() for transaction in list(data.groupby(['Member_number', 'Date']))]
len(all_transactions) | code |
72083691/cell_27 | [
"text_plain_output_1.png"
] | fpgrowth_matrix.head() | code |
72083691/cell_37 | [
"text_html_output_1.png"
] | apriori_matrix.tail() | code |
72083691/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.shape
data.head() | code |
72083691/cell_36 | [
"text_plain_output_1.png"
] | apriori_matrix.head() | code |
106212906/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
total_restaurent = df_new['Area'].value_counts()
total_restaurent | code |
106212906/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
category = df_new['Category'].value_counts(ascending=False)
category | code |
106212906/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df_new.describe() | code |
106212906/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df_new['Cost for Two (in Rupees)'].unique() | code |
106212906/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df1 = df_new['Category'].value_counts()
df1
df2 = df1.iloc[1:]
df2 | code |
106212906/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df1 = df_new['Category'].value_counts()
df1
df2 = df1.iloc[1:]
df2
df2.to_csv('category_type')
df3 = pd.read_csv('category_type')
df3.columns = ['Category', 'Count']
df3 | code |
106212906/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].unique() | code |
106212906/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df1 = df_new['Category'].value_counts()
df1 | code |
106212906/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df_new.head() | code |
106212906/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.head() | code |
106212906/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new.head() | code |
106212906/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df.head() | code |
106212906/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape | code |
106212906/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df1 = df_new['Category'].value_counts()
df1
df2 = df1.iloc[1:]
df2
df2.to_csv('category_type')
df3 = pd.read_csv('category_type')
df3 | code |
106212906/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df_new.head() | code |
106212906/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df.info() | code |
106212906/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
category = df_new['Category'].value_counts(ascending=False)
category
category_less_then_100 = category[category < 100]
category_less_then_100
def handle_categgory(value):
if value in category_less_then_100:
return 'Others'
else:
return value
df_new['Category'] = df_new['Category'].apply(handle_categgory)
df_new['Category'].value_counts() | code |
106212906/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
Image('../input/swiggy/swiggy.jpg', width=750) | code |
106212906/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape | code |
106212906/cell_35 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
plt.xticks(rotation=90)
df1 = df_new['Category'].value_counts()
df1
df2 = df1.iloc[1:]
df2
df2.to_csv('category_type')
df3 = pd.read_csv('category_type')
df3.columns = ['Category', 'Count']
df3
sns.set_theme()
plt.figure(figsize=(10, 5))
sns.barplot(data=df3, x='Category', y='Count')
plt.xticks(rotation=45) | code |
106212906/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df_new['Cost for Two (in Rupees)'].value_counts() | code |
106212906/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
category = df_new['Category'].value_counts(ascending=False)
category
category_less_then_100 = category[category < 100]
category_less_then_100 | code |
106212906/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
df_new.head() | code |
106212906/cell_10 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df.head() | code |
106212906/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new.shape
df_new.drop_duplicates(inplace=True)
df_new.shape
df_new['Area'].dropna
plt.figure(figsize=(15, 9))
sns.countplot(df_new['Area']).set(title='Number of Restaurents')
plt.xticks(rotation=90) | code |
106212906/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/swiggy-bangalore/Swiggy Bangalore.csv')
df = df.drop(['Offer'], axis=1)
df_new = df.drop(['Rating', 'URL'], axis=1)
df_new['Category'].nunique() | code |
32065944/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
sns.clustermap(data=distance_matrix, col_linkage=Z, row_linkage=Z, cmap=plt.get_cmap('RdBu')) | code |
32065944/cell_34 | [
"text_plain_output_1.png"
] | import gc
del clustermap
del distance_matrix
del distances
del Z
gc.collect() | code |
32065944/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tfidf_vectorizer = TfidfVectorizer(input='content', lowercase=False, preprocessor=lambda text: text, tokenizer=lambda text: text.split(' '), token_pattern=None, analyzer='word', stop_words=None, ngram_range=(1, 1), max_features=10000, binary=False, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)
features = tfidf_vectorizer.fit_transform(df['text_simplified'])
features.shape
features = features.astype('float32').toarray()
sample_size = 0.1
sample_mask = np.random.choice(a=[True, False], size=len(features), p=[sample_size, 1 - sample_size])
features_sample = features[sample_mask]
features_sample.shape | code |
32065944/cell_40 | [
"image_output_1.png"
] | from itertools import chain
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
ax = tokens_count.plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count")
ax.grid(True)
ax = tokens_count[10000:].plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count"),
ax.grid(True)
dissimilarities = pd.Series(distance_matrix.flatten())
ax = dissimilarities.hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.8].hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.95].hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
cluster_count = df['cluster'].value_counts().sort_values()
ax = cluster_count.plot(kind='bar', figsize=(15, 5))
ax.set_xticks([])
ax.set_xlabel('Cluster id')
ax.set_ylabel('Count')
ax.grid(True) | code |
32065944/cell_29 | [
"image_output_1.png"
] | from itertools import chain
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
ax = tokens_count.plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count")
ax.grid(True)
ax = tokens_count[10000:].plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count"),
ax.grid(True)
dissimilarities = pd.Series(distance_matrix.flatten())
ax = dissimilarities.hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.8].hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.95].hist(bins=100, figsize=(15, 5))
ax.set_xlabel('Cosine dissimilarity')
ax.set_ylabel('Count')
ax.grid(True) | code |
32065944/cell_2 | [
"text_plain_output_1.png"
] | !pip install fastcluster | code |
32065944/cell_28 | [
"image_output_1.png"
] | from itertools import chain
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
ax = tokens_count.plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count")
ax.grid(True)
ax = tokens_count[10000:].plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count"),
ax.grid(True)
dissimilarities = pd.Series(distance_matrix.flatten())
ax = dissimilarities.hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.8].hist(bins=100, figsize=(15, 5))
ax.set_xlabel('Cosine dissimilarity')
ax.set_ylabel('Count')
ax.grid(True) | code |
32065944/cell_16 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tfidf_vectorizer = TfidfVectorizer(input='content', lowercase=False, preprocessor=lambda text: text, tokenizer=lambda text: text.split(' '), token_pattern=None, analyzer='word', stop_words=None, ngram_range=(1, 1), max_features=10000, binary=False, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)
features = tfidf_vectorizer.fit_transform(df['text_simplified'])
features.shape | code |
32065944/cell_43 | [
"image_output_1.png"
] | from itertools import chain
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
ax = tokens_count.plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count")
ax.grid(True)
ax = tokens_count[10000:].plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count"),
ax.grid(True)
dissimilarities = pd.Series(distance_matrix.flatten())
ax = dissimilarities.hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.8].hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.95].hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
cluster_count = df['cluster'].value_counts().sort_values()
ax = cluster_count.plot(kind='bar', figsize=(15, 5))
ax.set_xticks([])
ax.set_xlabel("Cluster id")
ax.set_ylabel("Count")
ax.grid(True)
noise_clusters = set(cluster_count[cluster_count <= 5].index)
noise_mask = df['cluster'].isin(noise_clusters)
df.loc[noise_mask, 'cluster'] = -1
cluster_count = df['cluster'].value_counts().sort_values()
ax = cluster_count.plot(kind='bar', figsize=(15, 5))
ax.set_xticks([])
ax.set_xlabel('Cluster id')
ax.set_ylabel('Count')
ax.grid(True) | code |
32065944/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from itertools import chain
from scipy.cluster.hierarchy import fcluster
from sklearn.feature_extraction.text import TfidfVectorizer
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
tfidf_vectorizer = TfidfVectorizer(input='content', lowercase=False, preprocessor=lambda text: text, tokenizer=lambda text: text.split(' '), token_pattern=None, analyzer='word', stop_words=None, ngram_range=(1, 1), max_features=10000, binary=False, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)
features = tfidf_vectorizer.fit_transform(df['text_simplified'])
features.shape
features = features.astype('float32').toarray()
sample_size = 0.1
sample_mask = np.random.choice(a=[True, False], size=len(features), p=[sample_size, 1 - sample_size])
features_sample = features[sample_mask]
features_sample.shape
sns.clustermap(data=distance_matrix, col_linkage=Z, row_linkage=Z, cmap=plt.get_cmap('RdBu'))
dissimilarities = pd.Series(distance_matrix.flatten())
clusters = fcluster(Z, t=0.999, criterion='distance')
clustermap = sns.clustermap(data=distance_matrix, col_linkage=Z, row_linkage=Z, cmap=plt.get_cmap('RdBu'))
cluster_mapping = dict(zip(range(len(features_sample)), clusters))
clustermap_clusters = pd.Series([cluster_mapping[id_] for id_ in list(clustermap.data2d.columns)])
for cluster in set(clusters):
cluster_range = list(clustermap_clusters[clustermap_clusters == cluster].index)
clustermap.ax_heatmap.add_patch(patches.Rectangle(xy=(np.min(cluster_range), np.min(cluster_range)), width=len(cluster_range), height=len(cluster_range), fill=False, edgecolor='lightgreen', lw=2))
print(f'There are {clustermap_clusters.nunique()} clusters.') | code |
32065944/cell_46 | [
"text_html_output_1.png"
] | from itertools import chain
from scipy.cluster.hierarchy import fcluster
from sklearn.feature_extraction.text import TfidfVectorizer
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
ax = tokens_count.plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count")
ax.grid(True)
ax = tokens_count[10000:].plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count"),
ax.grid(True)
tfidf_vectorizer = TfidfVectorizer(input='content', lowercase=False, preprocessor=lambda text: text, tokenizer=lambda text: text.split(' '), token_pattern=None, analyzer='word', stop_words=None, ngram_range=(1, 1), max_features=10000, binary=False, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)
features = tfidf_vectorizer.fit_transform(df['text_simplified'])
features.shape
features = features.astype('float32').toarray()
sample_size = 0.1
sample_mask = np.random.choice(a=[True, False], size=len(features), p=[sample_size, 1 - sample_size])
features_sample = features[sample_mask]
features_sample.shape
sns.clustermap(data=distance_matrix, col_linkage=Z, row_linkage=Z, cmap=plt.get_cmap('RdBu'))
dissimilarities = pd.Series(distance_matrix.flatten())
ax = dissimilarities.hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.8].hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
ax = dissimilarities[dissimilarities >= 0.95].hist(bins=100, figsize=(15, 5))
ax.set_xlabel("Cosine dissimilarity")
ax.set_ylabel("Count")
ax.grid(True)
clusters = fcluster(Z, t=0.999, criterion='distance')
clustermap = sns.clustermap(data=distance_matrix, col_linkage=Z, row_linkage=Z, cmap=plt.get_cmap('RdBu'))
cluster_mapping = dict(zip(range(len(features_sample)), clusters))
clustermap_clusters = pd.Series([cluster_mapping[id_] for id_ in list(clustermap.data2d.columns)])
for cluster in set(clusters):
cluster_range = list(clustermap_clusters[clustermap_clusters == cluster].index)
clustermap.ax_heatmap.add_patch(patches.Rectangle(xy=(np.min(cluster_range), np.min(cluster_range)), width=len(cluster_range), height=len(cluster_range), fill=False, edgecolor='lightgreen', lw=2))
cluster_count = df['cluster'].value_counts().sort_values()
ax = cluster_count.plot(kind='bar', figsize=(15, 5))
ax.set_xticks([])
ax.set_xlabel("Cluster id")
ax.set_ylabel("Count")
ax.grid(True)
noise_clusters = set(cluster_count[cluster_count <= 5].index)
noise_mask = df['cluster'].isin(noise_clusters)
df.loc[noise_mask, 'cluster'] = -1
columns = np.array(tfidf_vectorizer.get_feature_names())
top_k = 3
def describe(df: pd.DataFrame) -> pd.DataFrame:
order = features[df.index].mean(axis=0).argsort()[::-1][:top_k]
top_words = columns[order]
cluster_id = df['cluster'].iloc[0]
for i, word in enumerate(top_words):
df[f'word_{i + 1}'] = word if cluster_id != -1 else ''
return df
df = df.groupby('cluster').apply(describe)
df.filter(regex='text_simplified|word_\\d+', axis=1) | code |
32065944/cell_24 | [
"text_plain_output_1.png"
] | distances = squareform(distance_matrix, force='tovector')
Z = fastcluster.linkage(distances, method='complete', preserve_input=True) | code |
32065944/cell_14 | [
"image_output_1.png"
] | from itertools import chain
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
ax = tokens_count.plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count")
ax.grid(True)
ax = tokens_count[10000:].plot(figsize=(15, 5))
ax.set_xlabel('Token')
(ax.set_ylabel('Count'),)
ax.grid(True) | code |
32065944/cell_22 | [
"text_plain_output_1.png"
] | distance_matrix = pairwise_distances(features_sample, metric='cosine') | code |
32065944/cell_10 | [
"text_plain_output_1.png"
] | from itertools import chain
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count | code |
32065944/cell_27 | [
"image_output_1.png"
] | from itertools import chain
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
ax = tokens_count.plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count")
ax.grid(True)
ax = tokens_count[10000:].plot(figsize=(15, 5))
ax.set_xlabel("Token")
ax.set_ylabel("Count"),
ax.grid(True)
dissimilarities = pd.Series(distance_matrix.flatten())
ax = dissimilarities.hist(bins=100, figsize=(15, 5))
ax.set_xlabel('Cosine dissimilarity')
ax.set_ylabel('Count')
ax.grid(True) | code |
32065944/cell_12 | [
"text_plain_output_1.png"
] | from itertools import chain
import pandas as pd
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
ax = tokens_count.plot(figsize=(15, 5))
ax.set_xlabel('Token')
ax.set_ylabel('Count')
ax.grid(True) | code |
32065944/cell_36 | [
"text_plain_output_1.png"
] | from itertools import chain
from scipy.cluster.hierarchy import fcluster
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/cleaning-cord-19-metadata/cord_metadata_cleaned.csv')
df = df.dropna(subset=['text_simplified']).reset_index(drop=True)
tokens = df['text_simplified'].str.split(' ').tolist()
tokens = pd.Series(chain(*tokens))
tokens_count = tokens.value_counts()
tokens_count
tfidf_vectorizer = TfidfVectorizer(input='content', lowercase=False, preprocessor=lambda text: text, tokenizer=lambda text: text.split(' '), token_pattern=None, analyzer='word', stop_words=None, ngram_range=(1, 1), max_features=10000, binary=False, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)
features = tfidf_vectorizer.fit_transform(df['text_simplified'])
features.shape
features = features.astype('float32').toarray()
sample_size = 0.1
sample_mask = np.random.choice(a=[True, False], size=len(features), p=[sample_size, 1 - sample_size])
features_sample = features[sample_mask]
features_sample.shape
sns.clustermap(data=distance_matrix, col_linkage=Z, row_linkage=Z, cmap=plt.get_cmap('RdBu'))
dissimilarities = pd.Series(distance_matrix.flatten())
clusters = fcluster(Z, t=0.999, criterion='distance')
clustermap = sns.clustermap(data=distance_matrix, col_linkage=Z, row_linkage=Z, cmap=plt.get_cmap('RdBu'))
cluster_mapping = dict(zip(range(len(features_sample)), clusters))
clustermap_clusters = pd.Series([cluster_mapping[id_] for id_ in list(clustermap.data2d.columns)])
for cluster in set(clusters):
cluster_range = list(clustermap_clusters[clustermap_clusters == cluster].index)
clustermap.ax_heatmap.add_patch(patches.Rectangle(xy=(np.min(cluster_range), np.min(cluster_range)), width=len(cluster_range), height=len(cluster_range), fill=False, edgecolor='lightgreen', lw=2))
model = KNeighborsClassifier(n_neighbors=5, metric='cosine', n_jobs=-1)
model.fit(features_sample, clusters) | code |
90111983/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
print('Cases of nonconformity by gender: {}'.format(sum(df['total'] - df['male'] - df['female']))) | code |
90111983/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
df.head() | code |
90111983/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
plt.figure(figsize=(12, 5))
plt.title("Characteristics of China's population over the period of 70 years", fontweight='bold', fontsize=12)
plt.plot(df['year'], df['female'], linewidth=3, label='female')
plt.plot(df['year'], df['male'], linewidth=3, label='male')
plt.plot(df['year'], df['urban'], linewidth=3, linestyle='--', label='urban')
plt.plot(df['year'], df['rural'], linewidth=3, linestyle='--', label='rural')
plt.xlabel('year', fontweight='bold', fontsize=10)
plt.ylabel('population', fontweight='bold', fontsize=10)
plt.grid(axis='x', color='0.95')
plt.legend(title='Features:')
plt.show() | code |
90111983/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
df.describe() | code |
90111983/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
fig, ax1 = plt.subplots(figsize=(12, 5))
plt.title('The difference between the genders in total and percentage terms', fontweight='bold', fontsize = 12)
ax1.set_xlabel('year', fontsize = 10,fontweight='bold')
ax1.set_ylabel('total',fontweight='bold', fontsize = 10, color = 'green')
plt.plot(df['year'], df['male'] - df['female'], linewidth=3,label= 'total', color = 'green')
ax1.tick_params(axis='y')
ax2 = ax1.twinx()
ax2.set_ylabel('percent', fontweight='bold', fontsize = 10)
plt.plot(df['year'], (df['male'] - df['female'])/df['total']*100, linewidth=3, color = 'black', label= 'percent')
ax2.tick_params(axis='y')
ax2.yaxis.set_major_formatter(mtick.PercentFormatter())
fig.tight_layout()
fig, ax1 = plt.subplots(figsize=(12, 5))
plt.title('Changing of population growth', fontweight='bold', fontsize=12)
ax1.set_xlabel('year', fontsize=10, fontweight='bold')
ax1.set_ylabel('total number', fontweight='bold', fontsize=10, color='green')
plt.plot(df['year'], df['total'], linewidth=3, label='total', color='green')
ax1.tick_params(axis='y')
ax2 = ax1.twinx()
ax2.set_ylabel('total growth', fontweight='bold', fontsize=10)
plt.plot(df['year'], df['total'] - df['total'].shift(), linewidth=3, color='black', label='percent')
ax2.tick_params(axis='y')
plt.axhline(y=0, color='red', linestyle='--')
fig.tight_layout() | code |
90111983/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
fig, ax1 = plt.subplots(figsize=(12, 5))
plt.title('The difference between the genders in total and percentage terms', fontweight='bold', fontsize=12)
ax1.set_xlabel('year', fontsize=10, fontweight='bold')
ax1.set_ylabel('total', fontweight='bold', fontsize=10, color='green')
plt.plot(df['year'], df['male'] - df['female'], linewidth=3, label='total', color='green')
ax1.tick_params(axis='y')
ax2 = ax1.twinx()
ax2.set_ylabel('percent', fontweight='bold', fontsize=10)
plt.plot(df['year'], (df['male'] - df['female']) / df['total'] * 100, linewidth=3, color='black', label='percent')
ax2.tick_params(axis='y')
ax2.yaxis.set_major_formatter(mtick.PercentFormatter())
fig.tight_layout() | code |
90111983/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
df.info() | code |
90111983/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.head() | code |
90111983/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
print('Cases of nonconformity by territory: {}'.format(sum(df['total'] - df['urban'] - df['rural']))) | code |
90111983/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask] | code |
90111983/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
print('Cases of nonconformity by gender: {}'.format(sum(df['total'] - df['male'] - df['female']))) | code |
1006102/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/database.csv', low_memory=False)
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(13, 15))
crims_by_relationship = data[['Relationship', 'Record ID']].groupby('Relationship').count()
crimes_per_perpetrator_race = data[['Perpetrator Race', 'Record ID']].groupby('Perpetrator Race').count()
crimes_per_victime_race = data[['Victim Race', 'Record ID']].groupby('Victim Race').count()
crimes_per_type = data[['Crime Type', 'Record ID']].groupby('Crime Type').count()
crimes_per_perpetrator_race.plot(kind='bar', ax= ax1, title='crimes per perpetrator race')
crimes_per_victime_race.plot(kind='bar', ax= ax2, title='crimes per victime race')
crims_by_relationship.plot(kind='bar', ax= ax3, title='crimes by relationship')
crimes_per_type.plot(kind='bar', ax= ax4, title='crimes types')
data1 = data[['Relationship', 'Year', 'Record ID']].groupby(['Relationship', 'Year']).count().reset_index()
plt.plot(data1[data1.Relationship == 'Wife']['Year'].tolist(), data1[data1.Relationship == 'Wife']['Record ID'].tolist(), data1[data1.Relationship == 'Acquaintance']['Year'].tolist(), data1[data1.Relationship == 'Acquaintance']['Record ID'].tolist())
plt.show() | code |
1006102/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import sklearn.cluster as cluster
data = pd.read_csv('../input/database.csv', low_memory=False)
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(13, 15))
crims_by_relationship = data[['Relationship', 'Record ID']].groupby('Relationship').count()
crimes_per_perpetrator_race = data[['Perpetrator Race', 'Record ID']].groupby('Perpetrator Race').count()
crimes_per_victime_race = data[['Victim Race', 'Record ID']].groupby('Victim Race').count()
crimes_per_type = data[['Crime Type', 'Record ID']].groupby('Crime Type').count()
crimes_per_perpetrator_race.plot(kind='bar', ax= ax1, title='crimes per perpetrator race')
crimes_per_victime_race.plot(kind='bar', ax= ax2, title='crimes per victime race')
crims_by_relationship.plot(kind='bar', ax= ax3, title='crimes by relationship')
crimes_per_type.plot(kind='bar', ax= ax4, title='crimes types')
data1 = data[['Relationship', 'Year', 'Record ID']].groupby(['Relationship', 'Year']).count().reset_index()
victims = data[['Victim Sex', 'Victim Age', 'Victim Ethnicity', 'Victim Race']]
import seaborn as sns
import sklearn.cluster as cluster
def plot_clusters(data, algorithm, args, kwds):
labels = algorithm(*args, **kwds).fit_predict(data)
palette = sns.color_palette('deep', np.unique(labels).max() + 1)
colors = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]
plt.scatter(data.T[0], data.T[1], c=colors, **plot_kwds)
frame = plt.gca()
frame.axes.get_xaxis().set_visible(False)
frame.axes.get_yaxis().set_visible(False)
plt.title('Clusters found by {}'.format(str(algorithm.__name__)), fontsize=24)
plt.text(-0.5, 0.7, 'Clustering took {:.2f} s'.format(end_time - start_time), fontsize=14)
plot_clusters(victims, cluster.KMeans, (), {'n_clusters': 6}) | code |
1006102/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/database.csv', low_memory=False)
data.head() | code |
1006102/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006102/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/database.csv', low_memory=False)
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(13, 15))
crims_by_relationship = data[['Relationship', 'Record ID']].groupby('Relationship').count()
crimes_per_perpetrator_race = data[['Perpetrator Race', 'Record ID']].groupby('Perpetrator Race').count()
crimes_per_victime_race = data[['Victim Race', 'Record ID']].groupby('Victim Race').count()
crimes_per_type = data[['Crime Type', 'Record ID']].groupby('Crime Type').count()
crimes_per_perpetrator_race.plot(kind='bar', ax=ax1, title='crimes per perpetrator race')
crimes_per_victime_race.plot(kind='bar', ax=ax2, title='crimes per victime race')
crims_by_relationship.plot(kind='bar', ax=ax3, title='crimes by relationship')
crimes_per_type.plot(kind='bar', ax=ax4, title='crimes types') | code |
1006102/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/database.csv', low_memory=False)
victims = data[['Victim Sex', 'Victim Age', 'Victim Ethnicity', 'Victim Race']]
victims.head() | code |
89138671/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | y = data.pop('target')
X = data.drop(columns=['row_id']) | code |
89138671/cell_3 | [
"text_plain_output_1.png"
] | data = pd.read_pickle('../input/ump-train-picklefile/train.pkl')
data.drop(columns=['row_id'], inplace=True) | code |
89138671/cell_5 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_20.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_15.png",
"image_output_9.png",
"image_output_19.png"
] | for investment in np.random.choice(pd.unique(data['investment_id']), 20):
data[data['investment_id'] == investment].plot('time_id', 'target') | code |
34121141/cell_9 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
train_it = train_datagen.flow_from_directory('/kaggle/input/dataset/train/', class_mode='categorical', batch_size=10, target_size=(224, 224))
test_it = test_datagen.flow_from_directory('/kaggle/input/dataset/test/', class_mode='categorical', batch_size=5, target_size=(224, 224))
train_it.class_indices | code |
34121141/cell_7 | [
"text_plain_output_1.png"
] | from keras.applications.vgg16 import VGG16
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, BatchNormalization, Activation
from keras.layers import Flatten
from keras.models import Model, Sequential
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot
import sys
def define_model():
model = VGG16(include_top=False, input_shape=(224, 224, 3))
for layer in model.layers:
layer.trainable = False
flat1 = Flatten()(model.layers[-1].output)
class1 = Dense(256, activation='relu', kernel_initializer='he_uniform')(flat1)
output = Dense(166, activation='softmax')(class1)
model = Model(inputs=model.inputs, outputs=output)
opt = SGD(lr=0.001, momentum=0.9)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
return model
def summarize_diagnostics(history):
filename = sys.argv[0].split('/')[-1]
pyplot.close()
train_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
train_it = train_datagen.flow_from_directory('/kaggle/input/dataset/train/', class_mode='categorical', batch_size=10, target_size=(224, 224))
test_it = test_datagen.flow_from_directory('/kaggle/input/dataset/test/', class_mode='categorical', batch_size=5, target_size=(224, 224))
model = define_model()
model.summary()
history = model.fit_generator(train_it, steps_per_epoch=len(train_it), validation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=1)
_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)
print('> %.3f' % (acc * 100.0))
model.save('accuracy - %.3f' % (acc * 100.0) + '.h5')
summarize_diagnostics(history) | code |
34121141/cell_8 | [
"text_plain_output_1.png"
] | from keras.applications.vgg16 import VGG16
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, BatchNormalization, Activation
from keras.layers import Flatten
from keras.models import Model, Sequential
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot
import cv2
import numpy as np
import numpy as np # linear algebra
import sys
def define_model():
model = VGG16(include_top=False, input_shape=(224, 224, 3))
for layer in model.layers:
layer.trainable = False
flat1 = Flatten()(model.layers[-1].output)
class1 = Dense(256, activation='relu', kernel_initializer='he_uniform')(flat1)
output = Dense(166, activation='softmax')(class1)
model = Model(inputs=model.inputs, outputs=output)
opt = SGD(lr=0.001, momentum=0.9)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
return model
def summarize_diagnostics(history):
filename = sys.argv[0].split('/')[-1]
pyplot.close()
train_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
train_it = train_datagen.flow_from_directory('/kaggle/input/dataset/train/', class_mode='categorical', batch_size=10, target_size=(224, 224))
test_it = test_datagen.flow_from_directory('/kaggle/input/dataset/test/', class_mode='categorical', batch_size=5, target_size=(224, 224))
model = define_model()
model.summary()
history = model.fit_generator(train_it, steps_per_epoch=len(train_it), validation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=1)
_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)
model.save('accuracy - %.3f' % (acc * 100.0) + '.h5')
summarize_diagnostics(history)
import cv2
img = cv2.imread('/kaggle/input/dataset/test/Voltorb/Voltorb.14.png')
img = cv2.resize(img, (100, 100))
pred = model.predict(np.array([img]))
move_code = np.argmax(pred[0])
move_code | code |
128012372/cell_21 | [
"image_output_1.png"
] | from keras import regularizers
from keras.callbacks import ModelCheckpoint, CSVLogger, TensorBoard, EarlyStopping, ReduceLROnPlateau
from keras.layers import Conv2D, Dense, BatchNormalization, Activation, Dropout, MaxPooling2D, Flatten
from tensorflow.keras.optimizers import Adam, RMSprop, SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
import datetime
import os
import pandas as pd
import tensorflow as tf
tf.config.list_physical_devices('GPU')
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
train_datagen = ImageDataGenerator(rescale=1.0 / 255, zoom_range=0.4, horizontal_flip=True)
training_set = train_datagen.flow_from_directory(train_dir, batch_size=16, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
test_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_set = test_datagen.flow_from_directory(test_dir, batch_size=15, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
training_set.class_indices
def get_model(input_size, classes=2):
model = tf.keras.models.Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=input_size))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(2, 2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(0.01)))
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.01)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
return model
model_3 = get_model((row, col, 1), classes)
model_3.summary()
chk_path = 'model_3.h5'
log_dir = 'checkpoint/logs/' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
checkpoint = ModelCheckpoint(filepath=chk_path, save_best_only=True, verbose=1, mode='min', moniter='val_loss')
earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, restore_best_weights=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=6, verbose=1, min_delta=0.0001)
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
csv_logger = CSVLogger('training.log')
callbacks = [checkpoint, reduce_lr, csv_logger]
steps_per_epoch = training_set.n // training_set.batch_size
validation_steps = test_set.n // test_set.batch_size
hist = model_3.fit(x=training_set, validation_data=test_set, epochs=60, callbacks=callbacks, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) | code |
128012372/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
import os
import pandas as pd
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
train_datagen = ImageDataGenerator(rescale=1.0 / 255, zoom_range=0.4, horizontal_flip=True)
training_set = train_datagen.flow_from_directory(train_dir, batch_size=16, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
test_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_set = test_datagen.flow_from_directory(test_dir, batch_size=15, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
training_set.class_indices | code |
128012372/cell_9 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
test_count.transpose().plot(kind='bar') | code |
128012372/cell_25 | [
"text_plain_output_1.png"
] | from keras import regularizers
from keras.callbacks import ModelCheckpoint, CSVLogger, TensorBoard, EarlyStopping, ReduceLROnPlateau
from keras.layers import Conv2D, Dense, BatchNormalization, Activation, Dropout, MaxPooling2D, Flatten
from tensorflow.keras.optimizers import Adam, RMSprop, SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
import datetime
import os
import pandas as pd
import tensorflow as tf
tf.config.list_physical_devices('GPU')
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
train_datagen = ImageDataGenerator(rescale=1.0 / 255, zoom_range=0.4, horizontal_flip=True)
training_set = train_datagen.flow_from_directory(train_dir, batch_size=16, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
test_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_set = test_datagen.flow_from_directory(test_dir, batch_size=15, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
training_set.class_indices
def get_model(input_size, classes=2):
model = tf.keras.models.Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=input_size))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(2, 2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(0.01)))
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.01)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
return model
model_3 = get_model((row, col, 1), classes)
model_3.summary()
chk_path = 'model_3.h5'
log_dir = 'checkpoint/logs/' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
checkpoint = ModelCheckpoint(filepath=chk_path, save_best_only=True, verbose=1, mode='min', moniter='val_loss')
earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, restore_best_weights=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=6, verbose=1, min_delta=0.0001)
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
csv_logger = CSVLogger('training.log')
callbacks = [checkpoint, reduce_lr, csv_logger]
steps_per_epoch = training_set.n // training_set.batch_size
validation_steps = test_set.n // test_set.batch_size
hist = model_3.fit(x=training_set, validation_data=test_set, epochs=60, callbacks=callbacks, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps)
train_loss, train_accu = model_3.evaluate(training_set)
test_loss, test_accu = model_3.evaluate(test_set)
print('final train accuracy = {:.2f} , validation accuracy = {:.2f}'.format(train_accu * 100, test_accu * 100)) | code |
128012372/cell_23 | [
"text_plain_output_1.png"
] | from keras import regularizers
from keras.callbacks import ModelCheckpoint, CSVLogger, TensorBoard, EarlyStopping, ReduceLROnPlateau
from keras.layers import Conv2D, Dense, BatchNormalization, Activation, Dropout, MaxPooling2D, Flatten
from tensorflow.keras.optimizers import Adam, RMSprop, SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
import datetime
import matplotlib.pyplot as plt
import os
import pandas as pd
import tensorflow as tf
tf.config.list_physical_devices('GPU')
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
i = 1
for expression in os.listdir(train_dir):
img = load_img(train_dir + expression + '/' + os.listdir(train_dir + expression)[1])
plt.axis('off')
i += 1
train_datagen = ImageDataGenerator(rescale=1.0 / 255, zoom_range=0.4, horizontal_flip=True)
training_set = train_datagen.flow_from_directory(train_dir, batch_size=16, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
test_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_set = test_datagen.flow_from_directory(test_dir, batch_size=15, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
training_set.class_indices
def get_model(input_size, classes=2):
model = tf.keras.models.Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=input_size))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(2, 2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(0.01)))
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.01)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
return model
model_3 = get_model((row, col, 1), classes)
model_3.summary()
chk_path = 'model_3.h5'
log_dir = 'checkpoint/logs/' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
checkpoint = ModelCheckpoint(filepath=chk_path, save_best_only=True, verbose=1, mode='min', moniter='val_loss')
earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, restore_best_weights=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=6, verbose=1, min_delta=0.0001)
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
csv_logger = CSVLogger('training.log')
callbacks = [checkpoint, reduce_lr, csv_logger]
steps_per_epoch = training_set.n // training_set.batch_size
validation_steps = test_set.n // test_set.batch_size
hist = model_3.fit(x=training_set, validation_data=test_set, epochs=60, callbacks=callbacks, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps)
plt.figure(figsize=(14, 5))
plt.subplot(1, 2, 1)
plt.plot(hist.history['accuracy'])
plt.plot(hist.history['val_accuracy'])
plt.title('Model 1 Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend(['train', 'test'], loc='upper left')
plt.subplot(1, 2, 2)
plt.plot(hist.history['loss'])
plt.plot(hist.history['val_loss'])
plt.title('model 1 Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend(['train', 'test'], loc='upper left')
plt.show() | code |
128012372/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
print(train_count)
print(test_count) | code |
128012372/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import regularizers
from keras.layers import Conv2D, Dense, BatchNormalization, Activation, Dropout, MaxPooling2D, Flatten
from tensorflow.keras.optimizers import Adam, RMSprop, SGD
import os
import pandas as pd
import tensorflow as tf
tf.config.list_physical_devices('GPU')
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
def get_model(input_size, classes=2):
model = tf.keras.models.Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=input_size))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(2, 2))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(0.01)))
model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.01)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
return model
model_3 = get_model((row, col, 1), classes)
model_3.summary() | code |
128012372/cell_8 | [
"image_output_1.png"
] | import os
import pandas as pd
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
train_count.transpose().plot(kind='bar') | code |
128012372/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
from keras.layers import Conv2D, Dense, BatchNormalization, Activation, Dropout, MaxPooling2D, Flatten
from tensorflow.keras.optimizers import Adam, RMSprop, SGD
from keras import regularizers
from keras.callbacks import ModelCheckpoint, CSVLogger, TensorBoard, EarlyStopping, ReduceLROnPlateau
import datetime
import matplotlib.pyplot as plt
from tensorflow.keras.utils import plot_model | code |
128012372/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
print(classes) | code |
128012372/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
import matplotlib.pyplot as plt
import os
import pandas as pd
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
plt.figure(figsize=(14, 22))
i = 1
for expression in os.listdir(train_dir):
img = load_img(train_dir + expression + '/' + os.listdir(train_dir + expression)[1])
plt.subplot(1, 7, i)
plt.imshow(img)
plt.title(expression)
plt.axis('off')
i += 1
plt.show() | code |
128012372/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
import os
import pandas as pd
train_dir = '/kaggle/input/happy-sad-7125/train/'
test_dir = '/kaggle/input/happy-sad-7125/test/'
row, col = (48, 48)
classes = 2
def count_exp(path, set_):
dict_ = {}
for expression in os.listdir(path):
dir_ = path + expression
dict_[expression] = len(os.listdir(dir_))
df = pd.DataFrame(dict_, index=[set_])
return df
train_count = count_exp(train_dir, 'train')
test_count = count_exp(test_dir, 'test')
train_datagen = ImageDataGenerator(rescale=1.0 / 255, zoom_range=0.4, horizontal_flip=True)
training_set = train_datagen.flow_from_directory(train_dir, batch_size=16, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical')
test_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_set = test_datagen.flow_from_directory(test_dir, batch_size=15, target_size=(48, 48), shuffle=True, color_mode='grayscale', class_mode='categorical') | code |
128012372/cell_5 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.config.list_physical_devices('GPU') | code |
2017333/cell_21 | [
"text_plain_output_1.png"
] | from subprocess import check_output
print(check_output(['ls', '../working']).decode('utf8')) | code |
2017333/cell_13 | [
"text_html_output_1.png"
] | from sklearn import svm
from sklearn import svm
clf = svm.SVC()
clf.fit(X_train, Y_train)
clf.score(X_train, Y_train) | code |
2017333/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
PassengerIds = test['PassengerId']
PassengerIds | code |
2017333/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
train.head() | code |
2017333/cell_11 | [
"text_html_output_1.png"
] | X_train.head() | code |
2017333/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import svm
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
PassengerIds = test['PassengerId']
PassengerIds
train['Sex'] = train['Sex'].apply(lambda x: 1 if x == 'male' else 0)
train['Age'] = train['Age'].fillna(np.mean(train['Age']))
train['Fare'] = train['Fare'].fillna(np.mean(train['Fare']))
from sklearn import svm
clf = svm.SVC()
clf.fit(X_train, Y_train)
clf.score(X_train, Y_train)
clf.fit(X_test, Y_test)
clf.score(X_test, Y_test)
test['Sex'] = test['Sex'].apply(lambda x: 1 if x == 'male' else 0)
test['Age'] = test['Age'].fillna(np.mean(test['Age']))
test['Fare'] = test['Fare'].fillna(np.mean(test['Fare']))
test = test[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']]
results = clf.predict(test)
submission_df = {'PassengerId': PassengerIds, 'Survived': results}
submission = pd.DataFrame(submission_df)
submission | code |
2017333/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2017333/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
train['Sex'] = train['Sex'].apply(lambda x: 1 if x == 'male' else 0)
train = train[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']]
train.head() | code |
2017333/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import svm
from sklearn import svm
clf = svm.SVC()
clf.fit(X_train, Y_train)
clf.score(X_train, Y_train)
clf.fit(X_test, Y_test)
clf.score(X_test, Y_test) | code |
2017333/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.head() | code |
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