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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
18149558/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
print('Items set shape', items.shape) | code |
18149558/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
test.head() | code |
18149558/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
print('Shops set shape', shops.shape) | code |
18149558/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test) | code |
18149558/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
print('Train DS:', train.columns.values)
print('Test DS:', test.columns.values)
print('Item cats DS:', items_cats.columns.values)
print('Items DS:', items.columns.values)
print('Shops DS:', shops.columns.values) | code |
18149558/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df['m1'] = train_df.groupby(['shop_id', 'item_id']).item_cnt_day.shift()
train_df['m1'].fillna(0, inplace=True)
train_df
train_df['m2'] = train_df.groupby(['shop_id', 'item_id']).m1.shift()
train_df['m2'].fillna(0, inplace=True)
train_df.rename(columns={'item_cnt_day': 'item_cnt_month'}, inplace=True)
finalDf = train_df[['shop_id', 'item_id', 'date_block_num', 'm1', 'm2', 'item_cnt_month']].reset_index()
finalDf.drop(['index'], axis=1, inplace=True)
newTest = pd.merge_asof(test, finalDf, left_index=True, right_index=True, on=['shop_id', 'item_id'])
y_train = finalDf['item_cnt_month']
newTest.drop(['item_cnt_month'], axis=1, inplace=True)
x_train = finalDf[['shop_id', 'item_id', 'm1', 'm2']]
x_test = newTest[['shop_id', 'item_id', 'm1', 'm2']]
x_test.shape
x_test_reshaped = x_test.values.reshape((x_test.values.shape[0], 1, x_test.values.shape[1]))
x_test_reshaped.shape | code |
18149558/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train.head() | code |
18149558/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df['m1'] = train_df.groupby(['shop_id', 'item_id']).item_cnt_day.shift()
train_df['m1'].fillna(0, inplace=True)
train_df
train_df['m2'] = train_df.groupby(['shop_id', 'item_id']).m1.shift()
train_df['m2'].fillna(0, inplace=True)
train_df.rename(columns={'item_cnt_day': 'item_cnt_month'}, inplace=True)
finalDf = train_df[['shop_id', 'item_id', 'date_block_num', 'm1', 'm2', 'item_cnt_month']].reset_index()
finalDf.drop(['index'], axis=1, inplace=True)
newTest = pd.merge_asof(test, finalDf, left_index=True, right_index=True, on=['shop_id', 'item_id'])
newTest.head() | code |
18149558/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test) | code |
18149558/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
train.columns.values | code |
18149558/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df['m1'] = train_df.groupby(['shop_id', 'item_id']).item_cnt_day.shift()
train_df['m1'].fillna(0, inplace=True)
train_df
train_df['m2'] = train_df.groupby(['shop_id', 'item_id']).m1.shift()
train_df['m2'].fillna(0, inplace=True)
train_df.rename(columns={'item_cnt_day': 'item_cnt_month'}, inplace=True)
train_df.head() | code |
18149558/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df['m1'] = train_df.groupby(['shop_id', 'item_id']).item_cnt_day.shift()
train_df['m1'].fillna(0, inplace=True)
train_df
train_df['m2'] = train_df.groupby(['shop_id', 'item_id']).m1.shift()
train_df['m2'].fillna(0, inplace=True)
train_df.rename(columns={'item_cnt_day': 'item_cnt_month'}, inplace=True)
finalDf = train_df[['shop_id', 'item_id', 'date_block_num', 'm1', 'm2', 'item_cnt_month']].reset_index()
finalDf.drop(['index'], axis=1, inplace=True)
newTest = pd.merge_asof(test, finalDf, left_index=True, right_index=True, on=['shop_id', 'item_id'])
y_train = finalDf['item_cnt_month']
newTest.drop(['item_cnt_month'], axis=1, inplace=True)
x_train = finalDf[['shop_id', 'item_id', 'm1', 'm2']]
x_test = newTest[['shop_id', 'item_id', 'm1', 'm2']]
x_test.shape | code |
18149558/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train) | code |
18149558/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
print('Testing set shape', test.shape) | code |
121151039/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train) | code |
121151039/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
data = pd.read_csv('/kaggle/input/wind-turbine-scada-dataset/T1.csv')
data = data[data['LV ActivePower (kW)'] > 0]
data = data.dropna()
data = pd.get_dummies(data)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data[['Wind Speed (m/s)', 'Wind Direction (°)']] = scaler.fit_transform(data[['Wind Speed (m/s)', 'Wind Direction (°)']])
print(data.head())
print(data.describe()) | code |
121151039/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/wind-turbine-scada-dataset/T1.csv')
print(data.head())
print(data.describe())
print(data.info()) | code |
88085617/cell_21 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores
from sklearn.model_selection import GridSearchCV
clf = GridSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df
clf.best_score_
clf.best_params_
from sklearn.model_selection import RandomizedSearchCV
clf = RandomizedSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10, n_iter=4)
clf.fit(iris.data, iris.target)
clf.cv_results_ | code |
88085617/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result) | code |
88085617/cell_23 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores
from sklearn.model_selection import GridSearchCV
clf = GridSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df
clf.best_score_
clf.best_params_
from sklearn.model_selection import RandomizedSearchCV
clf = RandomizedSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10, n_iter=4)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df[['param_C', 'param_kernel', 'param_max_iter', 'mean_test_score']]
clf.best_params_ | code |
88085617/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores
from sklearn.model_selection import GridSearchCV
clf = GridSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df
clf.best_score_
clf.best_params_ | code |
88085617/cell_7 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df | code |
88085617/cell_18 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores
from sklearn.model_selection import GridSearchCV
clf = GridSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df
clf.best_score_ | code |
88085617/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores
from sklearn.model_selection import GridSearchCV
clf = GridSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10)
clf.fit(iris.data, iris.target)
clf.cv_results_ | code |
88085617/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores
from sklearn.model_selection import GridSearchCV
clf = GridSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df | code |
88085617/cell_17 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores
from sklearn.model_selection import GridSearchCV
clf = GridSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df
df[['param_C', 'param_kernel', 'param_max_iter', 'mean_test_score']] | code |
88085617/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores | code |
88085617/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
result = cross_val_score(svm.SVC(kernel='rbf', C=5, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result)
kernels = ['rbf', 'linear']
C = [1, 10, 20, 30]
avg_scores = {}
for kval in kernels:
for cval in C:
cv_scores = cross_val_score(svm.SVC(kernel=kval, C=cval), iris.data, iris.target, cv=5)
avg_scores[kval + '_' + str(cval)] = np.mean(cv_scores)
avg_scores
from sklearn.model_selection import GridSearchCV
clf = GridSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df
clf.best_score_
clf.best_params_
from sklearn.model_selection import RandomizedSearchCV
clf = RandomizedSearchCV(svm.SVC(gamma='auto'), {'C': [1, 5, 10, 7, 3, 12], 'kernel': ['rbf', 'linear', 'sigmoid', 'poly'], 'max_iter': [1, 3, 5, 7, 9, 12, 25, 50, 100]}, cv=10, n_iter=4)
clf.fit(iris.data, iris.target)
clf.cv_results_
df = pd.DataFrame(clf.cv_results_)
df[['param_C', 'param_kernel', 'param_max_iter', 'mean_test_score']] | code |
88085617/cell_10 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df | code |
88085617/cell_12 | [
"text_html_output_1.png"
] | from sklearn import svm, datasets
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
from sklearn import svm, datasets
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['target'] = df['target'].apply(lambda x: iris.target_names[x])
result = cross_val_score(svm.SVC(kernel='linear', C=10, gamma='auto'), iris.data, iris.target, cv=5)
np.mean(result) | code |
33116172/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
nRowsRead = 1000
df2 = pd.read_csv('/kaggle/input/column_3C.csv', delimiter=',', nrows=nRowsRead)
df2.dataframeName = 'column_3C.csv'
nRow, nCol = df2.shape
df2.head(5) | code |
33116172/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
df1.head(5) | code |
33116172/cell_25 | [
"text_plain_output_1.png"
] | (y_test.shape, X_test.shape)
X_test.min() | code |
33116172/cell_23 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
import xgboost as xgb
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
model.save_model('model.bst')
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print('Accuracy: %.2f%%' % (accuracy * 100.0)) | code |
33116172/cell_30 | [
"text_plain_output_1.png"
] | from platform import python_version
from platform import python_version
print(python_version()) | code |
33116172/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
from sklearn.model_selection import train_test_split
from sklearn import model_selection
from sklearn.metrics import accuracy_score
X = df1[['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle', 'sacral_slope', 'pelvic_radius', 'degree_spondylolisthesis']]
Y = df1['class']
seed = 2020
test_size = 0.33
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
model.save_model('model.bst')
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
model = xgb.XGBClassifier()
kfold = KFold(n_splits=10, random_state=2020)
results = cross_val_score(model, X, Y, cv=kfold)
xgb.__version__ | code |
33116172/cell_26 | [
"text_plain_output_1.png"
] | (y_test.shape, X_test.shape)
X_test.min()
X_test.max() | code |
33116172/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
nRowsRead = 1000
df2 = pd.read_csv('/kaggle/input/column_3C.csv', delimiter=',', nrows=nRowsRead)
df2.dataframeName = 'column_3C.csv'
nRow, nCol = df2.shape
print(f'There are {nRow} rows and {nCol} columns') | code |
33116172/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
print(f'There are {nRow} rows and {nCol} columns') | code |
33116172/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
from sklearn.model_selection import train_test_split
from sklearn import model_selection
from sklearn.metrics import accuracy_score
X = df1[['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle', 'sacral_slope', 'pelvic_radius', 'degree_spondylolisthesis']]
Y = df1['class']
seed = 2020
test_size = 0.33
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
model.save_model('model.bst')
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
model = xgb.XGBClassifier()
kfold = KFold(n_splits=10, random_state=2020)
results = cross_val_score(model, X, Y, cv=kfold)
print('Accuracy: %.2f%% (%.2f%%)' % (results.mean() * 100, results.std() * 100)) | code |
33116172/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
nRowsRead = 1000
df1 = pd.read_csv('/kaggle/input/column_2C.csv', delimiter=',', nrows=nRowsRead)
df1.dataframeName = 'column_2C.csv'
nRow, nCol = df1.shape
nRowsRead = 1000
df2 = pd.read_csv('/kaggle/input/column_3C.csv', delimiter=',', nrows=nRowsRead)
df2.dataframeName = 'column_3C.csv'
nRow, nCol = df2.shape
sns.pairplot(df2, hue='class', size=3, diag_kind='kde') | code |
33116172/cell_24 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | (y_test.shape, X_test.shape) | code |
33116172/cell_27 | [
"text_plain_output_1.png"
] | (y_test.shape, X_test.shape)
X_test.min()
X_test.max()
X_test.mean() | code |
72075480/cell_42 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape
tf.sort_values('tf', ascending=False)
text = ' '.join((i for i in df.Review))
wordcloud = WordCloud().generate(text)
plt.axis('off')
wordcloud = WordCloud(max_font_size=50, max_words=100, background_color='white').generate(text)
plt.axis('off')
wordcloud.to_file('wordcloud.png')
df.head() | code |
72075480/cell_21 | [
"text_html_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'].apply(lambda x: TextBlob(x).words).head() | code |
72075480/cell_9 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape | code |
72075480/cell_34 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape
tf.sort_values('tf', ascending=False)
tf[tf['tf'] > 25].plot.bar(x='words', y='tf')
plt.show() | code |
72075480/cell_30 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape
tf['words'].nunique() | code |
72075480/cell_40 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from nltk.sentiment import SentimentIntensityAnalyzer
from textblob import Word, TextBlob
from warnings import filterwarnings
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape
tf.sort_values('tf', ascending=False)
text = ' '.join((i for i in df.Review))
wordcloud = WordCloud().generate(text)
plt.axis('off')
wordcloud = WordCloud(max_font_size=50, max_words=100, background_color='white').generate(text)
plt.axis('off')
wordcloud.to_file('wordcloud.png')
sia = SentimentIntensityAnalyzer()
sia.polarity_scores('The food was awesome')
df['Review'].apply(lambda x: x.upper())
df['Review'][0:10].apply(lambda x: sia.polarity_scores(x)) | code |
72075480/cell_29 | [
"text_html_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape | code |
72075480/cell_39 | [
"image_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape
tf.sort_values('tf', ascending=False)
text = ' '.join((i for i in df.Review))
wordcloud = WordCloud().generate(text)
plt.axis('off')
wordcloud = WordCloud(max_font_size=50, max_words=100, background_color='white').generate(text)
plt.axis('off')
wordcloud.to_file('wordcloud.png')
df['Review'].apply(lambda x: x.upper()) | code |
72075480/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72075480/cell_7 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.head() | code |
72075480/cell_32 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape
tf.sort_values('tf', ascending=False) | code |
72075480/cell_28 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.head() | code |
72075480/cell_8 | [
"text_html_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.info() | code |
72075480/cell_38 | [
"text_html_output_1.png"
] | from nltk.sentiment import SentimentIntensityAnalyzer
sia = SentimentIntensityAnalyzer()
sia.polarity_scores('The food was awesome') | code |
72075480/cell_3 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from PIL import Image
from nltk.corpus import stopwords
from nltk.sentiment import SentimentIntensityAnalyzer
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV, cross_validate
from sklearn.preprocessing import LabelEncoder
from textblob import Word, TextBlob
from wordcloud import WordCloud | code |
72075480/cell_31 | [
"text_html_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape
tf['tf'].describe([0.05, 0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 0.95, 0.99]).T | code |
72075480/cell_24 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
df['Review'].head() | code |
72075480/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from warnings import filterwarnings
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
df.head() | code |
72075480/cell_36 | [
"text_plain_output_1.png"
] | from textblob import Word, TextBlob
from warnings import filterwarnings
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
filterwarnings('ignore')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
df = pd.read_csv('../input/reviews/Restaurant_Reviews.tsv', delimiter='\t')
df.shape
drops = pd.Series(' '.join(df['Review']).split()).value_counts()[-250:]
df['Review'] = df['Review'].apply(lambda x: ' '.join((x for x in x.split() if x not in drops)))
df['Review'] = df['Review'].apply(lambda x: ' '.join([Word(word).lemmatize() for word in x.split()]))
tf = df['Review'].apply(lambda x: pd.value_counts(x.split(' '))).sum(axis=0).reset_index()
tf.columns = ['words', 'tf']
tf.shape
tf.sort_values('tf', ascending=False)
text = ' '.join((i for i in df.Review))
wordcloud = WordCloud().generate(text)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
wordcloud = WordCloud(max_font_size=50, max_words=100, background_color='white').generate(text)
plt.figure()
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
wordcloud.to_file('wordcloud.png') | code |
2014797/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True) | code |
2014797/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train.head(5) | code |
2014797/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = train.corr()
train.drop(train[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
test.drop(test[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
sex_variable = train.pivot_table(index='Sex', values='Survived')
survived = train[train['Survived'] == 1]
died = train[train['Survived'] == 0]
def category_age(df, cut_points, label_names):
df['Age'] = df['Age'].fillna(-0.5)
df['Age_categories'] = pd.cut(df['Age'], cut_points, labels=label_names)
return df
cut_points = [0, 5, 12, 18, 35, 60, 100]
label_names = ['Infant', 'Child', 'Teenager', 'Young Adult', 'Adult', 'Senior']
train = category_age(train, cut_points, label_names)
test = category_age(test, cut_points, label_names)
pivot = train.pivot_table(index='Age_categories', values='Survived')
def create_dummies(df, column_name):
dummies = pd.get_dummies(df[column_name], prefix=column_name)
df = pd.concat([df, dummies], axis=1)
return df
for column in ['Pclass', 'Sex', 'Age_categories']:
train = create_dummies(train, column)
test = create_dummies(test, column)
train = train.drop(train[['Pclass', 'Sex', 'Age', 'Age_categories']], axis=1)
test = test.drop(test[['Pclass', 'Sex', 'Age', 'Age_categories']], axis=1)
lr = LogisticRegression()
train_y = train['Survived']
train_x = train.drop('Survived', axis=1)
scores = cross_val_score(lr, train_x, train_y, cv=10)
scores.sort()
accuracy = scores.mean()
print(scores)
print(accuracy) | code |
2014797/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = train.corr()
train.drop(train[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
test.drop(test[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
sex_variable = train.pivot_table(index='Sex', values='Survived')
sex_variable.plot.bar()
plt.show() | code |
2014797/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = train.corr()
train.drop(train[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
test.drop(test[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
sex_variable = train.pivot_table(index='Sex', values='Survived')
survived = train[train['Survived'] == 1]
died = train[train['Survived'] == 0]
def category_age(df, cut_points, label_names):
df['Age'] = df['Age'].fillna(-0.5)
df['Age_categories'] = pd.cut(df['Age'], cut_points, labels=label_names)
return df
cut_points = [0, 5, 12, 18, 35, 60, 100]
label_names = ['Infant', 'Child', 'Teenager', 'Young Adult', 'Adult', 'Senior']
train = category_age(train, cut_points, label_names)
test = category_age(test, cut_points, label_names)
pivot = train.pivot_table(index='Age_categories', values='Survived')
def create_dummies(df, column_name):
dummies = pd.get_dummies(df[column_name], prefix=column_name)
df = pd.concat([df, dummies], axis=1)
return df
for column in ['Pclass', 'Sex', 'Age_categories']:
train = create_dummies(train, column)
test = create_dummies(test, column) | code |
2014797/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = train.corr()
train.drop(train[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
test.drop(test[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
train.head() | code |
2014797/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv') | code |
2014797/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = train.corr()
sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values) | code |
2014797/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = train.corr()
train.drop(train[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
test.drop(test[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
sex_variable = train.pivot_table(index='Sex', values='Survived')
survived = train[train['Survived'] == 1]
died = train[train['Survived'] == 0]
def category_age(df, cut_points, label_names):
df['Age'] = df['Age'].fillna(-0.5)
df['Age_categories'] = pd.cut(df['Age'], cut_points, labels=label_names)
return df
cut_points = [0, 5, 12, 18, 35, 60, 100]
label_names = ['Infant', 'Child', 'Teenager', 'Young Adult', 'Adult', 'Senior']
train = category_age(train, cut_points, label_names)
test = category_age(test, cut_points, label_names)
pivot = train.pivot_table(index='Age_categories', values='Survived')
pivot.plot.bar()
plt.show() | code |
2014797/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
train.head() | code |
2014797/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = train.corr()
train.drop(train[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
test.drop(test[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
sex_variable = train.pivot_table(index='Sex', values='Survived')
survived = train[train['Survived'] == 1]
died = train[train['Survived'] == 0]
survived['Age'].plot.hist(alpha=0.5, color='red', bins=50)
died['Age'].plot.hist(alpha=0.5, color='blue', bins=50)
plt.legend(['Survived', 'Died'])
plt.show() | code |
2014797/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train.info() | code |
2014797/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('...train.csv')
test = pd.read_csv('...test.csv')
train = train.drop(train[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
test = test.drop(test[['Cabin', 'Embarked', 'Name', 'Ticket']], axis=1)
train.dropna(axis=0, how='any', inplace=True)
corr = train.corr()
train.drop(train[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
test.drop(test[['Fare', 'SibSp', 'Parch']], axis=1, inplace=True)
sex_variable = train.pivot_table(index='Sex', values='Survived')
survived = train[train['Survived'] == 1]
died = train[train['Survived'] == 0]
def category_age(df, cut_points, label_names):
df['Age'] = df['Age'].fillna(-0.5)
df['Age_categories'] = pd.cut(df['Age'], cut_points, labels=label_names)
return df
cut_points = [0, 5, 12, 18, 35, 60, 100]
label_names = ['Infant', 'Child', 'Teenager', 'Young Adult', 'Adult', 'Senior']
train = category_age(train, cut_points, label_names)
test = category_age(test, cut_points, label_names)
pivot = train.pivot_table(index='Age_categories', values='Survived')
def create_dummies(df, column_name):
dummies = pd.get_dummies(df[column_name], prefix=column_name)
df = pd.concat([df, dummies], axis=1)
return df
for column in ['Pclass', 'Sex', 'Age_categories']:
train = create_dummies(train, column)
test = create_dummies(test, column)
train = train.drop(train[['Pclass', 'Sex', 'Age', 'Age_categories']], axis=1)
test = test.drop(test[['Pclass', 'Sex', 'Age', 'Age_categories']], axis=1) | code |
17131726/cell_9 | [
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_df.columns.values)
train_df.isna().sum()
from sklearn.preprocessing import LabelEncoder
labelEncoder = LabelEncoder()
train_df.team_1_name = labelEncoder.fit_transform(train_df.team_1_name)
train_df.team_2_name = labelEncoder.fit_transform(train_df.team_2_name)
train_df.drop(['id', 'queue_id', 'team_1_win', 'team_2_win', 'winning_team'], axis=1)
data = train_df.game_duration
values, base = np.histogram(data, bins=40)
cumulative = np.cumsum(values)
plt.plot(base[:-1], cumulative, c='blue')
plt.show() | code |
17131726/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_df.columns.values) | code |
17131726/cell_6 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_df.columns.values)
train_df.isna().sum()
from sklearn.preprocessing import LabelEncoder
labelEncoder = LabelEncoder()
train_df.team_1_name = labelEncoder.fit_transform(train_df.team_1_name)
train_df.team_2_name = labelEncoder.fit_transform(train_df.team_2_name)
train_df.head() | code |
17131726/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
train_df.head() | code |
17131726/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
17131726/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_df.columns.values)
train_df.isna().sum()
from sklearn.preprocessing import LabelEncoder
labelEncoder = LabelEncoder()
train_df.team_1_name = labelEncoder.fit_transform(train_df.team_1_name)
train_df.team_2_name = labelEncoder.fit_transform(train_df.team_2_name)
train_df.drop(['id', 'queue_id', 'team_1_win', 'team_2_win', 'winning_team'], axis=1)
train_def.head() | code |
17131726/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/matchesheader.csv')
names_col = list(train_df.columns.values)
new_names_col = map(lambda name: name.strip(), names_col)
train_df.columns = new_names_col
list(train_df.columns.values)
train_df.isna().sum() | code |
128022699/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd
df = pd.read_csv('/content/drive/MyDrive/PRML LABs/PRML Major Project/audio_dataset.csv')
df | code |
128022699/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import os
import re
import pandas as pd
import librosa
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score
from scipy.fft import fft
import seaborn as sns
import matplotlib.pyplot as plt
import os
import re
import pandas as pd
import librosa
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score, classification_report
from scipy.fft import fft
import seaborn as sns
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as f
import torch.optim as optim
from torch.utils.data import DataLoader, random_split, TensorDataset | code |
90151187/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_dat', 'customer_id', 'article_id']]
train.to_parquet('train.pqt', index=False)
tmp = train.groupby('customer_id').t_dat.max().reset_index()
tmp.columns = ['customer_id', 'max_dat']
train = train.merge(tmp, on=['customer_id'], how='left')
train['diff_dat'] = (train.max_dat - train.t_dat).dt.days
train = train.loc[train['diff_dat'] <= 6]
tmp = train.groupby(['customer_id', 'article_id'])['t_dat'].agg('count').reset_index()
tmp.columns = ['customer_id', 'article_id', 'ct']
train = train.merge(tmp, on=['customer_id', 'article_id'], how='left')
train = train.sort_values(['ct', 't_dat'], ascending=False)
train = train.drop_duplicates(['customer_id', 'article_id'])
train = train.sort_values(['ct', 't_dat'], ascending=False)
import pandas as pd, numpy as np
train = train.to_pandas()
pairs = np.load('../input/hmitempairs/pairs_cudf.npy', allow_pickle=True).item()
train['article_id2'] = train.article_id.map(pairs)
train2 = train[['customer_id', 'article_id2']].copy()
train2 = train2.loc[train2.article_id2.notnull()]
train2 = train2.drop_duplicates(['customer_id', 'article_id2'])
train2 = train2.rename({'article_id2': 'article_id'}, axis=1)
train = train[['customer_id', 'article_id']]
train = pd.concat([train, train2], axis=0, ignore_index=True)
train.article_id = train.article_id.astype('int32')
train = train.drop_duplicates(['customer_id', 'article_id'])
train.article_id = ' 0' + train.article_id.astype('str')
preds = cudf.DataFrame(train.groupby('customer_id').article_id.sum().reset_index())
preds.columns = ['customer_id', 'prediction']
preds.head() | code |
90151187/cell_4 | [
"text_html_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_dat', 'customer_id', 'article_id']]
train.to_parquet('train.pqt', index=False)
print(train.shape)
train.head() | code |
90151187/cell_6 | [
"text_plain_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_dat', 'customer_id', 'article_id']]
train.to_parquet('train.pqt', index=False)
tmp = train.groupby('customer_id').t_dat.max().reset_index()
tmp.columns = ['customer_id', 'max_dat']
train = train.merge(tmp, on=['customer_id'], how='left')
train['diff_dat'] = (train.max_dat - train.t_dat).dt.days
train = train.loc[train['diff_dat'] <= 6]
print('Train shape:', train.shape) | code |
90151187/cell_8 | [
"text_html_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_dat', 'customer_id', 'article_id']]
train.to_parquet('train.pqt', index=False)
tmp = train.groupby('customer_id').t_dat.max().reset_index()
tmp.columns = ['customer_id', 'max_dat']
train = train.merge(tmp, on=['customer_id'], how='left')
train['diff_dat'] = (train.max_dat - train.t_dat).dt.days
train = train.loc[train['diff_dat'] <= 6]
tmp = train.groupby(['customer_id', 'article_id'])['t_dat'].agg('count').reset_index()
tmp.columns = ['customer_id', 'article_id', 'ct']
train = train.merge(tmp, on=['customer_id', 'article_id'], how='left')
train = train.sort_values(['ct', 't_dat'], ascending=False)
train = train.drop_duplicates(['customer_id', 'article_id'])
train = train.sort_values(['ct', 't_dat'], ascending=False)
train.head() | code |
90151187/cell_15 | [
"text_plain_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_dat', 'customer_id', 'article_id']]
train.to_parquet('train.pqt', index=False)
tmp = train.groupby('customer_id').t_dat.max().reset_index()
tmp.columns = ['customer_id', 'max_dat']
train = train.merge(tmp, on=['customer_id'], how='left')
train['diff_dat'] = (train.max_dat - train.t_dat).dt.days
train = train.loc[train['diff_dat'] <= 6]
tmp = train.groupby(['customer_id', 'article_id'])['t_dat'].agg('count').reset_index()
tmp.columns = ['customer_id', 'article_id', 'ct']
train = train.merge(tmp, on=['customer_id', 'article_id'], how='left')
train = train.sort_values(['ct', 't_dat'], ascending=False)
train = train.drop_duplicates(['customer_id', 'article_id'])
train = train.sort_values(['ct', 't_dat'], ascending=False)
import pandas as pd, numpy as np
train = train.to_pandas()
pairs = np.load('../input/hmitempairs/pairs_cudf.npy', allow_pickle=True).item()
train['article_id2'] = train.article_id.map(pairs)
train2 = train[['customer_id', 'article_id2']].copy()
train2 = train2.loc[train2.article_id2.notnull()]
train2 = train2.drop_duplicates(['customer_id', 'article_id2'])
train2 = train2.rename({'article_id2': 'article_id'}, axis=1)
train = train[['customer_id', 'article_id']]
train = pd.concat([train, train2], axis=0, ignore_index=True)
train.article_id = train.article_id.astype('int32')
train = train.drop_duplicates(['customer_id', 'article_id'])
train.article_id = ' 0' + train.article_id.astype('str')
preds = cudf.DataFrame(train.groupby('customer_id').article_id.sum().reset_index())
preds.columns = ['customer_id', 'prediction']
train = cudf.read_parquet('train.pqt')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train.loc[train.t_dat >= cudf.to_datetime('2020-09-16')]
top12 = ' 0' + ' 0'.join(train.article_id.value_counts().to_pandas().index.astype('str')[:12])
print("Last week's top 12 popular items:")
print(top12) | code |
90151187/cell_17 | [
"text_html_output_1.png"
] | import cudf
train = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
train['customer_id'] = train['customer_id'].str[-16:].str.hex_to_int().astype('int64')
train['article_id'] = train.article_id.astype('int32')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train[['t_dat', 'customer_id', 'article_id']]
train.to_parquet('train.pqt', index=False)
tmp = train.groupby('customer_id').t_dat.max().reset_index()
tmp.columns = ['customer_id', 'max_dat']
train = train.merge(tmp, on=['customer_id'], how='left')
train['diff_dat'] = (train.max_dat - train.t_dat).dt.days
train = train.loc[train['diff_dat'] <= 6]
tmp = train.groupby(['customer_id', 'article_id'])['t_dat'].agg('count').reset_index()
tmp.columns = ['customer_id', 'article_id', 'ct']
train = train.merge(tmp, on=['customer_id', 'article_id'], how='left')
train = train.sort_values(['ct', 't_dat'], ascending=False)
train = train.drop_duplicates(['customer_id', 'article_id'])
train = train.sort_values(['ct', 't_dat'], ascending=False)
import pandas as pd, numpy as np
train = train.to_pandas()
pairs = np.load('../input/hmitempairs/pairs_cudf.npy', allow_pickle=True).item()
train['article_id2'] = train.article_id.map(pairs)
train2 = train[['customer_id', 'article_id2']].copy()
train2 = train2.loc[train2.article_id2.notnull()]
train2 = train2.drop_duplicates(['customer_id', 'article_id2'])
train2 = train2.rename({'article_id2': 'article_id'}, axis=1)
train = train[['customer_id', 'article_id']]
train = pd.concat([train, train2], axis=0, ignore_index=True)
train.article_id = train.article_id.astype('int32')
train = train.drop_duplicates(['customer_id', 'article_id'])
train.article_id = ' 0' + train.article_id.astype('str')
preds = cudf.DataFrame(train.groupby('customer_id').article_id.sum().reset_index())
preds.columns = ['customer_id', 'prediction']
train = cudf.read_parquet('train.pqt')
train.t_dat = cudf.to_datetime(train.t_dat)
train = train.loc[train.t_dat >= cudf.to_datetime('2020-09-16')]
top12 = ' 0' + ' 0'.join(train.article_id.value_counts().to_pandas().index.astype('str')[:12])
sub = cudf.read_csv('../input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
sub = sub[['customer_id']]
sub['customer_id_2'] = sub['customer_id'].str[-16:].str.hex_to_int().astype('int64')
sub = sub.merge(preds.rename({'customer_id': 'customer_id_2'}, axis=1), on='customer_id_2', how='left').fillna('')
del sub['customer_id_2']
sub.prediction = sub.prediction + top12
sub.prediction = sub.prediction.str.strip()
sub.prediction = sub.prediction.str[:131]
sub.to_csv(f'submission.csv', index=False)
sub.head() | code |
72120060/cell_40 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from xgboost import XGBRegressor
import skopt
knn_reg = KNeighborsRegressor(n_jobs=-1)
mlp_reg = MLPRegressor()
rf_reg = RandomForestRegressor(n_jobs=-1)
gb_reg = GradientBoostingRegressor()
xgb_reg = XGBRegressor(n_jobs=-1)
lgbm_reg = LGBMRegressor(n_jobs=-1)
cat_reg = CatBoostRegressor()
search_space = [skopt.space.Integer(4, 12, name='max_depth'), skopt.space.Integer(50, 200, name='n_estimators'), skopt.space.Integer(17, 24, name='max_features'), skopt.space.Real(0.0, 1.0, name='min_impurity_decrease'), skopt.space.Categorical(categories=[True, False], name='bootstrap')]
evaluator = Params_Evaluate(X_train, X_val, Y_train, Y_val)
evaluator.select_model(rf_reg)
@skopt.utils.use_named_args(search_space)
def objective(**params):
return evaluator.evaluate_params(params)
def to_named_params(results, search_space):
params = results.x
param_dict = {}
params_list = [(dimension.name, param) for dimension, param in zip(search_space, params)]
for item in params_list:
param_dict[item[0]] = item[1]
return param_dict
search_space_xgb = [skopt.space.Integer(4, 12, name='max_depth'), skopt.space.Real(0.0, 1.0, name='eta'), skopt.space.Real(0.0, 1.0, name='subsample'), skopt.space.Categorical(categories=['gbtree', 'dart'], name='booster')]
best_params_gxb = to_named_params(results_xgb, search_space_xgb)
best_xgb_reg = xgb_reg.set_params(**best_params_gxb)
best_xgb_reg | code |
72120060/cell_39 | [
"text_plain_output_1.png"
] | results_xgb = skopt.forest_minimize(objective, search_space_xgb, **HPO_params) | code |
72120060/cell_41 | [
"text_plain_output_1.png"
] | import skopt
search_space = [skopt.space.Integer(4, 12, name='max_depth'), skopt.space.Integer(50, 200, name='n_estimators'), skopt.space.Integer(17, 24, name='max_features'), skopt.space.Real(0.0, 1.0, name='min_impurity_decrease'), skopt.space.Categorical(categories=[True, False], name='bootstrap')]
def to_named_params(results, search_space):
params = results.x
param_dict = {}
params_list = [(dimension.name, param) for dimension, param in zip(search_space, params)]
for item in params_list:
param_dict[item[0]] = item[1]
return param_dict
best_params = to_named_params(results, search_space)
best_params | code |
72120060/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72120060/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from warnings import filterwarnings
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
from warnings import filterwarnings
filterwarnings('ignore', category=DeprecationWarning)
filterwarnings('ignore', category=FutureWarning)
filterwarnings('ignore', category=UserWarning) | code |
72120060/cell_16 | [
"text_plain_output_1.png"
] | from catboost import CatBoostRegressor
from lightgbm import LGBMRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import numpy as np # linear algebra
def check_rmse(model, x_val, y_val):
pred = model.predict(x_val)
return np.sqrt(mean_squared_error(y_val, pred))
knn_reg = KNeighborsRegressor(n_jobs=-1)
mlp_reg = MLPRegressor()
rf_reg = RandomForestRegressor(n_jobs=-1)
gb_reg = GradientBoostingRegressor()
xgb_reg = XGBRegressor(n_jobs=-1)
lgbm_reg = LGBMRegressor(n_jobs=-1)
cat_reg = CatBoostRegressor()
models = [knn_reg, mlp_reg, rf_reg, gb_reg, xgb_reg, lgbm_reg, cat_reg]
models_name = ['knn_reg', 'mlp_reg', 'rf_reg', 'gb_reg', 'xgb_reg', 'lgbm_reg', 'cat_reg']
rmse_error = []
for i, model in enumerate(models):
model.fit(X_train, Y_train)
rmse = check_rmse(model, X_val, Y_val)
rmse_error.append(rmse)
plt.barh(models_name, rmse_error)
plt.ylabel('Models')
plt.xlabel('RMSE')
plt.show() | code |
72120060/cell_27 | [
"image_output_1.png"
] | code |
|
2007618/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
def priceOverTime(data, label):
"""Plot price over time"""
priceOverTime(newdf3, 'California')
priceOverTime(newdf4, 'Colorado')
priceOverTime(newdf5, 'Michigan')
def priceOverTime2(data, label):
data.groupby(data.Date.dt.year)['MedianSoldPrice_AllHomes'].mean().plot(kind='bar', figsize=(10, 6), color='grey', edgecolor='black', linewidth=2)
plt.suptitle(label, fontsize=12)
plt.ylabel('MedianSoldPrice_AllHomes')
plt.xlabel('Year')
plt.show()
priceOverTime2(newdf6, 'San Francisco')
priceOverTime2(newdf7, 'Denver')
priceOverTime2(newdf8, 'Detroit') | code |
2007618/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
def priceOverTime(data, label):
"""Plot price over time"""
data.groupby(newdf.Date.dt.year)['MedianSoldPrice_AllHomes'].mean().plot(kind='bar', figsize=(10, 6), color='grey', edgecolor='black', linewidth=2)
plt.suptitle(label, fontsize=12)
plt.ylabel('MedianSoldPrice_AllHomes')
plt.xlabel('Year')
plt.show()
priceOverTime(newdf3, 'California')
priceOverTime(newdf4, 'Colorado')
priceOverTime(newdf5, 'Michigan') | code |
2007618/cell_3 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
state_data = '../input/State_time_series.csv'
df = pd.read_csv(state_data)
city_data = '../input/City_time_series.csv'
dfCity = pd.read_csv(city_data)
State_house = pd.read_csv('../input/State_time_series.csv', parse_dates=['Date'])
States = ['California', 'Colorado', 'Michigan']
newdf = df.loc[df['RegionName'].isin(States)]
newdf.Date = pd.to_datetime(newdf.Date)
newdf2 = newdf.loc[newdf['Date'].dt.year == 2016]
newdf3 = df.loc[df['RegionName'] == 'California']
newdf4 = df.loc[df['RegionName'] == 'Colorado']
newdf5 = df.loc[df['RegionName'] == 'Michigan']
newdf6 = dfCity.loc[dfCity['RegionName'] == 'san_franciscosan_franciscoca']
newdf6.Date = pd.to_datetime(newdf6.Date)
newdf7 = dfCity.loc[dfCity['RegionName'] == 'denverdenverco']
newdf7.Date = pd.to_datetime(newdf7.Date)
newdf8 = dfCity.loc[dfCity['RegionName'] == 'detroitwaynemi']
newdf8.Date = pd.to_datetime(newdf8.Date) | code |
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