path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
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stringclasses 1
value |
|---|---|---|---|
33096822/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
type(avo['Date'].iloc[0])
avo['Date'] = pd.to_datetime(avo['Date'])
type(avo['Date'].iloc[0])
avo['Month'] = avo['Date'].apply(lambda x: x.month)
avo.groupby('Month').mean()['AveragePrice'].sort_values(ascending=False).index[0]
avo.drop(avo.loc[avo['region'] == 'TotalUS'].index, inplace=True)
avo.groupby('region').sum()['Total Volume']
avo.groupby('region').sum()['Total Volume'].idxmax()
avo.drop(avo.loc[avo['region'] == 'TotalUS'].index, inplace=True)
avo['Revenue'] = avo['AveragePrice'] * avo['Total Volume']
c = avo.groupby('type').sum()['Revenue']['conventional']
o = avo.groupby('type').sum()['Revenue']['organic']
o - c
a = pd.DataFrame(avo.groupby(['region', 'type']).sum()['Total Volume'].values.reshape((-1, 2)))
a['ratio'] = a[1] / a[0]
a['region'] = avo['region'].unique()
a.set_index('region', inplace=True)
a
plt.figure(figsize=(5, 12))
a['ratio'].sort_values().plot.barh() | code |
33096822/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
avo['Month'] = avo['Date'].apply(lambda x: x.month)
avo.groupby('Month').mean()['AveragePrice'].sort_values(ascending=False).index[0]
avo.drop(avo.loc[avo['region'] == 'TotalUS'].index, inplace=True)
avo.groupby('region').sum()['Total Volume']
avo.groupby('region').sum()['Total Volume'].idxmax()
avo.drop(avo.loc[avo['region'] == 'TotalUS'].index, inplace=True)
avo['Revenue'] = avo['AveragePrice'] * avo['Total Volume']
c = avo.groupby('type').sum()['Revenue']['conventional']
o = avo.groupby('type').sum()['Revenue']['organic']
o - c | code |
33096822/cell_12 | [
"text_html_output_1.png"
] | avo['Month'] = avo['Date'].apply(lambda x: x.month)
avo.groupby('Month').mean()['AveragePrice'].sort_values(ascending=False).index[0]
avo.drop(avo.loc[avo['region'] == 'TotalUS'].index, inplace=True)
avo.groupby('region').sum()['Total Volume']
avo.groupby('region').sum()['Total Volume'].idxmax() | code |
33096822/cell_5 | [
"text_plain_output_1.png"
] | import seaborn as sns
sns.pairplot(avo) | code |
72065541/cell_13 | [
"text_html_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0] | code |
72065541/cell_4 | [
"image_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
print('The shape of the dataset is {}.\n\n'.format(train.shape))
train.head(10) | code |
72065541/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
train.drop(['Id', 'Alley', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu'], axis=1, inplace=True)
train.shape
sns.set_theme(style="white")
corr = train.corr()
f, ax = plt.subplots(figsize=(30, 30))
mask = np.triu(np.ones_like(corr, dtype=bool))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(corr, annot=True, mask = mask, cmap=cmap)
plt.show()
corr['SalePrice'].sort_values(ascending=False) | code |
72065541/cell_20 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
[col for col in test.columns if test[col].isnull().sum() != 0]
missing_val_count_by_column = test.isnull().sum()
Id = test['Id']
test.drop(['Id', 'Alley', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu'], axis=1, inplace=True)
test.shape | code |
72065541/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.describe() | code |
72065541/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
train.drop(['Id', 'Alley', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu'], axis=1, inplace=True)
train.shape
sns.set_theme(style="white")
corr = train.corr()
f, ax = plt.subplots(figsize=(30, 30))
mask = np.triu(np.ones_like(corr, dtype=bool))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(corr, annot=True, mask = mask, cmap=cmap)
plt.show()
quantitative_features = [col for col in train.columns if train[col].dtypes != 'object']
categorical_features = [col for col in train.columns if train[col].dtypes == 'object']
sns.histplot(data=train, x='SalePrice', stat='count', bins=10, kde=True)
plt.title('Sale Price Distribution')
plt.show() | code |
72065541/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
print('The shape of the dataset is {}.\n\n'.format(test.shape))
test.head(5) | code |
72065541/cell_19 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
train.drop(['Id', 'Alley', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu'], axis=1, inplace=True)
train.shape | code |
72065541/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.info() | code |
72065541/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
[col for col in test.columns if test[col].isnull().sum() != 0] | code |
72065541/cell_17 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
[col for col in test.columns if test[col].isnull().sum() != 0]
missing_val_count_by_column = test.isnull().sum()
print(missing_val_count_by_column[missing_val_count_by_column > 0]) | code |
72065541/cell_14 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
print(missing_val_count_by_column[missing_val_count_by_column > 0]) | code |
72065541/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
train.drop(['Id', 'Alley', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu'], axis=1, inplace=True)
train.shape
sns.set_theme(style='white')
corr = train.corr()
f, ax = plt.subplots(figsize=(30, 30))
mask = np.triu(np.ones_like(corr, dtype=bool))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(corr, annot=True, mask=mask, cmap=cmap)
plt.show() | code |
72065541/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
train.drop(['Id', 'Alley', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu'], axis=1, inplace=True)
train.shape
sns.set_theme(style="white")
corr = train.corr()
f, ax = plt.subplots(figsize=(30, 30))
mask = np.triu(np.ones_like(corr, dtype=bool))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(corr, annot=True, mask = mask, cmap=cmap)
plt.show()
quantitative_features = [col for col in train.columns if train[col].dtypes != 'object']
categorical_features = [col for col in train.columns if train[col].dtypes == 'object']
train[quantitative_features].hist(bins=50, figsize=(20, 17))
plt.show() | code |
72065541/cell_36 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))
train.drop_duplicates(inplace=True)
test = pd.read_csv(os.path.join(dataset_path, 'test.csv'))
[col for col in train.columns if train[col].isnull().sum() != 0]
missing_val_count_by_column = train.isnull().sum()
[col for col in test.columns if test[col].isnull().sum() != 0]
missing_val_count_by_column = test.isnull().sum()
Id = test['Id']
test.drop(['Id', 'Alley', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu'], axis=1, inplace=True)
test.shape
X_train['BsmtQual'].fillna('No Basement', inplace=True)
X_train['BsmtCond'].fillna('No Basement', inplace=True)
X_train['BsmtExposure'].fillna('No Basement', inplace=True)
X_train['BsmtFinType1'].fillna('No Basement', inplace=True)
X_train['BsmtFinType2'].fillna('No Basement', inplace=True)
X_train['GarageType'].fillna('No Garage', inplace=True)
X_train['GarageFinish'].fillna('No Garage', inplace=True)
X_train['GarageQual'].fillna('No Garage', inplace=True)
X_train['GarageCond'].fillna('No Garage', inplace=True)
X_valid['BsmtQual'].fillna('No Basement', inplace=True)
X_valid['BsmtCond'].fillna('No Basement', inplace=True)
X_valid['BsmtExposure'].fillna('No Basement', inplace=True)
X_valid['BsmtFinType1'].fillna('No Basement', inplace=True)
X_valid['BsmtFinType2'].fillna('No Basement', inplace=True)
X_valid['GarageType'].fillna('No Garage', inplace=True)
X_valid['GarageFinish'].fillna('No Garage', inplace=True)
X_valid['GarageQual'].fillna('No Garage', inplace=True)
X_valid['GarageCond'].fillna('No Garage', inplace=True)
test['BsmtQual'].fillna('No Basement', inplace=True)
test['BsmtCond'].fillna('No Basement', inplace=True)
test['BsmtExposure'].fillna('No Basement', inplace=True)
test['BsmtFinType1'].fillna('No Basement', inplace=True)
test['BsmtFinType2'].fillna('No Basement', inplace=True)
test['GarageType'].fillna('No Garage', inplace=True)
test['GarageFinish'].fillna('No Garage', inplace=True)
test['GarageQual'].fillna('No Garage', inplace=True)
test['GarageCond'].fillna('No Garage', inplace=True) | code |
128044716/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d | code |
128044716/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b | code |
128044716/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import numpy as np
import time
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b
c = np.array([[111, 112, 113], [121, 122, 123], [131, 132, 133]])
c
d = np.array([a, b, c])
d
a = np.array([[[311, 312, 313], [321, 322, 323], [331, 332, 333]], [[211, 212, 213], [221, 222, 223], [231, 232, 233]], [[111, 112, 113], [121, 122, 123], [131, 132, 133]]])
a
a.ndim
initial_time = time.time()
a = 0
for i in range(1000000):
a = a + i
print(a)
final_time = time.time()
diff1 = final_time - initial_time
diff1 | code |
128044716/cell_30 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b
c = np.array([[111, 112, 113], [121, 122, 123], [131, 132, 133]])
c
d = np.array([a, b, c])
d
a = np.array([[[311, 312, 313], [321, 322, 323], [331, 332, 333]], [[211, 212, 213], [221, 222, 223], [231, 232, 233]], [[111, 112, 113], [121, 122, 123], [131, 132, 133]]])
a | code |
128044716/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c | code |
128044716/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b
c = np.array([[111, 112, 113], [121, 122, 123], [131, 132, 133]])
c
d = np.array([a, b, c])
d
d.shape
d.size | code |
128044716/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b
c = np.array([[111, 112, 113], [121, 122, 123], [131, 132, 133]])
c | code |
128044716/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2) | code |
128044716/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b | code |
128044716/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a | code |
128044716/cell_28 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b
c = np.array([[111, 112, 113], [121, 122, 123], [131, 132, 133]])
c
d = np.array([a, b, c])
d
d.shape | code |
128044716/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a | code |
128044716/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape | code |
128044716/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim | code |
128044716/cell_35 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
import numpy as np
import time
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b
c = np.array([[111, 112, 113], [121, 122, 123], [131, 132, 133]])
c
d = np.array([a, b, c])
d
a = np.array([[[311, 312, 313], [321, 322, 323], [331, 332, 333]], [[211, 212, 213], [221, 222, 223], [231, 232, 233]], [[111, 112, 113], [121, 122, 123], [131, 132, 133]]])
a
a.ndim
initial_time = time.time()
a = 0
for i in range(1000000):
a = a + i
final_time = time.time()
diff1 = final_time - initial_time
diff1
initial_time = time.time()
n = np.arange(1000000)
s = np.sum(n)
print(s)
final_time = time.time()
diff2 = final_time - initial_time
diff2 | code |
128044716/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b
c = np.array([[111, 112, 113], [121, 122, 123], [131, 132, 133]])
c
d = np.array([a, b, c])
d
a = np.array([[[311, 312, 313], [321, 322, 323], [331, 332, 333]], [[211, 212, 213], [221, 222, 223], [231, 232, 233]], [[111, 112, 113], [121, 122, 123], [131, 132, 133]]])
a
a.ndim | code |
128044716/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a | code |
128044716/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a | code |
128044716/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c) | code |
128044716/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
c | code |
128044716/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
c = np.arange(10)
np.arange(10, 20, 2)
a.shape
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
a
a.shape
a.ndim
a = np.ones([3, 4])
a
b = np.zeros([3, 5])
b
c = np.eye(3)
c
d = np.diag([1, 2, 3])
d
np.diag(c)
a = np.array([[311, 312, 313], [321, 322, 323], [331, 332, 333]])
a
b = np.array([[211, 212, 213], [221, 222, 223], [231, 232, 233]])
b
c = np.array([[111, 112, 113], [121, 122, 123], [131, 132, 133]])
c
d = np.array([a, b, c])
d | code |
128044716/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
a = np.array([2, 3, 4, 5])
a.shape | code |
128020226/cell_13 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import display
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import os
import pandas as pd
import torch
import torch.nn as nn
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target = 'yield'
cols = [col for col in test_df.columns]
fig, axes = plt.subplots(4, 4, figsize=(16, 16))
for i, col in enumerate(cols):
axes[i//4, i%4].hist(train_df[col], bins=20)
axes[i//4, i%4].set_title(col, fontsize=14)
plt.show()
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class BlueberryDataset(Dataset):
def __init__(self, X, y=None, train=False):
self.train = train
if self.train:
self.X = X
self.y = y
else:
self.X = X
def __len__(self):
return len(self.X)
def __getitem__(self, idx):
if self.train:
x = torch.tensor(self.X.iloc[idx]).to(dtype=torch.float32)
y = torch.tensor(self.y.iloc[idx]).to(dtype=torch.float32)
return (x, y)
else:
x = torch.tensor(self.X.iloc[idx]).to(dtype=torch.float32)
return x
train_dataset = BlueberryDataset(X_train, y_train, train=True)
train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_dataset = BlueberryDataset(X_val, y_val, train=True)
val_dataloader = DataLoader(val_dataset, batch_size=128, shuffle=True)
test_dataset = BlueberryDataset(test_df, train=False)
test_dataloader = DataLoader(test_dataset, batch_size=128, shuffle=False)
class SimpleModel(nn.Module):
def __init__(self, in_features=test_df.shape[1], out_features=1):
super().__init__()
self.fc1 = nn.Sequential(nn.Linear(in_features, 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, 16), nn.ReLU(), nn.Linear(16, out_features))
def forward(self, x):
return self.fc1(x)
model = SimpleModel().to(device)
sample = torch.randn(64, test_df.shape[1]).to(device)
criterion = nn.L1Loss()
optimizer = torch.optim.AdamW(params=model.parameters(), lr=0.001)
EPOCHS = 50
for epoch in range(EPOCHS):
model.train()
train_loss = 0
for i, (X_tr, y_tr) in enumerate(train_dataloader):
X_tr = X_tr.to(device)
y_tr = y_tr.to(device).reshape(-1, 1)
y_pred = model(X_tr)
loss = criterion(y_pred, y_tr)
train_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
val_loss = 0
model.eval()
with torch.inference_mode():
for X_v, y_v in val_dataloader:
X_v = X_v.to(device)
y_v = y_v.to(device).reshape(-1, 1)
val_pred = model(X_v)
loss = criterion(val_pred, y_v)
val_loss += loss.item()
train_loss /= len(train_dataloader)
val_loss /= len(val_dataloader)
if epoch % 10 == 9:
print(f'Epoch: {epoch + 1} | Train MAE: {train_loss:.5f} | Valid MAE: {val_loss:.5f}') | code |
128020226/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from IPython.display import display
import os
import pandas as pd
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target = 'yield'
print('Train data shape:', train_df.shape)
print()
print('Null values in train data')
print('Column #', end='')
display(train_df.isna().sum())
print('Duplicates in train data:', train_df.duplicated().sum()) | code |
128020226/cell_6 | [
"text_plain_output_1.png"
] | from IPython.display import display
import matplotlib.pyplot as plt
import os
import pandas as pd
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target = 'yield'
cols = [col for col in test_df.columns]
fig, axes = plt.subplots(4, 4, figsize=(16, 16))
for i, col in enumerate(cols):
axes[i // 4, i % 4].hist(train_df[col], bins=20)
axes[i // 4, i % 4].set_title(col, fontsize=14)
plt.show() | code |
128020226/cell_11 | [
"text_plain_output_1.png"
] | from IPython.display import display
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import os
import pandas as pd
import torch
import torch.nn as nn
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target = 'yield'
cols = [col for col in test_df.columns]
fig, axes = plt.subplots(4, 4, figsize=(16, 16))
for i, col in enumerate(cols):
axes[i//4, i%4].hist(train_df[col], bins=20)
axes[i//4, i%4].set_title(col, fontsize=14)
plt.show()
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class BlueberryDataset(Dataset):
def __init__(self, X, y=None, train=False):
self.train = train
if self.train:
self.X = X
self.y = y
else:
self.X = X
def __len__(self):
return len(self.X)
def __getitem__(self, idx):
if self.train:
x = torch.tensor(self.X.iloc[idx]).to(dtype=torch.float32)
y = torch.tensor(self.y.iloc[idx]).to(dtype=torch.float32)
return (x, y)
else:
x = torch.tensor(self.X.iloc[idx]).to(dtype=torch.float32)
return x
train_dataset = BlueberryDataset(X_train, y_train, train=True)
train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_dataset = BlueberryDataset(X_val, y_val, train=True)
val_dataloader = DataLoader(val_dataset, batch_size=128, shuffle=True)
test_dataset = BlueberryDataset(test_df, train=False)
test_dataloader = DataLoader(test_dataset, batch_size=128, shuffle=False)
class SimpleModel(nn.Module):
def __init__(self, in_features=test_df.shape[1], out_features=1):
super().__init__()
self.fc1 = nn.Sequential(nn.Linear(in_features, 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, 16), nn.ReLU(), nn.Linear(16, out_features))
def forward(self, x):
return self.fc1(x)
model = SimpleModel().to(device)
sample = torch.randn(64, test_df.shape[1]).to(device)
print(sample.shape)
print(model(sample).shape) | code |
128020226/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import torch
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | code |
128020226/cell_3 | [
"image_output_1.png"
] | import os
import pandas as pd
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target = 'yield'
train_df.info() | code |
128020226/cell_14 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import display
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import os
import pandas as pd
import torch
import torch.nn as nn
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target = 'yield'
cols = [col for col in test_df.columns]
fig, axes = plt.subplots(4, 4, figsize=(16, 16))
for i, col in enumerate(cols):
axes[i//4, i%4].hist(train_df[col], bins=20)
axes[i//4, i%4].set_title(col, fontsize=14)
plt.show()
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class BlueberryDataset(Dataset):
def __init__(self, X, y=None, train=False):
self.train = train
if self.train:
self.X = X
self.y = y
else:
self.X = X
def __len__(self):
return len(self.X)
def __getitem__(self, idx):
if self.train:
x = torch.tensor(self.X.iloc[idx]).to(dtype=torch.float32)
y = torch.tensor(self.y.iloc[idx]).to(dtype=torch.float32)
return (x, y)
else:
x = torch.tensor(self.X.iloc[idx]).to(dtype=torch.float32)
return x
train_dataset = BlueberryDataset(X_train, y_train, train=True)
train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_dataset = BlueberryDataset(X_val, y_val, train=True)
val_dataloader = DataLoader(val_dataset, batch_size=128, shuffle=True)
test_dataset = BlueberryDataset(test_df, train=False)
test_dataloader = DataLoader(test_dataset, batch_size=128, shuffle=False)
class SimpleModel(nn.Module):
def __init__(self, in_features=test_df.shape[1], out_features=1):
super().__init__()
self.fc1 = nn.Sequential(nn.Linear(in_features, 64), nn.ReLU(), nn.Linear(64, 64), nn.ReLU(), nn.Linear(64, 16), nn.ReLU(), nn.Linear(16, out_features))
def forward(self, x):
return self.fc1(x)
model = SimpleModel().to(device)
sample = torch.randn(64, test_df.shape[1]).to(device)
criterion = nn.L1Loss()
optimizer = torch.optim.AdamW(params=model.parameters(), lr=0.001)
EPOCHS = 50
for epoch in range(EPOCHS):
model.train()
train_loss = 0
for i, (X_tr, y_tr) in enumerate(train_dataloader):
X_tr = X_tr.to(device)
y_tr = y_tr.to(device).reshape(-1, 1)
y_pred = model(X_tr)
loss = criterion(y_pred, y_tr)
train_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
val_loss = 0
model.eval()
with torch.inference_mode():
for X_v, y_v in val_dataloader:
X_v = X_v.to(device)
y_v = y_v.to(device).reshape(-1, 1)
val_pred = model(X_v)
loss = criterion(val_pred, y_v)
val_loss += loss.item()
train_loss /= len(train_dataloader)
val_loss /= len(val_dataloader)
results = []
model.eval()
with torch.inference_mode():
for X_ts in test_dataloader:
test_pred = model(X_ts)
results.extend(test_pred.reshape(-1).tolist()) | code |
128020226/cell_5 | [
"text_plain_output_1.png"
] | from IPython.display import display
import os
import pandas as pd
DATA_PATH = '/kaggle/input/playground-series-s3e14/'
TRAIN_FILE = 'train.csv'
TEST_FILE = 'test.csv'
train_df = pd.read_csv(os.path.join(DATA_PATH, TRAIN_FILE), index_col=0)
test_df = pd.read_csv(os.path.join(DATA_PATH, TEST_FILE), index_col=0)
target = 'yield'
print('Test data shape:', test_df.shape)
print()
print('Null values in test data')
print('Column #', end='')
display(test_df.isna().sum())
print('Duplicates in train data:', test_df.duplicated().sum()) | code |
33120852/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum()
train_data.Embarked.unique()
train_data.loc[train_data.Embarked == 'S'].count()
train_data.loc[train_data.Embarked == 'S'] | code |
33120852/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.head() | code |
33120852/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum() | code |
33120852/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum()
train_data.Embarked.unique() | code |
33120852/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum()
train_data.Embarked.unique()
train_data.loc[train_data.Embarked == 'S'].count()
train_data.loc[train_data.Embarked == 'S']
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.Embarked.fillna(value='S', inplace=True)
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.isna().sum()
train_data.head() | code |
33120852/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 |
33120852/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum()
train_data.Embarked.unique()
train_data.loc[train_data.Embarked == 'S'].count()
train_data.loc[train_data.Embarked == 'S']
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.Embarked.fillna(value='S', inplace=True)
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.isna().sum() | code |
33120852/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum() | code |
33120852/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum()
train_data.Embarked.unique()
train_data.loc[train_data.Embarked == 'S'].count()
train_data.loc[train_data.Embarked == 'S']
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.loc[train_data.Embarked.isna()].Embarked | code |
33120852/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum()
train_data.Embarked.unique()
train_data.loc[train_data.Embarked == 'S'].count()
train_data.loc[train_data.Embarked == 'S']
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.loc[train_data.Embarked.isna()].Embarked
train_data.Embarked.fillna(value='S', inplace=True)
train_data.loc[train_data.Embarked.isna()].Embarked | code |
33120852/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum()
train_data.Embarked.unique()
train_data.loc[train_data.Embarked == 'S'].count()
train_data.loc[train_data.Embarked == 'S']
train_data.loc[train_data.Embarked.isna()].Embarked | code |
33120852/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum() | code |
33120852/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape
train_data.isnull().sum()
train_data.drop(columns='Cabin', axis=1, inplace=True)
train_data.isnull().sum()
train_data.Age.fillna(value=train_data.Age.mean(), inplace=True)
train_data.isnull().sum()
train_data.Embarked.unique()
train_data.loc[train_data.Embarked == 'S'].count() | code |
33120852/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
train_data.shape | code |
16144913/cell_3 | [
"text_plain_output_1.png"
] | !ls ../input | code |
333538/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
data['Age'].dropna().describe() | code |
333538/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 |
333538/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
bins = np.arange(data['Income'].dropna().min(), data['Income'].dropna().max(), 2000)
plt.hist(data['Income'].dropna(), bins=bins, alpha=0.5, color='#EDD834', label='Income') | code |
333538/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
data['Income'].dropna().describe() | code |
333538/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
bins = np.arange(data['Income'].dropna().min(), data['Income'].dropna().max(), 2000)
bins_age = np.arange(data['Age'].dropna().min(), data['Age'].dropna().max())
plt.hist(data['Age'].dropna(), bins=bins_age, alpha=0.5, color='#EDD834', label='Age') | code |
73061409/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape | code |
73061409/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(238, 103, 48, 0.85)', line=dict(color='rgb(25, 20, 20)', width=1.0)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Leagues')
values = data['Stage'].value_counts().tolist()
names = list(dict(data['Stage'].value_counts()).keys())
values = data['Season'].value_counts().tolist()
names = list(dict(data['Season'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(29, 66, 138, 0.75)', line=dict(color='rgb(25, 20, 20)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Seasons')
values = data['Team'].value_counts().tolist()[:10]
names = list(dict(data['Team'].value_counts()).keys())[:10]
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(85, 37, 130, 0.85)', line=dict(color='rgb(253, 185, 39)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Top-10 Teams')
values = data['Player'].value_counts().tolist()[:10]
names = list(dict(data['Player'].value_counts()).keys())[:10]
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(206, 17, 65, 0.85)', line=dict(color='rgb(6, 25, 34)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Top-10 Players')
plt.style.use("seaborn")
fig, ax =plt.subplots(2,1, figsize=(20,15))
fig.suptitle("Height and Weight Distribution of the Players", fontsize=25, y=0.93)
sns.histplot(x = data["height_cm"], kde=True, ax=ax[0], color="navy", bins=20);
ax[0].set_xlabel("Player Height in cm.",fontsize=15);
sns.histplot(x = data["weight_kg"], kde=True, ax=ax[1], color="darkred", bins=20);
ax[1].set_xlabel("Player Weight in kg.",fontsize=15);
values = data['high_school'].value_counts().tolist()[:215]
names = list(dict(data['high_school'].value_counts()).keys())[:15]
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(0, 101, 58, 0.85)', line=dict(color='rgb(255, 194, 32)', width=1.5)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='High Schools of the Players')
fig.show() | code |
73061409/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(238, 103, 48, 0.85)', line=dict(color='rgb(25, 20, 20)', width=1.0)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Leagues')
fig.show() | code |
73061409/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(238, 103, 48, 0.85)', line=dict(color='rgb(25, 20, 20)', width=1.0)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Leagues')
values = data['Stage'].value_counts().tolist()
names = list(dict(data['Stage'].value_counts()).keys())
values = data['Season'].value_counts().tolist()
names = list(dict(data['Season'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(29, 66, 138, 0.75)', line=dict(color='rgb(25, 20, 20)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Seasons')
values = data['Team'].value_counts().tolist()[:10]
names = list(dict(data['Team'].value_counts()).keys())[:10]
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(85, 37, 130, 0.85)', line=dict(color='rgb(253, 185, 39)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Top-10 Teams')
values = data['Player'].value_counts().tolist()[:10]
names = list(dict(data['Player'].value_counts()).keys())[:10]
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(206, 17, 65, 0.85)', line=dict(color='rgb(6, 25, 34)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Top-10 Players')
fig.show() | code |
73061409/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(238, 103, 48, 0.85)', line=dict(color='rgb(25, 20, 20)', width=1.0)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Leagues')
values = data['Stage'].value_counts().tolist()
names = list(dict(data['Stage'].value_counts()).keys())
values = data['Season'].value_counts().tolist()
names = list(dict(data['Season'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(29, 66, 138, 0.75)', line=dict(color='rgb(25, 20, 20)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Seasons')
values = data['Team'].value_counts().tolist()[:10]
names = list(dict(data['Team'].value_counts()).keys())[:10]
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(85, 37, 130, 0.85)', line=dict(color='rgb(253, 185, 39)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Top-10 Teams')
values = data['Player'].value_counts().tolist()[:10]
names = list(dict(data['Player'].value_counts()).keys())[:10]
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(206, 17, 65, 0.85)', line=dict(color='rgb(6, 25, 34)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Top-10 Players')
plt.style.use('seaborn')
fig, ax = plt.subplots(2, 1, figsize=(20, 15))
fig.suptitle('Height and Weight Distribution of the Players', fontsize=25, y=0.93)
sns.histplot(x=data['height_cm'], kde=True, ax=ax[0], color='navy', bins=20)
ax[0].set_xlabel('Player Height in cm.', fontsize=15)
sns.histplot(x=data['weight_kg'], kde=True, ax=ax[1], color='darkred', bins=20)
ax[1].set_xlabel('Player Weight in kg.', fontsize=15) | code |
73061409/cell_14 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(238, 103, 48, 0.85)', line=dict(color='rgb(25, 20, 20)', width=1.0)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Leagues')
values = data['Stage'].value_counts().tolist()
names = list(dict(data['Stage'].value_counts()).keys())
values = data['Season'].value_counts().tolist()
names = list(dict(data['Season'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(29, 66, 138, 0.75)', line=dict(color='rgb(25, 20, 20)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Seasons')
values = data['Team'].value_counts().tolist()[:10]
names = list(dict(data['Team'].value_counts()).keys())[:10]
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(85, 37, 130, 0.85)', line=dict(color='rgb(253, 185, 39)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Top-10 Teams')
fig.show() | code |
73061409/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(238, 103, 48, 0.85)', line=dict(color='rgb(25, 20, 20)', width=1.0)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Leagues')
values = data['Stage'].value_counts().tolist()
names = list(dict(data['Stage'].value_counts()).keys())
px.pie(data, values=values, names=names, title='Basketball Match Stages', color_discrete_sequence=['#ee6730', '#1d428a', '#c8102e']) | code |
73061409/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.shape
values = data['League'].value_counts().tolist()
names = list(dict(data['League'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(238, 103, 48, 0.85)', line=dict(color='rgb(25, 20, 20)', width=1.0)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Leagues')
values = data['Stage'].value_counts().tolist()
names = list(dict(data['Stage'].value_counts()).keys())
values = data['Season'].value_counts().tolist()
names = list(dict(data['Season'].value_counts()).keys())
fig = go.Bar(x=names, y=values, marker=dict(color='rgba(29, 66, 138, 0.75)', line=dict(color='rgb(25, 20, 20)', width=1.25)))
layout = go.Layout()
fig = go.Figure(data=fig, layout=layout)
fig.update_layout(title_text='Basketball Seasons')
fig.show() | code |
73061409/cell_5 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/basketball-players-stats-per-season-49-leagues/players_stats_by_season_full_details.csv')
data.head() | code |
88092308/cell_21 | [
"text_plain_output_1.png"
] | from implicit.evaluation import mean_average_precision_at_k
from scipy.sparse import coo_matrix
import implicit
import numpy as np
import pandas as pd
transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = articles['article_id'].unique().tolist()
customer_ids = dict(list(enumerate(all_customers)))
article_ids = dict(list(enumerate(all_articles)))
transactions['customer_id'] = transactions['customer_id'].map({u: uidx for uidx, u in customer_ids.items()})
transactions['article_id'] = transactions['article_id'].map({i: iidx for iidx, i in article_ids.items()})
del customers, articles
row = transactions['customer_id'].values
col = transactions['article_id'].values
data = np.ones(transactions.shape[0])
coo_train = coo_matrix((data, (row, col)), shape=(len(all_customers), len(all_articles)))
coo_train
def to_customer_article_coo(transactions):
""" Turn a dataframe with transactions into a COO sparse articles x customers matrix"""
row = transactions['customer_id'].values
col = transactions['article_id'].values
data = np.ones(transactions.shape[0])
coo = coo_matrix((data, (row, col)), shape=(len(all_customers), len(all_articles)))
return coo
def split_data(transactions, validation_days=7):
""" Split a pandas dataframe into training and validation data, using <<validation_days>>
"""
validation_cut = transactions['t_dat'].max() - pd.Timedelta(validation_days)
df_train = transactions[transactions['t_dat'] < validation_cut]
df_val = transactions[transactions['t_dat'] >= validation_cut]
return (df_train, df_val)
def get_val_matrices(transactions, validation_days=7):
""" Split into training and validation and create various matrices
Returns a dictionary with the following keys:
coo_train: training data in COO sparse format and as (customers x articles)
csr_train: training data in CSR sparse format and as (customers x articles)
csr_val: validation data in CSR sparse format and as (customers x articles)
"""
df_train, df_val = split_data(transactions, validation_days=validation_days)
coo_train = to_customer_article_coo(df_train)
coo_val = to_customer_article_coo(df_val)
csr_train = coo_train.tocsr()
csr_val = coo_val.tocsr()
return {'coo_train': coo_train, 'csr_train': csr_train, 'csr_val': csr_val}
def validate(matrices, factors=200, iterations=20, regularization=0.01, show_progress=True):
""" Train an ALS model with <<factors>> (embeddings dimension)
for <<iterations>> over matrices and validate with MAP@12
"""
coo_train, csr_train, csr_val = (matrices['coo_train'], matrices['csr_train'], matrices['csr_val'])
model = implicit.als.AlternatingLeastSquares(factors=factors, iterations=iterations, regularization=regularization, random_state=7, use_gpu=True)
model.fit(coo_train, show_progress=show_progress)
map12 = mean_average_precision_at_k(model, csr_train, csr_val, K=12, show_progress=show_progress, num_threads=4)
return map12
transactions_customers = transactions['customer_id'].unique().tolist()
len(transactions_customers) | code |
88092308/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
transactions['t_dat'].max() | code |
88092308/cell_4 | [
"text_plain_output_1.png"
] | transactions = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv', dtype={'article_id': str}, parse_dates=['t_dat'])
sample_submission = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/sample_submission.csv')
customers = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv', dtype={'article_id': str}) | code |
88092308/cell_6 | [
"text_plain_output_1.png"
] | customers | code |
88092308/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | # Installing latest implicit library for ALS
!pip install --upgrade implicit | code |
88092308/cell_11 | [
"text_plain_output_1.png"
] | from scipy.sparse import coo_matrix
import numpy as np
transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = articles['article_id'].unique().tolist()
customer_ids = dict(list(enumerate(all_customers)))
article_ids = dict(list(enumerate(all_articles)))
transactions['customer_id'] = transactions['customer_id'].map({u: uidx for uidx, u in customer_ids.items()})
transactions['article_id'] = transactions['article_id'].map({i: iidx for iidx, i in article_ids.items()})
del customers, articles
row = transactions['customer_id'].values
col = transactions['article_id'].values
data = np.ones(transactions.shape[0])
coo_train = coo_matrix((data, (row, col)), shape=(len(all_customers), len(all_articles)))
coo_train | code |
88092308/cell_19 | [
"text_plain_output_1.png"
] | from implicit.evaluation import mean_average_precision_at_k
from scipy.sparse import coo_matrix
import implicit
import numpy as np
import pandas as pd
transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = articles['article_id'].unique().tolist()
customer_ids = dict(list(enumerate(all_customers)))
article_ids = dict(list(enumerate(all_articles)))
transactions['customer_id'] = transactions['customer_id'].map({u: uidx for uidx, u in customer_ids.items()})
transactions['article_id'] = transactions['article_id'].map({i: iidx for iidx, i in article_ids.items()})
del customers, articles
row = transactions['customer_id'].values
col = transactions['article_id'].values
data = np.ones(transactions.shape[0])
coo_train = coo_matrix((data, (row, col)), shape=(len(all_customers), len(all_articles)))
coo_train
def to_customer_article_coo(transactions):
""" Turn a dataframe with transactions into a COO sparse articles x customers matrix"""
row = transactions['customer_id'].values
col = transactions['article_id'].values
data = np.ones(transactions.shape[0])
coo = coo_matrix((data, (row, col)), shape=(len(all_customers), len(all_articles)))
return coo
def split_data(transactions, validation_days=7):
""" Split a pandas dataframe into training and validation data, using <<validation_days>>
"""
validation_cut = transactions['t_dat'].max() - pd.Timedelta(validation_days)
df_train = transactions[transactions['t_dat'] < validation_cut]
df_val = transactions[transactions['t_dat'] >= validation_cut]
return (df_train, df_val)
def get_val_matrices(transactions, validation_days=7):
""" Split into training and validation and create various matrices
Returns a dictionary with the following keys:
coo_train: training data in COO sparse format and as (customers x articles)
csr_train: training data in CSR sparse format and as (customers x articles)
csr_val: validation data in CSR sparse format and as (customers x articles)
"""
df_train, df_val = split_data(transactions, validation_days=validation_days)
coo_train = to_customer_article_coo(df_train)
coo_val = to_customer_article_coo(df_val)
csr_train = coo_train.tocsr()
csr_val = coo_val.tocsr()
return {'coo_train': coo_train, 'csr_train': csr_train, 'csr_val': csr_val}
def validate(matrices, factors=200, iterations=20, regularization=0.01, show_progress=True):
""" Train an ALS model with <<factors>> (embeddings dimension)
for <<iterations>> over matrices and validate with MAP@12
"""
coo_train, csr_train, csr_val = (matrices['coo_train'], matrices['csr_train'], matrices['csr_val'])
model = implicit.als.AlternatingLeastSquares(factors=factors, iterations=iterations, regularization=regularization, random_state=7, use_gpu=True)
model.fit(coo_train, show_progress=show_progress)
map12 = mean_average_precision_at_k(model, csr_train, csr_val, K=12, show_progress=show_progress, num_threads=4)
return map12
coo_train = to_customer_article_coo(transactions)
csr_train = coo_train.tocsr()
def train(coo_train, factors=200, iterations=15, regularization=0.01, show_progress=True):
model = implicit.als.AlternatingLeastSquares(factors=factors, iterations=iterations, regularization=regularization, random_state=7, use_gpu=True)
model.fit(coo_train, show_progress=show_progress)
return model
best_params
model = train(coo_train, **best_params) | code |
88092308/cell_18 | [
"text_plain_output_1.png"
] | from implicit.evaluation import mean_average_precision_at_k
from scipy.sparse import coo_matrix
import implicit
import numpy as np
import pandas as pd
transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = articles['article_id'].unique().tolist()
customer_ids = dict(list(enumerate(all_customers)))
article_ids = dict(list(enumerate(all_articles)))
transactions['customer_id'] = transactions['customer_id'].map({u: uidx for uidx, u in customer_ids.items()})
transactions['article_id'] = transactions['article_id'].map({i: iidx for iidx, i in article_ids.items()})
del customers, articles
row = transactions['customer_id'].values
col = transactions['article_id'].values
data = np.ones(transactions.shape[0])
coo_train = coo_matrix((data, (row, col)), shape=(len(all_customers), len(all_articles)))
coo_train
def to_customer_article_coo(transactions):
""" Turn a dataframe with transactions into a COO sparse articles x customers matrix"""
row = transactions['customer_id'].values
col = transactions['article_id'].values
data = np.ones(transactions.shape[0])
coo = coo_matrix((data, (row, col)), shape=(len(all_customers), len(all_articles)))
return coo
def split_data(transactions, validation_days=7):
""" Split a pandas dataframe into training and validation data, using <<validation_days>>
"""
validation_cut = transactions['t_dat'].max() - pd.Timedelta(validation_days)
df_train = transactions[transactions['t_dat'] < validation_cut]
df_val = transactions[transactions['t_dat'] >= validation_cut]
return (df_train, df_val)
def get_val_matrices(transactions, validation_days=7):
""" Split into training and validation and create various matrices
Returns a dictionary with the following keys:
coo_train: training data in COO sparse format and as (customers x articles)
csr_train: training data in CSR sparse format and as (customers x articles)
csr_val: validation data in CSR sparse format and as (customers x articles)
"""
df_train, df_val = split_data(transactions, validation_days=validation_days)
coo_train = to_customer_article_coo(df_train)
coo_val = to_customer_article_coo(df_val)
csr_train = coo_train.tocsr()
csr_val = coo_val.tocsr()
return {'coo_train': coo_train, 'csr_train': csr_train, 'csr_val': csr_val}
def validate(matrices, factors=200, iterations=20, regularization=0.01, show_progress=True):
""" Train an ALS model with <<factors>> (embeddings dimension)
for <<iterations>> over matrices and validate with MAP@12
"""
coo_train, csr_train, csr_val = (matrices['coo_train'], matrices['csr_train'], matrices['csr_val'])
model = implicit.als.AlternatingLeastSquares(factors=factors, iterations=iterations, regularization=regularization, random_state=7, use_gpu=True)
model.fit(coo_train, show_progress=show_progress)
map12 = mean_average_precision_at_k(model, csr_train, csr_val, K=12, show_progress=show_progress, num_threads=4)
return map12
coo_train = to_customer_article_coo(transactions)
csr_train = coo_train.tocsr()
def train(coo_train, factors=200, iterations=15, regularization=0.01, show_progress=True):
model = implicit.als.AlternatingLeastSquares(factors=factors, iterations=iterations, regularization=regularization, random_state=7, use_gpu=True)
model.fit(coo_train, show_progress=show_progress)
return model
best_params | code |
88092308/cell_8 | [
"text_plain_output_1.png"
] | transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape | code |
88092308/cell_15 | [
"text_html_output_1.png"
] | best_map12 = 0
for factors in [40, 50, 60, 100, 200, 500, 1000]:
for iterations in [3, 12, 14, 15, 20]:
for regularization in [0.01]:
map12 = validate(matrices, factors, iterations, regularization, show_progress=False)
if map12 > best_map12:
best_map12 = map12
best_params = {'factors': factors, 'iterations': iterations, 'regularization': regularization}
print(f'Best MAP@12 found. Updating: {best_params}')
del matrices | code |
88092308/cell_16 | [
"text_html_output_1.png"
] | best_params | code |
88092308/cell_22 | [
"text_plain_output_1.png"
] | df_preds = submit(model, csr_train, transactions_customers, heng_df) | code |
88092308/cell_10 | [
"text_plain_output_1.png"
] | transactions = transactions[transactions['t_dat'] > '2020-09-14']
transactions.shape
all_customers = customers['customer_id'].unique().tolist()
all_articles = articles['article_id'].unique().tolist()
customer_ids = dict(list(enumerate(all_customers)))
article_ids = dict(list(enumerate(all_articles)))
transactions['customer_id'] = transactions['customer_id'].map({u: uidx for uidx, u in customer_ids.items()})
transactions['article_id'] = transactions['article_id'].map({i: iidx for iidx, i in article_ids.items()})
del customers, articles | code |
88092308/cell_12 | [
"text_html_output_1.png"
] | model = implicit.als.AlternatingLeastSquares(factors=10, iterations=2, use_gpu=True, calculate_training_loss=True, random_state=7)
model.fit(coo_train) | code |
88092308/cell_5 | [
"text_plain_output_1.png"
] | articles | code |
104121949/cell_21 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age2 = {'a': 3, 'b': 6, 'c': 9}
age.update(age2)
age | code |
104121949/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
marks | code |
104121949/cell_25 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age2 = {'a': 3, 'b': 6, 'c': 9}
age.update(age2)
age.pop('c')
age.clear()
age | code |
104121949/cell_4 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
age | code |
104121949/cell_23 | [
"text_plain_output_1.png"
] | age = {}
type(age)
age = {'Rahul': 23, 'Joe': 15, 'Venkat': 32}
a = age.get('Rohit')
age2 = {'a': 3, 'b': 6, 'c': 9}
age.update(age2)
age.pop('c')
age | code |
104121949/cell_2 | [
"text_plain_output_1.png"
] | age = {}
type(age) | code |
104121949/cell_11 | [
"text_plain_output_1.png"
] | marks = {'Rahul': 23, 'Joe': 15, 'Venkat': {'Section1': 12, 'Section2': 15, 'Section3': 22}}
marks.keys() | code |
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