path
stringlengths 13
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sequencelengths 1
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|---|---|---|---|
106208751/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
col_remove = ['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating']
data_clear = data.drop(labels=col_remove, axis=1)
data_clear.isnull().sum()
data_clear = data_clear.dropna(axis=0)
data_clear.head() | code |
106208751/cell_5 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')
data.info()
data.head() | code |
89139202/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
print(df_train['day'].value_counts())
print(df_train['month'].value_counts()) | code |
89139202/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
print(df_train.isnull().sum())
print(df_train.isna().sum())
print(f'Duplicates: {df_train.duplicated().sum()}') | code |
89139202/cell_2 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
print(df_train.head())
df_train.drop(['row_id'], axis=1, inplace=True)
print(df_train.head())
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv') | code |
89139202/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
fig, ax = plt.subplots(2, 2, figsize=(15, 15))
ax[0, 0].hist(df_train['congestion'], bins=100)
ax[0, 0].set_xlabel('Congestion')
ax[0, 1].hist(df_train['x'])
ax[0, 1].set_xlabel('x')
ax[1, 0].hist(df_train['y'])
ax[1, 0].set_xlabel('y')
ax[1,1].hist(df_train['direction'], bins=df_train['direction'].nunique(), align='right')
ax[1, 1].set_xlabel('direction')
fig.show()
fig, ax = plt.subplots(1, 2, figsize=(15, 7))
ax[0].hist2d(df_train['congestion'], df_train['day'], bins = [50, 7], cmap=mpl.cm.Blues)
ax[0].set_xlabel('Congestion')
ax[0].set_ylabel('Weekday')
ax[1].hist2d(df_train['congestion'], df_train['month'], bins = [50, 12], cmap=mpl.cm.Blues, range=([0, 100], [0, 11]))
ax[1].set_xlabel('Congestion')
ax[1].set_ylabel('Month')
plt.show()
fig, ax = plt.subplots(3, 3, figsize=(30, 30))
unique_directions = df_train['direction'].unique()
title_map = {
0 : 'Monday',
1 : 'Tuesday',
2 : 'Wednesday',
3 : 'Thursday',
4 : 'Friday',
5 : 'Saturday',
6 : 'Sunday',
}
ax = ax.ravel()
for i in range(7):
day_view = df_train[df_train['day'] == i]
for direction in unique_directions:
direction_view = day_view[day_view['direction'] == direction]
ax[i].hist(direction_view['congestion'], label=direction, bins=50)
ax[i].legend(loc='best')
ax[i].set_xlabel('Congestion')
ax[i].set_ylabel('Count/bin')
ax[i].set_title(title_map[i])
ax = ax.reshape(3, 3)
plt.show()
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
sns.heatmap(df_train.corr(), cmap=sns.color_palette('vlag', as_cmap=True), square=True, ax=ax, annot=True)
plt.show() | code |
89139202/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
df_train.describe() | code |
89139202/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
fig, ax = plt.subplots(2, 2, figsize=(15, 15))
ax[0, 0].hist(df_train['congestion'], bins=100)
ax[0, 0].set_xlabel('Congestion')
ax[0, 1].hist(df_train['x'])
ax[0, 1].set_xlabel('x')
ax[1, 0].hist(df_train['y'])
ax[1, 0].set_xlabel('y')
ax[1,1].hist(df_train['direction'], bins=df_train['direction'].nunique(), align='right')
ax[1, 1].set_xlabel('direction')
fig.show()
fig, ax = plt.subplots(1, 2, figsize=(15, 7))
ax[0].hist2d(df_train['congestion'], df_train['day'], bins=[50, 7], cmap=mpl.cm.Blues)
ax[0].set_xlabel('Congestion')
ax[0].set_ylabel('Weekday')
ax[1].hist2d(df_train['congestion'], df_train['month'], bins=[50, 12], cmap=mpl.cm.Blues, range=([0, 100], [0, 11]))
ax[1].set_xlabel('Congestion')
ax[1].set_ylabel('Month')
plt.show() | code |
89139202/cell_12 | [
"text_html_output_1.png"
] | import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
fig, ax = plt.subplots(2, 2, figsize=(15, 15))
ax[0, 0].hist(df_train['congestion'], bins=100)
ax[0, 0].set_xlabel('Congestion')
ax[0, 1].hist(df_train['x'])
ax[0, 1].set_xlabel('x')
ax[1, 0].hist(df_train['y'])
ax[1, 0].set_xlabel('y')
ax[1,1].hist(df_train['direction'], bins=df_train['direction'].nunique(), align='right')
ax[1, 1].set_xlabel('direction')
fig.show()
fig, ax = plt.subplots(1, 2, figsize=(15, 7))
ax[0].hist2d(df_train['congestion'], df_train['day'], bins = [50, 7], cmap=mpl.cm.Blues)
ax[0].set_xlabel('Congestion')
ax[0].set_ylabel('Weekday')
ax[1].hist2d(df_train['congestion'], df_train['month'], bins = [50, 12], cmap=mpl.cm.Blues, range=([0, 100], [0, 11]))
ax[1].set_xlabel('Congestion')
ax[1].set_ylabel('Month')
plt.show()
fig, ax = plt.subplots(3, 3, figsize=(30, 30))
unique_directions = df_train['direction'].unique()
title_map = {0: 'Monday', 1: 'Tuesday', 2: 'Wednesday', 3: 'Thursday', 4: 'Friday', 5: 'Saturday', 6: 'Sunday'}
ax = ax.ravel()
for i in range(7):
day_view = df_train[df_train['day'] == i]
for direction in unique_directions:
direction_view = day_view[day_view['direction'] == direction]
ax[i].hist(direction_view['congestion'], label=direction, bins=50)
ax[i].legend(loc='best')
ax[i].set_xlabel('Congestion')
ax[i].set_ylabel('Count/bin')
ax[i].set_title(title_map[i])
ax = ax.reshape(3, 3)
plt.show() | code |
89139202/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-mar-2022/train.csv')
df_train['time'] = pd.to_datetime(df_train['time'])
df_train.drop(['row_id'], axis=1, inplace=True)
df_test = pd.read_csv('../input/tabular-playground-series-mar-2022/test.csv')
fig, ax = plt.subplots(2, 2, figsize=(15, 15))
ax[0, 0].hist(df_train['congestion'], bins=100)
ax[0, 0].set_xlabel('Congestion')
ax[0, 1].hist(df_train['x'])
ax[0, 1].set_xlabel('x')
ax[1, 0].hist(df_train['y'])
ax[1, 0].set_xlabel('y')
ax[1, 1].hist(df_train['direction'], bins=df_train['direction'].nunique(), align='right')
ax[1, 1].set_xlabel('direction')
fig.show() | code |
74044709/cell_6 | [
"text_plain_output_1.png"
] | pip install openai | code |
74044709/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 |
74044709/cell_8 | [
"text_plain_output_1.png"
] | """prompt=text
t=True
while t:
person=str(input('Morty:'))
prompt+='Morty:'+person+'
'
prompt+='Rick:'
output=model(prompt)
prompt+=output+'
'
print('Rick:',output)
if person=='bey':
print('Rick:ok I'm done, go away')
t=False
""" | code |
74044709/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('/kaggle/input/rickmorty-scripts/RickAndMortyScripts.csv')
data.head() | code |
74044709/cell_12 | [
"text_html_output_1.png"
] | """prompt=newtext
t=True
while t:
person=str(input('Morty:'))
prompt+='Morty:'+person+'
'
prompt+='Rick:'
output=model(prompt)
prompt+=output+'
'
print('Rick:',output)
if person=='bey':
print('Rick:ok I'm done, go away')
t=False""" | code |
90143425/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group', 'Group_Num']] = train.PassengerId.str.split('_', expand=True)
test[['Group', 'Group_Num']] = test.PassengerId.str.split('_', expand=True)
for i in train.columns:
print(f'{i}: {train[i].nunique()}') | code |
90143425/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group', 'Group_Num']] = train.PassengerId.str.split('_', expand=True)
test[['Group', 'Group_Num']] = test.PassengerId.str.split('_', expand=True)
train.head() | code |
90143425/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group', 'Group_Num']] = train.PassengerId.str.split('_', expand=True)
test[['Group', 'Group_Num']] = test.PassengerId.str.split('_', expand=True)
mode_list = ['HomePlanet', 'CryoSleep', 'Destination', 'VIP', 'Deck', 'Side']
for i in mode_list:
train[i] = train[i].fillna(train[i].mode()[0])
test[i] = test[i].fillna(train[i].mode()[0])
median_list = ['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']
for i in median_list:
train[i] = train[i].fillna(train[i].median())
test[i] = test[i].fillna(train[i].median())
for i in train.columns:
if train[i].isna().sum() > 0:
print(f'{i}: {train[i].isna().sum()}') | code |
90143425/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90143425/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group', 'Group_Num']] = train.PassengerId.str.split('_', expand=True)
test[['Group', 'Group_Num']] = test.PassengerId.str.split('_', expand=True)
for i in train.columns:
if train[i].isna().sum() > 0:
print(f'{i}: {train[i].isna().sum()}')
print('\n', 'test NaNs', '\n')
for i in test.columns:
if test[i].isna().sum() > 0:
print(f'{i}: {test[i].isna().sum()}') | code |
90143425/cell_16 | [
"text_html_output_1.png"
] | from category_encoders.ordinal import OrdinalEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from category_encoders.ordinal import OrdinalEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
oe = OrdinalEncoder()
scaler = StandardScaler()
logit = LogisticRegression()
pipe = Pipeline([('Encoder', oe), ('Scaler', scaler), ('Logistic Regression', logit)])
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
print(classification_report(y_test, y_pred)) | code |
90143425/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train.info() | code |
90143425/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train[['Group', 'Group_Num']] = train.PassengerId.str.split('_', expand=True)
test[['Group', 'Group_Num']] = test.PassengerId.str.split('_', expand=True)
mode_list = ['HomePlanet', 'CryoSleep', 'Destination', 'VIP', 'Deck', 'Side']
for i in mode_list:
train[i] = train[i].fillna(train[i].mode()[0])
test[i] = test[i].fillna(train[i].mode()[0])
for i in train.columns:
if train[i].isna().sum() > 0:
print(f'{i}: {train[i].isna().sum()}') | code |
90143425/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
train[['Deck', 'Cabin_Num', 'Side']] = train.Cabin.str.split('/', expand=True)
test[['Deck', 'Cabin_Num', 'Side']] = test.Cabin.str.split('/', expand=True)
train.head() | code |
106198899/cell_4 | [
"text_plain_output_1.png"
] | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras import layers
pre_trained_model = InceptionV3(input_shape=(256, 256, 3), include_top=False, weights=None)
for layer in pre_trained_model.layers:
layer.trainable = False | code |
106198899/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras import layers
pre_trained_model = InceptionV3(input_shape=(256, 256, 3), include_top=False, weights=None)
for layer in pre_trained_model.layers:
layer.trainable = False
pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output | code |
106198899/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_df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')
test_df = pd.read_csv('../input/mayo-clinic-strip-ai/test.csv')
train_df.head() | code |
106198899/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
"import os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))" | code |
106198899/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras import Model
from tensorflow.keras import layers
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras import layers
pre_trained_model = InceptionV3(input_shape=(256, 256, 3), include_top=False, weights=None)
for layer in pre_trained_model.layers:
layer.trainable = False
pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
last_output = last_layer.output
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras import Model
x = layers.Flatten()(last_output)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dropout(0.2)(x)
x = layers.Dense(1, activation='sigmoid')(x)
model = Model(pre_trained_model.input, x)
model.summary() | code |
106198899/cell_15 | [
"text_plain_output_1.png"
] | from openslide import OpenSlide
from tqdm import tqdm
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
train_df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')
test_df = pd.read_csv('../input/mayo-clinic-strip-ai/test.csv')
train_df_sample = train_df.iloc[:754].copy()
def preprocess(image_path):
slide = OpenSlide(image_path)
region = (1000, 1000)
size = (5000, 5000)
image = slide.read_region(region, 0, size)
image = image.convert('RGB')
image = tf.image.resize(image, (256, 256))
image = np.array(image)
return image
x_train = []
for i in tqdm(train_df_sample['file_path']):
x1 = preprocess(i)
x_train.append(x1)
x_train = np.array(x_train)
y_train = np.array(train_df_sample['target'])
X_train, X_test, Y_train, Y_test = train_test_split(x_train, y_train, test_size=0.2) | code |
106198899/cell_3 | [
"text_plain_output_1.png"
] | """!wget --no-check-certificate https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5""" | code |
106198899/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from openslide import OpenSlide
from tqdm import tqdm
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
train_df = pd.read_csv('../input/mayo-clinic-strip-ai/train.csv')
test_df = pd.read_csv('../input/mayo-clinic-strip-ai/test.csv')
train_df_sample = train_df.iloc[:754].copy()
def preprocess(image_path):
slide = OpenSlide(image_path)
region = (1000, 1000)
size = (5000, 5000)
image = slide.read_region(region, 0, size)
image = image.convert('RGB')
image = tf.image.resize(image, (256, 256))
image = np.array(image)
return image
x_train = []
for i in tqdm(train_df_sample['file_path']):
x1 = preprocess(i)
x_train.append(x1) | code |
106198899/cell_5 | [
"text_html_output_1.png"
] | from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras import layers
pre_trained_model = InceptionV3(input_shape=(256, 256, 3), include_top=False, weights=None)
for layer in pre_trained_model.layers:
layer.trainable = False
pre_trained_model.summary() | code |
106211900/cell_11 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import transforms
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/digit-recognizer/train.csv')
test_set = pd.read_csv('../input/digit-recognizer/test.csv')
valid_size = 0.1
data_nums = len(train_set)
indices = list(range(data_nums))
np.random.shuffle(indices)
splits = int(np.floor(valid_size * data_nums))
train_ind, valid_ind = (indices[splits:], indices[:splits])
train_s = SubsetRandomSampler(train_ind)
valid_s = SubsetRandomSampler(valid_ind)
transforms_dic = {'train': transforms.Compose([transforms.ToPILImage(), transforms.RandomAffine(8, translate=(0, 0.1), scale=(0.8, 1.2)), transforms.ToTensor()]), 'valid': transforms.Compose([transforms.ToTensor()]), 'test': transforms.Compose([transforms.ToTensor()])}
train = Datamnist(train_set, transforms_dic['train'])
valid = Datamnist(train_set, transforms_dic['valid'])
test = Datamnist(test_set, transforms_dic['test'], labels=False)
train_loader = DataLoader(train, batch_size=128, sampler=train_s)
valid_loader = DataLoader(valid, sampler=valid_s)
test_loader = DataLoader(test)
Trainmodel = Runmodel(train_loader, valid_loader)
Trainmodel.train()
Trainmodel.predict(test_loader)
print('ok') | code |
106211900/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torchvision import transforms
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/digit-recognizer/train.csv')
test_set = pd.read_csv('../input/digit-recognizer/test.csv')
valid_size = 0.1
data_nums = len(train_set)
indices = list(range(data_nums))
np.random.shuffle(indices)
splits = int(np.floor(valid_size * data_nums))
train_ind, valid_ind = (indices[splits:], indices[:splits])
train_s = SubsetRandomSampler(train_ind)
valid_s = SubsetRandomSampler(valid_ind)
transforms_dic = {'train': transforms.Compose([transforms.ToPILImage(), transforms.RandomAffine(8, translate=(0, 0.1), scale=(0.8, 1.2)), transforms.ToTensor()]), 'valid': transforms.Compose([transforms.ToTensor()]), 'test': transforms.Compose([transforms.ToTensor()])}
train = Datamnist(train_set, transforms_dic['train'])
valid = Datamnist(train_set, transforms_dic['valid'])
test = Datamnist(test_set, transforms_dic['test'], labels=False)
train_loader = DataLoader(train, batch_size=128, sampler=train_s)
valid_loader = DataLoader(valid, sampler=valid_s)
test_loader = DataLoader(test)
Trainmodel = Runmodel(train_loader, valid_loader)
Trainmodel.train() | code |
1004711/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
1004711/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
1004711/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
1004711/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
train_df[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False) | code |
105197876/cell_9 | [
"text_html_output_1.png"
] | from matplotlib.offsetbox import AnchoredText
from scipy.signal import periodogram
from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
import numpy as np
import optuna
import pandas as pd
import seaborn as sns
import xgboost as xgb
def seasonal_plot(X, y, period, freq, ax=None):
if ax is None:
_, ax = plt.subplots()
palette = sns.color_palette("husl", n_colors=X[period].nunique(),)
ax = sns.lineplot(
x=freq,
y=y,
hue=period,
data=X,
ci=False,
ax=ax,
palette=palette,
legend=False,
)
ax.set_title(f"Seasonal Plot ({period}/{freq})")
for line, name in zip(ax.lines, X[period].unique()):
y_ = line.get_ydata()[-1]
ax.annotate(
name,
xy=(1, y_),
xytext=(6, 0),
color=line.get_color(),
xycoords=ax.get_yaxis_transform(),
textcoords="offset points",
size=14,
va="center",
)
return ax
def plot_periodogram(ts, detrend='linear', ax=None):
from scipy.signal import periodogram
fs = pd.Timedelta("1Y") / pd.Timedelta("1D")
freqencies, spectrum = periodogram(
ts,
fs=fs,
detrend=detrend,
window="boxcar",
scaling='spectrum',
)
if ax is None:
_, ax = plt.subplots()
ax.step(freqencies, spectrum, color="purple")
ax.set_xscale("log")
ax.set_xticks([1, 2, 4, 6, 12, 26, 52, 104])
ax.set_xticklabels(
[
"Annual (1)",
"Semiannual (2)",
"Quarterly (4)",
"Bimonthly (6)",
"Monthly (12)",
"Biweekly (26)",
"Weekly (52)",
"Semiweekly (104)",
],
rotation=30,
)
ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
ax.set_ylabel("Variance")
ax.set_title("Periodogram")
return ax
def lagplot(x, y=None, lag=1, standardize=False, ax=None, **kwargs):
from matplotlib.offsetbox import AnchoredText
x_ = x.shift(lag)
if standardize:
x_ = (x_ - x_.mean()) / x_.std()
if y is not None:
y_ = (y - y.mean()) / y.std() if standardize else y
else:
y_ = x
corr = y_.corr(x_)
if ax is None:
fig, ax = plt.subplots()
scatter_kws = dict(
alpha=0.75,
s=3,
)
line_kws = dict(color='C3', )
ax = sns.regplot(x=x_,
y=y_,
scatter_kws=scatter_kws,
line_kws=line_kws,
lowess=True,
ax=ax,
**kwargs)
at = AnchoredText(
f"{corr:.2f}",
prop=dict(size="large"),
frameon=True,
loc="upper left",
)
at.patch.set_boxstyle("square, pad=0.0")
ax.add_artist(at)
ax.set(title=f"Lag {lag}", xlabel=x_.name, ylabel=y_.name)
return ax
def plot_lags(x, y=None, lags=6, nrows=1, lagplot_kwargs={}, **kwargs):
import math
kwargs.setdefault('nrows', nrows)
kwargs.setdefault('ncols', math.ceil(lags / nrows))
kwargs.setdefault('figsize', (kwargs['ncols'] * 2, nrows * 2 + 0.5))
fig, axs = plt.subplots(sharex=True, sharey=True, squeeze=False, **kwargs)
for ax, k in zip(fig.get_axes(), range(kwargs['nrows'] * kwargs['ncols'])):
if k + 1 <= lags:
ax = lagplot(x, y, lag=k + 1, ax=ax, **lagplot_kwargs)
ax.set_title(f"Lag {k + 1}", fontdict=dict(fontsize=14))
ax.set(xlabel="", ylabel="")
else:
ax.axis('off')
plt.setp(axs[-1, :], xlabel=x.name)
plt.setp(axs[:, 0], ylabel=y.name if y is not None else x.name)
fig.tight_layout(w_pad=0.1, h_pad=0.1)
return fig
train_df = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='date', parse_dates=['date']).drop('row_id', axis=1).to_period(freq='D')
test_df = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='date', parse_dates=['date']).drop('row_id', axis=1).to_period(freq='D')
sub_df = pd.read_csv('../input/tabular-playground-series-sep-2022/sample_submission.csv')
train_df_xgb = train_df.copy()
train_df_xgb['num_sold'] = np.array(train_df['num_sold']) - np.array(train_df['num_sold_predicted'])
train_df_xgb['day'] = train_df_xgb.index.dayofweek
train_df_xgb['week'] = train_df_xgb.index.week
train_df_xgb['dayofyear'] = train_df_xgb.index.dayofyear
train_df_xgb['year'] = train_df_xgb.index.year
train_df_xgb['month'] = train_df_xgb.index.month
train_df_xgb['amount_time'] = train_df_xgb['month'] * 100 + train_df_xgb['day']
train_df_xgb['special_days'] = train_df_xgb['amount_time'].isin([101, 1228, 1229, 1230, 1231]).astype(int)
train_df_xgb['month'] = np.cos(0.5236 * train_df_xgb['month'])
test_df['day'] = test_df.index.dayofweek
test_df['week'] = test_df.index.week
test_df['dayofyear'] = test_df.index.dayofyear
test_df['year'] = test_df.index.year
test_df['month'] = test_df.index.month
test_df['amount_time'] = test_df['month'] * 100 + test_df['day']
test_df['special_days'] = test_df['amount_time'].isin([101, 1228, 1229, 1230, 1231]).astype(int)
test_df['month'] = np.cos(0.5236 * test_df['month'])
def xgb_model(train_data, test_data):
X = train_data.copy()
target = X.pop('num_sold')
X_train, X_valid, y_train, y_valid = train_test_split(X, target)
xgb_train = xgb.DMatrix(X_train, label=y_train)
xgb_eval = xgb.DMatrix(X_valid, label=y_valid)
def objective(trial, xgb_train, xgb_eval):
params = {'tree_method': trial.suggest_categorical('tree_method', ['gpu_hist']), 'objective': trial.suggest_categorical('objective', ['reg:squarederror']), 'eta': trial.suggest_float('eta', 0.001, 0.3, log=True), 'gamma': trial.suggest_float('gamma', 0.001, 1000, log=True), 'max_depth': trial.suggest_int('max_depth', 3, 12), 'grow_policy': trial.suggest_categorical('grow_policy', ['depthwise', 'lossguide']), 'min_child_weight': trial.suggest_float('min_child_weight', 0.001, 1000, log=True), 'lambda': trial.suggest_float('lambda', 0.001, 100, log=True), 'alpha': trial.suggest_float('alpha', 0.001, 100, log=True), 'colsample_bytree': trial.suggest_float('colsample_bytree', 0.9, 1.0, step=0.05), 'colsample_bylevel': trial.suggest_float('colsample_bylevel', 0.8, 1.0, step=0.05), 'colsample_bynode': trial.suggest_float('colsample_bynode', 0.7, 1.0, step=0.05), 'subsample': trial.suggest_float('subsample', 0.5, 1.0, step=0.05), 'eval_metric': trial.suggest_categorical('eval_metric', ['rmse'])}
num_round = 1000
evallist = [(xgb_eval, 'eval')]
model = xgb.train(params, xgb_train, num_round, evallist, early_stopping_rounds=10, verbose_eval=1000)
return model.best_score
study = optuna.create_study(direction='minimize', study_name='Xgboost')
func = lambda trial: objective(trial, xgb_train, xgb_eval)
study.optimize(func, n_trials=20)
best_params = study.best_params
evallist = [(xgb_eval, 'eval')]
best_model = xgb.train(best_params, xgb_train, 2000, evallist, early_stopping_rounds=100, verbose_eval=2000)
xgb_test = xgb.DMatrix(test_data)
test_preds = best_model.predict(xgb_test)
y_preds = best_model.predict(xgb.DMatrix(X, label=target))
return (test_preds, y_preds)
test_preds, y_preds = xgb_model(pd.get_dummies(train_df_xgb.drop('num_sold_predicted', axis=1)), pd.get_dummies(test_df.drop('num_sold', axis=1)))
xgb_y_pred = pd.Series(y_preds, index=train_df.index)
xgb_y_fore = pd.Series(test_preds, index=test_df.index)
_, ax = plt.subplots(figsize=(14, 6))
xgb_y_pred.groupby('date').mean().plot(ax=ax, label='Seasonal Learned by model')
xgb_y_fore.groupby('date').mean().plot(ax=ax, label='Seasonal Forecast by model', color='C3')
train_df_xgb.groupby('date').num_sold.mean().plot(ax=ax, label='Actual Seasonal')
_ = ax.legend() | code |
105197876/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.offsetbox import AnchoredText
from scipy.signal import periodogram
import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def seasonal_plot(X, y, period, freq, ax=None):
if ax is None:
_, ax = plt.subplots()
palette = sns.color_palette("husl", n_colors=X[period].nunique(),)
ax = sns.lineplot(
x=freq,
y=y,
hue=period,
data=X,
ci=False,
ax=ax,
palette=palette,
legend=False,
)
ax.set_title(f"Seasonal Plot ({period}/{freq})")
for line, name in zip(ax.lines, X[period].unique()):
y_ = line.get_ydata()[-1]
ax.annotate(
name,
xy=(1, y_),
xytext=(6, 0),
color=line.get_color(),
xycoords=ax.get_yaxis_transform(),
textcoords="offset points",
size=14,
va="center",
)
return ax
def plot_periodogram(ts, detrend='linear', ax=None):
from scipy.signal import periodogram
fs = pd.Timedelta("1Y") / pd.Timedelta("1D")
freqencies, spectrum = periodogram(
ts,
fs=fs,
detrend=detrend,
window="boxcar",
scaling='spectrum',
)
if ax is None:
_, ax = plt.subplots()
ax.step(freqencies, spectrum, color="purple")
ax.set_xscale("log")
ax.set_xticks([1, 2, 4, 6, 12, 26, 52, 104])
ax.set_xticklabels(
[
"Annual (1)",
"Semiannual (2)",
"Quarterly (4)",
"Bimonthly (6)",
"Monthly (12)",
"Biweekly (26)",
"Weekly (52)",
"Semiweekly (104)",
],
rotation=30,
)
ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
ax.set_ylabel("Variance")
ax.set_title("Periodogram")
return ax
def lagplot(x, y=None, lag=1, standardize=False, ax=None, **kwargs):
from matplotlib.offsetbox import AnchoredText
x_ = x.shift(lag)
if standardize:
x_ = (x_ - x_.mean()) / x_.std()
if y is not None:
y_ = (y - y.mean()) / y.std() if standardize else y
else:
y_ = x
corr = y_.corr(x_)
if ax is None:
fig, ax = plt.subplots()
scatter_kws = dict(
alpha=0.75,
s=3,
)
line_kws = dict(color='C3', )
ax = sns.regplot(x=x_,
y=y_,
scatter_kws=scatter_kws,
line_kws=line_kws,
lowess=True,
ax=ax,
**kwargs)
at = AnchoredText(
f"{corr:.2f}",
prop=dict(size="large"),
frameon=True,
loc="upper left",
)
at.patch.set_boxstyle("square, pad=0.0")
ax.add_artist(at)
ax.set(title=f"Lag {lag}", xlabel=x_.name, ylabel=y_.name)
return ax
def plot_lags(x, y=None, lags=6, nrows=1, lagplot_kwargs={}, **kwargs):
import math
kwargs.setdefault('nrows', nrows)
kwargs.setdefault('ncols', math.ceil(lags / nrows))
kwargs.setdefault('figsize', (kwargs['ncols'] * 2, nrows * 2 + 0.5))
fig, axs = plt.subplots(sharex=True, sharey=True, squeeze=False, **kwargs)
for ax, k in zip(fig.get_axes(), range(kwargs['nrows'] * kwargs['ncols'])):
if k + 1 <= lags:
ax = lagplot(x, y, lag=k + 1, ax=ax, **lagplot_kwargs)
ax.set_title(f"Lag {k + 1}", fontdict=dict(fontsize=14))
ax.set(xlabel="", ylabel="")
else:
ax.axis('off')
plt.setp(axs[-1, :], xlabel=x.name)
plt.setp(axs[:, 0], ylabel=y.name if y is not None else x.name)
fig.tight_layout(w_pad=0.1, h_pad=0.1)
return fig
train_df = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='date', parse_dates=['date']).drop('row_id', axis=1).to_period(freq='D')
test_df = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='date', parse_dates=['date']).drop('row_id', axis=1).to_period(freq='D')
sub_df = pd.read_csv('../input/tabular-playground-series-sep-2022/sample_submission.csv')
print('describtion of train data:')
display(train_df.describe(include='object'))
print('describtion of test data:')
display(test_df.describe(include='object')) | code |
105197876/cell_3 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from matplotlib.offsetbox import AnchoredText
from scipy.signal import periodogram
import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
def seasonal_plot(X, y, period, freq, ax=None):
if ax is None:
_, ax = plt.subplots()
palette = sns.color_palette("husl", n_colors=X[period].nunique(),)
ax = sns.lineplot(
x=freq,
y=y,
hue=period,
data=X,
ci=False,
ax=ax,
palette=palette,
legend=False,
)
ax.set_title(f"Seasonal Plot ({period}/{freq})")
for line, name in zip(ax.lines, X[period].unique()):
y_ = line.get_ydata()[-1]
ax.annotate(
name,
xy=(1, y_),
xytext=(6, 0),
color=line.get_color(),
xycoords=ax.get_yaxis_transform(),
textcoords="offset points",
size=14,
va="center",
)
return ax
def plot_periodogram(ts, detrend='linear', ax=None):
from scipy.signal import periodogram
fs = pd.Timedelta("1Y") / pd.Timedelta("1D")
freqencies, spectrum = periodogram(
ts,
fs=fs,
detrend=detrend,
window="boxcar",
scaling='spectrum',
)
if ax is None:
_, ax = plt.subplots()
ax.step(freqencies, spectrum, color="purple")
ax.set_xscale("log")
ax.set_xticks([1, 2, 4, 6, 12, 26, 52, 104])
ax.set_xticklabels(
[
"Annual (1)",
"Semiannual (2)",
"Quarterly (4)",
"Bimonthly (6)",
"Monthly (12)",
"Biweekly (26)",
"Weekly (52)",
"Semiweekly (104)",
],
rotation=30,
)
ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
ax.set_ylabel("Variance")
ax.set_title("Periodogram")
return ax
def lagplot(x, y=None, lag=1, standardize=False, ax=None, **kwargs):
from matplotlib.offsetbox import AnchoredText
x_ = x.shift(lag)
if standardize:
x_ = (x_ - x_.mean()) / x_.std()
if y is not None:
y_ = (y - y.mean()) / y.std() if standardize else y
else:
y_ = x
corr = y_.corr(x_)
if ax is None:
fig, ax = plt.subplots()
scatter_kws = dict(
alpha=0.75,
s=3,
)
line_kws = dict(color='C3', )
ax = sns.regplot(x=x_,
y=y_,
scatter_kws=scatter_kws,
line_kws=line_kws,
lowess=True,
ax=ax,
**kwargs)
at = AnchoredText(
f"{corr:.2f}",
prop=dict(size="large"),
frameon=True,
loc="upper left",
)
at.patch.set_boxstyle("square, pad=0.0")
ax.add_artist(at)
ax.set(title=f"Lag {lag}", xlabel=x_.name, ylabel=y_.name)
return ax
def plot_lags(x, y=None, lags=6, nrows=1, lagplot_kwargs={}, **kwargs):
import math
kwargs.setdefault('nrows', nrows)
kwargs.setdefault('ncols', math.ceil(lags / nrows))
kwargs.setdefault('figsize', (kwargs['ncols'] * 2, nrows * 2 + 0.5))
fig, axs = plt.subplots(sharex=True, sharey=True, squeeze=False, **kwargs)
for ax, k in zip(fig.get_axes(), range(kwargs['nrows'] * kwargs['ncols'])):
if k + 1 <= lags:
ax = lagplot(x, y, lag=k + 1, ax=ax, **lagplot_kwargs)
ax.set_title(f"Lag {k + 1}", fontdict=dict(fontsize=14))
ax.set(xlabel="", ylabel="")
else:
ax.axis('off')
plt.setp(axs[-1, :], xlabel=x.name)
plt.setp(axs[:, 0], ylabel=y.name if y is not None else x.name)
fig.tight_layout(w_pad=0.1, h_pad=0.1)
return fig
train_df = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='date', parse_dates=['date']).drop('row_id', axis=1).to_period(freq='D')
test_df = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='date', parse_dates=['date']).drop('row_id', axis=1).to_period(freq='D')
sub_df = pd.read_csv('../input/tabular-playground-series-sep-2022/sample_submission.csv')
print('shape of train data: ', train_df.shape)
display(train_df)
print('shape of test data', test_df.shape)
display(test_df)
print('shape of sample submission data: ', sub_df.shape)
display(sub_df) | code |
105197876/cell_10 | [
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from matplotlib.offsetbox import AnchoredText
from scipy.signal import periodogram
from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
import numpy as np
import optuna
import pandas as pd
import seaborn as sns
import xgboost as xgb
def seasonal_plot(X, y, period, freq, ax=None):
if ax is None:
_, ax = plt.subplots()
palette = sns.color_palette("husl", n_colors=X[period].nunique(),)
ax = sns.lineplot(
x=freq,
y=y,
hue=period,
data=X,
ci=False,
ax=ax,
palette=palette,
legend=False,
)
ax.set_title(f"Seasonal Plot ({period}/{freq})")
for line, name in zip(ax.lines, X[period].unique()):
y_ = line.get_ydata()[-1]
ax.annotate(
name,
xy=(1, y_),
xytext=(6, 0),
color=line.get_color(),
xycoords=ax.get_yaxis_transform(),
textcoords="offset points",
size=14,
va="center",
)
return ax
def plot_periodogram(ts, detrend='linear', ax=None):
from scipy.signal import periodogram
fs = pd.Timedelta("1Y") / pd.Timedelta("1D")
freqencies, spectrum = periodogram(
ts,
fs=fs,
detrend=detrend,
window="boxcar",
scaling='spectrum',
)
if ax is None:
_, ax = plt.subplots()
ax.step(freqencies, spectrum, color="purple")
ax.set_xscale("log")
ax.set_xticks([1, 2, 4, 6, 12, 26, 52, 104])
ax.set_xticklabels(
[
"Annual (1)",
"Semiannual (2)",
"Quarterly (4)",
"Bimonthly (6)",
"Monthly (12)",
"Biweekly (26)",
"Weekly (52)",
"Semiweekly (104)",
],
rotation=30,
)
ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
ax.set_ylabel("Variance")
ax.set_title("Periodogram")
return ax
def lagplot(x, y=None, lag=1, standardize=False, ax=None, **kwargs):
from matplotlib.offsetbox import AnchoredText
x_ = x.shift(lag)
if standardize:
x_ = (x_ - x_.mean()) / x_.std()
if y is not None:
y_ = (y - y.mean()) / y.std() if standardize else y
else:
y_ = x
corr = y_.corr(x_)
if ax is None:
fig, ax = plt.subplots()
scatter_kws = dict(
alpha=0.75,
s=3,
)
line_kws = dict(color='C3', )
ax = sns.regplot(x=x_,
y=y_,
scatter_kws=scatter_kws,
line_kws=line_kws,
lowess=True,
ax=ax,
**kwargs)
at = AnchoredText(
f"{corr:.2f}",
prop=dict(size="large"),
frameon=True,
loc="upper left",
)
at.patch.set_boxstyle("square, pad=0.0")
ax.add_artist(at)
ax.set(title=f"Lag {lag}", xlabel=x_.name, ylabel=y_.name)
return ax
def plot_lags(x, y=None, lags=6, nrows=1, lagplot_kwargs={}, **kwargs):
import math
kwargs.setdefault('nrows', nrows)
kwargs.setdefault('ncols', math.ceil(lags / nrows))
kwargs.setdefault('figsize', (kwargs['ncols'] * 2, nrows * 2 + 0.5))
fig, axs = plt.subplots(sharex=True, sharey=True, squeeze=False, **kwargs)
for ax, k in zip(fig.get_axes(), range(kwargs['nrows'] * kwargs['ncols'])):
if k + 1 <= lags:
ax = lagplot(x, y, lag=k + 1, ax=ax, **lagplot_kwargs)
ax.set_title(f"Lag {k + 1}", fontdict=dict(fontsize=14))
ax.set(xlabel="", ylabel="")
else:
ax.axis('off')
plt.setp(axs[-1, :], xlabel=x.name)
plt.setp(axs[:, 0], ylabel=y.name if y is not None else x.name)
fig.tight_layout(w_pad=0.1, h_pad=0.1)
return fig
train_df = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv', index_col='date', parse_dates=['date']).drop('row_id', axis=1).to_period(freq='D')
test_df = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv', index_col='date', parse_dates=['date']).drop('row_id', axis=1).to_period(freq='D')
sub_df = pd.read_csv('../input/tabular-playground-series-sep-2022/sample_submission.csv')
train_df_xgb = train_df.copy()
train_df_xgb['num_sold'] = np.array(train_df['num_sold']) - np.array(train_df['num_sold_predicted'])
train_df_xgb['day'] = train_df_xgb.index.dayofweek
train_df_xgb['week'] = train_df_xgb.index.week
train_df_xgb['dayofyear'] = train_df_xgb.index.dayofyear
train_df_xgb['year'] = train_df_xgb.index.year
train_df_xgb['month'] = train_df_xgb.index.month
train_df_xgb['amount_time'] = train_df_xgb['month'] * 100 + train_df_xgb['day']
train_df_xgb['special_days'] = train_df_xgb['amount_time'].isin([101, 1228, 1229, 1230, 1231]).astype(int)
train_df_xgb['month'] = np.cos(0.5236 * train_df_xgb['month'])
test_df['day'] = test_df.index.dayofweek
test_df['week'] = test_df.index.week
test_df['dayofyear'] = test_df.index.dayofyear
test_df['year'] = test_df.index.year
test_df['month'] = test_df.index.month
test_df['amount_time'] = test_df['month'] * 100 + test_df['day']
test_df['special_days'] = test_df['amount_time'].isin([101, 1228, 1229, 1230, 1231]).astype(int)
test_df['month'] = np.cos(0.5236 * test_df['month'])
def xgb_model(train_data, test_data):
X = train_data.copy()
target = X.pop('num_sold')
X_train, X_valid, y_train, y_valid = train_test_split(X, target)
xgb_train = xgb.DMatrix(X_train, label=y_train)
xgb_eval = xgb.DMatrix(X_valid, label=y_valid)
def objective(trial, xgb_train, xgb_eval):
params = {'tree_method': trial.suggest_categorical('tree_method', ['gpu_hist']), 'objective': trial.suggest_categorical('objective', ['reg:squarederror']), 'eta': trial.suggest_float('eta', 0.001, 0.3, log=True), 'gamma': trial.suggest_float('gamma', 0.001, 1000, log=True), 'max_depth': trial.suggest_int('max_depth', 3, 12), 'grow_policy': trial.suggest_categorical('grow_policy', ['depthwise', 'lossguide']), 'min_child_weight': trial.suggest_float('min_child_weight', 0.001, 1000, log=True), 'lambda': trial.suggest_float('lambda', 0.001, 100, log=True), 'alpha': trial.suggest_float('alpha', 0.001, 100, log=True), 'colsample_bytree': trial.suggest_float('colsample_bytree', 0.9, 1.0, step=0.05), 'colsample_bylevel': trial.suggest_float('colsample_bylevel', 0.8, 1.0, step=0.05), 'colsample_bynode': trial.suggest_float('colsample_bynode', 0.7, 1.0, step=0.05), 'subsample': trial.suggest_float('subsample', 0.5, 1.0, step=0.05), 'eval_metric': trial.suggest_categorical('eval_metric', ['rmse'])}
num_round = 1000
evallist = [(xgb_eval, 'eval')]
model = xgb.train(params, xgb_train, num_round, evallist, early_stopping_rounds=10, verbose_eval=1000)
return model.best_score
study = optuna.create_study(direction='minimize', study_name='Xgboost')
func = lambda trial: objective(trial, xgb_train, xgb_eval)
study.optimize(func, n_trials=20)
best_params = study.best_params
evallist = [(xgb_eval, 'eval')]
best_model = xgb.train(best_params, xgb_train, 2000, evallist, early_stopping_rounds=100, verbose_eval=2000)
xgb_test = xgb.DMatrix(test_data)
test_preds = best_model.predict(xgb_test)
y_preds = best_model.predict(xgb.DMatrix(X, label=target))
return (test_preds, y_preds)
test_preds, y_preds = xgb_model(pd.get_dummies(train_df_xgb.drop('num_sold_predicted', axis=1)), pd.get_dummies(test_df.drop('num_sold', axis=1)))
xgb_y_pred = pd.Series(y_preds, index=train_df.index)
xgb_y_fore = pd.Series(test_preds, index=test_df.index)
_, ax = plt.subplots(figsize=(14, 6))
# ax = y.plot(color='0.25', style='.', title="Num Sold - Seasonal Forecast")
xgb_y_pred.groupby('date').mean().plot(ax=ax, label="Seasonal Learned by model")
xgb_y_fore.groupby('date').mean().plot(ax=ax, label="Seasonal Forecast by model", color='C3')
train_df_xgb.groupby('date').num_sold.mean().plot(ax = ax, label='Actual Seasonal')
_ = ax.legend()
sub_df['num_sold'] = np.array(test_df['num_sold']) + np.array(xgb_y_fore)
sub_df.to_csv('submission.csv', index=False)
sub_df | code |
105197876/cell_5 | [
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_35.png",
"application_vnd.jupyter.stderr_output_9.png",
"text_plain_output_30.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
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"text_plain_output_20.png",
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"application_vnd.jupyter.stderr_output_25.png",
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"text_plain_output_32.png",
"text_plain_output_10.png",
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"application_vnd.jupyter.stderr_output_3.png",
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"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_38.png",
"text_plain_output_16.png",
"application_vnd.jupyter.stderr_output_15.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_output_17.png",
"text_plain_output_26.png",
"text_plain_output_34.png",
"application_vnd.jupyter.stderr_output_41.png",
"text_plain_output_42.png",
"text_plain_output_28.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_29.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png",
"text_plain_output_12.png",
"application_vnd.jupyter.stderr_output_39.png",
"application_vnd.jupyter.stderr_output_21.png",
"application_vnd.jupyter.stderr_output_37.png"
] | _, ax = plt.subplots(12, 4, figsize=(14, 50))
test_df['num_sold'] = 0
train_df['num_sold_predicted'] = 0
for country, i in zip(train_df['country'].unique(), range(6)):
for store, k in zip(train_df['store'].unique(), range(2)):
for product, j in zip(train_df['product'].unique(), range(4)):
temp_df = None
temp_roll = None
temp_df = train_df.loc[(train_df['country'] == country) & (train_df['store'] == store) & (train_df['product'] == product), ['num_sold']]
temp_roll = temp_df.rolling(window=365, center=True, min_periods=183).mean()
temp_df.plot(style='.', color='0.5', ax=ax[i * 2 + k, j], title=f'{country} {store} {product}')
temp_roll.plot(ax=ax[i * 2 + k, j], linewidth=3, label='rolling')
fourier = CalendarFourier(freq='A', order=10)
dp = DeterministicProcess(index=temp_df.index, constant=True, order=1, seasonal=True, additional_terms=[fourier], drop=True)
X = dp.in_sample()
y = temp_df['num_sold']
model = LinearRegression(fit_intercept=False)
model.fit(X, y)
y_pred = pd.Series(model.predict(X), index=X.index)
y_pred.plot(ax=ax[i * 2 + k, j], linewidth=0.5, label='Trend fitted')
X_fore = dp.out_of_sample(steps=365)
y_fore = pd.Series(model.predict(X_fore), index=X_fore.index)
y_fore.plot(ax=ax[i * 2 + k, j], linewidth=0.5, label='Trend Forecast', color='C3')
test_df.loc[(test_df['country'] == country) & (test_df['store'] == store) & (test_df['product'] == product), ['num_sold']] = y_fore
train_df.loc[(train_df['country'] == country) & (train_df['store'] == store) & (train_df['product'] == product), ['num_sold_predicted']] = y_pred | code |
105193549/cell_42 | [
"text_html_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0])
type(ast.literal_eval(a[0]))
df_titles['genres2'] = df_titles['genres'].apply(ast.literal_eval)
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], genres2_length), 'genres3': np.concatenate(df_titles['genres2'].values)})
df_titles_normalised
np.concatenate(df_titles['genres2'].values)
np.repeat(df_titles['title'], genres2_length)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5).loc[:5, ['title', 'genres']]
df_titles
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], list(map(len, df_titles['genres'].apply(ast.literal_eval).values))), 'genres': np.concatenate(df_titles['genres'].apply(ast.literal_eval).values)})
df_titles_normalised
df_titles_normalised.loc[df_titles_normalised['genres'] == 'drama', :]
df_titles_normalised.loc[df_titles_normalised['genres'] == 'war', :]
df_titles_normalised.loc[df_titles_normalised['genres'].isin(['war', 'drama']), :] | code |
105193549/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a | code |
105193549/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], genres2_length), 'genres3': np.concatenate(df_titles['genres2'].values)})
df_titles_normalised | code |
105193549/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length | code |
105193549/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], genres2_length), 'genres3': np.concatenate(df_titles['genres2'].values)})
df_titles_normalised
np.concatenate(df_titles['genres2'].values)
np.repeat(df_titles['title'], genres2_length) | code |
105193549/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['genres2'].values | code |
105193549/cell_40 | [
"text_plain_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0])
type(ast.literal_eval(a[0]))
df_titles['genres2'] = df_titles['genres'].apply(ast.literal_eval)
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], genres2_length), 'genres3': np.concatenate(df_titles['genres2'].values)})
df_titles_normalised
np.concatenate(df_titles['genres2'].values)
np.repeat(df_titles['title'], genres2_length)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5).loc[:5, ['title', 'genres']]
df_titles
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], list(map(len, df_titles['genres'].apply(ast.literal_eval).values))), 'genres': np.concatenate(df_titles['genres'].apply(ast.literal_eval).values)})
df_titles_normalised
df_titles_normalised.loc[df_titles_normalised['genres'] == 'drama', :] | code |
105193549/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], genres2_length), 'genres3': np.concatenate(df_titles['genres2'].values)})
df_titles_normalised
np.concatenate(df_titles['genres2'].values) | code |
105193549/cell_41 | [
"text_html_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0])
type(ast.literal_eval(a[0]))
df_titles['genres2'] = df_titles['genres'].apply(ast.literal_eval)
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], genres2_length), 'genres3': np.concatenate(df_titles['genres2'].values)})
df_titles_normalised
np.concatenate(df_titles['genres2'].values)
np.repeat(df_titles['title'], genres2_length)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5).loc[:5, ['title', 'genres']]
df_titles
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], list(map(len, df_titles['genres'].apply(ast.literal_eval).values))), 'genres': np.concatenate(df_titles['genres'].apply(ast.literal_eval).values)})
df_titles_normalised
df_titles_normalised.loc[df_titles_normalised['genres'] == 'drama', :]
df_titles_normalised.loc[df_titles_normalised['genres'] == 'war', :] | code |
105193549/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
type(a[0]) | code |
105193549/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['genres'].values | code |
105193549/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles | code |
105193549/cell_18 | [
"text_plain_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0])
type(ast.literal_eval(a[0]))
df_titles['genres2'] = df_titles['genres'].apply(ast.literal_eval)
df_titles | code |
105193549/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['title'] | code |
105193549/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['genres2'].values | code |
105193549/cell_15 | [
"text_plain_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0]) | code |
105193549/cell_16 | [
"text_plain_output_1.png"
] | import ast
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0])
type(ast.literal_eval(a[0])) | code |
105193549/cell_38 | [
"text_plain_output_1.png"
] | import ast
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
ast.literal_eval(a[0])
type(ast.literal_eval(a[0]))
df_titles['genres2'] = df_titles['genres'].apply(ast.literal_eval)
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], genres2_length), 'genres3': np.concatenate(df_titles['genres2'].values)})
df_titles_normalised
np.concatenate(df_titles['genres2'].values)
np.repeat(df_titles['title'], genres2_length)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5).loc[:5, ['title', 'genres']]
df_titles
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], list(map(len, df_titles['genres'].apply(ast.literal_eval).values))), 'genres': np.concatenate(df_titles['genres'].apply(ast.literal_eval).values)})
df_titles_normalised | code |
105193549/cell_3 | [
"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 |
105193549/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
genres2_length | code |
105193549/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
a[0] | code |
105193549/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
df_titles['genres2'].values | code |
105193549/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
a = df_titles['genres'].values
a
a[0] | code |
105193549/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles | code |
105193549/cell_36 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5)
df_titles
df_titles = df_titles.loc[:5, ['title', 'genres']]
df_titles
genres2_length = list(map(len, df_titles['genres2'].values))
genres2_length
df_titles_normalised = pd.DataFrame({'title': np.repeat(df_titles['title'], genres2_length), 'genres3': np.concatenate(df_titles['genres2'].values)})
df_titles_normalised
df_titles = pd.read_csv('../input/netflix-tv-shows-and-movies/titles.csv', nrows=5).loc[:5, ['title', 'genres']]
df_titles | code |
48162408/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)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
train_f.shape
train_f.columns | code |
48162408/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
test_f.shape
test_f.head() | code |
48162408/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
stargs_name = list(targ_score.columns[1:])
scored_targets = train[list(targ_score.columns[1:])].sum(axis = 1)
nscored_targets = train[list(targ_nscore.columns[1:])].sum(axis = 1)
fig,axes = plt.subplots(figsize = (32,8),ncols = 2)
sns.countplot(scored_targets,ax = axes[0])
sns.countplot(nscored_targets,ax = axes[1])
# scored_targets
for i in range(2):
axes[i].tick_params(axis = 'x',labelsize =20)
axes[i].tick_params(axis = 'y', labelsize = 20)
axes[0].set_title(f'Training set unique scored per sample',size = 22 , pad = 22)
axes[1].set_title(f'Training set unique not scored per sample',size = 22 , pad = 22)
plt.show()
targ_score.shape | code |
48162408/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import log_loss
from sklearn.model_selection import KFold
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
stargs_name = list(targ_score.columns[1:])
scored_targets = train[list(targ_score.columns[1:])].sum(axis = 1)
nscored_targets = train[list(targ_nscore.columns[1:])].sum(axis = 1)
fig,axes = plt.subplots(figsize = (32,8),ncols = 2)
sns.countplot(scored_targets,ax = axes[0])
sns.countplot(nscored_targets,ax = axes[1])
# scored_targets
for i in range(2):
axes[i].tick_params(axis = 'x',labelsize =20)
axes[i].tick_params(axis = 'y', labelsize = 20)
axes[0].set_title(f'Training set unique scored per sample',size = 22 , pad = 22)
axes[1].set_title(f'Training set unique not scored per sample',size = 22 , pad = 22)
plt.show()
test_f.shape
train_f.shape
train_f.columns
def preprocess(df):
df = df.copy()
df.loc[:, 'cp_type'] = df.loc[:, 'cp_type'].map({'trt_cp': 0, 'ctl_vehicle': 1})
df.loc[:, 'cp_dose'] = df.loc[:, 'cp_dose'].map({'D1': 0, 'D2': 1})
del df['sig_id']
return df
train = preprocess(train_f)
test = preprocess(test_f)
del targ_score['sig_id']
targ_score.shape
def metric(y_true, y_pred):
metrics = []
metrics.append(log_loss(y_true, y_pred.astype(float), labels=[0, 1]))
return np.mean(metrics)
cols = targ_score.columns
submission = sample.copy()
submission.loc[:, cols] = 0
submission
N_splits = 5
off_loss = 0
for c, columns in enumerate(cols, 1):
y = targ_score[columns]
total_loss = 0
for fn, (trn_idx, val_idx) in enumerate(KFold(n_splits=N_splits, shuffle=True).split(train)):
X_train, X_val = (train.iloc[trn_idx], train.iloc[val_idx])
y_train, y_val = (y.iloc[trn_idx], y.iloc[val_idx])
model = XGBRegressor(tree_method='gpu_hist', min_child_weight=1, learning_rate=0.015, colsample_bytree=0.65, gamma=3.69, max_delta_step=2.07, max_depth=10, n_estimators=207, subsample=1)
model.fit(X_train, y_train)
pred = model.predict(X_val)
loss = metric(y_val, pred)
total_loss += loss
predictions = model.predict(test)
submission[columns] += predictions / N_splits
off_loss += total_loss / N_splits
submission | code |
48162408/cell_11 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
test_f.shape | code |
48162408/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 |
48162408/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
stargs_name = list(targ_score.columns[1:])
scored_targets = train[list(targ_score.columns[1:])].sum(axis=1)
nscored_targets = train[list(targ_nscore.columns[1:])].sum(axis=1)
fig, axes = plt.subplots(figsize=(32, 8), ncols=2)
sns.countplot(scored_targets, ax=axes[0])
sns.countplot(nscored_targets, ax=axes[1])
for i in range(2):
axes[i].tick_params(axis='x', labelsize=20)
axes[i].tick_params(axis='y', labelsize=20)
axes[0].set_title(f'Training set unique scored per sample', size=22, pad=22)
axes[1].set_title(f'Training set unique not scored per sample', size=22, pad=22)
plt.show() | code |
48162408/cell_28 | [
"text_html_output_1.png"
] | from sklearn.metrics import log_loss
from sklearn.model_selection import KFold
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
stargs_name = list(targ_score.columns[1:])
scored_targets = train[list(targ_score.columns[1:])].sum(axis = 1)
nscored_targets = train[list(targ_nscore.columns[1:])].sum(axis = 1)
fig,axes = plt.subplots(figsize = (32,8),ncols = 2)
sns.countplot(scored_targets,ax = axes[0])
sns.countplot(nscored_targets,ax = axes[1])
# scored_targets
for i in range(2):
axes[i].tick_params(axis = 'x',labelsize =20)
axes[i].tick_params(axis = 'y', labelsize = 20)
axes[0].set_title(f'Training set unique scored per sample',size = 22 , pad = 22)
axes[1].set_title(f'Training set unique not scored per sample',size = 22 , pad = 22)
plt.show()
test_f.shape
train_f.shape
train_f.columns
def preprocess(df):
df = df.copy()
df.loc[:, 'cp_type'] = df.loc[:, 'cp_type'].map({'trt_cp': 0, 'ctl_vehicle': 1})
df.loc[:, 'cp_dose'] = df.loc[:, 'cp_dose'].map({'D1': 0, 'D2': 1})
del df['sig_id']
return df
train = preprocess(train_f)
test = preprocess(test_f)
del targ_score['sig_id']
targ_score.shape
def metric(y_true, y_pred):
metrics = []
metrics.append(log_loss(y_true, y_pred.astype(float), labels=[0, 1]))
return np.mean(metrics)
cols = targ_score.columns
submission = sample.copy()
submission.loc[:, cols] = 0
submission
N_splits = 5
off_loss = 0
for c, columns in enumerate(cols, 1):
y = targ_score[columns]
total_loss = 0
for fn, (trn_idx, val_idx) in enumerate(KFold(n_splits=N_splits, shuffle=True).split(train)):
X_train, X_val = (train.iloc[trn_idx], train.iloc[val_idx])
y_train, y_val = (y.iloc[trn_idx], y.iloc[val_idx])
model = XGBRegressor(tree_method='gpu_hist', min_child_weight=1, learning_rate=0.015, colsample_bytree=0.65, gamma=3.69, max_delta_step=2.07, max_depth=10, n_estimators=207, subsample=1)
model.fit(X_train, y_train)
pred = model.predict(X_val)
loss = metric(y_val, pred)
total_loss += loss
predictions = model.predict(test)
submission[columns] += predictions / N_splits
off_loss += total_loss / N_splits
off_loss / 100 | code |
48162408/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
stargs_name = list(targ_score.columns[1:])
scored_targets = train[list(targ_score.columns[1:])].sum(axis = 1)
nscored_targets = train[list(targ_nscore.columns[1:])].sum(axis = 1)
fig,axes = plt.subplots(figsize = (32,8),ncols = 2)
sns.countplot(scored_targets,ax = axes[0])
sns.countplot(nscored_targets,ax = axes[1])
# scored_targets
for i in range(2):
axes[i].tick_params(axis = 'x',labelsize =20)
axes[i].tick_params(axis = 'y', labelsize = 20)
axes[0].set_title(f'Training set unique scored per sample',size = 22 , pad = 22)
axes[1].set_title(f'Training set unique not scored per sample',size = 22 , pad = 22)
plt.show()
fig, axes = plt.subplots(figsize=(24, 24), nrows=3, ncols=2)
sns.countplot(train_f['cp_type'], ax=axes[0][0])
sns.countplot(test_f['cp_type'], ax=axes[0][1])
sns.countplot(train_f['cp_time'], ax=axes[1][0])
sns.countplot(test_f['cp_time'], ax=axes[1][1])
sns.countplot(train_f['cp_dose'], ax=axes[2][0])
sns.countplot(test_f['cp_dose'], ax=axes[2][1])
for i, f in enumerate(['cp_type', 'cp_time', 'cp_dose']):
for j, d in enumerate(['training', 'test']):
axes[i][j].set_title(f'{d} Set {f} Distribution', size=20, pad=15) | code |
48162408/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)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
train.head() | code |
48162408/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)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
drug.head() | code |
48162408/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
stargs_name = list(targ_score.columns[1:])
scored_targets = train[list(targ_score.columns[1:])].sum(axis = 1)
nscored_targets = train[list(targ_nscore.columns[1:])].sum(axis = 1)
fig,axes = plt.subplots(figsize = (32,8),ncols = 2)
sns.countplot(scored_targets,ax = axes[0])
sns.countplot(nscored_targets,ax = axes[1])
# scored_targets
for i in range(2):
axes[i].tick_params(axis = 'x',labelsize =20)
axes[i].tick_params(axis = 'y', labelsize = 20)
axes[0].set_title(f'Training set unique scored per sample',size = 22 , pad = 22)
axes[1].set_title(f'Training set unique not scored per sample',size = 22 , pad = 22)
plt.show()
len(targ_score) | code |
48162408/cell_10 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
len(train_f) - len(test_f) | code |
48162408/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import log_loss
from sklearn.model_selection import KFold
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
stargs_name = list(targ_score.columns[1:])
scored_targets = train[list(targ_score.columns[1:])].sum(axis = 1)
nscored_targets = train[list(targ_nscore.columns[1:])].sum(axis = 1)
fig,axes = plt.subplots(figsize = (32,8),ncols = 2)
sns.countplot(scored_targets,ax = axes[0])
sns.countplot(nscored_targets,ax = axes[1])
# scored_targets
for i in range(2):
axes[i].tick_params(axis = 'x',labelsize =20)
axes[i].tick_params(axis = 'y', labelsize = 20)
axes[0].set_title(f'Training set unique scored per sample',size = 22 , pad = 22)
axes[1].set_title(f'Training set unique not scored per sample',size = 22 , pad = 22)
plt.show()
test_f.shape
train_f.shape
train_f.columns
def preprocess(df):
df = df.copy()
df.loc[:, 'cp_type'] = df.loc[:, 'cp_type'].map({'trt_cp': 0, 'ctl_vehicle': 1})
df.loc[:, 'cp_dose'] = df.loc[:, 'cp_dose'].map({'D1': 0, 'D2': 1})
del df['sig_id']
return df
train = preprocess(train_f)
test = preprocess(test_f)
del targ_score['sig_id']
targ_score.shape
def metric(y_true, y_pred):
metrics = []
metrics.append(log_loss(y_true, y_pred.astype(float), labels=[0, 1]))
return np.mean(metrics)
cols = targ_score.columns
submission = sample.copy()
submission.loc[:, cols] = 0
submission
N_splits = 5
off_loss = 0
for c, columns in enumerate(cols, 1):
y = targ_score[columns]
total_loss = 0
for fn, (trn_idx, val_idx) in enumerate(KFold(n_splits=N_splits, shuffle=True).split(train)):
print('Fold :', fn + 1)
X_train, X_val = (train.iloc[trn_idx], train.iloc[val_idx])
y_train, y_val = (y.iloc[trn_idx], y.iloc[val_idx])
model = XGBRegressor(tree_method='gpu_hist', min_child_weight=1, learning_rate=0.015, colsample_bytree=0.65, gamma=3.69, max_delta_step=2.07, max_depth=10, n_estimators=207, subsample=1)
model.fit(X_train, y_train)
pred = model.predict(X_val)
loss = metric(y_val, pred)
total_loss += loss
predictions = model.predict(test)
submission[columns] += predictions / N_splits
off_loss += total_loss / N_splits
print('Model ' + str(c) + ':Loss = ' + str(total_loss / N_splits)) | code |
48162408/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)
sample = pd.read_csv('../input/lish-moa/sample_submission.csv')
test_f = pd.read_csv('../input/lish-moa/test_features.csv')
train_f = pd.read_csv('../input/lish-moa/train_features.csv')
drug = pd.read_csv('../input/lish-moa/train_drug.csv')
targ_nscore = pd.read_csv('../input/lish-moa/train_targets_nonscored.csv')
targ_score = pd.read_csv('../input/lish-moa/train_targets_scored.csv')
train = train_f.merge(targ_score, on='sig_id', how='left')
train = train.merge(targ_nscore, on='sig_id', how='left')
train_f.shape | code |
1004150/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
poke[poke['Dual'] == 0] | code |
1004150/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004150/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
poke['Type 2'].fillna('No Type') | code |
1004150/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
poke.head(10) | code |
1004150/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
print(poke.sample(20))
print('===============================================')
print(poke.dtypes) | code |
1004150/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
poke = pd.read_csv('../input/Pokemon.csv')
print('%0.2f percent of the Pokemon dont have a secondary type' % (poke['Type 2'].isnull().sum() / len(poke) * 100))
print('Roughly %0.2f percent are legendary types.' % (len(poke[poke['Legendary'] == True]) * 100 / len(poke))) | code |
129005548/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)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.info() | code |
129005548/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns | code |
129005548/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 |
129005548/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset_index()
gender.rename(columns={'age': 'amount'}, inplace=True)
plt.title('Diabetes patients per gender', size=30)
sns.barplot(data=gender, x='gender', y='amount') | code |
129005548/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset_index()
gender.rename(columns={'age': 'amount'}, inplace=True)
diabetes = df[df['diabetes'] == 1]
plt.figure(figsize=(15, 10))
sns.stripplot(diabetes, x='smoking_history', y='bmi', hue='gender') | code |
129005548/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset_index()
gender.rename(columns={'age': 'amount'}, inplace=True)
diabetes = df[df['diabetes'] == 1]
plt.figure(figsize=(15, 10))
sns.stripplot(diabetes, y='age', x='smoking_history', hue='heart_disease') | code |
129005548/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.head() | code |
129005548/cell_14 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset_index()
gender.rename(columns={'age': 'amount'}, inplace=True)
diabetes = df[df['diabetes'] == 1]
sns.stripplot(diabetes, y='age', x='hypertension', hue='gender') | code |
129005548/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset_index()
gender.rename(columns={'age': 'amount'}, inplace=True)
diabetes = df[df['diabetes'] == 1]
sns.scatterplot(diabetes, x='HbA1c_level', y='blood_glucose_level', hue='gender') | code |
129005548/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender = pd.pivot_table(df[df['diabetes'] == 1], index='gender', values='age', aggfunc=len).reset_index()
gender.rename(columns={'age': 'amount'}, inplace=True)
diabetes = df[df['diabetes'] == 1]
sns.stripplot(diabetes, y='age', x='heart_disease', hue='gender') | code |
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