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18153807/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
object_list = train.select_dtypes(include=['object']).columns
display(train[object_list].sample(10).T)
for f in object_list:
print('Unique in column ', f, ' is -> ', len(train[f].unique()))
float_list = train.select_dtypes(include=['float64']).columns
display(train[float_list].sample(10).T)
int_list = train.select_dtypes(include=['int64']).columns
one_columns = []
for f in int_list:
if len(train[f].unique()) == 1:
one_columns.append(f)
train.drop(columns=one_columns, inplace=True)
test.drop(columns=one_columns, inplace=True) | code |
18153807/cell_6 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
object_list = train.select_dtypes(include=['object']).columns
float_list = train.select_dtypes(include=['float64']).columns
int_list = train.select_dtypes(include=['int64']).columns
one_columns = []
for f in int_list:
if len(train[f].unique()) == 1:
one_columns.append(f)
train.drop(columns=one_columns, inplace=True)
test.drop(columns=one_columns, inplace=True)
for f in object_list:
le = LabelEncoder()
le.fit(list(train[f].values) + list(test[f].values))
train[f] = le.transform(list(train[f].values))
test[f] = le.transform(list(test[f].values))
Y = train['y']
train.drop(columns=['y'], inplace=True, axis=1)
combine = pd.concat([train, test])
for f in object_list:
temp = pd.get_dummies(combine[f])
combine = pd.concat([combine, temp], axis=1)
train = combine[:train.shape[0]]
test = combine[train.shape[0]:]
print(train.shape)
print(test.shape)
print(Y.shape)
train_columns = train.columns | code |
18153807/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import warnings
warnings.filterwarnings('ignore') | code |
18153807/cell_8 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
object_list = train.select_dtypes(include=['object']).columns
float_list = train.select_dtypes(include=['float64']).columns
int_list = train.select_dtypes(include=['int64']).columns
one_columns = []
for f in int_list:
if len(train[f].unique()) == 1:
one_columns.append(f)
train.drop(columns=one_columns, inplace=True)
test.drop(columns=one_columns, inplace=True)
for f in object_list:
le = LabelEncoder()
le.fit(list(train[f].values) + list(test[f].values))
train[f] = le.transform(list(train[f].values))
test[f] = le.transform(list(test[f].values))
Y = train['y']
train.drop(columns=['y'], inplace=True, axis=1)
combine = pd.concat([train, test])
for f in object_list:
temp = pd.get_dummies(combine[f])
combine = pd.concat([combine, temp], axis=1)
train = combine[:train.shape[0]]
test = combine[train.shape[0]:]
train_columns = train.columns
def df_column_uniquify(df):
df_columns = df.columns
new_columns = []
for item in df_columns:
counter = 0
newitem = item
while newitem in new_columns:
counter += 1
newitem = '{}_{}'.format(item, counter)
new_columns.append(newitem)
df.columns = new_columns
return df
train = df_column_uniquify(train)
test = df_column_uniquify(test)
train['y'] = Y
original_col = list(test.drop(columns=object_list).columns)
display(train.head())
display(test.head()) | code |
130011087/cell_13 | [
"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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
sns.scatterplot(data=coffee_code_df, x='CodingHours', y='CoffeeCupsPerDay')
plt.xlabel('Coding Hours')
plt.ylabel('Coffee Cups per Day')
plt.title('Coding Hours vs Coffee Cups per Day')
plt.show() | code |
130011087/cell_11 | [
"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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
sns.countplot(data=coffee_code_df, x='Gender')
plt.xlabel('Gender')
plt.ylabel('Count')
plt.title('Gender Distribution')
plt.show() | code |
130011087/cell_19 | [
"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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
plt.xticks(rotation=45)
sns.boxplot(data=coffee_code_df, x='AgeRange', y='CodingHours')
plt.xlabel('Age Range')
plt.ylabel('Coding Hours')
plt.title('Distribution of Coding Hours by Age Range')
plt.show() | code |
130011087/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130011087/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df.describe()
coffee_code_df.info() | code |
130011087/cell_15 | [
"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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
sns.countplot(data=coffee_code_df, x='CoffeeTime')
plt.xlabel('Coffee Time')
plt.ylabel('Frequency')
plt.title('Frequency of Coffee Time')
plt.xticks(rotation=45)
plt.show() | code |
130011087/cell_17 | [
"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
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df = coffee_code_df.dropna(how='any')
plt.xticks(rotation=45)
sns.barplot(data=coffee_code_df, x='Gender', y='CoffeeCupsPerDay')
plt.xlabel('Gender')
plt.ylabel('Average Coffee Cups per Day')
plt.title('Average Coffee Cups per Day by Gender')
plt.show() | code |
130011087/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
coffee_code_df = pd.read_csv('/kaggle/input/coffee-and-code-dataset/CoffeeAndCodeLT2018 - CoffeeAndCodeLT2018.csv')
coffee_code_df.head(5) | code |
18142262/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import re
import nltk
import spacy
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
df['text_lower'] = df['text'].str.lower()
df.head() | code |
18142262/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import re
import nltk
import spacy
full_df = pd.read_csv('../input/twcs/twcs.csv', nrows=5000)
df = full_df[['text']]
full_df.head() | code |
33120214/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 |
33120214/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from datetime import timedelta
from matplotlib.dates import WeekdayLocator, DateFormatter
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
covid19 = pd.read_csv('/kaggle/input/hospital-resources-during-covid19-pandemic/Hospitalization_all_locs.csv', parse_dates=['date'], usecols=['location_name', 'date', 'allbed_mean', 'ICUbed_mean', 'InvVen_mean'])
covid19.rename(columns={'location_name': 'state'}, inplace=True)
states_list = ['New York', 'Louisiana', 'Washington', 'California', 'Alabama']
covid19 = covid19[covid19['state'].isin(states_list)].copy()
covid19['Resources'] = covid19.loc[:, ['allbed_mean', 'ICUbed_mean', 'InvVen_mean']].sum(axis=1).div(1000)
fig, ax = plt.subplots(figsize=(20, 10))
for st in states_list:
ax.plot(covid19[covid19.state == st].date, covid19[covid19.state == st].Resources, label=st)
ax.xaxis.set_major_locator(WeekdayLocator())
ax.xaxis.set_major_formatter(DateFormatter('%b %d'))
min_date = covid19.date[covid19.Resources != 0].min().date() - timedelta(days=7)
max_date = covid19.date[covid19.Resources != 0].max().date() + timedelta(days=7)
ax.set_xlim(min_date, max_date)
fig.autofmt_xdate()
ax.legend()
font_size = 14
plt.title('The hospital resources needed for COVID-19 patients across 5 different US States', fontsize=font_size + 2)
plt.ylabel('Total Resource Count (k)', fontsize=font_size)
plt.xlabel('Date', fontsize=font_size)
plt.show()
fig.savefig('Hospital_resource_use.png', bbox_inches='tight') | code |
34117774/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
def create_model(img_rows, img_cols, channels):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu', input_shape=(img_rows, img_cols, channels)))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
return model
model = create_model(img_rows, img_cols, channels)
model.fit(X_train, y_train, batch_size=32, epochs=32, validation_data=(X_val, y_val))
print('Base accuracy on regular images: ', model.evaluate(X_test, y_test, verbose=0)) | code |
34117774/cell_26 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
labels = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
def create_model(img_rows, img_cols, channels):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu', input_shape=(img_rows, img_cols, channels)))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
return model
model = create_model(img_rows, img_cols, channels)
model.fit(X_train, y_train, batch_size=32, epochs=32, validation_data=(X_val, y_val))
def adversarial_pattern(image, label):
image = tf.cast(image, tf.float32)
with tf.GradientTape() as tape:
tape.watch(image)
prediction = model(image)
loss = tf.keras.losses.MSE(label, prediction)
gradient = tape.gradient(loss, image)
signed_grad = tf.sign(gradient)
return signed_grad
image = X_train[11]
image_label = y_train[11]
perturbations = adversarial_pattern(image.reshape((1, img_rows, img_cols, channels)), image_label).numpy()
adversarial = image + perturbations * 0.1
print('The true label was: {}'.format(labels[model.predict(image.reshape((1, img_rows, img_cols, channels))).argmax()]))
print('The prediction after the attack is: {}'.format(labels[model.predict(adversarial).argmax()])) | code |
34117774/cell_11 | [
"text_plain_output_1.png"
] | import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
print('y_train set shape of {}'.format(y_train.shape))
print('y_test set shape of {}'.format(y_test.shape)) | code |
34117774/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
def create_model(img_rows, img_cols, channels):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu', input_shape=(img_rows, img_cols, channels)))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
return model
model = create_model(img_rows, img_cols, channels)
model.fit(X_train, y_train, batch_size=32, epochs=32, validation_data=(X_val, y_val)) | code |
34117774/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
labels = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
def create_model(img_rows, img_cols, channels):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu', input_shape=(img_rows, img_cols, channels)))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
return model
model = create_model(img_rows, img_cols, channels)
model.fit(X_train, y_train, batch_size=32, epochs=32, validation_data=(X_val, y_val))
def adversarial_pattern(image, label):
image = tf.cast(image, tf.float32)
with tf.GradientTape() as tape:
tape.watch(image)
prediction = model(image)
loss = tf.keras.losses.MSE(label, prediction)
gradient = tape.gradient(loss, image)
signed_grad = tf.sign(gradient)
return signed_grad
image = X_train[11]
image_label = y_train[11]
perturbations = adversarial_pattern(image.reshape((1, img_rows, img_cols, channels)), image_label).numpy()
adversarial = image + perturbations * 0.1
print('Base accuracy on adversarial images: {}'.format(model.evaluate(X_adversarial_test, y_adversarial_test, verbose=0))) | code |
34117774/cell_28 | [
"image_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import matplotlib.pyplot as plt
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
labels = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
def create_model(img_rows, img_cols, channels):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu', input_shape=(img_rows, img_cols, channels)))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
return model
model = create_model(img_rows, img_cols, channels)
model.fit(X_train, y_train, batch_size=32, epochs=32, validation_data=(X_val, y_val))
def adversarial_pattern(image, label):
image = tf.cast(image, tf.float32)
with tf.GradientTape() as tape:
tape.watch(image)
prediction = model(image)
loss = tf.keras.losses.MSE(label, prediction)
gradient = tape.gradient(loss, image)
signed_grad = tf.sign(gradient)
return signed_grad
image = X_train[11]
image_label = y_train[11]
perturbations = adversarial_pattern(image.reshape((1, img_rows, img_cols, channels)), image_label).numpy()
adversarial = image + perturbations * 0.1
plt.imshow(image.reshape((img_rows, img_cols))) | code |
34117774/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | print('X_train set shape of {}'.format(X_train.shape))
print('X_test set shape of {}'.format(X_test.shape))
print('y_train set shape of {}'.format(y_train.shape))
print('y_test set shape of {}'.format(y_test.shape)) | code |
34117774/cell_14 | [
"text_plain_output_1.png"
] | import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
print('X_train set shape of {}'.format(X_train.shape))
print('X_val set shape of {}'.format(X_val.shape))
print('y_train set shape of {}'.format(y_train.shape))
print('y_val set shape of {}'.format(y_val.shape)) | code |
34117774/cell_10 | [
"text_plain_output_1.png"
] | img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
print('X_train set shape of {}'.format(X_train.shape))
print('X_test set shape of {}'.format(X_test.shape)) | code |
34117774/cell_27 | [
"text_plain_output_1.png"
] | from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten, Activation
import matplotlib.pyplot as plt
import tensorflow as tf
configuration = tf.compat.v1.ConfigProto()
configuration.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=configuration)
img_rows, img_cols, channels = (28, 28, 1)
num_classes = 10
X_train = X_train / 255
X_test = X_test / 255
X_train = X_train.reshape((-1, img_rows, img_cols, channels))
X_test = X_test.reshape((-1, img_rows, img_cols, channels))
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
def create_model(img_rows, img_cols, channels):
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu', input_shape=(img_rows, img_cols, channels)))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(64, kernel_size=(3, 3), strides=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(32))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
return model
model = create_model(img_rows, img_cols, channels)
model.fit(X_train, y_train, batch_size=32, epochs=32, validation_data=(X_val, y_val))
def adversarial_pattern(image, label):
image = tf.cast(image, tf.float32)
with tf.GradientTape() as tape:
tape.watch(image)
prediction = model(image)
loss = tf.keras.losses.MSE(label, prediction)
gradient = tape.gradient(loss, image)
signed_grad = tf.sign(gradient)
return signed_grad
image = X_train[11]
image_label = y_train[11]
perturbations = adversarial_pattern(image.reshape((1, img_rows, img_cols, channels)), image_label).numpy()
adversarial = image + perturbations * 0.1
if channels == 1:
plt.imshow(adversarial.reshape((img_rows, img_cols)))
else:
plt.imshow(adversarial.reshape((img_rows, img_cols, channels))) | code |
121148913/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
print(f'The dataset consist of {len(raw_behaviour)} number of interactions.')
raw_behaviour.head() | code |
121148913/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from collections import Counter
from torch.utils.data import Dataset, DataLoader
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
news = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/news.tsv', sep='\t', names=['itemId', 'category', 'subcategory', 'title', 'abstract', 'url', 'title_entities', 'abstract_entities'])
def process_impression(impression_list):
list_of_strings = impression_list.split()
click = [x.split('-')[0] for x in list_of_strings if x.split('-')[1] == '1']
non_click = [x.split('-')[0] for x in list_of_strings if x.split('-')[1] == '0']
return (click, non_click)
raw_behaviour['click'], raw_behaviour['noclicks'] = zip(*raw_behaviour['impressions'].map(process_impression))
raw_behaviour['epochhrs'] = pd.to_datetime(raw_behaviour['timestamp']).values.astype(np.int64) / 1000000.0 / 1000 / 3600
raw_behaviour['epochhrs'] = raw_behaviour['epochhrs'].round()
raw_behaviour = raw_behaviour.explode('click').reset_index(drop=True)
click_history = raw_behaviour[['userId', 'click_history']].drop_duplicates().dropna()
click_history['click_history'] = click_history.click_history.map(lambda x: x.split())
click_history = click_history.explode('click_history').rename(columns={'click_history': 'click'})
click_history['epochhrs'] = raw_behaviour.epochhrs.min()
click_history['noclicks'] = pd.Series([[] for _ in range(len(click_history.index))])
raw_behaviour = pd.concat([raw_behaviour, click_history], axis=0).reset_index(drop=True)
min_click_cutoff = 100
raw_behaviour = raw_behaviour[raw_behaviour.groupby('click')['userId'].transform('size') >= min_click_cutoff].reset_index(drop=True)
click_set = set(raw_behaviour['click'].unique())
raw_behaviour['noclicks'] = raw_behaviour['noclicks'].apply(lambda impressions: [impression for impression in impressions if impression in click_set])
behaviour = raw_behaviour[['epochhrs', 'userId', 'click', 'noclicks']].copy()
test_time_th = behaviour['epochhrs'].quantile(0.9)
train = behaviour[behaviour['epochhrs'] < test_time_th].copy()
ind2item = {idx + 1: itemid for idx, itemid in enumerate(train.click.unique())}
item2ind = {itemid: idx for idx, itemid in ind2item.items()}
train['noclicks'] = train['noclicks'].map(lambda list_of_items: [item2ind.get(l, 0) for l in list_of_items])
train['click'] = train['click'].map(lambda item: item2ind.get(item, 0))
ind2user = {idx + 1: userid for idx, userid in enumerate(train['userId'].unique())}
user2ind = {userid: idx for idx, userid in ind2user.items()}
train['userIdx'] = train['userId'].map(lambda x: user2ind.get(x, 0))
valid = behaviour[behaviour['epochhrs'] >= test_time_th].copy()
valid['click'] = valid['click'].map(lambda item: item2ind.get(item, 0))
valid['noclicks'] = valid['noclicks'].map(lambda list_of_items: [item2ind.get(l, 0) for l in list_of_items])
valid['userIdx'] = valid['userId'].map(lambda x: user2ind.get(x, 0))
class MindDataset(Dataset):
def __init__(self, df):
self.data = {'userIdx': torch.tensor(df.userIdx.values.astype(np.int64)), 'click': torch.tensor(df.click.values.astype(np.int64))}
def __len__(self):
return len(self.data['userIdx'])
def __getitem__(self, idx):
return {key: val[idx] for key, val in self.data.items()}
bs = 1024
ds_train = MindDataset(train)
train_loader = DataLoader(ds_train, batch_size=bs, shuffle=True)
ds_valid = MindDataset(valid)
valid_loader = DataLoader(ds_valid, batch_size=bs, shuffle=False)
batch = next(iter(train_loader))
class NewsMF(pl.LightningModule):
def __init__(self, num_users, num_items, dim=10):
super().__init__()
self.dim = dim
self.useremb = nn.Embedding(num_embeddings=num_users, embedding_dim=dim)
self.itememb = nn.Embedding(num_embeddings=num_items, embedding_dim=dim)
self.num_users = num_users
self.num_items = num_items
def step(self, batch, batch_idx, phase='train'):
batch_size = batch['userIdx'].size(0)
uservec = self.useremb(batch['userIdx'])
itemvec_click = self.itememb(batch['click'])
neg_sample = torch.randint_like(batch['click'], 1, self.num_items)
itemvec_noclick = self.itememb(neg_sample)
score_click = torch.sigmoid((uservec * itemvec_click).sum(-1).unsqueeze(-1))
score_noclick = torch.sigmoid((uservec * itemvec_noclick).sum(-1).unsqueeze(-1))
scores_all = torch.concat((score_click, score_noclick), dim=1)
target_all = torch.concat((torch.ones_like(score_click), torch.zeros_like(score_noclick)), dim=1)
loss = F.binary_cross_entropy(scores_all, target_all)
return loss
def training_step(self, batch, batch_idx):
return self.step(batch, batch_idx, 'train')
def validation_step(self, batch, batch_idx):
return self.step(batch, batch_idx, 'val')
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
return optimizer
mf_model = NewsMF(num_users=len(ind2user) + 1, num_items=len(ind2item) + 1, dim=50)
trainer = pl.Trainer(max_epochs=50, accelerator='gpu')
trainer.fit(model=mf_model, train_dataloaders=train_loader)
itememb = mf_model.itememb.weight.detach()
news['ind'] = news['itemId'].map(item2ind)
news = news.sort_values('ind').reset_index(drop=True)
news['n_click_training'] = news['ind'].map(dict(Counter(train.click)))
news.sort_values('n_click_training', ascending=False).head(15) | code |
121148913/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
news = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/news.tsv', sep='\t', names=['itemId', 'category', 'subcategory', 'title', 'abstract', 'url', 'title_entities', 'abstract_entities'])
print(f'The article data consist in total of {len(news)} number of articles.')
news.head() | code |
121148913/cell_15 | [
"text_html_output_1.png",
"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)
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
news = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/news.tsv', sep='\t', names=['itemId', 'category', 'subcategory', 'title', 'abstract', 'url', 'title_entities', 'abstract_entities'])
def process_impression(impression_list):
list_of_strings = impression_list.split()
click = [x.split('-')[0] for x in list_of_strings if x.split('-')[1] == '1']
non_click = [x.split('-')[0] for x in list_of_strings if x.split('-')[1] == '0']
return (click, non_click)
raw_behaviour['click'], raw_behaviour['noclicks'] = zip(*raw_behaviour['impressions'].map(process_impression))
raw_behaviour['epochhrs'] = pd.to_datetime(raw_behaviour['timestamp']).values.astype(np.int64) / 1000000.0 / 1000 / 3600
raw_behaviour['epochhrs'] = raw_behaviour['epochhrs'].round()
raw_behaviour = raw_behaviour.explode('click').reset_index(drop=True)
click_history = raw_behaviour[['userId', 'click_history']].drop_duplicates().dropna()
click_history['click_history'] = click_history.click_history.map(lambda x: x.split())
click_history = click_history.explode('click_history').rename(columns={'click_history': 'click'})
click_history['epochhrs'] = raw_behaviour.epochhrs.min()
click_history['noclicks'] = pd.Series([[] for _ in range(len(click_history.index))])
raw_behaviour = pd.concat([raw_behaviour, click_history], axis=0).reset_index(drop=True)
min_click_cutoff = 100
raw_behaviour = raw_behaviour[raw_behaviour.groupby('click')['userId'].transform('size') >= min_click_cutoff].reset_index(drop=True)
click_set = set(raw_behaviour['click'].unique())
raw_behaviour['noclicks'] = raw_behaviour['noclicks'].apply(lambda impressions: [impression for impression in impressions if impression in click_set])
behaviour = raw_behaviour[['epochhrs', 'userId', 'click', 'noclicks']].copy()
print('Number of interactions in the behaviour dataset:', behaviour.shape[0])
print('Number of users in the behaviour dataset:', behaviour.userId.nunique())
print('Number of articles in the behaviour dataset:', behaviour.click.nunique())
behaviour.head() | code |
121148913/cell_22 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
news = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/news.tsv', sep='\t', names=['itemId', 'category', 'subcategory', 'title', 'abstract', 'url', 'title_entities', 'abstract_entities'])
def process_impression(impression_list):
list_of_strings = impression_list.split()
click = [x.split('-')[0] for x in list_of_strings if x.split('-')[1] == '1']
non_click = [x.split('-')[0] for x in list_of_strings if x.split('-')[1] == '0']
return (click, non_click)
raw_behaviour['click'], raw_behaviour['noclicks'] = zip(*raw_behaviour['impressions'].map(process_impression))
raw_behaviour['epochhrs'] = pd.to_datetime(raw_behaviour['timestamp']).values.astype(np.int64) / 1000000.0 / 1000 / 3600
raw_behaviour['epochhrs'] = raw_behaviour['epochhrs'].round()
raw_behaviour = raw_behaviour.explode('click').reset_index(drop=True)
click_history = raw_behaviour[['userId', 'click_history']].drop_duplicates().dropna()
click_history['click_history'] = click_history.click_history.map(lambda x: x.split())
click_history = click_history.explode('click_history').rename(columns={'click_history': 'click'})
click_history['epochhrs'] = raw_behaviour.epochhrs.min()
click_history['noclicks'] = pd.Series([[] for _ in range(len(click_history.index))])
raw_behaviour = pd.concat([raw_behaviour, click_history], axis=0).reset_index(drop=True)
min_click_cutoff = 100
raw_behaviour = raw_behaviour[raw_behaviour.groupby('click')['userId'].transform('size') >= min_click_cutoff].reset_index(drop=True)
click_set = set(raw_behaviour['click'].unique())
raw_behaviour['noclicks'] = raw_behaviour['noclicks'].apply(lambda impressions: [impression for impression in impressions if impression in click_set])
behaviour = raw_behaviour[['epochhrs', 'userId', 'click', 'noclicks']].copy()
test_time_th = behaviour['epochhrs'].quantile(0.9)
train = behaviour[behaviour['epochhrs'] < test_time_th].copy()
ind2item = {idx + 1: itemid for idx, itemid in enumerate(train.click.unique())}
item2ind = {itemid: idx for idx, itemid in ind2item.items()}
train['noclicks'] = train['noclicks'].map(lambda list_of_items: [item2ind.get(l, 0) for l in list_of_items])
train['click'] = train['click'].map(lambda item: item2ind.get(item, 0))
ind2user = {idx + 1: userid for idx, userid in enumerate(train['userId'].unique())}
user2ind = {userid: idx for idx, userid in ind2user.items()}
train['userIdx'] = train['userId'].map(lambda x: user2ind.get(x, 0))
valid = behaviour[behaviour['epochhrs'] >= test_time_th].copy()
valid['click'] = valid['click'].map(lambda item: item2ind.get(item, 0))
valid['noclicks'] = valid['noclicks'].map(lambda list_of_items: [item2ind.get(l, 0) for l in list_of_items])
valid['userIdx'] = valid['userId'].map(lambda x: user2ind.get(x, 0))
class MindDataset(Dataset):
def __init__(self, df):
self.data = {'userIdx': torch.tensor(df.userIdx.values.astype(np.int64)), 'click': torch.tensor(df.click.values.astype(np.int64))}
def __len__(self):
return len(self.data['userIdx'])
def __getitem__(self, idx):
return {key: val[idx] for key, val in self.data.items()}
bs = 1024
ds_train = MindDataset(train)
train_loader = DataLoader(ds_train, batch_size=bs, shuffle=True)
ds_valid = MindDataset(valid)
valid_loader = DataLoader(ds_valid, batch_size=bs, shuffle=False)
batch = next(iter(train_loader))
class NewsMF(pl.LightningModule):
def __init__(self, num_users, num_items, dim=10):
super().__init__()
self.dim = dim
self.useremb = nn.Embedding(num_embeddings=num_users, embedding_dim=dim)
self.itememb = nn.Embedding(num_embeddings=num_items, embedding_dim=dim)
self.num_users = num_users
self.num_items = num_items
def step(self, batch, batch_idx, phase='train'):
batch_size = batch['userIdx'].size(0)
uservec = self.useremb(batch['userIdx'])
itemvec_click = self.itememb(batch['click'])
neg_sample = torch.randint_like(batch['click'], 1, self.num_items)
itemvec_noclick = self.itememb(neg_sample)
score_click = torch.sigmoid((uservec * itemvec_click).sum(-1).unsqueeze(-1))
score_noclick = torch.sigmoid((uservec * itemvec_noclick).sum(-1).unsqueeze(-1))
scores_all = torch.concat((score_click, score_noclick), dim=1)
target_all = torch.concat((torch.ones_like(score_click), torch.zeros_like(score_noclick)), dim=1)
loss = F.binary_cross_entropy(scores_all, target_all)
return loss
def training_step(self, batch, batch_idx):
return self.step(batch, batch_idx, 'train')
def validation_step(self, batch, batch_idx):
return self.step(batch, batch_idx, 'val')
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
return optimizer
mf_model = NewsMF(num_users=len(ind2user) + 1, num_items=len(ind2item) + 1, dim=50)
trainer = pl.Trainer(max_epochs=50, accelerator='gpu')
trainer.fit(model=mf_model, train_dataloaders=train_loader) | code |
121148913/cell_12 | [
"text_html_output_1.png",
"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)
raw_behaviour = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/behaviors.tsv', sep='\t', names=['impressionId', 'userId', 'timestamp', 'click_history', 'impressions'])
news = pd.read_csv('/kaggle/input/mind-news-dataset/MINDsmall_train/news.tsv', sep='\t', names=['itemId', 'category', 'subcategory', 'title', 'abstract', 'url', 'title_entities', 'abstract_entities'])
def process_impression(impression_list):
list_of_strings = impression_list.split()
click = [x.split('-')[0] for x in list_of_strings if x.split('-')[1] == '1']
non_click = [x.split('-')[0] for x in list_of_strings if x.split('-')[1] == '0']
return (click, non_click)
raw_behaviour['click'], raw_behaviour['noclicks'] = zip(*raw_behaviour['impressions'].map(process_impression))
raw_behaviour['epochhrs'] = pd.to_datetime(raw_behaviour['timestamp']).values.astype(np.int64) / 1000000.0 / 1000 / 3600
raw_behaviour['epochhrs'] = raw_behaviour['epochhrs'].round()
raw_behaviour = raw_behaviour.explode('click').reset_index(drop=True)
click_history = raw_behaviour[['userId', 'click_history']].drop_duplicates().dropna()
click_history['click_history'] = click_history.click_history.map(lambda x: x.split())
click_history = click_history.explode('click_history').rename(columns={'click_history': 'click'})
click_history['epochhrs'] = raw_behaviour.epochhrs.min()
click_history['noclicks'] = pd.Series([[] for _ in range(len(click_history.index))])
raw_behaviour = pd.concat([raw_behaviour, click_history], axis=0).reset_index(drop=True)
min_click_cutoff = 100
print(f'Number of items that have less than {min_click_cutoff} clicks make up', np.round(np.mean(raw_behaviour.groupby('click').size() < min_click_cutoff) * 100, 3), '% of the total, and these will be removed.') | code |
49124155/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
target = train['Survived']
m = pd.DataFrame(test['PassengerId'])
print('Shape of train:', train.shape)
print('Shape of test:', test.shape) | code |
49124155/cell_2 | [
"text_plain_output_1.png"
] | test | code |
49124155/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 |
49124155/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
target = train['Survived']
m = pd.DataFrame(test['PassengerId'])
import seaborn as sns
import matplotlib.pyplot as plt
dataset = pd.concat([train.drop('Survived', axis=1), test])
dataset.isnull().sum()
a = dataset.groupby('Pclass')['Age'].median()
dataset['Age'] = dataset['Age'].fillna(dataset['Pclass'].map(a))
a = dataset.groupby('Pclass')['Fare'].median()
dataset['Fare'] = dataset['Fare'].fillna(dataset['Pclass'].map(a))
dataset['Embarked'].fillna('S', inplace=True)
dataset['Passenger'] = dataset['SibSp'] + dataset['Parch'] + 1
dataset | code |
49124155/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
target = train['Survived']
m = pd.DataFrame(test['PassengerId'])
import seaborn as sns
import matplotlib.pyplot as plt
dataset = pd.concat([train.drop('Survived', axis=1), test])
dataset.isnull().sum()
a = dataset.groupby('Pclass')['Age'].median()
dataset['Age'] = dataset['Age'].fillna(dataset['Pclass'].map(a))
a = dataset.groupby('Pclass')['Fare'].median()
dataset['Fare'] = dataset['Fare'].fillna(dataset['Pclass'].map(a))
dataset['Embarked'].fillna('S', inplace=True)
dataset['Name'].iloc[3].split()[1]
a = []
for i in range(len(dataset)):
a.append(dataset['Name'].iloc[i].split()[1])
a = pd.Series(a)
dataset['Title'] = a
a = [i for i in dataset.columns if dataset[i].dtypes == 'object']
b = dataset[a]
for i in b.columns:
dataset[i] = dataset[i].factorize()[0]
train = dataset[:len(train)]
test = dataset[len(train):]
from sklearn.ensemble import GradientBoostingClassifier
sky = GradientBoostingClassifier()
sky.fit(train, target) | code |
49124155/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
target = train['Survived']
m = pd.DataFrame(test['PassengerId'])
import seaborn as sns
import matplotlib.pyplot as plt
dataset = pd.concat([train.drop('Survived', axis=1), test])
dataset.isnull().sum() | code |
72082831/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
useful_features = [c for c in df_train.columns if c not in ('id', 'loss', 'kfold')]
df_train[useful_features] | code |
72082831/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
useful_features = [c for c in df_train.columns if c not in ('id', 'loss', 'kfold')]
df_train[useful_features]
from xgboost import XGBRegressor
xtrain = df_train[df_train.kfold != 3]
xvalid = df_train[df_train.kfold == 3]
ytrain = xtrain['loss']
xtrain = xtrain[useful_features]
yvalid = xvalid['loss']
xvalid = xvalid[useful_features]
model = XGBRegressor(n_estimators=500, random_state=3)
model.fit(xtrain, ytrain, early_stopping_rounds=5, eval_set=[(xvalid, yvalid)], verbose=False)
preds_valid = model.predict(xvalid)
print(mean_squared_error(yvalid, preds_valid, squared=False)) | code |
72082831/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabulardata-kfolds-created/train_folds.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_submission = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
sample_submission.head() | code |
90133716/cell_13 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
y = df['left']
df.drop(labels='left', axis=1, inplace=True)
from sklearn.model_selection import train_test_split
xtr, xte, ytr, yte = train_test_split(df, y, test_size=0.25)
(xtr.shape, xte.shape, ytr.shape, yte.shape)
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
rf.fit(xtr, ytr)
print(precision_score(ytr, rf.predict(xtr)))
print(classification_report(yte, rf.predict(xte))) | code |
90133716/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
y = df['left']
df.drop(labels='left', axis=1, inplace=True)
from sklearn.model_selection import train_test_split
xtr, xte, ytr, yte = train_test_split(df, y, test_size=0.25)
(xtr.shape, xte.shape, ytr.shape, yte.shape)
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, precision_score
lr = LogisticRegression()
lr.fit(xtr, ytr)
precision_score(ytr, lr.predict(xtr)) | code |
90133716/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
colormap = plt.cm.get_cmap('Greens')
fig, ax = plt.subplots(figsize=(12, 3))
plot = ax.pcolor(sales_salary.T, cmap=colormap, edgecolor='black')
ax.set_xlabel('sales')
ax.set_xticks(np.arange(len(sales_salary.index.values)) + 0.5)
ax.set_xticklabels(sales_salary.index.values)
ax.set_ylabel('salary')
ax.set_yticks(np.arange(len(sales_salary.columns.values)) + 0.5)
ax.set_yticklabels(sales_salary.columns.values)
cbar = fig.colorbar(plot)
cbar.ax.set_ylabel('quantity', rotation=360)
cbar.ax.get_yaxis().labelpad = 25 | code |
90133716/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
df['left'].describe() | code |
90133716/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
df.head() | code |
90133716/cell_11 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
y = df['left']
df.drop(labels='left', axis=1, inplace=True)
from sklearn.model_selection import train_test_split
xtr, xte, ytr, yte = train_test_split(df, y, test_size=0.25)
(xtr.shape, xte.shape, ytr.shape, yte.shape)
from sklearn.linear_model import LogisticRegressionCV
lr_cv = LogisticRegressionCV()
lr_cv.fit(xtr, ytr)
precision_score(ytr, lr_cv.predict(xtr))
pd.DataFrame(lr_cv.scores_[1]).T.plot() | code |
90133716/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
y = df['left']
df.drop(labels='left', axis=1, inplace=True)
from sklearn.model_selection import train_test_split
xtr, xte, ytr, yte = train_test_split(df, y, test_size=0.25)
(xtr.shape, xte.shape, ytr.shape, yte.shape) | code |
90133716/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
y = df['left']
df.drop(labels='left', axis=1, inplace=True)
from sklearn.model_selection import train_test_split
xtr, xte, ytr, yte = train_test_split(df, y, test_size=0.25)
(xtr.shape, xte.shape, ytr.shape, yte.shape)
from sklearn.linear_model import LogisticRegressionCV
lr_cv = LogisticRegressionCV()
lr_cv.fit(xtr, ytr)
precision_score(ytr, lr_cv.predict(xtr))
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
rf.fit(xtr, ytr)
print(rf.feature_importances_)
pd.DataFrame(rf.feature_importances_).plot(kind='barh') | code |
90133716/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary | code |
90133716/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
y = df['left']
df.drop(labels='left', axis=1, inplace=True)
from sklearn.model_selection import train_test_split
xtr, xte, ytr, yte = train_test_split(df, y, test_size=0.25)
(xtr.shape, xte.shape, ytr.shape, yte.shape)
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=3)
poly.fit(df)
xtr2 = poly.transform(xtr)
lr_cv2 = LogisticRegression()
lr_cv2.fit(xtr2, ytr)
xte2 = poly.transform(xte)
print(precision_score(ytr, lr_cv2.predict(xtr2)))
print(classification_report(yte, lr_cv2.predict(xte2))) | code |
90133716/cell_10 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
y = df['left']
df.drop(labels='left', axis=1, inplace=True)
from sklearn.model_selection import train_test_split
xtr, xte, ytr, yte = train_test_split(df, y, test_size=0.25)
(xtr.shape, xte.shape, ytr.shape, yte.shape)
from sklearn.linear_model import LogisticRegressionCV
lr_cv = LogisticRegressionCV()
lr_cv.fit(xtr, ytr)
precision_score(ytr, lr_cv.predict(xtr)) | code |
90133716/cell_12 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report, precision_score
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
y = df['left']
df.drop(labels='left', axis=1, inplace=True)
from sklearn.model_selection import train_test_split
xtr, xte, ytr, yte = train_test_split(df, y, test_size=0.25)
(xtr.shape, xte.shape, ytr.shape, yte.shape)
from sklearn.linear_model import LogisticRegressionCV
lr_cv = LogisticRegressionCV()
lr_cv.fit(xtr, ytr)
precision_score(ytr, lr_cv.predict(xtr))
from sklearn.metrics import classification_report
print(classification_report(yte, lr_cv.predict(xte))) | code |
90133716/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
sales_salary = pd.crosstab(df['sales'], df['salary'], normalize=False)
sales_salary = sales_salary[['low', 'medium', 'high']]
sales_salary['temp'] = sales_salary.index.values
sales_salary.iloc[0, 3] = 'it'
sales_salary.iloc[1, 3] = 'rand_d'
sales_salary.sort_values(by='temp', inplace=True)
sales_salary.set_index('temp', inplace=True)
sales_salary.index.name = 'sales'
sales_salary
df = df.join(pd.get_dummies(df['salary']))
df = df.join(pd.get_dummies(df['sales']), rsuffix='d')
df.drop(labels=['sales', 'salary'], inplace=True, axis=1)
df.head() | code |
33100747/cell_6 | [
"image_output_1.png"
] | from dateutil.relativedelta import relativedelta
from keras.layers.core import Dense
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/hourly-energy-consumption/AEP_hourly.csv')
from sklearn.preprocessing import MinMaxScaler
from dateutil.relativedelta import relativedelta
import datetime
data = data.sort_values(by=['Datetime'])
data['Datetime'] = data['Datetime'].astype('datetime64')
data = data.loc[data['Datetime'] >= data['Datetime'][len(data['Datetime']) - 1] - relativedelta(years=3)]
data.reset_index(inplace=True)
scaler = MinMaxScaler()
consumption = scaler.fit_transform(np.reshape(data['AEP_MW'].values, (-1, 1)))[:, 0]
ratio = 0.8
split = int(np.floor(ratio * len(data)))
input_length = 20
x_train = [consumption[i - input_length:i] for i in range(input_length, split)]
x_test = [consumption[i - input_length:i] for i in range(input_length + split, len(consumption))]
y_train = consumption[input_length:split]
y_test = consumption[input_length + split:]
x_train_lstm = np.reshape(x_train, (np.shape(x_train)[0], np.shape(x_train)[1], 1))
x_test_lstm = np.reshape(x_test, (np.shape(x_test)[0], np.shape(x_test)[1], 1))
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import Dense
lstm = Sequential()
layers = [LSTM(units=128, input_shape=(input_length, 1), activation='sigmoid', return_sequences=True), LSTM(units=128, activation='sigmoid'), Dense(1)]
for layer in layers:
lstm.add(layer)
lstm.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
history_lstm = lstm.fit(x_train_lstm, y_train, validation_data=(x_test_lstm, y_test), epochs=3, batch_size=32)
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(121)
plt.plot(history_lstm.history['loss'])
plt.plot(history_lstm.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epochs')
plt.legend(['train', 'val'], loc='upper left')
plt.subplot(122)
plt.plot(history_lstm.history['accuracy'])
plt.plot(history_lstm.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epochs')
plt.legend(['train', 'val'], loc='upper left')
plt.show() | code |
33100747/cell_2 | [
"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/hourly-energy-consumption/AEP_hourly.csv')
print(data.columns)
print(data.head) | code |
33100747/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 |
33100747/cell_7 | [
"image_output_1.png"
] | from dateutil.relativedelta import relativedelta
from keras.layers.core import Dense
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
import datetime
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/hourly-energy-consumption/AEP_hourly.csv')
from sklearn.preprocessing import MinMaxScaler
from dateutil.relativedelta import relativedelta
import datetime
data = data.sort_values(by=['Datetime'])
data['Datetime'] = data['Datetime'].astype('datetime64')
data = data.loc[data['Datetime'] >= data['Datetime'][len(data['Datetime']) - 1] - relativedelta(years=3)]
data.reset_index(inplace=True)
scaler = MinMaxScaler()
consumption = scaler.fit_transform(np.reshape(data['AEP_MW'].values, (-1, 1)))[:, 0]
ratio = 0.8
split = int(np.floor(ratio * len(data)))
input_length = 20
x_train = [consumption[i - input_length:i] for i in range(input_length, split)]
x_test = [consumption[i - input_length:i] for i in range(input_length + split, len(consumption))]
y_train = consumption[input_length:split]
y_test = consumption[input_length + split:]
x_train_lstm = np.reshape(x_train, (np.shape(x_train)[0], np.shape(x_train)[1], 1))
x_test_lstm = np.reshape(x_test, (np.shape(x_test)[0], np.shape(x_test)[1], 1))
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import Dense
lstm = Sequential()
layers = [LSTM(units=128, input_shape=(input_length, 1), activation='sigmoid', return_sequences=True), LSTM(units=128, activation='sigmoid'), Dense(1)]
for layer in layers:
lstm.add(layer)
lstm.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
history_lstm = lstm.fit(x_train_lstm, y_train, validation_data=(x_test_lstm, y_test), epochs=3, batch_size=32)
import matplotlib.pyplot as plt
predictions = lstm.predict(x_test_lstm)
first_date = data['Datetime'][len(data) - len(y_test)]
predicted_dates = [first_date + datetime.timedelta(hours=i) for i in range(len(x_test))]
plt.figure()
plt.plot(data['Datetime'], scaler.inverse_transform(np.reshape(consumption, (-1, 1))), color='b', alpha=0.7)
plt.plot(predicted_dates, scaler.inverse_transform(np.reshape(predictions, (-1, 1))), color='r', alpha=0.4)
plt.xlabel('Datetime')
plt.ylabel('Energy consumption in MegaWatt')
plt.title('American energy consumption evolution over time')
plt.legend(['true data', 'prediction'])
plt.show() | code |
33100747/cell_3 | [
"text_plain_output_1.png"
] | from dateutil.relativedelta import relativedelta
from sklearn.preprocessing import MinMaxScaler
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('/kaggle/input/hourly-energy-consumption/AEP_hourly.csv')
from sklearn.preprocessing import MinMaxScaler
from dateutil.relativedelta import relativedelta
import datetime
data = data.sort_values(by=['Datetime'])
data['Datetime'] = data['Datetime'].astype('datetime64')
data = data.loc[data['Datetime'] >= data['Datetime'][len(data['Datetime']) - 1] - relativedelta(years=3)]
data.reset_index(inplace=True)
print(data)
scaler = MinMaxScaler()
consumption = scaler.fit_transform(np.reshape(data['AEP_MW'].values, (-1, 1)))[:, 0]
print(consumption) | code |
33100747/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from dateutil.relativedelta import relativedelta
from keras.layers.core import Dense
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
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('/kaggle/input/hourly-energy-consumption/AEP_hourly.csv')
from sklearn.preprocessing import MinMaxScaler
from dateutil.relativedelta import relativedelta
import datetime
data = data.sort_values(by=['Datetime'])
data['Datetime'] = data['Datetime'].astype('datetime64')
data = data.loc[data['Datetime'] >= data['Datetime'][len(data['Datetime']) - 1] - relativedelta(years=3)]
data.reset_index(inplace=True)
scaler = MinMaxScaler()
consumption = scaler.fit_transform(np.reshape(data['AEP_MW'].values, (-1, 1)))[:, 0]
ratio = 0.8
split = int(np.floor(ratio * len(data)))
input_length = 20
x_train = [consumption[i - input_length:i] for i in range(input_length, split)]
x_test = [consumption[i - input_length:i] for i in range(input_length + split, len(consumption))]
y_train = consumption[input_length:split]
y_test = consumption[input_length + split:]
x_train_lstm = np.reshape(x_train, (np.shape(x_train)[0], np.shape(x_train)[1], 1))
x_test_lstm = np.reshape(x_test, (np.shape(x_test)[0], np.shape(x_test)[1], 1))
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import Dense
lstm = Sequential()
layers = [LSTM(units=128, input_shape=(input_length, 1), activation='sigmoid', return_sequences=True), LSTM(units=128, activation='sigmoid'), Dense(1)]
for layer in layers:
lstm.add(layer)
lstm.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
history_lstm = lstm.fit(x_train_lstm, y_train, validation_data=(x_test_lstm, y_test), epochs=3, batch_size=32) | code |
2013148/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns]
for i in columns:
X = X[~X[i].isnull()]
for i in range(1, 4):
X['Pclass_' + str(i)] = X['Pclass'] == i
del X['Pclass']
for i in X.Embarked.unique():
X[i] = X['Embarked'] == i
del X['Embarked']
for i in X.Sex.unique():
X[i] = X['Sex'] == i
del X['Sex']
y = pd.DataFrame({'Survived': X['Survived'], 'Not Survived': 1 - X['Survived']})
y.shape
del X['Survived']
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
net = tflearn.input_data(shape=[None, 9])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net)
model.fit(X, y, n_epoch=200, batch_size=16, show_metric=True) | code |
2013148/cell_11 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import Imputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns]
for i in columns:
X = X[~X[i].isnull()]
for i in range(1, 4):
X['Pclass_' + str(i)] = X['Pclass'] == i
del X['Pclass']
for i in X.Embarked.unique():
X[i] = X['Embarked'] == i
del X['Embarked']
for i in X.Sex.unique():
X[i] = X['Sex'] == i
del X['Sex']
y = pd.DataFrame({'Survived': X['Survived'], 'Not Survived': 1 - X['Survived']})
y.shape
del X['Survived']
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
net = tflearn.input_data(shape=[None, 9])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net)
model.fit(X, y, n_epoch=200, batch_size=16, show_metric=True)
df = pd.read_csv('../input/test.csv')
X_test = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
X_test = X_test[columns]
for i in range(1, 4):
X_test['Pclass_' + str(i)] = X_test['Pclass'] == i
del X_test['Pclass']
for i in X_test.Embarked.unique():
X_test[i] = X_test['Embarked'] == i
del X_test['Embarked']
for i in X_test.Sex.unique():
X_test[i] = X_test['Sex'] == i
del X_test['Sex']
X_test = np.array(X_test, dtype=np.float32)
from sklearn.preprocessing import Imputer
imputer = Imputer()
X_test = imputer.fit_transform(X_test)
pred = model.predict(X_test) | code |
2013148/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import tflearn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2013148/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import Imputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns]
for i in columns:
X = X[~X[i].isnull()]
for i in range(1, 4):
X['Pclass_' + str(i)] = X['Pclass'] == i
del X['Pclass']
for i in X.Embarked.unique():
X[i] = X['Embarked'] == i
del X['Embarked']
for i in X.Sex.unique():
X[i] = X['Sex'] == i
del X['Sex']
y = pd.DataFrame({'Survived': X['Survived'], 'Not Survived': 1 - X['Survived']})
y.shape
del X['Survived']
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
net = tflearn.input_data(shape=[None, 9])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net)
model.fit(X, y, n_epoch=200, batch_size=16, show_metric=True)
df = pd.read_csv('../input/test.csv')
X_test = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
X_test = X_test[columns]
for i in range(1, 4):
X_test['Pclass_' + str(i)] = X_test['Pclass'] == i
del X_test['Pclass']
for i in X_test.Embarked.unique():
X_test[i] = X_test['Embarked'] == i
del X_test['Embarked']
for i in X_test.Sex.unique():
X_test[i] = X_test['Sex'] == i
del X_test['Sex']
X_test = np.array(X_test, dtype=np.float32)
from sklearn.preprocessing import Imputer
imputer = Imputer()
X_test = imputer.fit_transform(X_test)
pred = model.predict(X_test)
predict = np.zeros(len(pred))
for i in range(len(pred)):
if pred[i][1] >= 0.5:
predict[i] = 1
y_test = pd.read_csv('../input/gender_submission.csv')
y_test = pd.DataFrame({'Survived': y_test['Survived'], 'Not Survived': 1 - y_test['Survived']})
y_test = np.array(y_test, dtype=np.float32)
test = pd.read_csv('../input/test.csv')
my_submission = pd.DataFrame({'PassengerId': test.PassengerId, 'Survived': predict.astype(int)})
my_submission.to_csv('submission.csv', index=False) | code |
2013148/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import Imputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns]
for i in columns:
X = X[~X[i].isnull()]
for i in range(1, 4):
X['Pclass_' + str(i)] = X['Pclass'] == i
del X['Pclass']
for i in X.Embarked.unique():
X[i] = X['Embarked'] == i
del X['Embarked']
for i in X.Sex.unique():
X[i] = X['Sex'] == i
del X['Sex']
y = pd.DataFrame({'Survived': X['Survived'], 'Not Survived': 1 - X['Survived']})
y.shape
del X['Survived']
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
net = tflearn.input_data(shape=[None, 9])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net)
model.fit(X, y, n_epoch=200, batch_size=16, show_metric=True)
df = pd.read_csv('../input/test.csv')
X_test = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
X_test = X_test[columns]
for i in range(1, 4):
X_test['Pclass_' + str(i)] = X_test['Pclass'] == i
del X_test['Pclass']
for i in X_test.Embarked.unique():
X_test[i] = X_test['Embarked'] == i
del X_test['Embarked']
for i in X_test.Sex.unique():
X_test[i] = X_test['Sex'] == i
del X_test['Sex']
X_test = np.array(X_test, dtype=np.float32)
from sklearn.preprocessing import Imputer
imputer = Imputer()
X_test = imputer.fit_transform(X_test)
pred = model.predict(X_test)
predict = np.zeros(len(pred))
for i in range(len(pred)):
if pred[i][1] >= 0.5:
predict[i] = 1
y_test = pd.read_csv('../input/gender_submission.csv')
y_test = pd.DataFrame({'Survived': y_test['Survived'], 'Not Survived': 1 - y_test['Survived']})
y_test = np.array(y_test, dtype=np.float32)
model.evaluate(X_test, y_test, batch_size=16) | code |
2013148/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import Imputer
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tflearn
df = pd.read_csv('../input/train.csv')
X = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']
X = X[columns]
for i in columns:
X = X[~X[i].isnull()]
for i in range(1, 4):
X['Pclass_' + str(i)] = X['Pclass'] == i
del X['Pclass']
for i in X.Embarked.unique():
X[i] = X['Embarked'] == i
del X['Embarked']
for i in X.Sex.unique():
X[i] = X['Sex'] == i
del X['Sex']
y = pd.DataFrame({'Survived': X['Survived'], 'Not Survived': 1 - X['Survived']})
y.shape
del X['Survived']
X = np.array(X, dtype=np.float32)
y = np.array(y, dtype=np.float32)
net = tflearn.input_data(shape=[None, 9])
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 32)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net)
model.fit(X, y, n_epoch=200, batch_size=16, show_metric=True)
df = pd.read_csv('../input/test.csv')
X_test = df.copy()
columns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
X_test = X_test[columns]
for i in range(1, 4):
X_test['Pclass_' + str(i)] = X_test['Pclass'] == i
del X_test['Pclass']
for i in X_test.Embarked.unique():
X_test[i] = X_test['Embarked'] == i
del X_test['Embarked']
for i in X_test.Sex.unique():
X_test[i] = X_test['Sex'] == i
del X_test['Sex']
X_test = np.array(X_test, dtype=np.float32)
from sklearn.preprocessing import Imputer
imputer = Imputer()
X_test = imputer.fit_transform(X_test)
pred = model.predict(X_test)
predict = np.zeros(len(pred))
for i in range(len(pred)):
if pred[i][1] >= 0.5:
predict[i] = 1 | code |
130020137/cell_13 | [
"text_plain_output_1.png"
] | from collections import deque
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
print('Jumlah Antrian : ', antrian)
antrian.append(6)
print('Nasabah ke ', 6)
print('Jumlah Antrian :', antrian)
antrian.append(7)
print('Nasabah ke ', 7)
print('Jumlah Antrian :', antrian)
out = antrian.popleft()
print('Nasabah yang keluar', out)
print('Jumlah Nasabah Sekarang :', antrian)
out = antrian.popleft()
print('Nasabah yang keluar', out)
print('Jumlah Nasabah Sekarang :', antrian)
out = antrian.popleft()
print('Nasabah yang keluar', out)
print('Jumlah Nasabah Sekarang :', antrian)
antrian.append(8)
print('Nasabah ke ', 8)
print('Jumlah Antrian :', antrian) | code |
130020137/cell_4 | [
"text_plain_output_1.png"
] | batubata = [1, 2, 3, 4, 5]
print(batubata)
batubata.append(6)
print('Batu Bata yang ditambah menjadi', 6)
print('Batu Bata yang diangkut', batubata)
batubata.append(7)
print('Batu Bata yang ditambah menjadi', 7)
print('Batu Bata yang diangkut', batubata)
batubatalelah = batubata.pop()
print('Batu bata yang dikeluarkan adalah :', batubatalelah)
print('Jumlah Batu Bata yang diangkut :', batubata) | code |
130020137/cell_6 | [
"text_plain_output_1.png"
] | sepedamotor = [1, 2, 3]
print('Jumlah Sepeda Motor :', sepedamotor)
sepedamotor.append(4)
print('Penambahan Sepeda motor menjadi', 4)
print('Jumlah Sepeda Motor : ', sepedamotor)
sepedamotor.pop()
print('Pengambilan Sepeda Motor', sepedamotor)
print('Jumlah Sepeda Motor : ', sepedamotor)
sepedamotor.pop()
print('Pengambilan Sepeda Motor ', sepedamotor)
print('Jumlah Sepeda Motor : ', sepedamotor) | code |
130020137/cell_2 | [
"text_plain_output_1.png"
] | buku = [1, 2, 3, 4, 5, 6]
print('Jumlah Buku Awal:', buku)
buku.append(7)
print('Penambahan Buku', 7)
print('Jumlah Buku : ', buku)
buku.append(8)
print('Penambahan Buku', 8)
print('Jumlah Buku : ', buku)
buku.pop()
print('Pengambilan Buku oleh Pelanggan', buku)
print('Jumlah Buku : ', buku) | code |
130020137/cell_19 | [
"text_plain_output_1.png"
] | from collections import deque
from collections import deque
from collections import deque
from collections import deque
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
antrian.append(6)
antrian.append(7)
out = antrian.popleft()
out = antrian.popleft()
out = antrian.popleft()
antrian.append(8)
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
npm = deque([200902001, 200902002, 200902003, 200902004, 200902005])
antrian.append(6)
antrian.append(7)
out = antrian.popleft()
outnpm = npm.popleft()
from collections import deque
antrian = deque([5, 6, 7, 8, 9])
antrian.append(10)
antrian.append(11)
out = antrian.popleft()
out = antrian.popleft()
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
print('jumlah antrian :', antrian)
antrian.append(6)
print('antrian ke ', 6)
print('jumlah antrian : ', antrian)
out = antrian.popleft()
print('antrian yang keluar ', out)
print('jumlah antrian :', antrian)
antrian.append(7)
print('antrian ke', 7)
print('jumlah antrian :', antrian)
out = antrian.popleft()
print('antrian yang keluar ', out)
print('jumlah antrian :', antrian) | code |
130020137/cell_8 | [
"text_plain_output_1.png"
] | baju = [1, 2, 3, 4, 5]
print('jumlah baju awal:', baju)
baju.append(6)
print('penambahan baju', 6)
print('jumlah baju : ', baju)
baju.pop()
print('pengambilan baju oleh sibapak', baju)
print('jumlah baju : ', baju) | code |
130020137/cell_15 | [
"text_plain_output_1.png"
] | from collections import deque
from collections import deque
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
antrian.append(6)
antrian.append(7)
out = antrian.popleft()
out = antrian.popleft()
out = antrian.popleft()
antrian.append(8)
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
npm = deque([200902001, 200902002, 200902003, 200902004, 200902005])
print('Jumlah Mahasiswa : ', antrian)
print('Nomor Antrian Mahasiswa :', npm)
antrian.append(6)
print('Mahasiswa ke : ', 6)
print('Nomor Npm : ', 200902006)
print('Jumlah Mahasiswa:', antrian)
antrian.append(7)
print('Mahasiswa ke : ', 7)
print('Nomor Npm : ', 200902007)
print('Jumlah Mahasiswa: ', antrian)
out = antrian.popleft()
outnpm = npm.popleft()
print('Mahasiswa yang ke: ', out)
print(npm)
print('Jumlah Mahasiswa Sekarang : ', antrian) | code |
130020137/cell_17 | [
"text_plain_output_1.png"
] | from collections import deque
from collections import deque
from collections import deque
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
antrian.append(6)
antrian.append(7)
out = antrian.popleft()
out = antrian.popleft()
out = antrian.popleft()
antrian.append(8)
from collections import deque
antrian = deque([1, 2, 3, 4, 5])
npm = deque([200902001, 200902002, 200902003, 200902004, 200902005])
antrian.append(6)
antrian.append(7)
out = antrian.popleft()
outnpm = npm.popleft()
from collections import deque
antrian = deque([5, 6, 7, 8, 9])
print('Jumlah Antrian : ', antrian)
antrian.append(10)
print('Pembeli ke ', 10)
print('Jumlah Antrian :', antrian)
antrian.append(11)
print('Pembeli ke ', 11)
print('Jumlah Antrian :', antrian)
out = antrian.popleft()
print('Pembeli yang keluar', out)
print('Jumlah Pembeli :', antrian)
out = antrian.popleft()
print('Pembeli yang keluar', out)
print('Jumlah Pembeli :', antrian) | code |
130020137/cell_10 | [
"text_plain_output_1.png"
] | baju = [1, 2, 3, 4, 5]
baju.append(6)
baju.pop()
baju = [5, 6, 7, 8, 9, 10]
print('jumlah baju awal:', baju)
lipatan = [5, 6, 7, 8, 9]
print('jumlah baju yang sudah dilipat:', lipatan)
lipatan.pop(4)
print('pengambilan baju oleh siadik : ', lipatan)
lipatan.append(10)
print('Akhir jumlah baju : ', lipatan) | code |
105204964/cell_21 | [
"image_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
data.isna().sum().sum()
data.loc[1292, 'WRI Category'] = 'Medium'
data.loc[1193, 'Vulnerability Category'] = 'Very Low'
data.loc[1202, 'Vulnerability Category'] = 'Very Low'
data.loc[1205, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, ' Lack of Adaptive Capacities'] = np.mean(data[' Lack of Adaptive Capacities'])
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
az.plot_kde(data[i],fill_kwargs={"alpha":0.5},ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
import warnings
warnings.filterwarnings("ignore")
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
sns.boxplot(data[i],ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
sns.despine()
ys = ['WRI','Exposure','Vulnerability','Susceptibility']
fig, ((a,b),(c,d)) = plt.subplots (2, 2, figsize=(12, 12))
for i,t in zip(ys,[a,b,c,d]):
sns.stripplot(x='Year', y=i, hue=i+' Category',data=data, ax = t)
t.legend(ncol=3)
t.set_title(i)
sns.despine()
ys = ['WRI', 'Exposure', 'Vulnerability', 'Susceptibility']
fig, ((a, b), (c, d)) = plt.subplots(2, 2, figsize=(12, 12))
for i, t in zip(ys, [a, b, c, d]):
sns.violinplot(x=i + ' Category', y=i, data=data.sort_values(by=i), ax=t)
t.legend(ncol=3)
t.set_title(i)
sns.despine() | code |
105204964/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
summary('Vulnerability', 'Vulnerability Category', 2016) | code |
105204964/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
data.isna().sum().sum()
data.isna().sum().sum() | code |
105204964/cell_4 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique())) | code |
105204964/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
data.isna().sum().sum()
data.loc[1292, 'WRI Category'] = 'Medium'
data.loc[1193, 'Vulnerability Category'] = 'Very Low'
data.loc[1202, 'Vulnerability Category'] = 'Very Low'
data.loc[1205, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, ' Lack of Adaptive Capacities'] = np.mean(data[' Lack of Adaptive Capacities'])
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
az.plot_kde(data[i],fill_kwargs={"alpha":0.5},ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
import warnings
warnings.filterwarnings("ignore")
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
sns.boxplot(data[i],ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
sns.despine()
ys = ['WRI', 'Exposure', 'Vulnerability', 'Susceptibility']
fig, ((a, b), (c, d)) = plt.subplots(2, 2, figsize=(12, 12))
for i, t in zip(ys, [a, b, c, d]):
sns.stripplot(x='Year', y=i, hue=i + ' Category', data=data, ax=t)
t.legend(ncol=3)
t.set_title(i)
sns.despine() | code |
105204964/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
data.head() | code |
105204964/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
summary('WRI', 'WRI Category', 2020) | code |
105204964/cell_19 | [
"text_html_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
data.isna().sum().sum()
data.loc[1292, 'WRI Category'] = 'Medium'
data.loc[1193, 'Vulnerability Category'] = 'Very Low'
data.loc[1202, 'Vulnerability Category'] = 'Very Low'
data.loc[1205, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, ' Lack of Adaptive Capacities'] = np.mean(data[' Lack of Adaptive Capacities'])
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
az.plot_kde(data[i],fill_kwargs={"alpha":0.5},ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
import warnings
warnings.filterwarnings("ignore")
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
sns.boxplot(data[i],ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
sns.catplot(x='Year', y='WRI', hue='WRI' + ' Category', data=data, kind='point')
sns.despine() | code |
105204964/cell_18 | [
"text_html_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
data.isna().sum().sum()
data.loc[1292, 'WRI Category'] = 'Medium'
data.loc[1193, 'Vulnerability Category'] = 'Very Low'
data.loc[1202, 'Vulnerability Category'] = 'Very Low'
data.loc[1205, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, ' Lack of Adaptive Capacities'] = np.mean(data[' Lack of Adaptive Capacities'])
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
az.plot_kde(data[i],fill_kwargs={"alpha":0.5},ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
import warnings
warnings.filterwarnings("ignore")
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
sns.boxplot(data[i],ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
sns.pairplot(data=data)
plt.show() | code |
105204964/cell_16 | [
"text_plain_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
data.isna().sum().sum()
data.loc[1292, 'WRI Category'] = 'Medium'
data.loc[1193, 'Vulnerability Category'] = 'Very Low'
data.loc[1202, 'Vulnerability Category'] = 'Very Low'
data.loc[1205, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, ' Lack of Adaptive Capacities'] = np.mean(data[' Lack of Adaptive Capacities'])
ys = ['WRI', 'Exposure', 'Vulnerability', 'Susceptibility', 'Lack of Coping Capabilities', ' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax, i in zip(axs.ravel(), ys):
az.plot_kde(data[i], fill_kwargs={'alpha': 0.5}, ax=ax)
ax.set_title(i)
sns.despine()
plt.show() | code |
105204964/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
data.describe() | code |
105204964/cell_17 | [
"text_html_output_1.png"
] | import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
data.isna().sum().sum()
data.loc[1292, 'WRI Category'] = 'Medium'
data.loc[1193, 'Vulnerability Category'] = 'Very Low'
data.loc[1202, 'Vulnerability Category'] = 'Very Low'
data.loc[1205, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, 'Vulnerability Category'] = 'Very Low'
data.loc[1858, ' Lack of Adaptive Capacities'] = np.mean(data[' Lack of Adaptive Capacities'])
ys = ['WRI','Exposure','Vulnerability','Susceptibility','Lack of Coping Capabilities',' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax,i in zip(axs.ravel(),ys):
az.plot_kde(data[i],fill_kwargs={"alpha":0.5},ax=ax)
ax.set_title(i)
sns.despine()
plt.show()
import warnings
warnings.filterwarnings('ignore')
ys = ['WRI', 'Exposure', 'Vulnerability', 'Susceptibility', 'Lack of Coping Capabilities', ' Lack of Adaptive Capacities']
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(15, 12))
for ax, i in zip(axs.ravel(), ys):
sns.boxplot(data[i], ax=ax)
ax.set_title(i)
sns.despine()
plt.show() | code |
105204964/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
data.isna().sum().sum()
data[pd.isnull(data).any(axis=1)] | code |
105204964/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
(len(data['Year'].unique()), np.sort(data['Year'].unique()), len(data['Region'].unique()))
data.isna().sum().sum()
def summary(col1, col2, year):
pivot = pd.pivot_table(data, values=[col1], index=['Year', col2], aggfunc={col1: [min, max, np.mean, np.std]})
pivot = pivot.sort_values(by=(col1, 'mean')).reindex(np.sort(data['Year'].unique()), level=0)
return pivot.loc[year, :]
summary('Vulnerability', 'Vulnerability Category', 2019) | code |
105204964/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import arviz as az
data = pd.read_csv('../input/global-disaster-risk-index-time-series-dataset/world_risk_index.csv')
data.isna().sum().sum() | code |
90155584/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
df_bank = marketing.groupby(['y']).apply(lambda x: x.sample(n=199, random_state=123))
df_bank.drop(columns='y', axis=1, inplace=True)
df_bank.reset_index(inplace=True)
df_bank.drop(columns='level_1', axis=1, inplace=True)
df_bank.groupby('y')['campaign'].count()
df_bank.groupby('y').agg({'campaign': ['median', 'mean']}) | code |
90155584/cell_9 | [
"text_plain_output_1.png"
] | N = 45211
e = 0.05
n = N / (1 + N * e ** 2)
n | code |
90155584/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5) | code |
90155584/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
marketing.describe() | code |
90155584/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 8))
sns.histplot(data=marketing, x='y', ax=ax1)
sns.boxplot(data=marketing, x='y', y='campaign', ax=ax2)
plt.show() | code |
90155584/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import scipy.stats as st
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
df_bank = marketing.groupby(['y']).apply(lambda x: x.sample(n=199, random_state=123))
df_bank.drop(columns='y', axis=1, inplace=True)
df_bank.reset_index(inplace=True)
df_bank.drop(columns='level_1', axis=1, inplace=True)
df_bank.groupby('y')['campaign'].count()
df_bank.groupby('y').agg({'campaign': ['median', 'mean']})
yes = df_bank[df_bank['y'] == 'yes']
no = df_bank[df_bank['y'] == 'no']
mannwhitneyu = st.mannwhitneyu(yes['campaign'], no['campaign'])
p_value = mannwhitneyu.pvalue
print('P-Value :', p_value)
if p_value >= 0.05:
print('Accept H0')
else:
print('Reject H0, Accept Ha') | code |
90155584/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
#Visualize the data
fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,8))
sns.histplot(data=marketing, x='y', ax=ax1)
sns.boxplot(data=marketing, x='y', y='campaign', ax=ax2)
plt.show()
df_bank = marketing.groupby(['y']).apply(lambda x: x.sample(n=199, random_state=123))
df_bank.drop(columns='y', axis=1, inplace=True)
df_bank.reset_index(inplace=True)
df_bank.drop(columns='level_1', axis=1, inplace=True)
df_bank.groupby('y')['campaign'].count()
df_bank.groupby('y').agg({'campaign': ['median', 'mean']})
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 8))
sns.barplot(data=df_bank, x='y', y='campaign', estimator=np.median, ax=ax1)
sns.barplot(data=df_bank, x='y', y='campaign', ax=ax2)
plt.show() | code |
90155584/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/bank-marketing/bank-full.csv')
marketing = df[['campaign', 'y']]
marketing.sample(5)
df_bank = marketing.groupby(['y']).apply(lambda x: x.sample(n=199, random_state=123))
df_bank.drop(columns='y', axis=1, inplace=True)
df_bank.reset_index(inplace=True)
df_bank.drop(columns='level_1', axis=1, inplace=True)
df_bank.groupby('y')['campaign'].count() | code |
32067430/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
team_stats = pd.read_csv('/kaggle/input/college-basketball-dataset/cbb.csv')
team_stats.head(5) | code |
32067430/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
team_stats = pd.read_csv('/kaggle/input/college-basketball-dataset/cbb.csv')
avg_off = team_stats['ADJOE'].mean()
avg_def = team_stats['ADJDE'].mean()
team_stats[team_stats['POSTSEASON'] == 'Champions']['ADJOE'].mean() - avg_off | code |
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