path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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|---|---|---|---|
106202407/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape) | code |
106202407/cell_10 | [
"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/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train_X = train.copy()
train_Y = train_X.pop('Transported')
def displayAllCateFeatInfo(df):
pass
def splitCabinForNewFeatures(df):
my_df = df.copy()
split_cabin_df = my_df.Cabin.str.split('/', expand=True)
my_df['CabinDeck'] = split_cabin_df[0]
my_df['CabinSide'] = split_cabin_df[2]
my_df.pop('Cabin')
return my_df
def splitNameToGenerateFamilyName(df):
my_df = df.copy()
split_name_df = my_df.Name.str.split(' ', expand=True)
my_df['FamilyName'] = split_name_df[1]
my_df.pop('Name')
return my_df
displayAllCateFeatInfo(splitNameToGenerateFamilyName(train_X)) | code |
105189461/cell_4 | [
"text_plain_output_1.png"
] | score1 = 100
score2 = 145.9
type(score1) | code |
105189461/cell_6 | [
"text_plain_output_1.png"
] | score1 = 100
score2 = 145.9
total_score = score1 + score2
print(total_score) | code |
105189461/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | score1 = 100
score2 = 145.9
total_score = score1 + score2
print('total score of tom is', total_score) | code |
105189461/cell_10 | [
"text_plain_output_1.png"
] | sale1 = input('sales in store1')
sale2 = input('sales in store2') | code |
105189461/cell_5 | [
"text_plain_output_1.png"
] | score1 = 100
score2 = 145.9
type(score2) | code |
74055897/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20)
data.drop(columns=['Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu', 'Id'], inplace=True)
corr = data.corr()
kot = corr[np.abs(corr) >= 0.7]
data.drop(columns=['GarageArea', 'GrLivArea', 'GarageYrBlt'], inplace=True)
y = data.SalePrice
data.drop(columns=['SalePrice'], inplace=True)
data.columns
def get_num_cat_features(type_features, data):
return data.select_dtypes(include=type_features)
numerics = ['int64', 'float64']
newdf_num = get_num_cat_features(numerics, data)
newdf_cat = data.select_dtypes(['object'])
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test.drop(columns=['Id', 'Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu'], inplace=True)
test.drop(columns=['GarageArea', 'GrLivArea', 'GarageYrBlt'], inplace=True)
test.fillna(test.mean())
numericstest = ['int64', 'float64']
newdf_numtest = get_num_cat_features(numerics, test)
test_num = get_num_cat_features(numerics, test)
newdf_cattest = test.select_dtypes(['object'])
trainTest = newdf_cat.append(newdf_cattest)
def encode_labels(data):
encoded_categoric_train_set = data.copy()
for c in data.columns:
data[c] = data[c].astype('category')
encoded_categoric_train_set[c] = data[c].cat.codes
return encoded_categoric_train_set
encoded_categoric_train_set = encode_labels(trainTest)
newdf_cat = encoded_categoric_train_set[:1460]
test = encoded_categoric_train_set[1460:]
test.shape | code |
74055897/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.info() | code |
74055897/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20)
data.drop(columns=['Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu', 'Id'], inplace=True)
corr = data.corr()
kot = corr[np.abs(corr) >= 0.7]
data.drop(columns=['GarageArea', 'GrLivArea', 'GarageYrBlt'], inplace=True)
y = data.SalePrice
data.drop(columns=['SalePrice'], inplace=True)
data.columns
def get_num_cat_features(type_features, data):
return data.select_dtypes(include=type_features)
numerics = ['int64', 'float64']
newdf_num = get_num_cat_features(numerics, data)
newdf_cat = data.select_dtypes(['object'])
newdf_num.shape
newdf_num.shape | code |
74055897/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False) | code |
74055897/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20)
data.drop(columns=['Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu', 'Id'], inplace=True)
plt.figure(figsize=(10, 10))
sns.heatmap(data.corr()) | code |
74055897/cell_7 | [
"text_plain_output_1.png"
] | import missingno
import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingno.bar(data) | code |
74055897/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20)
data.drop(columns=['Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu', 'Id'], inplace=True)
corr = data.corr()
kot = corr[np.abs(corr) >= 0.7]
data.drop(columns=['GarageArea', 'GrLivArea', 'GarageYrBlt'], inplace=True)
y = data.SalePrice
data.drop(columns=['SalePrice'], inplace=True)
data.columns
def get_num_cat_features(type_features, data):
return data.select_dtypes(include=type_features)
numerics = ['int64', 'float64']
newdf_num = get_num_cat_features(numerics, data)
newdf_cat = data.select_dtypes(['object'])
newdf_num.shape | code |
74055897/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20) | code |
74055897/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20)
data.drop(columns=['Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu', 'Id'], inplace=True)
corr = data.corr()
kot = corr[np.abs(corr) >= 0.7]
data.drop(columns=['GarageArea', 'GrLivArea', 'GarageYrBlt'], inplace=True)
y = data.SalePrice
data.drop(columns=['SalePrice'], inplace=True)
y | code |
74055897/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20)
data.drop(columns=['Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu', 'Id'], inplace=True)
corr = data.corr()
kot = corr[np.abs(corr) >= 0.7]
data.drop(columns=['GarageArea', 'GrLivArea', 'GarageYrBlt'], inplace=True)
y = data.SalePrice
data.drop(columns=['SalePrice'], inplace=True)
data.columns | code |
74055897/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20)
data.drop(columns=['Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu', 'Id'], inplace=True)
corr = data.corr()
kot = corr[np.abs(corr) >= 0.7]
data.drop(columns=['GarageArea', 'GrLivArea', 'GarageYrBlt'], inplace=True)
y = data.SalePrice
data.drop(columns=['SalePrice'], inplace=True)
data.columns
def get_num_cat_features(type_features, data):
return data.select_dtypes(include=type_features)
numerics = ['int64', 'float64']
newdf_num = get_num_cat_features(numerics, data)
newdf_cat = data.select_dtypes(['object'])
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test.drop(columns=['Id', 'Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu'], inplace=True)
test.drop(columns=['GarageArea', 'GrLivArea', 'GarageYrBlt'], inplace=True)
test.fillna(test.mean())
numericstest = ['int64', 'float64']
newdf_numtest = get_num_cat_features(numerics, test)
test_num = get_num_cat_features(numerics, test)
newdf_cattest = test.select_dtypes(['object'])
trainTest = newdf_cat.append(newdf_cattest)
test.info() | code |
74055897/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_values(ascending=False).head(20)
data.drop(columns=['Alley', 'Fence', 'PoolQC', 'MiscFeature', 'FireplaceQu', 'Id'], inplace=True)
corr = data.corr()
kot = corr[np.abs(corr) >= 0.7]
plt.figure(figsize=(12, 8))
sns.heatmap(kot, cmap='Reds') | code |
74055897/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False) | code |
128000744/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/road-accidents-rome-june2022/426c71f0-7181-417a-b149-33ba943382b0.csv', sep=';', encoding='latin-1')
df.columns | code |
128000744/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/road-accidents-rome-june2022/426c71f0-7181-417a-b149-33ba943382b0.csv', sep=';', encoding='latin-1')
df.columns
df[['NUM_MORTI']].sum() | code |
33101088/cell_6 | [
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.models import Sequential, load_model
from keras.preprocessing.image import ImageDataGenerator
from sklearn import preprocessing
from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np
import os
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
X_train = []
Y_train = []
X_test = []
for index, row in train_df.iterrows():
X_train.append(row.values[1:].reshape((28, 28, 1)))
Y_train.append(row['label'])
for index, row in test_df.iterrows():
X_test.append(row.values.reshape((28, 28, 1)))
X_train = np.array(X_train) / 255.0
Y_train = np.array(Y_train)
X_test = np.array(X_test) / 255.0
lb = preprocessing.LabelBinarizer()
lb.fit(Y_train)
Y_train = lb.transform(Y_train)
sss = StratifiedShuffleSplit(10, 0.2, random_state=15)
for train_idx, val_idx in sss.split(X_train, Y_train):
X_train_tmp, X_val = (X_train[train_idx], X_train[val_idx])
Y_train_tmp, Y_val = (Y_train[train_idx], Y_train[val_idx])
X_train = X_train_tmp
Y_train = Y_train_tmp
img_size = (28, 28, 1)
n_classes = 10
if os.path.exists('keras_model.h5'):
model = load_model('keras_model.h5')
else:
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=img_size, kernel_initializer='normal'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (5, 5), kernel_initializer='normal'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0, zoom_range=0.1, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=False, vertical_flip=False)
datagen.fit(X_train)
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=1000), epochs=20, validation_data=(X_val, Y_val), steps_per_epoch=X_train.shape[0] / 1000, verbose=1)
score, acc = model.evaluate(X_val, Y_val, verbose=1)
print('\nLoss:', score, '\nAcc:', acc)
model.save('keras_model.h5') | code |
33101088/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn import preprocessing
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator | code |
33101088/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.initializers import Ones, Zeros
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Input, Conv2DTranspose
from keras.models import Model
from keras.models import Sequential, load_model
from keras.preprocessing.image import ImageDataGenerator
from sklearn import preprocessing
from sklearn.model_selection import StratifiedShuffleSplit
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
X_train = []
Y_train = []
X_test = []
for index, row in train_df.iterrows():
X_train.append(row.values[1:].reshape((28, 28, 1)))
Y_train.append(row['label'])
for index, row in test_df.iterrows():
X_test.append(row.values.reshape((28, 28, 1)))
X_train = np.array(X_train) / 255.0
Y_train = np.array(Y_train)
X_test = np.array(X_test) / 255.0
lb = preprocessing.LabelBinarizer()
lb.fit(Y_train)
Y_train = lb.transform(Y_train)
sss = StratifiedShuffleSplit(10, 0.2, random_state=15)
for train_idx, val_idx in sss.split(X_train, Y_train):
X_train_tmp, X_val = (X_train[train_idx], X_train[val_idx])
Y_train_tmp, Y_val = (Y_train[train_idx], Y_train[val_idx])
X_train = X_train_tmp
Y_train = Y_train_tmp
img_size = (28, 28, 1)
n_classes = 10
if os.path.exists('keras_model.h5'):
model = load_model('keras_model.h5')
else:
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=img_size, kernel_initializer='normal'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (5, 5), kernel_initializer='normal'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(n_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0, zoom_range=0.1, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=False, vertical_flip=False)
datagen.fit(X_train)
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=1000), epochs=20, validation_data=(X_val, Y_val), steps_per_epoch=X_train.shape[0] / 1000, verbose=1)
score, acc = model.evaluate(X_val, Y_val, verbose=1)
model.save('keras_model.h5')
Y_test = model.predict(X_test)
Y_test = lb.inverse_transform(Y_test)
Y_test = [[y] for y in Y_test]
index = [[i] for i in range(1, X_test.shape[0] + 1)]
output_np = np.concatenate((index, Y_test), axis=1)
output_df = pd.DataFrame(data=output_np, columns=['ImageId', 'Label'])
output_df.to_csv('out.csv', index=False)
Y_train_label = lb.inverse_transform(Y_train)
Y_train_label[:30]
class_indices = [3, 5, 0, 22, 1, 9, 2, 28, 4, 7]
from keras import backend as K
K.set_learning_phase(1)
import tensorflow as tf
model = load_model('keras_model.h5')
layer_dict = dict([(layer.name, layer) for layer in model.layers])
def deprocess_image(x):
x -= x.mean()
x /= x.std() + 1e-05
x *= 0.1
x += 0.5
x = np.clip(x, 0, 1)
x *= 255
x = np.clip(x, 0, 255).astype('uint8')
return x
from keras.layers import Input, Conv2DTranspose
from keras.models import Model
from keras.initializers import Ones, Zeros
class SaliencyMask(object):
def __init__(self, model, output_index=0):
pass
def get_mask(self, input_image):
pass
def get_smoothed_mask(self, input_image, stdev_spread=0.2, nsamples=50):
stdev = stdev_spread * (np.max(input_image) - np.min(input_image))
total_gradients = np.zeros_like(input_image, dtype=np.float64)
for i in range(nsamples):
noise = np.random.normal(0, stdev, input_image.shape)
x_value_plus_noise = input_image + noise
total_gradients += self.get_mask(x_value_plus_noise)
return total_gradients / nsamples
class GradientSaliency(SaliencyMask):
def __init__(self, model, output_index=0):
input_tensors = [model.input]
gradients = model.optimizer.get_gradients(model.output[0][output_index], model.input)
self.compute_gradients = K.function(inputs=input_tensors, outputs=gradients)
def get_mask(self, input_image):
x_value = np.expand_dims(input_image, axis=0)
gradients = self.compute_gradients([x_value])[0][0]
return gradients
class VisualBackprop(SaliencyMask):
def __init__(self, model, output_index=0):
inps = [model.input]
outs = [layer.output for layer in model.layers]
self.forward_pass = K.function(inps, outs)
self.model = model
def get_mask(self, input_image):
x_value = np.expand_dims(input_image, axis=0)
visual_bpr = None
layer_outs = self.forward_pass([x_value, 0])
for i in range(len(self.model.layers) - 1, -1, -1):
if 'Conv2D' in str(type(self.model.layers[i])):
layer = np.mean(layer_outs[i], axis=3, keepdims=True)
layer = layer - np.min(layer)
layer = layer / (np.max(layer) - np.min(layer) + 1e-06)
if visual_bpr is not None:
if visual_bpr.shape != layer.shape:
visual_bpr = self._deconv(visual_bpr)
visual_bpr = visual_bpr * layer
else:
visual_bpr = layer
return visual_bpr[0]
def _deconv(self, feature_map):
x = Input(shape=(None, None, 1))
y = Conv2DTranspose(filters=1, kernel_size=(3, 3), strides=(2, 2), padding='same', kernel_initializer=Ones(), bias_initializer=Zeros())(x)
deconv_model = Model(inputs=[x], outputs=[y])
inps = [deconv_model.input]
outs = [deconv_model.layers[-1].output]
deconv_func = K.function(inps, outs)
return deconv_func([feature_map, 0])[0]
Y_train_label = lb.inverse_transform(Y_train)
fig, ax = plt.subplots(10, 2, figsize=(5, 25))
i = -1
for c in class_indices:
img = np.array(X_train[c])
i = i + 1
vanilla = GradientSaliency(model, Y_train_label[c])
mask = vanilla.get_mask(img)
filter_mask = (mask > 0.0).reshape((28, 28))
smooth_mask = vanilla.get_smoothed_mask(img)
filter_smoothed_mask = (smooth_mask > 0.0).reshape((28, 28))
fig.subplots_adjust(hspace=0.8)
ax[i, 0].imshow(img.reshape((28, 28)), cmap='gray')
cax = ax[i, 1].imshow(mask.reshape((28, 28)), cmap='jet')
fig.colorbar(cax, ax=ax[i, 1]) | code |
327813/cell_6 | [
"text_plain_output_1.png"
] | #machine learning
train_data = train_df.values
test_data = test_df.values
X_train = train_data[:,1:]
y_train = train_data[:,0]
X_test = test_data[:,1:]
idx = test_data[:,0]
#random forest classifier
rfc = RandomForestClassifier(n_estimators=100)
rfc.fit(X_train, y_train)
score_rfc = rfc.score(X_train, y_train)
out_rfc = rfc.predict(X_test)
print ("random forest classifier score: %f", score_rfc)
#logistic regression
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
score_logreg = logreg.score(X_train, y_train)
out_logreg = logreg.predict(X_test)
print ("logistic regression score: %f", score_logreg)
#SVM
svc = SVC()
svc.fit(X_train, y_train)
score_svc = svc.score(X_train, y_train)
out_svc = svc.predict(X_test)
print ("SVM score: %f", score_svc)
#knn classifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
score_knn = knn.score(X_train, y_train)
out_knn = knn.predict(X_test)
print ("knn score: %f", score_knn)
#write out predictions
#predictions_file = open("titanic_pred.csv", "wb")
#open_file_object = csv.writer(predictions_file)
#open_file_object.writerow(["PassengerId","Survived"])
#open_file_object.writerows(zip(idx, out_rfc))
#predictions_file.close() | code |
327813/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
if __name__ == '__main__':
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.head()
train_df.info()
test_df.info() | code |
105198337/cell_21 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | lr = create_model('lr')
tuned_lr = tune_model(lr)
plot_model(tuned_lr) | code |
105198337/cell_13 | [
"text_plain_output_1.png"
] | rf = create_model('rf')
tuned_rf = tune_model(rf)
evaluate_model(tuned_rf) | code |
105198337/cell_9 | [
"text_html_output_2.png"
] | top_model = compare_models(sort='AUC', fold=5, n_select=3) | code |
105198337/cell_4 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | !pip install --pre pycaret | code |
105198337/cell_23 | [
"image_png_output_1.png"
] | lr = create_model('lr')
tuned_lr = tune_model(lr)
predict_model(tuned_lr) | code |
105198337/cell_20 | [
"text_html_output_2.png"
] | lr = create_model('lr')
tuned_lr = tune_model(lr) | code |
105198337/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/text-sim-out/Output_0907.csv')
data.dtypes | code |
105198337/cell_11 | [
"text_plain_output_1.png"
] | rf = create_model('rf')
tuned_rf = tune_model(rf) | code |
105198337/cell_19 | [
"image_png_output_1.png"
] | lr = create_model('lr') | code |
105198337/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 |
105198337/cell_7 | [
"image_png_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/text-sim-out/Output_0907.csv')
data.dtypes
data.columns | code |
105198337/cell_18 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | top_model = compare_models(sort='AUC', fold=5, n_select=3)
top_model | code |
105198337/cell_8 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/text-sim-out/Output_0907.csv')
data.dtypes
data.columns
from pycaret.classification import *
setup(data=data[['label', 'bert_score', 'jarowinkler', 'levenshtein', 'ratcliff']], target='label') | code |
105198337/cell_15 | [
"text_html_output_2.png"
] | catboost = create_model('catboost')
tuned_catboost = tune_model(catboost) | code |
105198337/cell_16 | [
"text_html_output_2.png"
] | catboost = create_model('catboost')
tuned_catboost = tune_model(catboost)
interpret_model(tuned_catboost) | code |
105198337/cell_3 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/text-sim-out/Output_0907.csv')
data | code |
105198337/cell_17 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | catboost = create_model('catboost')
tuned_catboost = tune_model(catboost)
evaluate_model(tuned_catboost) | code |
105198337/cell_24 | [
"text_plain_output_1.png"
] | rf = create_model('rf')
tuned_rf = tune_model(rf)
catboost = create_model('catboost')
tuned_catboost = tune_model(catboost)
lr = create_model('lr')
tuned_lr = tune_model(lr)
blend = blend_models(estimator_list=[tuned_lr, tuned_catboost, tuned_rf]) | code |
105198337/cell_14 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | catboost = create_model('catboost') | code |
105198337/cell_22 | [
"image_output_1.png"
] | lr = create_model('lr')
tuned_lr = tune_model(lr)
evaluate_model(tuned_lr) | code |
105198337/cell_10 | [
"text_html_output_1.png"
] | rf = create_model('rf') | code |
105198337/cell_12 | [
"text_plain_output_1.png"
] | rf = create_model('rf')
tuned_rf = tune_model(rf)
predict_model(tuned_rf) | code |
128034494/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns | code |
128034494/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum()
from sklearn.impute import KNNImputer
knn = KNNImputer(n_neighbors=7, weights='distance')
features[col_float] = knn.fit_transform(features[col_float])
features.isna().sum()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
encoded = encoder.fit_transform(features[col_str])
new_features = pd.DataFrame(encoded)
new_features.head(3) | code |
128034494/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum()
from sklearn.impute import KNNImputer
knn = KNNImputer(n_neighbors=7, weights='distance')
features[col_float] = knn.fit_transform(features[col_float])
features.isna().sum() | code |
128034494/cell_79 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
target.value_counts()
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum()
from sklearn.impute import KNNImputer
knn = KNNImputer(n_neighbors=7, weights='distance')
features[col_float] = knn.fit_transform(features[col_float])
features.isna().sum()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
encoded = encoder.fit_transform(features[col_str])
new_features = pd.DataFrame(encoded)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
new_target = le.fit_transform(target)
new_target
from sklearn.metrics import roc_auc_score
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
def get_metrics(classifier, Xvalid, yvalid):
"""Function to Get the metrics of the given Classifier"""
y_train_pred = classifier.predict_proba(X_train)[:, 1]
y_valid_pred = classifier.predict_proba(Xvalid)[:, 1]
y_valid_predict = classifier.predict(Xvalid)
cm = confusion_matrix(yvalid, y_valid_predict)
dist = ConfusionMatrixDisplay(cm)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier()
dc.fit(X_train, y_train)
get_metrics(dc, X_valid, y_valid)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier(max_depth=4, criterion='gini', min_samples_split=2, min_samples_leaf=4)
dc.fit(X_train, y_train)
get_metrics(dc, X_valid, y_valid)
test[col_str] = si.transform(test[col_str])
test[col_float] = knn.transform(test[col_float])
test_encoded = pd.DataFrame(encoder.fit_transform(test[col_str]))
new_df_test = pd.DataFrame(test_encoded)
new_df_test[col_float] = test[col_float]
prediction = dc.predict(new_df_test)
prediction
prediction_decode = le.inverse_transform(prediction)
prediction_decode
submission = pd.DataFrame(columns=['id', 'Made_Purchase'])
submission['id'] = [i for i in range(len(prediction_decode))]
submission['Made_Purchase'] = prediction_decode
submission.head() | code |
128034494/cell_33 | [
"text_html_output_1.png"
] | print('training set shape: ', X_train.shape, y_train.shape)
print('Validation set shape: ', X_valid.shape, y_valid.shape) | code |
128034494/cell_74 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum()
from sklearn.impute import KNNImputer
knn = KNNImputer(n_neighbors=7, weights='distance')
features[col_float] = knn.fit_transform(features[col_float])
features.isna().sum()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
encoded = encoder.fit_transform(features[col_str])
new_features = pd.DataFrame(encoded)
test[col_str] = si.transform(test[col_str])
test[col_float] = knn.transform(test[col_float])
test_encoded = pd.DataFrame(encoder.fit_transform(test[col_str]))
new_df_test = pd.DataFrame(test_encoded)
new_df_test[col_float] = test[col_float]
new_df_test.head() | code |
128034494/cell_76 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum()
from sklearn.impute import KNNImputer
knn = KNNImputer(n_neighbors=7, weights='distance')
features[col_float] = knn.fit_transform(features[col_float])
features.isna().sum()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
encoded = encoder.fit_transform(features[col_str])
new_features = pd.DataFrame(encoded)
from sklearn.metrics import roc_auc_score
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
def get_metrics(classifier, Xvalid, yvalid):
"""Function to Get the metrics of the given Classifier"""
y_train_pred = classifier.predict_proba(X_train)[:, 1]
y_valid_pred = classifier.predict_proba(Xvalid)[:, 1]
y_valid_predict = classifier.predict(Xvalid)
cm = confusion_matrix(yvalid, y_valid_predict)
dist = ConfusionMatrixDisplay(cm)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier()
dc.fit(X_train, y_train)
get_metrics(dc, X_valid, y_valid)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier(max_depth=4, criterion='gini', min_samples_split=2, min_samples_leaf=4)
dc.fit(X_train, y_train)
get_metrics(dc, X_valid, y_valid)
test[col_str] = si.transform(test[col_str])
test[col_float] = knn.transform(test[col_float])
test_encoded = pd.DataFrame(encoder.fit_transform(test[col_str]))
new_df_test = pd.DataFrame(test_encoded)
new_df_test[col_float] = test[col_float]
prediction = dc.predict(new_df_test)
prediction | code |
128034494/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum()
from sklearn.impute import KNNImputer
knn = KNNImputer(n_neighbors=7, weights='distance')
features[col_float] = knn.fit_transform(features[col_float])
features.isna().sum()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
encoded = encoder.fit_transform(features[col_str])
new_features = pd.DataFrame(encoded)
new_features[col_float] = features[col_float]
new_features.head() | code |
128034494/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
train.info() | code |
128034494/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.head(3) | code |
128034494/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
print('String columns names : \n', col_str)
print()
print('Float columns names : \n', col_float) | code |
128034494/cell_59 | [
"text_plain_output_1.png"
] | from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
from sklearn.metrics import roc_auc_score
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
def get_metrics(classifier, Xvalid, yvalid):
"""Function to Get the metrics of the given Classifier"""
y_train_pred = classifier.predict_proba(X_train)[:, 1]
y_valid_pred = classifier.predict_proba(Xvalid)[:, 1]
y_valid_predict = classifier.predict(Xvalid)
cm = confusion_matrix(yvalid, y_valid_predict)
dist = ConfusionMatrixDisplay(cm)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier()
dc.fit(X_train, y_train)
get_metrics(dc, X_valid, y_valid)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier(max_depth=4, criterion='gini', min_samples_split=2, min_samples_leaf=4)
dc.fit(X_train, y_train)
get_metrics(dc, X_valid, y_valid) | code |
128034494/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
target.value_counts()
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
new_target = le.fit_transform(target)
new_target | code |
128034494/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape | code |
128034494/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
target.value_counts() | code |
128034494/cell_77 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
target.value_counts()
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum()
from sklearn.impute import KNNImputer
knn = KNNImputer(n_neighbors=7, weights='distance')
features[col_float] = knn.fit_transform(features[col_float])
features.isna().sum()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
encoded = encoder.fit_transform(features[col_str])
new_features = pd.DataFrame(encoded)
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
new_target = le.fit_transform(target)
new_target
from sklearn.metrics import roc_auc_score
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
def get_metrics(classifier, Xvalid, yvalid):
"""Function to Get the metrics of the given Classifier"""
y_train_pred = classifier.predict_proba(X_train)[:, 1]
y_valid_pred = classifier.predict_proba(Xvalid)[:, 1]
y_valid_predict = classifier.predict(Xvalid)
cm = confusion_matrix(yvalid, y_valid_predict)
dist = ConfusionMatrixDisplay(cm)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier()
dc.fit(X_train, y_train)
get_metrics(dc, X_valid, y_valid)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier(max_depth=4, criterion='gini', min_samples_split=2, min_samples_leaf=4)
dc.fit(X_train, y_train)
get_metrics(dc, X_valid, y_valid)
test[col_str] = si.transform(test[col_str])
test[col_float] = knn.transform(test[col_float])
test_encoded = pd.DataFrame(encoder.fit_transform(test[col_str]))
new_df_test = pd.DataFrame(test_encoded)
new_df_test[col_float] = test[col_float]
prediction = dc.predict(new_df_test)
prediction
prediction_decode = le.inverse_transform(prediction)
prediction_decode | code |
128034494/cell_46 | [
"text_html_output_1.png"
] | from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
from sklearn.metrics import roc_auc_score
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
def get_metrics(classifier, Xvalid, yvalid):
"""Function to Get the metrics of the given Classifier"""
y_train_pred = classifier.predict_proba(X_train)[:, 1]
y_valid_pred = classifier.predict_proba(Xvalid)[:, 1]
y_valid_predict = classifier.predict(Xvalid)
cm = confusion_matrix(yvalid, y_valid_predict)
dist = ConfusionMatrixDisplay(cm)
from sklearn.tree import DecisionTreeClassifier
dc = DecisionTreeClassifier()
dc.fit(X_train, y_train)
print('Training Score : ', dc.score(X_train, y_train))
get_metrics(dc, X_valid, y_valid) | code |
128034494/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
plt.figure(figsize=(20, 10))
sns.heatmap(var, annot=True) | code |
128034494/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum() | code |
128034494/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum() | code |
128034494/cell_71 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
features = train.drop('Made_Purchase', axis=1)
target.value_counts()
col_float = []
col_str = []
for i in features.columns:
if features[i].dtype == 'float64':
col_float.append(i)
else:
col_str.append(i)
from sklearn.impute import SimpleImputer
si = SimpleImputer(strategy='most_frequent')
features[col_str] = si.fit_transform(features[col_str])
features.isna().sum()
from sklearn.impute import KNNImputer
knn = KNNImputer(n_neighbors=7, weights='distance')
features[col_float] = knn.fit_transform(features[col_float])
features.isna().sum()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
encoded = encoder.fit_transform(features[col_str])
new_features = pd.DataFrame(encoded)
new_features[col_float] = features[col_float]
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
new_target = le.fit_transform(target)
new_target
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=200, criterion='entropy', max_depth=3, min_samples_leaf=2, min_samples_split=4)
rf.fit(new_features, new_target) | code |
33116653/cell_9 | [
"image_output_1.png"
] | import json
import os # To walk through the data files provided
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f = open(filename, 'r')
data = json.loads(f.read())
data['id'] = re.sub('(.*/)|(\\.json)', '', filename)
f.close()
return data
filename = testDirectory + '19bb5feb.json'
readTaskFile(filename)
def getGridSizeComparison(filename):
data = readTaskFile(filename)
trainSection = data['train']
ident = data['id']
numTrain = len(trainSection)
result = {}
for i in range(numTrain):
trainCase = trainSection[i]
trainCaseInput = trainCase['input']
trainCaseOutput = trainCase['output']
sameY = len(trainCaseInput) == len(trainCaseOutput)
sameX = len(trainCaseInput[0]) == len(trainCaseOutput[0])
result[ident + '_train_' + str(i)] = sameX and sameY
return result
filename = testDirectory + '19bb5feb.json'
getGridSizeComparison(filename)
def getResults(directory, f):
results = {}
for _, _, filenames in os.walk(directory):
for filename in filenames:
results.update(f(directory + filename))
return results
results = getResults(trainingDirectory, getGridSizeComparison)
print(results) | code |
33116653/cell_11 | [
"text_plain_output_1.png"
] | from matplotlib import colors
import json
import matplotlib.pyplot as plt
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f = open(filename, 'r')
data = json.loads(f.read())
data['id'] = re.sub('(.*/)|(\\.json)', '', filename)
f.close()
return data
filename = testDirectory + '19bb5feb.json'
readTaskFile(filename)
def getGridSizeComparison(filename):
data = readTaskFile(filename)
trainSection = data['train']
ident = data['id']
numTrain = len(trainSection)
result = {}
for i in range(numTrain):
trainCase = trainSection[i]
trainCaseInput = trainCase['input']
trainCaseOutput = trainCase['output']
sameY = len(trainCaseInput) == len(trainCaseOutput)
sameX = len(trainCaseInput[0]) == len(trainCaseOutput[0])
result[ident + '_train_' + str(i)] = sameX and sameY
return result
filename = testDirectory + '19bb5feb.json'
getGridSizeComparison(filename)
def plotTaskTraining(task):
"""
Plots the first train and test pairs of a specified task,
using same color scheme as the ARC app
"""
cmap = colors.ListedColormap(['#000000', '#0074D9', '#FF4136', '#2ECC40', '#FFDC00', '#AAAAAA', '#F012BE', '#FF851B', '#7FDBFF', '#870C25'])
norm = colors.Normalize(vmin=0, vmax=9)
nTrainingCases = len(task['train'])
fig, axs = plt.subplots(nTrainingCases, 2, figsize=(15, 15))
for i in range(nTrainingCases):
axs[i][0].imshow(task['train'][i]['input'], cmap=cmap, norm=norm)
axs[i][0].axis('off')
axs[i][0].set_title('Train Input')
axs[i][1].imshow(task['train'][i]['output'], cmap=cmap, norm=norm)
axs[i][1].axis('off')
axs[i][1].set_title('Train Output')
plt.tight_layout()
plt.show()
filename = testDirectory + '19bb5feb.json'
task = readTaskFile(filename)
plotTaskTraining(task) | code |
33116653/cell_7 | [
"text_plain_output_1.png"
] | import json
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f = open(filename, 'r')
data = json.loads(f.read())
data['id'] = re.sub('(.*/)|(\\.json)', '', filename)
f.close()
return data
filename = testDirectory + '19bb5feb.json'
readTaskFile(filename)
def getGridSizeComparison(filename):
data = readTaskFile(filename)
trainSection = data['train']
ident = data['id']
numTrain = len(trainSection)
result = {}
for i in range(numTrain):
trainCase = trainSection[i]
trainCaseInput = trainCase['input']
trainCaseOutput = trainCase['output']
sameY = len(trainCaseInput) == len(trainCaseOutput)
sameX = len(trainCaseInput[0]) == len(trainCaseOutput[0])
result[ident + '_train_' + str(i)] = sameX and sameY
return result
filename = testDirectory + '19bb5feb.json'
getGridSizeComparison(filename) | code |
33116653/cell_10 | [
"text_plain_output_1.png"
] | import json
import os # To walk through the data files provided
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f = open(filename, 'r')
data = json.loads(f.read())
data['id'] = re.sub('(.*/)|(\\.json)', '', filename)
f.close()
return data
filename = testDirectory + '19bb5feb.json'
readTaskFile(filename)
def getGridSizeComparison(filename):
data = readTaskFile(filename)
trainSection = data['train']
ident = data['id']
numTrain = len(trainSection)
result = {}
for i in range(numTrain):
trainCase = trainSection[i]
trainCaseInput = trainCase['input']
trainCaseOutput = trainCase['output']
sameY = len(trainCaseInput) == len(trainCaseOutput)
sameX = len(trainCaseInput[0]) == len(trainCaseOutput[0])
result[ident + '_train_' + str(i)] = sameX and sameY
return result
filename = testDirectory + '19bb5feb.json'
getGridSizeComparison(filename)
def getResults(directory, f):
results = {}
for _, _, filenames in os.walk(directory):
for filename in filenames:
results.update(f(directory + filename))
return results
results = getResults(trainingDirectory, getGridSizeComparison)
count = 0
for key, value in results.items():
if value:
count += 1
print('Proportion of training examples with the same grid size: ' + str(round(count / len(results), 2))) | code |
33116653/cell_5 | [
"text_plain_output_1.png"
] | import json
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f = open(filename, 'r')
data = json.loads(f.read())
data['id'] = re.sub('(.*/)|(\\.json)', '', filename)
f.close()
return data
filename = testDirectory + '19bb5feb.json'
readTaskFile(filename) | code |
129006229/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang.drop_duplicates()
lang.info() | code |
129006229/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang.info() | code |
129006229/cell_20 | [
"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
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang.drop_duplicates()
import matplotlib.pyplot as plt
subset_lang = lang[['LoR', 'Edu.day', 'Speaking']]
subset_lang = subset_lang.dropna()
plt.colorbar(label='Formal Education Days (Edu.day)')
import seaborn as sns
subset_lang = lang[['Sex', 'Speaking']]
subset_lang = lang[['AaA', 'LoR']]
subset_lang = lang[['Enroll', 'Speaking', 'Edu.day']]
sns.scatterplot(data=subset_lang, x='Enroll', y='Speaking', hue='Edu.day')
plt.xlabel('Enrollment Duration (Enroll)')
plt.ylabel('Speaking Proficiency Score')
plt.title('Relationship between Enroll, Edu.day, and Speaking Proficiency Score')
plt.show() | code |
129006229/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)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang.drop_duplicates()
import matplotlib.pyplot as plt
subset_lang = lang[['LoR', 'Edu.day', 'Speaking']]
subset_lang = subset_lang.dropna()
plt.scatter(subset_lang['LoR'], subset_lang['Speaking'], c=subset_lang['Edu.day'], cmap='viridis')
plt.xlabel('Length of Residence (LoR)')
plt.ylabel('Speaking Proficiency Score')
plt.colorbar(label='Formal Education Days (Edu.day)')
plt.title('Relationship between LoR, Edu.day, and Speaking Proficiency Score')
plt.show() | code |
129006229/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 |
129006229/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang.info() | code |
129006229/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang.drop_duplicates() | code |
129006229/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang | code |
129006229/cell_17 | [
"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
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang.drop_duplicates()
import matplotlib.pyplot as plt
subset_lang = lang[['LoR', 'Edu.day', 'Speaking']]
subset_lang = subset_lang.dropna()
plt.colorbar(label='Formal Education Days (Edu.day)')
import seaborn as sns
subset_lang = lang[['Sex', 'Speaking']]
subset_lang = lang[['AaA', 'LoR']]
plt.scatter(subset_lang['AaA'], subset_lang['LoR'])
plt.xlabel('Age at Arrival (AaA)')
plt.ylabel('Length of Residence (LoR)')
plt.title('Relationship between AaA and LoR')
plt.show() | code |
129006229/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang.drop_duplicates()
import matplotlib.pyplot as plt
subset_lang = lang[['LoR', 'Edu.day', 'Speaking']]
subset_lang = subset_lang.dropna()
plt.colorbar(label='Formal Education Days (Edu.day)')
import seaborn as sns
subset_lang = lang[['Sex', 'Speaking']]
sns.boxplot(x='Sex', y='Speaking', data=subset_lang)
plt.xlabel('Gender')
plt.ylabel('Speaking Proficiency Score')
plt.title('Distribution of Speaking Proficiency Scores by Gender')
plt.show() | code |
129006229/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang.describe() | code |
17121510/cell_9 | [
"image_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
import matplotlib.pyplot as plt
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LATENT_DIM1, latent_dim2=LATENT_DIM2)
trainer = ModelTrainer(vae, datagen, loss_fn='normal', lr=5e-05, decay=0.0001, beta=1)
trainer.fit(100, 2000, warm_up=True)
import matplotlib.pyplot as plt
plotter = VAEPlotter(trainer, datagen)
plotter.grid_plot() | code |
17121510/cell_6 | [
"image_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LATENT_DIM1, latent_dim2=LATENT_DIM2)
trainer = ModelTrainer(vae, datagen, loss_fn='normal', lr=5e-05, decay=0.0001, beta=1)
trainer.fit(100, 2000, warm_up=True) | code |
17121510/cell_2 | [
"text_plain_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter | code |
17121510/cell_1 | [
"text_plain_output_1.png"
] | !pip install csnl-vae-olaralex==1.92dev0 | code |
17121510/cell_7 | [
"image_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
import matplotlib.pyplot as plt
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LATENT_DIM1, latent_dim2=LATENT_DIM2)
trainer = ModelTrainer(vae, datagen, loss_fn='normal', lr=5e-05, decay=0.0001, beta=1)
trainer.fit(100, 2000, warm_up=True)
import matplotlib.pyplot as plt
plt.title('Model loss')
plt.plot(trainer.history.history['loss'])
plt.plot(trainer.history.history['val_loss'])
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.show() | code |
17121510/cell_3 | [
"text_plain_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl') | code |
17121510/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
import matplotlib.pyplot as plt
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LATENT_DIM1, latent_dim2=LATENT_DIM2)
trainer = ModelTrainer(vae, datagen, loss_fn='normal', lr=5e-05, decay=0.0001, beta=1)
trainer.fit(100, 2000, warm_up=True)
import matplotlib.pyplot as plt
plotter = VAEPlotter(trainer, datagen)
plotter.generate_samples() | code |
17121510/cell_5 | [
"text_plain_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LATENT_DIM1, latent_dim2=LATENT_DIM2)
trainer = ModelTrainer(vae, datagen, loss_fn='normal', lr=5e-05, decay=0.0001, beta=1) | code |
105183805/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import torchvision
print(torchvision.__version__) | code |
105183805/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
df = pd.read_csv('../input/plant-pathology-2021-fgvc8/train.csv')
df['labels'] = df['labels'].apply(lambda string: string.split(' '))
s = list(df['labels'])
mlb = MultiLabelBinarizer()
trainx = pd.DataFrame(mlb.fit_transform(s), columns=mlb.classes_, index=df.index)
trainx.insert(0, 'image', df['image'], True)
trainx | code |
105183805/cell_20 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from albumentations.pytorch.transforms import ToTensorV2
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
from sklearn.preprocessing import MultiLabelBinarizer
from torch.utils.data import Dataset, DataLoader
from transformers import get_cosine_schedule_with_warmup
import cv2
import cv2
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
df = pd.read_csv('../input/plant-pathology-2021-fgvc8/train.csv')
df['labels'] = df['labels'].apply(lambda string: string.split(' '))
s = list(df['labels'])
mlb = MultiLabelBinarizer()
trainx = pd.DataFrame(mlb.fit_transform(s), columns=mlb.classes_, index=df.index)
trainx.insert(0, 'image', df['image'], True)
trainx
t_df = pd.read_csv('../input/plant-pathology-2021-fgvc8/sample_submission.csv')
test_df = t_df.drop(['labels'], axis=1)
test_df
from sklearn.model_selection import train_test_split
train_df = trainx
train_df.reset_index(drop=True, inplace=True)
test_df.reset_index(drop=True, inplace=True)
class CustomDataset(Dataset):
def __init__(self, df, root_dir, transform=None, iftest=False):
self.df = df
self.root_dir = root_dir
self.transform = transform
self.iftest = iftest
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = self.root_dir + self.df.iloc[idx, 0]
image = cv2.imread(img_name, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if self.transform:
image = self.transform(image=image)['image']
if self.iftest:
return image
labels = torch.tensor(np.argmax(self.df.iloc[idx, 1:].values))
return (image, labels)
IMSIZE = 545
IMSIZE = EfficientNet.get_image_size('efficientnet-b5')
train_dataset = CustomDataset(df=train_df, root_dir='../input/plant-pathology-2021-fgvc8/train_images/', transform=Compose([augmentations.geometric.resize.Resize(height=IMSIZE, width=IMSIZE, always_apply=True), HorizontalFlip(p=0.5), VerticalFlip(p=0.5), ShiftScaleRotate(rotate_limit=25.0, p=0.7), OneOf([Emboss(p=1), Sharpen(p=1), Blur(p=1)], p=0.5), PiecewiseAffine(p=0.5), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), always_apply=True), ToTensorV2()]))
test_dataset = CustomDataset(df=test_df, root_dir='../input/plant-pathology-2021-fgvc8/test_images/', transform=Compose([augmentations.geometric.resize.Resize(height=IMSIZE, width=IMSIZE, always_apply=True), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), always_apply=True), ToTensorV2()]), iftest=True)
BATCH_SIZE = 1
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
use_cuda = torch.cuda.is_available()
if use_cuda:
device = 'cuda:0'
use_tpu = False
use_device = True
if use_tpu:
device = 'idk'
from efficientnet_pytorch import EfficientNet
model_efficient = EfficientNet.from_pretrained('efficientnet-b7')
ad = False
model_efficient._fc = nn.Sequential(nn.Linear(model_efficient._fc.in_features, 1000, bias=True), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(1000, 6, bias=True))
if use_device:
model_efficient = model_efficient.to(device)
NEPOCHS = 1
criterion_transfer = nn.CrossEntropyLoss()
learning_rate = 0.0008
optimizer_transfer = optim.AdamW(model_efficient.parameters(), learning_rate, weight_decay=0.001)
num_train_steps = int(len(train_dataset) / BATCH_SIZE * NEPOCHS)
from transformers import get_cosine_schedule_with_warmup
scheduler = get_cosine_schedule_with_warmup(optimizer_transfer, num_warmup_steps=len(train_dataset) / BATCH_SIZE * 5, num_training_steps=num_train_steps)
model_efficient.save('first_weights.h5')
print('Saved model to disk') | code |
105183805/cell_2 | [
"text_plain_output_1.png"
] | pip install --upgrade efficientnet-pytorch | code |
105183805/cell_19 | [
"text_plain_output_1.png"
] | from albumentations.pytorch.transforms import ToTensorV2
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MultiLabelBinarizer
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from tqdm import tqdm
from transformers import get_cosine_schedule_with_warmup
import cv2
import cv2
import numpy as np
import pandas as pd
import scipy
import torch
import torch.nn as nn
import torch.optim as optim
import tqdm
df = pd.read_csv('../input/plant-pathology-2021-fgvc8/train.csv')
df['labels'] = df['labels'].apply(lambda string: string.split(' '))
s = list(df['labels'])
mlb = MultiLabelBinarizer()
trainx = pd.DataFrame(mlb.fit_transform(s), columns=mlb.classes_, index=df.index)
trainx.insert(0, 'image', df['image'], True)
trainx
t_df = pd.read_csv('../input/plant-pathology-2021-fgvc8/sample_submission.csv')
test_df = t_df.drop(['labels'], axis=1)
test_df
from sklearn.model_selection import train_test_split
train_df = trainx
train_df.reset_index(drop=True, inplace=True)
test_df.reset_index(drop=True, inplace=True)
class CustomDataset(Dataset):
def __init__(self, df, root_dir, transform=None, iftest=False):
self.df = df
self.root_dir = root_dir
self.transform = transform
self.iftest = iftest
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = self.root_dir + self.df.iloc[idx, 0]
image = cv2.imread(img_name, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if self.transform:
image = self.transform(image=image)['image']
if self.iftest:
return image
labels = torch.tensor(np.argmax(self.df.iloc[idx, 1:].values))
return (image, labels)
IMSIZE = 545
IMSIZE = EfficientNet.get_image_size('efficientnet-b5')
train_dataset = CustomDataset(df=train_df, root_dir='../input/plant-pathology-2021-fgvc8/train_images/', transform=Compose([augmentations.geometric.resize.Resize(height=IMSIZE, width=IMSIZE, always_apply=True), HorizontalFlip(p=0.5), VerticalFlip(p=0.5), ShiftScaleRotate(rotate_limit=25.0, p=0.7), OneOf([Emboss(p=1), Sharpen(p=1), Blur(p=1)], p=0.5), PiecewiseAffine(p=0.5), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), always_apply=True), ToTensorV2()]))
test_dataset = CustomDataset(df=test_df, root_dir='../input/plant-pathology-2021-fgvc8/test_images/', transform=Compose([augmentations.geometric.resize.Resize(height=IMSIZE, width=IMSIZE, always_apply=True), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), always_apply=True), ToTensorV2()]), iftest=True)
BATCH_SIZE = 1
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
use_cuda = torch.cuda.is_available()
if use_cuda:
device = 'cuda:0'
use_tpu = False
use_device = True
if use_tpu:
device = 'idk'
def train(n_epochs, train_loader, valid_loader, model, optimizer, criterion, use_device, save_path, final_train=False, ifsched=False):
for epoch in range(1, n_epochs + 1):
train_loss = 0.0
valid_loss = 0.0
labels_for_acc = []
output_for_acc = []
labels_for_accv = []
output_for_accv = []
model.train()
for batch_idx, (data, target) in enumerate(tqdm(train_loader)):
if use_device:
data, target = (data.to(device), target.to(device))
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
train_loss += loss.item() * data.size(0)
loss.backward()
optimizer.step()
if ifsched:
scheduler.step()
labels_for_acc = np.concatenate((labels_for_acc, target.cpu().numpy()), 0)
output_for_acc = np.concatenate((output_for_acc, np.argmax(output.cpu().detach().numpy(), 1)), 0)
train_loss = train_loss / len(train_loader.dataset)
train_acc = accuracy_score(labels_for_acc, output_for_acc)
if not final_train:
with torch.no_grad():
model.eval()
for batch_idx, (data, target) in enumerate(valid_loader):
if use_device:
data, target = (data.to(device), target.to(device))
output = model(data)
loss = criterion(output, target)
valid_loss += loss.item() * data.size(0)
labels_for_accv = np.concatenate((labels_for_accv, target.cpu().numpy()), 0)
output_for_accv = np.concatenate((output_for_accv, np.argmax(output.cpu().detach().numpy(), 1)), 0)
valid_loss = valid_loss / len(valid_loader.dataset)
valid_acc = accuracy_score(labels_for_accv, output_for_accv)
from efficientnet_pytorch import EfficientNet
model_efficient = EfficientNet.from_pretrained('efficientnet-b7')
ad = False
model_efficient._fc = nn.Sequential(nn.Linear(model_efficient._fc.in_features, 1000, bias=True), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(1000, 6, bias=True))
if use_device:
model_efficient = model_efficient.to(device)
NEPOCHS = 1
criterion_transfer = nn.CrossEntropyLoss()
learning_rate = 0.0008
optimizer_transfer = optim.AdamW(model_efficient.parameters(), learning_rate, weight_decay=0.001)
num_train_steps = int(len(train_dataset) / BATCH_SIZE * NEPOCHS)
from transformers import get_cosine_schedule_with_warmup
scheduler = get_cosine_schedule_with_warmup(optimizer_transfer, num_warmup_steps=len(train_dataset) / BATCH_SIZE * 5, num_training_steps=num_train_steps)
def test(model, test_loader, use_device):
preds_for_output = np.zeros((1, 6))
with torch.no_grad():
model.eval()
for images in test_loader:
print(type(images))
if use_device:
images = images.to(device)
preds = model(images)
preds_for_output = np.concatenate((preds_for_output, preds.cpu().detach().numpy()), 0)
return preds_for_output
num_runs = 2
import scipy
subs = []
for i in range(num_runs):
out = test(model_efficient, test_loader, use_device)
output = pd.DataFrame(scipy.special.softmax(out, 1), columns=['complex', 'frog_eye_leaf_spot', 'healthy', 'powdery_mildew', 'rust', 'scab'])
output.drop(0, inplace=True)
output.reset_index(drop=True, inplace=True)
subs.append(output)
sub_eff = sum(subs) / num_runs | code |
105183805/cell_18 | [
"text_html_output_1.png"
] | from albumentations.pytorch.transforms import ToTensorV2
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MultiLabelBinarizer
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from tqdm import tqdm
from transformers import get_cosine_schedule_with_warmup
import cv2
import cv2
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import tqdm
df = pd.read_csv('../input/plant-pathology-2021-fgvc8/train.csv')
df['labels'] = df['labels'].apply(lambda string: string.split(' '))
s = list(df['labels'])
mlb = MultiLabelBinarizer()
trainx = pd.DataFrame(mlb.fit_transform(s), columns=mlb.classes_, index=df.index)
trainx.insert(0, 'image', df['image'], True)
trainx
t_df = pd.read_csv('../input/plant-pathology-2021-fgvc8/sample_submission.csv')
test_df = t_df.drop(['labels'], axis=1)
test_df
from sklearn.model_selection import train_test_split
train_df = trainx
train_df.reset_index(drop=True, inplace=True)
test_df.reset_index(drop=True, inplace=True)
class CustomDataset(Dataset):
def __init__(self, df, root_dir, transform=None, iftest=False):
self.df = df
self.root_dir = root_dir
self.transform = transform
self.iftest = iftest
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = self.root_dir + self.df.iloc[idx, 0]
image = cv2.imread(img_name, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if self.transform:
image = self.transform(image=image)['image']
if self.iftest:
return image
labels = torch.tensor(np.argmax(self.df.iloc[idx, 1:].values))
return (image, labels)
IMSIZE = 545
IMSIZE = EfficientNet.get_image_size('efficientnet-b5')
train_dataset = CustomDataset(df=train_df, root_dir='../input/plant-pathology-2021-fgvc8/train_images/', transform=Compose([augmentations.geometric.resize.Resize(height=IMSIZE, width=IMSIZE, always_apply=True), HorizontalFlip(p=0.5), VerticalFlip(p=0.5), ShiftScaleRotate(rotate_limit=25.0, p=0.7), OneOf([Emboss(p=1), Sharpen(p=1), Blur(p=1)], p=0.5), PiecewiseAffine(p=0.5), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), always_apply=True), ToTensorV2()]))
test_dataset = CustomDataset(df=test_df, root_dir='../input/plant-pathology-2021-fgvc8/test_images/', transform=Compose([augmentations.geometric.resize.Resize(height=IMSIZE, width=IMSIZE, always_apply=True), Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), always_apply=True), ToTensorV2()]), iftest=True)
BATCH_SIZE = 1
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
use_cuda = torch.cuda.is_available()
if use_cuda:
device = 'cuda:0'
use_tpu = False
use_device = True
if use_tpu:
device = 'idk'
def train(n_epochs, train_loader, valid_loader, model, optimizer, criterion, use_device, save_path, final_train=False, ifsched=False):
for epoch in range(1, n_epochs + 1):
train_loss = 0.0
valid_loss = 0.0
labels_for_acc = []
output_for_acc = []
labels_for_accv = []
output_for_accv = []
model.train()
for batch_idx, (data, target) in enumerate(tqdm(train_loader)):
if use_device:
data, target = (data.to(device), target.to(device))
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
train_loss += loss.item() * data.size(0)
loss.backward()
optimizer.step()
if ifsched:
scheduler.step()
labels_for_acc = np.concatenate((labels_for_acc, target.cpu().numpy()), 0)
output_for_acc = np.concatenate((output_for_acc, np.argmax(output.cpu().detach().numpy(), 1)), 0)
train_loss = train_loss / len(train_loader.dataset)
train_acc = accuracy_score(labels_for_acc, output_for_acc)
if not final_train:
with torch.no_grad():
model.eval()
for batch_idx, (data, target) in enumerate(valid_loader):
if use_device:
data, target = (data.to(device), target.to(device))
output = model(data)
loss = criterion(output, target)
valid_loss += loss.item() * data.size(0)
labels_for_accv = np.concatenate((labels_for_accv, target.cpu().numpy()), 0)
output_for_accv = np.concatenate((output_for_accv, np.argmax(output.cpu().detach().numpy(), 1)), 0)
valid_loss = valid_loss / len(valid_loader.dataset)
valid_acc = accuracy_score(labels_for_accv, output_for_accv)
from efficientnet_pytorch import EfficientNet
model_efficient = EfficientNet.from_pretrained('efficientnet-b7')
ad = False
model_efficient._fc = nn.Sequential(nn.Linear(model_efficient._fc.in_features, 1000, bias=True), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(1000, 6, bias=True))
if use_device:
model_efficient = model_efficient.to(device)
NEPOCHS = 1
criterion_transfer = nn.CrossEntropyLoss()
learning_rate = 0.0008
optimizer_transfer = optim.AdamW(model_efficient.parameters(), learning_rate, weight_decay=0.001)
num_train_steps = int(len(train_dataset) / BATCH_SIZE * NEPOCHS)
from transformers import get_cosine_schedule_with_warmup
scheduler = get_cosine_schedule_with_warmup(optimizer_transfer, num_warmup_steps=len(train_dataset) / BATCH_SIZE * 5, num_training_steps=num_train_steps)
train(NEPOCHS, train_loader, None, model_efficient, optimizer_transfer, criterion_transfer, use_device, 'model_transfer.pt', ifsched=True, final_train=True) | code |
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