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90118648/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/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test['Embarked'] = [1 if l == 'S' else 2 if l == 'C' else 3 for l in test['Embarked']]
test['Embarked'].value_counts() | code |
90118648/cell_27 | [
"text_plain_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.impute import KNNImputer
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')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predictors]
y_train = train['Survived']
X_test = test[predictors]
knn_imputer = KNNImputer()
X_train = pd.DataFrame(knn_imputer.fit_transform(X_train), columns=predictors)
X_train[['Sex', 'Embarked', 'Pclass', 'Title']] = X_train[['Sex', 'Embarked', 'Pclass', 'Title']].astype('category')
encoder = TargetEncoder(return_df=True)
X_train_te = encoder.fit_transform(X_train, y_train)
X_train_te.describe() | code |
90118648/cell_37 | [
"text_html_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
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')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predictors]
y_train = train['Survived']
X_test = test[predictors]
knn_imputer = KNNImputer()
X_train = pd.DataFrame(knn_imputer.fit_transform(X_train), columns=predictors)
X_test = pd.DataFrame(knn_imputer.transform(X_test), columns=predictors)
X_train[['Sex', 'Embarked', 'Pclass', 'Title']] = X_train[['Sex', 'Embarked', 'Pclass', 'Title']].astype('category')
X_test[['Sex', 'Embarked', 'Pclass', 'Title']] = X_test[['Sex', 'Embarked', 'Pclass', 'Title']].astype('category')
X_train_ohe = pd.get_dummies(X_train)
X_test_ohe = pd.get_dummies(X_test)
encoder = TargetEncoder(return_df=True)
X_train_te = encoder.fit_transform(X_train, y_train)
X_test_te = encoder.transform(X_test)
num = ['Age', 'Parch', 'SibSp', 'Fare']
power = PowerTransformer()
train_power = pd.DataFrame(power.fit_transform(X_train[num]), columns=num)
test_power = pd.DataFrame(power.transform(X_test[num]), columns=num)
X_train_ohe_power = pd.concat([train_power[num], X_train_ohe.drop(num, axis=1)], axis=1)
X_test_ohe_power = pd.concat([test_power[num], X_test_ohe.drop(num, axis=1)], axis=1)
X_train_te_power = pd.concat([train_power[num], X_train_te.drop(num, axis=1)], axis=1)
X_test_te_power = pd.concat([test_power[num], X_test_te.drop(num, axis=1)], axis=1)
rf = RandomForestClassifier(n_estimators=70, max_depth=4, random_state=42)
rf.fit(X_train_ohe_power, y_train)
y_rf = rf.predict(X_test_ohe_power)
results = pd.DataFrame({'PassengerId': test['PassengerId'], 'Survived': y_rf})
results.to_csv('submission.csv', index=False)
results['Survived'].value_counts(normalize=True) | code |
90118648/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')
test.info() | code |
90118648/cell_36 | [
"text_html_output_1.png"
] | from category_encoders import TargetEncoder
from sklearn.impute import KNNImputer
from sklearn.preprocessing import PowerTransformer
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')
predictors = ['Pclass', 'Sex', 'Age', 'Parch', 'SibSp', 'Fare', 'Embarked']
X_train = train[predictors]
y_train = train['Survived']
X_test = test[predictors]
knn_imputer = KNNImputer()
X_train = pd.DataFrame(knn_imputer.fit_transform(X_train), columns=predictors)
X_test = pd.DataFrame(knn_imputer.transform(X_test), columns=predictors)
X_train[['Sex', 'Embarked', 'Pclass', 'Title']] = X_train[['Sex', 'Embarked', 'Pclass', 'Title']].astype('category')
X_test[['Sex', 'Embarked', 'Pclass', 'Title']] = X_test[['Sex', 'Embarked', 'Pclass', 'Title']].astype('category')
X_train_ohe = pd.get_dummies(X_train)
X_test_ohe = pd.get_dummies(X_test)
encoder = TargetEncoder(return_df=True)
X_train_te = encoder.fit_transform(X_train, y_train)
X_test_te = encoder.transform(X_test)
num = ['Age', 'Parch', 'SibSp', 'Fare']
power = PowerTransformer()
train_power = pd.DataFrame(power.fit_transform(X_train[num]), columns=num)
test_power = pd.DataFrame(power.transform(X_test[num]), columns=num)
X_train_ohe_power = pd.concat([train_power[num], X_train_ohe.drop(num, axis=1)], axis=1)
X_test_ohe_power = pd.concat([test_power[num], X_test_ohe.drop(num, axis=1)], axis=1)
X_train_te_power = pd.concat([train_power[num], X_train_te.drop(num, axis=1)], axis=1)
X_test_te_power = pd.concat([test_power[num], X_test_te.drop(num, axis=1)], axis=1)
X_test_te_power.head() | code |
18118296/cell_4 | [
"text_html_output_1.png"
] | import os
data_dir = '../input'
os.listdir(f'{data_dir}') | code |
18118296/cell_6 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10) | code |
18118296/cell_29 | [
"text_html_output_1.png"
] | from bisect import bisect
from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier,
from sklearn.model_selection import KFold
import numpy as np
import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10)
train_df_raw.describe(include='all').T
test_df_raw.describe(include='all').T
train_df = train_df_raw
test_df = test_df_raw
all_df = [train_df, test_df]
category_maps = {}
def Categorify(df: pd.DataFrame, cat_names):
for cat_name in cat_names:
uniques = df[cat_name].unique()
category_maps[cat_name] = {i: uniques[i] for i in range(len(uniques))}
df[cat_name] = [np.where(uniques == key)[0][0] for key in df[cat_name]]
def Quantile(df: pd.DataFrame, quant_names, quants=[0.25, 0.5, 0.75]):
for quant_name in quant_names:
quant_col_name = f'{quant_name}_quantile'
quant_vals = [np.quantile(df[quant_name], quant) for quant in quants]
df[quant_col_name] = [bisect(quant_vals, x) for x in df[quant_name]]
SEED = 0
NFOLDS = 5
kf = KFold(n_splits=NFOLDS, random_state=SEED)
class SklearnHelper(object):
def __init__(self, name, clf, seed=0, params=None):
params['random_state'] = seed
self.name = name
self.clf = clf(**params)
def train(self, x_train, y_train):
self.clf.fit(x_train, y_train)
def predict(self, x):
return self.clf.predict(x)
def fit(self, x, y):
return self.clf.fit(x, y)
def feature_importances(self, x, y):
return self.clf.fit(x, y).feature_importances_
def get_oof(clf, x_train, y_train, x_test):
oof_train = np.zeros((x_train.shape[0],))
oof_test = np.zeros((x_test.shape[0],))
oof_test_skf = np.empty((NFOLDS, x_test.shape[0]))
for i, (train_index, test_index) in enumerate(kf.split(x_train, y_train)):
x_tr = x_train[train_index]
y_tr = y_train[train_index]
x_te = x_train[test_index]
clf.train(x_tr, y_tr)
oof_train[test_index] = clf.predict(x_te)
oof_test_skf[i, :] = clf.predict(x_test)
oof_test[:] = oof_test_skf.mean(axis=0)
return (oof_train.reshape(-1, 1), oof_test.reshape(-1, 1))
classifier_stack = [SklearnHelper('RandomForest', clf=RandomForestClassifier, seed=SEED, params={'n_jobs': -1, 'n_estimators': 500, 'max_depth': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'verbose': 0}), SklearnHelper('ExtraTrees', clf=ExtraTreesClassifier, seed=SEED, params={'n_jobs': -1, 'n_estimators': 500, 'max_depth': 8, 'min_samples_leaf': 2, 'verbose': 0}), SklearnHelper('AdaBoost', clf=AdaBoostClassifier, seed=SEED, params={'n_estimators': 500, 'learning_rate': 0.75}), SklearnHelper('GradientBoost', clf=GradientBoostingClassifier, seed=SEED, params={'n_estimators': 500, 'max_depth': 5, 'min_samples_leaf': 2, 'verbose': 0})]
dep_var = 'Survived'
drop_vars = ['PassengerId', 'Name', 'Ticket', 'Cabin']
x_train_df = train_df.drop(drop_vars, axis=1).drop(dep_var, axis=1)
x_train = x_train_df.values
y_train = train_df[dep_var].ravel()
x_test = test_df.drop(drop_vars, axis=1).values
oofs = {clf.name: get_oof(clf, x_train, y_train, x_test) for clf in classifier_stack}
print('Training is complete') | code |
18118296/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10)
train_df_raw.describe(include='all').T | code |
18118296/cell_32 | [
"text_html_output_1.png"
] | from bisect import bisect
from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier,
from sklearn.model_selection import KFold
import numpy as np
import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10)
train_df_raw.describe(include='all').T
test_df_raw.describe(include='all').T
train_df = train_df_raw
test_df = test_df_raw
all_df = [train_df, test_df]
category_maps = {}
def Categorify(df: pd.DataFrame, cat_names):
for cat_name in cat_names:
uniques = df[cat_name].unique()
category_maps[cat_name] = {i: uniques[i] for i in range(len(uniques))}
df[cat_name] = [np.where(uniques == key)[0][0] for key in df[cat_name]]
def Quantile(df: pd.DataFrame, quant_names, quants=[0.25, 0.5, 0.75]):
for quant_name in quant_names:
quant_col_name = f'{quant_name}_quantile'
quant_vals = [np.quantile(df[quant_name], quant) for quant in quants]
df[quant_col_name] = [bisect(quant_vals, x) for x in df[quant_name]]
SEED = 0
NFOLDS = 5
kf = KFold(n_splits=NFOLDS, random_state=SEED)
class SklearnHelper(object):
def __init__(self, name, clf, seed=0, params=None):
params['random_state'] = seed
self.name = name
self.clf = clf(**params)
def train(self, x_train, y_train):
self.clf.fit(x_train, y_train)
def predict(self, x):
return self.clf.predict(x)
def fit(self, x, y):
return self.clf.fit(x, y)
def feature_importances(self, x, y):
return self.clf.fit(x, y).feature_importances_
def get_oof(clf, x_train, y_train, x_test):
oof_train = np.zeros((x_train.shape[0],))
oof_test = np.zeros((x_test.shape[0],))
oof_test_skf = np.empty((NFOLDS, x_test.shape[0]))
for i, (train_index, test_index) in enumerate(kf.split(x_train, y_train)):
x_tr = x_train[train_index]
y_tr = y_train[train_index]
x_te = x_train[test_index]
clf.train(x_tr, y_tr)
oof_train[test_index] = clf.predict(x_te)
oof_test_skf[i, :] = clf.predict(x_test)
oof_test[:] = oof_test_skf.mean(axis=0)
return (oof_train.reshape(-1, 1), oof_test.reshape(-1, 1))
classifier_stack = [SklearnHelper('RandomForest', clf=RandomForestClassifier, seed=SEED, params={'n_jobs': -1, 'n_estimators': 500, 'max_depth': 6, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'verbose': 0}), SklearnHelper('ExtraTrees', clf=ExtraTreesClassifier, seed=SEED, params={'n_jobs': -1, 'n_estimators': 500, 'max_depth': 8, 'min_samples_leaf': 2, 'verbose': 0}), SklearnHelper('AdaBoost', clf=AdaBoostClassifier, seed=SEED, params={'n_estimators': 500, 'learning_rate': 0.75}), SklearnHelper('GradientBoost', clf=GradientBoostingClassifier, seed=SEED, params={'n_estimators': 500, 'max_depth': 5, 'min_samples_leaf': 2, 'verbose': 0})]
dep_var = 'Survived'
drop_vars = ['PassengerId', 'Name', 'Ticket', 'Cabin']
x_train_df = train_df.drop(drop_vars, axis=1).drop(dep_var, axis=1)
x_train = x_train_df.values
y_train = train_df[dep_var].ravel()
x_test = test_df.drop(drop_vars, axis=1).values
oofs = {clf.name: get_oof(clf, x_train, y_train, x_test) for clf in classifier_stack}
base_predictions_train = pd.DataFrame({key: oofs[key][0].ravel() for key in oofs})
base_predictions_train.head() | code |
18118296/cell_8 | [
"text_html_output_1.png"
] | import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10)
test_df_raw.describe(include='all').T | code |
18118296/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
data_dir = '../input'
os.listdir(f'{data_dir}')
train_df_raw = pd.read_csv(f'{data_dir}/train.csv', low_memory=False)
test_df_raw = pd.read_csv(f'{data_dir}/test.csv', low_memory=False)
train_df_raw.sample(10)
train_df_raw.describe(include='all').T
test_df_raw.describe(include='all').T
train_df = train_df_raw
test_df = test_df_raw
all_df = [train_df, test_df]
category_maps = {}
def Categorify(df: pd.DataFrame, cat_names):
for cat_name in cat_names:
uniques = df[cat_name].unique()
category_maps[cat_name] = {i: uniques[i] for i in range(len(uniques))}
df[cat_name] = [np.where(uniques == key)[0][0] for key in df[cat_name]]
cat_names = ['Sex', 'Embarked']
list(map(lambda df: Categorify(df, cat_names), all_df))
category_maps | code |
106211827/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import tensorflow as tf
from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
from transformers import WEIGHTS_NAME, CONFIG_NAME
import os | code |
106211827/cell_23 | [
"text_plain_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
import os
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import os
data_location = 'data'
if not os.path.exists(data_location):
os.makedirs(data_location)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
configuration = GPT2Config(vocab_size=tokenizer.vocab_size, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id)
model = TFGPT2LMHeadModel(configuration)
textfile = open('./data/poetry.txt', 'r', encoding='utf-8')
text = textfile.read()
textfile.close()
string_tokenized = tokenizer.encode(text)
examples = []
block_size = 100
BATCH_SIZE = 12
BUFFER_SIZE = 1000
for i in range(0, len(string_tokenized) - block_size + 1, block_size):
examples.append(string_tokenized[i:i + block_size])
inputs, labels = ([], [])
for ex in examples:
inputs.append(ex[:-1])
labels.append(ex[1:])
dataset = tf.data.Dataset.from_tensor_slices((inputs, labels))
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-05, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss)
model.fit(dataset, epochs=30)
save_location = './models'
if not os.path.exists(save_location):
os.makedirs(save_location)
model.save_pretrained(save_location)
tokenizer.save_pretrained(save_location) | code |
106211827/cell_11 | [
"text_plain_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2') | code |
106211827/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
import tensorflow as tf
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
configuration = GPT2Config(vocab_size=tokenizer.vocab_size, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id)
model = TFGPT2LMHeadModel(configuration)
textfile = open('./data/poetry.txt', 'r', encoding='utf-8')
text = textfile.read()
textfile.close()
string_tokenized = tokenizer.encode(text)
examples = []
block_size = 100
BATCH_SIZE = 12
BUFFER_SIZE = 1000
for i in range(0, len(string_tokenized) - block_size + 1, block_size):
examples.append(string_tokenized[i:i + block_size])
inputs, labels = ([], [])
for ex in examples:
inputs.append(ex[:-1])
labels.append(ex[1:])
dataset = tf.data.Dataset.from_tensor_slices((inputs, labels))
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
print('Done creating dataset') | code |
106211827/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 |
106211827/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | !pip install tokenizer
!pip install transformers | code |
106211827/cell_22 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
import tensorflow as tf
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
configuration = GPT2Config(vocab_size=tokenizer.vocab_size, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id)
model = TFGPT2LMHeadModel(configuration)
textfile = open('./data/poetry.txt', 'r', encoding='utf-8')
text = textfile.read()
textfile.close()
string_tokenized = tokenizer.encode(text)
examples = []
block_size = 100
BATCH_SIZE = 12
BUFFER_SIZE = 1000
for i in range(0, len(string_tokenized) - block_size + 1, block_size):
examples.append(string_tokenized[i:i + block_size])
inputs, labels = ([], [])
for ex in examples:
inputs.append(ex[:-1])
labels.append(ex[1:])
dataset = tf.data.Dataset.from_tensor_slices((inputs, labels))
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-05, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss)
model.fit(dataset, epochs=30) | code |
106211827/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
configuration = GPT2Config(vocab_size=tokenizer.vocab_size, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id)
model = TFGPT2LMHeadModel(configuration) | code |
128015730/cell_7 | [
"text_plain_output_1.png"
] | !python /kaggle/input/iot23bymyself/creatDataset.py | code |
128015730/cell_8 | [
"text_plain_output_1.png"
] | !zip -r /kaggle/working/dataImage imagesData | code |
16147726/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
import os
print(os.listdir('../input')) | code |
16147726/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import pickle
import pickle
with open('../input/X.pickle', 'rb') as fp:
X_feature = pickle.load(fp)
with open('../input/Y.pickle', 'rb') as fp:
Y_label = pickle.load(fp)
X_feature = X_feature / 255.0
model = Sequential()
model.add(Conv2D(64, (3, 3), input_shape=X_feature.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(4))
model.add(Activation('softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
model.fit(x=X_feature, y=Y_label, batch_size=20, epochs=50, validation_split=0.1, shuffle=True) | code |
1010749/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
for c in columns_with_null_values:
counter = raw_data[c].value_counts(dropna=False)
try:
counts = counter[None]
except:
counts = counter[float('nan')]
length = len(raw_data)
usable = length - counts
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
numerical_columns, categorical_columns = ([], [])
temporal_columns = ['GarageYrBlt', 'YearBuilt', 'YearRemodAdd', 'MoSold', 'YrSold']
from_int = ['MSSubClass']
categorical_columns = object_columns + from_int + temporal_columns
numerical_columns = list(filter(lambda x: x not in temporal_columns + ['MSSubClass'], int_columns)) + list(filter(lambda x: x not in temporal_columns, float_columns))
bounded_columns = ['OverallQual', 'OverallCond']
binary_columns = []
for c in categorical_columns:
classes = raw_data[c].unique()
length = len(classes)
if length == 2:
binary_columns.append(c)
has_zero_columns = []
for i in int_columns:
if 0 in raw_data[i].unique():
has_zero_columns.append(i)
counting_columns = ['BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars']
(categorical_columns, numerical_columns)
(binary_columns, temporal_columns)
(bounded_columns, has_zero_columns, counting_columns) | code |
1010749/cell_9 | [
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"image_output_632.png",
"image_output_510.png",
"image_output_259.png",
"image_output_604.png",
"image_output_168.png",
"image_output_258.png",
"image_output_407.png",
"image_output_719.png",
"image_output_646.png",
"image_output_236.png",
"image_output_497.png",
"image_output_154.png",
"image_output_102.png",
"image_output_656.png",
"image_output_644.png",
"image_output_653.png",
"image_output_176.png",
"image_output_321.png",
"image_output_706.png",
"image_output_175.png",
"image_output_567.png",
"image_output_124.png",
"image_output_505.png",
"image_output_88.png",
"image_output_272.png",
"image_output_33.png",
"image_output_140.png",
"image_output_449.png",
"image_output_490.png",
"image_output_569.png",
"image_output_450.png",
"image_output_714.png",
"image_output_345.png",
"image_output_358.png",
"image_output_690.png",
"image_output_87.png",
"image_output_255.png",
"image_output_540.png",
"image_output_50.png",
"image_output_455.png",
"image_output_675.png",
"image_output_15.png",
"image_output_267.png",
"image_output_99.png",
"image_output_49.png",
"image_output_197.png",
"image_output_624.png",
"image_output_100.png",
"image_output_129.png",
"image_output_493.png",
"image_output_491.png",
"image_output_691.png",
"image_output_444.png",
"image_output_166.png",
"image_output_76.png",
"image_output_223.png",
"image_output_9.png",
"image_output_19.png",
"image_output_371.png",
"image_output_79.png",
"image_output_215.png",
"image_output_61.png",
"image_output_622.png",
"image_output_396.png",
"image_output_203.png",
"image_output_563.png",
"image_output_390.png",
"image_output_414.png",
"image_output_38.png",
"image_output_334.png",
"image_output_113.png",
"image_output_26.png",
"image_output_460.png",
"image_output_446.png",
"image_output_600.png",
"image_output_376.png",
"image_output_674.png",
"image_output_264.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
for c in columns_with_null_values:
counter = raw_data[c].value_counts(dropna=False)
try:
counts = counter[None]
except:
counts = counter[float('nan')]
print(c)
length = len(raw_data)
usable = length - counts
print('%s/%s, %.1f Percent, %s usable rows' % (counts, length, counts / length * 100, usable)) | code |
1010749/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
for c in columns_with_null_values:
counter = raw_data[c].value_counts(dropna=False)
try:
counts = counter[None]
except:
counts = counter[float('nan')]
length = len(raw_data)
usable = length - counts
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
numerical_columns, categorical_columns = ([], [])
temporal_columns = ['GarageYrBlt', 'YearBuilt', 'YearRemodAdd', 'MoSold', 'YrSold']
from_int = ['MSSubClass']
categorical_columns = object_columns + from_int + temporal_columns
numerical_columns = list(filter(lambda x: x not in temporal_columns + ['MSSubClass'], int_columns)) + list(filter(lambda x: x not in temporal_columns, float_columns))
bounded_columns = ['OverallQual', 'OverallCond']
binary_columns = []
for c in categorical_columns:
classes = raw_data[c].unique()
length = len(classes)
if length == 2:
binary_columns.append(c)
counting_columns = ['BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars']
month_code = raw_data['MoSold'].unique()
def get_season(month_code):
m = month_code
if m in [3, 4, 5]:
return 'Spring'
elif m in [6, 7, 8]:
return 'Summer'
elif m in [9, 10, 11]:
return 'Autumn'
elif m in [12, 1, 2]:
return 'Winter'
seasons_series = raw_data['MoSold'].apply(get_season)
seasons_series.name = 'season'
year_sold = raw_data['YrSold']
year_built = raw_data['YearBuilt']
house_age_series = year_sold.subtract(year_built)
house_age_series.name = 'HouseAge'
garage_year_built = raw_data['GarageYrBlt']
year_built = raw_data['YearBuilt']
garage_age_series = year_sold.subtract(garage_year_built)
garage_age_series.name = 'GarageAge'
year_remod_add = raw_data['YearRemodAdd']
year_built = raw_data['YearBuilt']
remod_recency_series = year_sold.subtract(year_remod_add)
remod_recency_series.name = 'RemodRecency'
recent_df = raw_data
recent_df = recent_df.assign(garage_age=garage_age_series)
recent_df = recent_df.assign(remod_recency=remod_recency_series)
recent_df = recent_df.assign(house_age=house_age_series)
recent_df = recent_df.assign(season=seasons_series)
categorical_columns = categorical_columns + ['season']
numerical_columns = numerical_columns + ['garage_age', 'remod_recency', 'house_age']
recent_df[categorical_columns] = recent_df[categorical_columns].fillna('None')
import matplotlib.pyplot as plt
plt.style.use('ggplot')
for c in categorical_columns + bounded_columns + counting_columns:
if c in temporal_columns and c != 'MoSold':
continue
means = recent_df[['SalePrice', c]].groupby(c).mean().sort_values('SalePrice', ascending=False)
errors = recent_df[['SalePrice', c]].groupby(c).std()
barplot = means.plot.bar(yerr=errors)
plt.title(c)
plt.show() | code |
1010749/cell_40 | [
"image_output_11.png",
"image_output_24.png",
"image_output_46.png",
"image_output_25.png",
"image_output_47.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13.png",
"image_output_40.png",
"image_output_5.png",
"image_output_48.png",
"image_output_18.png",
"image_output_21.png",
"image_output_52.png",
"image_output_7.png",
"image_output_56.png",
"image_output_31.png",
"image_output_20.png",
"image_output_32.png",
"image_output_53.png",
"image_output_4.png",
"image_output_51.png",
"image_output_42.png",
"image_output_35.png",
"image_output_41.png",
"image_output_57.png",
"image_output_36.png",
"image_output_8.png",
"image_output_37.png",
"image_output_16.png",
"image_output_27.png",
"image_output_54.png",
"image_output_6.png",
"image_output_45.png",
"image_output_12.png",
"image_output_22.png",
"image_output_55.png",
"image_output_3.png",
"image_output_29.png",
"image_output_44.png",
"image_output_43.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_33.png",
"image_output_50.png",
"image_output_15.png",
"image_output_49.png",
"image_output_9.png",
"image_output_19.png",
"image_output_38.png",
"image_output_26.png"
] | from statsmodels.graphics.factorplots import interaction_plot
from statsmodels.stats.weightstats import ztest
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
for c in columns_with_null_values:
counter = raw_data[c].value_counts(dropna=False)
try:
counts = counter[None]
except:
counts = counter[float('nan')]
length = len(raw_data)
usable = length - counts
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
numerical_columns, categorical_columns = ([], [])
temporal_columns = ['GarageYrBlt', 'YearBuilt', 'YearRemodAdd', 'MoSold', 'YrSold']
from_int = ['MSSubClass']
categorical_columns = object_columns + from_int + temporal_columns
numerical_columns = list(filter(lambda x: x not in temporal_columns + ['MSSubClass'], int_columns)) + list(filter(lambda x: x not in temporal_columns, float_columns))
bounded_columns = ['OverallQual', 'OverallCond']
binary_columns = []
for c in categorical_columns:
classes = raw_data[c].unique()
length = len(classes)
if length == 2:
binary_columns.append(c)
counting_columns = ['BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars']
month_code = raw_data['MoSold'].unique()
def get_season(month_code):
m = month_code
if m in [3, 4, 5]:
return 'Spring'
elif m in [6, 7, 8]:
return 'Summer'
elif m in [9, 10, 11]:
return 'Autumn'
elif m in [12, 1, 2]:
return 'Winter'
seasons_series = raw_data['MoSold'].apply(get_season)
seasons_series.name = 'season'
year_sold = raw_data['YrSold']
year_built = raw_data['YearBuilt']
house_age_series = year_sold.subtract(year_built)
house_age_series.name = 'HouseAge'
garage_year_built = raw_data['GarageYrBlt']
year_built = raw_data['YearBuilt']
garage_age_series = year_sold.subtract(garage_year_built)
garage_age_series.name = 'GarageAge'
year_remod_add = raw_data['YearRemodAdd']
year_built = raw_data['YearBuilt']
remod_recency_series = year_sold.subtract(year_remod_add)
remod_recency_series.name = 'RemodRecency'
recent_df = raw_data
recent_df = recent_df.assign(garage_age=garage_age_series)
recent_df = recent_df.assign(remod_recency=remod_recency_series)
recent_df = recent_df.assign(house_age=house_age_series)
recent_df = recent_df.assign(season=seasons_series)
categorical_columns = categorical_columns + ['season']
numerical_columns = numerical_columns + ['garage_age', 'remod_recency', 'house_age']
recent_df[categorical_columns] = recent_df[categorical_columns].fillna('None')
import matplotlib.pyplot as plt
plt.style.use("ggplot")
for c in (categorical_columns + bounded_columns + counting_columns):
if c in temporal_columns and c != "MoSold":
continue
means = recent_df[["SalePrice",c]].groupby(c).mean().sort_values("SalePrice",ascending=False)
errors = recent_df[["SalePrice",c]].groupby(c).std()
barplot = means.plot.bar(yerr=errors)
plt.title(c)
plt.show()
data = recent_df
iv = 'CentralAir'
dv = 'SalePrice'
for c in categorical_columns + bounded_columns + counting_columns:
if c in temporal_columns:
continue
from statsmodels.stats.weightstats import ztest
ztest_dictionary = {}
for c in categorical_columns + bounded_columns + counting_columns:
if c in temporal_columns:
continue
subclasses = recent_df[c].unique()
delete = []
ttests, pvalues, combinations = ([], [], [])
for sc in subclasses:
for scsc in subclasses:
if scsc in delete or scsc == sc:
continue
sample_one = recent_df[recent_df[c] == sc]['SalePrice']
sample_two = recent_df[recent_df[c] == scsc]['SalePrice']
ttest = ztest(sample_one, sample_two)[0]
pvalue = ztest(sample_one, sample_two)[1]
combination = '%s * %s' % (sc, scsc)
ttests.append(ttest)
pvalues.append(pvalue)
combinations.append(combination)
d = {'t-test': ttests, 'p-value': pvalues}
ztest_dictionary[c] = pd.DataFrame(index=combinations, data=d)
from statsmodels.graphics.factorplots import interaction_plot
categorical_columnss = categorical_columns + counting_columns + bounded_columns
for c in categorical_columnss:
if c in temporal_columns:
continue
num = recent_df['SalePrice']
c1 = recent_df[c]
delete = []
for cc in categorical_columnss:
if cc in temporal_columns or cc == c or cc in delete:
continue
c2 = recent_df[cc]
c1_classes = len(recent_df[c])
c2_classes = len(recent_df[cc])
if c2_classes < c1_classes:
temp = c1
c1 = c2
c2 = temp
plt.style.use('ggplot')
fig = interaction_plot(c2, c1, num, ms=12)
plt.show()
delete.append(cc) | code |
1010749/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
print('Number of int64 columns: %s' % len(int_columns))
print('Number of float64 columns: %s' % len(float_columns))
print('Number of object columns: %s' % len(object_columns)) | code |
1010749/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
print('%s : %s, %s' % (feature_name, has_null, column_type))
print('\n %s/%s' % (len(columns_with_null_values), len(raw_data.columns))) | code |
1010749/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
has_zero_columns = []
for i in int_columns:
if 0 in raw_data[i].unique():
has_zero_columns.append(i)
print(has_zero_columns) | code |
1010749/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
for c in columns_with_null_values:
counter = raw_data[c].value_counts(dropna=False)
try:
counts = counter[None]
except:
counts = counter[float('nan')]
length = len(raw_data)
usable = length - counts
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
numerical_columns, categorical_columns = ([], [])
temporal_columns = ['GarageYrBlt', 'YearBuilt', 'YearRemodAdd', 'MoSold', 'YrSold']
from_int = ['MSSubClass']
categorical_columns = object_columns + from_int + temporal_columns
numerical_columns = list(filter(lambda x: x not in temporal_columns + ['MSSubClass'], int_columns)) + list(filter(lambda x: x not in temporal_columns, float_columns))
bounded_columns = ['OverallQual', 'OverallCond']
binary_columns = []
for c in categorical_columns:
classes = raw_data[c].unique()
length = len(classes)
if length == 2:
binary_columns.append(c)
binary_columns | code |
1010749/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
for c in columns_with_null_values:
counter = raw_data[c].value_counts(dropna=False)
try:
counts = counter[None]
except:
counts = counter[float('nan')]
length = len(raw_data)
usable = length - counts
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
numerical_columns, categorical_columns = ([], [])
temporal_columns = ['GarageYrBlt', 'YearBuilt', 'YearRemodAdd', 'MoSold', 'YrSold']
from_int = ['MSSubClass']
categorical_columns = object_columns + from_int + temporal_columns
numerical_columns = list(filter(lambda x: x not in temporal_columns + ['MSSubClass'], int_columns)) + list(filter(lambda x: x not in temporal_columns, float_columns))
bounded_columns = ['OverallQual', 'OverallCond']
binary_columns = []
for c in categorical_columns:
classes = raw_data[c].unique()
length = len(classes)
if length == 2:
binary_columns.append(c)
counting_columns = ['BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars']
month_code = raw_data['MoSold'].unique()
def get_season(month_code):
m = month_code
if m in [3, 4, 5]:
return 'Spring'
elif m in [6, 7, 8]:
return 'Summer'
elif m in [9, 10, 11]:
return 'Autumn'
elif m in [12, 1, 2]:
return 'Winter'
seasons_series = raw_data['MoSold'].apply(get_season)
seasons_series.name = 'season'
year_sold = raw_data['YrSold']
year_built = raw_data['YearBuilt']
house_age_series = year_sold.subtract(year_built)
house_age_series.name = 'HouseAge'
garage_year_built = raw_data['GarageYrBlt']
year_built = raw_data['YearBuilt']
garage_age_series = year_sold.subtract(garage_year_built)
garage_age_series.name = 'GarageAge'
year_remod_add = raw_data['YearRemodAdd']
year_built = raw_data['YearBuilt']
remod_recency_series = year_sold.subtract(year_remod_add)
remod_recency_series.name = 'RemodRecency'
recent_df = raw_data
recent_df = recent_df.assign(garage_age=garage_age_series)
recent_df = recent_df.assign(remod_recency=remod_recency_series)
recent_df = recent_df.assign(house_age=house_age_series)
recent_df = recent_df.assign(season=seasons_series)
categorical_columns = categorical_columns + ['season']
numerical_columns = numerical_columns + ['garage_age', 'remod_recency', 'house_age']
recent_df[categorical_columns] = recent_df[categorical_columns].fillna('None')
import matplotlib.pyplot as plt
plt.style.use("ggplot")
for c in (categorical_columns + bounded_columns + counting_columns):
if c in temporal_columns and c != "MoSold":
continue
means = recent_df[["SalePrice",c]].groupby(c).mean().sort_values("SalePrice",ascending=False)
errors = recent_df[["SalePrice",c]].groupby(c).std()
barplot = means.plot.bar(yerr=errors)
plt.title(c)
plt.show()
data = recent_df
iv = 'CentralAir'
dv = 'SalePrice'
for c in categorical_columns + bounded_columns + counting_columns:
if c in temporal_columns:
continue
data.boxplot(column=dv, by=c, vert=True, grid=True)
plt.xlabel('Sale Price')
plt.ylabel(c)
plt.title('')
plt.suptitle('')
plt.show() | code |
1010749/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
numerical_columns, categorical_columns = ([], [])
temporal_columns = ['GarageYrBlt', 'YearBuilt', 'YearRemodAdd', 'MoSold', 'YrSold']
from_int = ['MSSubClass']
categorical_columns = object_columns + from_int + temporal_columns
numerical_columns = list(filter(lambda x: x not in temporal_columns + ['MSSubClass'], int_columns)) + list(filter(lambda x: x not in temporal_columns, float_columns))
print(numerical_columns) | code |
1010749/cell_37 | [
"text_plain_output_1.png"
] | from statsmodels.stats.weightstats import ztest
import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
for c in columns_with_null_values:
counter = raw_data[c].value_counts(dropna=False)
try:
counts = counter[None]
except:
counts = counter[float('nan')]
length = len(raw_data)
usable = length - counts
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
numerical_columns, categorical_columns = ([], [])
temporal_columns = ['GarageYrBlt', 'YearBuilt', 'YearRemodAdd', 'MoSold', 'YrSold']
from_int = ['MSSubClass']
categorical_columns = object_columns + from_int + temporal_columns
numerical_columns = list(filter(lambda x: x not in temporal_columns + ['MSSubClass'], int_columns)) + list(filter(lambda x: x not in temporal_columns, float_columns))
bounded_columns = ['OverallQual', 'OverallCond']
binary_columns = []
for c in categorical_columns:
classes = raw_data[c].unique()
length = len(classes)
if length == 2:
binary_columns.append(c)
counting_columns = ['BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars']
month_code = raw_data['MoSold'].unique()
def get_season(month_code):
m = month_code
if m in [3, 4, 5]:
return 'Spring'
elif m in [6, 7, 8]:
return 'Summer'
elif m in [9, 10, 11]:
return 'Autumn'
elif m in [12, 1, 2]:
return 'Winter'
seasons_series = raw_data['MoSold'].apply(get_season)
seasons_series.name = 'season'
year_sold = raw_data['YrSold']
year_built = raw_data['YearBuilt']
house_age_series = year_sold.subtract(year_built)
house_age_series.name = 'HouseAge'
garage_year_built = raw_data['GarageYrBlt']
year_built = raw_data['YearBuilt']
garage_age_series = year_sold.subtract(garage_year_built)
garage_age_series.name = 'GarageAge'
year_remod_add = raw_data['YearRemodAdd']
year_built = raw_data['YearBuilt']
remod_recency_series = year_sold.subtract(year_remod_add)
remod_recency_series.name = 'RemodRecency'
recent_df = raw_data
recent_df = recent_df.assign(garage_age=garage_age_series)
recent_df = recent_df.assign(remod_recency=remod_recency_series)
recent_df = recent_df.assign(house_age=house_age_series)
recent_df = recent_df.assign(season=seasons_series)
categorical_columns = categorical_columns + ['season']
numerical_columns = numerical_columns + ['garage_age', 'remod_recency', 'house_age']
recent_df[categorical_columns] = recent_df[categorical_columns].fillna('None')
from statsmodels.stats.weightstats import ztest
ztest_dictionary = {}
for c in categorical_columns + bounded_columns + counting_columns:
if c in temporal_columns:
continue
subclasses = recent_df[c].unique()
delete = []
ttests, pvalues, combinations = ([], [], [])
for sc in subclasses:
for scsc in subclasses:
if scsc in delete or scsc == sc:
continue
sample_one = recent_df[recent_df[c] == sc]['SalePrice']
sample_two = recent_df[recent_df[c] == scsc]['SalePrice']
ttest = ztest(sample_one, sample_two)[0]
pvalue = ztest(sample_one, sample_two)[1]
combination = '%s * %s' % (sc, scsc)
ttests.append(ttest)
pvalues.append(pvalue)
combinations.append(combination)
d = {'t-test': ttests, 'p-value': pvalues}
ztest_dictionary[c] = pd.DataFrame(index=combinations, data=d) | code |
1010749/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
column_full_names = {'MSSubClass': 'The Building Class', 'MSZoning': 'The General Zoning Classification', 'SalePrice': 'Sale Price', 'LotFrontage': 'Linear feet of street connected to property', 'LotArea': 'Lot size in square feet', 'Street': 'Type of Road Access', 'Alley': 'Type of Alley Access', 'LotShape': 'General shape of property', 'LandContour': 'Flatness of The Property', 'Utilities': 'Type of utilitists available', 'LotConfig': 'Lot configuration', 'LandSlope': 'Slope of property', 'Neighborhood': 'Physical locations within Ames city limits', 'Condition1': 'Proximity to main road or railroad', 'Condition2': 'Proximity to main road or railroad (if a second is present)', 'BldgType': 'Type of dwelling', 'HouseStyle': 'Style of dwelling', 'OverallQual': 'Overall material and finish quality (1 - 10 Likert scale)', 'OverallCond': 'Overall condition rating (1 - 9 Likert scale)', 'RoofStyle': 'Type of roof', 'RoofMatl': 'Roof material', 'Exterior1st': 'Exterior covering on house', 'Exterior2nd': 'Exterior covering on house (if more than one material)', 'MasVnrType': 'Masonry veneer type', 'MasVnrArea': 'Masonry veneer area in square feet', 'ExterQual': 'Exterior material quality', 'ExterCond': 'Present condition of the material on the exterior', 'Foundation': 'Type of foundation', 'BsmtQual': 'Height of the basement', 'BsmtCond': 'General condition of the basement', 'BsmtExposure': 'Walkout or garden level basement walls', 'BsmtFinType1': 'Quality of basement finished area', 'BsmtFinSF1': 'Type 1 finished square feet', 'BsmtFinType2': 'Quality of second finished area (if present)', 'BsmtFinSF2': 'Type 2 finished square feet', 'BsmtUnfSF': 'Unfinished square feet of basement area', 'TotalBsmtSF': 'Total square feet of basement area', 'Heating': 'Type of heating', 'HeatingQC': 'Heating quality and condition', 'CentralAir': 'Central air conditioning', 'Electrical': 'Electrical system', '1stFlrSF': 'First Floor square feet', '2ndFlrSF': 'Second floor square feet', 'LowQualFinSF': 'Low quality finished square feet (all floors)', 'GrLivArea': 'Above grade (ground) living area square feet', 'BsmtFullBath': 'Basement full bathrooms', 'BsmtHalfBath': 'Basement half bathrooms', 'FullBath': 'Full bathrooms above grade', 'HalfBath': 'Half baths above grade', 'Bedroom': 'Number of bedrooms above basement level', 'Kitchen': 'Number of kitchens', 'KitchenQual': 'Kitchen quality', 'TotRmsAbvGrd': 'Total rooms above grade (does not include bathrooms)', 'Functional': 'Home functionality rating', 'Fireplaces': 'Number of fireplaces', 'FireplaceQu': 'Fireplace quality', 'GarageType': 'Garage location', 'GarageYrBlt': 'Year garage was built', 'GarageFinish': 'Interior finish of the garage', 'GarageCars': 'Size of garage in car capacity', 'GarageArea': 'Size of garage in square feet', 'GarageQual': 'Garage quality', 'GarageCond': 'Garage condition', 'PavedDrive': 'Paved driveway', 'WoodDeckSF': 'Wood deck area in square feet', 'OpenPorchSF': 'Open porch area in square feet', 'EnclosedPorch': 'Enclosed porch area in square feet', '3SsnPorch': 'Three season porch area in square feet', 'ScreenPorch': 'Screen porch area in square feet', 'PoolArea': 'Pool area in square feet', 'PoolQC': 'Pool quality', 'Fence': 'Fence quality', 'MiscFeature': 'Miscellaneous feature not covered in other categories', 'MiscVal': 'Value of miscellaneous feature ($)', 'MoSold': 'Month Sold', 'YrSold': 'Year Sold', 'YearBuilt': 'Original construction date', 'YearRemodAdd': 'Remodel date', 'SaleType': 'Type of sale', 'SaleCondition': 'Condition of sale'}
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
columns_with_null_values = []
for c in columns:
f = c
feature_name = str(f)
has_null = str(any(raw_data[f].isnull().values))
if has_null == 'True':
columns_with_null_values.append(f)
column_type = raw_data[f].dtype
column_names = raw_data.columns.drop('Id')
int_columns, float_columns, object_columns = ([], [], [])
for c in column_names:
column_dtype = str(raw_data.dtypes[c])
if column_dtype == 'int64':
int_columns.append(c)
elif column_dtype == 'object':
object_columns.append(c)
elif column_dtype == 'float64':
float_columns.append(c)
for f in float_columns:
print(f + ' ' + column_full_names[f]) | code |
1010749/cell_5 | [
"image_output_11.png",
"image_output_24.png",
"image_output_46.png",
"image_output_25.png",
"image_output_47.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13.png",
"image_output_40.png",
"image_output_5.png",
"image_output_48.png",
"image_output_18.png",
"image_output_21.png",
"image_output_52.png",
"image_output_7.png",
"image_output_56.png",
"image_output_31.png",
"image_output_20.png",
"image_output_32.png",
"image_output_53.png",
"image_output_4.png",
"image_output_51.png",
"image_output_42.png",
"image_output_35.png",
"image_output_41.png",
"image_output_36.png",
"image_output_8.png",
"image_output_37.png",
"image_output_16.png",
"image_output_27.png",
"image_output_54.png",
"image_output_6.png",
"image_output_45.png",
"image_output_12.png",
"image_output_22.png",
"image_output_55.png",
"image_output_3.png",
"image_output_29.png",
"image_output_44.png",
"image_output_43.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_33.png",
"image_output_50.png",
"image_output_15.png",
"image_output_49.png",
"image_output_9.png",
"image_output_19.png",
"image_output_38.png",
"image_output_26.png"
] | import pandas as pd
import pandas as pd
raw_data = pd.read_csv('../input/train.csv')
columns = raw_data.columns.tolist()
if 'Id' in columns:
columns.remove('Id')
print(columns)
print('Number of Features: %s' % len(columns)) | code |
50220357/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
plt.figure(figsize=(10, 10))
sn.heatmap(train.isnull(), yticklabels=False, cbar=False) | code |
50220357/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train['AgeGroup'] = pd.cut(train['Age'], bins, labels=labels)
test['AgeGroup'] = pd.cut(test['Age'], bins, labels=labels)
plt.figure(figsize=(10, 5))
sn.barplot(x='AgeGroup', y='Survived', data=train) | code |
50220357/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
test.describe(include='all') | code |
50220357/cell_44 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train['AgeGroup'] = pd.cut(train['Age'], bins, labels=labels)
test['AgeGroup'] = pd.cut(test['Age'], bins, labels=labels)
train.drop(['Cabin'], axis=1, inplace=True)
test.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.drop(['Ticket'], axis=1, inplace=True)
train.fillna({'Embarked': 'S'}, inplace=True)
combine = [train, test]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train['Title'], train['Sex'])
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace(['Countess', 'Sir'], 'Royal')
dataset['Title'] = dataset['Title'].replace(['Mlle', 'Ms'], 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
train[['Title', 'Survived']].groupby(['Title'], as_index=False).mean() | code |
50220357/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sn.barplot(x='SibSp', y='Survived', data=train)
print('Percentage of SibSp = 0 who survived:', train['Survived'][train['SibSp'] == 0].value_counts(normalize=True)[1] * 100)
print('Percentage of SibSp = 1 who survived:', train['Survived'][train['SibSp'] == 1].value_counts(normalize=True)[1] * 100)
print('Percentage of SibSp = 2 who survived:', train['Survived'][train['SibSp'] == 2].value_counts(normalize=True)[1] * 100)
print('Percentage of SibSp = 3 who survived:', train['Survived'][train['SibSp'] == 3].value_counts(normalize=True)[1] * 100) | code |
50220357/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train['AgeGroup'] = pd.cut(train['Age'], bins, labels=labels)
test['AgeGroup'] = pd.cut(test['Age'], bins, labels=labels)
sn.barplot(x='Parch', y='Survived', data=train)
plt.show() | code |
50220357/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train['AgeGroup'] = pd.cut(train['Age'], bins, labels=labels)
test['AgeGroup'] = pd.cut(test['Age'], bins, labels=labels)
sn.barplot(x='Pclass', y='Survived', data=train)
print('Percentage of Pclass = 1 who survived:', train['Survived'][train['Pclass'] == 1].value_counts(normalize=True)[1] * 100)
print('Percentage of Pclass = 2 who survived:', train['Survived'][train['Pclass'] == 2].value_counts(normalize=True)[1] * 100)
print('Percentage of Pclass = 3 who survived:', train['Survived'][train['Pclass'] == 3].value_counts(normalize=True)[1] * 100) | code |
50220357/cell_48 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train['AgeGroup'] = pd.cut(train['Age'], bins, labels=labels)
test['AgeGroup'] = pd.cut(test['Age'], bins, labels=labels)
train.drop(['Cabin'], axis=1, inplace=True)
test.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.drop(['Ticket'], axis=1, inplace=True)
train.fillna({'Embarked': 'S'}, inplace=True)
combine = [train, test]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train['Title'], train['Sex'])
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Royal': 5, 'Rare': 6}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
age_mapping = {'Baby': 1, 'Child': 2, 'Teenager': 3, 'Student': 4, 'Young Adult': 5, 'Adult': 6, 'Senior': 7}
train['AgeGroup'] = train['AgeGroup'].map(age_mapping)
test['AgeGroup'] = test['AgeGroup'].map(age_mapping)
train.head() | code |
50220357/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum() | code |
50220357/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
print(train.columns.values) | code |
50220357/cell_45 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train['AgeGroup'] = pd.cut(train['Age'], bins, labels=labels)
test['AgeGroup'] = pd.cut(test['Age'], bins, labels=labels)
train.drop(['Cabin'], axis=1, inplace=True)
test.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.drop(['Ticket'], axis=1, inplace=True)
train.fillna({'Embarked': 'S'}, inplace=True)
combine = [train, test]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train['Title'], train['Sex'])
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Royal': 5, 'Rare': 6}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train.head() | code |
50220357/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.head(10) | code |
50220357/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.drop(['Cabin'], axis=1, inplace=True)
test.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.drop(['Ticket'], axis=1, inplace=True)
print('Number of people embarking in Southampton (S):')
southampton = train[train['Embarked'] == 'S'].shape[0]
print(southampton)
print('Number of people embarking in Cherbourg (C):')
cherbourg = train[train['Embarked'] == 'C'].shape[0]
print(cherbourg)
print('Number of people embarking in Queenstown (Q):')
queenstown = train[train['Embarked'] == 'Q'].shape[0]
print(queenstown) | code |
50220357/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
sn.barplot(x='Sex', y='Survived', data=train)
print('Percentage of females who survived:', train['Survived'][train['Sex'] == 'female'].value_counts(normalize=True)[1] * 100)
print('Percentage of males who survived:', train['Survived'][train['Sex'] == 'male'].value_counts(normalize=True)[1] * 100) | code |
50220357/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train['AgeGroup'] = pd.cut(train['Age'], bins, labels=labels)
test['AgeGroup'] = pd.cut(test['Age'], bins, labels=labels)
train.drop(['Cabin'], axis=1, inplace=True)
test.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.drop(['Ticket'], axis=1, inplace=True)
train.fillna({'Embarked': 'S'}, inplace=True)
combine = [train, test]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train['Title'], train['Sex']) | code |
50220357/cell_53 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sn
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
pd.isnull(train).sum()
train['Age'] = train['Age'].fillna(-0.5)
test['Age'] = test['Age'].fillna(-0.5)
bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train['AgeGroup'] = pd.cut(train['Age'], bins, labels=labels)
test['AgeGroup'] = pd.cut(test['Age'], bins, labels=labels)
train.drop(['Cabin'], axis=1, inplace=True)
test.drop(['Cabin'], axis=1, inplace=True)
train.drop(['Ticket'], axis=1, inplace=True)
test.drop(['Ticket'], axis=1, inplace=True)
train.fillna({'Embarked': 'S'}, inplace=True)
combine = [train, test]
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)
pd.crosstab(train['Title'], train['Sex'])
title_mapping = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Royal': 5, 'Rare': 6}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
age_mapping = {'Baby': 1, 'Child': 2, 'Teenager': 3, 'Student': 4, 'Young Adult': 5, 'Adult': 6, 'Senior': 7}
train['AgeGroup'] = train['AgeGroup'].map(age_mapping)
test['AgeGroup'] = test['AgeGroup'].map(age_mapping)
train = train.drop(['Age'], axis=1)
test = test.drop(['Age'], axis=1)
train.drop(['Name'], axis=1, inplace=True)
test.drop(['Name'], axis=1, inplace=True)
sex_mapping = {'male': 0, 'female': 1}
train['Sex'] = train['Sex'].map(sex_mapping)
test['Sex'] = test['Sex'].map(sex_mapping)
train.head() | code |
50220357/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
train.describe(include='all') | code |
74052542/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns.values
features = list(train.columns)
list(enumerate(features)) | code |
74052542/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns.values
features = list(train.columns)
list(enumerate(features))
fig, ax = plt.subplots(figsize=(6, 6))
bars = ax.bar(train["claim"].value_counts().index,
train["claim"].value_counts().values,
color='darkorange',
edgecolor="black",
width=0.4)
ax.set_title("Claim (target) values distribution", fontsize=20, pad=15)
ax.set_ylabel("Amount of values", fontsize=14, labelpad=15)
ax.set_xlabel("Claim (target) value", fontsize=14, labelpad=10)
ax.set_xticks(train["claim"].value_counts().index)
ax.tick_params(axis="both", labelsize=12)
fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(9, 8), sharex=True)
ax[0].scatter(train['f10'], train['f15'], ec='k', color='skyblue')
ax[0].set_ylabel('f15')
ax[0].set_title('Relation b/w f10 & f15')
ax[1].scatter(train['f3'], train['f4'], ec='k', color='skyblue')
ax[1].set_xlabel('f3')
ax[1].set_ylabel('f4')
ax[1].set_title('Relation b/w f3 & f4')
plt.tight_layout()
plt.show() | code |
74052542/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
display(train.head())
display(test.head())
display(sub.head()) | code |
74052542/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 |
74052542/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
print('size of train: ', train.shape)
print('size of test: ', test.shape)
print('size of submission: ', sub.shape) | code |
74052542/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns.values
features = list(train.columns)
list(enumerate(features))
fig, ax = plt.subplots(figsize=(6, 6))
bars = ax.bar(train["claim"].value_counts().index,
train["claim"].value_counts().values,
color='darkorange',
edgecolor="black",
width=0.4)
ax.set_title("Claim (target) values distribution", fontsize=20, pad=15)
ax.set_ylabel("Amount of values", fontsize=14, labelpad=15)
ax.set_xlabel("Claim (target) value", fontsize=14, labelpad=10)
ax.set_xticks(train["claim"].value_counts().index)
ax.tick_params(axis="both", labelsize=12)
plt.figure(figsize=(7, 6))
sns.distplot(train['claim']) | code |
74052542/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
print(train.info())
print(test.info()) | code |
74052542/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns.values
features = list(train.columns)
list(enumerate(features))
train['claim'].value_counts() | code |
74052542/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns.values
features = list(train.columns)
list(enumerate(features))
fig, ax = plt.subplots(figsize=(6, 6))
bars = ax.bar(train['claim'].value_counts().index, train['claim'].value_counts().values, color='darkorange', edgecolor='black', width=0.4)
ax.set_title('Claim (target) values distribution', fontsize=20, pad=15)
ax.set_ylabel('Amount of values', fontsize=14, labelpad=15)
ax.set_xlabel('Claim (target) value', fontsize=14, labelpad=10)
ax.set_xticks(train['claim'].value_counts().index)
ax.tick_params(axis='both', labelsize=12) | code |
74052542/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns.values
features = list(train.columns)
list(enumerate(features))
train.describe() | code |
74052542/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns.values | code |
34122127/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cols_list = []
for j in range(8):
for i in range(8):
cols_list.append(f'S{i}R{j}')
cols_list.append('target')
df = pd.read_csv('/kaggle/input/emg-4/0.csv', header=None)
df.columns = cols_list
df | code |
34122127/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 |
34122127/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
cols_list = []
for j in range(8):
for i in range(8):
cols_list.append(f'S{i}R{j}')
cols_list.append('target')
df = pd.read_csv('/kaggle/input/emg-4/0.csv', header=None)
df.columns = cols_list
df
pd.wide_to_long(df.reset_index(), ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S0'], i=['index', 'target'], j='R', sep='R') | code |
106209373/cell_42 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(13, 8))
sns.countplot(x='arrival_date_month', data=hotel, hue='is_canceled', palette='ocean') | code |
106209373/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['is_canceled'].value_counts() | code |
106209373/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
hotel['arrival_date_year'].unique() | code |
106209373/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['country'].value_counts(normalize=True) | code |
106209373/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(13, 8))
sns.countplot(x='market_segment', data=hotel, hue='is_canceled', palette='prism_r') | code |
106209373/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['arrival_date_year'].unique() | code |
106209373/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel.head() | code |
106209373/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
confirmed_bookings['arrival_date_year'] = hotel['arrival_date_year']
Last = confirmed_bookings['arrival_date_year'].value_counts().sort_index()
Last | code |
106209373/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['arrival_date_month'].value_counts() | code |
106209373/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(10, 8))
sns.countplot(x='deposit_type', data=hotel, hue='is_canceled', palette='gist_rainbow_r') | code |
106209373/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(18, 9))
sns.lineplot(data=hotel, x='arrival_date_month', y='arrival_date_year') | code |
106209373/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['market_segment'].value_counts(normalize=True) | code |
106209373/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['customer_type'].value_counts() | code |
106209373/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(10, 8))
sns.countplot(data=hotel, x='total_of_special_requests', hue='is_canceled', palette='cool_r') | code |
106209373/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(7, 8))
sns.countplot(x='reservation_status', data=hotel, hue='is_canceled', palette='afmhot') | code |
106209373/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel.info() | code |
106209373/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['reservation_status'].value_counts(normalize=True) | code |
106209373/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
hotel['meal'].value_counts().unique | code |
106209373/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
print(round(100 * (hotel.isnull().sum() / len(hotel.index)), 2)) | code |
106209373/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hotel = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel = hotel.drop(['agent', 'company'], axis=1)
confirmed_bookings = hotel[hotel.is_canceled == '0']
plt.figure(figsize=(8, 8))
sns.countplot(data=hotel, x='hotel', hue='is_canceled', palette='Set1_r') | code |
2032996/cell_13 | [
"text_plain_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import numpy as np
import pandas as pd
import time
Time_0 = time.time()
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
y_train = train['log_price'] = np.log(train['price'] + 1)
def handle_missing(dataset):
dataset['category_name'].fillna(value='NA/NA/NA', inplace=True)
dataset['brand_name'].fillna(value='missing', inplace=True)
dataset['item_description'].fillna(value='missing', inplace=True)
return dataset
def split_cat(dataset):
dataset['cat1'], dataset['cat2'], dataset['cat3'] = zip(*dataset['category_name'].str.split('/', 2))
return dataset
def label_maker(dataset):
lb = LabelBinarizer(sparse_output=True)
cat1 = lb.fit_transform(dataset['cat1'])
cat2 = lb.fit_transform(dataset['cat2'])
cat3 = lb.fit_transform(dataset['cat3'])
brand_name = lb.fit_transform(dataset['brand_name'])
del lb
return (cat1, cat2, cat3, brand_name)
def get_dums(dataset):
X_dummies = csr_matrix(pd.get_dummies(dataset[['item_condition_id', 'shipping']], sparse=True).values)
return X_dummies
def text_processing(dataset):
MIN_DF_COUNT = 10
MAX_DF_COUNT = 10000
cv = CountVectorizer(min_df=MIN_DF_COUNT, max_df=MAX_DF_COUNT)
name = cv.fit_transform(dataset['name'])
MIN_DF_TF = 10
MAX_DF_TF = 51000
MAX_FEATURES_TF = 51000
tv = TfidfVectorizer(max_features=MAX_FEATURES_TF, min_df=MIN_DF_TF, max_df=MAX_DF_TF, ngram_range=(1, 3), stop_words='english')
description = tv.fit_transform(dataset['item_description'])
del cv, tv
return (name, description)
nrow_train = train.shape[0]
merge: pd.DataFrame = pd.concat([train, test])
submission: pd.DataFrame = test[['test_id']]
del train
del test
start_time = time.time()
merge = handle_missing(merge)
merge = split_cat(merge)
cat1, cat2, cat3, brand_name = label_maker(merge)
X_dummies = get_dums(merge)
name, description = text_processing(merge)
sparse_merge = hstack((cat1, cat3, cat3, brand_name, X_dummies, name, description)).tocsr()
X_train = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_train:]
def model_testing(model, X_test, y_test):
y_pred = model.predict(X_test)
error = rmsle(y_test, y_pred)
def rmsle(y, y0):
assert len(y) == len(y0)
return np.sqrt(np.mean(np.power(np.log1p(y) - np.log1p(y0), 2)))
ridge_model_1 = Ridge(alpha=5.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto', random_state=None)
ridge_model_2 = Ridge(alpha=5.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='sag', random_state=None)
ridge_model_3 = Ridge(alpha=5.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='lsqr', random_state=None)
gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=5, learning_rate=0.9, subsample=0.9)
start_time = time.time()
X_t, X_v, y_t, y_v = train_test_split(X_train, y_train, test_size=0.2)
ridge_model_1.fit(X_train, y_train)
model_testing(ridge_model_1, X_test=X_v, y_test=y_v)
def create_submission(model, test=X_test, submission=submission, path='./predictions.csv'):
predictions = model.predict(test)
predictions = pd.Series(np.exp(predictions) - 1)
submission['price'] = predictions
submission.to_csv(path, index=False)
start_time = time.time()
create_submission(ridge_model_1)
print('TIME:', time.time() - start_time)
print('TOTAL TIME:', time.time() - Time_0) | code |
2032996/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.sparse import csr_matrix, hstack
import time
import re
import math
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler, LabelBinarizer
from sklearn.cross_validation import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics import mean_squared_log_error
from sklearn.linear_model import Ridge
from sklearn.ensemble import GradientBoostingRegressor
import xgboost as xgb
seed = 90 | code |
2032996/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import numpy as np
import pandas as pd
import time
Time_0 = time.time()
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
y_train = train['log_price'] = np.log(train['price'] + 1)
def handle_missing(dataset):
dataset['category_name'].fillna(value='NA/NA/NA', inplace=True)
dataset['brand_name'].fillna(value='missing', inplace=True)
dataset['item_description'].fillna(value='missing', inplace=True)
return dataset
def split_cat(dataset):
dataset['cat1'], dataset['cat2'], dataset['cat3'] = zip(*dataset['category_name'].str.split('/', 2))
return dataset
def label_maker(dataset):
lb = LabelBinarizer(sparse_output=True)
cat1 = lb.fit_transform(dataset['cat1'])
cat2 = lb.fit_transform(dataset['cat2'])
cat3 = lb.fit_transform(dataset['cat3'])
brand_name = lb.fit_transform(dataset['brand_name'])
del lb
return (cat1, cat2, cat3, brand_name)
def get_dums(dataset):
X_dummies = csr_matrix(pd.get_dummies(dataset[['item_condition_id', 'shipping']], sparse=True).values)
return X_dummies
def text_processing(dataset):
MIN_DF_COUNT = 10
MAX_DF_COUNT = 10000
cv = CountVectorizer(min_df=MIN_DF_COUNT, max_df=MAX_DF_COUNT)
name = cv.fit_transform(dataset['name'])
MIN_DF_TF = 10
MAX_DF_TF = 51000
MAX_FEATURES_TF = 51000
tv = TfidfVectorizer(max_features=MAX_FEATURES_TF, min_df=MIN_DF_TF, max_df=MAX_DF_TF, ngram_range=(1, 3), stop_words='english')
description = tv.fit_transform(dataset['item_description'])
del cv, tv
return (name, description)
nrow_train = train.shape[0]
merge: pd.DataFrame = pd.concat([train, test])
submission: pd.DataFrame = test[['test_id']]
del train
del test
start_time = time.time()
merge = handle_missing(merge)
merge = split_cat(merge)
cat1, cat2, cat3, brand_name = label_maker(merge)
X_dummies = get_dums(merge)
name, description = text_processing(merge)
sparse_merge = hstack((cat1, cat3, cat3, brand_name, X_dummies, name, description)).tocsr()
X_train = sparse_merge[:nrow_train]
X_test = sparse_merge[nrow_train:]
def model_testing(model, X_test, y_test):
y_pred = model.predict(X_test)
error = rmsle(y_test, y_pred)
def rmsle(y, y0):
assert len(y) == len(y0)
return np.sqrt(np.mean(np.power(np.log1p(y) - np.log1p(y0), 2)))
ridge_model_1 = Ridge(alpha=5.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto', random_state=None)
ridge_model_2 = Ridge(alpha=5.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='sag', random_state=None)
ridge_model_3 = Ridge(alpha=5.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='lsqr', random_state=None)
gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=5, learning_rate=0.9, subsample=0.9)
start_time = time.time()
print('train test splitting...')
X_t, X_v, y_t, y_v = train_test_split(X_train, y_train, test_size=0.2)
print('training model...')
print('1')
ridge_model_1.fit(X_train, y_train)
model_testing(ridge_model_1, X_test=X_v, y_test=y_v)
print('training model...')
print('2')
print('training model...')
print('3')
print('TIME:', time.time() - start_time) | code |
2032996/cell_7 | [
"text_plain_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler,LabelBinarizer
import pandas as pd
import time
Time_0 = time.time()
train = pd.read_csv('../input/train.tsv', sep='\t')
test = pd.read_csv('../input/test.tsv', sep='\t')
def handle_missing(dataset):
dataset['category_name'].fillna(value='NA/NA/NA', inplace=True)
dataset['brand_name'].fillna(value='missing', inplace=True)
dataset['item_description'].fillna(value='missing', inplace=True)
return dataset
def split_cat(dataset):
dataset['cat1'], dataset['cat2'], dataset['cat3'] = zip(*dataset['category_name'].str.split('/', 2))
return dataset
def label_maker(dataset):
lb = LabelBinarizer(sparse_output=True)
cat1 = lb.fit_transform(dataset['cat1'])
cat2 = lb.fit_transform(dataset['cat2'])
cat3 = lb.fit_transform(dataset['cat3'])
brand_name = lb.fit_transform(dataset['brand_name'])
del lb
return (cat1, cat2, cat3, brand_name)
def get_dums(dataset):
X_dummies = csr_matrix(pd.get_dummies(dataset[['item_condition_id', 'shipping']], sparse=True).values)
return X_dummies
def text_processing(dataset):
MIN_DF_COUNT = 10
MAX_DF_COUNT = 10000
cv = CountVectorizer(min_df=MIN_DF_COUNT, max_df=MAX_DF_COUNT)
name = cv.fit_transform(dataset['name'])
MIN_DF_TF = 10
MAX_DF_TF = 51000
MAX_FEATURES_TF = 51000
tv = TfidfVectorizer(max_features=MAX_FEATURES_TF, min_df=MIN_DF_TF, max_df=MAX_DF_TF, ngram_range=(1, 3), stop_words='english')
description = tv.fit_transform(dataset['item_description'])
del cv, tv
return (name, description)
start_time = time.time()
print('Handle Missing...')
merge = handle_missing(merge)
print('splitting cat...')
merge = split_cat(merge)
print('making labels...')
cat1, cat2, cat3, brand_name = label_maker(merge)
print('getting dummies...')
X_dummies = get_dums(merge)
print('processing text...')
name, description = text_processing(merge)
print('stacking train...')
sparse_merge = hstack((cat1, cat3, cat3, brand_name, X_dummies, name, description)).tocsr()
print('TIME:', time.time() - start_time) | code |
129023624/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
train_df['contractions_converted'][9] | code |
129023624/cell_56 | [
"text_html_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict, KFold, GridSearchCV
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
def remove_mentions(data_df):
mentions_removed = re.sub('@[A-Za-z0-9_]+', '', data_df)
return mentions_removed
def remove_hashtags(data_df):
hashtags_removed = re.sub('#[A-Za-z0-9_]+', '', data_df)
return hashtags_removed
def remove_urls(data_df):
hashtags_removed = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[\n]|[$-_@.&+\\]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', data_df)
return hashtags_removed
def convert_contractions(data_df):
contractions_converted = contractions.fix(data_df)
return contractions_converted
train_df['text'] = train_df['text'].astype(str)
train_df['mentions_removed'] = train_df['text'].apply(remove_mentions).tolist()
train_df['hashtags_removed'] = train_df['mentions_removed'].apply(remove_hashtags).tolist()
train_df['url_removed'] = train_df['hashtags_removed'].apply(remove_urls).tolist()
train_df['lower_cased'] = train_df['url_removed'].apply(lambda x: x.lower())
train_df['contractions_converted'] = train_df['lower_cased'].apply(convert_contractions).tolist()
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
embedding_matrix = xlm_model.get_input_embeddings().weight
tokenized_feature_raw = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True)
token_sentence_length = [len(x) for x in tokenized_feature_raw['input_ids']]
avg_length = sum(token_sentence_length) / train_df.shape[0]
MAX_LEN = max(token_sentence_length)
import matplotlib.pyplot as plt
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
max_len = round(max(token_sentence_length))
max_len
tokenized_feature = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='tf')
padded_inputs = tokenized_feature['input_ids']
train_padded_docs = np.array(padded_inputs)
labels = np.array(train_df['target'])
def create_rnn_model(input_shape):
model_xlm = Sequential()
model_xlm.add(Embedding(250002, 1024, trainable=False, weights=[embedding_matrix.numpy()]))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GlobalMaxPool1D())
model_xlm.add(Dense(30, activation='relu'))
model_xlm.add(Dropout(0.4))
model_xlm.add(Dense(1, activation='sigmoid'))
return model_xlm
kfold = KFold(n_splits=10, shuffle=True, random_state=42)
model = create_rnn_model(X_train_xlm.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
batch_size = 64
epochs = 10
history_model_xlm = model.fit(X_train_xlm, y_train_xlm, validation_split=0.2, batch_size=batch_size, epochs=epochs)
y_pred = (model.predict(X_test_xlm) > 0.5).astype(int)
for train_index, test_index in kfold.split(train_padded_docs):
X_train, X_test = (train_padded_docs[train_index], train_padded_docs[test_index])
y_train, y_test = (labels[train_index], labels[test_index])
model = create_rnn_model(X_train.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=64, verbose=0)
loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
tokenized_feature_test_data = xlm_tokenizer.batch_encode_plus(test_df['contractions_converted'], add_special_tokens=True, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='tf')
padded_inputs_test = tokenized_feature_test_data['input_ids']
predictions = (model.predict(padded_inputs_test) > 0.5).astype(int)
sample_submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
sample_submission['target'] = predictions
sample_submission | code |
129023624/cell_34 | [
"text_plain_output_1.png"
] | from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
def remove_mentions(data_df):
mentions_removed = re.sub('@[A-Za-z0-9_]+', '', data_df)
return mentions_removed
def remove_hashtags(data_df):
hashtags_removed = re.sub('#[A-Za-z0-9_]+', '', data_df)
return hashtags_removed
def remove_urls(data_df):
hashtags_removed = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[\n]|[$-_@.&+\\]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', data_df)
return hashtags_removed
def convert_contractions(data_df):
contractions_converted = contractions.fix(data_df)
return contractions_converted
train_df['text'] = train_df['text'].astype(str)
train_df['mentions_removed'] = train_df['text'].apply(remove_mentions).tolist()
train_df['hashtags_removed'] = train_df['mentions_removed'].apply(remove_hashtags).tolist()
train_df['url_removed'] = train_df['hashtags_removed'].apply(remove_urls).tolist()
train_df['lower_cased'] = train_df['url_removed'].apply(lambda x: x.lower())
train_df['contractions_converted'] = train_df['lower_cased'].apply(convert_contractions).tolist()
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
tokenized_feature_raw = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True)
token_sentence_length = [len(x) for x in tokenized_feature_raw['input_ids']]
print('max: ', max(token_sentence_length))
print('min: ', min(token_sentence_length))
avg_length = sum(token_sentence_length) / train_df.shape[0]
MAX_LEN = max(token_sentence_length)
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 8))
plt.hist(token_sentence_length, rwidth=0.9)
plt.xlabel('Sequence Length', fontsize=18)
plt.ylabel('No of Samples', fontsize=18)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14) | code |
129023624/cell_44 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
embedding_matrix = xlm_model.get_input_embeddings().weight
def create_rnn_model(input_shape):
model_xlm = Sequential()
model_xlm.add(Embedding(250002, 1024, trainable=False, weights=[embedding_matrix.numpy()]))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GlobalMaxPool1D())
model_xlm.add(Dense(30, activation='relu'))
model_xlm.add(Dropout(0.4))
model_xlm.add(Dense(1, activation='sigmoid'))
return model_xlm
model = create_rnn_model(X_train_xlm.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
batch_size = 64
epochs = 10
history_model_xlm = model.fit(X_train_xlm, y_train_xlm, validation_split=0.2, batch_size=batch_size, epochs=epochs)
y_pred = (model.predict(X_test_xlm) > 0.5).astype(int) | code |
129023624/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.head() | code |
129023624/cell_29 | [
"image_output_1.png"
] | !pip install transformers | code |
129023624/cell_2 | [
"text_plain_output_1.png"
] | !pip install contractions | code |
129023624/cell_54 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Activation, Flatten, \
from keras.layers import LSTM, GRU, SimpleRNN
from keras.models import Sequential,Model,load_model
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict, KFold, GridSearchCV
from transformers import XLMRobertaTokenizer, TFAutoModel, TFAutoModelForSequenceClassification, TFXLMRobertaModel, XLMRobertaModel
import contractions
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
train_df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train_df.shape
nan_count = train_df.isna().sum()
nan_count
train_df.drop(['location'], axis=1)
train_df.duplicated(['text', 'target']).sum()
train_df = train_df.drop_duplicates(['text', 'target'])
train_df.shape
def remove_mentions(data_df):
mentions_removed = re.sub('@[A-Za-z0-9_]+', '', data_df)
return mentions_removed
def remove_hashtags(data_df):
hashtags_removed = re.sub('#[A-Za-z0-9_]+', '', data_df)
return hashtags_removed
def remove_urls(data_df):
hashtags_removed = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[\n]|[$-_@.&+\\]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', data_df)
return hashtags_removed
def convert_contractions(data_df):
contractions_converted = contractions.fix(data_df)
return contractions_converted
train_df['text'] = train_df['text'].astype(str)
train_df['mentions_removed'] = train_df['text'].apply(remove_mentions).tolist()
train_df['hashtags_removed'] = train_df['mentions_removed'].apply(remove_hashtags).tolist()
train_df['url_removed'] = train_df['hashtags_removed'].apply(remove_urls).tolist()
train_df['lower_cased'] = train_df['url_removed'].apply(lambda x: x.lower())
train_df['contractions_converted'] = train_df['lower_cased'].apply(convert_contractions).tolist()
MODEL_TYPE = 'xlm-roberta-large'
xlm_tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_TYPE)
xlm_model = TFAutoModel.from_pretrained(MODEL_TYPE)
embedding_matrix = xlm_model.get_input_embeddings().weight
tokenized_feature_raw = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True)
token_sentence_length = [len(x) for x in tokenized_feature_raw['input_ids']]
avg_length = sum(token_sentence_length) / train_df.shape[0]
MAX_LEN = max(token_sentence_length)
import matplotlib.pyplot as plt
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
max_len = round(max(token_sentence_length))
max_len
tokenized_feature = xlm_tokenizer.batch_encode_plus(train_df['contractions_converted'], add_special_tokens=True, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='tf')
padded_inputs = tokenized_feature['input_ids']
train_padded_docs = np.array(padded_inputs)
labels = np.array(train_df['target'])
def create_rnn_model(input_shape):
model_xlm = Sequential()
model_xlm.add(Embedding(250002, 1024, trainable=False, weights=[embedding_matrix.numpy()]))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GRU(512, return_sequences=True))
model_xlm.add(GlobalMaxPool1D())
model_xlm.add(Dense(30, activation='relu'))
model_xlm.add(Dropout(0.4))
model_xlm.add(Dense(1, activation='sigmoid'))
return model_xlm
kfold = KFold(n_splits=10, shuffle=True, random_state=42)
model = create_rnn_model(X_train_xlm.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
batch_size = 64
epochs = 10
history_model_xlm = model.fit(X_train_xlm, y_train_xlm, validation_split=0.2, batch_size=batch_size, epochs=epochs)
y_pred = (model.predict(X_test_xlm) > 0.5).astype(int)
for train_index, test_index in kfold.split(train_padded_docs):
X_train, X_test = (train_padded_docs[train_index], train_padded_docs[test_index])
y_train, y_test = (labels[train_index], labels[test_index])
model = create_rnn_model(X_train.shape[1:])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=64, verbose=0)
loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
tokenized_feature_test_data = xlm_tokenizer.batch_encode_plus(test_df['contractions_converted'], add_special_tokens=True, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='tf')
padded_inputs_test = tokenized_feature_test_data['input_ids']
predictions = (model.predict(padded_inputs_test) > 0.5).astype(int)
predictions | code |
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