Kaggle 6 - Binary Classification

import matplotlib.pyplot as plt
# Set Matplotlib defaults
plt.style.use('seaborn-whitegrid')
plt.rc('figure', autolayout=True)
plt.rc('axes', labelweight='bold', labelsize='large', titleweight='bold', titlesize=18, titlepad=10)
plt.rc('animation', html='html5')

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import make_pipeline
from sklearn.compose import make_column_transformer
from tensorflow import keras
from tensorflow.keras import layers
from keras.callbacks import EarlyStopping




# 1a. Read dataset 1 and separate the target
hotel = pd.read_csv('../input/dl-course-data/hotel.csv')
X = hotel.copy()
y = X.pop('is_canceled')

# 1b. A little transformation
X['arrival_date_month'] = \
    X['arrival_date_month'].map(
        {'January':1, 'February': 2, 'March':3,
         'April':4, 'May':5, 'June':6, 'July':7,
         'August':8, 'September':9, 'October':10,
         'November':11, 'December':12}
    )

# 1c. Features: numerical and categorical
features_num = [
    "lead_time", "arrival_date_week_number",
    "arrival_date_day_of_month", "stays_in_weekend_nights",
    "stays_in_week_nights", "adults", "children", "babies",
    "is_repeated_guest", "previous_cancellations",
    "previous_bookings_not_canceled", "required_car_parking_spaces",
    "total_of_special_requests", "adr",
]
features_cat = [
    "hotel", "arrival_date_month", "meal",
    "market_segment", "distribution_channel",
    "reserved_room_type", "deposit_type", "customer_type",
]

# 1d. Transformers and preprocessors
transformer_num = make_pipeline(
    SimpleImputer(strategy="constant"), # there are a few missing values
    StandardScaler(),
)
transformer_cat = make_pipeline(
    SimpleImputer(strategy="constant", fill_value="NA"),
    OneHotEncoder(handle_unknown='ignore'),
)

preprocessor = make_column_transformer(
    (transformer_num, features_num),
    (transformer_cat, features_cat),
)

# stratify - make sure classes are evenlly represented across splits
X_train, X_valid, y_train, y_valid = train_test_split(X, y, stratify=y, train_size=0.75)
X_train = preprocessor.fit_transform(X_train)
X_valid = preprocessor.transform(X_valid)
input_shape = [X_train.shape[1]]



# 2. Model for classification - Sigmoid activation function - Dropout + batch normalization
model = keras.Sequential([ layers.BatchNormalization(),
                           layers.Dense(256, activation='relu', input_shape=input_shape),
                           layers.BatchNormalization(),
                           layers.Dropout(0.3),
                           layers.Dense(256, activation='relu'),
                           layers.BatchNormalization(),
                           layers.Dropout(0.3),
                           layers.Dense(1, activation='sigmoid') ])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['binary_accuracy'])

early_stopping = EarlyStopping(patience=5, min_delta=0.001, restore_best_weights=True)

history = model.fit(X_train, y_train,
                    validation_data=(X_valid, y_valid),
                    batch_size=512,
                    epochs=200,
                    callbacks=[early_stopping] )

history_df = pd.DataFrame(history.history)
history_df.loc[:, ['loss', 'val_loss']].plot(title="Cross-entropy")
history_df.loc[:, ['binary_accuracy', 'val_binary_accuracy']].plot(title="Accuracy")
print("Though we can see the training loss continuing to fall, the early stopping callback prevented any overfitting. Moreover, the accuracy rose at the same rate as the cross-entropy fell, so it appears that minimizing cross-entropy was a good stand-in. All in all, it looks like this training was a success!", end="\n\n\n")