# Import our libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, precision_score
from sklearn.metrics import recall_score, f1_score
from sklearn.ensemble import BaggingClassifier, RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier

# Read in our dataset
df = pd.read_csv('smsspamcollection_SMSSpamCollection',
                 header=None,
                 names=['label', 'sms_message'])

# Fix our response value
df['label'] = df.label.map({'ham': 0, 'spam': 1})

# Split our dataset into training and testing data
X_train, X_test, y_train, y_test = train_test_split(df['sms_message'],
                                                    df['label'],
                                                    random_state=1)

# Instantiate the CountVectorizer method
count_vector = CountVectorizer()

# Fit the training data and then return the matrix
training_data = count_vector.fit_transform(X_train)

# Transform testing data and return the matrix. Note we are not fitting the
# testing data into the CountVectorizer()
testing_data = count_vector.transform(X_test)

# Instantiate our model
naive_bayes = MultinomialNB()

# Fit our model to the training data
naive_bayes.fit(training_data, y_train)

# Predict on the test data
predictions = naive_bayes.predict(testing_data)

# Score our model
print('Accuracy score: ', format(accuracy_score(y_test, predictions)))
print('Precision score: ', format(precision_score(y_test, predictions)))
print('Recall score: ', format(recall_score(y_test, predictions)))
print('F1 score: ', format(f1_score(y_test, predictions)))




# Instantiate a BaggingClassifier with:
# 200 weak learners (n_estimators) and everything else as default values
bagging = BaggingClassifier(n_estimators=200)


# Instantiate a RandomForestClassifier with:
# 200 weak learners (n_estimators) and everything else as default values
forest = RandomForestClassifier(n_estimators=200)

# Instantiate an a AdaBoostClassifier with:
# With 300 weak learners (n_estimators) and a learning_rate of 0.2
ada = AdaBoostClassifier(n_estimators=300, learning_rate=0.2)

# Fit your BaggingClassifier to the training data
bagging.fit(training_data, y_train)

# Fit your RandomForestClassifier to the training data
forest.fit(training_data, y_train)

# Fit your AdaBoostClassifier to the training data
ada.fit(training_data, y_train)


# Predict using BaggingClassifier on the test data
bag_result = bagging.predict(testing_data)

# Predict using RandomForestClassifier on the test data
forest_result = forest.predict(testing_data)

# Predict using AdaBoostClassifier on the test data
ada_result = ada.predict(testing_data)


def print_metrics(y_true, preds, model_name=None):
    '''
    INPUT:
    y_true - the y values that are actually true in the dataset (numpy array
    or pandas series)
    preds - the predictions for those values from some model (numpy array or
    pandas series)
    model_name - (str - optional) a name associated with the model if you
    would like to add it to the print statements

    OUTPUT:
    None - prints the accuracy, precision, recall, and F1 score
    '''
    if model_name is None:
        print('Accuracy score: ', format(accuracy_score(y_true, preds)))
        print('Precision score: ', format(precision_score(y_true, preds)))
        print('Recall score: ', format(recall_score(y_true, preds)))
        print('F1 score: ', format(f1_score(y_true, preds)))
        print('\n\n')

    else:
        print('Accuracy score for ' + model_name + ' :',
              format(accuracy_score(y_true, preds)))
        print('Precision score ' + model_name + ' :',
              format(precision_score(y_true, preds)))
        print('Recall score ' + model_name + ' :',
              format(recall_score(y_true, preds)))
        print('F1 score ' + model_name + ' :', format(f1_score(y_true, preds)))
        print('\n\n')


# Print Bagging scores
print_metrics(y_test, bag_result, model_name='Bagging')

# Print Random Forest scores
print_metrics(y_test, forest_result, model_name='Random Forest')

# Print AdaBoost scores
print_metrics(y_test, ada_result, model_name='AdaBoost')

# Naive Bayes Classifier scores
print_metrics(y_test, predictions, model_name='NB')