#!/usr/bin/python

import pickle
import numpy

numpy.random.seed(42)

# The words (features) and authors (labels), already largely processed.
# These files should have been created from the previous (Lesson 10)
# mini-project.
words_file = "../text_learning/your_word_data.pkl"
authors_file = "../text_learning/your_email_authors.pkl"
word_data = pickle.load(open(words_file, "r"))
authors = pickle.load(open(authors_file, "r"))

# test_size is the percentage of events assigned to the test set (the remainder go into training)
# feature matrices changed to dense representations for compatibility with
# classifier functions in versions 0.15.2 and earlier
from sklearn import cross_validation

# remove outliers from dataset
signature_words = ['sshacklensf', 'cgermannsf']
for i in range(len(word_data)):
    for word in signature_words:
        word_data[i] = word_data[i].replace(word, '')

features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(word_data, authors,
                                                                                             test_size=0.1,
                                                                                             random_state=42)

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words='english')
features_train = vectorizer.fit_transform(features_train)
features_test = vectorizer.transform(features_test).toarray()

# a classic way to overfit is to use a small number
# of data points and a large number of features;
# train on only 150 events to put ourselves in this regime
features_train = features_train[:150]
labels_train = labels_train[:150]


# your code goes here
from sklearn.tree import DecisionTreeClassifier
dt_classifier = DecisionTreeClassifier()
dt_classifier.fit(features_train, labels_train)

print 'accuracy', dt_classifier.score(features_test, labels_test)

for i in range(len(dt_classifier.feature_importances_)):
    if dt_classifier.feature_importances_[i] > 0.2:
        print dt_classifier.feature_importances_[i], vectorizer.get_feature_names()[i]
