%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from time import time
from sklearn.datasets import load_files
print("loading train dataset ...")
t = time()
news_train = load_files('datasets/mlcomp/379/train')
print("summary: {0} documents in {1} categories.".format(
len(news_train.data), len(news_train.target_names)))
print("done in {0} seconds".format(time() - t))
from sklearn.feature_extraction.text import TfidfVectorizer
print("vectorizing train dataset ...")
t = time()
vectorizer = TfidfVectorizer(encoding='latin-1')
X_train = vectorizer.fit_transform((d for d in news_train.data))
print("n_samples: %d, n_features: %d" % X_train.shape)
print("number of non-zero features in sample [{0}]: {1}".format(
news_train.filenames[0], X_train[0].getnnz()))
print("done in {0} seconds".format(time() - t))
from sklearn.naive_bayes import MultinomialNB
print("traning models ...".format(time() - t))
t = time()
y_train = news_train.target
clf = MultinomialNB(alpha=0.0001)
clf.fit(X_train, y_train)
train_score = clf.score(X_train, y_train)
print("train score: {0}".format(train_score))
print("done in {0} seconds".format(time() - t))
print("loading test dataset ...")
t = time()
news_test = load_files('datasets/mlcomp/379/test')
print("summary: {0} documents in {1} categories.".format(
len(news_test.data), len(news_test.target_names)))
print("done in {0} seconds".format(time() - t))
print("vectorizing test dataset ...")
t = time()
X_test = vectorizer.transform((d for d in news_test.data))
y_test = news_test.target
print("n_samples: %d, n_features: %d" % X_test.shape)
print("number of non-zero features in sample [{0}]: {1}".format(
news_test.filenames[0], X_test[0].getnnz()))
print("done in %fs" % (time() - t))
pred = clf.predict(X_test[0])
print("predict: {0} is in category {1}".format(
news_test.filenames[0], news_test.target_names[pred[0]]))
print("actually: {0} is in category {1}".format(
news_test.filenames[0], news_test.target_names[news_test.target[0]]))
print("predicting test dataset ...")
t = time()
pred = clf.predict(X_test)
print("done in %fs" % (time() - t))
from sklearn.metrics import classification_report
print("classification report on test set for classifier:")
print(clf)
print(classification_report(y_test, pred,
target_names=news_test.target_names))
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, pred)
print("confusion matrix:")
print(cm)
# Show confusion matrix
plt.figure(figsize=(8, 8), dpi=144)
plt.title('Confusion matrix of the classifier')
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.matshow(cm, fignum=1, cmap='gray')
plt.colorbar();
