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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import sklearn as sk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model.logistic import LogisticRegression
%matplotlib inline
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rouge = [[1,4], [2,3], [3,2], [4,1], [2,4], [3,3], [4,2] ]
bleu = [[1,2], [2,1], [3,0], [4,-1], [1,1], [2,0], [2,-1]]
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plt.scatter([x for x,y in rouge], [y for x,y in rouge], color='red')
plt.scatter([x for x,y in bleu], [y for x,y in bleu], color='blue')
Nous pouvons indiquer l'hyperplane séparateur (ci-bas).
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# Inspired by https://stackoverflow.com/questions/20045994/how-do-i-plot-the-decision-boundary-of-a-regression-using-matplotlib
# and http://stackoverflow.com/questions/28256058/plotting-decision-boundary-of-logistic-regression
X = np.array(rouge + bleu)
y = [1] * len(rouge) + [0] * len(bleu)
logreg = LogisticRegression()
logreg.fit(X, y)
xx, yy = np.mgrid[0:5:.01, -2:5:.01]
grid = np.c_[xx.ravel(), yy.ravel()]
probs = logreg.predict_proba(grid)[:, 1].reshape(xx.shape)
fig, ax = plt.subplots()
ax.contour(xx, yy, probs, levels=[.5], cmap="Greys", vmin=0, vmax=.6)
plt.scatter([x for x,y in rouge], [y for x,y in rouge], color='red')
plt.scatter([x for x,y in bleu], [y for x,y in bleu], color='blue')
plt.show()
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# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets
# Import some data to play with.
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
h = .02 # step size in the mesh
logreg = linear_model.LogisticRegression(C=1e5)
# We create an instance of Neighbours Classifier and fit the data.
logreg.fit(X, Y)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot.
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
# Plot also the training points.
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.show()
Il faut télécharger le corpus de spam ici. Unzippez-le dans le répertoire spam-corpus.
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df = pd.read_csv('spam-corpus/SMSSpamCollection', delimiter='\t', header=None)
print(df.head())
print('\n')
print('Number of spam messages: {n}'.format(n=df[df[0] == 'spam'][0].count()))
print('Number of ham messages: {n}'.format(n=df[df[0] == 'ham'][0].count()))
Il nous faut d'abord des critères (features). Puis nous allons utiliser TF-IDF pour trouver les mots les plus représentatifs des sms spam et ham.
fit_transform() pour les training data, mais transform() pour les test data?
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from sklearn.model_selection import train_test_split, cross_val_score
X_train_raw, X_test_raw, y_train, y_test = train_test_split(df[1], df[0])
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train_raw)
X_test = vectorizer.transform(X_test_raw)
Enfin, nous créeons un classifieur par régression logistique. Comme tout classifieur en scikit-learn, il nous propose fit() et predict(). Il faut toujours visualiser nos données et nos résultats, ce que nous faisons.
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classifier = LogisticRegression()
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)
num_to_show = 5
for msg, prediction in zip(X_test_raw[:num_to_show], predictions[:num_to_show]):
print('Prediction: {pred}.\nMessage: {msg}\n'.format(
pred=prediction, msg=msg))
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from sklearn.metrics import confusion_matrix
yy_test = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
yy_pred = [0, 1, 0, 0, 0, 0, 0, 1, 1, 1]
confusion = confusion_matrix(yy_test, yy_pred)
print(confusion)
plt.matshow(confusion)
plt.title('Confusion matrix')
plt.gray()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
# Ou si on voudrait que le noir nous montre les plus communs :
invert_colors = np.ones(confusion.shape) * confusion.max()
plt.matshow(invert_colors - confusion)
plt.title('Confusion matrix')
plt.gray()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
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good_scores = cross_val_score(classifier, X_train, y_train, cv=5)
random_X_train = np.random.rand(X_train.shape[0], X_train.shape[1])
bad_scores = cross_val_score(classifier, random_X_train, y_train, cv=5)
print('good', good_scores)
print('bad', bad_scores)