from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing

iris = datasets.load_iris()
X_iris, y_iris = iris.data, iris.target
#print X_iris.shape, y_iris.shape
#print X_iris[100], y_iris[100]

X, y = X_iris[:, :2], y_iris
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)
#print X_train.shape, y_train.shape
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

import matplotlib.pyplot as plt
colors = ['red', 'greenyellow', 'blue']

for i in xrange(len(colors)):
    xs = X_train[:, 0][y_train == i]
    ys = X_train[:, 1][y_train == i]
    plt.scatter(xs, ys, c=colors[i])

plt.legend(iris.target_names)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')

#plt.show()

from sklearn.linear_model import SGDClassifier
import numpy as np
clf = SGDClassifier()
clf.fit(X_train, y_train)

#print clf.coef_
#print clf.intercept_

x_min, x_max = X_train[:, 0].min() - .5, X_train[:, 0].max() + .5
y_min, y_max = X_train[:, 1].min() - .5, X_train[:, 1].max() + .5
xs = np.arange(x_min, x_max, 0.5)
fig, axes = plt.subplots(1, 3)
fig.set_size_inches(10,6)

for i in [0, 1, 2]:
    axes[i].set_aspect('equal')
    axes[i].set_title(iris.target_names[i] + ' versus the rest')
    axes[i].set_xlabel('Sepal length')
    axes[i].set_ylabel('Sepal width')
    axes[i].set_xlim(x_min, x_max)
    axes[i].set_ylim(y_min, y_max)
    plt.sca(axes[i])
    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=plt.cm.prism)
    ys = (-clf.intercept_[i] - xs * clf.coef_[i, 0]) / clf.coef_[i, 1]
    plt.plot(xs, ys, hold=True)

#plt.show()

print clf.predict(scaler.transform([[4.7, 3.1]]))
print clf.decision_function(scaler.transform([[4.7, 3.1]]))

from sklearn import metrics
y_train_pred = clf.predict(X_train)
print metrics.accuracy_score(y_train, y_train_pred)

y_pred = clf.predict(X_test)
print metrics.accuracy_score(y_test, y_pred)

print metrics.classification_report(y_test, y_pred, target_names=iris.target_names)
print metrics.confusion_matrix(y_test, y_pred)

from sklearn.cross_validation import cross_val_score, KFold
from sklearn.pipeline import Pipeline

clf = Pipeline([
    ('scaler', preprocessing.StandardScaler()),
    ('linear_model', SGDClassifier())
])

cv = KFold(X.shape[0], 5, shuffle=True, random_state=33)
scores = cross_val_score(clf, X, y, cv=cv)
print scores

from scipy.stats import sem
def mean_score(scores):
    return ("Mean score: {0: .3f} (+/- {1:.3f})").format(np.mean(scores), sem(scores))

print mean_score(scores)


