# Chapter 3:  A tour of machine learning classifiers using scikit-learn

# five main steps:
# 1.  selection of features
# 2.  choosing a performance metric
# 3.  choosing a classifier and optimization algorithm
# 4.  evaluating the performance of the model
# 5.  tuning the algorithm

# training a perceptron using scikit-learn

from sklearn import datasets
import numpy as np

iris = datasets.load_iris()

X = iris.data[:, [2,3]]
y = iris.target

# split data to evaluate model performance
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)

# normalize features
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

# train Perceptron using sklearn
from sklearn.linear_model import Perceptron
ppn = Perceptron(n_iter = 40, eta0 = 0.1, random_state = 0)
ppn.fit(X_train_std, y_train)

# make predictions on test data to evaluate model
y_pred = ppn.predict(X_test_std)
print('Misclassified samples: %d' % (y_test !=y_pred).sum())

# evaluate model using classification accuracy
from sklearn.metrics import accuracy_score
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))

# plot
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt

def plot_decision_regions(X, y, classifier, test_idx = None, resolution = 0.02):
    # set up marker generator and color map
    markers = ('s','x','o','^','v')
    colors = ('red','blue','lightgreen','gray','cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])
    
    # plot the decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    
    xx1,xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                          np.arange(x2_min, x2_max, resolution))
    
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    
    Z = Z.reshape(xx1.shape)

    plt.contourf(xx1, xx2, Z, alpha = 0.4, cmap = cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    # plot all samples
    X_test, y_test = X[test_idx, :], y[test_idx]
    
    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],
                    alpha = 0.8, c=cmap(idx),
                    marker = markers[idx], label = cl)
    
    # highlight test samples
    if test_idx:
        X_test, y_test = X[test_idx, :], y[test_idx]
        plt.scatter(X_test[:, 0], X_test[:, 1], c = '',
                    alpha = 1.0, linewidth = 1, marker = 'o',
                    s = 55, label = 'test set')
                    
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

plot_decision_regions(X = X_combined_std,
                      y = y_combined,
                      classifier = ppn,
                      test_idx = range(105, 150))

plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc = 'upper left')
plt.show()
    



