#p147

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)

from sklearn.preprocessing import LabelEncoder

X = df.iloc[:, 2:].values

y = df.loc[:, 1].values

print(np.unique(y))

le = LabelEncoder()

y = le.fit_transform(y)

print(np.unique(y))

#p148

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import PCA

from sklearn.linear_model import LogisticRegression

from sklearn.pipeline import Pipeline

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

pipe_lr = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(random_state=1))])

pipe_lr.fit(X_train, y_train)

print('Test Accuracy: %.3f' %pipe_lr.score(X_test, y_test))

#p153

from sklearn.cross_validation import StratifiedKFold

kfold = StratifiedKFold(y = y_train, n_folds=10, random_state=1)

scores = []

for k, (train, test) in enumerate(kfold):

pipe_lr.fit(X_train[train], y_train[train])

score = pipe_lr.score(X_train[test], y_train[test])

scores.append(score)

print('Fold: %s, Class dist. : %s Acc: %.3f' % (k+1, np.bincount(y_train[train]), score))

print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))

from sklearn.cross_validation import cross_val_score

scroes = cross_val_score(estimator=pipe_lr, X=X_train, y=y_train, cv=10, n_jobs=1)

print('CV accuracy scores: %s' %scores)

print('CV accuracy %.3f +/- %.3f' %(np.mean(scores), np.std(scores)))

#p155

import matplotlib.pyplot as plt

from sklearn.learning_curve import learning_curve

pipe_lr = Pipeline([('scl', StandardScaler()), ('clf', LogisticRegression(penalty='l2', random_state=0))])

train_sizes, train_scores, test_scores = learning_curve(estimator=pipe_lr, X=X_train, y=y_train, train_sizes=np.linspace(0.1, 1.0, 10), cv=10, n_jobs=1)

train_mean = np.mean(train_scores, axis=1)

train_std = np.std(train_scores, axis=1)

test_mean = np.mean(test_scores, axis=1)

test_std = np.std(test_scores, axis=1)

plt.plot(train_sizes, train_mean, color='blue', marker='o', markersize=5, label='training accuracy')

plt.fill_between(train_sizes, train_mean + train_std, train_mean - train_std, alpha=0.15, color='blue')

plt.plot(train_sizes, test_mean, color='green', linestyle='--', marker='s', markersize=5, label='validation accuracy')

plt.fill_between(train_sizes, test_mean + test_std, test_mean - test_std, alpha=0.15, color='green')

plt.grid()

plt.xlabel('Number of training samples')

plt.ylabel('Accuracy')

plt.legend(loc='lower right')

plt.ylim([0.8, 1.0])

plt.show()

#p156

from sklearn.learning_curve import validation_curve

param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]

train_scores, test_scores = validation_curve(estimator=pipe_lr, X=X_train, y=y_train, param_name='clf__C', param_range=param_range, cv=10)

train_mean = np.mean(train_scores, axis=1)

train_std = np.std(train_scores, axis=1)

test_mean = np.mean(test_scores, axis=1)

test_std=np.std(test_scores, axis=1)

plt.plot(param_range, train_mean, color='blue', marker='o', markersize=5, label='training accuracy')

plt.fill_between(param_range, train_mean + train_std, train_mean - train_std, alpha=0.15, color='blue')

plt.plot(param_range, test_mean, color='green', linestyle='--', marker='s', markersize=5, label='validation accuracy')

plt.fill_between(param_range, test_mean + test_std, test_mean - test_std, alpha=0.15, color='green')

plt.grid()

plt.xscale('log')

plt.legend(loc='lower right')

plt.xlabel('Parameter C')

plt.ylabel('Accuracy')

plt.ylim([0.8, 1.0])

plt.show()

print('========================================================')

#p158

from sklearn.grid_search import GridSearchCV

from sklearn.svm import SVC

pipe_svc = Pipeline([('scl', StandardScaler()), ('clf', SVC(random_state=1))])

param_range = [0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100,0, 1000,0]

param_grid = [{'clf__C':param_range, 'clf__kernel':['linear']}, {'clf__C':param_range, 'clf__gamma':param_range, 'clf__kernel':['rbf']}]

gs = GridSearchCV(estimator=pipe_svc, param_grid=param_grid, scoring='accuracy', cv=10, n_jobs=-1)

gs = gs.fit(X_train, y_train)

print(gs.best_score_)

print(gs.best_params_)

#p159

clf = gs.best_estimator_

clf.fit(X_train, y_train)

print('Test accuracy: %.3f' % clf.score(X_test, y_test))

#p160

gs = GridSearchCV(estimator=pipe_svc, param_grid=param_grid, scoring='accuracy', cv=10, n_jobs=-1)

scores = cross_val_score(gs, X, y, scoring='accuracy', cv=5)

pirnt('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))

#p161

from sklearn.tree import DecisionTreeClassifier

gs = GridSearchCV(estimator=DecisionTreeClassifier(random_state=0), param_grid=[{'max_depth': [1, 2, 3, 4, 5, 6, 7, None]}], scoring='accuracy' cv=5)

scores = cross_val_score(gs, X_train, y_train, Scoring='accuracy', cv=5)

print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))