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# Sebastian Raschka, 2015 (http://sebastianraschka.com)
# Python Machine Learning - Code Examples
# Chapter 7 - Combining Different Models for Ensemble Learning
# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
# License: MIT
# https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt
import math
import numpy as np
import pandas as pd
import operator
from scipy.misc import comb
import matplotlib.pyplot as plt
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.preprocessing import LabelEncoder
from sklearn.externals import six
from sklearn.base import clone
from sklearn.pipeline import _name_estimators
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import accuracy_score
from sklearn.grid_search import GridSearchCV
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import AdaBoostClassifier
from itertools import product
#############################################################################
print(50 * '=')
print('Section: Learning with ensembles')
print(50 * '-')
def ensemble_error(n_classifier, error):
    k_start = math.ceil(n_classifier / 2.0)
    probs = [comb(n_classifier, k) * error**k * (1-error)**(n_classifier - k)
             for k in range(k_start, n_classifier + 1)]
    return sum(probs)
print('Ensemble error', ensemble_error(n_classifier=11, error=0.25))
error_range = np.arange(0.0, 1.01, 0.01)
ens_errors = [ensemble_error(n_classifier=11, error=error)
              for error in error_range]
plt.plot(error_range,
         ens_errors,
         label='Ensemble error',
         linewidth=2)
plt.plot(error_range,
         error_range,
         linestyle='--',
         label='Base error',
         linewidth=2)
plt.xlabel('Base error')
plt.ylabel('Base/Ensemble error')
plt.legend(loc='upper left')
# plt.tight_layout()
# plt.savefig('./figures/ensemble_err.png', dpi=300)
#############################################################################
print(50 * '=')
print('Section: Implementing a simple majority vote classifier')
print(50 * '-')
np.argmax(np.bincount([0, 0, 1],
                      weights=[0.2, 0.2, 0.6]))
ex = np.array([[0.9, 0.1],
               [0.8, 0.2],
               [0.4, 0.6]])
p = np.average(ex,
               axis=0,
               weights=[0.2, 0.2, 0.6])
print('Averaged prediction', p)
print('np.argmax(p): ', np.argmax(p))
class MajorityVoteClassifier(BaseEstimator,
                             ClassifierMixin):
    """ A majority vote ensemble classifier
    Parameters
    ----------
    classifiers : array-like, shape = [n_classifiers]
      Different classifiers for the ensemble
    vote : str, {'classlabel', 'probability'} (default='label')
      If 'classlabel' the prediction is based on the argmax of
        class labels. Else if 'probability', the argmax of
        the sum of probabilities is used to predict the class label
        (recommended for calibrated classifiers).
    weights : array-like, shape = [n_classifiers], optional (default=None)
      If a list of `int` or `float` values are provided, the classifiers
      are weighted by importance; Uses uniform weights if `weights=None`.
    def __init__(self, classifiers, vote='classlabel', weights=None):
        self.classifiers = classifiers
        self.named_classifiers = {key: value for key, value
                                  in _name_estimators(classifiers)}
        self.vote = vote
        self.weights = weights
    def fit(self, X, y):
        """ Fit classifiers.
        Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            Matrix of training samples.
        y : array-like, shape = [n_samples]
            Vector of target class labels.
        Returns
        -------
        self : object
        """
        if self.vote not in ('probability', 'classlabel'):
            raise ValueError("vote must be 'probability' or 'classlabel'"
                             "; got (vote=%r)"
                             % self.vote)
        if self.weights and len(self.weights) != len(self.classifiers):
            raise ValueError('Number of classifiers and weights must be equal'
                             '; got %d weights, %d classifiers'
                             % (len(self.weights), len(self.classifiers)))
        # Use LabelEncoder to ensure class labels start with 0, which
        # is important for np.argmax call in self.predict
        self.lablenc_ = LabelEncoder()
        self.lablenc_.fit(y)
        self.classes_ = self.lablenc_.classes_
        self.classifiers_ = []
        for clf in self.classifiers:
            fitted_clf = clone(clf).fit(X, self.lablenc_.transform(y))
            self.classifiers_.append(fitted_clf)
        return self
    def predict(self, X):
        """ Predict class labels for X.
        Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            Matrix of training samples.
        Returns
        ----------
        maj_vote : array-like, shape = [n_samples]
            Predicted class labels.
        """
        if self.vote == 'probability':
            maj_vote = np.argmax(self.predict_proba(X), axis=1)
        else:  # 'classlabel' vote
            #  Collect results from clf.predict calls
            predictions = np.asarray([clf.predict(X)
                                      for clf in self.classifiers_]).T
            maj_vote = np.apply_along_axis(
                                      np.argmax(np.bincount(x,
                                                weights=self.weights)),
                                      arr=predictions)
        maj_vote = self.lablenc_.inverse_transform(maj_vote)
        return maj_vote
    def predict_proba(self, X):
        """ Predict class probabilities for X.
        Parameters
        ----------
        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
            Training vectors, where n_samples is the number of samples and
            n_features is the number of features.
        Returns
        ----------
        avg_proba : array-like, shape = [n_samples, n_classes]
            Weighted average probability for each class per sample.
        """
        probas = np.asarray([clf.predict_proba(X)
                             for clf in self.classifiers_])
        avg_proba = np.average(probas, axis=0, weights=self.weights)
        return avg_proba
    def get_params(self, deep=True):
        """ Get classifier parameter names for GridSearch"""
        if not deep:
            return super(MajorityVoteClassifier, self).get_params(deep=False)
        else:
            out = self.named_classifiers.copy()
            for name, step in six.iteritems(self.named_classifiers):
                for key, value in six.iteritems(step.get_params(deep=True)):
                    out['%s__%s' % (name, key)] = value
            return out
#############################################################################
print(50 * '=')
print('Section: Combining different algorithms for'
      ' classification with majority vote')
print(50 * '-')
iris = datasets.load_iris()
X, y = iris.data[50:, [1, 2]], iris.target[50:]
le = LabelEncoder()
y = le.fit_transform(y)
X_train, X_test, y_train, y_test =\
       train_test_split(X, y,
                        test_size=0.5,
                        random_state=1)
clf1 = LogisticRegression(penalty='l2',
                          random_state=0)
clf2 = DecisionTreeClassifier(max_depth=1,
                              criterion='entropy',
                              random_state=0)
clf3 = KNeighborsClassifier(n_neighbors=1,
                            metric='minkowski')
pipe1 = Pipeline([['sc', StandardScaler()],
                  ['clf', clf1]])
pipe3 = Pipeline([['sc', StandardScaler()],
                  ['clf', clf3]])
clf_labels = ['Logistic Regression', 'Decision Tree', 'KNN']
print('10-fold cross validation:\n')
for clf, label in zip([pipe1, clf2, pipe3], clf_labels):
    scores = cross_val_score(estimator=clf,
                             X=X_train,
                             y=y_train,
                             scoring='roc_auc')
    print("ROC AUC: %0.2f (+/- %0.2f) [%s]"
          % (scores.mean(), scores.std(), label))
mv_clf = MajorityVoteClassifier(classifiers=[pipe1, clf2, pipe3])
clf_labels += ['Majority Voting']
all_clf = [pipe1, clf2, pipe3, mv_clf]
for clf, label in zip(all_clf, clf_labels):
    scores = cross_val_score(estimator=clf,
                             X=X_train,
                             y=y_train,
                             scoring='roc_auc')
    print("ROC AUC: %0.2f (+/- %0.2f) [%s]"
          % (scores.mean(), scores.std(), label))
#############################################################################
print(50 * '=')
print('Section: Evaluating and tuning the ensemble classifier')
print(50 * '-')
colors = ['black', 'orange', 'blue', 'green']
linestyles = [':', '--', '-.', '-']
for clf, label, clr, ls \
        in zip(all_clf,
               clf_labels, colors, linestyles):
    # assuming the label of the positive class is 1
    y_pred = clf.fit(X_train,
                     y_train).predict_proba(X_test)[:, 1]
    fpr, tpr, thresholds = roc_curve(y_true=y_test,
                                     y_score=y_pred)
    roc_auc = auc(x=fpr, y=tpr)
    plt.plot(fpr, tpr,
             color=clr,
             linestyle=ls,
             label='%s (auc = %0.2f)' % (label, roc_auc))
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1],
         linestyle='--',
         color='gray',
         linewidth=2)
plt.xlim([-0.1, 1.1])
plt.ylim([-0.1, 1.1])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
# plt.tight_layout()
# plt.savefig('./figures/roc.png', dpi=300)
sc = StandardScaler()
X_train_std = sc.fit_transform(X_train)
all_clf = [pipe1, clf2, pipe3, mv_clf]
x_min = X_train_std[:, 0].min() - 1
x_max = X_train_std[:, 0].max() + 1
y_min = X_train_std[:, 1].min() - 1
y_max = X_train_std[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1))
f, axarr = plt.subplots(nrows=2, ncols=2,
                        sharex='col',
                        sharey='row',
                        figsize=(7, 5))
for idx, clf, tt in zip(product([0, 1], [0, 1]),
                        all_clf, clf_labels):
    clf.fit(X_train_std, y_train)
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.3)
    axarr[idx[0], idx[1]].scatter(X_train_std[y_train == 0, 0],
                                  X_train_std[y_train == 0, 1],
                                  c='blue',
                                  marker='^',
    axarr[idx[0], idx[1]].scatter(X_train_std[y_train == 1, 0],
                                  X_train_std[y_train == 1, 1],
                                  marker='o',
    axarr[idx[0], idx[1]].set_title(tt)
plt.text(-3.5, -4.5,
         s='Sepal width [standardized]',
         ha='center', va='center', fontsize=12)
plt.text(-10.5, 4.5,
         s='Petal length [standardized]',
         ha='center', va='center',
         fontsize=12, rotation=90)
# plt.tight_layout()
# plt.savefig('./figures/voting_panel', bbox_inches='tight', dpi=300)
print(mv_clf.get_params())
params = {'decisiontreeclassifier__max_depth': [1, 2],
          'pipeline-1__clf__C': [0.001, 0.1, 100.0]}
grid = GridSearchCV(estimator=mv_clf,
                    param_grid=params,
                    scoring='roc_auc')
grid.fit(X_train, y_train)
for params, mean_score, scores in grid.grid_scores_:
    print("%0.3f+/-%0.2f %r"
          % (mean_score, scores.std() / 2.0, params))
print('Best parameters: %s' % grid.best_params_)
print('Accuracy: %.2f' % grid.best_score_)
#############################################################################
print(50 * '=')
print('Section: Bagging -- Building an ensemble of'
      'classifiers from bootstrap samples')
print(50 * '-')
df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/'
                      'machine-learning-databases/wine/wine.data',
                      header=None)
df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
                   'Alcalinity of ash', 'Magnesium', 'Total phenols',
                   'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',
                   'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',
                   'Proline']
# drop 1 class
df_wine = df_wine[df_wine['Class label'] != 1]
y = df_wine['Class label'].values
X = df_wine[['Alcohol', 'Hue']].values
le = LabelEncoder()
y = le.fit_transform(y)
X_train, X_test, y_train, y_test =\
            train_test_split(X, y,
                             test_size=0.40,
                             random_state=1)
tree = DecisionTreeClassifier(criterion='entropy',
                              max_depth=None,
                              random_state=1)
bag = BaggingClassifier(base_estimator=tree,
                        n_estimators=500,
                        max_samples=1.0,
                        max_features=1.0,
                        bootstrap=True,
                        bootstrap_features=False,
                        n_jobs=1,
                        random_state=1)
tree = tree.fit(X_train, y_train)
y_train_pred = tree.predict(X_train)
y_test_pred = tree.predict(X_test)
tree_train = accuracy_score(y_train, y_train_pred)
tree_test = accuracy_score(y_test, y_test_pred)
print('Decision tree train/test accuracies %.3f/%.3f'
      % (tree_train, tree_test))
bag = bag.fit(X_train, y_train)
y_train_pred = bag.predict(X_train)
y_test_pred = bag.predict(X_test)
bag_train = accuracy_score(y_train, y_train_pred)
bag_test = accuracy_score(y_test, y_test_pred)
print('Bagging train/test accuracies %.3f/%.3f'
      % (bag_train, bag_test))
x_min = X_train[:, 0].min() - 1
x_max = X_train[:, 0].max() + 1
y_min = X_train[:, 1].min() - 1
y_max = X_train[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1))
f, axarr = plt.subplots(nrows=1, ncols=2,
                        sharex='col',
                        sharey='row',
                        figsize=(8, 3))
for idx, clf, tt in zip([0, 1],
                        [tree, bag],
                        ['Decision Tree', 'Bagging']):
    clf.fit(X_train, y_train)
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    axarr[idx].contourf(xx, yy, Z, alpha=0.3)
    axarr[idx].scatter(X_train[y_train == 0, 0],
                       X_train[y_train == 0, 1],
                       c='blue', marker='^')
    axarr[idx].scatter(X_train[y_train == 1, 0],
                       X_train[y_train == 1, 1],
                       c='red', marker='o')
    axarr[idx].set_title(tt)
axarr[0].set_ylabel('Alcohol', fontsize=12)
plt.text(10.2, -1.2,
         s='Hue',
         ha='center', va='center', fontsize=12)
# plt.tight_layout()
# plt.savefig('./figures/bagging_region.png',
#            dpi=300,
#            bbox_inches='tight')
#############################################################################
print(50 * '=')
print('Section: Leveraging weak learners via adaptive boosting')
print(50 * '-')
tree = DecisionTreeClassifier(criterion='entropy',
                              max_depth=1,
                              random_state=0)
ada = AdaBoostClassifier(base_estimator=tree,
                         n_estimators=500,
                         learning_rate=0.1,
                         random_state=0)
tree = tree.fit(X_train, y_train)
y_train_pred = tree.predict(X_train)
y_test_pred = tree.predict(X_test)
tree_train = accuracy_score(y_train, y_train_pred)
tree_test = accuracy_score(y_test, y_test_pred)
print('Decision tree train/test accuracies %.3f/%.3f'
      % (tree_train, tree_test))
ada = ada.fit(X_train, y_train)
y_train_pred = ada.predict(X_train)
y_test_pred = ada.predict(X_test)
ada_train = accuracy_score(y_train, y_train_pred)
ada_test = accuracy_score(y_test, y_test_pred)
print('AdaBoost train/test accuracies %.3f/%.3f'
      % (ada_train, ada_test))
x_min, x_max = X_train[:, 0].min() - 1, X_train[:, 0].max() + 1
y_min, y_max = X_train[:, 1].min() - 1, X_train[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1))
f, axarr = plt.subplots(1, 2, sharex='col', sharey='row', figsize=(8, 3))
for idx, clf, tt in zip([0, 1],
                        [tree, ada],
                        ['Decision Tree', 'AdaBoost']):
    clf.fit(X_train, y_train)
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    axarr[idx].contourf(xx, yy, Z, alpha=0.3)
    axarr[idx].scatter(X_train[y_train == 0, 0],
                       X_train[y_train == 0, 1],
                       c='blue', marker='^')
    axarr[idx].scatter(X_train[y_train == 1, 0],
                       X_train[y_train == 1, 1],
                       c='red', marker='o')
    axarr[idx].set_title(tt)
axarr[0].set_ylabel('Alcohol', fontsize=12)
plt.text(10.2, -1.2,
         s='Hue',
         ha='center', va='center', fontsize=12)
# plt.tight_layout()
# plt.savefig('./figures/adaboost_region.png',
#           dpi=300,
#           bbox_inches='tight')
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