#!/usr/bin/env python
# coding: utf-8

# Copyright (c) 2015 - 2017 [Sebastian Raschka](sebastianraschka.com)
# 
# https://github.com/rasbt/python-machine-learning-book
# 
# [MIT License](https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt)

# # Python Machine Learning - Code Examples

# # Chapter 3 - A Tour of Machine Learning Classifiers Using Scikit-Learn

# Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).

# In[1]:


get_ipython().run_line_magic('load_ext', 'watermark')
get_ipython().run_line_magic('watermark', "-a 'Sebastian Raschka' -u -d -p numpy,pandas,matplotlib,sklearn")


# *The use of `watermark` is optional. You can install this IPython extension via "`pip install watermark`". For more information, please see: https://github.com/rasbt/watermark.*

# ### Overview

# - [Choosing a classification algorithm](#Choosing-a-classification-algorithm)
# - [First steps with scikit-learn](#First-steps-with-scikit-learn)
#     - [Training a perceptron via scikit-learn](#Training-a-perceptron-via-scikit-learn)
# - [Modeling class probabilities via logistic regression](#Modeling-class-probabilities-via-logistic-regression)
#     - [Logistic regression intuition and conditional probabilities](#Logistic-regression-intuition-and-conditional-probabilities)
#     - [Learning the weights of the logistic cost function](#Learning-the-weights-of-the-logistic-cost-function)
#     - [Training a logistic regression model with scikit-learn](#Training-a-logistic-regression-model-with-scikit-learn)
#     - [Tackling overfitting via regularization](#Tackling-overfitting-via-regularization)
# - [Maximum margin classification with support vector machines](#Maximum-margin-classification-with-support-vector-machines)
#     - [Maximum margin intuition](#Maximum-margin-intuition)
#     - [Dealing with the nonlinearly separable case using slack variables](#Dealing-with-the-nonlinearly-separable-case-using-slack-variables)
#     - [Alternative implementations in scikit-learn](#Alternative-implementations-in-scikit-learn)
# - [Solving nonlinear problems using a kernel SVM](#Solving-nonlinear-problems-using-a-kernel-SVM)
#     - [Using the kernel trick to find separating hyperplanes in higher dimensional space](#Using-the-kernel-trick-to-find-separating-hyperplanes-in-higher-dimensional-space)
# - [Decision tree learning](#Decision-tree-learning)
#     - [Maximizing information gain – getting the most bang for the buck](#Maximizing-information-gain-–-getting-the-most-bang-for-the-buck)
#     - [Building a decision tree](#Building-a-decision-tree)
#     - [Combining weak to strong learners via random forests](#Combining-weak-to-strong-learners-via-random-forests)
# - [K-nearest neighbors – a lazy learning algorithm](#K-nearest-neighbors-–-a-lazy-learning-algorithm)
# - [Summary](#Summary)

# <br>
# <br>

# <br>
# <br>

# In[2]:


from IPython.display import Image
get_ipython().run_line_magic('matplotlib', 'inline')


# In[3]:


# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version


# # Choosing a classification algorithm

# ...

# # First steps with scikit-learn

# Loading the Iris dataset from scikit-learn. Here, the third column represents the petal length, and the fourth column the petal width of the flower samples. The classes are already converted to integer labels where 0=Iris-Setosa, 1=Iris-Versicolor, 2=Iris-Virginica.

# In[4]:


from sklearn import datasets
import numpy as np

iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target

print('Class labels:', np.unique(y))


# Splitting data into 70% training and 30% test data:

# In[5]:


if Version(sklearn_version) < '0.18':
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=0)


# Standardizing the features:

# In[6]:


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)


# <br>
# <br>

# ## Training a perceptron via scikit-learn

# Redefining the `plot_decision_region` function from chapter 2:

# In[7]:


from sklearn.linear_model import Perceptron

ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)
ppn.fit(X_train_std, y_train)


# In[8]:


y_test.shape


# In[9]:


y_pred = ppn.predict(X_test_std)
print('Misclassified samples: %d' % (y_test != y_pred).sum())


# In[10]:


from sklearn.metrics import accuracy_score

print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))


# In[11]:


from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import warnings


def versiontuple(v):
    return tuple(map(int, (v.split("."))))


def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):

    # setup 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())

    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], 
                    y=X[y == cl, 1],
                    alpha=0.6, 
                    c=cmap(idx),
                    edgecolor='black',
                    marker=markers[idx], 
                    label=cl)

    # highlight test samples
    if test_idx:
        # plot all samples
        if not versiontuple(np.__version__) >= versiontuple('1.9.0'):
            X_test, y_test = X[list(test_idx), :], y[list(test_idx)]
            warnings.warn('Please update to NumPy 1.9.0 or newer')
        else:
            X_test, y_test = X[test_idx, :], y[test_idx]

        plt.scatter(X_test[:, 0],
                    X_test[:, 1],
                    c='',
                    alpha=1.0,
                    edgecolor='black',
                    linewidths=1,
                    marker='o',
                    s=55, label='test set')


# Training a perceptron model using the standardized training data:

# In[12]:


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.tight_layout()
# plt.savefig('./figures/iris_perceptron_scikit.png', dpi=300)
plt.show()


# <br>
# <br>

# # Modeling class probabilities via logistic regression

# ...

# ### Logistic regression intuition and conditional probabilities

# In[13]:


import matplotlib.pyplot as plt
import numpy as np


def sigmoid(z):
    return 1.0 / (1.0 + np.exp(-z))

z = np.arange(-7, 7, 0.1)
phi_z = sigmoid(z)

plt.plot(z, phi_z)
plt.axvline(0.0, color='k')
plt.ylim(-0.1, 1.1)
plt.xlabel('z')
plt.ylabel('$\phi (z)$')

# y axis ticks and gridline
plt.yticks([0.0, 0.5, 1.0])
ax = plt.gca()
ax.yaxis.grid(True)

plt.tight_layout()
# plt.savefig('./figures/sigmoid.png', dpi=300)
plt.show()


# In[14]:


Image(filename='./images/03_03.png', width=500) 


# <br>
# <br>

# ### Learning the weights of the logistic cost function

# In[15]:


def cost_1(z):
    return - np.log(sigmoid(z))


def cost_0(z):
    return - np.log(1 - sigmoid(z))

z = np.arange(-10, 10, 0.1)
phi_z = sigmoid(z)

c1 = [cost_1(x) for x in z]
plt.plot(phi_z, c1, label='J(w) if y=1')

c0 = [cost_0(x) for x in z]
plt.plot(phi_z, c0, linestyle='--', label='J(w) if y=0')

plt.ylim(0.0, 5.1)
plt.xlim([0, 1])
plt.xlabel('$\phi$(z)')
plt.ylabel('J(w)')
plt.legend(loc='best')
plt.tight_layout()
# plt.savefig('./figures/log_cost.png', dpi=300)
plt.show()


# <br>
# <br>

# ### Training a logistic regression model with scikit-learn

# In[16]:


from sklearn.linear_model import LogisticRegression

lr = LogisticRegression(C=1000.0, random_state=0)
lr.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std, y_combined,
                      classifier=lr, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/logistic_regression.png', dpi=300)
plt.show()


# In[17]:


if Version(sklearn_version) < '0.17':
    lr.predict_proba(X_test_std[0, :])
else:
    lr.predict_proba(X_test_std[0, :].reshape(1, -1))


# <br>
# <br>

# ### Tackling overfitting via regularization

# In[18]:


Image(filename='./images/03_06.png', width=700) 


# In[19]:


weights, params = [], []
for c in np.arange(-5., 5.):
    lr = LogisticRegression(C=10.**c, random_state=0)
    lr.fit(X_train_std, y_train)
    weights.append(lr.coef_[1])
    params.append(10**c)

weights = np.array(weights)
plt.plot(params, weights[:, 0],
         label='petal length')
plt.plot(params, weights[:, 1], linestyle='--',
         label='petal width')
plt.ylabel('weight coefficient')
plt.xlabel('C')
plt.legend(loc='upper left')
plt.xscale('log')
# plt.savefig('./figures/regression_path.png', dpi=300)
plt.show()


# <br>
# <br>

# # Maximum margin classification with support vector machines

# In[20]:


Image(filename='./images/03_07.png', width=700) 


# ## Maximum margin intuition

# ...

# ## Dealing with the nonlinearly separable case using slack variables

# In[21]:


Image(filename='./images/03_08.png', width=600) 


# In[22]:


from sklearn.svm import SVC

svm = SVC(kernel='linear', C=1.0, random_state=0)
svm.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std, y_combined,
                      classifier=svm, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/support_vector_machine_linear.png', dpi=300)
plt.show()


# ## Alternative implementations in scikit-learn

# <br>
# <br>

# # Solving non-linear problems using a kernel SVM

# In[23]:


import matplotlib.pyplot as plt
import numpy as np

np.random.seed(0)
X_xor = np.random.randn(200, 2)
y_xor = np.logical_xor(X_xor[:, 0] > 0,
                       X_xor[:, 1] > 0)
y_xor = np.where(y_xor, 1, -1)

plt.scatter(X_xor[y_xor == 1, 0],
            X_xor[y_xor == 1, 1],
            c='b', marker='x',
            label='1')
plt.scatter(X_xor[y_xor == -1, 0],
            X_xor[y_xor == -1, 1],
            c='r',
            marker='s',
            label='-1')

plt.xlim([-3, 3])
plt.ylim([-3, 3])
plt.legend(loc='best')
plt.tight_layout()
# plt.savefig('./figures/xor.png', dpi=300)
plt.show()


# In[24]:


Image(filename='./images/03_11.png', width=700) 


# <br>
# <br>

# ## Using the kernel trick to find separating hyperplanes in higher dimensional space

# In[25]:


svm = SVC(kernel='rbf', random_state=0, gamma=0.10, C=10.0)
svm.fit(X_xor, y_xor)
plot_decision_regions(X_xor, y_xor,
                      classifier=svm)

plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/support_vector_machine_rbf_xor.png', dpi=300)
plt.show()


# In[26]:


from sklearn.svm import SVC

svm = SVC(kernel='rbf', random_state=0, gamma=0.2, C=1.0)
svm.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std, y_combined,
                      classifier=svm, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/support_vector_machine_rbf_iris_1.png', dpi=300)
plt.show()


# In[27]:


svm = SVC(kernel='rbf', random_state=0, gamma=100.0, C=1.0)
svm.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std, y_combined, 
                      classifier=svm, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/support_vector_machine_rbf_iris_2.png', dpi=300)
plt.show()


# <br>
# <br>

# # Decision tree learning

# In[28]:


Image(filename='./images/03_15.png', width=500) 


# <br>
# <br>

# ## Maximizing information gain - getting the most bang for the buck

# In[29]:


import matplotlib.pyplot as plt
import numpy as np


def gini(p):
    return p * (1 - p) + (1 - p) * (1 - (1 - p))


def entropy(p):
    return - p * np.log2(p) - (1 - p) * np.log2((1 - p))


def error(p):
    return 1 - np.max([p, 1 - p])

x = np.arange(0.0, 1.0, 0.01)

ent = [entropy(p) if p != 0 else None for p in x]
sc_ent = [e * 0.5 if e else None for e in ent]
err = [error(i) for i in x]

fig = plt.figure()
ax = plt.subplot(111)
for i, lab, ls, c, in zip([ent, sc_ent, gini(x), err], 
                          ['Entropy', 'Entropy (scaled)', 
                           'Gini Impurity', 'Misclassification Error'],
                          ['-', '-', '--', '-.'],
                          ['black', 'lightgray', 'red', 'green', 'cyan']):
    line = ax.plot(x, i, label=lab, linestyle=ls, lw=2, color=c)

ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15),
          ncol=3, fancybox=True, shadow=False)

ax.axhline(y=0.5, linewidth=1, color='k', linestyle='--')
ax.axhline(y=1.0, linewidth=1, color='k', linestyle='--')
plt.ylim([0, 1.1])
plt.xlabel('p(i=1)')
plt.ylabel('Impurity Index')
plt.tight_layout()
#plt.savefig('./figures/impurity.png', dpi=300, bbox_inches='tight')
plt.show()


# <br>
# <br>

# ## Building a decision tree

# In[30]:


from sklearn.tree import DecisionTreeClassifier

tree = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
tree.fit(X_train, y_train)

X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X_combined, y_combined, 
                      classifier=tree, test_idx=range(105, 150))

plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/decision_tree_decision.png', dpi=300)
plt.show()


# <br>
# <br>

# In[31]:


from sklearn.tree import export_graphviz

export_graphviz(tree, 
                out_file='tree.dot', 
                feature_names=['petal length', 'petal width'])


# In[32]:


Image(filename='./images/03_18.png', width=600) 


# **Note**
# 
# If you have scikit-learn 0.18 and pydotplus installed (e.g., you can install it via `pip install pydotplus`), you can also show the decision tree directly without creating a separate dot file as shown below. Also note that `sklearn 0.18` offers a few additional options to make the decision tree visually more appealing.

# In[33]:


import pydotplus


# In[34]:


from IPython.display import Image
from IPython.display import display

if Version(sklearn_version) >= '0.18':
    
    try:
        
        import pydotplus
        
        dot_data = export_graphviz(
        tree, 
        out_file=None,
        # the parameters below are new in sklearn 0.18
        feature_names=['petal length', 'petal width'],  
        class_names=['setosa', 'versicolor', 'virginica'],  
        filled=True,
        rounded=True)

        graph = pydotplus.graph_from_dot_data(dot_data)  
        display(Image(graph.create_png()))

    except ImportError:
        print('pydotplus is not installed.')


# <br>
# <br>

# ## Combining weak to strong learners via random forests

# In[35]:


from sklearn.ensemble import RandomForestClassifier

forest = RandomForestClassifier(criterion='entropy',
                                n_estimators=10, 
                                random_state=1,
                                n_jobs=2)
forest.fit(X_train, y_train)

plot_decision_regions(X_combined, y_combined, 
                      classifier=forest, test_idx=range(105, 150))

plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/random_forest.png', dpi=300)
plt.show()


# <br>
# <br>

# # K-nearest neighbors - a lazy learning algorithm

# In[36]:


Image(filename='./images/03_20.png', width=400) 


# In[37]:


from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(X_train_std, y_train)

plot_decision_regions(X_combined_std, y_combined, 
                      classifier=knn, test_idx=range(105, 150))

plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
# plt.savefig('./figures/k_nearest_neighbors.png', dpi=300)
plt.show()


# <br>
# <br>

# # Summary

# ...