import warnings
warnings.filterwarnings('ignore')

%matplotlib inline
%pylab inline

import pandas as pd
print(pd.__version__)

"""
Explanation: ML using KNN
End of explanation
"""


df = pd.read_csv('./insurance-customers-300.csv', sep=';')

y=df['group']

df.drop('group', axis='columns', inplace=True)

X = df.as_matrix()

df.describe()

"""
Explanation: First Step: Load Data and disassemble for our purposes
End of explanation
"""


# ignore this, it is just technical code
# should come from a lib, consider it to appear magically 
# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html

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

cmap_print = ListedColormap(['#AA8888', '#004000', '#FFFFDD'])
cmap_bold = ListedColormap(['#AA4444', '#006000', '#AAAA00'])
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#FFFFDD'])
font_size=25

def meshGrid(x_data, y_data):
    h = 1  # step size in the mesh
    x_min, x_max = x_data.min() - 1, x_data.max() + 1
    y_min, y_max = y_data.min() - 1, y_data.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    return (xx,yy)
    
def plotPrediction(clf, x_data, y_data, x_label, y_label, colors, title="", mesh=True, fname=None, print=False):
    xx,yy = meshGrid(x_data, y_data)
    plt.figure(figsize=(20,10))

    if clf and mesh:
        Z = clf.predict(np.c_[yy.ravel(), xx.ravel()])
        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
    
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    if print:
        plt.scatter(x_data, y_data, c=colors, cmap=cmap_print, s=200, marker='o', edgecolors='k')
    else:
        plt.scatter(x_data, y_data, c=colors, cmap=cmap_bold, s=80, marker='o', edgecolors='k')
    plt.xlabel(x_label, fontsize=font_size)
    plt.ylabel(y_label, fontsize=font_size)
    plt.title(title, fontsize=font_size)
    if fname:
        plt.savefig(fname)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)

X_train.shape, y_train.shape, X_test.shape, y_test.shape

X_train_kmh_age = X_train[:, :2]
X_test_kmh_age = X_test[:, :2]
X_train_2_dim = X_train_kmh_age
X_test_2_dim = X_test_kmh_age

from sklearn import neighbors
clf = neighbors.KNeighborsClassifier(1)
%time clf.fit(X_train_2_dim, y_train)

plotPrediction(clf, X_train_2_dim[:, 1], X_train_2_dim[:, 0], 
               'Age', 'Max Speed', y_train,
                title="Train Data Max Speed vs Age with Classification")

"""
Explanation: Second Step: First version using KNN
End of explanation
"""


clf.score(X_train_2_dim, y_train)

"""
Explanation: Look how great it is doing!
End of explanation
"""


plotPrediction(clf, X_test_2_dim[:, 1], X_test_2_dim[:, 0], 
               'Age', 'Max Speed', y_test,
                title="Test Data Max Speed vs Age with Prediction")

clf.score(X_test_2_dim, y_test)

"""
Explanation: But really?
End of explanation
"""


# http://scikit-learn.org/stable/modules/cross_validation.html
from sklearn.model_selection import cross_val_score

scores = cross_val_score(clf, X[:, :2], y, cv=10)

scores

# https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule
print("Accuracy: %0.2f (+/- %0.2f for 95 percent of runs)" % (scores.mean(), scores.std() * 2))

"""
Explanation: Cross Validation is a way to make the score more telling

we run the training and scoring many times (10 in our case)
each time we use a different part of the data for validation
this way we have many runs that take out a random factor
additionally we use all data for training
only works when training time is reasonably short
End of explanation
"""


