import sys
sys.path.append("./../..")

# example adapted from: http://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py
%reload_ext yellowbrick
from yellowbrick.classifier import DecisionViz

%matplotlib inline
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
         "Decision Tree", "Random Forest", "Neural Net", "AdaBoost",
         "Naive Bayes", "QDA"]

classifiers = [
    KNeighborsClassifier(3),
    SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),
    GaussianProcessClassifier(1.0 * RBF(1.0), warm_start=True),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    MLPClassifier(alpha=1),
    AdaBoostClassifier(),
    GaussianNB(),
    QuadraticDiscriminantAnalysis()]

X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                           random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)

data_set = make_moons(noise=0.3, random_state=0)

X, y = data_set
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42)

model = classifiers[2]
title = names[2]
    
viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.poof()

model = classifiers[3]
title = names[3]
    
viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.poof()

model = classifiers[4]
title = names[4]
    
viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.poof()

model = classifiers[5]
title = names[5]
    
viz = DecisionViz(model, title=title, features=['Feature Alpha', 'Feature Beta'], classes=['Class A', 'Class B'])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.poof()

model = classifiers[6]
title = names[6]
    
viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.poof()

model = classifiers[7]
title = names[7]
    
viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.poof()

model = classifiers[8]
title = names[8]
    
viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.poof()

model = classifiers[9]
title = names[9]
    
viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.poof()