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

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_score
from sklearn.model_selection import cross_validate


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# import the data using pandas read_csv
white = pd.read_csv('winequality-white.csv',sep=';',quotechar='"')
red = pd.read_csv('winequality-red.csv',sep=';',quotechar='"')


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# create dataframe for the chemical characteristics, which I'll label 'x'
# convert the dataframe into a matrix
x_df = white.iloc[:,:-1]
x = x_df.as_matrix()


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# create dataframe for the wine quality, which I'll label 'y'
# convert the dataframe into a numpy array, for sklearn to use
y_df = white["quality"].values
y = np.array([1 if i>=7 else 0 for i in y_df])


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# set aside an empty list in which to keep the importances scores
scores = []


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# create Random Forest Classifier model from sklean.ensemble
# fit the model to your x and y data
clf = RandomForestClassifier(n_estimators=11)
clf.fit(x,y)


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# after cross-validating the model 10 times using cross_val_score
# append the classification scores to the scores list
scores.append(cross_val_score(clf, x, y, cv=10))


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# print the classification scores to see how well Random Forest performed
print(cross_validate(clf,x,y,cv=10))
# looking at the train scores vs the test scores, it seems we achieved
# between 76.2% and 85.5% accuracy in the test, whereas training scores
# were a lot higher, between 99.2% to 99.7%.


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# now, let's compute the feature importances from the model
imp = clf.feature_importances_
names = x_df.columns


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# plot the RF variable importances in a horizontal bar chart
imp, names = zip(*sorted(zip(imp,names)))
plt.barh(range(len(names)), imp, align = 'center')
plt.yticks(range(len(names)), names)