import numpy as np
import pandas as pd
 
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.externals import joblib 
 
# 3. Load red wine data.
#dataset_url = inequality-red.csv'
data = pd.read_csv('winequality-red.csv',sep=';')
 
# 4. Split data into training and test sets
y = data.quality
X = data.drop('quality', axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    test_size=0.2, 
                                                    random_state=123, 
                                                    stratify=y)
 
# 5. Declare data preprocessing steps
pipeline = make_pipeline(preprocessing.StandardScaler(), 
                         RandomForestRegressor(n_estimators=100))
 
# 6. Declare hyperparameters to tune
hyperparameters = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'],
                  'randomforestregressor__max_depth': [None, 5, 3, 1]}
 
# 7. Tune model using cross-validation pipeline
clf = GridSearchCV(pipeline, hyperparameters, cv=10)
 
clf.fit(X_train, y_train)
 
# 8. Refit on the entire training set
# No additional code needed if clf.refit == True (default is True)
 
# 9. Evaluate model pipeline on test data
pred = clf.predict(X_test)
print (r2_score(y_test, pred))
print (mean_squared_error(y_test, pred))
 
# 10. Save model for future use
joblib.dump(clf, 'rf_regressor.pkl')
# To load: clf2 = joblib.load('rf_regressor.pkl')"

 