scikit-wine.py

import sklearn
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

#import sklearn.external.joblib as extjoblib
import joblib
dataset_url = 'http://mlr.cs.umass.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'
data = pd.read_csv(dataset_url)
#print(data.head())

data = pd.read_csv(dataset_url, sep=';')

#print(data.head())
print (data.shape)
#print(data.describe())

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)
X_train_scaled = preprocessing.scale(X_train)
#print(X_train_scaled)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train_scaled = scaler.transform(X_train)
#print(X_train_scaled.mean(axis=0))
#print(X_train_scaled.std(axis=0))
X_test_scaled = scaler.transform(X_test)
#print(X_test_scaled.mean(axis=0))
#print(X_test_scaled.std(axis=0))
pipeline = make_pipeline(preprocessing.StandardScaler(),RandomForestRegressor(n_estimators=100))
#print(pipeline.get_params())
hyperparameters = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'],
                  'randomforestregressor__max_depth': [None, 5, 3, 1]}

clf = GridSearchCV(pipeline, hyperparameters, cv=10)

# Fit and tune model
clf.fit(X_train, y_train)

#print(clf.best_params_)

#print(clf.refit)

y_pred = clf.predict(X_test)

print(r2_score(y_test, y_pred))
# 0.45044082571584243

print(mean_squared_error(y_test, y_pred))
# 0.35461593750000003

joblib.dump(clf, 'rf_regressor.pkl')

clf2 = joblib.load('rf_regressor.pkl')

# Predict data set using loaded model
clf2.predict(X_test)