def multiple_lineal_regression(file_path):
records = ReviewETL.load_file(file_path)
ratings = np.array([record['stars'] for record in records])
ETLUtils.drop_fields(['stars'], records)
data = np.array([record.values() for record in records])
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(data, ratings)
model = linear_model.LinearRegression(fit_intercept=True)
model.fit(data, ratings)
p = np.array([model.predict(xi) for xi in data])
e = p - ratings
total_error = np.dot(e, e)
rmse_train = np.sqrt(total_error / len(p))
kf = KFold(len(data), n_folds=10)
err = 0
for train, test in kf:
model.fit(data[train], ratings[train])
p = np.array([model.predict(xi) for xi in data[test]])
e = p - ratings[test]
err += np.dot(e, e)
rmse_10cv = np.sqrt(err / len(data))
print('RMSE on training: {}'.format(rmse_train))
print('RMSE on 10-fold CV: {}'.format(rmse_10cv))
def multiple_lineal_regression(file_path):
records = ReviewETL.load_file(file_path)
ratings = np.array([record['stars'] for record in records])
ETLUtils.drop_fields(['stars'], records)
data = np.array([record.values() for record in records])
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(data, ratings)
model = linear_model.LinearRegression(fit_intercept=True)
model.fit(data, ratings)
p = np.array([model.predict(xi) for xi in data])
e = p - ratings
total_error = np.dot(e, e)
rmse_train = np.sqrt(total_error / len(p))
kf = KFold(len(data), n_folds=10)
err = 0
for train, test in kf:
model.fit(data[train], ratings[train])
p = np.array([model.predict(xi) for xi in data[test]])
e = p - ratings[test]
err += np.dot(e, e)
rmse_10cv = np.sqrt(err / len(data))
print('RMSE on training: {}'.format(rmse_train))
print('RMSE on 10-fold CV: {}'.format(rmse_10cv))
def simple_lineal_regression(file_path):
records = ReviewETL.load_file(file_path)
data = [[record['review_count']] for record in records]
ratings = [record['stars'] for record in records]
num_testing_records = int(len(ratings) * 0.8)
training_data = data[:num_testing_records]
testing_data = data[num_testing_records:]
training_ratings = ratings[:num_testing_records]
testing_ratings = ratings[num_testing_records:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(training_data, training_ratings)
# The coefficients
print('Coefficients: \n', regr.coef_)
print('Intercept: \n', regr.intercept_)
# The root mean square error
print("RMSE: %.2f" % (np.mean(
(regr.predict(testing_data) - testing_ratings)**2))**0.5)
print('Variance score: %.2f' %
regr.score(testing_data, testing_ratings))
# Plot outputs
import pylab as pl
pl.scatter(testing_data, testing_ratings, color='black')
pl.plot(testing_data,
regr.predict(testing_data),
color='blue',
linewidth=3)
pl.xticks(())
pl.yticks(())
pl.show()
def simple_lineal_regression(file_path):
records = ReviewETL.load_file(file_path)
data = [[record['review_count']] for record in records]
ratings = [record['stars'] for record in records]
num_testing_records = int(len(ratings) * 0.8)
training_data = data[:num_testing_records]
testing_data = data[num_testing_records:]
training_ratings = ratings[:num_testing_records]
testing_ratings = ratings[num_testing_records:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(training_data, training_ratings)
# The coefficients
print('Coefficients: \n', regr.coef_)
print('Intercept: \n', regr.intercept_)
# The root mean square error
print("RMSE: %.2f"
% (np.mean(
(regr.predict(testing_data) - testing_ratings) ** 2)) ** 0.5)
print(
'Variance score: %.2f' % regr.score(testing_data, testing_ratings))
# Plot outputs
import pylab as pl
pl.scatter(testing_data, testing_ratings, color='black')
pl.plot(testing_data, regr.predict(testing_data), color='blue',
linewidth=3)
pl.xticks(())
pl.yticks(())
pl.show()
