def predict(train):
tr_train, tr_test = load_ml100k.get_train_test(train, random_state=34)
tr_predicted0 = regression.predict(tr_train)
tr_predicted1 = regression.predict(tr_train.T).T
tr_predicted2 = corrneighbours.predict(tr_train)
tr_predicted3 = corrneighbours.predict(tr_train.T).T
tr_predicted4 = norm.predict(tr_train)
tr_predicted5 = norm.predict(tr_train.T).T
stack_tr = np.array(
[
tr_predicted0[tr_test > 0],
tr_predicted1[tr_test > 0],
tr_predicted2[tr_test > 0],
tr_predicted3[tr_test > 0],
tr_predicted4[tr_test > 0],
tr_predicted5[tr_test > 0],
]
).T
lr = linear_model.LinearRegression()
lr.fit(stack_tr, tr_test[tr_test > 0])
stack_te = np.array(
[
tr_predicted0.ravel(),
tr_predicted1.ravel(),
tr_predicted2.ravel(),
tr_predicted3.ravel(),
tr_predicted4.ravel(),
tr_predicted5.ravel(),
]
).T
return lr.predict(stack_te).reshape(train.shape)
def predict(train):
tr_train, tr_test = load_ml100k.get_train_test(train, random_state=34)
tr_predicted0 = regression.predict(tr_train)
tr_predicted1 = regression.predict(tr_train.T).T
tr_predicted2 = corrneighbours.predict(tr_train)
tr_predicted3 = corrneighbours.predict(tr_train.T).T
tr_predicted4 = norm.predict(tr_train)
tr_predicted5 = norm.predict(tr_train.T).T
stack_tr = np.array([
tr_predicted0[tr_test > 0],
tr_predicted1[tr_test > 0],
tr_predicted2[tr_test > 0],
tr_predicted3[tr_test > 0],
tr_predicted4[tr_test > 0],
tr_predicted5[tr_test > 0],
]).T
lr = linear_model.LinearRegression()
lr.fit(stack_tr, tr_test[tr_test > 0])
stack_te = np.array([
tr_predicted0.ravel(),
tr_predicted1.ravel(),
tr_predicted2.ravel(),
tr_predicted3.ravel(),
tr_predicted4.ravel(),
tr_predicted5.ravel(),
]).T
return lr.predict(stack_te).reshape(train.shape)
def main(transpose_inputs=False):
train, test = get_train_test(random_state=12)
if transpose_inputs:
train = train.T
test = test.T
predicted = predict(train, plot_matrix=True)
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print('R2 score (binary {} neighbours): {:.1%}'.format(
('movie' if transpose_inputs else 'user'), r2))
def main(transpose_inputs=False):
from load_ml100k import get_train_test
train, test = get_train_test(random_state=12)
if transpose_inputs:
train = train.T
test = test.T
filled = predict(train)
r2 = metrics.r2_score(test[test > 0], filled[test > 0])
print('R2 score ({} regression): {:.1%}'.format(
('movie' if transpose_inputs else 'user'), r2))
def main(transpose_inputs=False):
train, test = get_train_test(random_state=12)
if transpose_inputs:
train = train.T
test = test.T
predicted = predict(train)
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print('R2 score (binary {} neighbours): {:.1%}'.format(
('movie' if transpose_inputs else 'user'),
r2))
def main(transpose_inputs=False):
from load_ml100k import get_train_test
from sklearn import metrics
train,test = get_train_test(random_state=12)
if transpose_inputs:
train = train.T
test = test.T
predicted = predict(train)
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print('R2 score ({} normalization): {:.1%}'.format(
('movie' if transpose_inputs else 'user'),
r2))
def main(transpose_inputs=False):
from load_ml100k import get_train_test
from sklearn import metrics
train, test = get_train_test(random_state=12)
if transpose_inputs:
train = train.T
test = test.T
predicted = predict(train)
r2_score = metrics.r2_score(test[test > 0], predicted[test > 0])
print('R2 score ({} normalization): {:.1%}'.format(
('movie' if transpose_inputs else 'user'), r2_score))
def stacked_predict(train_data):
# Stacked prediction: when fitting hyperparameters, though, we need two layers of training/testing splits: a first, higher-level split,
# and then, inside the training split, a second split to be able to fit the stacked learner.
tr_train, tr_test = load_ml100k.get_train_test(train_data, random_state=34)
# Call all the methods we previously defined:
# these have been implemented as functions:
tr_prediction_0 = regression.predict(tr_train)
tr_prediction_1 = regression.predict(tr_train.T).T
tr_prediction_2 = corr_neighbours.predict(tr_train)
tr_prediction_3 = corr_neighbours.predict(tr_train.T).T
tr_prediction_4 = normalization.predict(tr_train)
tr_prediction_5 = normalization.predict(tr_train.T).T
# Now assemble these predictions into a single array
stacked_learner = np.array([
tr_prediction_0[tr_test > 0],
tr_prediction_1[tr_test > 0],
tr_prediction_2[tr_test > 0],
tr_prediction_3[tr_test > 0],
tr_prediction_4[tr_test > 0],
tr_prediction_5[tr_test > 0],
]).T
# Fit a simple linear regression
linear_leaner = linear_model.LinearRegression()
linear_leaner.fit(stacked_learner, tr_test[tr_test > 0])
# apply the whole process to the testing split and evaluate
stacked_te = np.array([
tr_prediction_0.ravel(),
tr_prediction_1.ravel(),
tr_prediction_2.ravel(),
tr_prediction_3.ravel(),
tr_prediction_4.ravel(),
tr_prediction_5.ravel(),
]).T
return linear_leaner.predict(stacked_te).reshape(tr_train.shape)
def main():
train, test = load_ml100k.get_train_test(random_state=12)
predicted = predict(train)
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print("R2 stacked: {:.2%}".format(r2))
def main():
train, test = load_ml100k.get_train_test(random_state=12)
predicted = predict(train)
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print('R2 averaged: {:.2%}'.format(r2))
def main():
train,test = load_ml100k.get_train_test(random_state=12)
predicted = stacked_predict(train)
r2 = metrics.r2_score(test[test > 0], predicted[test > 0])
print('Results from ensemble_learner.py stacked prediction')
print('R2 stacked: {:.2%}'.format(r2))