BuildingMachineLearningSystemsWithPython/ch08/norm.py at master · luispedro/BuildingMachineLearningSystemsWithPython · GitHub

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import numpy as np

class NormalizePositive(object):

def __init__(self, axis=0):

self.axis = axis

def fit(self, features, y=None):

# count features that are greater than zero in axis `self.axis`:

if self.axis == 1:

features = features.T

binary = (features > 0)

count = binary.sum(axis=0)

# to avoid division by zero, set zero counts to one:

count[count == 0] = 1.

self.mean = features.sum(axis=0)/count

# Compute variance by average squared difference to the mean, but only

# consider differences where binary is True (i.e., where there was a

# true rating):

diff = (features - self.mean) * binary

diff **= 2

# regularize the estimate of std by adding 0.1

self.std = np.sqrt(0.1 + diff.sum(axis=0)/count)

return self

def transform(self, features):

if self.axis == 1:

features = features.T

binary = (features > 0)

features = features - self.mean

features /= self.std

features *= binary

if self.axis == 1:

features = features.T

return features

def inverse_transform(self, features, copy=True):

if copy:

features = features.copy()

if self.axis == 1:

features = features.T

features *= self.std

features += self.mean

if self.axis == 1:

features = features.T

return features

def fit_transform(self, features):

return self.fit(features).transform(features)

def predict(train):

norm = NormalizePositive()

train = norm.fit_transform(train)

return norm.inverse_transform(train * 0.)

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))

if __name__ == '__main__':

main()

main(transpose_inputs=True)

BuildingMachineLearningSystemsWithPython/ch08/norm.py at master · luispedro/BuildingMachineLearningSystemsWithPython · GitHub

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