def fit(self, x, y):
"""Fit model with batch stochastic gradient descent
Parameters
----------
x : array-like, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples]
Class labels of training samples
Returns
-------
self : an instance of self
"""
n_features = x.shape[1]
n_classes = len(numpy.unique(y))
train_set_x = theano.shared(numpy.asarray(x, dtype=theano.config.floatX), borrow=True)
train_set_y = theano.shared(numpy.asarray(y, dtype=numpy.int32), borrow=True)
self.model_ = LogisticRegressionModel(self.var_x, self.var_y, n_features, n_classes)
optimizer = MiniBatchGradientDescent(self.model_, self.n_iter, self.learning_rate, self.batch_size, 0)
optimizer.fit(train_set_x, train_set_y)
return self
class LogisticRegression(BaseEstimator, ClassifierMixin):
"""Linear logistic regression model
Parameters
----------
learning_rate : float
The learning rate. Defaults to 6
The learning rate is normalized with respect to the batch_size as learning_rate / batch_size
batch_size : integer
Number of samples to use for one iteration. Defaults to 600.
n_iter : integer
The number of gradient updates (aka epochs). Defaults to 1000.
"""
def __init__(self, learning_rate=6, batch_size=600, n_iter=1000):
self.batch_size = batch_size
self.learning_rate = learning_rate
self.n_iter = n_iter
self.model_ = None
self.var_x = T.matrix('x')
self.var_y = T.ivector('y')
def fit(self, x, y):
"""Fit model with batch stochastic gradient descent
Parameters
----------
x : array-like, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples]
Class labels of training samples
Returns
-------
self : an instance of self
"""
n_features = x.shape[1]
n_classes = len(numpy.unique(y))
train_set_x = theano.shared(numpy.asarray(x, dtype=theano.config.floatX), borrow=True)
train_set_y = theano.shared(numpy.asarray(y, dtype=numpy.int32), borrow=True)
self.model_ = LogisticRegressionModel(self.var_x, self.var_y, n_features, n_classes)
optimizer = MiniBatchGradientDescent(self.model_, self.n_iter, self.learning_rate, self.batch_size, 0)
optimizer.fit(train_set_x, train_set_y)
return self
def predict_proba(self, x):
"""Probability estimates for each class.
Parameters
----------
x : array-like, shape = [n_samples, n_features]
Samples of which class should be predicted
Returns
-------
p : array-like, shape = [n_samples, n_classes]
Returns the probability of the sample for each class in the model.
"""
n_features = self.model_.shape[0]
if x.shape[1] != n_features:
raise ValueError("X has %d features per sample; expecting %d"
% (x.shape[1], n_features))
test_set_x = theano.shared(numpy.asarray(x, dtype=theano.config.floatX), borrow=True)
pred_prob = self.model_.p_y_given_x
classify = theano.function(inputs=[], outputs=pred_prob, givens={self.var_x: test_set_x})
result = classify()
return result
def predict(self, x):
"""Predict class labels of samples in x
Parameters
----------
x : array-like, shape = [n_samples, n_features]
Samples of which class should be predicted
Returns
-------
c : array-like, shape = [n_samples]
Predicted class labels per sample
"""
n_features = self.model_.shape[0]
if x.shape[1] != n_features:
raise ValueError("X has %d features per sample; expecting %d"
% (x.shape[1], n_features))
test_set_x = theano.shared(numpy.asarray(x, dtype=theano.config.floatX), borrow=True)
y_pred = self.model_.predict_function()
classify = theano.function(inputs=[], outputs=y_pred, givens={self.var_x: test_set_x})
result = classify()
return numpy.asarray(result)
