class LearningModel(object):
"""
Represents the model that can be trained and later used to predict
keywords for unknown data
"""
def __init__(self, global_index, word2vec_model):
self.scaler = StandardScaler()
self.classifier = RankSVM(n_jobs=-1)
self.global_index = global_index
self.word2vec = word2vec_model
def maybe_fit_and_scale(self, matrix):
"""
If the scaler is not initialized, the fit() is performed on given data.
Exception is thrown if the data is not big enough. Input matrix is
scaled and returned.
:param matrix: matrix to be transformed
:return: scaled matrix
"""
if not hasattr(self.scaler, 'n_samples_seen_'):
if len(matrix) < 1000:
raise ValueError("Please user bigger batch size. "
"The feature matrix is too small "
"to fit the scaler.")
else:
self.scaler.fit(matrix)
return self.scaler.transform(matrix)
def partial_fit_classifier(self, input_matrix, output_vector):
"""
Fit the classifier on X, y matrices. Can be used for online training.
:param input_matrix: feature matrix
:param output_vector: vector of the same length as input_matrix
:return: None
"""
classes = np.array([0, 1], dtype=np.bool_)
# TODO Maybe initialize the classifier with this for balancing classes
# weights = compute_class_weight('balanced', classes, output_vector)
self.classifier = self.classifier.partial_fit(
input_matrix,
output_vector,
classes=classes,
)
def fit_classifier(self, input_matrix, output_vector):
"""
Fit the classifier on X, y matrices. Previous fit is discarded.
:param input_matrix: feature matrix
:param output_vector: vector of the same length as input_matrix
:return: None
"""
self.classifier = self.classifier.fit(input_matrix, output_vector)
def scale_and_predict(self, input_matrix):
"""
Predict output for given samples
:param input_matrix: a feature matrix
:return: matrix with predictions for each sample
"""
scaled_matrix = self.scaler.transform(input_matrix)
return self.classifier.predict(scaled_matrix)
def scale_and_predict_confidence(self, input_matrix):
"""
Predict confidence values for given samples
:param input_matrix: a feature matrix
:return: matrix with confidence values for each sample
"""
scaled_matrix = self.scaler.transform(input_matrix)
return self.classifier.decision_function(scaled_matrix)
def get_global_index(self):
""" Get the GlobalFrequencyIndex field. """
return self.global_index
def __init__(self, global_index, word2vec_model):
self.scaler = StandardScaler()
self.classifier = RankSVM(n_jobs=-1)
self.global_index = global_index
self.word2vec = word2vec_model
