def compute_lcbmf_one_user(user_id): file_name = "lcbmf_user_"+str(user_id) print "loading matrix user "+str(user_id)+"..." data_matrix = MDataExtractor.load_matrix(user_id) rows_labels = MDataExtractor.load_labels_vector(user_id) columns_labels = MDataExtractor.load_time_vector(user_id) importance_scores = MDataExtractor.load_importance_scores(user_id) print "user "+str(user_id)+" has "+str(len(rows_labels))+" features (rows) and "+str(len(columns_labels))+" realization (columns)" #compute the lcbmf lcbmf_comp = LCBMFComputer(data_matrix, "idf", "[0,1]", "positive, sum=1", 10) print "computing LCBMF for user "+str(user_id)+"..." lcbmf_comp.compute() print "constructing interpretable output for user "+str(user_id)+"..." lcbmf_comp.construct_rows_interpretable_output(rows_labels, disp_m) r_output = lcbmf_comp.rows_interpretable_output #write the result print "writing LCBMF result for user "+str(user_id)+"..." JsonLogsFileWriter.write(r_output, file_name)
def train(self, train_data_matrix): #transform of the matrix and save the transformations applied by feature (we do it here and not inside the LCBMFComupter class because we need to revert #back the transformation for the test set samples [transformed_trainset, self.transformation_scores_by_feature] = self.apply_pretransformation(train_data_matrix, self.pretransformation_name) #compute the a and b matrixes on the train set lcbmf = LCBMFComputer(transformed_trainset, None, self.a_constrains_name, self.b_constrains_name, self.k) [self.a_matrix, self.b_matrix] = lcbmf.compute()
def compute_lcbmf_one_user(user_id):
file_name = "lcbmf_user_" + str(user_id)
print "loading matrix user " + str(user_id) + "..."
data_matrix = MDataExtractor.load_matrix(user_id)
rows_labels = MDataExtractor.load_labels_vector(user_id)
columns_labels = MDataExtractor.load_time_vector(user_id)
importance_scores = MDataExtractor.load_importance_scores(user_id)
print "user " + str(user_id) + " has " + str(
len(rows_labels)) + " features (rows) and " + str(
len(columns_labels)) + " realization (columns)"
#compute the lcbmf
lcbmf_comp = LCBMFComputer(data_matrix, "idf", "[0,1]", "positive, sum=1",
10)
print "computing LCBMF for user " + str(user_id) + "..."
lcbmf_comp.compute()
print "constructing interpretable output for user " + str(user_id) + "..."
lcbmf_comp.construct_rows_interpretable_output(rows_labels, disp_m)
r_output = lcbmf_comp.rows_interpretable_output
#write the result
print "writing LCBMF result for user " + str(user_id) + "..."
JsonLogsFileWriter.write(r_output, file_name)
