def get_patient_predictions_rfe(self,expression_file,ic50_file,patient_directory,target_features,drug):
e_data,e_target,p_identifiers,p_data = dfm.get_cell_line_and_patient_expression_data_target_for_drug(expression_file,ic50_file,patient_directory,1.0,drug)
step_length = int(len(e_data.tolist()[0]) / 100) + 1
model = RFE(self.model,target_features,step=step_length)
model.fit(e_data,e_target)
predictions = model.predict(p_data)
all_features = dfm.get_cell_line_and_patient_expression_gene_intersection(dfm.get_cell_line_expression_frame(expression_file),dfm.get_patients_expression_frame(patient_directory))[0]
top_features = [all_features[i] for i in xrange(0,len(all_features)) if model.support_[i]]
return p_identifiers, predictions, top_features
def get_patient_predictions_threshold(self,expression_file,ic50_file,patient_directory,threshold,drug):
"""
Returns the predictions for which patients are likely to be sensitive to SMAPs and which are likely to be resistant.
First trains a given SVM model on expression data, and then uses the trained model to predict patient outcome.
Returns a list of patient identifiers, and a list of predictions about the patients response to a given drug.
"""
e_data,e_target,p_identifiers,p_data = dfm.get_cell_line_and_patient_expression_data_target_for_drug(expression_file,ic50_file,patient_directory,threshold,drug)
self.model.fit(e_data,e_target)
predictions = self.model.predict(p_data)
return p_identifiers,predictions
