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_predictions_full_CCLE_dataset_rfe(self,expression_file,ic50_file,target_features,drug):
scikit_data,scikit_target = dfm.get_expression_scikit_data_target_for_drug(expression_file,ic50_file,drug,normalized=True,trimmed=True,threshold=None)
step_length = int(len(scikit_data.tolist()[0]) / 100) + 1
model = RFE(self.model,target_features,step=step_length)
model.fit(scikit_data,scikit_target)
expression_frame = dfm.normalize_expression_frame(dfm.get_cell_line_expression_frame(expression_file))
cell_lines = expression_frame.columns
testing_data = dfm.get_scikit_data(expression_frame)
predictions = model.predict(testing_data)
top_features = [expression_frame.index[i] for i in xrange(0,len(expression_frame.index)) if model.support_[i]]
return cell_lines,predictions,top_features
