def backward_step(model,expression_frame,ic50_series,backward_step_size,forward_features_selected,backward_features_removed):
removable_features = set(expression_frame.index) - set(forward_features_selected) - set(backward_features_removed)
expression_frame = dfm.get_expression_frame_with_features(expression_frame,set(expression_frame.index) - set(backward_features_removed))
scikit_data,scikit_target = dfm.get_scikit_data_and_target(expression_frame,ic50_series)
model.fit(scikit_data,scikit_target)
coefs = model.coef_[0]
feature_names = list(expression_frame.index)
coefs,feature_names = zip(*sorted(zip(coefs,feature_names),key=lambda x : math.fabs(x[0])))
num_features_removed = 0
feature_index = 0
while(num_features_removed < backward_step_size and feature_index < len(feature_names)):
if(feature_names[feature_index] in removable_features):
backward_features_removed.append(feature_names[feature_index])
num_features_removed += 1
feature_index += 1
return backward_features_removed
def forward_step(model,expression_frame,ic50_series,forward_features_selected,backward_features_removed):
potential_features = set(expression_frame.index) - set(forward_features_selected) - set(backward_features_removed)
max_score = -1
best_feature = None
for feature in potential_features:
model = copy.copy(model)
model_features = set(forward_features_selected) & set(feature)
expression_frame = dfm.get_expression_frame_with_features(expression_frame,model_features)
scikit_data,scikit_target = dfm.get_scikit_data_and_target(expression_frame,ic50_series)
score = cv.cross_val_score(model,scikit_data,scikit_target,cv=5).mean()
if(score > max_score):
max_score = score
best_feature = feature
if(best_feature):
forward_features_selected.append(best_feature)
return forward_features_selected
else:
return forward_features_selected
