def get_predictions_full_CCLE_dataset_top_features(self,expression_file,ic50_file,num_features,drug):
expression_frame,ic50_series = dfm.get_expression_frame_and_ic50_series_for_drug(expression_file,ic50_file,drug,normalized=True,trimmed=True)
top_features = dfm.get_pval_top_n_features(expression_frame,ic50_series,num_features)
expression_frame = expression_frame.ix[top_features]
scikit_data,scikit_target = dfm.get_scikit_data_and_target(expression_frame,ic50_series)
cell_lines, testing_data = dfm.get_normalized_full_expression_identifiers_and_data(expression_file,expression_frame.index)
self.model.fit(scikit_data,scikit_target)
predictions = self.model.predict(testing_data)
return cell_lines,predictions,list(top_features)
def trim_X_num_features(X,y,train,num_features):
"""
Do calculations to trim X by taking the top num_features features based on p-value rank
"""
all_samples = pd.DataFrame(X)
all_labels = pd.Series(y)
train_samples,train_labels = get_training_samples_labels(all_samples,all_labels,train)
features = dfm.get_pval_top_n_features(train_samples.T,train_labels,num_features)
trimmed_all_samples = all_samples[features]
return np.array([list(trimmed_all_samples.ix[row]) for row in trimmed_all_samples.index])
