ML Test

def determine_feature_importance(df):
	#Determines the importance of individual features within a dataframe
	#Grab header for all feature values excluding score & ids
	features_list = df.columns.values[4::]
	print "Features List: \n", features_list
	
	#set X equal to all feature values, excluding Score & ID fields
	X = df.values[:,4::]
	
	#set y equal to all Score values
	y = df.values[:,0]
	
	#fit a random forest with near-default paramaters to determine feature importance
	print '\nCreating Random Forest Classifier...\n'
	forest = RandomForestClassifier(oob_score=True, n_estimators=10000)
	print '\nFitting Random Forest Classifier...\n'
	forest.fit(X,y)
	feature_importance = forest.feature_importances_
	print feature_importance
	
	#Make importances relative to maximum importance
	print "\nMaximum feature importance is currently: ", feature_importance.max()
	feature_importance = 100.0 * (feature_importance / feature_importance.max())
	print "\nNormalized feature importance: \n", feature_importance
	print "\nNormalized maximum feature importance: \n", feature_importance.max()
	print "\nTo do: set fi_threshold == max?"
	print "\nTesting: setting fi_threshhold == 1"
	fi_threshold=1
	
	#get indicies of all features over fi_threshold
	important_idx = np.where(feature_importance > fi_threshold)[0]
	print "\nRetrieved important_idx: ", important_idx
	
	#create a list of all feature names above fi_threshold
	important_features = features_list[important_idx]
	print "\n", important_features.shape[0], "Important features(>", fi_threshold, "% of max importance:\n", important_features
	
	#get sorted indices of important features
	sorted_idx = np.argsort(feature_importance[important_idx])[::-1]
	print "\nFeatures sorted by importance (DESC):\n", important_features[sorted_idx]
	
	#generate plot
	pos = np.arange(sorted_idx.shape[0]) + .5
	plt.subplot(1,2,2)
	plt.barh(pos,feature_importance[important_idx][sorted_idx[::-1]],align='center')
	plt.yticks(pos, important_features[sorted_idx[::-1]])
	plt.xlabel('Relative importance')
	plt.ylabel('Variable importance')
	plt.draw()
	plt.show()
	
	X = X[:, important_idx][:, sorted_idx]
	
	
	return "Feature importance determined"