Init random forest

classifier/classification_model_training/model_training.py

@@ -51,6 +51,17 @@ def simpleTrain(dataset, target, model='all'):
 	elif model == 'bayes':
 		return bayesAccuracy
 
+def randomForest(dataset, target):
+	from sklearn.ensemble import RandomForestClassifier
+	from sklearn.model_selection import train_test_split
+
+	trainingSet, testSet, trainingTarget, testTarget = train_test_split(dataset,
+		target, test_size=0.4, random_state=0)
+	clf = RandomForestClassifier(n_estimators=500, criterion = 'entropy',
+		n_jobs = -1, random_state = 4)
+	clf = clf.fit(trainingSet, trainingTarget)
+	print("Random forest accuracy: {0:.2f}".format(100*clf.score(testSet, testTarget)))
+
 def kFCrossValid(dataset, target, model = 'svm'):
 	from sklearn.model_selection import cross_val_score
 	from sklearn import metrics
@@ -73,6 +84,10 @@ def kFCrossValid(dataset, target, model = 'svm'):
 		# Naive Bayes
 		from sklearn.naive_bayes import GaussianNB
 		clf = GaussianNB()
+	elif model == 'rndForest':
+		from sklearn.ensemble import ExtraTreesClassifier
+		clf = ExtraTreesClassifier(n_estimators=1500, criterion = 'entropy',
+			n_jobs = -1, random_state = 4)
 	else:
 		print('Error. model specified not supported')
 		return None

classifier/pipeline.py

@@ -1,11 +1,18 @@
 import numpy as np
 from preprocessing.data_preprocessing import createSingleFeaturesArray, standardization, PCA
-from classification_model_training.model_training import simpleTrain
+from classification_model_training.model_training import simpleTrain, kFCrossValid
 
 dataset, target, featureKeys = createSingleFeaturesArray(
 	'feature_extraction/music_features/',
 	'feature_extraction/speech_features/')
 
 dataset = standardization(dataset)
+# dataset = PCA(dataset)
+print('Simple train accuracy achieved = ' + str(simpleTrain(dataset, target)))
+kFCrossValid(dataset, target, model = 'svm')
+kFCrossValid(dataset, target, model = 'rndForest')
+
 dataset = PCA(dataset)
-print('Max accuracy achieved = ' + str(simpleTrain(dataset, target)))
+print('Simple train accuracy achieved = ' + str(simpleTrain(dataset, target)))
+kFCrossValid(dataset, target, model = 'svm')
+kFCrossValid(dataset, target, model = 'rndForest')