Add LDA option to the classifier.

demos/classifier.py

@@ -35,6 +35,8 @@ import pandas as pd
 
 import openface
 
+from sklearn.pipeline import Pipeline
+from sklearn.lda import LDA
 from sklearn.preprocessing import LabelEncoder
 from sklearn.svm import SVC
 from sklearn.mixture import GMM
@@ -100,6 +102,11 @@ def train(args):
     elif args.classifier == 'GMM':
         clf = GMM(n_components=nClasses)
 
+    if args.ldaDim > 0:
+        clf_final = clf
+        clf = Pipeline([('lda', LDA(n_components=args.ldaDim)),
+                        ('clf', clf_final)])
+
     clf.fit(embeddings, labelsNum)
 
     fName = "{}/classifier.pkl".format(args.workDir)
@@ -147,6 +154,7 @@ if __name__ == '__main__':
     subparsers = parser.add_subparsers(dest='mode', help="Mode")
     trainParser = subparsers.add_parser('train',
                                         help="Train a new classifier.")
+    trainParser.add_argument('--ldaDim', type=int, default=-1)
     trainParser.add_argument('--classifier', type=str,
                              choices=['LinearSvm', 'GMM'],
                              help='The type of classifier to use.',