def predict_music(user):
"""
predicts the music based on the user choice
:parameter
user: (dic) Contains the user response for the songs
:return
selected_music: (int) Index of the recommended music based on user response using SVM
"""
connection = MongoClient(uri)
db = connection.get_default_database()
pos_feature = []
for p_index in user['pos_music']:
p_music = db.music_data.find_one({"_id": ObjectId(p_index)})
pos_feature.append(np.asarray(p_music['stFeatures']))
neg_feature = []
for n_index in user['neg_music']:
n_music = db.music_data.find_one({"_id": ObjectId(n_index)})
neg_feature.append(np.asarray(n_music['stFeatures']))
features = [(np.hstack(pos_feature)).T, (np.hstack(neg_feature)).T]
model = att.trainSVM_RBF(features, 0.1)
selected_music = user["av_music"][0]
selected_value = 0
for i in user["av_music"]:
s_music = db.music_data.find_one({"_id": ObjectId(i)})
feature = np.asarray(s_music['stFeatures'])
predict = [0, 0]
for j in range(feature.shape[1]):
predict[int(model.predict(feature[:, j].reshape(1, -1)))] += 1
if (predict[0] / (predict[0] + predict[1])) > selected_value:
selected_value = (predict[0] / (predict[0] + predict[1]))
selected_music = i
connection.close()
return selected_music
def Classify(clf, featuresAll, bestParam):
if clf == 'svm':
model = aT.trainSVM(featuresAll, bestParam)
elif clf == 'svm_rbf':
model = aT.trainSVM_RBF(featuresAll, bestParam)
elif clf == 'extratrees':
model = aT.trainExtraTrees(featuresAll, bestParam)
elif clf == 'randomforest':
model = aT.trainRandomForest(featuresAll, bestParam)
elif clf == 'knn':
model = aT.trainKNN(featuresAll, bestParam)
elif clf == 'gradientboosting':
model = aT.trainGradientBoosting(featuresAll, bestParam)
return model
def getTrainClassifier(f_train,classifier_name,param):
if classifier_name == AudioClassifierManager.__svmModelName:
classifier = aT.trainSVM(f_train, param)
elif classifier_name == AudioClassifierManager.__svmRbfModelName:
classifier = aT.trainSVM_RBF(f_train, param)
elif classifier_name == AudioClassifierManager.__knnModelName:
classifier = aT.trainKNN(f_train, param)
elif classifier_name == AudioClassifierManager.__randomforestModelName:
classifier = aT.trainRandomForest(f_train, param)
elif classifier_name == AudioClassifierManager.__gradientboostingModelName:
classifier = aT.trainGradientBoosting(f_train, param)
elif classifier_name == AudioClassifierManager.__extratreesModelName:
classifier = aT.trainExtraTrees(f_train, param)
else:
classifier = None
return classifier
def evaluateclassifier(features, class_names, n_exp, classifier_name, Params, parameterMode, perTrain=0.90):
'''
ARGUMENTS:
features: a list ([numOfClasses x 1]) whose elements containt numpy matrices of features.
each matrix features[i] of class i is [n_samples x numOfDimensions]
class_names: list of class names (strings)
n_exp: number of cross-validation experiments
classifier_name: svm or knn or randomforest
Params: list of classifier parameters (for parameter tuning during cross-validation)
parameterMode: 0: choose parameters that lead to maximum overall classification ACCURACY
1: choose parameters that lead to maximum overall f1 MEASURE
RETURNS:
bestParam: the value of the input parameter that optimizes the selected performance measure
'''
# feature normalization:
(features_norm, MEAN, STD) = normalizeFeatures(features)
#features_norm = features;
n_classes = len(features)
ac_all = []
f1_all = []
precision_classes_all = []
recall_classes_all = []
f1_classes_all = []
cms_all = []
# compute total number of samples:
n_samples_total = 0
for f in features:
n_samples_total += f.shape[0]
if n_samples_total > 1000 and n_exp > 50:
n_exp = 50
print("Number of training experiments changed to 50 due to high number of samples")
if n_samples_total > 2000 and n_exp > 10:
n_exp = 10
print("Number of training experiments changed to 10 due to high number of samples")
for Ci, C in enumerate(Params):
# for each param value
cm = numpy.zeros((n_classes, n_classes))
for e in range(n_exp):
# for each cross-validation iteration:
print("Param = {0:.5f} - classifier Evaluation "
"Experiment {1:d} of {2:d}".format(C, e+1, n_exp))
# split features:
f_train, f_test = randSplitFeatures(features_norm, perTrain)
# train multi-class svms:
if classifier_name == "svm":
classifier = trainSVM(f_train, C)
elif classifier_name == "svm_rbf":
classifier = trainSVM_RBF(f_train, C)
elif classifier_name == "knn":
classifier = trainKNN(f_train, C)
elif classifier_name == "randomforest":
classifier = trainRandomForest(f_train, C)
elif classifier_name == "gradientboosting":
classifier = trainGradientBoosting(f_train, C)
elif classifier_name == "extratrees":
classifier = trainExtraTrees(f_train, C)
elif classifier_name == "logisticregression":
classifier = trainLogisticRegression(f_train, C)
cmt = numpy.zeros((n_classes, n_classes))
for c1 in range(n_classes):
n_test_samples = len(f_test[c1])
res = numpy.zeros((n_test_samples, 1))
for ss in range(n_test_samples):
[res[ss], _] = classifierWrapperHead(classifier,
classifier_name,
f_test[c1][ss])
for c2 in range(n_classes):
cmt[c1][c2] = float(len(numpy.nonzero(res == c2)[0]))
cm = cm + cmt
cm = cm + 0.0000000010
rec = numpy.zeros((cm.shape[0], ))
pre = numpy.zeros((cm.shape[0], ))
for ci in range(cm.shape[0]):
rec[ci] = cm[ci, ci] / numpy.sum(cm[ci, :])
pre[ci] = cm[ci, ci] / numpy.sum(cm[:, ci])
precision_classes_all.append(pre)
recall_classes_all.append(rec)
f1 = 2 * rec * pre / (rec + pre)
f1_classes_all.append(f1)
ac_all.append(numpy.sum(numpy.diagonal(cm)) / numpy.sum(cm))
cms_all.append(cm)
f1_all.append(numpy.mean(f1))
print("\t\t", end="")
for i, c in enumerate(class_names):
if i == len(class_names)-1:
print("{0:s}\t\t".format(c), end="")
else:
print("{0:s}\t\t\t".format(c), end="")
print("OVERALL")
print("\tC", end="")
for c in class_names:
print("\tPRE\tREC\tf1", end="")
print("\t{0:s}\t{1:s}".format("ACC", "f1"))
best_ac_ind = numpy.argmax(ac_all)
best_f1_ind = numpy.argmax(f1_all)
for i in range(len(precision_classes_all)):
print("\t{0:.3f}".format(Params[i]), end="")
for c in range(len(precision_classes_all[i])):
print("\t{0:.1f}\t{1:.1f}\t{2:.1f}".format(100.0 * precision_classes_all[i][c],
100.0 * recall_classes_all[i][c],
100.0 * f1_classes_all[i][c]), end="")
print("\t{0:.1f}\t{1:.1f}".format(100.0 * ac_all[i], 100.0 * f1_all[i]), end="")
if i == best_f1_ind:
print("\t best f1", end="")
if i == best_ac_ind:
print("\t best Acc", end="")
print("")
if parameterMode == 0: # keep parameters that maximize overall classification accuracy:
print("Confusion Matrix:")
printConfusionMatrix(cms_all[best_ac_ind], class_names)
return Params[best_ac_ind]
elif parameterMode == 1: # keep parameters that maximize overall f1 measure:
print("Confusion Matrix:")
printConfusionMatrix(cms_all[best_f1_ind], class_names)
return Params[best_f1_ind]
def featureAndTrain(list_of_dirs, mt_win, mt_step, st_win, st_step,
classifier_type, model_name,
compute_beat=False, perTrain=0.90, feats=["gfcc", "mfcc"]):
'''
This function is used as a wrapper to segment-based audio feature extraction and classifier training.
ARGUMENTS:
list_of_dirs: list of paths of directories. Each directory contains a signle audio class whose samples are stored in seperate WAV files.
mt_win, mt_step: mid-term window length and step
st_win, st_step: short-term window and step
classifier_type: "svm" or "knn" or "randomforest" or "gradientboosting" or "extratrees"
model_name: name of the model to be saved
RETURNS:
None. Resulting classifier along with the respective model parameters are saved on files.
'''
# STEP A: Feature Extraction:
[features, classNames, _] = aF.dirsWavFeatureExtraction(list_of_dirs,
mt_win,
mt_step,
st_win,
st_step,
compute_beat=compute_beat,
feats=feats)
if len(features) == 0:
print("trainSVM_feature ERROR: No data found in any input folder!")
return
n_feats = features[0].shape[1]
feature_names = ["features" + str(d + 1) for d in range(n_feats)]
writeTrainDataToARFF(model_name, features, classNames, feature_names)
for i, f in enumerate(features):
if len(f) == 0:
print("trainSVM_feature ERROR: " + list_of_dirs[i] + " folder is empty or non-existing!")
return
# STEP B: classifier Evaluation and Parameter Selection:
if classifier_type == "svm" or classifier_type == "svm_rbf":
classifier_par = numpy.array([0.001, 0.01, 0.5, 1.0, 5.0, 10.0, 20.0])
elif classifier_type == "randomforest":
classifier_par = numpy.array([10, 25, 50, 100,200,500])
elif classifier_type == "knn":
classifier_par = numpy.array([1, 3, 5, 7, 9, 11, 13, 15])
elif classifier_type == "gradientboosting":
classifier_par = numpy.array([10, 25, 50, 100,200,500])
elif classifier_type == "extratrees":
classifier_par = numpy.array([10, 25, 50, 100,200,500])
elif classifier_type == "logisticregression":
classifier_par = numpy.array([0.01, 0.1, 1, 5])
# get optimal classifeir parameter:
features2 = []
for f in features:
fTemp = []
for i in range(f.shape[0]):
temp = f[i,:]
if (not numpy.isnan(temp).any()) and (not numpy.isinf(temp).any()) :
fTemp.append(temp.tolist())
else:
print("NaN Found! Feature vector not used for training")
features2.append(numpy.array(fTemp))
features = features2
bestParam = evaluateclassifier(features, classNames, 300, classifier_type, classifier_par, 0, perTrain) # Hier!!!!
print("Selected params: {0:.5f}".format(bestParam))
C = len(classNames)
[features_norm, MEAN, STD] = normalizeFeatures(features) # normalize features
MEAN = MEAN.tolist()
STD = STD.tolist()
featuresNew = features_norm
# STEP C: Save the classifier to file
if classifier_type == "svm":
classifier = trainSVM(featuresNew, bestParam)
elif classifier_type == "svm_rbf":
classifier = trainSVM_RBF(featuresNew, bestParam)
elif classifier_type == "randomforest":
classifier = trainRandomForest(featuresNew, bestParam)
elif classifier_type == "gradientboosting":
classifier = trainGradientBoosting(featuresNew, bestParam)
elif classifier_type == "extratrees":
classifier = trainExtraTrees(featuresNew, bestParam)
elif classifier_type == "logisticregression":
classifier = trainLogisticRegression(featuresNew, bestParam)
if classifier_type == "knn":
[X, Y] = listOfFeatures2Matrix(featuresNew)
X = X.tolist()
Y = Y.tolist()
fo = open(model_name, "wb")
cPickle.dump(X, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(Y, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(MEAN, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(STD, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(classNames, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(bestParam, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mt_win, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mt_step, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(st_win, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(st_step, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(compute_beat, fo, protocol=cPickle.HIGHEST_PROTOCOL)
fo.close()
elif classifier_type == "svm" or classifier_type == "svm_rbf" or \
classifier_type == "randomforest" or \
classifier_type == "gradientboosting" or \
classifier_type == "extratrees" or \
classifier_type == "logisticregression":
with open(model_name, 'wb') as fid:
cPickle.dump(classifier, fid)
fo = open(model_name + "MEANS", "wb")
cPickle.dump(MEAN, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(STD, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(classNames, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mt_win, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(mt_step, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(st_win, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(st_step, fo, protocol=cPickle.HIGHEST_PROTOCOL)
cPickle.dump(compute_beat, fo, protocol=cPickle.HIGHEST_PROTOCOL)
fo.close()
def main(rootName, modelType, classifierParam, signal_type):
CMall = numpy.zeros((2, 2))
if modelType != "svm" and modelType != "svm_rbf":
C = [int(classifierParam)]
else:
C = [(classifierParam)]
F1s = []
Accs = []
for ifold in range(0, 10): # for each fold
dirName = rootName + os.sep + "fold_{0:d}".format(
ifold) # get fold path name
classNamesTrain, featuresTrain = dirFeatureExtraction([
os.path.join(dirName, "train", "fail"),
os.path.join(dirName, "train", "success")
], signal_type) # TRAINING data feature extraction
bestParam = aT.evaluateClassifier(
featuresTrain, classNamesTrain, 2, modelType, C, 0,
0.90) # internal cross-validation (for param selection)
classNamesTest, featuresTest = dirFeatureExtraction([
os.path.join(dirName, "test", "fail"),
os.path.join(dirName, "test", "success")
], signal_type) # trainGradientBoosting data feature extraction
[featuresTrainNew, MEAN, STD] = aT.normalizeFeatures(
featuresTrain) # training features NORMALIZATION
if modelType == "svm": # classifier training
Classifier = aT.trainSVM(featuresTrainNew, bestParam)
elif modelType == "svm_rbf":
Classifier = aT.trainSVM_RBF(featuresTrainNew, bestParam)
elif modelType == "randomforest":
Classifier = aT.trainRandomForest(featuresTrainNew, bestParam)
elif modelType == "gradientboosting":
Classifier = aT.trainGradientBoosting(featuresTrainNew, bestParam)
elif modelType == "extratrees":
Classifier = aT.trainExtraTrees(featuresTrainNew, bestParam)
CM = numpy.zeros((2, 2)) # evaluation on testing data
for iC, f in enumerate(featuresTest): # for each class
for i in range(
f.shape[0]): # for each testing sample (feature vector)
curF = f[i, :] # get feature vector
curF = (curF - MEAN) / STD # normalize test feature vector
winnerClass = classNamesTrain[int(
aT.classifierWrapper(
Classifier, modelType,
curF)[0])] # classify and get winner class
trueClass = classNamesTest[iC] # get groundtruth class
CM[classNamesTrain.index(trueClass)][classNamesTrain.index(
winnerClass)] += 1 # update confusion matrix
CMall += CM # update overall confusion matrix
Recall, Precision, F1 = computePreRec(
CM, classNamesTrain) # get recall, precision and F1 (per class)
Acc = numpy.diagonal(CM).sum() / CM.sum() # get overall accuracy
F1s.append(numpy.mean(F1)) # append average F1
Accs.append(Acc) # append clasification accuracy
print
print "FINAL RESULTS"
print
print "----------------------------------"
print "fold\tacc\tf1"
print "----------------------------------"
for i in range(len(F1s)):
print "{0:d}\t{1:.1f}\t{2:.1f}".format(i, 100 * Accs[i], 100 * F1s[i])
Acc = numpy.diagonal(CMall).sum() / CMall.sum()
Recall, Precision, F1 = computePreRec(CMall, classNamesTrain)
print "----------------------------------"
print "{0:s}\t{1:.1f}\t{2:.1f}".format("Avg", 100 * numpy.mean(Accs),
100 * numpy.mean(F1s))
print "{0:s}\t{1:.1f}\t{2:.1f}".format("Av CM", 100 * Acc,
100 * numpy.mean(F1))
print "----------------------------------"
print
print "Overal Confusion matrix:"
aT.printConfusionMatrix(CMall, classNamesTrain)
print
print "FAIL Recall = {0:.1f}".format(100 *
Recall[classNamesTrain.index("fail")])
print "FAIL Precision = {0:.1f}".format(
100 * Precision[classNamesTrain.index("fail")])
print "SUCCESS Recall = {0:.1f}".format(
100 * Recall[classNamesTrain.index("success")])
print "SUCCESS Precision = {0:.1f}".format(
100 * Precision[classNamesTrain.index("success")])
return CMall, Acc, Recall, Precision, F1