Train_GMM.py

from __future__ import print_function
import numpy as np
from six.moves import cPickle as pickle
from sklearn.decomposition import PCA
from sklearn.mixture import GMM

##################load data#####################

print("Loading data")


all_data = pickle.load(open('dataset_standarized_all_10.pickle', 'rb'))
train_data = all_data['train_dataset']
test_data = all_data['test_dataset']

train_labels = all_data['train_labels']
test_labels = all_data['test_labels']

del all_data

# in case you want to select a portion of the features
# start_index=0
# end_index=87
# train_data=train_data[:,start_index:end_index,:]
# test_data=test_data[:,start_index:end_index,:]


input_width = train_data.shape[1]
input_height = train_data.shape[2]


# vectorize the data
def reformat(dataset):
    dataset = dataset.reshape(
        (-1, input_width * input_height)).astype(np.float32)
    return dataset


train_data = reformat(train_data)
test_data = reformat(test_data)

print("Shape of training set after vectorization:")
print(train_data.shape)

print("Shape of test set after vectorization:")
print(test_data.shape)

################## PCA #####################

print("Running PCA")

# run pca to reduce vector size to 900
pca = PCA(copy=True, iterated_power='auto', n_components=900, random_state=None,
          svd_solver='auto', tol=0.0, whiten=False)

pca.fit(train_data)

train_data = pca.transform(train_data)

test_data = pca.transform(test_data)

print("Shape of training set after pca:")
print(train_data.shape)

print("Shape of test set after pca:")
print(test_data.shape)

################## GMM #####################


# computes accuracy given the predictions and real labels
def accuracy(predictions, labels):
    batch_size = predictions.shape[0]
    sum = np.sum(predictions == labels)
    acc = (100.0 * sum) / batch_size
    return acc


n_classes = 10  # 10 genre classes

# Try GMMs using different types of covariances. I'm only letting 'full' as it performs better but can add different types to try
classifiers = dict((covar_type, GMM(n_components=n_classes,
                                    covariance_type=covar_type, init_params='wc', n_iter=5))
                   for covar_type in ['full'])


print("Training GMM")

for index, (name, classifier) in enumerate(classifiers.items()):
    # Since we have class labels for the training data, we can
    # initialize the GMM parameters in a supervised manner.
    classifier.means_ = np.array([train_data[train_labels == i].mean(axis=0)
                                  for i in range(n_classes)])

    # Train the other parameters using the EM algorithm.
    classifier.fit(train_data)

    # getting predictions of training set
    train_predictions = classifier.predict(train_data)

    print("Train Accuracy: %.1f%%" % accuracy(train_predictions, train_labels))

    # getting predictions of training set
    test_predictions = classifier.predict(test_data)

    print("Test Accuracy: %.1f%%" % accuracy(test_predictions, test_labels))