def test_single_task_dnn(self):
X_train, y_train, _, X_dev, y_dev, _ = load_proc_baseline_feature('BoAW', verbose=True)
assert X_train.shape[1] == X_dev.shape[1]
num_classes = max(max(y_train), max(y_dev))
test_dnn = SingleTaskDNN('BoAW', X_train.shape[1], num_classes)
test_dnn.build_model()
test_dnn.train_model(X_train, y_train, X_dev, y_dev)
test_dnn.evaluate_model(X_dev, y_dev)
def test_upsample(self):
X_train, y_train, train_inst, X_dev, y_dev, dev_inst = load_proc_baseline_feature(
'MFCC', verbose=True)
X_train, y_train, train_inst = upsample(X_train,
y_train,
train_inst,
verbose=True)
print(X_train.shape, y_train.shape, train_inst.shape)
from collections import Counter
print(Counter(y_train))
def run_MFCC(self):
"""run classifier on MFCC feature (single modality)
"""
print("\nbuilding a classifier on MFCC features (both frame-level and session-level)")
X_train, y_train, train_inst, X_dev, y_dev, dev_inst = load_proc_baseline_feature('MFCC', verbose=True)
if self.model_name == 'RF_cv':
y_train, y_dev = np.ravel(y_train), np.ravel(y_dev)
train_inst, dev_inst = np.ravel(train_inst), np.ravel(dev_inst)
X = np.vstack((X_train, X_dev))
y = np.hstack((y_train, y_dev))
inst = np.hstack((train_inst, dev_inst))
assert len(X) == len(y) == len(inst)
cv_ids = k_fold_cv(len(X))
cv_res = []
for (ids_train, ids_dev) in cv_ids:
X_train = X[ids_train]
y_train = y[ids_train]
X_dev = X[ids_dev]
y_dev = y[ids_dev]
dev_inst = inst[ids_dev]
RF_MFCC = RandomForest(self.feature_name, X_train, y_train, X_dev, y_dev, baseline=True, test=self.test)
RF_MFCC.run()
y_pred_train, y_pred_dev = RF_MFCC.evaluate()
_, session_res = get_UAR(y_pred_dev, y_dev, dev_inst, self.model_name, self.feature_name, 'baseline', baseline=True, test=True)
cv_res.append(session_res)
save_cv_results(cv_res, self.model_name, self.feature_name, 'baseline')
print("\nupsampling training data to address class imbalance")
X_train, y_train, train_inst = upsample(X_train, y_train, train_inst)
print("\nobtaining sparse matrix for better classification")
# X_train = sp.csr_matrix(np.vstack((X_train, X_dev)))
# X_dev = sp.csr_matrix(X_dev)
# y_train = np.hstack((y_train, y_dev))
X_train, X_dev = sp.csr_matrix(X_train), sp.csr_matrix(X_dev)
if self.model_name == 'SVM':
SVM_MFCC = LinearSVM(self.feature_name, X_train, y_train, X_dev, y_dev, baseline=True, test=self.test)
SVM_MFCC.run()
y_pred_train, y_pred_dev = SVM_MFCC.evaluate()
elif self.model_name == 'RF':
RF_MFCC = RandomForest(self.feature_name, X_train, y_train, X_dev, y_dev, baseline=True, test=self.test)
RF_MFCC.run()
y_pred_train, y_pred_dev = RF_MFCC.evaluate()
get_UAR(y_pred_train, y_train, train_inst, self.model_name, self.feature_name, 'baseline', baseline=True, train_set=True, test=self.test)
get_UAR(y_pred_dev, y_dev, dev_inst, self.model_name, self.feature_name, 'baseline', baseline=True, test=self.test)
if not self.test:
get_post_probability(y_pred_dev, y_dev, dev_inst, np.array([]), self.model_name, self.feature_name)
def run_AU(self):
"""run classifier on AU feature (single modality)
"""
print("\nbuilding a classifier on AU features (already session-level)")
X_train, y_train, _, X_dev, y_dev, _ = load_proc_baseline_feature('AU', verbose=True)
if self.model_name == 'RF_cv':
X = np.vstack((X_train, X_dev))
y = np.hstack((y_train, y_dev))
assert len(X) == len(y)
cv_ids = k_fold_cv(len(X))
cv_res = []
for (ids_train, ids_dev) in cv_ids:
X_train = X[ids_train]
y_train = y[ids_train]
X_dev = X[ids_dev]
y_dev = y[ids_dev]
RF_MFCC = RandomForest(self.feature_name, X_train, y_train, X_dev, y_dev, baseline=True, test=self.test)
RF_MFCC.run()
y_pred_train, y_pred_dev = RF_MFCC.evaluate()
_, session_res = get_UAR(y_pred_dev, y_dev, np.array([]), self.model_name, self.feature_name, 'baseline', baseline=True, test=True)
cv_res.append(session_res)
save_cv_results(cv_res, self.model_name, self.feature_name, 'baseline')
print("\nupsampling training data to address class imbalance")
X_train, y_train, _ = upsample(X_train, y_train, np.array([]))
print("\nobtaining sparse matrix for better classification")
# X_train = sp.csr_matrix(np.vstack((X_train, X_dev)))
# X_dev = sp.csr_matrix(X_dev)
# y_train = np.hstack((y_train, y_dev))
X_train, X_dev = sp.csr_matrix(X_train), sp.csr_matrix(X_dev)
if self.model_name == 'SVM':
SVM_AU = LinearSVM(self.feature_name, X_train, y_train, X_dev, y_dev, baseline=True, test=self.test)
SVM_AU.run()
y_pred_train, y_pred_dev = SVM_AU.evaluate()
session_prob = SVM_AU.get_session_probability()
elif self.model_name == 'RF':
RF_AU = RandomForest(self.feature_name, X_train, y_train, X_dev, y_dev, baseline=True, test=self.test)
RF_AU.run()
y_pred_train, y_pred_dev = RF_AU.evaluate()
session_prob = RF_AU.get_session_probability()
get_UAR(y_pred_train, y_train, np.array([]), self.model_name, self.feature_name, 'baseline', baseline=True, train_set=True, test=self.test)
get_UAR(y_pred_dev, y_dev, np.array([]), self.model_name, self.feature_name, 'baseline', baseline=True, test=self.test)
def test_multi_task_dnn(self):
X_train, y_train, inst_train, X_dev, y_dev, inst_dev = load_proc_baseline_feature('BoAW', verbose=True)
ymrs_dev, ymrs_train, _, _ = load_label()
self.assertEqual(X_train.shape[1], X_dev.shape[1])
num_classes = max(max(y_train), max(y_dev))
test_dnn = MultiTaskDNN('BoAW', X_train.shape[1], num_classes)
y_dev_r = test_dnn.prepare_regression_label(ymrs_dev.values[:, 1], inst_dev)
y_train_r = test_dnn.prepare_regression_label(ymrs_train.values[:, 1], inst_train)
self.assertEqual(len(y_dev_r), len(y_dev))
self.assertEqual(len(y_train_r), len(y_train))
test_dnn.build_model()
test_dnn.train_model(X_train, y_train, y_train_r, X_dev, y_dev, y_dev_r)
test_dnn.evaluate_model(X_train, y_train, y_train_r, X_dev, y_dev, y_dev_r)
def test_load_proc_baseline_feature(self):
load_proc_baseline_feature('Deep', verbose=True)
load_proc_baseline_feature('eGeMAPS', verbose=True)
load_proc_baseline_feature('MFCC', verbose=True)
load_proc_baseline_feature('BoAW', verbose=True)
load_proc_baseline_feature('BoVW', verbose=True)
load_proc_baseline_feature('AU', verbose=True)
