words_embed_tsr = self._word_embed(words_tsr.view(-1)).view(N, W, Ew)
# Apply dropout to word rep (N x W x Ew)
words_rep_tsr = self._word_dropout(words_embed_tsr)
# Apply bidirectional LSTM to word rep sequence (N x W x 2h)
(words_hidden_rep_tsr, _) = self._word_lstm(words_rep_tsr)
words_hidden_rep_tsr = words_hidden_rep_tsr.contiguous()
# Apply linear + softmax operation for sentence rep for all sentences (N x W x t)
word_probs_tsr = F.softmax(self._word_lin(
words_hidden_rep_tsr.view(N * W, self._h * 2)),
dim=1).view(N, W, t)
return word_probs_tsr
if __name__ == '__main__':
test_model_class(model_file_path=__file__,
model_class='PyBiLstm',
task=TaskType.POS_TAGGING,
dependencies={ModelDependency.PYTORCH: '0.4.1'},
train_dataset_uri='data/ptb_for_pos_tagging_train.zip',
test_dataset_uri='data/ptb_for_pos_tagging_test.zip',
queries=[['Ms.', 'Haag', 'plays', 'Elianti', '18', '.'],
[
'The', 'luxury', 'auto', 'maker', 'last',
'year', 'sold', '1,214', 'cars', 'in', 'the',
'U.S.'
]])
if __name__ == '__main__':
# model = SkLasso()
# model._reg = Lasso(0.1)
# model.train('data/home_rentals_train.zip')
# print(model.evaluate('data/home_rentals_test.zip'))
# queries = [[3358, 2, 1, 743, 10, 3230, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
# [3359, 1, 1, 533, 10, 1903, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [3360, 3, 2, 1186, 62, 4437, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
# ]
# print(model.predict(queries))
test_model_class(model_file_path=__file__,
model_class='SkBeyesianRidge',
task=TaskType.TABLE_REGRESSION,
dependencies={},
train_dataset_uri='data/home_rentals_train.zip',
test_dataset_uri='data/home_rentals_test.zip',
queries=[[
3358, 2, 1, 743, 10, 3230, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0
],
[
3359, 1, 1, 533, 10, 1903, 1, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
3360, 3, 2, 1186, 62, 4437, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0
]])
test_model_class(
model_file_path=__file__,
model_class='TfFeedForward',
task=TaskType.IMAGE_CLASSIFICATION,
dependencies={ModelDependency.TENSORFLOW: '1.12.0'},
train_dataset_uri=
'data/fashion_mnist_for_image_classification_train.zip',
test_dataset_uri='data/fashion_mnist_for_image_classification_test.zip',
queries=[[[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 1, 0, 0, 7, 0, 37, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 27, 84,
11, 0, 0, 0, 0, 0, 0, 119, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 88, 143,
110, 0, 0, 0, 0, 22, 93, 106, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 53, 129,
120, 147, 175, 157, 166, 135, 154, 168, 140, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 11, 137, 130,
128, 160, 176, 159, 167, 178, 149, 151, 144, 0, 0
],
[
0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 0, 3, 0, 0, 115, 114, 106,
137, 168, 153, 156, 165, 167, 143, 157, 158, 11, 0
],
[
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 89, 139, 90, 94,
153, 149, 131, 151, 169, 172, 143, 159, 169, 48, 0
],
[
0, 0, 0, 0, 0, 0, 2, 4, 1, 0, 0, 0, 98, 136, 110, 109,
110, 162, 135, 144, 149, 159, 167, 144, 158, 169, 119, 0
],
[
0, 0, 2, 2, 1, 2, 0, 0, 0, 0, 26, 108, 117, 99, 111, 117,
136, 156, 134, 154, 154, 156, 160, 141, 147, 156, 178, 0
],
[
3, 0, 0, 0, 0, 0, 0, 21, 53, 92, 117, 111, 103, 115, 129,
134, 143, 154, 165, 170, 154, 151, 154, 143, 138, 150,
165, 43
],
[
0, 0, 23, 54, 65, 76, 85, 118, 128, 123, 111, 113, 118,
127, 125, 139, 133, 136, 160, 140, 155, 161, 144, 155,
172, 161, 189, 62
],
[
0, 68, 94, 90, 111, 114, 111, 114, 115, 127, 135, 136,
143, 126, 127, 151, 154, 143, 148, 125, 162, 162, 144,
138, 153, 162, 196, 58
],
[
70, 169, 129, 104, 98, 100, 94, 97, 98, 102, 108, 106,
119, 120, 129, 149, 156, 167, 190, 190, 196, 198, 198,
187, 197, 189, 184, 36
],
[
16, 126, 171, 188, 188, 184, 171, 153, 135, 120, 126,
127, 146, 185, 195, 209, 208, 255, 209, 177, 245, 252,
251, 251, 247, 220, 206, 49
],
[
0, 0, 0, 12, 67, 106, 164, 185, 199, 210, 211, 210, 208,
190, 150, 82, 8, 0, 0, 0, 178, 208, 188, 175, 162, 158,
151, 11
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]]])
test_model_class(model_file_path=__file__,
model_class='XgbReg',
task=TaskType.TABULAR_REGRESSION,
dependencies={ModelDependency.XGBOOST: '0.90'},
queries=[{
'CRIM': {
370: 6.53876
},
'ZN': {
370: 0.0
},
'INDUS': {
370: 18.1
},
'CHAS': {
370: 1.0
},
'NOX': {
370: 0.631
},
'RM': {
370: 7.016
},
'AGE': {
370: 97.5
},
'DIS': {
370: 1.2024
},
'RAD': {
370: 24.0
},
'TAX': {
370: 666.0
},
'PTRATIO': {
370: 20.2
},
'B': {
370: 392.05
}
}],
train_dataset_uri=os.path.join(root,
'data/boston_train.csv'),
test_dataset_uri=os.path.join(root,
'data/boston_test.csv'))
objective='multi:softmax',
num_class=num_class)
return clf
if __name__ == '__main__':
test_model_class(model_file_path=__file__,
model_class='XgbClf',
task=TaskType.TABULAR_CLASSIFICATION,
dependencies={ModelDependency.XGBOOST: '0.90'},
train_dataset_uri=os.path.join(root,
'data/titanic_train.csv'),
test_dataset_uri=os.path.join(root,
'data/titanic_test.csv'),
queries=[{
'Pclass': {
499: 3
},
'Age': {
499: 24.0
},
'Sex_female': {
499: 0
},
'Sex_male': {
499: 1
}
}],
features=['Pclass', 'Sex', 'Age'],
target='Survived')
