def test_fit_and_predict_with_new_labels_set_via_method(self):
# Initialize model
invalid_entities = {"PAD": 0, "BACKGROUND": 1, "test3": 2}
cnn_model = CharacterLevelCnnModel(invalid_entities)
cnn_model._construct_model()
invalid_entities2 = {"PAD": 0, "BACKGROUND": 1}
cnn_model.set_label_mapping(invalid_entities2)
cnn_model._reconstruct_model()
cnn_model.set_label_mapping(self.label_mapping)
# data for model
data_gen = [[
np.array([['test']]), # x_data
np.zeros((1, 3400, max(self.label_mapping.values()) + 1))
]
# y_data
]
cv_gen = data_gen
cnn_model._construct_model()
# set different labels
cnn_model.set_label_mapping(self.label_mapping)
history, f1, f1_report = cnn_model.fit(data_gen, cv_gen)
# test predict on just the text
cnn_model.predict(data_gen[0][0])
def test_fit_and_predict_with_reset_weights(self, *mocks):
# model
cnn_model = CharacterLevelCnnModel(self.label_mapping)
# data for model
data_gen = [[
np.array([['test']]), # x_data
np.zeros((1, 3400, max(self.label_mapping.values()) + 1))
] # y_data
]
cv_gen = data_gen
# Basic Fit with Validation Data
history, f1, f1_report = cnn_model.fit(data_gen,
cv_gen,
reset_weights=True)
data_gen = [np.array([['test']])]
cnn_model.predict(data_gen)
def test_fit_and_predict_with_new_labels(self):
# Initialize model
cnn_model = CharacterLevelCnnModel(self.label_mapping)
# data for model
data_gen = [[
np.array([['test']]), # x_data
np.zeros((1, 3400, max(self.label_mapping.values()) + 1))
]
# y_data
]
cv_gen = data_gen
cnn_model._construct_model()
# fit with new labels
history, f1, f1_report = cnn_model.fit(
data_gen, cv_gen, label_mapping=self.label_mapping)
# predict after fitting on just the text
cnn_model.predict(data_gen[0][0])
def test_fit_and_predict_with_reset_weights(self, *mocks):
# model
cnn_model = CharacterLevelCnnModel(self.label_mapping)
# data for model
data_gen = [[
np.array([["test"]]), # x_data
np.zeros((1, 3400, max(self.label_mapping.values()) + 1)),
] # y_data
]
cv_gen = data_gen
# Basic Fit with Validation Data
with self.assertLogs("DataProfiler.labelers.character_level_cnn_model",
level="INFO") as logs:
history, f1, f1_report = cnn_model.fit(data_gen,
cv_gen,
reset_weights=True)
# Ensure info was logged during fit
self.assertTrue(len(logs.output))
data_gen = [np.array([["test"]])]
cnn_model.predict(data_gen)