def train():
settings = Settings()
batch_size = settings.get_training_parameters('batch_size')
epochs = settings.get_training_parameters('epochs')
model_builder = Model4Builder()
model = model_builder()
preprocessor = Preprocessor(model)
preprocessor.load_data(['category', 'is_top_submission'])
training_input = [preprocessor.training_data['category']]
validation_input = [preprocessor.validation_data['category']]
training_output = [preprocessor.training_data['is_top_submission']]
validation_output = [preprocessor.validation_data['is_top_submission']]
class_weights = calculate_class_weights(preprocessor.training_data['is_top_submission'],
[ol.name for ol in model.output_layers])
callbacks = CallbackBuilder(model, [CsvLogger, CsvPlotter, ConfigLogger, ModelSaver])()
model.fit(training_input, training_output, batch_size=batch_size, epochs=epochs,
callbacks=callbacks, validation_data=(validation_input, validation_output), class_weight=class_weights)
def train():
settings = Settings()
batch_size = settings.get_training_parameters('batch_size')
epochs = settings.get_training_parameters('epochs')
dictionary_size = settings.get_training_parameters('dictionary_size')
max_headline_length = settings.get_training_parameters('max_headline_length')
glove = Glove(dictionary_size)
glove.load_embedding()
model_builder = Model1Builder() \
.set_input('glove', glove) \
.set_parameter('max_headline_length', max_headline_length)
model = model_builder()
preprocessor = Preprocessor(model)
preprocessor.set_encoder('glove', glove)
preprocessor.set_parameter('max_headline_length', max_headline_length)
preprocessor.load_data(['headline', 'is_top_submission'])
training_input = [preprocessor.training_data['headline']]
validation_input = [preprocessor.validation_data['headline']]
training_output = [preprocessor.training_data['is_top_submission']]
validation_output = [preprocessor.validation_data['is_top_submission']]
class_weights = calculate_class_weights(preprocessor.training_data['is_top_submission'],
[ol.name for ol in model.output_layers])
callbacks = CallbackBuilder(model, [CsvLogger, CsvPlotter, ConfigLogger, ModelSaver])()
model.fit(training_input, training_output, batch_size=batch_size, epochs=epochs,
callbacks=callbacks, validation_data=(validation_input, validation_output), class_weight=class_weights)
def train():
settings = Settings()
batch_size = settings.get_training_parameters('batch_size')
epochs = settings.get_training_parameters('epochs')
max_headline_length = settings.get_training_parameters(
'max_headline_length')
max_article_length = settings.get_training_parameters('max_article_length')
headline_numeric_log = NumericLog(max_headline_length)
article_numeric_log = NumericLog(max_article_length)
model_builder = Model6Builder() \
.set_input('headline_numeric_log', headline_numeric_log) \
.set_input('article_numeric_log', article_numeric_log)
model = model_builder()
preprocessor = Preprocessor(model)
preprocessor.set_encoder('headline_numeric_log', headline_numeric_log)
preprocessor.set_encoder('article_numeric_log', article_numeric_log)
preprocessor.load_data([
'headline_log_representation', 'article_log_representation',
'is_top_submission'
])
training_input = [
preprocessor.training_data['headline_log_representation'],
preprocessor.training_data['article_log_representation']
]
validation_input = [
preprocessor.validation_data['headline_log_representation'],
preprocessor.validation_data['article_log_representation']
]
training_output = [preprocessor.training_data['is_top_submission']]
validation_output = [preprocessor.validation_data['is_top_submission']]
class_weights = calculate_class_weights(
preprocessor.training_data['is_top_submission'],
[ol.name for ol in model.output_layers])
callbacks = CallbackBuilder(
model, [CsvLogger, CsvPlotter, ConfigLogger, ModelSaver])()
model.fit(training_input,
training_output,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
validation_data=(validation_input, validation_output),
class_weight=class_weights)
def __init__(self, model, log_path):
super().__init__()
settings = Settings()
self.training_parameters = settings.get_training_parameters()
self.network_parameters = settings.get_network_parameters()
self.log_path = log_path
self.filename = '{}/{}'.format(log_path, 'config.txt')
model.summary(print_fn=self._handle_summary_print)
self.model = model
def calculate_correlations():
arg_parse = ArgumentParser()
arg_parse.add_argument('--model_1', type=str)
arg_parse.add_argument('--model_2', type=str)
arg_parse.add_argument('--model_3', type=str)
arg_parse.add_argument('--model_4', type=str)
arg_parse.add_argument('--model_5', type=str)
arg_parse.add_argument('--model_6', type=str)
arg_parse.add_argument('--model_7', type=str)
arguments = arg_parse.parse_args()
settings = Settings()
default_parameters = settings.get_training_parameters()
glove = Glove(default_parameters['dictionary_size'])
glove.load_embedding()
headline_numeric_log = NumericLog(
default_parameters['max_headline_length'])
article_numeric_log = NumericLog(default_parameters['max_article_length'])
print('load data...')
preprocessor = Preprocessor(None)
preprocessor.set_encoder('glove', glove)
preprocessor.set_encoder('headline_numeric_log', headline_numeric_log)
preprocessor.set_encoder('article_numeric_log', article_numeric_log)
preprocessor.set_parameter('max_headline_length',
default_parameters['max_headline_length'])
preprocessor.set_parameter('body_begin_length',
default_parameters['body_begin_length'])
preprocessor.load_data([
'headline', 'body_begin', 'category', 'minute', 'hour', 'day_of_week',
'day_of_year', 'headline_log_representation',
'article_log_representation', 'competitive_score'
])
custom_objects = {
'precision': precision,
'recall': recall,
'f1': f1,
}
print('load models...')
model_inputs = {}
model_inputs['model_1'] = [preprocessor.test_data['headline']]
model_inputs['model_2'] = [preprocessor.test_data['headline']]
model_inputs['model_3'] = [preprocessor.test_data['body_begin']]
model_inputs['model_4'] = [preprocessor.test_data['category']]
model_inputs['model_5'] = [
preprocessor.test_data[key]
for key in ['minute', 'hour', 'day_of_week', 'day_of_year']
]
model_inputs['model_6'] = [
preprocessor.test_data[key] for key in
['headline_log_representation', 'article_log_representation']
]
model_inputs['model_7'] = [preprocessor.test_data['competitive_score']]
print('predict...')
predictions = {}
for model_name in model_inputs.keys():
if hasattr(arguments, model_name) and getattr(arguments, model_name):
model = load_model(getattr(arguments, model_name),
custom_objects=custom_objects)
predictions[model_name] = np.round(
model.predict(model_inputs[model_name]))
print('calculate correlation...')
for model_name_1 in predictions.keys():
for model_name_2 in predictions.keys():
if model_name_1 != model_name_2:
correlation = np.corrcoef(predictions[model_name_1][:, -1],
predictions[model_name_2][:, -1])[0]
print(model_name_1, model_name_2, correlation[1])
