def predict(self, model, ignore_features):
logging.debug("Loading test set...")
_use_cols = misc_utils.get_cols_to_load(labels=True, load_int_cols=True, load_cat_cols=True,
ignore_features=ignore_features)
test_df = self.io_utils.load_validation_set(use_cols=_use_cols,
converters=misc_utils.get_converters(_use_cols),
chunk_size=self.settings['chunk_size'])
start = datetime.now()
logging.debug("Predicting probabilities...")
actual_labels = []
predicted_labels = []
if self.settings['chunk_size'] is not None:
for i, chunk in enumerate(test_df):
logging.debug("Processing chunk %d\t%s" % (i + 1, str(datetime.now() - start)))
y, predictions = self.__process_test_chunk(chunk, model, ignore_features)
actual_labels += y.tolist()
predicted_labels += self.__cap_predictions(predictions)
else:
y, predictions = self.__process_test_chunk(test_df, model, ignore_features)
actual_labels += y.tolist()
predicted_labels += self.__cap_predictions(predictions)
logging.debug("Finished predicting in \t%s" % str(datetime.now() - start))
return self.evaluate_model(actual_labels, predicted_labels)
def train(self, model, ignore_features):
logging.debug("Loading train set...")
_use_cols = misc_utils.get_cols_to_load(labels=True, load_int_cols=True, load_cat_cols=True,
ignore_features=ignore_features)
train_df = self.io_utils.load_train_set(use_cols=_use_cols, converters=misc_utils.get_converters(_use_cols),
chunk_size=self.settings['chunk_size'])
start = datetime.now()
logging.debug("Training Model...")
if self.settings['chunk_size'] is not None:
for i, chunk in enumerate(train_df):
logging.debug("Processing chunk %d\t%s" % (i + 1, str(datetime.now() - start)))
self.__process_train_chunk(chunk, model, ignore_features)
else:
self.__process_train_chunk(train_df, model, ignore_features)
logging.debug("Finished training in \t%s" % str(datetime.now() - start))
def __train_encoder(self, encoder_cache_file_name):
"""
Trains and caches the encoder in mini batch mode
:param encoder_cache_file_name:
:return: trained encoder object
"""
model = self.io_utils.get_model()
_use_cols = misc_utils.get_cols_to_load(labels=False, load_int_cols=False, load_cat_cols=True,
ignore_features=model['ignore_features'])
train_chunks = self.io_utils.load_train_set(use_cols=_use_cols, converters=misc_utils.get_converters(_use_cols),
chunk_size=1500000)
enc = OneHotEncoder()
start = datetime.now()
logging.debug("Training Encoder...")
for i, chunk in enumerate(train_chunks):
enc.partial_fit(chunk.as_matrix())
logging.debug("Trained encoder with chunk %d\t%s" % (i+1, str(datetime.now() - start)))
logging.debug("Finished training encoder in \t%s" % str(datetime.now() - start))
self.io_utils.save_to_cache(enc, encoder_cache_file_name)
return enc
