def train(self):
training_dataframe, model_definition, timeseries_cols = self._create_ludwig_dataframe(
'train')
if len(timeseries_cols) > 0:
training_dataframe, model_definition = self._translate_df_to_timeseries_format(
training_dataframe, model_definition, timeseries_cols, 'train')
with disable_console_output(True):
# <---- Ludwig currently broken, since mode can't be initialized without train_set_metadata and train_set_metadata can't be obtained without running train... see this issue for any updates on the matter: https://github.com/uber/ludwig/issues/295
#model.initialize_model(train_set_metadata={})
#train_stats = model.train_online(data_df=training_dataframe) # ??Where to add model_name?? ----> model_name=self.transaction.lmd['name']
ludwig_save_is_working = False
if not ludwig_save_is_working:
shutil.rmtree('results', ignore_errors=True)
if self.transaction.lmd['rebuild_model'] is True:
model = LudwigModel(model_definition)
merged_model_definition = model.model_definition
train_set_metadata = build_metadata(
training_dataframe,
(merged_model_definition['input_features'] +
merged_model_definition['output_features']),
merged_model_definition['preprocessing'])
model.initialize_model(train_set_metadata=train_set_metadata,
gpus=self.get_useable_gpus())
train_stats = model.train(
data_df=training_dataframe,
model_name=self.transaction.lmd['name'],
skip_save_model=ludwig_save_is_working,
skip_save_progress=True,
gpus=self.get_useable_gpus())
else:
model = LudwigModel.load(model_dir=self.get_model_dir())
train_stats = model.train(
data_df=training_dataframe,
model_name=self.transaction.lmd['name'],
skip_save_model=ludwig_save_is_working,
skip_save_progress=True,
gpus=self.get_useable_gpus())
for k in train_stats['train']:
if k not in self.transaction.lmd['model_accuracy']['train']:
self.transaction.lmd['model_accuracy']['train'][k] = []
self.transaction.lmd['model_accuracy']['test'][k] = []
elif k is not 'combined':
# We should be adding the accuracy here but we only have it for combined, so, for now use that, will only affect multi-output scenarios anyway
pass
else:
self.transaction.lmd['model_accuracy']['train'][k].extend(
train_stats['train'][k]['accuracy'])
self.transaction.lmd['model_accuracy']['test'][k].extend(
train_stats['test'][k]['accuracy'])
'''
@ TRAIN ONLINE BIT That's not working
model = LudwigModel.load(self.transaction.lmd['ludwig_data']['ludwig_save_path'])
for i in range(0,100):
train_stats = model.train_online(data_df=training_dataframe)
# The resulting train_stats are "None"... wonderful -_-
'''
ludwig_model_savepath = os.path.join(
CONFIG.MINDSDB_STORAGE_PATH,
self.transaction.lmd['name'] + '_ludwig_data')
if ludwig_save_is_working:
model.save(ludwig_model_savepath)
model.close()
else:
shutil.rmtree(ludwig_model_savepath, ignore_errors=True)
shutil.move(os.path.join('results',
os.listdir('results')[0]),
ludwig_model_savepath)
self.transaction.lmd['ludwig_data'] = {
'ludwig_save_path': ludwig_model_savepath
}
self.transaction.hmd['ludwig_data'] = {
'model_definition': model_definition
}
def train(self):
training_dataframe, model_definition, timeseries_cols, has_heavy_data, self.transaction.lmd[
'ludwig_tf_self_col_map'] = self._create_ludwig_dataframe('train')
if len(timeseries_cols) > 0:
training_dataframe, model_definition = self._translate_df_to_timeseries_format(
training_dataframe, model_definition, timeseries_cols, 'train')
with disable_console_output(True):
# <---- Ludwig currently broken, since mode can't be initialized without train_set_metadata and train_set_metadata can't be obtained without running train... see this issue for any updates on the matter: https://github.com/uber/ludwig/issues/295
#model.initialize_model(train_set_metadata={})
#train_stats = model.train_online(data_df=training_dataframe) # ??Where to add model_name?? ----> model_name=self.transaction.lmd['name']
ludwig_save_is_working = False
if not ludwig_save_is_working:
shutil.rmtree('results', ignore_errors=True)
if self.transaction.lmd['rebuild_model'] is True:
model = LudwigModel(model_definition)
merged_model_definition = model.model_definition
train_set_metadata = build_metadata(
training_dataframe,
(merged_model_definition['input_features'] +
merged_model_definition['output_features']),
merged_model_definition['preprocessing'])
model.initialize_model(train_set_metadata=train_set_metadata,
gpus=self._get_useable_gpus())
else:
model = LudwigModel.load(model_dir=self._get_model_dir())
split_by = int(20 * pow(10, 6))
if has_heavy_data:
split_by = 40
df_len = len(training_dataframe[training_dataframe.columns[0]])
if df_len > split_by:
i = 0
while i < df_len:
end = i + split_by
self.transaction.log.info(
f'Training with batch from index {i} to index {end}')
training_sample = training_dataframe.iloc[i:end]
training_sample = training_sample.reset_index()
if len(training_sample) < 1:
continue
train_stats = model.train(
data_df=training_sample,
model_name=self.transaction.lmd['name'],
skip_save_model=ludwig_save_is_working,
skip_save_progress=True,
gpus=self._get_useable_gpus())
i = end
else:
train_stats = model.train(
data_df=training_dataframe,
model_name=self.transaction.lmd['name'],
skip_save_model=ludwig_save_is_working,
skip_save_progress=True,
gpus=self._get_useable_gpus())
for k in train_stats['train']:
if k not in self.transaction.lmd['model_accuracy']['train']:
self.transaction.lmd['model_accuracy']['train'][k] = []
self.transaction.lmd['model_accuracy']['test'][k] = []
elif k != 'combined':
# We should be adding the accuracy here but we only have it for combined, so, for now use that, will only affect multi-output scenarios anyway
pass
else:
self.transaction.lmd['model_accuracy']['train'][k].extend(
train_stats['train'][k]['accuracy'])
self.transaction.lmd['model_accuracy']['test'][k].extend(
train_stats['test'][k]['accuracy'])
'''
@ TRAIN ONLINE BIT That's not working
model = LudwigModel.load(self.transaction.lmd['ludwig_data']['ludwig_save_path'])
for i in range(0,100):
train_stats = model.train_online(data_df=training_dataframe)
# The resulting train_stats are "None"... wonderful -_-
'''
ludwig_model_savepath = os.path.join(CONFIG.MINDSDB_STORAGE_PATH,
self.transaction.lmd['name'],
'ludwig_data')
Path(CONFIG.MINDSDB_STORAGE_PATH).joinpath(
self.transaction.lmd['name']).mkdir(mode=0o777,
exist_ok=True,
parents=True)
if ludwig_save_is_working:
model.save(ludwig_model_savepath)
model.close()
else:
shutil.rmtree(ludwig_model_savepath, ignore_errors=True)
shutil.move(os.path.join('results',
os.listdir('results')[0]),
ludwig_model_savepath)
self.transaction.lmd['ludwig_data'] = {
'ludwig_save_path': ludwig_model_savepath
}
self.transaction.hmd['ludwig_data'] = {
'model_definition': model_definition
}