def exec(self, args: dict):
source_id = prompt_for_source_id_if_needed(args,
self.prescience_client)
source = self.prescience_client.source(source_id)
download_directory = args.get('download')
cache = args.get('cache')
output = args.get('output')
preview = args.get('preview')
tree = args.get('tree')
if preview is not None:
df = self.prescience_client.source_dataframe(source_id=source_id)
TablePrinter.print_dataframe(df.head(10),
wanted_keys=preview,
output=output)
elif args.get('schema'):
source.schema().show(output)
elif download_directory is not None:
self.prescience_client.download_source(
source_id=source_id, output_directory=download_directory)
elif cache:
self.prescience_client.update_cache_source(source_id)
elif tree:
source.tree().show()
else:
source.show(output)
def show(self, output: OutputFormat = OutputFormat.TABLE):
"""
Show the current schema on stdout
"""
if isinstance(output, str):
output = OutputFormat(output)
if output == OutputFormat.JSON:
print(json.dumps(self.json))
if output == OutputFormat.HTML:
df = TablePrinter.get_table_dataframe(Field, self.fields())
return TablePrinter.print_html(df)
else:
print(TablePrinter.get_table(Field, self.fields()))
return self
def show(self, output: OutputFormat = OutputFormat.TABLE):
"""
Display the current algorithm configuration on std out
:param ouput: The output format
"""
if output == OutputFormat.JSON:
print(json.dumps(self.json_dict))
else:
description_dict = {
k: v
for k, v in self.json_dict.items()
if k not in ['hyperparameters']
}
TablePrinter.print_dict('ALGORITHMS', description_dict)
print(
TablePrinter.get_table(Hyperparameter,
self.get_hyperparameters()))
def show_arguments(self) -> 'ServingPayload':
"""
Display the filled argument with there validations inputs on std out
:return: The current serving payload
"""
evaluator_inputs = self.current_inputs_evaluators()
table = TablePrinter.get_table(Input, evaluator_inputs)
print(table.get_string(title=f'ARGUMENTS ({self.get_payload_id()})'))
return self
def show(self, output: OutputFormat = OutputFormat.TABLE):
"""
Show the current page on stdout
"""
if isinstance(output, str):
output = OutputFormat(output)
if output == OutputFormat.JSON:
print(json.dumps(self.json_dict, indent=4))
elif output == OutputFormat.HTML:
df = TablePrinter.get_table_dataframe(self.page_class, self.content)
return TablePrinter.print_html(df)
else:
table = TablePrinter.get_table(self.page_class, self.content)
print(table.get_string(title=colored(self.metadata.elements_type.upper(), 'yellow', attrs=['bold'])))
print(colored(f'page {self.metadata.page_number}/{self.metadata.total_pages}', 'yellow'))
return self
def show_result(self) -> 'ServingPayload':
"""
Display the result of the serving payload on std out
:return: The current serving payload
"""
if len(self.get_result_probabilities()) != 0:
from prescience_client.bean.serving.serving_payload_batch import ServingPayloadBatch
batch = ServingPayloadBatch(model_id=self.get_model_id(),
flow_type=self.flow_type,
batch_serving_payload=[self],
model_evaluator=self.model_evaluator,
prescience=self.prescience)
batch.show_result()
else:
TablePrinter.print_dict(
title=f'PREDICTIONS RESULTS FROM \'{self.get_model_id()}\'',
json_dict=self.get_result_dict())
return self
def show(self, output: OutputFormat = OutputFormat.TABLE):
"""
Show the current page on stdout
"""
if output == OutputFormat.JSON:
print(json.dumps(self.json_dict))
else:
table = TablePrinter.get_table(AlgorithmConfiguration,
self.get_algorithm_list())
print(
table.get_string(
title=colored('ALGORITHMS', 'yellow', attrs=['bold'])))
return self
def show(self, ouput: OutputFormat = OutputFormat.TABLE):
"""
Display the current configuration a a table in console standard output
"""
if ouput == OutputFormat.JSON:
print(json.dumps(self.__dict__))
else:
all_line = [ConfigLine(k, v) for k, v in self.projects.items()]
[
line.set_seleted() for line in all_line
if line.get_project() == self.get_current_project_name()
]
table = TablePrinter.get_table(ConfigLine, all_line)
print(table)
def exec(self, args: dict):
display_schema = args.get('schema')
dataset_id = prompt_for_dataset_id_if_needed(args,
self.prescience_client)
output = args.get('output')
download_train_directory = args.get('download_train')
download_test_directory = args.get('download_test')
preview = args.get('preview')
tree = args.get('tree')
cache = args.get('cache')
if display_schema:
self.prescience_client.dataset(dataset_id).schema().show(output)
elif preview is not None:
df = self.prescience_client.dataset_dataframe(
dataset_id=dataset_id, test_part=False)
TablePrinter.print_dataframe(df.head(100),
wanted_keys=preview,
output=output)
elif download_train_directory is not None:
self.prescience_client.download_dataset(
dataset_id=dataset_id,
output_directory=download_train_directory,
test_part=False)
elif download_test_directory is not None:
self.prescience_client.download_dataset(
dataset_id=dataset_id,
output_directory=download_test_directory,
test_part=True)
elif tree:
self.prescience_client.dataset(dataset_id).tree().show()
elif cache:
self.prescience_client.update_cache_dataset(dataset_id, True)
self.prescience_client.update_cache_dataset(dataset_id, False)
else:
self.prescience_client.dataset(dataset_id).show(output)