def update_data_table(filename, mode):
if filename is not None and os.path.isfile(filename):
if mode in ["all", "small"]:
df = file_to_pandas(filename)
if mode == "small":
df = df.head(50)
data_table = dash_table.DataTable(
id="table",
columns=[{
"name": c,
"id": c
} for c in df.columns],
data=df.to_dict("records"),
editable=False,
style_table={
"maxHeight": "500px",
"overflowY": "scroll"
},
)
attributes = list(df.columns)
else:
data_table = "Preview not enabled."
attributes = list(load_feature_metadata_from_file(filename))
target_options = [{"label": c, "value": c} for c in attributes]
default_target = attributes[-1]
return [data_table], target_options, default_target, False
return ["No data loaded"], [{"label": "-", "value": "a"}], "a", True
def run(dataset, config):
log.info("\n**** GAMA [v%s] ****", __version__)
log.info("sklearn == %s", sklearn.__version__)
log.info("category_encoders == %s", category_encoders.__version__)
is_classification = (config.type == 'classification')
# Mapping of benchmark metrics to GAMA metrics
metrics_mapping = dict(
acc='accuracy',
auc='roc_auc',
f1='f1',
logloss='neg_log_loss',
mae='neg_mean_absolute_error',
mse='neg_mean_squared_error',
msle='neg_mean_squared_log_error',
r2='r2',
rmse='neg_mean_squared_error',
)
scoring_metric = metrics_mapping[
config.metric] if config.metric in metrics_mapping else None
if scoring_metric is None:
raise ValueError("Performance metric {} not supported.".format(
config.metric))
training_params = {
k: v
for k, v in config.framework_params.items() if not k.startswith('_')
}
n_jobs = config.framework_params.get(
'_n_jobs', config.cores
) # useful to disable multicore, regardless of the dataset config
*_, did, fold = dataset.train_path.split('/')
fold = fold.split('.')[0].split('_')[-1]
log.info(
'Running GAMA with a maximum time of %ss on %s cores, optimizing %s.',
config.max_runtime_seconds, n_jobs, scoring_metric)
estimator = GamaClassifier if is_classification else GamaRegressor
kwargs = dict(n_jobs=n_jobs,
max_total_time=config.max_runtime_seconds,
scoring=scoring_metric,
random_state=config.seed,
**training_params)
version_leq_20_2_0 = version.parse(__version__) <= version.parse('20.2.0')
if version_leq_20_2_0:
log_file = os.path.join(config.output_dir, "logs",
'{}_{}.log'.format(did, fold))
utils.touch(log_file)
kwargs['keep_analysis_log'] = log_file
else:
kwargs['max_memory_mb'] = config.max_mem_size_mb
kwargs['output_directory'] = os.path.join(config.output_dir, "gama")
gama_automl = estimator(**kwargs)
data = file_to_pandas(dataset.train_path, encoding='utf-8')
x, y = data.loc[:,
data.columns != dataset.target], data.loc[:,
dataset.target]
with utils.Timer() as training_timer:
gama_automl.fit(x, y)
data = file_to_pandas(dataset.test_path, encoding='utf-8')
x, _ = data.loc[:,
data.columns != dataset.target], data.loc[:,
dataset.target]
log.info('Predicting on the test set.')
with utils.Timer() as predict_timer:
predictions = gama_automl.predict(x)
if is_classification:
probabilities = gama_automl.predict_proba(x)
else:
probabilities = None
return result(output_file=config.output_predictions_file,
predictions=predictions,
probabilities=probabilities,
target_is_encoded=False,
models_count=len(gama_automl._final_pop),
training_duration=training_timer.duration,
predict_duration=predict_timer.duration)
def test_file_to_pandas_arff(self):
df = file_to_pandas(ARFF_CJS)
_test_df_d23380(df)
def test_file_to_pandas_invalid(self):
with pytest.raises(ValueError, match="files supported."):
file_to_pandas("myfile.txt")
def test_file_to_pandas_csv(self):
df = file_to_pandas(CSV_CJS_FULL)
_test_df_d23380(df)