def _create_script_backup(self):
"""Create and save a copy of the script that initialized the Experiment if allowed to, and
if :attr:`source_script` ends with a ".py" extension"""
#################### Attempt to Copy Source Script if Allowed ####################
try:
if not self.source_script.endswith(".py"):
G.Env.result_paths["script_backup"] = None
if G.Env.result_paths["script_backup"] is not None:
try:
self._source_copy_helper()
except FileNotFoundError:
make_dirs(self.result_paths["script_backup"],
exist_ok=False)
self._source_copy_helper()
G.log(
"Created source backup: '{}'".format(self.source_script),
4)
else:
G.log(
"Skipped source backup: '{}'".format(self.source_script),
4)
#################### Exception Handling ####################
except AttributeError as _ex:
if G.Env is None:
raise EnvironmentInactiveError(extra="\n{!s}".format(_ex))
if not hasattr(G.Env, "result_paths"):
raise EnvironmentInvalidError(
extra=f"G.Env lacks 'result_paths' attr\n{_ex!s}")
raise
except KeyError as _ex:
if "script_backup" not in G.Env.result_paths:
raise EnvironmentInvalidError(
extra=
f"G.Env.result_paths lacks 'script_backup' key\n{_ex!s}")
raise
def save_result(self):
self.result = read_json(f"{self.result_paths['description']}/{self.experiment_id}.json")
make_dirs(self.result_path, exist_ok=True)
with open(f"{self.result_path}/{self.experiment_id}.yml", "w+") as f:
yaml.dump(self.result, f, default_flow_style=False, width=200)
def validate_parameters(self):
"""Ensure the provided parameters are valid and properly formatted"""
#################### root_results_path ####################
if self.root_results_path is None:
G.warn(
"Received root_results_path=None. Results will not be stored at all."
)
elif isinstance(self.root_results_path, str):
if not self.root_results_path.endswith(ASSETS_DIRNAME):
self.root_results_path = os.path.join(self.root_results_path,
ASSETS_DIRNAME)
self.result_paths["root"] = self.root_results_path
if not os.path.exists(self.root_results_path):
make_dirs(self.root_results_path, exist_ok=True)
else:
raise TypeError(
f"root_results_path must be None or str, not {self.root_results_path}"
)
#################### target_column ####################
if isinstance(self.target_column, str):
self.target_column = [self.target_column]
#################### file_blacklist ####################
self.file_blacklist = validate_file_blacklist(self.file_blacklist)
if self.root_results_path is None:
self.file_blacklist = "ALL"
#################### Train/Test Datasets ####################
if isinstance(self.train_dataset, str):
self.train_dataset = pd.read_csv(self.train_dataset)
if isinstance(self.test_dataset, str):
self.test_dataset = pd.read_csv(self.test_dataset)
#################### metrics_params/metrics_map ####################
if (self.metrics_map is not None) and ("metrics_map"
in self.metrics_params.keys()):
raise ValueError(
"`metrics_map` may be provided as a kwarg, or as a `metrics_params` key, but NOT BOTH. Received: "
+
f"\n `metrics_map`={self.metrics_map}\n `metrics_params`={self.metrics_params}"
)
else:
if self.metrics_map is None:
self.metrics_map = self.metrics_params["metrics_map"]
self.metrics_map = format_metrics_map(self.metrics_map)
self.metrics_params = {
**dict(metrics_map=self.metrics_map),
**self.metrics_params
}
#################### cross_validation_type ####################
if isinstance(self.cross_validation_type, str):
try:
self.cross_validation_type = sk_cv.__getattribute__(
self.cross_validation_type)
except AttributeError:
raise AttributeError(
f"'{self.cross_validation_type}' not in `sklearn.model_selection._split`"
)
#################### to_csv_params ####################
self.to_csv_params = {
k: v
for k, v in self.to_csv_params.items() if k != "path_or_buf"
}
#################### cross_experiment_params ####################
self.cross_experiment_params = dict(
cross_validation_type=self.cross_validation_type,
runs=self.runs,
global_random_seed=self.global_random_seed,
random_seeds=self.random_seeds,
random_seed_bounds=self.random_seed_bounds,
)
#################### experiment_callbacks ####################
if not isinstance(self.experiment_callbacks, list):
self.experiment_callbacks = [self.experiment_callbacks]
for cb in self.experiment_callbacks:
if not isclass(cb):
raise TypeError(
f"experiment_callbacks must be classes, not {type(cb)}: {cb}"
)
if cb.__name__ != "LambdaCallback":
raise ValueError(
f"experiment_callbacks must be LambdaCallback instances, not {cb}"
)