def list_trials(
experiment_path,
sort=None,
output=None,
filter_op=None,
info_keys=None,
limit=None,
desc=False,
):
"""Lists trials in the directory subtree starting at the given path.
Args:
experiment_path (str): Directory where trials are located.
Like Experiment.local_dir/Experiment.name/experiment*.json.
sort (list): Keys to sort by.
output (str): Name of file where output is saved.
filter_op (str): Filter operation in the format
"<column> <operator> <value>".
info_keys (list): Keys that are displayed.
limit (int): Number of rows to display.
desc (bool): Sort ascending vs. descending.
"""
_check_tabulate()
try:
checkpoints_df = ExperimentAnalysis(
experiment_path).dataframe() # last result
except TuneError as e:
raise click.ClickException("No trial data found!") from e
def key_filter(k):
return k in DEFAULT_CLI_KEYS or k.startswith(CONFIG_PREFIX)
col_keys = [k for k in checkpoints_df.columns if key_filter(k)]
if info_keys:
for k in info_keys:
if k not in checkpoints_df.columns:
raise click.ClickException("Provided key invalid: {}. "
"Available keys: {}.".format(
k, checkpoints_df.columns))
col_keys = [k for k in checkpoints_df.columns if k in info_keys]
if not col_keys:
raise click.ClickException("No columns to output.")
checkpoints_df = checkpoints_df[col_keys]
if "last_update_time" in checkpoints_df:
with pd.option_context("mode.use_inf_as_null", True):
datetime_series = checkpoints_df["last_update_time"].dropna()
datetime_series = datetime_series.apply(
lambda t: datetime.fromtimestamp(t).strftime(TIMESTAMP_FORMAT))
checkpoints_df["last_update_time"] = datetime_series
if "logdir" in checkpoints_df:
# logdir often too long to view in table, so drop experiment_path
checkpoints_df["logdir"] = checkpoints_df["logdir"].str.replace(
experiment_path, "")
if filter_op:
col, op, val = filter_op.split(" ")
col_type = checkpoints_df[col].dtype
if is_numeric_dtype(col_type):
val = float(val)
elif is_string_dtype(col_type):
val = str(val)
# TODO(Andrew): add support for datetime and boolean
else:
raise click.ClickException("Unsupported dtype for {}: {}".format(
val, col_type))
op = OPERATORS[op]
filtered_index = op(checkpoints_df[col], val)
checkpoints_df = checkpoints_df[filtered_index]
if sort:
for key in sort:
if key not in checkpoints_df:
raise click.ClickException("{} not in: {}".format(
key, list(checkpoints_df)))
ascending = not desc
checkpoints_df = checkpoints_df.sort_values(by=sort,
ascending=ascending)
if limit:
checkpoints_df = checkpoints_df[:limit]
print_format_output(checkpoints_df)
if output:
file_extension = os.path.splitext(output)[1].lower()
if file_extension in (".p", ".pkl", ".pickle"):
checkpoints_df.to_pickle(output)
elif file_extension == ".csv":
checkpoints_df.to_csv(output, index=False)
else:
raise click.ClickException(
"Unsupported filetype: {}".format(output))
click.secho("Output saved at {}".format(output), fg="green")
def list_trials(experiment_path,
sort=None,
output=None,
filter_op=None,
info_keys=None,
limit=None,
desc=False):
"""Lists trials in the directory subtree starting at the given path.
Args:
experiment_path (str): Directory where trials are located.
Corresponds to Experiment.local_dir/Experiment.name.
sort (list): Keys to sort by.
output (str): Name of file where output is saved.
filter_op (str): Filter operation in the format
"<column> <operator> <value>".
info_keys (list): Keys that are displayed.
limit (int): Number of rows to display.
desc (bool): Sort ascending vs. descending.
"""
_check_tabulate()
try:
checkpoints_df = ExperimentAnalysis(experiment_path).dataframe()
except TuneError:
print("No experiment state found!")
sys.exit(0)
if not info_keys:
info_keys = DEFAULT_EXPERIMENT_INFO_KEYS
col_keys = [k for k in list(info_keys) if k in checkpoints_df]
checkpoints_df = checkpoints_df[col_keys]
if "last_update_time" in checkpoints_df:
with pd.option_context("mode.use_inf_as_null", True):
datetime_series = checkpoints_df["last_update_time"].dropna()
datetime_series = datetime_series.apply(
lambda t: datetime.fromtimestamp(t).strftime(TIMESTAMP_FORMAT))
checkpoints_df["last_update_time"] = datetime_series
if "logdir" in checkpoints_df:
# logdir often too verbose to view in table, so drop experiment_path
checkpoints_df["logdir"] = checkpoints_df["logdir"].str.replace(
experiment_path, "")
if filter_op:
col, op, val = filter_op.split(" ")
col_type = checkpoints_df[col].dtype
if is_numeric_dtype(col_type):
val = float(val)
elif is_string_dtype(col_type):
val = str(val)
# TODO(Andrew): add support for datetime and boolean
else:
raise ValueError("Unsupported dtype for {}: {}".format(
val, col_type))
op = OPERATORS[op]
filtered_index = op(checkpoints_df[col], val)
checkpoints_df = checkpoints_df[filtered_index]
if sort:
if sort not in checkpoints_df:
raise KeyError("{} not in: {}".format(sort, list(checkpoints_df)))
ascending = not desc
checkpoints_df = checkpoints_df.sort_values(by=sort,
ascending=ascending)
if limit:
checkpoints_df = checkpoints_df[:limit]
print_format_output(checkpoints_df)
if output:
file_extension = os.path.splitext(output)[1].lower()
if file_extension in (".p", ".pkl", ".pickle"):
checkpoints_df.to_pickle(output)
elif file_extension == ".csv":
checkpoints_df.to_csv(output, index=False)
else:
raise ValueError("Unsupported filetype: {}".format(output))
print("Output saved at:", output)