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Formatted code for a consistent styling #3

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3 changes: 1 addition & 2 deletions src/ezautoml/__version__.py
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Original file line number Diff line number Diff line change
@@ -1,2 +1 @@

__version__= "0.7.1"
__version__ = "0.7.1"
108 changes: 83 additions & 25 deletions src/ezautoml/cli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,43 +18,84 @@
from ezautoml.__version__ import __version__

import warnings

warnings.filterwarnings("ignore", category=UserWarning)


def parse_args():
"""
Parses the command-line arguments.
"""
parser = argparse.ArgumentParser(
prog="ezautoml",
description="A Democratized, lightweight and modern framework for Python Automated Machine Learning.",
epilog="For more info, visit: https://github.com/eZWALT/eZAutoML"
epilog="For more info, visit: https://github.com/eZWALT/eZAutoML",
)

# Required arguments
parser.add_argument("--dataset", type=str, required=True, help="Path to the dataset file (CSV)")
parser.add_argument("--target", type=str, required=True, help="The target column name for prediction")
parser.add_argument(
"--task",
choices=["classification", "regression", "c", "r"],
required=True,
help="Task type: 'classification', 'regression', 'c' for classification, or 'r' for regression"
"--dataset", type=str, required=True, help="Path to the dataset file (CSV)"
)
parser.add_argument(
"--target",
type=str,
required=True,
help="The target column name for prediction",
)
parser.add_argument(
"--task",
choices=["classification", "regression", "c", "r"],
required=True,
help="Task type: 'classification', 'regression', 'c' for classification, or 'r' for regression",
)
# Optional arguments
parser.add_argument("--models", type=str, default="lgbm,xgb,rf", help="Comma-separated list of models to use (e.g., lr,rf,xgb). Use initials!")
parser.add_argument("--search", choices=["random", "optuna"], default="random", help="Optimization algorithm to perform")
parser.add_argument("--trials", type=int, default=10, help="Maximum number of trials inside an optimization algorithm")
parser.add_argument("--output", type=str, default=".", help="Directory to save the output models/results")
parser.add_argument("--save", action="store_true", help="Directory to save the output models/results")
parser.add_argument("--verbose", action="store_true", help="Increase logging verbosity")
parser.add_argument("--version", action="version", version=f"eZAutoML {__version__}", help="Show the current version")
parser.add_argument(
"--models",
type=str,
default="lgbm,xgb,rf",
help="Comma-separated list of models to use (e.g., lr,rf,xgb). Use initials!",
)
parser.add_argument(
"--search",
choices=["random", "optuna"],
default="random",
help="Optimization algorithm to perform",
)
parser.add_argument(
"--trials",
type=int,
default=10,
help="Maximum number of trials inside an optimization algorithm",
)
parser.add_argument(
"--output",
type=str,
default=".",
help="Directory to save the output models/results",
)
parser.add_argument(
"--save",
action="store_true",
help="Directory to save the output models/results",
)
parser.add_argument(
"--verbose", action="store_true", help="Increase logging verbosity"
)
parser.add_argument(
"--version",
action="version",
version=f"eZAutoML {__version__}",
help="Show the current version",
)

return parser.parse_args()


def sanitize_feature_names(df):
"""
Sanitizes column names by replacing non-alphanumeric characters with underscores.
"""
sanitized_columns = [re.sub(r'[^0-9a-zA-Z_]', '_', col) for col in df.columns]
sanitized_columns = [re.sub(r"[^0-9a-zA-Z_]", "_", col) for col in df.columns]
df.columns = sanitized_columns
return df

Expand All @@ -75,6 +116,7 @@ def load_and_prepare_data(dataset_path, target_column, task_type):

return X, y


def get_task_type_and_metrics(task):
"""
Returns the appropriate TaskType and metrics based on the task (classification or regression).
Expand All @@ -84,7 +126,7 @@ def get_task_type_and_metrics(task):
"c": "classification",
"r": "regression",
"classification": "classification",
"regression": "regression"
"regression": "regression",
}

task = task_mapping.get(task, task) # Get the corresponding full task name
Expand All @@ -94,40 +136,52 @@ def get_task_type_and_metrics(task):
metrics = MetricSet(
{
"accuracy": Metric(name="accuracy", fn=accuracy_score, minimize=False),
"f1_score": Metric(name="f1_score", fn=f1_score, minimize=False, default_kwargs={"average": "macro"})
"f1_score": Metric(
name="f1_score",
fn=f1_score,
minimize=False,
default_kwargs={"average": "macro"},
),
},
primary_metric_name="f1_score"
primary_metric_name="f1_score",
)
elif task == "regression":
task_type = TaskType.REGRESSION
metrics = MetricSet(
{
"mse": Metric(name="mse", fn=mean_squared_error, minimize=True),
"r2": Metric(name="r2", fn=r2_score, minimize=False)
"r2": Metric(name="r2", fn=r2_score, minimize=False),
},
primary_metric_name="mse"
primary_metric_name="mse",
)

return task_type, metrics


def select_optimizer(search_strategy):
"""
Returns the optimizer class based on the search strategy.
Displays a WIP message for Optuna and stops the execution.
"""
if search_strategy == "optuna":
log_wip_message() # Log the WIP message
raise SystemExit("Optuna optimizer is currently a Work In Progress. The process will be terminated.") # Terminate the process
raise SystemExit(
"Optuna optimizer is currently a Work In Progress. The process will be terminated."
) # Terminate the process
else:
optimizer_cls = RandomSearchOptimizer

return optimizer_cls


def log_wip_message():
"""
Logs a Work In Progress (WIP) message for the Optuna optimizer.
"""
logger.warning("Optuna optimizer is WIP (Work in Progress). Please proceed with caution.")
logger.warning(
"Optuna optimizer is WIP (Work in Progress). Please proceed with caution."
)


def save_results(ezautoml, output_dir):
"""
Expand All @@ -138,14 +192,15 @@ def save_results(ezautoml, output_dir):
ezautoml.history.to_json(os.path.join(output_dir, "history.json"))
ezautoml.history.to_csv(os.path.join(output_dir, "history_summary.csv"))


def main():
args = parse_args()

task_mapping = {
"c": "classification",
"r": "regression",
"classification": "classification",
"regression": "regression"
"regression": "regression",
}

task = task_mapping.get(args.task, args.task)
Expand All @@ -155,7 +210,9 @@ def main():
if X is None or y is None:
return

search_space_file = "classification_space" if task == "classification" else "regression_space"
search_space_file = (
"classification_space" if task == "classification" else "regression_space"
)
search_space = SearchSpace.from_builtin(search_space_file)

optimizer_cls = select_optimizer(args.search)
Expand All @@ -166,7 +223,7 @@ def main():
metrics=metrics,
optimizer_cls=optimizer_cls,
max_trials=args.trials,
verbose=args.verbose
verbose=args.verbose,
)

ezautoml.fit(X, y)
Expand All @@ -182,5 +239,6 @@ def main():
if args.save:
save_results(ezautoml, args.output)


if __name__ == "__main__":
main()
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