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BUG: Fix metric bug in Utilities #33

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Merged
dguijo merged 2 commits into from
Jul 27, 2025
Merged

BUG: Fix metric bug in Utilities #33

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0b2db96
BUG: Correct metric list usage for evaluation in Utilities
angelsevillamol Jul 26, 2025
ca5a298
BUG: Fix incorrect metrics import in Utilities
angelsevillamol Jul 26, 2025
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44 changes: 9 additions & 35 deletions orca_python/utilities/utilities.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,9 +14,9 @@
import pandas as pd
from pkg_resources import get_distribution, parse_version
from sklearn import preprocessing
from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV, StratifiedKFold

from orca_python.metrics import compute_metric, load_metric_as_scorer
from orca_python.results import Results


Expand Down Expand Up @@ -169,30 +169,19 @@ def run_experiment(self):
train_metrics = OrderedDict()
test_metrics = OrderedDict()
for metric_name in self.general_conf["metrics"]:

try:
# Loading metric from file
module = __import__("orca_python").metrics
metric = getattr(
module, self.general_conf["cv_metric"].lower().strip()
)

except AttributeError:
raise AttributeError(
"No metric named '%s'" % metric_name.strip().lower()
)

# Get train scores
train_score = metric(
partition["train_outputs"], train_predicted_y
train_score = compute_metric(
metric_name,
partition["train_outputs"],
train_predicted_y,
)
train_metrics[metric_name.strip() + "_train"] = train_score

# Get test scores
test_metrics[metric_name.strip() + "_test"] = np.nan
if "test_outputs" in partition:
test_score = metric(
partition["test_outputs"], test_predicted_y
test_score = compute_metric(
metric_name, partition["test_outputs"], test_predicted_y
)
test_metrics[metric_name.strip() + "_test"] = test_score

Expand Down Expand Up @@ -536,23 +525,8 @@ def _get_optimal_estimator(
optimal.refit_time_ = elapsed
return optimal

try:
module = __import__("orca_python").metrics
metric = getattr(module, self.general_conf["cv_metric"].lower().strip())

except AttributeError:

if not isinstance(self.general_conf["cv_metric"], str):
raise AttributeError("cv_metric must be string")

raise AttributeError(
"No metric named '%s' implemented"
% self.general_conf["cv_metric"].strip().lower()
)

# Making custom metrics compatible with sklearn
gib = module.greater_is_better(self.general_conf["cv_metric"].lower().strip())
scoring_function = make_scorer(metric, greater_is_better=gib)
metric_name = self.general_conf["cv_metric"].strip().lower()
scoring_function = load_metric_as_scorer(metric_name)

# Creating object to split train data for cross-validation
# This will make GridSearch have a pseudo-random beheaviour
Expand Down