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[SC-9912] Fix index issue when using input_grid #335

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Merged
AnilSorathiya merged 2 commits into from
Mar 11, 2025
Merged

[SC-9912] Fix index issue when using input_grid #335

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a5913cc
Fix index issue when using input_grid
juanmleng Mar 11, 2025
390454a
bug fix : show the results of all the features
AnilSorathiya Mar 11, 2025
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25 changes: 15 additions & 10 deletions validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ def _compute_metrics(
None: The computed metrics are appended to the `results` dictionary in-place.
"""
results["Slice"].append(str(region))
results["Shape"].append(df_region.shape[0])
results["Number of Records"].append(df_region.shape[0])
results["Feature"].append(feature_column)

# Check if df_region is an empty dataframe and if so, append 0 to all metrics
Expand Down Expand Up @@ -222,7 +222,7 @@ def WeakspotsDiagnosis(
thresholds = thresholds or DEFAULT_THRESHOLDS
thresholds = {k.title(): v for k, v in thresholds.items()}

results_headers = ["Slice", "Shape", "Feature"]
results_headers = ["Slice", "Number of Records", "Feature"]
results_headers.extend(metrics.keys())

figures = []
Expand All @@ -236,19 +236,20 @@ def WeakspotsDiagnosis(
feature_columns
+ [datasets[1].target_column, datasets[1].prediction_column(model)]
]

results_1 = pd.DataFrame()
results_2 = pd.DataFrame()
for feature in feature_columns:
bins = 10
if feature in datasets[0].feature_columns_categorical:
bins = len(df_1[feature].unique())
df_1["bin"] = pd.cut(df_1[feature], bins=bins)

results_1 = {k: [] for k in results_headers}
results_2 = {k: [] for k in results_headers}
r1 = {k: [] for k in results_headers}
r2 = {k: [] for k in results_headers}

for region, df_region in df_1.groupby("bin"):
_compute_metrics(
results=results_1,
results=r1,
metrics=metrics,
region=region,
df_region=df_region,
Expand All @@ -260,7 +261,7 @@ def WeakspotsDiagnosis(
(df_2[feature] > region.left) & (df_2[feature] <= region.right)
]
_compute_metrics(
results=results_2,
results=r2,
metrics=metrics,
region=region,
df_region=df_2_region,
Expand All @@ -271,8 +272,8 @@ def WeakspotsDiagnosis(

for metric in metrics.keys():
fig, df = _plot_weak_spots(
results_1=results_1,
results_2=results_2,
results_1=r1,
results_2=r2,
feature_column=feature,
metric=metric,
threshold=thresholds[metric],
Expand All @@ -284,14 +285,18 @@ def WeakspotsDiagnosis(
# rely on visual assessment for this test for now.
if not df[df[list(thresholds.keys())].lt(thresholds).any(axis=1)].empty:
passed = False
results_1 = pd.concat([results_1, pd.DataFrame(r1)])
results_2 = pd.concat([results_2, pd.DataFrame(r2)])

return (
pd.concat(
[
pd.DataFrame(results_1).assign(Dataset=datasets[0].input_id),
pd.DataFrame(results_2).assign(Dataset=datasets[1].input_id),
]
).sort_values(["Feature", "Dataset"]),
)
.reset_index(drop=True)
.sort_values(["Feature", "Dataset"]),
*figures,
passed,
)