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Issue #571: Compatibility with >= Numpy 2.0.0 (update of ptp method) #572

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merged 3 commits into from
Oct 2, 2024
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Issue #571: Compatibility with >= Numpy 2.0.0 (update of ptp method) #572

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86b2449
Replaced use of x.ptp() with np.ptp(x)
Chip2916 Oct 1, 2024
f271e3a
Replaced np.Inf with np.inf
Chip2916 Oct 1, 2024
67f53c9
Updated change log
Chip2916 Oct 1, 2024
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1 change: 1 addition & 0 deletions python/dalex/NEWS.md
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Expand Up @@ -2,6 +2,7 @@

### development

* replaced instances of x.ptp() (with np.ptp(x)) and np.Inf (with np.inf) to make dx compatible with Numpy>=2.0.0
* added a way to pass `sample_weight` to loss functions in `model_parts()` (variable importance) using `weights` from `dx.Explainer` ([#563](https://github.com/ModelOriented/DALEX/issues/563))
* fixed the visualization of `shap_wrapper` for `shap==0.45.0`

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2 changes: 1 addition & 1 deletion python/dalex/dalex/aspect/_model_aspect_importance/object.py
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Expand Up @@ -246,7 +246,7 @@ def plot(
dl = _result_df.loc[
_result_df.aspect_name != "_baseline_", "dropout_loss"
].to_numpy()
min_max_margin = dl.ptp() * 0.15
min_max_margin = np.ptp(dl) * 0.15
min_max = [dl.min() - min_max_margin, dl.max() + min_max_margin]

# take out full model
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4 changes: 2 additions & 2 deletions python/dalex/dalex/aspect/_model_triplot/object.py
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Expand Up @@ -231,7 +231,7 @@ def plot(
fig.data[0]["y"] = y_vals

## triplot
min_x_imp, max_x_imp = np.Inf, -np.Inf
min_x_imp, max_x_imp = np.inf, -np.inf
for data in hierarchical_importance_dendrogram_plot["data"][::-1]:
data["xaxis"] = "x2"
data["hoverinfo"] = "text"
Expand All @@ -241,7 +241,7 @@ def plot(
max_x_imp = np.max([max_x_imp, np.max(data["x"])])
min_max_margin_imp = (max_x_imp - min_x_imp) * 0.15

min_x_clust, max_x_clust = np.Inf, -np.Inf
min_x_clust, max_x_clust = np.inf, -np.inf
for data in hierarchical_clustering_dendrogram_plot["data"]:
data["xaxis"] = "x3"
data["hoverinfo"] = "text"
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4 changes: 2 additions & 2 deletions python/dalex/dalex/aspect/_model_triplot/plot.py
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Expand Up @@ -148,8 +148,8 @@ def plot_single_aspects_importance(
}
)

temp_min_max = [np.Inf, -np.Inf]
min_max_margin = _result.dropout_loss.values.ptp() * 0.15
temp_min_max = [np.inf, -np.inf]
min_max_margin = np.ptp(_result.dropout_loss.values) * 0.15
temp_min_max[0] = np.min(
[temp_min_max[0], baseline - min_max_margin]
)
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4 changes: 2 additions & 2 deletions python/dalex/dalex/aspect/_predict_aspect_importance/object.py
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Expand Up @@ -306,7 +306,7 @@ def plot(
vcolors = _theme.get_aspect_importance_colors()

if min_max is None:
temp_min_max = [np.Inf, -np.Inf]
temp_min_max = [np.inf, -np.inf]
else:
temp_min_max = min_max

Expand Down Expand Up @@ -404,7 +404,7 @@ def plot(

if min_max is None:
cum = _result.importance.values + baseline
min_max_margin = cum.ptp() * 0.15
min_max_margin = np.ptp(cum) * 0.15
temp_min_max[0] = np.min(
[
temp_min_max[0],
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4 changes: 2 additions & 2 deletions python/dalex/dalex/aspect/_predict_triplot/object.py
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Expand Up @@ -284,7 +284,7 @@ def plot(
line={"color": "#371ea3", "width": 1.5, "dash": "dot"},
)

min_x_imp, max_x_imp = np.Inf, -np.Inf
min_x_imp, max_x_imp = np.inf, -np.inf
for data in hierarchical_importance_dendrogram_plot["data"][::-1]:
data["xaxis"] = "x2"
data["hoverinfo"] = "text"
Expand All @@ -294,7 +294,7 @@ def plot(
max_x_imp = np.max([max_x_imp, np.max(data["x"])])
min_max_margin_imp = (max_x_imp - min_x_imp) * 0.15

min_x_clust, max_x_clust = np.Inf, -np.Inf
min_x_clust, max_x_clust = np.inf, -np.inf
for data in hierarchical_clustering_dendrogram_plot["data"]:
data["xaxis"] = "x3"
data["hoverinfo"] = "text"
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4 changes: 2 additions & 2 deletions python/dalex/dalex/aspect/_predict_triplot/plot.py
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Expand Up @@ -239,8 +239,8 @@ def plot_single_aspects_importance(
}
)

temp_min_max = [np.Inf, -np.Inf]
min_max_margin = _result.importance.values.ptp() * 0.15
temp_min_max = [np.inf, -np.inf]
min_max_margin = np.ptp(_result.importance.values) * 0.15
temp_min_max[0] = np.min(
[temp_min_max[0], _result.importance.values.min() - min_max_margin]
)
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2 changes: 1 addition & 1 deletion python/dalex/dalex/model_explanations/_aggregated_profiles/object.py
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Expand Up @@ -242,7 +242,7 @@ def plot(self,

# calculate y axis range to allow for fixedrange True
dl = _result_df['_yhat_'].to_numpy()
min_max_margin = dl.ptp() * 0.10
min_max_margin = np.ptp(dl) * 0.10
min_max = [dl.min() - min_max_margin, dl.max() + min_max_margin]

is_x_numeric = False if geom == 'bars' else pd.api.types.is_numeric_dtype(_result_df['_x_'])
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2 changes: 1 addition & 1 deletion python/dalex/dalex/model_explanations/_variable_importance/object.py
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Expand Up @@ -198,7 +198,7 @@ def plot(self,
_global_checks.global_raise_objects_class(objects, self.__class__)

dl = _result_df.loc[_result_df.variable != '_baseline_', 'dropout_loss'].to_numpy()
min_max_margin = dl.ptp() * 0.15
min_max_margin = np.ptp(dl) * 0.15
min_max = [dl.min() - min_max_margin, dl.max() + min_max_margin]

# take out full model
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4 changes: 2 additions & 2 deletions python/dalex/dalex/predict_explanations/_break_down/object.py
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Expand Up @@ -199,7 +199,7 @@ def plot(self,
vcolors = _theme.get_break_down_colors()

if min_max is None:
temp_min_max = [np.Inf, -np.Inf]
temp_min_max = [np.inf, -np.inf]
else:
temp_min_max = min_max

Expand Down Expand Up @@ -256,7 +256,7 @@ def plot(self,

if min_max is None:
cum = df.cumulative.values
min_max_margin = cum.ptp() * 0.15
min_max_margin = np.ptp(cum) * 0.15
temp_min_max[0] = np.min([temp_min_max[0], cum.min() - min_max_margin])
temp_min_max[1] = np.max([temp_min_max[1], cum.max() + min_max_margin])

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2 changes: 1 addition & 1 deletion python/dalex/dalex/predict_explanations/_ceteris_paribus/object.py
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Expand Up @@ -262,7 +262,7 @@ def plot(self,

# calculate y axis range to allow for fixedrange True
dl = _result_df['_yhat_'].to_numpy()
min_max_margin = dl.ptp() * 0.10
min_max_margin = np.ptp(dl) * 0.10
min_max = [dl.min() - min_max_margin, dl.max() + min_max_margin]

# create _x_
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4 changes: 2 additions & 2 deletions python/dalex/dalex/predict_explanations/_shap/object.py
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Expand Up @@ -205,7 +205,7 @@ def plot(self,
vcolors = _theme.get_break_down_colors()

if min_max is None:
temp_min_max = [np.Inf, -np.Inf]
temp_min_max = [np.inf, -np.inf]
else:
temp_min_max = min_max

Expand Down Expand Up @@ -263,7 +263,7 @@ def plot(self,

if min_max is None:
cum = df.contribution.values + prediction_baseline
min_max_margin = cum.ptp() * 0.15
min_max_margin = np.ptp(cum) * 0.15
temp_min_max[0] = np.min([temp_min_max[0], cum.min() - min_max_margin])
temp_min_max[1] = np.max([temp_min_max[1], cum.max() + min_max_margin])

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