Add what-if input range and rounding controls

RELEASE_NOTES.md

@@ -1,6 +1,11 @@
 # Release Notes
 
 
+## Version 0.5.7:
+
+### Bug Fixes
+- Allow FeatureInputComponent (what-if inputs) to customize numeric ranges and rounding, and apply min/max/step to inputs.
+
 ## Version 0.5.6:
 
 ### Bug Fixes

TODO.md

@@ -6,10 +6,9 @@
 - Rules: link an issue when possible; include size S/M/L; mark blockers.
 
 **Now**
-- [S/M][Components][#277] whatif input range/rounding customization.
+- [M][Explainers][#273] categorical columns with NaNs: sorting and column preservation.
 
 **Next**
-- [M][Explainers][#273] categorical columns with NaNs: sorting and column preservation.
 - [S][Explainers][#270] Autogluon integration (coerce predict_proba to ndarray).
 - [M][Hub][#269] add_dashboard endpoint fails after first request (Flask blueprint lifecycle).
 - [M/L][Components][#262] add filters for random transaction selection in whatif tab.

explainerdashboard/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.5.6"
+__version__ = "0.5.7"
 
 import logging
 import sys

explainerdashboard/dashboard_components/overview_components.py

@@ -8,6 +8,7 @@
 from math import ceil
 
 import numpy as np
+import pandas as pd
 from pandas.api.types import is_bool_dtype
 
 from dash import html, dcc, Input, Output
@@ -1091,6 +1092,8 @@ def __init__(
         n_input_cols=4,
         sort_features="shap",
         fill_row_first=True,
+        feature_input_ranges=None,
+        round=2,
         description=None,
         **kwargs,
     ):
@@ -1116,6 +1119,9 @@ def __init__(
                 is 'shap' to sort by mean absolute shap value.
             fill_row_first (bool, optional): if True most important features will
                 be on top row, if False they will be in most left column.
+            feature_input_ranges (dict, optional): dict mapping feature names to
+                (min, max) numeric ranges for input fields.
+            round (int, optional): number of decimals to round numeric ranges to.
             description (str, optional): Tooltip to display when hover over
                 component title. When None default text is shown.
 
@@ -1131,6 +1137,8 @@ def __init__(
             explainer, name="feature-input-index-" + self.name, **kwargs
         )
         self.index_name = "feature-input-index-" + self.name
+        self.feature_input_ranges = feature_input_ranges or {}
+        self.round = round
 
         self._feature_callback_inputs = [
             Input("feature-input-" + feature + "-input-" + self.name, "value")
@@ -1214,17 +1222,28 @@ def _generate_dash_input(self, col, onehot_cols, onehot_dict, cat_dict):
             )
         else:
             col_values = self.explainer.X[col][lambda x: x != self.explainer.na_fill]
-            if is_bool_dtype(col_values):
+            if col in self.feature_input_ranges:
+                min_range, max_range = self.feature_input_ranges[col]
+            elif is_bool_dtype(col_values):
                 min_range = int(col_values.min())
                 max_range = int(col_values.max())
             else:
-                min_range = np.round(col_values.min(), 2)
-                max_range = np.round(col_values.max(), 2)
+                min_range = np.round(col_values.min(), self.round)
+                max_range = np.round(col_values.max(), self.round)
+
+            if is_bool_dtype(col_values) or pd.api.types.is_integer_dtype(col_values):
+                step = 1
+            else:
+                step = 10 ** (-self.round)
             return html.Div(
                 [
                     dbc.Label(col),
                     dbc.Input(
-                        id="feature-input-" + col + "-input-" + self.name, type="number"
+                        id="feature-input-" + col + "-input-" + self.name,
+                        type="number",
+                        min=min_range,
+                        max=max_range,
+                        step=step,
                     ),
                     dbc.FormText(f"Range: {min_range}-{max_range}")
                     if not self.hide_range

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "explainerdashboard"
-version = "0.5.6"
+version = "0.5.7"
 description = "Quickly build Explainable AI dashboards that show the inner workings of so-called \"blackbox\" machine learning models."
 readme = "README.md"
 requires-python = ">=3.10"

tests/test_feature_input_component.py

@@ -24,3 +24,30 @@ def test_feature_input_component_handles_bool_columns(classifier_data):
 
     layout = component.layout()
     assert layout is not None
+
+
+def test_feature_input_component_respects_custom_range_and_rounding(classifier_data):
+    X_train, y_train, X_test, y_test = classifier_data
+
+    model = RandomForestClassifier(n_estimators=5, max_depth=2)
+    model.fit(X_train, y_train)
+
+    explainer = ClassifierExplainer(model, X_test, y_test)
+    component = FeatureInputComponent(
+        explainer, feature_input_ranges={"Age": (0, 50)}, round=1
+    )
+
+    age_div = next(
+        div
+        for div in component._feature_inputs
+        if getattr(div.children[0], "children", None) == "Age"
+    )
+    age_input = age_div.children[1]
+    range_text = age_div.children[2].children
+
+    props = age_input.to_plotly_json()["props"]
+
+    assert props.get("min") == 0
+    assert props.get("max") == 50
+    assert props.get("step") == 0.1
+    assert range_text == "Range: 0-50"