remove the InlineProvider from the documenation examples and force the documenation system to use the inline provider through environment variables

paulbkoch · paulbkoch · commit 88cf229003e5 · 2026-03-16T17:13:32.000-07:00
diff --git a/docs/benchmarks/ebm-classification-comparison.ipynb b/docs/benchmarks/ebm-classification-comparison.ipynb
@@ -386,13 +386,6 @@
     "record_df = pd.DataFrame.from_records(records)[['dataset_name', 'model_name', 'test_score_mean', 'test_score_std']]\n",
     "record_df.to_csv('ebm-perf-classification-overnight.csv')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/build.bat b/docs/build.bat
@@ -1,8 +1,12 @@
+@setlocal
+
+SET INTERPRET_DOCS_MODE=docs
+
 jupyter-book build --warningiserror --keep-going .\interpret\
 SET RETURNCODE=%ERRORLEVEL%
 
 copy .\extras\* .\interpret\_build\html\ /Y
 
 echo This file stops Github Pages from deleting directories that start with _> .\interpret\_build\html\.nojekyll
 
-exit /b %RETURNCODE%
+endlocal & exit /b %RETURNCODE%
diff --git a/docs/build.sh b/docs/build.sh
@@ -1,3 +1,5 @@
+export INTERPRET_DOCS_MODE=docs
+
 jupyter-book build --warningiserror --keep-going ./interpret/
 ret_code=$?
 
diff --git a/docs/excel_exporter/excel_exporter.ipynb b/docs/excel_exporter/excel_exporter.ipynb
@@ -557,14 +557,6 @@
    "source": [
     "workbook.close()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7a17d435-a231-4179-b602-c0bf75a0e203",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/interpret/aplr.ipynb b/docs/interpret/aplr.ipynb
@@ -24,19 +24,6 @@
     "The following code will train a logistic regression for the breast cancer dataset. The visualizations provided will be for both global and local explanations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "chemical-warrior",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/dpebm.ipynb b/docs/interpret/dpebm.ipynb
@@ -22,18 +22,6 @@
     "The following code will train a DPEBM classifier for the adult income dataset. The visualizations provided will be for both global and local explanations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bibliographic-mouse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/dt.ipynb b/docs/interpret/dt.ipynb
@@ -26,18 +26,6 @@
     "The following code will train an decision tree classifier for the breast cancer dataset. The visualizations provided will be for both global and local explanations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "surprising-smith",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/ebm-internals-classification.ipynb b/docs/interpret/ebm-internals-classification.ipynb
@@ -20,11 +20,7 @@
     "# boilerplate\n",
     "from interpret import show\n",
     "from interpret.glassbox import ExplainableBoostingClassifier\n",
-    "import numpy as np\n",
-    "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
+    "import numpy as np\n"
    ]
   },
   {
diff --git a/docs/interpret/ebm-internals-multiclass.ipynb b/docs/interpret/ebm-internals-multiclass.ipynb
@@ -20,11 +20,7 @@
     "# boilerplate\n",
     "from interpret import show\n",
     "from interpret.glassbox import ExplainableBoostingClassifier\n",
-    "import numpy as np\n",
-    "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
+    "import numpy as np\n"
    ]
   },
   {
diff --git a/docs/interpret/ebm-internals-regression.ipynb b/docs/interpret/ebm-internals-regression.ipynb
@@ -24,11 +24,7 @@
     "# boilerplate\n",
     "from interpret import show\n",
     "from interpret.glassbox import ExplainableBoostingRegressor\n",
-    "import numpy as np\n",
-    "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
+    "import numpy as np\n"
    ]
   },
   {
diff --git a/docs/interpret/ebm.ipynb b/docs/interpret/ebm.ipynb
@@ -53,18 +53,6 @@
     "The following code will train an EBM classifier for the adult income dataset. The visualizations provided will be for both global and local explanations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bibliographic-mouse",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/faq.ipynb b/docs/interpret/faq.ipynb
@@ -234,12 +234,11 @@
    "source": [
     "<h2>Can we enforce monotonicity for individual EBM terms?</h2>\n",
     "\n",
-    "It is possible to enforce monotonicity for individual terms in an EBM via two methods:\n:",
+    "It is possible to enforce monotonicity for individual terms in an EBM via two methods:\n",
     "\n",
     "- By setting `monotone_constraints` in the `ExplainableBoostingClassifier` or `ExplainableBoostingRegressor` constructor. This parameter is a list of integers, where each integer corresponds to the monotonicity constraint for the corresponding feature. A value of -1 enforces decreasing monotonicity, 0 enforces no monotonicity, and 1 enforces increasing monotonicity. For example, `monotone_constraints=[1, -1, 0]` would enforce increasing monotonicity for the first feature, decreasing monotonicity for the second feature, and no monotonicity for the third feature.\n",
     "\n",
-    "- Through postprocessing a graph. We generally recommend using [isotonic regression](https://scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html#sklearn.isotonic.IsotonicRegression) on each graph output to force positive or negative monotonicity. This can be done by calling the `monotonize` method on the EBM object.",
-    "\n",
+    "- Through postprocessing a graph. We generally recommend using [isotonic regression](https://scikit-learn.org/stable/modules/generated/sklearn.isotonic.IsotonicRegression.html#sklearn.isotonic.IsotonicRegression) on each graph output to force positive or negative monotonicity. This can be done by calling the `monotonize` method on the EBM object.\n",
     "Postprocessing is the recommended method, as it prevents the model from compensating for the monotonicity constraints by learning non-monotonic effects in other highly-correlated features."
    ]
   },
diff --git a/docs/interpret/framework.ipynb b/docs/interpret/framework.ipynb
@@ -125,18 +125,6 @@
     "For basic use cases, it is good to show an explanation one at a time. The rendered widget will provide a dropdown to select between visualizations. For example, in the event of a global explanation, it will provide an overview, along with graphs for each feature as shown with the code below:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "gross-river",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/index.ipynb b/docs/interpret/index.ipynb
@@ -48,10 +48,6 @@
     "import pandas as pd\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "df = pd.read_csv(\n",
     "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "    header=None)\n",
diff --git a/docs/interpret/lime.ipynb b/docs/interpret/lime.ipynb
@@ -37,18 +37,6 @@
     "The following code will train a blackbox pipeline for the breast cancer dataset. Aftewards it will interpret the pipeline and its decisions with LIME. The visualizations provided will be for local explanations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "recreational-carrier",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/lr.ipynb b/docs/interpret/lr.ipynb
@@ -26,18 +26,6 @@
     "The following code will train a logistic regression for the breast cancer dataset. The visualizations provided will be for both global and local explanations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "chemical-warrior",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/msa.ipynb b/docs/interpret/msa.ipynb
@@ -28,18 +28,6 @@
     "The following code will train a blackbox pipeline for the breast cancer dataset. Aftewards it will interpret the pipeline and its decisions with Morris method. The visualizations provided will be for global explanations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "arbitrary-schedule",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/pdp.ipynb b/docs/interpret/pdp.ipynb
@@ -32,18 +32,6 @@
     "The following code will train a blackbox pipeline for the breast cancer dataset. Aftewards it will interpret the pipeline and its decisions with Partial Dependence Plots. The visualizations provided will be for global explanations."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "quantitative-sarah",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/interpret/python/examples/custom-interactions.ipynb b/docs/interpret/python/examples/custom-interactions.ipynb
@@ -40,10 +40,6 @@
     "from sklearn.model_selection import train_test_split\n",
     "from interpret.glassbox import ExplainableBoostingClassifier\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "df = pd.read_csv(\n",
     "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "    header=None)\n",
diff --git a/docs/interpret/python/examples/differential-privacy.ipynb b/docs/interpret/python/examples/differential-privacy.ipynb
@@ -39,10 +39,6 @@
     "import pandas as pd\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "df = pd.read_csv(\n",
     "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "    header=None)\n",
diff --git a/docs/interpret/python/examples/explain-blackbox-classifiers.ipynb b/docs/interpret/python/examples/explain-blackbox-classifiers.ipynb
@@ -40,10 +40,6 @@
     "from sklearn.model_selection import train_test_split\n",
     "from interpret import show\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "df = pd.read_csv(\n",
     "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "    header=None)\n",
diff --git a/docs/interpret/python/examples/explain-blackbox-regressors.ipynb b/docs/interpret/python/examples/explain-blackbox-regressors.ipynb
@@ -41,10 +41,6 @@
     "from sklearn.model_selection import train_test_split\n",
     "from interpret import show\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "X, y = load_diabetes(return_X_y=True, as_frame=True)\n",
     "\n",
     "seed = 42\n",
diff --git a/docs/interpret/python/examples/group-importances.ipynb b/docs/interpret/python/examples/group-importances.ipynb
@@ -56,10 +56,6 @@
     "from sklearn.datasets import load_diabetes\n",
     "from interpret.glassbox import ExplainableBoostingRegressor\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "X, y = load_diabetes(return_X_y=True, as_frame=True)\n",
     "\n",
     "ebm = ExplainableBoostingRegressor()\n",
diff --git a/docs/interpret/python/examples/interpretable-classification.ipynb b/docs/interpret/python/examples/interpretable-classification.ipynb
@@ -41,10 +41,6 @@
     "from interpret import show\n",
     "from interpret.perf import ROC\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "df = pd.read_csv(\n",
     "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "    header=None)\n",
@@ -186,7 +182,7 @@
     "X = pd.get_dummies(X, prefix_sep='.').astype(float)\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)\n",
     "\n",
-    "lr = LogisticRegression(random_state=seed, penalty='l1', solver='liblinear')\n",
+    "lr = LogisticRegression(random_state=seed, l1_ratio=1, solver='liblinear')\n",
     "lr.fit(X_train, y_train)\n",
     "\n",
     "tree = ClassificationTree()\n",
diff --git a/docs/interpret/python/examples/interpretable-regression-synthetic.ipynb b/docs/interpret/python/examples/interpretable-regression-synthetic.ipynb
@@ -42,10 +42,6 @@
     "from interpret.utils import make_synthetic\n",
     "from interpret import show\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "seed = 42\n",
     "\n",
     "X, y, names, types = make_synthetic(classes=None, n_samples=50000, missing=False, seed=seed)\n",
diff --git a/docs/interpret/python/examples/interpretable-regression.ipynb b/docs/interpret/python/examples/interpretable-regression.ipynb
@@ -41,10 +41,6 @@
     "from interpret import show\n",
     "from interpret.perf import RegressionPerf\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "dataset = fetch_california_housing()\n",
     "X = dataset.data\n",
     "y = dataset.target\n",
diff --git a/docs/interpret/python/examples/merge-ebms.ipynb b/docs/interpret/python/examples/merge-ebms.ipynb
@@ -40,10 +40,6 @@
     "from sklearn.model_selection import train_test_split\n",
     "from interpret import show\n",
     "\n",
-    "from interpret import set_visualize_provider\n",
-    "from interpret.provider import InlineProvider\n",
-    "set_visualize_provider(InlineProvider())\n",
-    "\n",
     "df = pd.read_csv(\n",
     "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
     "    header=None)\n",
diff --git a/docs/interpret/shap.ipynb b/docs/interpret/shap.ipynb
diff --git a/python/interpret-core/interpret/provider/_environment.py b/python/interpret-core/interpret/provider/_environment.py
diff --git a/python/interpret-core/interpret/provider/_visualize.py b/python/interpret-core/interpret/provider/_visualize.py

Original file line number	Diff line number	Diff line change
`@@ -386,13 +386,6 @@`
`386`	`386`	`"record_df = pd.DataFrame.from_records(records)[['dataset_name', 'model_name', 'test_score_mean', 'test_score_std']]\n",`
`387`	`387`	`"record_df.to_csv('ebm-perf-classification-overnight.csv')"`
`388`	`388`	`]`
`389`		`- },`
`390`		`- {`
`391`		`- "cell_type": "code",`
`392`		`- "execution_count": null,`
`393`		`- "metadata": {},`
`394`		`- "outputs": [],`
`395`		`- "source": []`
`396`	`389`	`}`
`397`	`390`	`],`
`398`	`391`	`"metadata": {`
Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,5 @@`
	`1`	`+export INTERPRET_DOCS_MODE=docs`
	`2`	`+`
`1`	`3`	`jupyter-book build --warningiserror --keep-going ./interpret/`
`2`	`4`	`ret_code=$?`
`3`	`5`
Original file line number	Diff line number	Diff line change
`@@ -20,11 +20,7 @@`
`20`	`20`	`"# boilerplate\n",`
`21`	`21`	`"from interpret import show\n",`
`22`	`22`	`"from interpret.glassbox import ExplainableBoostingClassifier\n",`
`23`		`- "import numpy as np\n",`
`24`		`- "\n",`
`25`		`- "from interpret import set_visualize_provider\n",`
`26`		`- "from interpret.provider import InlineProvider\n",`
`27`		`- "set_visualize_provider(InlineProvider())"`
	`23`	`+ "import numpy as np\n"`
`28`	`24`	`]`
`29`	`25`	`},`
`30`	`26`	`{`
Original file line number	Diff line number	Diff line change
`@@ -24,11 +24,7 @@`
`24`	`24`	`"# boilerplate\n",`
`25`	`25`	`"from interpret import show\n",`
`26`	`26`	`"from interpret.glassbox import ExplainableBoostingRegressor\n",`
`27`		`- "import numpy as np\n",`
`28`		`- "\n",`
`29`		`- "from interpret import set_visualize_provider\n",`
`30`		`- "from interpret.provider import InlineProvider\n",`
`31`		`- "set_visualize_provider(InlineProvider())"`
	`27`	`+ "import numpy as np\n"`
`32`	`28`	`]`
`33`	`29`	`},`
`34`	`30`	`{`