sklearn.impute.MissingIndicator converter (#268)

* missing indicator with some problem * supun is awesome * upping coverage
2020-08-29 22:58:33 -07:00 · 2020-08-29 22:58:33 -07:00 · a745886376
--- a/doc/html/hummingbird/ml/operator_converters/index.html
+++ b/doc/html/hummingbird/ml/operator_converters/index.html
@ -1,4 +1,4 @@
-<!doctype html>
+vi<!doctype html>
 <html lang="en">
 <head>
 <meta charset="utf-8">
@ -60,6 +60,7 @@ from .sklearn import decision_tree  # noqa: E402
 from .sklearn import gbdt  # noqa: E402
 from .sklearn import iforest  # noqa: E402
 from .sklearn import linear as sklearn_linear  # noqa: E402
+from .sklearn import missing_indicator  # noqa: E402
 from .sklearn import mlp as sklearn_mlp  # noqa: E402
 from .sklearn import nb as sklearn_nb  # noqa: E402
 from .sklearn import normalizer as sklearn_normalizer  # noqa: E402
--- a/doc/html/hummingbird/ml/operator_converters/sklearn/missing_indicator.html
+++ b/doc/html/hummingbird/ml/operator_converters/sklearn/missing_indicator.html
@ -0,0 +1,260 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
+<meta name="generator" content="pdoc 0.8.1" />
+<title>hummingbird.ml.operator_converters.sklearn.missing_indicator API documentation</title>
+<meta name="description" content="Converter for scikit-learn MissingIndicator." />
+<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
+<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
+<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
+<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
+<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
+<style>.homelink{display:block;font-size:2em;font-weight:bold;color:#555;padding-bottom:.5em;border-bottom:1px solid silver}.homelink:hover{color:inherit}.homelink img{max-width:20%;max-height:5em;margin:auto;margin-bottom:.3em}</style>
+<link rel="canonical" href="https://microsoft.github.io/hummingbird/ml/operator_converters/sklearn/missing_indicator.html">
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>hummingbird.ml.operator_converters.sklearn.missing_indicator</code></h1>
+</header>
+<section id="section-intro">
+<p>Converter for scikit-learn MissingIndicator.</p>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+<a href="https://github.com/microsoft/hummingbird/blob/master/hummingbird/ml/operator_converters/sklearn/missing_indicator.py#L0-L56" class="git-link">Browse git</a>
+</summary>
+<pre><code class="python"># -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+
+&#34;&#34;&#34;
+Converter for scikit-learn MissingIndicator.
+&#34;&#34;&#34;
+from .._base_operator import BaseOperator
+import numpy as np
+from onnxconverter_common.registration import register_converter
+import torch
+
+
+class MissingIndicator(BaseOperator, torch.nn.Module):
+    &#34;&#34;&#34;
+    Class implementing Imputer operators in MissingIndicator.
+    &#34;&#34;&#34;
+
+    def __init__(self, sklearn_missing_indicator, device):
+        super(MissingIndicator, self).__init__()
+        self.transformer = True
+        self.missing_values = torch.nn.Parameter(
+            torch.tensor([sklearn_missing_indicator.missing_values], dtype=torch.float32), requires_grad=False
+        )
+        self.features = sklearn_missing_indicator.features
+        self.is_nan = True if (sklearn_missing_indicator.missing_values in [&#34;NaN&#34;, None, np.nan]) else False
+        self.column_indices = torch.nn.Parameter(torch.LongTensor(sklearn_missing_indicator.features_), requires_grad=False)
+
+    def forward(self, x):
+        if self.is_nan:
+            if self.features == &#34;all&#34;:
+                return torch.isnan(x).float()
+            else:
+                return torch.isnan(torch.index_select(x, 1, self.column_indices)).float()
+        else:
+            if self.features == &#34;all&#34;:
+                return torch.eq(x, self.missing_values).float()
+            else:
+                return torch.eq(torch.index_select(x, 1, self.column_indices), self.missing_values).float()
+
+
+def convert_sklearn_missing_indicator(operator, device, extra_config):
+    &#34;&#34;&#34;
+    Converter for `sklearn.impute.MissingIndicator`
+    Args:
+        operator: An operator wrapping a `sklearn.impute.MissingIndicator` model
+        device: String defining the type of device the converted operator should be run on
+        extra_config: Extra configuration used to select the best conversion strategy
+    Returns:
+        A PyTorch model
+    &#34;&#34;&#34;
+    return MissingIndicator(operator.raw_operator, device)
+
+
+register_converter(&#34;SklearnMissingIndicator&#34;, convert_sklearn_missing_indicator)</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-functions">Functions</h2>
+<dl>
+<dt id="hummingbird.ml.operator_converters.sklearn.missing_indicator.convert_sklearn_missing_indicator"><code class="name flex">
+<span>def <span class="ident">convert_sklearn_missing_indicator</span></span>(<span>operator, device, extra_config)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Converter for <code>sklearn.impute.MissingIndicator</code></p>
+<h2 id="args">Args</h2>
+<dl>
+<dt><strong><code>operator</code></strong></dt>
+<dd>An operator wrapping a <code>sklearn.impute.MissingIndicator</code> model</dd>
+<dt><strong><code>device</code></strong></dt>
+<dd>String defining the type of device the converted operator should be run on</dd>
+<dt><strong><code>extra_config</code></strong></dt>
+<dd>Extra configuration used to select the best conversion strategy</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>A PyTorch model</code></dt>
+<dd>&nbsp;</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+<a href="https://github.com/microsoft/hummingbird/blob/master/hummingbird/ml/operator_converters/sklearn/missing_indicator.py#L44-L54" class="git-link">Browse git</a>
+</summary>
+<pre><code class="python">def convert_sklearn_missing_indicator(operator, device, extra_config):
+    &#34;&#34;&#34;
+    Converter for `sklearn.impute.MissingIndicator`
+    Args:
+        operator: An operator wrapping a `sklearn.impute.MissingIndicator` model
+        device: String defining the type of device the converted operator should be run on
+        extra_config: Extra configuration used to select the best conversion strategy
+    Returns:
+        A PyTorch model
+    &#34;&#34;&#34;
+    return MissingIndicator(operator.raw_operator, device)</code></pre>
+</details>
+</dd>
+</dl>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="hummingbird.ml.operator_converters.sklearn.missing_indicator.MissingIndicator"><code class="flex name class">
+<span>class <span class="ident">MissingIndicator</span></span>
+<span>(</span><span>sklearn_missing_indicator, device)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Class implementing Imputer operators in MissingIndicator.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+<a href="https://github.com/microsoft/hummingbird/blob/master/hummingbird/ml/operator_converters/sklearn/missing_indicator.py#L16-L41" class="git-link">Browse git</a>
+</summary>
+<pre><code class="python">class MissingIndicator(BaseOperator, torch.nn.Module):
+    &#34;&#34;&#34;
+    Class implementing Imputer operators in MissingIndicator.
+    &#34;&#34;&#34;
+
+    def __init__(self, sklearn_missing_indicator, device):
+        super(MissingIndicator, self).__init__()
+        self.transformer = True
+        self.missing_values = torch.nn.Parameter(
+            torch.tensor([sklearn_missing_indicator.missing_values], dtype=torch.float32), requires_grad=False
+        )
+        self.features = sklearn_missing_indicator.features
+        self.is_nan = True if (sklearn_missing_indicator.missing_values in [&#34;NaN&#34;, None, np.nan]) else False
+        self.column_indices = torch.nn.Parameter(torch.LongTensor(sklearn_missing_indicator.features_), requires_grad=False)
+
+    def forward(self, x):
+        if self.is_nan:
+            if self.features == &#34;all&#34;:
+                return torch.isnan(x).float()
+            else:
+                return torch.isnan(torch.index_select(x, 1, self.column_indices)).float()
+        else:
+            if self.features == &#34;all&#34;:
+                return torch.eq(x, self.missing_values).float()
+            else:
+                return torch.eq(torch.index_select(x, 1, self.column_indices), self.missing_values).float()</code></pre>
+</details>
+<h3>Ancestors</h3>
+<ul class="hlist">
+<li>hummingbird.ml.operator_converters._base_operator.BaseOperator</li>
+<li>abc.ABC</li>
+<li>torch.nn.modules.module.Module</li>
+</ul>
+<h3>Methods</h3>
+<dl>
+<dt id="hummingbird.ml.operator_converters.sklearn.missing_indicator.MissingIndicator.forward"><code class="name flex">
+<span>def <span class="ident">forward</span></span>(<span>self, x)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the :class:<code>Module</code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+<a href="https://github.com/microsoft/hummingbird/blob/master/hummingbird/ml/operator_converters/sklearn/missing_indicator.py#L31-L41" class="git-link">Browse git</a>
+</summary>
+<pre><code class="python">def forward(self, x):
+    if self.is_nan:
+        if self.features == &#34;all&#34;:
+            return torch.isnan(x).float()
+        else:
+            return torch.isnan(torch.index_select(x, 1, self.column_indices)).float()
+    else:
+        if self.features == &#34;all&#34;:
+            return torch.eq(x, self.missing_values).float()
+        else:
+            return torch.eq(torch.index_select(x, 1, self.column_indices), self.missing_values).float()</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<header>
+<a class="homelink" rel="home" title="Hummingbird Home" href="https://github.com/microsoft/hummingbird"> Hummingbird
+</a>
+</header>
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="hummingbird.ml.operator_converters.sklearn" href="index.html">hummingbird.ml.operator_converters.sklearn</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-functions">Functions</a></h3>
+<ul class="">
+<li><code><a title="hummingbird.ml.operator_converters.sklearn.missing_indicator.convert_sklearn_missing_indicator" href="#hummingbird.ml.operator_converters.sklearn.missing_indicator.convert_sklearn_missing_indicator">convert_sklearn_missing_indicator</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="hummingbird.ml.operator_converters.sklearn.missing_indicator.MissingIndicator" href="#hummingbird.ml.operator_converters.sklearn.missing_indicator.MissingIndicator">MissingIndicator</a></code></h4>
+<ul class="">
+<li><code><a title="hummingbird.ml.operator_converters.sklearn.missing_indicator.MissingIndicator.forward" href="#hummingbird.ml.operator_converters.sklearn.missing_indicator.MissingIndicator.forward">forward</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.8.1</a>.</p>
+</footer>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
+<script>hljs.initHighlightingOnLoad()</script>
+</body>
+</html>
--- a/doc/html/hummingbird/ml/supported.html
+++ b/doc/html/hummingbird/ml/supported.html
@ -46,6 +46,7 @@ LogisticRegression,
 LogisticRegressionCV,
 MaxAbsScaler,
 MinMaxScaler,
+MissingIndicator,
 MLPClassifier,
 MultinomialNB,
 Normalizer,
@ -104,6 +105,7 @@ LogisticRegression,
 LogisticRegressionCV,
 MaxAbsScaler,
 MinMaxScaler,
+MissingIndicator,
 MLPClassifier,
 MultinomialNB,
 Normalizer,
@ -168,12 +170,17 @@ def _build_sklearn_operator_list():
        # SVM-based models
        from sklearn.svm import LinearSVC, SVC, NuSVC

+
+        # Imputers
+        from sklearn.impute import MissingIndicator
+
        # MLP Models
        from sklearn.neural_network import MLPClassifier

        # Naive Bayes Models
        from sklearn.naive_bayes import BernoulliNB, GaussianNB, MultinomialNB

+
        # Preprocessing
        from sklearn.preprocessing import (
            Binarizer,
@ -216,6 +223,8 @@ def _build_sklearn_operator_list():
            # SVM
            NuSVC,
            SVC,
+            # Imputers
+            MissingIndicator,
            # Preprocessing
            Binarizer,
            MaxAbsScaler,
@ -449,7 +458,9 @@ CONTAINER = &#34;container&#34;
 <details class="source">
 <summary>
 <span>Expand source code</span>
+
 <a href="https://github.com/microsoft/hummingbird/blob/master/hummingbird/ml/supported.py#L277-L290" class="git-link">Browse git</a>
+
 </summary>
 <pre><code class="python">def get_onnxml_api_operator_name(model_type):
    &#34;&#34;&#34;
@ -486,7 +497,9 @@ or an object with scikit-learn API (e.g., LightGBM)</dd>
 <details class="source">
 <summary>
 <span>Expand source code</span>
+
 <a href="https://github.com/microsoft/hummingbird/blob/master/hummingbird/ml/supported.py#L261-L274" class="git-link">Browse git</a>
+
 </summary>
 <pre><code class="python">def get_sklearn_api_operator_name(model_type):
    &#34;&#34;&#34;
--- a/hummingbird/ml/operator_converters/init.py
+++ b/hummingbird/ml/operator_converters/init.py
@ -30,6 +30,7 @@ from .sklearn import decision_tree  # noqa: E402
 from .sklearn import gbdt  # noqa: E402
 from .sklearn import iforest  # noqa: E402
 from .sklearn import linear as sklearn_linear  # noqa: E402
+from .sklearn import missing_indicator  # noqa: E402
 from .sklearn import mlp as sklearn_mlp  # noqa: E402
 from .sklearn import nb as sklearn_nb  # noqa: E402
 from .sklearn import normalizer as sklearn_normalizer  # noqa: E402
--- a/hummingbird/ml/operator_converters/sklearn/missing_indicator.py
+++ b/hummingbird/ml/operator_converters/sklearn/missing_indicator.py
@ -0,0 +1,57 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+# --------------------------------------------------------------------------
+
+"""
+Converter for scikit-learn MissingIndicator.
+"""
+from .._base_operator import BaseOperator
+import numpy as np
+from onnxconverter_common.registration import register_converter
+import torch
+
+
+class MissingIndicator(BaseOperator, torch.nn.Module):
+    """
+    Class implementing Imputer operators in MissingIndicator.
+    """
+
+    def __init__(self, sklearn_missing_indicator, device):
+        super(MissingIndicator, self).__init__()
+        self.transformer = True
+        self.missing_values = torch.nn.Parameter(
+            torch.tensor([sklearn_missing_indicator.missing_values], dtype=torch.float32), requires_grad=False
+        )
+        self.features = sklearn_missing_indicator.features
+        self.is_nan = True if (sklearn_missing_indicator.missing_values in ["NaN", None, np.nan]) else False
+        self.column_indices = torch.nn.Parameter(torch.LongTensor(sklearn_missing_indicator.features_), requires_grad=False)
+
+    def forward(self, x):
+        if self.is_nan:
+            if self.features == "all":
+                return torch.isnan(x).float()
+            else:
+                return torch.isnan(torch.index_select(x, 1, self.column_indices)).float()
+        else:
+            if self.features == "all":
+                return torch.eq(x, self.missing_values).float()
+            else:
+                return torch.eq(torch.index_select(x, 1, self.column_indices), self.missing_values).float()
+
+
+def convert_sklearn_missing_indicator(operator, device, extra_config):
+    """
+    Converter for `sklearn.impute.MissingIndicator`
+    Args:
+        operator: An operator wrapping a `sklearn.impute.MissingIndicator` model
+        device: String defining the type of device the converted operator should be run on
+        extra_config: Extra configuration used to select the best conversion strategy
+    Returns:
+        A PyTorch model
+    """
+    return MissingIndicator(operator.raw_operator, device)
+
+
+register_converter("SklearnMissingIndicator", convert_sklearn_missing_indicator)
--- a/hummingbird/ml/supported.py
+++ b/hummingbird/ml/supported.py
@ -31,6 +31,7 @@ LogisticRegression,
 LogisticRegressionCV,
 MaxAbsScaler,
 MinMaxScaler,
+MissingIndicator,
 MLPClassifier,
 MultinomialNB,
 Normalizer,
@ -95,6 +96,9 @@ def _build_sklearn_operator_list():
        # SVM-based models
        from sklearn.svm import LinearSVC, SVC, NuSVC

+        # Imputers
+        from sklearn.impute import MissingIndicator
+
        # MLP Models
        from sklearn.neural_network import MLPClassifier

@ -143,6 +147,8 @@ def _build_sklearn_operator_list():
            # SVM
            NuSVC,
            SVC,
+            # Imputers
+            MissingIndicator,
            # Preprocessing
            Binarizer,
            MaxAbsScaler,
--- a/tests/test_sklearn_missing_indicator.py
+++ b/tests/test_sklearn_missing_indicator.py
@ -0,0 +1,41 @@
+"""
+Tests sklearn Normalizer converter
+"""
+import unittest
+import warnings
+
+import numpy as np
+import torch
+
+from sklearn.impute import MissingIndicator
+
+import hummingbird.ml
+
+
+class TestSklearnMissingIndicator(unittest.TestCase):
+    def _test_sklearn_missing_indic(self, model, data):
+        data_tensor = torch.from_numpy(data)
+        torch_model = hummingbird.ml.convert(model, "torch")
+        self.assertIsNotNone(torch_model)
+
+        np.testing.assert_allclose(
+            model.transform(data), torch_model.transform(data_tensor), rtol=1e-06, atol=1e-06,
+        )
+
+    def test_missing_indicator_float_inputs(self):
+        for features in ["all", "missing-only"]:
+            model = MissingIndicator(features=features)
+            data = np.array([[1, 2], [np.nan, 3], [7, 6]], dtype=np.float32)
+            model.fit(data)
+            self._test_sklearn_missing_indic(model, data)
+
+    def test_missing_indicator_float_inputs_isnan_false(self):
+        for features in ["all", "missing-only"]:
+            model = MissingIndicator(features=features, missing_values=0)
+            data = np.array([[1, 2], [0, 3], [7, 6]], dtype=np.float32)
+            model.fit(data)
+            self._test_sklearn_missing_indic(model, data)
+
+
+if __name__ == "__main__":
+    unittest.main()