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<!DOCTYPE html>
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<title>Generating counterfactual explanations with any ML model — DiCE 0.11 documentation</title>
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<p class="caption" role="heading"><span class="caption-text">Getting Started:</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../readme.html">Diverse Counterfactual Explanations (DiCE) for ML</a></li>
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<p class="caption" role="heading"><span class="caption-text">Notebooks:</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="DiCE_getting_started.html">Quick introduction to generating counterfactual explanations using DiCE</a></li>
<li class="toctree-l1"><a class="reference internal" href="DiCE_feature_importances.html">Estimating local and global feature importance scores using DiCE</a></li>
<li class="toctree-l1"><a class="reference internal" href="DiCE_multiclass_classification_and_regression.html">Generating counterfactuals for multi-class classification and regression models</a></li>
<li class="toctree-l1"><a class="reference internal" href="DiCE_multiclass_classification_and_regression.html#Regression">Regression</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Generating counterfactual explanations with any ML model</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#1.-Independent-random-sampling-of-features">1. Independent random sampling of features</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#Loading-dataset">Loading dataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="#Training-a-custom-ML-model">Training a custom ML model</a></li>
<li class="toctree-l3"><a class="reference internal" href="#Generate-diverse-counterfactuals">Generate diverse counterfactuals</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#Selecting-the-features-to-vary">Selecting the features to vary</a></li>
<li class="toctree-l4"><a class="reference internal" href="#Choosing-feature-ranges">Choosing feature ranges</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#2.-Genetic-Algorithm">2. Genetic Algorithm</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#id1">Training a custom ML model</a></li>
<li class="toctree-l3"><a class="reference internal" href="#id2">Generate diverse counterfactuals</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#3.-Querying-a-KD-Tree">3. Querying a KD Tree</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#id3">Training a custom ML model</a></li>
<li class="toctree-l3"><a class="reference internal" href="#id4">Generate diverse counterfactuals</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#id5">Selecting the features to vary</a></li>
<li class="toctree-l4"><a class="reference internal" href="#Selecting-the-feature-ranges">Selecting the feature ranges</a></li>
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<li class="toctree-l1"><a class="reference internal" href="DiCE_with_private_data.html">Generating counterfactual explanations without access to training data</a></li>
<li class="toctree-l1"><a class="reference internal" href="DiCE_with_advanced_options.html">Advanced options to customize Counterfactual Explanations</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Package:</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../dice_ml.html">dice_ml package</a></li>
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<section id="Generating-counterfactual-explanations-with-any-ML-model">
<h1>Generating counterfactual explanations with any ML model<a class="headerlink" href="#Generating-counterfactual-explanations-with-any-ML-model" title="Permalink to this heading"></a></h1>
<p>The goal of this notebook is to show how to generate CFs for ML models using frameworks other than TensorFlow or PyTorch. We show how to generate diverse CFs by three methods: 1. Independent random sampling of features (<code class="docutils literal notranslate"><span class="pre">method_name='random')</span></code> 2. Genetic algorithm (<code class="docutils literal notranslate"><span class="pre">method_name='genetic')</span></code> 3. Querying a KD tree (<code class="docutils literal notranslate"><span class="pre">method_name='kdtree')</span></code></p>
<p>We use scikit-learn models for demonstration.</p>
<section id="1.-Independent-random-sampling-of-features">
<h2>1. Independent random sampling of features<a class="headerlink" href="#1.-Independent-random-sampling-of-features" title="Permalink to this heading"></a></h2>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># import DiCE
import dice_ml
from dice_ml.utils import helpers # helper functions
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import RandomForestClassifier
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%load_ext autoreload
%autoreload 2
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</div>
</div>
<section id="Loading-dataset">
<h3>Loading dataset<a class="headerlink" href="#Loading-dataset" title="Permalink to this heading"></a></h3>
<p>We use the “adult” income dataset from UCI Machine Learning Repository (<a class="reference external" href="https://archive.ics.uci.edu/ml/datasets/adult">https://archive.ics.uci.edu/ml/datasets/adult</a>). We transform the data as described in dice_ml.utils.helpers module.</p>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>age</th>
<th>workclass</th>
<th>education</th>
<th>marital_status</th>
<th>occupation</th>
<th>race</th>
<th>gender</th>
<th>hours_per_week</th>
<th>income</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>28</td>
<td>Private</td>
<td>Bachelors</td>
<td>Single</td>
<td>White-Collar</td>
<td>White</td>
<td>Female</td>
<td>60</td>
<td>0</td>
</tr>
<tr>
<th>1</th>
<td>30</td>
<td>Self-Employed</td>
<td>Assoc</td>
<td>Married</td>
<td>Professional</td>
<td>White</td>
<td>Male</td>
<td>65</td>
<td>1</td>
</tr>
<tr>
<th>2</th>
<td>32</td>
<td>Private</td>
<td>Some-college</td>
<td>Married</td>
<td>White-Collar</td>
<td>White</td>
<td>Male</td>
<td>50</td>
<td>0</td>
</tr>
<tr>
<th>3</th>
<td>20</td>
<td>Private</td>
<td>Some-college</td>
<td>Single</td>
<td>Service</td>
<td>White</td>
<td>Female</td>
<td>35</td>
<td>0</td>
</tr>
<tr>
<th>4</th>
<td>41</td>
<td>Self-Employed</td>
<td>Some-college</td>
<td>Married</td>
<td>White-Collar</td>
<td>White</td>
<td>Male</td>
<td>50</td>
<td>0</td>
</tr>
</tbody>
</table>
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</pre></div>
</div>
</div>
</section>
<section id="Training-a-custom-ML-model">
<h3>Training a custom ML model<a class="headerlink" href="#Training-a-custom-ML-model" title="Permalink to this heading"></a></h3>
<p>Below, we build an ML model using scikit-learn to demonstrate how our methods can work with any sklearn model.</p>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>target = dataset["income"]
# Split data into train and test
datasetX = dataset.drop("income", axis=1)
x_train, x_test, y_train, y_test = train_test_split(datasetX,
target,
test_size=0.2,
random_state=0,
stratify=target)
numerical = ["age", "hours_per_week"]
categorical = x_train.columns.difference(numerical)
# We create the preprocessing pipelines for both numeric and categorical data.
numeric_transformer = Pipeline(steps=[
('scaler', StandardScaler())])
categorical_transformer = Pipeline(steps=[
('onehot', OneHotEncoder(handle_unknown='ignore'))])
transformations = ColumnTransformer(
transformers=[
('num', numeric_transformer, numerical),
('cat', categorical_transformer, categorical)])
# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(steps=[('preprocessor', transformations),
('classifier', RandomForestClassifier())])
model = clf.fit(x_train, y_train)
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># provide the trained ML model to DiCE's model object
backend = 'sklearn'
m = dice_ml.Model(model=model, backend=backend)
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</div>
</div>
</section>
<section id="Generate-diverse-counterfactuals">
<h3>Generate diverse counterfactuals<a class="headerlink" href="#Generate-diverse-counterfactuals" title="Permalink to this heading"></a></h3>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># initiate DiCE
exp_random = dice_ml.Dice(d, m, method="random")
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># generate counterfactuals
dice_exp_random = exp_random.generate_counterfactuals(query_instances, total_CFs=2, desired_class="opposite", verbose=False)
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100%|█████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.98it/s]
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<th>marital_status</th>
<th>occupation</th>
<th>race</th>
<th>gender</th>
<th>hours_per_week</th>
<th>income</th>
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Diverse Counterfactual set (new outcome: 1)
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<th>marital_status</th>
<th>occupation</th>
<th>race</th>
<th>gender</th>
<th>hours_per_week</th>
<th>income</th>
</tr>
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<td>-</td>
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<td>-</td>
<td>-</td>
<td>1</td>
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<td>43</td>
<td>-</td>
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<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>-</td>
<td>1</td>
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<th>gender</th>
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<td>Self-Employed</td>
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<td>Other</td>
<td>Male</td>
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Diverse Counterfactual set (new outcome: 0)
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<th>marital_status</th>
<th>occupation</th>
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<th>gender</th>
<th>hours_per_week</th>
<th>income</th>
</tr>
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<td>-</td>
<td>-</td>
<td>0</td>
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</tbody>
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</div>
<p>It can be observed that the random sampling method produces less sparse CFs in contrast to current DiCE’s implementation. The sparsity issue with random sampling worsens with increasing <em>total_CFs</em></p>
<p>Further, different sets of counterfactuals can be generated with different random seeds.</p>
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# default random seed is 17
dice_exp_random = exp_random.generate_counterfactuals(query_instances,
total_CFs=4,
desired_class="opposite",
random_seed=9)
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100%|█████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2.49it/s]
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<table border="1" class="dataframe">
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<th>hours_per_week</th>
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