Merge branch 'main' into gaugup/AddSupport3.12

.github/workflows/python-publish.yml

@@ -1,18 +1,17 @@
-# This workflow will upload a Python Package using Twine when a release is created
-# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
-
-name: Upload Python Package
-
 on:
   release:
     types: [created]
   workflow_dispatch:
 
 jobs:
-  deploy:
-
+  pypi-publish:
+    name: upload release to PyPI
     runs-on: ubuntu-latest
-
+    # Specifying a GitHub environment is optional, but strongly encouraged
+    environment: release
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
     steps:
     - uses: actions/checkout@v3
     - name: Set up Python
@@ -24,9 +23,7 @@ jobs:
         python -m pip install --upgrade pip
         pip install setuptools wheel twine
     - name: Build and publish
-      env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
       run: |
         python setup.py sdist bdist_wheel
-        twine upload dist/*
+    - name: Publish package distributions to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1

dice_ml/data_interfaces/base_data_interface.py

@@ -2,6 +2,7 @@
 
 from abc import ABC, abstractmethod
 
+import pandas as pd
 from raiutils.exceptions import UserConfigValidationException
 
 from dice_ml.utils.exception import SystemException
@@ -71,6 +72,31 @@ def _validate_and_set_permitted_range(self, params, features_dict=None):
                     )
         self.permitted_range, _ = self.get_features_range(input_permitted_range, features_dict)
 
+    def ensure_consistent_type(self, output_df, query_instance):
+        qdf = self.query_instance_to_df(query_instance)
+        output_df = output_df.astype(qdf.dtypes.to_dict())
+        return output_df
+
+    def query_instance_to_df(self, query_instance):
+        if isinstance(query_instance, list):
+            if isinstance(query_instance[0], dict):  # prepare a list of query instances
+                test = pd.DataFrame(query_instance, columns=self.feature_names)
+
+            else:  # prepare a single query instance in list
+                query_instance = {'row1': query_instance}
+                test = pd.DataFrame.from_dict(
+                    query_instance, orient='index', columns=self.feature_names)
+
+        elif isinstance(query_instance, dict):
+            test = pd.DataFrame({k: [v] for k, v in query_instance.items()}, columns=self.feature_names)
+
+        elif isinstance(query_instance, pd.DataFrame):
+            test = query_instance.copy()
+
+        else:
+            raise ValueError("Query instance should be a dict, a pandas dataframe, a list, or a list of dicts")
+        return test
+
     @abstractmethod
     def __init__(self, params):
         """The init method needs to be implemented by the inherting classes."""

dice_ml/data_interfaces/private_data_interface.py

@@ -351,8 +351,7 @@ def prepare_df_for_ohe_encoding(self):
 
         return df
 
-    def prepare_query_instance(self, query_instance):
-        """Prepares user defined test input(s) for DiCE."""
+    def query_instance_to_df(self, query_instance):
         if isinstance(query_instance, list):
             if isinstance(query_instance[0], dict):  # prepare a list of query instances
                 test = pd.DataFrame(query_instance, columns=self.feature_names)
@@ -370,7 +369,11 @@ def prepare_query_instance(self, query_instance):
 
         else:
             raise ValueError("Query instance should be a dict, a pandas dataframe, a list, or a list of dicts")
+        return test
 
+    def prepare_query_instance(self, query_instance):
+        """Prepares user defined test input(s) for DiCE."""
+        test = self.query_instance_to_df(query_instance)
         test = test.reset_index(drop=True)
         return test
 

dice_ml/data_interfaces/public_data_interface.py

@@ -451,24 +451,7 @@ def prepare_df_for_ohe_encoding(self):
 
     def prepare_query_instance(self, query_instance):
         """Prepares user defined test input(s) for DiCE."""
-        if isinstance(query_instance, list):
-            if isinstance(query_instance[0], dict):  # prepare a list of query instances
-                test = pd.DataFrame(query_instance, columns=self.feature_names)
-
-            else:  # prepare a single query instance in list
-                query_instance = {'row1': query_instance}
-                test = pd.DataFrame.from_dict(
-                    query_instance, orient='index', columns=self.feature_names)
-
-        elif isinstance(query_instance, dict):
-            test = pd.DataFrame({k: [v] for k, v in query_instance.items()}, columns=self.feature_names)
-
-        elif isinstance(query_instance, pd.DataFrame):
-            test = query_instance.copy()
-
-        else:
-            raise ValueError("Query instance should be a dict, a pandas dataframe, a list, or a list of dicts")
-
+        test = self.query_instance_to_df(query_instance)
         test = test.reset_index(drop=True)
         # encode categorical and numerical columns
         test = self._set_feature_dtypes(test,

dice_ml/explainer_interfaces/explainer_base.py

@@ -194,6 +194,14 @@ def generate_counterfactuals(self, query_instances, total_CFs,
                 posthoc_sparsity_algorithm=posthoc_sparsity_algorithm,
                 verbose=verbose,
                 **kwargs)
+            res.test_instance_df = self.data_interface.ensure_consistent_type(
+                    res.test_instance_df, query_instance)
+            if res.final_cfs_df is not None and len(res.final_cfs_df) > 0:
+                res.final_cfs_df = self.data_interface.ensure_consistent_type(
+                    res.final_cfs_df, query_instance)
+            if res.final_cfs_df_sparse is not None and len(res.final_cfs_df_sparse) > 0:
+                res.final_cfs_df_sparse = self.data_interface.ensure_consistent_type(
+                    res.final_cfs_df_sparse, query_instance)
             cf_examples_arr.append(res)
         self._check_any_counterfactuals_computed(cf_examples_arr=cf_examples_arr)
 

dice_ml/utils/helpers.py

@@ -138,7 +138,17 @@ def load_outcome_not_last_column_dataset():
 
 
 def load_custom_testing_dataset_binary():
-    data = [['a', 1, 0], ['b', 5, 1], ['c', 2, 0], ['a', 3, 0], ['c', 4, 1]]
+    data = [
+        ['a', 1, 0],
+        ['b', 5, 1],
+        ['c', 2, 0],
+        ['a', 3, 0],
+        ['c', 4, 1],
+        ['c', 10, 0],
+        ['a', 7, 0],
+        ['c', 8, 1],
+        ['b', 10, 1],
+    ]
     return pd.DataFrame(data, columns=['Categorical', 'Numerical', 'Outcome'])
 
 

docs/_modules/dice_ml/constants.html

@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>dice_ml.constants &mdash; DiCE 0.9 documentation</title>
+  <title>dice_ml.constants &mdash; DiCE 0.11 documentation</title>
       <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -50,8 +50,6 @@
 <li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_model_agnostic_CFs.html">Generating counterfactual explanations with any ML model</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_with_private_data.html">Generating counterfactual explanations without access to training data</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_with_advanced_options.html">Advanced options to customize Counterfactual Explanations</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_getting_started_feasible.html">Generate feasible counterfactual explanations using a VAE</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_getting_started_feasible.html#Adding-feasibility-constraints">Adding feasibility constraints</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Package:</span></p>
 <ul>

docs/_modules/dice_ml/counterfactual_explanations.html

@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>dice_ml.counterfactual_explanations &mdash; DiCE 0.10 documentation</title>
+  <title>dice_ml.counterfactual_explanations &mdash; DiCE 0.11 documentation</title>
       <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>

docs/_modules/dice_ml/data.html

@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>dice_ml.data &mdash; DiCE 0.9 documentation</title>
+  <title>dice_ml.data &mdash; DiCE 0.11 documentation</title>
       <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
       <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
   <!--[if lt IE 9]>
@@ -50,8 +50,6 @@
 <li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_model_agnostic_CFs.html">Generating counterfactual explanations with any ML model</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_with_private_data.html">Generating counterfactual explanations without access to training data</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_with_advanced_options.html">Advanced options to customize Counterfactual Explanations</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_getting_started_feasible.html">Generate feasible counterfactual explanations using a VAE</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../notebooks/DiCE_getting_started_feasible.html#Adding-feasibility-constraints">Adding feasibility constraints</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Package:</span></p>
 <ul>
@@ -95,23 +93,23 @@ <h1>Source code for dice_ml.data</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="Data"><a class="viewcode-back" href="../../dice_ml.html#dice_ml.data.Data">[docs]</a><span class="k">class</span> <span class="nc">Data</span><span class="p">(</span><span class="n">_BaseData</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Class containing all required information about the data for DiCE.&quot;&quot;&quot;</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Class containing all required information about the data for DiCE.&quot;&quot;&quot;</span>
 
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Init method</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Init method</span>
 
 <span class="sd">        :param **params: a dictionary of required parameters.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">decide_implementation_type</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
 
 <div class="viewcode-block" id="Data.decide_implementation_type"><a class="viewcode-back" href="../../dice_ml.html#dice_ml.data.Data.decide_implementation_type">[docs]</a>    <span class="k">def</span> <span class="nf">decide_implementation_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Decides if the Data class is for public or private data.&quot;&quot;&quot;</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Decides if the Data class is for public or private data.&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span> <span class="o">=</span> <span class="n">decide</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">params</span><span class="p">)</span></div></div>
 
 
 <div class="viewcode-block" id="decide"><a class="viewcode-back" href="../../dice_ml.html#dice_ml.data.decide">[docs]</a><span class="k">def</span> <span class="nf">decide</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Decides if the Data class is for public or private data.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Decides if the Data class is for public or private data.</span>
 
 <span class="sd">    To add new implementations of Data, add the class in data_interfaces</span>
 <span class="sd">    subpackage and import-and-return the class in an elif loop as shown</span>