examples/PDP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "230ce0fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -r ../requirements-examples.txt --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4c8a080",
   "metadata": {},
   "outputs": [],
   "source": [
    "from trustyai.model import feature, PredictionInput\n",
    "from trustyai.utils import TestModels\n",
    "from trustyai.explainers.pdp import PDPExplainer\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d66ee0",
   "metadata": {},
   "source": [
    "Initialize a simple regression model that accepts numerical features, sums up their values except for one of them, defined by the user (_feature-0_ in this case).\n",
    "Also, a set of _100_ inputs with randomly picked integers (between _-100_ and _100_) is generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "551b8be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TestModels.getSumSkipModel(0)\n",
    "\n",
    "no_of_features = 5\n",
    "data = []\n",
    "for i in range(100):\n",
    "    data.append(PredictionInput([feature(name=f\"f-num{i}\", value=np.random.randint(-100, 100), dtype=\"number\") for i in range(no_of_features)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c1e99c",
   "metadata": {},
   "source": [
    "Initialize _TrustyAI_ *Partial Dependence Plot Explainer*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "074e91f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdp_explainer = PDPExplainer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142ae026",
   "metadata": {},
   "source": [
    "Generate the partial dependence results on the model and generated data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a77ea32",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdp_results = pdp_explainer.explain(model, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0049f026",
   "metadata": {},
   "source": [
    "Visualize the partial dependence information in a `pd.DataFrame`.\n",
    "The resulting table contains feature values and each feature name as columns and marginal per-feature outputs as rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7d7b739",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>-97.0</th>\n",
       "      <th>-96.0</th>\n",
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       "      <th>-64.0</th>\n",
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       "      <td>-31.96</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>-118.28</td>\n",
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       "      <td>-110.28</td>\n",
       "      <td>-100.28</td>\n",
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       "      <td>-91.28</td>\n",
       "      <td>-85.28</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-94.29</td>\n",
       "      <td>-88.29</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-116.43</td>\n",
       "      <td>-112.43</td>\n",
       "      <td>-111.43</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-104.88</td>\n",
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       "      <td>-41.88</td>\n",
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       "      <td>0.12</td>\n",
       "      <td>19.12</td>\n",
       "      <td>20.12</td>\n",
       "      <td>21.12</td>\n",
       "      <td>39.12</td>\n",
       "      <td>46.12</td>\n",
       "      <td>49.12</td>\n",
       "      <td>57.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 155 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    -97.0   -96.0   -92.0   -91.0   -89.0   -79.0  -76.0   -75.0  -70.0  \\\n",
       "0  -31.96  -31.96  -31.96  -31.96  -31.96  -31.96 -31.96  -31.96 -31.96   \n",
       "1 -118.28     NaN -113.28     NaN -110.28 -100.28    NaN     NaN -91.28   \n",
       "2     NaN     NaN     NaN     NaN     NaN     NaN    NaN     NaN -94.29   \n",
       "3     NaN -116.43 -112.43 -111.43     NaN  -99.43    NaN     NaN    NaN   \n",
       "4     NaN     NaN     NaN -120.88     NaN     NaN    NaN -104.88 -99.88   \n",
       "\n",
       "   -64.0  ...  -12.0   15.0   30.0   49.0   50.0   51.0   69.0   76.0   79.0  \\\n",
       "0 -31.96  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1 -85.28  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2 -88.29  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "3 -84.43  ...    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "4    NaN  ... -41.88 -14.88   0.12  19.12  20.12  21.12  39.12  46.12  49.12   \n",
       "\n",
       "    87.0  \n",
       "0    NaN  \n",
       "1    NaN  \n",
       "2    NaN  \n",
       "3    NaN  \n",
       "4  57.12  \n",
       "\n",
       "[5 rows x 155 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdp_results.as_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d67ffd5",
   "metadata": {},
   "source": [
    "Plot the partial dependence information for each feature.\n",
    "The ignored feature is expected to have a constant output, regardless of its input value, because the model ignores it.\n",
    "Partial dependence for all other features will linearly increase with as the input value becomes bigger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ee0dfd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_results.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6090ee4c",
   "metadata": {},
   "source": [
    "Generate some additional random data, using _sklearn_ library, that can be more suited for classification tasks.\n",
    "Features are still numerical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e3898c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_random_dataframe_classifier():\n",
    "    X, _ = make_classification(n_samples=5000, n_features=5, n_classes=2,\n",
    "                               n_clusters_per_class=2, class_sep=2, flip_y=0, random_state=23)\n",
    "\n",
    "    return pd.DataFrame({\n",
    "        'x1': X[:, 0],\n",
    "        'x2': X[:, 1],\n",
    "        'x3': X[:, 2],\n",
    "        'x4': X[:, 3],\n",
    "        'x5': X[:, 4],\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73a5e496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.124064</td>\n",
       "      <td>1.608138</td>\n",
       "      <td>-0.404920</td>\n",
       "      <td>0.942188</td>\n",
       "      <td>-2.754050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.048919</td>\n",
       "      <td>-2.428100</td>\n",
       "      <td>0.427166</td>\n",
       "      <td>-1.466329</td>\n",
       "      <td>4.053088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.241751</td>\n",
       "      <td>-2.109687</td>\n",
       "      <td>-0.410332</td>\n",
       "      <td>-1.386671</td>\n",
       "      <td>3.250637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.299680</td>\n",
       "      <td>-1.386333</td>\n",
       "      <td>0.149882</td>\n",
       "      <td>-0.406165</td>\n",
       "      <td>3.351016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.226471</td>\n",
       "      <td>-0.992053</td>\n",
       "      <td>-0.475162</td>\n",
       "      <td>-0.327913</td>\n",
       "      <td>2.308332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1        x2        x3        x4        x5\n",
       "0 -2.124064  1.608138 -0.404920  0.942188 -2.754050\n",
       "1  3.048919 -2.428100  0.427166 -1.466329  4.053088\n",
       "2  2.241751 -2.109687 -0.410332 -1.386671  3.250637\n",
       "3  3.299680 -1.386333  0.149882 -0.406165  3.351016\n",
       "4  2.226471 -0.992053 -0.475162 -0.327913  2.308332"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = create_random_dataframe_classifier()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8baf4499",
   "metadata": {},
   "source": [
    "Initialize a simple classifier that performs linear combination of its weights and input features.\n",
    "The model returns `true` whether the output is bigger than a predetermined _threshold_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc2975cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_model = TestModels.getLinearThresholdModel([0.1, 0.2, 0.3, 0.4, 0.5], 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbef6f97",
   "metadata": {},
   "source": [
    "Generate results for partial dependence on the classification model and generated data and visualize them in a `pd.DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b98c2205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>-3.745417</th>\n",
       "      <th>-3.394707</th>\n",
       "      <th>-3.121121</th>\n",
       "      <th>-2.862176</th>\n",
       "      <th>-2.755705</th>\n",
       "      <th>-2.669231</th>\n",
       "      <th>-2.645467</th>\n",
       "      <th>-2.535498</th>\n",
       "      <th>-2.531641</th>\n",
       "      <th>-2.521502</th>\n",
       "      <th>...</th>\n",
       "      <th>2.915186</th>\n",
       "      <th>3.122571</th>\n",
       "      <th>3.277693</th>\n",
       "      <th>3.343708</th>\n",
       "      <th>3.582826</th>\n",
       "      <th>3.601798</th>\n",
       "      <th>3.626563</th>\n",
       "      <th>3.835093</th>\n",
       "      <th>4.640224</th>\n",
       "      <th>4.809713</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 501 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  -3.745417 -3.394707 -3.121121 -2.862176 -2.755705 -2.669231 -2.645467  \\\n",
       "0     false     false     false     false     false     false     false   \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "  -2.535498 -2.531641 -2.521502  ...  2.915186  3.122571  3.277693  3.343708  \\\n",
       "0     false     false     false  ...       NaN       NaN       NaN       NaN   \n",
       "1       NaN       NaN       NaN  ...       NaN       NaN       NaN       NaN   \n",
       "2       NaN       NaN       NaN  ...       NaN       NaN       NaN       NaN   \n",
       "3       NaN       NaN       NaN  ...       NaN       NaN       NaN       NaN   \n",
       "4       NaN       NaN       NaN  ...      true      true      true      true   \n",
       "\n",
       "   3.582826  3.601798  3.626563  3.835093  4.640224  4.809713  \n",
       "0       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "1       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "3       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "4      true      true      true      true      true      true  \n",
       "\n",
       "[5 rows x 501 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_pdp_results = pdp_explainer.explain(class_model, df)\n",
    "class_pdp_results.as_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852ff2bf",
   "metadata": {},
   "source": [
    "Plot the partial dependence information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00dd2870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_pdp_results.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}