docs/source/Quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "23ccb3d3-fcff-4077-bd05-c3d1d6ee01aa",
   "metadata": {},
   "source": [
    "# Quickstart "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450ed0f3-48cc-4205-99b0-91a385beb501",
   "metadata": {},
   "source": [
    "This notebook serves as a short tutorial on how to use the **clusterxplain** package for calculating feature importance values. We will therefore use the [iris](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html) dataset form [scikit-learn](https://scikit-learn.org/stable/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9128c97-16b9-4c40-bf42-7cab28d43f99",
   "metadata": {},
   "source": [
    "## Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c2dd0a28-9a91-4f9b-a611-34126dfb114b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans\n",
    "from cxplain.xkm import XkmExplainer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195017a5-0493-4807-a948-20c3a14258b1",
   "metadata": {},
   "source": [
    "## Fit a k-means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "356a27d6-5ec3-4656-8c2e-c1e249837a6d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2,\n",
       "       2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load iris data\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# set params\n",
    "n_clusters = 3\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "n_obs = X.shape[0]\n",
    "n_features = X.shape[1]\n",
    "feature_names = iris.feature_names\n",
    "\n",
    "# fit Kmeans\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=3, n_init=10).fit(X)\n",
    "cluster_centers = kmeans.cluster_centers_\n",
    "predictions = kmeans.predict(X)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c57c820-3c36-411e-a85b-fd1491656b05",
   "metadata": {},
   "source": [
    "## Explaining the Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e44dd374-da67-4633-a20b-8e60ddaf45fb",
   "metadata": {},
   "source": [
    "In order to explain the clustering produced above **clusterxplain** offers various *Explainers*:\n",
    "\n",
    "- `XkmExplainer`\n",
    "- `DecisionTreeExplainer`\n",
    "- `RandomForestExplainer`\n",
    "- `ExKMCExplainer`\n",
    "- `ShapExplainer`\n",
    "- `NeonExplainer`\n",
    "- `GradientExplainer`\n",
    "\n",
    "All explainers expose the same API by having a `fit()`, `explain()`, and `fit_explain()` method implemented. The `explain()` or `fit_explain()` return an instance of the `ExplainedClustering` class, which holds all calculated feature importance values and some convenience functions for plotting.\n",
    "\n",
    "This tutorial will showcase the *XKM* method, but every other explainer works the same. \n",
    "\n",
    "Note that not all explainers calculate point-wise and cluster-wise feature importances, only global ones. `XkmExplainer`, `ShapExplainer`, `NeonExplainer` and `GradientExplainer` calculate all three kinds of importance values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4f2a4e-3c3a-47c6-8c2c-e1edc418fa49",
   "metadata": {},
   "source": [
    "Note that the `XkmExplainer` comes in two flavours, namely `\"next_best\"` or `\"all\"`. Both flavours are described in (TBD). Basically `\"next_best\"` uses only the the distance to closest cluster on a feture dimension to calculate importance values. whereas `\"all\"` uses the distances to all clusters. Additionally you have to specify which metric you used for clustering, in our case of ordinary k-means this corresponds to `\"euclidean\"`, but you could also choose `\"manhatten\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "64cc4c75-83fd-4d66-bcac-565813d6dcfd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# fit the explainer and calculate feature importances\n",
    "explained_clustering = XkmExplainer(data=X, cluster_centers=cluster_centers, flavour=\"next_best\", distance_metric=\"euclidean\", cluster_predictions=predictions).fit_explain()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c985b5-b1b6-49d5-922e-21053763729d",
   "metadata": {},
   "source": [
    "The resulting instance of the `ExplainedClustering` class can then be used to plot the calculated importance values. You can also retrieve the importance values as dataframe by the property `explained_clustering.pointwise_relevance_df`,`explained_clustering.cluster_relevance_df`, `explained_clustering.global_relevance_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c48f0a0-6be1-4b1e-83b9-01809827f283",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot pointwise relevances for observations 1-10\n",
    "explained_clustering.show_pointwise_relevance(subset_index=list(range(10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4332e672-ab6a-43bd-b7f3-031be414d885",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot cluster-wise feature importances\n",
    "explained_clustering.show_cluster_relevance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2dabe084-bf21-413c-a4f8-ed548ba4722f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot global feature importances\n",
    "explained_clustering.show_global_relevance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6613dd20-b4e1-4a2e-aac7-882f69689b0d",
   "metadata": {
    "tags": []
   },
   "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>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assigned_clusters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.686290</td>\n",
       "      <td>0.221374</td>\n",
       "      <td>0.992866</td>\n",
       "      <td>0.980448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.328449</td>\n",
       "      <td>0.246249</td>\n",
       "      <td>0.711564</td>\n",
       "      <td>0.578215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.537812</td>\n",
       "      <td>0.084899</td>\n",
       "      <td>0.754957</td>\n",
       "      <td>0.621068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         R1        R2        R3        R4\n",
       "assigned_clusters                                        \n",
       "0                  0.686290  0.221374  0.992866  0.980448\n",
       "1                  0.328449  0.246249  0.711564  0.578215\n",
       "2                  0.537812  0.084899  0.754957  0.621068"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve cluster-wise importance values as df\n",
    "explained_clustering.cluster_relevance_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cxplain",
   "language": "python",
   "name": "cxplain"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}