{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30b22730",
   "metadata": {},
   "source": [
    "# Clutering Evalutation\n",
    "\n",
    "\n",
    "Today, we will learn how to  evaluate if a clustering model was effective or not.  In the process, we will also learn how to determine if we have the right number of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76bf2492",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ed28959",
   "metadata": {},
   "source": [
    "Extract out the features to `iris_X`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4dfc2b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df = sns.load_dataset('iris')\n",
    "iris_X = iris_df.drop(columns=['species'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5743a6da",
   "metadata": {},
   "source": [
    "First we will fit the model with the correct number of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9845d3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km3 = KMeans(n_clusters=3)\n",
    "km3.fit(iris_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95424580",
   "metadata": {},
   "source": [
    "When we use the fit method, it does not return the predictions, but it does store them in the estimator object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0db355a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 2, 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
       "       2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km3.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3bb6e55d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km2 = KMeans(n_clusters=2)\n",
    "km2.fit(iris_X)\n",
    "km4 = KMeans(n_clusters=4)\n",
    "km4.fit(iris_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baed5182",
   "metadata": {},
   "source": [
    "How do we tell if this is good?\n",
    "\n",
    "One way to intuitively think about a good clustering solution is that every point should be close to points in the same cluster and far from points in other clusters. By definition with Kmeans, they will always be clos*er* to points in the same cluster, but we also what that the clusters aren't just touching, but actually spaced apart, if the clustering actually captures meaningfully different groups.\n",
    "\n",
    "## Silhouette Score\n",
    "\n",
    "\n",
    "The [Silhouette score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html) computes a ratio of how close points are to points in the same cluster vs other clusters.\n",
    "\n",
    "$$ s = \\frac{b-a}{max(a,b)}$$\n",
    "\n",
    "\n",
    "a: The mean distance between a sample and all other points in the same class.\n",
    "\n",
    "\n",
    "b: The mean distance between a sample and all other points in the next nearest cluster.\n",
    "\n",
    "We can calculate this score for each point and get the average.\n",
    "\n",
    "If the cluster is really tight, all of the points are close, then a will be small.  \n",
    "If the clusters are really far apart, b will be large.\n",
    "If both of these are true then b-a will be close to the value of b and the denominator will be b, so the score will be 1.\n",
    "\n",
    "If the clusters are spread out and close together, then a and be will be close in value, and the s will be close 0. These are overlapping clusters, or possibly too may clusters for this data.\n",
    "\n",
    "\n",
    "\n",
    "Let's check our clustering solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0bae0728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5528190123564101"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.silhouette_score(iris_X, km3.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c5cae6",
   "metadata": {},
   "source": [
    "This looks pretty good. But we can also compare it for the other two models that we fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc834047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6810461692117467"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.silhouette_score(iris_X, km2.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2243a7d",
   "metadata": {},
   "source": [
    "We see in this case this actually fits better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8208b344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4974551890173759"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.silhouette_score(iris_X, km4.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9d258f",
   "metadata": {},
   "source": [
    "and 4 fits worse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c1fcd2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fe46ca5e700>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall22/BrownFall22/_build/jupyter_execute/notes/2022-11-04_16_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185ffbf5",
   "metadata": {},
   "source": [
    "If we look at the data, we see that it does look somewhat more like two clusters than 4.  \n",
    "\n",
    "\n",
    "\n",
    "## What's a good Silhouette Score?\n",
    "\n",
    "To think through what a good silhouette score is, we can apply the score to data that represents different scenarios.\n",
    "\n",
    "First, I'll re-sample some data like we used on Monday, but instead of applying K-means and checking the score of the actual algorithm, we'll add a\n",
    "few different scenarios and add that score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95c18a9c",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'string' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m K \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\n\u001b[1;32m      2\u001b[0m N \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m200\u001b[39m\n\u001b[0;32m----> 3\u001b[0m classes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mstring\u001b[49m\u001b[38;5;241m.\u001b[39mascii_uppercase[:K])\n\u001b[1;32m      4\u001b[0m mu \u001b[38;5;241m=\u001b[39m {c: i \u001b[38;5;28;01mfor\u001b[39;00m c, i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(classes,[[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m], [\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m6\u001b[39m], [\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m6\u001b[39m],[\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m2\u001b[39m]])}\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# sample random cluster assignments\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'string' is not defined"
     ]
    }
   ],
   "source": [
    "K = 4\n",
    "N = 200\n",
    "classes = list(string.ascii_uppercase[:K])\n",
    "mu = {c: i for c, i in zip(classes,[[2,2], [6,6], [2,6],[6,2]])}\n",
    "\n",
    "\n",
    "# sample random cluster assignments\n",
    "target = np.random.choice(classes,N)\n",
    "\n",
    "# sample points with different means according to those assignments\n",
    "data = [np.random.multivariate_normal(mu[c],.25*np.eye(2)) for c in target]\n",
    "feature_names = ['x' + str(i) for i in range(2)]\n",
    "df = pd.DataFrame(data = data,columns = feature_names)\n",
    "\n",
    "# save the true assignments\n",
    "df['true'] = target\n",
    "\n",
    "# random assignments, right number of clusters\n",
    "df['random'] = np.random.choice(classes,N)\n",
    "\n",
    "# random assignments, way too many clusters\n",
    "charsK4 = list(string.ascii_uppercase[:K*4])\n",
    "df['random10'] = np.random.choice(charsK4,N)\n",
    "\n",
    "# Kmeans with 2x number of clusters\n",
    "kmrK2 = KMeans(K*2)\n",
    "kmK2.fit(df[['x0','x1']])\n",
    "df['km' + str(K*2)] = kmK2.labels_\n",
    "\n",
    "# assign every point to its own cluster\n",
    "df['id'] = list(range(N))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f1b9e0b6",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m assignment_cols \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrue\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom10\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkm8\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 3\u001b[0m s \u001b[38;5;241m=\u001b[39m [metrics\u001b[38;5;241m.\u001b[39msilhouette_score(iris_X, df[col]) \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m assignment_cols]\n\u001b[1;32m      5\u001b[0m pd\u001b[38;5;241m.\u001b[39mSeries(data \u001b[38;5;241m=\u001b[39m s,index\u001b[38;5;241m=\u001b[39massignment_cols)\n",
      "Cell \u001b[0;32mIn[11], line 3\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m assignment_cols \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrue\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrandom10\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkm8\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 3\u001b[0m s \u001b[38;5;241m=\u001b[39m [metrics\u001b[38;5;241m.\u001b[39msilhouette_score(iris_X, \u001b[43mdf\u001b[49m[col]) \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m assignment_cols]\n\u001b[1;32m      5\u001b[0m pd\u001b[38;5;241m.\u001b[39mSeries(data \u001b[38;5;241m=\u001b[39m s,index\u001b[38;5;241m=\u001b[39massignment_cols)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "assignment_cols = ['true','random', 'random10','km8','id']\n",
    "\n",
    "s = [metrics.silhouette_score(iris_X, df[col]) for col in assignment_cols]\n",
    "\n",
    "pd.Series(data = s,index=assignment_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ebce43d1",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m [sns\u001b[38;5;241m.\u001b[39mpairplot(data \u001b[38;5;241m=\u001b[39mdf, hue\u001b[38;5;241m=\u001b[39mcol, \u001b[38;5;28mvars\u001b[39m\u001b[38;5;241m=\u001b[39m feature_names) \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m assignment_cols]\n",
      "Cell \u001b[0;32mIn[12], line 1\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m [sns\u001b[38;5;241m.\u001b[39mpairplot(data \u001b[38;5;241m=\u001b[39m\u001b[43mdf\u001b[49m, hue\u001b[38;5;241m=\u001b[39mcol, \u001b[38;5;28mvars\u001b[39m\u001b[38;5;241m=\u001b[39m feature_names) \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m assignment_cols]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "[sns.pairplot(data =df, hue=col, vars= feature_names) for col in assignment_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1522203",
   "metadata": {},
   "source": [
    "## Mutual Information\n",
    "\n",
    "\n",
    "````{margin}\n",
    "```{admonition} Further Reading\n",
    "Sklearn proides many [mutual information based scores](https://scikit-learn.org/stable/modules/clustering.html#mutual-information-based-scores). See the user guide for definitions of each, pros and cons and examples.\n",
    "```\n",
    "````\n",
    "When we know the truth, we can see if the learned clusters are related to the true groups, we can't compare them like accuracy but we can use a metric that is intuitively like a correlation for categorical variables, the mutual information.\n",
    "\n",
    "Formally mutual information uses the joint distribution between the true labels and the cluster assignments to see how much they co-occur.  If they're the exact same, then they have maximal mutual information.  If they're completely and independent, then they have 0 mutual information.\n",
    "Mutual information is related to entropy in physics.\n",
    "\n",
    "The [`mutual_info_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html#sklearn.metrics.mutual_info_score) method in the `metrics` module computes mutual information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38700345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5738298777087831"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mutual_info_score(iris_df['species'],km2.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "43e30938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8255910976103357"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mutual_info_score(iris_df['species'],km3.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aa3bee3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8846414796605857"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mutual_info_score(iris_df['species'],km4.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff92d3c3",
   "metadata": {},
   "source": [
    "When we know the truth, we can see if the learned clusters are related to the true groups, we can't compare them like accuracy but we can use a metric that is intuitively like a correlation for categorical variables, the mutual information.\n",
    "\n",
    "The `adjusted_mutual_info_score` methos in the `metrics` module computes a version of mutual information that is normalized to have good properties. Apply that to the two different clustering solutions and to a solution for K=4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1fc3140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7551191675800486"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.adjusted_mutual_info_score(iris_df['species'],km3.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a1d834d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.653838071376278"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.adjusted_mutual_info_score(iris_df['species'],km2.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bed9ffbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7151742230795869"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.adjusted_mutual_info_score(iris_df['species'],km4.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dd8a0d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4eafab18",
   "metadata": {},
   "source": [
    "## Questions After Class\n",
    "\n",
    "### If the silhouette score is 0 does it mean that both clusters are the almost the same?\n",
    "\n",
    "Yes\n",
    "\n",
    "### Are there any other scores to look at to determine best number of clusters?\n",
    "\n",
    "Any of the scores can be used in that way.  And, next week, we will learn how to optimize models."
   ]
  }
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