{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f0bd6c66",
   "metadata": {},
   "source": [
    "# Making Predictions in Generative Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1325d71d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score\n",
    "iris_df = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9213fdc4",
   "metadata": {},
   "source": [
    "We'll  load the data again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e60b4874",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a440266",
   "metadata": {},
   "source": [
    "Next we indicate the feature variables and target and split into test and train\n",
    "sets.  Well use 80% of the data for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f89bb691",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_vars = ['sepal_length', 'sepal_width','petal_length', 'petal_width',]\n",
    "target_var = 'species'\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris_df[feature_vars],\n",
    "                          iris_df[target_var], train_size=.8, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fc931ae",
   "metadata": {},
   "source": [
    "We can confirm the shape is as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d149a2f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c12d372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b3f5062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".8*150"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11a3854",
   "metadata": {},
   "source": [
    "Next we again initialize and fit the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ece7c7e3",
   "metadata": {},
   "outputs": [
    {
     "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>GaussianNB()</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\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c8cba16",
   "metadata": {},
   "source": [
    "We can compute predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73e33065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['virginica', 'versicolor', 'setosa', 'virginica', 'setosa',\n",
       "       'virginica', 'setosa', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'setosa', 'versicolor', 'versicolor', 'setosa',\n",
       "       'setosa', 'virginica', 'versicolor', 'setosa', 'setosa',\n",
       "       'virginica', 'setosa', 'setosa', 'versicolor', 'versicolor',\n",
       "       'setosa'], dtype='<U10')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = gnb.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231a8515",
   "metadata": {},
   "source": [
    "And score the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6c6eba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f4e7a4",
   "metadata": {},
   "source": [
    "We saw last week that when we fit the Gaussian Naive Bayes, it computes a mean\n",
    "$\\theta$ and variance $\\sigma$ and adds them to model parameters in attributes\n",
    "`gnb.theta_, gnb.sigma_`.\n",
    "\n",
    "When we use the predict method, it uses those parameters to calculate the\n",
    "likelihood of the sample according to a Gaussian distribution (normal) for each\n",
    "class and then calculates the probability of the sample belonging to each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ed52a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.63380783e-232, 2.18878438e-006, 9.99997811e-001],\n",
       "       [1.82640391e-082, 9.99998304e-001, 1.69618390e-006],\n",
       "       [1.00000000e+000, 7.10250510e-019, 3.65449801e-028],\n",
       "       [1.58508262e-305, 1.04649020e-006, 9.99998954e-001],\n",
       "       [1.00000000e+000, 8.59168655e-017, 4.22159374e-027],\n",
       "       [6.39815011e-321, 1.56450314e-010, 1.00000000e+000],\n",
       "       [1.00000000e+000, 1.09797313e-016, 5.30276557e-027],\n",
       "       [1.25122812e-146, 7.74052109e-001, 2.25947891e-001],\n",
       "       [5.34357526e-150, 9.07564955e-001, 9.24350453e-002],\n",
       "       [5.67261712e-093, 9.99882109e-001, 1.17891111e-004],\n",
       "       [2.38651144e-210, 5.29609631e-001, 4.70390369e-001],\n",
       "       [8.12047631e-132, 9.43762575e-001, 5.62374248e-002],\n",
       "       [5.25177109e-132, 9.98864361e-001, 1.13563851e-003],\n",
       "       [1.24498038e-139, 9.49838641e-001, 5.01613586e-002],\n",
       "       [4.08232760e-140, 9.88043864e-001, 1.19561365e-002],\n",
       "       [1.00000000e+000, 7.12837229e-019, 4.10162749e-029],\n",
       "       [4.19553996e-131, 9.87944980e-001, 1.20550201e-002],\n",
       "       [4.13286716e-111, 9.99942383e-001, 5.76167389e-005],\n",
       "       [1.00000000e+000, 2.24933112e-015, 3.63624519e-026],\n",
       "       [1.00000000e+000, 9.86750131e-016, 2.42355087e-025],\n",
       "       [1.85930865e-186, 1.66966805e-002, 9.83303319e-001],\n",
       "       [8.83060167e-130, 9.92757232e-001, 7.24276827e-003],\n",
       "       [1.00000000e+000, 4.26380344e-013, 4.34222344e-023],\n",
       "       [1.00000000e+000, 1.28045851e-016, 1.26708019e-027],\n",
       "       [2.43739221e-168, 1.83516225e-001, 8.16483775e-001],\n",
       "       [1.00000000e+000, 2.62431469e-018, 6.72573168e-029],\n",
       "       [1.00000000e+000, 3.20605389e-011, 1.52433420e-020],\n",
       "       [2.20964201e-110, 9.99291229e-001, 7.08771072e-004],\n",
       "       [1.39297338e-046, 9.99999972e-001, 2.81392389e-008],\n",
       "       [1.00000000e+000, 1.85943966e-013, 1.58833385e-023]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b17caa",
   "metadata": {},
   "source": [
    "These are hard to interpret as is, one option is to plot them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "77503c3c",
   "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>index</th>\n",
       "      <th>predicted_species</th>\n",
       "      <th>true_species</th>\n",
       "      <th>i,pred</th>\n",
       "      <th>i,true</th>\n",
       "      <th>correct</th>\n",
       "      <th>species</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0,virginica</td>\n",
       "      <td>0,virginica</td>\n",
       "      <td>True</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.633808e-232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>1,versicolor</td>\n",
       "      <td>1,versicolor</td>\n",
       "      <td>True</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.826404e-82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>2,setosa</td>\n",
       "      <td>2,setosa</td>\n",
       "      <td>True</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>3,virginica</td>\n",
       "      <td>3,virginica</td>\n",
       "      <td>True</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.585083e-305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>4,setosa</td>\n",
       "      <td>4,setosa</td>\n",
       "      <td>True</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index predicted_species true_species        i,pred        i,true  correct  \\\n",
       "0      0         virginica    virginica   0,virginica   0,virginica     True   \n",
       "1      1        versicolor   versicolor  1,versicolor  1,versicolor     True   \n",
       "2      2            setosa       setosa      2,setosa      2,setosa     True   \n",
       "3      3         virginica    virginica   3,virginica   3,virginica     True   \n",
       "4      4            setosa       setosa      4,setosa      4,setosa     True   \n",
       "\n",
       "  species    probability  \n",
       "0  setosa  1.633808e-232  \n",
       "1  setosa   1.826404e-82  \n",
       "2  setosa   1.000000e+00  \n",
       "3  setosa  1.585083e-305  \n",
       "4  setosa   1.000000e+00  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make the prbabilities into a dataframe labeled with classes & make the index a separate column\n",
    "prob_df = pd.DataFrame(data = gnb.predict_proba(X_test), columns = gnb.classes_ ).reset_index()\n",
    "# add the predictions\n",
    "prob_df['predicted_species'] = y_pred\n",
    "prob_df['true_species'] = y_test.values\n",
    "# for plotting, make a column that combines the index & prediction\n",
    "pred_text = lambda r: str( r['index']) + ',' + r['predicted_species']\n",
    "prob_df['i,pred'] = prob_df.apply(pred_text,axis=1)\n",
    "# same for ground truth\n",
    "true_text = lambda r: str( r['index']) + ',' + r['true_species']\n",
    "prob_df['correct'] = prob_df['predicted_species'] == prob_df['true_species']\n",
    "# a dd a column for which are correct\n",
    "prob_df['i,true'] = prob_df.apply(true_text,axis=1)\n",
    "prob_df_melted = prob_df.melt(id_vars =[ 'index', 'predicted_species','true_species','i,pred','i,true','correct'],value_vars = gnb.classes_,\n",
    "                             var_name = target_var, value_name = 'probability')\n",
    "prob_df_melted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412d40d8",
   "metadata": {},
   "source": [
    "Now we have a data frame where each rown is one the probability of one sample belonging to one class. So there's a total of `number_of_samples*number_of_classes` rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca226a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_df_melted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf840395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)*len(gnb.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49864d5b",
   "metadata": {},
   "source": [
    "One way to look at these is to, for each sample in the test set, make a bar chart of the probability it belongs to each class.  We added to the data frame information so that we can plot this with the true class in the title using `col = 'i,true'`\n",
    "\n",
    "````{margin}\n",
    "```{tip}\n",
    "I used `set_theme` to change both the fond size and the color palette.\n",
    "Seaborn has a [detailed guide](https://seaborn.pydata.org/tutorial/color_palettes.html#palette-tutorial)\n",
    "for choosing colors. The `colorblind` palette uses colors that are distinguishable under most common\n",
    "forms of color blindness.\n",
    "```\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a1767c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd4af264ca0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x2160 with 30 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-10-18_24_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(font_scale=2, palette= \"colorblind\")\n",
    "# plot a bar graph for each point labeled with the prediction\n",
    "sns.catplot(data =prob_df_melted, x = 'species', y='probability' ,col ='i,true',\n",
    "            col_wrap=5,kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfe1381",
   "metadata": {},
   "source": [
    "We see that most sampples have nearly all of their probability mass (all probabiilties in a distribution sum (or integrate if continuous) to 1, but a few samples are not.  \n",
    "\n",
    "```{admonition} Try it yourself\n",
    "Try adding a column that could change the headings to include an indicator of which are correct or not)\n",
    "```\n",
    "\n",
    "For now, we'll group and look at on average, what the distributions are for correct vs incorrect based on predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "75d89566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd4c038e9d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-10-18_26_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.25, palette= \"colorblind\")\n",
    "sns.catplot(data =prob_df_melted, x = 'species', y='probability' ,\n",
    "            col ='predicted_species',row ='correct', kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffcb0912",
   "metadata": {},
   "source": [
    "We see that the errors were all for versicolor, and on average the distribution is very uncertain for those samples.  Those samples are probably hard to distinguish. We could check by creating a data frame with the data and the information about predictions and correct values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "850d1743",
   "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>setosa</th>\n",
       "      <th>versicolor</th>\n",
       "      <th>virginica</th>\n",
       "      <th>predicted_species</th>\n",
       "      <th>true_species</th>\n",
       "      <th>i,pred</th>\n",
       "      <th>correct</th>\n",
       "      <th>i,true</th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.633808e-232</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>0,virginica</td>\n",
       "      <td>True</td>\n",
       "      <td>0,virginica</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.826404e-82</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>1,versicolor</td>\n",
       "      <td>True</td>\n",
       "      <td>1,versicolor</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          setosa  versicolor  virginica predicted_species true_species  \\\n",
       "0  1.633808e-232    0.000002   0.999998         virginica    virginica   \n",
       "1   1.826404e-82    0.999998   0.000002        versicolor   versicolor   \n",
       "\n",
       "         i,pred  correct        i,true  sepal_length  sepal_width  \\\n",
       "0   0,virginica     True   0,virginica           5.8          2.8   \n",
       "1  1,versicolor     True  1,versicolor           6.0          2.2   \n",
       "\n",
       "   petal_length  petal_width  \n",
       "0           5.1          2.4  \n",
       "1           4.0          1.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_data_df = pd.concat([prob_df,X_test.reset_index()],axis=1).drop(columns=['index'])\n",
    "prob_data_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a94dced9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "03dc0853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fd4ae680820>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1189.25x1000 with 20 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-10-18_30_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(prob_data_df,x_vars=feature_vars,y_vars= feature_vars,hue='true_species')\n",
    "\n",
    "g.map_diag(sns.kdeplot)\n",
    "g.map_offdiag(sns.scatterplot, size=prob_data_df[\"correct\"])\n",
    "\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e209f13",
   "metadata": {},
   "source": [
    "Here we see that the large dots (the incorrect ones) are all nearby to points of a different color.  They were in fact samples that are similar to the other species.  So again, this result makes sense and helps us see when classifiers that are a good fit for the data will still make mistakes.\n",
    "\n",
    "We can also look at the probabilites of the predicted sample using max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "532eeebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99999781, 0.9999983 , 1.        , 0.99999895, 1.        ,\n",
       "       1.        , 1.        , 0.77405211, 0.90756495, 0.99988211,\n",
       "       0.52960963, 0.94376258, 0.99886436, 0.94983864, 0.98804386,\n",
       "       1.        , 0.98794498, 0.99994238, 1.        , 1.        ,\n",
       "       0.98330332, 0.99275723, 1.        , 1.        , 0.81648378,\n",
       "       1.        , 1.        , 0.99929123, 0.99999997, 1.        ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_predicted = np.max(gnb.predict_proba(X_test),axis=1)\n",
    "p_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dddab35",
   "metadata": {},
   "source": [
    "We see here that most of the predictions are pretty confident.\n",
    "We can also use the probabilities to then compute predictions and compare these to what the `predict` method gave, to confirm that this is how the predict method works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d351f352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "9     True\n",
       "10    True\n",
       "11    True\n",
       "12    True\n",
       "13    True\n",
       "14    True\n",
       "15    True\n",
       "16    True\n",
       "17    True\n",
       "18    True\n",
       "19    True\n",
       "20    True\n",
       "21    True\n",
       "22    True\n",
       "23    True\n",
       "24    True\n",
       "25    True\n",
       "26    True\n",
       "27    True\n",
       "28    True\n",
       "29    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data = gnb.predict_proba(X_test), columns = gnb.classes_ ).idxmax(axis=1) ==y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82edca6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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