{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/huonglan/Documents/codeproject/IRL-MOOC\n"
     ]
    }
   ],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from utils.data_helper import *\n",
    "from utils.plot import *\n",
    "from environment.feature_world import *\n",
    "from models.maxent import *\n",
    "import utils.data_helper as data_helper\n",
    "import environment.feature_world as feature_world\n",
    "import importlib\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature list: ['competency_strength', 'competency_alignment', 'competency_anticipation', 'content_alignment', 'content_anticipation', 'student_speed', 'student_shape']\n",
      "(Number of students, number of weeks, number of features): (15503, 10, 7)\n"
     ]
    }
   ],
   "source": [
    "course_id = 'dsp-002'\n",
    "DATA_DIR = f\"data/mooc_features/eq_week-marras_et_al-dsp_002/\"\n",
    "path = f'results/features/{course_id}'\n",
    "features = np.load(DATA_DIR + \"feature_values.npz\")\n",
    "feature_list = json.load(open(DATA_DIR + \"settings.txt\", 'r'))['feature_names']\n",
    "data = features['feature_values']\n",
    "label_df = pd.read_csv(DATA_DIR + \"feature_labels.csv\")\n",
    "y = np.array(label_df['label-pass-fail']).astype(int)\n",
    "\n",
    "print('Feature list:', feature_list)\n",
    "print('(Number of students, number of weeks, number of features):', features['feature_values'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student pass the course: (943, 10, 7)\n",
      "Student fail the course: (14560, 10, 7)\n"
     ]
    }
   ],
   "source": [
    "data[:, :, -2] = np.log(data[:, :, -2]) \n",
    "pass_data = data[y==0]\n",
    "fail_data = data[y==1]\n",
    "print('Student pass the course:', pass_data.shape)\n",
    "print('Student fail the course:', fail_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nan values (PASS STUDENTS) per student\n",
      "competency_strength        5.708378\n",
      "competency_alignment       0.000000\n",
      "competency_anticipation    1.000000\n",
      "content_alignment          0.000000\n",
      "content_anticipation       1.000000\n",
      "student_speed              3.746554\n",
      "student_shape              5.708378\n",
      "dtype: float64\n",
      "Number of nan values (FAIL STUDENTS) per student\n",
      "competency_strength        9.787775\n",
      "competency_alignment       0.000000\n",
      "competency_anticipation    1.000000\n",
      "content_alignment          0.000000\n",
      "content_anticipation       1.000000\n",
      "student_speed              9.767102\n",
      "student_shape              9.787775\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "pass_feature_df = {}\n",
    "fail_feature_df = {}\n",
    "feature_df = {}\n",
    "for i in range(7):\n",
    "    pass_feature_df[feature_list[i]] = pass_data[:, :, i].reshape(-1)\n",
    "    fail_feature_df[feature_list[i]] = fail_data[:, :, i].reshape(-1)\n",
    "    feature_df[feature_list[i]] = data[:, :, i].reshape(-1)\n",
    "\n",
    "pass_feature_df = pd.DataFrame(pass_feature_df)\n",
    "fail_feature_df = pd.DataFrame(fail_feature_df)\n",
    "feature_df = pd.DataFrame(feature_df)\n",
    "print('Number of nan values (PASS STUDENTS) per student') \n",
    "print(pass_feature_df.isna().sum()/pass_data.shape[0])\n",
    "print('Number of nan values (FAIL STUDENTS) per student') \n",
    "print(fail_feature_df.isna().sum()/fail_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>competency_strength</th>\n",
       "      <th>competency_alignment</th>\n",
       "      <th>competency_anticipation</th>\n",
       "      <th>content_alignment</th>\n",
       "      <th>content_anticipation</th>\n",
       "      <th>student_speed</th>\n",
       "      <th>student_shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3090.000000</td>\n",
       "      <td>145600.000000</td>\n",
       "      <td>131040.000000</td>\n",
       "      <td>145600.000000</td>\n",
       "      <td>131040.000000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>3090.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.339209</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.051024</td>\n",
       "      <td>0.003517</td>\n",
       "      <td>6.762048</td>\n",
       "      <td>0.000971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.207187</td>\n",
       "      <td>0.011303</td>\n",
       "      <td>0.011330</td>\n",
       "      <td>0.194784</td>\n",
       "      <td>0.017023</td>\n",
       "      <td>1.008532</td>\n",
       "      <td>0.031149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.175000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.133398</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.820562</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.468750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.501082</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>9.237761</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       competency_strength  competency_alignment  competency_anticipation  \\\n",
       "count          3090.000000         145600.000000            131040.000000   \n",
       "mean              0.339209              0.000131                 0.000452   \n",
       "std               0.207187              0.011303                 0.011330   \n",
       "min               0.000000              0.000000                 0.000000   \n",
       "25%               0.175000              0.000000                 0.000000   \n",
       "50%               0.281250              0.000000                 0.000000   \n",
       "75%               0.468750              0.000000                 0.000000   \n",
       "max               1.000000              1.000000                 1.000000   \n",
       "\n",
       "       content_alignment  content_anticipation  student_speed  student_shape  \n",
       "count      145600.000000         131040.000000    3391.000000    3090.000000  \n",
       "mean            0.051024              0.003517       6.762048       0.000971  \n",
       "std             0.194784              0.017023       1.008532       0.031149  \n",
       "min             0.000000              0.000000       0.000000       0.000000  \n",
       "25%             0.000000              0.000000       6.133398       0.000000  \n",
       "50%             0.000000              0.000000       6.820562       0.000000  \n",
       "75%             0.000000              0.000000       7.501082       0.000000  \n",
       "max             1.000000              0.545455       9.237761       1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_feature_df.describe()\n",
    "fail_feature_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PASS-------------------\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAIL-------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"PASS-------------------\")\n",
    "visualize_skewness(pass_feature_df)\n",
    "print(\"FAIL-------------------\")\n",
    "visualize_skewness(fail_feature_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 percentile of non zero value: [0.2962962962962963, 0.5, 0.06666666666666667, 0.5, 0.05, 6.895682697747868, 1.0]\n",
      "Discretized by percentile\n",
      "50 percentile of non zero value: [0.2962962962962963, 0.5, 0.06666666666666667, 0.5, 0.05, 6.895682697747868, 1.0]\n",
      "Discretized by percentile\n",
      "50 percentile of non zero value: [0.2962962962962963, 0.5, 0.06666666666666667, 0.5, 0.05, 6.895682697747868, 1.0]\n",
      "Discretized by percentile\n"
     ]
    }
   ],
   "source": [
    "cluster_list = [3]*feature_df.shape[1]\n",
    "kmeans=[]\n",
    "mids = []\n",
    "for id in range(len(cluster_list)):\n",
    "    X = data[:, :, id]\n",
    "    X = np.reshape(X[~np.isnan(X)], (-1, 1))\n",
    "    model = KMeans(n_clusters = cluster_list[id], random_state=0).fit(X)\n",
    "    kmeans.append(model)\n",
    "    mids.append(np.percentile(X[X>0], 50))\n",
    "  \n",
    "filna_pass_data = data_helper.dealing_missing_value(pass_data)\n",
    "filna_fail_data = data_helper.dealing_missing_value(fail_data)\n",
    "filna_data = data_helper.dealing_missing_value(data)\n",
    "discretized_pass_data = data_helper.discretized_feature(clusters_list=cluster_list, feature=filna_pass_data, feature_list=feature_list, kmeans=kmeans, mids=mids, discretized_by_percentile=True)\n",
    "discretized_fail_data = data_helper.discretized_feature(clusters_list=cluster_list, feature=filna_fail_data, feature_list=feature_list, kmeans=kmeans, mids=mids, discretized_by_percentile=True)\n",
    "discretized_data = data_helper.discretized_feature(clusters_list=cluster_list, feature=filna_data, feature_list=feature_list, kmeans=kmeans, mids=mids, discretized_by_percentile=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pass data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cw/xp7t80310j1c47ynx9xz9y4m0000gn/T/ipykernel_62787/1442209741.py:2: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.\n",
      "  student_world = feature_world.FeatureWorld(size=size, samples_trajectory=discretized_data)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample trajectories: [[ 713    1 2349]\n",
      " [2349    1 2360]\n",
      " [2360    1 2574]\n",
      " [2574    1 2466]\n",
      " [2466    1 2700]\n",
      " [2700    1  728]\n",
      " [ 728    1 2745]\n",
      " [2745    0 2745]\n",
      " [2745    1  299]]\n",
      "Trajectories shape: 943\n",
      "Feature matrix shape: (4374, 24)\n",
      "Transition probability: (4374, 2, 4374)\n"
     ]
    }
   ],
   "source": [
    "size = np.append(cluster_list, 2)\n",
    "student_world = feature_world.FeatureWorld(size=size, samples_trajectory=discretized_data)\n",
    "features = student_world.state_features()\n",
    "trajectories = feature_table_to_trajectories(student_world, discretized_pass_data)\n",
    "print(\"Sample trajectories:\", trajectories[1])\n",
    "print(\"Trajectories shape:\", len(trajectories))\n",
    "print('Feature matrix shape:', features.shape)\n",
    "print('Transition probability:', student_world.p_transition.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transition probability matrix shape: (4374, 2, 4374)\n",
      "Init/30, Alpha: tensor([0.7200, 0.7800, 0.6900, 0.4400, 0.9600, 0.7000, 0.3200, 0.9300, 0.1600,\n",
      "        0.6100, 0.7500, 0.1700, 0.8000, 1.0000, 0.9300, 0.3100, 0.9900, 0.3300,\n",
      "        0.4100, 0.6500, 0.9600, 0.8200, 0.7800, 0.6400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Logging: Maximum Entropy IRL\n",
      "Max length of trajectories:  9\n",
      "Feature matrix shape: (4374, 24)\n",
      "Feature expectation: [5.34 1.85 1.8  8.26 0.31 0.44 4.81 2.03 2.16 5.7  1.76 1.53 6.59 0.71\n",
      " 1.7  3.28 2.74 2.99 8.89 0.11 0.   2.97 6.03 9.  ]\n",
      "Epoch 1/30, Alpha: tensor([0.6200, 0.8800, 0.7900, 0.3400, 0.8600, 0.8000, 0.2200, 1.0300, 0.2600,\n",
      "        0.7100, 0.6500, 0.2700, 0.9000, 0.9000, 0.8300, 0.2100, 1.0900, 0.4300,\n",
      "        0.5100, 0.5500, 0.8600, 0.9200, 0.6800, 0.5400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 2/30, Alpha: tensor([0.5200, 0.9800, 0.8800, 0.2400, 0.8300, 0.9000, 0.1200, 1.1200, 0.3600,\n",
      "        0.8100, 0.5600, 0.2800, 1.0000, 0.8000, 0.7300, 0.1100, 1.1900, 0.5300,\n",
      "        0.6100, 0.4500, 0.7600, 1.0200, 0.5800, 0.4400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 3/30, Alpha: tensor([0.4200, 1.0800, 0.9800, 0.1400, 0.8600, 0.9900, 0.0200, 1.2100, 0.4600,\n",
      "        0.9100, 0.4600, 0.2400, 1.1000, 0.7000, 0.6400, 0.0100, 1.2900, 0.6300,\n",
      "        0.7100, 0.3500, 0.6600, 1.1200, 0.4800, 0.3400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 4/30, Alpha: tensor([ 0.3300,  1.1700,  1.0800,  0.0400,  0.9200,  1.0900, -0.0800,  1.3100,\n",
      "         0.5600,  1.0100,  0.3700,  0.1800,  1.2000,  0.6100,  0.5400, -0.0900,\n",
      "         1.3900,  0.7300,  0.8000,  0.2600,  0.5600,  1.2200,  0.3900,  0.2400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 5/30, Alpha: tensor([ 0.2300,  1.2600,  1.1700, -0.0600,  0.9900,  1.1900, -0.1800,  1.3900,\n",
      "         0.6600,  1.1100,  0.2800,  0.1000,  1.3000,  0.5100,  0.4400, -0.1900,\n",
      "         1.4900,  0.8300,  0.9000,  0.1600,  0.4600,  1.3100,  0.2900,  0.1500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 6/30, Alpha: tensor([ 0.1400,  1.3400,  1.2700, -0.1600,  1.0700,  1.2900, -0.2800,  1.4800,\n",
      "         0.7600,  1.2000,  0.1900,  0.0200,  1.3900,  0.4200,  0.3400, -0.2900,\n",
      "         1.5900,  0.9300,  1.0000,  0.0600,  0.3700,  1.4000,  0.2000,  0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 7/30, Alpha: tensor([ 0.0500,  1.4200,  1.3600, -0.2600,  1.1500,  1.3900, -0.3800,  1.5700,\n",
      "         0.8600,  1.3000,  0.1100, -0.0700,  1.4900,  0.3300,  0.2400, -0.3900,\n",
      "         1.6900,  1.0200,  1.0900, -0.0300,  0.2700,  1.4900,  0.1100, -0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 8/30, Alpha: tensor([-0.0400,  1.4900,  1.4600, -0.3600,  1.2400,  1.4900, -0.4800,  1.6600,\n",
      "         0.9600,  1.3900,  0.0400, -0.1600,  1.5900,  0.2400,  0.1400, -0.4800,\n",
      "         1.7900,  1.1200,  1.1900, -0.1200,  0.1700,  1.5800,  0.0200, -0.1100],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 9/30, Alpha: tensor([-0.1300,  1.5700,  1.5600, -0.4600,  1.3300,  1.5900, -0.5800,  1.7500,\n",
      "         1.0600,  1.4700, -0.0200, -0.2600,  1.6800,  0.1600,  0.0400, -0.5800,\n",
      "         1.8900,  1.2200,  1.2800, -0.2100,  0.0800,  1.6700, -0.0700, -0.1600],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 10/30, Alpha: tensor([-0.2200,  1.6300,  1.6500, -0.5600,  1.4200,  1.6900, -0.6800,  1.8400,\n",
      "         1.1600,  1.5600, -0.0700, -0.3600,  1.7800,  0.0800, -0.0600, -0.6800,\n",
      "         1.9900,  1.3100,  1.3800, -0.3000, -0.0200,  1.7500, -0.1500, -0.1800],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 11/30, Alpha: tensor([-0.3100,  1.7000,  1.7500, -0.6600,  1.5100,  1.7900, -0.7800,  1.9300,\n",
      "         1.2600,  1.6300, -0.1100, -0.4500,  1.8700, -0.0000, -0.1600, -0.7800,\n",
      "         2.1000,  1.4100,  1.4700, -0.3900, -0.1100,  1.8400, -0.2400, -0.1900],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 12/30, Alpha: tensor([-0.3900,  1.7700,  1.8400, -0.7600,  1.6000,  1.8900, -0.8800,  2.0200,\n",
      "         1.3600,  1.7100, -0.1400, -0.5500,  1.9600, -0.0800, -0.2500, -0.8800,\n",
      "         2.2000,  1.5100,  1.5600, -0.4800, -0.2000,  1.9200, -0.3200, -0.1800],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 13/30, Alpha: tensor([-0.4800,  1.8300,  1.9400, -0.8600,  1.6800,  1.9800, -0.9800,  2.1100,\n",
      "         1.4600,  1.7700, -0.1700, -0.6500,  2.0600, -0.1500, -0.3500, -0.9800,\n",
      "         2.3000,  1.6000,  1.6600, -0.5700, -0.3000,  2.0100, -0.4100, -0.1600],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 14/30, Alpha: tensor([-0.5700,  1.9000,  2.0300, -0.9500,  1.7700,  2.0800, -1.0700,  2.2100,\n",
      "         1.5600,  1.8300, -0.1800, -0.7500,  2.1500, -0.2200, -0.4400, -1.0800,\n",
      "         2.4000,  1.7000,  1.7500, -0.6500, -0.3900,  2.0900, -0.4900, -0.1300],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 15/30, Alpha: tensor([-0.6600,  1.9600,  2.1300, -1.0500,  1.8500,  2.1800, -1.1700,  2.3000,\n",
      "         1.6600,  1.8900, -0.1800, -0.8500,  2.2300, -0.2900, -0.5400, -1.1800,\n",
      "         2.5100,  1.7900,  1.8400, -0.7400, -0.4800,  2.1800, -0.5800, -0.1000],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 16/30, Alpha: tensor([-0.7400,  2.0300,  2.2200, -1.1500,  1.9400,  2.2800, -1.2700,  2.3900,\n",
      "         1.7600,  1.9400, -0.1700, -0.9600,  2.3200, -0.3600, -0.6300, -1.2800,\n",
      "         2.6100,  1.8900,  1.9300, -0.8200, -0.5700,  2.2600, -0.6600, -0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 17/30, Alpha: tensor([-0.8300,  2.0900,  2.3100, -1.2500,  2.0200,  2.3800, -1.3700,  2.4800,\n",
      "         1.8600,  1.9800, -0.1500, -1.0600,  2.4100, -0.4300, -0.7300, -1.3800,\n",
      "         2.7100,  1.9900,  2.0300, -0.9100, -0.6600,  2.3400, -0.7500, -0.0000],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 18/30, Alpha: tensor([-0.9200,  2.1500,  2.4100, -1.3400,  2.1100,  2.4700, -1.4700,  2.5700,\n",
      "         1.9600,  2.0200, -0.1300, -1.1600,  2.5000, -0.4900, -0.8200, -1.4800,\n",
      "         2.8100,  2.0800,  2.1200, -0.9900, -0.7500,  2.4300, -0.8300,  0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 19/30, Alpha: tensor([-1.0000,  2.2100,  2.5000, -1.4400,  2.2100,  2.5700, -1.5700,  2.6600,\n",
      "         2.0600,  2.0600, -0.1000, -1.2600,  2.5900, -0.5500, -0.9200, -1.5700,\n",
      "         2.9100,  2.1800,  2.2100, -1.0700, -0.8400,  2.5100, -0.9100,  0.0900],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 20/30, Alpha: tensor([-1.0900,  2.2700,  2.6000, -1.5500,  2.3100,  2.6700, -1.6700,  2.7500,\n",
      "         2.1600,  2.0900, -0.0700, -1.3600,  2.6800, -0.6100, -1.0200, -1.6700,\n",
      "         3.0200,  2.2700,  2.2900, -1.1400, -0.9200,  2.5900, -0.9900,  0.1100],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 21/30, Alpha: tensor([-1.1800,  2.3300,  2.6900, -1.6500,  2.4100,  2.7700, -1.7700,  2.8300,\n",
      "         2.2500,  2.1200, -0.0300, -1.4600,  2.7700, -0.6700, -1.1100, -1.7700,\n",
      "         3.1200,  2.3700,  2.3800, -1.2200, -1.0100,  2.6800, -1.0800,  0.1200],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 22/30, Alpha: tensor([-1.2600,  2.3800,  2.7800, -1.7500,  2.5100,  2.8800, -1.8700,  2.9100,\n",
      "         2.3500,  2.1500,  0.0100, -1.5600,  2.8700, -0.7300, -1.2100, -1.8700,\n",
      "         3.2200,  2.4600,  2.4700, -1.2900, -1.1000,  2.7600, -1.1600,  0.1200],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 23/30, Alpha: tensor([-1.3500,  2.4300,  2.8800, -1.8600,  2.6200,  2.9800, -1.9600,  2.9900,\n",
      "         2.4500,  2.1700,  0.0500, -1.6600,  2.9600, -0.7900, -1.3100, -1.9700,\n",
      "         3.3200,  2.5600,  2.5500, -1.3500, -1.1800,  2.8400, -1.2400,  0.1100],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 24/30, Alpha: tensor([-1.4300,  2.4800,  2.9700, -1.9600,  2.7300,  3.0800, -2.0600,  3.0700,\n",
      "         2.5500,  2.1900,  0.1000, -1.7600,  3.0500, -0.8500, -1.4100, -2.0700,\n",
      "         3.4300,  2.6500,  2.6300, -1.4200, -1.2600,  2.9200, -1.3200,  0.0800],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 25/30, Alpha: tensor([-1.5200,  2.5300,  3.0700, -2.0700,  2.8400,  3.1900, -2.1600,  3.1500,\n",
      "         2.6500,  2.2200,  0.1500, -1.8600,  3.1400, -0.9100, -1.5100, -2.1700,\n",
      "         3.5300,  2.7500,  2.7100, -1.4800, -1.3400,  3.0000, -1.4000,  0.0600],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 26/30, Alpha: tensor([-1.6000,  2.5800,  3.1600, -2.1700,  2.9500,  3.2900, -2.2600,  3.2200,\n",
      "         2.7500,  2.2400,  0.2000, -1.9600,  3.2400, -0.9700, -1.6100, -2.2700,\n",
      "         3.6300,  2.8400,  2.7900, -1.5400, -1.4200,  3.0800, -1.4800,  0.0200],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 27/30, Alpha: tensor([-1.6800,  2.6300,  3.2500, -2.2800,  3.0600,  3.4000, -2.3600,  3.2900,\n",
      "         2.8500,  2.2600,  0.2500, -2.0600,  3.3300, -1.0300, -1.7100, -2.3600,\n",
      "         3.7400,  2.9300,  2.8700, -1.6000, -1.5000,  3.1600, -1.5600, -0.0200],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 28/30, Alpha: tensor([-1.7700,  2.6700,  3.3500, -2.3900,  3.1700,  3.5000, -2.4600,  3.3600,\n",
      "         2.9500,  2.2800,  0.3000, -2.1600,  3.4300, -1.0800, -1.8100, -2.4600,\n",
      "         3.8400,  3.0300,  2.9500, -1.6500, -1.5800,  3.2400, -1.6400, -0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 29/30, Alpha: tensor([-1.8500,  2.7100,  3.4400, -2.5000,  3.2900,  3.6100, -2.5500,  3.4300,\n",
      "         3.0500,  2.3000,  0.3600, -2.2600,  3.5200, -1.1400, -1.9100, -2.5600,\n",
      "         3.9400,  3.1200,  3.0300, -1.7100, -1.6600,  3.3200, -1.7200, -0.0600],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 30/30, Alpha: tensor([-1.9300,  2.7600,  3.5300, -2.6100,  3.4000,  3.7100, -2.6500,  3.4900,\n",
      "         3.1500,  2.3200,  0.4100, -2.3600,  3.6200, -1.2000, -2.0100, -2.6600,\n",
      "         4.0400,  3.2200,  3.1100, -1.7600, -1.7400,  3.4000, -1.8000, -0.0600],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Result Max Ent IRL: Alpha [-1.93  2.76  3.53 -2.61  3.4   3.71 -2.65  3.49  3.15  2.32  0.41 -2.36\n",
      "  3.62 -1.2  -2.01 -2.66  4.04  3.22  3.11 -1.76 -1.74  3.4  -1.8  -0.06]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "transition_probability = student_world.p_transition\n",
    "print('transition probability matrix shape:', transition_probability.shape)\n",
    "history_pass = irl(feature_matrix=features, transition_probability=transition_probability,\n",
    "        trajectories=trajectories, epochs=30, learning_rate=0.1, show=True, seed=52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.9336348   2.756731    3.534947   -2.6079633   3.402755    3.714083\n",
      " -2.652383    3.4921045   3.1531706   2.3227699   0.40805984 -2.3579395\n",
      "  3.6185162  -1.2025634  -2.0117438  -2.6601813   4.0438666   3.2151768\n",
      "  3.1060753  -1.7564987  -1.7352827   3.4022462  -1.8030449  -0.06497482]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pass_train_loss, pass_alpha, pass_reward = history_pass['train loss'], history_pass['alpha'], history_pass['reward']\n",
    "plt.plot(range(len(pass_train_loss)), pass_train_loss)\n",
    "print(pass_alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.rcParams['font.family'] = 'Times New Roman'\n",
    "plt.rcParams['font.size'] = 24\n",
    "plt.rcParams['axes.titlesize'] = 28\n",
    "plt.rcParams['axes.labelsize'] = 24\n",
    "plt.rcParams['xtick.labelsize'] = 20\n",
    "plt.rcParams['ytick.labelsize'] = 20\n",
    "plt.rcParams['legend.fontsize'] = 20\n",
    "plt.rcParams['figure.titlesize'] = 28  \n",
    "\n",
    "final_feature_list = np.append(feature_list, 'is_missing')\n",
    "from utils import plot\n",
    "importlib.reload(plot)\n",
    "plot.plot_feature_alpha(pass_alpha, size, final_feature_list, title='Passing students', course_id=course_id)\n",
    "# plot.plot_each_feature_alpha(pass_alpha, size, final_feature_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fail data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cw/xp7t80310j1c47ynx9xz9y4m0000gn/T/ipykernel_62787/869915459.py:2: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.\n",
      "  student_world = FeatureWorld(size=size, samples_trajectory=discretized_data)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample trajectories: [[2376    1 2187]\n",
      " [2187    1 2214]\n",
      " [2214    1 2187]\n",
      " [2187    0 2187]\n",
      " [2187    0 2187]\n",
      " [2187    0 2187]\n",
      " [2187    0 2187]\n",
      " [2187    0 2187]\n",
      " [2187    0 2187]]\n",
      "Feature matrix shape: (4374, 24)\n",
      "Transition probability: (4374, 2, 4374)\n",
      "Trajectories shape: 14560\n",
      "Init/20, Alpha: tensor([0.7200, 0.7800, 0.6900, 0.4400, 0.9600, 0.7000, 0.3200, 0.9300, 0.1600,\n",
      "        0.6100, 0.7500, 0.1700, 0.8000, 1.0000, 0.9300, 0.3100, 0.9900, 0.3300,\n",
      "        0.4100, 0.6500, 0.9600, 0.8200, 0.7800, 0.6400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Logging: Maximum Entropy IRL\n",
      "Max length of trajectories:  9\n",
      "Feature matrix shape: (4374, 24)\n",
      "Feature expectation: [8.79 0.11 0.1  9.   0.   0.   8.94 0.05 0.01 8.2  0.42 0.37 8.41 0.35\n",
      " 0.24 8.77 0.12 0.11 9.   0.   0.   0.13 8.87 9.  ]\n",
      "Epoch 1/20, Alpha: tensor([0.8200, 0.6800, 0.5900, 0.5400, 0.8600, 0.6000, 0.4200, 0.8300, 0.0600,\n",
      "        0.7100, 0.6500, 0.0700, 0.9000, 0.9000, 0.8300, 0.2100, 0.8900, 0.4300,\n",
      "        0.5100, 0.5500, 0.8600, 0.7200, 0.8800, 0.5400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 2/20, Alpha: tensor([ 0.8200,  0.6100,  0.5100,  0.6400,  0.7600,  0.5000,  0.5100,  0.7300,\n",
      "        -0.0400,  0.8100,  0.5500, -0.0000,  0.9500,  0.8200,  0.7800,  0.1200,\n",
      "         0.8100,  0.5300,  0.6100,  0.4500,  0.7600,  0.6500,  0.8700,  0.4400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 3/20, Alpha: tensor([ 0.7800,  0.5600,  0.4400,  0.7400,  0.6600,  0.4000,  0.6100,  0.6300,\n",
      "        -0.1200,  0.9100,  0.4600, -0.0400,  0.9600,  0.7700,  0.7800,  0.0300,\n",
      "         0.7500,  0.6300,  0.7100,  0.3500,  0.6600,  0.6000,  0.8200,  0.3400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 4/20, Alpha: tensor([ 0.7200,  0.5200,  0.3900,  0.8400,  0.5600,  0.3000,  0.7000,  0.5400,\n",
      "        -0.1900,  1.0100,  0.3600, -0.0300,  0.9500,  0.7300,  0.8000, -0.0700,\n",
      "         0.7000,  0.7300,  0.8100,  0.2500,  0.5600,  0.5600,  0.7500,  0.2400],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 5/20, Alpha: tensor([ 0.6500,  0.4800,  0.3600,  0.9400,  0.4600,  0.2000,  0.7900,  0.4400,\n",
      "        -0.2500,  1.1100,  0.2600, -0.0100,  0.9300,  0.7100,  0.8400, -0.1600,\n",
      "         0.6700,  0.8200,  0.9000,  0.1500,  0.4600,  0.5300,  0.6800,  0.1500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 6/20, Alpha: tensor([ 0.5800,  0.4200,  0.3300,  1.0300,  0.3600,  0.1000,  0.8700,  0.3500,\n",
      "        -0.2900,  1.2000,  0.1700,  0.0400,  0.8900,  0.6900,  0.8800, -0.2500,\n",
      "         0.6500,  0.9000,  1.0000,  0.0600,  0.3600,  0.4800,  0.6100,  0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 7/20, Alpha: tensor([ 0.5100,  0.3600,  0.3000,  1.1200,  0.2700,  0.0100,  0.9500,  0.2600,\n",
      "        -0.3200,  1.2900,  0.0700,  0.0900,  0.8400,  0.6800,  0.9400, -0.3400,\n",
      "         0.6400,  0.9600,  1.0800, -0.0400,  0.2700,  0.4200,  0.5500, -0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 8/20, Alpha: tensor([ 0.4400,  0.2900,  0.2700,  1.2000,  0.1800, -0.0900,  1.0300,  0.1700,\n",
      "        -0.3300,  1.3800, -0.0200,  0.1500,  0.7900,  0.6900,  0.9800, -0.4300,\n",
      "         0.6500,  1.0000,  1.1700, -0.1300,  0.1800,  0.3500,  0.5000, -0.1100],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 9/20, Alpha: tensor([ 0.3800,  0.2200,  0.2400,  1.2700,  0.0800, -0.1700,  1.1000,  0.0900,\n",
      "        -0.3300,  1.4700, -0.1100,  0.2100,  0.7300,  0.6900,  1.0000, -0.5100,\n",
      "         0.6700,  1.0200,  1.2400, -0.2000,  0.0800,  0.2800,  0.4600, -0.1600],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 10/20, Alpha: tensor([ 0.3200,  0.1400,  0.2100,  1.3400, -0.0100, -0.2500,  1.1600, -0.0000,\n",
      "        -0.3100,  1.5500, -0.1900,  0.2600,  0.6800,  0.7000,  1.0000, -0.5800,\n",
      "         0.6900,  1.0200,  1.3200, -0.2700, -0.0000,  0.2000,  0.4300, -0.1800],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 11/20, Alpha: tensor([ 0.2700,  0.0500,  0.1700,  1.4100, -0.0900, -0.3100,  1.2300, -0.0800,\n",
      "        -0.2900,  1.6400, -0.2700,  0.3200,  0.6300,  0.7200,  0.9700, -0.6500,\n",
      "         0.7200,  1.0000,  1.3800, -0.3400, -0.0900,  0.1100,  0.4200, -0.1900],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 12/20, Alpha: tensor([ 0.2300, -0.0300,  0.1200,  1.4600, -0.1600, -0.3700,  1.2800, -0.1600,\n",
      "        -0.2600,  1.7100, -0.3500,  0.3700,  0.5900,  0.7300,  0.9200, -0.7100,\n",
      "         0.7500,  0.9700,  1.4400, -0.3900, -0.1600,  0.0300,  0.4100, -0.1800],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 13/20, Alpha: tensor([ 0.1900, -0.1100,  0.0700,  1.5200, -0.2300, -0.4300,  1.3300, -0.2300,\n",
      "        -0.2300,  1.7900, -0.4300,  0.4300,  0.5600,  0.7300,  0.8700, -0.7800,\n",
      "         0.7700,  0.9400,  1.5000, -0.4400, -0.2300, -0.0600,  0.4200, -0.1600],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 14/20, Alpha: tensor([ 0.1500, -0.1700,  0.0100,  1.5700, -0.2900, -0.4700,  1.3700, -0.2900,\n",
      "        -0.1900,  1.8600, -0.5000,  0.4800,  0.5400,  0.7200,  0.8100, -0.8400,\n",
      "         0.7900,  0.9200,  1.5500, -0.4900, -0.2900, -0.1500,  0.4200, -0.1300],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 15/20, Alpha: tensor([ 0.1100, -0.2300, -0.0500,  1.6100, -0.3500, -0.5100,  1.4100, -0.3400,\n",
      "        -0.1500,  1.9200, -0.5700,  0.5300,  0.5200,  0.7000,  0.7500, -0.9000,\n",
      "         0.8100,  0.9000,  1.6000, -0.5300, -0.3500, -0.2200,  0.4200, -0.1000],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 16/20, Alpha: tensor([ 0.0700, -0.2700, -0.0800,  1.6500, -0.4000, -0.5500,  1.4400, -0.3900,\n",
      "        -0.1100,  1.9900, -0.6300,  0.5900,  0.5100,  0.6800,  0.6900, -0.9700,\n",
      "         0.8200,  0.8900,  1.6500, -0.5700, -0.4000, -0.2900,  0.4200, -0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 17/20, Alpha: tensor([ 0.0200, -0.3100, -0.1000,  1.6900, -0.4500, -0.5800,  1.4600, -0.4400,\n",
      "        -0.0600,  2.0500, -0.6900,  0.6500,  0.5000,  0.6400,  0.6300, -1.0300,\n",
      "         0.8300,  0.8800,  1.6900, -0.6000, -0.4500, -0.3500,  0.4200, -0.0000],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 18/20, Alpha: tensor([-0.0200, -0.3300, -0.1000,  1.7200, -0.4900, -0.6100,  1.4800, -0.4700,\n",
      "        -0.0200,  2.1000, -0.7500,  0.7000,  0.4900,  0.6100,  0.5900, -1.1000,\n",
      "         0.8300,  0.8900,  1.7300, -0.6200, -0.4900, -0.4100,  0.4100,  0.0500],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 19/20, Alpha: tensor([-0.0600, -0.3500, -0.0900,  1.7600, -0.5300, -0.6300,  1.4800, -0.5100,\n",
      "         0.0300,  2.1500, -0.8000,  0.7600,  0.4800,  0.5700,  0.5600, -1.1600,\n",
      "         0.8300,  0.9000,  1.7600, -0.6500, -0.5300, -0.4600,  0.4100,  0.0900],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Epoch 20/20, Alpha: tensor([-0.0900, -0.3600, -0.0700,  1.7800, -0.5700, -0.6500,  1.4900, -0.5300,\n",
      "         0.0400,  2.2000, -0.8500,  0.8200,  0.4600,  0.5400,  0.5300, -1.2300,\n",
      "         0.8200,  0.9200,  1.8000, -0.6700, -0.5700, -0.5000,  0.4000,  0.1100],\n",
      "       grad_fn=<RoundBackward1>)\n",
      "Result Max Ent IRL: Alpha [-0.09 -0.36 -0.07  1.78 -0.57 -0.65  1.49 -0.53  0.04  2.2  -0.85  0.82\n",
      "  0.46  0.54  0.53 -1.23  0.82  0.92  1.8  -0.67 -0.57 -0.5   0.4   0.11]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "size = np.append(cluster_list, 2)\n",
    "student_world = FeatureWorld(size=size, samples_trajectory=discretized_data)\n",
    "features = student_world.state_features()\n",
    "trajectories = feature_table_to_trajectories(student_world, discretized_fail_data)\n",
    "print(\"Sample trajectories:\", trajectories[1])\n",
    "print('Feature matrix shape:', features.shape)\n",
    "print('Transition probability:', student_world.p_transition.shape)\n",
    "print(\"Trajectories shape:\", len(trajectories))\n",
    "history_fail = irl(feature_matrix=features, transition_probability=student_world.p_transition,\n",
    "        trajectories=trajectories, epochs=20, learning_rate=0.1, show=True, seed=52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.08539744 -0.36277092 -0.07278982  1.7837163  -0.56548226 -0.65062577\n",
      "  1.4861197  -0.53088325  0.04412325  2.2027986  -0.8522022   0.8193183\n",
      "  0.45605215  0.5373029   0.53481436 -1.232846    0.8244648   0.9174636\n",
      "  1.7965715  -0.66698056 -0.5670972  -0.5007596   0.4022622   0.10855999]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fail_train_loss, fail_alpha, fail_reward = history_fail['train loss'], history_fail['alpha'], history_fail['reward']\n",
    "plt.plot(range(len(fail_train_loss)), fail_train_loss)\n",
    "print(fail_alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAKBA/xehiFVFgIfZRQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_feature_list = np.append(feature_list, 'is_missing')\n",
    "plot_feature_alpha(fail_alpha, size, final_feature_list, title='Failing students', course_id=course_id)\n",
    "plot_each_feature_alpha(fail_alpha, size, final_feature_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save data to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fail_df = pd.DataFrame(fail_reward)\n",
    "fail_df.to_csv(f'{path}/fail_reward.csv', index=False)\n",
    "pd.DataFrame(fail_alpha).to_csv(f'{path}/fail_alpha.csv', index=False)\n",
    "\n",
    "pass_df = pd.DataFrame(pass_reward)\n",
    "pass_df.to_csv(f'{path}/pass_reward.csv', index=False)\n",
    "pd.DataFrame(pass_alpha).to_csv(f'{path}/pass_alpha.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reward_matrix_to_feature_value(reward, feature_list, topn=10):\n",
    "    df = pd.DataFrame(columns=feature_list)\n",
    "    mapping = {0: 'zero', 1: 'medium', 2: 'high', 3: 'very high'}\n",
    "    # Initiate the list\n",
    "    reward = reward.reshape(-1)\n",
    "    topn_reward = np.argsort(reward, axis=0)[-topn:]\n",
    "    for val in reversed(topn_reward):\n",
    "        point = student_world.state_index_to_point(val)\n",
    "        features = [mapping[x] for x in point[:-1]]\n",
    "        features.append('True' if point[-1] == 1 else 'False')\n",
    "        df.loc[len(df)] = features   \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pass_reward = pd.read_csv(f'{path}/pass_reward.csv').to_numpy()\n",
    "fail_reward = pd.read_csv(f'{path}/fail_reward.csv').to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>competency_strength</th>\n",
       "      <th>competency_alignment</th>\n",
       "      <th>competency_anticipation</th>\n",
       "      <th>content_alignment</th>\n",
       "      <th>content_anticipation</th>\n",
       "      <th>student_speed</th>\n",
       "      <th>student_shape</th>\n",
       "      <th>is_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>medium</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>medium</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  competency_strength competency_alignment competency_anticipation  \\\n",
       "0                high                 high                  medium   \n",
       "1                high               medium                  medium   \n",
       "2                high                 high                    high   \n",
       "3                high               medium                    high   \n",
       "4              medium                 high                  medium   \n",
       "5                high                 high                  medium   \n",
       "6              medium               medium                  medium   \n",
       "7              medium                 high                    high   \n",
       "8                high               medium                  medium   \n",
       "9                high                 high                    high   \n",
       "\n",
       "  content_alignment content_anticipation student_speed student_shape  \\\n",
       "0              zero                 zero        medium          zero   \n",
       "1              zero                 zero        medium          zero   \n",
       "2              zero                 zero        medium          zero   \n",
       "3              zero                 zero        medium          zero   \n",
       "4              zero                 zero        medium          zero   \n",
       "5              zero                 zero          high          zero   \n",
       "6              zero                 zero        medium          zero   \n",
       "7              zero                 zero        medium          zero   \n",
       "8              zero                 zero          high          zero   \n",
       "9              zero                 zero          high          zero   \n",
       "\n",
       "  is_missing  \n",
       "0      False  \n",
       "1      False  \n",
       "2      False  \n",
       "3      False  \n",
       "4      False  \n",
       "5      False  \n",
       "6      False  \n",
       "7      False  \n",
       "8      False  \n",
       "9      False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topn_pass = reward_matrix_to_feature_value(pass_reward, final_feature_list)\n",
    "display(topn_pass)\n",
    "topn_pass.to_csv(f'{path}/topn_reward_pass.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
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       "        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>competency_strength</th>\n",
       "      <th>competency_alignment</th>\n",
       "      <th>competency_anticipation</th>\n",
       "      <th>content_alignment</th>\n",
       "      <th>content_anticipation</th>\n",
       "      <th>student_speed</th>\n",
       "      <th>student_shape</th>\n",
       "      <th>is_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>high</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>zero</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>zero</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  competency_strength competency_alignment competency_anticipation  \\\n",
       "0                high                 zero                    zero   \n",
       "1                high                 zero                    zero   \n",
       "2                zero                 zero                    zero   \n",
       "3                zero                 zero                    zero   \n",
       "4                high                 zero                    zero   \n",
       "5                high                 zero                    zero   \n",
       "6                zero                 zero                    zero   \n",
       "7                high                 zero                    zero   \n",
       "8                zero                 zero                    zero   \n",
       "9                zero                 zero                    zero   \n",
       "\n",
       "  content_alignment content_anticipation student_speed student_shape  \\\n",
       "0              zero               medium          high          zero   \n",
       "1              zero                 high          high          zero   \n",
       "2              zero               medium          high          zero   \n",
       "3              zero                 high          high          zero   \n",
       "4              zero                 zero          high          zero   \n",
       "5              zero               medium        medium          zero   \n",
       "6              zero                 zero          high          zero   \n",
       "7              zero                 high        medium          zero   \n",
       "8              zero               medium        medium          zero   \n",
       "9              zero                 high        medium          zero   \n",
       "\n",
       "  is_missing  \n",
       "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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topn_fail = reward_matrix_to_feature_value(fail_reward, final_feature_list)\n",
    "display(topn_fail)\n",
    "topn_fail.to_csv(f'{path}/topn_reward_fail.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'results/features/dsp_002/topn_reward_pass.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresults/features/dsp_002\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m topn_pass \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mpath\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/topn_reward_pass.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m topn_fail \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/topn_reward_fail.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m display(topn_pass[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompetency_strength\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompetency_anticipation\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis_missing\u001b[39m\u001b[38;5;124m'\u001b[39m]])\n",
      "File \u001b[0;32m~/miniconda3/envs/irl/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/irl/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/miniconda3/envs/irl/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/irl/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/miniconda3/envs/irl/lib/python3.12/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'results/features/dsp_002/topn_reward_pass.csv'"
     ]
    }
   ],
   "source": [
    "path=f'results/features/dsp_002'\n",
    "topn_pass = pd.read_csv(f'{path}/topn_reward_pass.csv')\n",
    "topn_fail = pd.read_csv(f'{path}/topn_reward_fail.csv')\n",
    "display(topn_pass[['competency_strength', 'competency_anticipation', 'is_missing']])\n",
    "display(topn_fail[['competency_strength', 'competency_anticipation', 'is_missing']])"
   ]
  }
 ],
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