{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cfc0a1ce",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Optimize MOOC learner pathways\n",
    "\n",
    "In this section, we propose a learning item recommendation system based on\n",
    "consensus clustering (MultiCons) and collaborative filtering.\n",
    "\n",
    "The approach consists of first grouping learners into homogeneous groups based on\n",
    "their profile and learning activities in the course using consensus clustering.\n",
    "\n",
    "Then, for each student cluster, collaborative filtering is applied to recommend\n",
    "previously unexplored learning items.\n",
    "\n",
    "We measure the quality of our approach by first training a decision tree classifier\n",
    "predicting certification success, then comparing changes in success predictions when\n",
    "students whose failure had previously been correctly predicted follow the\n",
    "recommendations.\n",
    "\n",
    "Finally, we compare the quality of our approach to a baseline method that applies\n",
    "collaborative filtering on the full dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e4c1d28",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import Markdown, display\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cluster import OPTICS, AgglomerativeClustering, Birch, KMeans\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold, train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from surprise import Dataset, KNNWithMeans, Reader\n",
    "from surprise.model_selection import GridSearchCV as SupGridSearchCV\n",
    "\n",
    "from multicons import MultiCons\n",
    "\n",
    "from oulad import filter_by_module_presentation, get_oulad\n",
    "\n",
    "%load_ext oulad.capture"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aaa2c81",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Preparing the dataset\n",
    "\n",
    "In this section we:\n",
    " - Load the OULAD dataset\n",
    " - Select a subset related to a course session\n",
    " - Prepare the student interaction and student profile feature tables\n",
    "\n",
    "### Loading OULAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b627d102",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture oulad\n",
    "oulad = get_oulad()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27753cb7",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Selecting one course session\n",
    "\n",
    "We start by selecting one OULAD course session.\n",
    "\n",
    "We choose the `BBB` course from the `2013J` session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eaaec84e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CODE_MODULE = \"BBB\"\n",
    "CODE_PRESENTATION = \"2013J\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e48d211a",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### The `student_item` table\n",
    "\n",
    "It represents all student interactions with course items of the selected course\n",
    "session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "230ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "       id_student id_site  sum_click\n",
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       "\n",
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    }
   ],
   "source": [
    "%%capture -ns optimize_mooc_learner_pathways student_registration student_item\n",
    "student_registration = (\n",
    "    filter_by_module_presentation(\n",
    "        oulad.student_registration, CODE_MODULE, CODE_PRESENTATION\n",
    "    )\n",
    "    # Remove students that unregistered before the course started.\n",
    "    .query(\"~(date_unregistration < 0)\")\n",
    "    .drop([\"date_unregistration\"], axis=1)\n",
    "    .set_index(\"id_student\")\n",
    ")\n",
    "\n",
    "student_item = (\n",
    "    filter_by_module_presentation(oulad.student_vle, CODE_MODULE, CODE_PRESENTATION)\n",
    "    .query(\"id_student in @student_registration.index\")\n",
    "    .drop([\"date\"], axis=1)\n",
    "    .groupby([\"id_site\", \"id_student\"])\n",
    "    .sum()\n",
    "    .reset_index()\n",
    "    # We convert the `id_site` column to string type as the values of `id_site` will\n",
    "    # be used as column names in the `student_profile` table.\n",
    "    # student_item.id_site = student_item.id_site.astype(str)\n",
    "    .astype({\"id_site\": str, \"sum_click\": float})[\n",
    "        [\"id_student\", \"id_site\", \"sum_click\"]\n",
    "    ]\n",
    ")\n",
    "display(student_item)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf80807",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### The `student_profile` table\n",
    "\n",
    "It contains student demographic data along with course registration information,\n",
    "course item interactions, and the final result.\n",
    "\n",
    "It also lets us identify students that have no interaction records or have\n",
    "unregistered before the course started, which we exclude.\n",
    "\n",
    "We consider students marked with a final result of `Withdrawn` and `Fail` as failed\n",
    "and students marked with `Pass` or `Distinction` as succeded.\n",
    "\n",
    "Finally, we encode all ordinal categorical values to nummerical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "995ee115",
   "metadata": {},
   "outputs": [
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       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1851 rows × 328 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            gender  highest_education  imd_band  age_band  studied_credits  \\\n",
       "id_student                                                                   \n",
       "23798          0.0               0.50      55.0       0.0               60   \n",
       "27759          0.0               0.25      45.0       0.5              120   \n",
       "30091          1.0               0.50      15.0       0.0               60   \n",
       "31014          1.0               0.25      85.0       0.5              120   \n",
       "31849          1.0               0.25      65.0       0.5              120   \n",
       "...            ...                ...       ...       ...              ...   \n",
       "2680344        1.0               0.75      85.0       0.5               60   \n",
       "2680885        1.0               0.25      55.0       0.0               60   \n",
       "2691100        1.0               0.25       5.0       0.0              120   \n",
       "2691566        1.0               0.25       5.0       0.0               60   \n",
       "2693772        1.0               0.25      35.0       0.0               60   \n",
       "\n",
       "            disability  final_result  date_registration  703721  703722  ...  \\\n",
       "id_student                                                               ...   \n",
       "23798              0.0          True              -27.0     1.0     1.0  ...   \n",
       "27759              1.0         False              -43.0     1.0     1.0  ...   \n",
       "30091              1.0          True             -145.0     1.0     1.0  ...   \n",
       "31014              0.0         False              -43.0     1.0     1.0  ...   \n",
       "31849              0.0          True             -128.0     1.0     1.0  ...   \n",
       "...                ...           ...                ...     ...     ...  ...   \n",
       "2680344            0.0          True              -25.0     1.0     1.0  ...   \n",
       "2680885            1.0          True             -141.0     1.0     1.0  ...   \n",
       "2691100            0.0          True             -141.0     1.0     1.0  ...   \n",
       "2691566            0.0         False             -109.0     1.0     0.0  ...   \n",
       "2693772            0.0         False              -49.0     1.0     0.0  ...   \n",
       "\n",
       "            704231  704232  704233  704234  704235  704236  704237  704238  \\\n",
       "id_student                                                                   \n",
       "23798          0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "27759          0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "30091          0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "31014          0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "31849          0.0     0.0     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "...            ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2680344        0.0     0.0     0.0     0.0     1.0     0.0     0.0     0.0   \n",
       "2680885        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2691100        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2691566        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2693772        0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "            704239  704240  \n",
       "id_student                  \n",
       "23798          0.0     0.0  \n",
       "27759          0.0     0.0  \n",
       "30091          0.0     0.0  \n",
       "31014          0.0     0.0  \n",
       "31849          0.0     0.0  \n",
       "...            ...     ...  \n",
       "2680344        0.0     0.0  \n",
       "2680885        0.0     0.0  \n",
       "2691100        0.0     0.0  \n",
       "2691566        0.0     0.0  \n",
       "2693772        0.0     0.0  \n",
       "\n",
       "[1851 rows x 328 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "student_activity = student_item.pivot_table(\n",
    "    values=\"sum_click\",\n",
    "    index=\"id_student\",\n",
    "    columns=\"id_site\",\n",
    "    fill_value=0.0,\n",
    ")\n",
    "student_profile = (\n",
    "    filter_by_module_presentation(oulad.student_info, CODE_MODULE, CODE_PRESENTATION)\n",
    "    .set_index(\"id_student\")\n",
    "    .drop([\"num_of_prev_attempts\", \"region\"], axis=1)\n",
    "    .join(student_registration, how=\"inner\")\n",
    "    .join((student_activity > 0).astype(float), how=\"inner\")\n",
    "    .fillna(0.0)\n",
    "    .replace(\n",
    "        {\n",
    "            \"age_band\": {\"0-35\": \"0.0\", \"35-55\": \"0.5\", \"55<=\": \"1.0\"},\n",
    "            \"disability\": {\"N\": \"0.0\", \"Y\": \"1.0\"},\n",
    "            \"gender\": {\"M\": \"0.0\", \"F\": \"1.0\"},\n",
    "            \"highest_education\": {\n",
    "                \"No Formal quals\": \"0.0\",\n",
    "                \"Lower Than A Level\": \"0.25\",\n",
    "                \"A Level or Equivalent\": \"0.5\",\n",
    "                \"HE Qualification\": \"0.75\",\n",
    "                \"Post Graduate Qualification\": \"1.0\",\n",
    "            },\n",
    "            \"imd_band\": {\n",
    "                # Using 0.0 instead of np.nan as NA's have been filled with zeros.\n",
    "                0.0: \"0.0\",\n",
    "                \"0-10%\": \"5.0\",\n",
    "                # The OULAD data set is missing the `%` in the `10-20` imd_band.\n",
    "                \"10-20\": \"15.0\",\n",
    "                \"20-30%\": \"25.0\",\n",
    "                \"30-40%\": \"35.0\",\n",
    "                \"40-50%\": \"45.0\",\n",
    "                \"50-60%\": \"55.0\",\n",
    "                \"60-70%\": \"65.0\",\n",
    "                \"70-80%\": \"75.0\",\n",
    "                \"80-90%\": \"85.0\",\n",
    "                \"90-100%\": \"95.0\",\n",
    "            },\n",
    "            \"final_result\": {\n",
    "                \"Withdrawn\": \"\",\n",
    "                \"Fail\": \"\",\n",
    "                \"Pass\": \"1\",\n",
    "                \"Distinction\": \"1\",\n",
    "            },\n",
    "        }\n",
    "    )\n",
    "    .astype(\n",
    "        {\n",
    "            \"age_band\": float,\n",
    "            \"disability\": float,\n",
    "            \"gender\": float,\n",
    "            \"highest_education\": float,\n",
    "            \"imd_band\": float,\n",
    "            \"final_result\": bool,\n",
    "        }\n",
    "    )\n",
    ")\n",
    "display(student_profile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2df28d1",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Train/Test split\n",
    "\n",
    "In this section we split the `student_profile` table into training and\n",
    "testing sets and standartize feature values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbefb1eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "RANDOM_STATE = 0\n",
    "feature_table = student_profile.drop([\"final_result\"], axis=1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    feature_table,\n",
    "    student_profile.final_result,\n",
    "    test_size=0.2,\n",
    "    random_state=RANDOM_STATE,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b27370",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Final result prediction\n",
    "\n",
    "Next, we train a decision tree model to predict the student `final_result` outcome.\n",
    "The model will be used at the final step to evaluate the quality of the\n",
    "recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a37e6bca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Decision Tree"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Precision: 0.8475"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Decision Tree Parameters: {'criterion': 'gini', 'max_depth': 9, 'min_samples_leaf': 6, 'min_samples_split': 13, 'random_state': 0, 'splitter': 'best'}"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Out of 147 failing students, the model predicted correctly 113 failing students (76.87%)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%capture -ns optimize_mooc_learner_pathways gs_classifier\n",
    "# Hyperparameter search space\n",
    "hyperparameters = {\n",
    "    \"criterion\": [\"gini\"],  # [\"gini\", \"entropy\", \"log_loss\"],\n",
    "    \"splitter\": [\"best\"],  # [\"random\", \"best\"],\n",
    "    \"max_depth\": [9],  # [None, *list(range(1, 20))],\n",
    "    \"min_samples_split\": [13],  # range(2, 20),\n",
    "    \"min_samples_leaf\": [6],  # range(1, 20),\n",
    "    \"random_state\": [RANDOM_STATE],\n",
    "}\n",
    "\n",
    "# Train Decision tree\n",
    "gs_classifier = GridSearchCV(\n",
    "    DecisionTreeClassifier(),\n",
    "    hyperparameters,\n",
    "    scoring=\"precision\",\n",
    "    n_jobs=-1,\n",
    "    error_score=\"raise\",\n",
    "    cv=StratifiedKFold(n_splits=3, shuffle=True, random_state=RANDOM_STATE),\n",
    ").fit(x_train, y_train)\n",
    "\n",
    "display(Markdown(\"#### Decision Tree\"))\n",
    "plt.figure(figsize=(20, 10))\n",
    "plot_tree(\n",
    "    gs_classifier.best_estimator_,\n",
    "    feature_names=x_train.columns.values.tolist(),\n",
    "    filled=True,\n",
    ")\n",
    "plt.show()\n",
    "\n",
    "display(Markdown(f\"Precision: {gs_classifier.score(x_test, y_test):.4f}\"))\n",
    "display(Markdown(f\"Decision Tree Parameters: {gs_classifier.best_params_}\"))\n",
    "predictions = gs_classifier.predict(x_test)\n",
    "display(\n",
    "    Markdown(\n",
    "        f\"Out of {(~y_test).sum()} failing students, the model predicted \"\n",
    "        f\"correctly {(~predictions[~y_test]).sum()} failing students \"\n",
    "        f\"({100 * (~predictions[~y_test]).sum() / (~y_test).sum():.2f}%)\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829a3d2c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Consensus clustering\n",
    "\n",
    "### Base clusterings\n",
    "\n",
    "At this stage we train our base clustering models which will be used in the MultiCons\n",
    "Consensus algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06fa9d1d-faa1-4b0d-93e1-ce8149097743",
   "metadata": {
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MultiCons: selected merging_threshold=0.5 with score: 0.39'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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   ],
   "source": [
    "%%capture -ns optimize_mooc_learner_pathways base_clusterings consensus\n",
    "base_clusterings = [\n",
    "    KMeans(\n",
    "        n_clusters=18, max_iter=4000, n_init=\"auto\", random_state=RANDOM_STATE\n",
    "    ).fit_predict(feature_table),\n",
    "    AgglomerativeClustering(n_clusters=19).fit_predict(feature_table),\n",
    "    GaussianMixture(n_components=19, random_state=RANDOM_STATE).fit_predict(\n",
    "        feature_table\n",
    "    ),\n",
    "    Birch(n_clusters=8, threshold=0.3).fit_predict(np.ascontiguousarray(feature_table)),\n",
    "    OPTICS(min_samples=11).fit_predict(feature_table),\n",
    "]\n",
    "\n",
    "\n",
    "def search_best_merging_threshold(clusterings, mt_range):\n",
    "    \"\"\"Loops over mt_range and returns the most similar fitted MultiCons instance.\"\"\"\n",
    "    max_score = 0\n",
    "    selected_consensus = None\n",
    "    for merging_threshold in mt_range:\n",
    "        multicons = MultiCons(\n",
    "            consensus_function=\"consensus_function_12\",\n",
    "            merging_threshold=merging_threshold,\n",
    "        ).fit(clusterings)\n",
    "        score = multicons.ensemble_similarity[multicons.recommended]\n",
    "        if score > max_score:\n",
    "            max_score = score\n",
    "            selected_consensus = multicons\n",
    "    return selected_consensus\n",
    "\n",
    "\n",
    "consensus = search_best_merging_threshold(base_clusterings, [0.5, 0.75])\n",
    "display(\n",
    "    f\"MultiCons: selected merging_threshold={consensus.merging_threshold} \"\n",
    "    f\"with score: {consensus.ensemble_similarity[consensus.recommended]:0.2f}\"\n",
    ")\n",
    "display(consensus.cons_tree())\n",
    "display(\n",
    "    pd.DataFrame(\n",
    "        {\"multicons\": consensus.labels_, \"final_result\": student_profile.final_result}\n",
    "    )\n",
    "    .groupby([\"multicons\", \"final_result\"])\n",
    "    .size()\n",
    "    .to_frame()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f762b3cd",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Collaborative filtering\n",
    "\n",
    "Next, for each consensus group we train a collaborative filtering model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa5e1caf",
   "metadata": {
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture -ns optimize_mooc_learner_pathways recommenders_mc\n",
    "def get_trained_recommenders(labels, algo, parameters) -> dict:\n",
    "    \"\"\"Returns a dictionary of trained recommenders by label.\"\"\"\n",
    "    recommenders = {}\n",
    "    for label in np.unique(labels):\n",
    "        mask = labels == label\n",
    "        subset = student_item[student_item.id_student.isin(feature_table.index[mask])]\n",
    "        reader = Reader(rating_scale=(0, subset.sum_click.max()))\n",
    "        data = Dataset.load_from_df(subset, reader)\n",
    "        grid_search = SupGridSearchCV(algo, parameters, cv=3, refit=True, n_jobs=-1)\n",
    "        grid_search.fit(data)\n",
    "        # display(Markdown(\"Label=%s RMSE=%.3f\" % (label, gs.best_score[\"rmse\"])))\n",
    "        recommenders[label] = grid_search\n",
    "    return recommenders\n",
    "\n",
    "\n",
    "sim_options = {\n",
    "    \"name\": [\"msd\"],  # [\"msd\", \"cosine\"],\n",
    "    \"min_support\": [4],  # [3, 4, 5],\n",
    "    \"user_based\": [False],  # [False, True],\n",
    "}\n",
    "param_grid = {\"sim_options\": sim_options, \"verbose\": [False]}\n",
    "recommenders_mc = get_trained_recommenders(consensus.labels_, KNNWithMeans, param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1421fee0",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Recommendation\n",
    "\n",
    "At this stage we generate recommedations for students that were predicted as failling.\n",
    "We simulate students to follow N recommendations and measure whether it changes the\n",
    "estimated success rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1809f042",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>multicons_collaborative_filtering</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.405405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.837838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.216216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.675676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35.135135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43.243243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50.675676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55.405405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60.810811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62.162162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64.189189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64.189189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>68.243243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    multicons_collaborative_filtering\n",
       "1                            5.405405\n",
       "2                            8.108108\n",
       "3                           12.837838\n",
       "4                           16.216216\n",
       "5                           25.675676\n",
       "6                           35.135135\n",
       "7                           43.243243\n",
       "8                           50.675676\n",
       "9                           55.405405\n",
       "10                          60.810811\n",
       "11                          62.162162\n",
       "12                          64.189189\n",
       "13                          64.189189\n",
       "14                          68.243243"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%capture -ns optimize_mooc_learner_pathways results_mc\n",
    "def get_recommendation_results(labels, recommenders):\n",
    "    \"\"\"Returns the percentages of succeeding students by recommendation count.\"\"\"\n",
    "    final_result_predictions = []\n",
    "    student_ids = y_test[~predictions].index\n",
    "    label_by_student = pd.Series(labels, index=feature_table.index)[student_ids]\n",
    "    for student_id in student_ids:\n",
    "        algo = recommenders[label_by_student[student_id]]\n",
    "        student = x_test.loc[student_id]\n",
    "\n",
    "        side_id_prediction = {\"site_id\": [], \"prediction\": []}\n",
    "        for site_id in student_activity.columns[student[student_activity.columns] == 0]:\n",
    "            prediction = algo.predict(student_id, site_id)\n",
    "            if prediction.details.get(\"was_impossible\"):\n",
    "                continue\n",
    "            prediction = int(prediction.est)\n",
    "            if prediction:\n",
    "                side_id_prediction[\"site_id\"].append(site_id)\n",
    "                side_id_prediction[\"prediction\"].append(prediction)\n",
    "\n",
    "        recommendations = pd.Series(\n",
    "            side_id_prediction[\"prediction\"], index=side_id_prediction[\"site_id\"]\n",
    "        ).sort_values(ascending=False)\n",
    "        following_recommendation_students = []\n",
    "        for recommendation_follow_count in range(1, 15):\n",
    "            new_student = student.copy()\n",
    "            new_student.loc[recommendations.index[:recommendation_follow_count]] = 1\n",
    "            following_recommendation_students.append(new_student)\n",
    "\n",
    "        final_result_predictions.append(\n",
    "            gs_classifier.predict(pd.DataFrame(following_recommendation_students))\n",
    "        )\n",
    "\n",
    "    return (\n",
    "        pd.DataFrame(final_result_predictions, columns=range(1, 15))\n",
    "        .sum()\n",
    "        .mul(100)\n",
    "        .div(len(student_ids))\n",
    "    )\n",
    "\n",
    "\n",
    "results_mc = get_recommendation_results(consensus.labels_, recommenders_mc)\n",
    "recommendation_improvement_rate_mc_cf_df = pd.DataFrame(\n",
    "    results_mc, columns=[\"multicons_collaborative_filtering\"], index=range(1, 15)\n",
    ")\n",
    "display(recommendation_improvement_rate_mc_cf_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4984268c",
   "metadata": {},
   "source": [
    "## Validation\n",
    "\n",
    "Finally, we compare the quality of our approach witha baseline method that applies\n",
    "collaborative filtering on the full dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d9aa76c7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>multicons_collaborative_filtering</th>\n",
       "      <th>collaborative_filtering</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.405405</td>\n",
       "      <td>0.675676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.108108</td>\n",
       "      <td>6.081081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.837838</td>\n",
       "      <td>12.162162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.216216</td>\n",
       "      <td>18.243243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.675676</td>\n",
       "      <td>25.675676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35.135135</td>\n",
       "      <td>35.810811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43.243243</td>\n",
       "      <td>43.918919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50.675676</td>\n",
       "      <td>53.378378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55.405405</td>\n",
       "      <td>54.054054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60.810811</td>\n",
       "      <td>59.459459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62.162162</td>\n",
       "      <td>62.837838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64.189189</td>\n",
       "      <td>66.891892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64.189189</td>\n",
       "      <td>68.243243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>68.243243</td>\n",
       "      <td>72.297297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    multicons_collaborative_filtering  collaborative_filtering\n",
       "1                            5.405405                 0.675676\n",
       "2                            8.108108                 6.081081\n",
       "3                           12.837838                12.162162\n",
       "4                           16.216216                18.243243\n",
       "5                           25.675676                25.675676\n",
       "6                           35.135135                35.810811\n",
       "7                           43.243243                43.918919\n",
       "8                           50.675676                53.378378\n",
       "9                           55.405405                54.054054\n",
       "10                          60.810811                59.459459\n",
       "11                          62.162162                62.837838\n",
       "12                          64.189189                66.891892\n",
       "13                          64.189189                68.243243\n",
       "14                          68.243243                72.297297"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%capture -ns optimize_mooc_learner_pathways recommenders_cf results_cf\n",
    "single_cluster = np.zeros(student_profile.shape[0])\n",
    "recommenders_cf = get_trained_recommenders(single_cluster, KNNWithMeans, param_grid)\n",
    "results_cf = get_recommendation_results(single_cluster, recommenders_cf)\n",
    "recommendation_improvement_rate_mc_cf_df[\"collaborative_filtering\"] = results_cf\n",
    "display(recommendation_improvement_rate_mc_cf_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22c17f7d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recommendation_improvement_rate_mc_cf_df.plot(\n",
    "    title=\"Precentage of succeding students by number of applied recommendations\",\n",
    "    xlabel=\"Recommendation count\",\n",
    "    ylabel=\"Success percentage\",\n",
    "    xticks=recommendation_improvement_rate_mc_cf_df.index,\n",
    ")\n",
    "display()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}