RecSys_PyTorch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf4e2680-7cb8-43ae-9f0f-68fbb0a54db1",
   "metadata": {},
   "source": [
    "# Building an Educational Content Recommender System in PyTorch\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Welcome to this PyTorch tutorial focused on creating a recommender system for educational content. The goal is to predict the most effective distribution of study time across various content types, enhancing student learning experiences. We'll utilize a dataset reflecting student interactions with different educational materials to train our model.\n",
    "\n",
    "## Understanding the Dataset\n",
    "\n",
    "Our dataset comprises various elements capturing student interactions with educational content. The dataset includes student identifiers, types of educational content, and interaction details, providing rich information for model training. Importantly, we'll use 'LearnerProfile' only for validation, not for training the model.\n",
    "\n",
    "### Key Elements\n",
    "\n",
    "- **StudentID**: Provides unique identification for personalized recommendations.\n",
    "- **TopicID and ContentID**: Connects content to specific educational topics.\n",
    "- **ContentType**: Reflects the variety of educational materials.\n",
    "- **InteractionStatus and Duration**: Indicates engagement depth with content.\n",
    "- **TopicPerformanceScore**: Measures student understanding, aiding in gauging content effectiveness.\n",
    "\n",
    "### Loading the Data\n",
    "\n",
    "We begin by loading the dataset into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce9d7087-86cc-4633-9be0-a67b77dfab87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T03:45:59.443468Z",
     "iopub.status.busy": "2024-01-29T03:45:59.443188Z",
     "iopub.status.idle": "2024-01-29T03:46:00.126626Z",
     "shell.execute_reply": "2024-01-29T03:46:00.125592Z",
     "shell.execute_reply.started": "2024-01-29T03:45:59.443441Z"
    }
   },
   "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>StudentID</th>\n",
       "      <th>LearnerProfile</th>\n",
       "      <th>TopicID</th>\n",
       "      <th>ContentID</th>\n",
       "      <th>ContentType</th>\n",
       "      <th>InteractionStatus</th>\n",
       "      <th>InteractionDuration</th>\n",
       "      <th>TopicPerformanceScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Synthesizers</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lecture Notes</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.531156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Synthesizers</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Educational Games</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.531156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Synthesizers</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Audio Lectures</td>\n",
       "      <td>1</td>\n",
       "      <td>18.181458</td>\n",
       "      <td>92.531156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Synthesizers</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Infographics and Visual Aids</td>\n",
       "      <td>1</td>\n",
       "      <td>23.665718</td>\n",
       "      <td>92.531156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Synthesizers</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Examples and Analogies</td>\n",
       "      <td>1</td>\n",
       "      <td>17.906003</td>\n",
       "      <td>92.531156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89995</th>\n",
       "      <td>500</td>\n",
       "      <td>Visual and Auditory Learners</td>\n",
       "      <td>30</td>\n",
       "      <td>176</td>\n",
       "      <td>Educational Games</td>\n",
       "      <td>1</td>\n",
       "      <td>17.905029</td>\n",
       "      <td>95.591516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89996</th>\n",
       "      <td>500</td>\n",
       "      <td>Visual and Auditory Learners</td>\n",
       "      <td>30</td>\n",
       "      <td>177</td>\n",
       "      <td>Audio Lectures</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>95.591516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89997</th>\n",
       "      <td>500</td>\n",
       "      <td>Visual and Auditory Learners</td>\n",
       "      <td>30</td>\n",
       "      <td>178</td>\n",
       "      <td>Infographics and Visual Aids</td>\n",
       "      <td>1</td>\n",
       "      <td>23.738050</td>\n",
       "      <td>95.591516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89998</th>\n",
       "      <td>500</td>\n",
       "      <td>Visual and Auditory Learners</td>\n",
       "      <td>30</td>\n",
       "      <td>179</td>\n",
       "      <td>Examples and Analogies</td>\n",
       "      <td>1</td>\n",
       "      <td>15.367639</td>\n",
       "      <td>95.591516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89999</th>\n",
       "      <td>500</td>\n",
       "      <td>Visual and Auditory Learners</td>\n",
       "      <td>30</td>\n",
       "      <td>180</td>\n",
       "      <td>Instructional Videos</td>\n",
       "      <td>1</td>\n",
       "      <td>45.413550</td>\n",
       "      <td>95.591516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       StudentID                LearnerProfile  TopicID  ContentID  \\\n",
       "0              1                  Synthesizers        1          1   \n",
       "1              1                  Synthesizers        1          2   \n",
       "2              1                  Synthesizers        1          3   \n",
       "3              1                  Synthesizers        1          4   \n",
       "4              1                  Synthesizers        1          5   \n",
       "...          ...                           ...      ...        ...   \n",
       "89995        500  Visual and Auditory Learners       30        176   \n",
       "89996        500  Visual and Auditory Learners       30        177   \n",
       "89997        500  Visual and Auditory Learners       30        178   \n",
       "89998        500  Visual and Auditory Learners       30        179   \n",
       "89999        500  Visual and Auditory Learners       30        180   \n",
       "\n",
       "                        ContentType  InteractionStatus  InteractionDuration  \\\n",
       "0                     Lecture Notes                  0             0.000000   \n",
       "1                 Educational Games                  0             0.000000   \n",
       "2                    Audio Lectures                  1            18.181458   \n",
       "3      Infographics and Visual Aids                  1            23.665718   \n",
       "4            Examples and Analogies                  1            17.906003   \n",
       "...                             ...                ...                  ...   \n",
       "89995             Educational Games                  1            17.905029   \n",
       "89996                Audio Lectures                  0             0.000000   \n",
       "89997  Infographics and Visual Aids                  1            23.738050   \n",
       "89998        Examples and Analogies                  1            15.367639   \n",
       "89999          Instructional Videos                  1            45.413550   \n",
       "\n",
       "       TopicPerformanceScore  \n",
       "0                  92.531156  \n",
       "1                  92.531156  \n",
       "2                  92.531156  \n",
       "3                  92.531156  \n",
       "4                  92.531156  \n",
       "...                      ...  \n",
       "89995              95.591516  \n",
       "89996              95.591516  \n",
       "89997              95.591516  \n",
       "89998              95.591516  \n",
       "89999              95.591516  \n",
       "\n",
       "[90000 rows x 8 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "csv_file_path = 'data/content_recommender_data.csv'\n",
    "df = pd.read_csv(csv_file_path)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac236ee0-205b-4721-a306-0cd5d924bf5f",
   "metadata": {},
   "source": [
    "## Objective\n",
    "\n",
    "To determine the optimal distribution of time that a given student should spend on a particular type of learning content. Therefore, providing insight into what type of content should be recommended next.\n",
    "\n",
    "## Approach\n",
    "\n",
    "1. **Model Inputs**:\n",
    "   - `StudentID`: To capture individual student characteristics.\n",
    "   - `TopicPerformanceScore`: The student's performance level on a given topic.\n",
    "\n",
    "2. **Model Output**:\n",
    "   - `InteractionDuration` The distribution of time spent across the different content types.\n",
    "\n",
    "3. **Training Phase**:\n",
    "   - The model learns from actual student data.\n",
    "   - The `TopicPerformanceScore` is sourced from the dataset, representing actual performance scores.\n",
    "   - The model learns the relationship between student characteristics, performance scores, and the distribution of time spent on content types.\n",
    "\n",
    "4. **Inference Phase**:\n",
    "   - You set `TopicPerformanceScore` to an ideal level (like 1).\n",
    "   - The model then predicts the `InteractionDuration` vector.\n",
    "   - This vector suggests an optimal study strategy for each content type to achieve the best possible outcome on a new topic.\n",
    "\n",
    "5. **Application**:\n",
    "   - By predicting the optimal distribution of time spent on particular content types for a given student. The recommender system can reccomend the content type that the student is lacking on their current topic."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ace8d19-3a1b-4610-a033-95143354b1ce",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "Effective preprocessing is vital for preparing the dataset for our recommender system. We focus on creating 'InteractionDistribution' vectors and splitting the dataset into training and validation sets. This process ensures our model learns from a representative sample of student interactions while allowing for independent evaluation of its performance.\n",
    "\n",
    "### Steps Involved:\n",
    "\n",
    "1. **Generating Interaction Distributions**: Transforming raw interaction data into normalized distribution vectors representing the proportion of time spent on each content type.\n",
    "2. **Data Splitting**: Carefully dividing the dataset to ensure distinct student representations in both training and validation datasets, thereby preventing data leakage and ensuring model generalizability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbb5b3a2-9509-49e9-be5c-165c75274a0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T03:46:00.128460Z",
     "iopub.status.busy": "2024-01-29T03:46:00.128072Z",
     "iopub.status.idle": "2024-01-29T03:46:14.039159Z",
     "shell.execute_reply": "2024-01-29T03:46:14.038015Z",
     "shell.execute_reply.started": "2024-01-29T03:46:00.128433Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Create a function to generate the interaction distribution vector\n",
    "def create_interaction_distribution(df):\n",
    "    # Group by StudentID and TopicID\n",
    "    grouped = df.groupby(['StudentID', 'TopicID'])\n",
    "\n",
    "    # Initialize a dictionary to store the interaction distribution vectors\n",
    "    interaction_distributions = []\n",
    "\n",
    "    for (student_id, topic_id), group in grouped:\n",
    "        # Calculate total interaction duration per content type\n",
    "        total_duration = group.groupby('ContentType')['InteractionDuration'].sum()\n",
    "\n",
    "        # Normalize the vector so it sums to 1\n",
    "        normalized_duration = total_duration / (total_duration.sum()+1e-6)\n",
    "\n",
    "        # Create a dictionary entry for each student-topic pair\n",
    "        interaction_distributions.append({\n",
    "            'StudentID': student_id,\n",
    "            'TopicID': topic_id,\n",
    "            'TopicPerformanceScore': group['TopicPerformanceScore'].iloc[0],\n",
    "            'InteractionDistribution': normalized_duration\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(interaction_distributions)\n",
    "\n",
    "# Apply the function to the dataset\n",
    "interaction_df = create_interaction_distribution(df)\n",
    "\n",
    "# Split data \n",
    "train_df, validate_df = train_test_split(interaction_df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Remove from memory\n",
    "del interaction_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0945d14-5d98-484e-bd11-1a5d6e5c72ae",
   "metadata": {},
   "source": [
    "## DataLoader Construction\n",
    "\n",
    "Efficient data handling and batching are critical for model training and validation. We utilize PyTorch's DataLoader for streamlined data processing, enabling efficient and manageable batches of data to be fed into our model during training and evaluation phases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "520388f7-9f9f-402f-8f2a-e73fbb9887e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T03:46:14.040936Z",
     "iopub.status.busy": "2024-01-29T03:46:14.040509Z",
     "iopub.status.idle": "2024-01-29T03:46:15.848972Z",
     "shell.execute_reply": "2024-01-29T03:46:15.847824Z",
     "shell.execute_reply.started": "2024-01-29T03:46:14.040910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training set has 12000 samples and the validation set has 3000 samples.\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "class ContentRecommenderDataset(Dataset):\n",
    "    def __init__(self, dataframe, keys):\n",
    "        self.dataframe = dataframe\n",
    "        self.keys = keys  # Preset order of content types\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.dataframe)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        # Subtract 1 from ID to start at 0 index for embedding\n",
    "        student_id = self.dataframe.iloc[idx]['StudentID'] - 1\n",
    "        \n",
    "        performance_score = torch.tensor(self.dataframe.iloc[idx]['TopicPerformanceScore'], dtype=torch.float32)/100\n",
    "\n",
    "        # Ensuring consistent order of content types\n",
    "        interaction_distribution = torch.tensor([self.dataframe.iloc[idx]['InteractionDistribution'][key] for key in self.keys], dtype=torch.float32)\n",
    "\n",
    "        return student_id, performance_score, interaction_distribution\n",
    "\n",
    "# Preset order of content types\n",
    "keys = sorted(train_df['InteractionDistribution'].values[0].keys())\n",
    "\n",
    "# Initialize the datasets with keys\n",
    "train_dataset = ContentRecommenderDataset(train_df, keys)\n",
    "val_dataset = ContentRecommenderDataset(validate_df, keys)\n",
    "\n",
    "# Create DataLoaders\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=True)\n",
    "\n",
    "print(f'The training set has {len(train_dataset)} samples and the validation set has {len(val_dataset)} samples.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca42445c-9d03-4208-87a4-7d0ea90fc9a9",
   "metadata": {},
   "source": [
    "## Model Architecture\n",
    "\n",
    "Our recommender system leverages a neural network architecture, incorporating embedding layers for categorical data and fully connected layers. This architecture is designed to capture complex relationships between student characteristics, content interactions, and performance outcomes.\n",
    "\n",
    "### Features:\n",
    "\n",
    "- **Student Embedding**: Captures unique student characteristics and learning patterns.\n",
    "- **Fully Connected Layers**: Integrates embeddings with continuous features like performance scores, culminating in output layers predicting interaction distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d1a02bb-7429-4534-bce0-b3d3a981e79d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T03:46:15.851067Z",
     "iopub.status.busy": "2024-01-29T03:46:15.850496Z",
     "iopub.status.idle": "2024-01-29T03:46:15.863262Z",
     "shell.execute_reply": "2024-01-29T03:46:15.862364Z",
     "shell.execute_reply.started": "2024-01-29T03:46:15.851035Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class ContentRecommenderModel(nn.Module):\n",
    "    def __init__(self, num_students, embedding_dim, num_content_types):\n",
    "        super(ContentRecommenderModel, self).__init__()\n",
    "        self.student_embedding = nn.Embedding(num_students, embedding_dim)\n",
    "        self.fc1 = nn.Linear(embedding_dim + 1, 64)  # Plus 1 for performance score\n",
    "        self.fc2 = nn.Linear(64, 64)\n",
    "        self.output = nn.Linear(64, num_content_types)\n",
    "\n",
    "    def forward(self, student_id, performance_score, out_distbn=False):\n",
    "        student_embed = self.student_embedding(student_id)\n",
    "        combined_input = torch.cat((student_embed, performance_score.unsqueeze(1)), dim=1)\n",
    "        x = F.relu(self.fc1(combined_input))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        interaction_distribution = self.output(x)\n",
    "        if out_distbn:\n",
    "            # Make logits sum to 1\n",
    "            interaction_distribution = torch.nn.functional.softmax(interaction_distribution, dim=1)\n",
    "        return interaction_distribution\n",
    "\n",
    "# Parameters for the model\n",
    "num_students = df['StudentID'].nunique()\n",
    "embedding_dim = 10  # This can be tuned\n",
    "num_content_types = len(keys)\n",
    "\n",
    "# Create an instance of the model\n",
    "model = ContentRecommenderModel(num_students, embedding_dim, num_content_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be74ae6-a571-4b34-bb74-019bf57c27bb",
   "metadata": {},
   "source": [
    "## Training and Validation\n",
    "\n",
    "We outline the process for training our model, including setting up the optimizer and defining a suitable loss function. \n",
    "\n",
    "### Key Components:\n",
    "\n",
    "- **Optimizer Setup**: Configuring an optimizer to update the model's parameters based on the computed gradients.\n",
    "- **Loss Function**: Choosing a loss function that aligns with our objective of predicting interaction distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "272046b9-61a9-4e24-a0d0-0d82110331b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T03:46:15.864431Z",
     "iopub.status.busy": "2024-01-29T03:46:15.864061Z",
     "iopub.status.idle": "2024-01-29T03:46:15.873181Z",
     "shell.execute_reply": "2024-01-29T03:46:15.872283Z",
     "shell.execute_reply.started": "2024-01-29T03:46:15.864391Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setting up the optimizer\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=0.01)\n",
    "\n",
    "# Defining the loss function\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5b1ed4-a177-47fe-a645-ee8da46f42f2",
   "metadata": {},
   "source": [
    "### Training Loop\n",
    "\n",
    "The training loop involves iterating over epochs, where the model learns from the training data and is evaluated against the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "daf070d2-26f1-4d18-b78f-299516074ac3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T03:46:15.874190Z",
     "iopub.status.busy": "2024-01-29T03:46:15.873900Z",
     "iopub.status.idle": "2024-01-29T04:05:35.396275Z",
     "shell.execute_reply": "2024-01-29T04:05:35.394922Z",
     "shell.execute_reply.started": "2024-01-29T03:46:15.874163Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 100/100, Train Loss: 1.523548992474874, Val Loss: 1.6593862442260092\r"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "num_epochs = 100\n",
    "train_losses, val_losses = [], []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # Training Phase\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    for student_id, performance_score, tgt_distbn in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(student_id, performance_score)\n",
    "        loss = criterion(outputs, tgt_distbn)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "    avg_train_loss = train_loss / len(train_loader)\n",
    "    train_losses.append(avg_train_loss)\n",
    "\n",
    "    # Validation Phase\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    with torch.no_grad():\n",
    "        for student_id, performance_score, tgt_distbn in val_loader:\n",
    "            outputs = model(student_id, performance_score)\n",
    "            loss = criterion(outputs, tgt_distbn)\n",
    "            val_loss += loss.item()\n",
    "    avg_val_loss = val_loss / len(val_loader)\n",
    "    val_losses.append(avg_val_loss)\n",
    "\n",
    "    print(f'Epoch {epoch+1}/{num_epochs}, Train Loss: {avg_train_loss}, Val Loss: {avg_val_loss}', end='\\r')\n",
    "\n",
    "# Plotting the training and validation losses\n",
    "plt.plot(range(1, num_epochs+1), train_losses, label='Train')\n",
    "plt.plot(range(1, num_epochs+1), val_losses, label='Validation')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8ed5c3-3743-47aa-8cb8-b92c4d6ff943",
   "metadata": {},
   "source": [
    "From the training progress, it appears that the model is over-fitting w.r.t the training set; however, I would imagine this has to do with the small size of the dataset. The neural network approach requires more data than simpler model-based methods or memory-based methods. Of course there are also ways to mitigate some of the over-fitting such as using dropout, larger batch size, etc.\n",
    "\n",
    "This is also a simple mock dataset and I would imagine with a more realistic dataset, there would be less overfitting as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060dac71-ffa2-482b-a0ab-a17845ade45d",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "After training, we assess the model's performance by visualizing the loss over epochs and analyzing the predicted interaction distributions for different learner profiles. This step ensures that our model's recommendations align with the unique characteristics and preferences of each learner type (which were not used during trainig).\n",
    "\n",
    "### Visualization:\n",
    "\n",
    "- Plotting training and validation losses to assess model convergence.\n",
    "- Analyzing predicted content interaction distributions for different learner profiles, validating the model's effectiveness in capturing distinct learning preferences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bc28e52a-da37-4652-9e45-ea3d52958e17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T04:05:35.401182Z",
     "iopub.status.busy": "2024-01-29T04:05:35.400826Z",
     "iopub.status.idle": "2024-01-29T04:05:36.332599Z",
     "shell.execute_reply": "2024-01-29T04:05:36.331235Z",
     "shell.execute_reply.started": "2024-01-29T04:05:35.401153Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Find one StudentID for each LearnerProfile\n",
    "unique_profiles = df['LearnerProfile'].unique()\n",
    "student_ids_for_profiles = {}\n",
    "\n",
    "# Number of profiles\n",
    "num_profiles = len(unique_profiles)\n",
    "cols = 2  # Number of columns in grid\n",
    "rows = num_profiles // cols + (num_profiles % cols > 0)  # Calculate rows needed\n",
    "\n",
    "# Create subplots\n",
    "fig, axes = plt.subplots(rows, cols, figsize=(15, 5 * rows))\n",
    "axes = axes.flatten()  # Flatten to simplify indexing\n",
    "\n",
    "for profile in unique_profiles:\n",
    "    student_ids_for_profiles[profile] = df[df['LearnerProfile'] == profile]['StudentID'].iloc[0]\n",
    "\n",
    "# Predict optimal distribution and plot histograms\n",
    "for i, (profile, student_id) in enumerate(student_ids_for_profiles.items()):\n",
    "    # Prepare input for model\n",
    "    performance_score = torch.tensor([1.0], dtype=torch.float32)  # Assuming best performance score\n",
    "    student_tensor = torch.tensor([student_id], dtype=torch.int) - 1 # Starting indexing at 0\n",
    "\n",
    "    # Predict the distribution\n",
    "    with torch.no_grad():\n",
    "        predicted_distribution = model(student_tensor, performance_score, out_distbn=True)\n",
    "\n",
    "     # Plotting in subplot\n",
    "    axes[i].bar(range(len(predicted_distribution[0])), predicted_distribution[0])\n",
    "    axes[i].set_title(f'Optimal Interaction Distribution for {profile}')\n",
    "    axes[i].set_ylabel('Time Spent')\n",
    "    axes[i].set_xticks(range(len(keys)))\n",
    "    axes[i].set_xticklabels(keys, rotation=70)\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25ced95-0aa6-4f3c-888e-fd01d709c397",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T03:31:03.686856Z",
     "iopub.status.busy": "2024-01-29T03:31:03.686197Z",
     "iopub.status.idle": "2024-01-29T03:31:03.695119Z",
     "shell.execute_reply": "2024-01-29T03:31:03.693748Z",
     "shell.execute_reply.started": "2024-01-29T03:31:03.686784Z"
    }
   },
   "source": [
    "## Conclusion and Future Directions\n",
    "\n",
    "Our model offers a foundational approach to recommending educational content. However, it currently lacks the capability to handle new student IDs not present in the training phase. \n",
    "\n",
    "To address this, there are a few strategies you could consider:\n",
    "\n",
    "1. **Cold Start Strategy**: Implement a cold start strategy for new students. This often involves using a generalized model or heuristic until enough data is collected about the new student. For instance, initially, you might recommend a balanced content type distribution until the model learns more about the student's preferences and performance.\n",
    "\n",
    "2. **Metadata-Based Embeddings**: If you have additional metadata about students (like their learning preferences, prior educational background, or demographic information), you could use this data to generate initial embeddings for new students. This approach can provide a starting point that's more personalized than a generic model.\n",
    "\n",
    "3. **Similarity-Based Assignment**: Another approach could be to find students in the training data who are similar to the new student based on available features. You can use their embedding as a proxy for the new student until more personalized data is collected.\n",
    "\n",
    "4. **Hybrid Approach**: Combine content-based and collaborative filtering approaches. This could involve using both the content being interacted with and the user's characteristics to predict the interaction duration.\n",
    "\n",
    "5. **Online Learning**: Continuously update the model with new data. As the model encounters new StudentIDs and their interactions, it can adjust and fine-tune the embeddings accordingly.\n",
    "\n",
    "Each of these strategies has its strengths and limitations, and the choice might depend on the specific constraints and objectives of your recommender system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00caf0b-f91e-4075-a9c6-6142477588d9",
   "metadata": {},
   "source": [
    "## Visualize the distribution of StudentID Embedding\n",
    "\n",
    "For instance, it could be possible to look at the student embeddings to see if they cluster depending on the different learning styles.\n",
    "\n",
    "The beginnings of such an exploration are shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6fb8aabf-9048-4e5a-b7e3-bbc552e9eda7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T14:38:59.149463Z",
     "iopub.status.busy": "2024-01-29T14:38:59.148901Z",
     "iopub.status.idle": "2024-01-29T14:38:59.157708Z",
     "shell.execute_reply": "2024-01-29T14:38:59.156404Z",
     "shell.execute_reply.started": "2024-01-29T14:38:59.149419Z"
    }
   },
   "outputs": [],
   "source": [
    "# Prepare input for model\n",
    "student_tensor = torch.arange(df['StudentID'].nunique())\n",
    "# Predict the embeddings\n",
    "with torch.no_grad():\n",
    "    student_embeddings = model.student_embedding(student_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0915160d-85db-4d3d-881e-d265a5418f01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T14:39:00.205790Z",
     "iopub.status.busy": "2024-01-29T14:39:00.205267Z",
     "iopub.status.idle": "2024-01-29T14:39:03.997595Z",
     "shell.execute_reply": "2024-01-29T14:39:03.996472Z",
     "shell.execute_reply.started": "2024-01-29T14:39:00.205759Z"
    }
   },
   "outputs": [],
   "source": [
    "import umap\n",
    "\n",
    "reducer = umap.UMAP()\n",
    "reducer.fit(student_embeddings)\n",
    "umap_embedding = reducer.transform(student_embeddings)\n",
    "\n",
    "student_profiles = []\n",
    "for student_id in student_tensor:\n",
    "    student_id = int(student_id) + 1\n",
    "    student_profiles.append(df[df['StudentID'] == student_id]['LearnerProfile'].iloc[0])\n",
    "student_profiles = np.array(student_profiles)\n",
    "\n",
    "student_profiles_nums = np.zeros((len(student_profiles),))\n",
    "profiles = {}\n",
    "for i, prof in enumerate(np.unique(student_profiles)):\n",
    "    profiles[prof] = i\n",
    "    student_profiles_nums[student_profiles==prof] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0f40f83c-819d-4d59-af70-da9713ff9cca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-29T14:39:04.064746Z",
     "iopub.status.busy": "2024-01-29T14:39:04.064476Z",
     "iopub.status.idle": "2024-01-29T14:39:04.340985Z",
     "shell.execute_reply": "2024-01-29T14:39:04.339904Z",
     "shell.execute_reply.started": "2024-01-29T14:39:04.064720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "# Define four distinct colors\n",
    "colors = ['#FF5733', '#33FF57', '#3357FF', '#FF33F6']  # Example colors: red, green, blue, pink\n",
    "# Create a custom colormap\n",
    "custom_cmap = ListedColormap(colors)\n",
    "\n",
    "pts = plt.scatter(umap_embedding[:, 0], umap_embedding[:, 1], c=student_profiles_nums, cmap=custom_cmap, s=5, vmin=0, vmax=len(profiles)-1)\n",
    "\n",
    "plt.gca().set_aspect('equal', 'datalim')\n",
    "# Colorbar\n",
    "cbar = plt.colorbar(pts, ticks=np.linspace(0, len(profiles)-1, 2*len(profiles)+1)[1::2])\n",
    "cbar.ax.set_yticklabels(profiles.keys())\n",
    "\n",
    "#plt.colorbar(pts)\n",
    "plt.title('UMAP Projection of Embeddings', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68c4356d-3878-4294-8716-ede3fde39dd1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}