LinUCB.ipynb

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      "source": [
        "#LinUCB contextual bandit algorithm\n",
        "**is used here to create an adaptive learning environment\n",
        "as learners improve**"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/"
        },
        "id": "ONJC_RCBnkt1",
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          "text": [
            "Preference scores: [0.70710678 0.70710678 0.70710678 1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.         1.         1.\n",
            " 1.         1.         1.         1.        ]\n",
            "Chosen subject based on preference: contest 4\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import logging\n",
        "import pickle\n",
        "\n",
        "# Configure logging\n",
        "logging.basicConfig(filename='linucb.log', level=logging.INFO, format='%(asctime)s - %(message)s')\n",
        "\n",
        "\n",
        "def save_context(context, filename=\"context.pkl\"):\n",
        "    with open(filename, \"wb\") as f:\n",
        "        pickle.dump(context, f)\n",
        "\n",
        "def load_context(filename=\"context.pkl\"):\n",
        "    try:\n",
        "        with open(filename, \"rb\") as f:\n",
        "            return pickle.load(f)\n",
        "    except FileNotFoundError:\n",
        "        return None\n",
        "\n",
        "\n",
        "# List of subjects\n",
        "contest_rank = [\n",
        "    'contest 1', 'contest 2', 'contest 3', 'contest 4', 'contest 5', 'contest 6', 'contest 7', 'contest 8', 'contest 9', 'contest 10',\n",
        "    'contest 11', 'contest 12', 'contest 13', 'contest 14', 'contest 15', 'contest 16', 'contest 17', 'contest 18', 'contest 19', 'contest 20',\n",
        "    'contest 21', 'contest 22', 'contest 23', 'contest 24', 'contest 25', 'contest 26', 'contest 27', 'contest 28', 'contest 29', 'contest 30',\n",
        "    'contest 31', 'contest 32', 'contest 33', 'contest 34', 'contest 35', 'contest 36', 'contest 37', 'contest 38', 'contest 39', 'contest 40'\n",
        "]\n",
        "\n",
        "\n",
        "class LinUCB:\n",
        "    def __init__(self, n_actions, n_features, alpha=1.0):\n",
        "        self.n_actions = n_actions\n",
        "        self.n_features = n_features\n",
        "        self.alpha = alpha\n",
        "\n",
        "        # Initialize parameters\n",
        "        self.A = np.array([np.identity(n_features) for _ in range(n_actions)])  # action covariance matrix\n",
        "        self.b = np.array([np.zeros(n_features) for _ in range(n_actions)])  # action reward vector\n",
        "        self.theta = np.array([np.zeros(n_features) for _ in range(n_actions)])  # action parameter vector\n",
        "\n",
        "        # Initialize interaction counts\n",
        "        self.interaction_counts = np.zeros(n_actions)\n",
        "\n",
        "    def predict(self, context):\n",
        "        context = np.array(context)  # Convert list to ndarray\n",
        "        p = np.zeros(self.n_actions)\n",
        "        for a in range(self.n_actions):\n",
        "            self.theta[a] = np.dot(np.linalg.inv(self.A[a]), self.b[a])  # theta_a = A_a^-1 * b_a\n",
        "            p[a] = np.dot(self.theta[a], context) + self.alpha * np.sqrt(np.dot(context, np.dot(np.linalg.inv(self.A[a]), context)))\n",
        "        return p\n",
        "\n",
        "    def update(self, action, context, reward):\n",
        "        context = np.array(context)  # Convert list to ndarray if necessary\n",
        "        context = context.reshape(-1)  # Ensure context is a flat array\n",
        "        self.A[action] += np.outer(context, context)  # A_a = A_a + x_t * x_t^T\n",
        "        self.b[action] += reward * context  # b_a = b_a + r_t * x_t\n",
        "        self.interaction_counts[action] += 1  # Increment interaction count for the chosen action\n",
        "\n",
        "        # Log the update\n",
        "        logging.info(f\"Action: {action}, Context: {context.tolist()}, Reward: {reward}\")\n",
        "        self.save_state()\n",
        "\n",
        "    def save_state(self, filename=\"model_state.pkl\"):\n",
        "        with open(filename, \"wb\") as f:\n",
        "            pickle.dump(self, f)\n",
        "\n",
        "    @staticmethod\n",
        "    def load_state(filename=\"model_state.pkl\"):\n",
        "        try:\n",
        "            with open(filename, \"rb\") as f:\n",
        "                return pickle.load(f)\n",
        "        except FileNotFoundError:\n",
        "            return None\n",
        "\n",
        "\n",
        "# Example usage\n",
        "\n",
        "# Suppose we have 40 subjects (actions) and each context is a 4-dimensional feature vector\n",
        "n_actions = len(subjects)\n",
        "n_features = 1\n",
        "alpha = 1.0\n",
        "\n",
        "# Try to load an existing model, otherwise initialize a new one\n",
        "modelll = LinUCB.load_state() or LinUCB(n_actions, n_features, alpha)\n",
        "\n",
        "# Example context vector for a user (e.g., user's preferences or history in some feature space)\n",
        "context = [1]\n",
        "\n",
        "\n",
        "# Predict the preference scores for each subject\n",
        "preference_scores = modelll.predict(context)\n",
        "print(\"Preference scores:\", preference_scores)\n",
        "\n",
        "# Select the action (subject) with the highest score\n",
        "chosen_action = np.argmax(preference_scores)\n",
        "print(\"Chosen subject based on preference:\", contest_rank[chosen_action])\n",
        "\n",
        "# print(type(subjects.index(subjects[chosen_action])))\n",
        "# Update the model with the chosen action, context, and reward (e.g., user clicked on the subject)\n",
        "answer = \"correct\"\n",
        "if answer == \"incorrect\":\n",
        "    reward = 1\n",
        "if answer == \"correct\":\n",
        "    reward = 0\n",
        "modelll.update(chosen_action, context, reward)"
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