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        "# Lesson 04: PyTorch MLP Forecaster\n",
        "\n",
        "**CS230 inspiration:** Deep neural networks, vectorization, programming frameworks, and practical implementation.\n",
        "\n",
        "**Demand forecasting focus:** Build a PyTorch MLP that predicts future demand from lagged tabular features.\n",
        "\n",
        "**Learning objectives**\n",
        "\n",
        "- Use Dataset/DataLoader style tensors for forecasting data.\n",
        "- Train and evaluate a compact MLP with MAE loss.\n",
        "- Compare neural performance against lesson 01 baselines.\n",
        "\n",
        "**Senior-readiness checkpoint:** You can write a clean PyTorch training loop and keep preprocessing identical across splits.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Cell 1 Explanation\n",
        "\n",
        "**What this cell does:** This setup cell imports the shared scientific Python stack and the curriculum helpers used by later cells. It also establishes the deterministic seed convention so repeated runs are comparable.\n",
        "\n",
        "**Why it matters:** A restart-and-run notebook should make its dependencies explicit before any modeling work begins. For forecasting, reproducibility is especially important because small data or seed changes can change validation conclusions.\n",
        "\n",
        "**Key operations to notice:** confirm that shared helper imports match the lesson's code path; check that `SEED` is defined before any generated data or model training\n",
        "\n",
        "**What to inspect:** Check that imports are grouped by purpose: general analysis packages, forecasting data helpers, metrics, plotting utilities, and any lesson-specific libraries.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from demand_forecasting_curriculum.data import (\n",
        "    DEFAULT_FEATURE_COLUMNS,\n",
        "    generate_daily_demand,\n",
        "    make_sequence_dataset,\n",
        "    make_supervised_table,\n",
        "    time_series_split,\n",
        ")\n",
        "from demand_forecasting_curriculum.metrics import (\n",
        "    interval_coverage,\n",
        "    mase,\n",
        "    pinball_loss,\n",
        "    smape,\n",
        "    wmape,\n",
        ")\n",
        "from demand_forecasting_curriculum.plotting import plot_predictions, plot_series\n",
        "\n",
        "SEED = 42\n",
        "np.random.seed(SEED)\n",
        "\n",
        "import torch\n",
        "from demand_forecasting_curriculum.modeling import (\n",
        "    MLPRegressor,\n",
        "    Standardizer,\n",
        "    make_regression_loader,\n",
        "    predict_regression_model,\n",
        "    set_seed,\n",
        "    train_regression_model,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Cell 2 Explanation\n",
        "\n",
        "**What this cell does:** This cell advances **PyTorch MLP Forecaster** by working on synthetic demand generation, time-based splitting, supervised feature construction. It creates or updates `df`, `supervised`, `train`, `valid`, `test`, `feature_cols` and prepares evidence that later cells use rather than hiding state in prose.\n",
        "\n",
        "**Why it matters:** The goal is tied to this lesson's forecasting focus: Build a PyTorch MLP that predicts future demand from lagged tabular features. Each object is kept inspectable so the learner can connect code, assumptions, and modeling consequences.\n",
        "\n",
        "**Key operations to notice:** creates a synthetic retail panel with known demand drivers; turns time series rows into forecast-origin training examples; keeps train, validation, and test windows ordered by date; fits scaling parameters on training data only\n",
        "\n",
        "**What to inspect:** Before moving on, inspect shapes, date ranges, metric values, segment behavior, or generated tables. If a value looks surprisingly good, surprisingly bad, or unavailable at forecast time, treat that as a modeling clue.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "set_seed(SEED)\n",
        "df = generate_daily_demand(n_stores=4, n_items=6, periods=540, seed=SEED)\n",
        "supervised = make_supervised_table(df, horizon=7)\n",
        "train, valid, test = time_series_split(supervised, valid_days=60, test_days=60)\n",
        "\n",
        "feature_cols = [\"store_id\", \"item_id\", *DEFAULT_FEATURE_COLUMNS]\n",
        "X_train_raw = train[feature_cols].to_numpy(dtype=np.float32)\n",
        "X_valid_raw = valid[feature_cols].to_numpy(dtype=np.float32)\n",
        "y_train = np.log1p(train[\"y\"].to_numpy(dtype=np.float32))\n",
        "y_valid = np.log1p(valid[\"y\"].to_numpy(dtype=np.float32))\n",
        "\n",
        "scaler = Standardizer.fit(X_train_raw)\n",
        "X_train = scaler.transform(X_train_raw).astype(np.float32)\n",
        "X_valid = scaler.transform(X_valid_raw).astype(np.float32)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Cell 3 Explanation\n",
        "\n",
        "**What this cell does:** This cell advances **PyTorch MLP Forecaster** by working on model training, model definition. It creates or updates `train_loader`, `valid_loader`, `model`, `history` and prepares evidence that later cells use rather than hiding state in prose.\n",
        "\n",
        "**Why it matters:** The goal is tied to this lesson's forecasting focus: Build a PyTorch MLP that predicts future demand from lagged tabular features. Each object is kept inspectable so the learner can connect code, assumptions, and modeling consequences.\n",
        "\n",
        "**Key operations to notice:** wraps NumPy arrays in PyTorch DataLoaders; instantiates a feed-forward demand forecasting model; runs the shared PyTorch training loop; creates an inspectable table for assumptions, metrics, or decisions\n",
        "\n",
        "**What to inspect:** Before moving on, inspect shapes, date ranges, metric values, segment behavior, or generated tables. If a value looks surprisingly good, surprisingly bad, or unavailable at forecast time, treat that as a modeling clue.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "train_loader = make_regression_loader(X_train, y_train, batch_size=256, shuffle=True)\n",
        "valid_loader = make_regression_loader(X_valid, y_valid, batch_size=512, shuffle=False)\n",
        "\n",
        "model = MLPRegressor(n_features=X_train.shape[1], hidden_sizes=(96, 48), dropout=0.05)\n",
        "history = train_regression_model(\n",
        "    model,\n",
        "    train_loader,\n",
        "    valid_loader,\n",
        "    epochs=8,\n",
        "    learning_rate=1e-3,\n",
        "    weight_decay=1e-4,\n",
        ")\n",
        "pd.DataFrame(history)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Cell 4 Explanation\n",
        "\n",
        "**What this cell does:** This cell advances **PyTorch MLP Forecaster** by working on forecast metrics. It creates or updates `pred_log`, `pred`, `actual` and prepares evidence that later cells use rather than hiding state in prose.\n",
        "\n",
        "**Why it matters:** The goal is tied to this lesson's forecasting focus: Build a PyTorch MLP that predicts future demand from lagged tabular features. Each object is kept inspectable so the learner can connect code, assumptions, and modeling consequences.\n",
        "\n",
        "**Key operations to notice:** generates validation or test predictions from a trained model; evaluates scale-weighted forecast error; evaluates symmetric percentage forecast error\n",
        "\n",
        "**What to inspect:** Before moving on, inspect shapes, date ranges, metric values, segment behavior, or generated tables. If a value looks surprisingly good, surprisingly bad, or unavailable at forecast time, treat that as a modeling clue.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pred_log = predict_regression_model(model, X_valid)\n",
        "pred = np.expm1(pred_log)\n",
        "actual = np.expm1(y_valid)\n",
        "\n",
        "{\"valid_wmape\": wmape(actual, pred), \"valid_smape\": smape(actual, pred)}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Cell 5 Explanation\n",
        "\n",
        "**What this cell does:** This cell advances **PyTorch MLP Forecaster** by working on visualization. It creates or updates `plot_frame`, `ax` and prepares evidence that later cells use rather than hiding state in prose.\n",
        "\n",
        "**Why it matters:** The goal is tied to this lesson's forecasting focus: Build a PyTorch MLP that predicts future demand from lagged tabular features. Each object is kept inspectable so the learner can connect code, assumptions, and modeling consequences.\n",
        "\n",
        "**Key operations to notice:** visualizes model behavior or data quality\n",
        "\n",
        "**What to inspect:** Before moving on, inspect shapes, date ranges, metric values, segment behavior, or generated tables. If a value looks surprisingly good, surprisingly bad, or unavailable at forecast time, treat that as a modeling clue.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "plot_frame = valid.assign(prediction=pred).query(\"store_id == 0 and item_id == 0\").sort_values(\"date\")\n",
        "ax = plot_predictions(plot_frame[\"date\"], plot_frame[\"y\"], plot_frame[\"prediction\"])\n",
        "ax.set_title(\"PyTorch MLP forecast\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Lesson Recap\n",
        "\n",
        "In this lesson, we used 5 runnable code cells to work through **PyTorch MLP Forecaster**. The practical focus was: Build a PyTorch MLP that predicts future demand from lagged tabular features.\n",
        "\n",
        "We did this because deep learning for demand forecasting is not only about choosing an architecture. The learner needs to understand the data contract, the target and horizon, the validation path, the metric, and the operational decision supported by the forecast.\n",
        "\n",
        "The core takeaways were:\n",
        "\n",
        "- Use Dataset/DataLoader style tensors for forecasting data.\n",
        "- Train and evaluate a compact MLP with MAE loss.\n",
        "- Compare neural performance against lesson 01 baselines.\n",
        "\n",
        "By the end, you should be able to explain not just what the code produced, but why each step was necessary and what could go wrong if the same step were skipped or performed with leakage.\n",
        "\n",
        "**Senior-readiness reminder:** You can write a clean PyTorch training loop and keep preprocessing identical across splits.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Exercises\n",
        "\n",
        "- Switch the loss from MAE to MSE in the helper and inspect bias toward high-volume series.\n",
        "- Increase dropout and explain whether validation WMAPE improves or worsens.\n",
        "- Add `stockout` as a feature and decide whether it is available at forecast time.\n"
      ]
    }
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