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        "# Lesson 14: Interpretability and Diagnostics\n",
        "\n",
        "**CS230 inspiration:** What is going on inside my model, reading research papers, and model diagnostics.\n",
        "\n",
        "**Demand forecasting focus:** Use permutation importance, partial dependence style sweeps, and residual checks.\n",
        "\n",
        "**Learning objectives**\n",
        "\n",
        "- Estimate which features a model relies on.\n",
        "- Detect suspicious reliance on unavailable or unstable features.\n",
        "- Communicate diagnostics to business and engineering partners.\n",
        "\n",
        "**Senior-readiness checkpoint:** You can use interpretability to find modeling risk, not just produce a pretty chart.\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",
        "from sklearn.ensemble import HistGradientBoostingRegressor\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Cell 2 Explanation\n",
        "\n",
        "**What this cell does:** This cell advances **Interpretability and Diagnostics** by working on synthetic demand generation, time-based splitting, supervised feature construction, forecast metrics. It creates or updates `df`, `supervised`, `train`, `valid`, `_`, `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: Use permutation importance, partial dependence style sweeps, and residual checks. 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; trains a strong tabular baseline; evaluates scale-weighted 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": [
        "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, _ = time_series_split(supervised, valid_days=60, test_days=60)\n",
        "feature_cols = [\"store_id\", \"item_id\", *DEFAULT_FEATURE_COLUMNS]\n",
        "\n",
        "model = HistGradientBoostingRegressor(max_iter=120, learning_rate=0.04, random_state=SEED)\n",
        "model.fit(train[feature_cols], train[\"y\"])\n",
        "baseline_pred = model.predict(valid[feature_cols])\n",
        "baseline_score = wmape(valid[\"y\"], baseline_pred)\n",
        "baseline_score\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Cell 3 Explanation\n",
        "\n",
        "**What this cell does:** This cell advances **Interpretability and Diagnostics** by working on forecast metrics. It creates or updates `rng`, `importance_rows`, `importance` 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: Use permutation importance, partial dependence style sweeps, and residual checks. Each object is kept inspectable so the learner can connect code, assumptions, and modeling consequences.\n",
        "\n",
        "**Key operations to notice:** evaluates scale-weighted forecast error; 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": [
        "rng = np.random.default_rng(SEED)\n",
        "importance_rows = []\n",
        "for col in feature_cols:\n",
        "    shuffled = valid[feature_cols].copy()\n",
        "    shuffled[col] = rng.permutation(shuffled[col].to_numpy())\n",
        "    score = wmape(valid[\"y\"], model.predict(shuffled))\n",
        "    importance_rows.append({\"feature\": col, \"wmape_delta\": score - baseline_score})\n",
        "\n",
        "importance = pd.DataFrame(importance_rows).sort_values(\"wmape_delta\", ascending=False)\n",
        "importance.head(10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Cell 4 Explanation\n",
        "\n",
        "**What this cell does:** This cell advances **Interpretability and Diagnostics** by working on visualization. It creates or updates `sweep`, `price_grid`, `response`, `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: Use permutation importance, partial dependence style sweeps, and residual checks. 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": [
        "sweep = valid[feature_cols].copy()\n",
        "price_grid = np.linspace(valid[\"price\"].quantile(0.05), valid[\"price\"].quantile(0.95), 20)\n",
        "response = []\n",
        "for price in price_grid:\n",
        "    sweep[\"price\"] = price\n",
        "    response.append(model.predict(sweep).mean())\n",
        "\n",
        "ax = pd.Series(response, index=price_grid).plot(figsize=(7, 3))\n",
        "ax.set_xlabel(\"Counterfactual price\")\n",
        "ax.set_ylabel(\"Average predicted demand\")\n",
        "ax.set_title(\"Partial dependence style price sweep\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Lesson Recap\n",
        "\n",
        "In this lesson, we used 4 runnable code cells to work through **Interpretability and Diagnostics**. The practical focus was: Use permutation importance, partial dependence style sweeps, and residual checks.\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",
        "- Estimate which features a model relies on.\n",
        "- Detect suspicious reliance on unavailable or unstable features.\n",
        "- Communicate diagnostics to business and engineering partners.\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 use interpretability to find modeling risk, not just produce a pretty chart.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Exercises\n",
        "\n",
        "- Compute permutation importance separately for promoted and non-promoted rows.\n",
        "- Look for a feature whose importance would worry you in production.\n",
        "- Write a one-paragraph diagnostic summary for a demand planning stakeholder.\n"
      ]
    }
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