{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "(mmm_evaluation)=\n", "# Model Evaluation in PyMC-Marketing\n", "\n", "This notebook demonstrates how to evaluate Marketing Mix Models using PyMC-Marketing's evaluation metrics and functions. We'll cover:\n", "\n", "1. Standard evaluation metrics (RMSE, MAE, MAPE)\n", "2. Normalized metrics (NRMSE, NMAE)\n", "3. Calculating and visualizing metric distributions and summaries of those distributions\n", "4. Creating evaluation plots (prior vs posterior plots)\n", "\n", "First, let's import the necessary libraries:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn.metrics import (\n", " root_mean_squared_error,\n", ")\n", "\n", "from pymc_marketing.mmm import (\n", " MMM,\n", " GeometricAdstock,\n", " LogisticSaturation,\n", ")\n", "from pymc_marketing.mmm.evaluation import (\n", " calculate_metric_distributions,\n", " compute_summary_metrics,\n", " summarize_metric_distributions,\n", ")\n", "\n", "az.style.use(\"arviz-darkgrid\")\n", "plt.rcParams[\"figure.figsize\"] = [12, 7]\n", "plt.rcParams[\"figure.dpi\"] = 100\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "%config InlineBackend.figure_format = \"retina\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "seed: int = sum(map(ord, \"mmm-evaluation\"))\n", "rng: np.random.Generator = np.random.default_rng(seed=seed)\n", "hdi_prob: float = 0.89 # change this to whatever HDI you want" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting up a Demo Model\n", "\n", "Let's first create a simple MMM model using the example dataset:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2f46c5724cae428786abf194dc9ca01e", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3500 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "516d27e9998a483daa9b9d81c22bc4e5", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3500 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ef49ae5c66354337aefda57656290ae4", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3500 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "03f7dae4d2ad4b2d8fb865c1fb1fd6a8", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/3500 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Sampling: [y]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5bb25137ec014883bb62c5fb618958b4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load example data\n", "data_url = \"https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/data/mmm_example.csv\"\n", "data = pd.read_csv(data_url, parse_dates=[\"date_week\"])\n", "\n", "X = data.drop(\"y\", axis=1)\n", "y = data[\"y\"]\n", "\n", "# Create and fit the model\n", "mmm = MMM(\n", " adstock=GeometricAdstock(l_max=8),\n", " saturation=LogisticSaturation(),\n", " date_column=\"date_week\",\n", " channel_columns=[\"x1\", \"x2\"],\n", " control_columns=[\n", " \"event_1\",\n", " \"event_2\",\n", " \"t\",\n", " ],\n", " yearly_seasonality=2,\n", ")\n", "\n", "fit_kwargs = {\n", " \"tune\": 1_500,\n", " \"chains\": 4,\n", " \"draws\": 2_000,\n", " \"nuts_sampler\": \"numpyro\",\n", " \"target_accept\": 0.92,\n", " \"random_seed\": rng,\n", "}\n", "_ = mmm.fit(X, y, **fit_kwargs)\n", "\n", "# Generate posterior predictive samples\n", "posterior_preds = mmm.sample_posterior_predictive(X, random_seed=rng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding the Evaluation Metrics\n", "\n", "PyMC-Marketing provides several metrics for evaluating your models:\n", "\n", "1. Standard metrics from scikit-learn:\n", " - RMSE (Root Mean Squared Error)\n", " - MAE (Mean Absolute Error)\n", " - MAPE (Mean Absolute Percentage Error)\n", "\n", "2. Bayesian R-Squared (from `arviz.az.r2_score`)\n", "\n", "3. Normalized metrics:\n", " - NRMSE (Normalized Root Mean Squared Error), such as is used by Robyn\n", " - NMAE (Normalized Mean Absolute Error)\n", "\n", "Let's calculate these metrics for our model:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "R_SQUARED:\n", " mean: 0.9057\n", " median: 0.9062\n", " std: 0.0100\n", " min: 0.8605\n", " max: 0.9343\n", " 89%_hdi_lower: 0.8902\n", " 89%_hdi_upper: 0.9217\n", "\n", "RMSE:\n", " mean: 351.3628\n", " median: 350.5780\n", " std: 19.7836\n", " min: 286.7718\n", " max: 431.5480\n", " 89%_hdi_lower: 320.3752\n", " 89%_hdi_upper: 382.8984\n", "\n", "NRMSE:\n", " mean: 0.0686\n", " median: 0.0685\n", " std: 0.0039\n", " min: 0.0560\n", " max: 0.0843\n", " 89%_hdi_lower: 0.0626\n", " 89%_hdi_upper: 0.0748\n", "\n", "MAE:\n", " mean: 281.3655\n", " median: 280.6294\n", " std: 16.6181\n", " min: 223.4076\n", " max: 346.8520\n", " 89%_hdi_lower: 256.0472\n", " 89%_hdi_upper: 308.4454\n", "\n", "NMAE:\n", " mean: 0.0550\n", " median: 0.0548\n", " std: 0.0032\n", " min: 0.0436\n", " max: 0.0678\n", " 89%_hdi_lower: 0.0500\n", " 89%_hdi_upper: 0.0602\n", "\n", "MAPE:\n", " mean: 0.0556\n", " median: 0.0554\n", " std: 0.0033\n", " min: 0.0443\n", " max: 0.0683\n", " 89%_hdi_lower: 0.0503\n", " 89%_hdi_upper: 0.0608\n" ] } ], "source": [ "# Calculate metrics for all posterior samples\n", "results = compute_summary_metrics(\n", " y_true=mmm.y,\n", " y_pred=posterior_preds.y.to_numpy(),\n", " metrics_to_calculate=[\n", " \"r_squared\",\n", " \"rmse\",\n", " \"nrmse\",\n", " \"mae\",\n", " \"nmae\",\n", " \"mape\",\n", " ],\n", " hdi_prob=hdi_prob,\n", ")\n", "\n", "# Print results in a formatted way\n", "for metric, stats in results.items():\n", " print(f\"\\n{metric.upper()}:\")\n", " for stat, value in stats.items():\n", " print(f\" {stat}: {value:.4f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`compute_summary_metrics` actually combines the steps of two other functions:\n", "\n", "1. `calculate_metric_distributions`\n", "2. `summarize_metric_distributions`\n", "\n", "The metric distributions (unsummarised) can sometimes be useful on their own, e.g. if you'd like to visualise the distribution of a metric." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RMSE Summary:\n", " Mean: 351.3628\n", " Median: 350.5780\n", " Standard Deviation: 19.7836\n", " 89% HDI: [320.3752, 382.8984]\n", "\n", "MAE Summary:\n", " Mean: 281.3655\n", " Median: 280.6294\n", " Standard Deviation: 16.6181\n", " 89% HDI: [256.0472, 308.4454]\n", "\n", "R_SQUARED Summary:\n", " Mean: 0.9057\n", " Median: 0.9062\n", " Standard Deviation: 0.0100\n", " 89% HDI: [0.8902, 0.9217]\n" ] } ], "source": [ "# Calculate distributions for multiple metrics\n", "metric_distributions = calculate_metric_distributions(\n", " y_true=mmm.y,\n", " y_pred=posterior_preds.y.to_numpy(),\n", " metrics_to_calculate=[\"rmse\", \"mae\", \"r_squared\"],\n", ")\n", "\n", "# Summarize the distributions\n", "summaries = summarize_metric_distributions(metric_distributions, hdi_prob=0.89)\n", "\n", "# Create a nice display of the summaries\n", "for metric, summary in summaries.items():\n", " print(f\"\\n{metric.upper()} Summary:\")\n", " print(f\" Mean: {summary['mean']:.4f}\")\n", " print(f\" Median: {summary['median']:.4f}\")\n", " print(f\" Standard Deviation: {summary['std']:.4f}\")\n", " print(\n", " f\" 89% HDI: [{summary['89%_hdi_lower']:.4f}, {summary['89%_hdi_upper']:.4f}]\"\n", " )" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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