From feecebbab688d8d8733eb706beba2edbb123ab12 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Sat, 15 Jul 2023 10:38:54 +0200 Subject: [PATCH 1/2] DOC link Notes and formula fix --- src/model_diagnostics/calibration/identification.py | 4 ++-- src/model_diagnostics/calibration/plots.py | 2 +- src/model_diagnostics/scoring/plots.py | 4 ++-- src/model_diagnostics/scoring/scoring.py | 7 ++++--- 4 files changed, 9 insertions(+), 8 deletions(-) diff --git a/src/model_diagnostics/calibration/identification.py b/src/model_diagnostics/calibration/identification.py index 16ddbaa..41a1f77 100644 --- a/src/model_diagnostics/calibration/identification.py +++ b/src/model_diagnostics/calibration/identification.py @@ -25,8 +25,8 @@ def identification_function( ) -> np.ndarray: r"""Canonical identification function. - Identification functions act as generalised residuals. See Notes for further - details. + Identification functions act as generalised residuals. See [Notes](#notes) for + further details. Parameters ---------- diff --git a/src/model_diagnostics/calibration/plots.py b/src/model_diagnostics/calibration/plots.py index 74e7b72..f517aeb 100644 --- a/src/model_diagnostics/calibration/plots.py +++ b/src/model_diagnostics/calibration/plots.py @@ -42,7 +42,7 @@ def plot_reliability_diagram( predictions `y_pred` (x-axis). The conditional expectation is estimated via isotonic regression (PAV algorithm) of `y_obs` on `y_pred`. - See Notes for further details. + See [Notes](#notes) for further details. Parameters ---------- diff --git a/src/model_diagnostics/scoring/plots.py b/src/model_diagnostics/scoring/plots.py index 2e82cf8..e37d688 100644 --- a/src/model_diagnostics/scoring/plots.py +++ b/src/model_diagnostics/scoring/plots.py @@ -32,7 +32,7 @@ def plot_murphy_diagram( over a range of their free parameter `eta`. This shows, if a model dominates all others over a wide class of scoring functions or if the ranking is very much dependent on the choice of scoring function. - See Notes for further details. + See [Notes](#notes) for further details. Parameters ---------- @@ -66,7 +66,7 @@ def plot_murphy_diagram( Notes ----- - For details, refer to [Ehm2015]. + For details, refer to `[Ehm2015]`. References ---------- diff --git a/src/model_diagnostics/scoring/scoring.py b/src/model_diagnostics/scoring/scoring.py index d041679..ab6a2de 100644 --- a/src/model_diagnostics/scoring/scoring.py +++ b/src/model_diagnostics/scoring/scoring.py @@ -1,4 +1,5 @@ -"""The scoring module provides scoring functions and the score decomposition. +"""The scoring module provides scoring functions, also known as loss functions, +and a score decomposition. Each scoring function is implemented as a class that needs to be instantiated before calling the `__call__` methode, e.g. `SquaredError()(y_obs=[1], y_pred=[2])`. """ @@ -576,7 +577,7 @@ class ElementaryScore(_BaseScoringFunction): The elementary scoring function is consistent for the specified `functional` for all values of `eta` and is the main ingredient for Murphy diagrams. - See Notes for further details. + See [Notes](#notes) for further details. Parameters ---------- @@ -599,7 +600,7 @@ class ElementaryScore(_BaseScoringFunction): The elementary scoring or loss function is given by \[ - S_\eta^h(y, z) = (\mathbf{1}\{\eta \le z\} - \mathbf{1}\{\eta \le y\}) + S_\eta(y, z) = (\mathbf{1}\{\eta \le z\} - \mathbf{1}\{\eta \le y\}) V(y, \eta) \] From 9283b2aacc418f3a27c149d5b12e7d5be66ea0aa Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Sat, 15 Jul 2023 11:12:34 +0200 Subject: [PATCH 2/2] DOC add quantile regression example --- docs/examples/quantile_regression.ipynb | 417 ++++++++++++++++++++++++ mkdocs.yml | 1 + 2 files changed, 418 insertions(+) create mode 100644 docs/examples/quantile_regression.ipynb diff --git a/docs/examples/quantile_regression.ipynb b/docs/examples/quantile_regression.ipynb new file mode 100644 index 0000000..6a6a2fb --- /dev/null +++ b/docs/examples/quantile_regression.ipynb @@ -0,0 +1,417 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "10d13d40-71e5-4165-8b90-7a7dca12cfa7", + "metadata": {}, + "source": [ + "# Quantile Regression on Synthetic Data\n", + "This notebooks shows the diagnostic tools applied to quantile regression.\n", + "For details, see https://arxiv.org/abs/2202.12780.\n", + "\n", + "## 1. Data and Models\n", + "We start by creating an artificial dataset with an asymmetric distribution such that mean and median are very different.\n", + "Then, we fit a linear model and gradient boosted trees for the **75%-quantile**." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "066effb0-f042-4f88-a244-8c248af8a91f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best alpha for linear quantile regression = 0.0018329807108324356\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import polars as pl\n", + "from sklearn.ensemble import HistGradientBoostingRegressor\n", + "from sklearn.linear_model import QuantileRegressor\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.preprocessing import SplineTransformer\n", + "\n", + "\n", + "quantile_level = 0.75\n", + "\n", + "def create_data(n, rng): \n", + " x = np.linspace(start=0, stop=10, num=200)\n", + " y_true_mean = 10 + 0.5 * x * np.sin(x)\n", + " a = 5\n", + " y = y_true_mean + 10 * (rng.pareto(a, size=x.shape[0]) - 1 / (a - 1))\n", + " return y, x\n", + "\n", + "rng = np.random.RandomState(42)\n", + "y_train, x_train = create_data(200, rng)\n", + "y_test, x_test = create_data(100, rng)\n", + "X_train, X_test = x_train[:, np.newaxis], x_test[:, np.newaxis]\n", + "\n", + "m_linear = GridSearchCV(\n", + " estimator=make_pipeline(\n", + " SplineTransformer(degree=3, n_knots=20),\n", + " QuantileRegressor(quantile=quantile_level, solver=\"highs\"),\n", + " ),\n", + " param_grid={\"quantileregressor__alpha\": np.logspace(-4, 0, 20)},\n", + " cv=5,\n", + ").fit(X_train, y_train)\n", + "print(\n", + " \"best alpha for linear quantile regression = \"\n", + " f\"{m_linear.best_params_['quantileregressor__alpha']}\"\n", + ")\n", + "m_hgbt = HistGradientBoostingRegressor(\n", + " loss=\"quantile\", quantile=quantile_level\n", + ").fit(X_train, y_train)\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(x_train, y_train, color=\"black\", alpha=0.5)\n", + "ax.plot(x_train, m_linear.predict(X_train), label=f\"LinReg Quantile: {quantile_level}\")\n", + "ax.plot(x_train, m_hgbt.predict(X_train), label=f\"HGBT Quantile: {quantile_level}\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "c0451c5d-5b12-434b-b409-ff6fd1fad39f", + "metadata": {}, + "source": [ + "## 2. Calibration Assessment\n", + "First, we look at the calibration in total, i.e. neither conditioning on a feature nor on the predictions.\n", + "We want to predict the 75%-quantile, so we expect that 25% of the data lie above and 75% below the model predictions.\n", + "This is what the [identification function](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/identification/#model_diagnostics.calibration.identification.identification_function) for quantiles actually calculates such that a value of 0 is ideal.\n", + "Note that the identification function is implicitly called in [`compute_bias`](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/identification/#model_diagnostics.calibration.identification.compute_bias) as well as [`plot_bias`](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/plots/#model_diagnostics.calibration.plots.plot_bias)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "380b0581-9e4e-4093-ac6d-cfe0775bf588", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"linear_quant_r…0.005200200.00.0304880.869899
"hgbt_quant_reg…0.0200200.00.0306951.0
" + ], + "text/plain": [ + "shape: (2, 6)\n", + "┌──────────────────┬───────────┬────────────┬──────────────┬─────────────┬──────────┐\n", + "│ model ┆ bias_mean ┆ bias_count ┆ bias_weights ┆ bias_stderr ┆ p_value │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ f64 ┆ u32 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞══════════════════╪═══════════╪════════════╪══════════════╪═════════════╪══════════╡\n", + "│ linear_quant_reg ┆ 0.005 ┆ 200 ┆ 200.0 ┆ 0.030488 ┆ 0.869899 │\n", + "│ hgbt_quant_reg ┆ 0.0 ┆ 200 ┆ 200.0 ┆ 0.030695 ┆ 1.0 │\n", + "└──────────────────┴───────────┴────────────┴──────────────┴─────────────┴──────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Bias Plot on Training Set')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from model_diagnostics.calibration import compute_bias, plot_bias, plot_reliability_diagram\n", + "\n", + "df = pl.DataFrame({\n", + " \"linear_quant_reg\": m_linear.predict(X_train),\n", + " \"hgbt_quant_reg\": m_hgbt.predict(X_train),\n", + "})\n", + "\n", + "display(compute_bias(y_obs=y_train, y_pred=df, functional=\"quantile\", level=quantile_level))\n", + "\n", + "ax = plot_bias(\n", + " y_obs=y_train, y_pred=df, functional=\"quantile\", level=quantile_level,confidence_level=1 - 0.9\n", + ")\n", + "ax.set_title(\"Bias Plot on Training Set\")" + ] + }, + { + "cell_type": "markdown", + "id": "eb63cca7-3d69-4086-968c-b989da3db3c9", + "metadata": {}, + "source": [ + "By setting a very small confidence level $\\alpha$, such that $1-\\alpha$ is in between the p-values, we see that the confidence interval of the linear model does not reach the 0-line.\n", + "\n", + "We go on by investiating auto-calibration with the classical tool: a reliability diagram." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ab66b8c7-1b80-497c-8a20-3921c6fd0202", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.98 s, sys: 88.6 ms, total: 2.07 s\n", + "Wall time: 1.96 s\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Reliability Diagram on Training Set')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "ax = plot_reliability_diagram(\n", + " y_train,\n", + " df,\n", + " functional=\"quantile\",\n", + " level=quantile_level,\n", + " n_bootstrap=100,\n", + ")\n", + "ax.set_ylim(None, 20)\n", + "ax.set_title(\"Reliability Diagram on Training Set\")" + ] + }, + { + "cell_type": "markdown", + "id": "8d5f454a-3686-4a61-b789-47522bc92936", + "metadata": {}, + "source": [ + "The reliability diagram shows reasonable auto-calibration for both models.\n", + "For large predicted values, the linear model seems to be a bit better.\n", + "\n", + "We conclude the assessment of calibration by a bias plot conditional on our single feature." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "55b6f3dc-9610-4f10-8225-70ea007189df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Bias Plot conditional on prediction on Training Set')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plot_bias(y_obs=y_train, y_pred=df, feature=x_train, confidence_level=0.9)\n", + "ax.set_title(\"Bias Plot conditional on prediction on Training Set\")" + ] + }, + { + "cell_type": "markdown", + "id": "cfb41dd8-9ff6-4bc9-9518-47e9796efd88", + "metadata": {}, + "source": [ + "Here, the HGBT model seems a bit better calibrated.\n", + "\n", + "## 3. Comparison of Model Performance\n", + "\n", + "We use the standard loss function for quantile regression: the pinball loss.\n", + "Furthermore, as is standard, we do this analysis out-of-sample, i.e. using the test data set.\n", + "\n", + "We report not only the pinball loss or score, but also the additive score decomposition: `score = miscalibration - discrimination + uncertainty`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a9c52877-4fb9-4f08-9657-9343ebaefc6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (2, 5)
modelmiscalibrationdiscriminationuncertaintyscore
strf64f64f64f64
"linear_quant_r…0.0884210.2606331.0751950.902983
"hgbt_quant_reg…0.0560780.2068181.0751950.924455
" + ], + "text/plain": [ + "shape: (2, 5)\n", + "┌──────────────────┬────────────────┬────────────────┬─────────────┬──────────┐\n", + "│ model ┆ miscalibration ┆ discrimination ┆ uncertainty ┆ score │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞══════════════════╪════════════════╪════════════════╪═════════════╪══════════╡\n", + "│ linear_quant_reg ┆ 0.088421 ┆ 0.260633 ┆ 1.075195 ┆ 0.902983 │\n", + "│ hgbt_quant_reg ┆ 0.056078 ┆ 0.206818 ┆ 1.075195 ┆ 0.924455 │\n", + "└──────────────────┴────────────────┴────────────────┴─────────────┴──────────┘" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from model_diagnostics.scoring import decompose, plot_murphy_diagram, PinballLoss\n", + "\n", + "\n", + "pbl = PinballLoss(level=quantile_level)\n", + "df = pl.DataFrame({\n", + " \"linear_quant_reg\": m_linear.predict(X_test),\n", + " \"hgbt_quant_reg\": m_hgbt.predict(X_test),\n", + "})\n", + "decompose(y_obs=y_test, y_pred=df, scoring_function=pbl).sort(\"score\")" + ] + }, + { + "cell_type": "markdown", + "id": "5b9d7b59-dd93-4b8b-94b0-38ed51d1ce04", + "metadata": {}, + "source": [ + "The linear model has a better (lower) out-of-sample score (or loss).\n", + "While the HGBT has a better miscalibration term (smaller is better), the dominating term is the discrimination (larger is better) where the linear model is clearly superior.\n", + "\n", + "Does the ranking of the models change with the choice of a scoring function?\n", + "This can be analysed by means of the Murphy diagram.\n", + "The x-axis `eta` specifies different scoring/loss functions, all consistent for the 75%-quantile." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6241d423-0898-4244-a23e-1cdac9c3dcb6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_murphy_diagram(y_obs=y_test, y_pred=df, functional=\"quantile\", level=quantile_level)" + ] + }, + { + "cell_type": "markdown", + "id": "e1fc6aa9-3a5a-4512-91ee-29e541bfeb8f", + "metadata": {}, + "source": [ + "The linear model seems in deed be superior over a range of `eta` values." + ] + } + ], + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/mkdocs.yml b/mkdocs.yml index b78cb06..75ce23e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -35,6 +35,7 @@ nav: - About: index.md - Examples: - Regression on Workers' Compensation: examples/regression_on_workers_compensation.ipynb + - Quantile Regression: examples/quantile_regression.ipynb - API Reference: reference/ # defer to gen-files + literate-nav - Development: development.md - Release Notes: https://github.com/lorentzenchr/model-diagnostics/releases