diff --git a/.github/workflows/checks.yaml b/.github/workflows/checks.yaml new file mode 100644 index 0000000..828f8c5 --- /dev/null +++ b/.github/workflows/checks.yaml @@ -0,0 +1,44 @@ +name: Checks +on: + workflow_dispatch: + pull_request: + +# Cancel any ongoing run of this workflow on the same PR or ref +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Install uv + uses: astral-sh/setup-uv@v6 + - name: Run pre-commit + run: uv run pre-commit + tests: + strategy: + fail-fast: false + matrix: + python-version: ['3.11', '3.12', '3.13', '3.14'] + os: ['ubuntu-latest', 'windows-latest', 'macos-latest'] + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v4 + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + python-version: ${{ matrix.python-version }} + - name: Set up Python + run: uv python install + - name: Install Miniforge + uses: conda-incubator/setup-miniconda@v3 + with: + auto-update-conda: true + miniforge-version: latest + conda-remove-defaults: true + - name: Install khiops + run: conda install --quiet --yes --name test khiops-core=11.0.0b.0 + - name: Run tests + run: conda run --name test uv run pytest src/tests diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 0000000..3ff23e4 --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,43 @@ +name: Docs +on: + workflow_dispatch: + +# Cancel any ongoing run of this workflow on the same PR or ref +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +# Required permissions to deploy in GH pages +permissions: + id-token: write + pages: write + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Setup uv + uses: astral-sh/setup-uv@v6 + - name: Setup Python + run: uv python install + - name: Setup Just + uses: extractions/setup-just@v3 + - name: Build docs + run: | + mkdir docs/_static + just docs + - name: Upload the package as an artifact + uses: actions/upload-pages-artifact@v4 + with: + path: ./docs/_build/html + publish: + if: github.ref_type == 'tag' + needs: [build] + runs-on: ubuntu-latest + steps: + - name: Setup Pages + uses: actions/configure-pages@v5 + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 669c628..199c922 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,7 +1,19 @@ repos: -- repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.4 - hooks: - - id: ruff-check - args: [ --fix ] - - id: ruff-format + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.12.4 + hooks: + - id: ruff-check + args: [ --fix ] + - id: ruff-format + - repo: https://github.com/mwouts/jupytext + rev: v1.17.3 + hooks: + - id: jupytext + args: [--update, --from, py:percent, --to, notebook] + files: docs/quickstart.py + - repo: https://github.com/rhysd/actionlint + rev: v1.7.8 + hooks: + - id: actionlint + name: actionlint + diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..467c46c --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,10 @@ +# Changelog + +## [0.1] - 2025-10-17 +### Added +- `KhalibClassifier` class +- `calibration_error` function +- `build_reliability_diagram` function + +[Unreleased]: https://github.com/yourusername/yourproject/compare/0.1...HEAD +[1.0.0]: https://github.com/yourusername/yourproject/releases/tag/0.1 diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..6da7bb3 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,28 @@ +BSD 3-Clause License + +Copyright (c) 2025 The khalib contributors. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 8607296..c059400 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,44 @@ -# khalib: Classifier Calibration with Khiops -khalib is a classifier calibration method based on the [Khiops][khiops-org] supervised -discretization algorithm (MODL). + +# khalib +`khalib` is a classifier probability calibration package powered by the [Khiops][khiops-org] AutoML +suite. + + +## Features +- `KhalibClassifier`: A scikit-learn estimator to calibrate classifiers with a similar interface + fashion as [CalibratedClassifierCV][sk-calclf]. +- `calibration_error` : A function to estimate the Estimated Calibration Error (ECE). +- `build_reliability_diagram` : A function that builds a reliability diagram. + +These features are based on Khiops's non-parametric supervised histograms, so there is no need to +specify the number and width of the bins, as they are automatically estimated from data. + + + +See the [documentation][khalib-docs] for more information. + + +## Installation + +*Note: We'll improve this installation procedure soon!* + +- Make sure you have installed [Khiops 11 Beta][khiops11-setup] +- Execute + +```bash +pip install https://github.com/KhiopsLab/khalib/archive/refs/tags/0.1.zip +``` + + + + +## Documentation + +See https://khiopslab.github.io/khalib/ [khiops-org]: https://khiops.org +[khiops11-setup]: https://khiops.org/11.0.0-b.0/setup/ +[sk-calclf]: https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html +[khalib-docs]: https://khiopslab.github.io/khalib + diff --git a/docs/api.rst b/docs/api.rst new file mode 100644 index 0000000..36171c0 --- /dev/null +++ b/docs/api.rst @@ -0,0 +1,32 @@ +API Reference +============= + +.. currentmodule:: khalib + +.. autoclass:: KhalibClassifier + :members: + :inherited-members: + :exclude-members: set_score_request + :member-order: bysource + + +.. autofunction:: calibration_error + +.. autofunction:: build_reliability_diagram + +.. autoclass:: Histogram + :members: + :member-order: bysource + + +.. autofunction:: calibrate_binary + + + + +.. rubric:: References + +.. [RCSM22] Roelofs, R., Cain, N., Shlens, J. & Mozer, M.C.. (2022). Mitigating Bias + in Calibration Error Estimation . Proceedings of The 25th International Conference on + Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research + 151:4036-4054 Available from https://proceedings.mlr.press/v151/roelofs22a.html. diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..f44651a --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,54 @@ +project = "khalib" +author = "Felipe Olmos" +release = "0.1" + +extensions = [ + "myst_nb", # activates myst_parser as well + "numpydoc", + "sphinx.ext.autodoc", + "sphinx_copybutton", + "sphinx_paramlinks", + "sphinx.ext.intersphinx", +] + +templates_path = ["_templates"] +exclude_patterns = [] + +html_theme = "furo" +html_static_path = ["_static"] +html_title = f"
{project} {release}
" +default_role = "obj" + +# Extensions configs + +## autosummary +autosummary_generate = True +autosummary_ignore_module_all = False + +## autodoc +autodoc_default_options = { + "members": True, + "inherited-members": False, + "private-members": False, + "show-inheritance": True, + "special-members": False, +} + + +## myst_nb +nb_execution_allow_errors = False # Enable this to debug notebooks +nb_execution_show_tb: True + + +## numpydoc +numpydoc_show_class_members = False + + +## intersphinx +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "pandas": ("https://pandas.pydata.org/pandas-docs/dev", None), + "sklearn": ("https://scikit-learn.org/stable", None), + "numpy": ("https://numpy.org/doc/stable", None), + "matplotlib": ("https://matplotlib.org/stable", None), +} diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..4e6cd9c --- /dev/null +++ b/docs/index.md @@ -0,0 +1,44 @@ +```{include} ../README.md +:start-after: +:end-before: +``` + +```{include} ../README.md +:start-after: +:end-before: +``` + +## How does it work + +`khalib` proposes histogram-based calibration and its error estimation. Its differentiating factor +is that uses [Khiops][khiops-org] to construct the histogram in which {math}`P(Y = 1 | S)` is +estimated. These histograms have the following properties: +- They balance class purity, model complexity and data fitness. +- They are non-parametric: The optimal histogram is searched without constraint in number of bins or + bin width. This implies that the user doesn't need to set a number of bins nor their widths. + + +[khiops-org]: https://khiops.org +[khiops11-setup]: https://khiops.org/11.0.0-b.0/setup/ +[sk-calclf]: https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html +[khalib-docs]: https://khiopsml.github.io/khalib + + +See the [Quickstart](quickstart) and [API reference](api) to learn how to use the library. + + +```{toctree} +:hidden: + +Home +Quickstart +API Reference +``` + +```{toctree} +:caption: See Also +:hidden: + +Khiops +``` + diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb new file mode 100644 index 0000000..8316f2d --- /dev/null +++ b/docs/quickstart.ipynb @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "721d9059", + "metadata": {}, + "source": [ + " # Quickstart\n", + "\n", + "\n", + " {download}`⬇️ Download this Notebook <./quickstart.ipynb>`\n", + "\n", + " ## KhalibClassifier Scikit-Learn Estimator\n", + "\n", + " We first create our train, calibration and test datasets. We use 45k as test to\n", + " ensure good error estimations. The rest is divided evenly into train and calibration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c5a14fe", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_classification\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "X, y = make_classification(\n", + " n_samples=50_000, n_features=20, n_informative=2, n_redundant=10, random_state=42\n", + ")\n", + "X_train, X_not_train, y_train, y_not_train = train_test_split(\n", + " X, y, train_size=2500, random_state=42\n", + ")\n", + "X_calib, X_test, y_calib, y_test = train_test_split(\n", + " X_not_train, y_not_train, train_size=2500, random_state=42\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "11e47c79", + "metadata": {}, + "source": [ + "We now train a `GaussianNB` classifier. This kind of model is usually uncalibrated\n", + "because data never fullfill its hypotheses. We also estimate its expected calibration\n", + "error (ECE):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24800655", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "\n", + "import khalib\n", + "\n", + "# Compute the positive scores with a Gaussian Naive Bayes model\n", + "gnb = GaussianNB()\n", + "gnb.fit(X_train, y_train)\n", + "y_scores_test = gnb.predict_proba(X_test)[:, 1]\n", + "\n", + "# Compute and display the ECE\n", + "ece_test = khalib.calibration_error(y_scores_test, y_test)\n", + "print(\"RAW GNB ECE:\", ece_test)" + ] + }, + { + "cell_type": "markdown", + "id": "e761991c", + "metadata": {}, + "source": [ + "To calibrate our `GaussianNB` we create an instance of `KhalibClassifier` with it:" + ] + }, + { + "cell_type": "markdown", + "id": "fefe95a7", + "metadata": {}, + "source": [ + "We can also plot the reliability diagram using the `build_reliability_diagram`\n", + "function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ed2da8f", + "metadata": {}, + "outputs": [], + "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", + "_ = khalib.build_reliability_diagram(y_scores_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "faf3526f", + "metadata": {}, + "source": [ + "We now calibrate the model with a `KhalibClassifier` object. It uses the uncalibrated\n", + "model as parameter. We then `fit` it on the `calib` split." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4ab538f", + "metadata": {}, + "outputs": [], + "source": [ + "# Train the calibrated classifier and obtain the calibrated scores\n", + "calib_gnb = khalib.KhalibClassifier(gnb)\n", + "calib_gnb.fit(X_calib, y_calib)\n", + "y_calib_scores_test = calib_gnb.predict_proba(X_test)[:, 1]\n", + "\n", + "# Compute the ECE\n", + "calib_ece_test = khalib.calibration_error(y_calib_scores_test, y_test)\n", + "print(\"CALIB ECE:\", calib_ece_test)\n", + "print(\"Reduction:\", (ece_test - calib_ece_test) / ece_test)" + ] + }, + { + "cell_type": "markdown", + "id": "081da17b", + "metadata": {}, + "source": [ + "We observe that `khalib` reduced the ECE by ~90%. We now plot the reliability diagram\n", + "for the calibrated scores. The `reliability_diagram` uses a heuristic to detect when\n", + "the scores are distributed as Dirac deltas and changes the visualization accordingly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bcade36", + "metadata": {}, + "outputs": [], + "source": [ + "_ = khalib.build_reliability_diagram(y_calib_scores_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "b136103a", + "metadata": {}, + "source": [ + "## calibrate_binary function + Histogram class\n", + "\n", + "We can achieve the same result \"manually\" by using the function `calibrate_binary`\n", + "which calibrates the scores with a `Histogram` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75463478", + "metadata": {}, + "outputs": [], + "source": [ + "# Obtain the scores on the calib split and build a supervised histogram with it\n", + "y_scores_calib = gnb.predict_proba(X_calib)[:, 1]\n", + "hist = khalib.Histogram.from_data(y_scores_calib, y=y_calib)\n", + "\n", + "# Calibrate the scores of the test split\n", + "calib_hist_y_test_scores = khalib.calibrate_binary(\n", + " y_scores_test, hist, only_positive=True\n", + ")\n", + "\n", + "# Print the error and plot the reliability diagram\n", + "calib_hist_ece_test = khalib.calibration_error(calib_hist_y_test_scores, y=y_test)\n", + "print(\"CALIB HIST ECE:\", calib_hist_ece_test)\n", + "print(\"Reduction :\", (ece_test - calib_ece_test) / ece_test)\n", + "_ = khalib.build_reliability_diagram(calib_hist_y_test_scores, y_test)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/quickstart.py b/docs/quickstart.py new file mode 100644 index 0000000..f5e6c23 --- /dev/null +++ b/docs/quickstart.py @@ -0,0 +1,114 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.17.3 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Quickstart +# +# +# {download}`⬇️ Download this Notebook <./quickstart.ipynb>` +# +# ## KhalibClassifier Scikit-Learn Estimator +# +# We first create our train, calibration and test datasets. We use 45k as test to +# ensure good error estimations. The rest is divided evenly into train and calibration. + +# %% +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split + +X, y = make_classification( + n_samples=50_000, n_features=20, n_informative=2, n_redundant=10, random_state=42 +) +X_train, X_not_train, y_train, y_not_train = train_test_split( + X, y, train_size=2500, random_state=42 +) +X_calib, X_test, y_calib, y_test = train_test_split( + X_not_train, y_not_train, train_size=2500, random_state=42 +) + +# %% [markdown] +# We now train a `GaussianNB` classifier. This kind of model is usually uncalibrated +# because data never fullfill its hypotheses. We also estimate its expected calibration +# error (ECE): + +# %% +from sklearn.naive_bayes import GaussianNB + +import khalib + +# Compute the positive scores with a Gaussian Naive Bayes model +gnb = GaussianNB() +gnb.fit(X_train, y_train) +y_scores_test = gnb.predict_proba(X_test)[:, 1] + +# Compute and display the ECE +ece_test = khalib.calibration_error(y_scores_test, y_test) +print("RAW GNB ECE:", ece_test) + + +# %% [markdown] +# To calibrate our `GaussianNB` we create an instance of `KhalibClassifier` with it: + +# %% [markdown] +# We can also plot the reliability diagram using the `build_reliability_diagram` +# function: + +# %% +# %config InlineBackend.figure_formats = ['svg'] +_ = khalib.build_reliability_diagram(y_scores_test, y_test) + +# %% [markdown] +# We now calibrate the model with a `KhalibClassifier` object. It uses the uncalibrated +# model as parameter. We then `fit` it on the `calib` split. + +# %% +# Train the calibrated classifier and obtain the calibrated scores +calib_gnb = khalib.KhalibClassifier(gnb) +calib_gnb.fit(X_calib, y_calib) +y_calib_scores_test = calib_gnb.predict_proba(X_test)[:, 1] + +# Compute the ECE +calib_ece_test = khalib.calibration_error(y_calib_scores_test, y_test) +print("CALIB ECE:", calib_ece_test) +print("Reduction:", (ece_test - calib_ece_test) / ece_test) + +# %% [markdown] +# We observe that `khalib` reduced the ECE by ~90%. We now plot the reliability diagram +# for the calibrated scores. The `reliability_diagram` uses a heuristic to detect when +# the scores are distributed as Dirac deltas and changes the visualization accordingly: + +# %% +_ = khalib.build_reliability_diagram(y_calib_scores_test, y_test) + +# %% [markdown] +# ## calibrate_binary function + Histogram class +# +# We can achieve the same result "manually" by using the function `calibrate_binary` +# which calibrates the scores with a `Histogram` object. + +# %% +# Obtain the scores on the calib split and build a supervised histogram with it +y_scores_calib = gnb.predict_proba(X_calib)[:, 1] +hist = khalib.Histogram.from_data(y_scores_calib, y=y_calib) + +# Calibrate the scores of the test split +calib_hist_y_test_scores = khalib.calibrate_binary( + y_scores_test, hist, only_positive=True +) + +# Print the error and plot the reliability diagram +calib_hist_ece_test = khalib.calibration_error(calib_hist_y_test_scores, y=y_test) +print("CALIB HIST ECE:", calib_hist_ece_test) +print("Reduction :", (ece_test - calib_ece_test) / ece_test) +_ = khalib.build_reliability_diagram(calib_hist_y_test_scores, y_test) diff --git a/justfile b/justfile index e6acf96..f26187f 100644 --- a/justfile +++ b/justfile @@ -2,7 +2,13 @@ alias pc := pre-commit pre-commit: uv run pre-commit run --all-files +alias d := docs +docs: + (cd docs && rm -rf _build && uv run sphinx-build --nitpicky . _build/html) + alias t := test [positional-arguments] @test *args: uv run pytest src/tests "$@" + + diff --git a/pyproject.toml b/pyproject.toml index 0ff7e2a..d516eae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,15 +4,19 @@ build-backend = "setuptools.build_meta" [project] name = "khalib" -version = "0.1b0" +version = "0.1" description = "Classifier Calibration with Khiops" authors = [{ name = "Felipe Olmos", email = "luisfelipe.olmosmarchant@orange.com" }] requires-python = ">=3.11" dependencies = [ "khiops>=11.0.0.0b0", - "numpy>=1.26.0", + "numpy>=2.0.0", "scikit-learn>=1.5.0", + "matplotlib>=3.9.0", ] +license = "BSD-3-Clause" +license-files = ["LICENSE.md"] + [tools.setuptools] packages = ["khcalib"] @@ -23,6 +27,7 @@ markers = [ ] [tool.ruff.lint] +exclude = ["docs/quickstart.py"] select = [ "E", # pycodestyle errors "F", # pyflakes @@ -60,7 +65,18 @@ khiops = { url = "https://github.com/KhiopsML/khiops-python/releases/download/11 [dependency-groups] dev = [ - "ipython>=9.4.0", + "furo>=2025.7.19", + "ipython>=8.27.0", + "jupyterlab>=4.4.9", + "jupyterlab-myst>=2.4.2", + "jupytext>=1.17.3", + "matplotlib>=3.10.6", + "myst-nb>=1.3.0", + "myst-parser>=4.0.1", + "numpydoc>=1.9.0", "pre-commit>=4.3.0", "pytest>=8.4.1", + "sphinx>=8.2.3", + "sphinx-copybutton>=0.5.2", + "sphinx-paramlinks>=0.6.0", ] diff --git a/src/khalib.py b/src/khalib.py index a837ba2..44ed919 100644 --- a/src/khalib.py +++ b/src/khalib.py @@ -1,3 +1,5 @@ +"""Classifier calibration with Khiops""" + import bisect import math import os @@ -11,6 +13,8 @@ from khiops import core as kh from khiops.core.internals.runner import KhiopsLocalRunner from khiops.sklearn import KhiopsClassifier +from matplotlib import pyplot as plt +from matplotlib.ticker import LogLocator from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin from sklearn.exceptions import NotFittedError from sklearn.model_selection import train_test_split @@ -24,7 +28,7 @@ def build_single_core_khiops_runner(): """Build a Khiops runner with one core - Running with a single core is faster for most of the calibration use-cases + Running with a single core is faster for most of the calibration use-cases. """ default_max_cores = os.environ.get("KHIOPS_PROC_NUMBER") os.environ["KHIOPS_PROC_NUMBER"] = "1" @@ -53,15 +57,63 @@ def single_core_khiops_runner(): @dataclass() class Histogram: + """A histogram with optional target variable statistics + + .. note:: + To obtain instances from real data, prefer the factory method `from_data` rather + than the base constructor. + + Attributes + ---------- + breakpoints : list[float] + The breakpoints defining the histogram in increasing order. + freqs : list of float + The frequencies of each bin. + densities : list[float] + The densities of each bin. + classes : list + The class labels. + target_freqs : list[tuple], optional + The target frequencies in each bin. Each tuple is has the same size as + :paramref:`classes`. + target_probas : list[tuple], optional + The target conditional probabilites in each bin. Each tuple is has the same size + as :paramref:`classes`. + """ + breakpoints: list[float] freqs: list[int] + densities: list[float] = field(init=False) target_freqs: list[tuple] = field(default_factory=list) classes: list = field(default_factory=list) - densities: list[float] = field(init=False) target_probas: list[tuple] = field(init=False, default_factory=list) + @property + def n_bins(self) -> int: + """Number of histogram bins.""" + return max(len(self.breakpoints) - 1, 0) + + @property + def bins(self) -> list[tuple]: + """List of histogram bins.""" + return [tuple(self.breakpoints[i : i + 2]) for i in range(self.n_bins)] + + @property + def classes_type(self) -> type | None: + """Type of the target classes (only for histograms built with y).""" + if self.classes: + return type(self.classes[0]) + return None + @classmethod - def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): + def from_data( + cls, + x, + y=None, + method: str = "khiops", + max_bins: int = 0, + use_finest: bool = False, + ) -> "Histogram": """Computes a histogram of an 1D vector via Khiops Parameters @@ -70,10 +122,10 @@ def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): Input scores. y : array-like of shape (n_samples,) or (n_samples, 1), optional Target values. - method : {"modl", "eq-freq", "eq-width"}, default="modl" + method : {"khiops", "eq-freq", "eq-width"}, default="khiops" Histogram method: - - "modl": A non-parametric regularized histogram method. + - "khiops": A non-parametric regularized histogram method. - "eq-freq": All bins have the same number of elements. If many instances have too many values the algorithm will put it in its own bin, which will be larger than the other ones. @@ -84,11 +136,12 @@ def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): The maximum number of bins to be created. The algorithms usually create this number of bins but they may create less. The default value 0 means: - - For "modl": that there is no limit to the number of intervals. + - For "khiops": that there is no limit to the number of intervals. - For "eq-freq" or "eq-width": that 10 is the maximum number of intervals. - use_finest: _Unsupervised 'modl' histogram only_: - If `True` it chooses the finest instead of the most interpretable. + use_finest: bool, default=False + *Unsupervised 'khiops' histogram only*: If `True` it builds the finest + histogram instead of the most interpretable. Returns ------- @@ -100,7 +153,7 @@ def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): raise ValueError(f"x must be 1-D but it has shape {x.shape}.") if y is not None and len(y.shape) > 1 and y.shape[1] > 1: raise ValueError(f"y must be 1-D but it has shape {y.shape}.") - valid_methods = ["modl", "eq-freq", "eq-width"] + valid_methods = ["khiops", "eq-freq", "eq-width"] if method not in valid_methods: raise ValueError(f"method must be in {valid_methods}. It is '{method}'") if max_bins < 0: @@ -108,11 +161,11 @@ def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): # Set the y vector to be used by khiops # This is necessary because for the "eq-freq" and "eq-width" methods if - # target is set then it uses 'modl'. - y_khiops = y if method == "modl" else None + # target is set then it uses 'khiops'. + y_khiops = y if method == "khiops" else None # Transform the binning methods to the Khiops names - if method == "modl": + if method == "khiops": khiops_method = "MODL" elif method == "eq-freq": khiops_method = "EqualFrequency" @@ -121,7 +174,7 @@ def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): else: raise ValueError( f"Unknown binning method '{method}'. " - "Choose between 'modl', 'eq-freq' and 'eq-width'" + "Choose between 'khiops', 'eq-freq' and 'eq-width'" ) # Create Khiops dictionary @@ -180,6 +233,9 @@ def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): do_data_preparation_only=True, ) results = kh.read_analysis_results_file(f"{work_dir}/report.khj") + # import shutil + + # shutil.copy(f"{work_dir}/report.khj", "tmp.khj") # Initialize the histogram if y is not None: @@ -216,45 +272,50 @@ def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): for tfreqs in dg.part_target_frequencies ] # Unsupervised khiops execution - # Case 'eq-freq' or 'eq-width' - elif score_stats.modl_histograms is None: - # Create the breakpoints - breakpoints = [part.lower_bound for part in dg.dimensions[0].partition] - breakpoints.append(dg.dimensions[0].partition[-1].upper_bound) - - # Recover the frequencies - freqs = dg.frequencies.copy() - # Case 'modl': Recover the histogram from the modl_histogram field else: - if use_finest: - histogram_index = -1 + # Case 'eq-freq' or 'eq-width' + if score_stats.modl_histograms is None: + # Create the breakpoints + breakpoints = [ + part.lower_bound for part in dg.dimensions[0].partition + ] + breakpoints.append(dg.dimensions[0].partition[-1].upper_bound) + + # Recover the frequencies + freqs = dg.frequencies.copy() + # Case 'khiops': Recover the histogram from the modl_histogram field else: - histogram_index = ( - score_stats.modl_histograms.information_rates.index(100) - ) - breakpoints = score_stats.modl_histograms.histograms[ - histogram_index - ].bounds - freqs = score_stats.modl_histograms.histograms[ - histogram_index - ].frequencies - - # Compute the supervised fields if y was provided - if y is not None: - # Compute the class and bin indexes - y_indexes = le.transform(y) - x_bin_indexes = ( - np.searchsorted(breakpoints[:-1], x, side="left") - 1 - ).reshape(1, -1) - x_bin_indexes[x_bin_indexes < 0] = 0 - # Note: reshape is needed for the 1-column matrix case if not the - # iterator below does not work - - # Compute the target frequencies - target_freqs = [[0 for _ in le.classes_] for _ in breakpoints[:-1]] - for y_score_bin_index, y_index in np.nditer([x_bin_indexes, y_indexes]): - target_freqs[y_score_bin_index][y_index] += 1 - target_freqs = [tuple(freqs) for freqs in target_freqs] + if use_finest: + histogram_index = -1 + else: + histogram_index = ( + score_stats.modl_histograms.information_rates.index(100) + ) + breakpoints = score_stats.modl_histograms.histograms[ + histogram_index + ].bounds + freqs = score_stats.modl_histograms.histograms[ + histogram_index + ].frequencies + + # Compute the supervised fields if y was provided + if y is not None: + # Compute the class and bin indexes + y_indexes = le.transform(y) + x_bin_indexes = ( + np.searchsorted(breakpoints[:-1], x, side="left") - 1 + ).reshape(1, -1) + x_bin_indexes[x_bin_indexes < 0] = 0 + # Note: reshape is needed for the 1-column matrix case if not the + # iterator below does not work + + # Compute the target frequencies + target_freqs = [[0 for _ in le.classes_] for _ in breakpoints[:-1]] + for y_score_bin_index, y_index in np.nditer( + [x_bin_indexes, y_indexes] + ): + target_freqs[y_score_bin_index][y_index] += 1 + target_freqs = [tuple(freqs) for freqs in target_freqs] # Otherwise there is just one interval else: # Non-informative variable: histogram with only the bin (min, max) @@ -281,7 +342,9 @@ def from_data(cls, x, y=None, method="modl", max_bins=0, use_finest=False): ) @classmethod - def from_data_and_breakpoints(cls, x, breakpoints: list[float], y=None): + def from_data_and_breakpoints( + cls, x, breakpoints: list[float], y=None + ) -> "Histogram": """Builds a histogram from a list of breakpoints and data Parameters @@ -380,7 +443,18 @@ def __post_init__(self): self.target_probas = target_probas def find(self, value: float) -> int: - """Returns the histogram bin index for a value""" + """Returns the histogram bin index for a value + + Parameters + ---------- + value : float + The value to look up in the histogram bins. + + Returns + ------- + int + The index of the bin containing the value. + """ if value <= self.breakpoints[0]: return 0 elif value >= self.breakpoints[-1]: @@ -389,40 +463,37 @@ def find(self, value: float) -> int: return bisect.bisect_right(self.breakpoints, value) - 1 def vfind(self, values): - """Returns the histogram bin indexes for a value sequence""" + """Returns the histogram bin indexes for a value sequence + + Parameters + ---------- + value : :external:term:`array-like` + The values to look up in the histogram bins. + + Returns + ------- + :external:term:`array-like` + The indexes of the bins containing the values. + """ indexes = np.searchsorted(self.breakpoints[:-1], values, side="right") - 1 indexes[np.array(values) < self.breakpoints[0]] = 0 indexes[np.array(values) > self.breakpoints[-2]] = self.n_bins - 1 return indexes - @property - def n_bins(self): - """Number of histogram bins""" - return max(len(self.breakpoints) - 1, 0) - - @property - def bins(self): - """List of histogram bins""" - return [tuple(self.breakpoints[i : i + 2]) for i in range(self.n_bins)] - - @property - def classes_type(self): - """Type of the target classes (only for histograms built with y)""" - if self.classes: - return type(self.classes[0]) - return None - def calibration_error( y_scores, y, method: str = "label-bin", - histogram_method: str = "modl", + histogram_method: str = "khiops", max_bins: int = 0, multi_class_method: str = "top-label", histogram: Histogram | None = None, ): - """Estimates the ECE for a pair of score and label vectors + r"""Estimates the ECE via binning + + The default binning method "khiops" finds a regularized histogram for which the + distribution of the target as pure as possible. Parameters ---------- @@ -436,13 +507,15 @@ def calibration_error( multi_class_method : {"top-label", "classwise"}, default="top-label" Multi-class ECE estimation method: - - "top-label": Estimates the ECE for the predicted class. - - "classwise": Estimates the ECE for each class in a 1-vs-rest and the averages - it. - histogram_method : {"modl", "eq-freq", "eq-width"}, default="modl" + - "top-label": Estimates the ECE for the confidence scores (ie. the predicted + class score). + - "classwise": Estimates the ECE for each class in a one-vs-rest fashion and the + averages it. + histogram_method : {"khiops", "eq-freq", "eq-width"}, default="khiops" Histogram method: - - "modl": A non-parametric regularized histogram method. + - "khiops": A non-parametric regularized histogram method. It finds the best + histogram such that the distribution of the target is constant in each bin. - "eq-freq": All bins have the same number of elements. If many instances have too many values the algorithm will put it in its own bin, which will be larger than the other ones. @@ -453,11 +526,65 @@ def calibration_error( The maximum number of bins to be created. The algorithms usually create this number of bins but they may create less. The default value 0 means: - - For "modl": that there is no limit to the number of intervals. + - For "khiops": that there is no limit to the number of intervals. - For "eq-freq" or "eq-width": that 10 is the maximum number of intervals. histogram : `Histogram`, optional A ready-made histogram. If set then it is used for the ECE computation and the parameters histogram_method and max_bins are ignored. + + + Notes + ----- + We present the formulas for the two binary ECE estimators used in this function, as + described in [RCSM22]_. Both approximate the theoretical calibration error: + + .. math:: + \mathbb{E}\left[ \left| \mathbb{P}\left[ Y = 1 | S \right] - S \right| \right] + + where the pair :math:`(S, Y)` is a random vector taking values in :math:`[0, 1] + \times \lbrace{0, 1\rbrace}`. The random variable :math:`S` represent the + (normalized) scores coming from a classifier, and :math:`Y` the target. + + Let + :math:`\lbrace {B_i} \rbrace _{i=I}^I` a binning of :math:`[0,1]` and :math:`\lbrace + (s_n, y_n) \rbrace_{n=1}^N` a sample following the distribution of :math:`(S, Y)`. + + + The **label-bin** ECE estimation is given by: + + .. math:: + \text{ECE}_{\text{lb}} = \frac{1}{N} \sum_{n=1}^N \sum_{i = 1}^I + \mathbb{1}_{s_n \in B_i} \left| s_n - \bar{y_i} \right| + + where :math:`\bar{y_i}` is the sample conditional expectation of :math:`Y` in the + :math:`i`-th bin: + + .. math:: + \frac{\sum_{n=1}^N \mathbb{1}_{s_n \in B_i} \cdot y_n}{|B_i|}. + + On the other hand, the **bin** ECE estimation is given by: + + .. math:: + \text{ECE}_{\text{bin}} = \sum_{i = 1}^I + \frac{\left| B_i \right| }{N} \left| \bar{s_i} - \bar{y_i} \right| + + where :math:`\bar{y_i}` is as above, and :math:`\bar{s_i}` is the sample + conditional expectation of :math:`S` in the :math:`i`-th bin: + + .. math:: + \frac{\sum_{n=1}^N \mathbb{1}_{s_n \in B_i} \cdot s_n}{|B_i|}. + + + The **bin** estimator is a lower bound for the real ECE for any binning used. And, + for a given binning, it is a lower bound of the **label-bin** estimator. + + The **bin** estimator is the most common literature, usually called the *plugin* + estimator, or even shown as the defitinion ECE. Despite this fact, we choose the + **label-bin** estimator as default method because it is exact when the conditional + distribution :math:`\mathbb{P}\left[ Y = 1 | S = s \right]` is piecewise constant on + :math:`s`. These piecewise constant models are exactly the hypothesis space of any + histogram estimation of the ECE. + """ if len(y_scores.shape) > 2: raise ValueError( @@ -525,7 +652,7 @@ def _binary_ece( y_scores, y, method: str = "label-bin", - histogram_method: str = "modl", + histogram_method: str = "khiops", max_bins: int = 0, histogram: Histogram | None = None, ): @@ -570,21 +697,47 @@ class KhalibClassifier(ClassifierMixin, MetaEstimatorMixin, BaseEstimator): This estimator uses the Khiops regularized histogram construction method (MODL) to calibrate the probabilities/scores coming from a classifier. - For binary classifiers the calibrated scores are calculated + For binary classifiers, it fits a Khiops histogram for the scores for the positive + class. Then, for a given score, it estimates its calibrated probability as the + positive class probability of the bin in which the score falls. + + For multi-class classifiers, it fits khiops histogram for each class in a + one-vs-rest fashion. Then, given a score vector, it estimates its calibrated + probabilities by applying in each one-vs-rest the binary method described above and + then normalizing to one. + + This class emulates closely the `~sklearn.calibration.CalibratedClassifierCV` + interface but without cross-validation, as the Khiops models are regularized and do + not need it. + + .. _KhiopsClassifier: https://khiops.org/11.0.0-b.0/api-docs/python-api/api/sklearn/generated/khiops.sklearn.estimators.html#khiops.sklearn.estimators.KhiopsClassifier Parameters ---------- estimator : estimator instance, default=None The classifier whose output need to be calibrated to provide more accurate - `predict_proba` outputs. If not provided, it trains a `KhiopsClassifier`. If - provided, but the estimator is no fitted then, on `fit`, it will be fitted with - `train_size` elements. + `predict_proba` outputs. If not provided, it trains a `KhiopsClassifier`_. If + provided, but the estimator is not fitted then, on `fit`, it will + be fitted with :paramref:`train_size` elements. train_size : float or int, default=0.75 - Same parameter as `test_train_split`. Only used when `estimator` is not provided - or non-fitted. + Same parameter as `~sklearn.model_selection.train_test_split`. Only used when + :paramref:`estimator` is not provided or not fitted. random_state : int, default=None - Same parameter as `test_train_split`. Only used when `estimator` is not provided - or non-fitted. + Same parameter as `~sklearn.model_selection.train_test_split`. Only used when + :paramref:`estimator` is not provided or not fitted. + + + Attributes + ---------- + fitted_estimator_ : + The underlying fitted classifier. It is the same instance as + :paramref:`estimator` if it was provided, otherwise it is an instance of + `KhiopsClassifier`_. + histograms_ : list of size 1 or n_classes + In the binary case: a list of size 1 with the `Histogram` instance for the + positive class. In the multi class case: A list of size n_classes containing the + one-vs-rest `Histogram` for each class. + """ def __init__(self, estimator=None, train_size=0.75, random_state=None): @@ -594,7 +747,20 @@ def __init__(self, estimator=None, train_size=0.75, random_state=None): self.random_state = random_state def fit(self, X, y): # noqa: N803 - """Fits the calibrated model""" + """Fits the calibrated model + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,) + Target values. + + Returns + ------- + self + The calling estimator instance. + """ # Check/initialize fitted estimator if self.estimator is None: self.fitted_estimator_ = KhiopsClassifier() @@ -633,13 +799,27 @@ def fit(self, X, y): # noqa: N803 calibrated_probas = np.empty(scores.shape) for k, histogram in enumerate(self.histograms_): - calibrated_probas[:, k] = map_scores_to_positive_class_proba( + calibrated_probas[:, k] = calibrate_binary( scores[:, k], histogram, only_positive=True ) return self def predict_proba(self, X): # noqa: N803 - """Estimates the calibrated classification probabilities""" + """Estimates the calibrated classification probabilities + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + + Returns + ------- + P : array-like of shape (n_samples, n_classes) + The estimated calibrated probabilities for each class. + """ + # Check that is fitted + check_is_fitted(self) + # Estimate the uncalibrated scores scores, _ = _get_response_values( self.fitted_estimator_, @@ -649,14 +829,12 @@ def predict_proba(self, X): # noqa: N803 # Binary classification if len(self.histograms_) == 1: - calibrated_probas = map_scores_to_positive_class_proba( - scores, self.histograms_[0] - ) + calibrated_probas = calibrate_binary(scores, self.histograms_[0]) # Multiclass classification: One-vs-Rest else: calibrated_probas = np.empty(scores.shape) for k, histogram in enumerate(self.histograms_): - calibrated_probas[:, k] = map_scores_to_positive_class_proba( + calibrated_probas[:, k] = calibrate_binary( scores[:, k], histogram, only_positive=True ) @@ -667,17 +845,19 @@ def predict_proba(self, X): # noqa: N803 return calibrated_probas -def map_scores_to_positive_class_proba( - y_scores, histogram: Histogram, only_positive: bool = False -): - """Maps scores to the target probability of the histogram's positive class +def calibrate_binary(y_scores, histogram: Histogram, only_positive: bool = False): + """Calibrates a binary score with an histogram + + This function may be used to calibrate binary classification scores without the use + of the `KhalibClassifier` estimator. Parameters ---------- y_scores : array-like of shape (n_samples,) or (n_samples, 1) - Input scores. + Scores for the positive class. histogram : `Histogram` - A histogram with target statistics (ie `target_freqs` must be non-empty). + A histogram with target statistics (ie. :paramref:`Histogram.target_freqs` must + be non-empty). only_positive : bool, default=False If ``True`` it returns only 1-D array with the probabilities of the positive class. Otherwise it returns a 2-D array with the probabilities for both classes. @@ -702,3 +882,212 @@ def map_scores_to_positive_class_proba( calibrated_probas[it.index][1] = histogram.target_probas[bin_i][1] return calibrated_probas + + +def build_reliability_diagram( + y_scores, + y, + dirac_threshold=1.0e-06, + log_plot_threshold=3.0, + min_density_bar_width=2.5e-03, +): + """Builds a reliability diagram with the target score distribution below + + To build the diagram this function uses Khiops supervised histograms. To build the + score distribution, it uses Khiops unsupervised histograms. Using the latter, it + implements a heuristic to dectect when the scores is distributed as a sum of diracs + and changes visualization accordingly. + + + Parameters + ---------- + y_scores : array-like of shape (n_samples,) or (n_samples, 1) or (n_samples, 2) + Scores/probabilities. If it is a 2-D array the column indexed as 1 will be used + for the estimation. + y : array-like of shape (n_samples,) or (n_samples, n_classes) + Target values (class labels). + dirac_threshold : float, default=1.0e-06 + If a bin in the scores' unsupervised histogram is lower than this + 'dirac_threshold' then it is considered a dirac mass. + log_plot_threshold : float, default=3.0 + *Density plot only:* If the log-difference between the maximal and minimal + positive density values is larger than 'log_plot_threshold' then the density + plot uses a log scale in the y-axis. + min_density_bar_width : float, default=5.0e-03 + *Density plot only:* If a bin of the scores' unsupervised histogram has a width + lower than 'min_density_bar_width' then it is plotted as having a width of + 'min_density_bar_width'. + + Returns + ------- + tuple + A 2-tuple containing: + + - A `matplotlib.figure.Figure`. + - A `dict` containing two `matplotlib.axes.Axes` with keys, "reliability + diagram" and "score_distribution". + """ + # Check the input parameters + if len(y_scores.shape) > 2: + raise ValueError( + "'y_scores' must be either a 1-D or 2-D array-like object, " + f"but its shape is {y_scores.shape}" + ) + if len(y.shape) > 1: + raise ValueError( + f"'y_scores' must be a 1-D array-like object, but its shape is {y.shape}" + ) + if dirac_threshold <= 0.0: + raise ValueError("'dirac_threshold' must be positive") + if log_plot_threshold <= 0.0: + raise ValueError("'log_plot_threshold' must be positive") + if min_density_bar_width <= 0.0: + raise ValueError("'min_density_bar_width' must be positive") + + # Create the base plot + fig, axs = plt.subplot_mosaic( + [["reliability_diagram"], ["score_distribution"]], + figsize=(6, 6), + height_ratios=(4, 1), + sharex=True, + ) + fig.subplots_adjust(hspace=0) + fig.suptitle("Reliability Diagram") + + # Build a unsupervised histogram to to detect the dirac masses case + uhist_y_scores = Histogram.from_data(y_scores, use_finest=True) + dirac_indexes = [] + if uhist_y_scores.freqs[1] == 0 and ( + uhist_y_scores.bins[0][1] - uhist_y_scores.bins[0][0] < dirac_threshold + ): + dirac_indexes.append(True) + else: + dirac_indexes.append(False) + for i in range(1, uhist_y_scores.n_bins - 1): + cur_left, cur_right = uhist_y_scores.bins[i] + if ( + uhist_y_scores.freqs[i - 1] == 0 + and uhist_y_scores.freqs[i + 1] == 0 + and (cur_right - cur_left < dirac_threshold) + ): + dirac_indexes.append(True) + else: + dirac_indexes.append(False) + if uhist_y_scores.freqs[-2] == 0 and ( + uhist_y_scores.bins[-1][1] - uhist_y_scores.bins[-1][0] < dirac_threshold + ): + dirac_indexes.append(True) + else: + dirac_indexes.append(False) + + # Compute the supervised score histogram + hist_y_scores = Histogram.from_data(y_scores, y) + + # Case where are only dirac masses + n_nonzero_bins = sum(freq > 0 for freq in uhist_y_scores.freqs) + if sum(dirac_indexes) == n_nonzero_bins: + # Compute the dirac masses plot data + dirac_masses = [ + freq / sum(uhist_y_scores.freqs) + for freq, is_dirac in zip(uhist_y_scores.freqs, dirac_indexes, strict=False) + if is_dirac + ] + dirac_locations = [ + (left + right) / 2 + for (left, right), is_dirac in zip( + uhist_y_scores.bins, dirac_indexes, strict=False + ) + if is_dirac + ] + indexes = hist_y_scores.vfind(dirac_locations) + target_probas = [hist_y_scores.target_probas[i][1] for i in indexes] + axs["reliability_diagram"].scatter( + dirac_locations, target_probas, color="tab:red", alpha=0.8 + ) + axs["reliability_diagram"].plot( + (0, 1), (0, 1), color="tab:grey", linestyle="dotted" + ) + for loc, mass in zip(dirac_locations, dirac_masses, strict=False): + axs["score_distribution"].annotate( + "", + xy=(loc, mass), + xytext=(loc, 0.0), + label="dirac masses", + arrowprops={ + "arrowstyle": "-|>", + "shrinkA": 0, + "shrinkB": 0, + "color": "tab:blue", + }, + ) + axs["score_distribution"].grid(color="gainsboro") + axs["score_distribution"].set_xlabel("Score Positive Class") + axs["score_distribution"].set_ylabel("Probability") + # Case where there are only proper densities or a mix of both + else: + # Plot the reliability diagram + x = [] + for intv in hist_y_scores.bins: + x.extend(intv) + x[0] = 0 + x[-1] = 1 + y = [] + for probas in hist_y_scores.target_probas: + y.extend((probas[1], probas[1])) + axs["reliability_diagram"].plot(x, y) + axs["reliability_diagram"].plot(x, x, linestyle="dotted", color="gray") + axs["reliability_diagram"].fill_between(x, x, y, color="tab:red", alpha=0.2) + axs["reliability_diagram"].set_ylabel("Positive Class Probability") + axs["reliability_diagram"].grid(color="gainsboro") + + # Recompute the density, this time we took the most interpretable histogram + uhist_y_scores = Histogram.from_data(y_scores) + + # Plot the density + density_bar_starts = [ + left + for (left, _), freq in zip( + uhist_y_scores.bins, uhist_y_scores.freqs, strict=True + ) + if freq > 0 + ] + density_bar_widths = [ + right - left + if right - left >= min_density_bar_width + else min_density_bar_width + for (left, right), freq in zip( + uhist_y_scores.bins, uhist_y_scores.freqs, strict=True + ) + if freq > 0 + ] + density_bar_heights = [ + density + for density, freq in zip( + uhist_y_scores.densities, + uhist_y_scores.freqs, + strict=True, + ) + if freq > 0 + ] + density_bar_log_range = None + if density_bar_heights: + density_bar_log_range = math.log10(max(density_bar_heights)) - math.log10( + min(density_bar_heights) + ) + axs["score_distribution"].bar( + x=density_bar_starts, + height=density_bar_heights, + width=density_bar_widths, + align="edge", + label="Density", + ) + axs["score_distribution"].grid(color="gainsboro") + axs["score_distribution"].set_xlabel("Score Positive Class") + axs["score_distribution"].set_ylabel("Density") + if density_bar_log_range > log_plot_threshold: + axs["score_distribution"].set_yscale("log") + axs["score_distribution"].yaxis.set_major_locator( + LogLocator(base=10, subs=[1], numticks=5) + ) + + return fig, axs diff --git a/src/tests/data/histogram/ref/test_happy_path_modl-bool-True.json b/src/tests/data/histogram/ref/test_happy_path_khiops-bool-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_happy_path_modl-bool-True.json rename to src/tests/data/histogram/ref/test_happy_path_khiops-bool-True.json diff --git a/src/tests/data/histogram/ref/test_happy_path_modl-float-True.json b/src/tests/data/histogram/ref/test_happy_path_khiops-float-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_happy_path_modl-float-True.json rename to src/tests/data/histogram/ref/test_happy_path_khiops-float-True.json diff --git a/src/tests/data/histogram/ref/test_happy_path_modl-int-False.json b/src/tests/data/histogram/ref/test_happy_path_khiops-int-False.json similarity index 100% rename from src/tests/data/histogram/ref/test_happy_path_modl-int-False.json rename to src/tests/data/histogram/ref/test_happy_path_khiops-int-False.json diff --git a/src/tests/data/histogram/ref/test_happy_path_modl-int-True.json b/src/tests/data/histogram/ref/test_happy_path_khiops-int-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_happy_path_modl-int-True.json rename to src/tests/data/histogram/ref/test_happy_path_khiops-int-True.json diff --git a/src/tests/data/histogram/ref/test_happy_path_modl-intnl-True.json b/src/tests/data/histogram/ref/test_happy_path_khiops-intnl-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_happy_path_modl-intnl-True.json rename to src/tests/data/histogram/ref/test_happy_path_khiops-intnl-True.json diff --git a/src/tests/data/histogram/ref/test_happy_path_modl-str-True.json b/src/tests/data/histogram/ref/test_happy_path_khiops-str-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_happy_path_modl-str-True.json rename to src/tests/data/histogram/ref/test_happy_path_khiops-str-True.json diff --git a/src/tests/data/histogram/ref/test_no_info_target_modl.json b/src/tests/data/histogram/ref/test_no_info_target_khiops.json similarity index 100% rename from src/tests/data/histogram/ref/test_no_info_target_modl.json rename to src/tests/data/histogram/ref/test_no_info_target_khiops.json diff --git a/src/tests/data/histogram/ref/test_single_value_score_modl-bool-True.json b/src/tests/data/histogram/ref/test_single_value_score_khiops-bool-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_single_value_score_modl-bool-True.json rename to src/tests/data/histogram/ref/test_single_value_score_khiops-bool-True.json diff --git a/src/tests/data/histogram/ref/test_single_value_score_modl-float-True.json b/src/tests/data/histogram/ref/test_single_value_score_khiops-float-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_single_value_score_modl-float-True.json rename to src/tests/data/histogram/ref/test_single_value_score_khiops-float-True.json diff --git a/src/tests/data/histogram/ref/test_single_value_score_modl-int-False.json b/src/tests/data/histogram/ref/test_single_value_score_khiops-int-False.json similarity index 100% rename from src/tests/data/histogram/ref/test_single_value_score_modl-int-False.json rename to src/tests/data/histogram/ref/test_single_value_score_khiops-int-False.json diff --git a/src/tests/data/histogram/ref/test_single_value_score_modl-int-True.json b/src/tests/data/histogram/ref/test_single_value_score_khiops-int-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_single_value_score_modl-int-True.json rename to src/tests/data/histogram/ref/test_single_value_score_khiops-int-True.json diff --git a/src/tests/data/histogram/ref/test_single_value_score_modl-intnl-True.json b/src/tests/data/histogram/ref/test_single_value_score_khiops-intnl-True.json similarity index 100% rename from src/tests/data/histogram/ref/test_single_value_score_modl-intnl-True.json rename to src/tests/data/histogram/ref/test_single_value_score_khiops-intnl-True.json diff --git a/src/tests/test_all.py b/src/tests/test_all.py index 0bd6d3d..2dbf2a4 100644 --- a/src/tests/test_all.py +++ b/src/tests/test_all.py @@ -90,11 +90,11 @@ class TestHistogram: ("eq-width", "int", False), ("eq-width", "int", True), ("eq-width", "intnl", True), - ("modl", "bool", True), - ("modl", "float", True), - ("modl", "int", False), - ("modl", "int", True), - ("modl", "intnl", True), + ("khiops", "bool", True), + ("khiops", "float", True), + ("khiops", "int", False), + ("khiops", "int", True), + ("khiops", "intnl", True), ] @pytest.mark.parametrize(("method", "y_fixture", "use_y"), all_cases) @@ -123,7 +123,7 @@ def test_single_value_score( histogram = Histogram.from_data(y_scores, y=y, method=method) assert histogram == ref_histogram - @pytest.mark.parametrize("method", ["eq-freq", "eq-width", "modl"]) + @pytest.mark.parametrize("method", ["eq-freq", "eq-width", "khiops"]) def test_no_info_target(self, y_fixtures, y_scores_fixtures, ref_histogram, method): # Prepare the input data for the histogram y = y_fixtures["random"] @@ -155,8 +155,8 @@ def test_find_vfind_coherence(self, y_fixtures, y_scores_fixtures): ) @pytest.mark.parametrize("y_scores_fixture", ["original", "constant"]) - @pytest.mark.parametrize("method", ["eq-freq", "eq-width", "modl"]) - def test_manual_vs_modl_coherence( + @pytest.mark.parametrize("method", ["eq-freq", "eq-width", "khiops"]) + def test_manual_vs_khiops_coherence( self, y_fixtures, y_scores_fixture, y_scores_fixtures, method ): # Prepare the input data for the histogram @@ -318,25 +318,6 @@ def test_accuracy_binary_ece( def _clf_data_builder(n_train, n_calib, n_test, n_classes, n_features, n_informative): # Note, we build this way to ensure reproducibility for the not_fitted/None case - # x_not_test, y_not_test = make_classification( - # n_samples=n_train 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