From d7e0c0979c650587064f22785679aa5d89c00228 Mon Sep 17 00:00:00 2001 From: Jonathan Dan Date: Mon, 12 Jun 2023 10:43:32 +0200 Subject: [PATCH] Make compatible with pip packaging system --- README.md | 18 +- example.ipynb | 212 ------------------ pyproject.toml | 28 +++ src/timescoring/__init__.py | 0 .../timescoring/annotations.py | 0 scoring.py => src/timescoring/scoring.py | 2 +- .../timescoring/visualization.py | 4 +- test.py => tests/test.py | 4 +- 8 files changed, 44 insertions(+), 224 deletions(-) delete mode 100644 example.ipynb create mode 100644 pyproject.toml create mode 100644 src/timescoring/__init__.py rename annotations.py => src/timescoring/annotations.py (100%) rename scoring.py => src/timescoring/scoring.py (99%) rename visualization.py => src/timescoring/visualization.py (99%) rename test.py => tests/test.py (99%) diff --git a/README.md b/README.md index 617eefa..c6bdb2f 100644 --- a/README.md +++ b/README.md @@ -20,11 +20,15 @@ Both methods are illustrated in the following figures : ![Illustration of sample based scoring.](https://user-images.githubusercontent.com/747240/244666630-cdfe12cc-22a2-4b23-be15-3e60dbedb437.png) ![Illustration of event based scoring.](https://user-images.githubusercontent.com/747240/244666619-8dd90008-79af-4836-8769-daa204bbe16c.png) -## Code +## Installation + +The package can be installed through pip using the following command : -An example usage of the library is provided in `example.ipynb`. +`pip install timescoring` + +## Code -The library provides three classes : +The `timescoring` package provides three classes : - `annotation.Annotation` : store annotations - `scoring.SampleScoring(ref, hyp)` : Compute sample based scoring @@ -58,7 +62,7 @@ Scores are provided as attributes of the scoring class. The following metrics ca ```python ## Loading Annotations ## -from annotations import Annotation +from timescoring.annotations import Annotation # Annotation objects can be instantiated from a binary mask @@ -82,15 +86,15 @@ labels = Annotation(events, fs, numSamples) ## Computing performance score ## -import scoring -import visualization +from timescoring import scoring +from timescoring import visualization fs = 1 duration = 66*60 ref = Annotation([(8*60, 12*60), (30*60, 35*60), (48*60, 50*60)], fs, duration) hyp = Annotation([(8*60, 12*60), (28*60, 32*60), (50.5*60, 51*60), (60*60, 62*60)], fs, duration) scores = scoring.SampleScoring(ref, hyp) -figSamples = visualization.plotSampleScoring(ref, hyp, param) +figSamples = visualization.plotSampleScoring(ref, hyp) # Scores can also be computed per event param = scoring.EventScoring.Parameters( diff --git a/example.ipynb b/example.ipynb deleted file mode 100644 index 1e36666..0000000 --- a/example.ipynb +++ /dev/null @@ -1,212 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Example usage of the Performance Metric library" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Annotations\n", - "\n", - "The library allows to compare two sets of annotations of a binary classification problem on a time series. In our application field this corresponds to labeling of epileptic seizures in an EEG recording.\n", - "\n", - "Annotations are stored an can be loaded in one of two representations :\n", - "- Binary mask at the sampling frequency of labels (e.g. `11111100000111111111000000`)\n", - "- List of (start, stop) tuples representing the different events. These evens are stored in seconds (e.g. `[(0, 6), (11, 20)]`) " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Annotation objects contain a representation as a mask and as a list of events:\n", - "[False True True False False False True True True False]\n", - "[(1.0, 3.0), (6.0, 9.0)]\n" - ] - } - ], - "source": [ - "from annotations import Annotation\n", - "\n", - "# Annotation objects can be instantiated from a binary mask\n", - "\n", - "fs = 1\n", - "mask = [0,1,1,0,0,0,1,1,1,0]\n", - "\n", - "labels = Annotation(mask, fs)\n", - "\n", - "print('Annotation objects contain a representation as a mask and as a list of events:')\n", - "print(labels.mask)\n", - "print(labels.events)\n", - "\n", - "\n", - "# The Annotation object can also be instantiated from a list of events\n", - "fs = 1\n", - "numSamples = 10 # In this case the duration of the recording in samples should be provided\n", - "events = [(1, 3), (6, 9)]\n", - "\n", - "labels = Annotation(events, fs, numSamples)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scoring\n", - "\n", - "The scoring module takes a reference annotation (ground-truth labels) and a hypothesis (e.g. output of an ML pipeline) and computes seizure detection performance metrics.\n", - "\n", - "The class implements two types of scoring algorithms :\n", - "- Sample based scoring : Computes detections and errors on a sample by sample basis at the sampling frequency of the labels\n", - "- Event based scoring : Computes detections and errors per event" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Sample scoring\n", - "- Sensitivity : nan \n", - "- Precision : 0.00 \n", - "- F1-score : nan \n", - "- FP/24h : 86400.00 \n", - "\n", - "# Event scoring\n", - "- Sensitivity : 0.50 \n", - "- Precision : 1.00 \n", - "- F1-score : 0.67 \n", - "- FP/24h : 0.00 \n", - "\n" - ] - } - ], - "source": [ - "import scoring\n", - "\n", - "fs = 10\n", - "ref = Annotation([1,1,1,0,0,0,1,1,1,0], fs)\n", - "hyp = Annotation([0,1,1,0,1,1,0,0,1,0], fs)\n", - "scores = scoring.SampleScoring(ref, hyp)\n", - "\n", - "\n", - "print(\"# Sample scoring\\n\" +\n", - " \"- Sensitivity : {:.2f} \\n\".format(scores.sensitivity) + \n", - " \"- Precision : {:.2f} \\n\".format(scores.precision) + \n", - " \"- F1-score : {:.2f} \\n\".format(scores.f1) + \n", - " \"- FP/24h : {:.2f} \\n\".format(scores.fpRate))\n", - "\n", - "\n", - "# Scores can also be computed per event\n", - "param = scoring.EventScoring.Parameters(\n", - " toleranceStart=0,\n", - " toleranceEnd=0,\n", - " minOverlap=0.66,\n", - " maxEventDuration=5*60,\n", - " minDurationBetweenEvents=0)\n", - "scores = scoring.EventScoring(ref, hyp, param)\n", - "\n", - "print(\"# Event scoring\\n\" +\n", - " \"- Sensitivity : {:.2f} \\n\".format(scores.sensitivity) + \n", - " \"- Precision : {:.2f} \\n\".format(scores.precision) + \n", - " \"- F1-score : {:.2f} \\n\".format(scores.f1) + \n", - " \"- FP/24h : {:.2f} \\n\".format(scores.fpRate))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization\n", - "\n", - "Visualization is provided by :\n", - "- `plotSampleScoring`\n", - "- `plotEventScoring`\n", - "\n", - "Both functions take a reference and hypothesis Annotation object as an input and return a `matplotlib.pyplot.figure` object as output." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import visualization\n", - "import matplotlib.pyplot as plt\n", - "\n", - "param = scoring.EventScoring.Parameters(\n", - " toleranceStart=0,\n", - " toleranceEnd=0, \n", - " minOverlap=0,\n", - " maxEventDuration=5*60)\n", - "\n", - "visualization.plotSampleScoring(ref, hyp)\n", - "visualization.plotEventScoring(ref, hyp, param)\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "performance", - "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" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..fd054ea --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,28 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "timescoring" +version = "0.0.1" +authors = [ + { name="Jonathan Dan", email="jonathan.dan@epfl.ch" }, + { name="Una Pale", email="una.pale@epfl.ch" }, + { name="PEDESITE" } +] +description = "Library for measuring performance of time series classification" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "matplotlib>=3.7.1", + "numpy>=1.24.3 ", + "nptyping>=2.5.0"] +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] + +[project.urls] +"Homepage" = "https://github.com/esl-epfl/epilepsy_performance_metrics" +"Bug Tracker" = "https://github.com/esl-epfl/epilepsy_performance_metrics/issues" \ No newline at end of file diff --git a/src/timescoring/__init__.py b/src/timescoring/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/annotations.py b/src/timescoring/annotations.py similarity index 100% rename from annotations.py rename to src/timescoring/annotations.py diff --git a/scoring.py b/src/timescoring/scoring.py similarity index 99% rename from scoring.py rename to src/timescoring/scoring.py index 51648b3..ac9d0bf 100644 --- a/scoring.py +++ b/src/timescoring/scoring.py @@ -6,7 +6,7 @@ import numpy as np -from annotations import Annotation +from .annotations import Annotation class _Scoring: """" Base class for different scoring methods. The class provides the common diff --git a/visualization.py b/src/timescoring/visualization.py similarity index 99% rename from visualization.py rename to src/timescoring/visualization.py index 026f5a8..d756fc1 100644 --- a/visualization.py +++ b/src/timescoring/visualization.py @@ -1,8 +1,8 @@ import matplotlib.pyplot as plt import numpy as np -from annotations import Annotation -import scoring +from .annotations import Annotation +from . import scoring def plotSampleScoring(ref : Annotation, hyp : Annotation, fs : int = 1) -> plt.figure: """Build an overview plot showing the outcome of sample scoring. diff --git a/test.py b/tests/test.py similarity index 99% rename from test.py rename to tests/test.py index a23fbd7..f4843bb 100644 --- a/test.py +++ b/tests/test.py @@ -8,8 +8,8 @@ import numpy as np -from annotations import Annotation -import scoring +from src.timescoring.annotations import Annotation +from src.timescoring import scoring class TestAnnotation(unittest.TestCase): def assertListOfTupleEqual(expected, actual, message):