diff --git a/omnibin/__init__.py b/omnibin/__init__.py
index 1403539b4961b0239810c54b0e56cfc16e40c4f3..6b5136c0ab16fe25ccc7ce9af179edaad43438c5 100644
--- a/omnibin/__init__.py
+++ b/omnibin/__init__.py
@@ -1,4 +1,4 @@
from .metrics import generate_binary_classification_report, ColorScheme
-__version__ = "0.1.6"
+__version__ = "0.1.7"
__all__ = ["generate_binary_classification_report"]
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 7e283cc1cfd4c67a38879f4c824f969117000e74..fb311cbaf6cfd5cedbbae3e76defcbbc2591e490 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
[project]
name = "omnibin"
-version = "0.1.6"
+version = "0.1.7"
description = "A package for generating comprehensive binary classification reports with visualizations and confidence intervals"
readme = "README.md"
requires-python = ">=3.11"
diff --git a/space/space/omnibin/utils.py b/space/space/omnibin/utils.py
index c48af978eceb5eb72f99d12d6dbb341fe8a99fa2..33276dfb8d0a0e9e8d0873c0e5f28c531bd7f350 100644
--- a/space/space/omnibin/utils.py
+++ b/space/space/omnibin/utils.py
@@ -129,6 +129,7 @@ def calculate_metrics_summary(y_true, y_scores, best_thresh):
"Sensitivity": recall_score(y_true, y_pred_opt),
"Specificity": recall_score(y_true, y_pred_opt, pos_label=0),
"PPV": precision_score(y_true, y_pred_opt, zero_division=0),
+ "NPV": precision_score(y_true, y_pred_opt, pos_label=0, zero_division=0),
"MCC": matthews_corrcoef(y_true, y_pred_opt),
"F1 Score": f1_score(y_true, y_pred_opt),
"AUC-ROC": roc_auc_score(y_true, y_scores),
@@ -142,6 +143,7 @@ def calculate_confidence_intervals(y_true, y_scores, best_thresh, n_bootstrap=10
"Sensitivity": lambda yt, ys: recall_score(yt, ys >= best_thresh),
"Specificity": lambda yt, ys: recall_score(yt, ys >= best_thresh, pos_label=0),
"PPV": lambda yt, ys: precision_score(yt, ys >= best_thresh, zero_division=0),
+ "NPV": lambda yt, ys: precision_score(yt, ys >= best_thresh, pos_label=0, zero_division=0),
"MCC": lambda yt, ys: matthews_corrcoef(yt, ys >= best_thresh),
"F1 Score": lambda yt, ys: f1_score(yt, ys >= best_thresh),
"AUC-ROC": lambda yt, ys: roc_auc_score(yt, ys),
diff --git a/space/space/results/classification_report.pdf b/space/space/results/classification_report.pdf
index 6f42303f67412c4476f85b7884b59c010458ef1d..c4965b6d2a69439b1ca83f2f6bfd12ee9570cc3d 100644
Binary files a/space/space/results/classification_report.pdf and b/space/space/results/classification_report.pdf differ
diff --git a/space/space/results/plots/calibration.png b/space/space/results/plots/calibration.png
index f7c01a3c5c9fef74942c4099f05cd05a045b3987..6a0cc40265ed5d18db5dc8cbd4234520004b6bdb 100644
Binary files a/space/space/results/plots/calibration.png and b/space/space/results/plots/calibration.png differ
diff --git a/space/space/results/plots/confusion_matrix.png b/space/space/results/plots/confusion_matrix.png
index 0b478f8c734ae6f382c1bb83b101cdf4a0355827..62056c6787c2ca8f9ed131a6993c4ed14d095363 100644
Binary files a/space/space/results/plots/confusion_matrix.png and b/space/space/results/plots/confusion_matrix.png differ
diff --git a/space/space/results/plots/metrics_summary.png b/space/space/results/plots/metrics_summary.png
index d12df16853fee7c0770444108ef111b9653f651f..ae26b706484784c2d1d522a3f4b257c124bfee7d 100644
Binary files a/space/space/results/plots/metrics_summary.png and b/space/space/results/plots/metrics_summary.png differ
diff --git a/space/space/results/plots/metrics_threshold.png b/space/space/results/plots/metrics_threshold.png
index 065fcd9b7628d23439b760827af8a0d2bc0e4d0a..d1030e70e7546f48f8a7bc82370632c0a6419aa1 100644
Binary files a/space/space/results/plots/metrics_threshold.png and b/space/space/results/plots/metrics_threshold.png differ
diff --git a/space/space/results/plots/prediction_distribution.png b/space/space/results/plots/prediction_distribution.png
index ca236325a1ef6cdc35fa0bd6a22fe274efcf9b9d..136f2571853cf19ed33caa699a86fb32b99a923b 100644
Binary files a/space/space/results/plots/prediction_distribution.png and b/space/space/results/plots/prediction_distribution.png differ
diff --git a/space/space/results/plots/roc_pr.png b/space/space/results/plots/roc_pr.png
index 8a9be95b7653bc3ebe65c63b285716e29ff33415..54a10b97fdfe453afc9fbc109ef66e4d75d0c95d 100644
Binary files a/space/space/results/plots/roc_pr.png and b/space/space/results/plots/roc_pr.png differ
diff --git a/space/space/space/space/space/.github/workflows/deploy.yml b/space/space/space/space/space/.github/workflows/deploy.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ba3f387f0fc1eeb02143c84491f6532ac8896159
--- /dev/null
+++ b/space/space/space/space/space/.github/workflows/deploy.yml
@@ -0,0 +1,44 @@
+name: Deploy to Hugging Face Space
+
+on:
+ push:
+ branches:
+ - main
+
+jobs:
+ deploy:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout source repo
+ uses: actions/checkout@v3
+
+ - name: Clone HF Space repo
+ run: |
+ git config --global user.email "kitamura.felipe@gmail.com"
+ git config --global user.name "kitamura-felipe"
+ git clone https://huggingface.co/spaces/felipekitamura/omnibin space
+ rsync -av --exclude='.git' ./ space/
+
+ echo '---' > temp_readme.md
+ echo 'title: Omnibin' >> temp_readme.md
+ echo 'emoji: ⚡' >> temp_readme.md
+ echo 'colorFrom: pink' >> temp_readme.md
+ echo 'colorTo: yellow' >> temp_readme.md
+ echo 'sdk: gradio' >> temp_readme.md
+ echo 'sdk_version: 5.29.0' >> temp_readme.md
+ echo 'app_file: app.py' >> temp_readme.md
+ echo 'pinned: false' >> temp_readme.md
+ echo 'license: mit' >> temp_readme.md
+ echo 'short_description: A Python package for generating comprehensive binary classi' >> temp_readme.md
+ echo '---' >> temp_readme.md
+ echo '' >> temp_readme.md
+ cat README.md >> temp_readme.md
+ mv temp_readme.md space/README.md
+
+ cd space
+ git add .
+ git commit -m "Automated update from GitHub" || echo "No changes"
+ git push https://USER:$HF_TOKEN@huggingface.co/spaces/felipekitamura/omnibin HEAD:main
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
diff --git a/space/space/space/space/space/.github/workflows/test.yml b/space/space/space/space/space/.github/workflows/test.yml
new file mode 100644
index 0000000000000000000000000000000000000000..4bcb014846c2a8353f41872eb3b703ad3f697a95
--- /dev/null
+++ b/space/space/space/space/space/.github/workflows/test.yml
@@ -0,0 +1,26 @@
+name: Run Tests
+
+on:
+ push:
+ branches: [main]
+
+jobs:
+ test:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v3
+
+ - name: Set up Python
+ uses: actions/setup-python@v4
+ with:
+ python-version: '3.10'
+
+ - name: Install dependencies
+ run: |
+ pip install -r requirements.txt
+ pip install pytest
+
+ - name: Run tests
+ run: pytest tests/
diff --git a/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml b/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml
index 5d7f0dd73b3a39507870f9d263b725025c8485b5..aedcc2760dec0e235f0ef0cf6b1b712e199330ff 100644
--- a/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml
+++ b/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml
@@ -3,11 +3,33 @@ name: Deploy to Hugging Face Space
on:
push:
branches:
- - main # or the branch you want to trigger deployment
+ - main
jobs:
+ test:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout source repo
+ uses: actions/checkout@v3
+
+ - name: Set up Python
+ uses: actions/setup-python@v4
+ with:
+ python-version: '3.10'
+
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r requirements.txt
+ pip install pytest
+
+ - name: Run tests
+ run: pytest tests/
+
deploy:
runs-on: ubuntu-latest
+ needs: test # 👈 only runs if 'test' job passes
steps:
- name: Checkout source repo
@@ -35,6 +57,7 @@ jobs:
echo '' >> temp_readme.md
cat README.md >> temp_readme.md
mv temp_readme.md space/README.md
+
cd space
git add .
git commit -m "Automated update from GitHub" || echo "No changes"
diff --git a/space/space/space/space/space/space/space/.gitignore b/space/space/space/space/space/space/space/.gitignore
index 418a11687e6a1488cb8b6cc8cfea2cc15c716664..61ca016b11a2b27d30d35093dc8bcf5be5bca69f 100644
--- a/space/space/space/space/space/space/space/.gitignore
+++ b/space/space/space/space/space/space/space/.gitignore
@@ -48,4 +48,5 @@ htmlcov/
*.xlsx
*.xls
*.db
-*.sqlite3
\ No newline at end of file
+*.sqlite3
+plots/
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/README.md b/space/space/space/space/space/space/space/README.md
index 65ce218f2d2a160e66582da44c7b4a0d0ac0b1f0..6e51d298908af09fc89f34ab7d5b26e9ac1e2ed8 100644
--- a/space/space/space/space/space/space/space/README.md
+++ b/space/space/space/space/space/space/space/README.md
@@ -1,4 +1,6 @@
-[](https://github.com/kitamura-felipe/omnibin/actions/workflows/update-hf-space.yml)
+[](https://github.com/kitamura-felipe/omnibin/actions/workflows/test.yml)
+[](https://github.com/kitamura-felipe/omnibin/actions/workflows/deploy.yml)
+
# Omnibin
diff --git a/space/space/space/space/space/space/space/space/space/README.md b/space/space/space/space/space/space/space/space/space/README.md
index 1f102d5a6fbe98e89efc7f0d0a36741cedbe08dd..65ce218f2d2a160e66582da44c7b4a0d0ac0b1f0 100644
--- a/space/space/space/space/space/space/space/space/space/README.md
+++ b/space/space/space/space/space/space/space/space/space/README.md
@@ -1,4 +1,4 @@
-[](https://github.com/felipekitamura/omnibin/actions/workflows/update-hf-space.yml)
+[](https://github.com/kitamura-felipe/omnibin/actions/workflows/update-hf-space.yml)
# Omnibin
diff --git a/space/space/space/space/space/space/space/space/space/space/README.md b/space/space/space/space/space/space/space/space/space/space/README.md
index 4b306bf66aa050d19edb2645ac64df2e6ab1c35f..1f102d5a6fbe98e89efc7f0d0a36741cedbe08dd 100644
--- a/space/space/space/space/space/space/space/space/space/space/README.md
+++ b/space/space/space/space/space/space/space/space/space/space/README.md
@@ -1,3 +1,5 @@
+[](https://github.com/felipekitamura/omnibin/actions/workflows/update-hf-space.yml)
+
# Omnibin
A Python package for generating comprehensive binary classification reports with visualizations and confidence intervals.
diff --git a/space/space/space/space/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml b/space/space/space/space/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml
index 155fe85ecf8c05469f2c260bf22aab27f2610fa3..5d7f0dd73b3a39507870f9d263b725025c8485b5 100644
--- a/space/space/space/space/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml
+++ b/space/space/space/space/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml
@@ -19,6 +19,22 @@ jobs:
git config --global user.name "kitamura-felipe"
git clone https://huggingface.co/spaces/felipekitamura/omnibin space
rsync -av --exclude='.git' ./ space/
+
+ echo '---' > temp_readme.md
+ echo 'title: Omnibin' >> temp_readme.md
+ echo 'emoji: ⚡' >> temp_readme.md
+ echo 'colorFrom: pink' >> temp_readme.md
+ echo 'colorTo: yellow' >> temp_readme.md
+ echo 'sdk: gradio' >> temp_readme.md
+ echo 'sdk_version: 5.29.0' >> temp_readme.md
+ echo 'app_file: app.py' >> temp_readme.md
+ echo 'pinned: false' >> temp_readme.md
+ echo 'license: mit' >> temp_readme.md
+ echo 'short_description: A Python package for generating comprehensive binary classi' >> temp_readme.md
+ echo '---' >> temp_readme.md
+ echo '' >> temp_readme.md
+ cat README.md >> temp_readme.md
+ mv temp_readme.md space/README.md
cd space
git add .
git commit -m "Automated update from GitHub" || echo "No changes"
diff --git a/space/space/space/space/space/space/space/space/space/space/space/README.md b/space/space/space/space/space/space/space/space/space/space/space/README.md
index bf81bd221342c5d9193b829926f6fa7faddb2219..4b306bf66aa050d19edb2645ac64df2e6ab1c35f 100644
--- a/space/space/space/space/space/space/space/space/space/space/space/README.md
+++ b/space/space/space/space/space/space/space/space/space/space/space/README.md
@@ -97,18 +97,3 @@ Here are examples of the visualizations generated by Omnibin:
### Metrics Summary
-
-
-
----
-title: Omnibin
-emoji: ⚡
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: A Python package for generating comprehensive binary classi
----
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/README.md b/space/space/space/space/space/space/space/space/space/space/space/space/README.md
index 3c18c272877ba223eb36cad737e80f04415d9765..bf81bd221342c5d9193b829926f6fa7faddb2219 100644
--- a/space/space/space/space/space/space/space/space/space/space/space/space/README.md
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/README.md
@@ -1,16 +1,3 @@
----
-title: Omnibin
-emoji: ⚡
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: A Python package for generating comprehensive binary classi
----
-
# Omnibin
A Python package for generating comprehensive binary classification reports with visualizations and confidence intervals.
@@ -109,4 +96,19 @@ Here are examples of the visualizations generated by Omnibin:
### Metrics Summary
-
\ No newline at end of file
+
+
+
+
+---
+title: Omnibin
+emoji: ⚡
+colorFrom: pink
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.29.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: A Python package for generating comprehensive binary classi
+---
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml
new file mode 100644
index 0000000000000000000000000000000000000000..155fe85ecf8c05469f2c260bf22aab27f2610fa3
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/.github/workflows/update-hf-space.yml
@@ -0,0 +1,27 @@
+name: Deploy to Hugging Face Space
+
+on:
+ push:
+ branches:
+ - main # or the branch you want to trigger deployment
+
+jobs:
+ deploy:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout source repo
+ uses: actions/checkout@v3
+
+ - name: Clone HF Space repo
+ run: |
+ git config --global user.email "kitamura.felipe@gmail.com"
+ git config --global user.name "kitamura-felipe"
+ git clone https://huggingface.co/spaces/felipekitamura/omnibin space
+ rsync -av --exclude='.git' ./ space/
+ cd space
+ git add .
+ git commit -m "Automated update from GitHub" || echo "No changes"
+ git push https://USER:$HF_TOKEN@huggingface.co/spaces/felipekitamura/omnibin HEAD:main
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/.gitignore b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..418a11687e6a1488cb8b6cc8cfea2cc15c716664
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/.gitignore
@@ -0,0 +1,51 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Distribution
+dist/
+build/
+update.bat
+
+
+# Coverage
+.coverage
+htmlcov/
+
+# Results and data
+*.xlsx
+*.xls
+*.db
+*.sqlite3
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/CHANGELOG.md b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..c1c7f06c206f8387bd9b68d2ea4634dbeeff8859
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/CHANGELOG.md
@@ -0,0 +1,15 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [0.1.0] - 2024-03-19
+
+### Added
+- Initial release
+- Basic binary classification metrics and visualizations
+- Comprehensive reporting functionality
+- Confidence interval calculations
+- Example usage and documentation
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/LICENSE b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..206421a1c363f8b67f0ffc968e29f148434c6b1c
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Felipe Campos Kitamura
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/data/scores.csv b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/data/scores.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1c612fb5667550ac4c93931d21321ed8d766adf9
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/data/scores.csv
@@ -0,0 +1,10201 @@
+y_pred,y_true
+0.6108777193397277,1
+0.9414566422007212,1
+0.9601427666215183,1
+0.9945732823840646,1
+0.526910845164245,1
+0.817536078656719,1
+0.9531242479103951,1
+0.9022708644316214,1
+0.9137806971384371,1
+0.9589073324045465,1
+0.8126999356017819,1
+0.9202403430450075,1
+0.9666927872049409,1
+0.7398566829429536,1
+0.9994507348128117,1
+0.8699429209074441,1
+0.7169458224355099,1
+0.6206740439285562,1
+0.7782477065486615,1
+0.999510755737531,1
+0.6222805141006532,1
+0.9101386250699285,1
+0.9392654671510053,1
+0.7042143713691886,1
+0.9899063783622869,1
+0.9984884971208096,1
+0.9893956939823622,1
+0.6173053052869797,1
+0.5994133923310844,1
+0.7627981122461411,1
+0.8228259660465228,1
+0.9681260365399877,1
+0.9693876939167403,1
+0.9838581059841184,1
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diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/example.py b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/example.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fc1412450d48f281869fbf4987315a6813fe575
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/example.py
@@ -0,0 +1,36 @@
+import pandas as pd
+import numpy as np
+import os
+from omnibin import generate_binary_classification_report, ColorScheme
+
+# Define paths
+RESULTS_DIR = os.path.join(os.path.dirname(__file__), "results")
+
+# Ensure results directory exists
+os.makedirs(RESULTS_DIR, exist_ok=True)
+
+# Generate random data
+data = pd.DataFrame({
+ 'y_true': (y:=np.random.choice([0,1],1000,p:=[.9,.1])),
+ 'y_pred': np.where(
+ y,
+ np.random.beta(3,1.5,1000)*.9+.1, # Positive cases: less skewed towards 1.0
+ np.random.beta(1.5,3,1000)*.9+.1 # Negative cases: less skewed towards 0.1
+ )
+})
+
+y_true = data['y_true'].values
+y_scores = data['y_pred'].values
+
+# Generate comprehensive classification report
+report_path = generate_binary_classification_report(
+ y_true=y_true,
+ y_scores=y_scores,
+ output_path=os.path.join(RESULTS_DIR, "classification_report.pdf"),
+ n_bootstrap=1000,
+ random_seed=42, # Set a fixed random seed for reproducibility
+ dpi=72,
+ color_scheme=ColorScheme.DEFAULT
+)
+
+print(f"Report generated and saved to: {report_path}")
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/pyproject.toml b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..def608be3c97f6335da2391461b8baf32416eb15
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/pyproject.toml
@@ -0,0 +1,31 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "omnibin"
+version = "0.1.5"
+description = "A package for generating comprehensive binary classification reports with visualizations and confidence intervals"
+readme = "README.md"
+requires-python = ">=3.11"
+license = "MIT"
+authors = [
+ { name = "Felipe Campos Kitamura", email = "kitamura.felipe@gmail.com" }
+]
+classifiers = [
+ "Programming Language :: Python :: 3",
+ "License :: OSI Approved :: MIT License",
+ "Operating System :: OS Independent",
+]
+dependencies = [
+ "numpy>=1.21.0",
+ "pandas>=1.3.0",
+ "scikit-learn>=1.0.0",
+ "matplotlib>=3.4.0",
+ "scipy>=1.7.0",
+ "seaborn>=0.11.0",
+]
+
+[project.urls]
+Homepage = "https://github.com/kitamura-felipe/omnibin"
+Repository = "https://github.com/kitamura-felipe/omnibin.git"
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/results/classification_report.pdf b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/results/classification_report.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..6f42303f67412c4476f85b7884b59c010458ef1d
Binary files /dev/null and b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/results/classification_report.pdf differ
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/.gitattributes b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
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@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.npz filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/README.md b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3c18c272877ba223eb36cad737e80f04415d9765
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/README.md
@@ -0,0 +1,112 @@
+---
+title: Omnibin
+emoji: ⚡
+colorFrom: pink
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.29.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: A Python package for generating comprehensive binary classi
+---
+
+# Omnibin
+
+A Python package for generating comprehensive binary classification reports with visualizations and confidence intervals.
+
+## Try it Online
+
+You can try Omnibin directly in your browser through our [Hugging Face Space](https://felipekitamura-omnibin.hf.space).
+
+## Installation
+
+```bash
+pip install omnibin
+```
+
+## Usage
+
+```python
+import pandas as pd
+from omnibin import generate_binary_classification_report, ColorScheme
+
+# Load your data
+data = pd.read_csv("data/scores.csv")
+y_true = data['y_true'].values
+y_scores = data['y_pred'].values
+
+# Generate comprehensive classification report
+report_path = generate_binary_classification_report(
+ y_true=y_true, # Array of true binary labels (0 or 1)
+ y_scores=y_scores, # Array of predicted probabilities or scores
+ output_path="classification_report.pdf", # Path to save the PDF report
+ n_bootstrap=1000, # Number of bootstrap iterations for confidence intervals
+ random_seed=42, # Random seed for reproducibility
+ dpi=300, # DPI for plot resolution
+ color_scheme=ColorScheme.DEFAULT # Color scheme for plots (DEFAULT, MONOCHROME, or VIBRANT)
+)
+```
+
+## Input Format
+
+The input data should be provided as:
+- `y_true`: Array of true binary labels (0 or 1)
+- `y_pred`: Array of predicted probabilities or scores
+
+## Features
+
+- Generates a comprehensive PDF report with:
+ - ROC curve with confidence bands
+ - Precision-Recall curve with confidence bands
+ - Metrics vs. threshold plots
+ - Confusion matrix at optimal threshold
+ - Calibration plot
+ - Summary table with confidence intervals
+- Calculates optimal threshold using Youden's J statistic
+- Provides confidence intervals using bootstrapping
+- Supports both probability and score-based predictions
+
+## Metrics Included
+
+- Accuracy
+- Sensitivity (Recall)
+- Specificity
+- Positive Predictive Value (Precision)
+- Matthews Correlation Coefficient
+- F1 Score
+- AUC-ROC
+- AUC-PR
+
+All metrics include 95% confidence intervals calculated through bootstrapping.
+
+## Output
+
+The package generates a PDF report containing:
+1. ROC and Precision-Recall curves with confidence bands
+2. Metrics plotted across different thresholds
+3. Confusion matrix at the optimal threshold
+4. Calibration plot
+5. Summary table with all metrics and their confidence intervals
+
+## Example
+
+Here are examples of the visualizations generated by Omnibin:
+
+### ROC and Precision-Recall Curves
+
+
+### Metrics vs Threshold
+
+
+### Confusion Matrix
+
+
+### Calibration Plot
+
+
+### Prediction Distribution
+
+
+### Metrics Summary
+
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/app.py b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0a4b237291b2cfb632f4ad3bb39d1f9cebb5048
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/app.py
@@ -0,0 +1,90 @@
+import gradio as gr
+import pandas as pd
+import os
+import shutil
+from omnibin import generate_binary_classification_report, ColorScheme
+
+# Define results directory
+RESULTS_DIR = "/tmp/results"
+
+# Map string color schemes to enum values
+COLOR_SCHEME_MAP = {
+ "DEFAULT": ColorScheme.DEFAULT,
+ "MONOCHROME": ColorScheme.MONOCHROME,
+ "VIBRANT": ColorScheme.VIBRANT
+}
+
+def process_csv(csv_file, n_bootstrap=1000, dpi=72, color_scheme="DEFAULT"):
+ # Convert string color scheme to enum
+ color_scheme_enum = COLOR_SCHEME_MAP[color_scheme]
+
+ # Read the CSV file
+ df = pd.read_csv(csv_file.name)
+
+ # Check if required columns exist
+ required_columns = ['y_true', 'y_pred']
+ if not all(col in df.columns for col in required_columns):
+ raise ValueError("CSV file must contain 'y_true' and 'y_pred' columns")
+
+ # Clean up results directory if it exists
+ if os.path.exists(RESULTS_DIR):
+ shutil.rmtree(RESULTS_DIR)
+
+ # Create fresh results directory
+ os.makedirs(RESULTS_DIR, exist_ok=True)
+
+ # Generate the report
+ report_path = generate_binary_classification_report(
+ y_true=df['y_true'].values,
+ y_scores=df['y_pred'].values,
+ output_path=os.path.join(RESULTS_DIR, "classification_report.pdf"),
+ n_bootstrap=n_bootstrap,
+ random_seed=42,
+ dpi=dpi,
+ color_scheme=color_scheme_enum
+ )
+
+ # Get paths to individual plots
+ plots_dir = os.path.join(RESULTS_DIR, "plots")
+ plot_paths = {
+ "ROC and PR Curves": os.path.join(plots_dir, "roc_pr.png"),
+ "Metrics vs Threshold": os.path.join(plots_dir, "metrics_threshold.png"),
+ "Confusion Matrix": os.path.join(plots_dir, "confusion_matrix.png"),
+ "Calibration Plot": os.path.join(plots_dir, "calibration.png"),
+ "Prediction Distribution": os.path.join(plots_dir, "prediction_distribution.png"),
+ "Metrics Summary": os.path.join(plots_dir, "metrics_summary.png")
+ }
+
+ # Return both the PDF and the plot images
+ return report_path, *plot_paths.values()
+
+# Create the Gradio interface
+iface = gr.Interface(
+ fn=process_csv,
+ inputs=[
+ gr.File(label="Upload CSV file with 'y_true' and 'y_pred' columns"),
+ gr.Number(label="Number of Bootstrap Iterations", value=1000, minimum=100, maximum=10000),
+ gr.Number(label="DPI", value=72, minimum=50, maximum=300),
+ gr.Dropdown(label="Color Scheme", choices=["DEFAULT", "MONOCHROME", "VIBRANT"], value="DEFAULT")
+ ],
+ outputs=[
+ gr.File(label="Classification Report PDF"),
+ gr.Image(label="ROC and PR Curves"),
+ gr.Image(label="Metrics vs Threshold"),
+ gr.Image(label="Confusion Matrix"),
+ gr.Image(label="Calibration Plot"),
+ gr.Image(label="Prediction Distribution"),
+ gr.Image(label="Metrics Summary")
+ ],
+ title="Binary Classification Report Generator",
+ description="Upload a CSV file containing 'y_true' and 'y_pred' columns to generate a binary classification report.\n\n"
+ "'y_true': reference standard (0s or 1s).\n\n"
+ "'y_pred': model prediction (continuous value between 0 and 1).\n\n"
+ "This application takes approximately 35 seconds to generate the report.\n",
+
+ examples=[["scores.csv", 1000, 72, "DEFAULT"]],
+ cache_examples=False
+)
+
+if __name__ == "__main__":
+ iface.launch()
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin-0.1.4-py3-none-any.whl b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin-0.1.4-py3-none-any.whl
new file mode 100644
index 0000000000000000000000000000000000000000..67d8dbc13caead0ad7537765083fc920554f60e3
Binary files /dev/null and b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin-0.1.4-py3-none-any.whl differ
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/__init__.py b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a464f8efafbe988fc39f83e0dcdb57444fb725b
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/__init__.py
@@ -0,0 +1,4 @@
+from .metrics import generate_binary_classification_report, ColorScheme
+
+__version__ = "0.1.5"
+__all__ = ["generate_binary_classification_report"]
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/metrics.py b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..77f69882317f6131aefa077d63ee583c9f899400
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/metrics.py
@@ -0,0 +1,62 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tqdm import tqdm
+import os
+from sklearn.metrics import (
+ accuracy_score, recall_score, precision_score, f1_score, roc_auc_score,
+ average_precision_score, confusion_matrix, matthews_corrcoef, roc_curve,
+ precision_recall_curve
+)
+from sklearn.calibration import calibration_curve
+from matplotlib.backends.backend_pdf import PdfPages
+from enum import Enum
+from .utils import (
+ ColorScheme, calculate_metrics_by_threshold, bootstrap_curves,
+ calculate_optimal_threshold, calculate_metrics_summary,
+ calculate_confidence_intervals, create_output_directories,
+ plot_roc_pr_curves, plot_metrics_threshold, plot_confusion_matrix,
+ plot_calibration, plot_metrics_summary, plot_prediction_distribution
+)
+
+def generate_binary_classification_report(y_true, y_scores, output_path="omnibin_report.pdf", n_bootstrap=1000, random_seed=42, dpi=300, color_scheme=ColorScheme.DEFAULT):
+ # Set random seed for reproducibility
+ if random_seed is not None:
+ np.random.seed(random_seed)
+
+ # Set DPI for all figures
+ plt.rcParams['figure.dpi'] = dpi
+
+ # Get color scheme
+ colors = color_scheme.value
+
+ # Calculate metrics and optimal threshold
+ metrics_df = calculate_metrics_by_threshold(y_true, y_scores)
+ best_thresh = calculate_optimal_threshold(y_true, y_scores)
+ metrics_summary = calculate_metrics_summary(y_true, y_scores, best_thresh)
+ conf_intervals = calculate_confidence_intervals(y_true, y_scores, best_thresh, n_bootstrap)
+
+ # Create output directories
+ plots_dir = create_output_directories(output_path)
+
+ # Calculate confidence intervals for curves
+ tpr_ci, precision_ci, common_fpr, common_recall = bootstrap_curves(y_true, y_scores, n_boot=n_bootstrap)
+
+ with PdfPages(output_path) as pdf:
+ # Generate and save all plots
+ plots = [
+ plot_roc_pr_curves(y_true, y_scores, tpr_ci, precision_ci, common_fpr, common_recall, colors, dpi, plots_dir),
+ plot_metrics_threshold(metrics_df, colors, dpi, plots_dir),
+ plot_confusion_matrix(y_true, y_scores, best_thresh, colors, dpi, plots_dir),
+ plot_calibration(y_true, y_scores, colors, dpi, plots_dir),
+ plot_metrics_summary(metrics_summary, conf_intervals, dpi, plots_dir),
+ plot_prediction_distribution(y_true, y_scores, best_thresh, colors, dpi, plots_dir)
+ ]
+
+ # Save all plots to PDF
+ for plot in plots:
+ pdf.savefig(plot, dpi=dpi)
+ plt.close(plot)
+
+ return output_path
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/utils.py b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c48af978eceb5eb72f99d12d6dbb341fe8a99fa2
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/omnibin/utils.py
@@ -0,0 +1,263 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tqdm import tqdm
+from sklearn.metrics import (
+ accuracy_score, recall_score, precision_score, f1_score, roc_auc_score,
+ average_precision_score, confusion_matrix, matthews_corrcoef, roc_curve,
+ precision_recall_curve
+)
+from sklearn.calibration import calibration_curve
+from enum import Enum
+import os
+
+class ColorScheme(Enum):
+ DEFAULT = {
+ 'positive_class': 'tab:blue',
+ 'negative_class': 'tab:orange',
+ 'roc_curve': 'tab:blue',
+ 'pr_curve': 'tab:blue',
+ 'threshold_line': 'black',
+ 'calibration_curve': 'tab:blue',
+ 'calibration_reference': 'gray',
+ 'metrics_colors': ['tab:blue', 'tab:red', 'tab:green', 'tab:purple', 'tab:orange', 'tab:brown', 'tab:pink'],
+ 'cmap': 'Blues'
+ }
+
+ MONOCHROME = {
+ 'positive_class': '#404040',
+ 'negative_class': '#808080',
+ 'roc_curve': '#000000',
+ 'pr_curve': '#000000',
+ 'threshold_line': '#000000',
+ 'calibration_curve': '#000000',
+ 'calibration_reference': '#808080',
+ 'metrics_colors': ['#000000', '#404040', '#606060', '#808080', '#A0A0A0', '#C0C0C0', '#E0E0E0'],
+ 'cmap': 'Greys'
+ }
+
+ VIBRANT = {
+ 'positive_class': '#FF6B6B',
+ 'negative_class': '#4ECDC4',
+ 'roc_curve': '#FF6B6B',
+ 'pr_curve': '#4ECDC4',
+ 'threshold_line': '#2C3E50',
+ 'calibration_curve': '#FF6B6B',
+ 'calibration_reference': '#95A5A6',
+ 'metrics_colors': ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEEAD', '#D4A5A5', '#9B59B6'],
+ 'cmap': 'Greens'
+ }
+
+def calculate_metrics_by_threshold(y_true, y_scores):
+ """Calculate various metrics across different thresholds."""
+ thresholds = np.linspace(0, 1, 100)
+ metrics_by_threshold = []
+
+ for t in tqdm(thresholds, desc="Calculating metrics across thresholds"):
+ y_pred = (y_scores >= t).astype(int)
+ acc = accuracy_score(y_true, y_pred)
+ sens = recall_score(y_true, y_pred)
+ spec = recall_score(y_true, y_pred, pos_label=0)
+ ppv = precision_score(y_true, y_pred, zero_division=0)
+ mcc = matthews_corrcoef(y_true, y_pred)
+ f1 = f1_score(y_true, y_pred)
+ metrics_by_threshold.append([t, acc, sens, spec, ppv, mcc, f1])
+
+ return pd.DataFrame(metrics_by_threshold, columns=[
+ "Threshold", "Accuracy", "Sensitivity", "Specificity",
+ "PPV", "MCC", "F1 Score"
+ ])
+
+def bootstrap_metric(metric_func, y_true, y_scores, n_boot=1000):
+ """Calculate bootstrap confidence intervals for a given metric."""
+ stats = []
+ for _ in tqdm(range(n_boot), desc="Bootstrap iterations", leave=False):
+ indices = np.random.choice(range(len(y_true)), len(y_true), replace=True)
+ try:
+ stats.append(metric_func(y_true[indices], y_scores[indices]))
+ except:
+ continue
+ return np.percentile(stats, [2.5, 97.5])
+
+def bootstrap_curves(y_true, y_scores, n_boot=1000):
+ """Calculate bootstrap confidence intervals for ROC and PR curves."""
+ tprs = []
+ fprs = []
+ precisions = []
+ recalls = []
+
+ base_fpr, base_tpr, _ = roc_curve(y_true, y_scores)
+ base_precision, base_recall, _ = precision_recall_curve(y_true, y_scores)
+
+ common_fpr = np.linspace(0, 1, 100)
+ common_recall = np.linspace(0, 1, 100)
+
+ for _ in tqdm(range(n_boot), desc="Bootstrap iterations for curves", leave=False):
+ indices = np.random.choice(range(len(y_true)), len(y_true), replace=True)
+ try:
+ fpr, tpr, _ = roc_curve(y_true[indices], y_scores[indices])
+ tpr_interp = np.interp(common_fpr, fpr, tpr)
+ tprs.append(tpr_interp)
+
+ precision, recall, _ = precision_recall_curve(y_true[indices], y_scores[indices])
+ sort_idx = np.argsort(recall)
+ recall = recall[sort_idx]
+ precision = precision[sort_idx]
+ precision_interp = np.interp(common_recall, recall, precision)
+ precisions.append(precision_interp)
+ except:
+ continue
+
+ tpr_ci = np.percentile(tprs, [2.5, 97.5], axis=0)
+ precision_ci = np.percentile(precisions, [2.5, 97.5], axis=0)
+
+ return tpr_ci, precision_ci, common_fpr, common_recall
+
+def calculate_optimal_threshold(y_true, y_scores):
+ """Calculate the optimal threshold using ROC curve."""
+ fpr, tpr, roc_thresholds = roc_curve(y_true, y_scores)
+ j_scores = tpr - fpr
+ return roc_thresholds[np.argmax(j_scores)]
+
+def calculate_metrics_summary(y_true, y_scores, best_thresh):
+ """Calculate summary metrics at the optimal threshold."""
+ y_pred_opt = (y_scores >= best_thresh).astype(int)
+
+ return {
+ "Accuracy": accuracy_score(y_true, y_pred_opt),
+ "Sensitivity": recall_score(y_true, y_pred_opt),
+ "Specificity": recall_score(y_true, y_pred_opt, pos_label=0),
+ "PPV": precision_score(y_true, y_pred_opt, zero_division=0),
+ "MCC": matthews_corrcoef(y_true, y_pred_opt),
+ "F1 Score": f1_score(y_true, y_pred_opt),
+ "AUC-ROC": roc_auc_score(y_true, y_scores),
+ "AUC-PR": average_precision_score(y_true, y_scores)
+ }
+
+def calculate_confidence_intervals(y_true, y_scores, best_thresh, n_bootstrap=1000):
+ """Calculate confidence intervals for all metrics."""
+ metric_functions = {
+ "Accuracy": lambda yt, ys: accuracy_score(yt, ys >= best_thresh),
+ "Sensitivity": lambda yt, ys: recall_score(yt, ys >= best_thresh),
+ "Specificity": lambda yt, ys: recall_score(yt, ys >= best_thresh, pos_label=0),
+ "PPV": lambda yt, ys: precision_score(yt, ys >= best_thresh, zero_division=0),
+ "MCC": lambda yt, ys: matthews_corrcoef(yt, ys >= best_thresh),
+ "F1 Score": lambda yt, ys: f1_score(yt, ys >= best_thresh),
+ "AUC-ROC": lambda yt, ys: roc_auc_score(yt, ys),
+ "AUC-PR": lambda yt, ys: average_precision_score(yt, ys)
+ }
+
+ return {
+ name: bootstrap_metric(func, y_true, y_scores, n_boot=n_bootstrap)
+ for name, func in metric_functions.items()
+ }
+
+def create_output_directories(output_path):
+ """Create necessary output directories for plots and PDF."""
+ output_dir = os.path.dirname(output_path)
+ if output_dir:
+ os.makedirs(output_dir, exist_ok=True)
+
+ plots_dir = os.path.join(output_dir, "plots")
+ os.makedirs(plots_dir, exist_ok=True)
+
+ return plots_dir
+
+def plot_roc_pr_curves(y_true, y_scores, tpr_ci, precision_ci, common_fpr, common_recall, colors, dpi, plots_dir):
+ """Generate ROC and PR curves with confidence intervals."""
+ plt.figure(figsize=(12, 5), dpi=dpi)
+
+ plt.subplot(1, 2, 1)
+ fpr, tpr, _ = roc_curve(y_true, y_scores)
+ plt.plot(fpr, tpr, label="ROC curve", color=colors['roc_curve'])
+ plt.fill_between(common_fpr, tpr_ci[0], tpr_ci[1], alpha=0.3, color=colors['roc_curve'])
+ plt.plot([0, 1], [0, 1], "k--")
+ plt.xlabel("False Positive Rate")
+ plt.ylabel("True Positive Rate")
+ plt.title("ROC Curve")
+ plt.legend()
+
+ plt.subplot(1, 2, 2)
+ precision, recall, _ = precision_recall_curve(y_true, y_scores)
+ plt.plot(recall, precision, label="PR curve", color=colors['pr_curve'])
+ plt.fill_between(common_recall, precision_ci[0], precision_ci[1], alpha=0.3, color=colors['pr_curve'])
+ plt.xlabel("Recall")
+ plt.ylabel("Precision")
+ plt.title("Precision-Recall Curve")
+ plt.legend()
+
+ plt.savefig(os.path.join(plots_dir, "roc_pr.png"), dpi=dpi, bbox_inches='tight')
+ return plt.gcf()
+
+def plot_metrics_threshold(metrics_df, colors, dpi, plots_dir):
+ """Generate metrics vs threshold plot."""
+ plt.figure(figsize=(10, 6), dpi=dpi)
+ for i, col in enumerate(metrics_df.columns[1:]):
+ plt.plot(metrics_df["Threshold"], metrics_df[col], label=col,
+ color=colors['metrics_colors'][i % len(colors['metrics_colors'])])
+ plt.xlabel("Threshold")
+ plt.ylabel("Metric Value")
+ plt.title("Metrics Across Thresholds")
+ plt.legend()
+
+ plt.savefig(os.path.join(plots_dir, "metrics_threshold.png"), dpi=dpi, bbox_inches='tight')
+ return plt.gcf()
+
+def plot_confusion_matrix(y_true, y_scores, best_thresh, colors, dpi, plots_dir):
+ """Generate confusion matrix plot."""
+ cm = confusion_matrix(y_true, y_scores >= best_thresh)
+ plt.figure(figsize=(5, 4), dpi=dpi)
+ sns.heatmap(cm, annot=True, fmt="d", cmap=colors['cmap'], cbar=False, annot_kws={"size": 12})
+ plt.title("Confusion Matrix (Optimal Threshold)", fontsize=12)
+ plt.xlabel("Predicted Label", fontsize=12)
+ plt.ylabel("True Label", fontsize=12)
+
+ plt.savefig(os.path.join(plots_dir, "confusion_matrix.png"), dpi=dpi, bbox_inches='tight')
+ return plt.gcf()
+
+def plot_calibration(y_true, y_scores, colors, dpi, plots_dir):
+ """Generate calibration plot."""
+ plt.figure(figsize=(6, 6), dpi=dpi)
+ prob_true, prob_pred = calibration_curve(y_true, y_scores, n_bins=10, strategy='uniform')
+ plt.plot(prob_pred, prob_true, marker='o', label='Calibration curve', color=colors['calibration_curve'])
+ plt.plot([0, 1], [0, 1], linestyle='--', color=colors['calibration_reference'])
+ plt.xlabel('Predicted Probability')
+ plt.ylabel('True Probability')
+ plt.title('Calibration Plot')
+ plt.legend()
+
+ plt.savefig(os.path.join(plots_dir, "calibration.png"), dpi=dpi, bbox_inches='tight')
+ return plt.gcf()
+
+def plot_metrics_summary(metrics_summary, conf_intervals, dpi, plots_dir):
+ """Generate metrics summary table plot."""
+ fig, ax = plt.subplots(figsize=(8, 6), dpi=dpi)
+ ax.axis("off")
+ table_data = [
+ [k, f"{v:.3f}", f"[{conf_intervals[k][0]:.3f}, {conf_intervals[k][1]:.3f}]"]
+ for k, v in metrics_summary.items()
+ ]
+ table = ax.table(cellText=table_data, colLabels=["Metric", "Value", "95% CI"], loc="center")
+ table.auto_set_font_size(False)
+ table.set_fontsize(10)
+ table.scale(1.2, 1.2)
+ ax.set_title("Performance Metrics at Optimal Threshold", fontweight="bold")
+
+ plt.savefig(os.path.join(plots_dir, "metrics_summary.png"), dpi=dpi, bbox_inches='tight')
+ return plt.gcf()
+
+def plot_prediction_distribution(y_true, y_scores, best_thresh, colors, dpi, plots_dir):
+ """Generate prediction distribution histogram."""
+ plt.figure(figsize=(10, 6), dpi=dpi)
+ plt.hist(y_scores[y_true == 1], bins=50, alpha=0.5, label='Positive Class', color=colors['positive_class'])
+ plt.hist(y_scores[y_true == 0], bins=50, alpha=0.5, label='Negative Class', color=colors['negative_class'])
+ plt.axvline(x=best_thresh, color=colors['threshold_line'], linestyle='--',
+ label=f'Optimal Threshold ({best_thresh:.3f})')
+ plt.xlabel('Predicted Probability')
+ plt.ylabel('Count')
+ plt.title('Distribution of Predictions')
+ plt.legend()
+
+ plt.savefig(os.path.join(plots_dir, "prediction_distribution.png"), dpi=dpi, bbox_inches='tight')
+ return plt.gcf()
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/requirements.txt b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19f47fa8107a5979c47da81962d6de48a042c284
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/requirements.txt
@@ -0,0 +1,6 @@
+pandas
+numpy
+gradio
+matplotlib
+seaborn
+scikit-learn
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/scores.csv b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/scores.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1c612fb5667550ac4c93931d21321ed8d766adf9
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/scores.csv
@@ -0,0 +1,10201 @@
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diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/tests/__init__.py b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c422d607982b40966f4525cb2a7db3d6523b2cd4
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/tests/__init__.py
@@ -0,0 +1,3 @@
+"""
+Unit tests for the omnibin package.
+"""
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/tests/test_metrics.py b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/tests/test_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea0fa4745f5166bc970332d11c69ff7aa80fc406
--- /dev/null
+++ b/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/space/tests/test_metrics.py
@@ -0,0 +1,141 @@
+import unittest
+import os
+import pandas as pd
+import numpy as np
+from omnibin.metrics import generate_binary_classification_report
+
+class TestMetrics(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls):
+ """Set up test data"""
+ # Create synthetic test data
+ np.random.seed(42)
+ n_samples = 1000
+ cls.y_true = np.random.binomial(1, 0.3, n_samples)
+ cls.y_scores = np.random.beta(2, 5, n_samples)
+
+ # Adjust scores to have some correlation with true labels
+ cls.y_scores[cls.y_true == 1] = np.random.beta(5, 2, sum(cls.y_true == 1))
+
+ # Create test output directory
+ cls.test_output_dir = "test_outputs"
+ os.makedirs(cls.test_output_dir, exist_ok=True)
+
+ def test_report_generation(self):
+ """Test the main report generation function"""
+ output_path = os.path.join(self.test_output_dir, "test_report.pdf")
+
+ # Generate report
+ result_path = generate_binary_classification_report(
+ y_true=self.y_true,
+ y_scores=self.y_scores,
+ output_path=output_path,
+ n_bootstrap=100 # Use smaller number for testing
+ )
+
+ # Test that file was created
+ self.assertTrue(os.path.exists(result_path))
+ self.assertTrue(os.path.getsize(result_path) > 0)
+
+ # Clean up
+ os.remove(result_path)
+
+ def test_input_validation(self):
+ """Test input validation"""
+ # Test with invalid y_true values
+ with self.assertRaises(ValueError):
+ generate_binary_classification_report(
+ y_true=np.array([0, 1, 2]), # Invalid label
+ y_scores=np.array([0.1, 0.5, 0.9])
+ )
+
+ # Test with invalid y_scores values
+ with self.assertRaises(ValueError):
+ generate_binary_classification_report(
+ y_true=np.array([0, 1, 0]),
+ y_scores=np.array([-0.1, 1.5, 0.9]) # Values outside [0,1]
+ )
+
+ # Test with mismatched lengths
+ with self.assertRaises(ValueError):
+ generate_binary_classification_report(
+ y_true=np.array([0, 1]),
+ y_scores=np.array([0.1, 0.5, 0.9])
+ )
+
+ def test_bootstrap_consistency(self):
+ """Test that bootstrap results are consistent"""
+ output_path1 = os.path.join(self.test_output_dir, "test_report1.pdf")
+ output_path2 = os.path.join(self.test_output_dir, "test_report2.pdf")
+
+ # Generate two reports with same data and seed
+ np.random.seed(42)
+ generate_binary_classification_report(
+ y_true=self.y_true,
+ y_scores=self.y_scores,
+ output_path=output_path1,
+ n_bootstrap=100
+ )
+
+ np.random.seed(42)
+ generate_binary_classification_report(
+ y_true=self.y_true,
+ y_scores=self.y_scores,
+ output_path=output_path2,
+ n_bootstrap=100
+ )
+
+ # Compare file sizes (they should be similar)
+ size1 = os.path.getsize(output_path1)
+ size2 = os.path.getsize(output_path2)
+ self.assertAlmostEqual(size1, size2, delta=1000) # Allow small differences due to PDF compression
+
+ # Clean up
+ os.remove(output_path1)
+ os.remove(output_path2)
+
+ def test_edge_cases(self):
+ """Test edge cases"""
+ # Test with all positive labels
+ output_path = os.path.join(self.test_output_dir, "all_positive.pdf")
+ generate_binary_classification_report(
+ y_true=np.ones(100),
+ y_scores=np.random.random(100),
+ output_path=output_path,
+ n_bootstrap=100
+ )
+ self.assertTrue(os.path.exists(output_path))
+ os.remove(output_path)
+
+ # Test with all negative labels
+ output_path = os.path.join(self.test_output_dir, "all_negative.pdf")
+ generate_binary_classification_report(
+ y_true=np.zeros(100),
+ y_scores=np.random.random(100),
+ output_path=output_path,
+ n_bootstrap=100
+ )
+ self.assertTrue(os.path.exists(output_path))
+ os.remove(output_path)
+
+ # Test with perfect predictions
+ output_path = os.path.join(self.test_output_dir, "perfect.pdf")
+ generate_binary_classification_report(
+ y_true=np.array([0, 1, 0, 1]),
+ y_scores=np.array([0.1, 0.9, 0.2, 0.8]),
+ output_path=output_path,
+ n_bootstrap=100
+ )
+ self.assertTrue(os.path.exists(output_path))
+ os.remove(output_path)
+
+ @classmethod
+ def tearDownClass(cls):
+ """Clean up test outputs"""
+ if os.path.exists(cls.test_output_dir):
+ for file in os.listdir(cls.test_output_dir):
+ os.remove(os.path.join(cls.test_output_dir, file))
+ os.rmdir(cls.test_output_dir)
+
+if __name__ == '__main__':
+ unittest.main()
\ No newline at end of file
diff --git a/space/space/space/space/space/space/space/tests/test_metrics.py b/space/space/space/space/space/space/space/tests/test_metrics.py
index ea0fa4745f5166bc970332d11c69ff7aa80fc406..23636b46b2434e72f6e3ff88338c84adf9287f5e 100644
--- a/space/space/space/space/space/space/space/tests/test_metrics.py
+++ b/space/space/space/space/space/space/space/tests/test_metrics.py
@@ -2,6 +2,8 @@ import unittest
import os
import pandas as pd
import numpy as np
+import warnings
+from sklearn.exceptions import UndefinedMetricWarning
from omnibin.metrics import generate_binary_classification_report
class TestMetrics(unittest.TestCase):
@@ -109,12 +111,15 @@ class TestMetrics(unittest.TestCase):
# Test with all negative labels
output_path = os.path.join(self.test_output_dir, "all_negative.pdf")
- generate_binary_classification_report(
- y_true=np.zeros(100),
- y_scores=np.random.random(100),
- output_path=output_path,
- n_bootstrap=100
- )
+ with warnings.catch_warnings():
+ warnings.filterwarnings("ignore", category=UserWarning)
+ warnings.filterwarnings("ignore", category=UndefinedMetricWarning)
+ generate_binary_classification_report(
+ y_true=np.zeros(100),
+ y_scores=np.random.random(100),
+ output_path=output_path,
+ n_bootstrap=100
+ )
self.assertTrue(os.path.exists(output_path))
os.remove(output_path)
@@ -134,7 +139,13 @@ class TestMetrics(unittest.TestCase):
"""Clean up test outputs"""
if os.path.exists(cls.test_output_dir):
for file in os.listdir(cls.test_output_dir):
- os.remove(os.path.join(cls.test_output_dir, file))
+ file_path = os.path.join(cls.test_output_dir, file)
+ if os.path.isfile(file_path):
+ os.remove(file_path)
+ elif os.path.isdir(file_path):
+ for subfile in os.listdir(file_path):
+ os.remove(os.path.join(file_path, subfile))
+ os.rmdir(file_path)
os.rmdir(cls.test_output_dir)
if __name__ == '__main__':