bugfix in bayesian cc, merged

.github/workflows/ci.yml

@@ -20,15 +20,16 @@ jobs:
     env:
       QUAPY_TESTS_OMIT_LARGE_DATASETS: True
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip setuptools wheel
-        python -m pip install -e .[bayes,composable,tests]
+        python -m pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
+        python -m pip install -e .[bayes,tests]
     - name: Test with unittest
       run: python -m unittest
 
@@ -38,15 +39,18 @@ jobs:
     runs-on: ubuntu-latest
     if: github.ref == 'refs/heads/master'
     steps:
-    - uses: actions/checkout@v1
-    - name: Build documentation
-      uses: ammaraskar/sphinx-action@master
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v5
       with:
-        pre-build-command: |
-          apt-get --allow-releaseinfo-change update -y && apt-get install -y git && git --version
-          python -m pip install --upgrade pip setuptools wheel "jax[cpu]"
-          python -m pip install -e .[composable,neural,docs]
-        docs-folder: "docs/"
+        python-version: 3.11
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip setuptools wheel "jax[cpu]"
+        python -m pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
+        python -m pip install -e .[neural,docs]
+    - name: Build documentation
+      run: sphinx-build -M html docs/source docs/build
     - name: Publish documentation
       run: |
         git clone ${{ github.server_url }}/${{ github.repository }}.git --branch gh-pages --single-branch __gh-pages/

.github/workflows/pylint.yml

@@ -1,23 +0,0 @@
-name: Pylint
-
-on: [push]
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.8", "3.9", "3.10"]
-    steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install pylint
-    - name: Analysing the code with pylint
-      run: |
-        pylint $(git ls-files '*.py')

.gitignore

@@ -167,3 +167,4 @@ TweetSentQuant
 
 
 *.png
+.idea

CHANGE_LOG.txt

@@ -1,9 +1,10 @@
+Change Log 0.1.10
+-----------------
+
+- ...
+
 Change Log 0.1.9
 ----------------
-- [TODO] add LeQua2024 and normalized match distance to qp.error
-- [TODO] add CDE-iteration and Bayes-CDE methods
-- [TODO] add Friedman's method and DeBias
-- [TODO] check ignore warning stuff (check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings)
 
 - Added LeQua 2024 datasets and normalized match distance to qp.error
 

README.md

@@ -45,19 +45,18 @@ of the test set.
 
 ```python
 import quapy as qp
-from sklearn.linear_model import LogisticRegression
 
-dataset = qp.datasets.fetch_twitter('semeval16')
+dataset = qp.datasets.fetch_UCIBinaryDataset("yeast")
+training, test = dataset.train_test
 
 # create an "Adjusted Classify & Count" quantifier
-model = qp.method.aggregative.ACC(LogisticRegression())
-model.fit(dataset.training)
+model = qp.method.aggregative.ACC()
+model.fit(training)
 
-estim_prevalence = model.quantify(dataset.test.instances)
-true_prevalence  = dataset.test.prevalence()
+estim_prevalence = model.quantify(test.X)
+true_prevalence  = test.prevalence()
 
 error = qp.error.mae(true_prevalence, estim_prevalence)
-
 print(f'Mean Absolute Error (MAE)={error:.3f}')
 ```
 

TODO.txt

@@ -1,3 +1,8 @@
-- Test the return_type="index" in protocols and finish the "distributin_samples.py" example
-- Add EDy (an implementation is available at quantificationlib)
--
+- [TODO] Test the return_type="index" in protocols and finish the "distributin_samples.py" example
+- [TODO] Add EDy (an implementation is available at quantificationlib)
+- [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
+- [TODO] add HistNetQ
+- [TODO] add CDE-iteration and Bayes-CDE methods
+- [TODO] add Friedman's method and DeBias
+- [TODO] check ignore warning stuff
+    check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings

docs/source/conf.py

@@ -11,9 +11,14 @@ import sys
 from os.path import join
 quapy_path = join(pathlib.Path(__file__).parents[2].resolve().as_posix(), 'quapy')
 wiki_path = join(pathlib.Path(__file__).parents[0].resolve().as_posix(), 'wiki')
+source_path = pathlib.Path(__file__).parents[2].resolve().as_posix()
 print(f'quapy path={quapy_path}')
+print(f'quapy source path={source_path}')
 sys.path.insert(0, quapy_path)
 sys.path.insert(0, wiki_path)
+sys.path.insert(0, source_path)
+
+print(sys.path)
 
 
 project = 'QuaPy: A Python-based open-source framework for quantification'

docs/source/manuals/methods.md

@@ -447,7 +447,7 @@ The [](quapy.method.composable) module allows the composition of quantification
 ```sh
 pip install --upgrade pip setuptools wheel
 pip install "jax[cpu]"
-pip install quapy[composable]
+pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
 ```
 
 ### Basics

examples/14.composable_methods.py

@@ -2,6 +2,13 @@
 This example illustrates the composition of quantification methods from
 arbitrary loss functions and feature transformations. It will extend the basic
 example on the usage of quapy with this composition.
+
+This example requires the installation of qunfold, the back-end of QuaPy's
+composition module:
+
+    pip install --upgrade pip setuptools wheel
+    pip install "jax[cpu]"
+    pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
 """
 
 import numpy as np

examples/4b.lequa2024_experiments.py

@@ -33,9 +33,9 @@ quantifier = KDEyML(classifier=LogisticRegression())
 
 # model selection
 param_grid = {
-    'classifier__C': np.logspace(-3, 3, 7),          # classifier-dependent: inverse of regularization strength
-    'classifier__class_weight': ['balanced', None],  # classifier-dependent: weights of each class
-    'bandwidth': np.linspace(0.01, 0.2, 20)          # quantifier-dependent: bandwidth of the kernel
+    'classifier__C': np.logspace(-3, 3, 7),       # classifier-dependent: inverse of regularization strength
+    'classifier__class_weight': ['balanced', None],         # classifier-dependent: weights of each class
+    'bandwidth': np.linspace(0.01, 0.2, 20)  # quantifier-dependent: bandwidth of the kernel
 }
 model_selection = GridSearchQ(quantifier, param_grid, protocol=val_generator, error='mrae', refit=False, verbose=True)
 quantifier = model_selection.fit(training)

quapy/data/datasets.py

@@ -637,7 +637,7 @@ def fetch_UCIMulticlassDataset(
         if n_train > max_train_instances:
             train_prop = (max_train_instances / n)
 
-    data = Dataset(*data.split_stratified(train_prop, random_state=0))
+    data = Dataset(*data.split_stratified(train_prop, random_state=0), name=dataset_name)
     
     if standardize:
         data = standardizer(data)

quapy/method/composable.py

@@ -1,45 +1,57 @@
 """This module allows the composition of quantification methods from loss functions and feature transformations. This functionality is realized through an integration of the qunfold package: https://github.com/mirkobunse/qunfold."""
 
-import qunfold
-from qunfold.quapy import QuaPyWrapper
-from qunfold.sklearn import CVClassifier
-from qunfold import (
-    LeastSquaresLoss, # losses
-    BlobelLoss,
-    EnergyLoss,
-    HellingerSurrogateLoss,
-    CombinedLoss,
-    TikhonovRegularization,
-    TikhonovRegularized,
-    ClassTransformer, # transformers
-    HistogramTransformer,
-    DistanceTransformer,
-    KernelTransformer,
-    EnergyKernelTransformer,
-    LaplacianKernelTransformer,
-    GaussianKernelTransformer,
-    GaussianRFFKernelTransformer,
-)
+_import_error_message = """qunfold, the back-end of quapy.method.composable, is not properly installed.
 
-__all__ = [ # control public members, e.g., for auto-documentation in sphinx; omit QuaPyWrapper
-    "ComposableQuantifier",
-    "CVClassifier",
-    "LeastSquaresLoss",
-    "BlobelLoss",
-    "EnergyLoss",
-    "HellingerSurrogateLoss",
-    "CombinedLoss",
-    "TikhonovRegularization",
-    "TikhonovRegularized",
-    "ClassTransformer",
-    "HistogramTransformer",
-    "DistanceTransformer",
-    "KernelTransformer",
-    "EnergyKernelTransformer",
-    "LaplacianKernelTransformer",
-    "GaussianKernelTransformer",
-    "GaussianRFFKernelTransformer",
-]
+To fix this error, call:
+
+    pip install --upgrade pip setuptools wheel
+    pip install "jax[cpu]"
+    pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
+"""
+
+try:
+    import qunfold
+    from qunfold.quapy import QuaPyWrapper
+    from qunfold.sklearn import CVClassifier
+    from qunfold import (
+        LeastSquaresLoss, # losses
+        BlobelLoss,
+        EnergyLoss,
+        HellingerSurrogateLoss,
+        CombinedLoss,
+        TikhonovRegularization,
+        TikhonovRegularized,
+        ClassTransformer, # transformers
+        HistogramTransformer,
+        DistanceTransformer,
+        KernelTransformer,
+        EnergyKernelTransformer,
+        LaplacianKernelTransformer,
+        GaussianKernelTransformer,
+        GaussianRFFKernelTransformer,
+    )
+
+    __all__ = [ # control public members, e.g., for auto-documentation in sphinx; omit QuaPyWrapper
+        "ComposableQuantifier",
+        "CVClassifier",
+        "LeastSquaresLoss",
+        "BlobelLoss",
+        "EnergyLoss",
+        "HellingerSurrogateLoss",
+        "CombinedLoss",
+        "TikhonovRegularization",
+        "TikhonovRegularized",
+        "ClassTransformer",
+        "HistogramTransformer",
+        "DistanceTransformer",
+        "KernelTransformer",
+        "EnergyKernelTransformer",
+        "LaplacianKernelTransformer",
+        "GaussianKernelTransformer",
+        "GaussianRFFKernelTransformer",
+    ]
+except ImportError as e:
+    raise ImportError(_import_error_message) from e
 
 def ComposableQuantifier(loss, transformer, **kwargs):
     """A generic quantification / unfolding method that solves a linear system of equations.

setup.py

@@ -125,7 +125,6 @@ setup(
     # projects.
     extras_require={  # Optional
        'bayes': ['jax', 'jaxlib', 'numpyro'],
-       'composable': ['qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4'],
        'neural': ['torch'],
        'tests': ['certifi'],
        'docs' : ['sphinx-rtd-theme', 'myst-parser'],