Merge pull request #42 from mirkobunse/devel

Fix PyPI: replace the direct extra dependency quapy[composable] with documentation on how to install through git

.github/workflows/ci.yml

@@ -28,7 +28,8 @@ jobs:
     - 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
 
@@ -46,7 +47,8 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip setuptools wheel "jax[cpu]"
-        python -m pip install -e .[composable,neural,docs]
+        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

KDEy/experiments.py

@@ -1,108 +0,0 @@
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-from os.path import join
-import quapy as qp
-from quapy.protocol import UPP
-from quapy.method.aggregative import KDEyML
-
-DEBUG = True
-
-qp.environ["SAMPLE_SIZE"] = 100 if DEBUG else 500
-val_repeats  = 100 if DEBUG else 500
-test_repeats = 100 if DEBUG else 500
-if DEBUG:
-    qp.environ["DEFAULT_CLS"] = LogisticRegression()
-
-test_results = {}
-val_choice = {}
-
-bandwidth_range = np.linspace(0.01, 0.20, 20)
-if DEBUG:
-    bandwidth_range = np.linspace(0.01, 0.20, 10)
-
-def datasets():
-    for dataset_name in qp.datasets.UCI_MULTICLASS_DATASETS[:4]:
-        dataset = qp.datasets.fetch_UCIMulticlassDataset(dataset_name)
-        if DEBUG:
-            dataset = dataset.reduce(random_state=0)
-        yield dataset
-
-
-def experiment_dataset(dataset):
-    train, test = dataset.train_test
-    test_gen = UPP(test, repeats=test_repeats)
-
-    # bandwidth chosen during model selection in validation
-    train_tr, train_va = train.split_stratified(random_state=0)
-    kdey = KDEyML(random_state=0)
-    modsel = qp.model_selection.GridSearchQ(
-        model=kdey,
-        param_grid={'bandwidth': bandwidth_range},
-        protocol=UPP(train_va, repeats=val_repeats),
-        refit=False,
-        n_jobs=-1
-    ).fit(train_tr)
-    chosen_bandwidth = modsel.best_params_['bandwidth']
-    modsel_choice = float(chosen_bandwidth)
-
-    # results in test
-    print(f"testing KDEy in {dataset.name}")
-    dataset_results = []
-    for b in bandwidth_range:
-        kdey = KDEyML(bandwidth=b, random_state=0)
-        kdey.fit(train)
-
-        mae = qp.evaluation.evaluate(kdey, protocol=test_gen, error_metric='mae', verbose=True)
-        print(f'bandwidth={b}: {mae:.5f}')
-        dataset_results.append((float(b), float(mae)))
-
-    return modsel_choice, dataset_results
-
-def plot_bandwidth(val_choice, test_results):
-    for dataset_name in val_choice.keys():
-        import matplotlib.pyplot as plt
-
-        bandwidths, results = zip(*test_results[dataset_name])
-
-        # Crear la gráfica
-        plt.figure(figsize=(8, 6))
-
-        # Graficar los puntos de datos
-        plt.plot(bandwidths, results, marker='o')
-
-        # Agregar la línea vertical en bandwidth_chosen
-        plt.axvline(x=val_choice[dataset_name], color='r', linestyle='--', label=f'Bandwidth elegido: {val_choice[dataset_name]}')
-
-        # Agregar etiquetas y título
-        plt.xlabel('Bandwidth')
-        plt.ylabel('Resultado')
-        plt.title('Gráfica de Bandwidth vs Resultado')
-
-        # Mostrar la leyenda
-        plt.legend()
-
-        # Mostrar la gráfica
-        plt.grid(True)
-        plt.show()
-
-
-for dataset in datasets():
-    if DEBUG:
-        result_path = f'./results/debug/{dataset.name}.pkl'
-    else:
-        result_path = f'./results/{dataset.name}.pkl'
-
-    modsel_choice, dataset_results = qp.util.pickled_resource(result_path, experiment_dataset, dataset)
-    val_choice[dataset.name] = modsel_choice
-    test_results[dataset.name] = dataset_results
-
-    print(f'Dataset = {dataset.name}')
-    print(modsel_choice)
-    print(dataset_results)
-
-plot_bandwidth(val_choice, test_results)
-
-
-
-
-

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

quapy/method/composable.py

@@ -1,5 +1,15 @@
 """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_error_message = """qunfold, the back-end of quapy.method.composable, is not properly installed.
+
+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
@@ -40,6 +50,8 @@ __all__ = [ # control public members, e.g., for auto-documentation in sphinx; om
         "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'],