Merge pull request #42 from mirkobunse/devel
Fix PyPI: replace the direct extra dependency quapy[composable] with documentation on how to install through git
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a271fe1231
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@ -28,7 +28,8 @@ jobs:
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip setuptools wheel
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python -m pip install -e .[bayes,composable,tests]
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python -m pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
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python -m pip install -e .[bayes,tests]
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- name: Test with unittest
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run: python -m unittest
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@ -46,7 +47,8 @@ jobs:
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip setuptools wheel "jax[cpu]"
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python -m pip install -e .[composable,neural,docs]
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python -m pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
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python -m pip install -e .[neural,docs]
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- name: Build documentation
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run: sphinx-build -M html docs/source docs/build
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- name: Publish documentation
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@ -1,108 +0,0 @@
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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from os.path import join
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import quapy as qp
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from quapy.protocol import UPP
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from quapy.method.aggregative import KDEyML
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DEBUG = True
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qp.environ["SAMPLE_SIZE"] = 100 if DEBUG else 500
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val_repeats = 100 if DEBUG else 500
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test_repeats = 100 if DEBUG else 500
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if DEBUG:
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qp.environ["DEFAULT_CLS"] = LogisticRegression()
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test_results = {}
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val_choice = {}
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bandwidth_range = np.linspace(0.01, 0.20, 20)
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if DEBUG:
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bandwidth_range = np.linspace(0.01, 0.20, 10)
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def datasets():
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for dataset_name in qp.datasets.UCI_MULTICLASS_DATASETS[:4]:
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dataset = qp.datasets.fetch_UCIMulticlassDataset(dataset_name)
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if DEBUG:
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dataset = dataset.reduce(random_state=0)
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yield dataset
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def experiment_dataset(dataset):
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train, test = dataset.train_test
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test_gen = UPP(test, repeats=test_repeats)
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# bandwidth chosen during model selection in validation
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train_tr, train_va = train.split_stratified(random_state=0)
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kdey = KDEyML(random_state=0)
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modsel = qp.model_selection.GridSearchQ(
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model=kdey,
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param_grid={'bandwidth': bandwidth_range},
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protocol=UPP(train_va, repeats=val_repeats),
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refit=False,
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n_jobs=-1
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).fit(train_tr)
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chosen_bandwidth = modsel.best_params_['bandwidth']
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modsel_choice = float(chosen_bandwidth)
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# results in test
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print(f"testing KDEy in {dataset.name}")
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dataset_results = []
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for b in bandwidth_range:
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kdey = KDEyML(bandwidth=b, random_state=0)
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kdey.fit(train)
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mae = qp.evaluation.evaluate(kdey, protocol=test_gen, error_metric='mae', verbose=True)
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print(f'bandwidth={b}: {mae:.5f}')
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dataset_results.append((float(b), float(mae)))
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return modsel_choice, dataset_results
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def plot_bandwidth(val_choice, test_results):
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for dataset_name in val_choice.keys():
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import matplotlib.pyplot as plt
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bandwidths, results = zip(*test_results[dataset_name])
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# Crear la gráfica
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plt.figure(figsize=(8, 6))
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# Graficar los puntos de datos
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plt.plot(bandwidths, results, marker='o')
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# Agregar la línea vertical en bandwidth_chosen
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plt.axvline(x=val_choice[dataset_name], color='r', linestyle='--', label=f'Bandwidth elegido: {val_choice[dataset_name]}')
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# Agregar etiquetas y título
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plt.xlabel('Bandwidth')
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plt.ylabel('Resultado')
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plt.title('Gráfica de Bandwidth vs Resultado')
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# Mostrar la leyenda
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plt.legend()
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# Mostrar la gráfica
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plt.grid(True)
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plt.show()
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for dataset in datasets():
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if DEBUG:
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result_path = f'./results/debug/{dataset.name}.pkl'
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else:
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result_path = f'./results/{dataset.name}.pkl'
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modsel_choice, dataset_results = qp.util.pickled_resource(result_path, experiment_dataset, dataset)
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val_choice[dataset.name] = modsel_choice
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test_results[dataset.name] = dataset_results
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print(f'Dataset = {dataset.name}')
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print(modsel_choice)
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print(dataset_results)
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plot_bandwidth(val_choice, test_results)
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@ -447,7 +447,7 @@ The [](quapy.method.composable) module allows the composition of quantification
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```sh
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pip install --upgrade pip setuptools wheel
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pip install "jax[cpu]"
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pip install quapy[composable]
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pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
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```
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### Basics
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@ -2,6 +2,13 @@
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This example illustrates the composition of quantification methods from
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arbitrary loss functions and feature transformations. It will extend the basic
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example on the usage of quapy with this composition.
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This example requires the installation of qunfold, the back-end of QuaPy's
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composition module:
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pip install --upgrade pip setuptools wheel
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pip install "jax[cpu]"
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pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
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"""
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import numpy as np
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@ -1,45 +1,57 @@
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"""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."""
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import qunfold
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from qunfold.quapy import QuaPyWrapper
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from qunfold.sklearn import CVClassifier
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from qunfold import (
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LeastSquaresLoss, # losses
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BlobelLoss,
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EnergyLoss,
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HellingerSurrogateLoss,
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CombinedLoss,
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TikhonovRegularization,
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TikhonovRegularized,
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ClassTransformer, # transformers
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HistogramTransformer,
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DistanceTransformer,
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KernelTransformer,
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EnergyKernelTransformer,
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LaplacianKernelTransformer,
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GaussianKernelTransformer,
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GaussianRFFKernelTransformer,
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)
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_import_error_message = """qunfold, the back-end of quapy.method.composable, is not properly installed.
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__all__ = [ # control public members, e.g., for auto-documentation in sphinx; omit QuaPyWrapper
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"ComposableQuantifier",
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"CVClassifier",
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"LeastSquaresLoss",
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"BlobelLoss",
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"EnergyLoss",
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"HellingerSurrogateLoss",
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"CombinedLoss",
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"TikhonovRegularization",
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"TikhonovRegularized",
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"ClassTransformer",
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"HistogramTransformer",
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"DistanceTransformer",
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"KernelTransformer",
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"EnergyKernelTransformer",
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"LaplacianKernelTransformer",
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"GaussianKernelTransformer",
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"GaussianRFFKernelTransformer",
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]
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To fix this error, call:
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pip install --upgrade pip setuptools wheel
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pip install "jax[cpu]"
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pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4"
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"""
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try:
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import qunfold
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from qunfold.quapy import QuaPyWrapper
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from qunfold.sklearn import CVClassifier
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from qunfold import (
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LeastSquaresLoss, # losses
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BlobelLoss,
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EnergyLoss,
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HellingerSurrogateLoss,
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CombinedLoss,
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TikhonovRegularization,
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TikhonovRegularized,
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ClassTransformer, # transformers
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HistogramTransformer,
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DistanceTransformer,
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KernelTransformer,
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EnergyKernelTransformer,
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LaplacianKernelTransformer,
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GaussianKernelTransformer,
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GaussianRFFKernelTransformer,
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)
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__all__ = [ # control public members, e.g., for auto-documentation in sphinx; omit QuaPyWrapper
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"ComposableQuantifier",
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"CVClassifier",
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"LeastSquaresLoss",
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"BlobelLoss",
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"EnergyLoss",
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"HellingerSurrogateLoss",
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"CombinedLoss",
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"TikhonovRegularization",
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"TikhonovRegularized",
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"ClassTransformer",
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"HistogramTransformer",
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"DistanceTransformer",
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"KernelTransformer",
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"EnergyKernelTransformer",
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"LaplacianKernelTransformer",
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"GaussianKernelTransformer",
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"GaussianRFFKernelTransformer",
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]
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except ImportError as e:
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raise ImportError(_import_error_message) from e
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def ComposableQuantifier(loss, transformer, **kwargs):
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"""A generic quantification / unfolding method that solves a linear system of equations.
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