forked from moreo/QuaPy
pip package
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1
TODO.txt
1
TODO.txt
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@ -2,7 +2,6 @@ Packaging:
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==========================================
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Documentation with sphinx
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Document methods with paper references
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allow for "pip install"
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unit-tests
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New features:
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@ -10,7 +10,7 @@ from . import model_selection
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from . import classification
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from quapy.method.base import isprobabilistic, isaggregative
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__version__ = '0.1'
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__version__ = '0.1.4'
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environ = {
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'SAMPLE_SIZE': None,
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@ -1,28 +1,32 @@
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from copy import deepcopy
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from typing import Union
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import f1_score, make_scorer, accuracy_score
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from sklearn.model_selection import GridSearchCV, cross_val_predict
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from tqdm import tqdm
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import numpy as np
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from joblib import Parallel, delayed
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import GridSearchCV, cross_val_predict
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import quapy as qp
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from quapy.data import LabelledCollection
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from quapy import functional as F
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from quapy.data import LabelledCollection
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from quapy.evaluation import evaluate
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from quapy.model_selection import GridSearchQ
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from . import neural
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from .base import BaseQuantifier
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from quapy.method.aggregative import CC, ACC, PCC, PACC, HDy, EMQ
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QuaNet = neural.QuaNetTrainer
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try:
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from . import neural
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except ModuleNotFoundError:
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neural = None
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from .base import BaseQuantifier
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from quapy.method.aggregative import CC, ACC, PACC, HDy, EMQ
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if neural:
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QuaNet = neural.QuaNetTrainer
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else:
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QuaNet = "QuaNet is not available due to missing torch package"
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class Ensemble(BaseQuantifier):
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VALID_POLICIES = {'ave', 'ptr', 'ds'} | qp.error.QUANTIFICATION_ERROR_NAMES
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"""
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@ -65,9 +69,9 @@ class Ensemble(BaseQuantifier):
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if self.verbose:
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print('[Ensemble]' + msg)
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def fit(self, data: qp.data.LabelledCollection, val_split: Union[qp.data.LabelledCollection, float]=None):
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def fit(self, data: qp.data.LabelledCollection, val_split: Union[qp.data.LabelledCollection, float] = None):
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self.sout('Fit')
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if self.policy=='ds' and not data.binary:
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if self.policy == 'ds' and not data.binary:
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raise ValueError(f'ds policy is only defined for binary quantification, but this dataset is not binary')
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if val_split is None:
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val_split = self.val_split
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@ -132,7 +136,7 @@ class Ensemble(BaseQuantifier):
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tests = [m[3] for m in self.ensemble]
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scores = []
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for i, model in enumerate(self.ensemble):
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scores.append(evaluate(model[0], tests[:i] + tests[i+1:], error, self.n_jobs))
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scores.append(evaluate(model[0], tests[:i] + tests[i + 1:], error, self.n_jobs))
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order = np.argsort(scores)
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self.ensemble = _select_k(self.ensemble, order, k=self.red_size)
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@ -168,7 +172,7 @@ class Ensemble(BaseQuantifier):
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lr_base = LogisticRegression(class_weight='balanced', max_iter=1000)
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optim = GridSearchCV(
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lr_base, param_grid={'C': np.logspace(-4,4,9)}, cv=5, n_jobs=self.n_jobs, refit=True
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lr_base, param_grid={'C': np.logspace(-4, 4, 9)}, cv=5, n_jobs=self.n_jobs, refit=True
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).fit(X, y)
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posteriors = cross_val_predict(
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@ -204,8 +208,8 @@ class Ensemble(BaseQuantifier):
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def get_probability_distribution(posterior_probabilities, bins=8):
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assert posterior_probabilities.shape[1]==2, 'the posterior probabilities do not seem to be for a binary problem'
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posterior_probabilities = posterior_probabilities[:,1] # take the positive posteriors only
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assert posterior_probabilities.shape[1] == 2, 'the posterior probabilities do not seem to be for a binary problem'
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posterior_probabilities = posterior_probabilities[:, 1] # take the positive posteriors only
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distribution, _ = np.histogram(posterior_probabilities, bins=bins, range=(0, 1), density=True)
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return distribution
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@ -223,7 +227,7 @@ def _delayed_new_instance(args):
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if val_split is not None:
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if isinstance(val_split, float):
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assert 0 < val_split < 1, 'val_split should be in (0,1)'
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data, val_split = data.split_stratified(train_prop=1-val_split)
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data, val_split = data.split_stratified(train_prop=1 - val_split)
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sample_index = data.sampling_index(sample_size, *prev)
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sample = data.sampling_from_index(sample_index)
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@ -255,7 +259,7 @@ def _draw_simplex(ndim, min_val, max_trials=100):
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:return: a sample from the ndim-dimensional simplex that is uniform in S(ndim)-R where S(ndim) is the simplex
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and R is the simplex subset containing dimensions lower than min_val
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"""
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if min_val >= 1/ndim:
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if min_val >= 1 / ndim:
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raise ValueError(f'no sample can be draw from the {ndim}-dimensional simplex so that '
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f'all its values are >={min_val} (try with a larger value for min_pos)')
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trials = 0
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@ -300,14 +304,15 @@ def _check_error(error):
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f'the name of an error function in {qp.error.ERROR_NAMES}')
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def ensembleFactory(learner, base_quantifier_class, param_grid=None, optim=None, param_model_sel:dict=None, **kwargs):
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if optim is not None:
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if param_grid is None:
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raise ValueError(f'param_grid is None but optim was requested.')
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if param_model_sel is None:
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raise ValueError(f'param_model_sel is None but optim was requested.')
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error = _check_error(optim)
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return _instantiate_ensemble(learner, base_quantifier_class, param_grid, error, param_model_sel, **kwargs)
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def ensembleFactory(learner, base_quantifier_class, param_grid=None, optim=None, param_model_sel: dict = None,
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**kwargs):
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if optim is not None:
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if param_grid is None:
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raise ValueError(f'param_grid is None but optim was requested.')
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if param_model_sel is None:
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raise ValueError(f'param_model_sel is None but optim was requested.')
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error = _check_error(optim)
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return _instantiate_ensemble(learner, base_quantifier_class, param_grid, error, param_model_sel, **kwargs)
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def ECC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
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@ -327,4 +332,4 @@ def EHDy(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
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def EEMQ(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
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return ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, **kwargs)
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return ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, **kwargs)
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@ -100,6 +100,12 @@ def test_ensemble_method(base_method, learner, dataset: Dataset, policy):
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def test_quanet_method():
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try:
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import quapy.classification.neural
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except ModuleNotFoundError:
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print('skipping QuaNet test due to missing torch package')
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return
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dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
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dataset = Dataset(dataset.training.sampling(100, *dataset.training.prevalence()),
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dataset.test.sampling(100, *dataset.test.prevalence()))
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@ -0,0 +1,164 @@
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from setuptools import setup, find_packages
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import pathlib
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here = pathlib.Path(__file__).parent.resolve()
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long_description = (here / 'README.md').read_text(encoding='utf-8')
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def get_version(rel_path):
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init_content = (here / rel_path).read_text(encoding='utf-8')
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for line in init_content.split('\n'):
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if line.startswith('__version__'):
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delim = '"' if '"' in line else "'"
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return line.split(delim)[1]
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else:
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raise RuntimeError("Unable to find version string.")
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# Arguments marked as "Required" below must be included for upload to PyPI.
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# Fields marked as "Optional" may be commented out.
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setup(
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# This is the name of your project. The first time you publish this
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# package, this name will be registered for you. It will determine how
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# users can install this project, e.g.:
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#
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# $ pip install sampleproject
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#
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# And where it will live on PyPI: https://pypi.org/project/sampleproject/
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#
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# There are some restrictions on what makes a valid project name
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# specification here:
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# https://packaging.python.org/specifications/core-metadata/#name
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name='QuaPy', # Required
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# Versions should comply with PEP 440:
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# https://www.python.org/dev/peps/pep-0440/
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#
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# For a discussion on single-sourcing the version across setup.py and the
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# project code, see
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# https://packaging.python.org/en/latest/single_source_version.html
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version=get_version("quapy/__init__.py"), # Required
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# This is a one-line description or tagline of what your project does. This
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# corresponds to the "Summary" metadata field:
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# https://packaging.python.org/specifications/core-metadata/#summary
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description='QuaPy: a framework for Quantification in Python', # Optional
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# This is an optional longer description of your project that represents
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# the body of text which users will see when they visit PyPI.
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#
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# Often, this is the same as your README, so you can just read it in from
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# that file directly (as we have already done above)
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#
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# This field corresponds to the "Description" metadata field:
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# https://packaging.python.org/specifications/core-metadata/#description-optional
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long_description=long_description, # Optional
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# Denotes that our long_description is in Markdown; valid values are
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# text/plain, text/x-rst, and text/markdown
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#
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# Optional if long_description is written in reStructuredText (rst) but
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# required for plain-text or Markdown; if unspecified, "applications should
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# attempt to render [the long_description] as text/x-rst; charset=UTF-8 and
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# fall back to text/plain if it is not valid rst" (see link below)
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#
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# This field corresponds to the "Description-Content-Type" metadata field:
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# https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
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long_description_content_type='text/markdown', # Optional (see note above)
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# This should be a valid link to your project's main homepage.
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#
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# This field corresponds to the "Home-Page" metadata field:
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# https://packaging.python.org/specifications/core-metadata/#home-page-optional
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url='https://github.com/HLT-ISTI/QuaPy', # Optional
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maintainer='Alejandro Moreo',
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maintainer_email='alejandro.moreo@isti.cnr.it',
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classifiers=[
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'Development Status :: 4 - Beta',
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'Intended Audience :: Developers',
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'Intended Audience :: Science/Research',
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'Programming Language :: Python',
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'Topic :: Software Development',
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'Topic :: Scientific/Engineering',
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'License :: OSI Approved :: BSD License',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: 3.9',
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'Programming Language :: Python :: 3 :: Only',
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],
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keywords='machine learning, quantification, classification, prevalence estimation, priors estimate',
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# When your source code is in a subdirectory under the project root, e.g.
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# `src/`, it is necessary to specify the `package_dir` argument.
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#package_dir={'': 'src'}, # Optional
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# You can just specify package directories manually here if your project is
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# simple. Or you can use find_packages().
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#
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# Alternatively, if you just want to distribute a single Python file, use
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# the `py_modules` argument instead as follows, which will expect a file
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# called `my_module.py` to exist:
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#
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# py_modules=["my_module"],
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#
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packages=find_packages(include=['quapy', 'quapy.*']), # Required
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python_requires='>=3.6, <4',
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install_requires=['scikit-learn', 'pandas', 'tqdm', 'matplotlib'],
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# List additional groups of dependencies here (e.g. development
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# dependencies). Users will be able to install these using the "extras"
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# syntax, for example:
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#
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# $ pip install sampleproject[dev]
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#
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# Similar to `install_requires` above, these must be valid existing
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# projects.
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# extras_require={ # Optional
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# 'dev': ['check-manifest'],
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# 'test': ['coverage'],
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# },
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# If there are data files included in your packages that need to be
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# installed, specify them here.
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# package_data={ # Optional
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# 'sample': ['package_data.dat'],
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# },
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# Although 'package_data' is the preferred approach, in some case you may
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# need to place data files outside of your packages. See:
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# http://docs.python.org/distutils/setupscript.html#installing-additional-files
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#
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# In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
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# data_files=[('my_data', ['data/data_file'])], # Optional
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# To provide executable scripts, use entry points in preference to the
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# "scripts" keyword. Entry points provide cross-platform support and allow
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# `pip` to create the appropriate form of executable for the target
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# platform.
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#
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# For example, the following would provide a command called `sample` which
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# executes the function `main` from this package when invoked:
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# entry_points={ # Optional
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# 'console_scripts': [
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# 'sample=sample:main',
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# ],
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# },
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project_urls={ # Optional
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'Contributors': 'https://github.com/HLT-ISTI/QuaPy/graphs/contributors',
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'Bug Reports': 'https://github.com/HLT-ISTI/QuaPy/issues',
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'Documentation': 'https://github.com/HLT-ISTI/QuaPy/wiki',
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'Source': 'https://github.com/HLT-ISTI/QuaPy/',
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},
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)
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