From 79fbbd9d80d03fc121cb5331b51d54b286e085a3 Mon Sep 17 00:00:00 2001
From: Andrea Esuli <andrea@esuli.it>
Date: Mon, 10 May 2021 13:36:35 +0200
Subject: [PATCH] pip package
---
TODO.txt | 1 -
quapy/__init__.py | 2 +-
quapy/method/meta.py | 61 ++++++++------
quapy/tests/test_methods.py | 6 ++
setup.py | 164 ++++++++++++++++++++++++++++++++++++
5 files changed, 204 insertions(+), 30 deletions(-)
create mode 100644 setup.py
diff --git a/TODO.txt b/TODO.txt
index 2e153a2..2496a4b 100644
--- a/TODO.txt
+++ b/TODO.txt
@@ -2,7 +2,6 @@ Packaging:
==========================================
Documentation with sphinx
Document methods with paper references
-allow for "pip install"
unit-tests
New features:
diff --git a/quapy/__init__.py b/quapy/__init__.py
index cffae1c..7fea635 100644
--- a/quapy/__init__.py
+++ b/quapy/__init__.py
@@ -10,7 +10,7 @@ from . import model_selection
from . import classification
from quapy.method.base import isprobabilistic, isaggregative
-__version__ = '0.1'
+__version__ = '0.1.4'
environ = {
'SAMPLE_SIZE': None,
diff --git a/quapy/method/meta.py b/quapy/method/meta.py
index e74c969..fc3efe3 100644
--- a/quapy/method/meta.py
+++ b/quapy/method/meta.py
@@ -1,28 +1,32 @@
from copy import deepcopy
from typing import Union
+import numpy as np
+from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score, make_scorer, accuracy_score
+from sklearn.model_selection import GridSearchCV, cross_val_predict
from tqdm import tqdm
-import numpy as np
-from joblib import Parallel, delayed
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import GridSearchCV, cross_val_predict
-
import quapy as qp
-from quapy.data import LabelledCollection
from quapy import functional as F
+from quapy.data import LabelledCollection
from quapy.evaluation import evaluate
from quapy.model_selection import GridSearchQ
-from . import neural
-from .base import BaseQuantifier
-from quapy.method.aggregative import CC, ACC, PCC, PACC, HDy, EMQ
-QuaNet = neural.QuaNetTrainer
+try:
+ from . import neural
+except ModuleNotFoundError:
+ neural = None
+from .base import BaseQuantifier
+from quapy.method.aggregative import CC, ACC, PACC, HDy, EMQ
+
+if neural:
+ QuaNet = neural.QuaNetTrainer
+else:
+ QuaNet = "QuaNet is not available due to missing torch package"
class Ensemble(BaseQuantifier):
-
VALID_POLICIES = {'ave', 'ptr', 'ds'} | qp.error.QUANTIFICATION_ERROR_NAMES
"""
@@ -65,9 +69,9 @@ class Ensemble(BaseQuantifier):
if self.verbose:
print('[Ensemble]' + msg)
- def fit(self, data: qp.data.LabelledCollection, val_split: Union[qp.data.LabelledCollection, float]=None):
+ def fit(self, data: qp.data.LabelledCollection, val_split: Union[qp.data.LabelledCollection, float] = None):
self.sout('Fit')
- if self.policy=='ds' and not data.binary:
+ if self.policy == 'ds' and not data.binary:
raise ValueError(f'ds policy is only defined for binary quantification, but this dataset is not binary')
if val_split is None:
val_split = self.val_split
@@ -132,7 +136,7 @@ class Ensemble(BaseQuantifier):
tests = [m[3] for m in self.ensemble]
scores = []
for i, model in enumerate(self.ensemble):
- scores.append(evaluate(model[0], tests[:i] + tests[i+1:], error, self.n_jobs))
+ scores.append(evaluate(model[0], tests[:i] + tests[i + 1:], error, self.n_jobs))
order = np.argsort(scores)
self.ensemble = _select_k(self.ensemble, order, k=self.red_size)
@@ -168,7 +172,7 @@ class Ensemble(BaseQuantifier):
lr_base = LogisticRegression(class_weight='balanced', max_iter=1000)
optim = GridSearchCV(
- lr_base, param_grid={'C': np.logspace(-4,4,9)}, cv=5, n_jobs=self.n_jobs, refit=True
+ lr_base, param_grid={'C': np.logspace(-4, 4, 9)}, cv=5, n_jobs=self.n_jobs, refit=True
).fit(X, y)
posteriors = cross_val_predict(
@@ -204,8 +208,8 @@ class Ensemble(BaseQuantifier):
def get_probability_distribution(posterior_probabilities, bins=8):
- assert posterior_probabilities.shape[1]==2, 'the posterior probabilities do not seem to be for a binary problem'
- posterior_probabilities = posterior_probabilities[:,1] # take the positive posteriors only
+ assert posterior_probabilities.shape[1] == 2, 'the posterior probabilities do not seem to be for a binary problem'
+ posterior_probabilities = posterior_probabilities[:, 1] # take the positive posteriors only
distribution, _ = np.histogram(posterior_probabilities, bins=bins, range=(0, 1), density=True)
return distribution
@@ -223,7 +227,7 @@ def _delayed_new_instance(args):
if val_split is not None:
if isinstance(val_split, float):
assert 0 < val_split < 1, 'val_split should be in (0,1)'
- data, val_split = data.split_stratified(train_prop=1-val_split)
+ data, val_split = data.split_stratified(train_prop=1 - val_split)
sample_index = data.sampling_index(sample_size, *prev)
sample = data.sampling_from_index(sample_index)
@@ -255,7 +259,7 @@ def _draw_simplex(ndim, min_val, max_trials=100):
:return: a sample from the ndim-dimensional simplex that is uniform in S(ndim)-R where S(ndim) is the simplex
and R is the simplex subset containing dimensions lower than min_val
"""
- if min_val >= 1/ndim:
+ if min_val >= 1 / ndim:
raise ValueError(f'no sample can be draw from the {ndim}-dimensional simplex so that '
f'all its values are >={min_val} (try with a larger value for min_pos)')
trials = 0
@@ -300,14 +304,15 @@ def _check_error(error):
f'the name of an error function in {qp.error.ERROR_NAMES}')
-def ensembleFactory(learner, base_quantifier_class, param_grid=None, optim=None, param_model_sel:dict=None, **kwargs):
- if optim is not None:
- if param_grid is None:
- raise ValueError(f'param_grid is None but optim was requested.')
- if param_model_sel is None:
- raise ValueError(f'param_model_sel is None but optim was requested.')
- error = _check_error(optim)
- return _instantiate_ensemble(learner, base_quantifier_class, param_grid, error, param_model_sel, **kwargs)
+def ensembleFactory(learner, base_quantifier_class, param_grid=None, optim=None, param_model_sel: dict = None,
+ **kwargs):
+ if optim is not None:
+ if param_grid is None:
+ raise ValueError(f'param_grid is None but optim was requested.')
+ if param_model_sel is None:
+ raise ValueError(f'param_model_sel is None but optim was requested.')
+ error = _check_error(optim)
+ return _instantiate_ensemble(learner, base_quantifier_class, param_grid, error, param_model_sel, **kwargs)
def ECC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
@@ -327,4 +332,4 @@ def EHDy(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
def EEMQ(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
- return ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, **kwargs)
\ No newline at end of file
+ return ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, **kwargs)
diff --git a/quapy/tests/test_methods.py b/quapy/tests/test_methods.py
index 186b7c0..bcf721c 100644
--- a/quapy/tests/test_methods.py
+++ b/quapy/tests/test_methods.py
@@ -100,6 +100,12 @@ def test_ensemble_method(base_method, learner, dataset: Dataset, policy):
def test_quanet_method():
+ try:
+ import quapy.classification.neural
+ except ModuleNotFoundError:
+ print('skipping QuaNet test due to missing torch package')
+ return
+
dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
dataset = Dataset(dataset.training.sampling(100, *dataset.training.prevalence()),
dataset.test.sampling(100, *dataset.test.prevalence()))
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..898ff46
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,164 @@
+from setuptools import setup, find_packages
+import pathlib
+
+here = pathlib.Path(__file__).parent.resolve()
+
+long_description = (here / 'README.md').read_text(encoding='utf-8')
+
+
+def get_version(rel_path):
+ init_content = (here / rel_path).read_text(encoding='utf-8')
+ for line in init_content.split('\n'):
+ if line.startswith('__version__'):
+ delim = '"' if '"' in line else "'"
+ return line.split(delim)[1]
+ else:
+ raise RuntimeError("Unable to find version string.")
+# Arguments marked as "Required" below must be included for upload to PyPI.
+# Fields marked as "Optional" may be commented out.
+
+setup(
+ # This is the name of your project. The first time you publish this
+ # package, this name will be registered for you. It will determine how
+ # users can install this project, e.g.:
+ #
+ # $ pip install sampleproject
+ #
+ # And where it will live on PyPI: https://pypi.org/project/sampleproject/
+ #
+ # There are some restrictions on what makes a valid project name
+ # specification here:
+ # https://packaging.python.org/specifications/core-metadata/#name
+ name='QuaPy', # Required
+
+ # Versions should comply with PEP 440:
+ # https://www.python.org/dev/peps/pep-0440/
+ #
+ # For a discussion on single-sourcing the version across setup.py and the
+ # project code, see
+ # https://packaging.python.org/en/latest/single_source_version.html
+ version=get_version("quapy/__init__.py"), # Required
+
+ # This is a one-line description or tagline of what your project does. This
+ # corresponds to the "Summary" metadata field:
+ # https://packaging.python.org/specifications/core-metadata/#summary
+ description='QuaPy: a framework for Quantification in Python', # Optional
+
+ # This is an optional longer description of your project that represents
+ # the body of text which users will see when they visit PyPI.
+ #
+ # Often, this is the same as your README, so you can just read it in from
+ # that file directly (as we have already done above)
+ #
+ # This field corresponds to the "Description" metadata field:
+ # https://packaging.python.org/specifications/core-metadata/#description-optional
+ long_description=long_description, # Optional
+
+ # Denotes that our long_description is in Markdown; valid values are
+ # text/plain, text/x-rst, and text/markdown
+ #
+ # Optional if long_description is written in reStructuredText (rst) but
+ # required for plain-text or Markdown; if unspecified, "applications should
+ # attempt to render [the long_description] as text/x-rst; charset=UTF-8 and
+ # fall back to text/plain if it is not valid rst" (see link below)
+ #
+ # This field corresponds to the "Description-Content-Type" metadata field:
+ # https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
+ long_description_content_type='text/markdown', # Optional (see note above)
+
+ # This should be a valid link to your project's main homepage.
+ #
+ # This field corresponds to the "Home-Page" metadata field:
+ # https://packaging.python.org/specifications/core-metadata/#home-page-optional
+ url='https://github.com/HLT-ISTI/QuaPy', # Optional
+
+ maintainer='Alejandro Moreo',
+
+ maintainer_email='alejandro.moreo@isti.cnr.it',
+
+ classifiers=[
+ 'Development Status :: 4 - Beta',
+
+ 'Intended Audience :: Developers',
+ 'Intended Audience :: Science/Research',
+ 'Programming Language :: Python',
+ 'Topic :: Software Development',
+ 'Topic :: Scientific/Engineering',
+
+ 'License :: OSI Approved :: BSD License',
+
+ 'Programming Language :: Python :: 3',
+ 'Programming Language :: Python :: 3.6',
+ 'Programming Language :: Python :: 3.7',
+ 'Programming Language :: Python :: 3.8',
+ 'Programming Language :: Python :: 3.9',
+ 'Programming Language :: Python :: 3 :: Only',
+ ],
+
+ keywords='machine learning, quantification, classification, prevalence estimation, priors estimate',
+
+ # When your source code is in a subdirectory under the project root, e.g.
+ # `src/`, it is necessary to specify the `package_dir` argument.
+ #package_dir={'': 'src'}, # Optional
+
+ # You can just specify package directories manually here if your project is
+ # simple. Or you can use find_packages().
+ #
+ # Alternatively, if you just want to distribute a single Python file, use
+ # the `py_modules` argument instead as follows, which will expect a file
+ # called `my_module.py` to exist:
+ #
+ # py_modules=["my_module"],
+ #
+ packages=find_packages(include=['quapy', 'quapy.*']), # Required
+
+ python_requires='>=3.6, <4',
+
+ install_requires=['scikit-learn', 'pandas', 'tqdm', 'matplotlib'],
+
+ # List additional groups of dependencies here (e.g. development
+ # dependencies). Users will be able to install these using the "extras"
+ # syntax, for example:
+ #
+ # $ pip install sampleproject[dev]
+ #
+ # Similar to `install_requires` above, these must be valid existing
+ # projects.
+ # extras_require={ # Optional
+ # 'dev': ['check-manifest'],
+ # 'test': ['coverage'],
+ # },
+
+ # If there are data files included in your packages that need to be
+ # installed, specify them here.
+ # package_data={ # Optional
+ # 'sample': ['package_data.dat'],
+ # },
+
+ # Although 'package_data' is the preferred approach, in some case you may
+ # need to place data files outside of your packages. See:
+ # http://docs.python.org/distutils/setupscript.html#installing-additional-files
+ #
+ # In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
+ # data_files=[('my_data', ['data/data_file'])], # Optional
+
+ # To provide executable scripts, use entry points in preference to the
+ # "scripts" keyword. Entry points provide cross-platform support and allow
+ # `pip` to create the appropriate form of executable for the target
+ # platform.
+ #
+ # For example, the following would provide a command called `sample` which
+ # executes the function `main` from this package when invoked:
+ # entry_points={ # Optional
+ # 'console_scripts': [
+ # 'sample=sample:main',
+ # ],
+ # },
+
+ project_urls={ # Optional
+ 'Contributors': 'https://github.com/HLT-ISTI/QuaPy/graphs/contributors',
+ 'Bug Reports': 'https://github.com/HLT-ISTI/QuaPy/issues',
+ 'Documentation': 'https://github.com/HLT-ISTI/QuaPy/wiki',
+ 'Source': 'https://github.com/HLT-ISTI/QuaPy/',
+ },
+)