diff --git a/docs/build/html/Datasets.html b/docs/build/html/Datasets.html index 6af836e..9c9eaa7 100644 --- a/docs/build/html/Datasets.html +++ b/docs/build/html/Datasets.html @@ -2,23 +2,26 @@ - + - - Datasets — QuaPy 0.1.6 documentation + + + Datasets — QuaPy 0.1.7 documentation + + - - + + - - - -
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quapy.tests package

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quapy.tests.test_base module

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quapy.tests.test_datasets module

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quapy.tests.test_methods module

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Module contents

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- - - - \ No newline at end of file diff --git a/docs/build/html/readme.html b/docs/build/html/readme.html deleted file mode 100644 index c223f24..0000000 --- a/docs/build/html/readme.html +++ /dev/null @@ -1,129 +0,0 @@ - - - - - - - - - Getting Started — QuaPy 0.1.6 documentation - - - - - - - - - - - - - - - - - -
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Getting Started

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QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation) written in Python.

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Installation

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>>> pip install quapy
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- - - - \ No newline at end of file diff --git a/docs/build/html/readme2.html b/docs/build/html/readme2.html deleted file mode 100644 index e5ff4a6..0000000 --- a/docs/build/html/readme2.html +++ /dev/null @@ -1,92 +0,0 @@ - - - - - - - - - <no title> — QuaPy 0.1.6 documentation - - - - - - - - - - - - - - - -
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.. include:: ../../README.md

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- - - - \ No newline at end of file diff --git a/docs/build/html/search.html b/docs/build/html/search.html index 2090979..480e246 100644 --- a/docs/build/html/search.html +++ b/docs/build/html/search.html @@ -2,11 +2,11 @@ - + - Search — QuaPy 0.1.6 documentation + Search — QuaPy 0.1.7 documentation @@ -14,7 +14,9 @@ + + @@ -37,7 +39,7 @@
  • modules |
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  • modules |
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+from model_selection import GridSearchQ +from quapy.method.aggregative import PACC, AggregativeProbabilisticQuantifier +from quapy.protocol import APP +import numpy as np +from sklearn.linear_model import LogisticRegression + + +# Define a custom quantifier: for this example, we will consider a new quantification algorithm that uses a +# logistic regressor for generating posterior probabilities, and then applies a custom threshold value to the +# posteriors. Since the quantifier internally uses a classifier, it is an aggregative quantifier; and since it +# relies on posterior probabilities, then it is a probabilistic aggregative quantifier. Note also it has an +# internal hyperparameter (let say, alpha) which is the decision threshold. Let's also assume the quantifier +# is binary, for simplicity. + +class MyQuantifier(AggregativeProbabilisticQuantifier, BinaryQuantifier): + def __init__(self, classifier, alpha=0.5): + self.alpha = alpha + # aggregative quantifiers have an internal self.classifier attribute + self.classifier = classifier + + def fit(self, data: LabelledCollection, fit_classifier=True): + assert fit_classifier, 'this quantifier needs to fit the classifier!' + self.classifier.fit(*data.Xy) + return self + + # in general, we would need to implement the method quantify(self, instances) but, since this method is of + # type aggregative, we can simply implement the method aggregate, which has the following interface + def aggregate(self, classif_predictions: np.ndarray): + # the posterior probabilities have already been generated by the quantify method; we only need to + # specify what to do with them + positive_probabilities = classif_predictions[:, 1] + crisp_decisions = positive_probabilities > self.alpha + pos_prev = crisp_decisions.mean() + neg_prev = 1-pos_prev + return np.asarray([neg_prev, pos_prev]) + + +if __name__ == '__main__': + + qp.environ['SAMPLE_SIZE'] = 100 + + # define an instance of our custom quantifier + quantifier = MyQuantifier(LogisticRegression(), alpha=0.5) + + # load the IMDb dataset + train, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test + train, val = train.split_stratified(train_prop=0.75) + + # model selection + # let us assume we want to explore our hyperparameter alpha along with one hyperparameter of the classifier + param_grid = { + 'alpha': np.linspace(0,1,11), # quantifier-dependent hyperparameter + 'classifier__C': np.logspace(-2,2,5) # classifier-dependent hyperparameter + } + quantifier = GridSearchQ(quantifier, param_grid, protocol=APP(val), n_jobs=-1, verbose=True).fit(train) + + # evaluation + mae = qp.evaluation.evaluate(quantifier, protocol=APP(test), error_metric='mae') + + print(f'MAE = {mae:.4f}') + + # final remarks: this method is only for demonstration purposes and makes little sense in general. The method relies + # on an hyperparameter alpha for binarizing the posterior probabilities. A much better way for fulfilling this + # goal would be to calibrate the classifier (LogisticRegression is already reasonably well calibrated) and then + # simply cut at 0.5. + diff --git a/quapy/CHANGE_LOG.txt b/quapy/CHANGE_LOG.txt index f2deea0..3fb21f6 100644 --- a/quapy/CHANGE_LOG.txt +++ b/quapy/CHANGE_LOG.txt @@ -1,11 +1,19 @@ -# main changes in 0.1.7 +Change Log 0.1.7 +--------------------- -- Protocols are now abstracted as AbstractProtocol. There is a new class extending AbstractProtocol called +- Protocols are now abstracted as instances of AbstractProtocol. There is a new class extending AbstractProtocol called AbstractStochasticSeededProtocol, which implements a seeding policy to allow replicate the series of samplings. - There are some examples of protocols, APP, NPP, USimplexPP, CovariateShiftPP (experimental). + There are some examples of protocols, APP, NPP, USimplexPP, DomainMixer (experimental). The idea is to start the sampling by simply calling the __call__ method. This change has a great impact in the framework, since many functions in qp.evaluation, qp.model_selection, - and sampling functions in LabelledCollection make use of the old functions. + and sampling functions in LabelledCollection relied of the old functions. E.g., the functionality of + qp.evaluation.artificial_prevalence_report or qp.evaluation.natural_prevalence_report is now obtained by means of + qp.evaluation.report which takes a protocol as an argument. I have not maintained compatibility with the old + interfaces because I did not really like them. Check the wiki guide and the examples for more details. + + check guides + + check examples - ACC, PACC, Forman's threshold variants have been parallelized. @@ -51,47 +59,31 @@ multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy method aligned with the Firat's formulation. +- internal method properties "binary", "aggregative", and "probabilistic" have been removed; these conditions are + checked via isinstance + +- quantifiers (i.e., classes that inherit from BaseQuantifier) are not forced to implement classes_ or n_classes; + these can be used anyway internally, but the framework will not suppose (nor impose) that a quantifier implements + them + +- qp.evaluation.prediction has been optimized so that, if a quantifier is of type aggregative, and if the evaluation + protocol is of type OnLabelledCollection, then the computation is faster. In this specific case, the predictions + are issued only once and for all, and not for each sample. An exception to this (which is implement also), is + when the number of instances across all samples is anyway smaller than the number of instances in the original + labelled collection; in this case the heuristic is of no help, and is therefore not applied. + +- the distinction between "classify" and "posterior_probabilities" has been removed in Aggregative quantifiers, + so that probabilistic classifiers return posterior probabilities, while non-probabilistic quantifiers + return crisp decisions. + Things to fix: -- calibration with recalibration methods has to be fixed for exact_train_prev in EMQ (conflicts with clone, deepcopy, etc.) -- clean functions like binary, aggregative, probabilistic, etc; those should be resolved via isinstance(): - this is not working; I don't know how to make the isinstance work. Looks like there is some problem with the - path of the imported class wrt the path of the class that arrives from another module... -- clean classes_ and n_classes from methods (maybe not from aggregative ones, but those have to be used only - internally and not imposed in any abstract class) -- optimize "qp.evaluation.prediction" for aggregative methods (pre-classification) +-------------- +- OneVsAll is duplicated (in aggregative and in general), and is not well documented. It is not working either. + Check method def __parallel(self, func, *args, **kwargs) in aggregative.OneVsAll - update unit tests -- Policies should be able to set their output to "labelled_collection" or "instances_prevalence" or something similar. -- Policies should implement the "gen()" one, taking a reader function as an input, and a folder path maybe -- Review all documentation, redo the Sphinx doc, update Wikis... +- update Wikis... - Resolve the OneVsAll thing (it is in base.py and in aggregative.py) -- Better handle the environment (e.g., with n_jobs) -- test cross_generate_predictions and cancel cross_generate_predictions_depr - Add a proper log? -- test LoadSamplesFromDirectory (in protocols.py) -- improve plots? -- I have removed the distinction between "classify" and "posterior_probabilities" in the Aggregative quantifiers, - so that probabilistic classifiers actually return posterior probabilities, while non-probabilistic quantifiers - return instead crisp decisions. The idea was to unify the quantification function (i.e., now it is always - classify & aggregate, irrespective of the class). However, this has caused a problem with OneVsAll. This has to - be checked, since it is now innecessarily complicated (it also has old references to .probabilistic, and all this - stuff). -- Check method def __parallel(self, func, *args, **kwargs) in aggregative.OneVsAll +- improve plots +- documentation of protocols is incomplete -New features: -- Add LeQua2022 to datasets (everything automatic, and with proper protocols "gen") -- Add an "experimental room", with scripts to quickly test new ideas and see results. - -# 0.1.7 -# change the LabelledCollection API (removing protocol-related samplings) -# need to change the two references to the above in the wiki / doc, and code examples... -# removed artificial_prevalence_sampling from functional - -# also: some parameters in the init could be used to indicate that the method should return a tuple with -# unlabelled instances and the vector of prevalence values (and not a LabelledCollection). -# Or: this can be done in a different function; i.e., we use one function (now __call__) to return -# LabelledCollections, and another new one for returning the other output, which is more general for -# evaluation purposes. - -# the so-called "gen" function has to be implemented as a protocol. The problem here is that this function -# should be able to return only unlabelled instances plus a vector of prevalences (and not LabelledCollections). -# This was coded as different functions in 0.1.6 diff --git a/quapy/__init__.py b/quapy/__init__.py index 54b1603..47a7388 100644 --- a/quapy/__init__.py +++ b/quapy/__init__.py @@ -23,9 +23,28 @@ environ = { } -def get_njobs(n_jobs): +def _get_njobs(n_jobs): + """ + If `n_jobs` is None, then it returns `environ['N_JOBS']`; if otherwise, returns `n_jobs`. + + :param n_jobs: the number of `n_jobs` or None if not specified + :return: int + """ return environ['N_JOBS'] if n_jobs is None else n_jobs +def _get_sample_size(sample_size): + """ + If `sample_size` is None, then it returns `environ['SAMPLE_SIZE']`; if otherwise, returns `sample_size`. + If none of these are set, then a ValueError exception is raised. + + :param sample_size: integer or None + :return: int + """ + sample_size = environ['SAMPLE_SIZE'] if sample_size is None else sample_size + if sample_size is None: + raise ValueError('neither sample_size nor qp.environ["SAMPLE_SIZE"] have been specified') + return sample_size + diff --git a/quapy/classification/calibration.py b/quapy/classification/calibration.py index 69a7e14..f35bb97 100644 --- a/quapy/classification/calibration.py +++ b/quapy/classification/calibration.py @@ -12,12 +12,18 @@ import numpy as np class RecalibratedProbabilisticClassifier: + """ + Abstract class for (re)calibration method from `abstention.calibration`, as defined in + `Alexandari, A., Kundaje, A., & Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration + is hard-to-beat at label shift adaptation. In International Conference on Machine Learning (pp. 222-232). PMLR. + `_: + """ pass class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabilisticClassifier): """ - Applies a (re)calibration method from abstention.calibration, as defined in + Applies a (re)calibration method from `abstention.calibration`, as defined in `Alexandari et al. paper `_: :param classifier: a scikit-learn probabilistic classifier @@ -25,7 +31,7 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi :param val_split: indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the training instances (the rest is used for training). In any case, the classifier is retrained in the whole - training set afterwards. + training set afterwards. Default value is 5. :param n_jobs: indicate the number of parallel workers (only when val_split is an integer); default=None :param verbose: whether or not to display information in the standard output """ @@ -38,6 +44,13 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi self.verbose = verbose def fit(self, X, y): + """ + Fits the calibration for the probabilistic classifier. + + :param X: array-like of shape `(n_samples, n_features)` with the data instances + :param y: array-like of shape `(n_samples,)` with the class labels + :return: self + """ k = self.val_split if isinstance(k, int): if k < 2: @@ -49,6 +62,15 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi return self.fit_cv(X, y) def fit_cv(self, X, y): + """ + Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all + training instances via cross-validation, and then retrains the classifier on all training instances. + The posterior probabilities thus generated are used for calibrating the outpus of the classifier. + + :param X: array-like of shape `(n_samples, n_features)` with the data instances + :param y: array-like of shape `(n_samples,)` with the class labels + :return: self + """ posteriors = cross_val_predict( self.classifier, X, y, cv=self.val_split, n_jobs=self.n_jobs, verbose=self.verbose, method='predict_proba' ) @@ -58,6 +80,16 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi return self def fit_tr_val(self, X, y): + """ + Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a + training and a validation set, and then uses the training samples to learn classifier which is then used + to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate + the classifier. The classifier is not retrained on the whole dataset. + + :param X: array-like of shape `(n_samples, n_features)` with the data instances + :param y: array-like of shape `(n_samples,)` with the class labels + :return: self + """ Xtr, Xva, ytr, yva = train_test_split(X, y, test_size=self.val_split, stratify=y) self.classifier.fit(Xtr, ytr) posteriors = self.classifier.predict_proba(Xva) @@ -66,32 +98,49 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi return self def predict(self, X): + """ + Predicts class labels for the data instances in `X` + + :param X: array-like of shape `(n_samples, n_features)` with the data instances + :return: array-like of shape `(n_samples,)` with the class label predictions + """ return self.classifier.predict(X) def predict_proba(self, X): + """ + Generates posterior probabilities for the data instances in `X` + + :param X: array-like of shape `(n_samples, n_features)` with the data instances + :return: array-like of shape `(n_samples, n_classes)` with posterior probabilities + """ posteriors = self.classifier.predict_proba(X) return self.calibration_function(posteriors) @property def classes_(self): + """ + Returns the classes on which the classifier has been trained on + + :return: array-like of shape `(n_classes)` + """ return self.classifier.classes_ class NBVSCalibration(RecalibratedProbabilisticClassifierBase): """ - Applies the No-Bias Vector Scaling (NBVS) calibration method from abstention.calibration, as defined in + Applies the No-Bias Vector Scaling (NBVS) calibration method from `abstention.calibration`, as defined in `Alexandari et al. paper `_: :param classifier: a scikit-learn probabilistic classifier :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the training instances (the rest is used for training). In any case, the classifier is retrained in the whole - training set afterwards. + training set afterwards. Default value is 5. :param n_jobs: indicate the number of parallel workers (only when val_split is an integer) :param verbose: whether or not to display information in the standard output """ - def __init__(self, classifier, val_split=5, n_jobs=1, verbose=False): + def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False): self.classifier = classifier self.calibrator = NoBiasVectorScaling(verbose=verbose) self.val_split = val_split @@ -101,19 +150,19 @@ class NBVSCalibration(RecalibratedProbabilisticClassifierBase): class BCTSCalibration(RecalibratedProbabilisticClassifierBase): """ - Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from abstention.calibration, as defined in + Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from `abstention.calibration`, as defined in `Alexandari et al. paper `_: :param classifier: a scikit-learn probabilistic classifier :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the training instances (the rest is used for training). In any case, the classifier is retrained in the whole - training set afterwards. + training set afterwards. Default value is 5. :param n_jobs: indicate the number of parallel workers (only when val_split is an integer) :param verbose: whether or not to display information in the standard output """ - def __init__(self, classifier, val_split=5, n_jobs=1, verbose=False): + def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False): self.classifier = classifier self.calibrator = TempScaling(verbose=verbose, bias_positions='all') self.val_split = val_split @@ -123,19 +172,19 @@ class BCTSCalibration(RecalibratedProbabilisticClassifierBase): class TSCalibration(RecalibratedProbabilisticClassifierBase): """ - Applies the Temperature Scaling (TS) calibration method from abstention.calibration, as defined in + Applies the Temperature Scaling (TS) calibration method from `abstention.calibration`, as defined in `Alexandari et al. paper `_: :param classifier: a scikit-learn probabilistic classifier :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the training instances (the rest is used for training). In any case, the classifier is retrained in the whole - training set afterwards. + training set afterwards. Default value is 5. :param n_jobs: indicate the number of parallel workers (only when val_split is an integer) :param verbose: whether or not to display information in the standard output """ - def __init__(self, classifier, val_split=5, n_jobs=1, verbose=False): + def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False): self.classifier = classifier self.calibrator = TempScaling(verbose=verbose) self.val_split = val_split @@ -145,19 +194,19 @@ class TSCalibration(RecalibratedProbabilisticClassifierBase): class VSCalibration(RecalibratedProbabilisticClassifierBase): """ - Applies the Vector Scaling (VS) calibration method from abstention.calibration, as defined in + Applies the Vector Scaling (VS) calibration method from `abstention.calibration`, as defined in `Alexandari et al. paper `_: :param classifier: a scikit-learn probabilistic classifier :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the training instances (the rest is used for training). In any case, the classifier is retrained in the whole - training set afterwards. + training set afterwards. Default value is 5. :param n_jobs: indicate the number of parallel workers (only when val_split is an integer) :param verbose: whether or not to display information in the standard output """ - def __init__(self, classifier, val_split=5, n_jobs=1, verbose=False): + def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False): self.classifier = classifier self.calibrator = VectorScaling(verbose=verbose) self.val_split = val_split diff --git a/quapy/classification/svmperf.py b/quapy/classification/svmperf.py index 2f6ad90..176b102 100644 --- a/quapy/classification/svmperf.py +++ b/quapy/classification/svmperf.py @@ -94,6 +94,7 @@ class SVMperf(BaseEstimator, ClassifierMixin): def predict(self, X): """ Predicts labels for the instances `X` + :param X: array-like of shape `(n_samples, n_features)` instances to classify :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of instances in `X` diff --git a/quapy/data/datasets.py b/quapy/data/datasets.py index b35343b..241cd04 100644 --- a/quapy/data/datasets.py +++ b/quapy/data/datasets.py @@ -554,7 +554,31 @@ def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float): def fetch_lequa2022(task, data_home=None): """ + Loads the official datasets provided for the `LeQua `_ competition. + In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification + problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide raw documents instead. + Tasks T1A and T2A are binary sentiment quantification problems, while T2A and T2B are multiclass quantification + problems consisting of estimating the class prevalence values of 28 different merchandise products. + We refer to the `Esuli, A., Moreo, A., Sebastiani, F., & Sperduti, G. (2022). + A Detailed Overview of LeQua@ CLEF 2022: Learning to Quantify. + `_ for a detailed description + on the tasks and datasets. + + The datasets are downloaded only once, and stored for fast reuse. + + See `lequa2022_experiments.py` provided in the example folder, that can serve as a guide on how to use these + datasets. + + + :param task: a string representing the task name; valid ones are T1A, T1B, T2A, and T2B + :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default + ~/quay_data/ directory) + :return: a tuple `(train, val_gen, test_gen)` where `train` is an instance of + :class:`quapy.data.base.LabelledCollection`, `val_gen` and `test_gen` are instances of + :class:`quapy.protocol.SamplesFromDir`, i.e., are sampling protocols that return a series of samples + labelled by prevalence. """ + from quapy.data._lequa2022 import load_raw_documents, load_vector_documents, SamplesFromDir assert task in LEQUA2022_TASKS, \ diff --git a/quapy/data/preprocessing.py b/quapy/data/preprocessing.py index a987900..e65ccf7 100644 --- a/quapy/data/preprocessing.py +++ b/quapy/data/preprocessing.py @@ -88,7 +88,7 @@ def standardize(dataset: Dataset, inplace=False): :param dataset: a :class:`quapy.data.base.Dataset` object :param inplace: set to True if the transformation is to be applied inplace, or to False (default) if a new :class:`quapy.data.base.Dataset` is to be returned - :return: + :return: an instance of :class:`quapy.data.base.Dataset` """ s = StandardScaler(copy=not inplace) training = s.fit_transform(dataset.training.instances) @@ -110,7 +110,7 @@ def index(dataset: Dataset, min_df=5, inplace=False, **kwargs): :param min_df: minimum number of occurrences below which the term is replaced by a `UNK` index :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default) :param kwargs: the rest of parameters of the transformation (as for sklearn's - `CountVectorizer _`) + `CountVectorizer _`) :return: a new :class:`quapy.data.base.Dataset` (if inplace=False) or a reference to the current :class:`quapy.data.base.Dataset` (inplace=True) consisting of lists of integer values representing indices. """ @@ -147,7 +147,8 @@ class IndexTransformer: contains, and that would be generated by sklearn's `CountVectorizer `_ - :param kwargs: keyworded arguments from `CountVectorizer `_ + :param kwargs: keyworded arguments from + `CountVectorizer `_ """ def __init__(self, **kwargs): @@ -179,7 +180,7 @@ class IndexTransformer: """ # given the number of tasks and the number of jobs, generates the slices for the parallel processes assert self.unk != -1, 'transform called before fit' - n_jobs = qp.get_njobs(n_jobs) + n_jobs = qp._get_njobs(n_jobs) indexed = map_parallel(func=self._index, args=X, n_jobs=n_jobs) return np.asarray(indexed) diff --git a/quapy/depr_evaluation.py b/quapy/depr_evaluation.py deleted file mode 100644 index 0846ab0..0000000 --- a/quapy/depr_evaluation.py +++ /dev/null @@ -1,439 +0,0 @@ -from typing import Union, Callable, Iterable -import numpy as np -from tqdm import tqdm -import inspect - -import quapy as qp -from quapy.data import LabelledCollection -from quapy.method.base import BaseQuantifier -from quapy.util import temp_seed -import quapy.functional as F -import pandas as pd - - -def artificial_prevalence_prediction( - model: BaseQuantifier, - test: LabelledCollection, - sample_size, - n_prevpoints=101, - repeats=1, - eval_budget: int = None, - n_jobs=1, - random_seed=42, - verbose=False): - """ - Performs the predictions for all samples generated according to the Artificial Prevalence Protocol (APP). - The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g., - [0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of - prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ..., - [1, 0, 0] prevalence values of size `sample_size` will be considered). The number of samples for each valid - combination of prevalence values is indicated by `repeats`. - - :param model: the model in charge of generating the class prevalence estimations - :param test: the test set on which to perform APP - :param sample_size: integer, the size of the samples - :param n_prevpoints: integer, the number of different prevalences to sample (or set to None if eval_budget - is specified; default 101, i.e., steps of 1%) - :param repeats: integer, the number of repetitions for each prevalence (default 1) - :param eval_budget: integer, if specified, sets a ceil on the number of evaluations to perform. For example, if - there are 3 classes, `repeats=1`, and `eval_budget=20`, then `n_prevpoints` will be set to 5, since this - will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]) and - since setting `n_prevpoints=6` would produce more than 20 evaluations. - :param n_jobs: integer, number of jobs to be run in parallel (default 1) - :param random_seed: integer, allows to replicate the samplings. The seed is local to the method and does not affect - any other random process (default 42) - :param verbose: if True, shows a progress bar - :return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples - `(n_prevpoints*repeats)` and `n` the number of classes. The first one contains the true prevalence values - for the samples generated while the second one contains the prevalence estimations - """ - - n_prevpoints, _ = qp.evaluation._check_num_evals(test.n_classes, n_prevpoints, eval_budget, repeats, verbose) - - with temp_seed(random_seed): - indexes = list(test.artificial_sampling_index_generator(sample_size, n_prevpoints, repeats)) - - return _predict_from_indexes(indexes, model, test, n_jobs, verbose) - - -def natural_prevalence_prediction( - model: BaseQuantifier, - test: LabelledCollection, - sample_size, - repeats, - n_jobs=1, - random_seed=42, - verbose=False): - """ - Performs the predictions for all samples generated according to the Natural Prevalence Protocol (NPP). - The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural - prevalence of the collection. - - :param model: the model in charge of generating the class prevalence estimations - :param test: the test set on which to perform NPP - :param sample_size: integer, the size of the samples - :param repeats: integer, the number of samples to generate - :param n_jobs: integer, number of jobs to be run in parallel (default 1) - :param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect - any other random process (default 42) - :param verbose: if True, shows a progress bar - :return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples - `(repeats)` and `n` the number of classes. The first one contains the true prevalence values - for the samples generated while the second one contains the prevalence estimations - """ - - with temp_seed(random_seed): - indexes = list(test.natural_sampling_index_generator(sample_size, repeats)) - - return _predict_from_indexes(indexes, model, test, n_jobs, verbose) - - -def gen_prevalence_prediction(model: BaseQuantifier, gen_fn: Callable, eval_budget=None): - """ - Generates prevalence predictions for a custom protocol defined as a generator function that yields - samples at each iteration. The sequence of samples is processed exhaustively if `eval_budget=None` - or up to the `eval_budget` iterations if specified. - - :param model: the model in charge of generating the class prevalence estimations - :param gen_fn: a generator function yielding one sample at each iteration - :param eval_budget: a maximum number of evaluations to run. Set to None (default) for exploring the - entire sequence - :return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples - generated and `n` the number of classes. The first one contains the true prevalence values - for the samples generated while the second one contains the prevalence estimations - """ - if not inspect.isgenerator(gen_fn()): - raise ValueError('param "gen_fun" is not a callable returning a generator') - - if not isinstance(eval_budget, int): - eval_budget = -1 - - true_prevalences, estim_prevalences = [], [] - for sample_instances, true_prev in gen_fn(): - true_prevalences.append(true_prev) - estim_prevalences.append(model.quantify(sample_instances)) - eval_budget -= 1 - if eval_budget == 0: - break - - true_prevalences = np.asarray(true_prevalences) - estim_prevalences = np.asarray(estim_prevalences) - - return true_prevalences, estim_prevalences - - -def _predict_from_indexes( - indexes, - model: BaseQuantifier, - test: LabelledCollection, - n_jobs=1, - verbose=False): - - if model.aggregative: #isinstance(model, qp.method.aggregative.AggregativeQuantifier): - # print('\tinstance of aggregative-quantifier') - quantification_func = model.aggregate - if model.probabilistic: # isinstance(model, qp.method.aggregative.AggregativeProbabilisticQuantifier): - # print('\t\tinstance of probabilitstic-aggregative-quantifier') - preclassified_instances = model.posterior_probabilities(test.instances) - else: - # print('\t\tinstance of hard-aggregative-quantifier') - preclassified_instances = model.classify(test.instances) - test = LabelledCollection(preclassified_instances, test.labels) - else: - # print('\t\tinstance of base-quantifier') - quantification_func = model.quantify - - def _predict_prevalences(index): - sample = test.sampling_from_index(index) - true_prevalence = sample.prevalence() - estim_prevalence = quantification_func(sample.instances) - return true_prevalence, estim_prevalence - - pbar = tqdm(indexes, desc='[artificial sampling protocol] generating predictions') if verbose else indexes - results = qp.util.parallel(_predict_prevalences, pbar, n_jobs=n_jobs) - - true_prevalences, estim_prevalences = zip(*results) - true_prevalences = np.asarray(true_prevalences) - estim_prevalences = np.asarray(estim_prevalences) - - return true_prevalences, estim_prevalences - - -def artificial_prevalence_report( - model: BaseQuantifier, - test: LabelledCollection, - sample_size, - n_prevpoints=101, - repeats=1, - eval_budget: int = None, - n_jobs=1, - random_seed=42, - error_metrics:Iterable[Union[str,Callable]]='mae', - verbose=False): - """ - Generates an evaluation report for all samples generated according to the Artificial Prevalence Protocol (APP). - The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g., - [0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of - prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ..., - [1, 0, 0] prevalence values of size `sample_size` will be considered). The number of samples for each valid - combination of prevalence values is indicated by `repeats`. - Te report takes the form of a - pandas' `dataframe `_ - in which the rows correspond to different samples, and the columns inform of the true prevalence values, - the estimated prevalence values, and the score obtained by each of the evaluation measures indicated. - - :param model: the model in charge of generating the class prevalence estimations - :param test: the test set on which to perform APP - :param sample_size: integer, the size of the samples - :param n_prevpoints: integer, the number of different prevalences to sample (or set to None if eval_budget - is specified; default 101, i.e., steps of 1%) - :param repeats: integer, the number of repetitions for each prevalence (default 1) - :param eval_budget: integer, if specified, sets a ceil on the number of evaluations to perform. For example, if - there are 3 classes, `repeats=1`, and `eval_budget=20`, then `n_prevpoints` will be set to 5, since this - will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]) and - since setting `n_prevpoints=6` would produce more than 20 evaluations. - :param n_jobs: integer, number of jobs to be run in parallel (default 1) - :param random_seed: integer, allows to replicate the samplings. The seed is local to the method and does not affect - any other random process (default 42) - :param error_metrics: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a - callable error function; optionally, a list of strings or callables can be indicated, if the results - are to be evaluated with more than one error metric. Default is "mae" - :param verbose: if True, shows a progress bar - :return: pandas' dataframe with rows corresponding to different samples, and with columns informing of the - true prevalence values, the estimated prevalence values, and the score obtained by each of the evaluation - measures indicated. - """ - - true_prevs, estim_prevs = artificial_prevalence_prediction( - model, test, sample_size, n_prevpoints, repeats, eval_budget, n_jobs, random_seed, verbose - ) - return _prevalence_report(true_prevs, estim_prevs, error_metrics) - - -def natural_prevalence_report( - model: BaseQuantifier, - test: LabelledCollection, - sample_size, - repeats=1, - n_jobs=1, - random_seed=42, - error_metrics:Iterable[Union[str,Callable]]='mae', - verbose=False): - """ - Generates an evaluation report for all samples generated according to the Natural Prevalence Protocol (NPP). - The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural - prevalence of the collection. - Te report takes the form of a - pandas' `dataframe `_ - in which the rows correspond to different samples, and the columns inform of the true prevalence values, - the estimated prevalence values, and the score obtained by each of the evaluation measures indicated. - - :param model: the model in charge of generating the class prevalence estimations - :param test: the test set on which to perform NPP - :param sample_size: integer, the size of the samples - :param repeats: integer, the number of samples to generate - :param n_jobs: integer, number of jobs to be run in parallel (default 1) - :param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect - any other random process (default 42) - :param error_metrics: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a - callable error function; optionally, a list of strings or callables can be indicated, if the results - are to be evaluated with more than one error metric. Default is "mae" - :param verbose: if True, shows a progress bar - :return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples - `(repeats)` and `n` the number of classes. The first one contains the true prevalence values - for the samples generated while the second one contains the prevalence estimations - - """ - - true_prevs, estim_prevs = natural_prevalence_prediction( - model, test, sample_size, repeats, n_jobs, random_seed, verbose - ) - return _prevalence_report(true_prevs, estim_prevs, error_metrics) - - -def gen_prevalence_report(model: BaseQuantifier, gen_fn: Callable, eval_budget=None, - error_metrics:Iterable[Union[str,Callable]]='mae'): - """ - GGenerates an evaluation report for a custom protocol defined as a generator function that yields - samples at each iteration. The sequence of samples is processed exhaustively if `eval_budget=None` - or up to the `eval_budget` iterations if specified. - Te report takes the form of a - pandas' `dataframe `_ - in which the rows correspond to different samples, and the columns inform of the true prevalence values, - the estimated prevalence values, and the score obtained by each of the evaluation measures indicated. - - :param model: the model in charge of generating the class prevalence estimations - :param gen_fn: a generator function yielding one sample at each iteration - :param eval_budget: a maximum number of evaluations to run. Set to None (default) for exploring the - entire sequence - :return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples - generated. The first one contains the true prevalence values - for the samples generated while the second one contains the prevalence estimations - """ - true_prevs, estim_prevs = gen_prevalence_prediction(model, gen_fn, eval_budget) - return _prevalence_report(true_prevs, estim_prevs, error_metrics) - - -def _prevalence_report( - true_prevs, - estim_prevs, - error_metrics: Iterable[Union[str, Callable]] = 'mae'): - - if isinstance(error_metrics, str): - error_metrics = [error_metrics] - - error_names = [e if isinstance(e, str) else e.__name__ for e in error_metrics] - error_funcs = [qp.error.from_name(e) if isinstance(e, str) else e for e in error_metrics] - assert all(hasattr(e, '__call__') for e in error_funcs), 'invalid error functions' - - df = pd.DataFrame(columns=['true-prev', 'estim-prev'] + error_names) - for true_prev, estim_prev in zip(true_prevs, estim_prevs): - series = {'true-prev': true_prev, 'estim-prev': estim_prev} - for error_name, error_metric in zip(error_names, error_funcs): - score = error_metric(true_prev, estim_prev) - series[error_name] = score - df = df.append(series, ignore_index=True) - - return df - - -def artificial_prevalence_protocol( - model: BaseQuantifier, - test: LabelledCollection, - sample_size, - n_prevpoints=101, - repeats=1, - eval_budget: int = None, - n_jobs=1, - random_seed=42, - error_metric:Union[str,Callable]='mae', - verbose=False): - """ - Generates samples according to the Artificial Prevalence Protocol (APP). - The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g., - [0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of - prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ..., - [1, 0, 0] prevalence values of size `sample_size` will be considered). The number of samples for each valid - combination of prevalence values is indicated by `repeats`. - - :param model: the model in charge of generating the class prevalence estimations - :param test: the test set on which to perform APP - :param sample_size: integer, the size of the samples - :param n_prevpoints: integer, the number of different prevalences to sample (or set to None if eval_budget - is specified; default 101, i.e., steps of 1%) - :param repeats: integer, the number of repetitions for each prevalence (default 1) - :param eval_budget: integer, if specified, sets a ceil on the number of evaluations to perform. For example, if - there are 3 classes, `repeats=1`, and `eval_budget=20`, then `n_prevpoints` will be set to 5, since this - will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]) and - since setting `n_prevpoints=6` would produce more than 20 evaluations. - :param n_jobs: integer, number of jobs to be run in parallel (default 1) - :param random_seed: integer, allows to replicate the samplings. The seed is local to the method and does not affect - any other random process (default 42) - :param error_metric: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a - callable error function - :param verbose: set to True (default False) for displaying some information on standard output - :return: yields one sample at a time - """ - - if isinstance(error_metric, str): - error_metric = qp.error.from_name(error_metric) - - assert hasattr(error_metric, '__call__'), 'invalid error function' - - true_prevs, estim_prevs = artificial_prevalence_prediction( - model, test, sample_size, n_prevpoints, repeats, eval_budget, n_jobs, random_seed, verbose - ) - - return error_metric(true_prevs, estim_prevs) - - -def natural_prevalence_protocol( - model: BaseQuantifier, - test: LabelledCollection, - sample_size, - repeats=1, - n_jobs=1, - random_seed=42, - error_metric:Union[str,Callable]='mae', - verbose=False): - """ - Generates samples according to the Natural Prevalence Protocol (NPP). - The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural - prevalence of the collection. - - :param model: the model in charge of generating the class prevalence estimations - :param test: the test set on which to perform NPP - :param sample_size: integer, the size of the samples - :param repeats: integer, the number of samples to generate - :param n_jobs: integer, number of jobs to be run in parallel (default 1) - :param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect - any other random process (default 42) - :param error_metric: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a - callable error function - :param verbose: if True, shows a progress bar - :return: yields one sample at a time - """ - - if isinstance(error_metric, str): - error_metric = qp.error.from_name(error_metric) - - assert hasattr(error_metric, '__call__'), 'invalid error function' - - true_prevs, estim_prevs = natural_prevalence_prediction( - model, test, sample_size, repeats, n_jobs, random_seed, verbose - ) - - return error_metric(true_prevs, estim_prevs) - - -def evaluate(model: BaseQuantifier, test_samples:Iterable[LabelledCollection], error_metric:Union[str, Callable], n_jobs:int=-1): - """ - Evaluates a model on a sequence of test samples in terms of a given error metric. - - :param model: the model in charge of generating the class prevalence estimations - :param test_samples: an iterable yielding one sample at a time - :param error_metric: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a - callable error function - :param n_jobs: integer, number of jobs to be run in parallel (default 1) - :return: the score obtained using `error_metric` - """ - if isinstance(error_metric, str): - error_metric = qp.error.from_name(error_metric) - scores = qp.util.parallel(_delayed_eval, ((model, Ti, error_metric) for Ti in test_samples), n_jobs=n_jobs) - return np.mean(scores) - - -def _delayed_eval(args): - model, test, error = args - prev_estim = model.quantify(test.instances) - prev_true = test.prevalence() - return error(prev_true, prev_estim) - - -def _check_num_evals(n_classes, n_prevpoints=None, eval_budget=None, repeats=1, verbose=False): - if n_prevpoints is None and eval_budget is None: - raise ValueError('either n_prevpoints or eval_budget has to be specified') - elif n_prevpoints is None: - assert eval_budget > 0, 'eval_budget must be a positive integer' - n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, repeats) - eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, repeats) - if verbose: - print(f'setting n_prevpoints={n_prevpoints} so that the number of ' - f'evaluations ({eval_computations}) does not exceed the evaluation ' - f'budget ({eval_budget})') - elif eval_budget is None: - eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, repeats) - if verbose: - print(f'{eval_computations} evaluations will be performed for each ' - f'combination of hyper-parameters') - else: - eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, repeats) - if eval_computations > eval_budget: - n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, repeats) - new_eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, repeats) - if verbose: - print(f'the budget of evaluations would be exceeded with ' - f'n_prevpoints={n_prevpoints}. Chaning to n_prevpoints={n_prevpoints}. This will produce ' - f'{new_eval_computations} evaluation computations for each hyper-parameter combination.') - return n_prevpoints, eval_computations - diff --git a/quapy/error.py b/quapy/error.py index 2047929..c0cd157 100644 --- a/quapy/error.py +++ b/quapy/error.py @@ -11,11 +11,6 @@ def from_name(err_name): """ assert err_name in ERROR_NAMES, f'unknown error {err_name}' callable_error = globals()[err_name] - # if err_name in QUANTIFICATION_ERROR_SMOOTH_NAMES: - # eps = __check_eps() - # def bound_callable_error(y_true, y_pred): - # return callable_error(y_true, y_pred, eps) - # return bound_callable_error return callable_error diff --git a/quapy/functional.py b/quapy/functional.py index 3ee46ff..a1f0ba2 100644 --- a/quapy/functional.py +++ b/quapy/functional.py @@ -70,7 +70,7 @@ def HellingerDistance(P, Q): The HD for two discrete distributions of `k` bins is defined as: .. math:: - HD(P,Q) = \\frac{ 1 }{ \\sqrt{ 2 } } \\sqrt{ \sum_{i=1}^k ( \\sqrt{p_i} - \\sqrt{q_i} )^2 } + HD(P,Q) = \\frac{ 1 }{ \\sqrt{ 2 } } \\sqrt{ \\sum_{i=1}^k ( \\sqrt{p_i} - \\sqrt{q_i} )^2 } :param P: real-valued array-like of shape `(k,)` representing a discrete distribution :param Q: real-valued array-like of shape `(k,)` representing a discrete distribution @@ -78,11 +78,21 @@ def HellingerDistance(P, Q): """ return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2)) + def TopsoeDistance(P, Q, epsilon=1e-20): - """ Topsoe """ - return np.sum(P*np.log((2*P+epsilon)/(P+Q+epsilon)) + - Q*np.log((2*Q+epsilon)/(P+Q+epsilon))) + Topsoe distance between two (discretized) distributions `P` and `Q`. + The Topsoe distance for two discrete distributions of `k` bins is defined as: + + .. math:: + Topsoe(P,Q) = \\sum_{i=1}^k \\left( p_i \\log\\left(\\frac{ 2 p_i + \\epsilon }{ p_i+q_i+\\epsilon }\\right) + + q_i \\log\\left(\\frac{ 2 q_i + \\epsilon }{ p_i+q_i+\\epsilon }\\right) \\right) + + :param P: real-valued array-like of shape `(k,)` representing a discrete distribution + :param Q: real-valued array-like of shape `(k,)` representing a discrete distribution + :return: float + """ + return np.sum(P*np.log((2*P+epsilon)/(P+Q+epsilon)) + Q*np.log((2*Q+epsilon)/(P+Q+epsilon))) def uniform_prevalence_sampling(n_classes, size=1): @@ -136,7 +146,6 @@ def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True): .. math:: ACC(p) = \\frac{ p - fpr }{ tpr - fpr } - :param prevalence_estim: float, the estimated value for the positive class :param tpr: float, the true positive rate of the classifier :param fpr: float, the false positive rate of the classifier @@ -184,7 +193,7 @@ def __num_prevalence_combinations_depr(n_prevpoints:int, n_classes:int, n_repeat :param n_prevpoints: integer, number of prevalence points. :param n_repeats: integer, number of repetitions for each prevalence combination :return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the - number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0] + number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0] """ __cache={} def __f(nc,np): @@ -216,7 +225,7 @@ def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1 :param n_prevpoints: integer, number of prevalence points. :param n_repeats: integer, number of repetitions for each prevalence combination :return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the - number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0] + number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0] """ N = n_prevpoints-1 C = n_classes @@ -230,7 +239,7 @@ def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repe that the number of valid prevalence values generated as combinations of prevalence points (points in a `n_classes`-dimensional simplex) do not exceed combinations_budget. - :param combinations_budget: integer, maximum number of combinatios allowed + :param combinations_budget: integer, maximum number of combinations allowed :param n_classes: integer, number of classes :param n_repeats: integer, number of repetitions for each prevalence combination :return: the largest number of prevalence points that generate less than combinations_budget valid prevalences @@ -248,6 +257,7 @@ def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repe def check_prevalence_vector(p, raise_exception=False, toleranze=1e-08): """ Checks that p is a valid prevalence vector, i.e., that it contains values in [0,1] and that the values sum up to 1. + :param p: the prevalence vector to check :return: True if `p` is valid, False otherwise """ @@ -265,3 +275,4 @@ def check_prevalence_vector(p, raise_exception=False, toleranze=1e-08): raise ValueError('the prevalence vector does not sum up to 1') return False return True + diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py index 87b682e..a9a93cb 100644 --- a/quapy/method/aggregative.py +++ b/quapy/method/aggregative.py @@ -76,7 +76,7 @@ class AggregativeQuantifier(BaseQuantifier): by the classifier. :param instances: array-like - :return: `np.ndarray` of shape `(self.n_classes_,)` with class prevalence estimates. + :return: `np.ndarray` of shape `(n_classes)` with class prevalence estimates. """ classif_predictions = self.classify(instances) return self.aggregate(classif_predictions) @@ -87,7 +87,7 @@ class AggregativeQuantifier(BaseQuantifier): Implements the aggregation of label predictions. :param classif_predictions: `np.ndarray` of label predictions - :return: `np.ndarray` of shape `(self.n_classes_,)` with class prevalence estimates. + :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates. """ ... @@ -113,19 +113,6 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier): def classify(self, instances): return self.classifier.predict_proba(instances) - # def set_params(self, **parameters): - # if isinstance(self.classifier, CalibratedClassifierCV): - # if self.classifier.get_params().get('base_estimator') == 'deprecated': - # key_prefix = 'estimator__' # this has changed in the newer versions of sklearn - # else: - # key_prefix = 'base_estimator__' - # parameters = {key_prefix + k: v for k, v in parameters.items()} - # elif isinstance(self.classifier, RecalibratedClassifier): - # parameters = {'estimator__' + k: v for k, v in parameters.items()} - # - # self.classifier.set_params(**parameters) - # return self - # Helper # ------------------------------------ @@ -198,7 +185,7 @@ def cross_generate_predictions( n_jobs ): - n_jobs = qp.get_njobs(n_jobs) + n_jobs = qp._get_njobs(n_jobs) if isinstance(val_split, int): assert fit_classifier == True, \ @@ -305,7 +292,7 @@ class CC(AggregativeQuantifier): Computes class prevalence estimates by counting the prevalence of each of the predicted labels. :param classif_predictions: array-like with label predictions - :return: `np.ndarray` of shape `(self.n_classes_,)` with class prevalence estimates. + :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates. """ return F.prevalence_from_labels(classif_predictions, self.classes_) @@ -328,7 +315,7 @@ class ACC(AggregativeQuantifier): def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None): self.classifier = classifier self.val_split = val_split - self.n_jobs = qp.get_njobs(n_jobs) + self.n_jobs = qp._get_njobs(n_jobs) def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None): """ @@ -435,7 +422,7 @@ class PACC(AggregativeProbabilisticQuantifier): def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None): self.classifier = classifier self.val_split = val_split - self.n_jobs = qp.get_njobs(n_jobs) + self.n_jobs = qp._get_njobs(n_jobs) def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None): """ @@ -660,6 +647,20 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier): return np.asarray([1 - class1_prev, class1_prev]) +def _get_divergence(divergence: Union[str, Callable]): + if isinstance(divergence, str): + if divergence=='HD': + return F.HellingerDistance + elif divergence=='topsoe': + return F.TopsoeDistance + else: + raise ValueError(f'unknown divergence {divergence}') + elif callable(divergence): + return divergence + else: + raise ValueError(f'argument "divergence" not understood; use a str or a callable function') + + class DyS(AggregativeProbabilisticQuantifier, BinaryQuantifier): """ `DyS framework `_ (DyS). @@ -765,25 +766,13 @@ class SMM(AggregativeProbabilisticQuantifier, BinaryQuantifier): return np.asarray([1 - class1_prev, class1_prev]) -def _get_divergence(divergence: Union[str, Callable]): - if isinstance(divergence, str): - if divergence=='HD': - return F.HellingerDistance - elif divergence=='topsoe': - return F.TopsoeDistance - else: - raise ValueError(f'unknown divergence {divergence}') - elif callable(divergence): - return divergence - else: - raise ValueError(f'argument "divergence" not understood; use a str or a callable function') class DistributionMatching(AggregativeProbabilisticQuantifier): """ Generic Distribution Matching quantifier for binary or multiclass quantification. This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters. - :param classifier: a sklearn's Estimator that generates a probabilistic classifier + :param classifier: a `sklearn`'s Estimator that generates a probabilistic classifier :param val_split: indicates the proportion of data to be used as a stratified held-out validation set to model the validation distribution. This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of @@ -799,7 +788,6 @@ class DistributionMatching(AggregativeProbabilisticQuantifier): """ def __init__(self, classifier, val_split=0.4, nbins=8, divergence: Union[str, Callable]='HD', cdf=False, n_jobs=None): - self.classifier = classifier self.val_split = val_split self.nbins = nbins @@ -1020,7 +1008,7 @@ class ThresholdOptimization(AggregativeQuantifier, BinaryQuantifier): def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None): self.classifier = classifier self.val_split = val_split - self.n_jobs = qp.get_njobs(n_jobs) + self.n_jobs = qp._get_njobs(n_jobs) def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None): self._check_binary(data, "Threshold Optimization") @@ -1277,7 +1265,7 @@ class OneVsAll(AggregativeQuantifier): assert isinstance(self.binary_quantifier, AggregativeQuantifier), \ f'{self.binary_quantifier} does not seem to be of type Aggregative' self.binary_quantifier = binary_quantifier - self.n_jobs = qp.get_njobs(n_jobs) + self.n_jobs = qp._get_njobs(n_jobs) def fit(self, data: LabelledCollection, fit_classifier=True): assert not data.binary, \ diff --git a/quapy/method/base.py b/quapy/method/base.py index 459130c..a80f7b7 100644 --- a/quapy/method/base.py +++ b/quapy/method/base.py @@ -32,29 +32,10 @@ class BaseQuantifier(BaseEstimator): Generate class prevalence estimates for the sample's instances :param instances: array-like - :return: `np.ndarray` of shape `(self.n_classes_,)` with class prevalence estimates. + :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates. """ ... - # @abstractmethod - # def set_params(self, **parameters): - # """ - # Set the parameters of the quantifier. - # - # :param parameters: dictionary of param-value pairs - # """ - # ... - # - # @abstractmethod - # def get_params(self, deep=True): - # """ - # Return the current parameters of the quantifier. - # - # :param deep: for compatibility with sklearn - # :return: a dictionary of param-value pairs - # """ - # ... - class BinaryQuantifier(BaseQuantifier): """ @@ -77,7 +58,7 @@ class OneVsAllGeneric: assert isinstance(binary_quantifier, BaseQuantifier), \ f'{binary_quantifier} does not seem to be a Quantifier' self.binary_quantifier = binary_quantifier - self.n_jobs = qp.get_njobs(n_jobs) + self.n_jobs = qp._get_njobs(n_jobs) def fit(self, data: LabelledCollection, **kwargs): assert not data.binary, \ diff --git a/quapy/method/meta.py b/quapy/method/meta.py index 82d3a35..ba682ee 100644 --- a/quapy/method/meta.py +++ b/quapy/method/meta.py @@ -84,7 +84,7 @@ class Ensemble(BaseQuantifier): self.red_size = red_size self.policy = policy self.val_split = val_split - self.n_jobs = qp.get_njobs(n_jobs) + self.n_jobs = qp._get_njobs(n_jobs) self.post_proba_fn = None self.verbose = verbose self.max_sample_size = max_sample_size @@ -147,7 +147,7 @@ class Ensemble(BaseQuantifier): with the abstract class). Instead, use `Ensemble(GridSearchQ(q),...)`, with `q` a Quantifier (recommended), or `Ensemble(Q(GridSearchCV(l)))` with `Q` a quantifier class that has a classifier `l` optimized for - classification (not recommended). + classification (not recommended). :param parameters: dictionary :return: raises an Exception @@ -163,10 +163,12 @@ class Ensemble(BaseQuantifier): with the abstract class). Instead, use `Ensemble(GridSearchQ(q),...)`, with `q` a Quantifier (recommended), or `Ensemble(Q(GridSearchCV(l)))` with `Q` a quantifier class that has a classifier `l` optimized for - classification (not recommended). + classification (not recommended). + :param deep: for compatibility with scikit-learn :return: raises an Exception """ + raise NotImplementedError() def _accuracy_policy(self, error_name): diff --git a/quapy/method/non_aggregative.py b/quapy/method/non_aggregative.py index f70a0c6..0a8680d 100644 --- a/quapy/method/non_aggregative.py +++ b/quapy/method/non_aggregative.py @@ -21,7 +21,6 @@ class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier): :param data: the training sample :return: self """ - self._classes_ = data.classes_ self.estimated_prevalence = data.prevalence() return self @@ -34,29 +33,3 @@ class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier): """ return self.estimated_prevalence - @property - def classes_(self): - """ - Number of classes - - :return: integer - """ - - return self._classes_ - - def get_params(self, deep=True): - """ - Does nothing, since this learner has no parameters. - - :param deep: for compatibility with sklearn - :return: `None` - """ - return None - - def set_params(self, **parameters): - """ - Does nothing, since this learner has no parameters. - - :param parameters: dictionary of param-value pairs (ignored) - """ - pass diff --git a/quapy/model_selection.py b/quapy/model_selection.py index 3cb22c7..b8b9282 100644 --- a/quapy/model_selection.py +++ b/quapy/model_selection.py @@ -49,7 +49,7 @@ class GridSearchQ(BaseQuantifier): self.protocol = protocol self.refit = refit self.timeout = timeout - self.n_jobs = qp.get_njobs(n_jobs) + self.n_jobs = qp._get_njobs(n_jobs) self.verbose = verbose self.__check_error(error) assert isinstance(protocol, AbstractProtocol), 'unknown protocol' diff --git a/quapy/protocol.py b/quapy/protocol.py index b30165f..1dec78b 100644 --- a/quapy/protocol.py +++ b/quapy/protocol.py @@ -11,13 +11,17 @@ from glob import glob class AbstractProtocol(metaclass=ABCMeta): + """ + Abstract parent class for sample generation protocols. + """ @abstractmethod def __call__(self): """ - Implements the protocol. Yields one sample at a time + Implements the protocol. Yields one sample at a time along with its prevalence - :return: yields one sample at a time + :return: yields a tuple `(sample, prev) at a time, where `sample` is a set of instances + and in which `prev` is an `nd.array` with the class prevalence values """ ... @@ -32,9 +36,10 @@ class AbstractProtocol(metaclass=ABCMeta): class AbstractStochasticSeededProtocol(AbstractProtocol): """ - An AbstractStochasticSeededProtocol is a protocol that generates, via any random procedure (e.g., - via random sapling), sequences of `LabelledCollection` samples. The protocol abstraction enforces - the object to be instantiated using a seed, so that the sequence can be completely replicated. + An `AbstractStochasticSeededProtocol` is a protocol that generates, via any random procedure (e.g., + via random sampling), sequences of :class:`quapy.data.base.LabelledCollection` samples. + The protocol abstraction enforces + the object to be instantiated using a seed, so that the sequence can be fully replicated. In order to make this functionality possible, the classes extending this abstraction need to implement only two functions, :meth:`samples_parameters` which generates all the parameters needed for extracting the samples, and :meth:`sample` that, given some parameters as input, @@ -128,7 +133,8 @@ class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol): combination of prevalence values is indicated by `repeats`. :param data: a `LabelledCollection` from which the samples will be drawn - :param sample_size: integer, number of instances in each sample + :param sample_size: integer, number of instances in each sample; if None (default) then it is taken from + qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised. :param n_prevalences: the number of equidistant prevalence points to extract from the [0,1] interval for the grid (default is 21) :param repeats: number of copies for each valid prevalence vector (default is 10) @@ -138,10 +144,11 @@ class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol): to "labelled_collection" to get instead instances of LabelledCollection """ - def __init__(self, data:LabelledCollection, sample_size, n_prevalences=21, repeats=10, smooth_limits_epsilon=0, random_state=None, return_type='sample_prev'): + def __init__(self, data:LabelledCollection, sample_size=None, n_prevalences=21, repeats=10, + smooth_limits_epsilon=0, random_state=None, return_type='sample_prev'): super(APP, self).__init__(random_state) self.data = data - self.sample_size = sample_size + self.sample_size = qp._get_sample_size(sample_size) self.n_prevalences = n_prevalences self.repeats = repeats self.smooth_limits_epsilon = smooth_limits_epsilon @@ -191,17 +198,18 @@ class NPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol): samples uniformly at random, therefore approximately preserving the natural prevalence of the collection. :param data: a `LabelledCollection` from which the samples will be drawn - :param sample_size: integer, the number of instances in each sample + :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from + qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised. :param repeats: the number of samples to generate. Default is 100. :param random_state: allows replicating samples across runs (default None) :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or to "labelled_collection" to get instead instances of LabelledCollection """ - def __init__(self, data:LabelledCollection, sample_size, repeats=100, random_state=None, return_type='sample_prev'): + def __init__(self, data:LabelledCollection, sample_size=None, repeats=100, random_state=None, return_type='sample_prev'): super(NPP, self).__init__(random_state) self.data = data - self.sample_size = sample_size + self.sample_size = qp._get_sample_size(sample_size) self.repeats = repeats self.random_state = random_state self.collator = OnLabelledCollectionProtocol.get_collator(return_type) @@ -230,17 +238,19 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol) combinations of the grid values of APP makes this endeavour intractable. :param data: a `LabelledCollection` from which the samples will be drawn - :param sample_size: integer, the number of instances in each sample + :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from + qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised. :param repeats: the number of samples to generate. Default is 100. :param random_state: allows replicating samples across runs (default None) :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or to "labelled_collection" to get instead instances of LabelledCollection """ - def __init__(self, data: LabelledCollection, sample_size, repeats=100, random_state=None, return_type='sample_prev'): + def __init__(self, data: LabelledCollection, sample_size=None, repeats=100, random_state=None, + return_type='sample_prev'): super(USimplexPP, self).__init__(random_state) self.data = data - self.sample_size = sample_size + self.sample_size = qp._get_sample_size(sample_size) self.repeats = repeats self.random_state = random_state self.collator = OnLabelledCollectionProtocol.get_collator(return_type) @@ -259,32 +269,7 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol) return self.repeats -# class LoadSamplesFromDirectory(AbstractProtocol): -# -# def __init__(self, folder_path, loader_fn, classes=None, **loader_kwargs): -# assert exists(folder_path), f'folder {folder_path} does not exist' -# assert callable(loader_fn), f'the passed load_fn does not seem to be callable' -# self.folder_path = folder_path -# self.loader_fn = loader_fn -# self.classes = classes -# self.loader_kwargs = loader_kwargs -# self._list_files = None -# -# def __call__(self): -# for file in self.list_files: -# yield LabelledCollection.load(file, loader_func=self.loader_fn, classes=self.classes, **self.loader_kwargs) -# -# @property -# def list_files(self): -# if self._list_files is None: -# self._list_files = sorted(glob(self.folder_path, '*')) -# return self._list_files -# -# def total(self): -# return len(self.list_files) - - -class CovariateShiftPP(AbstractStochasticSeededProtocol): +class DomainMixer(AbstractStochasticSeededProtocol): """ Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence. @@ -311,10 +296,10 @@ class CovariateShiftPP(AbstractStochasticSeededProtocol): mixture_points=11, random_state=None, return_type='sample_prev'): - super(CovariateShiftPP, self).__init__(random_state) + super(DomainMixer, self).__init__(random_state) self.A = domainA self.B = domainB - self.sample_size = sample_size + self.sample_size = qp._get_sample_size(sample_size) self.repeats = repeats if prevalence is None: self.prevalence = domainA.prevalence() diff --git a/quapy/tests/test_methods.py b/quapy/tests/test_methods.py index bcf721c..f13907c 100644 --- a/quapy/tests/test_methods.py +++ b/quapy/tests/test_methods.py @@ -4,6 +4,7 @@ from sklearn.linear_model import LogisticRegression from sklearn.svm import LinearSVC import quapy as qp +from quapy.method.base import BinaryQuantifier from quapy.data import Dataset, LabelledCollection from quapy.method import AGGREGATIVE_METHODS, NON_AGGREGATIVE_METHODS, EXPLICIT_LOSS_MINIMIZATION_METHODS from quapy.method.aggregative import ACC, PACC, HDy @@ -21,7 +22,7 @@ learners = [LogisticRegression, LinearSVC] def test_aggregative_methods(dataset: Dataset, aggregative_method, learner): model = aggregative_method(learner()) - if model.binary and not dataset.binary: + if isinstance(model, BinaryQuantifier) and not dataset.binary: print(f'skipping the test of binary model {type(model)} on non-binary dataset {dataset}') return @@ -45,7 +46,7 @@ def test_elm_methods(dataset: Dataset, elm_method): print('Missing SVMperf binary program, skipping test') return - if model.binary and not dataset.binary: + if isinstance(model, BinaryQuantifier) and not dataset.binary: print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') return @@ -64,7 +65,7 @@ def test_elm_methods(dataset: Dataset, elm_method): def test_non_aggregative_methods(dataset: Dataset, non_aggregative_method): model = non_aggregative_method() - if model.binary and not dataset.binary: + if isinstance(model, BinaryQuantifier) and not dataset.binary: print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') return @@ -85,7 +86,7 @@ def test_non_aggregative_methods(dataset: Dataset, non_aggregative_method): def test_ensemble_method(base_method, learner, dataset: Dataset, policy): qp.environ['SAMPLE_SIZE'] = len(dataset.training) model = Ensemble(quantifier=base_method(learner()), size=5, policy=policy, n_jobs=-1) - if model.binary and not dataset.binary: + if isinstance(model, BinaryQuantifier) and not dataset.binary: print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') return @@ -120,7 +121,7 @@ def test_quanet_method(): from quapy.method.meta import QuaNet model = QuaNet(learner, sample_size=len(dataset.training), device='cuda') - if model.binary and not dataset.binary: + if isinstance(model, BinaryQuantifier) and not dataset.binary: print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') return @@ -138,7 +139,7 @@ def models_to_test_for_str_label_names(): models = list() learner = LogisticRegression for method in AGGREGATIVE_METHODS.difference(EXPLICIT_LOSS_MINIMIZATION_METHODS): - models.append(method(learner())) + models.append(method(learner(random_state=0))) for method in NON_AGGREGATIVE_METHODS: models.append(method()) return models @@ -156,6 +157,7 @@ def test_str_label_names(model): dataset.test.sampling(1000, *dataset.test.prevalence())) qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True) + numpy.random.seed(0) model.fit(dataset.training) int_estim_prevalences = model.quantify(dataset.test.instances) @@ -168,7 +170,8 @@ def test_str_label_names(model): ['one' if label == 1 else 'zero' for label in dataset.training.labels]), LabelledCollection(dataset.test.instances, ['one' if label == 1 else 'zero' for label in dataset.test.labels])) - + assert all(dataset_str.training.classes_ == dataset_str.test.classes_), 'wrong indexation' + numpy.random.seed(0) model.fit(dataset_str.training) str_estim_prevalences = model.quantify(dataset_str.test.instances) diff --git a/quapy/tests/test_modsel.py b/quapy/tests/test_modsel.py index d54dcbe..180f680 100644 --- a/quapy/tests/test_modsel.py +++ b/quapy/tests/test_modsel.py @@ -5,9 +5,9 @@ from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC import quapy as qp -from method.aggregative import PACC -from model_selection import GridSearchQ -from protocol import APP +from quapy.method.aggregative import PACC +from quapy.model_selection import GridSearchQ +from quapy.protocol import APP import time @@ -20,7 +20,7 @@ class ModselTestCase(unittest.TestCase): data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10) training, validation = data.training.split_stratified(0.7, random_state=1) - param_grid = {'C': np.logspace(-3,3,7)} + param_grid = {'classifier__C': np.logspace(-3,3,7)} app = APP(validation, sample_size=100, random_state=1) q = GridSearchQ( q, param_grid, protocol=app, error='mae', refit=True, timeout=-1, verbose=True @@ -28,8 +28,8 @@ class ModselTestCase(unittest.TestCase): print('best params', q.best_params_) print('best score', q.best_score_) - self.assertEqual(q.best_params_['C'], 10.0) - self.assertEqual(q.best_model().get_params()['C'], 10.0) + self.assertEqual(q.best_params_['classifier__C'], 10.0) + self.assertEqual(q.best_model().get_params()['classifier__C'], 10.0) def test_modsel_parallel(self): @@ -39,7 +39,7 @@ class ModselTestCase(unittest.TestCase): training, validation = data.training.split_stratified(0.7, random_state=1) # test = data.test - param_grid = {'C': np.logspace(-3,3,7)} + param_grid = {'classifier__C': np.logspace(-3,3,7)} app = APP(validation, sample_size=100, random_state=1) q = GridSearchQ( q, param_grid, protocol=app, error='mae', refit=True, timeout=-1, n_jobs=-1, verbose=True @@ -47,8 +47,8 @@ class ModselTestCase(unittest.TestCase): print('best params', q.best_params_) print('best score', q.best_score_) - self.assertEqual(q.best_params_['C'], 10.0) - self.assertEqual(q.best_model().get_params()['C'], 10.0) + self.assertEqual(q.best_params_['classifier__C'], 10.0) + self.assertEqual(q.best_model().get_params()['classifier__C'], 10.0) def test_modsel_parallel_speedup(self): class SlowLR(LogisticRegression): @@ -61,7 +61,7 @@ class ModselTestCase(unittest.TestCase): data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10) training, validation = data.training.split_stratified(0.7, random_state=1) - param_grid = {'C': np.logspace(-3, 3, 7)} + param_grid = {'classifier__C': np.logspace(-3, 3, 7)} app = APP(validation, sample_size=100, random_state=1) tinit = time.time() @@ -95,7 +95,7 @@ class ModselTestCase(unittest.TestCase): training, validation = data.training.split_stratified(0.7, random_state=1) # test = data.test - param_grid = {'C': np.logspace(-3,3,7)} + param_grid = {'classifier__C': np.logspace(-3,3,7)} app = APP(validation, sample_size=100, random_state=1) q = GridSearchQ( q, param_grid, protocol=app, error='mae', refit=True, timeout=3, n_jobs=-1, verbose=True diff --git a/quapy/tests/test_protocols.py b/quapy/tests/test_protocols.py index dea3290..1510fee 100644 --- a/quapy/tests/test_protocols.py +++ b/quapy/tests/test_protocols.py @@ -1,7 +1,7 @@ import unittest import numpy as np from quapy.data import LabelledCollection -from quapy.protocol import APP, NPP, USimplexPP, CovariateShiftPP, AbstractStochasticSeededProtocol +from quapy.protocol import APP, NPP, USimplexPP, DomainMixer, AbstractStochasticSeededProtocol def mock_labelled_collection(prefix=''): @@ -94,7 +94,7 @@ class TestProtocols(unittest.TestCase): def test_covariate_shift_replicate(self): dataA = mock_labelled_collection('domA') dataB = mock_labelled_collection('domB') - p = CovariateShiftPP(dataA, dataB, sample_size=10, mixture_points=11, random_state=1) + p = DomainMixer(dataA, dataB, sample_size=10, mixture_points=11, random_state=1) samples1 = samples_to_str(p) samples2 = samples_to_str(p) @@ -104,7 +104,7 @@ class TestProtocols(unittest.TestCase): def test_covariate_shift_not_replicate(self): dataA = mock_labelled_collection('domA') dataB = mock_labelled_collection('domB') - p = CovariateShiftPP(dataA, dataB, sample_size=10, mixture_points=11) + p = DomainMixer(dataA, dataB, sample_size=10, mixture_points=11) samples1 = samples_to_str(p) samples2 = samples_to_str(p) diff --git a/quapy/util.py b/quapy/util.py index 50a640d..6f8543d 100644 --- a/quapy/util.py +++ b/quapy/util.py @@ -22,7 +22,7 @@ def _get_parallel_slices(n_tasks, n_jobs): def map_parallel(func, args, n_jobs): """ - Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then + Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and `n_jobs`=2, then func is applied in two parallel processes to args[0:50] and to args[50:99]. func is a function that already works with a list of arguments. @@ -128,6 +128,7 @@ def create_if_not_exist(path): def get_quapy_home(): """ Gets the home directory of QuaPy, i.e., the directory where QuaPy saves permanent data, such as dowloaded datasets. + This directory is `~/quapy_data` :return: a string representing the path """ @@ -162,7 +163,7 @@ def save_text_file(path, text): def pickled_resource(pickle_path:str, generation_func:callable, *args): """ - Allows for fast reuse of resources that are generated only once by calling generation_func(*args). The next times + Allows for fast reuse of resources that are generated only once by calling generation_func(\\*args). The next times this function is invoked, it loads the pickled resource. Example: >>> def some_array(n): # a mock resource created with one parameter (`n`) @@ -191,10 +192,6 @@ class EarlyStop: """ A class implementing the early-stopping condition typically used for training neural networks. - :param patience: the number of (consecutive) times that a monitored evaluation metric (typically obtaind in a - held-out validation split) can be found to be worse than the best one obtained so far, before flagging the - stopping condition. An instance of this class is `callable`, and is to be used as follows: - >>> earlystop = EarlyStop(patience=2, lower_is_better=True) >>> earlystop(0.9, epoch=0) >>> earlystop(0.7, epoch=1) @@ -206,14 +203,14 @@ class EarlyStop: >>> earlystop.best_epoch # is 1 >>> earlystop.best_score # is 0.7 - + :param patience: the number of (consecutive) times that a monitored evaluation metric (typically obtaind in a + held-out validation split) can be found to be worse than the best one obtained so far, before flagging the + stopping condition. An instance of this class is `callable`, and is to be used as follows: :param lower_is_better: if True (default) the metric is to be minimized. - :ivar best_score: keeps track of the best value seen so far :ivar best_epoch: keeps track of the epoch in which the best score was set :ivar STOP: flag (boolean) indicating the stopping condition :ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call - """ def __init__(self, patience, lower_is_better=True): @@ -243,4 +240,5 @@ class EarlyStop: else: self.patience -= 1 if self.patience <= 0: - self.STOP = True \ No newline at end of file + self.STOP = True +