diff --git a/LeQua2022/baselinesSVD_T1A.py b/LeQua2022/baselinesSVD_T1A.py deleted file mode 100644 index 576a7ee..0000000 --- a/LeQua2022/baselinesSVD_T1A.py +++ /dev/null @@ -1,76 +0,0 @@ -import pickle - -import numpy as np -from sklearn.linear_model import LogisticRegression -from tqdm import tqdm -import pandas as pd - -import quapy as qp -from quapy.data import LabelledCollection -from quapy.method.aggregative import * -import quapy.functional as F -from data import * -import os -import constants - -from sklearn.decomposition import TruncatedSVD - - -# LeQua official baselines for task T1A (Binary/Vector) -# ===================================================== - -predictions_path = os.path.join('predictions', 'T1A') -os.makedirs(predictions_path, exist_ok=True) - -models_path = os.path.join('models', 'T1A') -os.makedirs(models_path, exist_ok=True) - -pathT1A = './data/T1A/public' -T1A_devvectors_path = os.path.join(pathT1A, 'dev_vectors') -T1A_devprevalence_path = os.path.join(pathT1A, 'dev_prevalences.csv') -T1A_trainpath = os.path.join(pathT1A, 'training_vectors.txt') - -train = LabelledCollection.load(T1A_trainpath, load_binary_vectors) -nF = train.instances.shape[1] -svd = TruncatedSVD(n_components=300) -train.instances = svd.fit_transform(train.instances) - -qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE - -print(f'number of classes: {len(train.classes_)}') -print(f'number of training documents: {len(train)}') -print(f'training prevalence: {F.strprev(train.prevalence())}') -print(f'training matrix shape: {train.instances.shape}') - -true_prevalence = ResultSubmission.load(T1A_devprevalence_path) - -for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]: - - # classifier = CalibratedClassifierCV(LogisticRegression()) - classifier = LogisticRegression() - model = quantifier(classifier).fit(train) - quantifier_name = model.__class__.__name__ - - predictions = ResultSubmission(categories=['negative', 'positive']) - for samplename, sample in tqdm(gen_load_samples_T1(T1A_devvectors_path, nF), - desc=quantifier_name, total=len(true_prevalence)): - sample = svd.transform(sample) - predictions.add(samplename, model.quantify(sample)) - - predictions.dump(os.path.join(predictions_path, quantifier_name + '.svd.csv')) - pickle.dump(model, open(os.path.join(models_path, quantifier_name+'.svd.pkl'), 'wb'), protocol=pickle.HIGHEST_PROTOCOL) - - mae, mrae = evaluate_submission(true_prevalence, predictions) - print(f'{quantifier_name} mae={mae:.3f} mrae={mrae:.3f}') - -""" -validation -CC 0.1862 1.9587 -ACC 0.0394 0.2669 -PCC 0.1789 2.1383 -PACC 0.0354 0.1587 -EMQ 0.0224 0.0960 -HDy 0.0467 0.2121 -""" - - diff --git a/LeQua2022/baselines_T1.py b/LeQua2022/baselines_T1.py new file mode 100644 index 0000000..3a52361 --- /dev/null +++ b/LeQua2022/baselines_T1.py @@ -0,0 +1,91 @@ +import argparse +import pickle +from sklearn.linear_model import LogisticRegression as LR +from quapy.method.aggregative import * +import quapy.functional as F +from data import * +import os +import constants + + +# LeQua official baselines for task T1B (Multiclass/Vector) +# ========================================================= + +def baselines(): + yield CC(LR(n_jobs=-1)), "CC" + yield ACC(LR(n_jobs=-1)), "ACC" + yield PCC(LR(n_jobs=-1)), "PCC" + yield PACC(LR(n_jobs=-1)), "PACC" + yield EMQ(CalibratedClassifierCV(LR(), n_jobs=-1)), "SLD" + yield HDy(LR(n_jobs=-1)) if args.task == 'T1A' else OneVsAll(HDy(LR()), n_jobs=-1), "HDy" + + +def main(args): + + models_path = qp.util.create_if_not_exist(os.path.join(args.modeldir, args.task)) + + path_dev_vectors = os.path.join(args.datadir, 'dev_vectors') + path_dev_prevs = os.path.join(args.datadir, 'dev_prevalences.csv') + path_train = os.path.join(args.datadir, 'training_vectors.txt') + + qp.environ['SAMPLE_SIZE'] = constants.SAMPLE_SIZE[args.task] + + train = LabelledCollection.load(path_train, load_binary_vectors) + nF = train.instances.shape[1] + + print(f'number of classes: {len(train.classes_)}') + print(f'number of training documents: {len(train)}') + print(f'training prevalence: {F.strprev(train.prevalence())}') + print(f'training matrix shape: {train.instances.shape}') + + param_grid = { + 'C': np.logspace(-3,3,7), + 'class_weight': ['balanced', None] + } + + def gen_samples(): + return gen_load_samples_T1(path_dev_vectors, nF, ground_truth_path=path_dev_prevs, return_id=False) + + for quantifier, q_name in baselines(): + print(f'{q_name}: Model selection') + quantifier = qp.model_selection.GridSearchQ( + quantifier, + param_grid, + sample_size=None, + protocol='gen', + error=qp.error.mae, + refit=False, + verbose=True + ).fit(train, gen_samples) + + print(f'{q_name} got MAE={quantifier.best_score_:.3f} (hyper-params: {quantifier.best_params_})') + + model_path = os.path.join(models_path, q_name+'.pkl') + print(f'saving model in {model_path}') + pickle.dump(quantifier.best_model(), open(model_path, 'wb'), protocol=pickle.HIGHEST_PROTOCOL) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='LeQua2022 Task T1A/T1B baselines') + parser.add_argument('task', metavar='TASK', type=str, choices=['T1A', 'T1B'], + help='Task name (T1A, T1B)') + parser.add_argument('datadir', metavar='DATA-PATH', type=str, + help='Path of the directory containing "dev_prevalences.csv", "training_vectors.txt", and ' + 'the directory "dev_vectors"') + parser.add_argument('modeldir', metavar='MODEL-PATH', type=str, + help='Path where to save the models. ' + 'A subdirectory named will be automatically created.') + args = parser.parse_args() + + if not os.path.exists(args.datadir): + raise FileNotFoundError(f'path {args.datadir} does not exist') + if not os.path.isdir(args.datadir): + raise ValueError(f'path {args.datadir} is not a valid directory') + if not os.path.exists(os.path.join(args.datadir, "dev_prevalences.csv")): + raise FileNotFoundError(f'path {args.datadir} does not contain "dev_prevalences.csv" file') + if not os.path.exists(os.path.join(args.datadir, "training_vectors.txt")): + raise FileNotFoundError(f'path {args.datadir} does not contain "training_vectors.txt" file') + if not os.path.exists(os.path.join(args.datadir, "dev_vectors")): + raise FileNotFoundError(f'path {args.datadir} does not contain "dev_vectors" folder') + + main(args) diff --git a/LeQua2022/baselines_T1A.py b/LeQua2022/baselines_T1A.py deleted file mode 100644 index 48d42d4..0000000 --- a/LeQua2022/baselines_T1A.py +++ /dev/null @@ -1,71 +0,0 @@ -import pickle - -import numpy as np -from sklearn.linear_model import LogisticRegression -from tqdm import tqdm -import pandas as pd - -import quapy as qp -from quapy.data import LabelledCollection -from quapy.method.aggregative import * -import quapy.functional as F -from data import * -import os -import constants - - -# LeQua official baselines for task T1A (Binary/Vector) -# ===================================================== - -predictions_path = os.path.join('predictions', 'T1A') -os.makedirs(predictions_path, exist_ok=True) - -models_path = os.path.join('models', 'T1A') -os.makedirs(models_path, exist_ok=True) - -pathT1A = './data/T1A/public' -T1A_devvectors_path = os.path.join(pathT1A, 'dev_vectors') -T1A_devprevalence_path = os.path.join(pathT1A, 'dev_prevalences.csv') -T1A_trainpath = os.path.join(pathT1A, 'training_vectors.txt') - -train = LabelledCollection.load(T1A_trainpath, load_binary_vectors) -nF = train.instances.shape[1] - -qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE - -print(f'number of classes: {len(train.classes_)}') -print(f'number of training documents: {len(train)}') -print(f'training prevalence: {F.strprev(train.prevalence())}') -print(f'training matrix shape: {train.instances.shape}') - -true_prevalence = ResultSubmission.load(T1A_devprevalence_path) - -for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]: - - # classifier = CalibratedClassifierCV(LogisticRegression(C=1)) - classifier = LogisticRegression(C=1) - model = quantifier(classifier).fit(train) - quantifier_name = model.__class__.__name__ - - predictions = ResultSubmission(categories=['negative', 'positive']) - for samplename, sample in tqdm(gen_load_samples_T1(T1A_devvectors_path, nF), - desc=quantifier_name, total=len(true_prevalence)): - predictions.add(samplename, model.quantify(sample)) - - predictions.dump(os.path.join(predictions_path, quantifier_name + '.csv')) - pickle.dump(model, open(os.path.join(models_path, quantifier_name+'.pkl'), 'wb'), protocol=pickle.HIGHEST_PROTOCOL) - - mae, mrae = evaluate_submission(true_prevalence, predictions) - print(f'{quantifier_name} mae={mae:.3f} mrae={mrae:.3f}') - -""" -validation -CC 0.1862 1.9587 -ACC 0.0394 0.2669 -PCC 0.1789 2.1383 -PACC 0.0354 0.1587 -EMQ 0.0224 0.0960 -HDy 0.0467 0.2121 -""" - - diff --git a/LeQua2022/baselines_T1Amodsel.py b/LeQua2022/baselines_T1Amodsel.py deleted file mode 100644 index 12d658a..0000000 --- a/LeQua2022/baselines_T1Amodsel.py +++ /dev/null @@ -1,81 +0,0 @@ -import pickle -from sklearn.linear_model import LogisticRegression -from quapy.method.aggregative import * -import quapy.functional as F -from data import * -import os -import constants - - -# LeQua official baselines for task T1A (Binary/Vector) -# ===================================================== - -predictions_path = os.path.join('predictions', 'T1A') -os.makedirs(predictions_path, exist_ok=True) - -models_path = os.path.join('models', 'T1A') -os.makedirs(models_path, exist_ok=True) - -pathT1A = './data/T1A/public' -T1A_devvectors_path = os.path.join(pathT1A, 'dev_vectors') -T1A_devprevalence_path = os.path.join(pathT1A, 'dev_prevalences.csv') -T1A_trainpath = os.path.join(pathT1A, 'training_vectors.txt') - -train = LabelledCollection.load(T1A_trainpath, load_binary_vectors) -nF = train.instances.shape[1] - -qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE - -print(f'number of classes: {len(train.classes_)}') -print(f'number of training documents: {len(train)}') -print(f'training prevalence: {F.strprev(train.prevalence())}') -print(f'training matrix shape: {train.instances.shape}') - -true_prevalence = ResultSubmission.load(T1A_devprevalence_path) - -param_grid = { - 'C': np.logspace(-3,3,7), - 'class_weight': ['balanced', None] -} - - -def gen_samples(): - return gen_load_samples_T1(T1A_devvectors_path, nF, ground_truth_path=T1A_devprevalence_path, return_id=False) - - -for quantifier in [EMQ]: # [CC, ACC, PCC, PACC, EMQ, HDy]: - if quantifier == EMQ: - classifier = CalibratedClassifierCV(LogisticRegression(), n_jobs=-1) - else: - classifier = LogisticRegression() - model = quantifier(classifier) - print(f'{model.__class__.__name__}: Model selection') - model = qp.model_selection.GridSearchQ( - model, - param_grid, - sample_size=None, - protocol='gen', - error=qp.error.mae, - refit=False, - verbose=True - ).fit(train, gen_samples) - - quantifier_name = model.best_model().__class__.__name__ - print(f'{quantifier_name} mae={model.best_score_:.3f} (params: {model.best_params_})') - - pickle.dump(model.best_model(), - open(os.path.join(models_path, quantifier_name+'.pkl'), 'wb'), - protocol=pickle.HIGHEST_PROTOCOL) - - -""" -validation -CC 0.1862 1.9587 -ACC 0.0394 0.2669 -PCC 0.1789 2.1383 -PACC 0.0354 0.1587 -EMQ 0.0224 0.0960 -HDy 0.0467 0.2121 -""" - - diff --git a/LeQua2022/baselines_T1B.py b/LeQua2022/baselines_T1B.py deleted file mode 100644 index 1344bbc..0000000 --- a/LeQua2022/baselines_T1B.py +++ /dev/null @@ -1,55 +0,0 @@ -import pickle - -import numpy as np -from sklearn.linear_model import LogisticRegression -from tqdm import tqdm -import pandas as pd - -import quapy as qp -from quapy.data import LabelledCollection -from quapy.method.aggregative import * -import quapy.functional as F -from data import * -import os -import constants - -predictions_path = os.path.join('predictions', 'T1B') # multiclass - vector -os.makedirs(predictions_path, exist_ok=True) - -pathT1B = './data/T1B/public' -T1B_devvectors_path = os.path.join(pathT1B, 'dev_vectors') -T1B_devprevalence_path = os.path.join(pathT1B, 'dev_prevalences.csv') -T1B_trainpath = os.path.join(pathT1B, 'training_vectors.txt') -T1B_catmap = os.path.join(pathT1B, 'training_vectors_label_map.txt') - -train = LabelledCollection.load(T1B_trainpath, load_binary_vectors) -nF = train.instances.shape[1] - -qp.environ['SAMPLE_SIZE'] = constants.T1B_SAMPLE_SIZE - -print(f'number of classes: {len(train.classes_)}') -print(f'number of training documents: {len(train)}') -print(f'training prevalence: {F.strprev(train.prevalence())}') -print(f'training matrix shape: {train.instances.shape}') - -true_prevalence = ResultSubmission.load(T1B_devprevalence_path) - -cat2code, categories = load_category_map(T1B_catmap) - -for quantifier in [PACC]: # [CC, ACC, PCC, PACC, EMQ]: - - classifier = CalibratedClassifierCV(LogisticRegression()) - model = quantifier(classifier).fit(train) - quantifier_name = model.__class__.__name__ - - predictions = ResultSubmission(categories=categories) - for samplename, sample in tqdm(gen_load_samples_T1(T1B_devvectors_path, nF), - desc=quantifier_name, total=len(true_prevalence)): - predictions.add(samplename, model.quantify(sample)) - - predictions.dump(os.path.join(predictions_path, quantifier_name + '.csv')) - mae, mrae = evaluate_submission(true_prevalence, predictions) - print(f'{quantifier_name} mae={mae:.3f} mrae={mrae:.3f}') - - - diff --git a/LeQua2022/constants.py b/LeQua2022/constants.py index 11a78ce..7036eff 100644 --- a/LeQua2022/constants.py +++ b/LeQua2022/constants.py @@ -9,4 +9,11 @@ T1B_SAMPLE_SIZE = 1000 T2A_SAMPLE_SIZE = 250 T2B_SAMPLE_SIZE = 1000 +SAMPLE_SIZE={ + 'T1A': T1A_SAMPLE_SIZE, + 'T1B': T1B_SAMPLE_SIZE, + 'T2A': T2A_SAMPLE_SIZE, + 'T2A': T2B_SAMPLE_SIZE +} + ERROR_TOL = 1E-3 diff --git a/LeQua2022/data.py b/LeQua2022/data.py index bcea49f..e4a1095 100644 --- a/LeQua2022/data.py +++ b/LeQua2022/data.py @@ -34,27 +34,23 @@ def load_category_map(path): def load_binary_vectors(path, nF=None): - return sklearn.datasets.load_svmlight_file(path, n_features=nF) + X, y = sklearn.datasets.load_svmlight_file(path, n_features=nF) + y = y.astype(int) + return X, y def __gen_load_samples_with_groudtruth(path_dir:str, return_id:bool, ground_truth_path:str, load_fn, **load_kwargs): true_prevs = ResultSubmission.load(ground_truth_path) for id, prevalence in true_prevs.iterrows(): sample, _ = load_fn(os.path.join(path_dir, f'{id}.txt'), **load_kwargs) - if return_id: - yield id, sample, prevalence - else: - yield sample, prevalence + yield (id, sample, prevalence) if return_id else (sample, prevalence) def __gen_load_samples_without_groudtruth(path_dir:str, return_id:bool, load_fn, **load_kwargs): nsamples = len(glob(os.path.join(path_dir, '*.txt'))) for id in range(nsamples): sample, _ = load_fn(os.path.join(path_dir, f'{id}.txt'), **load_kwargs) - if return_id: - yield id, sample - else: - yield sample + yield (id, sample) if return_id else sample def gen_load_samples_T1(path_dir:str, nF:int, ground_truth_path:str = None, return_id=True): @@ -68,6 +64,17 @@ def gen_load_samples_T1(path_dir:str, nF:int, ground_truth_path:str = None, retu yield r +def genSVD_load_samples_T1(load_fn, path_dir:str, nF:int, ground_truth_path:str = None, return_id=True): + if ground_truth_path is None: + # the generator function returns tuples (filename:str, sample:csr_matrix) + gen_fn = __gen_load_samples_without_groudtruth(path_dir, return_id, load_fn, nF=nF) + else: + # the generator function returns tuples (filename:str, sample:csr_matrix, prevalence:ndarray) + gen_fn = __gen_load_samples_with_groudtruth(path_dir, return_id, ground_truth_path, load_fn, nF=nF) + for r in gen_fn: + yield r + + def gen_load_samples_T2A(path_dir:str, ground_truth_path:str = None): # for ... : yield pass diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt index 18a0623..608daab 100644 --- a/docs/build/html/_sources/index.rst.txt +++ b/docs/build/html/_sources/index.rst.txt @@ -78,7 +78,7 @@ Features Methods Model Selection Plotting - API Developer documentation + API Developers documentation diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index d1eb190..5d2c96f 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -230,8 +230,6 @@
  • compute_table() (quapy.method.aggregative.ThresholdOptimization method)
  • compute_tpr() (quapy.method.aggregative.ThresholdOptimization method) -
    • -
  • conv_block() (quapy.classification.neural.CNNnet method)
  • counts() (quapy.data.base.LabelledCollection method)
    • @@ -337,7 +335,7 @@
  • fetch_UCILabelledCollection() (in module quapy.data.datasets)
    • -
  • fit() (quapy.classification.methods.PCALR method) +
  • fit() (quapy.classification.methods.LowRankLogisticRegression method) - + @@ -673,8 +669,6 @@
  • PACC (class in quapy.method.aggregative)
  • parallel() (in module quapy.util) -
    • -
  • PCALR (class in quapy.classification.methods)
  • PCC (class in quapy.method.aggregative)
    • @@ -686,7 +680,7 @@
  • (quapy.method.aggregative.OneVsAll method)
    • -
  • predict() (quapy.classification.methods.PCALR method) +
  • predict() (quapy.classification.methods.LowRankLogisticRegression method)
    • -
  • predict_proba() (quapy.classification.methods.PCALR method) +
  • predict_proba() (quapy.classification.methods.LowRankLogisticRegression method)
    • (quapy.classification.neural.NeuralClassifierTrainer method) @@ -952,7 +946,7 @@
    • se() (in module quapy.error)
      • -
    • set_params() (quapy.classification.methods.PCALR method) +
    • set_params() (quapy.classification.methods.LowRankLogisticRegression method)
      • (quapy.classification.neural.NeuralClassifierTrainer method) @@ -1032,7 +1026,7 @@
      • training_helper() (in module quapy.method.aggregative)
        • -
      • transform() (quapy.classification.methods.PCALR method) +
      • transform() (quapy.classification.methods.LowRankLogisticRegression method)
        • -
      • API Developer documentation
          +
        • API Developers documentation
          • diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index 76b8d31..318d517 100644 Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ diff --git a/docs/build/html/quapy.classification.html b/docs/build/html/quapy.classification.html index a5a1ace..34fe110 100644 --- a/docs/build/html/quapy.classification.html +++ b/docs/build/html/quapy.classification.html @@ -59,69 +59,108 @@

          quapy.classification.methods module

          -
          -class quapy.classification.methods.PCALR(n_components=100, **kwargs)
          +
          +class quapy.classification.methods.LowRankLogisticRegression(n_components=100, **kwargs)

          Bases: sklearn.base.BaseEstimator

          -

          An example of a classification method that also generates embedded inputs, as those required for QuaNet. -This example simply combines a Principal Component Analysis (PCA) with Logistic Regression (LR).

          -
          -
          -fit(X, y)
          -
          - -
          -
          -get_params()
          -

          Get parameters for this estimator.

          +

          An example of a classification method (i.e., an object that implements fit, predict, and predict_proba) +that also generates embedded inputs (i.e., that implements transform), as those required for +quapy.method.neural.QuaNet. This is a mock method to allow for easily instantiating +quapy.method.neural.QuaNet on array-like real-valued instances. +The transformation consists of applying sklearn.decomposition.TruncatedSVD +while classification is performed using sklearn.linear_model.LogisticRegression on the low-rank space

          Parameters
          -

          deep (bool, default=True) – If True, will return the parameters for this estimator and -contained subobjects that are estimators.

          +
            +

          • n_components – the number of principal components to retain

            • +

          • kwargs – parameters for the +Logistic Regression classifier

            • +
          +
          +
          +
          +
          +fit(X, y)
          +

          Fit the model according to the given training data. The fit consists of +fitting TruncatedSVD and Logistic Regression.

          +
          +
          Parameters
          +
            +

          • X – array-like of shape (n_samples, n_features) with the instances

            • +

          • y – array-like of shape (n_samples, n_classes) with the class labels

            • +
          Returns
          -

          params – Parameter names mapped to their values.

          -
          -
          Return type
          -

          dict

          +

          self

          -
          -predict(X)
          -
          +
          +get_params()
          +

          Get hyper-parameters for this estimator

          +
          +
          Returns
          +

          a dictionary with parameter names mapped to their values

          +
          +
          +
          -
          -predict_proba(X)
          -
          +
          +predict(X)
          +

          Predicts labels for the instances X

          +
          +
          Parameters
          +

          X – array-like of shape (n_samples, n_features) instances to classify

          +
          +
          Returns
          +

          a numpy array of length n containing the label predictions, where n is the number of +instances in X

          +
          +
          +
          -
          -set_params(**params)
          +
          +predict_proba(X)
          +

          Predicts posterior probabilities for the instances X

          +
          +
          Parameters
          +

          X – array-like of shape (n_samples, n_features) instances to classify

          +
          +
          Returns
          +

          array-like of shape (n_samples, n_classes) with the posterior probabilities

          +
          +
          +
          + +
          +
          +set_params(**params)

          Set the parameters of this estimator.

          -

          The method works on simple estimators as well as on nested objects -(such as Pipeline). The latter have -parameters of the form <component>__<parameter> so that it’s -possible to update each component of a nested object.

          Parameters
          -

          **params (dict) – Estimator parameters.

          -
          -
          Returns
          -

          self – Estimator instance.

          -
          -
          Return type
          -

          estimator instance

          +

          parameters – a **kwargs dictionary with the estimator parameters for +Logistic Regression +and eventually also n_components for PCA

          -
          -transform(X)
          -
          +
          +transform(X)
          +

          Returns the low-rank approximation of X with n_components dimensions

          +
          +
          Parameters
          +

          X – array-like of shape (n_samples, n_features) instances to embed

          +
          +
          Returns
          +

          array-like of shape (n_samples, n_components) with the embedded instances

          +
          +
          +
          @@ -132,25 +171,63 @@ possible to update each component of a nested object.

          class quapy.classification.neural.CNNnet(vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, kernel_heights=[3, 5, 7], stride=1, padding=0, drop_p=0.5)

          Bases: quapy.classification.neural.TextClassifierNet

          -
          -
          -conv_block(input, conv_layer)
          -
          - +

          An implementation of quapy.classification.neural.TextClassifierNet based on +Convolutional Neural Networks.

          +
          +
          Parameters
          +
            +

          • vocabulary_size – the size of the vocabulary

            • +

          • n_classes – number of target classes

            • +

          • embedding_size – the dimensionality of the word embeddings space (default 100)

            • +

          • hidden_size – the dimensionality of the hidden space (default 256)

            • +

          • repr_size – the dimensionality of the document embeddings space (default 100)

            • +

          • kernel_heights – list of kernel lengths (default [3,5,7]), i.e., the number of +consecutive tokens that each kernel covers

            • +

          • stride – convolutional stride (default 1)

            • +

          • stride – convolutional pad (default 0)

            • +

          • drop_p – drop probability for dropout (default 0.5)

            • +
          +
          +
          document_embedding(input)
          -
          +

          Embeds documents (i.e., performs the forward pass up to the +next-to-last layer).

          +
          +
          Parameters
          +

          input – a batch of instances, typically generated by a torch’s DataLoader +instance (see quapy.classification.neural.TorchDataset)

          +
          +
          Returns
          +

          a torch tensor of shape (n_samples, n_dimensions), where +n_samples is the number of documents, and n_dimensions is the +dimensionality of the embedding

          +
          +
          +
          get_params()
          -
          +

          Get hyper-parameters for this estimator

          +
          +
          Returns
          +

          a dictionary with parameter names mapped to their values

          +
          +
          +
          property vocabulary_size
          -
          +

          Return the size of the vocabulary

          +
          +
          Returns
          +

          integer

          +
          +
          +
          @@ -158,25 +235,60 @@ possible to update each component of a nested object.

          class quapy.classification.neural.LSTMnet(vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, lstm_class_nlayers=1, drop_p=0.5)

          Bases: quapy.classification.neural.TextClassifierNet

          +

          An implementation of quapy.classification.neural.TextClassifierNet based on +Long Short Term Memory networks.

          +
          +
          Parameters
          +
            +

          • vocabulary_size – the size of the vocabulary

            • +

          • n_classes – number of target classes

            • +

          • embedding_size – the dimensionality of the word embeddings space (default 100)

            • +

          • hidden_size – the dimensionality of the hidden space (default 256)

            • +

          • repr_size – the dimensionality of the document embeddings space (default 100)

            • +

          • lstm_class_nlayers – number of LSTM layers (default 1)

            • +

          • drop_p – drop probability for dropout (default 0.5)

            • +
          +
          +
          document_embedding(x)
          -
          +

          Embeds documents (i.e., performs the forward pass up to the +next-to-last layer).

          +
          +
          Parameters
          +

          x – a batch of instances, typically generated by a torch’s DataLoader +instance (see quapy.classification.neural.TorchDataset)

          +
          +
          Returns
          +

          a torch tensor of shape (n_samples, n_dimensions), where +n_samples is the number of documents, and n_dimensions is the +dimensionality of the embedding

          +
          +
          +
          get_params()
          -
          - -
          -
          -init_hidden(set_size)
          -
          +

          Get hyper-parameters for this estimator

          +
          +
          Returns
          +

          a dictionary with parameter names mapped to their values

          +
          +
          +
          property vocabulary_size
          -
          +

          Return the size of the vocabulary

          +
          +
          Returns
          +

          integer

          +
          +
          +
          @@ -184,45 +296,135 @@ possible to update each component of a nested object.

          class quapy.classification.neural.NeuralClassifierTrainer(net: quapy.classification.neural.TextClassifierNet, lr=0.001, weight_decay=0, patience=10, epochs=200, batch_size=64, batch_size_test=512, padding_length=300, device='cpu', checkpointpath='../checkpoint/classifier_net.dat')

          Bases: object

          +

          Trains a neural network for text classification.

          +
          +
          Parameters
          +
            +

          • net – an instance of TextClassifierNet implementing the forward pass

            • +

          • lr – learning rate (default 1e-3)

            • +

          • weight_decay – weight decay (default 0)

            • +

          • patience – number of epochs that do not show any improvement in validation +to wait before applying early stop (default 10)

            • +

          • epochs – maximum number of training epochs (default 200)

            • +

          • batch_size – batch size for training (default 64)

            • +

          • batch_size_test – batch size for test (default 512)

            • +

          • padding_length – maximum number of tokens to consider in a document (default 300)

            • +

          • device – specify ‘cpu’ (default) or ‘cuda’ for enabling gpu

            • +

          • checkpointpath – where to store the parameters of the best model found so far +according to the evaluation in the held-out validation split (default ‘../checkpoint/classifier_net.dat’)

            • +
          +
          +
          property device
          -
          +

          Gets the device in which the network is allocated

          +
          +
          Returns
          +

          device

          +
          +
          +
          fit(instances, labels, val_split=0.3)
          -
          +

          Fits the model according to the given training data.

          +
          +
          Parameters
          +
            +

          • instances – list of lists of indexed tokens

            • +

          • labels – array-like of shape (n_samples, n_classes) with the class labels

            • +

          • val_split – proportion of training documents to be taken as the validation set (default 0.3)

            • +
          +
          +
          Returns
          +

          +
          +
          +
          get_params()
          -
          +

          Get hyper-parameters for this estimator

          +
          +
          Returns
          +

          a dictionary with parameter names mapped to their values

          +
          +
          +
          predict(instances)
          -
          +

          Predicts labels for the instances

          +
          +
          Parameters
          +

          instances – list of lists of indexed tokens

          +
          +
          Returns
          +

          a numpy array of length n containing the label predictions, where n is the number of +instances in X

          +
          +
          +
          predict_proba(instances)
          -
          +

          Predicts posterior probabilities for the instances

          +
          +
          Parameters
          +

          X – array-like of shape (n_samples, n_features) instances to classify

          +
          +
          Returns
          +

          array-like of shape (n_samples, n_classes) with the posterior probabilities

          +
          +
          +
          reset_net_params(vocab_size, n_classes)
          -
          +

          Reinitialize the network parameters

          +
          +
          Parameters
          +
            +

          • vocab_size – the size of the vocabulary

            • +

          • n_classes – the number of target classes

            • +
          +
          +
          +
          set_params(**params)
          -
          +

          Set the parameters of this trainer and the learner it is training. +In this current version, parameter names for the trainer and learner should +be disjoint.

          +
          +
          Parameters
          +

          params – a **kwargs dictionary with the parameters

          +
          +
          +
          transform(instances)
          -
          +

          Returns the embeddings of the instances

          +
          +
          Parameters
          +

          instances – list of lists of indexed tokens

          +
          +
          Returns
          +

          array-like of shape (n_samples, embed_size) with the embedded instances, +where embed_size is defined by the classification network

          +
          +
          +
          @@ -230,49 +432,95 @@ possible to update each component of a nested object.

          class quapy.classification.neural.TextClassifierNet

          Bases: torch.nn.modules.module.Module

          +

          Abstract Text classifier (torch.nn.Module)

          dimensions()
          -
          +

          Gets the number of dimensions of the embedding space

          +
          +
          Returns
          +

          integer

          +
          +
          +
          abstract document_embedding(x)
          -
          +

          Embeds documents (i.e., performs the forward pass up to the +next-to-last layer).

          +
          +
          Parameters
          +

          x – a batch of instances, typically generated by a torch’s DataLoader +instance (see quapy.classification.neural.TorchDataset)

          +
          +
          Returns
          +

          a torch tensor of shape (n_samples, n_dimensions), where +n_samples is the number of documents, and n_dimensions is the +dimensionality of the embedding

          +
          +
          +
          forward(x)
          -

          Defines the computation performed at every call.

          -

          Should be overridden by all subclasses.

          -
          -

          Note

          -

          Although the recipe for forward pass needs to be defined within -this function, one should call the Module instance afterwards -instead of this since the former takes care of running the -registered hooks while the latter silently ignores them.

          -
          +

          Performs the forward pass.

          +
          +
          Parameters
          +

          x – a batch of instances, typically generated by a torch’s DataLoader +instance (see quapy.classification.neural.TorchDataset)

          +
          +
          Returns
          +

          a tensor of shape (n_instances, n_classes) with the decision scores +for each of the instances and classes

          +
          +
          abstract get_params()
          -
          +

          Get hyper-parameters for this estimator

          +
          +
          Returns
          +

          a dictionary with parameter names mapped to their values

          +
          +
          +
          predict_proba(x)
          -
          +

          Predicts posterior probabilities for the instances in x

          +
          +
          Parameters
          +

          x – a torch tensor of indexed tokens with shape (n_instances, pad_length) +where n_instances is the number of instances in the batch, and pad_length +is length of the pad in the batch

          +
          +
          Returns
          +

          array-like of shape (n_samples, n_classes) with the posterior probabilities

          +
          +
          +
          property vocabulary_size
          -
          +

          Return the size of the vocabulary

          +
          +
          Returns
          +

          integer

          +
          +
          +
          xavier_uniform()
          -
          +

          Performs Xavier initialization of the network parameters

          +
          @@ -280,10 +528,36 @@ registered hooks while the latter silently ignores them.

          class quapy.classification.neural.TorchDataset(instances, labels=None)

          Bases: torch.utils.data.dataset.Dataset

          +

          Transforms labelled instances into a Torch’s torch.utils.data.DataLoader object

          +
          +
          Parameters
          +
            +

          • instances – list of lists of indexed tokens

            • +

          • labels – array-like of shape (n_samples, n_classes) with the class labels

            • +
          +
          +
          asDataloader(batch_size, shuffle, pad_length, device)
          -
          +

          Converts the labelled collection into a Torch DataLoader with dynamic padding for +the batch

          +
          +
          Parameters
          +
            +

          • batch_size – batch size

            • +

          • shuffle – whether or not to shuffle instances

            • +

          • pad_length – the maximum length for the list of tokens (dynamic padding is +applied, meaning that if the longest document in the batch is shorter than +pad_length, then the batch is padded up to its length, and not to pad_length.

            • +

          • device – whether to allocate tensors in cpu or in cuda

            • +
          +
          +
          Returns
          +

          a torch.utils.data.DataLoader object

          +
          +
          +
          @@ -294,38 +568,79 @@ registered hooks while the latter silently ignores them.

          class quapy.classification.svmperf.SVMperf(svmperf_base, C=0.01, verbose=False, loss='01')

          Bases: sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin

          +

          A wrapper for the SVM-perf package by Thorsten Joachims. +When using losses for quantification, the source code has to be patched. See +the installation documentation +for further details.

          +

          References:

          +
          +
          +
          +
          Parameters
          +
            +

          • svmperf_base – path to directory containing the binary files svm_perf_learn and svm_perf_classify

            • +

          • C – trade-off between training error and margin (default 0.01)

            • +

          • verbose – set to True to print svm-perf std outputs

            • +

          • loss – the loss to optimize for. Available losses are “01”, “f1”, “kld”, “nkld”, “q”, “qacc”, “qf1”, “qgm”, “mae”, “mrae”.

            • +
          +
          +
          decision_function(X, y=None)
          -
          +

          Evaluate the decision function for the samples in X.

          +
          +
          Parameters
          +
            +

          • X – array-like of shape (n_samples, n_features) containing the instances to classify

            • +

          • y – unused

            • +
          +
          +
          Returns
          +

          array-like of shape (n_samples,) containing the decision scores of the instances

          +
          +
          +
          fit(X, y)
          -
          +

          Trains the SVM for the multivariate performance loss

          +
          +
          Parameters
          +
            +

          • X – training instances

            • +

          • y – a binary vector of labels

            • +
          +
          +
          Returns
          +

          self

          +
          +
          +
          predict(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

          +
          +
          set_params(**parameters)
          -

          Set the parameters of this estimator.

          -

          The method works on simple estimators as well as on nested objects -(such as Pipeline). The latter have -parameters of the form <component>__<parameter> so that it’s -possible to update each component of a nested object.

          +

          Set the hyper-parameters for svm-perf. Currently, only the C parameter is supported

          Parameters
          -

          **params (dict) – Estimator parameters.

          -
          -
          Returns
          -

          self – Estimator instance.

          -
          -
          Return type
          -

          estimator instance

          +

          parameters – a **kwargs dictionary {‘C’: <float>}

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b/quapy/classification/methods.py index b313f57..01f4654 100644 --- a/quapy/classification/methods.py +++ b/quapy/classification/methods.py @@ -3,10 +3,18 @@ from sklearn.decomposition import TruncatedSVD from sklearn.linear_model import LogisticRegression -class PCALR(BaseEstimator): +class LowRankLogisticRegression(BaseEstimator): """ - An example of a classification method that also generates embedded inputs, as those required for QuaNet. - This example simply combines a Principal Component Analysis (PCA) with Logistic Regression (LR). + An example of a classification method (i.e., an object that implements `fit`, `predict`, and `predict_proba`) + that also generates embedded inputs (i.e., that implements `transform`), as those required for + :class:`quapy.method.neural.QuaNet`. This is a mock method to allow for easily instantiating + :class:`quapy.method.neural.QuaNet` on array-like real-valued instances. + The transformation consists of applying :class:`sklearn.decomposition.TruncatedSVD` + while classification is performed using :class:`sklearn.linear_model.LogisticRegression` on the low-rank space. + + :param n_components: the number of principal components to retain + :param kwargs: parameters for the + `Logistic Regression `__ classifier """ def __init__(self, n_components=100, **kwargs): @@ -14,35 +22,76 @@ class PCALR(BaseEstimator): self.learner = LogisticRegression(**kwargs) def get_params(self): + """ + Get hyper-parameters for this estimator. + + :return: a dictionary with parameter names mapped to their values + """ params = {'n_components': self.n_components} params.update(self.learner.get_params()) return params def set_params(self, **params): - if 'n_components' in params: - self.n_components = params['n_components'] - del params['n_components'] - self.learner.set_params(**params) + """ + Set the parameters of this estimator. + + :param parameters: a `**kwargs` dictionary with the estimator parameters for + `Logistic Regression `__ + and eventually also `n_components` for `TruncatedSVD` + """ + params_ = dict(params) + if 'n_components' in params_: + self.n_components = params_['n_components'] + del params_['n_components'] + self.learner.set_params(**params_) def fit(self, X, y): - self.learner.fit(X, y) + """ + Fit the model according to the given training data. The fit consists of + fitting `TruncatedSVD` and then `LogisticRegression` on the low-rank representation. + + :param X: array-like of shape `(n_samples, n_features)` with the instances + :param y: array-like of shape `(n_samples, n_classes)` with the class labels + :return: `self` + """ nF = X.shape[1] self.pca = None if nF > self.n_components: - self.pca = TruncatedSVD(self.n_components).fit(X, y) + self.pca = TruncatedSVD(self.n_components).fit(X) + X = self.transform(X) + self.learner.fit(X, y) self.classes_ = self.learner.classes_ return self - def predict(self, X): - # X = self.transform(X) + """ + Predicts labels for the instances `X` embedded into the low-rank space. + + :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` + """ + X = self.transform(X) return self.learner.predict(X) def predict_proba(self, X): - # X = self.transform(X) + """ + Predicts posterior probabilities for the instances `X` embedded into the low-rank space. + + :param X: array-like of shape `(n_samples, n_features)` instances to classify + :return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities + """ + X = self.transform(X) return self.learner.predict_proba(X) def transform(self, X): + """ + Returns the low-rank approximation of `X` with `n_components` dimensions, or `X` unaltered if + `n_components` >= `X.shape[1]`. + + :param X: array-like of shape `(n_samples, n_features)` instances to embed + :return: array-like of shape `(n_samples, n_components)` with the embedded instances + """ if self.pca is None: return X return self.pca.transform(X) diff --git a/quapy/classification/neural.py b/quapy/classification/neural.py index 7823165..0d576c5 100644 --- a/quapy/classification/neural.py +++ b/quapy/classification/neural.py @@ -16,6 +16,22 @@ from quapy.util import EarlyStop class NeuralClassifierTrainer: + """ + Trains a neural network for text classification. + + :param net: an instance of `TextClassifierNet` implementing the forward pass + :param lr: learning rate (default 1e-3) + :param weight_decay: weight decay (default 0) + :param patience: number of epochs that do not show any improvement in validation + to wait before applying early stop (default 10) + :param epochs: maximum number of training epochs (default 200) + :param batch_size: batch size for training (default 64) + :param batch_size_test: batch size for test (default 512) + :param padding_length: maximum number of tokens to consider in a document (default 300) + :param device: specify 'cpu' (default) or 'cuda' for enabling gpu + :param checkpointpath: where to store the parameters of the best model found so far + according to the evaluation in the held-out validation split (default '../checkpoint/classifier_net.dat') + """ def __init__(self, net: 'TextClassifierNet', @@ -45,23 +61,36 @@ class NeuralClassifierTrainer: 'device': torch.device(device) } self.learner_hyperparams = self.net.get_params() - self.checkpointpath = checkpointpath self.classes_ = np.asarray([0, 1]) print(f'[NeuralNetwork running on {device}]') - os.makedirs(Path(checkpointpath).parent, exist_ok=True) def reset_net_params(self, vocab_size, n_classes): + """Reinitialize the network parameters + + :param vocab_size: the size of the vocabulary + :param n_classes: the number of target classes + """ self.net = self.net.__class__(vocab_size, n_classes, **self.learner_hyperparams) self.net = self.net.to(self.trainer_hyperparams['device']) self.net.xavier_uniform() def get_params(self): + """Get hyper-parameters for this estimator + + :return: a dictionary with parameter names mapped to their values + """ return {**self.net.get_params(), **self.trainer_hyperparams} def set_params(self, **params): + """Set the parameters of this trainer and the learner it is training. + In this current version, parameter names for the trainer and learner should + be disjoint. + + :param params: a `**kwargs` dictionary with the parameters + """ trainer_hyperparams = self.trainer_hyperparams learner_hyperparams = self.net.get_params() for key, val in params.items(): @@ -81,6 +110,10 @@ class NeuralClassifierTrainer: @property def device(self): + """ Gets the device in which the network is allocated + + :return: device + """ return next(self.net.parameters()).device def _train_epoch(self, data, status, pbar, epoch): @@ -132,6 +165,14 @@ class NeuralClassifierTrainer: f'macroF1={100 * self.status["va"]["f1"]:.2f}%') def fit(self, instances, labels, val_split=0.3): + """ + Fits the model according to the given training data. + + :param instances: list of lists of indexed tokens + :param labels: array-like of shape `(n_samples, n_classes)` with the class labels + :param val_split: proportion of training documents to be taken as the validation set (default 0.3) + :return: + """ train, val = LabelledCollection(instances, labels).split_stratified(1-val_split) opt = self.trainer_hyperparams checkpoint = self.checkpointpath @@ -169,9 +210,22 @@ class NeuralClassifierTrainer: return self def predict(self, instances): + """ + Predicts labels for the instances + + :param instances: list of lists of indexed tokens + :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of + instances in `X` + """ return np.argmax(self.predict_proba(instances), axis=-1) def predict_proba(self, instances): + """ + Predicts posterior probabilities for the instances + + :param X: array-like of shape `(n_samples, n_features)` instances to classify + :return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities + """ self.net.eval() opt = self.trainer_hyperparams with torch.no_grad(): @@ -182,6 +236,13 @@ class NeuralClassifierTrainer: return np.concatenate(positive_probs) def transform(self, instances): + """ + Returns the embeddings of the instances + + :param instances: list of lists of indexed tokens + :return: array-like of shape `(n_samples, embed_size)` with the embedded instances, + where `embed_size` is defined by the classification network + """ self.net.eval() embeddings = [] opt = self.trainer_hyperparams @@ -193,6 +254,12 @@ class NeuralClassifierTrainer: class TorchDataset(torch.utils.data.Dataset): + """ + Transforms labelled instances into a Torch's :class:`torch.utils.data.DataLoader` object + + :param instances: list of lists of indexed tokens + :param labels: array-like of shape `(n_samples, n_classes)` with the class labels + """ def __init__(self, instances, labels=None): self.instances = instances @@ -205,6 +272,18 @@ class TorchDataset(torch.utils.data.Dataset): return {'doc': self.instances[index], 'label': self.labels[index] if self.labels is not None else None} def asDataloader(self, batch_size, shuffle, pad_length, device): + """ + Converts the labelled collection into a Torch DataLoader with dynamic padding for + the batch + + :param batch_size: batch size + :param shuffle: whether or not to shuffle instances + :param pad_length: the maximum length for the list of tokens (dynamic padding is + applied, meaning that if the longest document in the batch is shorter than + `pad_length`, then the batch is padded up to its length, and not to `pad_length`. + :param device: whether to allocate tensors in cpu or in cuda + :return: a :class:`torch.utils.data.DataLoader` object + """ def collate(batch): data = [torch.LongTensor(item['doc'][:pad_length]) for item in batch] data = pad_sequence(data, batch_first=True, padding_value=qp.environ['PAD_INDEX']).to(device) @@ -220,37 +299,97 @@ class TorchDataset(torch.utils.data.Dataset): class TextClassifierNet(torch.nn.Module, metaclass=ABCMeta): + """ + Abstract Text classifier (`torch.nn.Module`) + """ @abstractmethod - def document_embedding(self, x): ... + def document_embedding(self, x): + """Embeds documents (i.e., performs the forward pass up to the + next-to-last layer). + + :param x: a batch of instances, typically generated by a torch's `DataLoader` + instance (see :class:`quapy.classification.neural.TorchDataset`) + :return: a torch tensor of shape `(n_samples, n_dimensions)`, where + `n_samples` is the number of documents, and `n_dimensions` is the + dimensionality of the embedding + """ + ... def forward(self, x): + """Performs the forward pass. + + :param x: a batch of instances, typically generated by a torch's `DataLoader` + instance (see :class:`quapy.classification.neural.TorchDataset`) + :return: a tensor of shape `(n_instances, n_classes)` with the decision scores + for each of the instances and classes + """ doc_embedded = self.document_embedding(x) return self.output(doc_embedded) def dimensions(self): + """Gets the number of dimensions of the embedding space + + :return: integer + """ return self.dim def predict_proba(self, x): + """ + Predicts posterior probabilities for the instances in `x` + + :param x: a torch tensor of indexed tokens with shape `(n_instances, pad_length)` + where `n_instances` is the number of instances in the batch, and `pad_length` + is length of the pad in the batch + :return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities + """ logits = self(x) return torch.softmax(logits, dim=1).detach().cpu().numpy() def xavier_uniform(self): + """ + Performs Xavier initialization of the network parameters + """ for p in self.parameters(): if p.dim() > 1 and p.requires_grad: torch.nn.init.xavier_uniform_(p) @abstractmethod - def get_params(self): ... + def get_params(self): + """ + Get hyper-parameters for this estimator + + :return: a dictionary with parameter names mapped to their values + """ + ... @property - def vocabulary_size(self): ... + def vocabulary_size(self): + """ + Return the size of the vocabulary + + :return: integer + """ + ... class LSTMnet(TextClassifierNet): + """ + An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on + Long Short Term Memory networks. + + :param vocabulary_size: the size of the vocabulary + :param n_classes: number of target classes + :param embedding_size: the dimensionality of the word embeddings space (default 100) + :param hidden_size: the dimensionality of the hidden space (default 256) + :param repr_size: the dimensionality of the document embeddings space (default 100) + :param lstm_class_nlayers: number of LSTM layers (default 1) + :param drop_p: drop probability for dropout (default 0.5) + """ def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, lstm_class_nlayers=1, drop_p=0.5): + super().__init__() self.vocabulary_size_ = vocabulary_size self.n_classes = n_classes @@ -270,7 +409,7 @@ class LSTMnet(TextClassifierNet): self.doc_embedder = torch.nn.Linear(hidden_size, self.dim) self.output = torch.nn.Linear(self.dim, n_classes) - def init_hidden(self, set_size): + def __init_hidden(self, set_size): opt = self.hyperparams var_hidden = torch.zeros(opt['lstm_class_nlayers'], set_size, opt['hidden_size']) var_cell = torch.zeros(opt['lstm_class_nlayers'], set_size, opt['hidden_size']) @@ -279,21 +418,55 @@ class LSTMnet(TextClassifierNet): return var_hidden, var_cell def document_embedding(self, x): + """Embeds documents (i.e., performs the forward pass up to the + next-to-last layer). + + :param x: a batch of instances, typically generated by a torch's `DataLoader` + instance (see :class:`quapy.classification.neural.TorchDataset`) + :return: a torch tensor of shape `(n_samples, n_dimensions)`, where + `n_samples` is the number of documents, and `n_dimensions` is the + dimensionality of the embedding + """ embedded = self.word_embedding(x) - rnn_output, rnn_hidden = self.lstm(embedded, self.init_hidden(x.size()[0])) + rnn_output, rnn_hidden = self.lstm(embedded, self.__init_hidden(x.size()[0])) abstracted = self.dropout(F.relu(rnn_hidden[0][-1])) abstracted = self.doc_embedder(abstracted) return abstracted def get_params(self): + """ + Get hyper-parameters for this estimator + + :return: a dictionary with parameter names mapped to their values + """ return self.hyperparams @property def vocabulary_size(self): + """ + Return the size of the vocabulary + + :return: integer + """ return self.vocabulary_size_ class CNNnet(TextClassifierNet): + """ + An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on + Convolutional Neural Networks. + + :param vocabulary_size: the size of the vocabulary + :param n_classes: number of target classes + :param embedding_size: the dimensionality of the word embeddings space (default 100) + :param hidden_size: the dimensionality of the hidden space (default 256) + :param repr_size: the dimensionality of the document embeddings space (default 100) + :param kernel_heights: list of kernel lengths (default [3,5,7]), i.e., the number of + consecutive tokens that each kernel covers + :param stride: convolutional stride (default 1) + :param stride: convolutional pad (default 0) + :param drop_p: drop probability for dropout (default 0.5) + """ def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, kernel_heights=[3, 5, 7], stride=1, padding=0, drop_p=0.5): @@ -320,19 +493,28 @@ class CNNnet(TextClassifierNet): self.doc_embedder = torch.nn.Linear(len(kernel_heights) * hidden_size, self.dim) self.output = nn.Linear(self.dim, n_classes) - def conv_block(self, input, conv_layer): + def __conv_block(self, input, conv_layer): conv_out = conv_layer(input) # conv_out.size() = (batch_size, out_channels, dim, 1) activation = F.relu(conv_out.squeeze(3)) # activation.size() = (batch_size, out_channels, dim1) max_out = F.max_pool1d(activation, activation.size()[2]).squeeze(2) # maxpool_out.size() = (batch_size, out_channels) return max_out def document_embedding(self, input): + """Embeds documents (i.e., performs the forward pass up to the + next-to-last layer). + + :param input: a batch of instances, typically generated by a torch's `DataLoader` + instance (see :class:`quapy.classification.neural.TorchDataset`) + :return: a torch tensor of shape `(n_samples, n_dimensions)`, where + `n_samples` is the number of documents, and `n_dimensions` is the + dimensionality of the embedding + """ input = self.word_embedding(input) input = input.unsqueeze(1) # input.size() = (batch_size, 1, num_seq, embedding_length) - max_out1 = self.conv_block(input, self.conv1) - max_out2 = self.conv_block(input, self.conv2) - max_out3 = self.conv_block(input, self.conv3) + max_out1 = self.__conv_block(input, self.conv1) + max_out2 = self.__conv_block(input, self.conv2) + max_out3 = self.__conv_block(input, self.conv3) all_out = torch.cat((max_out1, max_out2, max_out3), 1) # all_out.size() = (batch_size, num_kernels*out_channels) abstracted = self.dropout(F.relu(all_out)) # (batch_size, num_kernels*out_channels) @@ -340,10 +522,20 @@ class CNNnet(TextClassifierNet): return abstracted def get_params(self): + """ + Get hyper-parameters for this estimator + + :return: a dictionary with parameter names mapped to their values + """ return self.hyperparams @property def vocabulary_size(self): + """ + Return the size of the vocabulary + + :return: integer + """ return self.vocabulary_size_ diff --git a/quapy/classification/svmperf.py b/quapy/classification/svmperf.py index b5a4e85..2f6ad90 100644 --- a/quapy/classification/svmperf.py +++ b/quapy/classification/svmperf.py @@ -1,17 +1,29 @@ import random import subprocess -import tempfile from os import remove, makedirs from os.path import join, exists from subprocess import PIPE, STDOUT -import shutil - import numpy as np from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.datasets import dump_svmlight_file class SVMperf(BaseEstimator, ClassifierMixin): + """A wrapper for the `SVM-perf package `__ by Thorsten Joachims. + When using losses for quantification, the source code has to be patched. See + the `installation documentation `__ + for further details. + + References: + + * `Esuli et al.2015 `__ + * `Barranquero et al.2015 `__ + + :param svmperf_base: path to directory containing the binary files `svm_perf_learn` and `svm_perf_classify` + :param C: trade-off between training error and margin (default 0.01) + :param verbose: set to True to print svm-perf std outputs + :param loss: the loss to optimize for. Available losses are "01", "f1", "kld", "nkld", "q", "qacc", "qf1", "qgm", "mae", "mrae". + """ # losses with their respective codes in svm_perf implementation valid_losses = {'01':0, 'f1':1, 'kld':12, 'nkld':13, 'q':22, 'qacc':23, 'qf1':24, 'qgm':25, 'mae':26, 'mrae':27} @@ -24,10 +36,22 @@ class SVMperf(BaseEstimator, ClassifierMixin): self.loss = loss def set_params(self, **parameters): + """ + Set the hyper-parameters for svm-perf. Currently, only the `C` parameter is supported + + :param parameters: a `**kwargs` dictionary `{'C': }` + """ assert list(parameters.keys()) == ['C'], 'currently, only the C parameter is supported' self.C = parameters['C'] def fit(self, X, y): + """ + Trains the SVM for the multivariate performance loss + + :param X: training instances + :param y: a binary vector of labels + :return: `self` + """ assert self.loss in SVMperf.valid_losses, \ f'unsupported loss {self.loss}, valid ones are {list(SVMperf.valid_losses.keys())}' @@ -68,11 +92,24 @@ class SVMperf(BaseEstimator, ClassifierMixin): return self 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` + """ confidence_scores = self.decision_function(X) predictions = (confidence_scores > 0) * 1 return predictions def decision_function(self, X, y=None): + """ + Evaluate the decision function for the samples in `X`. + + :param X: array-like of shape `(n_samples, n_features)` containing the instances to classify + :param y: unused + :return: array-like of shape `(n_samples,)` containing the decision scores of the instances + """ assert hasattr(self, 'tmpdir'), 'predict called before fit' assert self.tmpdir is not None, 'model directory corrupted' assert exists(self.model), 'model not found' diff --git a/quapy/model_selection.py b/quapy/model_selection.py index 35f87b9..f4ff185 100644 --- a/quapy/model_selection.py +++ b/quapy/model_selection.py @@ -91,8 +91,8 @@ class GridSearchQ(BaseQuantifier): if self.protocol=='npp' and (self.eval_budget is None or self.eval_budget <= 0): raise ValueError(f'when protocol="npp" the parameter eval_budget should be ' f'indicated (and should be >0).') - if self.n_prevpoints != 1: - print('[warning] n_prevpoints has been set and will be ignored for the selected protocol') + if self.n_repetitions != 1: + print('[warning] n_repetitions has been set and will be ignored for the selected protocol') def _sout(self, msg): if self.verbose: @@ -165,7 +165,6 @@ class GridSearchQ(BaseQuantifier): params_values = list(self.param_grid.values()) model = self.model - n_jobs = self.n_jobs if self.timeout > 0: def handler(signum, frame): @@ -174,7 +173,6 @@ class GridSearchQ(BaseQuantifier): signal.signal(signal.SIGALRM, handler) - self._sout(f'starting optimization with n_jobs={n_jobs}') self.param_scores_ = {} self.best_score_ = None some_timeouts = False diff --git a/quapy/util.py b/quapy/util.py index 96c5835..9eafdfa 100644 --- a/quapy/util.py +++ b/quapy/util.py @@ -83,6 +83,7 @@ def download_file_if_not_exists(url, archive_path): def create_if_not_exist(path): os.makedirs(path, exist_ok=True) + return path def get_quapy_home():