From 164f7d8d5cbb70744a2dfaea117cea8965d06589 Mon Sep 17 00:00:00 2001 From: Alex Moreo Date: Wed, 15 Dec 2021 16:39:57 +0100 Subject: [PATCH] documenting quanet --- README.md | 4 ++ docs/build/html/genindex.html | 6 -- docs/build/html/objects.inv | Bin 2619 -> 2591 bytes docs/build/html/quapy.method.html | 111 ++++++++++++++++++++++++------ docs/build/html/searchindex.js | 2 +- quapy/method/neural.py | 102 +++++++++++++++++++++++---- 6 files changed, 184 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index 2e51244..0f9ee0d 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,10 @@ used for evaluating quantification methods. QuaPy also integrates commonly used datasets and offers visualization tools for facilitating the analysis and interpretation of results. +### Last updates: + +* A detailed developer API documentation is now available [here]()! + ### Installation ```commandline diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 6ba0ab0..04850d7 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -292,8 +292,6 @@
  • ensembleFactory() (in module quapy.method.meta)
  • EPACC() (in module quapy.method.meta) -
    • -
  • epoch() (quapy.method.neural.QuaNetTrainer method)
  • EPSILON (quapy.method.aggregative.EMQ attribute)
    • @@ -390,8 +388,6 @@
  • gen_prevalence_prediction() (in module quapy.evaluation)
  • gen_prevalence_report() (in module quapy.evaluation) -
    • -
  • get_aggregative_estims() (quapy.method.neural.QuaNetTrainer method)
  • get_nprevpoints_approximation() (in module quapy.functional)
    • @@ -452,8 +448,6 @@
  • index() (in module quapy.data.preprocessing)
  • IndexTransformer (class in quapy.data.preprocessing) -
    • -
  • init_hidden() (quapy.method.neural.QuaNetModule method)
  • isaggregative() (in module quapy.method.base)
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    class quapy.method.neural.QuaNetModule(doc_embedding_size, n_classes, stats_size, lstm_hidden_size=64, lstm_nlayers=1, ff_layers=[1024, 512], bidirectional=True, qdrop_p=0.5, order_by=0)

    Bases: torch.nn.modules.module.Module

    +

    Implements the QuaNet forward pass. +See QuaNetTrainer for training QuaNet.

    +
    +
    Parameters
    +
      +

    • doc_embedding_size – integer, the dimensionality of the document embeddings

      • +

    • n_classes – integer, number of classes

      • +

    • stats_size – integer, number of statistics estimated by simple quantification methods

      • +

    • lstm_hidden_size – integer, hidden dimensionality of the LSTM cell

      • +

    • lstm_nlayers – integer, number of LSTM layers

      • +

    • ff_layers – list of integers, dimensions of the densely-connected FF layers on top of the +quantification embedding

      • +

    • bidirectional – boolean, whether or not to use bidirectional LSTM

      • +

    • qdrop_p – float, dropout probability

      • +

    • order_by – integer, class for which the document embeddings are to be sorted

      • +
    +
    +
    property device
    @@ -1775,17 +1793,62 @@ registered hooks while the latter silently ignores them.

    -
    -
    -init_hidden()
    -
    -
    class quapy.method.neural.QuaNetTrainer(learner, sample_size, n_epochs=100, tr_iter_per_poch=500, va_iter_per_poch=100, lr=0.001, lstm_hidden_size=64, lstm_nlayers=1, ff_layers=[1024, 512], bidirectional=True, qdrop_p=0.5, patience=10, checkpointdir='../checkpoint', checkpointname=None, device='cuda')

    Bases: quapy.method.base.BaseQuantifier

    +

    Implementation of QuaNet, a neural network for +quantification. This implementation uses PyTorch and can take advantage of GPU +for speeding-up the training phase.

    +

    Example:

    +
    >>> import quapy as qp
+>>> from quapy.method.meta import QuaNet
+>>> from quapy.classification.neural import NeuralClassifierTrainer, CNNnet
+>>>
+>>> # use samples of 100 elements
+>>> qp.environ['SAMPLE_SIZE'] = 100
+>>>
+>>> # load the kindle dataset as text, and convert words to numerical indexes
+>>> dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
+>>> qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
+>>>
+>>> # the text classifier is a CNN trained by NeuralClassifierTrainer
+>>> cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)
+>>> learner = NeuralClassifierTrainer(cnn, device='cuda')
+>>>
+>>> # train QuaNet (QuaNet is an alias to QuaNetTrainer)
+>>> model = QuaNet(learner, qp.environ['SAMPLE_SIZE'], device='cuda')
+>>> model.fit(dataset.training)
+>>> estim_prevalence = model.quantify(dataset.test.instances)
+
    +
    +
    +
    Parameters
    +
      +

    • learner – an object implementing fit (i.e., that can be trained on labelled data), +predict_proba (i.e., that can generate posterior probabilities of unlabelled examples) and +transform (i.e., that can generate embedded representations of the unlabelled instances).

      • +

    • sample_size – integer, the sample size

      • +

    • n_epochs – integer, maximum number of training epochs

      • +

    • tr_iter_per_poch – integer, number of training iterations before considering an epoch complete

      • +

    • va_iter_per_poch – integer, number of validation iterations to perform after each epoch

      • +

    • lr – float, the learning rate

      • +

    • lstm_hidden_size – integer, hidden dimensionality of the LSTM cells

      • +

    • lstm_nlayers – integer, number of LSTM layers

      • +

    • ff_layers – list of integers, dimensions of the densely-connected FF layers on top of the +quantification embedding

      • +

    • bidirectional – boolean, indicates whether the LSTM is bidirectional or not

      • +

    • qdrop_p – float, dropout probability

      • +

    • patience – integer, number of epochs showing no improvement in the validation set before stopping the +training phase (early stopping)

      • +

    • checkpointdir – string, a path where to store models’ checkpoints

      • +

    • checkpointname – string (optional), the name of the model’s checkpoint

      • +

    • device – string, indicate “cpu” or “cuda”

      • +
    +
    +
    property classes_
    @@ -1800,17 +1863,14 @@ registered hooks while the latter silently ignores them.

    clean_checkpoint()
    -
    +

    Removes the checkpoint

    +
    clean_checkpoint_dir()
    -
    - -
    -
    -epoch(data: quapy.data.base.LabelledCollection, posteriors, iterations, epoch, early_stop, train)
    -
    +

    Removes anything contained in the checkpoint directory

    +
    @@ -1819,10 +1879,10 @@ registered hooks while the latter silently ignores them.

    Parameters
      -

    • data – the training data on which to train QuaNet. If fit_learner=True, the data will be split in +

    • data – the training data on which to train QuaNet. If fit_learner=True, the data will be split in 40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If -fit_learner=False, the data will be split in 66/34 for training QuaNet and validating it, respectively.

      • -

    • fit_learner – if true, trains the classifier on a split containing 40% of the data

      • +fit_learner=False, the data will be split in 66/34 for training QuaNet and validating it, respectively.

      +

    • fit_learner – if True, trains the classifier on a split containing 40% of the data

    Returns
    @@ -1831,11 +1891,6 @@ fit_learner=False, the data will be split in 66/34 for training QuaNet and valid
    -
    -
    -get_aggregative_estims(posteriors)
    -
    -
    get_params(deep=True)
    @@ -1852,7 +1907,7 @@ fit_learner=False, the data will be split in 66/34 for training QuaNet and valid
    -quantify(instances, *args)
    +quantify(instances)

    Generate class prevalence estimates for the sample’s instances

    Parameters
    @@ -1880,7 +1935,19 @@ fit_learner=False, the data will be split in 66/34 for training QuaNet and valid
    quapy.method.neural.mae_loss(output, target)
    -
    +

    Torch-like wrapper for the Mean Absolute Error

    +
    +
    Parameters
    +
      +

    • output – predictions

      • +

    • target – ground truth values

      • +
    +
    +
    Returns
    +

    mean absolute error loss

    +
    +
    +
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index 558e447..bf1f375 100644 --- a/quapy/method/neural.py +++ b/quapy/method/neural.py @@ -11,6 +11,53 @@ from quapy.util import EarlyStop class QuaNetTrainer(BaseQuantifier): + """ + Implementation of `QuaNet `_, a neural network for + quantification. This implementation uses `PyTorch `_ and can take advantage of GPU + for speeding-up the training phase. + + Example: + + >>> import quapy as qp + >>> from quapy.method.meta import QuaNet + >>> from quapy.classification.neural import NeuralClassifierTrainer, CNNnet + >>> + >>> # use samples of 100 elements + >>> qp.environ['SAMPLE_SIZE'] = 100 + >>> + >>> # load the kindle dataset as text, and convert words to numerical indexes + >>> dataset = qp.datasets.fetch_reviews('kindle', pickle=True) + >>> qp.data.preprocessing.index(dataset, min_df=5, inplace=True) + >>> + >>> # the text classifier is a CNN trained by NeuralClassifierTrainer + >>> cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes) + >>> learner = NeuralClassifierTrainer(cnn, device='cuda') + >>> + >>> # train QuaNet (QuaNet is an alias to QuaNetTrainer) + >>> model = QuaNet(learner, qp.environ['SAMPLE_SIZE'], device='cuda') + >>> model.fit(dataset.training) + >>> estim_prevalence = model.quantify(dataset.test.instances) + + :param learner: an object implementing `fit` (i.e., that can be trained on labelled data), + `predict_proba` (i.e., that can generate posterior probabilities of unlabelled examples) and + `transform` (i.e., that can generate embedded representations of the unlabelled instances). + :param sample_size: integer, the sample size + :param n_epochs: integer, maximum number of training epochs + :param tr_iter_per_poch: integer, number of training iterations before considering an epoch complete + :param va_iter_per_poch: integer, number of validation iterations to perform after each epoch + :param lr: float, the learning rate + :param lstm_hidden_size: integer, hidden dimensionality of the LSTM cells + :param lstm_nlayers: integer, number of LSTM layers + :param ff_layers: list of integers, dimensions of the densely-connected FF layers on top of the + quantification embedding + :param bidirectional: boolean, indicates whether the LSTM is bidirectional or not + :param qdrop_p: float, dropout probability + :param patience: integer, number of epochs showing no improvement in the validation set before stopping the + training phase (early stopping) + :param checkpointdir: string, a path where to store models' checkpoints + :param checkpointname: string (optional), the name of the model's checkpoint + :param device: string, indicate "cpu" or "cuda" + """ def __init__(self, learner, @@ -28,6 +75,7 @@ class QuaNetTrainer(BaseQuantifier): checkpointdir='../checkpoint', checkpointname=None, device='cuda'): + assert hasattr(learner, 'transform'), \ f'the learner {learner.__class__.__name__} does not seem to be able to produce document embeddings ' \ f'since it does not implement the method "transform"' @@ -64,10 +112,10 @@ class QuaNetTrainer(BaseQuantifier): """ Trains QuaNet. - :param data: the training data on which to train QuaNet. If fit_learner=True, the data will be split in + :param data: the training data on which to train QuaNet. If `fit_learner=True`, the data will be split in 40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If - fit_learner=False, the data will be split in 66/34 for training QuaNet and validating it, respectively. - :param fit_learner: if true, trains the classifier on a split containing 40% of the data + `fit_learner=False`, the data will be split in 66/34 for training QuaNet and validating it, respectively. + :param fit_learner: if True, trains the classifier on a split containing 40% of the data :return: self """ self._classes_ = data.classes_ @@ -125,8 +173,8 @@ class QuaNetTrainer(BaseQuantifier): checkpoint = self.checkpoint for epoch_i in range(1, self.n_epochs): - self.epoch(train_data_embed, train_posteriors, self.tr_iter, epoch_i, early_stop, train=True) - self.epoch(valid_data_embed, valid_posteriors, self.va_iter, epoch_i, early_stop, train=False) + self._epoch(train_data_embed, train_posteriors, self.tr_iter, epoch_i, early_stop, train=True) + self._epoch(valid_data_embed, valid_posteriors, self.va_iter, epoch_i, early_stop, train=False) early_stop(self.status['va-loss'], epoch_i) if early_stop.IMPROVED: @@ -139,7 +187,7 @@ class QuaNetTrainer(BaseQuantifier): return self - def get_aggregative_estims(self, posteriors): + def _get_aggregative_estims(self, posteriors): label_predictions = np.argmax(posteriors, axis=-1) prevs_estim = [] for quantifier in self.quantifiers.values(): @@ -150,10 +198,10 @@ class QuaNetTrainer(BaseQuantifier): return prevs_estim - def quantify(self, instances, *args): + def quantify(self, instances): posteriors = self.learner.predict_proba(instances) embeddings = self.learner.transform(instances) - quant_estims = self.get_aggregative_estims(posteriors) + quant_estims = self._get_aggregative_estims(posteriors) self.quanet.eval() with torch.no_grad(): prevalence = self.quanet.forward(embeddings, posteriors, quant_estims) @@ -162,7 +210,7 @@ class QuaNetTrainer(BaseQuantifier): prevalence = prevalence.numpy().flatten() return prevalence - def epoch(self, data: LabelledCollection, posteriors, iterations, epoch, early_stop, train): + def _epoch(self, data: LabelledCollection, posteriors, iterations, epoch, early_stop, train): mse_loss = MSELoss() self.quanet.train(mode=train) @@ -181,7 +229,7 @@ class QuaNetTrainer(BaseQuantifier): for it, index in enumerate(pbar): sample_data = data.sampling_from_index(index) sample_posteriors = posteriors[index] - quant_estims = self.get_aggregative_estims(sample_posteriors) + quant_estims = self._get_aggregative_estims(sample_posteriors) ptrue = torch.as_tensor([sample_data.prevalence()], dtype=torch.float, device=self.device) if train: self.optim.zero_grad() @@ -236,9 +284,15 @@ class QuaNetTrainer(BaseQuantifier): f'the parameters of QuaNet or the learner {self.learner.__class__.__name__}') def clean_checkpoint(self): + """ + Removes the checkpoint + """ os.remove(self.checkpoint) def clean_checkpoint_dir(self): + """ + Removes anything contained in the checkpoint directory + """ import shutil shutil.rmtree(self.checkpointdir, ignore_errors=True) @@ -248,10 +302,33 @@ class QuaNetTrainer(BaseQuantifier): def mae_loss(output, target): + """ + Torch-like wrapper for the Mean Absolute Error + + :param output: predictions + :param target: ground truth values + :return: mean absolute error loss + """ return torch.mean(torch.abs(output - target)) class QuaNetModule(torch.nn.Module): + """ + Implements the `QuaNet `_ forward pass. + See :class:`QuaNetTrainer` for training QuaNet. + + :param doc_embedding_size: integer, the dimensionality of the document embeddings + :param n_classes: integer, number of classes + :param stats_size: integer, number of statistics estimated by simple quantification methods + :param lstm_hidden_size: integer, hidden dimensionality of the LSTM cell + :param lstm_nlayers: integer, number of LSTM layers + :param ff_layers: list of integers, dimensions of the densely-connected FF layers on top of the + quantification embedding + :param bidirectional: boolean, whether or not to use bidirectional LSTM + :param qdrop_p: float, dropout probability + :param order_by: integer, class for which the document embeddings are to be sorted + """ + def __init__(self, doc_embedding_size, n_classes, @@ -262,6 +339,7 @@ class QuaNetModule(torch.nn.Module): bidirectional=True, qdrop_p=0.5, order_by=0): + super().__init__() self.n_classes = n_classes @@ -289,7 +367,7 @@ class QuaNetModule(torch.nn.Module): def device(self): return torch.device('cuda') if next(self.parameters()).is_cuda else torch.device('cpu') - def init_hidden(self): + def _init_hidden(self): directions = 2 if self.bidirectional else 1 var_hidden = torch.zeros(self.nlayers * directions, 1, self.hidden_size) var_cell = torch.zeros(self.nlayers * directions, 1, self.hidden_size) @@ -315,7 +393,7 @@ class QuaNetModule(torch.nn.Module): embeded_posteriors = embeded_posteriors.unsqueeze(0) self.lstm.flatten_parameters() - _, (rnn_hidden,_) = self.lstm(embeded_posteriors, self.init_hidden()) + _, (rnn_hidden,_) = self.lstm(embeded_posteriors, self._init_hidden()) rnn_hidden = rnn_hidden.view(self.nlayers, self.ndirections, 1, self.hidden_size) quant_embedding = rnn_hidden[0].view(-1) quant_embedding = torch.cat((quant_embedding, statistics))