testing IFCB dataset
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<li class="toctree-l4"><a class="reference internal" href="quapy.classification.html">quapy.classification package</a></li>
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<li class="toctree-l4 current"><a class="current reference internal" href="#">quapy.data package</a></li>
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@ -627,30 +626,31 @@ otherwise.</p>
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<span id="quapy-data-datasets-module"></span><h2>quapy.data.datasets module<a class="headerlink" href="#module-quapy.data.datasets" title="Link to this heading"></a></h2>
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<dl class="py function">
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<dt class="sig sig-object py" id="quapy.data.datasets.fetch_IFCB">
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<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_IFCB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">single_sample_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_IFCB"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_IFCB" title="Link to this definition"></a></dt>
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<dd><p>Loads the IFCB dataset for quantification <<a class="reference external" href="https://zenodo.org/records/10036244">https://zenodo.org/records/10036244</a>>`. For more
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information on this dataset check the zenodo site.
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This dataset is based on the data available publicly at <<a class="reference external" href="https://github.com/hsosik/WHOI-Plankton">https://github.com/hsosik/WHOI-Plankton</a>>.
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The scripts for the processing are available at <<a class="reference external" href="https://github.com/pglez82/IFCB_Zenodo">https://github.com/pglez82/IFCB_Zenodo</a>></p>
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<p>Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.</p>
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<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_IFCB</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">single_sample_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_IFCB"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_IFCB" title="Link to this definition"></a></dt>
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<dd><p>Loads the IFCB dataset for quantification from <a class="reference external" href="https://zenodo.org/records/10036244">Zenodo</a> (for more
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information on this dataset, please follow the zenodo link).
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This dataset is based on the data available publicly at
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<a class="reference external" href="https://github.com/hsosik/WHOI-Plankton">WHOI-Plankton repo</a>.
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The scripts for the processing are available at <a class="reference external" href="https://github.com/pglez82/IFCB_Zenodo">P. González’s repo</a>.
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Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.</p>
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<p>The datasets are downloaded only once, and stored for fast reuse.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>single_sample_train</strong> – boolean. If True (default), it returns the train dataset as an instance of
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<li><p><strong>single_sample_train</strong> – a boolean. If true, it will return the train dataset as a
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<a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (all examples together).
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If False, a generator of training samples will be returned.
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Each example in the training set has an individual class label.</p></li>
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If false, a generator of training samples will be returned. Each example in the training set has an individual label.</p></li>
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<li><p><strong>for_model_selection</strong> – if True, then returns a split 30% of the training set (86 out of 286 samples) to be used for model selection;
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if False, then returns the full training set as training set and the test set as the test set</p></li>
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<li><p><strong>data_home</strong> – specify the quapy home directory where collections will be dumped (leave empty to use the default
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~/quay_data/ directory)</p></li>
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</ul>
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</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p>a tuple <cite>(train, test_gen)</cite> where <cite>train</cite> is an instance of
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<a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a>, if <cite>single_sample_train</cite> is True or
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<code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data._ifcb.IFCBTrainSamplesFromDir</span></code> otherwise, i.e. a sampling protocol that
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returns a series of samples labelled example by example.
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test_gen is an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data._ifcb.IFCBTestSamples</span></code>,
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<a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a>, if <cite>single_sample_train</cite> is true or
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<code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data._ifcb.IFCBTrainSamplesFromDir</span></code>, i.e. a sampling protocol that returns a series of samples
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labelled example by example. test_gen will be a <code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data._ifcb.IFCBTestSamples</span></code>,
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i.e., a sampling protocol that returns a series of samples labelled by prevalence.</p>
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</dd>
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</dl>
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@ -22,7 +22,6 @@
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<script src="_static/js/theme.js"></script>
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<link rel="index" title="Index" href="genindex.html" />
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<link rel="search" title="Search" href="search.html" />
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</head>
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@ -52,7 +51,6 @@
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<li class="toctree-l4"><a class="reference internal" href="quapy.classification.html">quapy.classification package</a></li>
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<li class="toctree-l4"><a class="reference internal" href="quapy.data.html">quapy.data package</a></li>
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<li class="toctree-l4 current"><a class="current reference internal" href="#">quapy.method package</a></li>
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<li class="toctree-l4"><a class="reference internal" href="quapy.tests.html">quapy.tests package</a></li>
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</ul>
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</li>
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<li class="toctree-l3"><a class="reference internal" href="quapy.html#submodules">Submodules</a></li>
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@ -2820,7 +2818,6 @@ any quantification method should beat.</p>
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</div>
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<hr/>
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@ -1,29 +1,49 @@
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import numpy as np
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import quapy as qp
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from sklearn.linear_model import LogisticRegression
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from quapy.model_selection import GridSearchQ
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from quapy.evaluation import evaluation_report
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def newLR():
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return LogisticRegression(n_jobs=-1)
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print('Quantifying the IFCB dataset with PACC\n')
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# model selection
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print('loading dataset for model selection...', end='')
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train, val_gen = qp.datasets.fetch_IFCB(for_model_selection=True, single_sample_train=True)
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print('[done]')
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print(f'\ttraining size={len(train)}, features={train.X.shape[1]}, classes={train.n_classes}')
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print(f'\tvalidation samples={val_gen.total()}')
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quantifiers = [
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('CC', qp.method.aggregative.CC(newLR())),
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('ACC', qp.method.aggregative.ACC(newLR())),
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('PCC', qp.method.aggregative.PCC(newLR())),
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('PACC', qp.method.aggregative.PACC(newLR())),
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('HDy', qp.method.aggregative.DMy(newLR())),
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('EMQ', qp.method.aggregative.EMQ(newLR()))
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]
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print('model selection starts')
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quantifier = qp.method.aggregative.PACC(LogisticRegression())
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mod_sel = GridSearchQ(
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quantifier,
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param_grid={
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'classifier__C': np.logspace(-3,3,7),
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'classifier__class_weight': [None, 'balanced']
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},
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protocol=val_gen,
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refit=False,
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n_jobs=-1,
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verbose=True,
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raise_errors=True
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).fit(train)
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for quant_name, quantifier in quantifiers:
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print(f'model selection chose hyperparameters: {mod_sel.best_params_}')
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quantifier = mod_sel.best_model_
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print("Experiment with "+quant_name)
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train, test_gen = qp.datasets.fetch_IFCB()
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print('loading dataset for test...', end='')
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train, test_gen = qp.datasets.fetch_IFCB(for_model_selection=False, single_sample_train=True)
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print('[done]')
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print(f'\ttraining size={len(train)}, features={train.X.shape[1]}, classes={train.n_classes}')
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print(f'\ttest samples={test_gen.total()}')
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print('training on the whole dataset before test')
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quantifier.fit(train)
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print('testing...')
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report = evaluation_report(quantifier, protocol=test_gen, error_metrics=['mae'], verbose=True)
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print(report.mean())
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@ -4,6 +4,7 @@ import math
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from quapy.protocol import AbstractProtocol
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from pathlib import Path
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def get_sample_list(path_dir):
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"""Gets a sample list finding the csv files in a directory
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@ -19,6 +20,7 @@ def get_sample_list(path_dir):
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samples.append(filename)
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return samples
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def generate_modelselection_split(samples, split=0.3):
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"""This function generates a train/test split for model selection
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without the use of random numbers so the split is always the same
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@ -37,6 +39,7 @@ def generate_modelselection_split(samples, split=0.3):
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train = [item for i, item in enumerate(samples) if i not in test_indices]
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return train, test
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class IFCBTrainSamplesFromDir(AbstractProtocol):
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def __init__(self, path_dir:str, classes: list, samples: list = None):
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@ -64,6 +67,7 @@ class IFCBTrainSamplesFromDir(AbstractProtocol):
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"""
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return len(self.samples)
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class IFCBTestSamples(AbstractProtocol):
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def __init__(self, path_dir:str, test_prevalences: pd.DataFrame, samples: list = None, classes: list=None):
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return train, val_gen, test_gen
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def fetch_IFCB(single_sample_train=True, for_model_selection=False, data_home=None):
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"""
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Loads the IFCB dataset for quantification <https://zenodo.org/records/10036244>`. For more
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information on this dataset check the zenodo site.
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This dataset is based on the data available publicly at <https://github.com/hsosik/WHOI-Plankton>.
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The scripts for the processing are available at <https://github.com/pglez82/IFCB_Zenodo>
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Loads the IFCB dataset for quantification from `Zenodo <https://zenodo.org/records/10036244>`_ (for more
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information on this dataset, please follow the zenodo link).
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This dataset is based on the data available publicly at
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`WHOI-Plankton repo <https://github.com/hsosik/WHOI-Plankton>`_.
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The scripts for the processing are available at `P. González's repo <https://github.com/pglez82/IFCB_Zenodo>`_.
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Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.
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The datasets are downloaded only once, and stored for fast reuse.
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@ -60,6 +60,19 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
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"""
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pass
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def _check_non_empty_classes(self, data: LabelledCollection):
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"""
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Asserts all classes have positive instances.
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:param data: LabelledCollection
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:return: Nothing. May raise an exception.
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"""
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sample_prevs = data.prevalence()
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empty_classes = np.argwhere(sample_prevs==0).flatten()
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if len(empty_classes)>0:
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empty_class_names = data.classes_[empty_classes]
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raise ValueError(f'classes {empty_class_names} have no training examples')
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def fit(self, data: LabelledCollection, fit_classifier=True, val_split=None):
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"""
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Trains the aggregative quantifier. This comes down to training a classifier and an aggregation function.
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@ -93,6 +106,9 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
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self._check_classifier(adapt_if_necessary=(self._classifier_method() == 'predict_proba'))
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if fit_classifier:
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self._check_non_empty_classes(data)
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if predict_on is None:
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predict_on = self.val_split
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@ -100,7 +116,6 @@ class AggregativeQuantifier(BaseQuantifier, ABC):
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if fit_classifier:
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self.classifier.fit(*data.Xy)
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predictions = None
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elif isinstance(predict_on, float):
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if fit_classifier:
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if not (0. < predict_on < 1.):
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