forked from moreo/QuaPy
sketching readme system by Lu and King, Hopings and King
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@ -1,3 +1,9 @@
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Change Log 0.1.9
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----------------
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<...>
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Change Log 0.1.8
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----------------
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@ -11,7 +11,7 @@ from . import util
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from . import model_selection
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from . import classification
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__version__ = '0.1.8'
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__version__ = '0.1.9'
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environ = {
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'SAMPLE_SIZE': None,
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@ -1,5 +1,6 @@
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from typing import Union, Callable
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import numpy as np
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from sklearn.feature_extraction.text import CountVectorizer
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from quapy.functional import get_divergence
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from quapy.data import LabelledCollection
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@ -146,6 +147,53 @@ class DMx(BaseQuantifier):
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return F.argmin_prevalence(loss, n_classes, method=self.search)
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class ReadMe(BaseQuantifier):
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def __init__(self, bootstrap_trials=100, bootstrap_range=100, bagging_trials=100, bagging_range=25, **vectorizer_kwargs):
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self.bootstrap_trials = bootstrap_trials
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self.bootstrap_range = bootstrap_range
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self.bagging_trials = bagging_trials
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self.bagging_range = bagging_range
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self.vectorizer_kwargs = vectorizer_kwargs
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def fit(self, data: LabelledCollection):
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X, y = data.Xy
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self.vectorizer = CountVectorizer(binary=True, **self.vectorizer_kwargs)
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X = self.vectorizer.fit_transform(X)
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self.class_conditional_X = {i: X[y==i] for i in range(data.classes_)}
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def quantify(self, instances):
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X = self.vectorizer.transform(instances)
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# number of features
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num_docs, num_feats = X.shape
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# bootstrap
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p_boots = []
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for _ in range(self.bootstrap_trials):
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docs_idx = np.random.choice(num_docs, size=self.bootstra_range, replace=False)
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class_conditional_X = {i: X[docs_idx] for i, X in self.class_conditional_X.items()}
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Xboot = X[docs_idx]
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# bagging
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p_bags = []
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for _ in range(self.bagging_trials):
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feat_idx = np.random.choice(num_feats, size=self.bagging_range, replace=False)
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class_conditional_Xbag = {i: X[:, feat_idx] for i, X in class_conditional_X.items()}
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Xbag = Xboot[:,feat_idx]
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p = self.std_constrained_linear_ls(Xbag, class_conditional_Xbag)
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p_bags.append(p)
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p_boots.append(np.mean(p_bags, axis=0))
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p_mean = np.mean(p_boots, axis=0)
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p_std = np.std(p_bags, axis=0)
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return p_mean
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def std_constrained_linear_ls(self, X, class_cond_X: dict):
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pass
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def _get_features_range(X):
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feat_ranges = []
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