forked from moreo/QuaPy
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
from sklearn.base import BaseEstimator
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import numpy as np
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import quapy as qp
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import quapy.functional as F
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from data import LabelledCollection
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from method.aggregative import ACC
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from method.base import BaseQuantifier
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from tqdm import tqdm
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import seaborn as sns
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import matplotlib.pyplot as plt
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data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=10)
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class DecisionStump(BaseEstimator):
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def __init__(self, feat_id):
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self.feat_id = feat_id
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self.classes_ = np.asarray([0,1], dtype=int)
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def fit(self, X, y):
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return self
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def predict(self, X):
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return (X[:,self.feat_id].toarray().flatten()>0).astype(int)
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class QuantificationStump(BaseQuantifier):
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def __init__(self, feat_id):
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self.feat_id = feat_id
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def fit(self, data: LabelledCollection):
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self.qs = ACC(DecisionStump(self.feat_id))
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self.qs.fit(data, fit_learner=False, val_split=data)
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self.classes = data.classes_
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return self
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def quantify(self, instances):
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return self.qs.quantify(instances)
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def set_params(self, **parameters):
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raise NotImplemented()
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def get_params(self, deep=True):
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raise NotImplemented()
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@property
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def classes_(self):
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return self.classes
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train, dev = data.training.split_stratified()
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test = data.test.sampling(1000, 0.3, 0.7)
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print(f'test prevalence = {F.strprev(test.prevalence())}')
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nF = train.instances.shape[1]
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qs_scores = []
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qs = np.asarray([QuantificationStump(i).fit(train) for i in tqdm(range(nF))])
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scores = np.zeros(shape=(nF, 11*5))
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for j, dev_sample in tqdm(enumerate(dev.artificial_sampling_generator(500, n_prevalences=11, repeats=5)), total=11*5):
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sample_prev = dev_sample.prevalence()
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for i, qs_i in enumerate(qs):
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estim_prev = qs_i.quantify(dev.instances)
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error = qp.error.ae(sample_prev, estim_prev)
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scores[i,j] = error
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k=250
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scores = scores.mean(axis=1)
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order = np.argsort(scores)
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qs = qs[order][:k]
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prevs = np.asarray([qs_i.quantify(test.instances)[1] for qs_i in tqdm(qs)])
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print(f'test estimation mean {prevs.mean():.3f}, median = {np.median(prevs)}')
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# sns.histplot(data=prevs, binwidth=3)
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# An "interface" to matplotlib.axes.Axes.hist() method
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# n, bins, patches = plt.hist(x=prevs, bins='auto', alpha=0.7)
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# plt.grid(axis='y', alpha=0.75)
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# plt.xlabel('Value')
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# plt.ylabel('Frequency')
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# plt.title('My Very Own Histogram')
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# maxfreq = n.max()
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# Set a clean upper y-axis limit.
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# plt.ylim(ymax=np.ceil(maxfreq / 10) * 10 if maxfreq % 10 else maxfreq + 10)
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# plt.show() |