forked from moreo/QuaPy
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
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from sklearn.model_selection import GridSearchCV
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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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sample_size = 500
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qp.environ['SAMPLE_SIZE'] = sample_size
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def gen_data():
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data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)
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models = [
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qp.method.aggregative.CC,
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qp.method.aggregative.ACC,
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qp.method.aggregative.PCC,
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qp.method.aggregative.PACC,
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qp.method.aggregative.HDy,
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qp.method.aggregative.EMQ,
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qp.method.meta.ECC,
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qp.method.meta.EACC,
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qp.method.meta.EHDy,
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]
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method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
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for Quantifier in models:
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print(f'training {Quantifier.__name__}')
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lr = LogisticRegression(max_iter=1000, class_weight='balanced')
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# lr = GridSearchCV(lr, param_grid={'C':np.logspace(-3,3,7)}, n_jobs=-1)
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model = Quantifier(lr).fit(data.training)
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true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(
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model, data.test, sample_size, n_repetitions=20, n_prevpoints=11)
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method_names.append(Quantifier.__name__)
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true_prevs.append(true_prev)
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estim_prevs.append(estim_prev)
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tr_prevs.append(data.training.prevalence())
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return method_names, true_prevs, estim_prevs, tr_prevs
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method_names, true_prevs, estim_prevs, tr_prevs = qp.util.pickled_resource('./plots/plot_data.pkl', gen_data)
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qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, n_bins=11, savepath='./plots/err_drift.png')
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qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, savepath='./plots/bin_diag.png')
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qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, savepath='./plots/bin_bias.png')
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qp.plot.binary_bias_bins(method_names, true_prevs, estim_prevs, nbins=11, savepath='./plots/bin_bias_bin.png')
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