74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
import numpy as np
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from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
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from sklearn.svm import SVC, LinearSVC
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from commons import *
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from table import Table
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from tqdm import tqdm
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np.set_printoptions(linewidth=np.inf)
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def classifiers():
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yield 'LR-opt', LogisticRegressionCV(Cs=10)
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yield 'LR-opt-bal', LogisticRegressionCV(class_weight='balanced', Cs=10)
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yield 'LR-def', LogisticRegression()
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yield 'SVM-linear', LinearSVC()
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yield 'SVM-rbf', SVC(kernel='rbf')
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survey_y = './data/survey_y.csv'
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Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
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preprocessor = Preprocessor()
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Xtr = preprocessor.fit_transform(Xtr)
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trains = get_dataset_by_area(Atr, Xtr, ytr)
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n_areas = len(trains)
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areas = [Ai for Ai, _, _ in trains]
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tables = []
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text_outputs = []
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benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
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methods = [f'tr-{Ai}' for Ai in areas] # areas on which a quantifier is trained
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for cls_name, c in classifiers():
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table = Table(name=cls_name, benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local', lower_is_better=False)
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table.format.mean_prec = 4
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table.format.show_std = False
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table.format.stat_test = False
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table.format.remove_zero = True
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for i, (Ai, Xi, yi) in tqdm(enumerate(trains), total=n_areas):
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c.fit(Xi, yi)
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for j, (Aj, Xj, yj) in enumerate(trains):
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if i==j: continue
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pred_labels = c.predict(Xj)
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true_labels = yj
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acc = (pred_labels==true_labels).mean()
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table.add(benchmark=f'te-{Aj}', method=f'tr-{Ai}', v=acc)
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for test in benchmarks:
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values = table.get_benchmark_values(test)
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table.add(benchmark=test, method='Best', v=max(values))
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table.add(benchmark=test, method='Worst', v=min(values))
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table.add(benchmark=test, method='AVE', v=np.mean(values))
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tables.append(table)
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text_outputs.append(f'{cls_name} got mean {table.all_mean():.5f}')
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Table.LatexPDF(f'./results/classifier/doc.pdf', tables)
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with open(f'./results/classifier/output.txt', 'tw') as foo:
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foo.write('\n'.join(text_outputs))
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