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34 lines
1.0 KiB
34 lines
1.0 KiB
from classifier.linear_model import MultiClassPerceptron
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from utils.csv import read_csv_file
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from eval.metrics import f1_score
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import utils.constants as const
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import os
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project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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train_file_path = project_root+'/data/tsv/train.tsv'
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test_file_path = project_root+'/data/tsv/test.tsv'
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X_train_inst = read_csv_file(train_file_path, '\t')
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labels = set([inst.true_label for inst in X_train_inst])
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X_test_inst = read_csv_file(test_file_path, '\t')
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epochs = int(len(X_train_inst)*0.95)
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clf = MultiClassPerceptron(epochs=epochs, learning_rate=1, random_state=10)
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clf.fit(X_train=X_train_inst, labels=list(labels))
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y_test = clf.predict(X_test_inst)
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y_true = [inst.true_label for inst in X_test_inst]
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f1_score_micro = f1_score(y_true, y_test, labels, const.AVG_MICRO)
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f1_score_macro = f1_score(y_true, y_test, labels, const.AVG_MACRO)
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f1_score_none = f1_score(y_true, y_test, labels, None)
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for result in f1_score_micro+f1_score_macro+f1_score_none:
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result.print_result()
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