62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
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from sklearn.linear_model import LogisticRegression
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import pandas as pd
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import numpy as np
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from stop_words import get_stop_words
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from numpy import random
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stop_words = get_stop_words('polish')
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v = TfidfVectorizer(stop_words=None)
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naive_bayes=MultinomialNB()
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ball_train = pd.read_csv('train/train.tsv', sep='\t', error_bad_lines=False, header=None)
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ball_dev_expected = pd.read_csv('dev-0/expected.tsv', sep='\t', error_bad_lines=False, header=None)
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y_train = pd.DataFrame(ball_train[0])
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x_train = pd.DataFrame(ball_train[1])
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x_np=x_train.to_numpy()
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x_np = [str(item) for item in x_np]
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x_train=v.fit_transform(x_np)
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naive_bayes.fit(x_train, y_train)
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ball_dev_in = pd.read_csv('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None)
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X_dev = pd.DataFrame(ball_dev_in)
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X_dev_np=X_dev.to_numpy()
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X_dev_np = [str(item) for item in X_dev_np]
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X_dev=v.transform(X_dev_np)
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model = LogisticRegression() # definicja modelu
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model.fit(x_train, y_train) # dopasowanie modelu
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Y_dev_predictedNB = naive_bayes.predict(X_dev)
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Y_dev_predicted_baseline=np.ones_like(Y_dev_predictedNB)
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Y_dev_predicted_random=random.choice([0,1],size=len(Y_dev_predictedNB))
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Y_dev_predictedLogReg=model.predict(X_dev)
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print(f1_score(ball_dev_expected, Y_dev_predicted_baseline))
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print(f1_score(ball_dev_expected, Y_dev_predictedLogReg))
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print(f1_score(ball_dev_expected, Y_dev_predicted_random))
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print(f1_score(ball_dev_expected, Y_dev_predictedNB))
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print()
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print(accuracy_score(ball_dev_expected, Y_dev_predicted_baseline))
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print(accuracy_score(ball_dev_expected, Y_dev_predictedLogReg))
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print(accuracy_score(ball_dev_expected, Y_dev_predicted_random))
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print(accuracy_score(ball_dev_expected, Y_dev_predictedNB))
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print()
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print(precision_score(ball_dev_expected, Y_dev_predicted_baseline))
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print(precision_score(ball_dev_expected, Y_dev_predictedLogReg))
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print(precision_score(ball_dev_expected, Y_dev_predicted_random))
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print(precision_score(ball_dev_expected, Y_dev_predictedNB))
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print()
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print(recall_score(ball_dev_expected, Y_dev_predicted_baseline))
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print(recall_score(ball_dev_expected, Y_dev_predictedLogReg))
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print(recall_score(ball_dev_expected, Y_dev_predicted_random))
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print(recall_score(ball_dev_expected, Y_dev_predictedNB))
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