first commit

main.py

@@ -0,0 +1,61 @@
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
+from sklearn.linear_model import LogisticRegression
+import pandas as pd
+import numpy as np
+from stop_words import get_stop_words
+from numpy import random
+
+stop_words = get_stop_words('polish')
+
+v = TfidfVectorizer(stop_words=None)
+naive_bayes=MultinomialNB()
+
+ball_train = pd.read_csv('train/train.tsv', sep='\t', error_bad_lines=False, header=None)
+ball_dev_expected = pd.read_csv('dev-0/expected.tsv', sep='\t', error_bad_lines=False, header=None)
+
+y_train = pd.DataFrame(ball_train[0])
+x_train = pd.DataFrame(ball_train[1])
+x_np=x_train.to_numpy()
+x_np = [str(item) for item in x_np]
+
+x_train=v.fit_transform(x_np)
+
+naive_bayes.fit(x_train, y_train)
+
+ball_dev_in = pd.read_csv('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None)
+
+X_dev = pd.DataFrame(ball_dev_in)
+X_dev_np=X_dev.to_numpy()
+X_dev_np = [str(item) for item in X_dev_np]
+X_dev=v.transform(X_dev_np)
+
+model = LogisticRegression()  # definicja modelu
+model.fit(x_train, y_train)  # dopasowanie modelu
+
+
+Y_dev_predictedNB = naive_bayes.predict(X_dev)
+Y_dev_predicted_baseline=np.ones_like(Y_dev_predictedNB)
+Y_dev_predicted_random=random.choice([0,1],size=len(Y_dev_predictedNB))
+Y_dev_predictedLogReg=model.predict(X_dev)
+
+print(f1_score(ball_dev_expected, Y_dev_predicted_baseline))
+print(f1_score(ball_dev_expected, Y_dev_predictedLogReg))
+print(f1_score(ball_dev_expected, Y_dev_predicted_random))
+print(f1_score(ball_dev_expected, Y_dev_predictedNB))
+print()
+print(accuracy_score(ball_dev_expected, Y_dev_predicted_baseline))
+print(accuracy_score(ball_dev_expected, Y_dev_predictedLogReg))
+print(accuracy_score(ball_dev_expected, Y_dev_predicted_random))
+print(accuracy_score(ball_dev_expected, Y_dev_predictedNB))
+print()
+print(precision_score(ball_dev_expected, Y_dev_predicted_baseline))
+print(precision_score(ball_dev_expected, Y_dev_predictedLogReg))
+print(precision_score(ball_dev_expected, Y_dev_predicted_random))
+print(precision_score(ball_dev_expected, Y_dev_predictedNB))
+print()
+print(recall_score(ball_dev_expected, Y_dev_predicted_baseline))
+print(recall_score(ball_dev_expected, Y_dev_predictedLogReg))
+print(recall_score(ball_dev_expected, Y_dev_predicted_random))
+print(recall_score(ball_dev_expected, Y_dev_predictedNB))