regresja

run.py

@@ -0,0 +1,35 @@
+from sklearn.linear_model import LinearRegression
+from sklearn.feature_extraction.text import TfidfVectorizer
+import pandas as pd
+
+def regresja(path):
+
+    # dnae do modelu
+    train = pd.read_csv('train/train.tsv', sep='\t', header=None)
+    y_train = train[0]
+    x_train = train[4]
+
+    # dane do predykcji
+    x_predict = []
+    with open(f"{path}/in.tsv", encoding='utf-8') as f:
+        for line in f:
+            x_predict.append(line)
+
+    # tfidf
+    vectorizer = TfidfVectorizer()
+    x_train = vectorizer.fit_transform(x_train)
+
+    x_predict = vectorizer.transform(x_predict)
+
+    # model regresji
+    model = LinearRegression()
+    model.fit(x_train, y_train)
+
+    # przewidywanie wyniku
+    y_predict = model.predict(x_predict)
+    pd.DataFrame(y_predict).to_csv(f"{path}/out.tsv", header=False, index=None)
+
+
+regresja("dev-0")
+regresja("dev-1")
+regresja("test-A")