s444476 add solution

run.py

@@ -0,0 +1,38 @@
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+
+def linear_regression(train_path, predict_path, out_path):
+    print("Wczytywanie zbioru treningowego")
+    train = pd.read_csv(train_path, sep='\t', header=None)
+    X_train = train[4]
+    Y_train = train[0]
+
+    print("Wczytywanie pliku do predykcji")
+    pred_x = []
+    with open(predict_path, encoding='utf-8') as f:
+        for line in f:
+            pred_x.append(line)
+
+    print("Wektoryzacja")
+    vectorizer = TfidfVectorizer()
+    X_train = vectorizer.fit_transform(X_train)
+    pred_x = vectorizer.transform(pred_x)
+
+    print("Uczenie modelu")
+    model = LinearRegression()
+    model.fit(X_train, Y_train)
+
+    print("Predykcja wyników")
+    pred_y = model.predict(pred_x)
+
+
+    print("Zapis do pliku")
+    pd.DataFrame(pred_y).to_csv(out_path, header=False, index=None)
+
+linear_regression("train/train.tsv", "dev-0/in.tsv", "dev-0/out.tsv")
+linear_regression("train/train.tsv", "dev-1/in.tsv", "dev-1/out.tsv")
+linear_regression("train/train.tsv", "test-A/in.tsv", "test-A/out.tsv")
+
+

testing.py

@@ -0,0 +1,16 @@
+from sklearn.metrics import mean_squared_error
+import pandas as pd
+
+def RMSE(exp, pred):
+    expected = exp
+    predicted = pred
+
+    data_exp = pd.read_csv(expected, header=0, sep='\t')
+    data_pred = pd.read_csv(predicted, header=0, sep='\t')
+
+    rmse = mean_squared_error(data_exp, data_pred, squared=False)
+    
+    return rmse
+
+print(RMSE("dev-0/expected.tsv", "dev-0/out.tsv"))
+print(RMSE("dev-1/expected.tsv", "dev-1/out.tsv"))