change config.txt

config.txt

@@ -1 +1 @@
---precision 1
+--metric RMSE --precision 1

main.py

@@ -0,0 +1,51 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+
+with open('names') as f_names:
+    names = f_names.read().rstrip('\n').split('\t')
+
+
+cars_train = pd.read_csv('train/train.tsv', sep='\t', names=names)
+
+cars_train = pd.get_dummies(cars_train, columns=['engineType'])
+
+
+y_train = pd.DataFrame(cars_train['price'])
+cars_train.drop('price', inplace=True, axis=1)
+cars_train.drop('brand', inplace=True, axis=1)
+x_train = pd.DataFrame(cars_train)
+
+model = LinearRegression()
+model.fit(x_train, y_train)
+
+names.remove('price')
+
+cars_dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=names)
+with open('dev-0/expected.tsv', 'r') as dev_exp_f:
+    Y_dev = np.array([float(x.rstrip('\n')) for x in dev_exp_f.readlines()])
+
+cars_dev = pd.get_dummies(cars_dev, columns=['engineType'])
+cars_dev.drop('brand', inplace=True, axis=1)
+X_dev = pd.DataFrame(cars_dev)
+
+Y_dev_predicted = model.predict(X_dev)
+print(Y_dev_predicted)
+pd.DataFrame(Y_dev_predicted).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)
+
+
+cars_test=pd.read_csv('test-A/in.tsv', sep='\t', names=names)
+cars_test = pd.get_dummies(cars_test, columns=['engineType'])
+cars_test.drop('brand', inplace=True, axis=1)
+X_test = pd.DataFrame(cars_test)
+
+Y_test_predicted = model.predict(X_test)
+pd.DataFrame(Y_test_predicted).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)
+
+error = np.sqrt(mean_squared_error(Y_dev, Y_dev_predicted))
+print(error)