dev0_rmse.txt

@@ -1 +0,0 @@
-1165319253.2984157

main.py

@@ -1,62 +0,0 @@
-import pandas as pd
-from pandas import DataFrame
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error
-import numpy as np
-
-df = pd.read_csv("train/train.tsv", header=None, sep="\t", error_bad_lines=False, names=['price', 'mileage', 'year',
-                                                                                         'brand', 'engineType',
-                                                                                         'engineCapacity'])
-dev0 = pd.read_csv("dev-0/in.tsv", header=None, sep="\t", error_bad_lines=False, names=['mileage', 'year',
-                                                                                        'brand', 'engineType',
-                                                                                        'engineCapacity'])
-testA = pd.read_csv("test-A/in.tsv", header=None, sep="\t", error_bad_lines=False, names=['mileage', 'year',
-                                                                                          'brand', 'engineType',
-                                                                                          'engineCapacity'])
-expected = pd.read_csv("dev-0/expected.tsv", header=None, sep="\t", error_bad_lines=False, names=['price'])
-
-df = df[['price', 'year', 'mileage', 'engineCapacity']]
-
-min_val = np.min(df)
-max_val = np.max(df)
-df = (df - min_val) / (max_val - min_val)
-
-Y = df[['price']]
-X = df[['year', 'mileage', 'engineCapacity']]
-
-model = LinearRegression().fit(X, Y)
-
-dev0 = (dev0[['year', 'mileage', 'engineCapacity']] - min_val) / (max_val - min_val)
-testA = (testA[['year', 'mileage', 'engineCapacity']] - min_val) / (max_val - min_val)
-
-predicted_dev0 = model.predict(dev0[['year', 'mileage', 'engineCapacity']])
-predicted_testA = model.predict(testA[['year', 'mileage', 'engineCapacity']])
-
-predicted_denormalized = []
-for pred in predicted_dev0:
-    denorm = pred[0] * (max_val[0] - min_val[0]) + min_val[0]
-    predicted_denormalized.append(denorm)
-
-with open("dev-0/out.tsv", "w") as file:
-    for pred in predicted_denormalized:
-        file.write(str(pred) + "\n")
-
-predicted_denormalizedA = []
-for pred in predicted_testA:
-    denorm = pred[0] * (max_val[0] - min_val[0]) + min_val[0]
-    predicted_denormalizedA.append(denorm)
-
-with open("test-A/out.tsv", "w") as file:
-    for pred in predicted_denormalizedA:
-        file.write(str(pred) + "\n")
-
-
-predicted_denormalized = DataFrame(predicted_denormalized, columns=['pred'])
-error = mean_squared_error(expected, predicted_denormalized)
-for exp, pred in zip(expected.values, predicted_denormalized.values):
-    print(exp, pred)
-
-f = open("dev0_rmse.txt", "w")
-f.write(str(error))
-f.close()
-print(error)