main.py

@@ -1,80 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error
-import numpy as np
-
-
-# Read column names
-col_names = []
-with open('names') as f:
-    col_names = f.read().strip().split('\t')
-
-# Read data
-dev = pd.read_table('dev-0/in.tsv', error_bad_lines=False,
-                    header=None, names=col_names[1:])
-test = pd.read_table('test-A/in.tsv', error_bad_lines=False,
-                     header=None,  names=col_names[1:])
-train = pd.read_table('train/train.tsv', error_bad_lines=False,
-                      header=None,  names=col_names)
-dev_expected = pd.read_table('dev-0/expected.tsv', error_bad_lines=False,
-                             header=None)
-
-# Create dummies for columns
-for c in train.select_dtypes(include=object).columns.values:
-    train[c] = train[c].astype("category").cat.codes
-for c in dev.select_dtypes(include=object).columns.values:
-    dev[c] = dev[c].astype("category").cat.codes
-for c in test.select_dtypes(include=object).columns.values:
-    test[c] = test[c].astype("category").cat.codes
-
-# Sprawdzanie ile jest odstających wartości dla price
-# fig, ax = plt.subplots(1, 2)
-# fig.set_figheight(15)
-# fig.set_figwidth(20)
-# ax[0].boxplot(train['price'])
-# ax[0].set_title('price')
-# ax[1].boxplot(train['mileage'])
-# ax[1].set_title('mileage')
-# plt.show()
-
-# Usunięcie odstających wartości
-priceMin = 0
-for price in train['price']:
-    if price < 1000:
-        priceMin += 1
-# print("Price min cut: " + str(priceMin))
-
-priceMax = 0
-for price in train['price']:
-    if price > 1000000:
-        priceMin += 1
-# print("Price max cut: " + str(priceMax))
-
-mileageMin = 0
-for m in train['mileage']:
-    if m < 100:
-        mileageMin += 1
-# print("Mileage min cut: " + str(mileageMin))
-
-train = train.loc[(train['price'] > 1000)]
-train = train.loc[(train['mileage'] > 100)]
-
-# Split train set to X and Y
-X_train = train.loc[:, train.columns != 'price']
-Y_train = train['price']
-
-# Create Linear regresion model
-clf = LinearRegression().fit(X_train, Y_train)
-
-# # Predict
-dev_p = clf.predict(dev)
-test_p = clf.predict(test)
-
-# # Accuracy
-score = mean_squared_error(dev_p, dev_expected, squared=False)
-print("RMSE: " + str(score))
-
-# # Save to files
-dev_p.tofile('./dev-0/out.tsv', sep='\n')
-test_p.tofile('./test-A/out.tsv', sep='\n')