import pandas as pd
import matplotlib.pyplot as plt

# 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)
test_expected = pd.read_table('dev-0/expected.tsv', error_bad_lines=False,
                              header=None)

# Create dummies for brand
train = pd.get_dummies(train, columns=['engineType'])

# 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.loc[:, train.columns != 'price']
Y = train['price']

# print(train)
# print(col_names)