forked from s464914/ium_464914
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import pandas as pd
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.preprocessing import StandardScaler
|
|
|
|
def split(data):
|
|
forest_train, forest_test = train_test_split(data, test_size=0.2, random_state=1)
|
|
forest_train, forest_val = train_test_split(forest_train, test_size=0.25, random_state=1)
|
|
return forest_train, forest_test, forest_val
|
|
|
|
def normalization(data):
|
|
scaler = StandardScaler()
|
|
columns_to_normalize = data.columns[~data.columns.str.startswith('Soil_Type')]
|
|
columns_to_normalize = columns_to_normalize.to_list()
|
|
columns_to_normalize.remove('Cover_Type')
|
|
data[columns_to_normalize] = scaler.fit_transform(data[columns_to_normalize])
|
|
return data
|
|
|
|
def preprocessing(data):
|
|
#shuffle
|
|
data = data.sample(frac = 1)
|
|
return data
|
|
|
|
data = pd.read_csv("covtype.csv")
|
|
forest_train, forest_test, forest_val = split(data)
|
|
|
|
forest_train = preprocessing(forest_train)
|
|
forest_test = preprocessing(forest_test)
|
|
forest_val = preprocessing(forest_val)
|
|
|
|
forest_train = normalization(forest_train)
|
|
forest_test = normalization(forest_test)
|
|
forest_val = normalization(forest_val)
|
|
|
|
forest_train.to_csv('forest_train.csv', encoding='utf-8', index=False)
|
|
forest_test.to_csv('forest_test.csv', encoding='utf-8', index=False)
|
|
forest_val.to_csv('forest_val.csv', encoding='utf-8', index=False)
|
|
|
|
|