import pandas as pd

from sklearn import preprocessing
from sklearn.model_selection import train_test_split


df = pd.read_csv('smart_grid_stability_augmented.csv')
min_max_scaler = preprocessing.MinMaxScaler()
df_norm_array = min_max_scaler.fit_transform(df.iloc[:, 0:-1])
df_norm = pd.DataFrame(data=df_norm_array,
                       columns=df.columns[:-1])
df_norm['stabf'] = df['stabf']

train, testAndValid = train_test_split(
    df_norm,
    test_size=0.2,
    random_state=42,
    stratify=df_norm['stabf'])

test, valid = train_test_split(
    testAndValid,
    test_size=0.5,
    random_state=42,
    stratify=testAndValid['stabf'])


def namestr(obj, namespace):
    return [name for name in namespace if namespace[name] is obj]


dataset = df_norm
for x in [dataset, train, test, valid]:
    print([q for q in namestr(x, globals()) if len(q) == max([len(w) for w in namestr(x, globals())])][-1])
    print("size:", len(x))
    print(x.describe(include='all'))
    print("class distribution", x.value_counts('stabf'))