[imblearn] SMOTE dealing with inbalanced datasets

import numpy as np
import pandas as pd
from pandas import DataFrame, Series

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

from imblearn.over_sampling import SMOTE
df = make_classification(n_samples = 100000, n_features = 10, n_informative = 2, n_redundant = 0,
                         n_repeated = 0, n_classes = 2, n_clusters_per_class = 2, weights = [0.9999, 0.0001],
                         flip_y = 0, class_sep = 1.0, hypercube = True, shift = 0.0,
                         scale = 1.0, shuffle = True, random_state = 71)

df_raw = DataFrame(df[0], columns = ['var1', 'var2', 'var3', 'var4', 'var5', 'var6', 'var7', 'var8', 'var9', 'var10'])
df_raw['Class'] = df[1]

print(df_raw['Class'].value_counts())

X = df_raw.iloc[:, 0:10]
y = df_raw['Class']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 71)

smote = SMOTE(sampling_strategy=0.1, random_state=72)
X_train_resampled, y_train_resampled = smote.fit_sample(X_train, y_train)

print(X_train_resampled.shape)
print(y_train_resampled.shape)