欠采样 numpy 阵列

Question

我有一个包含 10192 个“0”样本和 2512 个“1”样本的火车。
我在片场应用了 PCA 来降低维度。
我想对这个 numpy 数组进行欠采样。
这是我的代码：

df = read_csv("train.csv")
X = df.drop(['label'], axis = 1)
y = df['label']
from sklearn.model_selection import train_test_split

X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size = 0.2, random_state = 42)
model = PCA(n_components = 19)
model.fit(X_train)
X_train_pca = model.transform(X_train)
X_validation_pca = model.transform(X_validation)

X_train = np.array(X_train_pca)
X_validation = np.array(X_validation_pca)
y_train = np.array(y_train)
y_validation = np.array(y_validation)

如何从 X_train numpy 数组中对“0”class 进行欠采样？

Answer 1

将 csv 导入df后尝试：

# class count
count_class_0, count_class_1 = df.label.value_counts()

# separate according to `label`
df_class_0 = df[df['label'] == 0]
df_class_1 = df[df['label'] == 1]

# sample only from class 0 quantity of rows of class 1
df_class_0_under = df_class_0.sample(count_class_1)
df_test_under = pd.concat([df_class_0_under, df_class_1], axis=0)

然后对df_test_under数据框执行所有计算。

或者使用RandomUnderSampler ：

from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler(random_state=0)
X_resampled, y_resampled = rus.fit_resample(X, y)