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对于回归问题，我有一个训练数据集： - 3 个具有高斯分布的变量 - 20 个具有均匀分布的变量。

我所有的变量都是连续的，在 [0;1] 之间。

问题是用于对我的回归模型进行评分的测试数据对所有变量具有均匀分布。实际上，我在尾部分布上的结果很差，所以我想对我的训练集进行过采样，以复制最稀有的行。

所以我的想法是在我的训练集上引导（使用带替换的抽样），以获得一组与测试集具有相同分布的数据。

为了做到这一点，我的想法（不知道它是否是一个好的！）是为我的 3 个变量添加 3 个带有间隔的列，并使用这些列对重采样进行分层。

示例：首先，生成数据

from scipy.stats import truncnorm
def get_truncated_normal(mean=0.5, sd=0.15, min_value=0, max_value=1):
    return truncnorm(
        (min_value - mean) / sd, (max_value - mean) / sd, loc=mean, scale=sd)

generator = get_truncated_normal()


import numpy as np
from sklearn.preprocessing import MinMaxScaler
S1 = generator.rvs(1000)
S2 = generator.rvs(1000)
S3 = generator.rvs(1000)
u = np.random.uniform(0, 1, 1000)

然后检查分布：

import seaborn as sns
sns.distplot(u);
sns.distplot(S2);

没关系，所以我会添加类别列

import pandas as pd
df = pd.DataFrame({'S1':S1,'S2':S2,'S3':S3,'Unif':u})

BINS_NUMBER = 10
df['S1_range'] = pd.cut(df.S1, 
                            bins=BINS_NUMBER, 
                            precision=6,
                            right=True, 
                            include_lowest=True)
df['S2_range'] = pd.cut(df.S2, 
                            bins=BINS_NUMBER, 
                            precision=6,
                            right=True, 
                            include_lowest=True)
df['S3_range'] = pd.cut(df.S3, 
                            bins=BINS_NUMBER, 
                            precision=6,
                            right=True, 
                            include_lowest=True)

支票

df.groupby('S1_range').size()

S1_range
(0.022025899999999998, 0.116709]      3
(0.116709, 0.210454]                 15
(0.210454, 0.304199]                 64
(0.304199, 0.397944]                152
(0.397944, 0.491689]                254
(0.491689, 0.585434]                217
(0.585434, 0.679179]                173
(0.679179, 0.772924]                 86
(0.772924, 0.866669]                 30
(0.866669, 0.960414]                  6
dtype: int64

这对我有好处。所以现在我将尝试重新采样，但它没有按预期工作

from sklearn.utils import resample
df_resampled = resample(df,replace=True,n_samples=1000, stratify=df['S1_range'])

df_resampled.groupby('S1_range').size()

S1_range
(0.022025899999999998, 0.116709]      3
(0.116709, 0.210454]                 15
(0.210454, 0.304199]                 64
(0.304199, 0.397944]                152
(0.397944, 0.491689]                254
(0.491689, 0.585434]                217
(0.585434, 0.679179]                173
(0.679179, 0.772924]                 86
(0.772924, 0.866669]                 30
(0.866669, 0.960414]                  6
dtype: int64

所以它不起作用，我在输出中得到与输入相同的分布......

你能帮助我吗 ？也许这不是这样做的好方法？

谢谢 ！！

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2 回答 2

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与其从头开始编写代码来重新采样连续数据，不如利用库来重新采样回归数据。

尽管流行的库（不平衡学习等）专注于分类（分类）变量，但最近有一个 Python 库（称为 resreg - RESampling for REgression）允许您对连续数据进行重新采样（resreg GitHub 页面）

此外，您可能希望在正态分布变量的尾部生成合成数据点，而不是自举，因为这样做可能会产生更好的结果（请参阅本文）。与用于分类的 SMOTE 类似，它在特征之间进行插值，您可以使用 resreg 包中的 SMOTER（用于回归的 SMOTE）在回归/连续数据中生成合成值。

下面是一个示例，说明如何使用 resreg 通过几行代码实现重采样：


import numpy as np
import resreg


cl = np.percentile(y,10)  # Oversample values less than the 10th percentile
ch = np.percentile(y,90)  # Oversample values less than the 10th percentile


# Assign relevance scores to indicate which samples in your dataset are
# to be resampled. Values below cl and above ch are assigned a relevance 
# value above 0.5, other values are assigned a relevance value above 0.5

relevance = resreg.sigmoid_relevance(X, y, cl=cl, ch=ch)


# Resample the relevant values (i.e relevance >= 0.5) by interpolating 
# between nearest k-neighbors (k=5). By setting over='balance', the 
# relevant values are oversampled so that the number of relevant and
# irrelevant values are equal

X_res, y_res = resreg.smoter(X, y, relevance=relevance, relevance_threshold=0.5, k=5, over='balance', random_state=0)
于 2020-10-08T20:27:57.707 回答
0

我的解决方案：

def create_sampled_data_set(n_samples_by_bin=1000,
                            n_bins=10,
                            replace=True,
                            save_csv=True):
    """In order to have the same distribution for S1..S3 between training
    set and test set, this function will generate a new
    training set resampled

    Return: (X_train, y_train)
    """
    def stratified_sample_df_(df, col, n_samples, replace=True):
        if replace:
            n = n_samples
        else:
            n = min(n_samples, df[col].value_counts().min())

        df_ = df.groupby(col).apply(lambda x: x.sample(n, replace=replace))
        df_.index = df_.index.droplevel(0)
        return df_

    X_train, y_train = load_data_for_train()

    # merge the dataframe for the sampling. Target will be removed after
    X_train = pd.merge(
        X_train, y_train[['Target']], left_index=True, right_index=True)
    del y_train

    # build a categorical feature, from S1..S3 distribution
    disc = KBinsDiscretizer(n_bins=n_bins, encode='ordinal', strategy='kmeans')
    disc.fit(X_train[['S1', 'S2', 'S3']])
    y_bin = disc.transform(X_train[['S1', 'S2', 'S3']])
    del disc
    vint = np.vectorize(np.int)
    y_bin = vint(y_bin)

    y_concat = []
    for i in range(len(y_bin)):
        a = y_bin[i, 0].astype('str')
        b = y_bin[i, 1].astype('str')
        c = y_bin[i, 2].astype('str')
        y_concat.append(a + ';' + b + ';' + c)
    del y_bin

    X_train['S_Class'] = y_concat
    del y_concat

    X_train_resampled = stratified_sample_df_(
        X_train, 'S_Class', n_samples_by_bin)
    del X_train
    y_train_resampled = X_train_resampled[['Target']].copy()
    y_train_resampled.rename(
        columns={y_train_resampled.columns[0]: 'Target'}, inplace=True)

    X_train_resampled = X_train_resampled.drop(['S_Class', 'Target'], axis=1)

    # save in file for further usage
    if save_csv:
        X_train_resampled.to_csv(
            "./data/training_input_resampled.csv", sep=",")
        y_train_resampled.to_csv(
            "./data/training_output_resampled.csv", sep=",")

    return(X_train_resampled,
           y_train_resampled)
于 2020-01-01T16:11:26.470 回答