Question

我正在使用线性回归分类器来预测一些值。我已经弄清楚了基本内容，现在看起来像这样：

import time as ti
import pandas as pd 
import numpy as np
from matplotlib import pyplot as plt 
import csv
from sklearn.datasets import load_boston
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from scipy.interpolate import * 
import datetime

data = pd.read_csv(r"C:\Users\simon\Desktop\Datenbank\visualisierung\includes\csv.csv")         
x = np.array(data["day"])   
y = np.array(data["balance"])

reg = linear_model.LinearRegression()
X_train, X_test, y_train, y_test, i_train, i_test = train_test_split(x, y, data.index, test_size=0.2, random_state=4)

X_train = X_train.reshape(-1, 1)
X_test = X_test.reshape(-1, 1)

i_train = i_train.values.reshape(-1, 1)
i_test = i_test.values.reshape(-1, 1)


reg.fit(i_train, y_train)

print(reg.score(i_test, y_test))

252128,6/6/19
252899,7/6/19
253670,8/6/19
254441,9/6/19

我总共有27行。

由于某种原因它不起作用。

UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.

dtype和形状为：

X_train, X_test = object #dtype
X_train = (21,)  #shape
X_test = (6,)    #shape

y_train, y_test = int64 #dtype
y_train, y_test = (1, 21) #shape

i_train, i_test = int64 #dtype
i_train, i_test = (1, 21) #shape

X_train，X_test，y_train，y_test，i_train，i_test都是：

<class 'numpy.ndarray'>

我可以想象那是因为我没有足够的例子。

为什么会发生这种情况，我该如何预防？

Answer 1

根据sklearn documentation的建议：

X : array-like or sparse matrix, shape (n_samples, n_features)
    Training data

y : array_like, shape (n_samples, n_targets)
    Target values. Will be cast to X’s dtype if necessary

因此，如果您的数据集仅包含1个特征，则需要使用以下方法重塑训练和测试集：

X_train = X_train.reshape(-1, 1)
X_test = X_test.reshape(-1, 1)
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)

，其余代码应正常工作。

按照OP的规范，数据集似乎是一个时间序列。线性回归不能正确地对数据建模，但是，作为一个有趣的玩具示例，您可以将日期转换为POSIX时间，分割数据并测试不同的算法。

假设您的数据集：

    balance day
0   252128  6/6/19
1   252899  7/6/19
2   253670  8/6/19
3   254441  9/6/19
4   255944  10/6/19
5   256041  11/6/19
6   256670  12/6/19
7   257441  13/6/19
8   258128  14/6/19
9   258899  15/6/19
10  259670  16/6/19
11  260241  17/6/19
12  260444  18/6/19
13  260341  19/6/19
14  260670  20/6/19
15  261441  21/6/19

您可以通过以下方式修改代码：

import pandas as pd
from sklearn import linear_model

data = pd.read_csv('csv.csv')

X = pd.to_datetime(data['day'])
# convert to POSIX time by dividing by 10**9
X = X.astype("int64").values.reshape(-1, 1) // 10**9
y = data['balance']

# split the data
X_train = X[:12]
y_train = y[:12]
X_test = X[-4:]
y_test = y[-4:]

reg.fit(X_train, y_train)

print(reg.score(X_test, y_test))

reg.predict(X_test)

您会得到什么？一个非常糟糕的解决方案。

少于两个样本的R ^ 2得分定义不明确。 Python Sklearn

1 个答案: