R^2 score is not well-defined with less than two samples. Python Sklearn

Question

I am using a Linear Regression classifier to predict some values. I already figured the basic part of the out and now it looks like this:

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

I have 27 rows of those in total.

It doesn't work for some reason.

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

The dtypes and shapes are:

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 are all a:

<class 'numpy.ndarray'>

I could imagine that thats because i dont have enough examples.

Why does this happen and how can i prevent it?

tell us about the arrays, x, X_train etc. Shape and dtype. — hpaulj
Review the required dimensions. Often first dimension is samples, 2nd is features — hpaulj
Why do you pass the indices i_train to the fit function instead of the features X_train? — AlCorreia
because for the linear regression classifier the x achse isnt really mattering and for some reason classifiers only take floats as input — python_beginner

sentence sentence · Accepted Answer · 2019-06-23T19:14:51

As suggested by sklearn documentation:

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

Therefore, if your dataset consists of only 1 feature, you need to reshape your training and test sets using:

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)

and the rest of your code should work properly.

After OP's specifications, the dataset seems to be a time series. Linear Regression is not going to properly model your data, but, as a toy example to have fun with, you can convert dates to POSIX time, split the data, and test different algorithms.

Assuming you dataset:

    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

you can modify the code this way:

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)

What do you get? A very poor solution.

R^2 score is not well-defined with less than two samples. Python Sklearn

1 Answers