[英]Sklearn LogisticRegression solver needs 2 classes of data
I'm trying to run a Logistic Regression via sklearn: 我正在尝试通过sklearn运行Logistic回归:
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
import datetime as dt
import pandas as pd
import numpy as np
import talib
import matplotlib.pyplot as plt
import seaborn as sns
col_names = ['dates','prices']
# load dataset
df = pd.read_csv("DJI2.csv", header=None, names=col_names)
df.drop('dates', axis=1, inplace=True)
print(df.shape)
df['3day MA'] = df['prices'].shift(1).rolling(window = 3).mean()
df['10day MA'] = df['prices'].shift(1).rolling(window = 10).mean()
df['30day MA'] = df['prices'].shift(1).rolling(window = 30).mean()
df['Std_dev']= df['prices'].rolling(5).std()
df['RSI'] = talib.RSI(df['prices'].values, timeperiod = 9)
df['Price_Rise'] = np.where(df['prices'].shift(-1) > df['prices'], 1, 0)
df = df.dropna()
xCols = ['3day MA', '10day MA', '30day MA', 'Std_dev', 'RSI', 'prices']
X = df[xCols]
X = X.astype('int')
Y = df['Price_Rise']
Y = Y.astype('int')
logreg = LogisticRegression()
for i in range(len(X)):
#Without this case below I get: ValueError: Found array with 0 sample(s) (shape=(0, 6)) while a minimum of 1 is required.
if(i == 0):
continue
logreg.fit(X[:i], Y[:i])
However, when i try to run this code I get the following error: 但是,当我尝试运行此代码时,出现以下错误:
ValueError:
This solver needs samples of at least 2 classes in the data, but the data contains only one class: 58
The shape of my X data is: (27779, 6)
The shape of my Y data is: (27779,)
我的X数据的形状为:
(27779, 6)
我的Y数据的形状为: (27779,)
Here is a df.head(3)
example to see what my data looks like: 这是一个
df.head(3)
示例,以查看我的数据是什么样的:
prices 3day MA 10day MA 30day MA Std_dev RSI Price_Rise
30 58.11 57.973333 57.277 55.602333 0.247123 81.932338 1
31 58.42 58.043333 57.480 55.718667 0.213542 84.279674 1
32 58.51 58.216667 57.667 55.774000 0.249139 84.919586 0
I've tried searching for where I am getting this issue from myself, but I've only managed to find these two answers, both of which discuss the issue as a bug in sklearn, however they are both approx. 我曾尝试搜索自己从何处获得此问题,但我仅设法找到了这 两个答案, 这 两个问题都作为sklearn中的错误进行了讨论,但是两者都差不多。 two years old so I do not think that I am having the same issue.
两岁,所以我不认为我遇到了同样的问题。
You should make sure you have two unique values in Y[:i]. 您应该确保在Y [:i]中有两个唯一值。 So before your loop, add something like:
因此,在循环之前,请添加以下内容:
starting_i = 0
for i in range(len(X)):
if np.unique(Y[:i]) == 2:
starting_i = i
Then just check that starting_i isn't 0 before running your main loop. 然后只需在运行主循环之前检查start_i不为0。 Or even simpler, you can find the first occurrence where Y[i] != Y[0].
或更简单地说,您可以找到第一个出现的地方,其中Y [i]!= Y [0]。
if i in range (0,3):
continue
Fixed this issue. 解决了此问题。 Y[:i] was not unique before i = 3.
Y [:i]在i = 3之前不是唯一的。
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