Sklearn LogisticRegression solver needs 2 classes of data

Question

I'm trying to run a Logistic Regression via sklearn:

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:

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,)

Here is a df.head(3) example to see what my data looks like:

     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. two years old so I do not think that I am having the same issue.

Answer 1

You should make sure you have two unique values in Y[:i]. So before your loop, add something like:

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. Or even simpler, you can find the first occurrence where Y[i] != Y[0].

Answer 2

if i in range (0,3): 
    continue

Fixed this issue. Y[:i] was not unique before i = 3.