ytsaig

import lightgbm as lgb
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error

def custom_subsample(n, frac):
    """Subsample frac*n indices."""
    return np.random.choice(n, int(n*frac), replace=False)

# load data

ytsaig

import numpy as np
from sklearn import datasets
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import OneHotEncoder
import xgboost as xgb


def softmax(z):
    z -= np.max(z)
    sm = (np.exp(z).T / np.sum(np.exp(z), axis=1)).T