Uplift Curves when random is negative with normalization about causalml HOT 2 CLOSED

import pandas as pd
from causalml.metrics import *


y = np.random.normal(-0.5, 2, 1000)
x1 = np.random.normal(-10, 10, 1000)
x2 = np.random.normal(0, 10, 1000)
x3 = np.random.normal(10, 10 , 1000)
w = np.random.randint(2, size=1000)

df = pd.DataFrame([y, w, x1, x2, x3]).T
df.columns = ['y', 'w', 'model1', 'model2', 'model3']

plot_gain(df, outcome_col='y', treatment_col='w',
              normalize=False, random_seed=42, n=100, figsize=(8, 8))

plot_gain(df, outcome_col='y', treatment_col='w',
              normalize=True, random_seed=42, n=100, figsize=(8, 8))