MaxHalford

import keras


class EvalCallback(keras.callbacks.Callback):

    def __init__(self, X_val, y_val, metric):
        super().__init__()
        self.X_val = X_val
        self.y_val = y_val
        self.metric = metric

MaxHalford

import collections
import itertools


df = pd.DataFrame({
    'a': [1, 0, 0, 0],
    'b': [0, 1, 0, 0],
    'c': [0, 0, 1, 0],
    'd': [0, 0, 0, 1],
    'e': [1, 1, 0, 0],

MaxHalford

import glob

import imageio
from keras import layers
from keras import losses
from keras import models
from keras import optimizers
import numpy as np
import pandas as pd

MaxHalford

"""
Rating percentage index (RPI).

Example taken from https://www.wikiwand.com/en/Rating_percentage_index#/Basketball_formula
"""

X = pd.DataFrame(
    data=[
        (2010, 'UConn', 64, 'Kansas', 57),
        (2010, 'UConn', 82, 'Duke', 68),

MaxHalford

MaxHalford

import lightgbm as lgb
from sklearn import datasets
from sklearn import model_selection

X, y = datasets.load_boston(return_X_y=True)
X_fit, X_val, y_fit, y_val

model = lgb.LGBMRegressor()
model.fit(X_fit, y_fit)

MaxHalford

MaxHalford

import numpy as np

goal_fracs = np.array([.1, .9])
goal_fracs = np.array([.04, .04, .11, .14, .67])
counts = np.zeros_like(goal_fracs)
n = 1
tol = .001

a = 0

MaxHalford

>>> from creme import stats

>>> mean = stats.Mean()

>>> mean.update(5)
>>> mean.update(10)
>>> mean
7.5

>>> mean.update(6)

MaxHalford

>>> from creme import feature_extraction
>>> from creme import stats

>>> X = [
...     {'place': 'Taco Bell', 'revenue': 42},
...     {'place': 'Burger King', 'revenue': 16},
...     {'place': 'Burger King', 'revenue': 24},
...     {'place': 'Taco Bell', 'revenue': 58},
...     {'place': 'Burger King', 'revenue': 20},
...     {'place': 'Taco Bell', 'revenue': 50}