MaxHalford

import numpy as np


combos = []

def generate(remainder, n, current_combo=[], i=0, step=0.1):
    if n == 1:
        combos.append(np.round(current_combo + [remainder], 1))
        return
    for p in np.arange(step, remainder - (n-2) * step - step / 10, step):

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import math

import matplotlib.pyplot as plt
import numpy as np
from scipy import stats


# Generate some data
a = 1.99

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curl -O https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh
bash Anaconda3-5.1.0-Linux-x86_64.sh  # Say "yes" and "yes"

anaconda3/bin/pip install kaggle
anaconda3/bin/pip install lightgbm

mkdir .kaggle
echo "{"username":"maxhalford", "key":"secret"}" >> ~/.kaggle/kaggle.json
chmod 600 ~/.kaggle/kaggle.json

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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

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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],

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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

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import itertools

from keras import layers as l
from keras import models as m
from keras import preprocessing as p
import numpy as np
import pandas as pd


def generate_series(paths, prepare, chunk_size=1e6):

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"""
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),

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