DavidWalz

import botorch
import gpytorch
import matplotlib.pyplot as plt
import torch
from botorch.acquisition.monte_carlo import qExpectedImprovement
from botorch.fit import fit_gpytorch_model
from botorch.models import ModelListGP, SingleTaskGP
from botorch.models.gpytorch import GPyTorchModel
from functools import partial
from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood

DavidWalz

import mlflow
import numpy as np
import matplotlib.pyplot as plt


# get runs
exp_name = "experiment_name"
exp_id = mlflow.get_experiment_by_name(exp_name).experiment_id
runs = mlflow.search_runs(exp_id)

DavidWalz

import scipy.stats
def clopper_pearson(k,n,alpha=0.32):
    """
    http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval
    alpha confidence intervals for a binomial distribution of k expected successes on n trials
    Clopper Pearson intervals are a conservative estimate.
    """
    lo = scipy.stats.beta.ppf(alpha/2, k, n-k+1)
    hi = scipy.stats.beta.ppf(1 - alpha/2, k+1, n-k)
    return lo, hi