abhijeet-talaulikar

def BayesianMMM(splits="W"):

    if splits == "Q":
      time_series = pd.PeriodIndex(dates, freq='Q').astype(str).str[-1].astype(int).values
    elif splits == "H":
      time_series = pd.PeriodIndex(dates, freq='Q').astype(str).str[-1].map({'1':1, '2':1, '3':2, '4':2}).values
    elif splits == "YoY":
      time_series = np.array([1]*52 + [2]*52)
    else:
      time_series = np.arange(104)

egemenzeytinci

from rfpimp import permutation_importances
from sklearn.base import clone
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import pandas as pd


def imp_df(column_names, importances):
    data = {

vlavorini

from scipy.stats import beta
import numpy as np
from calc_prob import calc_prob_between

#This is the known data: imporessions and conversions for the Control and Test set
imps_ctrl,convs_ctrl=16500, 30 
imps_test, convs_test=17000, 50

#here we create the Beta functions for the two sets
a_C, b_C = convs_ctrl+1, imps_ctrl-convs_ctrl+1

mnguyenngo

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

def z_val(sig_level=0.05, two_tailed=True):
    """Returns the z value for a given significance level"""
    z_dist = scs.norm()
    if two_tailed:
        sig_level = sig_level/2
        area = 1 - sig_level