mdbecker

from multiprocessing import Pool as MPool
from time import sleep
import datetime
import multiprocessing
import random

def time_request():
    from gevent import monkey; monkey.patch_socket
    from jsonrequester import JsonRequester

mdbecker

{
 "metadata": {
  "name": "Data Philly"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

mdbecker

Keybase proof

I hereby claim:

• I am mdbecker on github.
• I am mdbecker (https://keybase.io/mdbecker) on keybase.
• I have a public key whose fingerprint is 3269 BEE3 B3B2 23ED 1478 2F5B 73DE 3334 40FF FBF7

To claim this, I am signing this object:

mdbecker

from sklearn import metrics

def binary_cv_metrics(y, preds, m):
    ACC = metrics.accuracy_score(y,preds)
    cm = metrics.confusion_matrix(y,preds)

    m['confusion_matrix'] = cm
    m['Accuracy'] = ACC
    m['F1 score'] = metrics.f1_score(y,preds)
    m['FPR'] = cm[0,1]/(cm[0,:].sum()*1.0)

mdbecker

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt


class _MiniBoxPlotter(sns.categorical._ViolinPlotter):

    def draw_violins(self, ax):
        """Draw the violins onto `ax`."""
        for i, group_data in enumerate(self.plot_data):

mdbecker

from os import getpid, kill
from time import sleep
import re
import signal

from notebook.notebookapp import list_running_servers
from requests import get
from requests.compat import urljoin
import ipykernel
import json

mdbecker

{"date":{"4":1576826679362,"5":1576826680953,"6":1576826682705,"7":1576826715094,"8":1576826738398,"9":1576826749536,"10":1576826964746,"11":1576827009901,"12":1576827049302,"13":1576827049369,"14":1576827067127,"15":1576827067174,"16":1576827067715,"17":1576827071028,"18":1576827128560,"19":1576827181988,"20":1576827228449,"21":1576827233823,"22":1576827236225,"23":1576827244532,"24":1576827326470,"25":1576827331045,"26":1576827338079,"27":1576827342801,"28":1576827342887,"29":1576827362202,"30":1576827369175,"31":1576827406098,"32":1576827475226,"33":1576827479353,"34":1576827479381,"35":1576827481299,"36":1576827481300,"37":1576827484089,"38":1576827484095,"39":1576827495704,"40":1576827501289,"41":1576827508178,"42":1576827515849,"154":1576832407342,"155":1576832407392,"156":1576832428810,"157":1576832428828,"158":1576832429440,"179":1576840413638,"180":1576840413746,"181":1576840440551,"182":1576840440565,"183":1576840441194,"184":1576841274254,"185":1576841274351,"186":1576841285635,"187":1576841285658,

mdbecker

initial_susceptible # defaults to 3,600,000 https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/presentation.py#L200-L206
initial_infected # https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/models.py#L25-L27
initial_recovered # https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/models.py#L34
beta = # https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/models.py#L42-L45
gamma = # https://github.com/CodeForPhilly/chime/blob/2895a9c4ddcf42b3c96bcf7e03a7e2a15f4983de/src/penn_chime/models.py#L39
n_days = # User input, default to 60 or something

def sir(s, i, r, beta, gama, n):
    """The SIR model, one time step."""
    s_n = (-beta * s * i) + s

mdbecker

"""
Fixes https://github.com/scikit-learn/scikit-learn/issues/12052
CalibratedClassifierGroupCV is a drop in replacment for CalibratedClassifierCV that supports GroupKFold cv.

This is based off of https://github.com/scikit-learn/scikit-learn/blob/0.24.1/sklearn/calibration.py.
If you are using a different version of sklearn, you can make similar modifications to your version.

Example usage:

```