mdbecker

import hashlib
import json
import random
from datetime import datetime

import diffusers
import piexif
import torch
from diffusers import FluxPipeline
from PIL import Image

mdbecker

import mailbox
import csv
import email.utils
from collections import defaultdict
import argparse
from pathlib import Path
from bs4 import BeautifulSoup
from tqdm import tqdm

# Constants for field names

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:

```

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

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{"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,

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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

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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):

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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

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

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  "name": "Data Philly"
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 "worksheets": [
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   "cells": [
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