geblanco

# !pip install requests urllib3 geopy pandas folium
# partially taken from:
# https://towardsdatascience.com/pythons-geocoding-convert-a-list-of-addresses-into-a-map-f522ef513fd6\

import requests
import json
import urllib
from geopy.geocoders import Nominatim, GoogleV3
import pandas as pd
import folium

geblanco

# extracted from: https://github.com/scikit-learn/scikit-learn/blob/0fb307bf3/sklearn/metrics/_classification.py#L1825
def classification_report(data_dict):
    """Build a text report showing the main classification metrics.
    Read more in the :ref:`User Guide <classification_report>`.
    Parameters
    ----------
    report : string
        Text summary of the precision, recall, F1 score for each class.
        Dictionary returned if output_dict is True. Dictionary has the

geblanco

5a6,8
> import json
> 
> import sklearn.metrics as metrics
6a10,11
> from sklearn.metrics import precision_recall_curve
> from dvc.api import make_checkpoint
10c15
< if len(sys.argv) != 3:
---

geblanco

// requires canvas, npm i canvas

var Canvas = require('canvas')
var fs     = require('fs')

// Constants
var DIM = { width : 24, border : 1.3, font : 14 }
var COLOR = { light: '#ec9a97', text:'#3189cc' , border:'#e13d35' }

function paintCircularText(filePath=null, text=null, fill=false, border=true, callback=null){

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"""Official evaluation script for SQuAD version 2.0.

In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable.
"""
import argparse
import collections
import json

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import os
import sys
import json

languages = dict(
  python=dict(ext='py', comment='#'),
  javascript=dict(ext='js', comment='//')
)

if len(sys.argv) < 2: