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from sklearn.metrics import roc_curve, auc
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y[:, i], preds[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
    
# Compute micro-average ROC curve and ROC area

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import os
from IPython.core.display import Javascript
from IPython.display import display
import datetime
display(Javascript('Jupyter.notebook.kernel.execute(\
                       "this_notebook = " + "\'"\
                       +Jupyter.notebook.notebook_name+"\'");'))

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select B.*, A.*
from B
left outer join A  ON
(A.[id1] = B.[id1] or (coalesce(A.[id1],B.[id1]) is null)) 

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# matching functions from https://alexwlchan.net/2017/07/listing-s3-keys/
# https://alexwlchan.net/2018/01/listing-s3-keys-redux/

import boto3
import datetime
import os



def get_matching_s3_objects(bucket, prefix='', suffix=''):

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# Run this from the bash command prompt.  Make sure that jq is installed https://github.com/stedolan/jq/wiki/Installation
# json_temp.json has the file in the form [{...}, {...}, {...}] and coverts to {...}\n{...}\n

jq -c '.[]' json_temp.json > json_temp.jsonl

# From within python can do this:
pip install jsonlines

import json
import jsonlines

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# From https://stackoverflow.com/questions/14495176/compress-file-on-s3

aws s3 sync s3://your-pics .

for i in `find | grep -E "\.jpg$|\.jpg$"`; do gzip  "$i" ; echo $i;  done

aws s3 sync . s3://your-pics --content-encoding gzip --dryrun

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This is Bostock's interactive Reingold-Tilford Tree with data representing the rules of a simple sklearn decision tree. Click on nodes to expand or collapse.

For more, see the complete write-up.