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# Image processing function

# Usage: In the Terminal, run: bash images.sh

# Get user input and resize images
function process () {
  # rename
  a=1
  for i in *.${ext}
  do

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1. Clone your fork:

git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

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class Vertex(object):
    """represent vertices in a tree/graph."""
    def __init__(self, name):
        self.name = name
        self.children = {}
        self.status = None
        
    def add_child(self, child):
        key = child.name
        self.children[key] = child

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def create_tokens(source):
    """convert source to tokens.
    
    convert a string "(first (list 1 (+ 2 3) 9))" into a python list 
    ['(', 'first', '(', 'list', '1', '(', '+', '2', '3', ')', '9', ')', ')'].
    
    this conversion allows us to recursively inspect each element in the list.
    """
    return source.replace('(',' ( ').replace(')',' ) ').split()

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"""
graph examples collected from https://www.cs.hmc.edu/~keller/courses/cs60/s98/examples/acyclic/

in the first example, we have an acylic graph. there exists k such its adjacency 
matrix m^k == 0. because we cant produce a path of arbitrarily high length (eg
greater than k), the graph does not have a cycle.

in the second example, we have a cyclic graph. there is no k where its adjacency 
matrix m^k == 0.

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# we know the following graph is nilpontent
m = np.array([[0, 1, 0, 0, 0, 0], 
              [0, 0, 1, 1, 0, 0], 
              [0, 0, 0, 0, 0, 0], 
              [0, 0, 0, 0, 1, 1], 
              [0, 0, 0, 0, 0, 1], 
              [0, 0, 1, 0, 0, 0]])
              
for k in range(len(m)):
    print(m)

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Generating image embeddings for Hub

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Updates Ranker

Overview

The Updates Ranker ranks notification types (eg thank you coupons).

Surface

The Updates Ranker will be used in the Deal tab.

Runbook

Generate User and Global Notification Features

Notification features (eg user click rate for various notification types, triggers, and sources) are generated via a Sparkly job. These features are moved to FBv2 via the recs_ranking_updates_features_fbv2 DAG

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import requests
import json
import numpy as np

url = 'http://mmx-recsys-ingress.flop.search-infra.etsy-searchinfra-gke-prod-2.etsycloud.com/apiv2/general/rec_set/dynamic/recs'

headers = {
    'accept': 'application/json',
    'Content-Type': 'application/json',
}

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from glob import glob
import json
from tqdm import tqdm


# raw treatment -> gs://etsy-recsys-ml-dev-data-nxsn/user/vtang/updates-ranker-v1/metrics/prod_updates_2
prod_fp = glob("./prod_updates_2/*")
prod_updates = {}  # key = notification_feed_id, value = notification_type of candidate position 0

for fp in tqdm(prod_fp):