sbalnojan

Privacy Policy version 1.0 for the Death Day Countdown/ Fuzzy Time Clock iOS app

The Death Day Countdown/ Fuzzy Time Clock iOS app does not collect any user data of any kind. The app does not access the Internet at all.

sbalnojan

import os
from sqlalchemy import create_engine, func
from sqlalchemy.orm.session import sessionmaker

import psycopg2
import logging

def get_db_connection():
    PGHOST = os.getenv("DB_HOST","localhost")
    PGDATABASE = "postgres"

sbalnojan

containers:
  build-env:
    image: python:3.7
    volumes:
      - local: .
        container: /src
        options: cached
      - local: .pip-cache
        container: /src/.pip-cache
        options: cached

sbalnojan

containers:
  build-env:
    image: python:3.7
    volumes:
      - local: .
        container: /src
        options: cached
      - local: .pip-cache
        container: /src/.pip-cache
        options: cached

sbalnojan

containers:
  build-env:
    image: python:3.7
    working_directory: /src
    environment:
      PYTHONPATH: "/src"
    run_as_current_user:
      enabled: true
      home_directory: /home/container-user

sbalnojan

print("finally, let's do some ranking...")
entity_count = 8
scores, _, _ = comparator(
    comparator.prepare(torch.tensor(src_embedding.reshape([1,1,10]))).expand(1, entity_count, 10),
    comparator.prepare(torch.tensor(dest_embeddings.reshape([1,8,10]))),
    torch.empty(1, 0, 10),  # Left-hand side negatives, not needed
    torch.empty(1, 0, 10),  # Right-hand side negatives, not needed
)
permutation = scores.flatten().argsort(descending=True)

sbalnojan

print("Now let's do some simple things within torch:")

from torchbiggraph.model import DotComparator
src_entity_offset = dictionary["entities"]["user_id"].index("0")  # France
dest_1_entity_offset = dictionary["entities"]["user_id"].index("7")  # Paris
dest_2_entity_offset = dictionary["entities"]["user_id"].index("1")  # Paris
rel_type_index = dictionary["relations"].index("follow") # note we only have one...

with h5py.File("model/example_2/embeddings_user_id_0.v10.h5", "r") as hf:
    src_embedding = hf["embeddings"][src_entity_offset, :]

sbalnojan

import os
import random

"""
adapted from https://github.com/facebookresearch/PyTorch-BigGraph/blob/master/torchbiggraph/examples/livejournal.py
"""
FILENAMES = {
    'train': 'train.txt',
    'test': 'test.txt',
}

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import json
import h5py

with open(os.path.join(DATA_DIR,"dictionary.json"), "rt") as tf:
    dictionary = json.load(tf)

user_id = "0"
offset = dictionary["entities"]["user_id"].index(user_id)
print("our offset for user_id " , user_id, " is: ", offset)

sbalnojan

from torchbiggraph.eval import do_eval

eval_path = [convert_path(os.path.join(DATA_DIR, FILENAMES['test']))]
eval_config = attr.evolve(train_config, edge_paths=eval_path)

do_eval(eval_config)