amrakm

grep -Hrn "import re" . --include \*.ipynb

amrakm

from psycopg2.extras import execute_batch

def commit(self,vals)->None:
        conn  = self.conn
        cursor = conn.cursor()
        sql = """                                                                                                                                                                                           
        update "movies"                                                                                                                                                                                     
        set imdb_vote_count = %(votes)s,                                                                                                                                                                    
        imdb_rating = %(rating)s,                                                                                                                                                                           
        "updatedAt" = now()                                                

amrakm

# Create logger
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

# Create STDERR handler
handler = logging.StreamHandler(sys.stderr)
# ch.setLevel(logging.DEBUG)

# Create formatter and add it to the handler
formatter = logging.Formatter('%(name)s - %(levelname)s - %(message)s')

amrakm

import tensorflow as tf
strategy = tf.distribute.MirroredStrategy()
print('Number of devices: {}'.format(strategy.num_replicas_in_sync)) # it prints 2 .. correct


with strategy.scope():

    embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder-cmlm/multilingual-base/1")

amrakm

import transformers
import torch
from collections import defaultdict
from transformers import pipeline


text_batch = """Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of machine learning which is about analyzing any text and handling predictive analysis."""

kw_list = ['computer', 'machine learning', 'knowledge', 'language']

amrakm

import psycopg2
import logging

    
def execute_sql(sql, conn_str):
    
    conn =  psycopg2.connect(conn_str)
    cursor = conn.cursor()
    
    try:

amrakm

#source: https://stackoverflow.com/a/42012264


def melt_list_column(df, lst_col):
    
    return pd.DataFrame({
                col:np.repeat(df[col].values, df[lst_col].str.len())
                for col in df.columns.difference([lst_col])
            }).assign(**{lst_col:np.concatenate(df[lst_col].values)})[df.columns.tolist()]

amrakm

#source: stackoverflow answer, can't find the link anymore

import numpy as np

a = np.array([[[10,2],
               [5,3],
               [4,4]],

              [[7,6],
               [4,2],

amrakm

#source: https://www.maartengrootendorst.com/blog/concept/

import random
import nltk
nltk.download("wordnet")
from nltk.corpus import wordnet as wn

all_nouns = [word for synset in wn.all_synsets('n') for word in synset.lemma_names() 
             if "_" not in word]
selected_nouns = random.sample(all_nouns, 50_000)

amrakm

from scipy.sparse import coo_matrix



def create_sparse_matrix(data, iid2cf_idx, uid2cf_idx, item_id_column, user_id_column):
    
    max_item_idx = max(iid2cf_idx.values())
    max_user_idx = max(uid2cf_idx.values())