bhavsarpratik

PUT /test_index

{
	"mappings": {
		"properties": {
			"title": {
				"type": "text",
				"analyzer": "synonym_analyzer",
				"search_analyzer": "synonym_analyzer"
			},

bhavsarpratik

PUT /test_index

{
	"mappings": {
		"properties": {
			"title": {
				"type": "text",
				"analyzer": "synonym_analyzer",
				"search_analyzer": "synonym_analyzer"
			}

bhavsarpratik

# https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-synonym-tokenfilter.html
# Explicit mappings match any token sequence on the LHS of "=>"
# and replace with all alternatives on the RHS.  These types of mappings
# ignore the expand parameter in the schema.
# Examples:
i-pod, i pod => ipod,
sea biscuit, sea biscit => seabiscuit

# Equivalent synonyms may be separated with commas and give
# no explicit mapping.  In this case the mapping behavior will

bhavsarpratik

import os
from abc import ABCMeta, abstractmethod


class DataProcessor(metaclass=ABCMeta):
    """Base processor to be used for all preparation."""
    def __init__(self, input_directory, output_directory):
        self.input_directory = input_directory
        self.output_directory = output_directory

bhavsarpratik

[loggers]
keys=root

[logger_root]
level=INFO
handlers=screen,file

[formatters]
keys=simple

bhavsarpratik

import datetime
import json
import logging
import ntpath
import os


def create_folder(directory):
    try:
        if not os.path.exists(directory):

bhavsarpratik

from fastprogress.fastprogress import master_bar, progress_bar
from time import sleep
mb = master_bar(range(10))
for i in mb:
    for j in progress_bar(range(100), parent=mb):
        sleep(0.01)
        mb.child.comment = f'second bar stat'
    mb.first_bar.comment = f'first bar stat'
    mb.write(f'Finished loop {i}.')

bhavsarpratik

from tqdm import tqdm
import time

tqdm.pandas()

df['col'] = df['col'].progress_apply(lambda x: x**2)

text = ""
for char in tqdm(["a", "b", "c", "d"]):
    time.sleep(0.25)

bhavsarpratik

import json
import os

from sklearn.metrics import (accuracy_score, classification_report,
                             confusion_matrix, f1_score, fbeta_score)

def get_metrics(y, y_pred, beta=2, average_method='macro', y_encoder=None):
    if y_encoder:
        y = y_encoder.inverse_transform(y)
        y_pred = y_encoder.inverse_transform(y_pred)

bhavsarpratik

def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)