Analytics India Magazine analyticsindiamagazine

Analytics India Magazine chronicles technological progress in the space of analytics, artificial intelligence, data science & big data.

analyticsindiamagazine / app.py

Created December 9, 2019 05:29

	#Importing necessary packages
	import numpy as np
	from flask import Flask, request, render_template
	import pickle
	from fastai.tabular import *
	import os

	#Saving the working directory and model directory
	cwd = os.getcwd()
	path = cwd + '/model'

analyticsindiamagazine / index.html

Created December 9, 2019 05:27

	<!DOCTYPE html>
	<html >
	<!--From https://codepen.io/frytyler/pen/EGdtg-->
	<head>
	<meta charset="UTF-8">
	<title>Find The Resale Price Of Your Car Now!!</title>
	<meta name="viewport" content="width=device-width, initial-scale=1">
	<link href='https://fonts.googleapis.com/css?family=Pacifico' rel='stylesheet' type='text/css'>
	<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Raleway">
	<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">

analyticsindiamagazine / SimpleTransformers_classification.ipynb

Created December 3, 2019 09:43

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analyticsindiamagazine / ST_default_args.py

Created December 3, 2019 09:24

	self.args = {
	"output_dir": "outputs/",
	"cache_dir": "cache_dir/",
	"fp16": True,
	"fp16_opt_level": "O1",
	"max_seq_length": 128,
	"train_batch_size": 8,
	"gradient_accumulation_steps": 1,
	"eval_batch_size": 8,
	"num_train_epochs": 1,

analyticsindiamagazine / testing_prep.py

Last active December 2, 2019 03:26

	#TESTING
	max_seq_length = 128 #This number will determine the number of tokens

	#An example for tokenization
	s1 = train['STORY'].iloc[0]
	stokens1 = tokenizer.tokenize(s1)
	stokens1 = ["[CLS]"] + stokens1 + ["[SEP]"]

	input_ids1 = get_ids(stokens1, tokenizer, max_seq_length)
	input_masks1 = get_masks(stokens1, max_seq_length)

analyticsindiamagazine / bclassifier.py

Created November 29, 2019 10:04

	input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,
	name="input_word_ids")
	input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,
	name="input_mask")
	segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32,
	name="segment_ids")
	bert_inputs = [input_word_ids, input_mask, segment_ids]

	pooled_output, _ = bert_layer(bert_inputs)

analyticsindiamagazine / dataprep.py

Last active December 2, 2019 03:35

	def get_masks(tokens, max_seq_length):
	"""Mask for padding"""
	if len(tokens)>max_seq_length:
	#Cutting down the excess length
	tokens = tokens[0:max_seq_length]
	return [1]*len(tokens)
	else :
	return [1]len(tokens) + [0] (max_seq_length - len(tokens))

	def get_segments(tokens, max_seq_length):

analyticsindiamagazine / XAI_WHAT_IF.ipynb

Created November 27, 2019 11:20

How To Implement Explainable AI With What-If Tool For Model Comparison

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analyticsindiamagazine / WIT_final.py

Created November 27, 2019 06:28

	# Importing The WIT essentials
	from witwidget.notebook.visualization import WitConfigBuilder
	from witwidget.notebook.visualization import WitWidget

	#Setting the WIT tool box height in pixels
	tool_height_in_px = 600

	# Configuring the WIT tool with the test examples and the trained classifier
	config_builder = WitConfigBuilder(test_examples[0:num_datapoints]).set_estimator_and_feature_spec(
	classifier, feature_spec).set_compare_estimator_and_feature_spec(

analyticsindiamagazine / validation_data.py

Last active November 27, 2019 06:26

	#Setting the number of datapoints to choose
	num_datapoints = len(val)

	# Preprocessing The validation data
	make_label_column_numeric(val, label_column, classes)
	test_examples = df_to_examples(val[0:num_datapoints])