VIII : Build and deploy data science products: Machine translation application -Build and deploy using Flask


One measure of success will be the degree to which you build up others

This is the last post of the series and in this post we finally build and deploy our application we painstakingly developed over the past 7 posts . This series comprises of 8 posts.

  1. Understand the landscape of solutions available for machine translation
  2. Explore sequence to sequence model architecture for machine translation.
  3. Deep dive into the LSTM model with worked out numerical example.
  4. Understand the back propagation algorithm for a LSTM model worked out with a numerical example.
  5. Build a prototype of the machine translation model using a Google colab / Jupyter notebook.
  6. Build the production grade code for the training module using Python scripts.
  7. Building the Machine Translation application -From Prototype to Production : Inference process
  8. Building the Machine Translation application: Build and deploy using Flask : ( This post)

Over the last two posts we covered the factory model and saw how we could build the model during the training phase. We also saw how the model was used for inference. In this section we will take the results of these predictions and build an app using flask. We will progressively work through the different processes of building the application.

Folder Structure

In our journey so far we progressively built many files which were required for the training phase and the inference phase. Now we are getting into the deployment phase were we want to deploy the code we have built into an application. Many of the files which we have built during the earlier phases may not be required anymore in this phase. In addition, we want the application we deploy as light as possible for its performance. For this purpose it is always a good idea to create a seperate folder structure and a new virtual environment for deploying our application. We will only select the necessary files for the deployment purpose. Our final folder structure for this phase will look as follows

Let us progressively build this folder structure and the required files for building our machine translation application.

Setting up and Installing FLASK

When building an application in FLASK , it is always a good practice to create a virtual environment and then complete the application build process within the virtual environment. This way we can ensure that only application specific libraries and packages are deployed into the hosting service. You will see later on that creating a seperate folder and a new virtual environment will be vital for deploying the application in Heroku.

Let us first create a separate folder in our drive and then create a virtual environment within that folder. In a Linux based system, a seperate folder can be created as follows

$ mkdir mtApp

Once the new directory is created let us change directory into the mtApp directory and then create a virtual environment. A virtual environment can be created on Linux with Python3 with the below script

mtApp $ python3 -m venv mtApp

Here the second mtApp is the name of our virtual environment. Do not get confused with the directory we created with the same name. The virtual environment which we created can be activated as below

mtApp $ source mtApp/bin/activate

Once the virtual environment is enabled we will get the following prompt.

(mtApp) ~$

In addition you will notice that a new folder created with the same name as the virtual environment

Our next task is to install all the libraries which are required within the virtual environment we created.

(mtApp) ~$ pip install flask

(mtApp) ~$ pip install tensorflow

(mtApp) ~$ pip install gunicorn

That takes care of all the installations which are required to run our application. Let us now look through the individual folders and the files within it.

There would be three subfolders under the main application folder MTapp. The first subfolder factoryModel is a subset of the corrsponding folder we maintained during the training phase. The second subfolder mtApp is the one created when the virtual environment was created. We dont have to do anything with that folder. The third folder templates is a folder specifically for the flask application. The file is the driver file for the flask application. Let us now looks into each of the folders.

Folder 1 : factoryModel:

The subfolders and files under the factoryModel folder are as shown below. These subfolders and its files are the same as what we have seen during the training phase.

The config folder contains the file and the configuration file we used during the training and inference phases.

The output folder contains only a subset of the complete output folder we saw during the inference phase. We need only those files which are required to translate an input German string to English string. The model file we use is the one generated after the training phase.

The utils folder has the same helperFunctions script which we used during the training and inference phase.

Folder 2 : Templates :

The templates folder has two html templates which are required to visualise the outputs from the flask application. We will talk more about the contents of the html file in a short while along with our discussions on the flask app.

Flask Application

Now its time to get to the main part of this article, which is, building the script for the flask application. The code base for the functionalities of the application will be the same as what we have seen during the inference phase. The difference would be in terms of how we use the predictions and visualise them on to the web browser using the flask application.

Let us now open a new file and name is Let us start building the code in this file

This is the script for flask application

from tensorflow.keras.models import load_model
from factoryModel.config import mt_config as confFile
from factoryModel.utils.helperFunctions import *
from flask import Flask,request,render_template

# Initializing the flask application
app = Flask(__name__)

## Define the file path to the model
modelPath = confFile.MODEL_PATH

# Load the model from the file path
model = load_model(modelPath)

Lines 5-8 imports the required libraries for creating the application

Lines 11 creates the application object ‘app’ as an instance of the class ‘Flask’. The (__name__) variable passed to the Flask class is a predefined variable used in Python to set the name of the module in which it is used.

Line 14 we load the configuration file from the config folder.

Line 17 The model which we created during the training phase is loaded using the load_model() function in Keras.

Next we will load the required pickle files we saved after the training process. In lines 20-22 we intialize the paths to all the files and variables we saved as pickle files during the training phase. These paths are defined in the configuration file. Once the paths are initialized the required files and variables are loaded from the respecive pickle files in lines 24-27. We use the load_files() function we defined in the helper function script for loading the pickle files. You can notice that these steps are same as the ones we used during the inference process.

In the next lines we will explore the visualisation processes for flask application.

def home():
	return render_template('home.html')

Lines 29:31 is a feature called the ‘decorator’. A decorator is used to modify the function which comes after it. The function which follows the decorator is a very simple function which returns the html template for our landing page. The landing page of the application is a simple text box where the source language (German) has to be entered. The purpose of the decorator is to build a mapping between the function and the url for the landing page. The URL’s are defined through another important component called ‘routes’ . ‘Routes’ modules are objects which configures the webpages which receives inputs and displays the returned outputs. There are two ‘routes’ which are required for this application, one corresponding to the home page (‘/’) and the second one mapping to another webpage called ‘/translate. The way the decorator, the route and the associated function works together is as follows. The decorator first defines the relationship between the function and the route. The function returns the landing page and route shows the location where the landing page has to be displayed.

Next we will explore the next decorator which return the predictions

@app.route('/translate', methods=['POST', 'GET'])
def get_translation():
    if request.method == 'POST':

        result = request.form
        # Get the German sentence from the Input site
        gerSentence = str(result['input_text'])
        # Converting the text into the required format for prediction
        # Step 1 : Converting to an array
        gerAr = [gerSentence]
        # Clean the input sentence
        cleanText = cleanInput(gerAr)
        # Step 2 : Converting to sequences and padding them
        # Encode the inputsentence as sequence of integers
        seq1 = encode_sequences(Ger_tokenizer, int(Ger_stdlen), cleanText)
        # Step 3 : Get the translation
        translation = generatePredictions(model,Eng_tokenizer,seq1)
        # prediction = model.predict(seq1,verbose=0)[0]

        return render_template('result.html', trans=translation)

Line 33. Our application is designed to accept German sentences as input, translate it to English sentences using the model we built and output the prediction back to the webpage. By default, the routes decorator only receives input i.e ‘GET’ requests. In order to return the predicted words, we have to define a new method in the decorator route called ‘POST’. This is done through the parameters methods=['POST','GET'] in the decorator.

Line 34. is the main function which translates the input German sentences to English sentences and then display the predictions on to the webpage.

Line 35, defines the ‘if’ method to ascertain that there is a ‘POST’ method which is involved in the operation. The next line is where we define the web form which is used for getting the inputs from the application. Web forms are like templates which are used for receiving inputs from the users and also returning the output.

In Line 37 we define the request.form into a new variable called result. All the outputs from the web forms will be accessible through the variable result.There are two web forms which we use in the application ‘home.html’ and ‘result.html’.

By default the webforms have to reside in a folder called Templates. Before we proceed with the rest of the code within the function we have to understand the webforms. Therefore let us build them. Open a new file and name it home.html and copy the following code.

<!DOCTYPE html>

<title>Machine Translation APP</title>
<form action = "/translate" method= "POST">

	<h3> German Sentence: </h3>

	<th> <input name='input_text' type="text" value = " " /> </th>

	<p><input type = "submit" value = "submit" /></p>


The prediction process in our application is initiated when we get the input German text from the ‘home.html’ form. In ‘home.html’ we define the variable name ( ‘input_text’ : line 10 in home.html) for getting the German text as input. A default value can also be mentioned using the variable value which will be over written when a new text is given as input. We also specify a submit button for submitting the input German sentence through the form, line 12.

Line 39 : As seen in line 37, the inputs from the web form will be stored in the variable result. Now to access the input text which is stored in a variable called ‘input_text’ within home.html, we have to call it as ‘input_text’ from the result variable ( result['input_text']. This input text is there by stored into a variable ‘gerSentence’ as a string.

Line 42 the string object we received from the earlier line is converted to a list as required during prediction process.

Line 44, we clean the input text using the cleanInput() function we import from the helperfunctions. After cleaning the text we need to convert the input text into a sequence of integers which is done in line 47. Finally in line 49, we generate the predicted English sentences.

For visualizing the translation we use the second html template result.html. Let us quickly review the template

<!DOCTYPE html>
<title>Machine Translation APP</title>

          <h3> English Translation:  </h3>
                <th> {{ trans }} </th>

This template is a very simple one where the only varible of interest is on line 8 which is the variable trans.

The translation generated is relayed to result.html in line 51 by assigning the translation to the parameter trans .

if __name__ == '__main__':
    app.debug = True

Finally to run the app, the method has to be invoked as in line 56.

Let us now execute the application on the terminal. To execute the application run $ python on the terminal. Always ensure that the terminal is pointing to the virtual environment we initialized earlier.

When the command is executed you should expect to get the following screen

Click the url or copy the url on a browser to see the application you build come live on your browser.

Congratulations you have your application running on the browser. Keep entering the German sentences you want to translate and see how the application performs.

Deploying the application

You have come a long way from where you began. You have now built an application using your deep learning model. Now the next question is where to go from here. The obvious route is to deploy the application on a production server so that your application is accessible to users on the web. We have different deployment options available. Some popular ones are

  • Heroku
  • Google APP engine
  • AWS
  • Azure
  • Python Anywhere …… etc.

What ever be the option you choose, deploying an application of this size will be best achieved by subscribing a paid service on any of these options. However just to go through the motions and demonstrate the process let us try to deploy the application on the free option of Heroku.

Deployment Process on Heroku

Heroku offers a free version for deployment however there are restrictions on the size of the application which can be hosted as a free service. Unfortunately our application would be much larger than the one allowed on the free version. However, here I would like to demonstrate the process of deploying the application on Heroku.

Step 1 : Creating the Heroku account.

The first step in the process is to create an account with Heroku. This can be done through the link Once an account is created we get access to a dashboard which lists all the applications which we host in the platform.

Step 2 : Configuring git

Configuring ‘git’ is vital for deploying applications to Heroku. Git has to be installed first to our local system to make the deployment work. Git can be installed by following instructions in the link

Once ‘git’ is installed it has to be configured with your user name and email id.

$ git config –global “”

$ git config –global

Step 3 : Installing Heroku CLI

The next step is to install the Heroku CLI and the logging in to the Heroku CLI. The detailed steps which are involved for installing the Heroku CLI are given in this link

If you are using Ubantu system you can install Heroku CLI using the script below

$ sudo snap install heroku --classic

Once Heroku is installed we need to log into the CLI once. This is done in the terminal with the following command

$ heroku login

Step 4 : Creating the Procfile and requirements.txt

There is a file called ‘Procfile’ in the root folder of the application which gives instructions on starting the application.

Procfile and requirements.txt in the application folder

The file can be created using any text editor and should be saved in the name ‘Procfile’. No extension should be specified for the file. The contents of the file should be as follows

web: gunicorn app:app --log-file

Another important pre-requisite for the Heroku application is a file called ‘requirements.txt’. This is a file which lists down all the dependencies which needs to be installed for running the application. The requirements.txt file can be created using the below command.

$ pip freeze > requirements.txt

Step 5 : Initializing git and copying the required dependent files to Heroku

The above steps creates the basic files which are required for running the application. The next task is to initialize git on the folder. To initialize git we need to go into the root folder where the file exists and then initialize it with the below command

$ git init

Step 6 : Create application instance in Heroku

In order for git to push the application file to the remote Heroku server, an instance of the application needs to be created in Heroku. The command for creating the application instance is as shown below.

$ heroku create {application name}

Please replace the braces with the application name of your choice. For example if the application name you choose is 'gerengtran', it has to be enabled as follows

$ heroku create gerengtran

Step 7 : Pushing the application files to remote server

Once git is initialized and an instance of the application is created in Heroku, the application files can be set up in remote Heroku server by the following commands.

$ heroku git:remote -a {application name}

Please note that ‘application_name’ is the name of the application which you have chosen earlier. What ever name you choose will be the name of the application in Heroku. The external link to your application will be in the name which you choose here.

Step 8 : Deploying the application and making it available as a web app

The final step of the process is to complete the deployment on Heroku and making the application available as a web app. This process starts with the command to add all the changes which you made to git.

$ git add .

Please note that there is a full stop( ‘.’ ) as part of the script after ‘add’ with a space in between .

After adding all the changes, we need to commit all the changes before finally deploying the application.

$ git commit -am "First submission"

The deployment will be completed with the below script after which the application will be up and running as a web app.

$ git push heroku master

When the files are pushed, if the deployment is successful you will get a url which is the link to the application. Alternatively, you can also go to Heroku console and activate your application. Below is the view of your console with all the applications listed. The application with the red box is the application which has been deployed

If you click on the link of the application ( red box) you get the link where the application can be open.

When the open app button is clicked the application is opened in a browser.

Wrapping up the series

With this we have achieved a good milestone of building an application and deploying it on the web for others to consume. I am a strong believer that learning data science should be to enrich products and services. And the best way to learn how to enrich products and services is to build it yourselves at a smaller scale. I hope you would have gained a lot of confidence by building your application and then deploying them on the web. Before we bid adeau, to this series let us summarise what we have achieved in this series and list of the next steps

In this series we first understood the solution landscape of machine translation applications and then understood different architecture choices. In the third and fourth posts we dived into the mathematics of a LSTM model where we worked out a toy example for deriving the forward pass and backpropagation. In the subsequent posts we got down to the tasks of building our application. First we built a prototype and then converted it into production grade code. Finally we wrapped the functionalities we developed in a Flask application and understood the process of deploying it on Heroku.

You have definitely come a long way.

However looking back are there avenues for improvement ? Absolutely !!!

First of all the model we built is a simple one. Machine translation is a complex process which requires lot more sophisticated models for better results. Some of the model choices you can try out are the following

  1. Change the model architecture. Experiment with different number of units and number of layers. Try variations like bidirectional LSTM
  2. Use attention mechanisms on the LSTM layers. Attention mechanism is see to have given good performance on machine translation tasks
  3. Move away from sequence to sequence models and use state of the art models like Transformers.

The second set of optimizations you can try out are on the vizualisations of the flask application. The templates which are used here are very basic templates. You can further experiment with different templates and make the application visually attractive.

The final improvement areas are in the choices of deployment platforms. I would urge you to try out other deployment choices and let me know the results.

I hope all of you enjoyed this series. I definitely enjoyed writing this post. Hope it benefits you and enable you to improve upon the methods used here.

I will be back again with more practical application building series like this. Watch this space for more

You can download the code for the deployment process from the following link

Do you want to Climb the Machine Learning Knowledge Pyramid ?

Knowledge acquisition is such a liberating experience. The more you invest in your knowledge enhacement, the more empowered you become. The best way to acquire knowledge is by practical application or learn by doing. If you are inspired by the prospect of being empowerd by practical knowledge in Machine learning, I would recommend two books I have co-authored. The first one is specialised in deep learning with practical hands on exercises and interactive video and audio aids for learning

This book is accessible using the following links

The Deep Learning Workshop on Amazon

The Deep Learning Workshop on Packt

The second book equips you with practical machine learning skill sets. The pedagogy is through practical interactive exercises and activities.

This book can be accessed using the following links

The Data Science Workshop on Amazon

The Data Science Workshop on Packt

Enjoy your learning experience and be empowered !!!!

VI : Build and deploy data science products: Machine translation application – From prototype to production. Introduction to the factory model


This is the sixth part of the series where we continue on our pursuit to build a machine translation application. In this post we embark on a transformation process where in we transform our prototype into a production grade code.

This series comprises of 8 posts.

  1. Understand the landscape of solutions available for machine translation
  2. Explore sequence to sequence model architecture for machine translation.
  3. Deep dive into the LSTM model with worked out numerical example.
  4. Understand the back propagation algorithm for a LSTM model worked out with a numerical example.
  5. Build a prototype of the machine translation model using a Google colab / Jupyter notebook.
  6. Build the production grade code for the training module using Python scripts.( This post)
  7. Building the Machine Translation application -From Prototype to Production : Inference process
  8. Build the machine translation application using Flask and understand the process to deploy the application on Heroku

In this section we will see how we can take the prototype which we built in the last article into a production ready code. In the prototype building phase we were developing our code on a Jupyter/Colab notebook. However if we have to build an application and deploy it, notebooks would not be very effective. We have to convert the code we built on the notebook into production grade code using python scripts. We will be progressively building the scripts using a process, I call, as the factory model. Let us see what a factory model is.

Factory Model

A Factory model is a modularized process of generating business outcomes using machine learning models. There are some distinct phases in the process which includes

  1. Ingestion/Extraction process : Process of getting data from source systems/locations
  2. Transformation process : Transformation process entails transforming raw data ingested from multiple sources into a form fit for the desired business outcome
  3. Preprocessing process: This process involves basic level of cleaning of the transformed data.
  4. Feature engineering process : Feature engineering is the process of converting the preprocessed data into features which are required for model training.
  5. Training process : This is the phase where the models are built from the featurized data.
  6. Inference process : The models which were built during the training phase is then utilized to generate the desired business outcomes during the inference process.
  7. Deployment process : The results of the inference process will have to be consumed by some process. The consumer of the inferences could be a BI report or a web service or an ERP application or any downstream applications. There is a whole set of process which is involved in enabling the down stream systems to consume the results of the inference process. All these steps are called the deployment process.

Needless to say all these processes are supported by an infrastructure layer which is also called the data engineering layer. This layer looks at the most efficient and effective way of running all these processes through modularization and parallelization.

All these processes have to be designed seamlessly to get the business outcomes in the most effective and efficient way. To take an analogy its like running a factory where raw materials gets converted into a finished product and thereby gets consumed by the end customers. In our case, the raw material is the data, the product is the model generated from the training phase and the consumers are any business process which uses the outcomes generated from the model.

Let us now see how we can execute the factory model to generate the business outcomes.

Project Structure

Before we dive deep into the scripts, let us look at our project structure.

Our root folder is the Machine Translation folder which contains two sub folders Data and factoryModel. The Data subfolder contains the raw data. The factoryModel folder contains different subfolders containing scripts for our processes. We will be looking at each of these scripts in detail in the subsequent sections. Finally we have two driver files which is the driver file for the training process and which is the driver file for the inference process.

Let us first dive into the training phase scripts.

Training Phase

The first part of the factory model is the training phase which comprises of all the processes till the creation of the model. We will start off by building the supporting files and folders before we get into the driver file. We will first start with the configuration file.

Configuration file

When we were working with the notebook files, we were at a liberty to change the pararmeters we wanted to vary, say for example the path to the input file or some hyperparameters like the number of dimensions of the embedding vector, on the notebook itself. However when an application is in production we would not have the luxury to change the parameters and hyperparameters directly in the code base. To get over this problem we use the configuration files. We consolidate all the parameters and hyperparameters of the model on to the configuration file. All processes will pick the parameters from the configuration file for further processing.

The configuration file will be inside the config folder. Let us now build the configuration file.

Open a word editor like notepad++ or any other editor of your choice and open a new file and name it Let us start adding the below code in this file.

This is the configuration file for storing all the application parameters

import os
from os import path

# This is the base path to the Machine Translation folder
BASE_PATH = '/media/acer/7DC832E057A5BDB1/JMJTL/Tomslabs/BayesianQuest/MT/MachineTranslation'
# Define the path where data is stored
DATA_PATH = path.sep.join([BASE_PATH,'Data/deu.txt'])

Lines 5 and 6, we import the necessary library packages.

Line 10, we define the base path for the application. You need to change this path based on your specific path to the application. Once the base path is set, the rest of the paths will be derived out from it. In Line 12, we define the path to the raw data set folder. Note that we just join the name of the data folder and the raw text file with the base path to get the data path. We will be using the data path to read in the raw data.

In the config folder there will be another file named . This is a special file which tells Python to treat the config folder as part of the package. This file inside this folder will be an empty file with no code in it

Loading Data

The next helper files we will build are those for loading raw files and preprocessing. The code we use for these purposes are the same code which we used for building the prototype. This file will reside in the dataLoader folder

In your text editor open a new file and name it as and then add the below code into it

Factory Model for Machine translation preprocessing.
This is the script for loading the data and preprocessing data

import string
import re
from pickle import dump
from unicodedata import normalize
from numpy import array

# Creating the class to load data and then do the preprocessing as sequence of steps

class textLoader:
	def __init__(self , preprocessors = None):
		# This init method is to store the text preprocessing pipeline
		self.preprocessors = preprocessors
		# Initializing the preprocessors as an empty list of the preprocessors are None
		if self.preprocessors is None:
			self.preprocessors = []

	def loadDoc(self,filepath):
		# This is the function to read the file from the path provided
		# Open the file
		file = open(filepath,mode = 'rt',encoding = 'utf-8')
		# Reading the text
		text =
		#Once the file is read, applying the preprocessing steps one by one
		if self.preprocessors is not None:
			# Looping over all the preprocessing steps and applying them on the text data
			for p in self.preprocessors:
				text = p.preprocess(text)
		# Closing the file
		# Returning the text after all the preprocessing
		return text

Before addressing the code block line by line, let us get a big picture perspective of what we are trying to accomplish. When working with text you would have realised that different sources of raw text requires different preprocessing treatments. A preprocessing method which we have used for one circumstance may not be warranted in a different one. So in this code block we are building a template called textLoader, which reads in raw data and then applies different preprocessing steps like a pipeline as the situation warrants. Each of the individual preprocessing steps would be defined seperately. The textLoader class first reads in the data and then applies the selected preprocessing one after the other. Let us now dive into the details of the code.

Lines 6 to 10 imports all the necessary library packages for the process.

Line 14 we define the textLoader class. The constructor in line 15 takes the text preprocessor pipeline as the input. The prepreprocessors are given as lists. The default value is taken as None. The preprocessors provided in the constructor is initialized in line 17. Lines 19-20 initializes an empty list if the preprocessor argument is none. If you havent got a handle of why the preprocessors are defined this way, it is ok. This will be more clear when we define the actual preprocessors. Just hang on till then.

From line 22 we start the first function within this class. This function is to read the raw text and the apply the processing pipeline. Lines 25 – 27, where we open the text file and read the text is the same as what we defined during the prototype phase in the last post. We do a check to see if we have defined any preprocessor pipeline in line 29. If there are any pipeline defined those are applied on the text one by one in lines 31-32. The method .preprocess is specific to each of the preprocessor in the pipeline. This method would be clear once we take a look at each of the preprocessors. We finally close the raw file and the return the processed text in lines 35-38.

The file inside this folder will contain the following line for importing the textLoader class from the file for any calling script.

from .datasetloader import textLoader

Processing Data : Preprocessing pipeline construction

Next we will create the files for preprocessing the text. In the last section we saw how the raw data was loaded and then preprocessing pipeline was applied. In this section we look into the preprocessing pipeline. The folder structure will be as shown in the figure.

There would be three preprocessors classes for processing the raw data.

  • SentenceSplit : Preprocessor to split raw text into pair of English and German sentences. This class is inside the file
  • cleanData : Preprocessor to apply cleaning steps like removing punctuations, removing whitespaces which is included in the file.
  • TrainMaker : Preprocessor to tokenize text and then finally prepare the train and validation sets contined in the file

Let us now dive into each of the preprocessors.

Open a new file and name it Add the following code to this file.

Script for preprocessing of text for Machine Translation
This is the class for splitting the text into sentences

import string
from numpy import array

class SentenceSplit:
	def __init__(self,nrecords):
		# Creating the constructor for splitting the sentences
		# nrecords is the parameter which defines how many records you want to take from the data set
		self.nrecords = nrecords
	# Creating the new function for splitting the text
	def preprocess(self,text):
		sen = text.strip().split('\n')
		sen = [i.split('\t') for i in sen]
		# Saving into an array
		sen = array(sen)
		# Return only the first two columns as the third column is metadata. Also select the number of rows required
		return sen[:self.nrecords,:2]

This is the first or our preprocessors. This preprocessor splits the raw text and finally outputs an array of English and German sentence pairs.

After we import the required packages in lines 6-7, we define the class in line 9. We pass a variable nrecords to the constructor to subset the raw text and select number of rows we want to include for training.

The preprocess function starts in line 16. This is the function which we were accessing in line 32 of the textLoader class which we discussed in the last section. The rest is the same code we have used in the prototype building phase which includes

  • Splitting the text into sentences in line 17
  • Splitting each sentece on tab spaces to get the German and English sentences ( line 18)

Finally we convert the processed sentences into an array and return only the first two columns of the array. Please note that the third column contains metadata of each line and therefore we exclude it from the returned array. We also subset the array based on the number of records we want.

Now that the first preprocessor is complete,let us now create the second preprocessor.

Open a new file and name it and copy the below code.

Script for preprocessing data for Machine Translation application
This is the class for removing the punctuations from sentences and also converting it to lower cases

import string
from numpy import array
from unicodedata import normalize

class cleanData:
	def __init__(self):
		# Creating the constructor for removing punctuations and lowering the text
	# Creating the function for removing the punctuations and converting to lowercase
	def preprocess(self,lines):
		cleanArray = list()
		for docs in lines:
			cleanDocs = list()
			for line in docs:
				# Normalising unicode characters
				line = normalize('NFD', line).encode('ascii', 'ignore')
				line = line.decode('UTF-8')
				# Tokenize on white space
				line = line.split()
				# Removing punctuations from each token
				line = [word.translate(str.maketrans('', '', string.punctuation)) for word in line]
				# convert to lower case
				line = [word.lower() for word in line]
				# Remove tokens with numbers in them
				line = [word for word in line if word.isalpha()]
				# Store as string
				cleanDocs.append(' '.join(line))
		return array(cleanArray)

This preprocessor is to clean the array of German and English sentences we received from the earlier preprocessor. The cleaning steps are the same as what we have seen in the previous post. Let us quickly dive in and understand the code block.

We start of by defining the cleanData class in line 10. The preprocess method starts in line 16 with the array from the previous preprocessing step as the input. We define two placeholder lists in line 17 and line 19. In line 20 we loop through each of the sentence pair of the array and the carry out the following cleaning operations

  • Lines 22-23, normalise the text
  • Line 25 : Split the text to remove the whitespaces
  • Line 27 : Remove punctuations from each sentence
  • Line 29: Convert the text to lower case
  • Line 31: Remove numbers from text

Finally in line 33 all the tokens are joined together and appended into the cleanDocs list. In line 34 all the individual sentences are appended into the cleanArray list and converted into an array which is returned in line 35.

Let us now explore the third preprocessor.

Open a new file and name it . This file is pretty long and therefore we will go over it function by function. Let us explore the file in detail

This class has methods for tokenizing the text and preparing train and test sets

import string
import numpy as np
from numpy import array
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split

class TrainMaker:
	def __init__(self):
		# Creating the constructor for creating the tokenizers
	# Creating an internal function for tokenizing the text	
	def tokenMaker(self,text):
		tokenizer = Tokenizer()
		return tokenizer	

We down load all the required packages in lines 5-10, after which we define the constructor in lines 13-16. There is nothing going on in the constructor so we can conveniently pass it over.

The first function starts on line 19. This is a function we are familiar with in the previous post. This function fits the tokenizer function on text. The first step is to instantiate the tokenizer object in line 20 and then fit the tokenizer object on the provided text in line 21. Finally the tokenizer object which is fit on the text is returned in line 22. This function will be used for creating the tokenizer dictionaries for both English and German text.

The next function which we will see is the sequenceMaker. In the previous post we saw how we convert text as sequence of integers. The sequenceMaker function is used for this task.

	# Creating an internal function for encoding and padding sequences
	def sequenceMaker(self,tokenizer,stdlen,text):
		# Encoding sequences as integers
		seq = tokenizer.texts_to_sequences(text)
		# Padding the sequences with respect standard length
		seq = pad_sequences(seq,maxlen=stdlen,padding = 'post')
		return seq

The inputs to the sequenceMaker function on line 26 are the tokenizer , the maximum length of a sequence and the raw text which needs to be converted to sequences. First the text is converted to sequences of integers in line 28. As the sequences have to be of standard legth, they are padded to the maximum length in line 30. The standard length integer sequences is then returned in line 31.

	# Creating another function to find the maximum length of the sequences	
	def qntLength(self,lines):
		doc_len = []
		# Getting the length of all the language sentences
		[doc_len.append(len(line.split())) for line in lines]
		return np.quantile(doc_len, .975)

The next function we will define is the function to find the quantile length of the sentences. As seen from the previous post we made the standard length of the sequences equal to the 97.5 % quantile length of the respective text corpus. The function starts in line 34 where the complete text is given as input. We then create a placeholder in line 35. In line 37 we parse through each of the line and the find the total length of the sentence. The length of each sentence is stored in the placeholder list we created earlier. Finally in line 38, the 97.5 quantile of the length is returned to get the standard length.

	# Creating the function for creating tokenizers and also creating the train and test sets from the given text
	def preprocess(self,docArray):
		# Creating tokenizer forEnglish sentences
		eng_tokenizer = self.tokenMaker(docArray[:,0])
		# Finding the vocabulary size of the tokenizer
		eng_vocab_size = len(eng_tokenizer.word_index) + 1
		# Creating tokenizer for German sentences
		deu_tokenizer = self.tokenMaker(docArray[:,1])
		# Finding the vocabulary size of the tokenizer
		deu_vocab_size = len(deu_tokenizer.word_index) + 1
		# Finding the maximum length of English and German sequences
		eng_length = self.qntLength(docArray[:,0])
		ger_length = self.qntLength(docArray[:,1])
		# Splitting the train and test set
		train,test = train_test_split(docArray,test_size = 0.1,random_state = 123)
		# Calling the sequence maker function to create sequences of both train and test sets
		# Training data
		trainX = self.sequenceMaker(deu_tokenizer,int(ger_length),train[:,1])
		trainY = self.sequenceMaker(eng_tokenizer,int(eng_length),train[:,0])
		# Validation data
		testX = self.sequenceMaker(deu_tokenizer,int(ger_length),test[:,1])
		testY = self.sequenceMaker(eng_tokenizer,int(eng_length),test[:,0])
		return eng_tokenizer,eng_vocab_size,deu_tokenizer,deu_vocab_size,docArray,trainX,trainY,testX,testY,eng_length,ger_length

We tie all the earlier functions in the preprocess method starting in line 41. The input to this function is the English, German sentence pair as array. The various processes under this function are

  • Line 43 : Tokenizing English sentences using the tokenizer function created in line 19
  • Line 45 : We find the vocabulary size for the English corpus
  • Lines 47-49 the above two processes are repeated for German corpus
  • Lines 51-52 : The standard lengths of the English and German senetences are found out
  • Line 54 : The array is split to train and test sets.
  • Line 57 : The input sequences for the training set is created using the sequenceMaker() function. Please note that the German sentences are the input variable ( TrainX).
  • Line 58 : The target sequence which is the English sequence is created in this step.
  • Lines 60-61: The input and target sequences are created for the test set

All the variables and the train and test sets are returned in line 62

The file inside this folder will contain the following lines

from .splitsentences import SentenceSplit
from .datacleaner import cleanData
from .tokenizer import TrainMaker

That takes us to the end of the preprocessing steps. Let us now start the model building process.

Model building Scripts

Open a new file and name it . Copy the following code into the file.

This is the script and template for different models.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import RepeatVector
from tensorflow.keras.layers import TimeDistributed

class ModelBuilding:
	def EncDecbuild(in_vocab,out_vocab, in_timesteps,out_timesteps,units):
		# Initializing the model with Sequential class
		model = Sequential()
		# Initiating the embedding layer for the text
		model.add(Embedding(in_vocab, units, input_length=in_timesteps, mask_zero=True))
		# Adding the first LSTM layer
		# Using the RepeatVector to map the input sequence length to output sequence length
		# Adding the second layer of LSTM 
		model.add(LSTM(units, return_sequences=True))
		# Adding the fully connected layer with a softmax layer for getting the probability
		model.add(TimeDistributed(Dense(out_vocab, activation='softmax')))
		# Compiling the model
		model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
		# Printing the summary of the model
		return model

The model building scripts is straight forward. Here we implement the encoder decoder model we described extensively in the last post.

We start by importing all the necessary packages in lines 5-10. We then get to the meat of the model by defining the ModelBuilding class in line 12. The model we are using for our application is defined through a function EncDecbuild in line 14. The inputs to the function are the

  • in_vocab : This is the size of the German vocabulary
  • out_vocab : This is the size of the Enblish vocabulary
  • in_timesteps : The standard sequence length of the German sentences
  • out_timesteps : Standard sequence length of Enblish sentences
  • units : Number of hidden units for the LSTM layers.

The progressive building of the model was covered extensively in the last post. Let us quickly run through the same here

  • Line 16 we initialize the sequential class
  • The next layer is the Embedding layer defined in line 18. This layer converts the text to word embedding vectors. The inputs are the German vocabulary size, the dimension required for the word embeddings and the sequence length of the input sequences. In this example we have kept the dimension of the word embedding same as the number of units of LSTM. However this is a parameter which can be experimented with.
  • Line 20, we initialize our first LSTM unit.
  • We then perform the Repeat vector operation in Line 22 so as to make the mapping between the encoder time steps and decoder time steps
  • We add our second LSTM layer for the decoder part in Line 24.
  • The next layer is the dense layer whose output size is equal to the English vocabulary size.(Line 26)
  • Finally we compile the model using ‘adam’ optimizer and then summarise the model in lines 28-30

So far we explored the file ecosystem for our application. Next we will tie all these together in the driver program.

Driver Program

Open a new file and name it and start adding the following code blocks.

This is the driver file which controls the complete training process

from factoryModel.config import mt_config as confFile
from factoryModel.preprocessing import SentenceSplit,cleanData,TrainMaker
from factoryModel.dataLoader import textLoader
from factoryModel.models import ModelBuilding
from tensorflow.keras.callbacks import ModelCheckpoint
from factoryModel.utils.helperFunctions import *

## Define the file path to input data set
filePath = confFile.DATA_PATH

print('[INFO] Starting the preprocessing phase')

## Load the raw file and process the data
ss = SentenceSplit(50000)
cd = cleanData()
tm = TrainMaker()

Let us first look at the library file importing part. In line 5 we import the configuration file which we defined earlier. Please note the folder structure we implemented for the application. The configuration file is imported from the config folder which is inside the folder named factoryModel. Similary in line 6 we import all three preprocessing classes from the preprocessing folder. In line 7 we import the textLoader class from the dataLoader folder and finally in line 8 we import the ModelBuilding class from the models folder.

The first task we will do is to get the path of the files which we defined in the configuration file. We get the path to the raw data in line 13.

Lines 18-20, we instantiate the preprocessor classes starting with the SentenceSplit, cleanData and finally the trainMaker classes. Please note that we pass a parameter to the SentenceSplit(50000) class to indicate that we want only 50000 rows of the raw data, for processing.

Having seen the three preprocessing classes, let us now see how these preprocessors are tied together in a pipeline to be applied sequentially on the raw text. This is achieved in next code block

# Initializing the data set loader class and then executing the processing methods
tL = textLoader(preprocessors = [ss,cd,tm])
# Load the raw data, preprocess it and create the train and test sets
eng_tokenizer,eng_vocab_size,deu_tokenizer,deu_vocab_size,text,trainX,trainY,testX,testY,eng_length,ger_length = tL.loadDoc(filePath)

Line 21 we instantiate the textLoader class. Please note that all the preprocessing classes are given sequentially in a list as the parameters to this class. This way we ensure that each of the preprocessors are implemented one after the other when we implement the textLoader class. Please take some time to review the class textLoader earlier in the post to understand the dynamics of the loading and preprocessing steps.

In Line 23 we implement the loadDoc function which takes the path of the data set as the input. There are lots of processes which goes on in this method.

  • First loads the raw text using the file path provided.
  • On the raw text which is loaded, the three preprocessors are implemented one after the other
  • The last preprocessing step returns all the required data sets like the train and test sets along with the variables we require for modelling.

We now come to the end of the preprocessing step. Next we take the preprocessed data and train the model.

Training the model

We have already built all the necessary scripts required for training. We will tie all those pieces together in the training phase. Enter the following lines of code in our script

### Initiating the training phase #########
# Initialise the model
model = ModelBuilding.EncDecbuild(int(deu_vocab_size),int(eng_vocab_size),int(ger_length),int(eng_length),256)
# Define the checkpoints
checkpoint = ModelCheckpoint('model.h5',monitor = 'val_loss',verbose = 1, save_best_only = True,mode = 'min')
# Fit the model on the training data set,trainY,epochs = 50,batch_size = 64,validation_data=(testX,testY),callbacks = [checkpoint],verbose = 2)

In line 34, we initialize the model object. Please note that when we built the script ModelBuilding was the name of the class and EncDecbuild was the method or function under the class. This is how we initialize the model object in line 34. The various parameter we give are the German and English vocabulary sizes, sequence lenghts of the German and English senteces and the number of units for LSTM ( which is what we adopt for the embedding size also). We define the checkpoint variables in line 36.

We start the model fitting in line 38. At the end of the training process the best model is saved in the path we have defined in the configuration file.

Saving the other files and variables

Once the training is done the model file is stored as a 'model.h5‘ file. However we need to save other files and variables as pickle files so that we utilise them during our inference process. We will create a script where we store all such utility functions for saving data. This script will reside in the utils folder. Open a new file and name it and copy the following code.

This script lists down all the helper functions which are required for processing raw data

from pickle import load
from numpy import argmax
from tensorflow.keras.models import load_model
from pickle import dump

def save_clean_data(data,filename):
    print('Saved: %s' % filename)

Lines 5-8 we import all the necessary packages.

The first function we will be creating is to dump any files as pickle files which is initiated in line 10. The parameters are the data and the filename of the data we want to save.

Line 11 dumps the data as pickle file with the file name we have provided. We will be using this utility function to save all the files and variables after the training phase.

In our training driver file add the following lines

### Saving the tokenizers and other variables as pickle files

Lines 42-52, we save all the variables we received from line 24 as pickle files.

Executing the script

Now that we have completed all the scripts, let us go ahead and execute the scripts. Open a terminal and give the following command line arguments to run the script.

$ python

All the scripts will be executed and finally the model files and other variables will be stored on disk. We will be using all the saved files in the inference phase. We will address the inference phase in the next post of the series.

Go to article 7 of this series : From prototype to production: Inference Process

You can download the notebook for the prototype using the following link

Do you want to Climb the Machine Learning Knowledge Pyramid ?

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This book is accessible using the following links

The Deep Learning Workshop on Amazon

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The second book equips you with practical machine learning skill sets. The pedagogy is through practical interactive exercises and activities.

This book can be accessed using the following links

The Data Science Workshop on Amazon

The Data Science Workshop on Packt

Enjoy your learning experience and be empowered !!!!

Logic of Logistic Regression – Part III



In our previous post on logistic regression we defined the concept of parameters and had a first hand glimpse on the dynamics between the data set and the parameters to obtain our first set of predictions. In this part we will go further into how we optimize the parameters in order to improve the accuracy of our predictions. We will be dealing with the following concepts

  1. Deciphering the prediction errors
  2. Minimizing errors through gradient descent and finding optimized parameters
  3. Prediction with the optimized set of parameters.

Deciphering Prediction Errors

Let us revisit the toy example we discussed in our last post and dissect the below table which represented the dynamics of prediction.


To recap, let us list down our discussions in  the last post on the dynamics involved in the above table.

  • We first assumed an initial set of parameters
  • Multiplied the parameters with the respective features ( columns 2,3 &4) to get the weighted sum.
  • Converted the weighted sum into predictions ( column 6) by applying the activation function (sigmoid function).

Let us take a moment to reflect on what the predictions really mean ? The predictions are in fact the probabilities of the customer  buying the insurance policy. For example, for the first customer, we are predicting that the probability that the customer will buy the insurance policy is almost 17.9%.

However when we talk about predictions the first thing which comes to our mind is the veracity of those predictions. How close to reality are the first set of predictions which we made ? If we recall, in our last discussion on the training set, we introduced a new column called the labels. The labels in fact is the reality !! For example looking at the labels column we know that the first two customers did not buy the insurance policy ( label of ‘0’) and the next two bought the insurance policy. The veracity of our predictions can be realized by comparing our predictions with the reality manifested in the labels. By comparing we can see that the first and last customer predictions are somewhat close to reality and the middle ones are pretty off target. In ideal state, we want the first two predictions to be close to zero and the last two pretty close to ‘1’. However, what we predicted have obviously deviated from the reality. Such deviations are the errors we have inherited in our predictions.  However we need to note that the calculation of error for a classification problem like ours is a little mathematically oriented and is not as straight forward as subtracting the probability from the labels. For the sake of simplicity let us not get into those mathematical calculations and stick to our understanding that there  some errors inherited for each example. From the errors of each example we  can find the average error by summing up errors of all examples and dividing it by the number of examples ( 4 in our case). In machine learning parlance the average error so obtained can also be called the ‘Cost’.

Now that we know that there are ‘Cost’ involved in our predictions, our aim should be to minimize the cost so that our predictions are as close to the reality as possible.However the million dollar question is how do we minimize the cost ? What are the levers we have to reduce our costs ? Going back to our toy example, the two entities we have played around to get the predictions are the ‘data’ and the ‘parameters’ . We cannot change the given data because it is fixed. So all we have got to play around with is the parameters which we assumed. We have to try to change our parameters systematically so that we minimize the costs and get our predictions as close to the reality as possible. One of the ways we do this is by a procedure called gradient descent.

Gradient Descent

To understand the concept of gradient descent let us look at some graphical representations.


A pictorial representation of the cost function will look as the above. In the ‘X’ axis we have our parameters and in the ‘Y’ axis we have the cost. From the figure we can see that there are some set of parameters,’P’ with which we can get to the minimum cost ‘C_min’. Our aim is to find those parameters which will give us the minimum cost.

Let us represent the initial parameters we assumed as P_initial. For this set of parameters let us denote the  cost we derived as C1, as given in the figure. We can see from the figure that by moving the P value to the left ( decreasing the parameters ) by some value we can get to the minimum value of cost. Alternatively, if our initial ‘P’ value were to be on the left side of the graph, we would have to move to the right ( increase the value of parameters ) to get to the minimum cost. The procedure for achieving this is called the gradient descent.

The idea behind gradient descent is represented pictorially as below.


We decrease the parameters by small steps in an iterative fashion so as to get to the minimum cost. To find out  the “small steps” which I mentioned in the previous line we use a trick we learned in high school calculus called partial derivative. By taking the partial derivative at each point of the cost curve we get a value by which we have to reduce the parameters. With the new set of reduced parameters we find the new cost. Again we find the partial derivative at the new cost level to get the next steps which we have to take, and this process continues till we reach the minimum cost. An analogy to this process is like this. Suppose we are on top of a hill, blindfolded, and we want to find our way down the hill. The way we can do this is by feeling the ground with our foot to find those spots which are lower than the ones where we are currently and then move to the new spot. From the new spot we repeat the process till we finally reach the bottom of the hill. Gradient descent works somewhat similar to this.


Summarizing our discussions on gradient descent, these are the steps we take to get the optimum parameters.

  1. First start of with the assumed random parameters.
  2. Find the cost ( errors ) associated with the assumed parameters.
  3. Find the small steps we have to take to alter our parameters, by taking partial derivative of the cost.
  4. Reduce the parameters by the small steps and get a new set of parameters
  5. Find the new cost associated with the new parameters.
  6. Repeat the processes 3,4 & 5 till we get the most optimized cost.

The optimized parameters which we finally get are called the learned parameters.Getting to this optimized parameters is the most involved part of machine learning. Once we learn the parameters using, the training set, we are all set to do predictions which is the objective of any machine learning process.

Doing Predictions

Having learned our set of optimized parameters from the training set, we are now equipped with enough ammunition to do predictions. For doing predictions we take a new set of data called the test set. However there is a difference between the training set and test set. The test set will not have any labels. Our job is to predict the labels from the parameters we have learned. So in the insurance company example, the test set would be the new set of leads which the sales team generated. We have to predict the likelihood of these leads, buying an insurance policy. The way we do the prediction is as follows.

  • We take the optimized set of parameters learned from the training set
  • Multiply the parameters with the respective features ( columns 2,3 &4) to get the weighted sum.
  • Convert the weighted sum into probabilities ( column 6) by applying the activation function (sigmoid function).
  • We take a threshold point ( say 0.5). So any probability less than the threshold point is predicted as ‘0’ ( Will not buy) and anything greater that the threshold point is predicted as ‘1’.

The threshold point which we take to make a decision on our predictions is called the decision boundary.Needless to say, the logistic regression is the basic model among a vast set of powerful classification algorithms. The significance of logistic regression is that it is the building block for the development of powerful algorithms like Support Vector machines, Neural Networks etc. Having said that there are many problem areas where we have to go for simple algorithms like logistic regression. Having dealt with the basic building blocks of classification problems we will have further discussions on some of the most powerful algorithms in future posts. Until then watch out this space for more.

Logic of Logistic Regression – Part II


In the first part of this series on Logistic Regression, we set the stage for unveiling the logic behind logistic regression. We stopped our discussion by identifying three dynamic forces at play which determines the quality of predictions,

  1. Weights or parameters which we learn
  2. The activation function, and
  3. The decision boundary

In this second, part of the series we will look deeper into the first two of those dynamic forces.

Concept of Parameters

In the first part of this series when we were discussing the example we assumed a set of parameters i.e W(age) = 8 ; W(income) = 3 and W(propensity) = 10. Quite naturally, a  question lot of people asked me was, where did we get those values from ? Well, as far as that example was concerned, it was just some assumed values. However in the world of machine learning, the parameters is its Holy Grail. The cardinal purpose of the algorithms and theorems of machine learning is to enable the pursuit of the right set of parameters. But why is it that the parameters, so important ? To answer this let us look at what the parameters help us achieve.

Let us revisit the toy data set which we used in the first part. Let us first understand this data set before we get into understanding the parameters.

As can be seen, this data set consists of rows and columns. The data along the columns ( Age, Income & Propensity) are called its  features and the ones along the rows are the examples. In short each customer record in this data set is an example.

Now that we have seen the data set, let us now see the dynamics between the parameters and the data.

The role of the parameter is to act as a weighting factor for each of the features. In other words each feature will have a unique parameter playing the role of a weight. Our example data set has three features and therefore the number of parameters we will have is also three. In general if there are ‘n’ features there should be at least ‘n’ parameters ( However, in practice we will have n+1 parameters where the additional parameter is called the bias term. We will ignore that for the time being).  Please note here that the number of parameters does not depend on the number of examples.

Having looked at the anatomy of the data set and parameters, let us look at how the parameters are learned from a given data set.

Learning Parameters from data

The data set which is used for learning parameters is called a training set. There is a subtle difference between a training set and the one shown above. For the training set we will have an additional column and this additional column is for the labels or dependent variables.


The above data set is an example for a training set. The ‘labels’ column represent the results or outcome for each record. The records with ‘0’ are negative examples and those with ‘1’ are the positive examples. In this context the negative example would mean those customers who did not buy an insurance policy and the positive examples are the ones who bought them. The labels can also be interpreted from the perspective of probability of buying. So all the negative examples are the ones where the probability of sales is low i.e near 0% and the positive ones are those with high probability i.e near 100%. In real life a training set can be made from the historical data of customers in the organisation i.e who are the customers ? How many of them bought a policy ? How many did not ? etc.

The way, we go about the task of learning the parameters from the training set is as follows

  • Random Assumption of Parameters: To start off, we randomly select some arbitrary values for the parameters. For eg. let us assume the following values for the parameters ; W(age) = 1 ; W(income) = 1 and W(propensity) = 1
  • Scaling of the data : Once that we have assumed the parameters let us do some modification on the training data setIf we note the values for each features, the scale of values for each feature vary quite a bit. The values of feature ‘Age’ are all two digit numbers, the values of ‘Income’ are four digit numbers etc. In machine learning, when the values falls within different scales, the accuracy of prediction gets affected. So it is a good practice to normalize the data. One popular way is to subtract each value with the average of the feature and then divide by the range( difference between the maximum value and minimum value). Let us see this in action,with the feature ‘Age’                                                                                                                                           Average value of ‘Age’ = (28+32+36+ 46)/ 4 = 35.5                                                                         Range of ‘Age’ = 46 – 28 = 18                                                                                                                Scaled value for the first data (28) = 28 – 35.5 / 18 = -0.4167                                                  Similarly we do it for the complete data set. The scaled data set is as represented below.    Please note that we do not scale the labels.                                                                                                                                                          scale
  • Prediction with initial parameters : Once the data is scaled,  we go to the next step of using the assumed parameters for prediction. As mentioned earlier, the parameters are like weights which needs to be applied on each feature of the data. Therefore the first step in arriving at a prediction is to multiply the parameters with the corresponding feature and adding up the weighted features for each example. The same is carried out as below. Please note that the labels are not involved in any of these operations.   Weight   Let us study the above column closely. The weighted sum column which is got by applying the parameter on each feature and adding them up, is the value which finally determines the prediction. However for a classification problem the most intuitive way of representing the prediction is in terms of probabilities. As you know, when you represent a value as a probability it has to be within the range of ‘0’ and ‘1’. However if you note our weighted sum column, most of the values are outside the range of 0 & 1. So our challenge would be to apply some mathematical operation to represent them as a probability. The mathematical operation we use for this purpose is called the Activation Function.  One of the most common activation function used in classification problems is the  Sigmoid function . By applying this function on the weighted sum column we convert it into numbers which can be interpreted as probabilities. activation The new data set after applying the activation function is as represented above. Note that the probabilities column is our actual prediction and it can be interpreted as the probability that the  customer will buy the insurance policy. So for the first customer there is only 17.88% chance for buying the policy and for the last customer there is a high chance ( 81.4 %) for him/her to buy the policy.                                                                                                                                                                                                                                   Now that we have seen how we apply the activation function to get the prediction, we are a step closer to our final goal of learning the right parameters which gives the most accurate prediction. This all important step called the gradient descent will be explained in the next part of the post. Please watch out this space for the most important part of our logistic regression problem.

Bayesian Inference – A naive perspective

Many people have been asking me on the unusual name I have given for this Blog – “Bayesian Quest”. Well, the name is inspired from one of the important theorems in statistics ‘The Bayes Theorem’. There is also a branch in statistics called Bayesian Inference whose foundation is  the Bayes Theorem. Bayesian Inference has shot into prominence in this age of ‘Big Data’ and is therefore widely used in machine learning. This week, I will give a perspective on Bayes Theorem.

The essence of Statistics is to draw inference on an unknown population, from samples. Let me elaborate this with an example.  Suppose you are part of an agency specializing in predicting poll outcomes of general elections. To publish the most accurate predictions, the ideal method would be to ask  all the eligible voters within your country  which party they are going to vote. Obviously we all know that this is not possible as the cost and time required to conduct such a survey will be prohibitively expensive. So what do you, as a Psephologist do ? That’s where statistics and statistical inference methods comes in handy. What you would do in such a scenario is to select representative samples of people  from across the country and ask them questions on their voting preferences. In statistical parlance this is called sampling. The idea behind sampling is that, the sample sizes so selected( if selected carefully) will reflect the mood and voting preferences of the general population.  This act of inferring the unknown parameters of the population from the known parameters of the sample is the essence of statistics.There are predominantly two philosophical approaches for doing statistical inference. The first one, which is the more classical of the two is called the Frequentist approach and the second the Bayesian approach.

Let us first see how a frequentist will approach the problem of predictions. For the sake of simplicity let us assume that there are only two political parties, party A and party B.Any party which gets more than 50% of popular votes wins in the election. A frequentist will start their inference by first defining a set of hypothesis. The first hypothesis, which is called the null hypothesis, will ascertain that party A will get more than 50% vote. The other hypothesis, called the alternate hypothesis, will state the contrary i.e. party A will not get more than 50% vote. Given these hypothesis, the next task is to test the validity of these hypothesis from the sample data. Please note here, that the two hypothesis are defined with respect to population(all the eligible voters in the country) and not the sample.

Let  our sample size consist of 100 people who were interviewed. Out of this sample 46 people said they will vote for party A, 38 people said that they will vote for party B and the balance 16 people were undecided. The task at hand is to predict whether party A will get more than 50% in the general election given the numbers we have observed in the sample. To do the inference the frequentist will calculate a probability statistic called the ‘P’ statistic. The ‘P’ statistic in this case can be defined as follows – It is the probability of observing 46 people from a sample of  100 people who would vote for party A, assuming 50% or more of the population will vote for party A. Confused ????? ………….. Let me simplify this a bit more. Suppose there is a definite mood among the public in favor of party A, then there is a high chance of seeing a sample where  40 people or 50 or even 60 people out of the 100 saying that they will vote for party A. However there is very low chance to see a sample with only 10 people out of 100 saying that they will vote for party A. Please remember that these chances are with respect to our hypothesis that party A is very popular. On the contrary if party A were very unpopular, then the chance of seeing  10 people out of 100 saying they will vote for party A, is very plausible. The chance or probability of seeing the number we saw in our sample under the condition that our hypothesis is true is the ‘P’ statistic. Once the ‘P’ statistic is calculated , it is then compared to a threshold value usually 5%. If the ‘P’ value is less than the threshold value we will junk our null hypothesis that 50% or more people will vote for party A and will go with the alternate hypothesis. On the contrary if the P value is more than 5% we will stick with our null hypothesis. This in short is how a frequentist will approach the problem.

A Bayesian will approach this problem in a different way. A Bayesian will take into account historical data of past elections and then assume the probability of party A getting more than 50% of popular vote. This assumption is called the Prior probability.Looking at the historical data of the past 10 elections,  we find that only in 4 of them party A has got more than 50% of votes. In that scenario we will assume the prior probability of party A getting more than 50% of votes as .4( 4 out of 10). Once we have assumed a prior probability, we then look at our observed sample data ( 46 out of 100 saying they will vote for party A) and determine the possibility of seeing such data under the assumed prior. This possibility is called the Likelihood. The likelihood and the prior is multiplied together to get the final probability called the posterior probability. The posterior probability is our updated belief based on the data we observed and also the historical prior we assumed. So if party A has higher posterior probability than party B, we will assume that Party A has higher chance of getting more than 50% of votes than party B. This is rather a very naive explanation to the Bayesian approach.

Now that you have seen both Bayesian and Frequentist approaches you might be tempted to ask which is the better among the two. Well this debate has been going on for many years and there is no right answer. It all depends on the context and the problem which is at hand. However, in the recent past Bayesian inference has gained a definite edge over the Frequentist methods due to its ability to update prior beliefs through observation of more data. In addition, computing power is also getting cheaper and faster making Bayesian inference much more fulfilling than Frequentist methods. I will get into more examples of Bayesian inference in a future post.