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Machine learning models are usually developed in a training environment (online or offline) and then can be deployed to be used with live data.  If you're working in Data Science and Machine learning projects, knowing how to deploy a model is one of the most important skills you'll need to have. Who is this article for? This article is for those who have created a machine learning model in a local machine and want to deploy and test the model within a short time. It's also for those who are looking for an alternative platform to deploy their machine learning models. Let's get started! 🚀 What does it mean to deploy a Machine Learning model? Model deployment is the process of integrating your model into an existing production environment. The model will receive input and predict an output. Machine learning models can be deployed in different environments and can be integrated with different web or mobile applications through an API. “Only when a model is fully integrated with the business systems, we can extract real value from its predictions”. — Christopher Samiullah There are different platforms that can help you deploy your machine learning model. But for most of these platforms, it takes a lot of time and resources to configure the environment and deploy your model. For example, Sagemaker offers a popular library and ML frameworks, but you still have to depend on them for new releases. This might mean that you won't be able to deploy your model on time. Let's say the Sagemaker platform has scikit-learn v0.24 in their environment and you want to train and deploy your model with scikit-learn v1.0.1. You will not be able to do it until Sagemaker upgrades to the new version of scikit learn (1.0.1). In this article, you will learn how to use Aibro to deploy your model quickly and easily. What is Aibro? is a serverless MLOps tool that makes Machine Learning cloud computing cheap, easy, and fast. The tool can help data scientists or machine learning engineers train and deploy machine learning models on cloud platforms within a short period of time. Aibro It currently supports AWS cloud platform and they are planning to support more cloud platforms like Google Cloud, Microsoft Azure, Alibaba Cloud and IBM Cloud. It also supports most of the popular machine learning frameworks in the market like TensorFlow, Pytorch, Scikit-learn, and XGboost. Another advantage of using Aibro is the ability to reduce cloud costs by 85% using an exclusive cost-saving strategy built for machine learning. After understanding Aibro and its services, let's create a simple model and then deploy it. Create a Simple Model The first step is to build the model. We are going to use the to build a model that can classify if a movie review is positive or negative. Here are the steps you should follow to do that. IMDB Movie dataset Import the Important packages We need to import Python packages to load the data, clean the data, create a machine learning model, and save the model for deployment. accuracy_score,
    classification_report,
    plot_confusion_matrix,
) ):
    nltk.download(dependency) # import important modules import numpy as np import pandas as pd # sklearn modules from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.naive_bayes import MultinomialNB # classifier from sklearn.metrics import ( from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer # text preprocessing modules from string import punctuation # text preprocessing modules from nltk.tokenize import word_tokenize import nltk from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer import re #regular expression # Download dependency for dependency in ( "brown" , "names" , "wordnet" , "averaged_perceptron_tagger" , "universal_tagset" , import warnings warnings.filterwarnings( "ignore" ) # seeding np.random.seed( 123 ) Load the dataset from the data folder: # load data data = pd.read_csv( "../data/labeledTrainData.tsv" , sep= '\t' ) And then show a sample of the dataset: data.head() # show top five rows of data Our dataset has 3 columns: Id — This is the id of the review Sentiment — either positive (1) or negative (0) Review — comment about the movie Next, let's check the shape of the dataset: data.shape # check the shape of the data (25000, 3) The dataset has 25,000 reviews. Now we need to check if the dataset has any missing values: # check missing values in data data.isnull(). sum () id 0 sentiment 0 review 0 dtype: int64 The output shows that our dataset does not have any missing values. How to Evaluate Class Distribution We can use the value_counts() method from the Pandas package to evaluate the class distribution from our dataset. data.sentiment.value_counts() # evalute news sentiment distribution 1 12500 0 12500 Name: sentiment, dtype: int64 In this dataset, we have an equal number of positive and negative reviews. How to Process the Data After analyzing the dataset, the next step is to preprocess the dataset into the right format before creating our machine learning model. The reviews in this dataset contain a lot of unnecessary words and characters that we don’t need when creating a machine learning model. We will clean the messages by removing stopwords, numbers, and punctuation. Then we will convert each word into its base form by using the lemmatization process in the NLTK package. The text_cleaning() function will handle all necessary steps to clean our dataset. text = text.split() text = text.split()
        lemmatizer = WordNetLemmatizer() stop_words =  stopwords.words( 'english' ) def text_cleaning ( text, remove_stop_words= True , lemmatize_words= True ): # Clean the text, with the option to remove stop_words and to lemmatize words # Clean the text text = re.sub( r"[^A-Za-z0-9]" , " " , text) text = re.sub( r"\'s" , " " , text) text =  re.sub( r'http\S+' , ' link ' , text) text = re.sub( r'\b\d+(?:\.\d+)?\s+' , '' , text) # remove numbers # Remove punctuation from text text = '' .join([c for c in text if c not in punctuation]) # Optionally, remove stop words if remove_stop_words: text = [w for w in text if not w in stop_words] text = " " .join(text) # Optionally, shorten words to their stems if lemmatize_words: lemmatized_words = [lemmatizer.lemmatize(word) for word in text] text = " " .join(lemmatized_words) # Return a list of words return (text) Now we can clean our dataset by using the text_cleaning() function: #clean the review data[ "cleaned_review" ] = data[ "review" ].apply(text_cleaning) Then split the data into features and target variables like this: y = data.sentiment.values #split features and target from  data X = data[ "cleaned_review" ] Our feature for training is the cleaned_review variable and the target is the sentiment variable. We then split our dataset into train and test data. The test size is 15% of the entire dataset. X_train, X_valid, y_train, y_valid = train_test_split(
    X,
    y, stratify=y,
) # split data into train and validate test_size= 0.15 , random_state= 42 , shuffle= True , How to Create a Model We will train the Multinomial algorithm to classify if a review is positive or negative. This is one of the most common algorithms used for text classification. Naive Bayes But before training the model, we need to transform our cleaned reviews into numerical values so that the model can understand the data. In this case, we will use the . TfidfVectorizer will help us to convert a collection of text documents to a matrix of TF-IDF features. TfidfVectorizer method from scikit-learn To apply this series of steps (pre-processing and training), we will use a from scikit-learn that sequentially applies a list of transforms and a final estimator. Pipeline class sentiment_classifier = Pipeline(steps=[ ]) # Create a classifier in pipeline ( 'pre_processing' ,TfidfVectorizer(lowercase= False )), ( 'naive_bayes' ,MultinomialNB()) Then we train our classifier like this: sentiment_classifier.fit(X_train,y_train) # train the sentiment classifier We then create a prediction from the validation set: y_preds = sentiment_classifier.predict(X_valid) # test model performance on valid data The model’s performance will be evaluated by using the accuracy_score evaluation metric. We use accuracy_score because we have an equal number of classes in the sentiment variable. accuracy_score(y_valid,y_preds) 0.8629 The accuracy of our model is around 86.29% which is good performance. How to Save the Model Pipeline We can save the model pipeline in the model’s directory by using the joblib Python package. #save model import joblib joblib.dump(sentiment_classifier, '../models/sentiment_model_pipeline.pkl' ) Now that we've built our model, let's learn how to deploy it with Aibro. Deployment WorkFlow – a Step by step Guide To deploy your model with Aibro, you need to prepare your model in the properly formatted machine learning model repository. You can quickly take a look at this repository , but we will build the same for the model we have created. https://github.com/AIpaca-Inc/Aibro-examples As you can see from the image of the deployment flow, Aibro will create an inference API from the formatted machine learning model repository, and you will receive a unique API URL and start to make a prediction from your model. All you need to do is to follow these steps. Step 1: Install the aibro Python library To install aibro, run the following command in your terminal: pip install aibro Step 2: Prepare the Model Repository The model repository will be formatted in the following structure. This folder will contain the model you have created. (a) model folder The data folder will have a JSON file that has an input value. For our case, the input will have a text value (review) as follows. (b) data folder { "data": "I loved it, the kids loved it. It shows them that anything is possible but more especially when you have that one person fighting for you. That one person who believes in you without fail. I appreciated the various life lessons included in the film about being humble and thankful but commanding respect at the same time despite where or what background you come from. Success doesn’t see age, race or gender but sadly opportunity often does. Will Smith doesn’t let the lack of opportunity beat them as a family and the family is a team. The bigger picture is always knowing that there is a team involved in most successful people." } Remember there is no restriction on how you want to format your input and output. Note: The python file should contain two python functions. (c) predict.py This function is responsible for loading the machine learning model from the model folder and returning it. In this tutorial, we will use the joblib package to load the model we have created. load_model(): def load_model (): #load model model = joblib.load( "model/sentiment_model_pipeline.pkl" ) return model This function will receive a model as the input and then load the data from the data folder. Finally, it will make predictions and return the result. run(): data = json.load(fp) def run ( model ): fp = open ( "data/data.json" , "r" ) review = text_cleaning(data[ "data" ]) result = { "data" : model.predict([review])} return result Therefore the predict.py will look as follows: data = json.load(fp) run(load_model()) # import important modules import json # load data import joblib # load model from clean import text_cleaning # function to clean the text def load_model (): #load model model = joblib.load( "model/sentiment_model_pipeline.pkl" ) return model def run ( model ): fp = open ( "data/data.json" , "r" ) review = text_cleaning(data[ "data" ]) result = { "data" : model.predict([review])} return result if __name__ == "__main__" : Aibro will first need to install the packages required to run your model before deploying the model itself. You can either manually write the packages and their version number in the requirements.txt or run the following command which will do the same: (d) requirements.txt pip list -- format =freeze > requirements.txt nltk==3.6.7 numpy==1.19.1 pandas==1.0.5 scikit_learn==0.23.1 joblib==1.0.0 It is also recommended to use the pipreqs Python package to generate requirements.txt. This is because it will include Python packages based on imports in your project instead of all packages in your environment. Note: $ pipreqs /home/aibro_project Successfully saved requirements file /home/aibro_project/requirements.txt You can also include other files or folders that will be used by the predict.py Python file. For example, in the model we have created, we will need to clean the input before making a prediction. (e) Other Artifacts The clean.py contains a Python function that will clean the text before making a prediction. nltk.download(dependency) nltk.download(dependency) nltk.download(dependency) text = text.split() text = text.split()
       lemmatizer = WordNetLemmatizer() # import packages import nltk # Download dependency corpora_list = [ "stopwords" , "names" , "brown" , "wordnet" ] for dependency in corpora_list: try : nltk.data.find( 'corpora/{}' . format (dependency)) except LookupError: taggers_list = [ "averaged_perceptron_tagger" , "universal_tagset" ] for dependency in taggers_list: try : nltk.data.find( 'taggers/{}' . format (dependency)) except LookupError: tokenizers_list = [ "punkt" ] for dependency in tokenizers_list: try : nltk.data.find( 'tokenizers/{}' . format (dependency)) except LookupError: from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer from nltk.tokenize import word_tokenize import re #regular expression from string import punctuation stop_words =  stopwords.words( 'english' ) # function to clean the text def text_cleaning ( text, remove_stop_words= True , lemmatize_words= True ): # Clean the text, with the option to remove stop_words and to lemmatize word # Clean the text text = re.sub( r"[^A-Za-z0-9]" , " " , text) text = re.sub( r"\'s" , " " , text) text =  re.sub( r'http\S+' , ' link ' , text) text = re.sub( r'\b\d+(?:\.\d+)?\s+' , '' , text) # remove numbers # Remove punctuation from text text = '' .join([c for c in text if c not in punctuation]) # Optionally, remove stop words if remove_stop_words: text = [w for w in text if not w in stop_words] text = " " .join(text) # Optionally, shorten words to their stems if lemmatize_words: lemmatized_words = [lemmatizer.lemmatize(word) for word in text] text = " " .join(lemmatized_words) # Return a list of words return (text) How to Test the Repo with Dryrun Before we deploy our model, we can test the repo using Dryrun. Dryrun will locally validate the repo structure and test if the inference result can be successfully returned. The following line of code will test the repo we have created: api_url = Inference.deploy( ) from aibro import Inference artifacts_path= "./sentiment_model_repo" , dryrun= True , The formatted model repository is saved at path “./sentiment_model_repo”. Note: The result shows that the prediction finished without errors. Now we can deploy the model. How to Create an Inference API with One Line of Code To deploy the model, you need to configure the following variables in the inference.deploy() method. The model name should be unique with respect to all current active inference jobs under . In this example, the model name will be "my_sentiment_classifier". (a) model_name your profile This is the id of the machine that will run our model. For this example we will use "c5.large.od".You can see the entire list in the . (b) machine_id_config marketplace This will be the path to your formatted machine learning model repository. For this example, the path is . (c) artifacts_path "./sentiment_model_repo" You can also add a description of your model deployment. (d) description Finally, the one-line code to create an inference API will look as follows. api_url = Inference.deploy( ) from aibro import Inference model_name = "my_movie_sentiment_classifier" , machine_id_config = "c5.large.od" , artifacts_path = "./sentiment_model_repo" , description= "my first inference job" , Once the deployment is finished, an API URL is returned with the syntax like this “ ” http://api.aipaca.ai/v1/{username}/{client_id}/{model_name}/predict if your inference job is public, {client_id} is filled out with "public". Otherwise, {client_id} should be filled out with one of your ' IDs. Note: clients In this tutorial, the API URL will be http://api.aipaca.ai/v1/DavisDavid/public/my_sentiment_classifier/predict How to Test an Aibro API We successfully deployed the model and got the API URL. Let’s test the model and see the result. We will use a Python package called requests to send a request to the API URL and get results. The posted data will replace everything in the data folder. Therefore, your posted data should have the same format as whatever you had in the data folder initially. Note: prediction = requests.post( data=review,
)
 
result = prediction.text import requests import json review = { "data" : "A truly beautiful film that will having you crying with joy and pride. The (few) poor reviews cite a lack of authenticity regarding Richards character and a lack of screen time for the other major family members, including Serena. While I admittedly don’t know exactly the kind of person and father Richard was" } "http://api.aipaca.ai/v1/DavisDavid/public/my_movie_sentiment_classifier/predict" , print (result) The prediction result is {‘data’: array([1])}. As you can see we managed to predict by using the API, and the model predicts that the review is . positive (1) Complete the inference job If you're no longer going to use your inference job, you should shut down the API to avoid unnecessary costs. You can shut it down by passing the inference job id in the Inference.complete() method. rom aibro.inference import Inference id = "inf_cd712f4a-4b59-4e44-8787-9c5b5450ff6d" Inference.complete(job_id= id ) You will receive an output that the inference job successfully completed. Final Thoughts In this article, you have learned the fastest and simplest way to deploy a Machine Learning model to the cloud by using Aibro. You don't need to take a lot of your time and resources to configure the environment – just install aibro and you are good to go. There is a free community edition you can use for your small ML projects. But Alpaca is also giving free credits to new users so you don't need to worry about the cloud costs. There are a lot of features from Aibro that you can use while deploying your model. To learn more you can visit our beautiful designed documentation pages and you can also join our community to get more help. here here You can download the source code used in this article here: https://github.com/Davisy/Aibro-ML-Model-Deployment If you learned something new or enjoyed reading this article, please share it so that others can see it.

How to Deploy Machine Learning Models to the Cloud Quickly and Easily

SYDNEY, Australia, November 21st, 2024/Chainwire/--The GMT DAO, an independent community organization of GMT holders, is excited to announce the launch of the GMT Burn Initiative—an event designed to place the future of GMT firmly in the hands of its community. Through this initiative, GMT holders will vote on the decision to burn up to 600 million GMT, equivalent to 100 million USD, marking this as one of the most ambitious token burns in the web3 ecosystem. The GMT Burn Initiative reflects the GMT DAO’s commitment to transparency, governance, and sustainable token management. This community-driven approach aligns with the DAO's goal to shape GMT’s future responsibly and collaboratively. How It Works: Lock and Vote: GMT holders can participate by visiting burngmt.com, where they can lock their tokens and cast their vote on various proposals, including the first of its kind “GMT Burn Initiative”. Participants will receive an exclusive soulbound ‘Make GMT Great Again’ Red Hat NFT as a token of acknowledgement for their involvement. This NFT will record metadata detailing the claimed and eligible airdrops, as well as the votes cast.
Earn Rewards: A total of 100 million GMT will be distributed at the end of the 60-day voting period as rewards to participants, contingent on achieving milestones.
On-Chain Transparency: The voting process is recorded on the blockchain, allowing anyone to verify results and ensuring the integrity and transparency of each decision.
Regular Voting Cycles: This burn initiative is just the beginning. Moving forward, GMT DAO will introduce new proposals and voting cycles every quarter, giving holders regular opportunities to shape the future of GMT via gmtdao.com. This initiative showcases a proactive effort to ensure GMT's sustainable future, emphasizing innovation and community engagement. The GMT DAO remains committed to true decentralization, empowering GMT holders to shape its direction. By enabling votes on key proposals, including a potential major token burn, the DAO highlights the power of collective input and its dedication to building a strong, thriving ecosystem. The GMT Burn Initiative marks a new chapter in GMT’s journey, giving every holder a voice. Voting, which starts at 100 GMT, is now open at burngmt.com, inviting all GMT holders to help shape the token's future. About GMT DAO The GMT DAO is an independent organization of GMT holders dedicated to overseeing GMT’s future through transparent and community-led governance. The DAO’s formation represents a commitment to empowering GMT holders with a voice in key decisions, from token management to ecosystem development. For more information on the GMT Burn Initiative and to participate, users can visit burngmt.com. About GMT GMT sits at the heart of the FSL ecosystem. Exclusively earned through STEPN, this token plays a crucial role across all FSL products. GMT operates on four blockchains—Solana, BNB Chain, Ethereum, and Polygon—and is pivotal in Gas Hero, where it's used to trade items and burn for NFT level-ups. On MOOAR, all items can be traded using GMT, and it’s also central to primary sale raffle mints with key partners and is used in the newly launched STEPN GO to buy Sneakers and perform various in app functions. Contact GMT DAO hello@gmtdao.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here SYDNEY, Australia, November 21st, 2024/Chainwire/--The GMT DAO, an independent community organization of GMT holders, is excited to announce the launch of the GMT Burn Initiative—an event designed to place the future of GMT firmly in the hands of its community. Through this initiative, GMT holders will vote on the decision to burn up to 600 million GMT, equivalent to 100 million USD, marking this as one of the most ambitious token burns in the web3 ecosystem. The GMT Burn Initiative reflects the GMT DAO’s commitment to transparency, governance, and sustainable token management. This community-driven approach aligns with the DAO's goal to shape GMT’s future responsibly and collaboratively. How It Works: Lock and Vote: GMT holders can participate by visiting burngmt.com, where they can lock their tokens and cast their vote on various proposals, including the first of its kind “GMT Burn Initiative”. Participants will receive an exclusive soulbound ‘Make GMT Great Again’ Red Hat NFT as a token of acknowledgement for their involvement. This NFT will record metadata detailing the claimed and eligible airdrops, as well as the votes cast. Earn Rewards: A total of 100 million GMT will be distributed at the end of the 60-day voting period as rewards to participants, contingent on achieving milestones. On-Chain Transparency: The voting process is recorded on the blockchain, allowing anyone to verify results and ensuring the integrity and transparency of each decision. Regular Voting Cycles: This burn initiative is just the beginning. Moving forward, GMT DAO will introduce new proposals and voting cycles every quarter, giving holders regular opportunities to shape the future of GMT via gmtdao.com. Lock and Vote: GMT holders can participate by visiting burngmt.com , where they can lock their tokens and cast their vote on various proposals, including the first of its kind “GMT Burn Initiative”. Participants will receive an exclusive soulbound ‘Make GMT Great Again’ Red Hat NFT as a token of acknowledgement for their involvement. This NFT will record metadata detailing the claimed and eligible airdrops, as well as the votes cast. burngmt.com burngmt.com Earn Rewards: A total of 100 million GMT will be distributed at the end of the 60-day voting period as rewards to participants, contingent on achieving milestones. On-Chain Transparency: The voting process is recorded on the blockchain, allowing anyone to verify results and ensuring the integrity and transparency of each decision. Regular Voting Cycles: This burn initiative is just the beginning. Moving forward, GMT DAO will introduce new proposals and voting cycles every quarter, giving holders regular opportunities to shape the future of GMT via gmtdao.com . gmtdao.com gmtdao.com This initiative showcases a proactive effort to ensure GMT's sustainable future, emphasizing innovation and community engagement. The GMT DAO remains committed to true decentralization, empowering GMT holders to shape its direction. By enabling votes on key proposals, including a potential major token burn, the DAO highlights the power of collective input and its dedication to building a strong, thriving ecosystem. The GMT Burn Initiative marks a new chapter in GMT’s journey, giving every holder a voice. Voting, which starts at 100 GMT, is now open at burngmt.com , inviting all GMT holders to help shape the token's future. burngmt.com burngmt.com About GMT DAO The GMT DAO is an independent organization of GMT holders dedicated to overseeing GMT’s future through transparent and community-led governance. The DAO’s formation represents a commitment to empowering GMT holders with a voice in key decisions, from token management to ecosystem development. For more information on the GMT Burn Initiative and to participate, users can visit burngmt.com . burngmt.com burngmt.com About GMT GMT sits at the heart of the FSL ecosystem. Exclusively earned through STEPN , this token plays a crucial role across all FSL products. STEPN STEPN GMT operates on four blockchains—Solana, BNB Chain, Ethereum, and Polygon—and is pivotal in Gas Hero , where it's used to trade items and burn for NFT level-ups. Gas Hero Gas Hero On MOOAR , all items can be traded using GMT, and it’s also central to primary sale raffle mints with key partners and is used in the newly launched STEPN GO to buy Sneakers and perform various in app functions. MOOAR MOOAR STEPN GO STEPN GO Contact GMT DAO hello@gmtdao.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

GMT DAO Launches Groundbreaking Burn Initiative With a Target Of Up To 600 Million GMT

GMT DAO стартира новаторска инициатива за изгаряне с цел до 600 милиона GMT

GMT DAO spouští průlomovou iniciativu Burn s cílem až 600 milionů GMT

GMT DAO lanza una innovadora iniciativa de quema con un objetivo de hasta 600 millones de GMT

GMT DAO Luncurkan Inisiatif Pembakaran Terobosan dengan Target Hingga 600 Juta GMT

GMT DAO、最大6億GMTを目標とした画期的なバーンイニシアチブを開始

GMT DAO pristato novatorišką deginimo iniciatyvą, kurios tikslas – iki 600 mln. GMT

GMT DAO лансира револуционерна иницијатива за изгореници со цел до 600 милиони GMT

GMT DAO Melancarkan Inisiatif Pembakaran Pecah Tanah Dengan Sasaran Sehingga 600 Juta GMT

GMT DAO GMT මිලියන 600 ක් දක්වා ඉලක්කයක් සමඟ බිම් කඩකිරීමේ පිළිස්සීමේ මුලපිරීම දියත් කරයි

GMT DAO lanserar banbrytande Burn-initiativ med ett mål på upp till 600 miljoner GMT

GMT DAO เปิดตัวโครงการ Burning สุดล้ำด้วยเป้าหมายสูงถึง 600 ล้าน GMT

GMT DAO, 600 Milyon GMT'ye Kadar Hedefle Çığır Açan Yakma Girişimini Başlattı

SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 22 of 26. VI. CONSENSYS ALSO ACTS AS A BROKER WITH RESPECT TO THE LIDO AND ROCKET POOL INVESTMENT CONTRACTS. 324. Through its MetaMask Staking program, Consensys also acts as a broker by effecting transactions in the Lido and Rocket Pool investment contracts for the account of others. 325. As alleged above, Consensys solicits potential investors, holds itself out as a place to buy and sell the investment contracts, and recommends which of the two investment contracts will offer the highest rewards. 326. Consensys then effects the transaction on the investor’s behalf. 327. Specifically, as noted, Consensys through the MetaMask Staking software handles the investor’s assets by removing the investor’s ETH from the investor’s MetaMask wallet and transferring it to the MM Staking Router Smart Contract, which, in turn, transfers it to the Lido or Rocket Pool proxy smart contract. 328. Finally, Consensys’s software, transfers the acquired token—stETH or rETH—into the investor’s MetaMask Wallet. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 22 of 26. SEC v. Consensys Software Inc. Court Filing, retrieved on June 28, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 22 of 26. HackerNoon’s Legal PDF Series here VI. CONSENSYS ALSO ACTS AS A BROKER WITH RESPECT TO THE LIDO AND ROCKET POOL INVESTMENT CONTRACTS. 324. Through its MetaMask Staking program, Consensys also acts as a broker by effecting transactions in the Lido and Rocket Pool investment contracts for the account of others. 325. As alleged above, Consensys solicits potential investors, holds itself out as a place to buy and sell the investment contracts, and recommends which of the two investment contracts will offer the highest rewards. 326. Consensys then effects the transaction on the investor’s behalf. 327. Specifically, as noted, Consensys through the MetaMask Staking software handles the investor’s assets by removing the investor’s ETH from the investor’s MetaMask wallet and transferring it to the MM Staking Router Smart Contract, which, in turn, transfers it to the Lido or Rocket Pool proxy smart contract. 328. Finally, Consensys’s software, transfers the acquired token—stETH or rETH—into the investor’s MetaMask Wallet. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on June 28, 2024, storage.courtlistener.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener.com

SEC Targets Consensys for Brokerage Activities in Lido and Rocket Pool Investments

Investigating Ad Targeting on YouTube: Implications and Legal Analysis

How Advertisers Use Placement-Based Targeting on Children's Videos

কিভাবে বিজ্ঞাপনদাতারা শিশুদের ভিডিওতে স্থান নির্ধারণ-ভিত্তিক লক্ষ্য নির্ধারণ ব্যবহার করে

So nutzen Werbetreibende platzierungsbasiertes Targeting für Kindervideos

Cómo utilizan los anunciantes la orientación basada en la ubicación en los vídeos infantiles

Comment les annonceurs utilisent le ciblage par emplacements sur les vidéos pour enfants

विज्ञापनदाता बच्चों के वीडियो पर प्लेसमेंट-आधारित लक्ष्यीकरण का उपयोग कैसे करते हैं

広告主が子供向け動画でプレースメントベースのターゲティングを使用する方法

광고주가 아동용 동영상에 게재위치 기반 타겟팅을 사용하는 방법

Como os anunciantes usam a segmentação baseada em canal em vídeos infantis

Как рекламодатели используют таргетинг на основе мест размещения в детских видео

Reklamverenler Çocuk Videolarında Yerleşime Dayalı Hedeflemeyi Nasıl Kullanıyor?

Cách nhà quảng cáo sử dụng nhắm mục tiêu dựa trên vị trí trên video dành cho trẻ em

广告商如何在儿童视频中使用基于展示位置的定位

4 Strategies for Targeting Children on YouTube Despite Advertising Restrictions

Marketing to Children Through Online Targeted Advertising: Targeting Mechanisms and Legal Analysis

Cyber Polygon Returns to Simulate an Advanced Targeted Attack on a Tech Company

One of my goals during the past year was to work on my communication skills each day. I worked on writing, speaking and or listening everyday. Even though I was working to improve my communication skills as a neurodiverse person, daily I was also facing situations in which other people could have done better honing their message to me. This article illustrates some ways your message can be more clear for others. I know certain things are hard for me communication wise. It's part of the baggage I am carrying as a neurodiverse person. Much of my life I did not know I had this problem. I want to share these helpful thoughts so that others can use to more clearly communicate positive messages with neurodiverse people. There are 3 areas I feel it can be a struggle to receive communication: Finding out things “through the grapevine” If your organization is relying heavily on implied messages or verbal communication about important topics, there is a good chance this information will never reach some of your employees or members. It may not seem immediately clear, but many people have different preferences for how they pick up information (similarly related to the book The 5 Love Languages). To reach more people your communication styles for sending your message should be more inclusive and more explicit. Maybe there can be a written notice as well as a verbal statement when you need to communicate important information in some cases. One thing I learned while in the workplace is that many people never correct their vision and they actually cannot read small print very well. You might be trying to primarily communicate in writing with a neurotypical person who actually can’t read your microscopic print on your memo. Relying on picking up only social cues to understand social situations Sometimes people with neurodiverse traits are never invited to the social situations where information is exchanged. You could be making important decisions around the water cooler or during an informal lunch gathering when half your team is actually there. Because sometimes people with fewer social abilities have fewer opportunities to practice social skills, it might be harder for them to pick up social cues even if they are trying. Making the opportunities to meet together more obvious can help. This might include having an open office hour for team members to talk to managers. People assume I should just “know” or “Why don’t you just get it?” No matter how frustrated you are with someone, yelling at them and telling them “You don’t have any social skills” is a failure on your part. You are struggling to communicate and send your message, the other neurodiverse person is having trouble receiving your message. Yelling at the person you are intending to influence positively is the opposite of communication.  Do you want the neurodiverse person to change and see your point of view? Telling them they are deficient does not help because they are generally really trying their best. If you are feeling frustrated with a neurodiverse person, back up and find patience first. This is worth it even if you don’t want to keep a relationship with them because people never remember what you say, they remember how you make them feel. Recently, I was a newcomer in a social club where some of these areas of weakness forced me to stop participating. The social club was focused on providing fundraising support to a nonprofit in my community. They did not hold regular monthly meetings to disseminate information, but instead relied on committee leaders to share important info. This implicit way of sharing information was very difficult for me. A friend in the group had to keep me updated on important things via text on occasion. Additionally, they relied heavily on email blasts to share information. I noticed that I could not find important emails when I needed information. I was assigned to 4 committees when I was only supposed to be on one. I did not know who my point person was for any of the committees I was in until one of the senior leaders in the club noticed I was not participating in any of the committees I had been assigned. We had a deliverable to create a fundraising gala with the help of all the committees. I noticed many of the senior leaders of the social club working on things, but I could not tell what would be the most helpful for my contribution. I did not feel I was developing any useful skills and I felt I was disappointing people. Most of the time I felt I was confused about my role and when I tried to reach out for help, people were too busy to explain or I was calling at a bad time. Having a means of asynchronous communication (my posting a message that I did not understand)  would have been better than requiring synchronous communication (my having to call someone to explain if I did not understand) What would have helped Using slack or discord server to share information and to connect people If you have a slack or discord server, there would have been several options for methods of communication for people struggling with gaining information.  Senior leaders that wanted to share information more clearly would be able to.  Rather than pushing information out in email blasts the information could be pulled from the slack or discord when people needed it. An example would be posting the information about the required dress code for the gala in a slack channel so that many people could access it when they needed rather than finding out about the dress code “through the grapevine.” The pinterest page for gala dressing ideas that was created could have been posted so that everyone would have had access to it at the same time and months in advance. A buddy system for sharing information and orienting new members Because I was a new person to this group and they had recently changed many of the requirements to participate, I would have been well served if I had a veteran member of the group be my buddy and help explain requirements to me as they came up. I have had a buddy in another social group when I was new and it made a considerable difference to have one friendly person in the group that I knew or who might answer questions. Even if the buddy is not very active or helpful, as long as they are “good enough” to explain some concepts or social faux paus it might help some neurodiverse individuals save face. Being more forgiving with meeting requirements for new members I found the social group I was in struggled to keep me engaged because of the aforementioned reasons, I had trouble finding the meetings I did get to attend very helpful or productive and my engagement slid downhill. I found that the way I was being communicated with distracted me from helping achieve the deliverable of creating a fundraising gala and even made me dread participating in the group. If it had not been for one of the group members occasionally texting me to remind me about where I was supposed to be, I don’t think I would have lasted the 10 months I did in the group. Your neurodiverse coworkers and community members are trying very hard to build a communication bridge to you! Many of them already know they struggle with this. My only suggestion: Meet them halfway. Use some of these ideas to build out your communication so that it can be used and digested by more people and so that you can keep more of your talent engaged. One of my goals during the past year was to work on my communication skills each day. I worked on writing, speaking and or listening everyday. Even though I was working to improve my communication skills as a neurodiverse person, daily I was also facing situations in which other people could have done better honing their message to me. This article illustrates some ways your message can be more clear for others. I know certain things are hard for me communication wise. It's part of the baggage I am carrying as a neurodiverse person. Much of my life I did not know I had this problem. I want to share these helpful thoughts so that others can use to more clearly communicate positive messages with neurodiverse people. There are 3 areas I feel it can be a struggle to receive communication: Finding out things “through the grapevine” Finding out things “through the grapevine” If your organization is relying heavily on implied messages or verbal communication about important topics, there is a good chance this information will never reach some of your employees or members. It may not seem immediately clear, but many people have different preferences for how they pick up information (similarly related to the book The 5 Love Languages). To reach more people your communication styles for sending your message should be more inclusive and more explicit. Maybe there can be a written notice as well as a verbal statement when you need to communicate important information in some cases. One thing I learned while in the workplace is that many people never correct their vision and they actually cannot read small print very well. You might be trying to primarily communicate in writing with a neurotypical person who actually can’t read your microscopic print on your memo. Relying on picking up only social cues to understand social situations Relying on picking up only social cues to understand social situations Sometimes people with neurodiverse traits are never invited to the social situations where information is exchanged. You could be making important decisions around the water cooler or during an informal lunch gathering when half your team is actually there. Because sometimes people with fewer social abilities have fewer opportunities to practice social skills, it might be harder for them to pick up social cues even if they are trying. Making the opportunities to meet together more obvious can help. This might include having an open office hour for team members to talk to managers. People assume I should just “know” or “Why don’t you just get it?” People assume I should just “know” or “Why don’t you just get it?” No matter how frustrated you are with someone, yelling at them and telling them “You don’t have any social skills” is a failure on your part. You are struggling to communicate and send your message, the other neurodiverse person is having trouble receiving your message. Yelling at the person you are intending to influence positively is the opposite of communication.  Do you want the neurodiverse person to change and see your point of view? Telling them they are deficient does not help because they are generally really trying their best. If you are feeling frustrated with a neurodiverse person, back up and find patience first. This is worth it even if you don’t want to keep a relationship with them because people never remember what you say, they remember how you make them feel. Recently, I was a newcomer in a social club where some of these areas of weakness forced me to stop participating. The social club was focused on providing fundraising support to a nonprofit in my community. They did not hold regular monthly meetings to disseminate information, but instead relied on committee leaders to share important info. This implicit way of sharing information was very difficult for me. A friend in the group had to keep me updated on important things via text on occasion. Additionally, they relied heavily on email blasts to share information. I noticed that I could not find important emails when I needed information. I was assigned to 4 committees when I was only supposed to be on one. I did not know who my point person was for any of the committees I was in until one of the senior leaders in the club noticed I was not participating in any of the committees I had been assigned. We had a deliverable to create a fundraising gala with the help of all the committees. I noticed many of the senior leaders of the social club working on things, but I could not tell what would be the most helpful for my contribution. I did not feel I was developing any useful skills and I felt I was disappointing people. Most of the time I felt I was confused about my role and when I tried to reach out for help, people were too busy to explain or I was calling at a bad time. Having a means of asynchronous communication (my posting a message that I did not understand)  would have been better than requiring synchronous communication (my having to call someone to explain if I did not understand) What would have helped Using slack or discord server to share information and to connect people Using slack or discord server to share information and to connect people If you have a slack or discord server, there would have been several options for methods of communication for people struggling with gaining information.  Senior leaders that wanted to share information more clearly would be able to.  Rather than pushing information out in email blasts the information could be pulled from the slack or discord when people needed it. An example would be posting the information about the required dress code for the gala in a slack channel so that many people could access it when they needed rather than finding out about the dress code “through the grapevine.” The pinterest page for gala dressing ideas that was created could have been posted so that everyone would have had access to it at the same time and months in advance. A buddy system for sharing information and orienting new members A buddy system for sharing information and orienting new members Because I was a new person to this group and they had recently changed many of the requirements to participate, I would have been well served if I had a veteran member of the group be my buddy and help explain requirements to me as they came up. I have had a buddy in another social group when I was new and it made a considerable difference to have one friendly person in the group that I knew or who might answer questions. Even if the buddy is not very active or helpful, as long as they are “good enough” to explain some concepts or social faux paus it might help some neurodiverse individuals save face. Being more forgiving with meeting requirements for new members Being more forgiving with meeting requirements for new members I found the social group I was in struggled to keep me engaged because of the aforementioned reasons, I had trouble finding the meetings I did get to attend very helpful or productive and my engagement slid downhill. I found that the way I was being communicated with distracted me from helping achieve the deliverable of creating a fundraising gala and even made me dread participating in the group. If it had not been for one of the group members occasionally texting me to remind me about where I was supposed to be, I don’t think I would have lasted the 10 months I did in the group. Your neurodiverse coworkers and community members are trying very hard to build a communication bridge to you! Many of them already know they struggle with this. My only suggestion: Meet them halfway. Use some of these ideas to build out your communication so that it can be used and digested by more people and so that you can keep more of your talent engaged.

How to improve your communication so that it hits neurodiverse targets

In my previous story, Part 1 of this topic — https://hackernoon.com/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1, we covered the derivation of the expression of WoE using maximum likelihood. Now, we will apply it practically on a random dataset. import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
from scipy.special import logit, expit We have imported some special functions - logit and expit. We will use them in our script because logit is the inverse logistic function, and expit is the logistic function. print(expit(0))
print(logit(0.5)) The next step is to generate the y-vector randomly. For this, we use the Bernoulli distribution (actually, we use a Binomial distribution where n = 1). Each Bernoulli trial will produce either a 1 or 0 based on the probability parameter. And the n samples we generate correspond to the n trials of the Bernoulli distribution. Note that Binomial distribution has two parameters — n and p. Therefore, randomly sampling from Binomial requires us to specify both n and p beforehand and the “size” which is the number of experiments. Each experiment comprises n trials which will produce the “number of successes” which is the random variable for the Binomial distribution. y = np.random.binomial(1, 0.01, 10000)
print(sum(y)) np.random.binomial(10000, 0.01, 10) It is evident that the array generated using Bernoulli is what we want which is the vector y with 1s and 0s. However, the array generated using Binomial is sum(y) or sum of events in each Binomial experiment (each experiment has n = 10000 trials) performed 10 times. We don’t want that for our analysis. Then we generate two distributions for the variable “x” for which we need to compute the “weight of evidence” or which we want to target encode. x_1 = np.random.normal(130, 15, 114)
x_0 = np.random.normal(100, 20, 9886)
plt.hist(x_1) plt.hist(x_0) Note that we have cleverly randomly sampled x_1 and x_0 such that there is a higher proportion of the number of events (1s) for the “middle portion” of x values. This will give us a monotonic trend as we will see later. But the intuition is that the event rate is lower for lower values of x and increases as values of x increase. This is ensured by our sampling technique of choosing the right mean and standard deviation for the Normal distributions for x_1 and x_0. Now, we sort the vector y to ensure that we “stack” x_0 and x_1 corresponding to y=0 and y=1, respectively. Finally, we convert the np arrays into a data frame. y = np.sort(y)
print(y)
x = np.concatenate((x_0, x_1), axis=None)
df = pd.DataFrame({'y':y, 'x':x}) From now on, our main WoE code begins. ####### which feature we want a WoE for?
col = 'x' We can “bin” our variable x in two ways — equally proportioned bins using the percentile method or user-specified bins. ####### create a new dataframe with the binned values of the feature 'col' using the percentile method
####### To avoid dealing with Pandas series, create a new df and set its column to the output from pd.qcut
df_bins = pd.DataFrame()
df_bins[col] = pd.qcut(df[col], 5)
print(df_bins[col].value_counts()) Using pd.qcut(), we see how the bins have been equally proportioned. We can also use pd.cut() and specify the bins that we want which may not be equally proportioned. Note that the “include lowest = True” ensures that the x.min() is included in the lowest interval. Thus, we see 23.999 as the lowest end point of the lowest bin to include x = 24 ####### create a new dataframe with the binned values of the feature 'col' using user defined bins
####### include lowest = True ensures that df[col].min() is included in the binned interval. 
df_bins = pd.DataFrame()
df_bins[col] = pd.cut(df[col], bins = [24, 95, 115, 188], include_lowest = True)
print(df_bins[col].value_counts()) That is how our binned data frame for x looks like We define a data frame for calculating WoE woe_df = pd.concat([df['y'], df_bins], axis=1) And we calculate WoE using the one-line code (note the use of the logit function discussed earlier). Please refer to my earlier article to find out the rationale behind the following script — https://hackernoon.com/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1 woe_x = logit(woe_df.groupby('x')['y'].sum() / woe_df.groupby('x')['y'].count()) - logit(woe_df['y'].sum() / len(woe_df)) It is interesting to see that the lowest bin has negative infinity WoE. We will see the reason for that later. But first, we plot the WoE values for x. fig, ax = plt.subplots()
woe_x.plot(ax=ax, color = 'r', linewidth = 3)
plt.xlabel('x')
plt.ylabel('WoE')
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.show() As expected, we see a nice monotonic trend of WoE values for x. This indicates that as x increases, the probability of event or outcome y occurring increases as well. The following is also a good and alternative method to calculate WoE values. It also lists down the entire “table” of calculations so it is helpful to look at. def calculate_woe_iv(dataset, feature, target):
    lst = []
    for i in range(dataset[feature].nunique()):
        val = list(dataset[feature].unique())[i]
        lst.append({
            'Value': val,
            'All': dataset[dataset[feature] == val].count()[feature],
            'Good': dataset[(dataset[feature] == val) & (target == 0)].count()[feature],
            'Bad': dataset[(dataset[feature] == val) & (target == 1)].count()[feature]
        })
        
    dset = pd.DataFrame(lst)
    dset['Distr_Good'] = dset['Good'] / dset['Good'].sum()
    dset['Distr_Bad'] = dset['Bad'] / dset['Bad'].sum()
    dset['WoE'] = np.log(dset['Distr_Bad'] / dset['Distr_Good'])
    dset = dset.replace({'WoE': {np.inf: 0, -np.inf: 0}})
    dset['IV'] = (dset['Distr_Bad'] - dset['Distr_Good']) * dset['WoE']
    iv = dset['IV'].sum()
    
    dset = dset.sort_values(by='Value') # sort by values and not by WoEs to detect non-monotonic trends
    
    return dset, iv We can also calculate the information value for the variable x which quantifies the predictive power of x in predicting y. We use the same bins as we used earlier. print('WoE and IV for variable: {}'.format(col))
woe_iv_df, iv = calculate_woe_iv(df_bins, col, woe_df['y'])
print(woe_iv_df)
print('IV score: {:.2f}'.format(iv))
print('\n') The above result shows that x is quite predictive as its IV is 1.75 which is a good value of IV. We also see that the lower bin has exactly 0 events. So clearly, its WoE had to be -infinity as we saw before. However, this code snippet function assigns a value “0” to it which is wrong per se but for plotting purposes, we can use that. We plot the WoE values again. fig,ax = plt.subplots()
woe_iv_df_plt = woe_iv_df[['Value', 'WoE']].set_index('Value')
woe_iv_df_plt.plot(ax=ax, color = 'r', linewidth = 3)
plt.xlabel('x')
plt.ylabel('WoE')
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.show() It is the same trend we saw earlier sans the lowest interval WoE value = 0. If we want to avoid that, we can use the “user-specified bins” using pd.cut instead of pd.qcut. If we use that, we will get the following plot In my previous story, Part 1 of this topic — https://hackernoon.com/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1 , we covered the derivation of the expression of WoE using maximum likelihood. Now, we will apply it practically on a random dataset. https://hackernoon.com/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1 import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
from scipy.special import logit, expit import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
from scipy.special import logit, expit We have imported some special functions - logit and expit. We will use them in our script because logit is the inverse logistic function, and expit is the logistic function. print(expit(0))
print(logit(0.5)) print(expit(0))
print(logit(0.5)) The next step is to generate the y-vector randomly. For this, we use the Bernoulli distribution (actually, we use a Binomial distribution where n = 1). Each Bernoulli trial will produce either a 1 or 0 based on the probability parameter. And the n samples we generate correspond to the n trials of the Bernoulli distribution. Note that Binomial distribution has two parameters — n and p. Therefore, randomly sampling from Binomial requires us to specify both n and p beforehand and the “size” which is the number of experiments. Each experiment comprises n trials which will produce the “number of successes” which is the random variable for the Binomial distribution. y = np.random.binomial(1, 0.01, 10000)
print(sum(y)) y = np.random.binomial(1, 0.01, 10000)
print(sum(y)) np.random.binomial(10000, 0.01, 10) np.random.binomial(10000, 0.01, 10) It is evident that the array generated using Bernoulli is what we want which is the vector y with 1s and 0s. However, the array generated using Binomial is sum(y) or sum of events in each Binomial experiment (each experiment has n = 10000 trials) performed 10 times. We don’t want that for our analysis. Then we generate two distributions for the variable “x” for which we need to compute the “weight of evidence” or which we want to target encode. x_1 = np.random.normal(130, 15, 114)
x_0 = np.random.normal(100, 20, 9886)
plt.hist(x_1) x_1 = np.random.normal(130, 15, 114)
x_0 = np.random.normal(100, 20, 9886)
plt.hist(x_1) plt.hist(x_0) plt.hist(x_0) Note that we have cleverly randomly sampled x_1 and x_0 such that there is a higher proportion of the number of events (1s) for the “middle portion” of x values. This will give us a monotonic trend as we will see later. But the intuition is that the event rate is lower for lower values of x and increases as values of x increase. This is ensured by our sampling technique of choosing the right mean and standard deviation for the Normal distributions for x_1 and x_0. Now, we sort the vector y to ensure that we “stack” x_0 and x_1 corresponding to y=0 and y=1, respectively. Finally, we convert the np arrays into a data frame. y = np.sort(y)
print(y)
x = np.concatenate((x_0, x_1), axis=None)
df = pd.DataFrame({'y':y, 'x':x}) y = np.sort(y)
print(y)
x = np.concatenate((x_0, x_1), axis=None)
df = pd.DataFrame({'y':y, 'x':x}) From now on, our main WoE code begins. ####### which feature we want a WoE for?
col = 'x' ####### which feature we want a WoE for?
col = 'x' We can “bin” our variable x in two ways — equally proportioned bins using the percentile method or user-specified bins. ####### create a new dataframe with the binned values of the feature 'col' using the percentile method
####### To avoid dealing with Pandas series, create a new df and set its column to the output from pd.qcut
df_bins = pd.DataFrame()
df_bins[col] = pd.qcut(df[col], 5)
print(df_bins[col].value_counts()) ####### create a new dataframe with the binned values of the feature 'col' using the percentile method
####### To avoid dealing with Pandas series, create a new df and set its column to the output from pd.qcut
df_bins = pd.DataFrame()
df_bins[col] = pd.qcut(df[col], 5)
print(df_bins[col].value_counts()) Using pd.qcut(), we see how the bins have been equally proportioned. We can also use pd.cut() and specify the bins that we want which may not be equally proportioned. Note that the “include lowest = True” ensures that the x.min() is included in the lowest interval. Thus, we see 23.999 as the lowest end point of the lowest bin to include x = 24 ####### create a new dataframe with the binned values of the feature 'col' using user defined bins
####### include lowest = True ensures that df[col].min() is included in the binned interval. 
df_bins = pd.DataFrame()
df_bins[col] = pd.cut(df[col], bins = [24, 95, 115, 188], include_lowest = True)
print(df_bins[col].value_counts()) ####### create a new dataframe with the binned values of the feature 'col' using user defined bins
####### include lowest = True ensures that df[col].min() is included in the binned interval. 
df_bins = pd.DataFrame()
df_bins[col] = pd.cut(df[col], bins = [24, 95, 115, 188], include_lowest = True)
print(df_bins[col].value_counts()) That is how our binned data frame for x looks like We define a data frame for calculating WoE woe_df = pd.concat([df['y'], df_bins], axis=1) woe_df = pd.concat([df['y'], df_bins], axis=1) And we calculate WoE using the one-line code (note the use of the logit function discussed earlier). Please refer to my earlier article to find out the rationale behind the following script — https://hackernoon.com/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1 https://hackernoon.com/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1 woe_x = logit(woe_df.groupby('x')['y'].sum() / woe_df.groupby('x')['y'].count()) - logit(woe_df['y'].sum() / len(woe_df)) woe_x = logit(woe_df.groupby('x')['y'].sum() / woe_df.groupby('x')['y'].count()) - logit(woe_df['y'].sum() / len(woe_df)) It is interesting to see that the lowest bin has negative infinity WoE. We will see the reason for that later. But first, we plot the WoE values for x. fig, ax = plt.subplots()
woe_x.plot(ax=ax, color = 'r', linewidth = 3)
plt.xlabel('x')
plt.ylabel('WoE')
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.show() fig, ax = plt.subplots()
woe_x.plot(ax=ax, color = 'r', linewidth = 3)
plt.xlabel('x')
plt.ylabel('WoE')
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.show() As expected, we see a nice monotonic trend of WoE values for x. This indicates that as x increases, the probability of event or outcome y occurring increases as well. The following is also a good and alternative method to calculate WoE values. It also lists down the entire “table” of calculations so it is helpful to look at. def calculate_woe_iv(dataset, feature, target):
    lst = []
    for i in range(dataset[feature].nunique()):
        val = list(dataset[feature].unique())[i]
        lst.append({
            'Value': val,
            'All': dataset[dataset[feature] == val].count()[feature],
            'Good': dataset[(dataset[feature] == val) & (target == 0)].count()[feature],
            'Bad': dataset[(dataset[feature] == val) & (target == 1)].count()[feature]
        })
        
    dset = pd.DataFrame(lst)
    dset['Distr_Good'] = dset['Good'] / dset['Good'].sum()
    dset['Distr_Bad'] = dset['Bad'] / dset['Bad'].sum()
    dset['WoE'] = np.log(dset['Distr_Bad'] / dset['Distr_Good'])
    dset = dset.replace({'WoE': {np.inf: 0, -np.inf: 0}})
    dset['IV'] = (dset['Distr_Bad'] - dset['Distr_Good']) * dset['WoE']
    iv = dset['IV'].sum()
    
    dset = dset.sort_values(by='Value') # sort by values and not by WoEs to detect non-monotonic trends
    
    return dset, iv def calculate_woe_iv(dataset, feature, target):
    lst = []
    for i in range(dataset[feature].nunique()):
        val = list(dataset[feature].unique())[i]
        lst.append({
            'Value': val,
            'All': dataset[dataset[feature] == val].count()[feature],
            'Good': dataset[(dataset[feature] == val) & (target == 0)].count()[feature],
            'Bad': dataset[(dataset[feature] == val) & (target == 1)].count()[feature]
        })
        
    dset = pd.DataFrame(lst)
    dset['Distr_Good'] = dset['Good'] / dset['Good'].sum()
    dset['Distr_Bad'] = dset['Bad'] / dset['Bad'].sum()
    dset['WoE'] = np.log(dset['Distr_Bad'] / dset['Distr_Good'])
    dset = dset.replace({'WoE': {np.inf: 0, -np.inf: 0}})
    dset['IV'] = (dset['Distr_Bad'] - dset['Distr_Good']) * dset['WoE']
    iv = dset['IV'].sum()
    
    dset = dset.sort_values(by='Value') # sort by values and not by WoEs to detect non-monotonic trends
    
    return dset, iv We can also calculate the information value for the variable x which quantifies the predictive power of x in predicting y. We use the same bins as we used earlier. print('WoE and IV for variable: {}'.format(col))
woe_iv_df, iv = calculate_woe_iv(df_bins, col, woe_df['y'])
print(woe_iv_df)
print('IV score: {:.2f}'.format(iv))
print('\n') print('WoE and IV for variable: {}'.format(col))
woe_iv_df, iv = calculate_woe_iv(df_bins, col, woe_df['y'])
print(woe_iv_df)
print('IV score: {:.2f}'.format(iv))
print('\n') The above result shows that x is quite predictive as its IV is 1.75 which is a good value of IV. We also see that the lower bin has exactly 0 events. So clearly, its WoE had to be -infinity as we saw before. However, this code snippet function assigns a value “0” to it which is wrong per se but for plotting purposes, we can use that. We plot the WoE values again. fig,ax = plt.subplots()
woe_iv_df_plt = woe_iv_df[['Value', 'WoE']].set_index('Value')
woe_iv_df_plt.plot(ax=ax, color = 'r', linewidth = 3)
plt.xlabel('x')
plt.ylabel('WoE')
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.show() fig,ax = plt.subplots()
woe_iv_df_plt = woe_iv_df[['Value', 'WoE']].set_index('Value')
woe_iv_df_plt.plot(ax=ax, color = 'r', linewidth = 3)
plt.xlabel('x')
plt.ylabel('WoE')
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
plt.show() It is the same trend we saw earlier sans the lowest interval WoE value = 0. If we want to avoid that, we can use the “user-specified bins” using pd.cut instead of pd.qcut. If we use that, we will get the following plot

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