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 How Text to Speech Voices Are Used In Data Science

To speak on voices, text to speech platforms are bringing versatility to a new scale by implementing voices that sound more personal and less like a robot. As these services gain traction, vocal quality, and implementation improve to give sounds that feel like they’re speaking to you from a human mouth.

The intention of most text to speech platforms have always been to provide experiences that users feel comfortable using. Voices are a huge part of that, so great strides have been taken to ensure that they sound right.

How Voices are Utilized

Voices in the text to speech are generated by a computer itself. As the computer-generated voices transcribe the text into oral responses, they make up what we hear as dialogue read to us. These voice clips initially had the problem of sounding robotic and unpersonable as they were pulled digitally together. Lately, though, the technology has improved to bring faster response time in transcribing words, as well as seamlessly stringing together. This has brought the advantage of making a computer-generated voice sound much more natural and human. As people seek to connect more with the works they read, having a human-sounding voice is a huge step in letting listeners relate to their works.

To give an example of where this works, you might have a GPS in your car. The GPS has a function where it will transcribe the car’s route and tell you each instruction. Some GPS companies have made full use of this feature and added fun voices to help entertain drivers. These include Darth Vader and Yoda from Star Wars or having Morgan Freeman and Homer Simpson narrate your route. Different voice types are utilized in services depending on the situation. Professional uses like customer service centers will keep automated voices sounding professional and courteous when assisting customers. Educational systems will keep softer and kinder sounding voices to help sound more friendly with students.

When compared to older solutions, the rise of vocal variety in Text to Speech services has taken huge leaps as more people see the value of having a voice that they can connect to. Expressive voices and emotional variance are being applied to voices to help further convey this, with happy or sad sounding voices being implemented wherever appropriate. As time goes on, these services will get better at the reading context within sentences to apply emotion and tone at the correct times, and improve overall vocal quality as well. These reinvent past methods by advancing the once static and robotic sounds that used to be commonplace among text to voice services.

More infrastructures adopt these services to expand their reach to consumers who might not have the capabilities to utilize their offerings.  Having clear and relatable voices matter because customers and users will be drawn to them considerably more than if they chose not to offer them at all. In the near future text to speech voices will develop even further, enhancing the way people of all kinds connect to the words they read.

As Businesses Struggle With ML, Automation Offers a Solution

In recent years, machine learning technology and the business solutions it enables has developed into a big business in and of itself. According to the industry analysts at IDC, spending on ML and AI technology is set to grow to almost $98 billion per year by 2023. In practical terms, that figure represents a business environment where ML technology has become a key priority for companies of every kind.

That doesn’t mean that the path to adopting ML technology is easy for businesses. Far from it. In fact, survey data seems to indicate that businesses are still struggling to get their machine learning efforts up and running. According to one such survey, it currently takes the average business as many as 90 days to deploy a single machine learning model. For 20% of businesses, that number is even higher.

From the data, it seems clear that something is missing in the methodologies that most companies rely on to make meaningful use of machine learning in their business workflows. A closer look at the situation reveals that the vast majority of data workers (analysts, data scientists, etc.) spend an inordinate amount of time on infrastructure work – and not on creating and refining machine learning models.

Streamlining the ML Adoption Process

To fix that problem, businesses need to turn to another growing area of technology: automation. By leveraging the latest in automation technology, it’s now possible to build an automated machine learning pipeline (AutoML pipeline) that cuts down on the repetitive tasks that slow down ML deployments and lets data workers get back to the work they were hired to do. With the right customized solution in place, a business’s ML team can:

  • Reduce the time spent on data collection, cleaning, and ingestion
  • Minimize human errors in the development of ML models
  • Decentralize the ML development process to create an ML-as-a-service model with increased accessibility for all business stakeholders

In short, an AutoML pipeline turns the high-effort functions of the ML development process into quick, self-adjusting steps handled exclusively by machines. In some use cases, an AutoML pipeline can even allow non-technical stakeholders to self-create ML solutions tailored to specific business use cases with no expert help required. In that way, it can cut ML costs, shorten deployment time, and allow data scientists to focus on tackling more complex modelling work to develop custom ML solutions that are still outside the scope of available automation techniques.

The Parts of an AutoML Pipeline

Although the frameworks and tools used to create an AutoML pipeline can vary, they all contain elements that conform to the following areas:

  • Data Preprocessing – Taking available business data from a variety of sources, cleaning it, standardizing it, and conducting missing value imputation
  • Feature Engineering – Identifying features in the raw data set to create hypotheses for the model to base predictions on
  • Model Selection – Choosing the right ML approach or hyperparameters to produce the desired predictions
  • Tuning Hyperparameters – Determining which hyperparameters help the model achieve optimal performance

As anyone familiar with ML development can tell you, the steps in the above process tend to represent the majority of the labour and time-intensive work that goes into creating a model that’s ready for real-world business use. It is also in those steps where the lion’s share of business ML budgets get consumed, and where most of the typical delays occur.

The Limitations and Considerations for Using AutoML

Given the scope of the work that can now become part of an AutoML pipeline, it’s tempting to imagine it as a panacea – something that will allow a business to reduce its reliance on data scientists going forward. Right now, though, the technology can’t do that. At this stage, AutoML technology is still best used as a tool to augment the productivity of business data teams, not to supplant them altogether.

To that end, there are some considerations that businesses using AutoML will need to keep in mind to make sure they get reliable, repeatable, and value-generating results, including:

  • Transparency – Businesses must establish proper vetting procedures to make sure they understand the models created by their AutoML pipeline, so they can explain why it’s making the choices or predictions it’s making. In some industries, such as in medicine or finance, this could even fall under relevant regulatory requirements.
  • Extensibility – Making sure the AutoML framework may be expanded and modified to suit changing business needs or to tackle new challenges as they arise.
  • Monitoring and Maintenance – Since today’s AutoML technology isn’t a set-it-and-forget-it proposition, it’s important to establish processes for the monitoring and maintenance of the deployment so it can continue to produce useful and reliable ML models.

The Bottom Line

As it stands today, the convergence of automation and machine learning holds the promise of delivering ML models at scale for businesses, which would greatly speed up the adoption of the technology and lower barriers to entry for those who have yet to embrace it. On the whole, that’s great news both for the businesses that will benefit from increased access to ML technology, as well as for the legions of data professionals tasked with making it all work.

It’s important to note, of course, that complete end-to-end ML automation with no human intervention is still a long way off. While businesses should absolutely explore building an automated machine learning pipeline to speed up development time in their data operations, they shouldn’t lose sight of the fact that they still need plenty of high-skilled data scientists and analysts on their teams. It’s those specialists that can make appropriate and productive use of the technology. Without them, an AutoML pipeline would accomplish little more than telling the business what it wants to hear.

The good news is that the AutoML tools that exist right now are sufficient to alleviate many of the real-world problems businesses face in their road to ML adoption. As they become more commonplace, there’s little doubt that the lead time to deploy machine learning models is going to shrink correspondingly – and that businesses will enjoy higher ROI and enhanced outcomes as a result.

Best machine learning algorithms you should know

Machine learning is a key technology tool businesses use to build tools that enhance their operations. To do that, they take advantage of machine learning algorithms that come in different shapes and sizes, servicing different purposes and working on different data sets. Choosing the right algorithm for the job is what makes machine learning and deep learning projects successful. That’s why being aware of all the different types of machine learning algorithms is so important – that’s how you get better results and build more advanced solutions.

Here’s an overview of the best machine learning algorithms you should know before starting your project.

What is meant by machine learning algorithms?

First things first, what is machine learning and how do algorithms fit into the picture? A machine learning (ML) algorithm is a process or set of procedures that allow a model to adapt to the data with a specific objective set as the goal.

An ML algorithm specifies how the data is transformed from the input to output, helping the model to learn the appropriate mapping from input to output. That model specifies the mapping functions and holds the parameters in place, while the machine learning algorithm updates the parameters to help the model match its goal.

What are the algorithms used in machine learning?

Algorithms can model problems in many different ways. The easiest way to differentiate between different ML algorithms is by comparing them by learning styles that they can adapt. Generally, machine learning algorithms can adapt to several learning styles that help to solve different problems.

Here are four learning styles in machine learning you need to know:

1 Supervised learning

In supervised learning, the input data serves as training data and comes with a known label or result – for example, the price at a time or spam/not-spam.

In this variant, the training process is critical for preparing a model that makes predictions and then is corrected when the predictions are wrong. The training process continues until the model achieves the appropriate level of accuracy. Classification and regression are examples of problems for this learning type.

 

2 Unsupervised learning

In unsupervised learning, input data isn’t labeled and doesn’t come with a known result. Data scientists prepare models by deducing the structures in the input data to extract general rules or reduce redundancy through mathematical processes. Unsupervised learning addresses problems such as association rule learning, dimensionality reduction, and clustering.

3 Semi-supervised learning

In this learning style, the input data is a mixture of labeled and unlabeled examples. The prediction problem is known, but the model needs to learn the structures for organizing data and making predictions on its own. This learning style is used to address problems such as regression and classification.

4 Reinforcement learning

One of three basic machine learning paradigms together with supervised learning and unsupervised learning, reinforcement learning (RL) is an area of machine learning that focuses on the ways in which software agents should take actions to maximize a specified notion of cumulative reward in a given environment.

The best machine learning algorithms you should know

1 Linear Regression

Linear regression is an algorithm that correlates between two variables in the data set, examining the input and output sets to show a relationship between them. For example, the algorithm can show how changing one of the input variables affects the other variable. The relationship is represented by plotting a line on the graph.

Linear regression is one of the most popular algorithms in machine learning because it’s transparent and requires no tuning to work. Practical applications of this algorithm are risk assessment or sales forecasting solutions.

2 Logistic regression

Logistic regression is a type of constrained Linear Regression with a non-linearity application after you apply weights. Note that this algorithm is used for classification, not regression. The algorithm restricts the outputs close to +/- classes (and 1 and 0 in the case of sigmoid) and can be trained with Gradient Descent or L-BFGS.

Logistic regression is used in Natural Language Processing (NLP) applications, where it often appears under the name of Maximum Entropy Classifier.

3 Principal component analysis (PCA or LDA)

Principal component analysis is an unsupervised method that helps data scientists to understand better the global properties of a data set that consists of vectors. It analyzes the covariance matrix of data points to learn which dimensions/data points have high variance among themselves and low covariance with others. The algorithm helps data scientists to get data points with reduced dimensions.

4 K-means clustering

K- means clustering is a type of unsupervised clustering algorithm that sorts data sets through defined clusters. It offers results in the form of groups based on internal patterns.

For example, you can use a K-means algorithm for sorting web results for the word “cat,” and it will show all the results in the form of groups. The main advantage of this algorithm is its accuracy as it provides data groupings faster than other algorithms.

 

5 Decision trees

A decision tree is made of various branches that represent the outcome of many decisions. This algorithm collects and graphs data in multiple branches to predict response variables on the basis of past decisions. It comes in handy for mapping our decisions and presents results visually to communicate findings easily.

Decision trees work best for smaller data sets and relatively low-stake decisions – otherwise, the long-tail visuals can be hard to decipher. The key advantage of this algorithm is that it allows showing multiple outcomes and tests without having to involve data scientists – it’s easy to use.

6 Random forests

A random forest consists of a great number of individual decision trees where they all operate as an ensemble. An individual tree in the random forest generates a class prediction – the class which receives the highest number of votes becomes the model’s prediction. Having many relatively uncorrelated models (trees) operating as a committee easily outperforms individual constituent models.

The low correlation between these models is the strength of this approach because it allows producing ensemble predictions that are far more accurate than individual predictions. Note that decisions trees protect each other from individual errors. While some trees may generate false predictions, others will generate the right ones – as a group; they will be able to move in the right direction.

7 Support Vector Machine

Support Vector Machines (SVMs) are linear models similar to linear or logistic regression we’ve discussed earlier. However, there’s one difference – they have a different margin-based loss function, which can be optimized by using methods such as L-BFGS or SGD. SVMs internally analyze data sets into classes, which is helpful for future classifications.

The main idea behind SVM is separating data into classes and maximizing the margins of entering future data into classes. This type of algorithm works best for training data. However, it can also serve as a tool for processing nonlinear data. The financial sector makes use of Support Vector Machines thanks to its accuracy in classifying both current and future data sets.

8 Apriori

The Apriori algorithm is used a lot in market analysis. It’s based on the principle of Apriori and checks for positive and negative correlations between products after analyzing values in data sets.

For example, if two values often correlate in a data set, the algorithm will conclude that A will often lead to B, referring to the information in data sets. For example, if customers often buy product A and product B together, this relation will hold a high percentage and help companies like Google or Amazon to predict product searches and purchases.

9 Naive Bayes Classifier

This handy classification technique is based on Bayes’ Theorem, which assumes independence among predictors. The algorithm will assume that the presence of a specific feature in a class is not related to the presence of any other feature in the same class.

For example, a fruit may be considered a banana if it’s yellow, curved, and about 15 cm long. These features depend on each other, and on the existence of hooter features, they all independently contribute to the probability that this fruit is a banana. That’s why the algorithm bears the name “Naive.”

The algorithm offers a model that is easy to build and helpful in handling very large data sets. It can outperform the most sophisticated classification methods.

10 K-Nearest Neighbors (KNN)

This is one of the simplest algorithm types used in machine learning for classification and regression. KNN algorithms classify new data points on the basis of similarity measures, such as the distance function. They perform classification by using a majority vote of the data points’ neighbors. They then assign data to the class, which has the nearest neighbors. Together with increasing the number of nearest neighbors (the value of k), the accuracy may increase as well.

11 Ordinary Least Squares Regression (OLSR)

Ordinary Least Squares Regression (OLSR) is a generalized linear modeling technique data scientists use for estimating unknown parameters that are part of a linear regression model. OLSR describes the relationship between a dependent variable and one or more of its independent variables.

The algorithm is applied in diverse fields such as economics, finance, medicine, and social sciences. Companies use it in machine learning and predictive analytics to dynamically predict specific outcomes on the basis of variables that change dynamically.

We hope that this machine learning algorithms list helps you pick the right tools of the trade for your next machine learning project. If you’d like to learn more about Machine Learning, Data Science and Web Development, visit the Sunscrapers company blog.

A Bird’s Eye View: How Machine Learning Can Help You Charge Your E-Scooters

Bird scooters in Columbus, Ohio

Bird scooters in Columbus, Ohio

Ever since I started using bike-sharing to get around in Seattle, I have become fascinated with geolocation data and the transportation sharing economy. When I saw this project leveraging the mobility data RESTful API from the Los Angeles Department of Transportation, I was eager to dive in and get my hands dirty building a data product utilizing a company’s mobility data API.

Unfortunately, the major bike and scooter providers (Bird, JUMP, Lime) don’t have publicly accessible APIs. However, some folks have seemingly been able to reverse-engineer the Bird API used to populate the maps in their Android and iOS applications.

One interesting feature of this data is the nest_id, which indicates if the Bird scooter is in a “nest” — a centralized drop-off spot for charged Birds to be released back into circulation.

I set out to ask the following questions:

  1. Can real-time predictions be made to determine if a scooter is currently in a nest?
  2. For non-nest scooters, can new nest location recommendations be generated from geospatial clustering?

To answer these questions, I built a full-stack machine learning web application, NestGenerator, which provides an automated recommendation engine for new nest locations. This application can help power Bird’s internal nest location generation that runs within their Android and iOS applications. NestGenerator also provides real-time strategic insight for Bird chargers who are enticed to optimize their scooter collection and drop-off route based on proximity to scooters and nest locations in their area.

Bird

The electric scooter market has seen substantial growth with Bird’s recent billion dollar valuation  and their $300 million Series C round in the summer of 2018. Bird offers electric scooters that top out at 15 mph, cost $1 to unlock and 15 cents per minute of use. Bird scooters are in over 100 cities globally and they announced in late 2018 that they eclipsed 10 million scooter rides since their launch in 2017.

Bird scooters in Tel Aviv, Israel

Bird scooters in Tel Aviv, Israel

With all of these scooters populating cities, there’s much-needed demand for people to charge them. Since they are electric, someone needs to charge them! A charger can earn additional income for charging the scooters at their home and releasing them back into circulation at nest locations. The base price for charging each Bird is $5.00. It goes up from there when the Birds are harder to capture.

Data Collection and Machine Learning Pipeline

The full data pipeline for building “NestGenerator”

Data

From the details here, I was able to write a Python script that returned a list of Bird scooters within a specified area, their geolocation, unique ID, battery level and a nest ID.

I collected scooter data from four cities (Atlanta, Austin, Santa Monica, and Washington D.C.) across varying times of day over the course of four weeks. Collecting data from different cities was critical to the goal of training a machine learning model that would generalize well across cities.

Once equipped with the scooter’s latitude and longitude coordinates, I was able to leverage additional APIs and municipal data sources to get granular geolocation data to create an original scooter attribute and city feature dataset.

Data Sources:

  • Walk Score API: returns a walk score, transit score and bike score for any location.
  • Google Elevation API: returns elevation data for all locations on the surface of the earth.
  • Google Places API: returns information about places. Places are defined within this API as establishments, geographic locations, or prominent points of interest.
  • Google Reverse Geocoding API: reverse geocoding is the process of converting geographic coordinates into a human-readable address.
  • Weather Company Data: returns the current weather conditions for a geolocation.
  • LocationIQ: Nearby Points of Interest (PoI) API returns specified PoIs or places around a given coordinate.
  • OSMnx: Python package that lets you download spatial geometries and model, project, visualize, and analyze street networks from OpenStreetMap’s APIs.

Feature Engineering

After extensive API wrangling, which included a four-week prolonged data collection phase, I was finally able to put together a diverse feature set to train machine learning models. I engineered 38 features to classify if a scooter is currently in a nest.

Full Feature Set

Full Feature Set

The features boiled down into four categories:

  • Amenity-based: parks within a given radius, gas stations within a given radius, walk score, bike score
  • City Network Structure: intersection count, average circuity, street length average, average streets per node, elevation level
  • Distance-based: proximity to closest highway, primary road, secondary road, residential road
  • Scooter-specific attributes: battery level, proximity to closest scooter, high battery level (> 90%) scooters within a given radius, total scooters within a given radius

 

Log-Scale Transformation

For each feature, I plotted the distribution to explore the data for feature engineering opportunities. For features with a right-skewed distribution, where the mean is typically greater than the median, I applied these log transformations to normalize the distribution and reduce the variability of outlier observations. This approach was used to generate a log feature for proximity to closest scooter, closest highway, primary road, secondary road, and residential road.

An example of a log transformation

Statistical Analysis: A Systematic Approach

Next, I wanted to ensure that the features I included in my model displayed significant differences when broken up by nest classification. My thinking was that any features that did not significantly differ when stratified by nest classification would not have a meaningful predictive impact on whether a scooter was in a nest or not.

Distributions of a feature stratified by their nest classification can be tested for statistically significant differences. I used an unpaired samples t-test with a 0.01% significance level to compute a p-value and confidence interval to determine if there was a statistically significant difference in means for a feature stratified by nest classification. I rejected the null hypothesis if a p-value was smaller than the 0.01% threshold and if the 99.9% confidence interval did not straddle zero. By rejecting the null-hypothesis in favor of the alternative hypothesis, it’s deemed there is a significant difference in means of a feature by nest classification.

Battery Level Distribution Stratified by Nest Classification to run a t-test

Battery Level Distribution Stratified by Nest Classification to run a t-test

Log of Closest Scooter Distribution Stratified by Nest Classification to run a t-test

Throwing Away Features

Using the approach above, I removed ten features that did not display statistically significant results.

Statistically Insignificant Features Removed Before Model Development

Model Development

I trained two models, a random forest classifier and an extreme gradient boosting classifier since tree-based models can handle skewed data, capture important feature interactions, and provide a feature importance calculation. I trained the models on 70% of the data collected for all four cities and reserved the remaining 30% for testing.

After hyper-parameter tuning the models for performance on cross-validation data it was time to run the models on the 30% of test data set aside from the initial data collection.

I also collected additional test data from other cities (Columbus, Fort Lauderdale, San Diego) not involved in training the models. I took this step to ensure the selection of a machine learning model that would generalize well across cities. The performance of each model on the additional test data determined which model would be integrated into the application development.

Performance on Additional Cities Test Data

The Random Forest Classifier displayed superior performance across the board

The Random Forest Classifier displayed superior performance across the board

I opted to move forward with the random forest model because of its superior performance on AUC score and accuracy metrics on the additional cities test data. AUC is the Area under the ROC Curve, and it provides an aggregate measure of model performance across all possible classification thresholds.

AUC Score on Test Data for each Model

AUC Score on Test Data for each Model

Feature Importance

Battery level dominated as the most important feature. Additional important model features were proximity to high level battery scooters, proximity to closest scooter, and average distance to high level battery scooters.

Feature Importance for the Random Forest Classifier

Feature Importance for the Random Forest Classifier

The Trade-off Space

Once I had a working machine learning model for nest classification, I started to build out the application using the Flask web framework written in Python. After spending a few days of writing code for the application and incorporating the trained random forest model, I had enough to test out the basic functionality. I could finally run the application locally to call the Bird API and classify scooter’s into nests in real-time! There was one huge problem, though. It took more than seven minutes to generate the predictions and populate in the application. That just wasn’t going to cut it.

The question remained: will this model deliver in a production grade environment with the goal of making real-time classifications? This is a key trade-off in production grade machine learning applications where on one end of the spectrum we’re optimizing for model performance and on the other end we’re optimizing for low latency application performance.

As I continued to test out the application’s performance, I still faced the challenge of relying on so many APIs for real-time feature generation. Due to rate-limiting constraints and daily request limits across so many external APIs, the current machine learning classifier was not feasible to incorporate into the final application.

Run-Time Compliant Application Model

After going back to the drawing board, I trained a random forest model that relied primarily on scooter-specific features which were generated directly from the Bird API.

Through a process called vectorization, I was able to transform the geolocation distance calculations utilizing NumPy arrays which enabled batch operations on the data without writing any “for” loops. The distance calculations were applied simultaneously on the entire array of geolocations instead of looping through each individual element. The vectorization implementation optimized real-time feature engineering for distance related calculations which improved the application response time by a factor of ten.

Feature Importance for the Run-time Compliant Random Forest Classifier

Feature Importance for the Run-time Compliant Random Forest Classifier

This random forest model generalized well on test-data with an AUC score of 0.95 and an accuracy rate of 91%. The model retained its prediction accuracy compared to the former feature-rich model, but it gained 60x in application performance. This was a necessary trade-off for building a functional application with real-time prediction capabilities.

Geospatial Clustering

Now that I finally had a working machine learning model for classifying nests in a production grade environment, I could generate new nest locations for the non-nest scooters. The goal was to generate geospatial clusters based on the number of non-nest scooters in a given location.

The k-means algorithm is likely the most common clustering algorithm. However, k-means is not an optimal solution for widespread geolocation data because it minimizes variance, not geodetic distance. This can create suboptimal clustering from distortion in distance calculations at latitudes far from the equator. With this in mind, I initially set out to use the DBSCAN algorithm which clusters spatial data based on two parameters: a minimum cluster size and a physical distance from each point. There were a few issues that prevented me from moving forward with the DBSCAN algorithm.

  1. The DBSCAN algorithm does not allow for specifying the number of clusters, which was problematic as the goal was to generate a number of clusters as a function of non-nest scooters.
  2. I was unable to hone in on an optimal physical distance parameter that would dynamically change based on the Bird API data. This led to suboptimal nest locations due to a distortion in how the physical distance point was used in clustering. For example, Santa Monica, where there are ~15,000 scooters, has a higher concentration of scooters in a given area whereas Brookline, MA has a sparser set of scooter locations.

An example of how sparse scooter locations vs. highly concentrated scooter locations for a given Bird API call can create cluster distortion based on a static physical distance parameter in the DBSCAN algorithm. Left:Bird scooters in Brookline, MA. Right:Bird scooters in Santa Monica, CA.

An example of how sparse scooter locations vs. highly concentrated scooter locations for a given Bird API call can create cluster distortion based on a static physical distance parameter in the DBSCAN algorithm. Left:Bird scooters in Brookline, MA. Right:Bird scooters in Santa Monica, CA.

Given the granularity of geolocation scooter data I was working with, geospatial distortion was not an issue and the k-means algorithm would work well for generating clusters. Additionally, the k-means algorithm parameters allowed for dynamically customizing the number of clusters based on the number of non-nest scooters in a given location.

Once clusters were formed with the k-means algorithm, I derived a centroid from all of the observations within a given cluster. In this case, the centroids are the mean latitude and mean longitude for the scooters within a given cluster. The centroids coordinates are then projected as the new nest recommendations.

NestGenerator showcasing non-nest scooters and new nest recommendations utilizing the K-Means algorithm

NestGenerator showcasing non-nest scooters and new nest recommendations utilizing the K-Means algorithm.

NestGenerator Application

After wrapping up the machine learning components, I shifted to building out the remaining functionality of the application. The final iteration of the application is deployed to Heroku’s cloud platform.

In the NestGenerator app, a user specifies a location of their choosing. This will then call the Bird API for scooters within that given location and generate all of the model features for predicting nest classification using the trained random forest model. This forms the foundation for map filtering based on nest classification. In the app, a user has the ability to filter the map based on nest classification.

Drop-Down Map View filtering based on Nest Classification

Drop-Down Map View filtering based on Nest Classification

Nearest Generated Nest

To see the generated nest recommendations, a user selects the “Current Non-Nest Scooters & Predicted Nest Locations” filter which will then populate the application with these nest locations. Based on the user’s specified search location, a table is provided with the proximity of the five closest nests and an address of the Nest location to help inform a Bird charger in their decision-making.

NestGenerator web-layout with nest addresses and proximity to nearest generated nests

NestGenerator web-layout with nest addresses and proximity to nearest generated nests

Conclusion

By accurately predicting nest classification and clustering non-nest scooters, NestGenerator provides an automated recommendation engine for new nest locations. For Bird, this application can help power their nest location generation that runs within their Android and iOS applications. NestGenerator also provides real-time strategic insight for Bird chargers who are enticed to optimize their scooter collection and drop-off route based on scooters and nest locations in their area.

Code

The code for this project can be found on my GitHub

Comments or Questions? Please email me an E-Mail!

 

Closing the AI-skills gap with Upskilling

Closing the AI-skills gap with Upskilling

Artificial Intelligent or as it is fancily referred as AI, has garnered huge popularity worldwide.  And given the career prospects it has, it definitely should. Almost everyone interested in technology sector has them rushing towards it, especially young and motivated fresh computer science graduates. Compared to other IT-related jobs AI pays way higher salary and have opportunities. According to a Glassdoor report, Data Scientist, one of the many related jobs, is the number one job with good salary, job openings and more. AI-related jobs include Data Scientists, Analysts, Machine Learning Engineer, NLP experts etc.

AI has found applications in almost every industry and thus it has picked up demand. Home assistants – Siri, Ok Google, Amazon Echo — chatbots, and more some of the popular applications of AI.

Increasing adoption of AI across Industry

The advantages of AI like increased productivity has increased its adoption among companies. According to Gartner, 37 percent of enterprise currently use AI in one way or the other. In fact, in the last four year adoption of AI technologies among companies has increased by 270 percent. In telecommunications, for instance, 52 percent of companies have chatbots deployed for better and smoother customer experience. Now, about 49 percent of businesses are now on their way to alter business models to integrate and adopt AI-driven processes. Further, industry leaders have gone beyond and voiced their concerns about companies that are lagging in AI adoption.

Unfortunately, it has been extremely difficult for employers to find right skilled or qualified candidates for AI-related positions. A reports suggests that there are total 300,000 AI professionals are available worldwide, while there’s demand for millions. In a recent survey conducted by Ernst & Young, 51 percent AI professionals told that lack of talent was the biggest impediment in AI adoption.

Further, O’Reilly, in 2018 conducted a survey, which found the lack of AI skills, among other things, was the major reason that was holding companies back from implementing AI.
The major reason for this is the lack of skills among people who aspire to get into AI-related jobs. According to a report, there demand for millions for jobs in AI. However, only a handful of qualified people are available.

Bridging the skill gap in AI-related jobs

Top companies and government around the world have taken up initiatives to close this gap. Google and Amazon, for instance, have dedicated facilities which trains in AI skills.  Google’s Brain Toronto is a dedicated facility to expand their talent in AI.  Similarly, Amazon has facility near University of Cambridge which is dedicated to AI. Most companies either already have a facility or are in the process of setting up one.

In addition to this, governments around the world are also taking initiatives to address the skill gap. For instance, government across the world are pushing towards AI advancement and are develop collaborative plans which aims at delivering more AI skilled professionals. Recently, the white house launched ai.gov which is further helping to promote AI in the US. The website will offer updates related to AI projects across different sectors.

Other than these, companies have taken this upon themselves to reskills their employees and prepare them for future roles. According to a report from Towards Data Science, about 63 percent of companies have in-house training programs to train employees in AI-related skills.

Overall, though there is demand for AI professionals, lack of skilled talent is a major problem.

Roles in Artificial Intelligence
Artificial Intelligence is the most dominant role for which companies hire across artificial Intelligence. Other than that, following are some of the popular roles:

  1. Machine learning Engineer: These are the people who make machines learn with complex algorithms. On advance level, Machine learning engineers are required to have good knowledge of computer vision. According to Indeed, in the last year, demand for Machine Learning Engineer has grown by 344 percent.
  2. NLP Experts: These experts are equipped with the understanding of making machines computer understand human language. Their expertise includes knowledge of how machines understand human language. Text-to-speech technologies are the common areas which require NLP experts. Demand for engineers who can program computers to understand human speech is growing continuously. It was the fast growing skills in Upwork’s list of in-demand freelancing skills. In Q4, 2016, it had grown 200 percent and since then has been on continuously growing.
  3. Big Data Engineers: This is majorly an analytics role. These gather huge amount of data available from sources and analyze it to derive insights and understand patter, which may be further used for machine learning, prediction modelling, natural language processing. In Mckinsey annual report 2018, it had reported that there was shortage of 190,000 big data professionals in the US alone.

Other roles like Data Scientists, Analysts, and more also in great demand. Then, again due to insufficient talent in the market, companies are struggling to hire for these roles.

Self-learning and upskilling
Artificial Intelligence is a continuously growing field and it has been advancing at a very fast pace, and it makes extremely difficult to keep up with in-demand skills. Hence, it is imperative to keep yourself up with demand of the industry, or it is just a matter of time before one becomes redundant.

On an individual level, learning new skills is necessary. One has to be agile and keep learning, and be ready to adapt new technologies. For this, AI training programs and certifications are ideal.  There are numerous AI programs which individuals can take to further learn new skills. AI certifications can immensely boost career opportunities. Certification programs offer a structured approach to learning which benefits in learning mostly practical and executional skills while keeping fluff away. It is more hands-on. Plus, certifications programs qualify only when one has passed practical test which is very advantageous in tech. AI certifications like AIE (Artificial Intelligence Engineer) are quite popular.

Online learning platforms also offer good a resource to learn artificial intelligence. Most schools haven’t yet adapted their curriculum to skill for AI, while most universities and grad schools are in their way to do so. In the meantime, online learning platforms offer a good way to learn AI skills, where one can start from basic and reach to advance skills.

Introduction to ROC Curve

The abbreviation ROC stands for Receiver Operating Characteristic. Its main purpose is to illustrate the diagnostic ability of classifier as the discrimination threshold is varied. It was developed during World War II when Radar operators had to decide if the blip on the screen is an enemy target, a friendly ship or just a noise.  For these purposes they measured the ability of a radar receiver operator to make these important distinctions, which was called the Receiver Operating Characteristic.

Later it was found useful in interpreting medical test results and then in Machine learning classification problems. In order to get an introduction to binary classification and terms like ‘precision’ and ‘recall’ one can look into my earlier blog  here.

True positive rate and false positive rate

Let’s imagine a situation where a fire alarm is installed in a kitchen. The alarm is supposed to emit a sound in case fire smoke is detected in the room. Unfortunately, there is a lot of cooking done in the kitchen and the alarm may trigger the sound too often. Thus, instead of serving a purpose the alarm becomes a nuisance due to a large number of false alarms. In statistical terms these types of errors are called type 1 errors, or false positives.

One way to deal with this problem is to simply decrease sensitivity of the device. We do this by increasing the trigger threshold at the alarm setting. But then, not every alarm should have the same threshold setting. Consider the same type of device but kept in a bedroom. With high threshold, the device might miss smoke from a real short-circuit in the wires which poses a real danger of fire. This kind of failure is called Type 2 error or a false negative. Although the two devices are the same, different types of threshold settings are optimal for different circumstances.

To specify this more formally, let us describe the performance of a binary classifier at a particular threshold by the following parameters:

 

These parameters take different values at different thresholds. Hence, they define the performance of the classifier at particular threshold. But we want to examine in overall how good a classifier is. Fortunately, there is a way to do that. We plot the True Positive Rate (TPR) and False Positive rate (FPR) at different thresholds and this plot is called ROC curve.

Let’s try to understand this with an example.

A case with a distinct population distribution

Let’s suppose there is a disease which can be identified with deficiency of some parameter (maybe a certain vitamin). The distribution of population with this disease has a mean vitamin concentration sharply distinct from the mean of a healthy population, as shown below.

This is result of dummy data simulating population of 2000 people,the link to the code is given  in the end of this blog.  As the two populations are distinctly separated (there is no  overlap between the two distributions), we can expect that a classifier would have an easy job distinquishing healthy from sick people. We can run a logistic regression classifier with a threshold of .5 and be 100% succesful in detecting the decease.

The confusion matrix may look something like this.

In this ideal case with a threshold  of  .5 we do not make a single wrong classification. The True positive rate and False positive rate are 1 and 0, respectively. But we can shift the threshold. In that case, we will  get different confusion matrices. First we plot threshold vs. TPR.

We see for most values of threshold the TPR is close to 1 which again proves data is easy to classify and the classifier is returning high probabilities  for the most of positives .

Similarly Let’s plot threshold vs. FPR.

For most of the data points FPR is close to zero. This is also good. Now its time to plot the ROC curve using these results (TPR vs FPR).

Let’s try to interpret  the results,  all the points lie on line x=0 and y=1, it means for all the points FPR is zero or TPR is one, making  the curve a square. which means the classifier does perfectly well.

Case with overlapping  population distribution

The above example was about a perfect classifer. However, life is often not so easy. Now let us consider another more realistic situation in which the parameter distribution of the population is not as distinct as in the previous case. Rather, the mean of the parameter with healthy and not healthy datapoints are close and the distributions overlap, as shown in the next figure.

If we set the threshold to 0.5, the confusion matrix may look like this.

Now, any new choice of threshold location will affect both false positives and false negatives. In fact, there is a trade-off. If we shift the threshold with the goal to reduce false negatives, false positives will increase. If we move the threshold to the other direction and reduce false positive, false negatives will increase.

The plots (TPR vs Threshold) , (FPR vs Threshold) are shown below

If we plot the ROC curve from these results, it looks like this:

From the curve we see the classifier does not perform as well as the earlier one.

What else can be infered from this curve? We first need to understand what the diagonal in this plot represent. The diagonal represents ‘Line of no discrimination’, which we obtain if we randomly guess. This is the ROC curve for the worst possible classifier. Therefore, by comparing the obtained ROC curve with the diagonal, we see how much better our classifer is from random guessing.

The further away ROC curve from the diagonal is (the closest it is to the top left corner) , better the classifier is.

Area Under the curve

The overall performance of the classifier is given by the area under the ROC curve and is usually denoted as AUC. Since TPR and FPR lie within the range of 0 to 1, the AUC also assumes values between 0 and 1. The higher the value of AUC, the better is the overall performance of the classifier.

Let’s see this for the two different distributions which we saw earlier.

As we know the classifier had worked perfectly in the first case with points at (0,1) the area under the curve is 1 which is perfect. In the latter case the classifier was not able to perform as good, the ROC curve is between the diagonal and left hand corner. The AUC as we can see is less than 1.

Some other general characteristics

There are still few points that needs to be discussed on a General ROC curve

  • The ROC curve does not provide information about the actual values of thresholds used for the classifier.
  • Performance of different classifiers can be compared using the AUC of different Classifier. The larger the AUC, the better the classifier.
  • The vertical distance of the ROC curve from the no discrimination line gives a measure of ‘INFORMEDNESS’. This is known as Youden’s J satistic. This statistics can take values between 0 and 1.

Youden’s  J statistic is defined for every point on the ROC curve . The point at which Youden’s  J satistics reaches its maximum for a given ROC curve can be used to guide the selection of the threshold to be used for that classifier.

I hope this post does the job of providing an understanding of ROC curves  and AUC. The  Python program for simulating the example given earlier can be found here .

Please feel free to adjust the mean of the distributions and see the changes in the plot.

How is automation changing data science and machine learning?

We have come a long way since the introduction of data science and machine learning. The recent study has found that the volume of business data doubles in less than 14 months. Today, the collection of data is no longer a problem, but the filtration, analysis, and maintenance of relevant information is a bigger issue.

We need to hire data science professionals, and they demand over $100k annually. Paying that sort of money for a professional is not feasible for every single organization, especially small and middle-sized companies. Google recently announced that it is going to make machine learning technology possible for every business.

The access to machine learning technology is now possible, even for small businesses due to automation. Google, Microsoft, and other companies have come up with automated machine learning tools that enable small businesses to use machine learning technology to enhance their business performance and profit.

Image Source: Google Cloud

With that said, the world still needs a lot of machine learning professionals. Many machine learning professionals prefer Python for machine learning due to its features and a wide range of libraries.

According to the Gartner report, around 40% of data science tasks will be automated by 2020. The data science tools can automate some parts of data science processes, but it is not complete automation.

With that said, it has been helping a lot to accelerate the tasks. We still need data science professionals to deal with real-world problems. The algorithms are not yet able to handle messy data. The significant chunk of data science professionals often prefers performing with data science with Python for sophisticated tasks.

Automation in Data Science

Let me show you the figure right at the beginning before moving forward.

Image Source: Wikipedia

If I had to use only one word to describe the entire data science process, I would use the word “headache.” According to the recent report, the median salary of data scientists easily surpasses $100k annually. The pay will be higher in the time to come.

One needs to pay a lot of money and invest a lot of time to get insights from the collected data. The data scientists need to spend almost 50-60% of their time in data processing and the rest of their time in modeling and deployment.

The cloud platforms like Amazon Web Services, Google, Microsoft Azure, and so on make the job more comfortable, but there is still a lot of work to maintain and extract useful insights from the collected data.

The data science process has lots of inefficiencies. At first, they need to spend over 50% of their total time on processing messy real-world data. After that, there could be a need to customize models, according to specific problems.

The significant contribution of automation is making a significant portion of data processing parts automated. Secondly, the automated platforms can make tracking of various models easier from multiple parameters. The time needed to launch the algorithm is minimal.

One example of an extensive tool to handle a data science project is Alteryx. IT has come up with powerful automated solutions that can drastically reduce the data processing and model development time for smoothening the entire data science workflow. The data science platform, Alteryx, is so amazing that its share price doubled in a span of little more than a year.

Some other great tools that can help you in data science automation are Rapidminer, H20.ai, KNIME, and so on. However, the lack of skilled data scientists can create a problem despite these tools. It is where the role of automated machine learning pops in.

How is Machine Learning Transformed with the entrance of Automation?

The traditional machine learning process was too complicated. One requires to have a lot of expensive machine learning professionals working for months to come up with models to process machine learning tasks.

Image Source: Medium

To make traditional machine learning work, one needs to gather data, standardize data, process features, create and train the machine learning model from problems, validate the models, and deploy the models at last.

You must have heard of how machine learning is only for corporations in the past. But, that has drastically changed in recent time, and it is all due to automation. Keep in mind that the above machine learning model is a simple one. There is a lot of extra works for complicated models. Even for the simple ones, you need to spend a lot of time and money, which makes it impossible for small and medium companies.

The automation in machine learning is all about automating the entire process to make machine learning easier. The only thing you need to do is feed data to the system (not a massive volume of data). You do not need even to cross the three-figure number of images to continue with automated machine learning platforms.

Microsoft has its automl platform along with Google. Other automl platforms can do the trick for you. Using those platforms do not cost you an arm and a leg. If you check out the price, you will be surprised.

There is no need for you to create or deploy models or even test the models. The algorithm will do the job for you. It takes examples and models of historical models to process the data and use a machine learning algorithm.

Even non-statistician can implement machine learning technology with limited data, thanks to automation in machine learning. You can make use of predictive analytics and can get easy solutions for simple prediction problems without scratching your head. Numerous libraries can assist you in the automated generation of machine learning pipelines.

How are the jobs of data scientists simplified by the introduction of automation in machine learning and data science?

It is true that the introduction of automation has drastically reduced the time for completing the tasks for data scientists. They no longer have to spend their valuable time in time-consuming, monotonous works that are necessary but do not provide a lot of value.

However, the need for skilled data scientists still exist, and it will always be there in the time to come. There are challenging works for data scientists that we cannot replace with machines, such as listening to clients, figuring out the root cause of business issues, development and selection of the right solution for the specific business problem.

Just like in other types of jobs, the advancement of automation technologies will modify the tasks that data scientists need to perform. They will be able to allocate more time on things that matter rather than monotonous tasks.

Final Verdict

The automation of machine learning and data science are in the beginning stage. However, they are already making a massive impact on the business world. The huge corporations are investing in Big Data and Machine Learning technologies. We can expect a considerable improvement in these technologies shortly.

Sooner, the competitive advantage of a business will depend on how well they can use the technologies, instead of access to machine learning or Big Data technologies.  I hope this article was valuable to you. If you want to add something or express your thoughts, feel free to leave a comment. I will gladly read and reply to your comment.

Predictive maintenance in Semiconductor Industry: Part 1

The process in the semiconductor industry is highly complicated and is normally under consistent observation via the monitoring of the signals coming from several sensors. Thus, it is important for the organization to detect the fault in the sensor as quickly as possible. There are existing traditional statistical based techniques however modern semiconductor industries have the ability to produce more data which is beyond the capability of the traditional process.

For this article, we will be using SECOM dataset which is available here.  A lot of work has already done on this dataset by different authors and there are also some articles available online. In this article, we will focus on problem definition, data understanding, and data cleaning.

This article is only the first of three parts, in this article we will discuss the business problem in hand and clean the dataset. In second part we will do feature engineering and in the last article we will build some models and evaluate them.

Problem definition

This data which is collected by these sensors not only contains relevant information but also a lot of noise. The dataset contains readings from 590. Among the 1567 examples, there are only 104 fail cases which means that out target variable is imbalanced. We will look at the distribution of the dataset when we look at the python code.

NOTE: For a detailed description regarding this cases study I highly recommend to read the following research papers:

  •  Kerdprasop, K., & Kerdprasop, N. A Data Mining Approach to Automate Fault Detection Model Development in the Semiconductor Manufacturing Process.
  • Munirathinam, S., & Ramadoss, B. Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process.

Data Understanding and Preparation

Let’s start exploring the dataset now. The first step as always is to import the required libraries.

import pandas as pd
import numpy as np

There are several ways to import the dataset, you can always download and then import from your working directory. However, I will directly import using the link. There are two datasets: one contains the readings from the sensors and the other one contains our target variable and a timestamp.

# Load dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/secom/secom.data"
names = ["feature" + str(x) for x in range(1, 591)]
secom_var = pd.read_csv(url, sep=" ", names=names, na_values = "NaN") 


url_l = "https://archive.ics.uci.edu/ml/machine-learning-databases/secom/secom_labels.data"
secom_labels = pd.read_csv(url_l,sep=" ",names = ["classification","date"],parse_dates = ["date"],na_values = "NaN")

The first step before doing the analysis would be to merge the dataset and we will us pandas library to merge the datasets in just one line of code.

#Data cleaning
#1. Combined the two datasets
secom_merged = pd.merge(secom_var, secom_labels,left_index=True,right_index=True)

Now let’s check out the distribution of the target variable

secom_merged.target.value_counts().plot(kind = 'bar')

Figure 1: Distribution of Target Variable

From Figure 1 it can be observed that the target variable is imbalanced and it is highly recommended to deal with this problem before the model building phase to avoid bias model. Xgboost is one of the models which can deal with imbalance classes but one needs to spend a lot of time to tune the hyper-parameters to achieve the best from the model.

The dataset in hand contains a lot of null values and the next step would be to analyse these null values and remove the columns having null values more than a certain percentage. This percentage is calculated based on 95th quantile of null values.

#2. Analyzing nulls
secom_rmNa.isnull().sum().sum()
secom_nulls = secom_rmNa.isnull().sum()/len(secom_rmNa)
secom_nulls.describe()
secom_nulls.hist()

Figure 2: Missing percentge in each column

Now we calculate the 95th percentile of the null values.

x = secom_nulls.quantile(0.95)
secom_rmNa = secom_merged[secom_merged.columns[secom_nulls < x]]

Figure 3: Missing percentage after removing columns with more then 45% Na

From figure 3 its visible that there are still missing values in the dataset and can be dealt by using many imputation methods. The most common method is to impute these values by mean, median or mode. There also exist few sophisticated techniques like K-nearest neighbour and interpolation.  We will be applying interpolation technique to our dataset. 

secom_complete = secom_rmNa.interpolate()

To prepare our dataset for analysis we should remove some more unwanted columns like columns with near zero variance. For this we can calulate number of unique values in each column and if there is only one unique value we can delete the column as it holds no information.

df = secom_complete.loc[:,secom_complete.apply(pd.Series.nunique) != 1]

## Let's check the shape of the df
df.shape
(1567, 444)

We have applied few data cleaning techniques and reduced the features from 590 to 444. However, In the next article we will apply some feature engineering techniques and adress problems like the curse of dimensionality and will also try to balance the target variable.

Bleiben Sie dran!!


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Sentiment Analysis of IMDB reviews

 

Download the Data Sources

The data sources used in this article can be downloaded here:

Language Detecting with sklearn by determining Letter Frequencies

Of course, there are better and more efficient methods to detect the language of a given text than counting its lettes. On the other hand this is a interesting little example to show the impressing ability of todays machine learning algorithms to detect hidden patterns in a given set of data.

For example take the sentence:

“Ceci est une phrase française.”

It’s not to hard to figure out that this sentence is french. But the (lowercase) letters of the same sentence in a random order look like this:

“eeasrsçneticuaicfhenrpaes”

Still sure it’s french? Regarding the fact that this string contains the letter “ç” some people could have remembered long passed french lessons back in school and though might have guessed right. But beside the fact that the french letter “ç” is also present for example in portuguese, turkish, catalan and a few other languages, this is still a easy example just to explain the problem. Just try to guess which language might have generated this:

“ogldviisnntmeyoiiesettpetorotrcitglloeleiengehorntsnraviedeenltseaecithooheinsnstiofwtoienaoaeefiitaeeauobmeeetdmsflteightnttxipecnlgtetgteyhatncdisaceahrfomseehmsindrlttdthoaranthahdgasaebeaturoehtrnnanftxndaeeiposttmnhgttagtsheitistrrcudf”

While this looks simply confusing to the human eye and it seems practically impossible to determine the language it was generated from, this string still contains as set of hidden but well defined patterns from which the language could be predictet with almost complete (ca. 98-99%) certainty.

First of all, we need a set of texts in the languages our model should be able to recognise. Luckily with the package NLTK there comes a big set of example texts which actually are protocolls of the european parliament and therefor are publicly availible in 11 differen languages:

  •  Danish
  •  Dutch
  •  English
  •  Finnish
  •  French
  •  German
  •  Greek
  •  Italian
  •  Portuguese
  •  Spanish
  •  Swedish

Because the greek version is not written with the latin alphabet, the detection of the language greek would just be too simple, so we stay with the other 10 languages availible. To give you a idea of the used texts, here is a little sample:

“Resumption of the session I declare resumed the session of the European Parliament adjourned on Friday 17 December 1999, and I would like once again to wish you a happy new year in the hope that you enjoyed a pleasant festive period.
Although, as you will have seen, the dreaded ‘millennium bug’ failed to materialise, still the people in a number of countries suffered a series of natural disasters that truly were dreadful.”

Train and Test

The following code imports the nessesary modules and reads the sample texts from a set of text files into a pandas.Dataframe object and prints some statistics about the read texts:

from pathlib import Path
import random
from collections import Counter, defaultdict
import numpy as np
import pandas as pd
from sklearn.neighbors import *
from matplotlib import pyplot as plt
from mpl_toolkits import mplot3d
%matplotlib inline


def read(file):
    '''Returns contents of a file'''
    with open(file, 'r', errors='ignore') as f:
        text = f.read()
    return text

def load_eu_texts():
    '''Read texts snipplets in 10 different languages into pd.Dataframe

    load_eu_texts() -> pd.Dataframe
    
    The text snipplets are taken from the nltk-data corpus.
    '''
    basepath = Path('/home/my_username/nltk_data/corpora/europarl_raw/langs/')
    df = pd.DataFrame(columns=['text', 'lang', 'len'])
    languages = [None]
    for lang in basepath.iterdir():
        languages.append(lang.as_posix())
        t = '\n'.join([read(p) for p in lang.glob('*')])
        d = pd.DataFrame()
        d['text'] = ''
        d['text'] = pd.Series(t.split('\n'))
        d['lang'] = lang.name.title()
        df = df.append(d.copy(), ignore_index=True)
    return df

def clean_eutextdf(df):
    '''Preprocesses the texts by doing a set of cleaning steps
    
    clean_eutextdf(df) -> cleaned_df
    '''
    # Cuts of whitespaces a the beginning and and
    df['text'] = [i.strip() for i in df['text']]
    # Generate a lowercase Version of the text column
    df['ltext'] = [i.lower() for i in df['text']]

    # Determining the length of each text
    df['len'] = [len(i) for i in df['text']]
    # Drops all texts that are not at least 200 chars long
    df = df.loc[df['len'] > 200]
    return df

# Execute the above functions to load the texts
df = clean_eutextdf(load_eu_texts())

# Print a few stats of the read texts
textline = 'Number of text snippplets: ' + str(df.shape[0])
print('\n' + textline + '\n' + ''.join(['_' for i in range(len(textline))]))
c = Counter(df['lang'])
for l in c.most_common():
    print('%-25s' % l[0] + str(l[1]))
df.sample(10)
Number of text snippplets: 56481
________________________________
French                   6466
German                   6401
Italian                  6383
Portuguese               6147
Spanish                  6016
Finnish                  5597
Swedish                  4940
Danish                   4914
Dutch                    4826
English                  4791
lang	len	text	ltext
135233	Finnish	346	Vastustan sitä , toisin kuin tämän parlamentin...	vastustan sitä , toisin kuin tämän parlamentin...
170400	Danish	243	Desuden ødelægger det centraliserede europæisk...	desuden ødelægger det centraliserede europæisk...
85466	Italian	220	In primo luogo , gli accordi di Sharm el-Sheik...	in primo luogo , gli accordi di sharm el-sheik...
15926	French	389	Pour ce qui est concrètement du barrage de Ili...	pour ce qui est concrètement du barrage de ili...
195321	English	204	Discretionary powers for national supervisory ...	discretionary powers for national supervisory ...
160557	Danish	304	Det er de spørgmål , som de lande , der udgør ...	det er de spørgmål , som de lande , der udgør ...
196310	English	355	What remains of the concept of what a company ...	what remains of the concept of what a company ...
110163	Portuguese	327	Actualmente , é do conhecimento dos senhores d...	actualmente , é do conhecimento dos senhores d...
151681	Danish	203	Dette er vigtigt for den tillid , som samfunde...	dette er vigtigt for den tillid , som samfunde...
200540	English	257	Therefore , according to proponents , such as ...	therefore , according to proponents , such as ...

Above you see a sample set of random rows of the created Dataframe. After removing very short text snipplets (less than 200 chars) we are left with 56481 snipplets. The function clean_eutextdf() then creates a lower case representation of the texts in the coloum ‘ltext’ to facilitate counting the chars in the next step.
The following code snipplet now extracs the features – in this case the relative frequency of each letter in every text snipplet – that are used for prediction:

def calc_charratios(df):
    '''Calculating ratio of any (alphabetical) char in any text of df for each lyric
    
    calc_charratios(df) -> list, pd.Dataframe
    '''
    CHARS = ''.join({c for c in ''.join(df['ltext']) if c.isalpha()})
    print('Counting Chars:')
    for c in CHARS:
        print(c, end=' ')
        df[c] = [r.count(c) for r in df['ltext']] / df['len']
    return list(CHARS), df

features, df = calc_charratios(df)

Now that we have calculated the features for every text snipplet in our dataset, we can split our data set in a train and test set:

def split_dataset(df, ratio=0.5):
    '''Split the dataset into a train and a test dataset
    
    split_dataset(featuredf, ratio) -> pd.Dataframe, pd.Dataframe
    '''
    df = df.sample(frac=1).reset_index(drop=True)
    traindf = df[:][:int(df.shape[0] * ratio)]
    testdf = df[:][int(df.shape[0] * ratio):]
    return traindf, testdf

featuredf = pd.DataFrame()
featuredf['lang'] = df['lang']
for feature in features:
    featuredf[feature] = df[feature]
traindf, testdf = split_dataset(featuredf, ratio=0.80)

x = np.array([np.array(row[1:]) for index, row in traindf.iterrows()])
y = np.array([l for l in traindf['lang']])
X = np.array([np.array(row[1:]) for index, row in testdf.iterrows()])
Y = np.array([l for l in testdf['lang']])

After doing that, we can train a k-nearest-neigbours classifier and test it to get the percentage of correctly predicted languages in the test data set. Because we do not know what value for k may be the best choice, we just run the training and testing with different values for k in a for loop:

def train_knn(x, y, k):
    '''Returns the trained k nearest neighbors classifier
    
    train_knn(x, y, k) -> sklearn.neighbors.KNeighborsClassifier
    '''
    clf = KNeighborsClassifier(k)
    clf.fit(x, y)
    return clf

def test_knn(clf, X, Y):
    '''Tests a given classifier with a testset and return result
    
    text_knn(clf, X, Y) -> float
    '''
    predictions = clf.predict(X)
    ratio_correct = len([i for i in range(len(Y)) if Y[i] == predictions[i]]) / len(Y)
    return ratio_correct

print('''k\tPercentage of correctly predicted language
__________________________________________________''')
for i in range(1, 16):
    clf = train_knn(x, y, i)
    ratio_correct = test_knn(clf, X, Y)
    print(str(i) + '\t' + str(round(ratio_correct * 100, 3)) + '%')
k	Percentage of correctly predicted language
__________________________________________________
1	97.548%
2	97.38%
3	98.256%
4	98.132%
5	98.221%
6	98.203%
7	98.327%
8	98.247%
9	98.371%
10	98.345%
11	98.327%
12	98.3%
13	98.256%
14	98.274%
15	98.309%

As you can see in the output the reliability of the language classifier is generally very high: It starts at about 97.5% for k = 1, increases for with increasing values of k until it reaches a maximum level of about 98.5% at k ≈ 10.

Using the Classifier to predict languages of texts

Now that we have trained and tested the classifier we want to use it to predict the language of example texts. To do that we need two more functions, shown in the following piece of code. The first one extracts the nessesary features from the sample text and predict_lang() predicts the language of a the texts:

def extract_features(text, features):
    '''Extracts all alphabetic characters and add their ratios as feature
    
    extract_features(text, features) -> np.array
    '''
    textlen = len(text)
    ratios = []
    text = text.lower()
    for feature in features:
        ratios.append(text.count(feature) / textlen)
    return np.array(ratios)

def predict_lang(text, clf=clf):
    '''Predicts the language of a given text and classifier
    
    predict_lang(text, clf) -> str
    '''
    extracted_features = extract_features(text, features)
    return clf.predict(np.array(np.array([extracted_features])))[0]

text_sample = df.sample(10)['text']

for example_text in text_sample:
    print('%-20s'  % predict_lang(example_text, clf) + '\t' + example_text[:60] + '...')
Italian             	Auspico che i progetti riguardanti i programmi possano contr...
English             	When that time comes , when we have used up all our resource...
Portuguese          	Creio que o Parlamento protesta muitas vezes contra este mét...
Spanish             	Sobre la base de esta posición , me parece que se puede enco...
Dutch               	Ik voel mij daardoor aangemoedigd omdat ik een brede consens...
Spanish             	Señor Presidente , Señorías , antes que nada , quisiera pron...
Italian             	Ricordo altresì , signora Presidente , che durante la preced...
Swedish             	Betänkande ( A5-0107 / 1999 ) av Berend för utskottet för re...
English             	This responsibility cannot only be borne by the Commissioner...
Portuguese          	A nossa leitura comum é que esse partido tem uma posição man...

With this classifier it is now also possible to predict the language of the randomized example snipplet from the introduction (which is acutally created from the first paragraph of this article):

example_text = "ogldviisnntmeyoiiesettpetorotrcitglloeleiengehorntsnraviedeenltseaecithooheinsnstiofwtoienaoaeefiitaeeauobmeeetdmsflteightnttxipecnlgtetgteyhatncdisaceahrfomseehmsindrlttdthoaranthahdgasaebeaturoehtrnnanftxndaeeiposttmnhgttagtsheitistrrcudf"
predict_lang(example_text)
'English'

The KNN classifier of sklearn also offers the possibility to predict the propability with which a given classification is made. While the probability distribution for a specific language is relativly clear for long sample texts it decreases noticeably the shorter the texts are.

def dict_invert(dictionary):
    ''' Inverts keys and values of a dictionary
    
    dict_invert(dictionary) -> collections.defaultdict(list)
    '''
    inverse_dict = defaultdict(list)
    for key, value in dictionary.items():
        inverse_dict[value].append(key)
    return inverse_dict

def get_propabilities(text, features=features):
    '''Prints the probability for every language of a given text
    
    get_propabilities(text, features)
    '''
    results = clf.predict_proba(extract_features(text, features=features).reshape(1, -1))
    for result in zip(clf.classes_, results[0]):
        print('%-20s' % result[0] + '%7s %%' % str(round(float(100 * result[1]), 4)))


example_text = 'ogldviisnntmeyoiiesettpetorotrcitglloeleiengehorntsnraviedeenltseaecithooheinsnstiofwtoienaoaeefiitaeeauobmeeetdmsflteightnttxipecnlgtetgteyhatncdisaceahrfomseehmsindrlttdthoaranthahdgasaebeaturoehtrnnanftxndaeeiposttmnhgttagtsheitistrrcudf'
print(example_text)
get_propabilities(example_text + '\n')
print('\n')
example_text2 = 'Dies ist ein kurzer Beispielsatz.'
print(example_text2)
get_propabilities(example_text2 + '\n')
ogldviisnntmeyoiiesettpetorotrcitglloeleiengehorntsnraviedeenltseaecithooheinsnstiofwtoienaoaeefiitaeeauobmeeetdmsflteightnttxipecnlgtetgteyhatncdisaceahrfomseehmsindrlttdthoaranthahdgasaebeaturoehtrnnanftxndaeeiposttmnhgttagtsheitistrrcudf
Danish                  0.0 %
Dutch                   0.0 %
English               100.0 %
Finnish                 0.0 %
French                  0.0 %
German                  0.0 %
Italian                 0.0 %
Portuguese              0.0 %
Spanish                 0.0 %
Swedish                 0.0 %


Dies ist ein kurzer Beispielsatz.
Danish                  0.0 %
Dutch                   0.0 %
English                 0.0 %
Finnish                 0.0 %
French              18.1818 %
German              72.7273 %
Italian              9.0909 %
Portuguese              0.0 %
Spanish                 0.0 %
Swedish                 0.0 %

Background and Insights

Why does a relative simple model like counting letters acutally work? Every language has a specific pattern of letter frequencies which can be used as a kind of fingerprint: While there are almost no y‘s in the german language this letter is quite common in english. In french the letter k is not very common because it is replaced with q in most cases.

For a better understanding look at the output of the following code snipplet where only three letters already lead to a noticable form of clustering:

projection='3d')
legend = []
X, Y, Z = 'e', 'g', 'h'

def iterlog(ln):
    retvals = []
    for n in ln:
        try:
            retvals.append(np.log(n))
        except:
            retvals.append(None)
    return retvals

for X in ['t']:
    ax = plt.axes(projection='3d')
    ax.xy_viewLim.intervalx = [-3.5, -2]
    legend = []
    for lang in [l for l in df.groupby('lang') if l[0] in {'German', 'English', 'Finnish', 'French', 'Danish'}]:
        sample = lang[1].sample(4000)

        legend.append(lang[0])
        ax.scatter3D(iterlog(sample[X]), iterlog(sample[Y]), iterlog(sample[Z]))

    ax.set_title('log(10) of the Relativ Frequencies of "' + X.upper() + "', '" + Y.upper() + '" and "' + Z.upper() + '"\n\n')
    ax.set_xlabel(X.upper())
    ax.set_ylabel(Y.upper())
    ax.set_zlabel(Z.upper())
    plt.legend(legend)
    plt.show()

 

Even though every single letter frequency by itself is not a very reliable indicator, the set of frequencies of all present letters in a text is a quite good evidence because it will more or less represent the letter frequency fingerprint of the given language. Since it is quite hard to imagine or visualize the above plot in more than three dimensions, I used a little trick which shows that every language has its own typical fingerprint of letter frequencies:

legend = []
fig = plt.figure(figsize=(15, 10))
plt.axes(yscale='log')
    
langs = defaultdict(list)

for lang in [l for l in df.groupby('lang') if l[0] in set(df['lang'])]:
    for feature in 'abcdefghijklmnopqrstuvwxyz':
        langs[lang[0]].append(lang[1][feature].mean())

mean_frequencies = {feature:df[feature].mean() for feature in 'abcdefghijklmnopqrstuvwxyz'}
for i in langs.items():
    legend.append(i[0])
    j = np.array(i[1]) / np.array([mean_frequencies[c] for c in 'abcdefghijklmnopqrstuvwxyz'])
    plt.plot([c for c in 'abcdefghijklmnopqrstuvwxyz'], j)
plt.title('Log. of relative Frequencies compared to the mean Frequency in all texts')
plt.xlabel('Letters')
plt.ylabel('(log(Lang. Frequencies / Mean Frequency)')
plt.legend(legend)
plt.grid()
plt.show()

What more?

Beside the fact, that letter frequencies alone, allow us to predict the language of every example text (at least in the 10 languages with latin alphabet we trained for) with almost complete certancy there is even more information hidden in the set of sample texts.

As you might know, most languages in europe belong to either the romanian or the indogermanic language family (which is actually because the romans conquered only half of europe). The border between them could be located in belgium, between france and germany and in swiss. West of this border the romanian languages, which originate from latin, are still spoken, like spanish, portouguese and french. In the middle and northern part of europe the indogermanic languages are very common like german, dutch, swedish ect. If we plot the analysed languages with a different colour sheme this border gets quite clear and allows us to take a look back in history that tells us where our languages originate from:

legend = []
fig = plt.figure(figsize=(15, 10))
plt.axes(yscale='linear')
    
langs = defaultdict(list)
for lang in [l for l in df.groupby('lang') if l[0] in {'German', 'English', 'French', 'Spanish', 'Portuguese', 'Dutch', 'Swedish', 'Danish', 'Italian'}]:
    for feature in 'abcdefghijklmnopqrstuvwxyz':
        langs[lang[0]].append(lang[1][feature].mean())

colordict = {l[0]:l[1] for l in zip([lang for lang in langs], ['brown', 'tomato', 'orangered',
                                                               'green', 'red', 'forestgreen', 'limegreen',
                                                               'darkgreen', 'darkred'])}
mean_frequencies = {feature:df[feature].mean() for feature in 'abcdefghijklmnopqrstuvwxyz'}
for i in langs.items():
    legend.append(i[0])
    j = np.array(i[1]) / np.array([mean_frequencies[c] for c in 'abcdefghijklmnopqrstuvwxyz'])
    plt.plot([c for c in 'abcdefghijklmnopqrstuvwxyz'], j, color=colordict[i[0]])
#     plt.plot([c for c in 'abcdefghijklmnopqrstuvwxyz'], i[1], color=colordict[i[0]])
plt.title('Log. of relative Frequencies compared to the mean Frequency in all texts')
plt.xlabel('Letters')
plt.ylabel('(log(Lang. Frequencies / Mean Frequency)')
plt.legend(legend)
plt.grid()
plt.show()

As you can see the more common letters, especially the vocals like a, e, i, o and u have almost the same frequency in all of this languages. Far more interesting are letters like q, k, c and w: While k is quite common in all of the indogermanic languages it is quite rare in romanic languages because the same sound is written with the letters q or c.
As a result it could be said, that even “boring” sets of data (just give it a try and read all the texts of the protocolls of the EU parliament…) could contain quite interesting patterns which – in this case – allows us to predict quite precisely which language a given text sample is written in, without the need of any translation program or to speak the languages. And as an interesting side effect, where certain things in history happend (or not happend): After two thousand years have passed, modern machine learning techniques could easily uncover this history because even though all these different languages developed, they still have a set of hidden but common patterns that since than stayed the same.