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There are some further details, e. It goes into the reasons for thinking about model interpretability as well as technical details on partial dependence plots, feature importance, feature interactions, LIME and SHAP.

It also discusses some much earlier, interesting model explanation approaches. These values reflect the optimal way of distributing credit in a multiplayer game based on how much each player contributes to some payoff in the game.

There is a great blog post by Cody Marie Wild that explains this in more detail, and also a double episode of the Linear Digressions podcast which is well worth a listen.

Maybe even more important than the sound theoretical underpinnings, SHAP has a good Python interface with great plots built in.

It plugs in to standard scikit-learn type predictors or really anything you want with little hassle. It is especially good for tree ensemble models random forest, gradient boosting.

This is done by aggregating gradients over a straight-line path between the point to explain and a reference points. It discusses under what conditions an explanation of a prediction is required by GDPR and when it is actually meaningful to the affected person.

They arrive at the conclusion that the most useful way to explain a prediction is a counterfactual that changes the input variables as little as possible while ending up with a different prediction.

The group that published LIME has extended their work after noticing that the LIME explanations can have unclear coverage, ie it is not clear whether a given explanation applies in a region where an unseen instance is located.

On instances where the anchor holds, the prediction is almost always the same. The degree to which it has to hold can be controlled with a parameter.

This tends to yield compact rules that are also easily understood by users. There is a Python interface for anchors.

A bit more than a year ago, I took the plunge and left my academic job to try my luck as a corporate data scientist, first at IBM obviously a very big company and now at Peltarion a startup which I still want to call small although it is growing rapidly.

So without further ado, I present my three last data science positions! At first I was hired as a general bioinformatics go-to person in a so-called core facility that does DNA sequencing, where I would be involved in a lot of different kinds of things: setting up data pipelines, deciding on quality control routines, trying to figure out what had gone wrong, delivering data to and communicating with customers, performing routine or custom analysis, and sometimes doing some actual research and writing papers.

After a while, I moved into a different role where my job was more explicitly to help researchers with data analysis, statistics and programming — more research-oriented and long-term work.

In a way, I was an academic data science consultant. Note: this is the type of role I have the most experience with, or the most data on, if you will, so I am more confident about the pronouncements here than in the other categories.

From one of these gigs I learned to build very complex processing pipelines with Snakemake. From another, I learned to build obscure functionality for web applications in Shiny.

For yet another customer, I suggested a way to use PCA and MDS to view their data from a global point of view which they had not considered, guiding them onto a path that eventually resulted in this Medium article.

After having been an academic consultant for quite a while, I decided to try to be a corporate one for a change. Since I was only there for six months, I only had time to participate in a handful of projects, which were mostly related to the manufacturing industry.

Fortunately, the knowledge of high-dimensional data that I had from biology came in good stead when working on these problems. It was not difficult to apply the skills I had obtained from academia in this setting.

Note: With my short experience, I have a hard time isolating out for some of the points below if they are true in general for consulting companies or company IBM in this case specific.

In the autumn of , I got an offer from a deep learning company, Peltarion, that I had applied to before starting at IBM. I decided to take it on the strength of the skills of my new colleagues, many of whom I knew from the Stockholm AI and machine learning scene.

As the company is a startup, I have worn many hats during the first six months, working in customer projects, writing documentation and blog posts, testing our deep learning platform, sitting together with beta testers, keeping an eye on competitors and so on.

Characteristics of data science in a startup setting: surely not representative of all startups…. Note: I suspect that the variance among startups is much higher than among academic groups or big consulting companies, so almost everything here is probably highly company-specific.

I hope you enjoyed this highly subjective look at different kinds of data scientist positions. Feel free to ask questions in the comments section or provide your own views on different roles.

At that event, we discussed various trends in data science and machine learning and at the end of it, I participated in a discussion group, led by professor Niklas Lavesson from Blekinge Institute of Technology, where we talked about model interpretability and explanation.

At the time, it felt like a fringe but interesting topic. Today, this topic seems to be all over the place. Ideas on interpreting machine learning.

It also talks about related things such as dimensionality reduction which I would not call model explanation per se, but which are still good to know.

Fast Forward Labs have announced a new report on interpretable machine learning. I have not read the actual report. Understanding Black-box Predictions via Influence Functions.

The paper of this name associated code here won a best-paper award at ICML again showing how hot this topic is! The authors use something called an influence function to quantify, roughly speaking, how much a perturbation of a single example in the training data set affects the resulting model.

In this way, they can identify the training data points most responsible for a given prediction. One might say that they have figured out a way to differentiate a predictive model with respect to data points in the training set.

I have tried it myself for a consulting gig and found it useful for understanding why a certain prediction was made.

The main implementation is in Python but there is also a good R port which is what I used when I tried it. LIME essentially builds a simplified local model around the data point you are interested in.

It does this by perturbing real training data points, obtaining the predicted label for those perturbed points, and fitting a sparse linear model to those points and labels.

As far as I have understood, that is! If anyone is interested, I might write another blog post illustrating how LIME can be used to understand why a certain prediction was made on a public dataset.

I might even try to explain the influence function paper if I get the time to try it and digest the math.

Many of us in the Nordics are a bit obsessed with the weather. Especially during summer, we keep checking different weather apps or newspaper prognoses to find out whether we will be able to go to the beach or have a barbecue party tomorrow.

In Sweden, the main source of predictions is the Swedish Meteorological and Hydrological Institute , but many also use for instance the Klart.

Various kinds of folk lore exists around these prognoses, for instance one often hears that the ones from the Norwegian Meteorological Institute at yr.

As a hobby project, we decided to test this claim, focusing on Stockholm as that is where we currently live.

The main task we considered was to predict the temperature in Stockholm Bromma, latitude However, we do have the measured temperature recorded hourly, so we can compare each forecast from either SMHI or YR to the actual temperature.

Measured temperatures were downloaded from here hourly using crontab. First, some summary statistics. On the whole, there are no dramatic differences between the two forecasting agencies.

It is clear that SMHI is not worse than YR on predicting the temperature in Stockholm 24h in advance probably not significantly better either, judging from some preliminary statistical tests conducted on the absolute deviations of the forecasts from the actual temperatures.

The median absolute deviation is 1, meaning that the most typical error is to get the temperature wrong by one degree Celsius in either direction.

The mean squared error is around 2. Here is a plot of SMHI predictions vs temperatures measured 24 hours later. The color indicates the density of points in that part of the plot.

Unfortunately, those data points are not exactly for the same times in the SMHI and YR datasets, because the two agencies do not publish forecasts for exactly the same times at least the way we collected the data.

Therefore we only have data points where both SMHI and YR had made forecasts for the same time point 24h into the future. Here is a plot of how those forecasts look.

However, the code can of course be adapted and the exercise can be repeated for other locations. We just thought it was a fun mini-project to check the claim that there was a big difference between the two national weather forecasting services.

If anyone is interested, I will put up code and data on GitHub. Leave a message here, on my Twitter or email.

Accuracy in predicting rain probably more useful. Accuracy as a function of how far ahead you look.

Note: this is a re-post of an analysis previously hosted at mindalyzer. Originally published in late December , this blog post was later followed up by this extended analysis on Follow the Data.

Authors: Mattias Östmar, mattiasostmar a gmail. Summary: We identify communities in the Swedish twitterverse by analyzing a large network of millions of reciprocal mentions in a sample of ,, tweets from , twitter accounts in and show that politically meaningful communities among others can be detected without having to read or search for specific words or phrases.

All images are licensed under Creative Commons CC-BY mention the source and the data is released under Creative Commons Zero which means you can freely download and use it for your own purposes, no matter what.

The underlaying tweets are restricted by Twitters Developer Agreement and Policy and cannot be shared due to their restrictions, which are mainly there to protect the privacy of all Twitter users.

A pipeline connecting the different code parts for reproducing this experiment is available at github. The API gives out tweets from before the polling starts as well, but Twitter does not document how those are selected.

A more in depth description of how the dataset was created and what it looks like can be found at mindalyzer.

From the full dataset of tweets, the tweets originating from was filtered out and a network of reciprocal mentions was created by parsing out any at-mentions e.

We look at reciprocal mention graphs, where a link between two users means that both have mentioned each other on Twitter at least once in the dataset i.

We take this as a proxy for a discussion happening between those two users. The mention graphs were generated using the NetworkX package for Python.

We naturally model the graph as undirected as both users sharing a link are interacting with each other, there is no notion of directionality and unweighted.

One could easily imagine a weighted version of the mention graph where the weight would represent the total number of reciprocal mentions between the users, but we did not feel that this was needed to achieve interesting results.

The final graph consisted of The average number of reciprocal mentions for nodes in the graph was The visualizations of the graphs were done in Gephi using the Fruchterman Reingold layout algoritm and thereafter adjusting the nodes with the Noverlap algorithm and finally the labels where adjusted with the algoritm Label adjust.

In order to find communities in the mention graph in other words, to cluster the mention graph , we use Infomap , an information-theory based approach to multi-level community detection that has been used for e.

This algorithm, which can be used both for directed and undirected, weighted and unweighted networks, allows for multi-level community detection, but here we only show results from a single partition into communities.

We also tried a multi-level decomposition, but did not feel that this added to the analysis presented here. Roughly speaking, a person involved in a lot of discussions with other users who are in turn highly ranked would get high scores by this measure.

For some clusters, it was enough to have a quick glance at the top ranked users to get a sense of what type of discourse that defines that cluster.

That way we also had words to judge the quaility of the clusters from. We took the top 20 communities in terms of size, collected the tweets during from each member in those clusters, and created a textual corpus out of that more specifically, a Dictionary using the Gensim package for Python.

Then, for each community, we tried to find the most over-represented words used by people in that community by calculating the TF-IDF term frequency-inverse document frequency for each word in each community, and looking at the top 10 words for each community.

For instance, communities representing Norwegian and Finnish users who presumably sometimes tweet in Swedish were trivial to identify.

It was also easy to spot a community dedicated to discussing the state of Swedish schools, another one devoted to the popular Swedish band The Fooo Conspiracy, and an immigration-critical cluster.

In fact we have defined dozens of thematically distinct communities and continue to find new ones. This corresponds almost eerily well to a set of Swedish Twitter users highlighted in large Swedish daily Svenska Dagbladet Försvarstwittrarna som blivit maktfaktor i debatten.

In fact, of their list of the top 10 defense bloggers, we find each and every one of them in our top Remember that our analysis uses no pre-existing knowledge of what we are looking for: the defense cluster just fell out of the mention graph decomposition.

You can also download a zoomable pdf. The most influential user in this community according to our analysis is sakine, Sakine Madon, who was also the most influential Twitter user in Mattias eigenvector centrality based analysis of the whole mention graph i.

One of the larger clusters consists of accounts clearly focused on immigration issues judging by the most distinguishing words.

This suggests that they have or at least had in the period up until an outsider position in the public discourse on Twitter that might or might not reflect such a position in the general public political discourse in Sweden.

There is much debate and worry about filter bubbles formed by algorithms that selects what people get to see. Research such as Credibility and trust of information in online environments suggests that the social filtering of content is a strong factor for influence.

Strong ties such as being part of a conversation graph such as this would most likely be an important factor in shaping of your world views.

Since we have the pipeline ready, we can easily redo it for when the data are in hand. Possibly this will reveal dynamical changes in what gets discussed on Twitter, and may give indications on how people are moving between different communities.

It could also be interesting to experiment with a weighted version of the graph, or to examine a hierarchical decomposition of the graph into multiple levels.

Graciously supported by The Swedish Memetic Society. I made a community decomposition of Swedish Twitter accounts in and and you can explore it in an online app.

As reported on this blog a couple of months ago , and also here. I have together with Mattias Östmar been investigating the community structure of Swedish Twitter users.

The analysis we posted then addressed data from and we basically just wanted to get a handle on what kind of information you can get from this type of analysis.

With the processing pipeline already set up, it was straightforward to repeat the analysis for the fresh data from as soon as Mattias had finished collecting it.

The nice thing about having data from two different years in that we can start to look at the dynamics — namely, how stable communities are, which communities are born or disappear, and how people move between them.

First of all, I made an app for exploring these data. The suggestions that are submitted are saved in a text file which I will review from time to time and update the community descriptions accordingly.

By looking at the data in the app, we can find out some pretty interesting things. I am not personally familiar with this account, but he must have done something to radically increase his reach in It turned out that the most stable communities i.

Among the larger communities in , we identified the one that was furthest from having a close equivalent in This was community 9, where the most influential account was thefooomusic.

This is a boy band whose popularity arguably hit a peak in The community closest to it in is community 24, but when we looked closer at that which you can also do in the app!

So in other words, the The Fooo fan cluster and the YouTuber cluster from merged into a mixed cluster in We were hoping to see some completely new communities appear in , but that did not really happen, at least not for the top communities.

Community 24, which was discussed above, was also dissimilar from all the communitites, but as described, we notice it has absorbed users from clusters 9 The Fooo and 84 YouTubers.

In our previous blog post on this topic, we presented a top list of defense Twitterers and compared that to a manually curated list from Swedish daily Svenska Dagbladet.

Here we will present our top list for One community we did not touch on in the last blog post is the green, environmental community. Of course, many parts of this analysis could be improved and there are some important caveats.

For example, the Infomap algorithm is not deterministic, which means that you are likely to get somewhat different results each time you run it.

For these data, we have run it a number of times and seen that you get results that are similar in a general sense each time in terms of community sizes, top influencers and so on , but it should be understood that some accounts even top influencers can in some cases move around between communities just because of this non-deterministic aspect of the algorithm.

Also, it is possible that the way we use to measure community similarity the Jaccard index, which is the ratio between the number of members in common between two communities and the number of members that are in any or both of the communities — or to put it in another way, the intersection divided by the union is too coarse, because it does not consider the influence of individual users.

I had some trouble coming up with a term to describe the three companies that I will discuss here: Arivale , Q and iCarbonX.

What they have in common in my opinion is that they. Arivale was founded by Leroy Hood , who is president of the Institute for Systems Biology and was involved in developing the automatization of DNA sequencing.

In connection with Arivale, Hood as talked about dense dynamic data clouds that will allow individuals to track their health status and make better lifestyle decisions.

They have different plans, including a 3, USD one-time plan. They sample blood, saliva and the gut microbiome and have special coaches who give feedback on findings, including genetic variants and how well you have done with your FitBit.

He has also been involved in a large number of other pioneering genomics projects. They also make the following point: We live in a world where we use millions of variables to predict what ad you will click on, what movie you might watch, whether you are creditworthy, the price of commodities, and even what the weather will be like next week.

Yet, we continue to conduct limited clinical studies where we try and reduce our understanding of human health and pathology to single variable differences in groups of people, when we have enormous evidence that the results of these studies are not necessarily relevant for each and every one of us.

What to make of these companies? They are certainly intriguing and exciting. On the other hand, the multi-omics aspect may prove helpful in a deep learning scenario if it turns out that information from different experiments can be combined some sort of transfer learning setting.

There are some related companies or projects that I do not discuss above. There are several academic projects along similar lines including one to which I am currently affiliated but this blog post is about commercial versions of molecular wellness monitoring.

Mattias hatched the idea to take a different perspective from looking at keywords or numbers of followers or tweets, and instead try to focus on engagement and interaction by looking at reciprocal mention graphs — graphs where two users get a link between them if both have mentioned each other at least once as happens by default when you reply to a tweet, for example.

He then applied an eigenvector centrality measure to that network and was able to measure the influence of each user in that way described in Swedish here.

In the present analysis we went further and tried to identify communities in the mention network by clustering the graph. After trying some different methods we eventually went with Infomap , a very general information-theory based method it handles both directed and undirected, weighted and unweighted networks, and can do multi-level decompositions that seems to work well for this purpose.

Infomap not only detects clusters but also ranks each user by a PageRank measure so that the centrality score comes for free.

We immediately recognized from scanning the top accounts in each cluster that there seemed to be definite themes to the clusters. But it was also possible to see at this point still by recognizing names of famous accounts that there were communities that seemed to be about national defence or the state of Swedish schools, for instance.

Still, knowing about famous accounts can only take us so far, so we did a relatively simple language analysis of the top 20 communities by size.

We took all the tweets from all users in those communities, built a corpus of words of those, and calculated the TF-IDFs for each word in each community.

In this way, we were able to identify words that were over-represented in a community with respect to the other communities.

The words that feel out of this analysis were in many cases very descriptive of the communities, and apart from the school and defence clusters we quickly identified an immigration-critical cluster, a cluster about stock trading, a sports cluster, a cluster about the boy band The Fooo Conspiracy, and many others.

In fact, we have since discovered that there are a lot of interesting and thematically very specific clusters beyond the top 20 which we are eager to explore!

As detailed in the analysis blog post, the list of top ranked accounts in our defence community was very close to a curated list of important defence Twitter accounts recently published by a major Swedish daily.

This probably means that we can identify the most important Swedish tweeps for many different topics without manual curation. This work was done on tweets from , but in mid-January we will repeat the analysis on data.

There is some code describing what we did on GitHub. By Mikael Huss mikaelhuss and Joel Westerberg tuxtux. How does it work? The original code and modifications As already mentioned, the code is available , and the authors show how to use it together with the forest covertype dataset.

There are also many parameters which need to be changed but which are in the main training loop file rather than the data helper file. In view of this, I also tried to generalize and streamline this process in my code.

I added some quick-and-dirty code for doing hyperparameter optimization, but so far only for classification. It is also worth mentioning that the example code from the authors only shows how to do classification, not regression, so that extra code also has to be written by the user.

I have added regression functionality with a simple mean squared error loss. You can point tensorboard at this folder to look at training and validation stats: tensorboard --logdir tflog and point your web browser to localhost Initially large learning rate is important, which should be gradually decayed until convergence.

Conclusions TabNet is an interesting architecture that seems promising for tabular data analysis. Who is this blog post for? CatBoost introduction CatBoost is my go-to package for modelling tabular data.

Some nice things about CatBoost: It handles cat egorical features get it? It typically requires very little parameter tuning. It avoids certain subtle types of data leakage that other methods may suffer from.

It is fast, and can be run on GPU if you want it to go even faster. Du kommenterar med ditt Twitter-konto. Du kommenterar med ditt Facebook-konto.

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