Tag Archives: scikit-learn

Machine Learning quiz – part 3 of 3

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In my last two posts I published part 1 and part 2 of this Machine Learning quiz. If you have not read them, please do (and cast your votes) before you read part 3 below.

QUIZ, part 3: vote responses and (some) answers

In part 1 I asked which predictions looked “better”: those from model A or those from model B (Figure 1)?

Figure 1

As a reminder, both model A and model B were trained to predict the same labeled facies picked by a geologist on core, shown on the left columns (they are identical) of the respective model panels. The right columns in each panels are the predictions.

The question is asked out of context, with no information given about the training process, and or difference in data manipulation (if any) and/or model algorithm used. Very unfair, I know!  And yet, ~78% of 54 respondent clearly indicated their preference for model A. My sense is that this is because model A looks overall smoother and has less of the extra misclassified thin layers.

Response 1

In part 2, I presented the two predictions, this time accompanied by a the confusion matrix for each model (Figure 2).

Figure 2

I asked again which model would be considered better [1] and this was the result:

Response 2a

Although there were far fewer votes (not as robust a statistical sample) I see that the proportion of votes is very similar to that in the previous response, and decidedly in favor of model A, again. However, the really interesting learning, and to me surprising, came from the next answer (Response 2b): about 82% of the 11 respondents believe the performance scores in the confusion matrix to be realistic.

Response 2b

Why was it a surprise? It is now time to reveal the trick…..

…which is that the scores in part 2, shown in the confusion matrices of Figure 2, were calculated on the whole well, for training and testing together!!

A few more details:

  • I used default parameters for both models
  • I used a single 70/30 train/test split (the same random split for both models) with no crossvalidation

which is, in essence, how to NOT do Machine Learning!

In Figure 3, I added a new column on the right of each prediction showing in red which part of the result is merely memorized, and in black which part is interpreted (noise?). Notice that for this particular well (the random 70/30 split was done on all wells together) the percentages are 72.5% and 27.5%.

I’ve also added the proper confusion matrix for each model, which used only the test set. These are more realistic (and poor) results.

Figure 3

So, going back to that last response: again, with 11 votes I don’t have solid statistics, but with that caveat in mind one might argue that this is a way you could be ‘sold’ unrealistic (as in over-optimistic) ML results.

At least you could sell them by being vague about the details to those not familiar with the task of machine classification of rock facies and its difficulties (see for example this paper for a great discussion about resolution limitations inherent  in using logs (machine) as opposed to core (human geologist).

Acknowledgments

A big thank you goes to Jesper (Way of the Geophysicist) for his encouragement and feedback, and for brainstorming with me on how to deliver this post series.

[1] notice that, as pointed out in part 2, model predictions were slightly different from those part 1 because I’d forgotten to set the random seed to be the same in the two pipelines; but not very much, the overall ‘look’ was very much the same.

Machine Learning quiz – part 2 of 3

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In my previous post I posted part 1 (of 3) of a Machine Learning quiz. If you have not read that post, please do, cast your vote, then come back and try part 2 below.

QUIZ, part 2

Just as a quick reminder, the image below shows the rock facies predicted from two models, which I just called A and B. Both were trained to predict the same labeled rock facies, picked by a geologist on core, which are shown on the left columns (they are identical) of the respective model panels. The right columns in each panels are the predictions.

*** Please notice that the models in this figure are (very slightly) different from part 1 because I’d forgotten to set the random seed to be the same in the two pipelines (yes, it happens, my apology). But they are not so different, so I left the image in part 1 unchanged and just updated this one.

Please answer the first question: which model predicts the labeled facies “better” (visually)?

Now study the performance as summarized in the confusion matrices for each model (the purple arrows indicate to which model each matrix belongs; I’ve highlighted in green the columns where each model does better, based on F1 (you don’t have to agree with my choice), and answer the second question (notice the differences are often a few 1/100s, or just one).

 

Machine Learning quiz – part 1 of 3

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Introduction

I’ve been meaning to write about the 2016 SEG Machine Learning Contest for some time. I am thinking of a short and not very structured series (i.e. I’ll jump all over the place) of 2, possibly 3 posts (with the exclusion of this quiz). It will mostly be a revisiting – and extension – of some work that team MandMs (Mark Dahl and I) did, but not necessarily posted. I will touch most certainly on cross-validation, learning curves, data imputation, maybe a few other topics.

Background on the 2016 ML contest

The goal of the SEG contest was for teams to train a machine learning algorithm to predict rock facies from well log data. Below is the (slightly modified) description of the data form the original notebook by Brendon Hall:

The data is originally from a class exercise from The University of Kansas on Neural Networks and Fuzzy Systems. This exercise is based on a consortium project to use machine learning techniques to create a reservoir model of the largest gas fields in North America, the Hugoton and Panoma Fields. For more info on the origin of the data, see Bohling and Dubois (2003) and Dubois et al. (2007).

This dataset is from nine wells (with 4149 examples), consisting of a set of seven predictor variables and a rock facies (class) for each example vector and validation (test) data (830 examples from two wells) having the same seven predictor variables in the feature vector. Facies are based on examination of cores from nine wells taken vertically at half-foot intervals. Predictor variables include five from wireline log measurements and two geologic constraining variables that are derived from geologic knowledge. These are essentially continuous variables sampled at a half-foot sample rate.

The seven predictor variables are:

The nine discrete facies (classes of rocks) are:

For some examples of the work during the contest, you can take a look at the original notebook, one of the submissions by my team, where we used Support Vector Classification to predict the facies, or a submission by the one of the top 4 teams, all of whom achieved the highest scores on the validation data with different combinations of Boosted Trees trained on augmented features alongside the original features.

QUIZ

Just before last Christmas, I run a little fun experiment to resume work with this dataset. I decided to turn the outcome into a quiz.

Below I present the predicted rock facies from two distinct models, which I call A and B. Both were trained to predict the same labeled facies picked by the geologist, which are shown on the left columns (they are identical) of the respective model panels. The right columns in each panels are the predictions. Which predictions are “better”?

Please be warned, the question is a trick one. As you can see, I am gently leading you to make a visual, qualitative assessment of “better-ness”, while being absolutely vague about the models and not giving any information about the training process, which is intentional, and – yes! – not very fair. But that’s the whole point of this quiz, which is really a teaser to the series.

Machine Learning in Python: classification using Support Vector Machines and Scikit-learn

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This post is a short extract, with minor modifications,  from my recently released article on the check the CSEG Recorder Machine Learning in Geoscience V: Introduction to Classification with SVMs.

Understanding classification with Support Vector Machines

Support Vector Machines are a popular type of algorithm used in classification, which is the process of  “…identifying to which of a set of categories (sub-populations) a new observation belongs (source: Wikipedia).

In classification, the output variable is a category, for example ‘sand’, or ‘shale’, and the main task of the process is the creation of a dividing boundary between the classes. This boundary will be a line in a bi-dimensional space (only two features used to classify), a surface in a three dimensional space (three features), and a hyper-plane in a higher- dimensional space. In this article I will use interchangeably the terms hyper-plane, boundary, and decision surface.

Defining the boundary may sound like a simple task, especially with two features (a bidimensional scatterplot), but it underlines the important concept of generalization, as pointed out by Jake VanderPlas in his Introduction to Scikit-Learn, because ”… in drawing this separating line, we have learned a model which can generalize to new data: if you were to drop a new point onto the plane which is unlabeled, this algorithm could now predict…” the class it belongs to.

Let’s use a toy classification problem to understand in more detail how in practice SVMs achieve the class separation and find the hyperplane. In the figure below I show an idealized version (with far fewer points) of a Vp/Vs ratio versus P-impedance crossplot from Amato del Monte (2017, Seismic rock physics, tutorial on The Leading Edge).  I’ve added three possible boundaries (dashed lines) separating the two classes.

Each boundary is valid, but are they equally good? Well, for the SVM classifier, they are not because the classifier looks for the boundary with the largest distance from the nearest point in either of the classes.

These points, called Support Vectors, are the most representative of each class, and typically the most difficult to classify. They are also the only ones that matter; if a Support Vector is moved, the boundary will also move. However, if any other point is moved, provided that it is not moved into the margin or across the boundary, it would have no effect on the boundary. This makes SVM classifiers insensitive to outliers (points very far away from the rest of the points in their class and from the boundary) and also less memory intensive than other classifiers (for example, the perceptron). The process of finding this boundary is referred to as “maximizing the margin”, where the margin is a corridor with no data points between the boundary and the support vectors. The larger this buffer, the lower the generalization error; conversely, small margins are almost invariably associated with over-fitting. We will see more on this in a subsequent section.

So, to go back to the question, which of the three proposed boundaries is the best one (and by “best” I am referring to the one that will generalize better to unseen data)? Based on what we’ve learned so far, it would have to be the green boundary. Indeed, the orange one is so close to its support vectors (the two points circled with orange) that it leaves virtually no margin; the purple boundary is slightly better (the support vectors are the points circled with purple) but its margin is still quite small compared to the green boundary.

Maximizing the margin is the goal of the SVM classifier, and it is a constrained optimization problem. I refer interested readers to Hearst (1998, Support Vector Machines, IEEE Intelligent Systems); however, I will quote a definition from that paper (with reference to Figure 1 and accompanying text) as it yields further understanding: “… the optimal hyper-plane is orthogonal to the shortest line connecting the convex hulls of the two classes, and intersects it half way”.

In the inset in the figure, I zoomed closer to the 4 points near the green boundary; I’ve also drawn the convex hulls for the classes, the margin, and the shortest orthogonal line, which is bisected by the hyper-plane. I have selected (by hand) the best hyper-plane already (the green one), but if you can imagine rotating a line to span all possible orientations in the empty space close to the two classes without intersecting either of the hulls and find the one with the largest margin, you’ve just done quadratic optimization in your head. Moreover, you’ve turned a crossplot into a decision surface (quoted from Sebastian Thrun,  Intro to Machine Learning, Udacity 120 course).

If you are interested in learning more about Support Vector Machines in an intuitive way, and then how to try classification in practice (using Python and the Scikit-learn library), read the full article here, check the GitHub repo, then read How good is what? (blog post by Evan Bianco of Agile Scientific) for an example and DIY evaluation of  classifier performance.

Machine Learning in Geoscience with Scikit-learn. Part 2: inferential statistics and domain knowledge to select features for oil prediction

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In the first post of this series I showed how to use Pandas, Seaborn, and Matplotlib to:

  • load a dataset
  • test, clean up, and summarize the data
  • start looking for relationships between variables using scatterplots and correlation coefficients

In this second post, I will expand on the latter point by introducing some tests and visualizations that will help highlight the possible criteria for choosing some variables, and dropping others. All in Python.

I will use a different dataset than that in the previous post. This one is from the paper “Many correlation coefficients, null hypotheses, and high value“(Lee Hunt, CSEG Recorder, December 2013).

The target to be predicted is oil production from a marine barrier sand. We have measured production (in tens of barrels per day) and 7 unknown (initially) predictors, at 21 wells.

Hang on tight, and read along, because it will be a wild ride!

I will show how to:

1) automatically flag linearly correlated predictors, so we can decide which might be dropped. In the example below (a matrix of pair-wise correlation coefficients between variables) we see that X2, and X7, the second and third best individual predictors of production (shown in the bottom row) are also highly correlated to X1, the best overall predictor.

2) automatically flag predictors that fail a critical r test

3) create a table to assess the probability that a certain correlation is spurious, in other words the probability of getting at least the correlation coefficient we got with our the sample, or even higher, purely by chance.

I will not recommend to run these tests and apply the criteria blindly. Rather, I will suggest how to use them to learn more about the data, and in conjunction with domain knowledge about the problem at hand (in this case oil production), make more informed choices about which variables should, and which should not be used.

And, of course, I will show how to make the prediction.

Have fun reading: get the Jupyter notebook on GitHub.

Machine learning in Planetary Science: compressing Pluto images with scikit-learn and PCA

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In a previous post I showed some of the beautiful new images of Pluto from New Horizon’s mission,  coloured using the new Matplotlib perceptual colormaps:

colormappedNew_Horizons_Pluto

More recently I was experimenting with Principal Component Analysis in scikit-learn, and one of the things I used it for was compression of some of these Pluto images. Below is an example of the first two components from the False Color Pluto image:

first_two

You can take a look at the Python code available on this Jupyter Notebook. There are of course better ways of compressing images, but this was a fun way to play around with PCA.

In a follow-up post I will use image registration and image processing techniques to reproduce from the raw channels NASA’s Psychedelic Pluto Image.

 

 

Machine learning in geoscience and planetary science with scikit-learn: series outline

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  • Machine learning in geoscience with scikit-learn. Part 3: the SEG ML contest
  • Machine Learning in Geoscience with Scikit-learn. Part 4: TBE