Adding a pruning method to the tree #941

sgenoud · 2012-07-10T11:10:26Z

I have added a pruning method to the decision trees method. The idea with decision tree is to build a huge one, prune it via a weakest link algorithm until it reaches a size that is reasonable (neither overfitting nor under fitting).

I also have build a helper function, cv_scores_vs_n_leaves, that computes the cross validated scores for different sizes of the tree. This can be plotted with a function such as

def plot_cross_validated_scores(scores):
    """Plots the cross validated scores versus the number of leaves of trees"""
    import matplotlib.pyplot as plt
    means = np.array([np.mean(s) for s in scores])
    stds = np.array([np.std(s) for s in scores])

    x = range(len(scores)+1, 1, -1)

    plt.plot(x, means)
    plt.plot(x, means+stds, lw=1, c='0.7')
    plt.plot(x, means-stds, lw=1, c='0.7')

    plt.xlabel('Number of leaves')
    plt.ylabel('Cross validated score')

Then we choose which size is the best for the data.

Just a couple of notes:

I need to add some tests (I am not sure how to make them)
This also needs to be documented

Before doing that work, I would gladly have some feedback on these modifications

jakevdp · 2012-07-10T14:14:40Z

sklearn/tree/tree.py

+        ----------
+        tree : binary tree object
+            The binary tree for which to compute the complexity costs.
+


The object doesn't need to be listed as a parameter here

sorry, I forgot to remove that when I refactored my code.

jakevdp · 2012-07-10T14:25:57Z

Nice addition. I haven't had a chance to try it out in detail, but I read over the code and it looks good.
One suggestion: in the pruning_order function, it would be more efficient if we were able to pass an argument like max_to_prune which would allow you to specify the maximum number of nodes you're interested in pruning. As it's currently written, I think the function will sort all the nodes each time it's called.

amueller · 2012-07-10T21:44:22Z

sklearn/tree/tree.py

@@ -266,6 +291,105 @@ def _add_leaf(self, parent, is_left_child, value, error, n_samples):

        return node_id

+    def _copy(self):


Is there a reason not to use clone here? Not sure if that copies the arrays, though. But I'm sure there is a method that does (for serialization for example).

clone clones the parameters of the Estimator object. Tree results from the fit of a DecisionTree -- it is not copied. Also DecisionTree has a Tree and is an Estimator, but a Tree is not an estimator (it inherits directly from object).

If someone knows a copying function, I will gladly use it.

copy or deepcopy from the copy module.

amueller · 2012-07-10T21:48:51Z

Thanks for your contribution.
I didn't look at the code in detail, so just some very general remarks.

It would be nice to have some mentioning in the narrative docs and maybe extent an already existing example to show how the pruning works and what effect is hast.

I'm not sure what would be a good test for the pruning but maybe you can come up with something, for example testing on some toy data with known expected outcome.

Looking forward to seeing this in action :)

mrjbq7 · 2012-07-10T22:18:53Z

sklearn/tree/tree.py

+    t_nodes = _get_terminal_nodes(children)
+    g_i = tree.init_error[t_nodes] - tree.best_error[t_nodes]
+
+    #alpha_i = np.min(g_i) / len(t_nodes)


If this isn't needed, should remove it...

jakevdp · 2012-07-10T22:19:11Z

I agree narrative docs and an example would be very helpful. You could modify these:

http://scikit-learn.org/dev/modules/tree.html (file is doc/modules/tree.rst)
http://scikit-learn.org/dev/auto_examples/tree/plot_tree_regression.html (file is examples/tree/plot_tree_regression.py)

You could add a fit to the plot which uses a pruned tree. Hopefully it would do better than the green line, but not over-fit as much as the red line.

Also, a new example with your plot_cross_validated_scores() function would be very useful. It could go in the same directory, and be included in the same narrative document.

Sorry to ask for more work when you've already done so much! Let us know if you need any help. I'm excited to see the results! 😁

mrjbq7 · 2012-07-10T22:19:36Z

sklearn/tree/tree.py

+
+        Parameters
+        ----------
+        n_leaves : binary tree object


Isn't this an int, what does it mean to be a binary tree object?

sgenoud · 2012-07-11T12:00:24Z

Thanks for all your feedback!

I will for sure make some docs, but I wanted to see if what I did was worth pulling before doing more work.

About my helper functions, one little details: I use _get_leaves in the new property leaves that I added to tree (and use it to compute the number of nodes to prune). I usually like to have small helper functions, it makes things easier for me to read afterwards. Nevertheless if merging them in seems a better design to most of you, I'll do it.

Also it would be nice if one of you plays a bit with the feature to have an external "test" on the function. I have used it for my own needs (and compared it in one case with some equivalent R function), but external confirmation is usually a good thing.

Correction of small details (documentation, commented code and return value)

glouppe · 2012-07-11T17:17:03Z

sklearn/tree/tree.py

+
+    def prune(self, n_leaves):
+        """
+        Prunes the tree in order to obtain the optimal tree with n_leaves


I would rather say the optimal subtree.

amueller · 2012-07-11T22:02:00Z

I just had a quick look at the plot_prune_boston.py example.
I noticed a couple of things:

You should add plt.show() at the end, so that people that just call the example also see something.
From the plot it is not immediately clear what one should see. I think you should write a small paragraph about what the example shows.
If I interpret the example correctly, the more leaves the better. This means not pruning is better then pruning (maybe 20 leaves is still pruned but I don't see performance deteriorating), right?
Finally, please run pep8 over it, there are some minor issues.

amueller · 2012-07-11T22:06:58Z

The synthetic example is pretty cool on the other hand and nicely illustrates the effect of pruning.

It leaves me with one question, though: are there cases when pruning is better than regularizing via "min_leaf" or "max_depth"?
The example shows nicely that regularizing is better than not regularizing, but it is not immediately clear what the benefit of one or the other way of regularizing is. Do you think it is possible to include this in the example somehow? At the moment, the example looks like "if you don't prune, you overfit' to me.

amueller · 2012-07-11T22:14:49Z

I don't know what the other tree-growers think, but I feel having a separate "prune" method breaks the API pretty badly. I think n_leaves should just be an additional parameter and prune should be _prune. If one want's more details, one can use cv_scores_vs_n_leaves. Which I would by the way rename to something like prune_path or similar, as this seems quite related to other regularization path functions.

amueller · 2012-07-11T22:20:41Z

I just realized that having a separate prune method means that the pruning can not be used together with GridSearchCV, which is basically a no-go.

sgenoud · 2012-07-12T07:58:21Z

If I interpret the example correctly, the more leaves the better. This means not pruning is better then pruning (maybe 20 leaves is still pruned but I don't see performance deteriorating), right?

I think the heuristics in this case is to go for the smallest tree with the value of the plateau. Actually R automatically uses such an heuristic. As you said, I should explain more how to read the graphs.

It leaves me with one question, though: are there cases when pruning is better than regularizing via "min_leaf" or "max_depth"?

I should probably show how the trees can be different in a case where we prune or not. Via pruning the resulting tree is "more optimal". It is possible that, while growing we do not go into a branch because the next step does not improve much the score, while the following step would improve it greatly -- but will not because this path is not chosen.

I think n_leaves should just be an additional parameter and prune should be _prune.

What do you think of a mixed approach? We could add an auto_prune argument to the fit method (in addition to n_leaves) and keep prune a method. This would mean that by default trees are grown and pruned, but users who want to be more picky about it can prune the trees themselves. Also, GridSearchCV would work.

Note that this would change the interface of this particular method (by that I mean that the default behaviour would become linked with pruning, while it was not so far). We would need to discuss a bit further what the default behaviour is (do we ask only for n_leaves by default grow a three with 3 more depth levels?).

Ok, if I sum it up, to follow better the API we need to change prune from an action that is made on a fitted Estimator to the default method for which trees are sized. This would have consequences on how we present it in the docs. I would propose the following:

Introducing a section that discusses methods of sizing a tree
Another section about the prune_path with the synthetic example

amueller · 2012-07-12T08:10:00Z

@sgenoud Thanks for your quick feedback. Before you get to work, I would prefer if someone else would also voice there opinion, so that you don't do any unnecessary work regarding refactoring.

If you could find an example that illustrates the benefits of pruning compared to other regularization methods, that would be great. For the synthetic example that you made, I fear that "more optimal" means "more overfitted" and for example "min_samples_leaf" does better. Maybe you should also mention somewhere that the pruning fits more strongly to the data.

As a default behavior, I would have imagined to have no pruning, to be backward compatible and also because the ensemble module doesn't need pruning.

amueller · 2012-07-20T23:01:30Z

@glouppe @pprett @bdholt1 comments on this?

glouppe · 2012-07-23T08:27:40Z

@amueller Not that much, basically instead of stoppping when the tree has exactly n_leaves, you instead pick the subtree that minimizes C(alpha, T). The same sequence of subtrees can be used if I recall correclty. (I admit though that I am not really an expert of post-pruning methods, I never usethem.)

glouppe · 2012-07-23T08:35:50Z

@sgenoud Yes I fully agree with you. However don't you think it would be better to implement the method that textbooks describe? Only not to confuse those that may have some background knowledge.

sgenoud · 2012-07-23T10:31:47Z

@glouppe It is probably a question of taste in the end. Is there a scikit-learn policy for this kind of choice?

I would propose to make the point of alpha and n_leaves being equivalent in the documentation. But to keep a simpler interface for the function based only on the number of leaves. Users familiar with the method will understand quickly that there is not much difference and be confused until they read the docs.

Finally, we could also argue that it would make pre and post pruning more similar.

amueller · 2012-07-23T10:39:12Z

I would favor n_leaves as it is much more intuitive. Also it is clear that the smallest change that has an effect is 1, and as @sgenoud said, it is more interpretable in the context of the other parameters.

glouppe · 2012-07-23T11:17:01Z

Okay then, I am fine with that.

Also renamed the helper function that plot the result of the function

sgenoud · 2012-07-24T16:04:00Z

I have tried to integrate your feedback, there is still two things to do:

edit the documentation
merge it with the master (the Tree object has been moved to a cython file I think)

sgenoud · 2012-07-25T08:53:46Z

@glouppe would you mind merging my modifications of the Tree object with your refactoring? I am no Cython expert and you are the one who refactored the object (and therefore know it well).

glouppe · 2012-07-26T06:49:03Z

@sgenoud I can do it, but it'll unfortunately have to wait until mid-Augustus. I am soon leaving for holidays and have a few remaining things to handle before.

amueller · 2012-07-27T21:11:28Z

@sgenoud maybe you can work on the doc in the meantime?

amueller · 2012-08-29T21:05:01Z

Anything new?

mrjbq7 · 2012-11-05T19:48:13Z

Need help with this? @sgenoud, what is the status of the patch?

sgenoud · 2012-11-11T13:22:16Z

Hi guys, sorry I have started a new job that takes me a lot of time. I will have more for this in December normally. I'll keep you posted

erg · 2013-04-03T18:20:43Z

This patch is basically completely busted now because it works on the old tree before things were ported to cython trees.

aflaxman · 2013-05-02T23:10:50Z

I would like to do some pruning, so I've made an attempt to update the code in this pull request. I'm not sure if it is the best way to do things now, but at least it brings us close to where we were before.

The updated code is here: https://github.com/aflaxman/scikit-learn/tree/tree_pruning

Is there still interest in this?

amueller · 2013-05-07T19:42:14Z

Yes, I think there is interest as long as it doesn't add overhead that can't be avoided for the forests.
I think having a pruning option for single trees would be great.
Maybe you should submit your own pull request with a reference to this one?

jessrosenfield · 2016-02-03T04:30:43Z

This seems to have gone stale. As a user of sklearn I'd love see pruning for decision trees. Are there any updates?

thomas4g · 2016-02-05T08:17:26Z

Echoing @jessrosenfield on this (I'm guessing she's working on the same machine learning project I am...) - any updates?

jakevdp · 2016-02-05T14:58:16Z

The tree and forest code has been changed considerably since this work was started; any new effort toward pruning would essentially have to start from scratch. Perhaps we should close this PR?

amueller · 2016-10-07T22:57:52Z

Closing in favor of #6557

Introduced pruning to decision trees

4a75a4a

jakevdp reviewed Jul 10, 2012
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Steve Genoud added 3 commits July 10, 2012 16:26

Merge branch 'master' of git://github.com/scikit-learn/scikit-learn

48fad0c

Fixed documentation of pruning_order function

c51698a

Added a max_to_prune argument to pruning_order

c139f6a

amueller reviewed Jul 10, 2012
View reviewed changes

mrjbq7 reviewed Jul 10, 2012
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Steve Genoud added 3 commits July 11, 2012 14:19

Incorporated feedback

35a8b3d

Correction of small details (documentation, commented code and return value)

Merge branch 'master' of git://github.com/scikit-learn/scikit-learn

4310876

Made n_output an optional value

0409c05

glouppe reviewed Jul 11, 2012
View reviewed changes

Steve Genoud added 3 commits July 11, 2012 19:43

Cleaned the function documentation (again)

89af373

Use shuffle and split to cross validate

45adf40

First draft of the documentation

9e41d88

pep8 corrections

92c6afb

sgenoud added 3 commits July 24, 2012 15:56

Renamed cv_scores_vs_n_leaves as pruned_path

acd4048

Also renamed the helper function that plot the result of the function

Added n_leaves to the DecisionTree estimator

98255d8

Moved the helper functions inside the Tree object

ea8e460

sgenoud closed this Nov 11, 2012

sgenoud reopened this Nov 11, 2012

aflaxman referenced this pull request May 2, 2013

Tree refactoring (19)

2fe48dc

larsmans force-pushed the master branch from 58a55ad to 4b82379 Compare August 25, 2014 21:50

MechCoder force-pushed the master branch from 6deaea0 to 3f49cee Compare November 3, 2014 12:36

maniteja123 mentioned this pull request Mar 18, 2016

add post-pruning for decision trees #6557

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eli-s-goldberg mentioned this pull request Apr 4, 2016

Trimming the tree eli-s-goldberg/Goldberg_et_al_2016#48

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amueller closed this Oct 7, 2016

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Adding a pruning method to the tree #941

Adding a pruning method to the tree #941

		@@ -266,6 +291,105 @@ def _add_leaf(self, parent, is_left_child, value, error, n_samples):

		return node_id

		def _copy(self):

Adding a pruning method to the tree #941

Adding a pruning method to the tree #941

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