My goal is to revise the split criterion of the regression tree algorithm so that the structure of the regression tree is independent of the output values of the learning sample.
It is obvious that the CART used by Random Forest does not satisfy, but Extra Trees may satisfy. In Pierre Geurts et al.(2006) 's opinion, in the extreme case, extremely randomized trees build totally randomized trees whose structures are independent of the output values of the learning sample. However, the score they used is dependent on the output.
In my limited experience with Extra Tree in Python and R, the score to find the best split variable is also reliable on the output. As a result, I can't use extra trees directly.
I have tried to revise the criterion in source code in R and Python, but it is difficult for me because it is written in C which I'm not familiar with.
Are there any other existing tree algorithms that satisfy independence? Thanks in advance.
I'm doing a decision tree, and I would like to force the algorithm to split the results into different classes after one node.
The problem is that in the trees that I get, after evaluating the condition (is X < than a certain value), I get two results of the same class (yes and yes, for example). I want to have "yes" and "no" as results for the evaluation of the node.
Here is the example of what I'm getting:
This is the code generating the tree and the plot:
clf = tree.DecisionTreeClassifier(max_depth=2)
clf = clf.fit(users_data, users_target)
dot_data = tree.export_graphviz(clf, out_file=None,
feature_names= feature_names,
class_names= target_names,
filled=True, rounded=True,
special_characters=True)
graph = graphviz.Source(dot_data)
graph
I expect to find "YES" and "NO" classes after the nodes. Now, I'm getting the same classes in the lasts levels after the respective conditions.
Thanks!
As is, you model indeed does look like it doesn't offer any further discrimination between the first and the second level nodes; so, if you are certain that this is (kind of) optimal for your case, you can simply ask it to stop there using max_depth=1 instead of 2:
clf = tree.DecisionTreeClassifier(max_depth=1)
Keep in mind however that in reality this can be far from optimal; have a look at the tree for the iris dataset from the scikit-learn docs:
where you can see that, further down the tree levels, nodes with class=versicolor emerge from what look like "pure" nodes of class=virginica (and vice versa).
So, before deciding to prune the tree beforehand to max_depth=1, you might want to check if leaving it to grow further (i.e. by not specifying the max_depth argument, thus leaving it in its default value of None), might be better for your case.
Everything depends on why exactly you are doing this (i.e. your business case): if it is an exploratory one, you might very well stop with max_depth=1; if it is a predictive one, you should consider which configuration maximizes an appropriate metric (most probably here, the accuracy).
Try using criterion = "entropy". I find this solves the problem
The splits of a decision tree are somewhat speculative, and they happen as long as the chosen criterion is decreased by the split. This, as you noticed, does not guarantee a particular split to result in different classes being the majority after the split. Limiting the depth of the tree is part of the reason you see a split not "played out" until it can reach nodes of distinct classes.
Pruning the tree should help. I was able to avoid a similar problem using a suitable value of the ccp_alpha parameter of the DecisionTreeClassifier. Here are my before- and after- trees.
I am using scikit-learn DecisionTreeClassifier to build a decision tree. Assume that a given decision tree has 6 leaf/terminal nodes (A, B, C, D, E and F). I now want to assign the original records coded as to which leaf/terminal node they would belong to (think of it as a form of feature engineering).
I would prefer not to score the records directly, but instead to build a collection of dummy variables from a variety of trees into a feature engineering pipeline.
Does anyone know of any easy approach for doing this?
Something similar is implemented under ensemble.RandomTreesEmbedding. Note that n_estimators denotes the number of decision trees.
See docs here.
all!
Could anybody give me an advice on Random Forest implementation in Python? Ideally I need something that outputs as much information about the classifiers as possible, especially:
which vectors from the train set are used to train each decision
trees
which features are selected at random in each node in each
tree, which samples from the training set end up in this node, which
feature(s) are selected for split and which threashold is used for
split
I have found quite some implementations, the most well known one is probably from scikit, but it is not clear how to do (1) and (2) there (see this question). Other implementations seem to have the same problems, except the one from openCV, but it is in C++ (python interface does not cover all methods for Random Forests).
Does anybody know something that satisfies (1) and (2)? Alternatively, any idea how to improve scikit implementation to get the features (1) and (2)?
Solved: checked the source code of sklearn.tree._tree.Tree. It has good comments (which fully describe the tree):
children_left : int*
children_left[i] holds the node id of the left child of node i.
For leaves, children_left[i] == TREE_LEAF. Otherwise,
children_left[i] > i. This child handles the case where
X[:, feature[i]] <= threshold[i].
children_right : int*
children_right[i] holds the node id of the right child of node i.
For leaves, children_right[i] == TREE_LEAF. Otherwise,
children_right[i] > i. This child handles the case where
X[:, feature[i]] > threshold[i].
feature : int*
feature[i] holds the feature to split on, for the internal node i.
threshold : double*
threshold[i] holds the threshold for the internal node i.
You can get nearly all the information in scikit-learn. What exactly was the problem? You can even visualize the trees using dot.
I don't think you can find out which split candidates were sampled at random, but you can find out which were selected in the end.
Edit: Look at the tree_ property of the decision tree. I agree, it is not very well documented. There really should be an example to visualize the leaf distributions etc. You can have a look at the visualization function to get an understanding of how to get to the properties.
I have a what I think is a simple machine learning question.
Here is the basic problem: I am repeatedly given a new object and a list of descriptions about the object. For example: new_object: 'bob' new_object_descriptions: ['tall','old','funny']. I then have to use some kind of machine learning to find previously handled objects that have the 10 or less most similar descriptions, for example, past_similar_objects: ['frank','steve','joe']. Next, I have an algorithm that can directly measure whether these objects are indeed similar to bob, for example, correct_objects: ['steve','joe']. The classifier is then given this feedback training of successful matches. Then this loop repeats with a new object.
a
Here's the pseudo-code:
Classifier=new_classifier()
while True:
new_object,new_object_descriptions = get_new_object_and_descriptions()
past_similar_objects = Classifier.classify(new_object,new_object_descriptions)
correct_objects = calc_successful_matches(new_object,past_similar_objects)
Classifier.train_successful_matches(object,correct_objects)
But, there are some stipulations that may limit what classifier can be used:
There will be millions of objects put into this classifier so classification and training needs to scale well to millions of object types and still be fast. I believe this disqualifies something like a spam classifier that is optimal for just two types: spam or not spam. (Update: I could probably narrow this to thousands of objects instead of millions, if that is a problem.)
Again, I prefer speed when millions of objects are being classified, over accuracy.
Update: The classifier should return the 10 (or fewer) most similar objects, based on feedback from past training. Without this limit, an obvious cheat would be for the classifier could just return all past objects :)
What are decent, fast machine learning algorithms for this purpose?
Note: The calc_successful_matches distance metric is extremely expensive to calculate and that's why I'm using a fast machine learning algorithm to try to guess which objects will be close before I actually do the expensive calculation.
An algorithm that seems to meet your requirements (and is perhaps similar to what John the Statistician is suggesting) is Semantic Hashing. The basic idea is that it trains a deep belief network (a type of neural network that some have called 'neural networks 2.0' and is a very active area of research right now) to create a hash of the list of descriptions of an object into binary number such that the Hamming distance between the numbers correspond to similar objects. Since this just requires bitwise operations it can be pretty fast, and since you can use it to create a nearest neighbor-style algorithm it naturally generalizes to a very large number of classes. This is very good state of the art stuff. Downside: it's not trivial to understand and implement, and requires some parameter tuning. The author provides some Matlab code here. A somewhat easier algorithm to implement and is closely related to this one is Locality Sensitive Hashing.
Now that you say that you have an expensive distance function you want to approximate quickly, I'm reminded of another very interesting algorithm that does this, Boostmap. This one uses boosting to create a fast metric which approximates an expensive to calculate metric. In a certain sense it's similar to the above idea but the algorithms used are different. The authors of this paper have several papers on related techniques, all pretty good quality (published in top conferences) that you might want to check out.
do you really need a machine learning algorithm for this? What is your metric for similarity? You've mentioned the dimensionality of the number of objects, what about the size of the trait set for each person? Are there a maximum number of trait types? I might try something like this:
1) Have a dictionary mapping trait to a list of names named map
for each person p
for each trait t in p
map[t].add(p);
2) then when I want to find the closest person, I'd take my dictionary and create a new temp one:
dictionary mapping name to count called cnt
for each trait t in my person of interest
for each person p in map[t]
cnt[p]++;
then the entry with the highest count is closest
The benefit here is the map is only created once. if the traits per person is small, and the types of available traits are large, then the algorithm should be fast.
You could use the vector space model (http://en.wikipedia.org/wiki/Vector_space_model). I think what you are trying to learn is how to weight terms in considering how close two object description vectors are to each other, say for example in terms of a simplified mutual information. This could be very efficient as you could hash from terms to vectors, which means you wouldn't have to compare objects without shared features. The naive model would then have an adjustable weight per term (this could either be per term per vector, per term overall, or both), as well as a threshold. The vector space model is a widely used technique (for example, in Apache Lucene, which you might be able to use for this problem), so you'll be able to find out a lot about it through further searches.
Let me give a very simple formulation of this in terms of your example. Given bob: ['tall','old','funny'], I retrieve
frank: ['young','short,'funny']
steve: ['tall','old','grumpy']
joe: ['tall','old']
as I am maintaining a hash from funny->{frank,...}, tall->{steve, joe,...}, and old->{steve, joe,...}
I calculate something like the overall mutual information: weight of shared tags/weight of bob's tags. If that weight is over the threshold, I include them in the list.
When training, if I make a mistake I modify the shared tags. If my error was including frank, I reduce the weight for funny, while if I make a mistake by not including Steve or Joe, I increase the weight for tall and old.
You can make this as sophisticated as you'd like, for example by including weights for conjunctions of terms.
SVM is pretty fast. LIBSVM for Python, in particular, provides a very decent implementation of Support Vector Machine for classification.
This project departs from typical classification applications in two notable ways:
Rather than outputting the class which the new object is thought to belong to (or possibly outputting an array of these classes, each with probability / confidence level), the "classifier" provides a list of "neighbors" which are "close enough" to the new object.
With each new classification, an objective function, independent from the classifier, provides the list of the correct "neighbors"; in turn the corrected list (a subset of the list provided by the classifier ?) is then used to train the classifier
The idea behind the second point is probably that future objects submitted to the classifier and with similar to the current object should get better "classified" (be associated with a more correct set of previously seen objects) since the on-going training re-enforces connections to positive (correct) matches, while weakening the connection to objects which the classifier initially got wrong.
These two characteristics introduce distinct problems.
- The fact that the output is a list of objects rather than a "prototype" (or category identifier of sorts) make it difficult to scale as the number of objects seen so far grows toward the millions of instances as suggested in the question.
- The fact that the training is done on the basis of a subset of the matches found by the classifier, may introduce over-fitting, whereby the classifier could become "blind" to features (dimensions) which it, accidentally, didn't weight as important/relevant, in the early parts of the training. (I may be assuming too much with regards to the objective function in charge of producing the list of "correct" objects)
Possibly, the scaling concern could be handled by having a two-step process, with a first classifier, based the K-Means algorithm or something similar, which would produce a subset of the overall object collection (of objects previously seen) as plausible matches for the current object (effectively filtering out say 70% or more of collection). These possible matches would then be evaluated on the basis of Vector Space Model (particularly relevant if the feature dimensions are based on factors rather than values) or some other models. The underlying assumption for this two-step process is that the object collection will effectively expose clusters (it may just be relatively evenly distributed along the various dimensions).
Another way to further limit the number of candidates to evaluate, as the size of the previously seen objects grows, is to remove near duplicates and to only compare with one of these (but to supply the full duplicate list in the result, assuming that if the new object is close to the "representative" of this near duplicate class, all members of the class would also match)
The issue of over-fitting is trickier to handle. A possible approach would be to [sometimes] randomly add objects to the matching list which the classifier would not normally include. The extra objects could be added on the basis of their distance relative distance to the new object (i.e. making it a bit more probable that a relatively close object be added)
What you describe is somewhat similar to the Locally Weighted Learning algorithm, which given a query instance, it trains a model locally around the neighboring instances weighted by their distances to the query one.
Weka (Java) has an implementation of this in weka.classifiers.lazy.LWL