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add metaexample for CHAIDTree Regression #5065

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merged 11 commits into from
Jun 25, 2020
Merged

add metaexample for CHAIDTree Regression #5065

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a3dcdfd
add metaexample for CHAIDTree Regression
Hephaestus12 Jun 12, 2020
b89ba4c
Fix tiny typo.
Hephaestus12 Jun 12, 2020
1f9edbc
set labels in constructor
Hephaestus12 Jun 12, 2020
95a2af7
remove prefix m from paramteres
Hephaestus12 Jun 13, 2020
40613e8
changed filename
Hephaestus12 Jun 19, 2020
7c843e2
add data and meta-example
Hephaestus12 Jun 23, 2020
4d666ac
make change in notebook
Hephaestus12 Jun 23, 2020
f735ae1
Re-add data for meta example.
Hephaestus12 Jun 25, 2020
ea5a144
changed local branch in data repo
Hephaestus12 Jun 25, 2020
b66ca9a
Edit SWIG interface.
Hephaestus12 Jun 25, 2020
ad7341f
squashed data commits
Hephaestus12 Jun 25, 2020
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2 changes: 1 addition & 1 deletion data
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Submodule data updated 1 files
+1 −0 testsuite/meta/regression/chaid_tree.dat
22 changes: 11 additions & 11 deletions doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb
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Expand Up @@ -1405,9 +1405,9 @@
"source": [
"def train_chaidtree(dependent_var_type,feature_types,num_bins,feats,labels):\n",
" # create CHAID tree object\n",
" c = sg.create_machine(\"CHAIDTree\", m_dependent_vartype=dependent_var_type,\n",
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perfect!

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I think you forgot some, I remember seeing one name in a "get" call...double check

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Are you referring to this:

shogun/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb

Line 613 in 123f512

" tree = sg.create_machine(\"C45ClassifierTree\", labels=labels, m_nominal=types)\n",

I don't think we should change this right? As it isn't related to CHAID tree? As then we will have to make some other change in the source code related to C45ClassifierTree

Also, the other one is :

shogun/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb

Line 643 in 123f512

" output_certainty=tree.get('m_certainty')\n",

This one too, is an instance of C45ClassifierTree. Should I change these two instances too?

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ah sorry. of course you are right!
We will see it in the CI for the notebooks

" m_feature_types=feature_types,\n",
" m_num_breakpoints=num_bins,\n",
" c = sg.create_machine(\"CHAIDTree\", dependent_vartype=dependent_var_type,\n",
" feature_types=feature_types,\n",
" num_breakpoints=num_bins,\n",
" labels=labels)\n",
" # train using training features\n",
" c.train(feats)\n",
Expand Down Expand Up @@ -1560,9 +1560,9 @@
"feature_types = np.array([2 for i in range(13)],dtype=np.int32) \n",
"\n",
"# setup CHAID tree - dependent variable is nominal(0), feature types set, number of bins(20)\n",
"chaid = sg.create_machine(\"CHAIDTree\", m_dependent_vartype=0,\n",
" m_feature_types=feature_types,\n",
" m_num_breakpoints=20)"
"chaid = sg.create_machine(\"CHAIDTree\", dependent_vartype=0,\n",
" feature_types=feature_types,\n",
" num_breakpoints=20)"
]
},
{
Expand Down Expand Up @@ -1644,10 +1644,10 @@
" feature_types[9]=1 \n",
"\n",
" # setup CHAID-tree\n",
" chaid = sg.create_machine(\"CHAIDTree\", m_dependent_vartype=2,\n",
" m_feature_types=feature_types,\n",
" m_num_breakpoints=10,\n",
" m_max_tree_depth=10)\n",
" chaid = sg.create_machine(\"CHAIDTree\", dependent_vartype=2,\n",
" feature_types=feature_types,\n",
" num_breakpoints=10,\n",
" max_tree_depth=10)\n",
"\n",
" # set evaluation criteria - mean squared error\n",
" accuracy = sg.create_evaluation(\"MeanSquaredError\")\n",
Expand Down Expand Up @@ -1727,4 +1727,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
}
}
3 changes: 1 addition & 2 deletions examples/meta/src/regression/cartree.sg.in
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Expand Up @@ -14,8 +14,7 @@ ft[0] = False
#![set_attribute_types]

#![create_machine]
Machine cartree = create_machine("CARTree", nominal=ft, mode=enum EProblemType.PT_REGRESSION, folds=5, apply_cv_pruning=True, seed=1)
cartree.set_labels(labels_train)
Machine cartree = create_machine("CARTree", labels=labels_train, nominal=ft, mode=enum EProblemType.PT_REGRESSION, folds=5, apply_cv_pruning=True, seed=1)
#![create_machine]

#![train_and_apply]
Expand Down
27 changes: 27 additions & 0 deletions examples/meta/src/regression/chaid_tree.sg.in
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@@ -0,0 +1,27 @@
File f_feats_train = read_csv("@SHOGUN_DATA@/regression_1d_linear_features_train.dat")
File f_feats_test = read_csv("@SHOGUN_DATA@/regression_1d_linear_features_test.dat")
File f_labels_train = read_csv("@SHOGUN_DATA@/regression_1d_linear_labels_train.dat")

#![create_features]
Features feats_train = create_features(f_feats_train)
Features feats_test = create_features(f_feats_test)
Labels labels_train = create_labels(f_labels_train)
#![create_features]

#![set_feature_types]
IntVector ft(1)
ft[0] = 2
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@karlnapf this is causing issues. In octave this becomes a scalar value :( I wrote a fix for this but it's in another branch, not yet merged.. Also this throws an error in the meta example, but ctest doesn't pick this up (I had this issue before) and I am not sure why. The test only fails when comparing the serialised outputs in the integration test, because this will not have serialised anything because of the exception thrown when you put ft

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So, unless we merge your fix this meta example will fail only in Octave, right? Could it be possible to merge this PR anyway, but somehow excluding it from testing with Octave (since it is broken atm)? Just to not have to put this on hold indefinitely...

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Maybe @Hephaestus12 can just fix it here? All you need to do is replace

shogun/src/interfaces/swig/shogun.i

Line 199 in 3041ea0

#ifdef SWIGR

with #if defined(SWIGR) || defined(SWIGOCTAVE)

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Yes, I'll do this.

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I hope this works :D

#![set_feature_types]

#![create_machine]
Machine chaidtree = create_machine("CHAIDTree", labels=labels_train, dependent_vartype=2, feature_types=ft, num_breakpoints=50)
#![create_machine]

#![train_and_apply]
chaidtree.train(feats_train)
Labels labels_predict = chaidtree.apply(feats_test)
#![train_and_apply]

#![extract_weights_labels]
RealVector labels_vector = labels_predict.get_real_vector("labels")
#![extract_weights_labels]
44 changes: 0 additions & 44 deletions examples/undocumented/python/regression_chaidtree.py
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51 changes: 0 additions & 51 deletions examples/undocumented/python/regression_randomforest.py
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20 changes: 10 additions & 10 deletions src/shogun/multiclass/tree/CHAIDTree.cpp
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Expand Up @@ -1392,14 +1392,14 @@ void CHAIDTree::init()
m_cont_breakpoints=SGMatrix<float64_t>();
m_num_breakpoints=0;

SG_ADD(&m_weights,"m_weights", "weights", ParameterProperties::READONLY);
SG_ADD(&m_weights_set,"m_weights_set", "weights set", ParameterProperties::READONLY);
SG_ADD(&m_feature_types,"m_feature_types", "feature types", ParameterProperties::SETTING);
SG_ADD(&m_dependent_vartype,"m_dependent_vartype", "dependent variable type", ParameterProperties::SETTING);
SG_ADD(&m_max_tree_depth,"m_max_tree_depth", "max tree depth", ParameterProperties::HYPER);
SG_ADD(&m_min_node_size,"m_min_node_size", "min node size", ParameterProperties::SETTING);
SG_ADD(&m_alpha_merge,"m_alpha_merge", "alpha-merge", ParameterProperties::HYPER);
SG_ADD(&m_alpha_split,"m_alpha_split", "alpha-split", ParameterProperties::HYPER);
SG_ADD(&m_cont_breakpoints,"m_cont_breakpoints", "breakpoints in continuous attributes", ParameterProperties::SETTING);
SG_ADD(&m_num_breakpoints,"m_num_breakpoints", "number of breakpoints", ParameterProperties::HYPER);
SG_ADD(&m_weights,"weights", "weights", ParameterProperties::READONLY);
SG_ADD(&m_weights_set,"weights_set", "weights set", ParameterProperties::READONLY);
SG_ADD(&m_feature_types,"feature_types", "feature types", ParameterProperties::SETTING);
SG_ADD(&m_dependent_vartype,"dependent_vartype", "dependent variable type", ParameterProperties::SETTING);
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This causes the notebooks to fail:
https://dev.azure.com/shogunml/shogun/_build/results?buildId=3629&view=logs&j=089c709a-44eb-5f6e-96e7-15e9ee1ff5bf&t=2da3e16b-a2b2-5f01-2cbe-a20d9528195b&l=1849

Should be simple to fix: open the notebook and edit the name in there as well :)

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How do I run test the notebooks on my local machine? Does make test do that?

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There is a script for doing that https://github.com/shogun-toolbox/shogun/blob/develop/scripts/test_notebooks.sh.

The link Heiko pasted here above will show also how to use it.

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It doesnt. You would have to compile shogun with the python interface, make sure you can load it from python, and then open the notebook in jupyter notebook.

However, you might be able to do a simple hack here:

  1. Open the notebook in a texteditor
  2. Search for m_
  3. If it is one of the varnames, change it to the values you updated them to
  4. Save the file in the texteditor and submit

As this is such a simple change, that should do it without the need for you running it locally

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Yes, I have already pushed the code after making those changes. :)

SG_ADD(&m_max_tree_depth,"max_tree_depth", "max tree depth", ParameterProperties::HYPER);
SG_ADD(&m_min_node_size,"min_node_size", "min node size", ParameterProperties::SETTING);
SG_ADD(&m_alpha_merge,"alpha_merge", "alpha-merge", ParameterProperties::HYPER);
SG_ADD(&m_alpha_split,"alpha_split", "alpha-split", ParameterProperties::HYPER);
SG_ADD(&m_cont_breakpoints,"cont_breakpoints", "breakpoints in continuous attributes", ParameterProperties::SETTING);
SG_ADD(&m_num_breakpoints,"num_breakpoints", "number of breakpoints", ParameterProperties::HYPER);
}