# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1226505278 18000
# Node ID 716c04512dbe9787272a437358507a928dcbb210
# Parent  2b0e10ac6929ce9a756d5ebf65b3f25024d9e505
init

diff -r 2b0e10ac6929 -r 716c04512dbe external/wrap_libsvm.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/external/wrap_libsvm.py	Wed Nov 12 10:54:38 2008 -0500
@@ -0,0 +1,99 @@
+"""Run an experiment using libsvm.
+"""
+import numpy
+from ..datasets import dataset_from_descr
+
+# libsvm currently has no python installation instructions/convention.
+#
+# This module uses a specific convention for libsvm's installation.
+# I base this on installing libsvm-2.88.
+# To install libsvm's python module, do three things:
+# 1. Build libsvm (run make in both the root dir and the python subdir).
+# 2. touch a '__init__.py' file in the python subdir
+# 3. add a symbolic link to a PYTHONPATH location that looks like this:
+#    libsvm -> <your root path>/libsvm-2.88/python/
+#
+# That is the sort of thing that this module expects from 'import libsvm'
+
+import libsvm
+
+def score_01(x, y, model):
+    assert len(x) == len(y)
+    size = len(x)
+    errors = 0
+    for i in range(size):
+        prediction = model.predict(x[i])
+        #probability = model.predict_probability
+        if (y[i] != prediction):
+            errors = errors + 1
+    return float(errors)/size
+
+#this is the dbdict experiment interface... if you happen to use dbdict
+class State(object):
+    #TODO: parametrize to get all the kernel types, not hardcode for RBF
+    dataset = 'MNIST_1k'
+    C = 10.0
+    kernel = 'RBF'
+    # rel_gamma is related to the procedure Jerome used. He mentioned why in
+    # quadratic_neurons/neuropaper/draft3.pdf.
+    rel_gamma = 1.0   
+
+    def __init__(self, **kwargs):
+        for k, v in kwargs:
+            setattr(self, k, type(getattr(self, k))(v))
+
+
+def dbdict_run_svm_experiment(state, channel=lambda *args, **kwargs:None):
+    """Parameters are described in state, and returned in state.
+
+    :param state: object instance to store parameters and return values
+    :param channel: not used
+
+    :returns: None
+
+    This is the kind of function that dbdict-run can use.
+
+    """
+    ((train_x, train_y), (valid_x, valid_y), (test_x, test_y)) = dataset_from_descr(state.dataset)
+
+    #libsvm needs stuff in int32 on a 32bit machine
+    #TODO: test this on a 64bit machine
+    train_y = numpy.asarray(train_y, dtype='int32')
+    valid_y = numpy.asarray(valid_y, dtype='int32')
+    test_y = numpy.asarray(test_y, dtype='int32')
+    problem = svm.svm_problem(train_y, train_x);
+
+    gamma0 = 0.5 / numpy.sum(numpy.var(train_x, axis=0))
+
+    param = svm.svm_parameter(C=state.C,
+            kernel_type=getattr(svm, state.kernel),
+            gamma=state.rel_gamma * gamma0)
+
+    model = svm.svm_model(problem, param) #this is the expensive part
+
+    state.train_01 = score_01(train_x, train_y, model)
+    state.valid_01 = score_01(valid_x, valid_y, model)
+    state.test_01 = score_01(test_x, test_y, model)
+
+    state.n_train = len(train_y)
+    state.n_valid = len(valid_y)
+    state.n_test = len(test_y)
+
+def run_svm_experiment(**kwargs):
+    """Python-friendly interface to dbdict_run_svm_experiment
+
+    Parameters are used to construct a `State` instance, which is returned after running
+    `dbdict_run_svm_experiment` on it.
+
+    .. code-block:: python
+        results = run_svm_experiment(dataset='MNIST_1k', C=100.0, rel_gamma=0.01)
+        print results.n_train
+        # 1000
+        print results.valid_01, results.test_01
+        # 0.14, 0.10  #.. or something...
+
+    """
+    state = State(**kwargs)
+    state_run_svm_experiment(state)
+    return state
+