scikit-learn · artsobolev · May 29, 2015 · May 29, 2015 · May 29, 2015 · May 29, 2015
diff --git a/benchmarks/bench_nca.py b/benchmarks/bench_nca.py
@@ -0,0 +1,61 @@
+"""
+A comparison of different optimizers for NCA
+
+Data: UC Irvine's Wine dataset.
+
+"""
+import numpy as np
+
+from sklearn.base import clone
+from sklearn import metrics
+from sklearn.metric_learning import NCATransformer
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.cross_validation import KFold
+
+from sklearn.utils.bench import total_seconds
+from sklearn.datasets import fetch_mldata, make_classification
+
+def get_params(learning_rate=1, loss='kl', max_iter=100, solver='adagrad',
+        random_state=0, verbose=2, n_init=10, method='semivectorized',
+        threshold=None):
+    return {
+            "learning_rate" : learning_rate,
+            "loss" : loss,
+            "max_iter" : max_iter,
+            "solver" : solver,
+            "random_state" : random_state,
+            "verbose" : verbose,
+            "n_init" : n_init,
+            "method" : method,
+            "threshold" : threshold,
+        }
+
+if __name__ == '__main__':
+
+    separator = "=" * 30 + "\n"
+    wine_ds = fetch_mldata('wine')
+
+    datasets = [
+            ('UCI Wine', (wine_ds.data, wine_ds.target)),
+            ('mk_cls', make_classification(n_samples=300, n_features=20, n_informative=2, n_classes=2))
+        ]
+
+    params = [
+            get_params(method='semivectorized'),
+            get_params(method='vectorized'),
+            get_params(method='semivectorized', threshold=0),
+            get_params(method='vectorized', threshold=0),
+        ]
+
+    for name, (X, y) in datasets:
+        print(separator)
+        print("Using {} dataset".format(name))
+        print(separator)
+
+        for args in params:
+            nca = NCATransformer(**args)
+            print(nca)
+            nca.fit(X, y)
+            print(separator)
+
diff --git a/sklearn/metric_learning/__init__.py b/sklearn/metric_learning/__init__.py
@@ -0,0 +1,9 @@
+"""
+The :mod:`sklearn.metric_learning` module includes models that find
+a metric that makes samples from the same class closer than samples
+from different classes.
+"""
+
+from .nca import NCATransformer
+
+__all__ = ['NCATransformer']