From 8bf560b0bc8f17b76707c91f6cb7aa41e2e8fa4d Mon Sep 17 00:00:00 2001
From: Yannick DAYER <yannick.dayer@idiap.ch>
Date: Tue, 7 Dec 2021 18:18:05 +0100
Subject: [PATCH] Added a k-means test for machine parameters
---
bob/learn/em/cluster/k_means.py | 4 +++-
bob/learn/em/test/test_kmeans.py | 41 ++++++++++++++++++++++++--------
2 files changed, 34 insertions(+), 11 deletions(-)
diff --git a/bob/learn/em/cluster/k_means.py b/bob/learn/em/cluster/k_means.py
index 72da1f1..7df4074 100644
--- a/bob/learn/em/cluster/k_means.py
+++ b/bob/learn/em/cluster/k_means.py
@@ -156,8 +156,10 @@ class KMeansMachine(BaseEstimator):
weights_count = np.bincount(closest_centroid_indices, minlength=n_cluster)
weights = weights_count / weights_count.sum()
- # Accumulate
+ # FIX for `too many indices for array` error if using `np.eye(n_cluster)` alone:
dask_compatible_eye = np.eye(n_cluster) * np.array(1, like=data)
+
+ # Accumulate
means_sum = np.sum(
dask_compatible_eye[closest_centroid_indices][:, :, None] * data[:, None],
axis=0,
diff --git a/bob/learn/em/test/test_kmeans.py b/bob/learn/em/test/test_kmeans.py
index 426b3e5..c39bd4b 100644
--- a/bob/learn/em/test/test_kmeans.py
+++ b/bob/learn/em/test/test_kmeans.py
@@ -6,6 +6,8 @@
# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
"""Tests the KMeans machine
+
+Tries each test with a numpy array and the equivalent dask array.
"""
import dask.array as da
@@ -46,14 +48,14 @@ def test_KMeansMachine():
km.centroids_ = means
# Distance and closest mean
- np.testing.assert_almost_equal(km.transform(mean)[0], 1)
- np.testing.assert_almost_equal(km.transform(mean)[1], 6)
+ np.testing.assert_almost_equal(km.transform(mean)[0], 1, decimal=10)
+ np.testing.assert_almost_equal(km.transform(mean)[1], 6, decimal=10)
(index, dist) = km.get_closest_centroid(mean)
assert index == 0, index
- np.testing.assert_almost_equal(dist, 1.0)
- np.testing.assert_almost_equal(km.get_min_distance(mean), 1)
+ np.testing.assert_almost_equal(dist, 1.0, decimal=10)
+ np.testing.assert_almost_equal(km.get_min_distance(mean), 1, decimal=10)
def test_KMeansMachine_var_and_weight():
@@ -68,11 +70,8 @@ def test_KMeansMachine_var_and_weight():
variances_result = np.array([[0.01, 1.0], [0.01555556, 0.00888889]])
weights_result = np.array([0.4, 0.6])
- np.testing.assert_almost_equal(variances, variances_result)
- np.testing.assert_almost_equal(weights, weights_result)
-
-
-np.set_printoptions(precision=9)
+ np.testing.assert_almost_equal(variances, variances_result, decimal=7)
+ np.testing.assert_equal(weights, weights_result)
def test_kmeans_fit():
@@ -89,7 +88,7 @@ def test_kmeans_fit():
[-1.07173464, -1.06200356, -1.00724920],
[0.99479125, 0.99665564, 0.97689017],
]
- np.testing.assert_almost_equal(centroids, expected)
+ np.testing.assert_almost_equal(centroids, expected, decimal=7)
def test_kmeans_fit_init_pp():
@@ -123,3 +122,25 @@ def test_kmeans_fit_init_random():
[0.99529015, 0.99570570, 0.97580858],
]
np.testing.assert_almost_equal(centroids, expected, decimal=7)
+
+def test_kmeans_parameters():
+ np.random.seed(0)
+ data1 = np.random.normal(loc=1, size=(2000, 3))
+ data2 = np.random.normal(loc=-1, size=(2000, 3))
+ data = np.concatenate([data1, data2], axis=0)
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ machine = KMeansMachine(
+ n_clusters=2,
+ init_method="k-means||",
+ convergence_threshold=1e-5,
+ max_iter=5,
+ random_state=0,
+ init_max_iter=5,
+ ).fit(data)
+ centroids = machine.centroids_[np.argsort(machine.centroids_[:, 0])]
+ expected = [
+ [-1.07173464, -1.06200356, -1.00724920],
+ [0.99479125, 0.99665564, 0.97689017],
+ ]
+ np.testing.assert_almost_equal(centroids, expected, decimal=7)
\ No newline at end of file
--
GitLab