Python sklearn.metrics.homogeneity_score() Examples

The following are 8 code examples of sklearn.metrics.homogeneity_score(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.metrics , or try the search function .
Example #1
Source File: k_means_plot.py    From machine-learning with GNU General Public License v3.0 6 votes vote down vote up 
def bench_k_means(estimator, name, data):
    t0 = time()
    estimator.fit(data)
    print('% 9s   %.2fs    %i   %.3f   %.3f   %.3f   %.3f   %.3f    %.3f'
          % (name, (time() - t0), estimator.inertia_,
             metrics.homogeneity_score(labels, estimator.labels_),
             metrics.completeness_score(labels, estimator.labels_),
             metrics.v_measure_score(labels, estimator.labels_),
             metrics.adjusted_rand_score(labels, estimator.labels_),
             metrics.adjusted_mutual_info_score(labels,  estimator.labels_),
             metrics.silhouette_score(data, estimator.labels_,
                                      metric='euclidean',
                                      sample_size=sample_size)))
Example #2
Source File: k_means_clustering.py    From FunUtils with MIT License 5 votes vote down vote up 
def bench_k_means(estimator, name, data):
    estimator.fit(data)
    # A short explanation for every score:
    # homogeneity:          each cluster contains only members of a single class (range 0 - 1)
    # completeness:         all members of a given class are assigned to the same cluster (range 0 - 1)
    # v_measure:            harmonic mean of homogeneity and completeness
    # adjusted_rand:        similarity of the actual values and their predictions,
    #                       ignoring permutations and with chance normalization
    #                       (range -1 to 1, -1 being bad, 1 being perfect and 0 being random)
    # adjusted_mutual_info: agreement of the actual values and predictions, ignoring permutations
    #                       (range 0 - 1, with 0 being random agreement and 1 being perfect agreement)
    # silhouette:           uses the mean distance between a sample and all other points in the same class,
    #                       as well as the mean distance between a sample and all other points in the nearest cluster
    #                       to calculate a score (range: -1 to 1, with the former being incorrect,
    #                       and the latter standing for highly dense clustering.
    #                       0 indicates overlapping clusters.
    print('%-9s \t%i \thomogeneity: %.3f \tcompleteness: %.3f \tv-measure: %.3f \tadjusted-rand: %.3f \t'
          'adjusted-mutual-info: %.3f \tsilhouette: %.3f'
          % (name, estimator.inertia_,
             metrics.homogeneity_score(y, estimator.labels_),
             metrics.completeness_score(y, estimator.labels_),
             metrics.v_measure_score(y, estimator.labels_),
             metrics.adjusted_rand_score(y, estimator.labels_),
             metrics.adjusted_mutual_info_score(y,  estimator.labels_),
             metrics.silhouette_score(data, estimator.labels_,
                                      metric='euclidean')))
Example #3
Source File: structural_tests.py    From drifter_ml with MIT License 5 votes vote down vote up 
def homogeneity_kmeans_scorer(self, min_similarity):
        return self.kmeans_scorer(
            metrics.homogeneity_score,
            min_similarity
        )
Example #4
Source File: structural_tests.py    From drifter_ml with MIT License 5 votes vote down vote up 
def homogeneity_dbscan_scorer(self, min_similarity):
        return self.dbscan_scorer(
            metrics.homogeneity_score,
            min_similarity
        )
Example #5
Source File: evaluate.py    From GrowingNeuralGas with MIT License 5 votes vote down vote up 
def evaluate_on_digits():
    digits = datasets.load_digits()
    data = digits.data
    target = digits.target
    gng = GrowingNeuralGas(data)
    gng.fit_network(e_b=0.05, e_n=0.006, a_max=8, l=100, a=0.5, d=0.995, passes=5, plot_evolution=False)
    clustered_data = gng.cluster_data()
    print('Found %d clusters.' % nx.number_connected_components(gng.network))
    target_infered = []
    for observation, cluster in clustered_data:
        target_infered.append(cluster)
    homogeneity = metrics.homogeneity_score(target, target_infered)
    print(homogeneity)
    gng.plot_clusters(gng.reduce_dimension(gng.cluster_data()))
Example #6
Source File: plot_kmeans_digits.py    From Computer-Vision-with-Python-3 with MIT License 5 votes vote down vote up 
def bench_k_means(estimator, name, data):
    t0 = time()
    estimator.fit(data)
    print('% 9s   %.2fs    %i   %.3f   %.3f   %.3f   %.3f   %.3f    %.3f'
          % (name, (time() - t0), estimator.inertia_,
             metrics.homogeneity_score(labels, estimator.labels_),
             metrics.completeness_score(labels, estimator.labels_),
             metrics.v_measure_score(labels, estimator.labels_),
             metrics.adjusted_rand_score(labels, estimator.labels_),
             metrics.adjusted_mutual_info_score(labels,  estimator.labels_),
             metrics.silhouette_score(data, estimator.labels_,
                                      metric='euclidean',
                                      sample_size=sample_size)))
Example #7
Source File: test_metrics.py    From pandas-ml with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_homogeneity_score(self):
        result = self.df.metrics.homogeneity_score()
        expected = metrics.homogeneity_score(self.target, self.pred)
        self.assertEqual(result, expected)
Example #8
Source File: test_cluster.py    From pandas-ml with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def test_KMeans_scores(self):
        digits = datasets.load_digits()
        df = pdml.ModelFrame(digits)

        scaled = pp.scale(digits.data)
        df.data = df.data.pp.scale()
        self.assert_numpy_array_almost_equal(df.data.values, scaled)

        clf1 = cluster.KMeans(init='k-means++', n_clusters=10,
                              n_init=10, random_state=self.random_state)
        clf2 = df.cluster.KMeans(init='k-means++', n_clusters=10,
                                 n_init=10, random_state=self.random_state)
        clf1.fit(scaled)
        df.fit_predict(clf2)

        expected = m.homogeneity_score(digits.target, clf1.labels_)
        self.assertEqual(df.metrics.homogeneity_score(), expected)

        expected = m.completeness_score(digits.target, clf1.labels_)
        self.assertEqual(df.metrics.completeness_score(), expected)

        expected = m.v_measure_score(digits.target, clf1.labels_)
        self.assertEqual(df.metrics.v_measure_score(), expected)

        expected = m.adjusted_rand_score(digits.target, clf1.labels_)
        self.assertEqual(df.metrics.adjusted_rand_score(), expected)

        expected = m.homogeneity_score(digits.target, clf1.labels_)
        self.assertEqual(df.metrics.homogeneity_score(), expected)

        expected = m.silhouette_score(scaled, clf1.labels_, metric='euclidean',
                                      sample_size=300, random_state=self.random_state)
        result = df.metrics.silhouette_score(metric='euclidean', sample_size=300,
                                             random_state=self.random_state)
        self.assertAlmostEqual(result, expected)