def compute_confusion_matrix_rand_index_clustering(clustering_algorithm, ys_labels_true, ys_labels_predicted, num_clusters, final_clustering = False):
num_examples = len(ys_labels_true)
confusion_matrix_rand_index_clustering = matrix_array_zeros((2, 2))
num_true_positives = 0
num_false_positives = 0
num_false_negatives = 0
num_true_negatives = 0
examples_pairs = list( iteration_combinations( range( 0, num_examples ), r = 2 ) )
num_examples_pairs = len(examples_pairs)
for example_pair in examples_pairs:
example_pair_first_index = example_pair[0]
example_pair_second_index = example_pair[1]
# The Example Pair are in the same Group (same True Label)
if(ys_labels_true[example_pair_first_index] == ys_labels_true[example_pair_second_index]):
# The Example Pair are in the same Cluster (same Predicted Label)
if(ys_labels_predicted[example_pair_first_index] == ys_labels_predicted[example_pair_second_index]):
num_true_positives = ( num_true_positives + 1 )
# The Example Pair are in different Clusters (different Predicted Labels)
else:
num_false_negatives = ( num_false_negatives + 1 )
# The Example Pair are in different Groups (different True Labels)
else:
# The Example Pair are in the same Cluster (same Predicted Label)
if(ys_labels_predicted[example_pair_first_index] == ys_labels_predicted[example_pair_second_index]):
num_false_positives = ( num_false_positives + 1 )
# The Example Pair are in different Clusters (different Predicted Labels)
else:
num_true_negatives = ( num_true_negatives + 1 )
confusion_matrix_rand_index_clustering[0][0] = int(num_true_positives)
confusion_matrix_rand_index_clustering[0][1] = int(num_false_positives)
confusion_matrix_rand_index_clustering[1][0] = int(num_false_negatives)
confusion_matrix_rand_index_clustering[1][1] = int(num_true_negatives)
return confusion_matrix_rand_index_clustering, num_examples_pairs
transformed_xs_images_matrix_isomap = isomap.fit_transform(xs_images_matrix)
# Create Data Frames for the 3 Features Extractions (PCA, TSNE and Isomap)
data_frame_transformed_extraction_pca, data_frame_columns_pca, data_frame_transformed_extraction_tsne, data_frame_columns_tsne, data_frame_transformed_extraction_isomap, data_frame_columns_isomap = create_data_frames_extraction(transformed_xs_images_matrix_pca, transformed_xs_images_matrix_tsne, transformed_xs_images_matrix_isomap, ys_labels_true, num_total_images_examples = 563, num_features_components = NUM_FEATURES_COMPONENTS)
# Initialize the Visualization/Plotting Style
intialize_plotting_style('seaborn-dark')
# Generate Analysis' Plots, for several Visualization Plots
generate_data_analysis_plots(data_frame_transformed_extraction_pca, data_frame_columns_pca, data_frame_transformed_extraction_tsne, data_frame_columns_tsne, data_frame_transformed_extraction_isomap, data_frame_columns_isomap, num_components = NUM_FEATURES_COMPONENTS)
# The final Features Extracted, to be used, in the Clustering methods,
# filled initially with zeros (0s)
xs_features = matrix_array_zeros( (num_total_images_examples, ( 3 * NUM_FEATURES_COMPONENTS ) ) )
xs_features = matrix_array_zeros( (num_total_images_examples, ( ( 3 * NUM_FEATURES_COMPONENTS ) + 1 ) ) )
# The final Features Extracted, to be used, in the Clustering methods,
# filled with the fitted and transformed data,
# from the PCA (Principal Component Analysis) Decomposition
xs_features[:, 0 : NUM_FEATURES_COMPONENTS] = transformed_xs_images_matrix_pca
# The final Features Extracted, to be used, in the Clustering methods,
# filled with the fitted and transformed data,
# from the TSNE (T-Distributed Stochastic Neighbor Embedding)
xs_features[:, NUM_FEATURES_COMPONENTS : ( 2 * NUM_FEATURES_COMPONENTS ) ] = transformed_xs_images_matrix_tsne
# The final Features Extracted, to be used, in the Clustering methods,
# filled with the fitted and transformed data,
# from the Isomap (Isometric Mapping)
def create_data_frames_extraction(transformed_xs_images_matrix_pca, transformed_xs_images_matrix_tsne, transformed_xs_images_matrix_isomap, ys_labels_true, num_total_images_examples = 563, num_features_components = 6):
# The Matrix for the Features and Labels, to be used to create the Data Frame,
# for the PCA (Principal Component Analysis) Features' Extraction method
transformed_data_images_matrix_pca = matrix_array_zeros( (num_total_images_examples, ( num_features_components + 1 ) ) )
# Fill the Matrix for the Features and Labels, to be used to create the Data Frame,
# for the PCA (Principal Component Analysis) Features' Extraction method, with the Features extracted
transformed_data_images_matrix_pca[:, 0 : -1] = transformed_xs_images_matrix_pca
# Fill the Matrix for the Features and Labels, to be used to create the Data Frame,
# for the PCA (Principal Component Analysis) Features' Extraction method, with the associated Labels
transformed_data_images_matrix_pca[:, -1] = ys_labels_true
# The Columns, with respect to the Identifiers of the Features and Labels,
# to be used in the Data Frame, for the PCA (Principal Component Analysis) method
data_frame_columns_pca = ['PCA - Feature 1', 'PCA - Feature 2', 'PCA - Feature 3',
'PCA - Feature 4', 'PCA - Feature 5', 'PCA - Feature 6',
'Celular Phase']
# The Data Frame of the Features Extracted, for the PCA (Principal Component Analysis) method
data_frame_extraction_pca = pandas_data_frame(transformed_data_images_matrix_pca, columns = data_frame_columns_pca)
# The Matrix for the Features and Labels, to be used to create the Data Frame,
# for the TSNE (T-Distributed Stochastic Neighbor Embedding) Features' Extraction method
transformed_data_images_matrix_tsne = matrix_array_zeros( (num_total_images_examples, ( num_features_components + 1 ) ) )
# Fill the Matrix for the Features and Labels, to be used to create the Data Frame,
# for the TSNE (T-Distributed Stochastic Neighbor Embedding) Features' Extraction method, with the Features extracted
transformed_data_images_matrix_tsne[:, 0 : -1] = transformed_xs_images_matrix_tsne
# Fill the Matrix for the Features and Labels, to be used to create the Data Frame,
# for the TSNE (T-Distributed Stochastic Neighbor Embedding) Features' Extraction method, with the associated Labels
transformed_data_images_matrix_tsne[:, -1] = ys_labels_true
# The Columns, with respect to the Identifiers of the Features and Labels,
# to be used in the Data Frame, for the TSNE (T-Distributed Stochastic Neighbor Embedding) method
data_frame_columns_tsne = ['TSNE - Feature 7', 'TSNE - Feature 8', 'TSNE - Feature 9',
'TSNE - Feature 10', 'TSNE - Feature 11', 'TSNE - Feature 12',
'Celular Phase']
# The Data Frame of the Features Extracted, for the TSNE (T-Distributed Stochastic Neighbor Embedding) method
data_frame_extraction_tsne = pandas_data_frame(transformed_data_images_matrix_tsne, columns = data_frame_columns_tsne)
# The Matrix for the Features and Labels, to be used to create the Data Frame,
# for the Isomap (Isometric Mapping) Features' Extraction method
transformed_data_images_matrix_isomap = matrix_array_zeros( (num_total_images_examples, ( num_features_components + 1 ) ) )
# Fill the Matrix for the Features and Labels, to be used to create the Data Frame,
# for the Isomap (Isometric Mapping) Features' Extraction method, with the Features extracted
transformed_data_images_matrix_isomap[:, 0 : -1] = transformed_xs_images_matrix_isomap
# Fill the Matrix for the Features and Labels, to be used to create the Data Frame,
# for the Isomap (Isometric Mapping) Features' Extraction method, with the associated Labels
transformed_data_images_matrix_isomap[:, -1] = ys_labels_true
# The Columns, with respect to the Identifiers of the Features and Labels,
# to be used in the Data Frame, for the Isomap (Isometric Mapping) method
data_frame_columns_isomap = ['Isomap - Feature 13', 'Isomap - Feature 14', 'Isomap - Feature 15',
'Isomap - Feature 16', 'Isomap - Feature 17', 'Isomap - Feature 18',
'Celular Phase']
# The Data Frame of the Features Extracted, for the Isomap (Isometric Mapping) method
data_frame_extraction_isomap = pandas_data_frame(transformed_data_images_matrix_isomap, columns = data_frame_columns_isomap)
return data_frame_extraction_pca, data_frame_columns_pca, data_frame_extraction_tsne, data_frame_columns_tsne, data_frame_extraction_isomap, data_frame_columns_isomap
def bisecting_k_means_clustering(xs_features_data,
examples_ids,
ys_labels_true,
final_max_num_clusters_and_iterations=2):
clusters_ids = [0]
clusters_data = [xs_features_data]
clusters_examples_ids = [examples_ids]
clusters_centroids_points = [0]
num_clusters = 1
tree_predictions_lists_with_offset = array_empty(
(len(xs_features_data), 0)).tolist()
tree_predictions_lists_without_offset = array_empty(
(len(xs_features_data), 0)).tolist()
ys_labels_predicted_with_offset = None
ys_labels_predicted_without_offset = None
current_iteration = 0
while (num_clusters < final_max_num_clusters_and_iterations):
cluster_index_with_more_examples = -1
num_max_examples_in_cluster = -1
for index_cluster in range(num_clusters):
num_examples_in_cluster = len(clusters_data[index_cluster])
if (num_examples_in_cluster > num_max_examples_in_cluster):
cluster_index_with_more_examples = index_cluster
num_max_examples_in_cluster = num_examples_in_cluster
cluster_id_to_be_divided = clusters_ids[
cluster_index_with_more_examples]
cluster_data_to_be_divided = clusters_data[
cluster_index_with_more_examples]
cluster_examples_ids_to_be_divided = clusters_examples_ids[
cluster_index_with_more_examples]
cluster_centroid_point_to_be_divided = clusters_centroids_points[
cluster_index_with_more_examples]
clusters_data.remove(cluster_data_to_be_divided)
clusters_examples_ids.remove(cluster_examples_ids_to_be_divided)
if (num_clusters == 1):
clusters_ids.pop(cluster_id_to_be_divided)
clusters_centroids_points.pop(0)
else:
cluster_centroid_point_index = 0
cluster_centroid_point_index_triggered = 0
for cluster_centroid_point in clusters_centroids_points:
if ((cluster_centroid_point_to_be_divided ==
cluster_centroid_point).all()):
cluster_centroid_point_index_triggered = cluster_centroid_point_index
cluster_centroid_point_index = (cluster_centroid_point_index +
1)
clusters_centroids_points.pop(
cluster_centroid_point_index_triggered)
if (num_clusters == 1):
two_sub_clusters_ids, two_sub_clusters_examples_ids, two_sub_clusters_data, two_sub_clusters_centroids, ys_labels_predicted_without_offset, ys_labels_predicted_with_offset, cluster_squared_error_sum_intertia = bissect_k_means_into_two_sub_clusters(
cluster_data_to_be_divided,
cluster_examples_ids_to_be_divided,
left_leaf_cluster_id_offset=0,
right_leaf_cluster_id_offset=0)
else:
two_sub_clusters_ids, two_sub_clusters_examples_ids, two_sub_clusters_data, two_sub_clusters_centroids, ys_labels_predicted_without_offset, ys_labels_predicted_with_offset, cluster_squared_error_sum_intertia = bissect_k_means_into_two_sub_clusters(
cluster_data_to_be_divided,
cluster_examples_ids_to_be_divided,
left_leaf_cluster_id_offset=cluster_id_to_be_divided,
right_leaf_cluster_id_offset=(num_clusters - 1))
ys_labels_predicted_with_offset_unique = array_sort(
array_unique_values(ys_labels_predicted_with_offset))
for sub_cluster_id in range(2):
clusters_ids.append(two_sub_clusters_ids[sub_cluster_id])
clusters_data.append(two_sub_clusters_data[sub_cluster_id])
clusters_examples_ids.append(
two_sub_clusters_examples_ids[sub_cluster_id])
clusters_centroids_points.append(
two_sub_clusters_centroids[sub_cluster_id])
for example_index in range(len(xs_features_data)):
if (example_index
in two_sub_clusters_examples_ids[sub_cluster_id]):
if (sub_cluster_id <
len(ys_labels_predicted_with_offset_unique)):
tree_predictions_lists_with_offset[
example_index].append(
ys_labels_predicted_with_offset_unique[
sub_cluster_id])
tree_predictions_lists_without_offset[
example_index].append(sub_cluster_id)
num_clusters = (num_clusters + 1)
current_iteration = (current_iteration + 1)
ys_final_labels_predicted = matrix_array_zeros(len(xs_features_data))
for example_index in range(len(xs_features_data)):
last_index = (len(tree_predictions_lists_with_offset[example_index]) -
1)
ys_final_labels_predicted[
example_index] = tree_predictions_lists_with_offset[example_index][
last_index]
clusters_centroids_points = array_matrix(clusters_centroids_points)
effective_num_clusters = len(
array_unique_values(ys_final_labels_predicted))
if (effective_num_clusters >= 2):
if (effective_num_clusters <= 26):
plot_clusters_centroids_and_radii("Bisecting-K-Means",
xs_features_data,
ys_final_labels_predicted,
clusters_centroids_points,
effective_num_clusters,
epsilon=None,
damping=None,
final_clustering=True)
plot_silhouette_analysis("Bisecting-K-Means",
xs_features_data,
ys_final_labels_predicted,
clusters_centroids_points,
effective_num_clusters,
epsilon=None,
damping=None,
final_clustering=True)
bisecting_k_means_final_clustering_silhouette_score, bisecting_k_means_final_clustering_precision_score, bisecting_k_means_final_clustering_recall_score, bisecting_k_means_final_clustering_rand_index_score, bisecting_k_means_final_clustering_f1_score, bisecting_k_means_final_clustering_adjusted_rand_score, bisecting_k_means_final_clustering_confusion_matrix_rand_index = compute_clustering_performance_metrics(
"Bisecting-K-Means",
xs_features_data,
ys_labels_true,
ys_final_labels_predicted,
effective_num_clusters,
final_clustering=True)
plot_confusion_matrix_rand_index_clustering_heatmap(
"Bisecting-K-Means",
bisecting_k_means_final_clustering_confusion_matrix_rand_index,
effective_num_clusters,
epsilon=None,
damping=None,
final_clustering=True)
xs_ids_examples = list(range(0, len(examples_ids)))
html_report_cluster_labels_hierarchical(
xs_ids_examples, tree_predictions_lists_without_offset,
"bisecting-k-means-hierarchical.html")
return clusters_ids, clusters_data, ys_final_labels_predicted, tree_predictions_lists_without_offset, num_clusters, effective_num_clusters
def k_means_pre_clustering(xs_features_data, ys_labels_true,
num_total_clusters):
clusters_squared_errors_sums_intertias = matrix_array_zeros(
num_total_clusters)
clusters_silhouette_scores = matrix_array_zeros((num_total_clusters - 1))
clusters_precision_scores = matrix_array_zeros((num_total_clusters - 1))
clusters_recall_scores = matrix_array_zeros((num_total_clusters - 1))
clusters_rand_index_scores = matrix_array_zeros((num_total_clusters - 1))
clusters_f1_scores = matrix_array_zeros((num_total_clusters - 1))
clusters_adjusted_rand_scores = matrix_array_zeros(
(num_total_clusters - 1))
for current_num_clusters in range(1, (num_total_clusters + 1)):
ys_labels_predicted, clusters_centroids, error_k_means_pre_clustering = k_means_clustering_method(
xs_features_data,
num_clusters=current_num_clusters,
max_iterations=300)
clusters_squared_errors_sums_intertias[(
current_num_clusters - 1)] = error_k_means_pre_clustering
plot_clusters_centroids_and_radii("K-Means",
xs_features_data,
ys_labels_predicted,
clusters_centroids,
num_clusters=current_num_clusters,
epsilon=None,
damping=None,
final_clustering=False)
if (current_num_clusters >= 2):
if (current_num_clusters <= 26):
plot_silhouette_analysis("K-Means",
xs_features_data,
ys_labels_predicted,
clusters_centroids,
current_num_clusters,
epsilon=None,
damping=None,
final_clustering=False)
silhouette_score, precision_score, recall_score, rand_index_score, f1_score, adjusted_rand_score, confusion_matrix_rand_index_clustering = compute_clustering_performance_metrics(
"K-Means",
xs_features_data,
ys_labels_true,
ys_labels_predicted,
current_num_clusters,
final_clustering=False)
plot_confusion_matrix_rand_index_clustering_heatmap(
"K-Means",
confusion_matrix_rand_index_clustering,
current_num_clusters,
epsilon=None,
damping=None,
final_clustering=False)
clusters_silhouette_scores[(current_num_clusters -
2)] = silhouette_score
clusters_precision_scores[(current_num_clusters -
2)] = precision_score
clusters_recall_scores[(current_num_clusters - 2)] = recall_score
clusters_rand_index_scores[(current_num_clusters -
2)] = rand_index_score
clusters_f1_scores[(current_num_clusters - 2)] = f1_score
clusters_adjusted_rand_scores[(current_num_clusters -
2)] = adjusted_rand_score
print_k_means_clustering_performance_metrics(
"K-Means", num_total_clusters, clusters_squared_errors_sums_intertias,
clusters_silhouette_scores, clusters_precision_scores,
clusters_recall_scores, clusters_rand_index_scores, clusters_f1_scores,
clusters_adjusted_rand_scores)
plot_clustering_scores("K-Means", num_total_clusters, None, None, None,
None, None, None, None, None,
clusters_silhouette_scores,
clusters_precision_scores, clusters_recall_scores,
clusters_rand_index_scores, clusters_f1_scores,
clusters_adjusted_rand_scores)
return clusters_squared_errors_sums_intertias, clusters_silhouette_scores, clusters_precision_scores, clusters_recall_scores, clusters_rand_index_scores, clusters_f1_scores, clusters_adjusted_rand_scores
def dbscan_pre_clustering(xs_features_data, ys_labels_true, num_closest_k_neighbors = 5, start_epsilon = 0.01, end_epsilon = 0.28, step_epsilon = 0.01):
current_epsilon_step = 0
num_epsilons_steps = int( ( end_epsilon - start_epsilon ) / step_epsilon )
clusters_epsilon_values = matrix_array_zeros( num_epsilons_steps )
clusters_num_centroids = matrix_array_zeros( num_epsilons_steps )
clusters_num_inliers = matrix_array_zeros( num_epsilons_steps )
clusters_num_outliers = matrix_array_zeros( num_epsilons_steps )
clusters_silhouette_scores = matrix_array_zeros( num_epsilons_steps )
clusters_precision_scores = matrix_array_zeros( num_epsilons_steps )
clusters_recall_scores = matrix_array_zeros( num_epsilons_steps )
clusters_rand_index_scores = matrix_array_zeros( num_epsilons_steps )
clusters_f1_scores = matrix_array_zeros( num_epsilons_steps )
clusters_adjusted_rand_scores = matrix_array_zeros( num_epsilons_steps )
for current_epsilon in a_range(start_epsilon, end_epsilon, step_epsilon):
ys_labels_predicted, clusters_centroids_indices, clusters_centroids_points, clusters_border_points, xs_features_data_inliers, xs_features_data_outliers = dbscan_clustering_method(xs_features_data, current_epsilon, num_closest_k_neighbors = num_closest_k_neighbors)
num_clusters_centroids = ( nan_max(ys_labels_predicted) + 1 )
plot_clusters_centroids_and_radii("DBSCAN", xs_features_data, ys_labels_predicted, clusters_centroids_points, num_clusters = num_clusters_centroids, epsilon = current_epsilon, damping = None, final_clustering = False)
clusters_epsilon_values[current_epsilon_step] = current_epsilon
clusters_num_centroids[current_epsilon_step] = num_clusters_centroids
clusters_num_inliers[current_epsilon_step] = len(xs_features_data_inliers)
clusters_num_outliers[current_epsilon_step] = len(xs_features_data_outliers)
if( num_clusters_centroids >= 2 ):
if( num_clusters_centroids <= 26 ):
plot_silhouette_analysis("DBSCAN", xs_features_data, ys_labels_predicted, clusters_centroids_points, num_clusters_centroids, epsilon = current_epsilon, damping = None, final_clustering = False)
silhouette_score, precision_score, recall_score, rand_index_score, f1_score, adjusted_rand_score, confusion_matrix_rand_index_clustering = compute_clustering_performance_metrics("K-Means", xs_features_data, ys_labels_true, ys_labels_predicted, num_clusters_centroids, final_clustering = False)
plot_confusion_matrix_rand_index_clustering_heatmap("DBSCAN", confusion_matrix_rand_index_clustering, num_clusters_centroids, epsilon = current_epsilon, damping = None, final_clustering = False)
clusters_silhouette_scores[current_epsilon_step] = silhouette_score
clusters_precision_scores[current_epsilon_step] = precision_score
clusters_recall_scores[current_epsilon_step] = recall_score
clusters_rand_index_scores[current_epsilon_step] = rand_index_score
clusters_f1_scores[current_epsilon_step] = f1_score
clusters_adjusted_rand_scores[current_epsilon_step] = adjusted_rand_score
current_epsilon_step = ( current_epsilon_step + 1 )
print_dbscan_clustering_performance_metrics("DBSCAN", start_epsilon, end_epsilon, step_epsilon, clusters_num_centroids, clusters_num_inliers, clusters_num_outliers, clusters_silhouette_scores, clusters_precision_scores, clusters_recall_scores, clusters_rand_index_scores, clusters_f1_scores, clusters_adjusted_rand_scores)
plot_clustering_scores("DBSCAN", num_clusters_centroids, start_epsilon, end_epsilon, step_epsilon, clusters_epsilon_values, None, None, None, None, clusters_silhouette_scores, clusters_precision_scores, clusters_recall_scores, clusters_rand_index_scores, clusters_f1_scores, clusters_adjusted_rand_scores)
return clusters_num_centroids, clusters_num_inliers, clusters_num_outliers, clusters_silhouette_scores, clusters_precision_scores, clusters_recall_scores, clusters_rand_index_scores, clusters_f1_scores, clusters_adjusted_rand_scores
def affinity_propagation_pre_clustering(xs_features_data, ys_labels_true, start_damping = 0.5, end_damping = 1.0, step_damping = 0.01):
current_damping_step = 0
some_damping = 0
some_damping_flag = False
num_damping_steps = int( ( end_damping - start_damping ) / step_damping )
clusters_damping_values = matrix_array_zeros( num_damping_steps )
clusters_num_centroids = matrix_array_zeros( num_damping_steps )
clusters_silhouette_scores = matrix_array_zeros( num_damping_steps )
clusters_precision_scores = matrix_array_zeros( num_damping_steps )
clusters_recall_scores = matrix_array_zeros( num_damping_steps )
clusters_rand_index_scores = matrix_array_zeros( num_damping_steps )
clusters_f1_scores = matrix_array_zeros( num_damping_steps )
clusters_adjusted_rand_scores = matrix_array_zeros( num_damping_steps )
for current_damping in a_range(start_damping, end_damping, step_damping):
ys_labels_predicted, clusters_centroids_indices, clusters_centroids_points = affinity_propagation_clustering_method(xs_features_data, damping_value = current_damping, max_iterations = 300)
num_clusters_centroids = ( nan_max(ys_labels_predicted) + 1 )
clusters_damping_values[current_damping_step] = current_damping
clusters_num_centroids[current_damping_step] = num_clusters_centroids
if( num_clusters_centroids >= 2 ):
if( num_clusters_centroids <= 26 ):
plot_clusters_centroids_and_radii("Affinity-Propagation", xs_features_data, ys_labels_predicted, clusters_centroids_points, num_clusters = num_clusters_centroids, epsilon = None, damping = current_damping, final_clustering = False)
plot_silhouette_analysis("Affinity-Propagation", xs_features_data, ys_labels_predicted, clusters_centroids_points, num_clusters_centroids, epsilon = None, damping = current_damping, final_clustering = False)
if(not some_damping_flag):
some_damping = current_damping
some_damping_flag = True
silhouette_score, precision_score, recall_score, rand_index_score, f1_score, adjusted_rand_score, confusion_matrix_rand_index_clustering = compute_clustering_performance_metrics("Affinity-Propagation", xs_features_data, ys_labels_true, ys_labels_predicted, num_clusters_centroids, final_clustering = False)
plot_confusion_matrix_rand_index_clustering_heatmap("Affinity-Propagation", confusion_matrix_rand_index_clustering, num_clusters_centroids, epsilon = None, damping = current_damping, final_clustering = False)
clusters_silhouette_scores[current_damping_step] = silhouette_score
clusters_precision_scores[current_damping_step] = precision_score
clusters_recall_scores[current_damping_step] = recall_score
clusters_rand_index_scores[current_damping_step] = rand_index_score
clusters_f1_scores[current_damping_step] = f1_score
clusters_adjusted_rand_scores[current_damping_step] = adjusted_rand_score
current_damping_step = ( current_damping_step + 1 )
print_affinity_propagation_clustering_performance_metrics("Affinity-Propagation", start_damping, end_damping, step_damping, clusters_num_centroids, clusters_silhouette_scores, clusters_precision_scores, clusters_recall_scores, clusters_rand_index_scores, clusters_f1_scores, clusters_adjusted_rand_scores)
plot_clustering_scores("Affinity-Propagation", num_clusters_centroids, None, None, None, None, start_damping, end_damping, step_damping, clusters_damping_values, clusters_silhouette_scores, clusters_precision_scores, clusters_recall_scores, clusters_rand_index_scores, clusters_f1_scores, clusters_adjusted_rand_scores)
return clusters_num_centroids, clusters_silhouette_scores, clusters_precision_scores, clusters_recall_scores, clusters_rand_index_scores, clusters_f1_scores, clusters_adjusted_rand_scores, some_damping