diff --git a/local/classification_kmeans.py b/local/classification_kmeans.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8265bda098237e27fef9b3313ef53c3f02419b7
--- /dev/null
+++ b/local/classification_kmeans.py
@@ -0,0 +1,208 @@
+# -*- coding: utf-8 -*-
+
+"""
+{Description}
+{License_info}
+"""
+
+import os, sys
+import numpy as np
+import json
+import matplotlib
+import matplotlib.pyplot as plt
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.metrics.pairwise import euclidean_distances
+from sklearn.metrics import pairwise_distances
+import faiss
+from sklearn.metrics import ConfusionMatrixDisplay
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import f1_score
+from sklearn.metrics import recall_score
+from sklearn.metrics import precision_score
+from sklearn.metrics import precision_recall_fscore_support
+from itertools import permutations
+
+plt.rcParams.update({'font.size': 15})
+
+def find_best_map(original_map, y_true, spks, distances):
+ print("-- (INFO) best map between y_pred and y_true (based on accuracy)")
+ keys, values = zip(*original_map.items())
+ value_perms = permutations(values)
+ best_map = None
+ best_accuracy = -np.inf
+ for value_perm in value_perms:
+ temp_map = dict(zip(keys, value_perm))
+ y_pred = []
+ col = []
+ for idx, (spk, distance) in enumerate(zip(spks, distances)):
+ clus_idx = np.argmin(distance)
+ y_pred.append(temp_map[clus_idx])
+ accuracy = accuracy_score(y_true, y_pred)
+ if accuracy > best_accuracy:
+ best_map = temp_map
+ best_accuracy = accuracy
+ print(f'Best map: {best_map}')
+ print(f'Best Accuracy: {best_accuracy}')
+ return best_map
+
+def plot_CM(y_true, y_pred, class_names):
+ np.set_printoptions(precision=2)
+ # Plot non-normalized confusion matrix
+ titles_options = [
+ ("confusion matrix without normalization", None),
+ ("normalized confusion matrix", "true"),
+ ]
+ for title, normalize in titles_options:
+ disp = ConfusionMatrixDisplay.from_predictions(
+ y_true,
+ y_pred,
+ display_labels=class_names,
+ labels=class_names,
+ cmap=plt.cm.Blues,
+ normalize=normalize,
+ colorbar=False,
+ values_format='.2f'
+ )
+ plt.xticks(rotation=90)
+ #disp.ax_.set_title(title)
+ print(f"-- (INFO) {title}")
+ print(disp.confusion_matrix)
+ return disp
+
+
+def main(X_path, y_path, pcaA_path, pcab_path, out_path, json_path, NC):
+ # load json ...
+ # dict with {"corpusA_speakingstyle": [spk1 spk2,...],
+ # "corpusB_speakingstyle": [spk1 spk2,...], ...}
+ with open(json_path, 'r') as f:
+ corpora = json.load(f)
+
+ X = np.load(X_path)
+ spks = np.load(y_path, allow_pickle=True)
+
+ A = np.load(pcaA_path)
+ b = np.load(pcab_path)
+ print('\nspkIDs:\n{}'.format(spks))
+ print(f'\ninput matrix X (shape: {np.shape(X)}):\n{X}')
+ print(f'\nprojection matrix A (shape: {np.shape(A)}):\n{A}')
+
+ # PROJECTION COMBINATIONS
+ proj_del = {2: ['PCA1', 'PCA2'],
+ 1: ['PCA1', 'PCA3'],
+ 0: ['PCA2', 'PCA3'],
+ 'none': ['PCA1', 'PCA2', 'PCA3']}
+
+ y_true = []
+ for key, spkIDs in corpora.items():
+ count = len(spkIDs)
+ y_true.append([key]*count)
+ y_true = [item for sublist in y_true for item in sublist]
+
+ y_pred_map = {}
+ for idx, key in enumerate(np.unique(y_true)):
+ y_pred_map[idx] = key
+
+ for pdel in proj_del.keys():
+ print("\n----\nProject with PCA dimensions {}\n----".format(proj_del[pdel]))
+ X_proj3D = np.dot(X, A) + b # [styles x 320^2] * [320^2 x 2] or [styles x corpora] + [corpora x 2]
+ if pdel == 'none':
+ X_proj = X_proj3D
+ else:
+ X_proj = np.delete(X_proj3D, pdel, 1)
+
+ print("... kmeans ...")
+ d = X_proj3D.shape[1]
+ kmeans = faiss.Kmeans(
+ d,
+ int(NC),
+ niter=10,
+ verbose=True,
+ gpu=False,
+ )
+ kmeans.train(X_proj3D)
+
+ if pdel == 'none':
+ centroids = kmeans.centroids
+ else:
+ centroids = np.delete(kmeans.centroids, pdel, 1)
+ print('-- (INFO) centroids (3D)\n{}'.format(kmeans.centroids))
+ print('-- (INFO) centroids (without dimension {})\n{}'.format(pdel, centroids))
+ print('-- (INFO) centroids shape: {}'.format(centroids.shape))
+
+ col, y_pred = [], []
+ col_map = {0: 'blue', 1: 'red', 2: 'green', 3: 'yellow', 4: 'purple', 5: 'olive', 6: 'pink'}
+ distances = pairwise_distances(X_proj, centroids, metric='euclidean')
+ y_pred_map = find_best_map(y_pred_map, y_true, spks, distances)
+ print("-- (INFO) resulting distances to k-means centroids")
+ for idx, (spk, distance) in enumerate(zip(spks, distances)):
+ print(f"{spk}: {distance}")
+ clus_idx = np.argmin(distance)
+ y_pred.append(y_pred_map[clus_idx])
+ col.append(col_map[clus_idx])
+
+ fig, ax = plt.subplots(figsize=(30,30))
+ print('-- (INFO) y_true:\n{}\n-- (INFO) y_pred:\n{}'.format(y_true,y_pred))
+ disp = plot_CM(y_true, y_pred, list(y_pred_map.values()))
+ ax = disp.ax_
+ for text in ax.texts:
+ value = float(text.get_text())
+ if value == 0.00:
+ text.set_text('0')
+ else:
+ text.set_text('{:.2f}'.format(value))
+ plt.tight_layout()
+ PCAlabel = '_'.join([s for s in proj_del[pdel]])
+ title='CM_{}_{}_nclus{}.png'.format(X_path.split('/')[-1].replace('.npy',''), PCAlabel, NC)
+ plt.savefig(os.path.join(out_path,title))
+
+ print("-- (INFO) summary scores")
+ print('Accuracy: {}'.format(accuracy_score(y_true, y_pred)))
+ print('Precision-score: {}'.format(precision_score(y_true, y_pred, average='weighted')))
+ print('Recall-score: {}'.format(recall_score(y_true, y_pred, average='weighted')))
+ print('F1-score: {}'.format(f1_score(y_true, y_pred, average='weighted')))
+
+if __name__ == "__main__":
+ ############################# FEATURES PATH ###########################
+ try:
+ X_path = sys.argv[1]
+ print("input matrix path is: " + X_path)
+ except:
+ print("ERROR: X_path not specified")
+ ############################# LABELS PATH ###########################
+ try:
+ y_path = sys.argv[2]
+ print("label vector path is: " + y_path)
+ except:
+ print("ERROR: y_path not specified")
+ ############################# PCA A PATH ###########################
+ try:
+ pcaA_path = sys.argv[3]
+ print("pca matrix A path is: " + pcaA_path)
+ except:
+ print("ERROR: pcaA_path not specified")
+ ############################# PCA b PATH ###########################
+ try:
+ pcab_path = sys.argv[4]
+ print("pca vector b path is: " + pcab_path)
+ except:
+ print("ERROR: pcab_path not specified")
+ ############################# OUT PATH ###########################
+ try:
+ out_path = sys.argv[5]
+ print("output path is: " + out_path)
+ except:
+ print("ERROR: out_path not specified")
+ ############################# JSON PATH ###########################
+ try:
+ json_path = sys.argv[6]
+ print("json path is: " + json_path)
+ except:
+ print("ERROR: json_path not specified")
+ ############################# NUMBER CLUSTERS ###########################
+ try:
+ NC = sys.argv[7]
+ print("NC (Number Clusters) is: " + NC)
+ except:
+ print("ERROR: NC (Number Clusters) not specified")
+
+ main(X_path, y_path, pcaA_path, pcab_path, out_path, json_path, NC)
\ No newline at end of file
diff --git a/run.sh b/run.sh
index ec094cc7fb564ae783c65d8f666af500a87d200a..5877ed72c804b72e8fbca80e71c6fc1520cdecd3 100755
--- a/run.sh
+++ b/run.sh
@@ -55,6 +55,7 @@ if [ $stage == 1 ] || [ $stage == 0 ]; then
--DATA_dir ${runDATA}
fi
+## COUNT CODEBOOK USAGE
if [ $stage == 2 ] || [ $stage == 0 ]; then
printf "\n... Count frequencies of codebooks ...\n"
python3 local/codebook_freqs.py $exp_dir \
@@ -67,6 +68,7 @@ if [ $stage == 2 ] || [ $stage == 0 ]; then
$exp_dir/data/${runDATA}.json
fi
+## CALCULATE SIMILARITY MATRIX
if [ $stage == 3 ] || [ $stage == 0 ]; then
printf "\n... Similarity Matrix ...\n"
python3 local/similarity_matrix.py \
@@ -76,6 +78,7 @@ if [ $stage == 3 ] || [ $stage == 0 ]; then
$exp_dir/data/${runDATA}.json
fi
+## PCA SPACE AND PLOTS
if [ $stage == 4 ] || [ $stage == 0 ]; then
printf "\n... PCA of similarity matrix ...\n"
python3 $FAIRSEQ/examples/wav2vec/unsupervised/scripts/pca.py \
@@ -90,4 +93,16 @@ if [ $stage == 4 ] || [ $stage == 0 ]; then
$exp_dir/numpy/pca/3_pca_b.npy \
$exp_dir/plots \
$exp_dir/data/${runDATA}.json
+fi
+
+if [ $stage == 5 ] || [ $stage == 0 ]; then
+ printf "\n... Classification with k-means in PCA space ...\n"
+ python3 local/classification_kmeans.py \
+ $exp_dir/numpy/similarity_matrix.npy \
+ $exp_dir/numpy/splits_labels.npy \
+ $exp_dir/numpy/pca/3_pca_A.npy \
+ $exp_dir/numpy/pca/3_pca_b.npy \
+ $exp_dir/plots \
+ $exp_dir/data/${runDATA}.json \
+ 2
fi
\ No newline at end of file