ICA for training IVE methods

evaluate_IVE.py

@@ -163,3 +163,4 @@ def execute_evaluation(db, classifiers, use_pca, seed, blocked_pca_features=0):
 
 
 execute_evaluation('diveface', ['mlp'], True, 0, 3)
+# ['mlp', 'svm_lin', 'svm_rbf', 'rf', 'gb', 'nb', 'et', 'log_reg']

main.py

@@ -6,7 +6,7 @@
 from xgboost import XGBClassifier
 from sklearn.preprocessing import StandardScaler
 import numpy as np
-from sklearn.decomposition import PCA
+from sklearn.decomposition import PCA, FastICA
 import pickle as pk
 import tqdm
 
@@ -20,16 +20,16 @@ def zeros_pca(x_total, mask):
 	return x_total
 
 
-def execute_all(model_feat_importance, use_pca, seed, blocked_pca_features=0):
+def execute_all(model_feat_importance, transform, seed, blocked_features=0):
 	# create
 	if not os.path.isfile('data/feret_df.csv'):
 		feret_df = manage_data.get_feret_df(r'data/myselection_embeddings', seed, save_files=True)
 	else:
 		feret_df = pd.read_csv('data/feret_df.csv')
 
-	folder = str(seed) + ('_pca' if use_pca else '_NO_pca')
-	if use_pca:
-		folder = folder + '_k=' + str(blocked_pca_features)
+	folder = str(seed) + ('_' + transform)
+	if transform == 'pca' or transform == 'ica':
+		folder = folder + '_k=' + str(blocked_features)
 	os.makedirs("results", exist_ok=True)
 	os.makedirs(os.path.join('results', folder), exist_ok=True)
 
@@ -41,7 +41,6 @@ def execute_all(model_feat_importance, use_pca, seed, blocked_pca_features=0):
 	num_steps = 1
 	num_eliminations = 3
 	num_epochs = 170
-	blocked_features = blocked_pca_features
 
 	# define classifier
 	n_estimators = 30
@@ -68,11 +67,16 @@ def execute_all(model_feat_importance, use_pca, seed, blocked_pca_features=0):
 	# get labels
 	y = manage_data.get_y_ready(feret_df, labels)
 
-	if use_pca:
+	if transform == 'pca':
 		# transform from original to PCA domain
-		pca = PCA(n_components=length_embeddings)
+		pca = PCA(n_components=length_embeddings, random_state=seed)
 		x_total = pca.fit_transform(x_total)
 		pk.dump(pca, open(os.path.join(os.path.join('results', folder), 'pca.pkl'), "wb"))
+	if transform == 'ica':
+		# transform from original to ICA domain
+		ica = FastICA(n_components=length_embeddings, random_state=seed, whiten='unit-variance', max_iter=1000)
+		x_total = ica.fit_transform(x_total)
+		pk.dump(ica, open(os.path.join(os.path.join('results', folder), 'ica.pkl'), "wb"))
 
 	x_first = np.copy(x_total)
 	x_second = np.copy(x_total)
@@ -108,7 +112,9 @@ def execute_all(model_feat_importance, use_pca, seed, blocked_pca_features=0):
 
 
 for seed in range(10):
-	execute_all('rf', False, seed)
+	execute_all('rf', 'NO_pca', seed)
 	for k in [0, 3, 5]:
-		execute_all('rf', True, seed, k)
-# execute_all('rf', ['mlp', 'svm_lin', 'svm_rbf', 'rf', 'gb', 'nb', 'et', 'log_reg'], True, 0, 3)
+		execute_all('rf', 'pca', seed, k)
+
+# for seed in range(10):
+# 	execute_all('rf', 'ica', seed)