features_obj = Features(pose_objects=pose_objects)
features_obj.compute_features()
# reduced_feature_matrix = features_obj.compute_pca()
# print(reduced_feature_matrix)
# print(len(reduced_feature_matrix),len(reduced_feature_matrix[0]))
# X = reduced_feature_matrix
X = features_obj.get_features()
Y = [obj.label for obj in pose_objects]
print(len(X), len(Y))
clf_rforest = Classification('RForest', X, Y)
clf_rforest.get_classifier_object()
clf_rforest.get_metrics()
pickle.dump(clf_rforest.get_classifier(), open('RForest_model.pkl', 'wb'))
print()
clf_svm = Classification('svm', X, Y)
clf_svm.get_classifier_object()
clf_svm.get_metrics()
pickle.dump(clf_svm.get_classifier(), open('SVM_model.pkl', 'wb'))
print()
clf_lda = Classification('lda', X, Y)
clf_lda.get_classifier_object()
clf_lda.get_metrics()
pickle.dump(clf_lda.get_classifier(), open('LDA_model.pkl', 'wb'))
clf_dt = Classification('logreg', X, Y)
clf_dt.get_classifier_object()
# clf_deci = Classification('DeciTree', x, y)
# clf_deci.get_classifier_object()
# clf_deci.get_metrics()
# pickle.dump(clf_deci.get_classifier(), open('DeciTree_model.pkl', 'wb'))
# print()
# clf_svm = Classification('svm', x, y)
# clf_svm.get_classifier_object()
# clf_svm.get_metrics()
# pickle.dump(clf_svm.get_classifier(), open('svm_model.pkl', 'wb'))
# print()
clf_rforest = Classification('RForest', x, y)
clf_rforest.get_classifier_object()
clf_rforest.get_metrics()
pickle.dump(clf_rforest.get_classifier(), open('RForest_model.pkl', 'wb'))
print()
clf = Classification('XGB', np.array(x), np.array(y))
clf.get_classifier_object()
clf.get_metrics()
pickle.dump(clf.get_classifier(), open('XGBoost_model.pkl', 'wb'))
print()
clf = Classification('NaiveBayes', np.array(x), np.array(y))
clf.get_classifier_object()
clf.get_metrics()
pickle.dump(clf.get_classifier(), open('NaiveBayes_model.pkl', 'wb'))
print()
clf = Classification('AdaBoost', np.array(x), np.array(y))