diff --git a/adnmtf/nmtf.py b/adnmtf/nmtf.py index 15d73ce..766168d 100644 --- a/adnmtf/nmtf.py +++ b/adnmtf/nmtf.py @@ -66,6 +66,7 @@ def __init__( self.leverage = leverage self.random_state = random_state self.verbose = verbose + np.random.seed(random_state) def fit_transform( self, @@ -260,9 +261,9 @@ def fit_transform( nmf_calculate_leverage = 0 nmf_use_robust_leverage = 0 - if self.random_state is not None: - random_seed = self.random_state - np.random.seed(random_seed) + # if self.random_state is not None: + # random_seed = self.random_state + # np.random.seed(random_seed) _, mt, mw, mb, mt_pct, mw_pct, diff, add_message, err_message, cancel_pressed = r_ntf_solve( m=m, @@ -516,9 +517,9 @@ def fit_transform( ntf_left_components = self.apply_left ntf_right_components = self.apply_right ntf_block_components = self.apply_block - if self.random_state is not None: - random_seed = self.random_state - np.random.seed(random_seed) + # if self.random_state is not None: + # random_seed = self.random_state + # np.random.seed(random_seed) my_status_box = get_status_box()(verbose=log_iter) @@ -600,9 +601,9 @@ def fit_transform( nmf_calculate_leverage = 0 nmf_use_robust_leverage = 0 - if self.random_state is not None: - random_seed = self.random_state - np.random.seed(random_seed) + # if self.random_state is not None: + # random_seed = self.random_state + # np.random.seed(random_seed) if update_w: nmf_fix_user_lhe = 0 diff --git a/adnmtf/nmtf_base.py b/adnmtf/nmtf_base.py index d7b8d2f..fd33217 100644 --- a/adnmtf/nmtf_base.py +++ b/adnmtf/nmtf_base.py @@ -68,7 +68,6 @@ def nmf_init(m, mmis, mt0, mw0, nc) -> Tuple[np.ndarray, np.ndarray]: mw = np.copy(mw0) if (mt.shape[0] == 0) or (mw.shape[0] == 0): # Note that if there are missing values, SVD is performed on matrix imputed with 0's - np.random.seed(3) if nc >= min(n, p): # arpack does not accept to factorize at full rank -> need to duplicate in both dimensions to force it work # noinspection PyTypeChecker @@ -76,15 +75,11 @@ def nmf_init(m, mmis, mt0, mw0, nc) -> Tuple[np.ndarray, np.ndarray]: np.concatenate((np.concatenate((m, m), axis=1), np.concatenate((m, m), axis=1)), axis=0), k=nc, v0=np.random.uniform(size=2 * min(n, p)), + random_state=0 ) d /= 2 - # svd causes mem allocation problem with large matrices - # t, d, w = np.linalg.svd(m) - # mt = t - # mw = w.T else: - t, d, w = svds(m, k=nc, v0=np.random.uniform(size=min(n, p))) - # t, d, w = np.linalg.svd(m) + t, d, w = svds(m, k=nc, v0=np.random.uniform(size=min(n, p)), random_state=0) mt = t[:n, :nc] mw = w[:nc, :p].T @@ -124,9 +119,6 @@ def nmf_init(m, mmis, mt0, mw0, nc) -> Tuple[np.ndarray, np.ndarray]: mw = mw * ((mt.T @ m) / ((mt.T @ mt) @ mw.T + precision)).T mt = mt * (m @ mw / (mt @ (mw.T @ mw) + precision)) - # np.savetxt("C:/Users/paul_/PycharmProjects/nmtf_private/tests/data/datatest_W.csv", mt) - # np.savetxt("C:/Users/paul_/PycharmProjects/nmtf_private/tests/data/datatest_H.csv", mw) - return mt, mw @@ -139,32 +131,6 @@ def init_ntf_type_1(m, mmis, n_blocks, nc, mt_nmf, mw_nmf, tolerance, log_iter, else: mt_nmf, mw_nmf = nmf_init(m=mstacked, mmis=mmis_stacked, mt0=mt_nmf, mw0=mw_nmf, nc=nc2) - # Quick NMF (canceled since multiplicative warm-up added in nmf_init) - # _, mt_nmf, mw_nmf, mb, diff, cancel_pressed = ntf_solve( - # m=mstacked, - # mmis=mmis_stacked, - # mt0=mt_nmf, - # mw0=mw_nmf, - # mb0=np.array([]), - # nc=nc2, - # tolerance=tolerance, - # log_iter=log_iter, - # status0=status0, - # max_iterations=10, - # nmf_fix_user_lhe=0, - # nmf_fix_user_rhe=0, - # nmf_fix_user_bhe=1, - # nmf_sparse_level=0, - # ntf_unimodal=0, - # ntf_smooth=0, - # ntf_left_components=0, - # ntf_right_components=0, - # ntf_block_components=0, - # n_blocks=1, - # nmf_priors=np.array([]), - # my_status_box=my_status_box, - # ) - # Factorize Left vectors and distribute multiple factors if nc2 < nc mt = np.zeros((n, nc)) mw = np.zeros((int(p / n_blocks), nc)) @@ -172,7 +138,7 @@ def init_ntf_type_1(m, mmis, n_blocks, nc, mt_nmf, mw_nmf, tolerance, log_iter, n_fact = int(np.ceil(nc / n_blocks)) for k in range(0, nc2): my_status_box.update_status(status="Start SVD...") - u, d, v = svds(np.reshape(mt_nmf[:, k], (int(p / n_blocks), n)).T, k=n_fact) + u, d, v = svds(np.reshape(mt_nmf[:, k], (int(p / n_blocks), n)).T, k=n_fact, random_state=0) v = v.T # svds returns singular vectors in reverse order u = u[:, ::-1] @@ -228,32 +194,6 @@ def init_ntf_type_2( else: mt_nmf, mw_nmf = nmf_init(m=m, mmis=mmis, mt0=mt_nmf, mw0=mw_nmf, nc=nc) - # Quick NMF (canceled since multiplicative warm-up added in nmf_init) - # _, mt_nmf, mw_nmf, mb, diff, cancel_pressed = ntf_solve( - # m=m, - # mmis=mmis, - # mt0=mt_nmf, - # mw0=mw_nmf, - # mb0=np.array([]), - # nc=nc, - # tolerance=tolerance, - # log_iter=log_iter, - # status0=status0, - # max_iterations=10, - # nmf_fix_user_lhe=0, - # nmf_fix_user_rhe=0, - # nmf_fix_user_bhe=1, - # nmf_sparse_level=0, - # ntf_unimodal=0, - # ntf_smooth=0, - # ntf_left_components=0, - # ntf_right_components=0, - # ntf_block_components=0, - # n_blocks=1, - # nmf_priors=np.array([]), - # my_status_box=my_status_box, - # ) - # Factorize Right vectors mt = np.zeros((n, nc)) mw = np.zeros((int(p / n_blocks), nc)) @@ -262,7 +202,7 @@ def init_ntf_type_2( for k in range(0, nc): my_status_box.update_status(status="Start SVD...") # noinspection PyTypeChecker - u, d, v = svds(np.reshape(mw_nmf[:, k], (int(p / n_blocks), n_blocks)), k=1) + u, d, v = svds(np.reshape(mw_nmf[:, k], (int(p / n_blocks), n_blocks)), k=1, random_state=0) v = v.T u = np.abs(u) v = np.abs(v)