def one_edge_tv(coord_ij, tria_ij, data_ij, Y_ij, params):
A_ij = tv.linear_operator_from_mesh(coord_ij, tria_ij)
X_train, X_test, y_train, y_test = train_test_split(data_ij,
Y_ij,
shuffle=True,
random_state=1,
test_size=0.33)
tv_reg = LinearRegressionL1L2TV(l1=params['l1'],
l2=params['l2'],
tv=params['tv'],
A=A_ij)
tv_reg.fit(X_train, y_train)
y_train_pred = tv_reg.predict(X_train)
y_test_pred = tv_reg.predict(X_test)
mse_test = mean_squared_error(y_test, y_test_pred)
r2_test = r2_score(y_test, y_test_pred_tv)
print('TRAIN MSE TV: {}, TEST MSE TV: {}'.format(
mean_squared_error(y_train, y_train_pred), mse_test))
print('TRAIN R2 TV: {}, TEST R2 TV: {}\n'.format(
r2_score(y_train, y_train_pred), r2_test))
return r2_test
def test_tvhelper_linear_operator_from_mesh(self):
import parsimony.functions.nesterov.tv as tv_helper
mesh_coord = np.array([[0, 0], [1, 0], [0, 1], [1, 1], [0, 2], [1, 2]])
mesh_triangles = np.array([[0 ,1, 3], [0, 2 ,3], [2, 3, 5], [2, 4, 5]])
A = tv_helper.linear_operator_from_mesh(mesh_coord, mesh_triangles)
a =[[np.where(l)[0].tolist() for l in a.toarray()] for a in A]
b = [[[], [0, 1], [0, 2], [0, 3], [2, 4], [2, 5]],
[[], [], [], [1, 3], [], [3, 5]],
[[], [], [], [2, 3], [], [4, 5]]]
assert a == b
def load_globals(config):
import mapreduce as GLOBAL # access to global variables
GLOBAL.DATA = GLOBAL.load_data(config["data"])
import brainomics.mesh_processing as mesh_utils
mesh_coord, mesh_triangles = mesh_utils.mesh_arrays(
config["structure"]["mesh"])
mask = np.load(config["structure"]["mask"])
GLOBAL.mesh_coord, GLOBAL.mesh_triangles, GLOBAL.mask = mesh_coord, mesh_triangles, mask
A = tv_helper.linear_operator_from_mesh(GLOBAL.mesh_coord,
GLOBAL.mesh_triangles, GLOBAL.mask)
GLOBAL.A = A
GLOBAL.CONFIG = config
def load_globals(config):
import mapreduce as GLOBAL # access to global variables
GLOBAL.DATA = GLOBAL.load_data(config["data"])
STRUCTURE = np.load(config["structure"])
A = tv_helper.A_from_mask(STRUCTURE)
N_COMP = config["N_COMP"]
GLOBAL.A, GLOBAL.STRUCTURE,GLOBAL.N_COMP = A, STRUCTURE,N_COMP
mesh_coord, mesh_triangles = mesh_utils.mesh_arrays(os.path.join(TEMPLATE_PATH, "lrh.pial.gii"))
mask = np.load(os.path.join(INPUT_BASE_DIR, "mask.npy"))
import parsimony.functions.nesterov.tv as tv_helper
Atv = tv_helper.linear_operator_from_mesh(mesh_coord, mesh_triangles, mask=mask)
GLOBAL.Atv = Atv
GLOBAL.FULL_RESAMPLE = config['full_resample']
np.save(os.path.join(OUTPUT, "mask.npy"), mask)
X = Xtot[:, mask]
assert X.shape == (280, 299806)
#############################################################################
X = np.hstack([Z, X])
assert X.shape == (280, 299809)
#Remove nan lines
X = X[np.logical_not(np.isnan(y)).ravel(), :]
y = y[np.logical_not(np.isnan(y))]
assert X.shape == (280, 299809)
np.save(os.path.join(OUTPUT, "X.npy"), X)
np.save(os.path.join(OUTPUT, "y.npy"), y)
#############################################################################
import parsimony.functions.nesterov.tv as nesterov_tv
from parsimony.utils.linalgs import LinearOperatorNesterov
Atv = nesterov_tv.linear_operator_from_mesh(cor,
tri,
mask,
calc_lambda_max=True)
Atv.save(os.path.join(OUTPUT, "Atv.npz"))
Atv_ = LinearOperatorNesterov(filename=os.path.join(OUTPUT, "Atv.npz"))
assert Atv.get_singular_values(0) == Atv_.get_singular_values(0)
assert np.allclose(Atv_.get_singular_values(0), 8.999, rtol=1e-03, atol=1e-03)
assert np.all([a.shape == (299806, 299806) for a in Atv])
MODELS["2d_l1l2tv_inexactfista"] = \
estimators.LogisticRegressionL1L2TVInexactFISTA(
l1, l2, tv, Al1tv,
algorithm_params=algorithm_params)
MODELS["2d_l1l2tv_inter_inexactfista"] = \
estimators.LogisticRegressionL1L2TVInexactFISTA(
l1, l2, tv, Al1tv,
penalty_start=1,
algorithm_params=algorithm_params)
## Get data structure from mesh
# build a cylinder mesh with the same topology than the 2D grid
xyz, tri = mesh.cylinder(shape[1], shape[0])
Atvmesh = nesterov_tv.linear_operator_from_mesh(xyz, tri)
MODELS["mesh_l1l2tv_conesta"] = \
estimators.LogisticRegressionL1L2TV(
l1, l2, tv, Atvmesh,
algorithm=algorithms.proximal.CONESTA(),
algorithm_params=algorithm_params)
MODELS["mesh_l1l2tv_inter_conesta"] = \
estimators.LogisticRegressionL1L2TV(
l1, l2, tv, Atvmesh,
penalty_start=1,
algorithm=algorithms.proximal.CONESTA(),
algorithm_params=algorithm_params)
Atvl1mesh = l1tv.linear_operator_from_mesh(xyz, tri)
MODELS["2d_l1l2tv_inexactfista"] = \
estimators.LogisticRegressionL1L2TVInexactFISTA(
l1, l2, tv, Al1tv,
algorithm_params=algorithm_params)
MODELS["2d_l1l2tv_inter_inexactfista"] = \
estimators.LogisticRegressionL1L2TVInexactFISTA(
l1, l2, tv, Al1tv,
penalty_start=1,
algorithm_params=algorithm_params)
## Get data structure from mesh
# build a cylinder mesh with the same topology than the 2D grid
xyz, tri = mesh.cylinder(shape[1], shape[0])
Atvmesh = nesterov_tv.linear_operator_from_mesh(xyz, tri)
MODELS["mesh_l1l2tv_conesta"] = \
estimators.LogisticRegressionL1L2TV(
l1, l2, tv, Atvmesh,
algorithm=algorithms.proximal.CONESTA(),
algorithm_params=algorithm_params)
MODELS["mesh_l1l2tv_inter_conesta"] = \
estimators.LogisticRegressionL1L2TV(
l1, l2, tv, Atvmesh,
penalty_start=1,
algorithm=algorithms.proximal.CONESTA(),
algorithm_params=algorithm_params)
Atvl1mesh = l1tv.linear_operator_from_mesh(xyz, tri)
global_pen = 0.1
tv_ratio =1e-05# 0.5
l1_ratio = 0.1
ltv = global_pen * tv_ratio
ll1 = l1_ratio * global_pen * (1 - tv_ratio)
ll2 = (1 - l1_ratio) * global_pen * (1 - tv_ratio)
assert(np.allclose(ll1 + ll2 + ltv, global_pen))
mesh_coord, mesh_triangles = mesh_utils.mesh_arrays(os.path.join(TEMPLATE_PATH, "lrh.pial.gii"))
mask = np.load(os.path.join(INPUT_BASE_DIR, "mask.npy"))
import parsimony.functions.nesterov.tv as tv_helper
Atv = tv_helper.linear_operator_from_mesh(mesh_coord, mesh_triangles, mask=mask)
# PARSIMONY
########################################
from parsimony.algorithms.utils import AlgorithmSnapshot
snapshot = AlgorithmSnapshot('/neurospin/brainomics/2014_pca_struct/adni/adni_time/enet_1e-6/',saving_period=1).save_conesta
mod = pca_tv.PCA_L1_L2_TV(n_components=3,
l1=ll1, l2=ll2, ltv=ltv,
Atv=Atv,
criterion="frobenius",
eps=1e-6,
inner_eps=1e-1,
max_iter=100,
inner_max_iter=int(1e4),
def init():
INPUT_DATA_X = os.path.join(WD_ORIGINAL, 'X.npy')
INPUT_DATA_y = os.path.join(WD_ORIGINAL, 'y.npy')
INPUT_MASK_PATH = os.path.join(WD_ORIGINAL, 'mask.npy')
INPUT_MESH_PATH = '/neurospin/brainomics/2013_adni/MCIc-CTL-FS_cs/lrh.pial.gii'
#INPUT_LINEAR_OPE_PATH = '/neurospin/brainomics/2016_schizConnect/analysis/NUSDAST/Freesurfer/data/30yo/Atv.npz'
# INPUT_CSV = '/neurospin/brainomics/2016_schizConnect/analysis/NUSDAST/Freesurfer/population_30yo.csv'
os.makedirs(WD, exist_ok=True)
shutil.copy(INPUT_DATA_X, WD)
shutil.copy(INPUT_DATA_y, WD)
shutil.copy(INPUT_MASK_PATH, WD)
shutil.copy(INPUT_MESH_PATH, WD)
#shutil.copy(INPUT_LINEAR_OPE_PATH, WD)
## Create config file
os.chdir(WD)
X = np.load("X.npy")
y = np.load("y.npy")
if not os.path.exists(os.path.join(WD, "Atv.npz")):
import brainomics.mesh_processing as mesh_utils
cor, tri = mesh_utils.mesh_arrays(os.path.join(WD, "lrh.pial.gii"))
mask = np.load(os.path.join(WD, 'mask.npy'))
import parsimony.functions.nesterov.tv as nesterov_tv
from parsimony.utils.linalgs import LinearOperatorNesterov
Atv = nesterov_tv.linear_operator_from_mesh(cor, tri, mask, calc_lambda_max=True)
Atv.save(os.path.join(WD, "Atv.npz"))
Atv_ = LinearOperatorNesterov(filename=os.path.join(WD, "Atv.npz"))
assert Atv.get_singular_values(0) == Atv_.get_singular_values(0)
assert np.allclose(Atv_.get_singular_values(0), 8.999, rtol=1e-03, atol=1e-03)
assert np.all([a.shape == (317089, 317089) for a in Atv])
if not os.path.exists(os.path.join(WD, "beta_start.npz")):
betas = dict()
import time
alphas = [.01, 0.1, 1.0, 10]
for alpha in alphas:
mod = estimators.RidgeLogisticRegression(l=alpha, class_weight="auto", penalty_start=penalty_start)
t_ = time.time()
mod.fit(X, y.ravel())
print(time.time() - t_) # 11564
betas["lambda_%.2f" % alpha] = mod.beta
np.savez(os.path.join(WD, "beta_start.npz"), **betas)
beta_start = np.load(os.path.join(WD, "beta_start.npz"))
assert np.all([np.all(beta_start[a] == betas[a]) for a in beta_start.keys()])
## Create config file
# ########################################################################
# Setting 1: 5cv + large range of parameters: cv_largerange
# with sub-sample training set with size 50, 100
# 5cv/cv0*[_sub50]/refit/*
# sub_sizes = [50, 100]
sub_sizes = []
cv_outer = [[tr, te] for tr, te in
StratifiedKFold(n_splits=NFOLDS_OUTER, random_state=42).split(np.zeros(y.shape[0]), y.ravel())]
# check we got the same CV than previoulsy
cv_old = json.load(open(os.path.join(WD_ORIGINAL, "config_modselectcv.json")))["resample"]
cv_outer_old = [cv_old[k] for k in ['cv%02d/refit' % i for i in range(NFOLDS_OUTER)]]
assert np.all([np.all(np.array(cv_outer_old[i][0]) == cv_outer[i][0]) for i in range(NFOLDS_OUTER)])
assert np.all([np.all(np.array(cv_outer_old[i][1]) == cv_outer[i][1]) for i in range(NFOLDS_OUTER)])
# check END
import collections
cv = collections.OrderedDict()
cv["refit/refit"] = [np.arange(len(y)), np.arange(len(y))]
for cv_outer_i, (tr_val, te) in enumerate(cv_outer):
# Simple CV
cv["cv%02d/refit" % (cv_outer_i)] = [tr_val, te]
# Nested CV
# cv_inner = StratifiedKFold(y[tr_val].ravel(), n_folds=NFOLDS_INNER, random_state=42)
# for cv_inner_i, (tr, val) in enumerate(cv_inner):
# cv["cv%02d/cvnested%02d" % ((cv_outer_i), cv_inner_i)] = [tr_val[tr], tr_val[val]]
# Sub-sample training set with size 50, 100
# => cv*_sub[50|100]/refit
grps = np.unique(y[tr_val]).astype(int)
ytr = y.copy()
ytr[te] = np.nan
g_idx = [np.where(ytr == g)[0] for g in grps]
assert np.all([np.all(ytr[g_idx[g]] == g) for g in grps])
g_size = np.array([len(g) for g in g_idx])
g_prop = g_size / g_size.sum()
for sub_size in sub_sizes:
# sub_size = sub_sizes[0]
sub_g_size = np.round(g_prop * sub_size).astype(int)
g_sub_idx = [np.random.choice(g_idx[g], sub_g_size[g], replace=False) for g in grps]
assert np.all([np.all(y[g_sub_idx[g]] == g) for g in grps])
tr_val_sub = np.concatenate(g_sub_idx)
assert len(tr_val_sub) == sub_size
assert np.all([idx in tr_val for idx in tr_val_sub])
assert np.all(np.logical_not([idx in te for idx in tr_val_sub]))
cv["cv%02d_sub%i/refit" % (cv_outer_i, sub_size)] = [tr_val_sub, te]
cv = {k:[cv[k][0].tolist(), cv[k][1].tolist()] for k in cv}
# Nested CV
# assert len(cv_largerange) == NFOLDS_OUTER * NFOLDS_INNER + NFOLDS_OUTER + 1
# Simple CV
# assert len(cv) == NFOLDS_OUTER + 1
# Simple CV + sub-sample training set with size 50, 100:
assert len(cv) == NFOLDS_OUTER * (1 + len(sub_sizes)) + 1
print(list(cv.keys()))
# Large grid of parameters
alphas = [0.001, 0.01, 0.1, 1.0]
# alphas = [.01, 0.1, 1.0] # first ran with this grid
tv_ratio = [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
l1l2_ratio = [0.1, 0.5, 0.9]
# l1l2_ratio = [0, 0.1, 0.5, 0.9, 1.0] # first ran with this grid
algos = ["enettv", "enetgn"]
params_enet_tvgn = [list(param) for param in itertools.product(algos, alphas, l1l2_ratio, tv_ratio)]
assert len(params_enet_tvgn) == 240 # old 300
params_enet = [list(param) for param in itertools.product(["enet"], alphas, l1l2_ratio, [0])]
assert len(params_enet) == 12 # old 15
params = params_enet_tvgn + params_enet
assert len(params) == 252 # 315
# Simple CV
# assert len(params) * len(cv) == 1890
# Simple CV + sub-sample training set with size 50, 100:
assert len(params) * len(cv) == 1512 # 5040
config = dict(data=dict(X="X.npy", y="y.npy"),
params=params, resample=cv,
structure_linear_operator_tv="Atv.npz",
beta_start="beta_start.npz",
map_output="5cv",
user_func=user_func_filename)
json.dump(config, open(os.path.join(WD, "config_cv_largerange.json"), "w"))
# Build utils files: sync (push/pull) and PBS
import brainomics.cluster_gabriel as clust_utils
cmd = "mapreduce.py --map %s/config_cv_largerange.json" % WD_CLUSTER
clust_utils.gabriel_make_qsub_job_files(WD, cmd,walltime = "250:00:00",
suffix="_cv_largerange",
freecores=2)
# ########################################################################
# Setting 2: dcv + reduced range of parameters: dcv_reducedrange
# 5cv/cv0*/cvnested0*/*
cv_outer = [[tr, te] for tr, te in
StratifiedKFold(n_splits=NFOLDS_OUTER, random_state=42).split(np.zeros(y.shape[0]), y.ravel())]
# check we got the same CV than previoulsy
cv_old = json.load(open(os.path.join(WD_ORIGINAL, "config_modselectcv.json")))["resample"]
cv_outer_old = [cv_old[k] for k in ['cv%02d/refit' % i for i in range(NFOLDS_OUTER)]]
assert np.all([np.all(np.array(cv_outer_old[i][0]) == cv_outer[i][0]) for i in range(NFOLDS_OUTER)])
assert np.all([np.all(np.array(cv_outer_old[i][1]) == cv_outer[i][1]) for i in range(NFOLDS_OUTER)])
# check END
import collections
cv = collections.OrderedDict()
cv["refit/refit"] = [np.arange(len(y)), np.arange(len(y))]
for cv_outer_i, (tr_val, te) in enumerate(cv_outer):
cv["cv%02d/refit" % (cv_outer_i)] = [tr_val, te]
cv_inner = StratifiedKFold(n_splits=NFOLDS_INNER, random_state=42).split(np.zeros(y[tr_val].shape[0]), y[tr_val].ravel())
for cv_inner_i, (tr, val) in enumerate(cv_inner):
cv["cv%02d/cvnested%02d" % ((cv_outer_i), cv_inner_i)] = [tr_val[tr], tr_val[val]]
cv = {k:[cv[k][0].tolist(), cv[k][1].tolist()] for k in cv}
#assert len(cv) == NFOLDS_OUTER + 1
assert len(cv) == NFOLDS_OUTER * NFOLDS_INNER + NFOLDS_OUTER + 1
print(list(cv.keys()))
# Reduced grid of parameters
alphas = [0.001, 0.01, 0.1, 1.0]
# alphas = [.01, 0.1] # original
tv_ratio = [0.2, 0.8]
l1l2_ratio = [0.1, 0.9]
algos = ["enettv", "enetgn"]
params_enet_tvgn = [list(param) for param in itertools.product(algos, alphas, l1l2_ratio, tv_ratio)]
assert len(params_enet_tvgn) == 32 # 16
params_enet = [list(param) for param in itertools.product(["enet"], alphas, l1l2_ratio, [0])]
assert len(params_enet) == 8 # 4
params = params_enet_tvgn + params_enet
assert len(params) == 40 # 20
assert len(params) * len(cv) == 1240 # 620
config = dict(data=dict(X="X.npy", y="y.npy"),
params=params, resample=cv,
structure_linear_operator_tv="Atv.npz",
beta_start="beta_start.npz",
map_output="5cv",
user_func=user_func_filename)
json.dump(config, open(os.path.join(WD, "config_dcv_reducedrange.json"), "w"))
# Build utils files: sync (push/pull) and PBS
import brainomics.cluster_gabriel as clust_utils
cmd = "mapreduce.py --map %s/config_dcv_reducedrange.json" % WD_CLUSTER
clust_utils.gabriel_make_qsub_job_files(WD, cmd,walltime = "250:00:00",
suffix="_dcv_reducedrange",
freecores=2)
