def mapper(key, output_collector):
"""
# debug mapper
config = json.load(open(os.path.join(WD, "config_cv_largerange.json"), "r"))
load_globals(config)
resample(config, 'refit/refit')
key = ('enettv', 0.01, 0.1, 0.3)
"""
import mapreduce as GLOBAL
Xtr = GLOBAL.DATA_RESAMPLED["X"][0]
Xte = GLOBAL.DATA_RESAMPLED["X"][1]
ytr = GLOBAL.DATA_RESAMPLED["y"][0]
yte = GLOBAL.DATA_RESAMPLED["y"][1]
# key = 'enettv_0.01_0.1_0.2'.split("_")
algo, alpha, l1l2ratio, tvratio = key[0], float(key[1]), float(key[2]), float(key[3])
tv = alpha * tvratio
l1 = alpha * float(1 - tv) * l1l2ratio
l2 = alpha * float(1 - tv) * (1- l1l2ratio)
print(key, algo, alpha, l1, l2, tv)
# alpha = float(key[0])
# l1, l2, tv = alpha * float(key[1]), alpha * float(key[2]), alpha * float(key[3])
# print("l1:%f, l2:%f, tv:%f" % (l1, l2, tv))
class_weight = "auto" # unbiased
# mask = np.ones(Xtr.shape[0], dtype=bool)
scaler = preprocessing.StandardScaler().fit(Xtr)
Xtr = scaler.transform(Xtr)
Xte = scaler.transform(Xte)
if algo == 'enettv':
conesta = algorithms.proximal.CONESTA(max_iter=10000)
mod = estimators.LogisticRegressionL1L2TV(l1, l2, tv, GLOBAL.Atv,
algorithm=conesta, class_weight=class_weight, penalty_start=penalty_start)
mod.fit(Xtr, ytr.ravel())
elif algo == 'enetgn':
fista = algorithms.proximal.FISTA(max_iter=5000)
mod = estimators.LogisticRegressionL1L2GraphNet(l1, l2, tv, GLOBAL.Agn,
algorithm=fista, class_weight=class_weight, penalty_start=penalty_start)
mod.fit(Xtr, ytr.ravel())
elif algo == 'enet':
fista = algorithms.proximal.FISTA(max_iter=5000)
mod = estimators.ElasticNetLogisticRegression(l1l2ratio, alpha,
algorithm=fista, class_weight=class_weight, penalty_start=penalty_start)
mod.fit(Xtr, ytr.ravel())
else:
raise Exception('Algo%s not handled' %algo)
#mod.fit(Xtr, ytr.ravel())
y_pred = mod.predict(Xte)
proba_pred = mod.predict_probability(Xte)
ret = dict(y_pred=y_pred, y_true=yte, proba_pred=proba_pred,beta=mod.beta)
if output_collector:
output_collector.collect(key, ret)
else:
return ret
def mapper(key, output_collector):
import mapreduce as GLOBAL
Xtr = GLOBAL.DATA_RESAMPLED["X"][0]
Xte = GLOBAL.DATA_RESAMPLED["X"][1]
ytr = GLOBAL.DATA_RESAMPLED["y"][0]
yte = GLOBAL.DATA_RESAMPLED["y"][1]
penalty_start = 3
alpha = float(key[0])
l1, l2, tv = alpha * float(key[1]), alpha * float(key[2]), alpha * float(key[3])
print("l1:%f, l2:%f, tv:%f" % (l1, l2, tv))
class_weight="auto" # unbiased
mask = np.ones(Xtr.shape[0], dtype=bool)
scaler = preprocessing.StandardScaler().fit(Xtr)
Xtr = scaler.transform(Xtr)
Xte=scaler.transform(Xte)
A = GLOBAL.A
conesta = algorithms.proximal.CONESTA(max_iter=500)
mod= estimators.LogisticRegressionL1L2TV(l1,l2,tv, A, algorithm=conesta,class_weight=class_weight,penalty_start=penalty_start)
mod.fit(Xtr, ytr.ravel())
y_pred = mod.predict(Xte)
proba_pred = mod.predict_probability(Xte)
ret = dict(y_pred=y_pred, y_true=yte, proba_pred=proba_pred, beta=mod.beta, mask=mask)
if output_collector:
output_collector.collect(key, ret)
else:
return ret
def mapper(key, output_collector):
import mapreduce as GLOBAL
Xtr = GLOBAL.DATA_RESAMPLED["X"][0]
Xte = GLOBAL.DATA_RESAMPLED["X"][1]
ytr = GLOBAL.DATA_RESAMPLED["y"][0]
yte = GLOBAL.DATA_RESAMPLED["y"][1]
alpha = float(key[0])
l1, l2, tv = alpha * float(key[1]), alpha * float(key[2]), alpha * float(
key[3])
print("l1:%f, l2:%f, tv:%f" % (l1, l2, tv))
print(key)
class_weight = "auto" # unbiased
print(output_collector.output_dir)
beta_start = GLOBAL.BETA_START.all()[os.path.basename(
output_collector.output_dir)]
mask = np.ones(Xtr.shape[0], dtype=bool)
scaler = preprocessing.StandardScaler().fit(Xtr)
Xtr = scaler.transform(Xtr)
Xte = scaler.transform(Xte)
A = GLOBAL.A
info = [
Info.converged, Info.num_iter, Info.time, Info.func_val, Info.mu,
Info.gap
]
conesta = algorithms.proximal.CONESTA()
algorithm_params = dict(max_iter=50000, info=info)
out_fista = os.path.join(WD, output_collector.output_dir,
"fista_ite_snapshots/")
out_conesta = os.path.join(WD, output_collector.output_dir,
"conesta_ite_snapshots/")
os.makedirs(out_fista, exist_ok=True)
os.makedirs(out_conesta, exist_ok=True)
snapshot_fista = AlgorithmSnapshot(out_fista, saving_period=1).save_fista
snapshot_conesta = AlgorithmSnapshot(out_conesta,
saving_period=1).save_conesta
algorithm_params["callback_fista"] = snapshot_fista
algorithm_params["callback_conesta"] = snapshot_conesta
mod= estimators.LogisticRegressionL1L2TV(l1,l2,tv, A, algorithm=conesta,\
algorithm_params=algorithm_params,\
class_weight=class_weight)
mod.fit(Xtr, ytr.ravel(), beta=beta_start)
y_pred = mod.predict(Xte)
proba_pred = mod.predict_probability(Xte)
ret = dict(y_pred=y_pred,
y_true=yte,
proba_pred=proba_pred,
beta=mod.beta,
mask=mask,
beta_start=beta_start)
if output_collector:
output_collector.collect(key, ret)
else:
return ret
def test_conesta_do_not_enter_loop_if_criterium_satisfied():
# beta_start = 0; with over penalized problem: should not enter loop
mod = estimators.LogisticRegressionL1L2TV(
l1*100, l2*100, tv*100, A,
algorithm=algorithms.proximal.CONESTA(),
algorithm_params=algorithm_params)
mod.fit(Xtr, ytr, beta=np.zeros((Xtr.shape[1], 1), dtype=float))
assert np.all(mod.beta == 0)
assert mod.get_info()['num_iter'] == 0
def fitting(p):
global_pen, l1_ratio, tv_ratio, = p[0], p[1], p[2]
ltv = global_pen * tv_ratio
ll1 = l1_ratio * global_pen * (1 - tv_ratio)
ll2 = (1 - l1_ratio) * global_pen * (1 - tv_ratio)
#l1, l2, tv= alpha * float(p[0]), alpha * float(p[1]), alpha * float(p[2])
clf = estimators.LogisticRegressionL1L2TV(ll1,
ll2,
ltv,
A,
algorithm=conesta)
n = 0
list_predict = list()
list_true = list()
list_proba_pred = list()
coef = np.zeros((23, 63966))
for i in range(1, 24):
test_bool = (subject == i)
train_bool = (subject != i)
Xtest = T[test_bool, :]
ytest = y[test_bool]
Xtrain = np.vstack((T_IMA_diff, T[train_bool, :]))
ytrain = np.hstack((y_IMA, y[train_bool]))
list_true.append(ytest.ravel())
scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
clf.fit(Xtrain, ytrain.ravel())
coef[n, :] = clf.beta[:, 0]
pred = (clf.predict(Xtest))
list_predict.append(pred)
proba_pred = clf.predict_probability(Xtest)
list_proba_pred.append(proba_pred)
n = n + 1
print n
true = np.concatenate(list_true)
pred = np.concatenate(list_predict)
proba_pred = np.concatenate(list_proba_pred)
precision, recall, f, s = precision_recall_fscore_support(true,
pred,
average=None)
acc = metrics.accuracy_score(true, pred)
auc = roc_auc_score(true, pred)
current = [
global_pen, l1_ratio, tv_ratio, acc, recall[0], recall[1],
precision[0], precision[1], auc
]
np.save(
os.path.join(BASE_PATH, 'toward_on', 'Logistic_L1_L2_TV_with_HC',
'betas_subj.npy'), coef)
return current
def mapper(key, output_collector):
import mapreduce as GLOBAL
Xtr = GLOBAL.DATA_RESAMPLED["X"][0]
Xte = GLOBAL.DATA_RESAMPLED["X"][1]
ytr = GLOBAL.DATA_RESAMPLED["y"][0]
yte = GLOBAL.DATA_RESAMPLED["y"][1]
print key, "Data shape:", Xtr.shape, Xte.shape, ytr.shape, yte.shape
STRUCTURE = GLOBAL.STRUCTURE
global_pen, l1_ratio, tv_ratio = key
ltv = global_pen * tv_ratio
ll1 = l1_ratio * global_pen * (1 - tv_ratio)
ll2 = (1 - l1_ratio) * global_pen * (1 - tv_ratio)
class_weight = "auto" # unbiased
mask = np.ones(Xtr.shape[0], dtype=bool)
T_IMA = np.load(
'/neurospin/brainomics/2016_classif_hallu_fmri/unsupervised_fmri/clustering_3rdcomp/cluster1/mapreduce/T_IMA.npy'
)
y_IMA = np.load(
'/neurospin/brainomics/2016_classif_hallu_fmri/unsupervised_fmri/clustering_3rdcomp/cluster1/mapreduce/y_IMA.npy'
)
T = GLOBAL.DATA["X"]
y = GLOBAL.DATA["y"]
Tdiff = np.mean(T_IMA, axis=0) - np.mean(T[y == 0], axis=0)
T_IMA_diff = T_IMA - Tdiff
Xtr = np.vstack((T_IMA_diff, Xtr))
ytr = np.hstack((y_IMA, ytr))
scaler = preprocessing.StandardScaler().fit(Xtr)
Xtr = scaler.transform(Xtr)
Xte = scaler.transform(Xte)
A = GLOBAL.A
conesta = algorithms.proximal.CONESTA(max_iter=500)
mod = estimators.LogisticRegressionL1L2TV(ll1,
ll2,
ltv,
A,
algorithm=conesta,
class_weight=class_weight)
mod.fit(Xtr, ytr.ravel())
y_pred = mod.predict(Xte)
ret = dict(y_pred=y_pred, y_true=yte, beta=mod.beta, mask=mask)
if output_collector:
output_collector.collect(key, ret)
else:
return ret
def mapper(key, output_collector):
import mapreduce as GLOBAL
X = GLOBAL.DATA["X"]
y = GLOBAL.DATA["y"]
start_vector = GLOBAL.DATA["start_vector"]
alpha = float(key[0])
l1, l2, tv = alpha * float(key[1]), alpha * float(key[2]), alpha * float(
key[3])
print("l1:%f, l2:%f, tv:%f" % (l1, l2, tv))
class_weight = "auto" # unbiased
mask = np.ones(X.shape[0], dtype=bool)
scaler = preprocessing.StandardScaler().fit(X)
X = scaler.transform(X)
A = GLOBAL.A
info = [
Info.converged, Info.num_iter, Info.time, Info.func_val, Info.mu,
Info.gap
]
conesta = algorithms.proximal.CONESTA()
algorithm_params = dict(max_iter=1000000, info=info)
out = os.path.join(WD_CLUSTER,GLOBAL.DIR,"0",str(key[0])+"_"+ str(key[1]) + "_" +\
str(key[2]) +"_"+str(key[3]),"conesta_ite_snapshots/")
os.makedirs(out, exist_ok=True)
snapshot = AlgorithmSnapshot(out, saving_period=1).save_conesta
algorithm_params["callback"] = snapshot
mod= estimators.LogisticRegressionL1L2TV(l1,l2,tv, A, algorithm=conesta,\
algorithm_params=algorithm_params,\
class_weight=class_weight,\
penalty_start=penalty_start,start_vector=start_vector)
mod.fit(X, y.ravel())
y_pred = mod.predict(X)
proba_pred = mod.predict_probability(X)
ret = dict(y_pred=y_pred,
y_true=y,
proba_pred=proba_pred,
beta=mod.beta,
mask=mask)
if output_collector:
output_collector.collect(key, ret)
else:
return ret
def resample(config, resample_nb):
import mapreduce as GLOBAL # access to global variables
#GLOBAL.DATA = GLOBAL.load_data(config["data"])
resample = config["resample"][resample_nb]
print "reslicing %d" % resample_nb
GLOBAL.DATA_RESAMPLED = {
k: [GLOBAL.DATA[k][idx, ...] for idx in resample]
for k in GLOBAL.DATA
}
print "done reslicing %d" % resample_nb
###############################
#weight computation for this fold
################################
Xtr = GLOBAL.DATA_RESAMPLED["X"][0]
ytr = GLOBAL.DATA_RESAMPLED["y"][0]
p = Xtr.shape[1]
groups = GLOBAL.groups
# Compute A matrix (as penalty_start is 1 we need to only p-1 columns)
Atv, n_compacts = parsimony.functions.nesterov.tv.A_from_shape(
(p - PENALTY_START, ))
eps = 1e-8
max_iter = 2600
info_conf = [Info.fvalue, Info.num_iter]
logr_tv = estimators.LogisticRegressionL1L2TV(
l1=0,
l2=0,
tv=0.001,
penalty_start=PENALTY_START,
A=Atv,
algorithm=explicit.StaticCONESTA(eps=eps,
max_iter=max_iter,
info=info_conf),
mean=False)
logr_tv.fit(Xtr, ytr)
beta_w = logr_tv.beta
# weights = [1./(np.linalg.norm(beta_w[group])) for group in groups]
weights = [np.sqrt(len(group)) for group in groups]
GLOBAL.ridge_coef = 1. / ((np.linalg.norm(beta_w)))
GLOBAL.weights = weights
# Store weights
output_dir = os.path.join(config['map_output'], str(resample_nb))
np.save(os.path.join(output_dir, "weights.npy"), GLOBAL.weights)
np.save(os.path.join(output_dir, "ridge_coef.npy"), GLOBAL.ridge_coef)
def mapper(key, output_collector):
import mapreduce as GLOBAL
Xtr = GLOBAL.DATA_RESAMPLED["X"][0]
Xte = GLOBAL.DATA_RESAMPLED["X"][1]
ytr = GLOBAL.DATA_RESAMPLED["y"][0]
yte = GLOBAL.DATA_RESAMPLED["y"][1]
T_IMA = GLOBAL.DATA_IMA["X_IMA"]
y_IMA = GLOBAL.DATA_IMA["y_IMA"]
T = GLOBAL.DATA["X"]
y = GLOBAL.DATA["y"]
Tdiff = np.mean(T_IMA, axis=0) - np.mean(T[y == 0], axis=0)
T_IMA_diff = T_IMA - Tdiff
Xtr = np.vstack((T_IMA_diff, Xtr))
ytr = np.hstack((y_IMA, ytr))
alpha = float(key[0])
l1, l2, tv = alpha * float(key[1]), alpha * float(key[2]), alpha * float(
key[3])
print "l1:%f, l2:%f, tv:%f" % (l1, l2, tv)
class_weight = "auto" # unbiased
mask = np.ones(Xtr.shape[0], dtype=bool)
scaler = preprocessing.StandardScaler().fit(Xtr)
Xtr = scaler.transform(Xtr)
Xte = scaler.transform(Xte)
A = GLOBAL.A
conesta = algorithms.proximal.CONESTA(max_iter=500)
mod = estimators.LogisticRegressionL1L2TV(l1,
l2,
tv,
A,
algorithm=conesta,
class_weight=class_weight)
mod.fit(Xtr, ytr.ravel())
y_pred = mod.predict(Xte)
ret = dict(y_pred=y_pred, y_true=yte, beta=mod.beta, mask=mask)
if output_collector:
output_collector.collect(key, ret)
else:
return ret
def fitting(p):
l1, l2, tv = alpha * float(p[0]), alpha * float(p[1]), alpha * float(p[2])
clf = estimators.LogisticRegressionL1L2TV(l1, l2, tv, A, algorithm=conesta)
n = 0
list_predict = list()
list_true = list()
list_proba_pred = list()
coef = np.zeros((23, sum(mask_bool)))
for i in range(1, 24):
test_bool = (subject == i)
train_bool = (subject != i)
Xtest = T[test_bool, :]
ytest = y[test_bool]
# Xtrain=T[train_bool,:]
# ytrain=y[train_bool]
Xtrain = np.vstack((T_IMA_diff, T[train_bool, :]))
ytrain = np.hstack((y_IMA, y[train_bool]))
list_true.append(ytest.ravel())
scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain = scaler.transform(Xtrain)
Xtest = scaler.transform(Xtest)
clf.fit(Xtrain, ytrain.ravel())
coef[n, :] = clf.beta[:, 0]
pred = (clf.predict(Xtest))
list_predict.append(pred)
proba_pred = clf.predict_probability(Xtest)
list_proba_pred.append(proba_pred)
n = n + 1
print n
true = np.concatenate(list_true)
pred = np.concatenate(list_predict)
proba_pred = np.concatenate(list_proba_pred)
precision, recall, f, s = precision_recall_fscore_support(true,
pred,
average=None)
acc = metrics.accuracy_score(true, pred)
auc = roc_auc_score(true, pred)
current = [
alpha, p[0], p[1], p[2], acc, recall[0], recall[1], precision[0],
precision[1], auc
]
return current
estimators.ElasticNetLogisticRegression(alpha=alpha / 10, l=.5,
penalty_start=1,
algorithm_params=algorithm_params)
## LogisticRegressionL1L2TV, Parsimony only
# Minimize:
# f(beta, X, y) = - loglik/n_train
# + k/2 * ||beta||^2_2
# + l * ||beta||_1
# + g * TV(beta)
A = nesterov_tv.linear_operator_from_shape(beta3d.shape)
l1, l2, tv = alpha * np.array((.05, .75, .2)) # l2, l1, tv penalties
MODELS["2d_l1l2tv_fista"] = \
estimators.LogisticRegressionL1L2TV(
l1, l2, tv, A,
algorithm=algorithms.proximal.FISTA(),
algorithm_params=algorithm_params)
MODELS["2d_l1l2tv_inter_fista"] = \
estimators.LogisticRegressionL1L2TV(
l1, l2, tv, A, penalty_start=1,
algorithm=algorithms.proximal.FISTA(),
algorithm_params=algorithm_params)
MODELS["2d_l1l2tv_static_conesta"] = \
estimators.LogisticRegressionL1L2TV(
l1, l2, tv, A,
algorithm=algorithms.proximal.StaticCONESTA(),
algorithm_params=algorithm_params)
# GraphNet
# l1, l2, gn = alpha * np.array((.05, .75, .2)) # l1, l2, gn penalties
l1, l2, gn = alpha * np.array((.33, .33, 33)) # l1, l2, gn penalties
A = sparse.vstack(nesterov_tv.linear_operator_from_shape(shape))
enetgn = estimators.LogisticRegressionL1L2GraphNet(l1, l2, gn, A)
yte_pred_enetgn = enetgn.fit(Xtr, ytr).predict(Xte)
_, recall_enetgn, _, _ = \
precision_recall_fscore_support(yte, yte_pred_enetgn, average=None)
# LogisticRegressionL1L2TV
l1, l2, tv = alpha * np.array((.05, .75, .2)) # l1, l2, tv penalties
# l1, l2, tv = alpha * np.array((.33, .33, 33)) # l1, l2, gn penalties
A = nesterov_tv.linear_operator_from_shape(beta3d.shape)
enettv = estimators.LogisticRegressionL1L2TV(l1,
l2,
tv,
A,
algorithm_params=dict(eps=1e-5))
yte_pred_enettv = enettv.fit(Xtr, ytr).predict(Xte)
_, recall_enettv, _, _ = \
precision_recall_fscore_support(yte, yte_pred_enettv, average=None)
###############################################################################
# Plot
plot = plt.subplot(231)
utils.plots.map2d(beta3d.reshape(shape), plot, title="beta star")
plot = plt.subplot(232)
utils.plots.map2d(ridge_sklrn.coef_.reshape(shape),
plot,
title="Ridge (sklrn) (%.2f, %.2f)" %
assert X_pra.shape[1] == 299864 scaler = preprocessing.StandardScaler().fit(X_scz) X_scz = scaler.transform(X_scz) X_pra = scaler.transform(X_pra) ##'enettv': tvratio=1.0 l1l2ratio = 0.1 alpha = 1.0 tv = alpha * tvratio l1 = alpha * float(1 - tv) * l1l2ratio l2 = alpha * float(1 - tv) * (1- l1l2ratio) conesta = algorithms.proximal.CONESTA(max_iter=10000) mod = estimators.LogisticRegressionL1L2TV(l1, l2, tv,Atv,\ algorithm=conesta, class_weight=class_weight, penalty_start=penalty_start) # #'enetgn': tvratio=0.8 l1l2ratio = 0.1 alpha = 0.1 tv = alpha * tvratio l1 = alpha * float(1 - tv) * l1l2ratio l2 = alpha * float(1 - tv) * (1- l1l2ratio) fista = algorithms.proximal.FISTA(max_iter=5000) mod = estimators.LogisticRegressionL1L2GraphNet(l1, l2, tv,Agn, algorithm=fista, class_weight=class_weight, penalty_start=penalty_start) #algo == 'enet': fista = algorithms.proximal.FISTA(max_iter=5000)
y_test_decfunc_pred = np.zeros(len(y))
y_train_pred = np.zeros(len(y))
coefs_cv = np.zeros((NFOLDS, X.shape[1]))
auc_test = list()
recalls_test = list()
acc_test = list()
for cv_i, (train, test) in enumerate(cv.split(X, y)):
#for train, test in cv.split(X, y, None):
print(cv_i)
X_train, X_test, y_train, y_test = X[train, :], X[test, :], y[train], y[test]
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
conesta = algorithms.proximal.CONESTA(max_iter=10000)
estimator = estimators.LogisticRegressionL1L2TV(l1, l2, tv, Atv, algorithm=conesta,
class_weight="auto", penalty_start=0)
estimator.fit(X_train, y_train.ravel())
# Store prediction for micro avg
y_test_pred[test] = estimator.predict(X_test).ravel()
y_test_prob_pred[test] = estimator.predict_probability(X_test).ravel()#[:, 1]
#y_test_decfunc_pred[test] = estimator.decision_function(X_test)
y_train_pred[train] = estimator.predict(X_train).ravel()
# Compute score for macro avg
auc_test.append(metrics.roc_auc_score(y_test, estimator.predict_probability(X_test).ravel()))
recalls_test.append(metrics.recall_score(y_test, estimator.predict(X_test).ravel(), average=None))
acc_test.append(metrics.accuracy_score(y_test, estimator.predict(X_test).ravel()))
coefs_cv[cv_i, :] = estimator.beta.ravel()
# Micro Avg
recall_test_microavg = metrics.recall_score(y, y_test_pred, average=None)
def mapper(key, output_collector):
import mapreduce as GLOBAL
Xtr = GLOBAL.DATA_RESAMPLED["X"][0]
Xte = GLOBAL.DATA_RESAMPLED["X"][1]
ytr = GLOBAL.DATA_RESAMPLED["y"][0]
yte = GLOBAL.DATA_RESAMPLED["y"][1]
# key = 'enettv_0.01_0.1_0.2'.split("_")
algo, alpha, l1l2ratio, tvratio = key[0], float(key[1]), float(
key[2]), float(key[3])
tv = alpha * tvratio
l1 = alpha * float(1 - tv) * l1l2ratio
l2 = alpha * float(1 - tv) * (1 - l1l2ratio)
print(key, algo, alpha, l1, l2, tv)
# alpha = float(key[0])
# l1, l2, tv = alpha * float(key[1]), alpha * float(key[2]), alpha * float(key[3])
# print("l1:%f, l2:%f, tv:%f" % (l1, l2, tv))
class_weight = "auto" # unbiased
beta_start = GLOBAL.beta_start["lambda_%.4f" % alpha]
print(beta_start.shape, Xtr.shape, beta_start.mean())
# mask = np.ones(Xtr.shape[0], dtype=bool)
scaler = preprocessing.StandardScaler().fit(Xtr)
Xtr = scaler.transform(Xtr)
Xte = scaler.transform(Xte)
if algo == 'enettv':
conesta = algorithms.proximal.CONESTA(max_iter=10000)
mod = estimators.LogisticRegressionL1L2TV(l1,
l2,
tv,
GLOBAL.Atv,
algorithm=conesta,
class_weight=class_weight,
penalty_start=penalty_start)
mod.fit(Xtr, ytr.ravel(), beta=beta_start)
elif algo == 'enetgn':
fista = algorithms.proximal.FISTA(max_iter=500)
mod = estimators.LogisticRegressionL1L2GraphNet(
l1,
l2,
tv,
GLOBAL.Agn,
algorithm=fista,
class_weight=class_weight,
penalty_start=penalty_start)
mod.fit(Xtr, ytr.ravel())
elif algo == 'enet':
fista = algorithms.proximal.FISTA(max_iter=500)
mod = estimators.ElasticNetLogisticRegression(
l1l2ratio,
alpha,
algorithm=fista,
class_weight=class_weight,
penalty_start=penalty_start)
mod.fit(Xtr, ytr.ravel())
else:
raise Exception('Algo%s not handled' % algo)
#mod.fit(Xtr, ytr.ravel())
y_pred = mod.predict(Xte)
proba_pred = mod.predict_probability(Xte)
ret = dict(y_pred=y_pred, y_true=yte, proba_pred=proba_pred,
beta=mod.beta) #, mask=mask)
if output_collector:
output_collector.collect(key, ret)
else:
return ret
#
# 4- build A matrix
import parsimony.functions.nesterov.gl as gl
import parsimony.algorithms.primaldual as explicit
import parsimony.estimators as estimators
Atv, n_compacts = parsimony.functions.nesterov.tv.A_from_shape((p_orig, ))
eps = 1e-8
max_iter = 2600
conts = 2 # will be removed next version current max_iter x cont
info_conf = [Info.fvalue, Info.num_iter]
logr_tv = estimators.LogisticRegressionL1L2TV(
l1=0,
l2=0,
tv=0.1,
A=Atv,
algorithm=explicit.StaticCONESTA(eps=eps,
max_iter=max_iter,
info=info_conf),
mean=False)
logr_tv.fit(X_orig, y)
beta_w = logr_tv.beta
# plt.plot(beta_w[1:])
# plt.show()
PENALTY_START = 1
extended_groups = groups
# + [[i] for i in range(PENALTY_START, p-1)]
#test avec tv
weights = [
1. / (np.linalg.norm(beta_w[group])) for group in extended_groups
