def get_workflow(self, n_features=int(1E03)):
random_state = 0
C_values = [1, 10]
k_values = 0
k_max = "auto"
n_folds_nested = 5
n_folds = 10
n_perms = 10
if k_max != "auto":
k_values = range_log2(np.minimum(int(k_max), n_features),
add_n=True)
else:
k_values = range_log2(n_features, add_n=True)
cls = Methods(*[
Pipe(SelectKBest(k=k), SVC(C=C, kernel="linear")) for C in C_values
for k in k_values
])
pipeline = CVBestSearchRefit(cls,
n_folds=n_folds_nested,
random_state=random_state)
wf = Perms(CV(pipeline, n_folds=n_folds),
n_perms=n_perms,
permute="y",
random_state=random_state)
return wf
def do_all(options):
if options.k_max != "auto":
k_values = range_log2(np.minimum(int(options.k_max),
options.n_features), add_n=True)
else:
k_values = range_log2(options.n_features, add_n=True)
C_values = [1, 10]
random_state = 0
#print options
#sys.exit(0)
if options.trace:
from epac import conf
conf.TRACE_TOPDOWN = True
## 1) Build dataset
## ================
X, y = datasets.make_classification(n_samples=options.n_samples,
n_features=options.n_features,
n_informative=options.n_informative)
## 2) Build Workflow
## =================
time_start = time.time()
## CV + Grid search of a pipeline with a nested grid search
cls = Methods(*[Pipe(SelectKBest(k=k),
SVC(kernel="linear", C=C))
for C in C_values
for k in k_values])
pipeline = CVBestSearchRefit(cls,
n_folds=options.n_folds_nested, random_state=random_state)
wf = Perms(CV(pipeline, n_folds=options.n_folds),
n_perms=options.n_perms,
permute="y",
random_state=random_state)
print "Time ellapsed, tree construction:", time.time() - time_start
## 3) Run Workflow
## ===============
time_fit_predict = time.time()
## Run on local machine
sfw_engine = SomaWorkflowEngine(
tree_root=wf,
num_processes=options.n_cores
)
## Run on cluster
# sfw_engine = SomaWorkflowEngine(
# tree_root=wf,
# num_processes=options.n_cores,
# resource_id="jl237561@gabriel",
# login="******")
# You can use soma_workflow_gui to track your progress
wf = sfw_engine.run(X=X, y=y)
print "Time ellapsed, fit predict:", time.time() - time_fit_predict
time_reduce = time.time()
## 4) Reduce Workflow
## ==================
print wf.reduce()
print "Time ellapsed, reduce:", time.time() - time_reduce
def do_all(options):
if options.k_max != "auto":
k_values = range_log2(np.minimum(int(options.k_max),
options.n_features),
add_n=True)
else:
k_values = range_log2(options.n_features, add_n=True)
C_values = [1, 10]
random_state = 0
#print options
#sys.exit(0)
if options.trace:
from epac import conf
conf.TRACE_TOPDOWN = True
## 1) Build dataset
## ================
X, y = datasets.make_classification(n_samples=options.n_samples,
n_features=options.n_features,
n_informative=options.n_informative)
## 2) Build Workflow
## =================
time_start = time.time()
## CV + Grid search of a pipeline with a nested grid search
cls = Methods(*[
Pipe(SelectKBest(k=k), SVC(kernel="linear", C=C)) for C in C_values
for k in k_values
])
pipeline = CVBestSearchRefit(cls,
n_folds=options.n_folds_nested,
random_state=random_state)
wf = Perms(CV(pipeline, n_folds=options.n_folds),
n_perms=options.n_perms,
permute="y",
random_state=random_state)
print "Time ellapsed, tree construction:", time.time() - time_start
## 3) Run Workflow
## ===============
time_fit_predict = time.time()
## Run on local machine
sfw_engine = SomaWorkflowEngine(tree_root=wf,
num_processes=options.n_cores)
## Run on cluster
# sfw_engine = SomaWorkflowEngine(
# tree_root=wf,
# num_processes=options.n_cores,
# resource_id="jl237561@gabriel",
# login="******")
wf = sfw_engine.run(X=X, y=y)
print "Time ellapsed, fit predict:", time.time() - time_fit_predict
time_reduce = time.time()
## 4) Reduce Workflow
## ==================
print wf.reduce()
print "Time ellapsed, reduce:", time.time() - time_reduce
def do_all(options):
if options.k_max != "auto":
k_values = range_log2(np.minimum(int(options.k_max), options.n_features), add_n=True)
else:
k_values = range_log2(options.n_features, add_n=True)
C_values = [1, 10]
random_state = 0
# print options
# sys.exit(0)
if options.trace:
from epac import conf
conf.TRACE_TOPDOWN = True
## 1) Build dataset
## ================
X, y = datasets.make_classification(
n_samples=options.n_samples, n_features=options.n_features, n_informative=options.n_informative
)
## 2) Build Workflow
## =================
time_start = time.time()
## CV + Grid search of a pipeline with a nested grid search
cls = Methods(*[Pipe(SelectKBest(k=k), SVC(kernel="linear", C=C)) for C in C_values for k in k_values])
pipeline = CVBestSearchRefit(cls, n_folds=options.n_folds_nested, random_state=random_state)
wf = Perms(CV(pipeline, n_folds=options.n_folds), n_perms=options.n_perms, permute="y", random_state=random_state)
print "Time ellapsed, tree construction:", time.time() - time_start
## 3) Export Workflow to soma_workflow_gui
## ===============
time_fit_predict = time.time()
if os.path.isdir(options.soma_workflow_dir):
shutil.rmtree(options.soma_workflow_dir)
sfw_engine = SomaWorkflowEngine(tree_root=wf, num_processes=options.n_cores)
sfw_engine.export_to_gui(options.soma_workflow_dir, X=X, y=y)
print "Time ellapsed, fit predict:", time.time() - time_fit_predict
# ## 6) Load Epac tree & Reduce
# ## ==========================
reduce_filename = os.path.join(options.soma_workflow_dir, "reduce.py")
f = open(reduce_filename, "w")
reduce_str = (
"""from epac.map_reduce.engine import SomaWorkflowEngine
wf = SomaWorkflowEngine.load_from_gui("%s")
print wf.reduce()
"""
% options.soma_workflow_dir
)
f.write(reduce_str)
f.close()
print "#First run\n" "soma_workflow_gui\n" "\t(1)Open %s\n" "\t(2)Submit\n" "\t(3)Transfer Input Files\n" "\t...wait...\n" "\t(4)Transfer Output Files\n" "#When done run:\npython %s" % (
os.path.join(options.soma_workflow_dir, sfw_engine.open_me_by_soma_workflow_gui),
reduce_filename,
)
def do_all(options):
if options.k_max != "auto":
k_values = range_log2(np.minimum(int(options.k_max),
options.n_features), add_n=True)
else:
k_values = range_log2(options.n_features, add_n=True)
C_values = [1, 10]
random_state = 0
#print options
#sys.exit(0)
if options.trace:
from epac import conf
conf.TRACE_TOPDOWN = True
## 1) Build dataset
## ================
X, y = datasets.make_classification(n_samples=options.n_samples,
n_features=options.n_features,
n_informative=options.n_informative)
## 2) Build Workflow
## =================
time_start = time.time()
## CV + Grid search of a pipeline with a nested grid search
cls = Methods(*[Pipe(SelectKBest(k=k),
SVC(kernel="linear", C=C))
for C in C_values
for k in k_values])
pipeline = CVBestSearchRefit(cls,
n_folds=options.n_folds_nested,
random_state=random_state)
wf = Perms(CV(pipeline, n_folds=options.n_folds),
n_perms=options.n_perms,
permute="y",
random_state=random_state)
print "Time ellapsed, tree construction:", time.time() - time_start
## 3) Run Workflow
## ===============
time_fit_predict = time.time()
wf.run(X=X, y=y)
print "Time ellapsed, fit predict:", time.time() - time_fit_predict
time_reduce = time.time()
## 4) Reduce Workflow
## ==================
print wf.reduce()
print "Time ellapsed, reduce:", time.time() - time_reduce
def get_workflow(self, n_features=int(1E03)):
random_state = 0
C_values = [1, 10]
k_values = 0
k_max = "auto"
n_folds_nested = 5
n_folds = 10
n_perms = 10
if k_max != "auto":
k_values = range_log2(np.minimum(int(k_max), n_features),
add_n=True)
else:
k_values = range_log2(n_features, add_n=True)
cls = Methods(*[Pipe(SelectKBest(k=k), SVC(C=C, kernel="linear"))
for C in C_values
for k in k_values])
pipeline = CVBestSearchRefit(cls,
n_folds=n_folds_nested,
random_state=random_state)
wf = Perms(CV(pipeline, n_folds=n_folds),
n_perms=n_perms,
permute="y",
random_state=random_state)
return wf
def do_all(options):
if options.k_max != "auto":
k_values = range_log2(np.minimum(int(options.k_max),
options.n_features),
add_n=True)
else:
k_values = range_log2(options.n_features, add_n=True)
C_values = [1, 10]
random_state = 0
#print options
#sys.exit(0)
if options.trace:
from epac import conf
conf.TRACE_TOPDOWN = True
## 1) Build dataset
## ================
X, y = datasets.make_classification(n_samples=options.n_samples,
n_features=options.n_features,
n_informative=options.n_informative)
## 2) Build Workflow
## =================
time_start = time.time()
## CV + Grid search of a pipeline with a nested grid search
cls = Methods(*[
Pipe(SelectKBest(k=k), SVC(kernel="linear", C=C)) for C in C_values
for k in k_values
])
pipeline = CVBestSearchRefit(cls,
n_folds=options.n_folds_nested,
random_state=random_state)
wf = Perms(CV(pipeline, n_folds=options.n_folds),
n_perms=options.n_perms,
permute="y",
random_state=random_state)
print "Time ellapsed, tree construction:", time.time() - time_start
## 3) Export Workflow to soma_workflow_gui
## ===============
time_fit_predict = time.time()
if os.path.isdir(options.soma_workflow_dir):
shutil.rmtree(options.soma_workflow_dir)
sfw_engine = SomaWorkflowEngine(tree_root=wf,
num_processes=options.n_cores)
sfw_engine.export_to_gui(options.soma_workflow_dir, X=X, y=y)
print "Time ellapsed, fit predict:", time.time() - time_fit_predict
# ## 6) Load Epac tree & Reduce
# ## ==========================
reduce_filename = os.path.join(options.soma_workflow_dir, "reduce.py")
f = open(reduce_filename, 'w')
reduce_str = """from epac.map_reduce.engine import SomaWorkflowEngine
wf = SomaWorkflowEngine.load_from_gui("%s")
print wf.reduce()
""" % options.soma_workflow_dir
f.write(reduce_str)
f.close()
print "#First run\n"\
"soma_workflow_gui\n"\
"\t(1)Open %s\n"\
"\t(2)Submit\n"\
"\t(3)Transfer Input Files\n"\
"\t...wait...\n"\
"\t(4)Transfer Output Files\n"\
"#When done run:\npython %s" % (
os.path.join(options.soma_workflow_dir,
sfw_engine.open_me_by_soma_workflow_gui),
reduce_filename)
n_features = int(1E03)
X, y = datasets.make_classification(n_samples=100,
n_features=n_features,
n_informative=2)
random_state = 0
C_values = [1, 10]
k_values = 0
k_max = "auto"
n_folds_nested = 5
n_folds = 10
n_perms = 10
if k_max != "auto":
k_values = range_log2(np.minimum(int(k_max), n_features),
add_n=True)
else:
k_values = range_log2(n_features, add_n=True)
cls = Methods(*[Pipe(SelectKBest(k=k), SVC(C=C, kernel="linear"))
for C in C_values
for k in k_values])
pipeline = CVBestSearchRefitParallel(cls,
n_folds=n_folds_nested,
random_state=random_state)
wf = Perms(CV(pipeline, n_folds=n_folds),
n_perms=n_perms,
permute="y",
random_state=random_state)
# wf.run(X=X, y=y)
#
res_cv_svms_auto = cv.reduce()
print res_cv_svms_auto
print res_cv_svms_auto["CVBestSearchRefit"]['y/test/score_recall']
# Re-fit on all data. Warning: biased !!!
svms_auto.run(X=X, y=y)
print svms_auto.best_params
print svms_auto.refited.estimator.coef_
##############################################################################
# Put everything together
# Pipeline, "Pipe": SelectKBest + StandardScaler + SVM l1 vs l2
from epac import range_log2
from epac import CVBestSearchRefit, Pipe, Methods, CV
k_values = range_log2(X.shape[1], add_n=True)
C_values = [.1, 1, 10, 100]
anova_svms = Methods(*[Pipe(SelectKBest(k=k), preprocessing.StandardScaler(),
Methods(*[SVM(C=C, penalty=penalty, class_weight='auto', dual=False)
for C in C_values for penalty in ['l1', 'l2']]))
for k in k_values])
# Take a look
print [l for l in anova_svms.walk_leaves()]
## k and C selection based on CV
anova_svms_auto = CVBestSearchRefit(anova_svms)
#anova_svm_all = Methods(anova_svm, anova_svm_cv)
cv = CV(anova_svms_auto, n_folds=n_folds)
time_fit_predict = time.time()
