diff --git a/summit/multiview_platform/exec_classif.py b/summit/multiview_platform/exec_classif.py
index 6c75194aaf2c10a58cfdd38ecd9e2fa8e071b27b..fd0499edbb1c30d7d7373d7149de3f2e4451f002 100644
--- a/summit/multiview_platform/exec_classif.py
+++ b/summit/multiview_platform/exec_classif.py
@@ -203,6 +203,7 @@ def get_random_hps_args(hps_args, classifier_name):
hps_dict["param_distributions"] = value
return hps_dict
+
def gen_single_monoview_arg_dictionary(classifier_name, arguments, nb_class,
view_index, view_name, hps_kwargs):
if classifier_name in arguments:
@@ -430,116 +431,6 @@ def benchmark_init(directory, classification_indices, labels, labels_dictionary,
return results_monoview, labels_names
-# def exec_one_benchmark(core_index=-1, labels_dictionary=None, directory=None,
-# classification_indices=None, args=None,
-# k_folds=None, random_state=None, hyper_param_search=None,
-# metrics=None, argument_dictionaries=None,
-# benchmark=None, views=None, views_indices=None, flag=None,
-# labels=None,
-# exec_monoview_multicore=exec_monoview_multicore,
-# exec_multiview_multicore=exec_multiview_multicore,):
-# """Used to run a benchmark using one core. ExecMonoview_multicore, initMultiviewArguments and
-# exec_multiview_multicore args are only used for tests"""
-#
-# results_monoview, labels_names = benchmark_init(directory,
-# classification_indices, labels,
-# labels_dictionary, k_folds)
-#
-# logging.debug("Start:\t monoview benchmark")
-# results_monoview += [
-# exec_monoview_multicore(directory, args["name"], labels_names,
-# classification_indices, k_folds,
-# core_index, args["file_type"], args["pathf"], random_state,
-# labels,
-# hyper_param_search=hyper_param_search,
-# metrics=metrics,
-# n_iter=args["hps_iter"], **argument)
-# for argument in argument_dictionaries["Monoview"]]
-# logging.debug("Done:\t monoview benchmark")
-#
-#
-# logging.debug("Start:\t multiview benchmark")
-# results_multiview = [
-# exec_multiview_multicore(directory, core_index, args["name"],
-# classification_indices, k_folds, args["file_type"],
-# args["pathf"], labels_dictionary, random_state,
-# labels, hyper_param_search=hyper_param_search,
-# metrics=metrics, n_iter=args["hps_iter"],
-# **arguments)
-# for arguments in argument_dictionaries["multiview"]]
-# logging.debug("Done:\t multiview benchmark")
-#
-# return [flag, results_monoview + results_multiview]
-#
-#
-# def exec_one_benchmark_multicore(nb_cores=-1, labels_dictionary=None,
-# directory=None, classification_indices=None,
-# args=None,
-# k_folds=None, random_state=None,
-# hyper_param_search=None, metrics=None,
-# argument_dictionaries=None,
-# benchmark=None, views=None, views_indices=None,
-# flag=None, labels=None,
-# exec_monoview_multicore=exec_monoview_multicore,
-# exec_multiview_multicore=exec_multiview_multicore,):
-# """Used to run a benchmark using multiple cores. ExecMonoview_multicore, initMultiviewArguments and
-# exec_multiview_multicore args are only used for tests"""
-#
-# results_monoview, labels_names = benchmark_init(directory,
-# classification_indices, labels,
-# labels_dictionary, k_folds)
-#
-# logging.debug("Start:\t monoview benchmark")
-# nb_experiments = len(argument_dictionaries["monoview"])
-# nb_multicore_to_do = int(math.ceil(float(nb_experiments) / nb_cores))
-# for step_index in range(nb_multicore_to_do):
-# results_monoview += (Parallel(n_jobs=nb_cores)(
-# delayed(exec_monoview_multicore)(directory, args["name"], labels_names,
-# classification_indices, k_folds,
-# core_index, args["file_type"], args["pathf"],
-# random_state, labels,
-# hyper_param_search=hyper_param_search,
-# metrics=metrics,
-# n_iter=args["hps_iter"],
-# **argument_dictionaries["monoview"][
-# core_index + step_index * nb_cores])
-# for core_index in
-# range(min(nb_cores, nb_experiments - step_index * nb_cores))))
-# logging.debug("Done:\t monoview benchmark")
-#
-# logging.debug("Start:\t multiview arguments initialization")
-# # argument_dictionaries = initMultiviewArguments(args, benchmark, views,
-# # views_indices,
-# # argument_dictionaries,
-# # random_state, directory,
-# # resultsMonoview,
-# # classification_indices)
-# logging.debug("Done:\t multiview arguments initialization")
-#
-# logging.debug("Start:\t multiview benchmark")
-# results_multiview = []
-# nb_experiments = len(argument_dictionaries["multiview"])
-# nb_multicore_to_do = int(math.ceil(float(nb_experiments) / nb_cores))
-# for step_index in range(nb_multicore_to_do):
-# results_multiview += Parallel(n_jobs=nb_cores)(
-# delayed(exec_multiview_multicore)(directory, core_index, args["name"],
-# classification_indices, k_folds,
-# args["file_type"], args["Base"]["pathf"],
-# labels_dictionary, random_state,
-# labels,
-# hyper_param_search=hyper_param_search,
-# metrics=metrics,
-# n_iter=args["hps_iter"],
-# **
-# argument_dictionaries["multiview"][
-# step_index * nb_cores + core_index])
-# for core_index in
-# range(min(nb_cores, nb_experiments - step_index * nb_cores)))
-# logging.debug("Done:\t multiview benchmark")
-#
-# return [flag, results_monoview + results_multiview]
-
-
def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
directory=None, classification_indices=None,
args=None,
@@ -548,7 +439,7 @@ def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
argument_dictionaries=None,
benchmark=None, views=None, views_indices=None,
flag=None, labels=None,
- track_tracebacks=False): # pragma: no cover
+ track_tracebacks=False, nb_cores=1): # pragma: no cover
results_monoview, labels_names = benchmark_init(directory,
classification_indices,
labels,
@@ -564,7 +455,7 @@ def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
results_monoview += [
exec_monoview(directory, X, Y, args["name"], labels_names,
classification_indices, k_folds,
- 1, args["file_type"], args["pathf"], random_state,
+ nb_cores, args["file_type"], args["pathf"], random_state,
hyper_param_search=hyper_param_search,
metrics=metrics,
**arguments)]
@@ -575,19 +466,8 @@ def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
"view_name"]] = traceback.format_exc()
else:
raise
-
logging.info("Done:\t monoview benchmark")
- logging.info("Start:\t multiview arguments initialization")
-
- # argument_dictionaries = initMultiviewArguments(args, benchmark, views,
- # views_indices,
- # argument_dictionaries,
- # random_state, directory,
- # resultsMonoview,
- # classification_indices)
- logging.info("Done:\t multiview arguments initialization")
-
logging.info("Start:\t multiview benchmark")
results_multiview = []
for arguments in argument_dictionaries["multiview"]:
@@ -595,7 +475,7 @@ def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
results_multiview += [
exec_multiview(directory, dataset_var, args["name"],
classification_indices,
- k_folds, 1, args["file_type"],
+ k_folds, nb_cores, args["file_type"],
args["pathf"], labels_dictionary, random_state,
labels,
hps_method=hyper_param_search,
@@ -660,26 +540,10 @@ def exec_benchmark(nb_cores, stats_iter,
"""
logging.info("Start:\t Executing all the needed benchmarks")
results = []
- # if nb_cores > 1:
- # if stats_iter > 1 or nb_multiclass > 1:
- # nb_exps_to_do = len(benchmark_arguments_dictionaries)
- # nb_multicore_to_do = range(int(math.ceil(float(nb_exps_to_do) / nb_cores)))
- # for step_index in nb_multicore_to_do:
- # results += (Parallel(n_jobs=nb_cores)(delayed(exec_one_benchmark)
- # (core_index=core_index,
- # **
- # benchmark_arguments_dictionaries[
- # core_index + step_index * nb_cores])
- # for core_index in range(
- # min(nb_cores, nb_exps_to_do - step_index * nb_cores))))
- # else:
- # results += [exec_one_benchmark_multicore(nb_cores=nb_cores, **
- # benchmark_arguments_dictionaries[0])]
- # else:
for arguments in benchmark_arguments_dictionaries:
benchmark_results = exec_one_benchmark_mono_core(
dataset_var=dataset_var,
- track_tracebacks=track_tracebacks,
+ track_tracebacks=track_tracebacks, nb_cores=nb_cores,
**arguments)
analyze_iterations([benchmark_results],
benchmark_arguments_dictionaries, stats_iter,
@@ -697,7 +561,6 @@ def exec_benchmark(nb_cores, stats_iter,
dataset_var.sample_ids,
dataset_var.get_labels())
logging.info("Done:\t Analyzing predictions")
- delete(benchmark_arguments_dictionaries, nb_cores, dataset_var)
return results_mean_stds
@@ -816,11 +679,5 @@ def exec_classif(arguments): # pragma: no cover
stats_iter_random_states, metrics,
argument_dictionaries, benchmark,
views, views_indices)
- results_mean_stds = exec_benchmark(
- nb_cores, stats_iter,
- benchmark_argument_dictionaries, directory, metrics,
- dataset_var,
- args["track_tracebacks"])
- # noise_results.append([noise_std, results_mean_stds])
- # plot_results_noise(directory, noise_results, metrics[0][0],
- # dataset_name)
+ exec_benchmark(nb_cores, stats_iter, benchmark_argument_dictionaries,
+ directory, metrics, dataset_var, args["track_tracebacks"])
diff --git a/summit/multiview_platform/monoview/exec_classif_mono_view.py b/summit/multiview_platform/monoview/exec_classif_mono_view.py
index 18d896bc353664555135950c21ffa4a137948b89..98b65e3dbfde3c8cbbbb51e6096065eb2ef0214e 100644
--- a/summit/multiview_platform/monoview/exec_classif_mono_view.py
+++ b/summit/multiview_platform/monoview/exec_classif_mono_view.py
@@ -36,8 +36,8 @@ def exec_monoview_multicore(directory, name, labels_names,
**args): # pragma: no cover
dataset_var = HDF5Dataset(
hdf5_file=h5py.File(path + name + str(dataset_file_index) + ".hdf5", "r"))
- neededViewIndex = args["view_index"]
- X = dataset_var.get_v(neededViewIndex)
+ needed_view_index = args["view_index"]
+ X = dataset_var.get_v(needed_view_index)
Y = labels
return exec_monoview(directory, X, Y, name, labels_names,
classification_indices, k_folds, 1, database_type,
@@ -61,8 +61,8 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
view_name, \
classifier_name, \
X, \
- learningRate, \
- labelsString, \
+ learning_rate, \
+ labels_string, \
output_file_name, \
directory, \
base_file_name = init_constants(args, X, classification_indices,
@@ -74,7 +74,7 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
"Info:\t Classification - Database:" + str(
database_name) + " View:" + str(
view_name) + " train ratio:"
- + str(learningRate) + ", CrossValidation k-folds: " + str(
+ + str(learning_rate) + ", CrossValidation k-folds: " + str(
k_folds.n_splits) + ", cores:"
+ str(nb_cores) + ", algorithm : " + classifier_name)
@@ -110,7 +110,7 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
random_state,
y=Y)
fit_beg = time.monotonic()
- classifier.fit(X_train, y_train) # NB_CORES=nbCores,
+ classifier.fit(X_train, y_train)
fit_duration = time.monotonic() - fit_beg
logging.info("Done:\t Training")
@@ -122,10 +122,10 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
# Filling the full prediction in the right order
full_pred = np.zeros(Y.shape, dtype=int) - 100
- for trainIndex, index in enumerate(classification_indices[0]):
- full_pred[index] = train_pred[trainIndex]
- for testIndex, index in enumerate(classification_indices[1]):
- full_pred[index] = test_pred[testIndex]
+ for train_index, index in enumerate(classification_indices[0]):
+ full_pred[index] = train_pred[train_index]
+ for test_index, index in enumerate(classification_indices[1]):
+ full_pred[index] = test_pred[test_index]
logging.info("Done:\t Predicting")
@@ -185,7 +185,8 @@ def init_constants(args, X, classification_indices, labels_names,
base_file_name = cl_type_string + '-' + name + "-" + view_name + "-"
output_file_name = os.path.join(directory, base_file_name)
secure_file_path(output_file_name)
- return kwargs, t_start, view_name, cl_type, X, learning_rate, labels_string, output_file_name, directory, base_file_name
+ return kwargs, t_start, view_name, cl_type, X, learning_rate, labels_string,\
+ output_file_name, directory, base_file_name
def init_train_test(X, Y, classification_indices):
diff --git a/summit/multiview_platform/monoview/monoview_utils.py b/summit/multiview_platform/monoview/monoview_utils.py
index c46c8b2e75c4e2b28ee2278fdd3f44316cbc5e44..2fbf53d3e077e013382663ceaa2af5ce57024531 100644
--- a/summit/multiview_platform/monoview/monoview_utils.py
+++ b/summit/multiview_platform/monoview/monoview_utils.py
@@ -13,8 +13,6 @@ __author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
-# __date__ = 2016 - 03 - 25
-
def change_label_to_minus(y):
"""
Change the label 0 to minus one
@@ -76,43 +74,6 @@ def gen_test_folds_preds(X_train, y_train, KFolds, estimator):
return test_folds_preds
-# class CustomRandint:
-# """Used as a distribution returning a integer between low and high-1.
-# It can be used with a multiplier agrument to be able to perform more complex generation
-# for example 10 e -(randint)"""
-#
-# def __init__(self, low=0, high=0, multiplier=""):
-# self.randint = randint(low, high)
-# self.multiplier = multiplier
-#
-# def rvs(self, random_state=None):
-# randinteger = self.randint.rvs(random_state=random_state)
-# if self.multiplier == "e-":
-# return 10 ** -randinteger
-# else:
-# return randinteger
-#
-# def get_nb_possibilities(self):
-# return self.randint.b - self.randint.a
-#
-#
-# class CustomUniform:
-# """Used as a distribution returning a float between loc and loc + scale..
-# It can be used with a multiplier agrument to be able to perform more complex generation
-# for example 10 e -(float)"""
-#
-# def __init__(self, loc=0, state=1, multiplier=""):
-# self.uniform = uniform(loc, state)
-# self.multiplier = multiplier
-#
-# def rvs(self, random_state=None):
-# unif = self.uniform.rvs(random_state=random_state)
-# if self.multiplier == 'e-':
-# return 10 ** -unif
-# else:
-# return unif
-
-
class BaseMonoviewClassifier(BaseClassifier):
def get_feature_importance(self, directory, base_file_name,
diff --git a/summit/multiview_platform/multiview/exec_multiview.py b/summit/multiview_platform/multiview/exec_multiview.py
index 1f3dcdc39b11f0d0e79c3f1629068bbfd72973b4..a594624c8f5aab3e332b6ef7d20776d91308500c 100644
--- a/summit/multiview_platform/multiview/exec_multiview.py
+++ b/summit/multiview_platform/multiview/exec_multiview.py
@@ -260,11 +260,8 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
logging.info("Done:\t Getting train/test split")
logging.info("Start:\t Getting classifiers modules")
- # classifierPackage = getattr(multiview_classifiers,
- # CL_type) # Permet d'appeler un module avec une string
classifier_module = getattr(multiview_classifiers, cl_type)
classifier_name = classifier_module.classifier_class_name
- # classifierClass = getattr(classifierModule, CL_type + "Class")
logging.info("Done:\t Getting classifiers modules")
logging.info("Start:\t Optimizing hyperparameters")
@@ -285,13 +282,6 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
hps.fit(dataset_var, dataset_var.get_labels(), )
classifier_config = hps.get_best_params()
hps.gen_report(output_file_name)
- # classifier_config = hyper_parameter_search.search_best_settings(
- # dataset_var, dataset_var.get_labels(), classifier_module,
- # classifier_name,
- # metrics[0], learning_indices, k_folds, random_state,
- # output_file_name, nb_cores=nb_cores, views_indices=views_indices,
- # searching_tool=hps_method, n_iter=n_iter,
- # classifier_config=classifier_config)
hps_duration = time.monotonic() - hps_beg
classifier = get_mc_estim(
getattr(classifier_module, classifier_name)(random_state=random_state,
@@ -325,8 +315,6 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
logging.info(
"Info:\t Classification duration " + str(extraction_time) + "s")
- # TODO: get better cltype
-
logging.info("Start:\t Result Analysis for " + cl_type)
times = (extraction_time, whole_duration)
result_analyzer = MultiviewResultAnalyzer(view_names=views,
diff --git a/summit/multiview_platform/multiview/multiview_utils.py b/summit/multiview_platform/multiview/multiview_utils.py
index 88c4ac534a3623a67cfe7acdc5903edb04e41587..6a26aaa37623b1b1f265e10af39ecdc3bef513fa 100644
--- a/summit/multiview_platform/multiview/multiview_utils.py
+++ b/summit/multiview_platform/multiview/multiview_utils.py
@@ -7,13 +7,6 @@ from .. import monoview_classifiers
from ..utils.base import BaseClassifier, ResultAnalyser
from ..utils.dataset import RAMDataset
-
-# class FakeEstimator():
-#
-# def predict(self, X, sample_indices=None, view_indices=None):
-# return np.zeros(sample_indices.shape[0])
-
-
class BaseMultiviewClassifier(BaseClassifier):
"""
BaseMultiviewClassifier base of Multiview classifiers
@@ -49,21 +42,6 @@ class BaseMultiviewClassifier(BaseClassifier):
'Used {} views to fit, and trying to predict on {}'.format(
self.used_views, view_indices))
- # def to_str(self, param_name):
- # if param_name in self.weird_strings:
- # string = ""
- # if "class_name" in self.weird_strings[param_name]:
- # string += self.get_params()[param_name].__class__.__name__
- # if "config" in self.weird_strings[param_name]:
- # string += "( with " + self.get_params()[
- # param_name].params_to_string() + ")"
- # else:
- # string += self.weird_strings[param_name](
- # self.get_params()[param_name])
- # return string
- # else:
- # return str(self.get_params()[param_name])
-
def accepts_multi_class(self, random_state, n_samples=10, dim=2,
n_classes=3, n_views=2):
if int(n_samples / n_classes) < 1:
diff --git a/summit/multiview_platform/result_analysis/error_analysis.py b/summit/multiview_platform/result_analysis/error_analysis.py
index 7ff5f06b9471ba08a15d2487e334285e1c6c5ef1..90d6b69756560a450b304db55abba3cea496f888 100644
--- a/summit/multiview_platform/result_analysis/error_analysis.py
+++ b/summit/multiview_platform/result_analysis/error_analysis.py
@@ -199,7 +199,6 @@ def plot_2d(data, classifiers_names, nb_classifiers, file_name, dataset_name, la
plt.close()
# The following part is used to generate an interactive graph.
if use_plotly:
- # [np.where(labels==i)[0] for i in np.unique(labels)]
hover_text = [[sample_ids[sample_index] + " failed " + str(
stats_iter - data[
sample_index, classifier_index]) + " time(s), labelled " + str(