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Commit cbef2800 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Removing warnings

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Pipeline #3574 failed
Stage: test
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config_files/config.yml
+ 1
− 1
View file @ cbef2800
# The base configuration of the benchmark
Base :
log: true
name: ["Plausible"]
name: ["plausible"]
label: "_"
type: ".hdf5"
views:
......
multiview_platform/mono_multi_view_classifiers/utils/configuration.py
+ 13
− 4
View file @ cbef2800
import builtins
from distutils.util import strtobool as tobool
import yaml
import os
def get_the_args(path_to_config_file="../config_files/config.yml"):
"""This is the main function for extracting the args for a '.yml' file"""
"""
The function for extracting the args for a '.yml' file.
Parameters
----------
path_to_config_file : str, path to the yml file containing the configuration
Returns
-------
yaml_config : dict, the dictionary conaining the configuration for the
benchmark
"""
with open(path_to_config_file, 'r') as stream:
yaml_config = yaml.safe_load(stream)
return yaml_config
multiview_platform/mono_multi_view_classifiers/utils/dataset.py
+ 30
− 30
View file @ cbef2800
......@@ -144,7 +144,7 @@ class Dataset():
-------
"""
self.nb_view = self.dataset.get("Metadata").attrs["nbView"]
self.nb_view = self.dataset["Metadata"].attrs["nbView"]
self.view_dict = self.get_view_dict()
def get_nb_examples(self):
......@@ -154,12 +154,12 @@ class Dataset():
-------
"""
return self.dataset.get("Metadata").attrs["datasetLength"]
return self.dataset["Metadata"].attrs["datasetLength"]
def get_view_dict(self):
view_dict = {}
for view_index in range(self.nb_view):
view_dict[self.dataset.get("View" + str(view_index)).attrs["name"]] = view_index
view_dict[self.dataset["View" + str(view_index)].attrs["name"]] = view_index
return view_dict
def get_label_names(self, decode=True, example_indices=None):
......@@ -167,11 +167,11 @@ class Dataset():
selected_labels = self.get_labels(example_indices)
if decode:
return [label_name.decode("utf-8")
for label, label_name in enumerate(self.dataset.get("Labels").attrs["names"])
for label, label_name in enumerate(self.dataset["Labels"].attrs["names"])
if label in selected_labels]
else:
return [label_name
for label, label_name in enumerate(self.dataset.get("Labels").attrs["names"])
for label, label_name in enumerate(self.dataset["Labels"].attrs["names"])
if label in selected_labels]
def init_example_indces(self, example_indices=None):
......@@ -183,21 +183,21 @@ class Dataset():
def get_v(self, view_index, example_indices=None):
example_indices = self.init_example_indces(example_indices)
if type(example_indices) is int:
return self.dataset.get("View" + str(view_index))[example_indices, :]
return self.dataset["View" + str(view_index)][example_indices, :]
else:
example_indices = np.array(example_indices)
sorted_indices = np.argsort(example_indices)
example_indices = example_indices[sorted_indices]
if not self.dataset.get("View" + str(view_index)).attrs["sparse"]:
return self.dataset.get("View" + str(view_index))[example_indices, :][
if not self.dataset["View" + str(view_index)].attrs["sparse"]:
return self.dataset["View" + str(view_index)][example_indices, :][
np.argsort(sorted_indices), :]
else:
sparse_mat = sparse.csr_matrix(
(self.dataset.get("View" + str(view_index)).get("data").value,
self.dataset.get("View" + str(view_index)).get("indices").value,
self.dataset.get("View" + str(view_index)).get("indptr").value),
shape=self.dataset.get("View" + str(view_index)).attrs["shape"])[
(self.dataset["View" + str(view_index)]["data"][()],
self.dataset["View" + str(view_index)]["indices"][()],
self.dataset["View" + str(view_index)]["indptr"][()]),
shape=self.dataset["View" + str(view_index)].attrs["shape"])[
example_indices, :][
np.argsort(sorted_indices), :]
......@@ -208,11 +208,11 @@ class Dataset():
def get_nb_class(self, example_indices=None):
example_indices = self.init_example_indces(example_indices)
return len(np.unique(self.dataset.get("Labels").value[example_indices]))
return len(np.unique(self.dataset["Labels"][example_indices]))
def get_labels(self, example_indices=None):
example_indices = self.init_example_indces(example_indices)
return self.dataset.get("Labels").value[example_indices]
return self.dataset["Labels"][example_indices]
def copy_view(self, target_dataset=None, source_view_name=None,
target_view_index=None, example_indices=None):
......@@ -220,7 +220,7 @@ class Dataset():
new_d_set = target_dataset.create_dataset("View"+str(target_view_index),
data=self.get_v(self.view_dict[source_view_name],
example_indices=example_indices))
for key, value in self.dataset.get("View"+str(self.view_dict[source_view_name])).attrs.items():
for key, value in self.dataset["View"+str(self.view_dict[source_view_name])].attrs.items():
new_d_set.attrs[key] = value
def init_view_names(self, view_names=None):
......@@ -240,15 +240,15 @@ class Dataset():
dataset_file_path = os.path.join(path,self.get_name()+"_temp_filter.hdf5")
new_dataset_file = h5py.File(dataset_file_path,"w")
self.dataset.copy("Metadata", new_dataset_file)
new_dataset_file.get("Metadata").attrs["datasetLength"] = len(example_indices)
new_dataset_file.get("Metadata").attrs["nbClass"] = np.unique(labels)
new_dataset_file["Metadata"].attrs["datasetLength"] = len(example_indices)
new_dataset_file["Metadata"].attrs["nbClass"] = np.unique(labels)
new_dataset_file.create_dataset("Labels", data=labels)
new_dataset_file.get("Labels").attrs["names"] = [label_name.encode()
new_dataset_file["Labels"].attrs["names"] = [label_name.encode()
if not isinstance(label_name, bytes)
else label_name
for label_name in label_names]
view_names = self.init_view_names(view_names)
new_dataset_file.get("Metadata").attrs["nbView"] = len(view_names)
new_dataset_file["Metadata"].attrs["nbView"] = len(view_names)
for new_index, view_name in enumerate(view_names):
self.copy_view(target_dataset=new_dataset_file,
source_view_name=view_name,
......@@ -271,18 +271,18 @@ class Dataset():
self.copy_view(target_dataset=noisy_dataset,
source_view_name=self.get_view_name(view_index),
target_view_index=view_index)
for view_index in range(noisy_dataset.get("Metadata").attrs["nbView"]):
for view_index in range(noisy_dataset["Metadata"].attrs["nbView"]):
view_key = "View" + str(view_index)
view_dset = noisy_dataset.get(view_key)
view_dset = noisy_dataset.get[view_key]
try:
view_limits = self.dataset[
"Metadata/View" + str(view_index) + "_limits"].value
"Metadata/View" + str(view_index) + "_limits"][()]
except:
import pdb;pdb.set_trace()
view_ranges = view_limits[:, 1] - view_limits[:, 0]
normal_dist = random_state.normal(0, noise_std, view_dset.value.shape)
normal_dist = random_state.normal(0, noise_std, view_dset[()].shape)
noise = normal_dist * view_ranges
noised_data = view_dset.value + noise
noised_data = view_dset[()] + noise
noised_data = np.where(noised_data < view_limits[:, 0],
view_limits[:, 0], noised_data)
noised_data = np.where(noised_data > view_limits[:, 1],
......@@ -439,11 +439,11 @@ def get_shape(dataset, view_index):
def get_value(dataset):
"""Used to get the value of a view in the HDF5 dataset even if it sparse"""
if not dataset.attrs["sparse"]:
return dataset.value
return dataset[()]
else:
sparse_mat = sparse.csr_matrix((dataset.get("data").value,
dataset.get("indices").value,
dataset.get("indptr").value),
sparse_mat = sparse.csr_matrix((dataset.get("data")[()],
dataset.get("indices")[()],
dataset.get("indptr")[()]),
shape=dataset.attrs["shape"])
return sparse_mat
......@@ -559,6 +559,6 @@ def input_(timeout=15):
def get_monoview_shared(path, name, view_name, labels_names, classification_indices):
"""ATM is not used with shared memory, but soon :)"""
hdf5_dataset_file = h5py.File(path + name + ".hdf5", "w")
X = hdf5_dataset_file.get(view_name).value
y = hdf5_dataset_file.get("Labels").value
X = hdf5_dataset_file.get(view_name)[()]
y = hdf5_dataset_file.get("Labels")[()]
return X, y
multiview_platform/mono_multi_view_classifiers/utils/get_multiview_db.py
+ 1
− 1
View file @ cbef2800
......@@ -36,7 +36,7 @@ def get_plausible_db_hdf5(features, path, file_name, nb_class=3,
label_names=["No".encode(), "Yes".encode(),
"Maybe".encode()],
random_state=None, full=True, add_noise=False,
noise_std=0.15, nb_view=3, nb_examples=5000,
noise_std=0.15, nb_view=3, nb_examples=100,
nb_features=10):
"""Used to generate a plausible dataset to test the algorithms"""
......
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