diff --git a/.idea/multiview_generator.iml b/.idea/multiview_generator.iml
index d6ebd4805981b8400db3e3291c74a743fef9a824..8427e77f22c557c911155af37a38d22c88ad3cf0 100644
--- a/.idea/multiview_generator.iml
+++ b/.idea/multiview_generator.iml
@@ -1,9 +1,10 @@
<?xml version="1.0" encoding="UTF-8"?>
<module type="JAVA_MODULE" version="4">
- <component name="NewModuleRootManager" inherit-compiler-output="true">
- <exclude-output />
+ <component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
+ <orderEntry type="library" name="R User Library" level="project" />
+ <orderEntry type="library" name="R Skeletons" level="application" />
</component>
</module>
\ No newline at end of file
diff --git a/generator/update_baptiste.py b/generator/update_baptiste.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3810c093504e269d928704de95cd36a52d810e8
--- /dev/null
+++ b/generator/update_baptiste.py
@@ -0,0 +1,286 @@
+import os
+import yaml
+import numpy as np
+from sklearn.datasets import make_classification
+from random import gauss
+from math import ceil, floor
+import pandas as pd
+import shutil
+import h5py
+
+class MultiviewDatasetGenetator():
+
+ def __init__(self, n_samples=100, n_views=2, n_classes=2,
+ Z_factor=2,
+ R=0,
+ n_clusters_per_class=1,
+ class_sep_factor=10,
+ n_informative_divid=2,
+ d=4,
+ D=10,
+ standard_deviation=2,
+ weights=None,
+ flip_y=0.0,
+ random_state=42, config_path=None):
+ if config_path is not None:
+ with open(config_path) as config_file:
+ args = yaml.safe_load(config_file)
+ self.__init__(**args)
+ else:
+ self.n_samples = n_samples
+ self.n_views = n_views
+ self.n_classes = n_classes
+ self.Z_factor = Z_factor
+ self.R = R
+ self.n_clusters_per_class = n_clusters_per_class
+ self.class_sep_factor = class_sep_factor
+ self.n_informative_divid = n_informative_divid
+ self.d = d
+ self.D = D
+ self.standard_deviation = standard_deviation
+ self.weights = weights
+ self.flip_y = flip_y
+ self.random_state = random_state
+
+ def generate(self):
+ if self.n_views < 2:
+ raise ValueError("n_views >= 2")
+ if self.n_classes < 2:
+ raise ValueError("n_classes >= 2")
+ if self.Z_factor < 1:
+ raise ValueError(
+ "Z_factor >= 1 pour le bon fonctionnement de l'algorithme")
+ if (self.R < 0) or (self.R > 1):
+ raise ValueError("0 <= R <= 1")
+ if self.n_clusters_per_class < 1:
+ raise ValueError("n_clusters_per_class >= 1")
+ if self.class_sep_factor < 0:
+ raise ValueError("class_sep_factor >= 0")
+ if self.n_informative_divid < 1:
+ raise ValueError("n_informative_divid >= 1")
+ if self.d < 1:
+ raise ValueError("d >= 1")
+ if (self.d + self.D) / 2 - 3 * self.standard_deviation < 1:
+ raise ValueError(
+ "Il faut que (d+D)/2 - 3*standard_deviation >= 1 pour avoir des valeurs positives non nulles lors de l'emploi de la loi normale")
+
+ # n_views dimension of view v values randomly from N((d+D)/2, standard_deviation^2)
+ d_v = np.random.normal(loc=(self.d + self.D) / 2,
+ scale=self.standard_deviation,
+ size=self.n_views)
+ d_v = list(d_v)
+ remove_list, add_list = [], []
+ for dim_view in d_v:
+ if dim_view < self.d or dim_view > self.D: # 1 <= d <= dim_view <= D
+ remove_list.append(dim_view)
+ add = -1
+ while add < self.d or add > self.D:
+ add = gauss((self.d + self.D) / 2, self.standard_deviation)
+ add_list.append(add)
+ d_v = [view for view in d_v if view not in remove_list] + add_list
+ d_v = [int(view) for view in d_v] # dimension of views = integer
+ # d_v = list of views dimension from the highest to the lowest
+ d_v.sort(reverse=True)
+ # Dimension of latent space Z (multiplied by Z_factor)
+ self.dim_Z = self.Z_factor * self.latent_space_dimension(d_v)
+ # Number of informative features
+ self.n_informative = round(self.dim_Z / self.n_informative_divid)
+ # Generation of latent space Z
+ self.Z, self.y = make_classification(n_samples=self.n_samples, n_features=self.dim_Z,
+ n_informative=self.n_informative, n_redundant=0,
+ n_repeated=0, n_classes=self.n_classes,
+ n_clusters_per_class=self.n_clusters_per_class,
+ weights=self.weights,
+ flip_y=self.flip_y,
+ class_sep=self.n_clusters_per_class * self.class_sep_factor,
+ random_state=self.random_state, shuffle=False)
+ I_q = np.arange(self.Z.shape[1])
+ meta_I_v = []
+ self.results = []
+ for view in range(n_views):
+ # choice d_v[view] numeros of Z columns uniformly from I_q
+ I_v = np.random.choice(I_q, size=d_v[view],
+ replace=False) # tirage dans I_q sans remise de taille d_v[view]
+ meta_I_v += list(I_v)
+ # projection of Z along the columns in I_v
+ X_v = self.projection( I_v)
+ self.results.append((X_v, I_v))
+ # remove R*d_v[view] columns numeros of I_v form I_q
+ elements_to_remove = np.random.choice(I_v,
+ size=floor(self.R * d_v[view]),
+ replace=False) # tirage dans I_v sans remise de taille floor(R*d_v[view])
+ I_q = np.setdiff1d(I_q,
+ elements_to_remove) # I_q less elements from elements_to_remove
+ self.unsued_dimensions_list = [column for column in I_q if
+ column not in meta_I_v]
+ self.unsued_dimensions_percent = round(
+ (len(self.unsued_dimensions_list) / self.dim_Z) * 100, 2)
+
+ def projection(self, chosen_columns_list):
+ """
+ Returns the projection of latent_space on the columns of chosen_columns_list (in chosen_columns_list order)
+
+ Parameters:
+ -----------
+ chosen_columns_list : list
+
+ Returns:
+ --------
+ an array of dimension (number of rows of latent_space, length of chosen_columns_list)
+ """
+ return self.Z[:, chosen_columns_list]
+
+ def latent_space_dimension(self, views_dimensions_list):
+ """
+ Returns the minimal dimension of latent space (enough to build the dataset) for generator_multiviews_dataset compared to views_dimensions_list
+
+ Parameters:
+ -----------
+ views_dimensions_list : list
+ R : float
+
+ Returns:
+ --------
+ an int
+ """
+ max_view_dimension = max(views_dimensions_list)
+ dimension = ceil(self.R * sum(views_dimensions_list))
+
+ if dimension < max_view_dimension:
+ dimension = max_view_dimension
+
+ reduced_dimension = dimension
+ remove_sum = 0
+
+ for num_view in range(1, len(views_dimensions_list)):
+ view_prec = views_dimensions_list[num_view - 1]
+ view_current = views_dimensions_list[num_view]
+ remove = floor(self.R * view_prec)
+ remove_sum += remove
+ if reduced_dimension - remove < view_current:
+ dimension += view_current - (reduced_dimension - remove)
+ reduced_dimension = dimension - remove_sum
+
+ return dimension
+
+ def to_csv(self, saving_path="."):
+ """
+ Create length of multiviews_list + 2 csv files to the indicated path
+ Files name :
+ latent_space.csv for latent_space
+ integer_labels.csv for integer_labels
+ view0.csv for multiviews_list[0]
+
+ Parameters:
+ -----------
+ path : str
+ latent_space : array
+ integer_labels : 1D array
+ multiviews_list : list of tuples
+
+ Returns:
+ --------
+ None
+ """
+ df_latent_space = pd.DataFrame(self.Z)
+ df_latent_space.to_csv(os.path.join(saving_path, 'latent_space.csv')
+ , index=False)
+
+ df_labels = pd.DataFrame(self.y)
+ df_labels.to_csv(os.path.join(saving_path, 'integer_labels.csv'),
+ index=False)
+
+ for view_index, view_tuple in enumerate(self.results):
+ df_view = pd.DataFrame(view_tuple[0], columns=view_tuple[1])
+ df_view.to_csv(os.path.join(saving_path,
+ 'view'+str(view_index)+'.csv'),
+ index=False)
+
+ def to_hdf5(self, saving_path=".", name="generated_dset"):
+
+ dataset_file = h5py.File(os.path.join(saving_path, name+".hdf5"), 'w')
+
+ labels_dataset = dataset_file.create_dataset("Labels",
+ shape=self.y.shape,
+ data=self.y)
+
+ labels_names = ["Label_1", "Label_0"]
+
+ labels_dataset.attrs["names"] = [
+ label_name.encode() if not isinstance(label_name, bytes)
+ else label_name for label_name in labels_names]
+
+ for view_index, (data, feature_indices) in enumerate(self.results):
+ df_dataset = dataset_file.create_dataset("View" + str(view_index),
+ shape=data.shape,
+ data=data)
+
+ df_dataset.attrs["sparse"] = False
+ df_dataset.attrs["name"] = "GeneratedView"+str(view_index)
+
+ meta_data_grp = dataset_file.create_group("Metadata")
+
+ meta_data_grp.attrs["nbView"] = len(self.results)
+ meta_data_grp.attrs["nbClass"] = np.unique(self.y)
+ meta_data_grp.attrs["datasetLength"] = \
+ self.results[0][0].shape[0]
+
+ meta_data_grp.create_dataset("example_ids", data=np.array(
+ ["gen_example_" + str(ex_indx) for ex_indx in
+ range(self.results[0][0].shape[0])]).astype(
+ np.dtype("S100")), dtype=np.dtype("S100"))
+
+ dataset_file.close()
+
+if __name__=="__main__":
+ n_samples = 100 # Number of samples in tha dataset
+ n_views = 4 # Number of views in the dataset
+ n_classes = 2 # Number of classes in the dataset
+ Z_factor = 2 # Z dim = latent_space_dim * z_factor
+ R = 0 # Precentage of non-redundant features in the view
+ n_clusters_per_class = 1 # Number of clusters for each class
+ class_sep_factor = 10000 # Separation between the different classes
+ n_informative_divid = 2 # Divides the number of informative features in the latent space
+ standard_deviation = 2
+ d = 4
+ D = 10
+ flip_y = 0.00
+ random_state = 42
+ weights = None # The proportions of examples in each class
+
+ path = "/home/baptiste/Documents/Datasets/Generated/metrics_dset/"
+ name = "metrics"
+ if not os.path.exists(path):
+ os.mkdir(path)
+
+ multiview_generator = MultiviewDatasetGenetator(n_samples=n_samples,
+ n_views=n_views,
+ n_classes=n_classes,
+ Z_factor=Z_factor,
+ R=R,
+ n_clusters_per_class=n_clusters_per_class,
+ class_sep_factor=class_sep_factor,
+ n_informative_divid=n_informative_divid,
+ d=d,
+ D=D,
+ standard_deviation=standard_deviation,
+ flip_y=flip_y,
+ weights=weights,
+ random_state=random_state)
+
+ multiview_generator.generate()
+ multiview_generator.to_hdf5(saving_path=path, name=name)
+
+ # for filename in os.listdir(path):
+ # file_path = os.path.join(path, filename)
+ # try:
+ # if os.path.isfile(file_path) or os.path.islink(file_path):
+ # os.unlink(file_path)
+ # elif os.path.isdir(file_path):
+ # shutil.rmtree(file_path)
+ # except Exception as e:
+ # print('Failed to delete %s. Reason: %s' % (file_path, e))
+ # changing_labels_indices = np.random.RandomState(random_state).choice(np.arange(y.shape[0]), n_outliers)
+ # print(changing_labels_indices)
+ # y[changing_labels_indices] = np.invert(y[changing_labels_indices].astype(bool)).astype(int)
+ # results_to_csv(path, Z, y, results)
\ No newline at end of file
diff --git a/late/__pycache__/multiviews_datasets_generator.cpython-36.pyc b/late/__pycache__/multiviews_datasets_generator.cpython-36.pyc
index 4cc36e52166be6df44fd7ed479f9ac3fbc60f93c..828277d0901732be8b003cb85e93cd846ecc6115 100644
Binary files a/late/__pycache__/multiviews_datasets_generator.cpython-36.pyc and b/late/__pycache__/multiviews_datasets_generator.cpython-36.pyc differ
diff --git a/late/execute.py b/late/execute.py
index 9538308c1ad26f9102a664b5f29a7c6340253cd6..a3810c093504e269d928704de95cd36a52d810e8 100644
--- a/late/execute.py
+++ b/late/execute.py
@@ -1,35 +1,286 @@
import os
+import yaml
import numpy as np
+from sklearn.datasets import make_classification
+from random import gauss
+from math import ceil, floor
+import pandas as pd
+import shutil
+import h5py
-from multiviews_datasets_generator import generator_multiviews_dataset, results_to_csv
-
-n_samples = 200 #Number of samples in tha dataset
-n_views = 4 # Number of views in the dataset
-n_classes = 2 # Number of classes in the dataset
-Z_factor = 1 # Z dim = latent_space_dim * z_factor
-R = 0 # Precentage of non-redundant features in the view
-n_clusters_per_class = 1 # Number of clusters for each class
-class_sep_factor = 100 # Separation between the different classes
-n_informative_divid = 1 # Divides the number of informative features in the latent space
-standard_deviation = 2
-d = 4
-D = 10
-random_state = 42
-n_outliers = 10
-
-path = "/home/baptiste/Documents/Datasets/Generated/outliers_dset/"
-if not os.path.exists(path):
- os.mkdir(path)
-
-Z, y, results, unsued_dimensions_percent, n_informative = generator_multiviews_dataset(n_samples, n_views, n_classes,
- Z_factor, R,
- n_clusters_per_class,
- class_sep_factor,
- n_informative_divid, d, D,
- standard_deviation)
-print(unsued_dimensions_percent)
-print(n_informative)
-print(Z.shape)
-changing_labels_indices = np.random.RandomState(random_state).choice(np.arange(y.shape[0]), n_outliers)
-y[changing_labels_indices] = np.invert(y[changing_labels_indices].astype(bool)).astype(int)
-results_to_csv(path, Z, y, results)
\ No newline at end of file
+class MultiviewDatasetGenetator():
+
+ def __init__(self, n_samples=100, n_views=2, n_classes=2,
+ Z_factor=2,
+ R=0,
+ n_clusters_per_class=1,
+ class_sep_factor=10,
+ n_informative_divid=2,
+ d=4,
+ D=10,
+ standard_deviation=2,
+ weights=None,
+ flip_y=0.0,
+ random_state=42, config_path=None):
+ if config_path is not None:
+ with open(config_path) as config_file:
+ args = yaml.safe_load(config_file)
+ self.__init__(**args)
+ else:
+ self.n_samples = n_samples
+ self.n_views = n_views
+ self.n_classes = n_classes
+ self.Z_factor = Z_factor
+ self.R = R
+ self.n_clusters_per_class = n_clusters_per_class
+ self.class_sep_factor = class_sep_factor
+ self.n_informative_divid = n_informative_divid
+ self.d = d
+ self.D = D
+ self.standard_deviation = standard_deviation
+ self.weights = weights
+ self.flip_y = flip_y
+ self.random_state = random_state
+
+ def generate(self):
+ if self.n_views < 2:
+ raise ValueError("n_views >= 2")
+ if self.n_classes < 2:
+ raise ValueError("n_classes >= 2")
+ if self.Z_factor < 1:
+ raise ValueError(
+ "Z_factor >= 1 pour le bon fonctionnement de l'algorithme")
+ if (self.R < 0) or (self.R > 1):
+ raise ValueError("0 <= R <= 1")
+ if self.n_clusters_per_class < 1:
+ raise ValueError("n_clusters_per_class >= 1")
+ if self.class_sep_factor < 0:
+ raise ValueError("class_sep_factor >= 0")
+ if self.n_informative_divid < 1:
+ raise ValueError("n_informative_divid >= 1")
+ if self.d < 1:
+ raise ValueError("d >= 1")
+ if (self.d + self.D) / 2 - 3 * self.standard_deviation < 1:
+ raise ValueError(
+ "Il faut que (d+D)/2 - 3*standard_deviation >= 1 pour avoir des valeurs positives non nulles lors de l'emploi de la loi normale")
+
+ # n_views dimension of view v values randomly from N((d+D)/2, standard_deviation^2)
+ d_v = np.random.normal(loc=(self.d + self.D) / 2,
+ scale=self.standard_deviation,
+ size=self.n_views)
+ d_v = list(d_v)
+ remove_list, add_list = [], []
+ for dim_view in d_v:
+ if dim_view < self.d or dim_view > self.D: # 1 <= d <= dim_view <= D
+ remove_list.append(dim_view)
+ add = -1
+ while add < self.d or add > self.D:
+ add = gauss((self.d + self.D) / 2, self.standard_deviation)
+ add_list.append(add)
+ d_v = [view for view in d_v if view not in remove_list] + add_list
+ d_v = [int(view) for view in d_v] # dimension of views = integer
+ # d_v = list of views dimension from the highest to the lowest
+ d_v.sort(reverse=True)
+ # Dimension of latent space Z (multiplied by Z_factor)
+ self.dim_Z = self.Z_factor * self.latent_space_dimension(d_v)
+ # Number of informative features
+ self.n_informative = round(self.dim_Z / self.n_informative_divid)
+ # Generation of latent space Z
+ self.Z, self.y = make_classification(n_samples=self.n_samples, n_features=self.dim_Z,
+ n_informative=self.n_informative, n_redundant=0,
+ n_repeated=0, n_classes=self.n_classes,
+ n_clusters_per_class=self.n_clusters_per_class,
+ weights=self.weights,
+ flip_y=self.flip_y,
+ class_sep=self.n_clusters_per_class * self.class_sep_factor,
+ random_state=self.random_state, shuffle=False)
+ I_q = np.arange(self.Z.shape[1])
+ meta_I_v = []
+ self.results = []
+ for view in range(n_views):
+ # choice d_v[view] numeros of Z columns uniformly from I_q
+ I_v = np.random.choice(I_q, size=d_v[view],
+ replace=False) # tirage dans I_q sans remise de taille d_v[view]
+ meta_I_v += list(I_v)
+ # projection of Z along the columns in I_v
+ X_v = self.projection( I_v)
+ self.results.append((X_v, I_v))
+ # remove R*d_v[view] columns numeros of I_v form I_q
+ elements_to_remove = np.random.choice(I_v,
+ size=floor(self.R * d_v[view]),
+ replace=False) # tirage dans I_v sans remise de taille floor(R*d_v[view])
+ I_q = np.setdiff1d(I_q,
+ elements_to_remove) # I_q less elements from elements_to_remove
+ self.unsued_dimensions_list = [column for column in I_q if
+ column not in meta_I_v]
+ self.unsued_dimensions_percent = round(
+ (len(self.unsued_dimensions_list) / self.dim_Z) * 100, 2)
+
+ def projection(self, chosen_columns_list):
+ """
+ Returns the projection of latent_space on the columns of chosen_columns_list (in chosen_columns_list order)
+
+ Parameters:
+ -----------
+ chosen_columns_list : list
+
+ Returns:
+ --------
+ an array of dimension (number of rows of latent_space, length of chosen_columns_list)
+ """
+ return self.Z[:, chosen_columns_list]
+
+ def latent_space_dimension(self, views_dimensions_list):
+ """
+ Returns the minimal dimension of latent space (enough to build the dataset) for generator_multiviews_dataset compared to views_dimensions_list
+
+ Parameters:
+ -----------
+ views_dimensions_list : list
+ R : float
+
+ Returns:
+ --------
+ an int
+ """
+ max_view_dimension = max(views_dimensions_list)
+ dimension = ceil(self.R * sum(views_dimensions_list))
+
+ if dimension < max_view_dimension:
+ dimension = max_view_dimension
+
+ reduced_dimension = dimension
+ remove_sum = 0
+
+ for num_view in range(1, len(views_dimensions_list)):
+ view_prec = views_dimensions_list[num_view - 1]
+ view_current = views_dimensions_list[num_view]
+ remove = floor(self.R * view_prec)
+ remove_sum += remove
+ if reduced_dimension - remove < view_current:
+ dimension += view_current - (reduced_dimension - remove)
+ reduced_dimension = dimension - remove_sum
+
+ return dimension
+
+ def to_csv(self, saving_path="."):
+ """
+ Create length of multiviews_list + 2 csv files to the indicated path
+ Files name :
+ latent_space.csv for latent_space
+ integer_labels.csv for integer_labels
+ view0.csv for multiviews_list[0]
+
+ Parameters:
+ -----------
+ path : str
+ latent_space : array
+ integer_labels : 1D array
+ multiviews_list : list of tuples
+
+ Returns:
+ --------
+ None
+ """
+ df_latent_space = pd.DataFrame(self.Z)
+ df_latent_space.to_csv(os.path.join(saving_path, 'latent_space.csv')
+ , index=False)
+
+ df_labels = pd.DataFrame(self.y)
+ df_labels.to_csv(os.path.join(saving_path, 'integer_labels.csv'),
+ index=False)
+
+ for view_index, view_tuple in enumerate(self.results):
+ df_view = pd.DataFrame(view_tuple[0], columns=view_tuple[1])
+ df_view.to_csv(os.path.join(saving_path,
+ 'view'+str(view_index)+'.csv'),
+ index=False)
+
+ def to_hdf5(self, saving_path=".", name="generated_dset"):
+
+ dataset_file = h5py.File(os.path.join(saving_path, name+".hdf5"), 'w')
+
+ labels_dataset = dataset_file.create_dataset("Labels",
+ shape=self.y.shape,
+ data=self.y)
+
+ labels_names = ["Label_1", "Label_0"]
+
+ labels_dataset.attrs["names"] = [
+ label_name.encode() if not isinstance(label_name, bytes)
+ else label_name for label_name in labels_names]
+
+ for view_index, (data, feature_indices) in enumerate(self.results):
+ df_dataset = dataset_file.create_dataset("View" + str(view_index),
+ shape=data.shape,
+ data=data)
+
+ df_dataset.attrs["sparse"] = False
+ df_dataset.attrs["name"] = "GeneratedView"+str(view_index)
+
+ meta_data_grp = dataset_file.create_group("Metadata")
+
+ meta_data_grp.attrs["nbView"] = len(self.results)
+ meta_data_grp.attrs["nbClass"] = np.unique(self.y)
+ meta_data_grp.attrs["datasetLength"] = \
+ self.results[0][0].shape[0]
+
+ meta_data_grp.create_dataset("example_ids", data=np.array(
+ ["gen_example_" + str(ex_indx) for ex_indx in
+ range(self.results[0][0].shape[0])]).astype(
+ np.dtype("S100")), dtype=np.dtype("S100"))
+
+ dataset_file.close()
+
+if __name__=="__main__":
+ n_samples = 100 # Number of samples in tha dataset
+ n_views = 4 # Number of views in the dataset
+ n_classes = 2 # Number of classes in the dataset
+ Z_factor = 2 # Z dim = latent_space_dim * z_factor
+ R = 0 # Precentage of non-redundant features in the view
+ n_clusters_per_class = 1 # Number of clusters for each class
+ class_sep_factor = 10000 # Separation between the different classes
+ n_informative_divid = 2 # Divides the number of informative features in the latent space
+ standard_deviation = 2
+ d = 4
+ D = 10
+ flip_y = 0.00
+ random_state = 42
+ weights = None # The proportions of examples in each class
+
+ path = "/home/baptiste/Documents/Datasets/Generated/metrics_dset/"
+ name = "metrics"
+ if not os.path.exists(path):
+ os.mkdir(path)
+
+ multiview_generator = MultiviewDatasetGenetator(n_samples=n_samples,
+ n_views=n_views,
+ n_classes=n_classes,
+ Z_factor=Z_factor,
+ R=R,
+ n_clusters_per_class=n_clusters_per_class,
+ class_sep_factor=class_sep_factor,
+ n_informative_divid=n_informative_divid,
+ d=d,
+ D=D,
+ standard_deviation=standard_deviation,
+ flip_y=flip_y,
+ weights=weights,
+ random_state=random_state)
+
+ multiview_generator.generate()
+ multiview_generator.to_hdf5(saving_path=path, name=name)
+
+ # for filename in os.listdir(path):
+ # file_path = os.path.join(path, filename)
+ # try:
+ # if os.path.isfile(file_path) or os.path.islink(file_path):
+ # os.unlink(file_path)
+ # elif os.path.isdir(file_path):
+ # shutil.rmtree(file_path)
+ # except Exception as e:
+ # print('Failed to delete %s. Reason: %s' % (file_path, e))
+ # changing_labels_indices = np.random.RandomState(random_state).choice(np.arange(y.shape[0]), n_outliers)
+ # print(changing_labels_indices)
+ # y[changing_labels_indices] = np.invert(y[changing_labels_indices].astype(bool)).astype(int)
+ # results_to_csv(path, Z, y, results)
\ No newline at end of file
diff --git a/late/multiviews_datasets_generator.py b/late/multiviews_datasets_generator.py
index 1cce9a032e6ba6d2f43e43b3cf5d82b03e2a414d..d3b9bc68bac1840c6e4fc896b32049b9b9933433 100644
--- a/late/multiviews_datasets_generator.py
+++ b/late/multiviews_datasets_generator.py
@@ -63,7 +63,11 @@ def projection(latent_space, chosen_columns_list):
return latent_space[:, chosen_columns_list]
-def generator_multiviews_dataset(n_samples=1000, n_views=3, n_classes=2, Z_factor=250, R=2/3, n_clusters_per_class=1, class_sep_factor=2, n_informative_divid=2, d=2, D=12, standard_deviation=2):
+def generator_multiviews_dataset(n_samples=1000, n_views=3, n_classes=2,
+ Z_factor=250, R=2/3, n_clusters_per_class=1,
+ class_sep_factor=2, n_informative_divid=2,
+ d=2, D=12, standard_deviation=2, weights=None,
+ random_state=42):
"""
Returns a generator multiviews dataset
@@ -149,9 +153,22 @@ def generator_multiviews_dataset(n_samples=1000, n_views=3, n_classes=2, Z_facto
# Number of informative features
n_informative = round(dim_Z/n_informative_divid)
# Generation of latent space Z
- Z, y = make_classification(n_samples=n_samples, n_features=dim_Z, n_informative=n_informative, n_redundant=0,
- n_repeated=0, n_classes=n_classes, n_clusters_per_class=n_clusters_per_class, weights=None,
- flip_y=0.00, class_sep=n_clusters_per_class*class_sep_factor, random_state=None)
+ print("n_samples :", n_samples)
+ print("dim_Z :", dim_Z)
+ print("n_informative :", n_informative)
+ print("n_redundant :", 0)
+ print("n_repeated :", 0)
+ print("n_classes :", n_classes)
+ print("n_clusters_per_class :", n_clusters_per_class)
+ print("class_sep :", n_clusters_per_class*class_sep_factor)
+
+
+ Z, y = make_classification(n_samples=n_samples, n_features=dim_Z, n_informative=n_informative, n_redundant=0,
+ n_repeated=0, n_classes=n_classes, n_clusters_per_class=n_clusters_per_class, weights=weights,
+ flip_y=0.00, class_sep=n_clusters_per_class*class_sep_factor, random_state=random_state, shuffle=False)
+ # Z, y = make_classification(n_samples=200, n_features=10, n_informative=2, n_redundant=0,
+ # n_repeated=0, n_classes=2, n_clusters_per_class=1, weights=None,
+ # flip_y=0, class_sep=100, random_state=random_state, shuffle=False)
I_q = np.array([i for i in range(Z.shape[1])]) # 1D-array of Z columns numero
meta_I_v = []