firts add

demo/result.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 27 16:14:14 2019
+
+@author: bernardet
+"""
+
+import parameters
+from multiviews_datasets import generator_multiviews_dataset, results_to_csv
+from test_classifier import score_multiviews_n_samples, graph_comparaison_classifier_scores_n_samples, score_multiviews_R, score_multiviews_Z_factor, score_multiviews_n_views_R, score_multiviews_class_sep, score_one_multiview_dataset, score_multiviews_n_informative_divided
+
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+n_samples = parameters.n_samples
+n_views = parameters.n_views
+n_classes = 3#parameters.n_classes
+Z_factor = parameters.Z_factor
+R = parameters.R
+n_clusters_per_class = 1#parameters.n_clusters_per_class
+class_sep_factor = 2#5#2#parameters.class_sep_factor
+n_informative_divid = 2#parameters.n_informative_divid
+cv = parameters.cv
+classifier = parameters.classifier
+classifier_dictionary = parameters.classifier_dictionary
+d = parameters.d
+D = parameters.D
+standard_deviation = parameters.standard_deviation
+path_data = parameters.path_data
+path_graph = parameters.path_graph
+n_samples_list = parameters.n_samples_list
+R_list = parameters.R_list
+Z_factor_list = parameters.Z_factor_list
+n_views_list = parameters.n_views_list
+class_sep_factor_list = parameters.class_sep_factor_list
+n_informative_divid_list = parameters.n_informative_divid_list
+
+
+# Generate one dataset
+#Z, y, multiviews_list, unsued_columns_percent = 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(Z, y, multiviews_list)
+
+# Register one multiview dataset
+#results_to_csv(path, Z, y, multiviews_list)
+
+# Score of one multiview dataset
+#df_dimensions, df_scores_means, df_scores_std = score_one_multiview_dataset(cv, classifier, classifier_dictionary, n_samples, n_views, n_classes, Z_factor, R, n_clusters_per_class, class_sep_factor, n_informative_divid, d, D, standard_deviation)
+#print(df_dimensions, df_scores_means, df_scores_std)
+
+# Scores of n_samples_list datasets
+#mean_samples, std_samples = score_multiviews_n_samples(n_samples_list, path_graph, cv, classifier, classifier_dictionary, n_views, n_classes, Z_factor, R, n_clusters_per_class, class_sep_factor, n_informative_divid, d, D, standard_deviation)
+#print(mean_samples, std_samples)
+
+# Plot scores classifier2 vs score classifier1
+classifier1 = "SVM"
+classifier2 = "NB"
+#graph_comparaison_classifier_scores_n_samples(classifier1, classifier2, n_samples_list, path_graph, cv, classifier_dictionary, n_views, n_classes, Z_factor, R, n_clusters_per_class, class_sep_factor, n_informative_divid, d, D, standard_deviation)
+
+# Scores of R_list datasets
+#mean_R, std_R = score_multiviews_R(R_list, path_graph, cv, classifier, classifier_dictionary, n_samples, n_views, n_classes, Z_factor, n_clusters_per_class, class_sep_factor, n_informative_divid, d, D, standard_deviation)
+#print(mean_R, std_R)
+
+# Scores of Z_factor_list datasets
+#mean_Z, std_Z, error_Z = score_multiviews_Z_factor(Z_factor_list, path_graph, cv, classifier, classifier_dictionary, n_samples, n_views, n_classes, R, n_clusters_per_class, class_sep_factor, n_informative_divid, d, D, standard_deviation)
+#print(mean_Z, std_Z, error_Z)
+
+# Scores divided by scores for R=1 (redundancy null) of n_views_list and R_list datasets
+#dict_n_views_R_ratio = score_multiviews_n_views_R(n_views_list, R_list, path_graph, cv, classifier, classifier_dictionary, n_samples, n_classes, Z_factor, n_clusters_per_class, class_sep_factor, n_informative_divid, d, D, standard_deviation)
+#print(dict_n_views_R_ratio)
+
+# Scores of class_sep_factor_list datasets 
+#df_mean, df_std = score_multiviews_class_sep(class_sep_factor_list, path_data, path_graph, cv, classifier, classifier_dictionary, n_views, n_samples, n_classes, Z_factor, R, n_clusters_per_class, n_informative_divid, d, D, standard_deviation)
+#print(df_mean, df_std)
+
+# Scores of n_informative_divid_list datasets 
+#mean_n_info, std_n_info = score_multiviews_n_informative_divided(n_informative_divid_list, path_graph, cv, classifier, classifier_dictionary, n_views, n_samples, n_classes, Z_factor, R, n_clusters_per_class, class_sep_factor, d, D, standard_deviation)
+#print(mean_n_info, std_n_info)
+
+
+Z_factor_list = [1, 3, 10, 25, 100, 250, 1000]
+path_graph = "/home/bernardet/Documents/StageL3/Graph/n_views_3_10_1_clus_2_n_info_div/"
+n_classes = 2
+n_clusters_per_class = 1
+class_sep_factor = 2
+n_informative_divid = 2
+for n_views in range(3, 11):
+    n_samples = 500*n_views
+    mean_Z, std_Z, error_Z = score_multiviews_Z_factor(Z_factor_list, path_graph, cv, classifier, classifier_dictionary, n_samples, n_views, n_classes, R, n_clusters_per_class, class_sep_factor, n_informative_divid, d, D, standard_deviation)

generator/__init__.py

+__version__ = '1.0.dev0'

generator/multiviews_datasets.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov 26 15:38:38 2019
+
+@author: bernardet
+"""
+
+from sklearn.datasets import make_classification
+from random import gauss
+from math import ceil, floor
+import numpy as np
+import pandas as pd
+
+
+def latent_space_dimension(views_dimensions_list, R):
+    """
+    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(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(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 projection(latent_space, chosen_columns_list):
+    """
+    Returns the projection of latent_space on the columns of chosen_columns_list (in chosen_columns_list order)
+    
+    Parameters:
+    -----------
+    latent_space : array
+    chosen_columns_list : list
+        
+    Returns:
+    --------
+    an array of dimension (number of rows of latent_space, length of chosen_columns_list)
+    """
+    return latent_space[:, chosen_columns_list]
+
+
+def 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):
+    """
+    Returns a generator multiviews dataset
+    
+    Parameters:
+    -----------
+    n_samples : int
+                dataset number of samples (number of rows of dataset)
+    n_views : int >= 2
+              dataset number of views
+              one view is a set of some features (columns) of the latent space
+    n_classes : int >= 2
+                dataset number of classes 
+    Z_factor : float >= 1
+               minimal dimension of the latent space (enough to build the dataset) is calculed then multiplied by Z_factor 
+    R : 0 <= float <= 1
+        R = 1 <> no possibility of redundancy between views
+        R = 0 <> maximal possibility of redundancy between views
+    n_clusters_per_class : int
+    class_sep_factor : float
+                       class_sep = n_clusters_per_class*class_sep_factor
+    n_informative_divid : float >= 1
+                          n_informative_divid raises <> number of non-informative features raises
+                          n_informative_divid = 1 <> no non-informative features, number of informative features = dimension of latent space
+                          number of informative features = round(dimension of latent space / n_informative_divid)
+    d : float >= 1
+        minimal dimension of views
+        dimension of views (int) chosen randomly from N((d+D)/2, standard_deviation^2) with d <= dimension of views <= D
+    D : float >= d
+        maximal dimension of views
+        dimension of views (int) chosen randomly from N((d+D)/2, standard_deviation^2) with d <= dimension of views <= D
+    standard_deviation : float
+                         standard deviation of the gaussian distribution N((d+D)/2, standard_deviation^2)
+                         dimension of views (int) chosen randomly from N((d+D)/2, standard_deviation^2) with d <= dimension of views <= D
+        
+    Returns:
+    --------
+    Z : an array of dimension(n_samples, R*n_views) = the generated samples
+    y : an array of dimension (n_samples) = the integer labels for class membership of each sample
+    a list of n_views tuples (X_v, I_v) with :
+        X_v = Z projected along d_v (= dimension of the v-ith views) columns in I_v
+        I_v = X_v columns numeros with numberring of Z columns numeros
+    unsued_dimensions_percent : percentage of unsued columns of latent space in views
+    n_informative : number of informative features (dimension of latent space - n_informative = number of non informative features)
+    """
+    
+    if n_views < 2:
+        raise ValueError("n_views >= 2")
+    if n_classes < 2:
+        raise ValueError("n_classes >= 2")
+    if Z_factor < 1:
+        raise ValueError("Z_factor >= 1 pour le bon fonctionnement de l'algorithme")
+    if d < 1:
+        raise ValueError("d >= 1")
+    if (d+D)/2 - 3*standard_deviation < 0:
+        raise ValueError("Il faut que (d+D)/2 - 3*standard_deviation >= 0 pour avoir des valeurs positives 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=(d+D)/2, scale=standard_deviation, size=n_views)
+    d_v = list(d_v)
+    remove_list, add_list = [], []
+    for dim_view in d_v:
+        if dim_view < d or dim_view > D:  # 1 <= d <= dim_view <= D
+            remove_list.append(dim_view)
+            add = -1
+            while add < d or add > D:
+                add = gauss((d+D)/2, 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)
+    dim_Z = Z_factor*latent_space_dimension(d_v, R)
+    # 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=0.01, class_sep=n_clusters_per_class*class_sep_factor, random_state=None)
+        
+    I_q = np.array([i for i in range(Z.shape[1])])  # 1D-array of Z columns numero
+    meta_I_v = []
+    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 = projection(Z, I_v)
+        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(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
+    unsued_dimensions_list = [column for column in I_q if column not in meta_I_v]
+    unsued_dimensions_percent = round((len(unsued_dimensions_list) / dim_Z)*100, 2)
+    return Z, y, results, unsued_dimensions_percent, n_informative
+
+
+def results_to_csv(path, latent_space, integer_labels, multiviews_list):
+    """
+    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(latent_space)
+    df_latent_space.to_csv(path+'latent_space.csv', index=False)
+    
+    df_labels = pd.DataFrame(integer_labels)
+    df_labels.to_csv(path+'integer_labels.csv', index=False)
+    
+    cpt = 0
+    for view_tuple in multiviews_list:
+        df_view = pd.DataFrame(view_tuple[0], columns=view_tuple[1])
+        df_view.to_csv(path+'view'+str(cpt)+'.csv', index=False)
+        cpt += 1

generator/parameters.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov 26 13:53:05 2019
+
+@author: bernardet
+"""
+from sklearn.svm import SVC
+from sklearn.naive_bayes import GaussianNB
+import numpy as np
+
+# General parameters
+n_samples = 1000
+# number of samples (int)
+n_views = 3
+# number of views >= 2 (int)
+n_classes = 2
+# number of classes >= 3 (int)
+Z_factor = 250
+# multiplication factor of Z dimension (default value = 1)
+R = 2/3
+# redondance (float)
+cv = 10
+# number of cross-validation splitting (int)
+n_clusters_per_class = 2
+# number of clusters per class >= 1 (int)
+class_sep_factor = 2
+# factor >= 1 as class_sep = n_clusters_per_class*class_sep_factor
+n_informative_divid = 1
+# factor >= 1 as number of informative features = round(dimension of latent space / n_informative_divid)
+classifier = "SVM"
+# name of classifier (str)
+classifier_dictionary={'SVM':SVC(kernel='linear'), 'NB':GaussianNB()}
+# dictionary of classifiers
+n_samples_list = [100, 500, 1000, 1500, 2000]#, 2500, 3000]#, 3500, 4000, 5000, 7000, 10000]
+# list of number of samples to test generator
+R_list = list(np.arange(0, 1.05, 0.05))
+# list of diverse R
+Z_factor_list = [1, 3, 10, 25, 100, 250, 1000]#[25, 50, 75, 100, 150, 200, 250, 500, 600, 750, 800, 900, 1000]
+# list of diverse Z_factor
+n_views_list = [n_view for n_view in range(2, 10)]
+# list of diverse n_views
+class_sep_factor_list = [2, 5, 10]
+# list of diverse class_sep_factor
+n_informative_divid_list = [1, 2, 3]
+# list of diverse n_informative_divid
+path_data = "/home/bernardet/Documents/StageL3/Data/"
+# path to register the multiview dataset
+path_graph = "/home/bernardet/Documents/StageL3/Graph/"
+# path to register scores graph
+
+
+
+# Parameters of gaussian distribution N((d+D)/2,  standard_deviation_2) :
+# d <= dim[v] <= D for all v
+# (d+D)/2 - 3*sqrt(standard_deviation_2) >= 0
+d = 4
+# < D, > 0
+D = 10
+# > d
+standard_deviation = 2
+# standard deviation of the gaussian distribution
+
+
+# make_classification parameters :
+
+# a trouver comment les utiliser
+part_informative = 0
+# proportion of informative features (float between 0 and 1)
+part_redundant = 1
+# proportion of redundant features (float between 0 and 1)
+# n_redundant >= 1 for redundant
+part_repeated = 1
+# # proportion of repeated features (float between 0 and 1)
+# n_repeated >= 1 for useless features and correlation
+
+
+weights = [0.7, 0.3]
+# proportion of samples assigned to each class (list) len(weights) = nbr_features
+# != [0.5, 0.5] / = [0.8, 0.2] for imbalance
+flip_y = 0.1
+# fraction of samples whose class are randomly exchanged (float) 
+# > 0 for noise