copyright + bug fix compute alpha

VERSION

+multimodal:0.0.dev0
+

copyright.py

@@ -50,7 +50,7 @@ def getStamp(date, iw_version):
     """ Return the corrected formated stamp """
     stamp = open("copyrightstamp.txt").read()
     stamp = stamp.replace("DATE", date)
-    stamp = stamp.replace("IW_VERSION", iw_version)
+    stamp = stamp.replace("MULTIMODAL_VERSION", multimodal_version)
     stamp = stamp.replace('\n', '\n# ')
     stamp = "# " + stamp
     stamp = stamp.replace("# \n", "#\n")

copyrightstamp.txt

@@ -34,7 +34,7 @@ Version:
 Licence:
 -------
 
-License: LGPLv3+
+License: New BSD License
 
 
 ######### COPYRIGHT #########

examples/usecase/plot_usecase_exampleMKL.py

@@ -19,7 +19,6 @@ from sklearn.tree import DecisionTreeClassifier
 from multimodal.datasets.base import load_dict, save_dict
 from multimodal.tests.data.get_dataset_path import get_dataset_path
 from multimodal.datasets.data_sample import MultiModalArray
-from multimodal.kernels.mvml import MVML
 from multimodal.kernels.lpMKL import MKL
 
 import numpy as np
@@ -50,7 +49,6 @@ if __name__ == '__main__':
     # file = get_dataset_path("digit_histogram.npy")
     file = get_dataset_path("digit_col_grad.npy")
     y = np.load(get_dataset_path("digit_y.npy"))
-    base_estimator = DecisionTreeClassifier(max_depth=4)
     dic_digit = load_dict(file)
     XX =MultiModalArray(dic_digit)
     X_train, X_test, y_train, y_test = train_test_split(XX, y)

license.txt

 New BSD License
 
-Copyright (c) 2020-15-01, The scit-multimodallearn developers.
+Copyright (c) 2020-15-01, The scikit-multimodallearn developers.
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

multimodal/__init__.py

-__version__ = '0.0.dev0'
+__version__ = "0.0.dev0"

multimodal/boosting/cumbo.py

@@ -311,16 +311,16 @@ class MuCumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
         zeta2 = zeta**2
         def F(x=None, z=None):
             if x is None:
-                # l'algorithme fonctionne de manière itérative
-                # il faut choisir un x initial, c'est ce qu'on fait ici
+                # iteratif algo
+                # choice x initial
                 return 0, matrix(1.0, (n_view*m, 1))
             if min(x) < 0.0:
-                return None   # cas impossible
-            # ici commence le code qui définit ce qu'est une itération
-            f = sum(matrix(coef * exp( matrix(zeta * x.T))   ))
-            Df = matrix(np.sum( zeta * coef * exp(matrix( zeta * x.T ) ), axis=0 )).T # -(x**-1).T
+                return None   # impossible
+            # begin iteration
+            f = sum(matrix(coef * exp( matrix(zeta * x.T))))
+            Df = matrix(np.sum( zeta * coef * exp(matrix( zeta * x.T)), axis=0)).T  # -(x**-1).T
             if z is None: return f, Df
-            H = spdiag(z[0] * matrix(np.sum(coef * zeta2 * exp( matrix(zeta* x.T) ), axis= 0))) ## beta**(-2))
+            H = spdiag(z[0] * matrix(np.sum(coef * zeta2 * exp( matrix(zeta* x.T)), axis=0)))  # beta**(-2))
             return f, Df, H
         try:
             solver = solvers.cp(F, A=A, b=b, G=G, h=h, dim={'l':2*n_view*m})['x']

multimodal/boosting/mumbo.py

@@ -221,12 +221,19 @@ class MumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
 
     def _compute_alphas(self, edges):
         """Compute values of confidence rate alpha given edge values."""
+        dim = edges.shape[0]
         np.where(edges > 1.0, edges, 1.0)
         alphas = 0.5 * np.log((1. + edges) / (1. - edges))
         if np.any(np.isinf(alphas)):
-            alphas[np.where(np.isinf(alphas))[0]] = 1.0
+            if isinstance(alphas, float):
+                alphas = 1.0
+            else:
+                alphas[np.where(np.isinf(alphas))[0]] = 1.0
         if np.any(np.isnan(alphas)):
-            alphas[np.where(np.isnan(alphas))[0]] = 1.0
+            if isinstance(alphas, float):
+                alphas = 1.0
+            else:
+                alphas[np.where(np.isnan(alphas))[0]] = 1.0
         return alphas
 
     def _compute_cost_global(self, label_score_global, best_predicted_classes,

multimodal/kernels/mvml.py

@@ -613,7 +613,7 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
 
         return A_new
 
-    def score(self, X, y=None):
+    def score(self, X, y):
         """Return the mean accuracy on the given test data and labels.
 
         Parameters

multimodal/tests/data/digit.py

+from sklearn import datasets
+import numpy as np
+import PIL
+import matplotlib.pyplot as plt
+import os
+import matplotlib.pyplot as plt
+from multimodal.datasets.base import load_dict, save_dict
+from multimodal.tests.data.get_dataset_path import get_dataset_path
+from multimodal.datasets.data_sample import MultiModalArray
+from multimodal.kernels.mvml import MVML
+#Load the digits dataset
+digits = datasets.load_digits()
+
+#Display the first digit
+plt.figure(1, figsize=(3, 3))
+plt.imshow(digits.images[-1], cmap=plt.cm.gray_r, interpolation='nearest')
+plt.show()
+colors = digits.data
+gradiant = np.gradient(digits.images, axis=[1,2])
+print(gradiant[0].shape)
+gradiant0 = gradiant[0].reshape(colors.shape[0], colors.shape[1])
+gradiant1 = gradiant[1].reshape(colors.shape[0], colors.shape[1])
+for ind in range(digits.images.shape[0]):
+    ima0 = digits.images[ind, :,:]
+    ima1 = gradiant[0][ind, :,:]
+    ima2 = gradiant[1][ind, :,:]
+    ama_pil0 = PIL.Image.fromarray(ima0, mode=None)
+    ama_pil1 = PIL.Image.fromarray(ima1, mode=None)
+    ama_pil2 = PIL.Image.fromarray(ima2, mode=None)
+    histo_color = np.asarray(ama_pil0.histogram())
+    histo_gradiant0 = np.asarray(ama_pil1.histogram())
+    histo_gradiant1 = np.asarray(ama_pil2.histogram())
+    if ind==0:
+        list_histogram_color = histo_color
+        list_histogram_gradiant0 = histo_gradiant0
+        list_histogram_gradiant1 = histo_gradiant1
+    else:
+        list_histogram_color = np.vstack((list_histogram_color, histo_color))
+        list_histogram_gradiant0 = np.vstack((list_histogram_gradiant0, histo_gradiant0))
+        list_histogram_gradiant1 = np.vstack((list_histogram_gradiant1, histo_gradiant1))
+
+dict_digit = {0: list_histogram_color, 1: list_histogram_gradiant0, 2: list_histogram_gradiant1}
+
+
+print(list_histogram_color.shape)
+print(list_histogram_gradiant0.shape)
+print(list_histogram_gradiant1.shape)
+file = get_dataset_path("digit_histogram.npy")
+save_dict(dict_digit, file)
+
+d2 = load_dict(file)
+
+figure = plt.figure(figsize=(27, 9))
+ax = plt.subplot(2,1,1)
+
+ax.scatter(list_histogram_color[:,3], list_histogram_color[:,4], c=digits.target, edgecolors='k')
+ax = plt.subplot(2,1,2)
+ax.scatter(list_histogram_color[:,0], list_histogram_color[:,1], c=digits.target, edgecolors='k')
+plt.show()
+
+mvml = MVML(lmbda=0.1, eta=1, nystrom_param=0.2)
+mvml.fit(d2, digits.target)

setup.py

 
-import os
+import os, re
+import shutil
 from setuptools import setup, find_packages
-
+from distutils.command.clean import clean as _clean
+from distutils.dir_util import remove_tree
+from distutils.command.sdist import sdist
 import multimodal
 
+try:
+    import numpy
+except:
+    raise 'Cannot build iw without numpy'
+    sys.exit()
+
+# --------------------------------------------------------------------
+# Clean target redefinition - force clean everything supprimer de la liste '^core\.*$',
+relist = ['^.*~$', '^#.*#$', '^.*\.aux$', '^.*\.pyc$', '^.*\.o$']
+reclean = []
+USE_COPYRIGHT = True
+try:
+    from copyright import writeStamp, eraseStamp
+except ImportError:
+    USE_COPYRIGHT = False
+
+###################
+# Get Multimodal version
+####################
+def get_version():
+    v_text = open('VERSION').read().strip()
+    v_text_formted = '{"' + v_text.replace('\n', '","').replace(':', '":"')
+    v_text_formted += '"}'
+    v_dict = eval(v_text_formted)
+    return v_dict["multimodal"]
+
+########################
+# Set Multimodal __version__
+########################
+def set_version(multimodal_dir, version):
+    filename = os.path.join(multimodal_dir, '__init__.py')
+    buf = ""
+    for line in open(filename, "rb"):
+        if not line.decode("utf8").startswith("__version__ ="):
+            buf += line.decode("utf8")
+    f = open(filename, "wb")
+    f.write(buf.encode("utf8"))
+    f.write(('__version__ = "%s"\n' % version).encode("utf8"))
+
+for restring in relist:
+    reclean.append(re.compile(restring))
+
+
+def wselect(args, dirname, names):
+    for n in names:
+        for rev in reclean:
+            if (rev.match(n)):
+                os.remove("%s/%s" %(dirname, n))
+        break
+
+
+######################
+# Custom clean command
+######################
+class clean(_clean):
+    def walkAndClean(self):
+        os.walk("..", wselect, [])
+        pass
+
+    def run(self):
+        clean.run(self)
+        if os.path.exists('build'):
+            shutil.rmtree('build')
+        for dirpath, dirnames, filenames in os.walk('iw'):
+            for filename in filenames:
+                if (filename.endswith('.so') or
+                        filename.endswith('.pyd') or
+                        filename.endswith('.dll') or
+                        filename.endswith('.pyc')):
+                    os.unlink(os.path.join(dirpath, filename))
+            for dirname in dirnames:
+                if dirname == '__pycache__':
+                    shutil.rmtree(os.path.join(dirpath, dirname))
+
+
+##############################
+# Custom sdist command
+##############################
+class m_sdist(sdist):
+    """ Build source package
+
+    WARNING : The stamping must be done on an default utf8 machine !
+    """
+
+    def run(self):
+        if USE_COPYRIGHT:
+            writeStamp()
+            sdist.run(self)
+            # eraseStamp()
+        else:
+            sdist.run(self)
 
 def setup_package():
     """Setup function"""
 
     name = 'scikit-multimodallearn'
-    version = multimodal.__version__
+    version = get_version()
+    multimodal_dir = 'multimodal'
+    set_version(multimodal_dir, version)
     description = 'A scikit-learn compatible package for multimodal Classifiers'
     here = os.path.abspath(os.path.dirname(__file__))
     with open(os.path.join(here, 'README.rst'), encoding='utf-8') as readme:
@@ -21,7 +117,7 @@ def setup_package():
         'Source': url,
         'Tracker': '{}/issues'.format(url)}
     author = 'Dominique Benielli and Sokol Koço and Florent Jaillet and Riikka Huusari ' \
-             'and Cécile Capponi and Hachem Kadri'
+             'and Baptiste Bauvin and Cécile Capponi and Hachem Kadri'
     author_email = 'contact.dev@lis-lab.fr'
     license = 'newBSD'
     classifiers = [