Refactored

README.md

@@ -18,7 +18,7 @@ And the following python modules :
 * [matplotlib](http://matplotlib.org/) - Used to plot results
 * [sklearn](http://scikit-learn.org/stable/) - Used for the monoview classifiers
 * [joblib](https://pypi.python.org/pypi/joblib) - Used to compute on multiple threads
-* [h5py](www.h5py.org) - Used to generate HDF5 datasets on hard drive and use them to spare RAM
+* [h5py](https://www.h5py.org) - Used to generate HDF5 datasets on hard drive and use them to spare RAM
 * [pickle](https://docs.python.org/3/library/pickle.html) - Used to store some results
 * ([graphviz](https://pypi.python.org/pypi/graphviz) - Used for decision tree interpretation)
 

docs/source/conf.py

 # -*- coding: utf-8 -*-
-from recommonmark.parser import CommonMarkParser
-from recommonmark.transform import AutoStructify
+
 # import os, sys
 #
 # MultiviewPlatform documentation build configuration file, created by
@@ -50,7 +49,6 @@ extensions = ['sphinx.ext.autodoc',
               'sphinx.ext.napoleon',
               'recommonmark']
 
-
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 
@@ -100,7 +98,6 @@ pygments_style = 'sphinx'
 # If true, `todo` and `todoList` produce output, else they produce nothing.
 todo_include_todos = True
 
-
 # -- Options for HTML output ----------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
@@ -119,13 +116,11 @@ html_theme = 'sphinx_rtd_theme'
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = []
 
-
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
 htmlhelp_basename = 'MultiviewPlatformdoc'
 
-
 # -- Options for LaTeX output ---------------------------------------------
 
 latex_elements = {
@@ -154,7 +149,6 @@ latex_documents = [
      u'Baptiste BAUVIN', 'manual'),
 ]
 
-
 # -- Options for manual page output ---------------------------------------
 
 # One entry per manual page. List of tuples
@@ -164,7 +158,6 @@ man_pages = [
      [author], 1)
 ]
 
-
 # -- Options for Texinfo output -------------------------------------------
 
 # Grouping the document tree into Texinfo files. List of tuples
@@ -176,9 +169,6 @@ texinfo_documents = [
      'Miscellaneous'),
 ]
 
-
-
-
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {'https://docs.python.org/': None}
 

docs/source/sphinxext/recommon.py

 from recommonmark.transform import AutoStructify
 
+
 def setup(app):
     app.add_transform(AutoStructify)

multiview_platform/MonoMultiViewClassifiers/Metrics/__init__.py

@@ -24,6 +24,7 @@ Define a getConfig function
 """
 
 import os
+
 for module in os.listdir(os.path.dirname(os.path.realpath(__file__))):
     if module in ['__init__.py'] or module[-3:] != '.py':
         continue

multiview_platform/MonoMultiViewClassifiers/Metrics/accuracy_score.py

@@ -14,7 +14,7 @@ __status__ = "Prototype"  # Production, Development, Prototype
 def score(y_true, y_pred, multiclass=False, **kwargs):
     """Arguments:
     y_true: real labels
-    y_pred predicted labels
+    y_pred: predicted labels
 
     Keyword Arguments:
     "0": weights to compute accuracy
@@ -39,7 +39,8 @@ def get_scorer(**kwargs):
         sample_weight = kwargs["0"]
     except:
         sample_weight = None
-    return make_scorer(metric, greater_is_better=True, sample_weight=sample_weight)
+    return make_scorer(metric, greater_is_better=True,
+                       sample_weight=sample_weight)
 
 
 def getConfig(**kwargs):
@@ -47,5 +48,6 @@ def getConfig(**kwargs):
         sample_weight = kwargs["0"]
     except:
         sample_weight = None
-    configString = "Accuracy score using " + str(sample_weight) + " as sample_weights (higher is better)"
+    configString = "Accuracy score using " + str(
+        sample_weight) + " as sample_weights (higher is better)"
     return configString

multiview_platform/MonoMultiViewClassifiers/Metrics/f1_score.py

@@ -32,7 +32,8 @@ def score(y_true, y_pred, multiclass=False, **kwargs):
         else:
             average = "binary"
 
-    score = metric(y_true, y_pred, sample_weight=sample_weight, labels=labels, pos_label=pos_label, average=average)
+    score = metric(y_true, y_pred, sample_weight=sample_weight, labels=labels,
+                   pos_label=pos_label, average=average)
 
     return score
 
@@ -54,7 +55,8 @@ def get_scorer(**kwargs):
         average = kwargs["3"]
     except:
         average = "binary"
-    return make_scorer(metric, greater_is_better=True, sample_weight=sample_weight, labels=labels,
+    return make_scorer(metric, greater_is_better=True,
+                       sample_weight=sample_weight, labels=labels,
                        pos_label=pos_label, average=average)
 
 
@@ -75,7 +77,9 @@ def getConfig(**kwargs):
         average = kwargs["3"]
     except:
         average = "binary"
-    configString = "F1 score using " + str(sample_weight) + " as sample_weights, " + str(labels) + " as labels, " + str(
+    configString = "F1 score using " + str(
+        sample_weight) + " as sample_weights, " + str(
+        labels) + " as labels, " + str(
         pos_label) \
                    + " as pos_label, " + average + " as average (higher is better)"
     return configString

multiview_platform/MonoMultiViewClassifiers/Metrics/fbeta_score.py

@@ -30,7 +30,8 @@ def score(y_true, y_pred, multiclass=False, **kwargs):
             average = "micro"
         else:
             average = "binary"
-    score = metric(y_true, y_pred, beta, sample_weight=sample_weight, labels=labels, pos_label=pos_label,
+    score = metric(y_true, y_pred, beta, sample_weight=sample_weight,
+                   labels=labels, pos_label=pos_label,
                    average=average)
     return score
 
@@ -56,7 +57,8 @@ def get_scorer(**kwargs):
         average = kwargs["4"]
     except:
         average = "binary"
-    return make_scorer(metric, greater_is_better=True, beta=beta, sample_weight=sample_weight, labels=labels,
+    return make_scorer(metric, greater_is_better=True, beta=beta,
+                       sample_weight=sample_weight, labels=labels,
                        pos_label=pos_label, average=average)
 
 
@@ -81,7 +83,9 @@ def getConfig(**kwargs):
         average = kwargs["3"]
     except:
         average = "binary"
-    configString = "F-beta score using " + str(sample_weight) + " as sample_weights, " + str(
+    configString = "F-beta score using " + str(
+        sample_weight) + " as sample_weights, " + str(
         labels) + " as labels, " + str(pos_label) \
-                   + " as pos_label, " + average + " as average, " + str(beta) + " as beta (higher is better)"
+                   + " as pos_label, " + average + " as average, " + str(
+        beta) + " as beta (higher is better)"
     return configString