Fake images

docs/source/tutorials/example0.rst

@@ -45,7 +45,8 @@ The file that regroups the accuracy scores is available in three versions :
 - and an :base_source:`an html interactive file <multiview_platform/examples/results/example_0/digits/result_example/digits-accuracy_score*.html>` :
 
 .. raw:: html
-    :file: images/example_0/acc.html
+    .. :file: images/example_0/acc.html
+    :file: images/fake.html
 
 These three files contain the same information : the two figures are bar plots of the score of each classifier with the score on the training set in light gray and the score on the testing set in black.
 
@@ -62,7 +63,8 @@ Once one has the scores of each classifier, an interesting analysis could be to
 This is possible with another result analysis, available in :base_source:`png <multiview_platform/examples/results/example_0/digits/result_example/digits-error_analysis_2D.png>`, :base_source:`csv <multiview_platform/examples/results/example_0/digits/result_example/digits_2D_plot_data.csv>` and :base_source:`html <multiview_platform/examples/results/example_0/digits/result_example/digits-error_analysis_2D.html>` :
 
 .. raw:: html
-    :file: images/example_0/err.html
+    .. :file: images/example_0/err.html
+    :file: images/fake.html
 
 This figure represents a matrix, with the examples in rows and classifiers in columns, with a white rectangle on row i, column j if classifier j failed to classify example i.
 

docs/source/tutorials/example1.rst

@@ -160,7 +160,8 @@ These files contain the scores of each classifier for the accuracy metric, order
 The html version is as follows :
 
 .. raw:: html
-    :file: ./images/example_1/accuracy.html
+    .. :file: ./images/example_1/accuracy.html
+    :file: images/fake.html
 
 This is a bar plot showing the score on the training set (light gray), and testing set (black) for each monoview classifier on each view and or each multiview classifier.
 
@@ -171,7 +172,8 @@ The ``.csv`` file is a matrix with the score on train stored in the first row an
 A similar graph ``*-accuracy_score*-class.html``, reports the error of each classifier on each class.
 
 .. raw:: html
-    :file: ./images/example_1/accuracy_class.html
+    .. :file: ./images/example_1/accuracy_class.html
+    :file: images/fake.html
 
 Here, for each classifier, 8 bars are plotted, one foe each class. It is clear that fore the monoview algorithms, in views 2 and 3, the third class is difficult, as showed in the error matrix.
 
@@ -191,7 +193,8 @@ The examples labelled as ``Mutual_error_*`` are mis-classified by most of the al
     It is highly recommended to zoom in the html figure to see each row.
 
 .. raw:: html
-    :file: ./images/example_1/error_2d.html
+    .. :file: ./images/example_1/error_2d.html
+    :file: images/fake.html
 
 
 
@@ -217,7 +220,8 @@ The data used to generate those matrices is available in ``*-2D_plot_data.csv``
 This file is a different way to visualize the same information as the two previous ones. Indeed, it is a bar plot, with a bar for each example, counting the ratio of classifiers that failed to classify this particular example.
 
 .. raw:: html
-    :file: ./images/example_1/bar.html
+    .. :file: ./images/example_1/bar.html
+    :file: images/fake.html
 
 All the spikes are the mutual error examples, the complementary ones are the 0.33 bars and the redundant are the empty spaces.
 

docs/source/tutorials/example2.rst

@@ -85,7 +85,8 @@ To run this example run,
 The results for accuracy metric are stored in ``multiview_platform/examples/results/example_2_1_1/doc_summit/``
 
 .. raw:: html
-    :file: ./images/example_2/2_1/low_train_acc.html
+    .. :file: ./images/example_2/2_1/low_train_acc.html
+    :file: images/fake.html
 
 These results were generated learning on 20% of the dataset and testing on 80% (see the :base_source:`config file <multiview_platform/examples/config_files/config_example_2_1_1.yml#L37>`).
 
@@ -104,7 +105,8 @@ Now, if you run :
 You should obtain these scores in ``multiview_platform/examples/results/example_2_1/doc_summit/`` :
 
 .. raw:: html
-    :file: ./images/example_2/2_1/high_train_accs.html
+    .. :file: ./images/example_2/2_1/high_train_accs.html
+    :file: images/fake.html
 
 
 Here we learned on 80% of the dataset and tested on 20%, so the line in the  :base_source:`config file <multiview_platform/examples/config_files/config_example_2_1_2.yml#L37>` has become ``split: 0.2``.
@@ -199,7 +201,8 @@ Here, we used :yaml:`split: 0.8` and the results are far better than :base_doc:`
 
 
 .. raw:: html
-    :file: ./images/example_2/2_2/acc_random_search.html
+    .. :file: ./images/example_2/2_2/acc_random_search.html
+    :file: images/fake.html
 
 
 
@@ -227,7 +230,8 @@ with different fold/draws settings :
 
 
 .. raw:: html
-    :file: ./images/durations.html
+    .. :file: ./images/durations.html
+    :file: images/fake.html
 
 .. note::