diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 5633883c9d96d5c4d875338703fe478e6b52b1af..1914f94ebfaadab87ac21602dd576eb61e3a83ba 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -23,7 +23,7 @@ Classification:
nb_class: 2
classes:
type: ["monoview"]
- algos_monoview: ["all", ]
+ algos_monoview: ["adaboost",]
algos_multiview: ["weighted_linear_early_fusion"]
stats_iter: 2
metrics: ["accuracy_score", "f1_score"]
diff --git a/docs/source/tutorials/example4.rst b/docs/source/tutorials/example4.rst
index 6fdd70a4a7f43c4c6d9de541a184b908e71a9588..a6ba2f79472ec133db56fb029f466386aaf0f538 100644
--- a/docs/source/tutorials/example4.rst
+++ b/docs/source/tutorials/example4.rst
@@ -118,7 +118,7 @@ In order to be able to analyze the results with more clarity, one can add the ex
Let's suppose that the objects we are trying to classify between 'Animal' and 'Object' are all bears, cars, planes, and birds. And that one has a ``.csv`` file with an ID for each of them (:python:`"bear_112", "plane_452", "bird_785", "car_369", ...` for example)
-Then as long as the IDs order conresspond to the example order in the lines of the previous matrices, to add the IDs in the hdf5 file, just add :
+Then as long as the IDs order corresponds to the example order in the lines of the previous matrices, to add the IDs in the hdf5 file, just add :
.. code-block:: python
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis.py
index 53178701c92c1b8c9e4607d34c513181f0493c9d..5cb0318d6d1ac691799645da1c9157b2e6d2f43f 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis.py
@@ -136,7 +136,7 @@ def plot_metric_scores(train_scores, test_scores, names, nb_results, metric_name
def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
- fileName, minSize=10,
+ fileName, minSize=10, labels=None,
width_denominator=2.0, height_denominator=20.0, stats_iter=1,
use_plotly=True, example_ids=None):
r"""Used to generate a 2D plot of the errors.
@@ -174,8 +174,8 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
aspect='auto')
plt.title('Errors depending on the classifier')
ticks = np.arange(0, nbClassifiers, 1)
- labels = classifiers_names
- plt.xticks(ticks, labels, rotation="vertical")
+ tick_labels = classifiers_names
+ plt.xticks(ticks, tick_labels, rotation="vertical")
cbar = fig.colorbar(cax, ticks=[-100 * stats_iter / 2, 0, stats_iter])
cbar.ax.set_yticklabels(['Unseen', 'Always Wrong', 'Always Right'])
@@ -183,22 +183,27 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
plt.close()
### The following part is used to generate an interactive graph.
if use_plotly:
- hover_text = [["Failed "+ str(stats_iter-data[i,j])+" time(s)"
+ label_index_list = [np.where(labels==i)[0] for i in np.unique(labels)]
+ print(label_index_list)
+ hover_text = [[example_ids[i] + " failed "+ str(stats_iter-data[i,j])+" time(s)"
for j in range(data.shape[1])]
for i in range(data.shape[0]) ]
- fig = plotly.graph_objs.Figure(data=plotly.graph_objs.Heatmap(
- x=list(classifiers_names),
- y=[_ for _ in example_ids],
- z=data,
- text=hover_text,
- hoverinfo=["y", "x", "text"],
- colorscale="Greys",
- colorbar=dict(tickvals=[0, stats_iter],
- ticktext=["Always Wrong", "Always Right"]),
- reversescale=True))
- fig.update_layout(
- xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
- yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
+ fig = plotly.subplots.make_subplots(rows=len(label_index_list), cols=1)
+ for row_index, label_index in enumerate(label_index_list):
+ fig.add_trace(plotly.graph_objs.Heatmap(
+ x=list(classifiers_names),
+ y=[example_ids[label_ind] for label_ind in label_index],
+ z=data[label_index, :],
+ text=hover_text,
+ hoverinfo=["y", "x", "text"],
+ colorscale="Greys",
+ colorbar=dict(tickvals=[0, stats_iter],
+ ticktext=["Always Wrong", "Always Right"]),
+ reversescale=True), row=row_index+1, col=1)
+ fig.update_yaxes(title_text="Label "+str(row_index), showticklabels=False, ticks='', row=row_index+1, col=1)
+ fig.update_xaxes(showticklabels=False, row=row_index+1, col=1)
+
+ fig.update_xaxes(showticklabels=True, row=len(label_index_list), col=1)
plotly.offline.plot(fig, filename=fileName + "error_analysis_2D.html", auto_open=False)
del fig
@@ -537,7 +542,7 @@ def gen_error_data(example_errors):
return nb_classifiers, nb_examples, classifiers_names, data_2d, error_on_examples
-def publishExampleErrors(example_errors, directory, databaseName, labels_names, example_ids):
+def publishExampleErrors(example_errors, directory, databaseName, labels_names, example_ids, labels):
logging.debug("Start:\t Biclass Label analysis figure generation")
base_file_name = directory + time.strftime(
@@ -552,7 +557,7 @@ def publishExampleErrors(example_errors, directory, databaseName, labels_names,
delimiter=",")
plot_2d(data_2d, classifiers_names, nb_classifiers, nb_examples,
- base_file_name, example_ids=example_ids)
+ base_file_name, example_ids=example_ids, labels=labels)
plot_errors_bar(error_on_examples, nb_classifiers, nb_examples,
base_file_name)
@@ -689,7 +694,7 @@ def analyze_biclass(results, benchmark_argument_dictionaries, stats_iter, metric
results = publishMetricsGraphs(metrics_scores, directory, database_name,
labels_names)
publishExampleErrors(example_errors, directory, database_name,
- labels_names, example_ids)
+ labels_names, example_ids, arguments["labels"])
publish_feature_importances(feature_importances, directory, database_name, labels_names)
if not str(classifierPositive) + str(classifierNegative) in biclass_results:
biclass_results[str(classifierPositive) + str(classifierNegative)] = {}