diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index b7aacdd3be4737f0d9b47fe6460a27c4c6d768cd..cdfcf30ba53ea9841566f0b198075f4baf8baa15 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -4,7 +4,9 @@ tests:
tags:
- docker
script:
- - pip3 install -e . --no-deps
+ - export LC_ALL=$(locale -a | grep en_US)
+ - export LANG=$(locale -a | grep en_US)
+ - pip3 install -e .
- pytest-3
coverage: '/^TOTAL.+?(\d+\%)$/'
artifacts:
diff --git a/README.rst b/README.rst
index fd9da8c86354eca883a8a3d5121d8e31a623c4de..b238fbc8f29fddfd975b4a79617851b54e1acc49 100644
--- a/README.rst
+++ b/README.rst
@@ -57,6 +57,7 @@ And the following python modules will be automatically installed :
* `pyyaml <https://pypi.org/project/PyYAML/>`_ - Used to read the config files,
* `plotly <https://plot.ly/>`_ - Used to generate interactive HTML visuals,
* `tabulate <https://pypi.org/project/tabulate/>`_ - Used to generated the confusion matrix.
+* `pyscm-ml <https://pypi.org/project/pyscm-ml/>`_ -
Installing
diff --git a/docker/Dockerfile_ubuntu_18.04 b/docker/Dockerfile_ubuntu_18.04
index 4e3bfa76f57ec17ee49037656c60792cbd59fd24..026c49a3542d5ad55a87625665f377fbca3c33f0 100644
--- a/docker/Dockerfile_ubuntu_18.04
+++ b/docker/Dockerfile_ubuntu_18.04
@@ -29,6 +29,7 @@ RUN apt-get install -y --no-install-recommends locales && \
update-locale en_US.UTF-8 && \
echo "export LC_ALL=$(locale -a | grep en_US)" >> /root/.bashrc && \
echo "export LANG=$(locale -a | grep en_US)" >> /root/.bashrc
+ENV LANGUAGE'en_US:en'
COPY requirements.txt .
RUN pip3 install -r requirements.txt
diff --git a/requirements.txt b/requirements.txt
index 5f4af0b8ad2e9bed3d8f79983591e7534071772d..273d8968b3cf03013d0653fb844de52e4f2502c1 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,4 +11,5 @@ pyyaml>=3.12
plotly>=4.2.1
matplotlib>=3.1.1
tabulate>=0.8.6
+pyscm-ml>=1.0.0
diff --git a/summit/multiview_platform/monoview_classifiers/adaboost.py b/summit/multiview_platform/monoview_classifiers/adaboost.py
index cd8ce3db0b769e7ad99032487d94da010988138b..412e9a19fe2c5b523bb358018dc8381acf488dd8 100644
--- a/summit/multiview_platform/monoview_classifiers/adaboost.py
+++ b/summit/multiview_platform/monoview_classifiers/adaboost.py
@@ -64,11 +64,12 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
[step_pred for step_pred in self.staged_predict(X)])
return pred
- def get_interpretation(self, directory, base_file_name, y_test,
+ def get_interpretation(self, directory, base_file_name, y_test, feature_ids,
multi_class=False): # pragma: no cover
interpretString = ""
interpretString += self.get_feature_importance(directory,
- base_file_name)
+ base_file_name,
+ feature_ids)
interpretString += "\n\n Estimator error | Estimator weight\n"
interpretString += "\n".join(
[str(error) + " | " + str(weight / sum(self.estimator_weights_)) for
diff --git a/summit/multiview_platform/monoview_classifiers/gradient_boosting.py b/summit/multiview_platform/monoview_classifiers/gradient_boosting.py
index e242dee80c6c1ef76daacd2c43d4c178b8f4c495..77242502ca370fabba2f51df167774a2c3ac24e2 100644
--- a/summit/multiview_platform/monoview_classifiers/gradient_boosting.py
+++ b/summit/multiview_platform/monoview_classifiers/gradient_boosting.py
@@ -76,14 +76,15 @@ class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
[step_pred for step_pred in self.staged_predict(X)])
return pred
- def get_interpretation(self, directory, base_file_name, y_test,
+ def get_interpretation(self, directory, base_file_name, y_test, feature_ids,
multi_class=False):
interpretString = ""
if multi_class:
return interpretString
else:
interpretString += self.get_feature_importance(directory,
- base_file_name)
+ base_file_name,
+ feature_ids)
step_test_metrics = np.array(
[self.plotted_metric.score(y_test, step_pred) for step_pred in
self.step_predictions])
diff --git a/summit/multiview_platform/monoview_classifiers/random_forest.py b/summit/multiview_platform/monoview_classifiers/random_forest.py
index c0ebaaa570e33e6d0fa2a92944a16b7f7ccecb99..f0d3578c3c81ac5f11c6baefd6b845d984986314 100644
--- a/summit/multiview_platform/monoview_classifiers/random_forest.py
+++ b/summit/multiview_platform/monoview_classifiers/random_forest.py
@@ -34,10 +34,11 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
["gini", "entropy"], [random_state]]
self.weird_strings = {}
- def get_interpretation(self, directory, base_file_name, y_test,
+ def get_interpretation(self, directory, base_file_name, y_test, feature_ids,
multiclass=False):
interpret_string = ""
interpret_string += self.get_feature_importance(directory,
- base_file_name)
+ base_file_name,
+ feature_ids)
return interpret_string
diff --git a/summit/multiview_platform/monoview_classifiers/scm.py b/summit/multiview_platform/monoview_classifiers/scm.py
new file mode 100644
index 0000000000000000000000000000000000000000..d094ceec2c8bc1f66896e2e83873c7f7fbcac03b
--- /dev/null
+++ b/summit/multiview_platform/monoview_classifiers/scm.py
@@ -0,0 +1,106 @@
+from pyscm.scm import SetCoveringMachineClassifier as scm
+
+import numpy as np
+
+from ..monoview.monoview_utils import BaseMonoviewClassifier
+from ..utils.hyper_parameter_search import CustomRandint, CustomUniform
+
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype" # Production, Development, Prototype
+
+
+# class Decis
+classifier_class_name = "SCM"
+
+class SCM(scm, BaseMonoviewClassifier):
+ """
+ SCM Classifier
+ Parameters
+ ----------
+ random_state (default : None)
+ model_type : string (default: "conjunction")
+ max_rules : int number maximum of rules (default : 10)
+ p : float value(default : 0.1 )
+
+ kwarg : others arguments
+
+ Attributes
+ ----------
+ param_names
+
+ distribs
+
+ classed_params
+
+ weird_strings
+
+ """
+
+ def __init__(self, random_state=None, model_type="conjunction",
+ max_rules=10, p=0.1, **kwargs):
+ """
+
+ Parameters
+ ----------
+ random_state
+ model_type
+ max_rules
+ p
+ kwargs
+ """
+ super(SCM, self).__init__(
+ random_state=random_state,
+ model_type=model_type,
+ max_rules=max_rules,
+ p=p
+ )
+ self.param_names = ["model_type", "max_rules", "p", "random_state"]
+ self.distribs = [["conjunction", "disjunction"],
+ CustomRandint(low=1, high=15),
+ CustomUniform(loc=0, state=1), [random_state]]
+ self.classed_params = []
+ self.weird_strings = {}
+
+ def fit(self, X, y, tiebreaker=None, iteration_callback=None, **fit_params):
+ self.n_features = X.shape[1]
+ scm.fit(self, X, y, tiebreaker=None, iteration_callback=None, **fit_params)
+ self.feature_importances_ = np.zeros(self.n_features)
+ # sum the rules importances :
+ # rules_importances = estim.get_rules_importances() #activate it when pyscm will implement importance
+ rules_importances = np.ones(len(
+ self.model_.rules)) # delete it when pyscm will implement importance
+ for rule, importance in zip(self.model_.rules, rules_importances):
+ self.feature_importances_[rule.feature_idx] += importance
+ self.feature_importances_ /= np.sum(self.feature_importances_)
+ return self
+
+ # def canProbas(self):
+ # """
+ # Used to know if the classifier can return label probabilities
+ #
+ # Returns
+ # -------
+ # return False in any case
+ # """
+ # return False
+
+ def get_interpretation(self, directory, base_file_name, y_test, feature_ids,
+ multi_class=False):
+ interpret_string = self.get_feature_importance(directory,
+ base_file_name,
+ feature_ids)
+ interpret_string += "Model used : " + str(self.model_)
+ return interpret_string
+
+
+
+def paramsToSet(nIter, random_state):
+ paramsSet = []
+ for _ in range(nIter):
+ paramsSet.append(
+ {"model_type": random_state.choice(["conjunction", "disjunction"]),
+ "max_rules": random_state.randint(1, 15),
+ "p": random_state.random_sample()})
+ return paramsSet
diff --git a/summit/multiview_platform/result_analysis/execution.py b/summit/multiview_platform/result_analysis/execution.py
index 279891e7d30ea3fa2262e25eea013a883d61db52..931d61863f3ae12128ce2c2c8c4933312045ff00 100644
--- a/summit/multiview_platform/result_analysis/execution.py
+++ b/summit/multiview_platform/result_analysis/execution.py
@@ -84,7 +84,7 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
sample_errors = get_sample_errors(labels, result)
feature_importances = get_feature_importances(result,
feature_ids=feature_ids,
- view_names=view_names)
+ view_names=view_names,)
durations = get_duration(result)
directory = arguments["directory"]
@@ -98,7 +98,7 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
publish_sample_errors(sample_errors, directory, database_name,
labels_names, sample_ids, labels)
publish_feature_importances(feature_importances, directory,
- database_name)
+ database_name, metric_scores=metrics_scores)
plot_durations(durations, directory, database_name)
iter_results["metrics_scores"][iter_index] = metrics_scores
@@ -129,7 +129,8 @@ def analyze_all(iter_results, stats_iter, directory, data_base_name,
publish_all_sample_errors(error_analysis, directory, stats_iter,
sample_ids, labels, data_base_name, label_names)
publish_feature_importances(feature_importances, directory,
- data_base_name, feature_importances_stds)
+ data_base_name, feature_importances_stds,
+ metric_scores=metrics_analysis)
plot_durations(duration_means, directory, data_base_name, duration_stds)
return results
diff --git a/summit/multiview_platform/result_analysis/feature_importances.py b/summit/multiview_platform/result_analysis/feature_importances.py
index 088cd8309f36539b370b6891313432f93246094d..36c0eb3514b0fa3db388af10803b60f2f245f011 100644
--- a/summit/multiview_platform/result_analysis/feature_importances.py
+++ b/summit/multiview_platform/result_analysis/feature_importances.py
@@ -7,7 +7,7 @@ import plotly
from ..monoview.monoview_utils import MonoviewResult
-def get_feature_importances(result, feature_ids=None, view_names=None):
+def get_feature_importances(result, feature_ids=None, view_names=None,):
r"""Extracts the feature importance from the monoview results and stores
them in a dictionnary :
feature_importance[view_name] is a pandas.DataFrame of size n_feature*n_clf
@@ -49,7 +49,7 @@ def get_feature_importances(result, feature_ids=None, view_names=None):
def publish_feature_importances(feature_importances, directory, database_name,
- feature_stds=None): # pragma: no cover
+ feature_stds=None, metric_scores=None): # pragma: no cover
importance_dfs = []
std_dfs = []
if not os.path.exists(os.path.join(directory, "feature_importances")):
@@ -67,22 +67,22 @@ def publish_feature_importances(feature_importances, directory, database_name,
importance_dfs.append(feature_importance.set_index(pd.Index([view_name+"-"+ind for ind in list(feature_importance.index)])))
- importance_dfs.append(pd.DataFrame(index=[view_name+"-br"],
- columns=feature_importance.columns,
- data=np.zeros((1, len(
- feature_importance.columns)))))
+ # importance_dfs.append(pd.DataFrame(index=[view_name+"-br"],
+ # columns=feature_importance.columns,
+ # data=np.zeros((1, len(
+ # feature_importance.columns)))))
std_dfs.append(feature_std.set_index(pd.Index([view_name+"-"+ind
for ind
in list(feature_std.index)])))
- std_dfs.append(pd.DataFrame(index=[view_name + "-br"],
- columns=feature_std.columns,
- data=np.zeros((1, len(
- feature_std.columns)))))
+ # std_dfs.append(pd.DataFrame(index=[view_name + "-br"],
+ # columns=feature_std.columns,
+ # data=np.zeros((1, len(
+ # feature_std.columns)))))
if len(importance_dfs)>0:
- feature_importances_df = pd.concat(importance_dfs[:-1])
+ feature_importances_df = pd.concat(importance_dfs)
feature_importances_df = feature_importances_df/feature_importances_df.sum(axis=0)
- feature_std_df = pd.concat(std_dfs[:-1])
+ feature_std_df = pd.concat(std_dfs)
if "mv" in feature_importances:
feature_importances_df = pd.concat([feature_importances_df,feature_importances["mv"].loc[(feature_importances["mv"] != 0).any(axis=1), :]], axis=1).fillna(0)
if feature_stds is not None:
@@ -94,10 +94,16 @@ def publish_feature_importances(feature_importances, directory, database_name,
feature_std_df = pd.concat([feature_std_df, fake], axis=1,).fillna(0)
plot_feature_importances(os.path.join(directory, "feature_importances",
database_name), feature_importances_df, feature_std_df)
+ if metric_scores is not None:
+ plot_feature_relevance(os.path.join(directory, "feature_importances",
+ database_name), feature_importances_df, feature_std_df, metric_scores)
def plot_feature_importances(file_name, feature_importance,
feature_std): # pragma: no cover
+ s = feature_importance.sum(axis=1)
+ s = s[s!=0]
+ feature_importance = feature_importance.loc[s.sort_values(ascending=False).index]
feature_importance.to_csv(file_name + "_dataframe.csv")
hover_text = [["-Feature :" + str(feature_name) +
"<br>-Classifier : " + classifier_name +
@@ -113,8 +119,8 @@ def plot_feature_importances(file_name, feature_importance,
z=feature_importance.values,
text=hover_text,
hoverinfo=["text"],
- colorscale="Greys",
- reversescale=False))
+ colorscale="Hot",
+ reversescale=True))
fig.update_layout(
xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
@@ -123,3 +129,20 @@ def plot_feature_importances(file_name, feature_importance,
plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)
del fig
+
+
+def plot_feature_relevance(file_name, feature_importance,
+ feature_std, metric_scores): # pragma: no cover
+ for metric, score_df in metric_scores.items():
+ if metric.endswith("*"):
+ if isinstance(score_df, dict):
+ score_df = score_df["mean"]
+ for score in score_df.columns:
+ if len(score.split("-"))>1:
+ algo, view = score.split("-")
+ feature_importance[algo].loc[[ind for ind in feature_importance.index if ind.startswith(view)]]*=score_df[score]['test']
+ else:
+ feature_importance[score] *= score_df[score]['test']
+ file_name+="_relevance"
+ plot_feature_importances(file_name, feature_importance,
+ feature_std)
diff --git a/summit/tests/test_multi_view/test_multiview_utils.py b/summit/tests/test_multi_view/test_multiview_utils.py
index 491d0134b2943be11436bf3bacef2eff6cb08614..99d725253c7a0341719913856259c00d65fba3a9 100644
--- a/summit/tests/test_multi_view/test_multiview_utils.py
+++ b/summit/tests/test_multi_view/test_multiview_utils.py
@@ -79,6 +79,7 @@ class TestFunctions(unittest.TestCase):
'knn',
'lasso',
'random_forest',
+ 'scm',
'sgd',
'svm_linear',
'svm_poly',
@@ -90,6 +91,7 @@ class TestFunctions(unittest.TestCase):
'gradient_boosting',
'knn',
'random_forest',
+ 'scm',
'svm_linear',
'svm_poly',
'svm_rbf'])