diff --git a/summit/examples/data/config_mv_mnist.yml b/summit/examples/data/config_mv_mnist.yml
new file mode 100644
index 0000000000000000000000000000000000000000..5a642345c9cad13a873a7a7bc682c563033c7f71
--- /dev/null
+++ b/summit/examples/data/config_mv_mnist.yml
@@ -0,0 +1,85 @@
+# The base configuration of the benchmark
+
+# Enable logging
+log: True
+# The name of each dataset in the directory on which the benchmark should be run
+name: "multiview_mnist"
+# A label for the result directory
+label: "mnist"
+# The type of dataset, currently supported ".hdf5", and ".csv"
+file_type: ".hdf5"
+# The views to use in the banchmark, an empty value will result in using all the views
+views:
+# The path to the directory where the datasets are stored, an absolute path is advised
+pathf: "examples/data/"
+# The niceness of the processes, useful to lower their priority
+nice: 0
+# The random state of the benchmark, useful for reproducibility
+random_state: 42
+# The number of parallel computing threads
+nb_cores: 4
+# Used to run the benchmark on the full dataset
+full: True
+# Used to be able to run more than one benchmark per minute
+debug: False
+# The directory in which the results will be stored, an absolute path is advised
+res_dir: "examples/results/example_3/"
+# If an error occurs in a classifier, if track_tracebacks is set to True, the
+# benchmark saves the traceback and continues, if it is set to False, it will
+# stop the benchmark and raise the error
+track_tracebacks: True
+
+# All the classification-realted configuration options
+
+# If the dataset is multiclass, will use this multiclass-to-biclass method
+multiclass_method: "oneVersusOne"
+# The ratio number of test exmaples/number of train samples
+split: 0.8
+# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
+nb_folds: 5
+# The number of classes to select in the dataset
+nb_class: 2
+# The name of the classes to select in the dataset
+classes:
+# The type of algorithms to run during the benchmark (monoview and/or multiview)
+type: ["monoview","multiview"]
+# The name of the monoview algorithms to run, ["all"] to run all the available classifiers
+algos_monoview: ["decision_tree", "adaboost", ]
+# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
+algos_multiview: ["early_fusion_decision_tree", "early_fusion_adaboost"]
+# The number of times the benchamrk is repeated with different train/test
+# split, to have more statistically significant results
+stats_iter: 5
+# The metrics that will be use din the result analysis
+metrics:
+ accuracy_score: {}
+ f1_score:
+ average: "micro"
+# The metric that will be used in the hyper-parameter optimization process
+metric_princ: "accuracy_score"
+# The type of hyper-parameter optimization method
+hps_type: 'Random'
+# The number of iteration in the hyper-parameter optimization process
+hps_args:
+ n_iter: 10
+
+decision_tree:
+ max_depth: 3
+
+adaboost:
+ base_estimator: "DecisionTreeClassifier"
+ n_estimators: 10
+
+weighted_linear_late_fusion:
+ classifiers_names: "decision_tree"
+ classifier_configs:
+ decision_tree:
+ max_depth: 2
+
+
+
+# The following arguments are classifier-specific, and are documented in each
+# of the corresponding modules.
+
+# In order to run multiple sets of parameters, use multiple values in the
+# following lists, and set hps_type to None.
diff --git a/summit/multiview_platform/monoview_classifiers/imbalance_bagging.py b/summit/multiview_platform/monoview_classifiers/imbalance_bagging.py
index 9dfa2e263a5ad0e54ecd7027c97fe423204749f5..525b52aa69411affa604807993413f85dab04433 100644
--- a/summit/multiview_platform/monoview_classifiers/imbalance_bagging.py
+++ b/summit/multiview_platform/monoview_classifiers/imbalance_bagging.py
@@ -12,9 +12,8 @@ class ImbalanceBagging(BaseMonoviewClassifier, BalancedBaggingClassifier):
def __init__(self, random_state=None, base_estimator="DecisionTreeClassifier",
n_estimators=10, sampling_strategy="auto",
- replacement=False, base_estimator_config=None):
- base_estimator = self.get_base_estimator(base_estimator,
- base_estimator_config)
+ replacement=False, base_estimator_config=None, **kwargs):
+ base_estimator = self.get_base_estimator(base_estimator, base_estimator_config, **kwargs)
super(ImbalanceBagging, self).__init__(random_state=random_state, base_estimator=base_estimator,
n_estimators=n_estimators,
sampling_strategy=sampling_strategy,
diff --git a/summit/multiview_platform/multiview/exec_multiview.py b/summit/multiview_platform/multiview/exec_multiview.py
index 254b11b3a676e0a82e3c7ef6abb6a4dc0a181753..96b2d2023653ad5b9715276f3845bac75b87d075 100644
--- a/summit/multiview_platform/multiview/exec_multiview.py
+++ b/summit/multiview_platform/multiview/exec_multiview.py
@@ -267,7 +267,6 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
logging.info("Start:\t Optimizing hyperparameters")
hps_beg = time.monotonic()
- print(dataset_var.view_dict)
if hps_method != "None":
hps_method_class = getattr(hyper_parameter_search, hps_method)
estimator = getattr(classifier_module, classifier_name)(
diff --git a/summit/multiview_platform/multiview_classifiers/early_fusion_imbalance_bagging.py b/summit/multiview_platform/multiview_classifiers/early_fusion_imbalance_bagging.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a1c5a2516ce29cf27a6217db56910f5dbd37df5
--- /dev/null
+++ b/summit/multiview_platform/multiview_classifiers/early_fusion_imbalance_bagging.py
@@ -0,0 +1,25 @@
+from .additions.early_fusion_from_monoview import BaseEarlyFusion
+from ..utils.base import base_boosting_estimators
+from ..utils.hyper_parameter_search import CustomRandint, CustomUniform
+
+classifier_class_name = "EarlyFusionImbalanceBagging"
+
+
+class EarlyFusionImbalanceBagging(BaseEarlyFusion):
+
+ def __init__(self, random_state=None, base_estimator="DecisionTreeClassifier",
+ n_estimators=10, sampling_strategy="auto",
+ replacement=False, base_estimator_config=None, **kwargs):
+ BaseEarlyFusion.__init__(self, random_state=random_state,
+ monoview_classifier="imbalance_bagging",
+ base_estimator=base_estimator,
+ n_estimators=n_estimators, sampling_strategy=sampling_strategy,
+ replacement=replacement, base_estimator_config=base_estimator_config, **kwargs)
+ self.param_names = ["n_estimators", "base_estimator",
+ "sampling_strategy", ]
+ self.classed_params = ["base_estimator"]
+ self.distribs = [CustomRandint(low=1, high=50),
+ base_boosting_estimators,
+ ["auto"]]
+ self.weird_strings = {"base_estimator": "class_name"}
+ self.base_estimator_config = base_estimator_config
\ No newline at end of file
diff --git a/summit/multiview_platform/multiview_classifiers/early_fusion_scm.py b/summit/multiview_platform/multiview_classifiers/early_fusion_scm.py
new file mode 100644
index 0000000000000000000000000000000000000000..370007ad864195b3d0c6df6c5ae6f537306556f9
--- /dev/null
+++ b/summit/multiview_platform/multiview_classifiers/early_fusion_scm.py
@@ -0,0 +1,20 @@
+from .additions.early_fusion_from_monoview import BaseEarlyFusion
+from ..utils.hyper_parameter_search import CustomUniform, CustomRandint
+
+classifier_class_name = "EarlyFusionSCM"
+
+
+class EarlyFusionSCM(BaseEarlyFusion):
+
+ def __init__(self, random_state=None, model_type="conjunction",
+ max_rules=10, p=0.1, **kwargs):
+ BaseEarlyFusion.__init__(self, random_state=None,
+ monoview_classifier="scm",
+ model_type=model_type,
+ max_rules=max_rules, p=p, **kwargs)
+ 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 = {}
\ No newline at end of file
diff --git a/summit/multiview_platform/result_analysis/feature_importances.py b/summit/multiview_platform/result_analysis/feature_importances.py
index 042e4c0d4d744e1681c1fc4e965892adc024ce61..b7fe1186e0070cf9f0fa65a8ad44f62621145a23 100644
--- a/summit/multiview_platform/result_analysis/feature_importances.py
+++ b/summit/multiview_platform/result_analysis/feature_importances.py
@@ -121,9 +121,9 @@ def plot_feature_importances(file_name, feature_importance,
hoverinfo=["text"],
colorscale="Hot",
reversescale=True))
- fig.update_layout(
- xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
- yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
+ # fig.update_layout(
+ # xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
+ # yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)
diff --git a/summit/multiview_platform/utils/base.py b/summit/multiview_platform/utils/base.py
index a32afbd039353852aedc0a0ba72bdc82ef5aef27..3e578e11099ef1c79352a4e73dfda6d8eb6aba8d 100644
--- a/summit/multiview_platform/utils/base.py
+++ b/summit/multiview_platform/utils/base.py
@@ -65,7 +65,14 @@ class BaseClassifier(BaseEstimator, ):
else:
return self.__class__.__name__ + " with no config."
- def get_base_estimator(self, base_estimator, estimator_config):
+ def get_base_estimator(self, base_estimator, estimator_config, **kwargs):
+ if not kwargs is None:
+ for key, value in kwargs.items():
+ if key.startswith("base_estimator__"):
+ if estimator_config is None:
+ estimator_config = {}
+ param_name = key.split("base_estimator__")[1]
+ estimator_config[param_name] = value
if estimator_config is None:
estimator_config = {}
if base_estimator is None:
diff --git a/summit/multiview_platform/utils/compression.py b/summit/multiview_platform/utils/compression.py
index a5ef2c4e1549e8bcc6107cde5151a8ba34292ad8..f27c14625552e25510208f02922be2fd0982a8d7 100644
--- a/summit/multiview_platform/utils/compression.py
+++ b/summit/multiview_platform/utils/compression.py
@@ -43,12 +43,5 @@ def remove_compressed(exp_path):
if __name__=="__main__":
- import os
- explore_files("/home/baptiste/Documents/Gitwork/summit/results/BioBanQ_mv_status/debug_started_2022_07_01-22_23_04_bal_acc/")
- # print(os.listdir("/home/baptiste/Documents/Gitwork/summit/results/"))
- # for dataset in os.listdir("/home/baptiste/Documents/Gitwork/summit/results/"):
- # print(dataset)
- # dataset_path = os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dataset)
- # for exp_name in os.listdir(dataset_path):
- # exp_path = os.path.join(dataset_path, exp_name)
- # explore_files(exp_path, compress=False)
\ No newline at end of file
+
+ simplify_plotly("/home/baptiste/Documents/Gitwork/summit/results/hepatitis/debug_started_2022_03_16-15_06_55__/hepatitis-mean_on_10_iter-balanced_accuracy_p.html")
diff --git a/summit/multiview_platform/utils/hyper_parameter_search.py b/summit/multiview_platform/utils/hyper_parameter_search.py
index b9a7b9faf313fa17c66a731f0eae1bd974e2f877..f55a90ef56981d81707d0254d05bc1fcd621a8c7 100644
--- a/summit/multiview_platform/utils/hyper_parameter_search.py
+++ b/summit/multiview_platform/utils/hyper_parameter_search.py
@@ -166,7 +166,6 @@ class Random(RandomizedSearchCV, HPSearch):
param_distributions=param_distributions,
refit=refit, n_jobs=n_jobs, scoring=scoring,
cv=cv, random_state=random_state)
-
self.framework = framework
self.available_indices = learning_indices
self.view_indices = view_indices
@@ -282,8 +281,6 @@ class CustomUniform(CustomDist):
return self.multiply(unif)
-
-
def format_params(params, pref=""):
if isinstance(params, dict):
dictionary = {}