name changed

.gitlab-ci.yml

@@ -23,7 +23,7 @@ doc:
         - export LC_ALL=$(locale -a | grep en_US)
         - export LANG=$(locale -a | grep en_US)
         - pip3 install -e . --no-deps
-        - sphinx-apidoc -o docs/source multiview_platform
+        - sphinx-apidoc -o docs/source summit
         - cd docs/source
         - sphinx-build -b html . ../build
         - cd ../..
@@ -45,7 +45,7 @@ pages:
         - export LANG=$(locale -a | grep en_US)
         - pip3 install -e . --no-deps
         - pytest-3
-        - sphinx-apidoc -o docs/source multiview_platform
+        - sphinx-apidoc -o docs/source summit
         - cd docs/source
         - sphinx-build -b html . ../build
         - cd ../..

README.md

@@ -71,7 +71,7 @@ to read it carefully before playing around with the parameters.
 
 You can create your own configuration file. In order to run the platform with it, run : 
 ```python
-from multiview_platform.execute import execute
+from summit.execute import execute
 execute(config_path="/absolute/path/to/your/config/file")
 ```
 

docs/source/conf.py

@@ -22,11 +22,11 @@ import os
 import sys
 
 sys.path.insert(0, os.path.abspath('.'))
-sys.path.insert(0, os.path.abspath('../../multiview_platform'))
+sys.path.insert(0, os.path.abspath('../../summit'))
 sys.path.insert(0, os.path.abspath('../..'))
 file_loc = os.path.split(__file__)[0]
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(file_loc), '.')))
-# import multiview_platform
+# import summit
 # -- General configuration ------------------------------------------------
 
 # If your documentation needs a minimal Sphinx version, state it here.

multiview_platform/__init__.py

-"""This is a test docstring to test stuff"""
-
-__version__ = "0.0.0.0"
-
-from . import mono_multi_view_classifiers, execute

multiview_platform/examples/config_files/config_example_0.yml

-# 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: "digits_doc"
-# A label for the resul directory
-label: "example_0"
-# 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: 1
-# 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_0/"
-# 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
-
-# The ratio of test examples/number of train examples
-split: 0.25
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class:
-# 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"]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_early_fusion", "weighted_linear_late_fusion",]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# 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: "None"
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-### Configuring the hyper-parameters for the classifiers
-
-decision_tree:
-  max_depth: 3
-
-weighted_linear_early_fusion:
-  monoview_classifier_name: "decision_tree"
-  monoview_classifier_config:
-    decision_tree:
-      max_depth: 6
-
-weighted_linear_late_fusion:
-  classifiers_names: "decision_tree"
-  classifier_configs:
-    decision_tree:
-      max_depth: 3

multiview_platform/examples/config_files/config_example_1.yml

-# 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: "doc_summit"
-# A label for the resul directory
-label: "example_1"
-# 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/mkljlj"
-# 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: 1
-# 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_1/"
-# 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
-
-# The ratio of test examples/number of train examples
-split: 0.35
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# The number of classes to select in the dataset
-nb_class:
-# 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"]
-# The names of the multiview algorithms to run, ["all"] to run all the available classifiers
-algos_multiview: ["weighted_linear_late_fusion",]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# 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: "None"
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-### Configuring the hyper-parameters for the classifiers
-
-decision_tree:
-  max_depth: 3
-
-weighted_linear_early_fusion:
-  monoview_classifier_name: "decision_tree"
-  monoview_classifier_config:
-    decision_tree:
-      max_depth: 6
-
-weighted_linear_late_fusion:
-  classifiers_names: "decision_tree"
-  classifier_configs:
-    decision_tree:
-      max_depth: 3

multiview_platform/examples/config_files/config_example_2_1_1.yml

-# 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: "doc_summit"
-# A label for the resul directory
-label: "example_2_1_1"
-# 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: 1
-# 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_2_1_1/"
-# 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 examples
-split: 0.8
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# 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: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# 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: None
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-decision_tree:
-  max_depth: 3
-
-adaboost:
-  base_estimator: "DecisionTreeClassifier"
-  n_estimators: 50
-
-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.

multiview_platform/examples/config_files/config_example_2_1_2.yml

-# 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: "doc_summit"
-# A label for the resul directory
-label: "example_2_1_2"
-# 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: 1
-# 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_2_1_2/"
-# 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 examples
-split: 0.2
-# The nubmer of folds in the cross validation process when hyper-paramter optimization is performed
-nb_folds: 2
-# 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: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# 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: None
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-decision_tree:
-  max_depth: 3
-
-adaboost:
-  base_estimator: "DecisionTreeClassifier"
-  n_estimators: 50
-
-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.

multiview_platform/examples/config_files/config_example_2_2_1.yml

-# 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: "doc_summit"
-# A label for the result directory
-label: "example_2_2_1"
-# 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: 1
-# 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_2_2_1/"
-# 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 examples
-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: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# 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: 5
-  equivalent_draws: True
-
-# 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.

multiview_platform/examples/config_files/config_example_2_3.yml

-# 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: "doc_summit"
-# A label for the result directory
-label: "example_2_3"
-# 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: 1
-# 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_2_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 examples
-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: ["weighted_linear_late_fusion", ]
-# The number of times the benchamrk is repeated with different train/test
-# split, to have more statistically significant results
-stats_iter: 1
-# 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: 'Grid'
-# The number of iteration in the hyper-parameter optimization process
-hps_args:
-  decision_tree:
-    max_depth: [1,2,3,4,5]
-
-  adaboost:
-    n_estimators: [10,15,20,25]
-
-  weighted_linear_late_fusion:
-    classifiers_names:
-      - ["decision_tree", "decision_tree", "decision_tree", "decision_tree"]
-      - ["adaboost", "adaboost", "adaboost", "adaboost",]
-
-    classifier_configs:
-      - decision_tree:
-          max_depth: 3
-        adaboost:
-          n_estimators: 10
-
-
-
-# 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.

multiview_platform/examples/config_files/config_example_3.yml

-# 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: "doc_summit"
-# A label for the result directory
-label: "example_3"
-# 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: 1
-# 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 examples
-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: ["weighted_linear_late_fusion", ]
-# 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: 'None'
-# The number of iteration in the hyper-parameter optimization process
-hps_args: {}
-
-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.

multiview_platform/execute.py

-"""This is the execution module, used to execute the code"""
-
-import os
-
-
-def execute(config_path=None):  # pragma: no cover
-    import sys
-
-    from multiview_platform.mono_multi_view_classifiers import exec_classif
-    if config_path is None:
-        exec_classif.exec_classif(sys.argv[1:])
-    else:
-        if config_path == "example 0":
-            config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_0.yml")
-        elif config_path == "example 1":
-            config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_1.yml")
-        elif config_path == "example 2.1.1":
-            config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_2_1_1.yml")
-        elif config_path == "example 2.1.2":
-            config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_2_1_2.yml")
-        elif config_path == "example 2.2":
-            config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_2_2.yml")
-        elif config_path == "example 2.3":
-            config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_2_3.yml")
-        elif config_path == "example 3":
-            config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "examples", "config_files", "config_example_3.yml")
-        exec_classif.exec_classif(["--config_path", config_path])
-
-
-if __name__ == "__main__":
-    execute()

multiview_platform/mono_multi_view_classifiers/__init__.py

-from . import exec_classif, result_analysis, metrics, monoview_classifiers, \
-    monoview, multiview, utils, multiview_classifiers
-
-__all__ = ['metrics', 'monoview', 'monoview_classifiers', 'multiview', 'utils']

multiview_platform/mono_multi_view_classifiers/metrics/__init__.py

-__version__ = "0.0.0.0"
-"""
-To be able to add another metric to the benchmark you must :
-
-Create a .py file named after the metric
-Define a score function
-    Input :
-        y_true : np array with the real labels
-        y_pred : np array with the predicted labels
-        kwargs : every argument that is specific to the metric
-    Returns:
-        score : the metric's score (float)
-Define a get_scorer function
-    Input :
-        kwargs : every argument that is specific to the metric
-    Returns :
-        scorer : an object similar to an sk-learn scorer
-Define a getConfig function
-    Input :
-        kwargs : every argument that is specific to the metric
-    Output :
-        config_string : A string that gives the name of the metric and explains how it is configured. Must end by
-                        (lower is better) or (higher is better) to be able to analyze the preds
-"""
-
-import os
-
-for module in os.listdir(os.path.dirname(os.path.realpath(__file__))):
-    if module in ['__init__.py'] or module[-3:] != '.py':
-        continue
-    __import__(module[:-3], locals(), globals(), [], 1)
-    pass
-del os

multiview_platform/mono_multi_view_classifiers/metrics/accuracy_score.py

-"""Functions :
- score: to get the accuracy score
- get_scorer: returns a sklearn scorer for grid search
-"""
-
-import warnings
-
-from sklearn.metrics import accuracy_score as metric
-from sklearn.metrics import make_scorer
-
-warnings.warn("the accuracy_score module  is deprecated", DeprecationWarning,
-              stacklevel=2)
-
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype"  # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=False, **kwargs):
-    """Arguments:
-    y_true: real labels
-    y_pred: predicted labels
-
-    Keyword Arguments:
-    "0": weights to compute accuracy
-
-    Returns:
-    Weighted accuracy score for y_true, y_pred"""
-    score = metric(y_true, y_pred, **kwargs)
-    return score
-
-
-def get_scorer(**kwargs):
-    """Keyword Arguments:
-    "0": weights to compute accuracy
-
-    Returns:
-    A weighted sklearn scorer for accuracy"""
-    return make_scorer(metric, greater_is_better=True,
-                       **kwargs)
-
-
-def get_config(**kwargs):
-    config_string = "Accuracy score using {}, (higher is better)".format(kwargs)
-    return config_string

multiview_platform/mono_multi_view_classifiers/metrics/f1_score.py

-"""Functions :
- score: to get the f1 score
- get_scorer: returns a sklearn scorer for grid search
-"""
-
-import warnings
-
-from sklearn.metrics import f1_score as metric
-from sklearn.metrics import make_scorer
-
-warnings.warn("the f1_score module  is deprecated", DeprecationWarning,
-              stacklevel=2)
-# Author-Info
-__author__ = "Baptiste Bauvin"
-__status__ = "Prototype"  # Production, Development, Prototype
-
-
-def score(y_true, y_pred, multiclass=True, average='micro', **kwargs):
-    score = metric(y_true, y_pred, average=average, **kwargs)
-    return score
-
-
-def get_scorer(average="micro", **kwargs):
-    return make_scorer(metric, greater_is_better=True, average=average,
-                       **kwargs)
-
-
-def get_config(average="micro", **kwargs, ):
-    config_string = "F1 score using average: {}, {} (higher is better)".format(
-        average, kwargs)
-    return config_string