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Commit c62f8b70 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Updated documentation

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docs/source/tutorials/example1.rst
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− 49
View file @ c62f8b70
...@@ -81,49 +81,49 @@ From here the result directory has the structure that follows : ...@@ -81,49 +81,49 @@ From here the result directory has the structure that follows :
.. code-block:: bash .. code-block:: bash
| started_1560_12_25-15_42 | started_1560_12_25-15_42
| ├── No-vs-Yes | ├── adaboost
| | ├── adaboost | | ├── ViewNumber0
| | | ├── ViewNumber0 | | | ├── *-summary.txt
| | | | ├── 1560_12_25-15_42-*-summary.txt | | | ├── <other classifier dependant files>
| | | | ├── <other classifier dependant files> | | ├── ViewNumber1
| | | ├── ViewNumber1 | | | ├── *-summary.txt
| | | | ├── 1560_12_25-15_42-*-summary.txt | | | ├── <other classifier dependant files>
| | | | ├── <other classifier dependant files> | | ├── ViewNumber2
| | | ├── ViewNumber2 | | | ├── *-summary.txt
| | | | ├── 1560_12_25-15_42-*-summary.txt | | | ├── <other classifier dependant files>
| | | | ├── <other classifier dependant files> | ├── decision_tree
| | ├── decision_tree | | ├── ViewNumber0
| | | ├── ViewNumber0
| | | | ├── <summary & classifier dependant files>
| | | ├── ViewNumber1
| | | | ├── <summary & classifier dependant files>
| | | ├── ViewNumber2
| | | | ├── <summary & classifier dependant files>
| | ├── [..
| | ├── ..]
| | ├── weighted_linear_late_fusion
| | | ├── <summary & classifier dependant files> | | | ├── <summary & classifier dependant files>
| | ├── [.. | | ├── ViewNumber1
| | ├── ..] | | | ├── <summary & classifier dependant files>
| | ├── train_labels.csv | | ├── ViewNumber2
| │ └── train_indices.csv | | | ├── <summary & classifier dependant files>
| ├── [..
| ├── ..]
| ├── weighted_linear_late_fusion
| | ├── <summary & classifier dependant files>
| ├── [..
| ├── ..]
| ├── train_labels.csv
| └── train_indices.csv
| ├── 1560_12_25-15_42-*-LOG.log | ├── 1560_12_25-15_42-*-LOG.log
| ├── config_file.yml | ├── config_file.yml
| ├── 1560_12_25-15_42-*-accuracy_score.png | ├── *-accuracy_score.png
| ├── 1560_12_25-15_42-*-accuracy_score.csv | ├── *-accuracy_score.csv
| ├── 1560_12_25-15_42-*-f1_score.png | ├── *-f1_score.png
| ├── 1560_12_25-15_42-*-f1_score.csv | ├── *-f1_score.csv
| ├── 1560_12_25-15_42-*-error_analysis_2D.png | ├── *-error_analysis_2D.png
| ├── 1560_12_25-15_42-*-error_analysis_2D.html | ├── *-error_analysis_2D.html
| ├── 1560_12_25-15_42-*-error_analysis_bar.png | ├── *-error_analysis_bar.png
| ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance.html | ├── feature_importances
| ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance_dataframe.csv | | ├── *-ViewNumber0-feature_importance.html
| ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance.html | | ├── *-ViewNumber0-feature_importance_dataframe.csv
| ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance_dataframe.csv | | ├── *-ViewNumber1-feature_importance.html
| ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance.html | | ├── *-ViewNumber1-feature_importance_dataframe.csv
| ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance_dataframe.csv | | ├── *-ViewNumber2-feature_importance.html
| ├── 1560_12_25-15_42-*-bar_plot_data.csv | | ├── *-ViewNumber2-feature_importance_dataframe.csv
| ├── 1560_12_25-15_42-*-2D_plot_data.csv | ├── *-bar_plot_data.csv
| ├── *-2D_plot_data.csv
| └── random_state.pickle | └── random_state.pickle
...@@ -131,7 +131,7 @@ The structure can seem complex, but it priovides a lot of information, from the ...@@ -131,7 +131,7 @@ The structure can seem complex, but it priovides a lot of information, from the
Let's comment each file : Let's comment each file :
``1560_12_25-15_42-*-accuracy_score.png`` and ``1560_12_25-15_42-*-accuracy_score.csv`` ``*-accuracy_score.png`` and ``*-accuracy_score.csv``
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
These files contain the scores of each classifier for the accuracy metric, ordered with le best ones on the right and These files contain the scores of each classifier for the accuracy metric, ordered with le best ones on the right and
...@@ -147,15 +147,15 @@ The image version is as follows : ...@@ -147,15 +147,15 @@ The image version is as follows :
The csv file is a matrix with the score on train stored in the first row and the score on test stored in the second one. Each classifier is presented in a row. It is loadable with pandas. The csv file is a matrix with the score on train stored in the first row and the score on test stored in the second one. Each classifier is presented in a row. It is loadable with pandas.
Similar files have been generated for the f1 metric (``1560_12_25-15_42-*-f1_score.png`` and ``1560_12_25-15_42-*-f1_score.csv``). Similar files have been generated for the f1 metric (``*-f1_score.png`` and ``*-f1_score.csv``).
``1560_12_25-15_42-*-error_analysis_2D.png`` and ``1560_12_25-15_42-*-error_analysis_2D.html`` ``*-error_analysis_2D.png`` and ``*-error_analysis_2D.html``
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
In these files, one can visualize the success or failure of each classifier on each example. In these files, one can visualize the success or failure of each classifier on each example.
Below, ``1560_12_25-15_42-*-error_analysis_2D.html`` is displayed. Below, ``*-error_analysis_2D.html`` is displayed.
It is the representation of a matrix, where the rows are the examples, and the columns are the classifiers. It is the representation of a matrix, where the rows are the examples, and the columns are the classifiers.
...@@ -185,9 +185,9 @@ It is an understandable behaviour as the Plausible dataset's view are generated ...@@ -185,9 +185,9 @@ It is an understandable behaviour as the Plausible dataset's view are generated
Morever, as confirmed by the accuracy graph, four monoview classifiers classified all the example to the same class, Morever, as confirmed by the accuracy graph, four monoview classifiers classified all the example to the same class,
and then, display a black half-column. and then, display a black half-column.
The data used to generate those matrices is available in ``1560_12_25-15_42-*-2D_plot_data.csv`` The data used to generate those matrices is available in ``*-2D_plot_data.csv``
``1560_12_25-15_42-*-error_analysis_bar.png`` ``*-error_analysis_bar.png``
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
This file is a different way to visualize the same information as the two previous ones. Indeed, it is a bar plot, This file is a different way to visualize the same information as the two previous ones. Indeed, it is a bar plot,
...@@ -198,7 +198,7 @@ with a bar for each example, counting the number of classifiers that failed to c ...@@ -198,7 +198,7 @@ with a bar for each example, counting the number of classifiers that failed to c
The bar plot showing for each example how many classifiers failed on it. The bar plot showing for each example how many classifiers failed on it.
The data used to generate this graph is available in ``1560_12_25-15_42-*-bar_plot_data.csv`` The data used to generate this graph is available in ``*-bar_plot_data.csv``
``config_file.yml`` ``config_file.yml``
<<<<<<<<<<<<<<<<<<< <<<<<<<<<<<<<<<<<<<
...@@ -218,7 +218,7 @@ The log file ...@@ -218,7 +218,7 @@ The log file
Classifier-dependant files Classifier-dependant files
<<<<<<<<<<<<<<<<<<<<<<<<<< <<<<<<<<<<<<<<<<<<<<<<<<<<
For each classifier, at least one file is generated, called ``1560_12_25-15_42-*-summary.txt``. For each classifier, at least one file is generated, called ``*-summary.txt``.
.. include:: ./images/summary.txt .. include:: ./images/summary.txt
:literal: :literal:
......
docs/source/tutorials/example3.rst
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− 67
View file @ c62f8b70
...@@ -44,83 +44,78 @@ The result directory will be structured as : ...@@ -44,83 +44,78 @@ The result directory will be structured as :
.. code-block:: bash .. code-block:: bash
| started_1560_12_25-15_42 | started_1560_12_25-15_42
| ├── No-vs-Yes
| | ├── 1560_12_25-15_42-*-accuracy_score.png
| | ├── 1560_12_25-15_42-*-accuracy_score.csv
| | ├── 1560_12_25-15_42-*-f1_score.png
| | ├── 1560_12_25-15_42-*-f1_score.csv
| | ├── 1560_12_25-15_42-*-error_analysis_2D.png
| | ├── 1560_12_25-15_42-*-error_analysis_2D.html
| | ├── 1560_12_25-15_42-*-error_analysis_bar.png
| | ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance.html
| | ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance_dataframe.csv
| | ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance.html
| | ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance_dataframe.csv
| | ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance.html
| | ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance_dataframe.csv
| | ├── 1560_12_25-15_42-*-bar_plot_data.csv
| | ├── 1560_12_25-15_42-*-2D_plot_data.csv
| ├── iter_1 | ├── iter_1
| | ├── No-vs-Yes | | ├── train_labels.csv
| | | ├── adaboost | | └── train_indices.csv
| | | | ├── ViewNumber0 | | ├── 1560_12_25-15_42-*-LOG.log
| | | | | ├── 1560_12_25-15_42-*-summary.txt | | ├── config_file.yml
| | | | | ├── <other classifier dependant files> | | ├── *-accuracy_score.png
| | | | ├── ViewNumber1 | | ├── *-accuracy_score.csv
| | | | | ├── 1560_12_25-15_42-*-summary.txt | | ├── *-f1_score.png
| | | | | ├── <other classifier dependant files> | | ├── *-f1_score.csv
| | ├── *-error_analysis_2D.png
| | ├── *-error_analysis_2D.html
| | ├── *-error_analysis_bar.png
| | ├── *-bar_plot_data.csv
| | ├── *-2D_plot_data.csv
| | ├── adaboost
| | | ├── ViewNumber0
| | | | ├── *-summary.txt
| | | | ├── <other classifier dependant files>
| | | ├── ViewNumber1
| | | | ├── *-summary.txt
| | | | ├── <other classifier dependant files>
| | | | ├── ViewNumber2 | | | | ├── ViewNumber2
| | | | | ├── 1560_12_25-15_42-*-summary.txt | | | | ├── *-summary.txt
| | | | | ├── <other classifier dependant files> | | | | ├── <other classifier dependant files>
| | | ├── decision_tree | | ├── decision_tree
| | | | ├── ViewNumber0 | | | ├── ViewNumber0
| | | | | ├── <summary & classifier dependant files> | | | | ├── <summary & classifier dependant files>
| | | | ├── ViewNumber1 | | | ├── ViewNumber1
| | | | | ├── <summary & classifier dependant files> | | | | ├── <summary & classifier dependant files>
| | | | ├── ViewNumber2 | | | ├── ViewNumber2
| | | | | ├── <summary & classifier dependant files>
| | | ├── [..
| | | ├── ..]
| | | ├── weighted_linear_late_fusion
| | | | ├── <summary & classifier dependant files> | | | | ├── <summary & classifier dependant files>
| | | | ├── [.. | | ├── [..
| | | | ├── ..] | | ├── ..]
| | | ├── train_labels.csv | | ├── weighted_linear_late_fusion
| | │ └── train_indices.csv | | | ├── <summary & classifier dependant files>
| | ├── [..
| | ├── ..]
| ├── iter_2
| | ├── [..
| | ├── ..]
| ├── [..
| ├── ..]
| ├── train_labels.csv
| └── train_indices.csv
| ├── 1560_12_25-15_42-*-LOG.log | ├── 1560_12_25-15_42-*-LOG.log
| ├── config_file.yml | ├── config_file.yml
| | ├── 1560_12_25-15_42-*-accuracy_score.png | ├── *-accuracy_score.png
| | ├── 1560_12_25-15_42-*-accuracy_score.csv | ├── *-accuracy_score.csv
| | ├── 1560_12_25-15_42-*-f1_score.png | ├── *-f1_score.png
| | ├── 1560_12_25-15_42-*-f1_score.csv | ├── *-f1_score.csv
| | ├── 1560_12_25-15_42-*-error_analysis_2D.png | ├── *-error_analysis_2D.png
| | ├── 1560_12_25-15_42-*-error_analysis_2D.html | ├── *-error_analysis_2D.html
| | ├── 1560_12_25-15_42-*-error_analysis_bar.png | ├── *-error_analysis_bar.png
| | ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance.html | ├── *-bar_plot_data.csv
| | ├── 1560_12_25-15_42-*-ViewNumber0-feature_importance_dataframe.csv | ├── *-2D_plot_data.csv
| | ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance.html | ├── feature_importances
| | ├── 1560_12_25-15_42-*-ViewNumber1-feature_importance_dataframe.csv | | ├── *-ViewNumber0-feature_importance.html
| | ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance.html | | ├── *-ViewNumber0-feature_importance_dataframe.csv
| | ├── 1560_12_25-15_42-*-ViewNumber2-feature_importance_dataframe.csv | | ├── *-ViewNumber1-feature_importance.html
| | ├── 1560_12_25-15_42-*-bar_plot_data.csv | | ├── *-ViewNumber1-feature_importance_dataframe.csv
| | ├── 1560_12_25-15_42-*-2D_plot_data.csv | | ├── *-ViewNumber2-feature_importance.html
| ├── iter_2 | | ├── *-ViewNumber2-feature_importance_dataframe.csv
| | ├── No-vs-Yes
| | | ├─[...
| | | ├─...]
| ├── iter_3
| ├── [...
| ├── ...]
| └── random_state.pickle | └── random_state.pickle
If you look closely, nearly all the files from Example 1 are in each ``iter_`` directory, and a new ``No-vs-Yes`` directory ha appeared, in which the main figures are saved. If you look closely, nearly all the files from Example 1 are in each ``iter_`` directory, and some files have appeared, in which the main figures are saved.
So, the files saved in ``started_1560_12_25-15_42/No-vs-Yes/`` are the one that show th mean results on all the statistical iterations. So, the files stored in ``started_1560_12_25-15_42/`` are the one that show the mean results on all the statistical iterations.
For example, ``started_1560_12_25-15_42/No-vs-Yes/1560_12_25-15_42-*-accuracy_score.png`` looks like : For example, ``started_1560_12_25-15_42/*-accuracy_score.png`` looks like :
.. figure:: ./images/accuracy_mean.png .. figure:: ./images/accuracy_mean.png
:scale: 25 :scale: 25
The main difference between this plot an the one from Example 1 is that here, the scores are means over all the satatisitcal iterations, and the standard deviations are plotted as vertical lines on top of the bars and printed after each score under the bars as "± <std>". The main difference between this plot an the one from Example 1 is that here, the scores are means over all the statistical iterations, and the standard deviations are plotted as vertical lines on top of the bars and printed after each score under the bars as "± <std>".
Then, each iteration's directory regroups all the results, structured as in Example 1. Then, each iteration's directory regroups all the results, structured as in Example 1.
......
multiview_platform/examples/config_files/config_example_1.yml
+ 60
− 173
View file @ c62f8b70
# The base configuration of the benchmark # The base configuration of the benchmark
Base :
# Enable logging # Enable logging
log: True log: True
# The name of each dataset in the directory on which the benchmark should be run # The name of each dataset in the directory on which the benchmark should be run
...@@ -7,7 +7,7 @@ Base : ...@@ -7,7 +7,7 @@ Base :
# A label for the resul directory # A label for the resul directory
label: "_" label: "_"
# The type of dataset, currently supported ".hdf5", and ".csv" # The type of dataset, currently supported ".hdf5", and ".csv"
type: ".hdf5" file_type: ".hdf5"
# The views to use in the banchmark, an empty value will result in using all the views # The views to use in the banchmark, an empty value will result in using all the views
views: views:
# The path to the directory where the datasets are stored, an absolute path is advised # The path to the directory where the datasets are stored, an absolute path is advised
...@@ -27,9 +27,13 @@ Base : ...@@ -27,9 +27,13 @@ Base :
noise_std: 0.0 noise_std: 0.0
# The directory in which the results will be stored, an absolute path is advised # The directory in which the results will be stored, an absolute path is advised
res_dir: "examples/results/example_1/" 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 # All the classification-realted configuration options
Classification:
# If the dataset is multiclass, will use this multiclass-to-biclass method # If the dataset is multiclass, will use this multiclass-to-biclass method
multiclass_method: "oneVersusOne" multiclass_method: "oneVersusOne"
# The ratio number of test exmaples/number of train examples # The ratio number of test exmaples/number of train examples
...@@ -64,120 +68,3 @@ Classification: ...@@ -64,120 +68,3 @@ Classification:
# In order to run multiple sets of parameters, use multiple values in the # In order to run multiple sets of parameters, use multiple values in the
# following lists, and set hps_type to None. # following lists, and set hps_type to None.
#####################################
# The Monoview Classifier arguments #
#####################################
random_forest:
n_estimators: [25]
max_depth: [3]
criterion: ["entropy"]
svm_linear:
C: [1]
svm_rbf:
C: [1]
svm_poly:
C: [1]
degree: [2]
adaboost:
n_estimators: [50]
base_estimator: ["DecisionTreeClassifier"]
decision_tree:
max_depth: [10]
criterion: ["gini"]
splitter: ["best"]
sgd:
loss: ["hinge"]
penalty: [l2]
alpha: [0.0001]
knn:
n_neighbors: [5]
weights: ["uniform"]
algorithm: ["auto"]
lasso:
alpha: [1]
max_iter: [2]
gradient_boosting:
n_estimators: [2]
######################################
# The Multiview Classifier arguments #
######################################
weighted_linear_early_fusion:
view_weights: [null]
monoview_classifier_name: ["decision_tree"]
monoview_classifier_config:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
entropy_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
disagree_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
double_fault_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
difficulty_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
majority_voting_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
bayesian_inference_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
weighted_linear_late_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
multiview_platform/examples/config_files/config_example_2_1_1.yml
+ 60
− 173
View file @ c62f8b70
# The base configuration of the benchmark # The base configuration of the benchmark
Base :
# Enable logging # Enable logging
log: True log: True
# The name of each dataset in the directory on which the benchmark should be run # The name of each dataset in the directory on which the benchmark should be run
...@@ -7,7 +7,7 @@ Base : ...@@ -7,7 +7,7 @@ Base :
# A label for the resul directory # A label for the resul directory
label: "_" label: "_"
# The type of dataset, currently supported ".hdf5", and ".csv" # The type of dataset, currently supported ".hdf5", and ".csv"
type: ".hdf5" file_type: ".hdf5"
# The views to use in the banchmark, an empty value will result in using all the views # The views to use in the banchmark, an empty value will result in using all the views
views: views:
# The path to the directory where the datasets are stored, an absolute path is advised # The path to the directory where the datasets are stored, an absolute path is advised
...@@ -27,9 +27,13 @@ Base : ...@@ -27,9 +27,13 @@ Base :
noise_std: 0.0 noise_std: 0.0
# The directory in which the results will be stored, an absolute path is advised # The directory in which the results will be stored, an absolute path is advised
res_dir: "examples/results/example_1/" 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 # All the classification-realted configuration options
Classification:
# If the dataset is multiclass, will use this multiclass-to-biclass method # If the dataset is multiclass, will use this multiclass-to-biclass method
multiclass_method: "oneVersusOne" multiclass_method: "oneVersusOne"
# The ratio number of test exmaples/number of train examples # The ratio number of test exmaples/number of train examples
...@@ -64,120 +68,3 @@ Classification: ...@@ -64,120 +68,3 @@ Classification:
# In order to run multiple sets of parameters, use multiple values in the # In order to run multiple sets of parameters, use multiple values in the
# following lists, and set hps_type to None. # following lists, and set hps_type to None.
#####################################
# The Monoview Classifier arguments #
#####################################
random_forest:
n_estimators: [25]
max_depth: [3]
criterion: ["entropy"]
svm_linear:
C: [1]
svm_rbf:
C: [1]
svm_poly:
C: [1]
degree: [2]
adaboost:
n_estimators: [50]
base_estimator: ["DecisionTreeClassifier"]
decision_tree:
max_depth: [10]
criterion: ["gini"]
splitter: ["best"]
sgd:
loss: ["hinge"]
penalty: [l2]
alpha: [0.0001]
knn:
n_neighbors: [5]
weights: ["uniform"]
algorithm: ["auto"]
lasso:
alpha: [1]
max_iter: [2]
gradient_boosting:
n_estimators: [2]
######################################
# The Multiview Classifier arguments #
######################################
weighted_linear_early_fusion:
view_weights: [null]
monoview_classifier_name: ["decision_tree"]
monoview_classifier_config:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
entropy_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
disagree_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
double_fault_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
difficulty_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
majority_voting_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
bayesian_inference_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
weighted_linear_late_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
multiview_platform/examples/config_files/config_example_2_1_2.yml
+ 61
− 174
View file @ c62f8b70
# The base configuration of the benchmark # The base configuration of the benchmark
Base :
# Enable logging # Enable logging
log: True log: True
# The name of each dataset in the directory on which the benchmark should be run # The name of each dataset in the directory on which the benchmark should be run
...@@ -7,7 +7,7 @@ Base : ...@@ -7,7 +7,7 @@ Base :
# A label for the resul directory # A label for the resul directory
label: "_" label: "_"
# The type of dataset, currently supported ".hdf5", and ".csv" # The type of dataset, currently supported ".hdf5", and ".csv"
type: ".hdf5" file_type: ".hdf5"
# The views to use in the banchmark, an empty value will result in using all the views # The views to use in the banchmark, an empty value will result in using all the views
views: views:
# The path to the directory where the datasets are stored, an absolute path is advised # The path to the directory where the datasets are stored, an absolute path is advised
...@@ -27,9 +27,13 @@ Base : ...@@ -27,9 +27,13 @@ Base :
noise_std: 0.0 noise_std: 0.0
# The directory in which the results will be stored, an absolute path is advised # The directory in which the results will be stored, an absolute path is advised
res_dir: "examples/results/example_1/" 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 # All the classification-realted configuration options
Classification:
# If the dataset is multiclass, will use this multiclass-to-biclass method # If the dataset is multiclass, will use this multiclass-to-biclass method
multiclass_method: "oneVersusOne" multiclass_method: "oneVersusOne"
# The ratio number of test exmaples/number of train examples # The ratio number of test exmaples/number of train examples
...@@ -64,120 +68,3 @@ Classification: ...@@ -64,120 +68,3 @@ Classification:
# In order to run multiple sets of parameters, use multiple values in the # In order to run multiple sets of parameters, use multiple values in the
# following lists, and set hps_type to None. # following lists, and set hps_type to None.
\ No newline at end of file
#####################################
# The Monoview Classifier arguments #
#####################################
random_forest:
n_estimators: [25]
max_depth: [3]
criterion: ["entropy"]
svm_linear:
C: [1]
svm_rbf:
C: [1]
svm_poly:
C: [1]
degree: [2]
adaboost:
n_estimators: [50]
base_estimator: ["DecisionTreeClassifier"]
decision_tree:
max_depth: [10]
criterion: ["gini"]
splitter: ["best"]
sgd:
loss: ["hinge"]
penalty: [l2]
alpha: [0.0001]
knn:
n_neighbors: [5]
weights: ["uniform"]
algorithm: ["auto"]
lasso:
alpha: [1]
max_iter: [2]
gradient_boosting:
n_estimators: [2]
######################################
# The Multiview Classifier arguments #
######################################
weighted_linear_early_fusion:
view_weights: [null]
monoview_classifier_name: ["decision_tree"]
monoview_classifier_config:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
entropy_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
disagree_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
double_fault_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
difficulty_fusion:
classifier_names: [["decision_tree"]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
majority_voting_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
bayesian_inference_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
weighted_linear_late_fusion:
classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
classifier_configs:
decision_tree:
max_depth: [1]
criterion: ["gini"]
splitter: ["best"]
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