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graphkit-learn/notebooks/get_dataset_attributes.ipynb

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Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
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Plus de choses sont changées...
7 years ago
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
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Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
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Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
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Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
​
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​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
Plus de choses sont changées...
7 years ago
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​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
Plus de choses sont changées...
7 years ago
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
​
​
​
​
​
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
Plus de choses sont changées...
7 years ago
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
​
Plus de choses sont changées...
7 years ago
​
​
1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
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Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
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1. [IMPORTANT] correct an error in dataset split section in function cross_validation_pre_computed. DON NOT use previous versions. They are all wrong. 2. save gram matrices and relative data when using function cross_validation_pre_computed, before cross validation step, in case that something goes wrong with CV. Parameter read_gm_from_file can be used to choose whether to read gram matrices from file. 3. add some test code to check if a gram matrix is symmetric and positive semi-definite.
6 years ago
Plus de choses sont changées...
7 years ago
​
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  1. {

  2.  "cells": [

  3.   {

  4.    "cell_type": "code",

  5.    "execution_count": 1,

  6.    "metadata": {

  7.     "scrolled": false

  8.    },

  9.    "outputs": [

  10.     {

  11.      "name": "stdout",

  12.      "output_type": "stream",

  13.      "text": [

  14.       "\n",

  15.       "Letter-med:\n",

  16.       "substructures : {'linear', 'non linear'}\n",

  17.       "node_labeled : False\n",

  18.       "edge_labeled : False\n",

  19.       "is_directed : False\n",

  20.       "dataset_size : 2250\n",

  21.       "ave_graph_size : 4.674666666666667\n",

  22.       "min_graph_size : 1\n",

  23.       "max_graph_size : 9\n",

  24.       "ave_graph_edge_num : 3.2057777777777776\n",

  25.       "min_graph_edge_num : 0\n",

  26.       "max_graph_edge_num : 7\n",

  27.       "ave_graph_degree : 2.012888888888889\n",

  28.       "min_graph_degree : 0\n",

  29.       "max_graph_degree : 4\n",

  30.       "node_label_num : 0\n",

  31.       "edge_label_num : 0\n",

  32.       "node_attr_dim : 2\n",

  33.       "edge_attr_dim : 0\n",

  34.       "class_number : 15\n",

  35.       "\n",

  36.       "\n",

  37.       "Mutagenicity:\n",

  38.       "substructures : {'linear', 'non linear'}\n",

  39.       "node_labeled : True\n",

  40.       "edge_labeled : True\n",

  41.       "is_directed : False\n",

  42.       "dataset_size : 4337\n",

  43.       "ave_graph_size : 30.317731150564907\n",

  44.       "min_graph_size : 4\n",

  45.       "max_graph_size : 417\n",

  46.       "ave_graph_edge_num : 30.76942587041734\n",

  47.       "min_graph_edge_num : 3\n",

  48.       "max_graph_edge_num : 112\n",

  49.       "ave_graph_degree : 3.75651371916071\n",

  50.       "min_graph_degree : 3\n",

  51.       "max_graph_degree : 4\n",

  52.       "node_label_num : 14\n",

  53.       "edge_label_num : 3\n",

  54.       "node_attr_dim : 0\n",

  55.       "edge_attr_dim : 0\n",

  56.       "class_number : 2\n",

  57.       "\n",

  58.       "\n",

  59.       "AIDS:\n",

  60.       "substructures : {'linear', 'non linear'}\n",

  61.       "node_labeled : True\n",

  62.       "edge_labeled : True\n",

  63.       "is_directed : False\n",

  64.       "dataset_size : 2000\n",

  65.       "ave_graph_size : 15.6925\n",

  66.       "min_graph_size : 2\n",

  67.       "max_graph_size : 95\n",

  68.       "ave_graph_edge_num : 16.195\n",

  69.       "min_graph_edge_num : 1\n",

  70.       "max_graph_edge_num : 103\n",

  71.       "ave_graph_degree : 3.322\n",

  72.       "min_graph_degree : 1\n",

  73.       "max_graph_degree : 6\n",

  74.       "node_label_num : 38\n",

  75.       "edge_label_num : 3\n",

  76.       "node_attr_dim : 4\n",

  77.       "edge_attr_dim : 0\n",

  78.       "class_number : 2\n",

  79.       "\n",

  80.       "\n",

  81.       "FIRSTMM_DB:\n",

  82.       "substructures : {'non linear'}\n",

  83.       "node_labeled : True\n",

  84.       "edge_labeled : False\n",

  85.       "is_directed : False\n",

  86.       "dataset_size : 41\n",

  87.       "ave_graph_size : 1377.2682926829268\n",

  88.       "min_graph_size : 134\n",

  89.       "max_graph_size : 5037\n",

  90.       "ave_graph_edge_num : 3074.0975609756097\n",

  91.       "min_graph_edge_num : 320\n",

  92.       "max_graph_edge_num : 10888\n",

  93.       "ave_graph_degree : 7.853658536585366\n",

  94.       "min_graph_degree : 6\n",

  95.       "max_graph_degree : 10\n",

  96.       "node_label_num : 5\n",

  97.       "edge_label_num : 0\n",

  98.       "node_attr_dim : 1\n",

  99.       "edge_attr_dim : 2\n",

  100.       "class_number : 11\n",

  101.       "\n",

  102.       "\n",

  103.       "MSRC9:\n",

  104.       "substructures : {'linear', 'non linear'}\n",

  105.       "node_labeled : True\n",

  106.       "edge_labeled : False\n",

  107.       "is_directed : False\n",

  108.       "dataset_size : 221\n",

  109.       "ave_graph_size : 40.57918552036199\n",

  110.       "min_graph_size : 25\n",

  111.       "max_graph_size : 55\n",

  112.       "ave_graph_edge_num : 97.9366515837104\n",

  113.       "min_graph_edge_num : 53\n",

  114.       "max_graph_edge_num : 145\n",

  115.       "ave_graph_degree : 10.158371040723981\n",

  116.       "min_graph_degree : 8\n",

  117.       "max_graph_degree : 16\n",

  118.       "node_label_num : 10\n",

  119.       "edge_label_num : 0\n",

  120.       "node_attr_dim : 0\n",

  121.       "edge_attr_dim : 0\n",

  122.       "class_number : 8\n",

  123.       "\n",

  124.       "\n",

  125.       "MSRC21:\n",

  126.       "substructures : {'linear', 'non linear'}\n",

  127.       "node_labeled : True\n",

  128.       "edge_labeled : False\n",

  129.       "is_directed : False\n",

  130.       "dataset_size : 563\n",

  131.       "ave_graph_size : 77.52042628774423\n",

  132.       "min_graph_size : 51\n",

  133.       "max_graph_size : 141\n",

  134.       "ave_graph_edge_num : 198.32326820603907\n",

  135.       "min_graph_edge_num : 121\n",

  136.       "max_graph_edge_num : 405\n",

  137.       "ave_graph_degree : 11.41563055062167\n",

  138.       "min_graph_degree : 8\n",

  139.       "max_graph_degree : 23\n",

  140.       "node_label_num : 22\n",

  141.       "edge_label_num : 0\n",

  142.       "node_attr_dim : 0\n",

  143.       "edge_attr_dim : 0\n",

  144.       "class_number : 20\n",

  145.       "\n",

  146.       "\n",

  147.       "SYNTHETIC:\n",

  148.       "substructures : {'linear', 'non linear'}\n",

  149.       "node_labeled : True\n",

  150.       "edge_labeled : False\n",

  151.       "is_directed : False\n",

  152.       "dataset_size : 300\n",

  153.       "ave_graph_size : 100.0\n",

  154.       "min_graph_size : 100\n",

  155.       "max_graph_size : 100\n",

  156.       "ave_graph_edge_num : 196.0\n",

  157.       "min_graph_edge_num : 196\n",

  158.       "max_graph_edge_num : 196\n",

  159.       "ave_graph_degree : 8.0\n",

  160.       "min_graph_degree : 8\n",

  161.       "max_graph_degree : 8\n",

  162.       "node_label_num : 8\n",

  163.       "edge_label_num : 0\n",

  164.       "node_attr_dim : 1\n",

  165.       "edge_attr_dim : 0\n",

  166.       "class_number : 2\n",

  167.       "\n",

  168.       "\n",

  169.       "BZR:\n",

  170.       "substructures : {'linear', 'non linear'}\n",

  171.       "node_labeled : True\n",

  172.       "edge_labeled : False\n",

  173.       "is_directed : False\n",

  174.       "dataset_size : 405\n",

  175.       "ave_graph_size : 35.75061728395062\n",

  176.       "min_graph_size : 13\n",

  177.       "max_graph_size : 57\n",

  178.       "ave_graph_edge_num : 38.358024691358025\n",

  179.       "min_graph_edge_num : 13\n",

  180.       "max_graph_edge_num : 60\n",

  181.       "ave_graph_degree : 3.8641975308641974\n",

  182.       "min_graph_degree : 3\n",

  183.       "max_graph_degree : 4\n",

  184.       "node_label_num : 10\n",

  185.       "edge_label_num : 0\n",

  186.       "node_attr_dim : 3\n",

  187.       "edge_attr_dim : 0\n",

  188.       "class_number : 2\n",

  189.       "\n",

  190.       "\n",

  191.       "COX2:\n",

  192.       "substructures : {'linear', 'non linear'}\n",

  193.       "node_labeled : True\n",

  194.       "edge_labeled : False\n",

  195.       "is_directed : False\n",

  196.       "dataset_size : 467\n",

  197.       "ave_graph_size : 41.224839400428266\n",

  198.       "min_graph_size : 32\n",

  199.       "max_graph_size : 56\n",

  200.       "ave_graph_edge_num : 43.44539614561028\n",

  201.       "min_graph_edge_num : 34\n",

  202.       "max_graph_edge_num : 59\n",

  203.       "ave_graph_degree : 4.0\n",

  204.       "min_graph_degree : 4\n",

  205.       "max_graph_degree : 4\n",

  206.       "node_label_num : 8\n",

  207.       "edge_label_num : 0\n",

  208.       "node_attr_dim : 3\n",

  209.       "edge_attr_dim : 0\n",

  210.       "class_number : 2\n",

  211.       "\n",

  212.       "\n",

  213.       "DHFR:\n",

  214.       "substructures : {'linear', 'non linear'}\n",

  215.       "node_labeled : True\n",

  216.       "edge_labeled : False\n",

  217.       "is_directed : False\n",

  218.       "dataset_size : 756\n",

  219.       "ave_graph_size : 42.42724867724868\n",

  220.       "min_graph_size : 20\n",

  221.       "max_graph_size : 71\n",

  222.       "ave_graph_edge_num : 44.544973544973544\n",

  223.       "min_graph_edge_num : 21\n",

  224.       "max_graph_edge_num : 73\n",

  225.       "ave_graph_degree : 3.955026455026455\n",

  226.       "min_graph_degree : 3\n",

  227.       "max_graph_degree : 4\n",

  228.       "node_label_num : 9\n",

  229.       "edge_label_num : 0\n",

  230.       "node_attr_dim : 3\n",

  231.       "edge_attr_dim : 0\n",

  232.       "class_number : 2\n",

  233.       "\n",

  234.       "\n",

  235.       "ENZYMES:\n",

  236.       "substructures : {'linear', 'non linear'}\n",

  237.       "node_labeled : True\n",

  238.       "edge_labeled : False\n",

  239.       "is_directed : False\n",

  240.       "dataset_size : 600\n",

  241.       "ave_graph_size : 32.63333333333333\n",

  242.       "min_graph_size : 2\n",

  243.       "max_graph_size : 126\n",

  244.       "ave_graph_edge_num : 62.13666666666666\n",

  245.       "min_graph_edge_num : 1\n",

  246.       "max_graph_edge_num : 149\n",

  247.       "ave_graph_degree : 6.086666666666667\n",

  248.       "min_graph_degree : 1\n",

  249.       "max_graph_degree : 9\n",

  250.       "node_label_num : 3\n",

  251.       "edge_label_num : 0\n",

  252.       "node_attr_dim : 18\n",

  253.       "edge_attr_dim : 0\n",

  254.       "class_number : 6\n",

  255.       "\n",

  256.       "\n",

  257.       "PROTEINS:\n",

  258.       "substructures : {'linear', 'non linear'}\n",

  259.       "node_labeled : True\n",

  260.       "edge_labeled : False\n",

  261.       "is_directed : False\n",

  262.       "dataset_size : 1113\n",

  263.       "ave_graph_size : 39.05750224618149\n",

  264.       "min_graph_size : 4\n",

  265.       "max_graph_size : 620\n",

  266.       "ave_graph_edge_num : 72.8158131176999\n",

  267.       "min_graph_edge_num : 5\n",

  268.       "max_graph_edge_num : 1049\n",

  269.       "ave_graph_degree : 5.794249775381851\n",

  270.       "min_graph_degree : 3\n",

  271.       "max_graph_degree : 25\n",

  272.       "node_label_num : 3\n",

  273.       "edge_label_num : 0\n",

  274.       "node_attr_dim : 1\n",

  275.       "edge_attr_dim : 0\n",

  276.       "class_number : 2\n",

  277.       "\n",

  278.       "\n",

  279.       "PROTEINS_full:\n",

  280.       "substructures : {'linear', 'non linear'}\n",

  281.       "node_labeled : True\n",

  282.       "edge_labeled : False\n",

  283.       "is_directed : False\n",

  284.       "dataset_size : 1113\n",

  285.       "ave_graph_size : 39.05750224618149\n",

  286.       "min_graph_size : 4\n",

  287.       "max_graph_size : 620\n",

  288.       "ave_graph_edge_num : 72.8158131176999\n",

  289.       "min_graph_edge_num : 5\n",

  290.       "max_graph_edge_num : 1049\n",

  291.       "ave_graph_degree : 5.794249775381851\n",

  292.       "min_graph_degree : 3\n",

  293.       "max_graph_degree : 25\n",

  294.       "node_label_num : 3\n",

  295.       "edge_label_num : 0\n",

  296.       "node_attr_dim : 29\n",

  297.       "edge_attr_dim : 0\n",

  298.       "class_number : 2\n",

  299.       "\n",

  300.       "\n",

  301.       "D&D:\n",

  302.       "substructures : {'linear', 'non linear'}\n",

  303.       "node_labeled : True\n",

  304.       "edge_labeled : False\n",

  305.       "is_directed : False\n",

  306.       "dataset_size : 1178\n",

  307.       "ave_graph_size : 284.3166383701188\n",

  308.       "min_graph_size : 30\n",

  309.       "max_graph_size : 5748\n",

  310.       "ave_graph_edge_num : 715.6587436332767\n",

  311.       "min_graph_edge_num : 63\n",

  312.       "max_graph_edge_num : 14267\n",

  313.       "ave_graph_degree : 9.509337860780985\n",

  314.       "min_graph_degree : 6\n",

  315.       "max_graph_degree : 19\n",

  316.       "node_label_num : 82\n",

  317.       "edge_label_num : 0\n",

  318.       "node_attr_dim : 0\n",

  319.       "edge_attr_dim : 0\n",

  320.       "class_number : 2\n",

  321.       "\n",

  322.       "\n",

  323.       "MUTAG:\n",

  324.       "substructures : {'linear', 'non linear'}\n",

  325.       "node_labeled : True\n",

  326.       "edge_labeled : True\n",

  327.       "is_directed : False\n",

  328.       "dataset_size : 188\n",

  329.       "ave_graph_size : 17.930851063829788\n",

  330.       "min_graph_size : 10\n",

  331.       "max_graph_size : 28\n",

  332.       "ave_graph_edge_num : 19.79255319148936\n",

  333.       "min_graph_edge_num : 10\n",

  334.       "max_graph_edge_num : 33\n",

  335.       "ave_graph_degree : 3.00531914893617\n",

  336.       "min_graph_degree : 3\n",

  337.       "max_graph_degree : 4\n",

  338.       "node_label_num : 7\n",

  339.       "edge_label_num : 11\n",

  340.       "node_attr_dim : 0\n",

  341.       "edge_attr_dim : 0\n",

  342.       "class_number : 2\n",

  343.       "\n",

  344.       "\n",

  345.       "Alkane:\n",

  346.       "substructures : {'linear', 'non linear'}\n",

  347.       "node_labeled : True\n",

  348.       "edge_labeled : False\n",

  349.       "is_directed : False\n",

  350.       "dataset_size : 150\n",

  351.       "ave_graph_size : 8.873333333333333\n",

  352.       "min_graph_size : 1\n",

  353.       "max_graph_size : 10\n",

  354.       "ave_graph_edge_num : 7.873333333333333\n",

  355.       "min_graph_edge_num : 0\n",

  356.       "max_graph_edge_num : 9\n",

  357.       "ave_graph_degree : 3.36\n",

  358.       "min_graph_degree : 0\n",

  359.       "max_graph_degree : 4\n",

  360.       "node_label_num : 2\n",

  361.       "edge_label_num : 1\n",

  362.       "node_attr_dim : 0\n",

  363.       "edge_attr_dim : 0\n",

  364.       "class_number : 123\n",

  365.       "\n",

  366.       "\n",

  367.       "Acyclic:\n",

  368.       "substructures : {'linear', 'non linear'}\n",

  369.       "node_labeled : True\n",

  370.       "edge_labeled : False\n",

  371.       "is_directed : False\n",

  372.       "dataset_size : 183\n",

  373.       "ave_graph_size : 8.153005464480874\n",

  374.       "min_graph_size : 3\n",

  375.       "max_graph_size : 11\n",

  376.       "ave_graph_edge_num : 7.1530054644808745\n",

  377.       "min_graph_edge_num : 2\n",

  378.       "max_graph_edge_num : 10\n",

  379.       "ave_graph_degree : 2.80327868852459\n",

  380.       "min_graph_degree : 2\n",

  381.       "max_graph_degree : 4\n",

  382.       "node_label_num : 3\n",

  383.       "edge_label_num : 1\n",

  384.       "node_attr_dim : 0\n",

  385.       "edge_attr_dim : 0\n",

  386.       "class_number : 148\n",

  387.       "\n",

  388.       "\n",

  389.       "MAO:\n",

  390.       "substructures : {'linear', 'non linear'}\n",

  391.       "node_labeled : True\n",

  392.       "edge_labeled : True\n",

  393.       "is_directed : False\n",

  394.       "dataset_size : 68\n",

  395.       "ave_graph_size : 18.38235294117647\n",

  396.       "min_graph_size : 11\n",

  397.       "max_graph_size : 27\n",

  398.       "ave_graph_edge_num : 19.63235294117647\n",

  399.       "min_graph_edge_num : 12\n",

  400.       "max_graph_edge_num : 29\n",

  401.       "ave_graph_degree : 3.0\n",

  402.       "min_graph_degree : 3\n",

  403.       "max_graph_degree : 3\n",

  404.       "node_label_num : 3\n",

  405.       "edge_label_num : 4\n",

  406.       "node_attr_dim : 0\n",

  407.       "edge_attr_dim : 0\n",

  408.       "class_number : 2\n",

  409.       "\n"

  410.      ]

  411.     },

  412.     {

  413.      "name": "stdout",

  414.      "output_type": "stream",

  415.      "text": [

  416.       "\n",

  417.       "PAH:\n",

  418.       "substructures : {'linear', 'non linear'}\n",

  419.       "node_labeled : False\n",

  420.       "edge_labeled : False\n",

  421.       "is_directed : False\n",

  422.       "dataset_size : 94\n",

  423.       "ave_graph_size : 20.70212765957447\n",

  424.       "min_graph_size : 10\n",

  425.       "max_graph_size : 28\n",

  426.       "ave_graph_edge_num : 24.425531914893618\n",

  427.       "min_graph_edge_num : 11\n",

  428.       "max_graph_edge_num : 34\n",

  429.       "ave_graph_degree : 3.0106382978723403\n",

  430.       "min_graph_degree : 3\n",

  431.       "max_graph_degree : 4\n",

  432.       "node_label_num : 1\n",

  433.       "edge_label_num : 1\n",

  434.       "node_attr_dim : 0\n",

  435.       "edge_attr_dim : 0\n",

  436.       "class_number : 2\n",

  437.       "\n",

  438.       "\n",

  439.       "NCI1:\n",

  440.       "substructures : {'linear', 'non linear'}\n",

  441.       "node_labeled : True\n",

  442.       "edge_labeled : False\n",

  443.       "is_directed : False\n",

  444.       "dataset_size : 4110\n",

  445.       "ave_graph_size : 29.8654501216545\n",

  446.       "min_graph_size : 3\n",

  447.       "max_graph_size : 111\n",

  448.       "ave_graph_edge_num : 32.3\n",

  449.       "min_graph_edge_num : 2\n",

  450.       "max_graph_edge_num : 119\n",

  451.       "ave_graph_degree : 3.3360097323600972\n",

  452.       "min_graph_degree : 2\n",

  453.       "max_graph_degree : 4\n",

  454.       "node_label_num : 37\n",

  455.       "edge_label_num : 0\n",

  456.       "node_attr_dim : 0\n",

  457.       "edge_attr_dim : 0\n",

  458.       "class_number : 2\n",

  459.       "\n",

  460.       "\n",

  461.       "NCI109:\n",

  462.       "substructures : {'linear', 'non linear'}\n",

  463.       "node_labeled : True\n",

  464.       "edge_labeled : False\n",

  465.       "is_directed : False\n",

  466.       "dataset_size : 4127\n",

  467.       "ave_graph_size : 29.681124303368065\n",

  468.       "min_graph_size : 4\n",

  469.       "max_graph_size : 111\n",

  470.       "ave_graph_edge_num : 32.13084565059365\n",

  471.       "min_graph_edge_num : 3\n",

  472.       "max_graph_edge_num : 119\n",

  473.       "ave_graph_degree : 3.343833292948873\n",

  474.       "min_graph_degree : 2\n",

  475.       "max_graph_degree : 5\n",

  476.       "node_label_num : 38\n",

  477.       "edge_label_num : 0\n",

  478.       "node_attr_dim : 0\n",

  479.       "edge_attr_dim : 0\n",

  480.       "class_number : 2\n",

  481.       "\n"

  482.      ]

  483.     },

  484.     {

  485.      "ename": "ModuleNotFoundError",

  486.      "evalue": "No module named 'tqdm'",

  487.      "output_type": "error",

  488.      "traceback": [

  489.       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",

  490.       "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",

  491.       "\u001b[0;32m<ipython-input-1-1e4da065c026>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mfilename_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset_y'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'dataset_y'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n\u001b[0m\u001b[1;32m     48\u001b[0m     attrs = get_dataset_attributes(\n\u001b[1;32m     49\u001b[0m         dataset, target=y, node_label='atom', edge_label='bond_type')\n",

  492.       "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/graphfiles.py\u001b[0m in \u001b[0;36mloadDataset\u001b[0;34m(filename, filename_y, extra_params)\u001b[0m\n\u001b[1;32m    377\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mextension\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"sdf\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m         \u001b[0;32mfrom\u001b[0m \u001b[0mtqdm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    380\u001b[0m         \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",

  493.       "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tqdm'"

  494.      ]

  495.     }

  496.    ],

  497.    "source": [

  498.     "import sys\n",

  499.     "sys.path.insert(0, \"../\")\n",

  500.     "from pygraph.utils.graphfiles import loadDataset\n",

  501.     "from pygraph.utils.graphdataset import get_dataset_attributes\n",

  502.     "\n",

  503.     "dslist = [\n",

  504.     "    {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n",

  505.     "    {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n",

  506.     "    {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'},\n",

  507.     "    {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'},\n",

  508.     "    {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'},\n",

  509.     "    {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'},\n",

  510.     "    {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'},\n",

  511.     "    {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'},\n",

  512.     "    {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'},\n",

  513.     "    {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'},\n",

  514.     "    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n",

  515.     "    {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'},\n",

  516.     "    {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'},\n",

  517.     "    {'name': 'D&D', 'dataset': '../datasets/D&D/DD.mat',\n",

  518.     "     'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}},\n",

  519.     "    {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',\n",

  520.     "        'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}},\n",

  521.     "    {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds',\n",

  522.     "        'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',},\n",

  523.     "    {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',},\n",

  524.     "    {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',},\n",

  525.     "    {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',},\n",

  526.     "    {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n",

  527.     "        'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},\n",

  528.     "    {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n",

  529.     "        'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},\n",

  530.     "    {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n",

  531.     "        'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',},\n",

  532.     "\n",

  533.     "#     # not working below\n",

  534.     "#     {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n",

  535.     "#     {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n",

  536.     "#     {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n",

  537.     "#     {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n",

  538.     "]\n",

  539.     "\n",

  540.     "for ds in dslist:\n",

  541.     "    dataset, y = loadDataset(\n",

  542.     "        ds['dataset'],\n",

  543.     "        filename_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n",

  544.     "        extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n",

  545.     "    attrs = get_dataset_attributes(\n",

  546.     "        dataset, target=y, node_label='atom', edge_label='bond_type')\n",

  547.     "    print()\n",

  548.     "    print(ds['name'] + ':')\n",

  549.     "    for atr in attrs:\n",

  550.     "        print(atr, ':', attrs[atr])\n",

  551.     "    print()"

  552.    ]

  553.   }

  554.  ],

  555.  "metadata": {

  556.   "kernelspec": {

  557.    "display_name": "Python 3",

  558.    "language": "python",

  559.    "name": "python3"

  560.   },

  561.   "language_info": {

  562.    "codemirror_mode": {

  563.     "name": "ipython",

  564.     "version": 3

  565.    },

  566.    "file_extension": ".py",

  567.    "mimetype": "text/x-python",

  568.    "name": "python",

  569.    "nbconvert_exporter": "python",

  570.    "pygments_lexer": "ipython3",

  571.    "version": "3.6.5"

  572.   }

  573.  },

  574.  "nbformat": 4,

  575.  "nbformat_minor": 2

  576. }

A Python package for graph kernels, graph edit distances and graph pre-image problem.