|
- # -*- coding: utf-8 -*-
- """model_selection_old.ipynb
-
- Automatically generated by Colaboratory.
-
- Original file is located at
- https://colab.research.google.com/drive/1uVkl7scNgEPrimX8ks6iEC5ijuhB8L_D
-
- **This script demonstrates how to compute a graph kernel.**
- ---
-
- **0. Install `graphkit-learn`.**
- """
-
- """**1. Perform model seletion and classification.**"""
-
- from gklearn.utils import model_selection_for_precomputed_kernel
- from gklearn.kernels import untilhpathkernel
- import numpy as np
-
- # Set parameters.
- datafile = '../../../datasets/MUTAG/MUTAG_A.txt'
- param_grid_precomputed = {'depth': np.linspace(1, 10, 10),
- 'k_func': ['MinMax', 'tanimoto'],
- 'compute_method': ['trie']}
- param_grid = {'C': np.logspace(-10, 10, num=41, base=10)}
-
- # Perform model selection and classification.
- model_selection_for_precomputed_kernel(
- datafile, # The path of dataset file.
- untilhpathkernel, # The graph kernel used for estimation.
- param_grid_precomputed, # The parameters used to compute gram matrices.
- param_grid, # The penelty Parameters used for penelty items.
- 'classification', # Or 'regression'.
- NUM_TRIALS=30, # The number of the random trials of the outer CV loop.
- ds_name='MUTAG', # The name of the dataset.
- n_jobs=1,
- verbose=True)
|
