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Project Description

HMC-loss

Abstruct

Python-implemented hierarchical multi-class validation metrics: HMC-loss . Original paper is (Bi&Kwok, 2012) .

Install

pip install hmc_loss

Requirement

  • numpy
  • Network X

How to use

This metrics is implemented like scikit-learn metrics.

from hmc_loss import hmc_loss_score, get_cost_list
import numpy as np

# Generate label data(2-D array of numpy)
true_label = np.random.randint(2, size(100, 100))
pred_label = np.random.randint(2, size(100, 100))

# Generate test graph(Di-Graph of NetworkX)
graph = nx.gnc_graph(100)
# Generate element list of graph node
label_list = list(range(100))
# Calculate cost of each node in graph
cost_list = get_cost_list(graph, 0, label_list)
# Calculate HMC-loss
hmc_loss_score(true_label, pred_label, graph, 0, label_list, cost_list, alpha=0.5, beta=1.5)

Licence

MIT

Author

Taske HAMANO

Release History

Release History

This version
History Node

1.0.0

History Node

0.4.0

History Node

0.3.1

History Node

0.3.0

History Node

0.2.0

History Node

0.1.0

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
hmc_loss-1.0.0.tar.gz (3.7 kB) Copy SHA256 Checksum SHA256 Source Apr 24, 2017

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