Advanced AI Training and Testing with CSA and HuffmanCodedPosAndEval
In this tutorial, we delve into advanced AI concepts, focusing on training and testing models using CSA (Computer Shogi Association) data alongside HuffmanCodedPosAndEval. We explore the process of filtering moves and ratings, incorporating test ratios for effective model evaluation. The tutorial provides insights into key Python libraries such as argparse, numpy, os, glob, and sklearn for data manipulation, training-test splitting, and model evaluation. The provided code snippets and visual aids offer a practical approach to enhance AI training techniques.
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Presentation Transcript
AI 5120108
import argparse from cshogi import HuffmanCodedPosAndEval, Board, BLACK, move16 from cshogi import CSA import numpy as np import os import glob from sklearn.model_selection import train_test_split parser = argparse.ArgumentParser() parser.add_argument('csa_dir') parser.add_argument('hcpe_train') parser.add_argument('hcpe_test') parser.add_argument('--filter_moves', type = int , default = 50) parser.add_argument('--filter_rating',type = int, default = 3500) parser.add_argument('--test_ratio', type = float, default= 0.1)
sage: FILTER_RATING] colab_kernel_launcher.py [-h] [--filter_moves FILTER_MOVES] [--filter_rating [--test_ratio TEST_RATIO] csa_dir hcpe_train hcpe_test colab_kernel_launcher.py: hcpe_test error: the following arguments are required: hcpe_train, An exception has occurred, use %tb to see the full traceback. SystemExit: 2 /usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py:3561: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D. warn("To exit: use 'exit', 'quit', or Ctrl-D.", stacklevel=1)
