def load_go_data(
self,
data_type='train', # <1>
num_samples=1000): # <2>
index = KGSIndex(data_directory=self.data_dir)
index.download_files() # <3>
sampler = Sampler(data_dir=self.data_dir)
data = sampler.draw_data(data_type, num_samples) # <4>
zip_names = set()
indices_by_zip_name = {}
for filename, index in data:
zip_names.add(filename) # <5>
if filename not in indices_by_zip_name:
indices_by_zip_name[filename] = []
indices_by_zip_name[filename].append(index) # <6>
for zip_name in zip_names:
base_name = zip_name.replace('.tar.gz', '')
data_file_name = base_name + data_type
if not os.path.isfile(self.data_dir + '/' + data_file_name):
self.process_zip(zip_name, data_file_name,
indices_by_zip_name[zip_name]) # <7>
features_and_labels = self.consolidate_games(data_type, data) # <8>
return features_and_labels
def load_go_data(self, data_type='train', num_samples=1000):
# download all games from KGS to local data directory
index = KGSIndex(data_directory=self.data_dir)
index.download_files()
# sampler instance selects the specified number of games for a data type
sampler = Sampler(data_dir=self.data_dir)
data = sampler.draw_data(data_type, num_samples)
# collect all zip files
zip_names = set()
indices_by_zip_name = {}
for filename, index in data:
zip_names.add(filename)
if filename not in indices_by_zip_name:
indices_by_zip_name[filename] = []
indices_by_zip_name[filename].append(index)
# group all SGF file indices by zip_file_name
for zip_name in zip_names:
base_name = zip_name.replace('.tar.gz', '')
data_file_name = base_name + data_type
# Process each zip file individually
if not os.path.isfile(self.data_dir + '/' + data_file_name):
self.process_zip(zip_name, data_file_name,
indices_by_zip_name[zip_name])
# Aggregate and return files
features_and_labels = self.consolidate_games(data_type, data)
return features_and_labels
def load_go_data(self,
data_type='train',
num_samples=1000,
download=False):
"""
棋譜データの読み込み
Parameters
----------
data_type : str
trainまたはtest
num_samples : int
読み込むゲームの数
download : bool
データのwebからのダウンロードを行うかどうか
Returns
-------
features_and_labels
[0]: 特徴量のリスト
[1]: ラベルのリスト
"""
# 必要なら棋譜データをダウンロード
if download:
index = KGSIndex(data_directory=self.data_dir)
index.download_files()
# Samplerによって必要数のゲーム数を含む(zipファイル名,ゲームインデックス)のリストを取得
# なお，Samplerは2014年より前の棋譜か後の棋譜かでtestとtrainを分けている
sampler = Sampler(data_dir=self.data_dir)
data = sampler.draw_data(data_type, num_samples)
# ファイル名のsetと「ファイル名->ゲームインデックスのリスト」となる辞書を作成
zip_names = set()
indices_by_zip_name = {}
for filename, index in data:
zip_names.add(filename)
if filename not in indices_by_zip_name:
indices_by_zip_name[filename] = []
indices_by_zip_name[filename].append(index)
# setに入っている必要なtar.gzを，未解凍なら解凍
for zip_name in zip_names:
base_name = zip_name.replace('.tar.gz', '')
data_file_name = base_name + data_type
if not os.path.isfile(self.data_dir + '/' + data_file_name):
self.process_zip(zip_name, data_file_name,
indices_by_zip_name[zip_name])
# 必要なデータが分かったので特徴量とラベルを取得する
features_and_labels = self.consolidate_games(data_type, data)
return features_and_labels
def load_go_data(self, data_type='train', num_samples=1000,
use_generator=False):
index = KGSIndex(data_directory=self.data_dir)
index.download_files()
sampler = Sampler(data_dir=self.data_dir, num_test_games=int(num_samples/10))
data = sampler.draw_data(data_type, num_samples)
self.map_to_workers(data_type, data) # <1>
if use_generator:
generator = DataGenerator(self.data_dir, data)
return generator # <2>
else:
features_and_labels = self.consolidate_games(data_type, data)
return features_and_labels # <3>
def load_go_data(self, data_type, num_samples,
use_generator=False):
index = KGSIndex(data_directory=self.data_dir)
index.download_files()
sampler = Sampler(data_dir=self.data_dir)
data = sampler.draw_data(data_type, num_samples)
self.map_to_workers(data_type, data) # <1>
if use_generator:
if self.data_dir is None:
self.data_dir = 'D:\\CODE\\Python\\Go\\code\\dlgo\\data\\tarfiles'
generator = DataGenerator(self.data_dir, data)
return generator # <2>
else:
features_and_labels = self.consolidate_games(data_type, data)
return features_and_labels # <3>
def load_go_data(self,
data_type='train',
num_samples=1000,
use_generator=True,
download=False):
"""
ファイルの読み込みを並列で行いながら特徴量とラベルを取得する
Parameters
----------
data_type : str
'train'または'test'
num_samples : int
取得する棋譜の数
use_generator : bool
yieldによるミニバッチの取得を行う
download : bool
ファイルのダウンロードを行う
Returns
-------
次のいずれか
generator : generator
ミニバッチの取得を行うgeneratorを返す
features_and_labels : tuple
特徴量とラベルを一度に取得する
"""
if download:
index = KGSIndex(data_directory=self.data_dir)
index.download_files()
sampler = Sampler(data_dir=self.data_dir)
data = sampler.draw_data(data_type, num_samples)
self.map_to_workers(data_type, data)
if use_generator:
# generatorではすべてのデータをメモリに持つわけではない
# なので取得データの保存は行わない
generator = DataGenerator(self.data_dir, data)
return generator
else:
features_and_labels = self.consolidate_games(data_type, data)
return features_and_labels
def load_go_data(self, data_type='train', num_samples=1000):
index = KGSIndex(data_directory=self.data_dir)
index.download_files()
sampler = Sampler(data_dir=self.data_dir)
data = sampler.draw_data(data_type, num_samples)
#print(data)
zip_names = set()
indices_by_zip_name = {}
for filename, index in data:
zip_names.add(filename)
if filename not in indices_by_zip_name:
indices_by_zip_name[filename] = []
indices_by_zip_name[filename].append(index)
for zip_name in zip_names:
basename = zip_name.replace('.tar.gz', '')
data_file_name = basename + data_type
if not os.path.isfile(self.data_dir + data_file_name):
#print(self.data_dir + data_file_name)
self.process_zip(zip_name, data_file_name,
indices_by_zip_name[zip_name])
def load_go_data(self,
data_type='train',
num_samples=1000,
use_generator=False):
index = KGSIndex(data_directory=self.data_dir)
index.download_files()
sampler = Sampler(data_dir=self.data_dir)
data = sampler.draw_data(data_type, num_samples)
# ワークロードをCPUにマップする
self.map_to_workers(data_type, data) # <1>
if use_generator:
generator = DataGenerator(self.data_dir, data)
# 囲碁データジェネレータを返すか
return generator # <2>
else:
features_and_labels = self.consolidate_games(data_type, data)
# 以前のように結合されたデータを返す
return features_and_labels # <3>
def load_go_data(self,
data_type='train',
num_samples=1000,
use_generator=False):
# Khoi tao KGSIndex()
index = KGSIndex(data_directory=self.data_dir)
# Download tat ca games tu KGS toi thu muc data_directory. Neu data co san, khong can download mot lan nua
index.download_files()
sampler = Sampler(data_dir=self.data_dir)
# Sample chon so luong games cu the cho data_type
data = sampler.draw_data(data_type, num_samples)
# Map workload to CPUs
self.map_to_workers(data_type, data)
if use_generator:
generator = DataGenerator(self.data_dir, data)
# Tra ve Go data generator
return generator
else:
features_and_labels = self.consolidate_games(data_type, data)
# Tra ve features va labels
return features_and_labels
def load_go_data(
self,
data_type='train', # <1>
num_samples=1000): # <2>
index = KGSIndex(data_directory=self.data_dir)
# KGSから全てのゲームをローカルのデータディレクトリにダウンロード。
# データがすでに利用可能な場合は、再度ダウンロードされない。
index.download_files() # <3>
sampler = Sampler(data_dir=self.data_dir)
# Sampleインスタンスは、選択されたデータ種別のために指定された数のゲームを選択する
data = sampler.draw_data(data_type, num_samples) # <4>
zip_names = set()
indices_by_zip_name = {}
for filename, index in data:
# データに含まれるすべてのzipファイル名をリストにまとめる
zip_names.add(filename) # <5>
if filename not in indices_by_zip_name:
indices_by_zip_name[filename] = []
# 全てのSGFファイルのインデックスをzipファイル名でグループ化する
indices_by_zip_name[filename].append(index) # <6>
for zip_name in zip_names:
base_name = zip_name.replace('.tar.gz', '')
data_file_name = base_name + data_type
if not os.path.isfile(self.data_dir + '/' + data_file_name):
# zipファイルは個別に処理される
self.process_zip(zip_name, data_file_name,
indices_by_zip_name[zip_name]) # <7>
# 各zipの特徴量とラベルが結合され、返される
features_and_labels = self.consolidate_games(data_type, data) # <8>
return features_and_labels
def load_go_data(self, data_type='train', # As `data_type` you can choose either 'train' or 'test'
num_samples=1000): # `num_samples` refers to the number of games to load data from
index = KGSIndex(data_directory=self.data_dir)
index.download_files() # download all games from KGS to our local data directory. If data is available, it won't be downloaded again
sampler = Sampler(data_dir=self.data_dir)
data = sampler.draw_data(data_type, num_samples) # The `Sampler` instance selects the specified number of games for a data type
zip_names = set()
indices_by_zip_name = {}
for filename, index in data:
zip_names.add(filename) # We collect all zip file names contained in the data in a list
if filename not in indices_by_zip_name:
indices_by_zip_name[filename] = []
indices_by_zip_name[filename].append(index) # Then we group all SGF file indices by zip file name
for zip_name in zip_names:
base_name = zip_name.replace('.tar.gz', '')
data_file_name = base_name + data_type
if not os.path.isfile(self.data_dir + '/' + data_file_name):
# The zip files are then processed individually
self.process_zip(zip_name, data_file_name, indices_by_zip_name[zip_name])
# Features and labels from each zip are then aggregated and returned
features_and_labels = self.consolidate_games(data_type, data)
return features_and_labels
import glob
import numpy as np
from dlgo.data.generator import DataGenerator
from dlgo.data.sampling import Sampler
data_dir = r'/home/nail/Code_Go/dlgo/data'
num_games = 20000
#====== T R A I N ========================================================================
sampler = Sampler(data_dir=data_dir, seed=0)
data = sampler.draw_data('train', num_games)
generator = DataGenerator(data_dir, data)
num_samples = 0
batch_size = 256
num_classes = 19 * 19
i = 0
for X, y in generator._generate(batch_size=batch_size,
num_classes=num_classes):
num_samples += X.shape[0]
i += 1
print('Train I = ', i, ' Num_samples = ', num_samples)
#======== T E S T ====================================================================
sampler = Sampler(data_dir=data_dir, seed=0)
data = sampler.draw_data('test', num_games)
test_generator = DataGenerator(data_dir, data)
num_samples_test = 0
i = 0
for X, y in test_generator._generate(batch_size=batch_size,
