def test_answer(self):
filepath = './test.h5'
# 数据为随机产生, 不具有任何实际意义.
_temp_arr = np.random.rand(3, 4)
# 创建并写入
w_parameters_gp = io.initialize_weights_file(filepath, mode='w', model_name='test')
w_compile_ds = w_parameters_gp['compile']
w_weights_ds = w_parameters_gp['weights']
w_compile_ds.attrs['module'] = 'classicML'
w_compile_ds.attrs['cml_version'] = io.cml_version
w_compile_ds.attrs['__version__'] = io.__version__
w_weights_ds.attrs['weights'] = _temp_arr
# 读取并核验
r_parameters_gp = io.initialize_weights_file(filepath, mode='r', model_name='test')
r_compile_ds = r_parameters_gp['compile']
r_weights_ds = r_parameters_gp['weights']
assert 'classicML' == r_compile_ds.attrs['module']
assert io.cml_version == r_compile_ds.attrs['cml_version']
assert io.__version__ == r_compile_ds.attrs['__version__']
assert _temp_arr.all() == r_weights_ds.attrs['weights'].all()
if os.path.exists(filepath):
os.remove(filepath)
def save_weights(self, filepath):
"""将模型权重保存为一个HDF5文件.
Arguments:
filepath: str, 权重文件保存的路径.
Raises:
TypeError: 模型权重保存失败.
Notes:
模型将不会保存关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath,
mode='w',
model_name='AveragedOneDependentEstimator')
# 保存模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
compile_ds.attrs['smoothing'] = self.smoothing
compile_ds.attrs['m'] = self.m
weights_ds.attrs['_attribute_list'] = np.void(
dumps(self._attribute_list))
except TypeError:
CLASSICML_LOGGER.error('模型权重保存失败, 请检查文件是否损坏')
raise TypeError('模型权重保存失败')
def load_weights(self, filepath):
"""加载模型参数.
Arguments:
filepath: str, 权重文件加载的路径.
Raises:
KeyError: 模型权重加载失败.
Notes:
模型将不会加载关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath,
mode='r',
model_name='AveragedOneDependentEstimator')
# 加载模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
self.smoothing = compile_ds.attrs['smoothing']
self.m = compile_ds.attrs['m']
self._attribute_list = loads(
weights_ds.attrs['_attribute_list'].tobytes())
# 标记加载完成
self.is_loaded = True
except KeyError:
CLASSICML_LOGGER.error('模型权重加载失败, 请检查文件是否损坏')
raise KeyError('模型权重加载失败')
def save_weights(self, filepath):
"""将模型权重保存为一个HDF5文件.
Arguments:
filepath: str, 权重文件保存的路径.
Raises:
TypeError: 模型权重保存失败.
References:
- [如何存储原始的二进制数据](https://docs.h5py.org/en/2.3/strings.html)
Notes:
模型将不会保存关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath, mode='w', model_name='DecisionTreeClassifier')
# 保存模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
compile_ds.attrs['criterion'] = self.criterion
if self.pruner is not None:
compile_ds.attrs['pruning'] = self.pruner.name
weights_ds.attrs['tree'] = np.void(dumps(self.tree))
except TypeError:
CLASSICML_LOGGER.error('模型权重保存失败, 请检查文件是否损坏')
raise TypeError('模型权重保存失败')
def load_weights(self, filepath):
"""加载模型参数.
Arguments:
filepath: str, 权重文件加载的路径.
Raises:
KeyError: 模型权重加载失败.
Notes:
模型将不会加载关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath, mode='r', model_name='DecisionTreeClassifier')
# 加载模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
self.criterion = compile_ds.attrs['criterion']
if self.pruner is not None:
self.pruner = get_pruner(compile_ds.attrs['pruning'])
self.tree = loads(weights_ds.attrs['tree'].tobytes())
# 标记加载完成
self.is_loaded = True
except KeyError:
CLASSICML_LOGGER.error('模型权重加载失败, 请检查文件是否损坏')
raise KeyError('模型权重加载失败')
def save_weights(self, filepath):
"""将模型权重保存为一个HDF5文件.
Arguments:
filepath: str, 权重文件保存的路径.
Raises:
TypeError: 模型权重保存失败.
Notes:
模型将不会保存关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath, mode='w', model_name='LogisticRegression')
# 保存模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
compile_ds.attrs['optimizer'] = self.optimizer.name
compile_ds.attrs['loss'] = self.loss.name
compile_ds.attrs['metric'] = self.metric.name
weights_ds.attrs['beta'] = self.beta
except TypeError:
CLASSICML_LOGGER.error('模型权重保存失败, 请检查文件是否损坏')
raise TypeError('模型权重保存失败')
def load_weights(self, filepath):
"""加载模型参数.
Arguments:
filepath: str, 权重文件加载的路径.
Raises:
KeyError: 模型权重加载失败.
Notes:
模型将不会加载关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath, mode='r', model_name='LogisticRegression')
# 加载模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
self.optimizer = get_optimizer(compile_ds.attrs['optimizer'])
self.loss = get_loss(compile_ds.attrs['loss'])
self.metric = get_metric(compile_ds.attrs['metric'])
self.beta = weights_ds.attrs['beta']
# 标记加载完成
self.is_loaded = True
except KeyError:
CLASSICML_LOGGER.error('模型权重加载失败, 请检查文件是否损坏')
raise KeyError('模型权重加载失败')
def save_weights(self, filepath):
"""将模型权重保存为一个HDF5文件.
Arguments:
filepath: str, 权重文件保存的路径.
Raises:
TypeError: 模型权重保存失败.
Notes:
模型将不会保存关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath, mode='w', model_name='SupportVectorClassifier')
# 保存模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
compile_ds.attrs['C'] = self.C
compile_ds.attrs['kernel'] = self.kernel.name
compile_ds.attrs['gamma'] = self.gamma
compile_ds.attrs['tol'] = self.tol
weights_ds.attrs['support'] = self.support
weights_ds.attrs['support_vector'] = self.support_vector
weights_ds.attrs['support_alpha'] = self.support_alpha
weights_ds.attrs['support_y'] = np.asarray(self.support_y,
dtype=np.float64)
weights_ds.attrs['b'] = np.float64(self.b)
except TypeError:
CLASSICML_LOGGER.error('模型权重保存失败, 请检查文件是否损坏')
raise TypeError('模型权重保存失败')
def load_weights(self, filepath):
"""加载模型参数.
Arguments:
filepath: str, 权重文件加载的路径.
Raises:
KeyError: 模型权重加载失败.
Notes:
模型将不会加载关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath, mode='r', model_name='SupportVectorClassifier')
# 加载模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
self.C = compile_ds.attrs['C']
self.gamma = compile_ds.attrs['gamma']
self.kernel = get_kernel(compile_ds.attrs['kernel'], self.gamma)
self.tol = compile_ds.attrs['tol']
self.support = weights_ds.attrs['support']
self.support_vector = weights_ds.attrs['support_vector']
self.support_alpha = weights_ds.attrs['support_alpha']
self.support_y = weights_ds.attrs['support_y']
self.b = weights_ds.attrs['b']
# 标记加载完成
self.is_loaded = True
except KeyError:
CLASSICML_LOGGER.error('模型权重加载失败, 请检查文件是否损坏')
raise KeyError('模型权重加载失败')
def save_weights(self, filepath):
"""将模型权重保存为一个HDF5文件.
Arguments:
filepath: str, 权重文件保存的路径.
Raises:
TypeError: 模型权重保存失败.
Notes:
模型将不会保存关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath,
mode='w',
model_name='RadialBasisFunctionNetwork')
# 保存模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
compile_ds.attrs['hidden_units'] = self.hidden_units
compile_ds.attrs['optimizer'] = self.optimizer.name
compile_ds.attrs['loss'] = self.loss.name
compile_ds.attrs['metric'] = self.metric.name
for parameter in self.parameters:
weights_ds.attrs[parameter] = self.parameters[parameter]
except TypeError:
CLASSICML_LOGGER.error('模型权重保存失败, 请检查文件是否损坏')
raise TypeError('模型权重保存失败')
def load_weights(self, filepath):
"""加载模型参数.
Arguments:
filepath: str, 权重文件加载的路径.
Raises:
KeyError: 模型权重加载失败.
Notes:
模型将不会加载关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath,
mode='r',
model_name='RadialBasisFunctionNetwork')
# 加载模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
self.hidden_units = compile_ds.attrs['hidden_units']
self.optimizer = get_optimizer(compile_ds.attrs['optimizer'])
self.loss = get_loss(compile_ds.attrs['loss'])
self.metric = get_metric(compile_ds.attrs['metric'])
for attr in weights_ds.attrs:
self.parameters[attr] = weights_ds.attrs[attr]
# 标记加载完成
self.is_loaded = True
except KeyError:
CLASSICML_LOGGER.error('模型权重加载失败, 请检查文件是否损坏')
raise KeyError('模型权重加载失败')
def save_weights(self, filepath):
"""将模型权重保存为一个HDF5文件.
Arguments:
filepath: str, 权重文件保存的路径.
Raises:
TypeError: 模型权重保存失败.
Notes:
模型将不会保存关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath, mode='w', model_name='NaiveBayesClassifier')
# 保存模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
compile_ds.attrs['smoothing'] = self.smoothing
weights_ds.attrs['p_0'] = self.p_0
weights_ds.attrs['p_1'] = self.p_1
weights_ds.attrs['pi_0'] = np.void(dumps(self.pi_0))
weights_ds.attrs['pi_1'] = np.void(dumps(self.pi_1))
except TypeError:
CLASSICML_LOGGER.error('模型权重保存失败, 请检查文件是否损坏')
raise TypeError('模型权重保存失败')
def load_weights(self, filepath):
"""加载模型参数.
Arguments:
filepath: str, 权重文件加载的路径.
Raises:
KeyError: 模型权重加载失败.
Notes:
模型将不会加载关于优化器的超参数.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(
filepath=filepath, mode='r', model_name='NaiveBayesClassifier')
# 加载模型参数.
try:
compile_ds = parameters_gp['compile']
weights_ds = parameters_gp['weights']
self.smoothing = compile_ds.attrs['smoothing']
self.p_0 = weights_ds.attrs['p_0']
self.p_1 = weights_ds.attrs['p_1']
self.pi_0 = loads(weights_ds.attrs['pi_0'].tobytes())
self.pi_1 = loads(weights_ds.attrs['pi_1'].tobytes())
# 标记加载完成
self.is_loaded = True
except KeyError:
CLASSICML_LOGGER.error('模型权重加载失败, 请检查文件是否损坏')
raise KeyError('模型权重加载失败')
def save_weights(self, filepath):
"""将模型权重保存为一个HDF5文件.
Arguments:
filepath: str, 权重文件保存的路径.
Raises:
TypeError: 模型权重保存失败.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(filepath=filepath,
mode='w',
model_name='LinearDiscriminantAnalysis')
# 保存模型参数.
try:
weights_ds = parameters_gp['weights']
weights_ds.attrs['w'] = self.w
weights_ds.attrs['mu_0'] = self.mu_0
weights_ds.attrs['mu_1'] = self.mu_1
except TypeError:
CLASSICML_LOGGER.error('模型权重保存失败, 请检查文件是否损坏')
raise TypeError('模型权重保存失败')
def load_weights(self, filepath):
"""加载模型参数.
Arguments:
filepath: str, 权重文件加载的路径.
Raises:
KeyError: 模型权重加载失败.
"""
# 初始化权重文件.
parameters_gp = io.initialize_weights_file(filepath=filepath,
mode='r',
model_name='LinearDiscriminantAnalysis')
# 加载模型参数.
try:
weights_ds = parameters_gp['weights']
self.w = weights_ds.attrs['w']
self.mu_0 = weights_ds.attrs['mu_0']
self.mu_1 = weights_ds.attrs['mu_1']
# 标记加载完成
self.is_loaded = True
except KeyError:
CLASSICML_LOGGER.error('模型权重加载失败, 请检查文件是否损坏')
raise KeyError('模型权重加载失败')