JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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use least squares to fit all default curves parameter seperately Returns ------- None def fit_theta(self): """use least squares to fit all default curves parameter seperately Returns ------- None """ x = range(1, self.point_num +...
filter the poor performing curve Returns ------- None def filter_curve(self): """filter the poor performing curve Returns ------- None """ avg = np.sum(self.trial_history) / self.point_num standard = avg * avg * self.poin...
return the predict y of 'model' when epoch = pos Parameters ---------- model: string name of the curve function model pos: int the epoch number of the position you want to predict Returns ------- int: The expected matr...
return the value of the f_comb when epoch = pos Parameters ---------- pos: int the epoch number of the position you want to predict sample: list sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk} Returns ------- int ...
normalize weight Parameters ---------- samples: list a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix, representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}} Returns ------- list ...
returns the value of sigma square, given the weight's sample Parameters ---------- sample: list sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk} Returns ------- float the value of sigma square, given the weight's sa...
returns the value of normal distribution, given the weight's sample and target position Parameters ---------- pos: int the epoch number of the position you want to predict sample: list sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk...
likelihood Parameters ---------- sample: list sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk} Returns ------- float likelihood def likelihood(self, samples): """likelihood Parameters -----...
priori distribution Parameters ---------- samples: list a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix, representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}} Returns ------- float ...
posterior probability Parameters ---------- samples: list a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix, representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}} Returns ------- ...
Adjust the weight of each function using mcmc sampling. The initial value of each weight is evenly distribute. Brief introduction: (1)Definition of sample: Sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk} (2)Definition of samples: Samples is...
predict the value of target position Parameters ---------- trial_history: list The history performance matrix of each trial. Returns ------- float expected final result performance of this hyperparameter config def predict(self, trial_hi...
Detect the outlier def _outlierDetection_threaded(inputs): ''' Detect the outlier ''' [samples_idx, samples_x, samples_y_aggregation] = inputs sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\ % (os.path.basename(__file__), samples_idx + 1, len(samples_x))) ...
Use Multi-thread to detect the outlier def outlierDetection_threaded(samples_x, samples_y_aggregation): ''' Use Multi-thread to detect the outlier ''' outliers = [] threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\ for samples_idx in range(0, len(sample...
deeper conv layer. def deeper_conv_block(conv_layer, kernel_size, weighted=True): '''deeper conv layer. ''' n_dim = get_n_dim(conv_layer) filter_shape = (kernel_size,) * 2 n_filters = conv_layer.filters weight = np.zeros((n_filters, n_filters) + filter_shape) center = tuple(map(lambda x: in...
deeper dense layer. def dense_to_deeper_block(dense_layer, weighted=True): '''deeper dense layer. ''' units = dense_layer.units weight = np.eye(units) bias = np.zeros(units) new_dense_layer = StubDense(units, units) if weighted: new_dense_layer.set_weights( (add_noise(we...
wider previous dense layer. def wider_pre_dense(layer, n_add, weighted=True): '''wider previous dense layer. ''' if not weighted: return StubDense(layer.input_units, layer.units + n_add) n_units2 = layer.units teacher_w, teacher_b = layer.get_weights() rand = np.random.randint(n_units...
wider previous conv layer. def wider_pre_conv(layer, n_add_filters, weighted=True): '''wider previous conv layer. ''' n_dim = get_n_dim(layer) if not weighted: return get_conv_class(n_dim)( layer.input_channel, layer.filters + n_add_filters, kernel_size=layer...
wider next conv layer. def wider_next_conv(layer, start_dim, total_dim, n_add, weighted=True): '''wider next conv layer. ''' n_dim = get_n_dim(layer) if not weighted: return get_conv_class(n_dim)(layer.input_channel + n_add, layer.filters, ...
wider batch norm layer. def wider_bn(layer, start_dim, total_dim, n_add, weighted=True): '''wider batch norm layer. ''' n_dim = get_n_dim(layer) if not weighted: return get_batch_norm_class(n_dim)(layer.num_features + n_add) weights = layer.get_weights() new_weights = [ add_no...
wider next dense layer. def wider_next_dense(layer, start_dim, total_dim, n_add, weighted=True): '''wider next dense layer. ''' if not weighted: return StubDense(layer.input_units + n_add, layer.units) teacher_w, teacher_b = layer.get_weights() student_w = teacher_w.copy() n_units_each_...
add noise to the layer. def add_noise(weights, other_weights): '''add noise to the layer. ''' w_range = np.ptp(other_weights.flatten()) noise_range = NOISE_RATIO * w_range noise = np.random.uniform(-noise_range / 2.0, noise_range / 2.0, weights.shape) return np.add(noise, weights)
initilize dense layer weight. def init_dense_weight(layer): '''initilize dense layer weight. ''' units = layer.units weight = np.eye(units) bias = np.zeros(units) layer.set_weights( (add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1]))) )
initilize conv layer weight. def init_conv_weight(layer): '''initilize conv layer weight. ''' n_filters = layer.filters filter_shape = (layer.kernel_size,) * get_n_dim(layer) weight = np.zeros((n_filters, n_filters) + filter_shape) center = tuple(map(lambda x: int((x - 1) / 2), filter_shape)) ...
initilize batch norm layer weight. def init_bn_weight(layer): '''initilize batch norm layer weight. ''' n_filters = layer.num_features new_weights = [ add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])), add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])), ...
parse log path def parse_log_path(args, trial_content): '''parse log path''' path_list = [] host_list = [] for trial in trial_content: if args.trial_id and args.trial_id != 'all' and trial.get('id') != args.trial_id: continue pattern = r'(?P<head>.+)://(?P<host>.+):(?P<path>...
use ssh client to copy data from remote machine to local machien def copy_data_from_remote(args, nni_config, trial_content, path_list, host_list, temp_nni_path): '''use ssh client to copy data from remote machine to local machien''' machine_list = nni_config.get_config('experimentConfig').get('machineList') ...
get path list according to different platform def get_path_list(args, nni_config, trial_content, temp_nni_path): '''get path list according to different platform''' path_list, host_list = parse_log_path(args, trial_content) platform = nni_config.get_config('experimentConfig').get('trainingServicePlatform')...
call cmds to start tensorboard process in local machine def start_tensorboard_process(args, nni_config, path_list, temp_nni_path): '''call cmds to start tensorboard process in local machine''' if detect_port(args.port): print_error('Port %s is used by another process, please reset port!' % str(args.por...
stop tensorboard def stop_tensorboard(args): '''stop tensorboard''' experiment_id = check_experiment_id(args) experiment_config = Experiments() experiment_dict = experiment_config.get_all_experiments() config_file_name = experiment_dict[experiment_id]['fileName'] nni_config = Config(config_file...
start tensorboard def start_tensorboard(args): '''start tensorboard''' experiment_id = check_experiment_id(args) experiment_config = Experiments() experiment_dict = experiment_config.get_all_experiments() config_file_name = experiment_dict[experiment_id]['fileName'] nni_config = Config(config_f...
The ratio is smaller the better def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad): ''' The ratio is smaller the better ''' ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value]) sigma = 0 return ratio, ...
Call selection def selection_r(x_bounds, x_types, clusteringmodel_gmm_good, clusteringmodel_gmm_bad, num_starting_points=100, minimize_constraints_fun=None): ''' Call selection ''' minimize_starting_points = [lib_data.rand(...
Select the lowest mu value def selection(x_bounds, x_types, clusteringmodel_gmm_good, clusteringmodel_gmm_bad, minimize_starting_points, minimize_constraints_fun=None): ''' Select the lowest mu value ''' results = lib_acquisition_fun...
Minimize constraints fun summation def _minimize_constraints_fun_summation(x): ''' Minimize constraints fun summation ''' summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX]) return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND
Load dataset, use 20newsgroups dataset def load_data(): '''Load dataset, use 20newsgroups dataset''' digits = load_digits() X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25) ss = StandardScaler() X_train = ss.fit_transform(X_train) ...
Get model according to parameters def get_model(PARAMS): '''Get model according to parameters''' model = SVC() model.C = PARAMS.get('C') model.keral = PARAMS.get('keral') model.degree = PARAMS.get('degree') model.gamma = PARAMS.get('gamma') model.coef0 = PARAMS.get('coef0') return ...
generate num hyperparameter configurations from search space using Bayesian optimization Parameters ---------- num: int the number of hyperparameter configurations Returns ------- list a list of hyperparameter configurations. Format: [[key1, valu...
Initialize Tuner, including creating Bayesian optimization-based parametric models and search space formations Parameters ---------- data: search space search space of this experiment Raises ------ ValueError Error: Search space is None ...
generate a new bracket def generate_new_bracket(self): """generate a new bracket""" logger.debug( 'start to create a new SuccessiveHalving iteration, self.curr_s=%d', self.curr_s) if self.curr_s < 0: logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate ne...
recerive the number of request and generate trials Parameters ---------- data: int number of trial jobs that nni manager ask to generate def handle_request_trial_jobs(self, data): """recerive the number of request and generate trials Parameters ---------- ...
get one trial job, i.e., one hyperparameter configuration. If this function is called, Command will be sent by BOHB: a. If there is a parameter need to run, will return "NewTrialJob" with a dict: { 'parameter_id': id of new hyperparameter 'parameter_source': 'algorithm'...
change json format to ConfigSpace format dict<dict> -> configspace Parameters ---------- data: JSON object search space of this experiment def handle_update_search_space(self, data): """change json format to ConfigSpace format dict<dict> -> configspace Parameters ...
receive the information of trial end and generate next configuaration. Parameters ---------- data: dict() it has three keys: trial_job_id, event, hyper_params trial_job_id: the id generated by training service event: the job's state hyper_params: ...
reveice the metric data and update Bayesian optimization with final result Parameters ---------- data: it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'. Raises ------ ValueError Data type not supported d...
Import additional data for tuning Parameters ---------- data: a list of dictionarys, each of which has at least two keys, 'parameter' and 'value' Raises ------ AssertionError data doesn't have required key 'parameter' and 'value' def handle_impo...
data_transforms for cifar10 dataset def data_transforms_cifar10(args): """ data_transforms for cifar10 dataset """ cifar_mean = [0.49139968, 0.48215827, 0.44653124] cifar_std = [0.24703233, 0.24348505, 0.26158768] train_transform = transforms.Compose( [ transforms.RandomCrop(3...
data_transforms for mnist dataset def data_transforms_mnist(args, mnist_mean=None, mnist_std=None): """ data_transforms for mnist dataset """ if mnist_mean is None: mnist_mean = [0.5] if mnist_std is None: mnist_std = [0.5] train_transform = transforms.Compose( [ ...
Compute the mean and std value of dataset. def get_mean_and_std(dataset): """Compute the mean and std value of dataset.""" dataloader = torch.utils.data.DataLoader( dataset, batch_size=1, shuffle=True, num_workers=2 ) mean = torch.zeros(3) std = torch.zeros(3) print("==> Computing mean ...
Init layer parameters. def init_params(net): """Init layer parameters.""" for module in net.modules(): if isinstance(module, nn.Conv2d): init.kaiming_normal(module.weight, mode="fan_out") if module.bias: init.constant(module.bias, 0) elif isinstance(modul...
EarlyStopping step on each epoch Arguments: metrics {float} -- metric value def step(self, metrics): """ EarlyStopping step on each epoch Arguments: metrics {float} -- metric value """ if self.best is None: self.best = metrics ret...
This can have false positives. For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7. def check_feasibility(x_bounds, lowerbound, upperbound): ''' This can have false positives. For examples, parameters can only be 0 or 5, and the summation constraint is between 6...
Key idea is that we try to move towards upperbound, by randomly choose one value for each parameter. However, for the last parameter, we need to make sure that its value can help us get above lowerbound def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100): ''' Key idea is that we try to...
Change '~' to user home directory def expand_path(experiment_config, key): '''Change '~' to user home directory''' if experiment_config.get(key): experiment_config[key] = os.path.expanduser(experiment_config[key])
Change relative path to absolute path def parse_relative_path(root_path, experiment_config, key): '''Change relative path to absolute path''' if experiment_config.get(key) and not os.path.isabs(experiment_config.get(key)): absolute_path = os.path.join(root_path, experiment_config.get(key)) prin...
Change the time to seconds def parse_time(time): '''Change the time to seconds''' unit = time[-1] if unit not in ['s', 'm', 'h', 'd']: print_error('the unit of time could only from {s, m, h, d}') exit(1) time = time[:-1] if not time.isdigit(): print_error('time format error!...
Parse path in config file def parse_path(experiment_config, config_path): '''Parse path in config file''' expand_path(experiment_config, 'searchSpacePath') if experiment_config.get('trial'): expand_path(experiment_config['trial'], 'codeDir') if experiment_config.get('tuner'): expand_pat...
Validate searchspace content, if the searchspace file is not json format or its values does not contain _type and _value which must be specified, it will not be a valid searchspace file def validate_search_space_content(experiment_config): '''Validate searchspace content, if the searchspace...
Validate whether the kubeflow operators are valid def validate_kubeflow_operators(experiment_config): '''Validate whether the kubeflow operators are valid''' if experiment_config.get('kubeflowConfig'): if experiment_config.get('kubeflowConfig').get('operator') == 'tf-operator': if experimen...
Validate whether the common values in experiment_config is valid def validate_common_content(experiment_config): '''Validate whether the common values in experiment_config is valid''' if not experiment_config.get('trainingServicePlatform') or \ experiment_config.get('trainingServicePlatform') not in ['...
check whether the file of customized tuner/assessor/advisor exists spec_key: 'tuner', 'assessor', 'advisor' def validate_customized_file(experiment_config, spec_key): ''' check whether the file of customized tuner/assessor/advisor exists spec_key: 'tuner', 'assessor', 'advisor' ''' if experimen...
Validate whether assessor in experiment_config is valid def parse_assessor_content(experiment_config): '''Validate whether assessor in experiment_config is valid''' if experiment_config.get('assessor'): if experiment_config['assessor'].get('builtinAssessorName'): experiment_config['assessor...
Valid whether useAnnotation and searchSpacePath is coexist spec_key: 'advisor' or 'tuner' builtin_name: 'builtinAdvisorName' or 'builtinTunerName' def validate_annotation_content(experiment_config, spec_key, builtin_name): ''' Valid whether useAnnotation and searchSpacePath is coexist spec_key: 'ad...
validate the trial config in pai platform def validate_pai_trial_conifg(experiment_config): '''validate the trial config in pai platform''' if experiment_config.get('trainingServicePlatform') == 'pai': if experiment_config.get('trial').get('shmMB') and \ experiment_config['trial']['shmMB'] > ex...
Validate whether experiment_config is valid def validate_all_content(experiment_config, config_path): '''Validate whether experiment_config is valid''' parse_path(experiment_config, config_path) validate_common_content(experiment_config) validate_pai_trial_conifg(experiment_config) experiment_confi...
get urls of local machine def get_local_urls(port): '''get urls of local machine''' url_list = [] for name, info in psutil.net_if_addrs().items(): for addr in info: if AddressFamily.AF_INET == addr.family: url_list.append('http://{}:{}'.format(addr.address, port)) re...
Parse an annotation string. Return an AST Expr node. code: annotation string (excluding '@') def parse_annotation(code): """Parse an annotation string. Return an AST Expr node. code: annotation string (excluding '@') """ module = ast.parse(code) assert type(module) is ast.Module, 'inter...
Parse an annotation function. Return the value of `name` keyword argument and the AST Call node. func_name: expected function name def parse_annotation_function(code, func_name): """Parse an annotation function. Return the value of `name` keyword argument and the AST Call node. func_name: expected ...
Parse `nni.variable` expression. Return the name argument and AST node of annotated expression. code: annotation string def parse_nni_variable(code): """Parse `nni.variable` expression. Return the name argument and AST node of annotated expression. code: annotation string """ name, call = p...
Parse `nni.function_choice` expression. Return the AST node of annotated expression and a list of dumped function call expressions. code: annotation string def parse_nni_function(code): """Parse `nni.function_choice` expression. Return the AST node of annotated expression and a list of dumped function ...
Convert all args to a dict such that every key and value in the dict is the same as the value of the arg. Return the AST Call node with only one arg that is the dictionary def convert_args_to_dict(call, with_lambda=False): """Convert all args to a dict such that every key and value in the dict is the same as t...
Wrap an AST Call node to lambda expression node. call: ast.Call node def make_lambda(call): """Wrap an AST Call node to lambda expression node. call: ast.Call node """ empty_args = ast.arguments(args=[], vararg=None, kwarg=None, defaults=[]) return ast.Lambda(args=empty_args, body=call)
Replace a node annotated by `nni.variable`. node: the AST node to replace annotation: annotation string def replace_variable_node(node, annotation): """Replace a node annotated by `nni.variable`. node: the AST node to replace annotation: annotation string """ assert type(node) is ast.Assign...
Replace a node annotated by `nni.function_choice`. node: the AST node to replace annotation: annotation string def replace_function_node(node, annotation): """Replace a node annotated by `nni.function_choice`. node: the AST node to replace annotation: annotation string """ target, funcs = p...
Annotate user code. Return annotated code (str) if annotation detected; return None if not. code: original user code (str) def parse(code): """Annotate user code. Return annotated code (str) if annotation detected; return None if not. code: original user code (str) """ try: ast_tree...
main function. def main(): ''' main function. ''' args = parse_args() if args.multi_thread: enable_multi_thread() if args.advisor_class_name: # advisor is enabled and starts to run if args.multi_phase: raise AssertionError('multi_phase has not been supporte...
Load yaml file content def get_yml_content(file_path): '''Load yaml file content''' try: with open(file_path, 'r') as file: return yaml.load(file, Loader=yaml.Loader) except yaml.scanner.ScannerError as err: print_error('yaml file format error!') exit(1) except Excep...
Detect if the port is used def detect_port(port): '''Detect if the port is used''' socket_test = socket.socket(socket.AF_INET,socket.SOCK_STREAM) try: socket_test.connect(('127.0.0.1', int(port))) socket_test.close() return True except: return False
Create the Gaussian Mixture Model def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34): ''' Create the Gaussian Mixture Model ''' samples = [samples_x[i] + [samples_y_aggregation[i]] for i in range(0, len(samples_x))] # Sorts so that we can get the top samples samples ...
Selecte R value def selection_r(acquisition_function, samples_y_aggregation, x_bounds, x_types, regressor_gp, num_starting_points=100, minimize_constraints_fun=None): ''' Selecte R value ''' minimize_startin...
selection def selection(acquisition_function, samples_y_aggregation, x_bounds, x_types, regressor_gp, minimize_starting_points, minimize_constraints_fun=None): ''' selection ''' outputs = None sys.stderr.write("[%s] Exercise \"%...
Reports intermediate result to Assessor. metric: serializable object. def report_intermediate_result(metric): """Reports intermediate result to Assessor. metric: serializable object. """ global _intermediate_seq assert _params is not None, 'nni.get_next_parameter() needs to be called before rep...
Reports final result to tuner. metric: serializable object. def report_final_result(metric): """Reports final result to tuner. metric: serializable object. """ assert _params is not None, 'nni.get_next_parameter() needs to be called before report_final_result' metric = json_tricks.dumps({ ...
get args from command line def get_args(): """ get args from command line """ parser = argparse.ArgumentParser("FashionMNIST") parser.add_argument("--batch_size", type=int, default=128, help="batch size") parser.add_argument("--optimizer", type=str, default="SGD", help="optimizer") parser.add_a...
build model from json representation def build_graph_from_json(ir_model_json): """build model from json representation """ graph = json_to_graph(ir_model_json) logging.debug(graph.operation_history) model = graph.produce_torch_model() return model
parse reveive msgs to global variable def parse_rev_args(receive_msg): """ parse reveive msgs to global variable """ global trainloader global testloader global net global criterion global optimizer # Loading Data logger.debug("Preparing data..") raw_train_data = torchvision.d...
train model on each epoch in trainset def train(epoch): """ train model on each epoch in trainset """ global trainloader global testloader global net global criterion global optimizer logger.debug("Epoch: %d", epoch) net.train() train_loss = 0 correct = 0 total = 0 ...
Freeze BatchNorm layers. def freeze_bn(self): '''Freeze BatchNorm layers.''' for layer in self.modules(): if isinstance(layer, nn.BatchNorm2d): layer.eval()
parse reveive msgs to global variable def parse_rev_args(receive_msg): """ parse reveive msgs to global variable """ global trainloader global testloader global net global criterion global optimizer # Loading Data logger.debug("Preparing data..") transform_train, transform_tes...
登录的统一接口 :param config_file 登录数据文件，若无则选择参数登录模式 :param user: 各家券商的账号或者雪球的用户名 :param password: 密码, 券商为加密后的密码，雪球为明文密码 :param account: [雪球登录需要]雪球手机号(邮箱手机二选一) :param portfolio_code: [雪球登录需要]组合代码 :param portfolio_market: [雪球登录需要]交易市场，可选['cn', 'us', 'hk'] 默认 'cn' de...
实现自动登录 :param limit: 登录次数限制 def autologin(self, limit=10): """实现自动登录 :param limit: 登录次数限制 """ for _ in range(limit): if self.login(): break else: raise exceptions.NotLoginError( "登录失败次数过多, 请检查密码是否正确 / 券商服务器是否处于维护中 /...
启动保持在线的进程 def keepalive(self): """启动保持在线的进程 """ if self.heart_thread.is_alive(): self.heart_active = True else: self.heart_thread.start()
读取 config def __read_config(self): """读取 config""" self.config = helpers.file2dict(self.config_path) self.global_config = helpers.file2dict(self.global_config_path) self.config.update(self.global_config)
默认提供最近30天的交割单, 通常只能返回查询日期内最新的 90 天数据。 :return: def exchangebill(self): """ 默认提供最近30天的交割单, 通常只能返回查询日期内最新的 90 天数据。 :return: """ # TODO 目前仅在华泰子类中实现 start_date, end_date = helpers.get_30_date() return self.get_exchangebill(start_date, end_date)
发起对 api 的请求并过滤返回结果 :param params: 交易所需的动态参数 def do(self, params): """发起对 api 的请求并过滤返回结果 :param params: 交易所需的动态参数""" request_params = self.create_basic_params() request_params.update(params) response_data = self.request(request_params) try: format_json...
格式化返回的值为正确的类型 :param response_data: 返回的数据 def format_response_data_type(self, response_data): """格式化返回的值为正确的类型 :param response_data: 返回的数据 """ if isinstance(response_data, list) and not isinstance( response_data, str ): return response_data ...
登陆客户端 :param config_path: 登陆配置文件，跟参数登陆方式二选一 :param user: 账号 :param password: 明文密码 :param exe_path: 客户端路径类似 r'C:\\htzqzyb2\\xiadan.exe', 默认 r'C:\\htzqzyb2\\xiadan.exe' :param comm_password: 通讯密码 :return: def prepare( self, config_path=None, user=No...
跟踪ricequant对应的模拟交易，支持多用户多策略 :param users: 支持easytrader的用户对象，支持使用 [] 指定多个用户 :param run_id: ricequant 的模拟交易ID，支持使用 [] 指定多个模拟交易 :param track_interval: 轮训模拟交易时间，单位为秒 :param trade_cmd_expire_seconds: 交易指令过期时间, 单位为秒 :param cmd_cache: 是否读取存储历史执行过的指令，防止重启时重复执行已经交易过的指令 :param entr...
登陆客户端 :param user: 账号 :param password: 明文密码 :param exe_path: 客户端路径类似 'C:\\中国银河证券双子星3.2\\Binarystar.exe', 默认 'C:\\中国银河证券双子星3.2\\Binarystar.exe' :param comm_password: 通讯密码, 华泰需要，可不设 :return: def login(self, user, password, exe_path, comm_password=None, **kwargs): ...
:param user: 用户名 :param password: 密码 :param exe_path: 客户端路径, 类似 :param comm_password: :param kwargs: :return: def login(self, user, password, exe_path, comm_password=None, **kwargs): """ :param user: 用户名 :param password: 密码 :param exe_pat...

use least squares to fit all default curves parameter seperately Returns ------- None def fit_theta(self): """use least squares to fit all default curves parameter seperately Returns ------- None """ x = range(1, self.point_num +...

filter the poor performing curve Returns ------- None def filter_curve(self): """filter the poor performing curve Returns ------- None """ avg = np.sum(self.trial_history) / self.point_num standard = avg * avg * self.poin...

return the predict y of 'model' when epoch = pos Parameters ---------- model: string name of the curve function model pos: int the epoch number of the position you want to predict Returns ------- int: The expected matr...

return the value of the f_comb when epoch = pos Parameters ---------- pos: int the epoch number of the position you want to predict sample: list sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk} Returns ------- int ...

normalize weight Parameters ---------- samples: list a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix, representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}} Returns ------- list ...

returns the value of sigma square, given the weight's sample Parameters ---------- sample: list sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk} Returns ------- float the value of sigma square, given the weight's sa...

returns the value of normal distribution, given the weight's sample and target position Parameters ---------- pos: int the epoch number of the position you want to predict sample: list sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk...

likelihood Parameters ---------- sample: list sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk} Returns ------- float likelihood def likelihood(self, samples): """likelihood Parameters -----...

priori distribution Parameters ---------- samples: list a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix, representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}} Returns ------- float ...

posterior probability Parameters ---------- samples: list a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix, representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}} Returns ------- ...

Adjust the weight of each function using mcmc sampling. The initial value of each weight is evenly distribute. Brief introduction: (1)Definition of sample: Sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk} (2)Definition of samples: Samples is...

predict the value of target position Parameters ---------- trial_history: list The history performance matrix of each trial. Returns ------- float expected final result performance of this hyperparameter config def predict(self, trial_hi...

Detect the outlier def _outlierDetection_threaded(inputs): ''' Detect the outlier ''' [samples_idx, samples_x, samples_y_aggregation] = inputs sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\ % (os.path.basename(__file__), samples_idx + 1, len(samples_x))) ...

Use Multi-thread to detect the outlier def outlierDetection_threaded(samples_x, samples_y_aggregation): ''' Use Multi-thread to detect the outlier ''' outliers = [] threads_inputs = [[samples_idx, samples_x, samples_y_aggregation]\ for samples_idx in range(0, len(sample...

deeper conv layer. def deeper_conv_block(conv_layer, kernel_size, weighted=True): '''deeper conv layer. ''' n_dim = get_n_dim(conv_layer) filter_shape = (kernel_size,) * 2 n_filters = conv_layer.filters weight = np.zeros((n_filters, n_filters) + filter_shape) center = tuple(map(lambda x: in...

deeper dense layer. def dense_to_deeper_block(dense_layer, weighted=True): '''deeper dense layer. ''' units = dense_layer.units weight = np.eye(units) bias = np.zeros(units) new_dense_layer = StubDense(units, units) if weighted: new_dense_layer.set_weights( (add_noise(we...

wider previous dense layer. def wider_pre_dense(layer, n_add, weighted=True): '''wider previous dense layer. ''' if not weighted: return StubDense(layer.input_units, layer.units + n_add) n_units2 = layer.units teacher_w, teacher_b = layer.get_weights() rand = np.random.randint(n_units...

wider previous conv layer. def wider_pre_conv(layer, n_add_filters, weighted=True): '''wider previous conv layer. ''' n_dim = get_n_dim(layer) if not weighted: return get_conv_class(n_dim)( layer.input_channel, layer.filters + n_add_filters, kernel_size=layer...

wider next conv layer. def wider_next_conv(layer, start_dim, total_dim, n_add, weighted=True): '''wider next conv layer. ''' n_dim = get_n_dim(layer) if not weighted: return get_conv_class(n_dim)(layer.input_channel + n_add, layer.filters, ...

wider batch norm layer. def wider_bn(layer, start_dim, total_dim, n_add, weighted=True): '''wider batch norm layer. ''' n_dim = get_n_dim(layer) if not weighted: return get_batch_norm_class(n_dim)(layer.num_features + n_add) weights = layer.get_weights() new_weights = [ add_no...

wider next dense layer. def wider_next_dense(layer, start_dim, total_dim, n_add, weighted=True): '''wider next dense layer. ''' if not weighted: return StubDense(layer.input_units + n_add, layer.units) teacher_w, teacher_b = layer.get_weights() student_w = teacher_w.copy() n_units_each_...

add noise to the layer. def add_noise(weights, other_weights): '''add noise to the layer. ''' w_range = np.ptp(other_weights.flatten()) noise_range = NOISE_RATIO * w_range noise = np.random.uniform(-noise_range / 2.0, noise_range / 2.0, weights.shape) return np.add(noise, weights)

initilize dense layer weight. def init_dense_weight(layer): '''initilize dense layer weight. ''' units = layer.units weight = np.eye(units) bias = np.zeros(units) layer.set_weights( (add_noise(weight, np.array([0, 1])), add_noise(bias, np.array([0, 1]))) )

initilize conv layer weight. def init_conv_weight(layer): '''initilize conv layer weight. ''' n_filters = layer.filters filter_shape = (layer.kernel_size,) * get_n_dim(layer) weight = np.zeros((n_filters, n_filters) + filter_shape) center = tuple(map(lambda x: int((x - 1) / 2), filter_shape)) ...

initilize batch norm layer weight. def init_bn_weight(layer): '''initilize batch norm layer weight. ''' n_filters = layer.num_features new_weights = [ add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])), add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])), ...

parse log path def parse_log_path(args, trial_content): '''parse log path''' path_list = [] host_list = [] for trial in trial_content: if args.trial_id and args.trial_id != 'all' and trial.get('id') != args.trial_id: continue pattern = r'(?P<head>.+)://(?P<host>.+):(?P<path>...

use ssh client to copy data from remote machine to local machien def copy_data_from_remote(args, nni_config, trial_content, path_list, host_list, temp_nni_path): '''use ssh client to copy data from remote machine to local machien''' machine_list = nni_config.get_config('experimentConfig').get('machineList') ...

get path list according to different platform def get_path_list(args, nni_config, trial_content, temp_nni_path): '''get path list according to different platform''' path_list, host_list = parse_log_path(args, trial_content) platform = nni_config.get_config('experimentConfig').get('trainingServicePlatform')...

call cmds to start tensorboard process in local machine def start_tensorboard_process(args, nni_config, path_list, temp_nni_path): '''call cmds to start tensorboard process in local machine''' if detect_port(args.port): print_error('Port %s is used by another process, please reset port!' % str(args.por...

stop tensorboard def stop_tensorboard(args): '''stop tensorboard''' experiment_id = check_experiment_id(args) experiment_config = Experiments() experiment_dict = experiment_config.get_all_experiments() config_file_name = experiment_dict[experiment_id]['fileName'] nni_config = Config(config_file...

start tensorboard def start_tensorboard(args): '''start tensorboard''' experiment_id = check_experiment_id(args) experiment_config = Experiments() experiment_dict = experiment_config.get_all_experiments() config_file_name = experiment_dict[experiment_id]['fileName'] nni_config = Config(config_f...

The ratio is smaller the better def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad): ''' The ratio is smaller the better ''' ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value]) sigma = 0 return ratio, ...

Call selection def selection_r(x_bounds, x_types, clusteringmodel_gmm_good, clusteringmodel_gmm_bad, num_starting_points=100, minimize_constraints_fun=None): ''' Call selection ''' minimize_starting_points = [lib_data.rand(...

Select the lowest mu value def selection(x_bounds, x_types, clusteringmodel_gmm_good, clusteringmodel_gmm_bad, minimize_starting_points, minimize_constraints_fun=None): ''' Select the lowest mu value ''' results = lib_acquisition_fun...

Minimize constraints fun summation def _minimize_constraints_fun_summation(x): ''' Minimize constraints fun summation ''' summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX]) return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND

Load dataset, use 20newsgroups dataset def load_data(): '''Load dataset, use 20newsgroups dataset''' digits = load_digits() X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25) ss = StandardScaler() X_train = ss.fit_transform(X_train) ...

Get model according to parameters def get_model(PARAMS): '''Get model according to parameters''' model = SVC() model.C = PARAMS.get('C') model.keral = PARAMS.get('keral') model.degree = PARAMS.get('degree') model.gamma = PARAMS.get('gamma') model.coef0 = PARAMS.get('coef0') return ...

generate num hyperparameter configurations from search space using Bayesian optimization Parameters ---------- num: int the number of hyperparameter configurations Returns ------- list a list of hyperparameter configurations. Format: [[key1, valu...

Initialize Tuner, including creating Bayesian optimization-based parametric models and search space formations Parameters ---------- data: search space search space of this experiment Raises ------ ValueError Error: Search space is None ...

generate a new bracket def generate_new_bracket(self): """generate a new bracket""" logger.debug( 'start to create a new SuccessiveHalving iteration, self.curr_s=%d', self.curr_s) if self.curr_s < 0: logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate ne...

recerive the number of request and generate trials Parameters ---------- data: int number of trial jobs that nni manager ask to generate def handle_request_trial_jobs(self, data): """recerive the number of request and generate trials Parameters ---------- ...

get one trial job, i.e., one hyperparameter configuration. If this function is called, Command will be sent by BOHB: a. If there is a parameter need to run, will return "NewTrialJob" with a dict: { 'parameter_id': id of new hyperparameter 'parameter_source': 'algorithm'...

change json format to ConfigSpace format dict<dict> -> configspace Parameters ---------- data: JSON object search space of this experiment def handle_update_search_space(self, data): """change json format to ConfigSpace format dict<dict> -> configspace Parameters ...

receive the information of trial end and generate next configuaration. Parameters ---------- data: dict() it has three keys: trial_job_id, event, hyper_params trial_job_id: the id generated by training service event: the job's state hyper_params: ...

reveice the metric data and update Bayesian optimization with final result Parameters ---------- data: it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'. Raises ------ ValueError Data type not supported d...

Import additional data for tuning Parameters ---------- data: a list of dictionarys, each of which has at least two keys, 'parameter' and 'value' Raises ------ AssertionError data doesn't have required key 'parameter' and 'value' def handle_impo...

data_transforms for cifar10 dataset def data_transforms_cifar10(args): """ data_transforms for cifar10 dataset """ cifar_mean = [0.49139968, 0.48215827, 0.44653124] cifar_std = [0.24703233, 0.24348505, 0.26158768] train_transform = transforms.Compose( [ transforms.RandomCrop(3...

data_transforms for mnist dataset def data_transforms_mnist(args, mnist_mean=None, mnist_std=None): """ data_transforms for mnist dataset """ if mnist_mean is None: mnist_mean = [0.5] if mnist_std is None: mnist_std = [0.5] train_transform = transforms.Compose( [ ...

Compute the mean and std value of dataset. def get_mean_and_std(dataset): """Compute the mean and std value of dataset.""" dataloader = torch.utils.data.DataLoader( dataset, batch_size=1, shuffle=True, num_workers=2 ) mean = torch.zeros(3) std = torch.zeros(3) print("==> Computing mean ...

Init layer parameters. def init_params(net): """Init layer parameters.""" for module in net.modules(): if isinstance(module, nn.Conv2d): init.kaiming_normal(module.weight, mode="fan_out") if module.bias: init.constant(module.bias, 0) elif isinstance(modul...

EarlyStopping step on each epoch Arguments: metrics {float} -- metric value def step(self, metrics): """ EarlyStopping step on each epoch Arguments: metrics {float} -- metric value """ if self.best is None: self.best = metrics ret...

This can have false positives. For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7. def check_feasibility(x_bounds, lowerbound, upperbound): ''' This can have false positives. For examples, parameters can only be 0 or 5, and the summation constraint is between 6...

Key idea is that we try to move towards upperbound, by randomly choose one value for each parameter. However, for the last parameter, we need to make sure that its value can help us get above lowerbound def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100): ''' Key idea is that we try to...

Change '~' to user home directory def expand_path(experiment_config, key): '''Change '~' to user home directory''' if experiment_config.get(key): experiment_config[key] = os.path.expanduser(experiment_config[key])

Change relative path to absolute path def parse_relative_path(root_path, experiment_config, key): '''Change relative path to absolute path''' if experiment_config.get(key) and not os.path.isabs(experiment_config.get(key)): absolute_path = os.path.join(root_path, experiment_config.get(key)) prin...

Change the time to seconds def parse_time(time): '''Change the time to seconds''' unit = time[-1] if unit not in ['s', 'm', 'h', 'd']: print_error('the unit of time could only from {s, m, h, d}') exit(1) time = time[:-1] if not time.isdigit(): print_error('time format error!...

Parse path in config file def parse_path(experiment_config, config_path): '''Parse path in config file''' expand_path(experiment_config, 'searchSpacePath') if experiment_config.get('trial'): expand_path(experiment_config['trial'], 'codeDir') if experiment_config.get('tuner'): expand_pat...

Validate searchspace content, if the searchspace file is not json format or its values does not contain _type and _value which must be specified, it will not be a valid searchspace file def validate_search_space_content(experiment_config): '''Validate searchspace content, if the searchspace...

Validate whether the kubeflow operators are valid def validate_kubeflow_operators(experiment_config): '''Validate whether the kubeflow operators are valid''' if experiment_config.get('kubeflowConfig'): if experiment_config.get('kubeflowConfig').get('operator') == 'tf-operator': if experimen...

Validate whether the common values in experiment_config is valid def validate_common_content(experiment_config): '''Validate whether the common values in experiment_config is valid''' if not experiment_config.get('trainingServicePlatform') or \ experiment_config.get('trainingServicePlatform') not in ['...

check whether the file of customized tuner/assessor/advisor exists spec_key: 'tuner', 'assessor', 'advisor' def validate_customized_file(experiment_config, spec_key): ''' check whether the file of customized tuner/assessor/advisor exists spec_key: 'tuner', 'assessor', 'advisor' ''' if experimen...

Validate whether assessor in experiment_config is valid def parse_assessor_content(experiment_config): '''Validate whether assessor in experiment_config is valid''' if experiment_config.get('assessor'): if experiment_config['assessor'].get('builtinAssessorName'): experiment_config['assessor...

Valid whether useAnnotation and searchSpacePath is coexist spec_key: 'advisor' or 'tuner' builtin_name: 'builtinAdvisorName' or 'builtinTunerName' def validate_annotation_content(experiment_config, spec_key, builtin_name): ''' Valid whether useAnnotation and searchSpacePath is coexist spec_key: 'ad...

validate the trial config in pai platform def validate_pai_trial_conifg(experiment_config): '''validate the trial config in pai platform''' if experiment_config.get('trainingServicePlatform') == 'pai': if experiment_config.get('trial').get('shmMB') and \ experiment_config['trial']['shmMB'] > ex...

Validate whether experiment_config is valid def validate_all_content(experiment_config, config_path): '''Validate whether experiment_config is valid''' parse_path(experiment_config, config_path) validate_common_content(experiment_config) validate_pai_trial_conifg(experiment_config) experiment_confi...

get urls of local machine def get_local_urls(port): '''get urls of local machine''' url_list = [] for name, info in psutil.net_if_addrs().items(): for addr in info: if AddressFamily.AF_INET == addr.family: url_list.append('http://{}:{}'.format(addr.address, port)) re...

Parse an annotation string. Return an AST Expr node. code: annotation string (excluding '@') def parse_annotation(code): """Parse an annotation string. Return an AST Expr node. code: annotation string (excluding '@') """ module = ast.parse(code) assert type(module) is ast.Module, 'inter...

Parse an annotation function. Return the value of `name` keyword argument and the AST Call node. func_name: expected function name def parse_annotation_function(code, func_name): """Parse an annotation function. Return the value of `name` keyword argument and the AST Call node. func_name: expected ...

Parse `nni.variable` expression. Return the name argument and AST node of annotated expression. code: annotation string def parse_nni_variable(code): """Parse `nni.variable` expression. Return the name argument and AST node of annotated expression. code: annotation string """ name, call = p...

Parse `nni.function_choice` expression. Return the AST node of annotated expression and a list of dumped function call expressions. code: annotation string def parse_nni_function(code): """Parse `nni.function_choice` expression. Return the AST node of annotated expression and a list of dumped function ...

Convert all args to a dict such that every key and value in the dict is the same as the value of the arg. Return the AST Call node with only one arg that is the dictionary def convert_args_to_dict(call, with_lambda=False): """Convert all args to a dict such that every key and value in the dict is the same as t...

Wrap an AST Call node to lambda expression node. call: ast.Call node def make_lambda(call): """Wrap an AST Call node to lambda expression node. call: ast.Call node """ empty_args = ast.arguments(args=[], vararg=None, kwarg=None, defaults=[]) return ast.Lambda(args=empty_args, body=call)

Replace a node annotated by `nni.variable`. node: the AST node to replace annotation: annotation string def replace_variable_node(node, annotation): """Replace a node annotated by `nni.variable`. node: the AST node to replace annotation: annotation string """ assert type(node) is ast.Assign...

Replace a node annotated by `nni.function_choice`. node: the AST node to replace annotation: annotation string def replace_function_node(node, annotation): """Replace a node annotated by `nni.function_choice`. node: the AST node to replace annotation: annotation string """ target, funcs = p...

Annotate user code. Return annotated code (str) if annotation detected; return None if not. code: original user code (str) def parse(code): """Annotate user code. Return annotated code (str) if annotation detected; return None if not. code: original user code (str) """ try: ast_tree...

main function. def main(): ''' main function. ''' args = parse_args() if args.multi_thread: enable_multi_thread() if args.advisor_class_name: # advisor is enabled and starts to run if args.multi_phase: raise AssertionError('multi_phase has not been supporte...

Load yaml file content def get_yml_content(file_path): '''Load yaml file content''' try: with open(file_path, 'r') as file: return yaml.load(file, Loader=yaml.Loader) except yaml.scanner.ScannerError as err: print_error('yaml file format error!') exit(1) except Excep...

Detect if the port is used def detect_port(port): '''Detect if the port is used''' socket_test = socket.socket(socket.AF_INET,socket.SOCK_STREAM) try: socket_test.connect(('127.0.0.1', int(port))) socket_test.close() return True except: return False

Create the Gaussian Mixture Model def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34): ''' Create the Gaussian Mixture Model ''' samples = [samples_x[i] + [samples_y_aggregation[i]] for i in range(0, len(samples_x))] # Sorts so that we can get the top samples samples ...

Selecte R value def selection_r(acquisition_function, samples_y_aggregation, x_bounds, x_types, regressor_gp, num_starting_points=100, minimize_constraints_fun=None): ''' Selecte R value ''' minimize_startin...

selection def selection(acquisition_function, samples_y_aggregation, x_bounds, x_types, regressor_gp, minimize_starting_points, minimize_constraints_fun=None): ''' selection ''' outputs = None sys.stderr.write("[%s] Exercise \"%...

Reports intermediate result to Assessor. metric: serializable object. def report_intermediate_result(metric): """Reports intermediate result to Assessor. metric: serializable object. """ global _intermediate_seq assert _params is not None, 'nni.get_next_parameter() needs to be called before rep...

Reports final result to tuner. metric: serializable object. def report_final_result(metric): """Reports final result to tuner. metric: serializable object. """ assert _params is not None, 'nni.get_next_parameter() needs to be called before report_final_result' metric = json_tricks.dumps({ ...

get args from command line def get_args(): """ get args from command line """ parser = argparse.ArgumentParser("FashionMNIST") parser.add_argument("--batch_size", type=int, default=128, help="batch size") parser.add_argument("--optimizer", type=str, default="SGD", help="optimizer") parser.add_a...

build model from json representation def build_graph_from_json(ir_model_json): """build model from json representation """ graph = json_to_graph(ir_model_json) logging.debug(graph.operation_history) model = graph.produce_torch_model() return model

parse reveive msgs to global variable def parse_rev_args(receive_msg): """ parse reveive msgs to global variable """ global trainloader global testloader global net global criterion global optimizer # Loading Data logger.debug("Preparing data..") raw_train_data = torchvision.d...

train model on each epoch in trainset def train(epoch): """ train model on each epoch in trainset """ global trainloader global testloader global net global criterion global optimizer logger.debug("Epoch: %d", epoch) net.train() train_loss = 0 correct = 0 total = 0 ...

Freeze BatchNorm layers. def freeze_bn(self): '''Freeze BatchNorm layers.''' for layer in self.modules(): if isinstance(layer, nn.BatchNorm2d): layer.eval()

parse reveive msgs to global variable def parse_rev_args(receive_msg): """ parse reveive msgs to global variable """ global trainloader global testloader global net global criterion global optimizer # Loading Data logger.debug("Preparing data..") transform_train, transform_tes...

登录的统一接口 :param config_file 登录数据文件，若无则选择参数登录模式 :param user: 各家券商的账号或者雪球的用户名 :param password: 密码, 券商为加密后的密码，雪球为明文密码 :param account: [雪球登录需要]雪球手机号(邮箱手机二选一) :param portfolio_code: [雪球登录需要]组合代码 :param portfolio_market: [雪球登录需要]交易市场，可选['cn', 'us', 'hk'] 默认 'cn' de...

实现自动登录 :param limit: 登录次数限制 def autologin(self, limit=10): """实现自动登录 :param limit: 登录次数限制 """ for _ in range(limit): if self.login(): break else: raise exceptions.NotLoginError( "登录失败次数过多, 请检查密码是否正确 / 券商服务器是否处于维护中 /...

启动保持在线的进程 def keepalive(self): """启动保持在线的进程 """ if self.heart_thread.is_alive(): self.heart_active = True else: self.heart_thread.start()

读取 config def __read_config(self): """读取 config""" self.config = helpers.file2dict(self.config_path) self.global_config = helpers.file2dict(self.global_config_path) self.config.update(self.global_config)

默认提供最近30天的交割单, 通常只能返回查询日期内最新的 90 天数据。 :return: def exchangebill(self): """ 默认提供最近30天的交割单, 通常只能返回查询日期内最新的 90 天数据。 :return: """ # TODO 目前仅在华泰子类中实现 start_date, end_date = helpers.get_30_date() return self.get_exchangebill(start_date, end_date)

发起对 api 的请求并过滤返回结果 :param params: 交易所需的动态参数 def do(self, params): """发起对 api 的请求并过滤返回结果 :param params: 交易所需的动态参数""" request_params = self.create_basic_params() request_params.update(params) response_data = self.request(request_params) try: format_json...

格式化返回的值为正确的类型 :param response_data: 返回的数据 def format_response_data_type(self, response_data): """格式化返回的值为正确的类型 :param response_data: 返回的数据 """ if isinstance(response_data, list) and not isinstance( response_data, str ): return response_data ...

登陆客户端 :param config_path: 登陆配置文件，跟参数登陆方式二选一 :param user: 账号 :param password: 明文密码 :param exe_path: 客户端路径类似 r'C:\\htzqzyb2\\xiadan.exe', 默认 r'C:\\htzqzyb2\\xiadan.exe' :param comm_password: 通讯密码 :return: def prepare( self, config_path=None, user=No...

跟踪ricequant对应的模拟交易，支持多用户多策略 :param users: 支持easytrader的用户对象，支持使用 [] 指定多个用户 :param run_id: ricequant 的模拟交易ID，支持使用 [] 指定多个模拟交易 :param track_interval: 轮训模拟交易时间，单位为秒 :param trade_cmd_expire_seconds: 交易指令过期时间, 单位为秒 :param cmd_cache: 是否读取存储历史执行过的指令，防止重启时重复执行已经交易过的指令 :param entr...

登陆客户端 :param user: 账号 :param password: 明文密码 :param exe_path: 客户端路径类似 'C:\\中国银河证券双子星3.2\\Binarystar.exe', 默认 'C:\\中国银河证券双子星3.2\\Binarystar.exe' :param comm_password: 通讯密码, 华泰需要，可不设 :return: def login(self, user, password, exe_path, comm_password=None, **kwargs): ...

:param user: 用户名 :param password: 密码 :param exe_path: 客户端路径, 类似 :param comm_password: :param kwargs: :return: def login(self, user, password, exe_path, comm_password=None, **kwargs): """ :param user: 用户名 :param password: 密码 :param exe_pat...