def get_query_with_settings(settings): """Function to get the query method""" method = settings.query_strategy if method in ['rand_max', 'rand_max_sampling']: settings.query_kwargs['rand_max_frac'] = 0.05 settings.query_kwargs = _unsafe_dict_update( settings.query_kwargs, settings.query_param) return get_query_strategy(method)
def lstm_base_model_defaults(settings, verbose=1): """ Set the lstm model defaults. """ model_kwargs = {} model_kwargs['backwards'] = True model_kwargs['dropout'] = 0.4 model_kwargs['optimizer'] = "rmsprop" model_kwargs['max_sequence_length'] = 1000 model_kwargs['verbose'] = verbose model_kwargs['lstm_out_width'] = 20 model_kwargs['dense_width'] = 128 upd_param = _unsafe_dict_update(model_kwargs, settings.model_param) settings.model_param = upd_param return upd_param
def lstm_fit_defaults(settings, verbose=1): """ Set the fit defaults and merge them with custom settings. """ # arguments to pass to the fit fit_kwargs = {} fit_kwargs['batch_size'] = 32 fit_kwargs['epochs'] = 10 fit_kwargs['verbose'] = verbose fit_kwargs['shuffle'] = False fit_kwargs['class_weight_inc'] = 30.0 settings.fit_kwargs = _unsafe_dict_update(fit_kwargs, settings.fit_param) _set_class_weight(fit_kwargs.pop('class_weight_inc'), fit_kwargs) return settings.fit_kwargs
def get_query_strategy(settings): """Function to get the query method""" method = settings.query_strategy if method in ['max', 'max_sampling']: return max_sampling, "Maximum inclusion sampling" if method in ['rand_max', 'rand_max_sampling']: settings.query_kwargs['rand_max_frac'] = 0.05 settings.query_kwargs = _unsafe_dict_update(settings.query_kwargs, settings.query_param) return rand_max_sampling, "Mix of random and max inclusion sampling" elif method in ['lc', 'sm', 'uncertainty', 'uncertainty_sampling']: return uncertainty_sampling, 'Least confidence / Uncertainty sampling' elif method == 'random': return random_sampling, 'Random' else: raise ValueError(f"Query strategy '{method}' not found.")
def __init__(self, balance_kwargs): self.balance_kwargs = self.default_kwargs() self.balance_kwargs = _unsafe_dict_update(self.balance_kwargs, balance_kwargs)
def __init__(self, param): self.name = "base" self.param = _unsafe_dict_update(self.default_param(), param)