def get_dimensionality_reduction_data(): """ Get features for all models This is used for dimensionality reduction, which is later used for scatter plotting. """ # we don't have costs for all values, should do that. data = [] experiment = 'Deterministic policy, regressed cost' seed = 3 checkpoint = 25000 seeds = DataReader.find_option_values('seed', experiment) for seed in seeds: checkpoints = DataReader.find_option_values( 'checkpoint', experiment, seed) for checkpoint in checkpoints: try: if data == []: data = DimensionalityReduction.get_model_failing_features( experiment, seed, checkpoint) else: data = np.concatenate([ data, DimensionalityReduction.get_model_failing_features( experiment, seed, checkpoint) ]) except Exception as e: print(checkpoint, 'failed', e) traceback.print_exc() data = sklearn.preprocessing.scale(data) return data
def seed_dropdown_change_callback(change): if self.ignore_updates: return if change.name == 'value' and change.new is not None: self.ignore_updates = True self.checkpoint_dropdown.options = \ DataReader.find_option_values( option='checkpoint', experiment=self.experiment_dropdown.value, seed=self.seed_dropdown.value ) self.checkpoint_dropdown.value = None self.checkpoint_dropdown.disabled = False self.ignore_updates = False
def experiment_dropdown_change_callback(change): if change.name == 'value' and change.new is not None: self.ignore_updates = True if level >= Picker.MODEL_LEVEL: self.seed_dropdown.options = \ DataReader.find_option_values( option='seed', experiment=self.experiment_dropdown.value ) self.seed_dropdown.value = None self.seed_dropdown.disabled = False self.checkpoint_dropdown.disabled = True self.checkpoint_dropdown.value = None self.ignore_updates = False if level == Picker.EXPERIMENT_LEVEL: self.call_callback(self.experiment_dropdown.value)