Source code for qiskit_experiments.data_processing.data_action

# This code is part of Qiskit.
#
# (C) Copyright IBM 2021.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
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"""Defines the steps that can be used to analyse data."""

from abc import ABC, abstractmethod
from collections import OrderedDict
from typing import Dict, Any

import numpy as np

from qiskit_experiments.framework.store_init_args import StoreInitArgs
from qiskit_experiments.framework import Options


[docs] class DataAction(ABC, StoreInitArgs): """Abstract action done on measured data to process it. Each subclass of DataAction must define the way it formats, validates and processes data. """ def __init__(self, validate: bool = True): """Create new node. Args: validate: If set to False the DataAction will not validate its input. """ self._validate = validate @abstractmethod def _process(self, data: np.ndarray) -> np.ndarray: """Applies the data processing step to the data. Args: data: A data array to process. This is a single numpy array containing all circuit results input to the data processor. If the elements are ufloat objects consisting of a nominal value and a standard error, then the error propagation is automatically computed. Returns: The processed data. """ def _format_data(self, data: np.ndarray) -> np.ndarray: """Format and validate the input. Check that the given data has the correct structure. This method may additionally change the data type, e.g. converting a list to a numpy array. Args: data: A data array to format. This is a single numpy array containing all circuit results input to the data processor. Returns: The data that has been validated and formatted. """ return data def __json_encode__(self) -> Dict[str, Any]: """Return the config dict for this node.""" return { "cls": type(self), "args": tuple(getattr(self, "__init_args__", OrderedDict()).values()), "kwargs": dict(getattr(self, "__init_kwargs__", OrderedDict())), } @classmethod def __json_decode__(cls, config: Dict[str, Any]) -> "DataAction": """Initialize a node from config dict.""" init_args = config.get("args", ()) init_kwargs = config.get("kwargs", {}) return cls(*init_args, **init_kwargs)
[docs] def __call__(self, data: np.ndarray) -> np.ndarray: """Call the data action of this node on the data. Args: data: A numpy array with arbitrary dtype. If the elements are ufloat objects consisting of a nominal value and a standard error, then the error propagation is done automatically. Returns: The processed data. """ return self._process(self._format_data(data))
def __repr__(self): """String representation of the node.""" return f"{self.__class__.__name__}(validate={self._validate})"
[docs] class TrainableDataAction(DataAction): """A base class for data actions that need training. .. note:: The parameters of trainable nodes computed during training should be listed in the class method :meth:`.TrainableDataAction._default_parameters`. These parameters are initialized at construction time and serialized together with the constructor arguments. All parameters defined in :meth:`.TrainableDataAction._default_parameters` should be assigned a `None` value to indicate that the node has not been trained. Parameter values can be updated with the :meth:`.set_parameters` method and refer to using the :meth:`.TrainableDataAction.parameters` method. This is required to correctly JSON serialize and deserialize a trainable node with parameters set during training. """ def __init__(self, validate: bool = True): """Create new node. Args: validate: If set to False the DataAction will not validate its input. """ super().__init__(validate=validate) self._parameters = self._default_parameters() @classmethod def _default_parameters(cls) -> Options: """Parameters of trainable nodes. The parameters defined here should be assigned a `None` to indicate that the node has not been trained. """ return Options() @property def parameters(self) -> Options: """Return the parameters of the trainable node.""" return self._parameters
[docs] def set_parameters(self, **fields): """Set parameters for training.""" for field in fields: if not hasattr(self._parameters, field): raise AttributeError(f"{field} is not a valid parameter for {type(self).__name__}.") self._parameters.update_options(**fields)
@property def is_trained(self) -> bool: """Return False if the DataAction needs to be trained. A node is considered trained if all its parameters are assigned, or do not have ``None`` values. Return: True if the data action has been trained. """ return all(p is not None for p in self.parameters.__dict__.values())
[docs] @abstractmethod def train(self, data: np.ndarray): """Train a DataAction. Certain data processing nodes, such as a SVD, require data to first train. Args: data: A data array for training. This is a single numpy array containing all circuit results input to the data processor :meth:`~qiskit_experiments.\ data_processing.data_processor.DataProcessor#train` method. """
def __json_encode__(self) -> Dict[str, Any]: """Return the config dict for this node.""" config = super().__json_encode__() config["params"] = self.parameters.__dict__ return config @classmethod def __json_decode__(cls, config: Dict[str, Any]) -> "TrainableDataAction": """Initialize a node from config dict.""" init_args = config.get("args", ()) init_kwargs = config.get("kwargs", {}) params = config.get("params", {}) instance = cls(*init_args, **init_kwargs) instance.set_parameters(**params) return instance def __repr__(self): """String representation of the node.""" options_str = f"validate={self._validate}" for pname, pval in self.parameters.__dict__.items(): options_str += f", {pname}={pval}" return f"{self.__class__.__name__}({options_str})"