Source code for gemseo.core.dataset

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                           documentation
#        :author: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""A generic dataset to store data in memory.

This module implements the concept of dataset
which is a key element for machine learning, post-processing, data analysis, ...

A :class:`.Dataset` uses its attribute :attr:`.Dataset.data`
to store :math:`N` series of data
representing the values of :math:`p` multidimensional features
belonging to different groups of features.

This attribute :attr:`.Dataset.data` is a dictionary of 2D numpy arrays,
whose rows are the samples, a.k.a. series, realizations or entries,
and columns are the variables, a.k.a. parameters or features.
The keys of this dictionary are
either the names of the groups of variables
or the names of the variables.
Thus, a :class:`.Dataset` is not only defined by the raw data stored
but also by the names, the sizes and the groups of the different variables.

A :class:`.Dataset` can be set
either from a file (:meth:`.Dataset.set_from_file`)
or from a numpy arrays (:meth:`.Dataset.set_from_array`),
and can be enriched from a group of variables (:meth:`.Dataset.add_group`)
or from a single variable (:meth:`.Dataset.add_variable`).

An :class:`.AbstractFullCache` or an :class:`.OptimizationProblem`
can also be exported to a :class:`.Dataset`
using :meth:`.AbstractFullCache.export_to_dataset`
and :meth:`.OptimizationProblem.export_to_dataset` respectively.

From a :class:`.Dataset`,
we can easily access its length and data,
either as 2D array or as dictionaries indexed by the variables names.
We can get either the whole data,
or the data associated to a group or the data associated to a list of variables.
It is also possible to export the :class:`.Dataset`
to an :class:`.AbstractFullCache` or a pandas DataFrame.
"""
from __future__ import annotations

import logging
import operator
from collections import namedtuple
from pathlib import Path
from typing import Any
from typing import Callable
from typing import ClassVar
from typing import Dict
from typing import Iterable
from typing import List
from typing import Mapping
from typing import NoReturn
from typing import Sequence
from typing import Tuple
from typing import Union

import numpy as np
from numpy import concatenate
from numpy import delete
from numpy import hstack
from numpy import isnan
from numpy import ndarray
from numpy import unique
from numpy import where
from pandas import DataFrame
from pandas import read_csv

from gemseo.caches.cache_factory import CacheFactory
from gemseo.core.cache import AbstractFullCache
from gemseo.post.dataset.dataset_plot import DatasetPlot
from gemseo.post.dataset.dataset_plot import DatasetPlotPropertyType
from gemseo.post.dataset.factory import DatasetPlotFactory
from gemseo.utils.data_conversion import concatenate_dict_of_arrays_to_array
from gemseo.utils.data_conversion import split_array_to_dict_of_arrays
from gemseo.utils.python_compatibility import singledispatchmethod
from gemseo.utils.string_tools import MultiLineString
from gemseo.utils.string_tools import pretty_repr

LOGGER = logging.getLogger(__name__)

LOGICAL_OPERATORS = {
    "<=": operator.le,
    "<": operator.lt,
    ">=": operator.ge,
    ">": operator.gt,
    "==": operator.eq,
    "!=": operator.ne,
}

EntryItemType = Union[int, Sequence[int], slice]
VariableItemType = Union[str, Iterable[str]]
ItemType = Union[
    EntryItemType, VariableItemType, Tuple[EntryItemType, VariableItemType]
]
AllDataType = Union[
    Dict[str, Union[Dict[str, ndarray], ndarray]],
    Tuple[Union[ndarray, Dict[str, ndarray]], List[str], Dict[str, int]],
]

ColumnName = namedtuple("ColumnName", "group,variable,component")


[docs]class Dataset: """A generic class to store data.""" name: str """The name of the dataset.""" data: dict[str, ndarray] """The data stored by variable names or group names. The values are NumPy arrays whose columns are features and rows are observations. """ sizes: dict[str, int] """The sizes of the variables.""" dimension: dict[str, int] """The dimensions of the groups of variables.""" length: int """The length of the dataset.""" strings_encoding: dict[str, dict[int, int]] """The encoding structure mapping the values of the string variables with integers. The keys are the names of the variables and the values are dictionaries whose keys are the components of the variables and the values are the integer values. """ metadata: dict[str, Any] """The metadata used to store any kind of information that are not variables, E.g. the mesh associated with a multi-dimensional variable. """ PARAMETER_GROUP: ClassVar[str] = "parameters" """The group name for the parameters.""" DESIGN_GROUP: ClassVar[str] = "design_parameters" """The group name for the design variables of an :class:`.OptimizationProblem`.""" FUNCTION_GROUP: ClassVar[str] = "functions" """The group name for the functions of an :class:`.OptimizationProblem`.""" INPUT_GROUP: ClassVar[str] = "inputs" """The group name for the input variables.""" OUTPUT_GROUP: ClassVar[str] = "outputs" """The group name for the output variables.""" GRADIENT_GROUP: ClassVar[str] = "gradients" """The group name for the gradients.""" DEFAULT_GROUP: ClassVar[str] = PARAMETER_GROUP """The default name to group the variables.""" DEFAULT_NAMES: ClassVar[dict[str, str]] = { PARAMETER_GROUP: "x", DESIGN_GROUP: "dp", FUNCTION_GROUP: "func", INPUT_GROUP: "in", OUTPUT_GROUP: "out", } """The default variable names for the different groups.""" HDF5_CACHE: ClassVar[str] = "HDF5Cache" """The name of the :class:`.HDF5Cache`.""" MEMORY_FULL_CACHE: ClassVar[str] = "MemoryFullCache" """The name of the :class:`.MemoryFullCache`.""" __GETITEM_ERROR_MESSAGE: ClassVar[str] = ( "You can get items from a dataset in one of the following ways: " "dataset[3] for the 4th sample, " "dataset['x'] for the variable 'x', " "dataset[['x', 'y']] for the variables 'x' and 'y', " "dataset[[0, 3]] for the 1st and 4th samples, " "dataset[(1, 'x')] for the variable 'x' of the 2nd sample, " "dataset[(1, ['x', 'y'])] for the variables 'x' and 'y' of the 2nd sample, " "dataset[([0, 3], 'x')] for the variable 'x' of the 1st and 4th samples, " "dataset[([0, 3], ['x', 'y'])] for the variables 'x' and 'y' " "of the 1st and 4th samples." ) def __init__( self, name: str | None = None, by_group: bool = True, ) -> None: """ Args: name: The name of the dataset. If None, use the name of the class. by_group: If True, store the data by group. Otherwise, store them by variables. """ self.name = name or self.__class__.__name__ self._names = {} # key = group, value = varnames self._groups = {} # key = varname, value = group self.sizes = {} # key = varname, value = varsize self._positions = {} self.dimension = {} # key = group, value = groupsize self.length = 0 self.data = {} self._group = by_group self.strings_encoding = {} self._cached_inputs = [] self._cached_outputs = [] self.metadata = {} self.__row_names = []
[docs] def remove( self, entries: list[int] | ndarray, ) -> None: """Remove entries. Args: entries: The entries to be removed, either indices or a boolean 1D array whose length is equal to the length of the dataset and elements to delete are coded True. """ if isinstance(entries, ndarray): entries = self.find(entries) self.length -= len(entries) for name, value in list(self.data.items()): self.data[name] = delete(value, entries, 0)
[docs] @staticmethod def find( comparison: ndarray, ) -> list[int]: """Find the entries for which a comparison is satisfied. This search uses a boolean 1D array whose length is equal to the length of the dataset. Args: comparison: A boolean vector whose length is equal to the number of samples. Returns: The indices of the entries for which the comparison is satisfied. """ return where(comparison)[0].tolist()
[docs] def is_nan(self) -> ndarray: """Check if an entry contains NaN. Returns: Whether any entries are NaN or not. """ return isnan(self.get_all_data(False)[0]).any(1)
[docs] def compare( self, value_1: str | float, logical_operator: str, value_2: str | float, component_1: int = 0, component_2: int = 0, ) -> ndarray: """Compare either a variable and a value or a variable and another variable. Args: value_1: The first value, either a variable name or a numeric value. logical_operator: The logical operator, either "==", "<", "<=", ">" or ">=". value_2: The second value, either a variable name or a numeric value. component_1: If value_1 is a variable name, component_1 corresponds to its component used in the comparison. component_2: If value_2 is a variable name, component_2 corresponds to its component used in the comparison. Returns: Whether the comparison is valid for the different entries. """ if value_1 not in self.variables and value_2 not in self.variables: raise ValueError( "Either value_1 ({}) or value_2 ({}) " "must be a variable name from the list: {}".format( value_1, value_2, self.variables ) ) if value_1 in self.variables: value_1 = self[value_1][:, component_1] if value_2 in self.variables: value_2 = self[value_2][:, component_2] try: result = LOGICAL_OPERATORS[logical_operator](value_1, value_2) except KeyError: raise ValueError( "{} is not a logical operator: " "use either '==', '<', '<=', '>' or '>='".format(logical_operator) ) return result
def _clean(self) -> None: """Remove all data from the dataset.""" self._names = {} self._groups = {} self.sizes = {} self._positions = {} self.dimension = {} self.length = 0 self.data = {} self.strings_encoding = {} self._cached_inputs = [] self._cached_outputs = [] self.metadata = {}
[docs] def is_group( self, name: str, ) -> bool: """Check if a name is a group name. Args: name: A name of a group. Returns: Whether the name is a group name. """ return name in self._names
[docs] def is_variable( self, name: str, ) -> bool: """Check if a name is a variable name. Args: name: A name of a variable. Returns: Whether the name is a variable name. """ return name in self._groups
[docs] def is_empty(self) -> bool: """Check if the dataset is empty. Returns: Whether the dataset is empty. """ return self.n_samples == 0
[docs] def get_names( self, group_name: str, ) -> list[str]: """Get the names of the variables of a group. Args: group_name: The name of the group. Returns: The names of the variables of the group. """ return self._names.get(group_name)
[docs] def get_group( self, variable_name: str, ) -> str: """Get the name of the group that contains a variable. Args: variable_name: The name of the variable. Returns: The group to which the variable belongs. """ return self._groups.get(variable_name)
@property def variables(self) -> list[str]: """The sorted names of the variables.""" return sorted(self._groups.keys()) @property def groups(self) -> list[str]: """The sorted names of the groups of variables.""" return sorted(self._names.keys()) def __str__(self) -> str: msg = MultiLineString() msg.add(self.name) msg.indent() msg.add("Number of samples: {}", self.n_samples) msg.add("Number of variables: {}", self.n_variables) msg.add("Variables names and sizes by group:") msg.indent() for group, varnames in sorted(self._names.items()): varnames = [f"{name} ({self.sizes[name]})" for name in varnames] if varnames: msg.add("{}: {}", group, pretty_repr(varnames)) total = sum(self.dimension.values()) msg.dedent() msg.add("Number of dimensions (total = {}) by group:", total) msg.indent() for group, size in sorted(self.dimension.items()): msg.add("{}: {}", group, size) return str(msg) def __check_new_variable( self, variable: str, ) -> None: """Check if a variable is defined. Args: variable: The name of the variable. """ if self.is_variable(variable): raise ValueError(f"{variable} is already defined.") if not isinstance(variable, str): raise TypeError(f"{variable} is not a string.") def __check_new_group(self, group): """Check if a group is defined. Args: group: The name of the group. """ if self.is_group(group): raise ValueError(f"{group} is already defined.") if not isinstance(group, str): raise TypeError(f"{group} is not a string.") def __check_length_consistency( self, length: int, ) -> None: """Check if a length is consistent with the length of the dataset and set it. Args: length: A length to be tested. Raises: ValueError: If the tested length is different from the dataset one. """ if self.length != 0 and length != self.length: raise ValueError( "The number of rows of data must be equal to the" " length of the dataset." ) self.length = length def __check_data_consistency( self, data: ndarray, ) -> None: """Check that a data array is consistent. It must me a 2D numpy array with length equal to the dataset one. Raises: ValueError: If the data is not a 2D numpy array. """ if not isinstance(data, ndarray) or len(data.shape) != 2: raise TypeError("data must be a 2D numpy array.") self.__check_length_consistency(data.shape[0]) @staticmethod def __check_variables_format( variables: list[str], ) -> None: """Check that the names of the variables are well formatted. Args: variables: The names of the variables. Raises: TypeError: When ``variables`` is not a list of string variable names. """ if not isinstance(variables, list) or any( [not isinstance(name, str) for name in variables] ): raise TypeError("variables must be a list of string variable names.") @staticmethod def __check_sizes_format( sizes: dict[str, int], variables: Iterable[int], dimension: int, ) -> None: """Check that the sizes of the variables are well specified. Args: sizes: The sizes of the variables. variables: The names of the variables. dimension: The data dimension. Raises: TypeError: When ``sizes`` is not a dictionary of positive integers. """ type_error = TypeError("sizes must be a dictionary of positive integers.") def is_size(size): return isinstance(size, int) and size > 0 if not isinstance(sizes, dict): raise type_error if any([not is_size(sizes.get(name)) for name in variables]): raise type_error total = sum(sizes[name] for name in variables) if total != dimension: raise ValueError( "The sum of the variable sizes ({}) must be equal " "to the data dimension ({}).".format(total, dimension) ) def __check_variables_sizes( self, variables: list[str], sizes: dict[str, int], dimension: int, ) -> None: """Check that the variables are well formatted. Args: sizes: The sizes of the variables. variables: The names of the variables. dimension: The data dimension. """ if variables is not None: self.__check_variables_format(variables) if sizes is not None: self.__check_sizes_format(sizes, variables, dimension) def __get_default_group_variables( self, group: str, dimension: int, pattern: str | None = None, ) -> tuple[list[str], dict[str, int], dict[str, str]]: """Create default names of the variables of a group. Args: group: The name of the group. dimension: The dimension of the group. pattern: A pattern to be used for the default name, e.g. 'x' will lead to 'x_0', 'x_1', ... If None, use :attr:`.Dataset.DEFAULT_NAMES` or the name of the group. Returns: The names, the sizes and the groups of the variables. """ pattern = pattern or self.DEFAULT_NAMES.get(group) or group variables = [f"{pattern}_{index}" for index in range(dimension)] sizes = {name: 1 for name in variables} groups = {name: group for name in variables} return variables, sizes, groups def __set_group_data( self, data: ndarray, group: str, variables: Iterable[str], sizes: dict[str, int], ) -> None: """Set the data related to a group. Args: data: The data. group: The name of the group. variables: The names of the variables. sizes: the sizes of the variables. """ if self._group: self.data[group] = data else: self.data.update(split_array_to_dict_of_arrays(data, sizes, variables)) def __set_variable_data( self, name: str, data: ndarray, group: str, ) -> None: """Set the data related to a variable. Args: name: The name of the variable. data: The data. group: The name of the group. """ if self._group: if not self.is_group(group): self.data[group] = data else: self.data[group] = hstack((self.data[group], data)) else: self.data[name] = data def __set_group_properties( self, group: str, variables: list[str], sizes: dict[str, int], cache_as_input: bool, ) -> None: """Set the properties related to a group. Args: group: The name of the group. variables: The names of the variables. sizes: The sizes of the variables. cache_as_input: If True, cache these data as inputs when the cache is exported to a cache. """ self.sizes.update(sizes) self._groups.update({name: group for name in variables}) self._names[group] = variables self.dimension[group] = sum(sizes[name] for name in variables) start = 0 for name in variables: self._positions[name] = [start, start + self.sizes[name] - 1] if self._group: start += self.sizes[name] if cache_as_input: self._cached_inputs.append(name) else: self._cached_outputs.append(name) def __set_variable_properties( self, variable: str, group: str, size: int, cache_as_input: bool, ) -> None: """Set the properties related to a variable. Args: variable: The name of the variable. group: The name of the group. size: The size of the variable. cache_as_input: If True, cache these data as inputs when the cache is exported to a cache. """ self.sizes[variable] = size self._groups[variable] = group if not self.is_group(group): self._names[group] = [variable] self.dimension[group] = self.sizes[variable] self._positions[variable] = [0, self.sizes[variable] - 1] else: self._names[group].append(variable) if self._group: ncols = self.dimension[group] self._positions[variable] = [ncols, ncols + self.sizes[variable] - 1] else: self._positions[variable] = [0, self.sizes[variable] - 1] self.dimension[group] += self.sizes[variable] if cache_as_input: self._cached_inputs.append(variable) else: self._cached_outputs.append(variable)
[docs] def add_group( self, group: str, data: ndarray, variables: list[str] | None = None, sizes: dict[str, int] | None = None, pattern: str | None = None, cache_as_input: bool = True, ) -> None: """Add data related to a group. Args: group: The name of the group of data to be added. data: The data to be added. variables: The names of the variables. If None, use default names based on a pattern. sizes: The sizes of the variables. If None, assume that all the variables have a size equal to 1. pattern: The name of the variable to be used as a pattern when variables is None. If None, use the :attr:`.Dataset.DEFAULT_NAMES` for this group if it exists. Otherwise, use the group name. cache_as_input: If True, cache these data as inputs when the cache is exported to a cache. """ self.__check_new_group(group) self.__check_data_consistency(data) self.__check_variables_sizes(variables, sizes, data.shape[1]) if variables is None or sizes is None: variables, sizes, _ = self.__get_default_group_variables( group, data.shape[1], pattern ) self.__set_group_data(data, group, variables, sizes) self.__set_group_properties(group, variables, sizes, cache_as_input)
[docs] def add_variable( self, name: str, data: ndarray, group: str = DEFAULT_GROUP, cache_as_input: bool = True, ) -> None: """Add data related to a variable. Args: name: The name of the variable to be stored. data: The data to be stored. group: The name of the group related to this variable. cache_as_input: If True, cache these data as inputs when the cache is exported to a cache. """ self.__check_new_variable(name) self.__check_data_consistency(data) self.__set_variable_data(name, data, group) self.__set_variable_properties(name, group, data.shape[1], cache_as_input)
def __convert_array_to_numeric( self, data: ndarray, ) -> tuple[ndarray, dict[int, dict[int, str]]]: """Convert an array to numeric by encoding the string elements. This method looks for the columns of the array containing string values and encodes them into integers. For instance, let us consider a column ``['blue', 'yellow', 'yellow', 'red', 'blue']``. The unique values, also called *tags*, are ``['blue', 'red', 'yellow']`` and the encoding rule is ``{0: 'blue', 1: 'red', 2: 'yello'}. Then, the column is replaced by ``[0, 2, 2, 1, 0]``. Args: data: The array. Returns: The array forced to float by encoding its string elements, and the encoding rules for the different columns. """ self.strings_encoding = {name: {} for name in self._groups} string_columns = [ column_index for column_index, column_value in enumerate(data[0]) if isinstance(column_value, str) ] if not string_columns: return data, {} columns_to_codes_to_tags = {} for string_column in string_columns: tags, codes = unique(data[:, string_column], return_inverse=True) columns_to_codes_to_tags[string_column] = dict(enumerate(tags)) data[:, string_column] = codes # Cast the array to float is its dtype is not numeric. # biufc = boolean, signed integer, unsigned integer, floating-point, # complex floating-point if data.dtype.kind in "biufc": return data, columns_to_codes_to_tags else: return data.astype("float"), columns_to_codes_to_tags
[docs] def set_from_array( self, data: ndarray, variables: list[str] | None = None, sizes: dict[str, int] | None = None, groups: dict[str, str] | None = None, default_name: str | None = None, ) -> None: """Set the dataset from an array. Args: data: The data to be stored. variables: The names of the variables. If None, use one default name per column of the array based on the pattern 'default_name'. sizes: The sizes of the variables. If None, assume that all the variables have a size equal to 1. groups: The groups of the variables. If None, use :attr:`.Dataset.DEFAULT_GROUP` for all the variables. default_name: The name of the variable to be used as a pattern when variables is None. If None, use the :attr:`.Dataset.DEFAULT_NAMES` for this group if it exists. Otherwise, use the group name. """ self._clean() self.__check_data_consistency(data) if variables is None: group = self.DEFAULT_GROUP get = self.__get_default_group_variables variables, sizes, groups = get(group, data.shape[1], default_name) else: self.__check_variables_format(variables) if sizes is None: sizes = {name: 1 for name in variables} self.__check_sizes_format(sizes, variables, data.shape[1]) if groups is None: groups = {name: self.DEFAULT_GROUP for name in variables} self.__check_groups_format(groups, variables) self.__set_data_properties(variables, sizes, groups) data, columns_to_codes_to_labels = self.__convert_array_to_numeric(data) self.__set_data(data, variables, columns_to_codes_to_labels)
def __check_groups_format( self, groups: dict[str, str], variables: Iterable[str], ) -> None: """Check the format of groups and update it if necessary. Args: groups: The names of the groups of the variables. variables: The names of the variables. """ type_error = TypeError( "groups must be a dictionary of the form {variable_name: group_name}." ) if not isinstance(groups, dict): raise type_error for name in variables: if groups.get(name) is None: groups.update({name: self.DEFAULT_GROUP}) elif not isinstance(groups[name], str): raise type_error def __set_data( self, data: ndarray, variables: Iterable[str], columns_to_codes_to_labels: dict[int, dict[int, str]], ) -> None: """Set data. Args: data: The data to be stored. variables: The names of the variables. columns_to_codes_to_labels: An encoding structure of the form: `{column_index: {code: label}}`, mapping the values of the string variables with integers for the different string columns of `data`. """ indices = {group: [] for group in self._names} start = 0 for variable in variables: end = start + self.sizes[variable] - 1 columns = list(range(start, end + 1)) start = end + 1 indices[self._groups[variable]] += columns for column in columns_to_codes_to_labels: if column in columns: index = columns.index(column) codes_to_labels = columns_to_codes_to_labels[column] self.strings_encoding[variable][index] = codes_to_labels if not self._group: self.data[variable] = data[:, columns] if self._group: for group in self._names: self.data[group] = data[:, indices[group]] def _set_variables_positions(self) -> None: """Set the positions of the variables.""" for variable_names in self._names.values(): start = 0 for variable_name in variable_names: self._positions[variable_name] = [ start, start + self.sizes[variable_name] - 1, ] if self._group: start += self.sizes[variable_name] else: start = 0 def __set_data_properties( self, variables: Iterable[str], sizes: Mapping[str, int], groups: Mapping[str, str], ) -> None: """Set the properties for the whole data. Args: variables: The names of the variables. sizes: The sizes of the variables. groups: The groups of the variables. """ for name in variables: if not self.is_group(groups[name]): self._names[groups[name]] = [name] else: self._names[groups[name]].append(name) self.sizes[name] = sizes[name] self._groups[name] = groups[name] for group, names in self._names.items(): self.dimension[group] = sum(self.sizes[name] for name in names) if group == self.OUTPUT_GROUP: self._cached_outputs += names else: self._cached_inputs += names self._set_variables_positions()
[docs] def set_from_file( self, filename: Path | str, variables: list[str] | None = None, sizes: dict[str, int] | None = None, groups: dict[str, str] | None = None, delimiter: str = ",", header: bool = True, ) -> None: """Set the dataset from a file. Args: filename: The name of the file containing the data. variables: The names of the variables. If None and `header` is True, read the names from the first line of the file. If None and `header` is False, use default names based on the patterns the :attr:`.Dataset.DEFAULT_NAMES` associated with the different groups. sizes: The sizes of the variables. If None, assume that all the variables have a size equal to 1. groups: The groups of the variables. If None, use :attr:`.Dataset.DEFAULT_GROUP` for all the variables. delimiter: The field delimiter. header: If True, read the names of the variables on the first line of the file. """ self._clean() if header: header = "infer" else: header = None data = read_csv(filename, delimiter=delimiter, header=header) if header and variables is None: variables = data.columns.values.tolist() self.set_from_array(data.values, variables, sizes, groups)
[docs] def set_metadata( self, name: str, value: Any, ) -> None: """Set a metadata attribute. Args: name: The name of the metadata attribute. value: The value of the metadata attribute. """ self.metadata[name] = value
@property def columns_names(self) -> list[str | ColumnName]: """The names of the columns of the dataset.""" return self.get_column_names()
[docs] def get_column_names( self, variables: Sequence[str] = None, as_tuple: bool = False, start: int = 0, ) -> list[str | ColumnName]: """Return the names of the columns of the dataset. If dim(x)=1, its column name is 'x' while if dim(y)=2, its column names are either 'x_0' and 'x_1' or ColumnName(group_name, 'x', '0') and ColumnName(group_name, 'x', '1'). Args: variables: The names of the variables. If ``None``, use all the variables. as_tuple: If True, return the names as named tuples. otherwise, return the names as strings. start: The first index for the components of a variable. E.g. with '0': 'x_0', 'x_1', ... Returns: The names of the columns of the dataset. """ column_names = [] for group, names in self._names.items(): for name in names: if variables and name not in variables: continue if as_tuple: column_names.extend( [ ColumnName(group, name, str(size + start)) for size in range(self.sizes[name]) ] ) else: if self.sizes[name] == 1: column_names.append(name) else: column_names.extend( [ f"{name}_{size + start}" for size in range(self.sizes[name]) ] ) return column_names
[docs] def get_data_by_group( self, group: str, as_dict: bool = False, ) -> ndarray | dict[str, ndarray]: """Get the data for a specific group name. Args: group: The name of the group. as_dict: If True, return values as dictionary. Returns: The data related to the group. """ if not self.is_group(group): raise ValueError(f"{group} is not an available group.") if group in self.data: data = self.data[group] if as_dict: data = split_array_to_dict_of_arrays( self.data[group], self.sizes, self._names[group] ) else: data = {name: self.data[name] for name in self._names[group]} if not as_dict: data = concatenate_dict_of_arrays_to_array(data, self._names[group]) return data
[docs] def get_data_by_names( self, names: str | Iterable[str], as_dict: bool = True, ) -> ndarray | dict[str, ndarray]: """Get the data for specific names of variables. Args: names: The names of the variables. as_dict: If True, return values as dictionary. Returns: The data related to the variables. """ if isinstance(names, str): names = [names] if not self._group: data = {name: self.data.get(name) for name in names} else: data = {} for name in names: indices = list( range(self._positions[name][0], self._positions[name][1] + 1) ) data[name] = self.data[self._groups[name]][:, indices] if not as_dict: data = concatenate_dict_of_arrays_to_array(data, names) return data
[docs] def get_all_data(self, by_group=True, as_dict=False) -> AllDataType: """Get all the data stored in the dataset. The data can be returned either as a dictionary indexed by the names of the variables, or as an array concatenating them, accompanied by the names and sizes of the variables. The data can also be classified by groups of variables. Args: by_group: If True, sort the data by group. as_dict: If True, return the data as a dictionary. Returns: All the data stored in the dataset. """ if by_group: data = { group: self.get_data_by_group(group, as_dict) for group in self._names } if not as_dict: data = (data, self._names, self.sizes) else: if not as_dict: data = concatenate( tuple(self.get_data_by_group(group) for group in self.groups), 1 ) names = [ name for group in self.groups for name in self.get_names(group) ] data = (data, names, self.sizes) else: data = {} for group in self._names: data.update(self.get_data_by_group(group, as_dict)) return data
@property def n_variables(self) -> int: """The number of variables.""" return len(self._groups)
[docs] def n_variables_by_group( self, group: str, ) -> int: """The number of variables for a group. Args: group: The name of a group. Returns: The group dimension. """ return len(self._names[group])
@property def n_samples(self) -> int: """The number of samples.""" return self.length def __len__(self) -> int: """The length of the dataset.""" return self.length def __bool__(self) -> bool: """True is the dataset is not empty.""" return not self.is_empty()
[docs] def export_to_dataframe( self, copy: bool = True, variable_names: Sequence[str] | None = None, ) -> DataFrame: """Export the dataset to a pandas Dataframe. Args: copy: If True, copy data. Otherwise, use reference. Returns: A pandas DataFrame containing the dataset. """ if variable_names is None: variable_names = self.variables # The column of a DataFrame is defined by three labels: # the group at which the variable belongs, # the name of the variable and # the components of the variable, # e.g. ("inputs", "x", "0"). group_labels = [] variable_labels = [] component_labels = [] for (group, variable, component) in self.get_column_names(as_tuple=True): if variable in variable_names: group_labels.append(group) variable_labels.append(variable) component_labels.append(component) # Reorder the variables according to variable_names variable_label_indices = [ variable_label_index for variable_name in variable_names for variable_label_index, variable_label_name in enumerate(variable_labels) if variable_label_name == variable_name ] group_labels = [group_labels[index] for index in variable_label_indices] component_labels = [component_labels[index] for index in variable_label_indices] variable_labels = [variable_labels[index] for index in variable_label_indices] columns = [group_labels, variable_labels, component_labels] data = self.get_data_by_names(variable_names, as_dict=False) dataframe = DataFrame(data, columns=columns, copy=copy) dataframe.index = self.row_names return dataframe
[docs] def export_to_cache( self, inputs: Iterable[str] | None = None, outputs: Iterable[str] | None = None, cache_type: str = MEMORY_FULL_CACHE, cache_hdf_file: str | None = None, cache_hdf_node_name: str | None = None, **options, ) -> AbstractFullCache: """Export the dataset to a cache. Args: inputs: The names of the inputs to cache. If None, use all inputs. outputs: The names of the outputs to cache. If None, use all outputs. cache_type: The type of cache to use. cache_hdf_file: The name of the HDF file to store the data. Required if the type of the cache is 'HDF5Cache'. cache_hdf_node_name: The name of the HDF node to store the discipline. If None, use the name of the dataset. Returns: A cache containing the dataset. """ if inputs is None: inputs = self._cached_inputs if outputs is None: outputs = self._cached_outputs create_cache = CacheFactory().create cache_hdf_node_name = cache_hdf_node_name or self.name if cache_type == self.HDF5_CACHE: cache = create_cache( cache_type, hdf_file_path=cache_hdf_file, hdf_node_path=cache_hdf_node_name, name=self.name, **options, ) else: cache = create_cache(cache_type, name=self.name, **options) for sample in self: input_data = {name: sample[name] for name in inputs} output_data = {name: sample[name] for name in outputs} cache.cache_outputs(input_data, output_data) return cache
[docs] def get_available_plots(self) -> list[str]: """Return the available plot methods.""" return DatasetPlotFactory().plots
[docs] def plot( self, name: str, show: bool = True, save: bool = False, file_path: str | Path | None = None, directory_path: str | Path | None = None, file_name: str | None = None, file_format: str | None = None, properties: Mapping[str, DatasetPlotPropertyType] | None = None, **options, ) -> DatasetPlot: """Plot the dataset from a :class:`.DatasetPlot`. See :meth:`.Dataset.get_available_plots` Args: name: The name of the post-processing, which is the name of a class inheriting from :class:`.DatasetPlot`. show: If True, display the figure. save: If True, save the figure. file_path: The path of the file to save the figures. If None, create a file path from ``directory_path``, ``file_name`` and ``file_format``. directory_path: The path of the directory to save the figures. If None, use the current working directory. file_name: The name of the file to save the figures. If None, use a default one generated by the post-processing. file_format: A file format, e.g. 'png', 'pdf', 'svg', ... If None, use a default file extension. properties: The general properties of a :class:`.DatasetPlot`. **options: The options for the post-processing. """ post = DatasetPlotFactory().create(name, dataset=self, **options) post.execute( save=save, show=show, file_path=file_path, directory_path=directory_path, file_name=file_name, file_format=file_format, properties=properties, ) return post
def __iter__(self) -> dict[str, ndarray]: for item in range(len(self)): yield self[item] @singledispatchmethod def __split_item(self, item) -> NoReturn: raise TypeError(self.__GETITEM_ERROR_MESSAGE) @__split_item.register def _(self, item: int): # type: (...) -> tuple[int, list[str]] return item, self.variables @__split_item.register def _( self, item: type(Ellipsis) ): # type: (...) -> tuple[type(Ellipsis), list[str]] return item, self.variables @__split_item.register def _(self, item: slice): # type: (...) -> tuple[slice, list[str]] return item, self.variables @__split_item.register def _(self, item: str): # type: (...) -> tuple[type(Ellipsis), str] return Ellipsis, item @__split_item.register def _(self, item: list): # type: (...) -> tuple[type(Ellipsis) | list[str]] if all(isinstance(name, str) for name in item): return Ellipsis, item if all(isinstance(entry, int) for entry in item): return item, self.variables raise TypeError(self.__GETITEM_ERROR_MESSAGE) @__split_item.register def _(self, item: tuple): # tuple[Any, Any] if len(item) != 2: raise TypeError(self.__GETITEM_ERROR_MESSAGE) return item def __getitem__( self, item: ItemType, ) -> dict[str, ndarray] | ndarray: entries, variables = self.__split_item(item) try: if isinstance(variables, str): return self.get_data_by_names([variables])[variables][entries, :] data = self.get_data_by_names(variables) return {name: value[entries, :] for name, value in data.items()} except IndexError: size = len(self) raise KeyError( f"Entries must be integers between {-size} and {size-1}; got {entries}." ) except KeyError as name: raise KeyError(f"There is not variable named {name} in the dataset.") except TypeError: raise TypeError(self.__GETITEM_ERROR_MESSAGE) @property def row_names(self) -> list[str]: """The names of the rows.""" return self.__row_names or [str(val) for val in range(len(self))] @row_names.setter def row_names( self, names: list[str], ) -> None: self.__row_names = names
[docs] def get_normalized_dataset( self, excluded_variables: Sequence[str] | None = None, excluded_groups: Sequence[str] | None = None, ) -> Dataset: """Get a normalized copy of the dataset. Args: excluded_variables: The names of the variables not to be normalized. If None, normalize all the variables. excluded_groups: The names of the groups not to be normalized. If None, normalize all the groups. Returns: A normalized dataset. """ excluded_groups = excluded_groups or [] excluded_variables = excluded_variables or [] dataset = Dataset(self.name, self._group) for group, names in self._names.items(): normalize_group = group not in excluded_groups for name in names: normalize_name = name not in excluded_variables data = self.get_data_by_names(name, False) if normalize_group and normalize_name: data = (data - np.min(data, 0)) / ( np.max(data, 0) - np.min(data, 0) ) dataset.add_variable(name, data, group, name in self._cached_inputs) return dataset
[docs] def transform_variable( self, name: str, transformation: Callable[[ndarray], ndarray] ) -> None: """Transform a variable. Args: name: The name of the variable, e.g. ``"foo"``. transformation: The function transforming the variable, e.g. ``"lambda x: np.exp(x)"``. """ if not self._group: self.data[name] = transformation(self.data[name]) else: group = self.get_group(name) indices = self._positions[name] self.data[group][indices] = transformation(self.data[group][indices])
[docs] def rename_variable(self, name: str, new_name: str) -> None: """Rename a variable. Args: name: The name of the variable. new_name: The new name of the variable. """ dictionaries = [ self._groups, self.sizes, self._positions, self.strings_encoding, ] if not self._group: dictionaries.append(self.data) for dict_ in dictionaries: if name in dict_: dict_[new_name] = dict_.pop(name) if name in self._cached_inputs: self._cached_inputs[self._cached_inputs.index(name)] = new_name if name in self._cached_outputs: self._cached_outputs[self._cached_outputs.index(name)] = new_name for _group, names in self._names.items(): if name in names: break names = self._names[_group] names[names.index(name)] = new_name self._names[_group] = names