Source code for

import warnings
import weakref

from math import log
from collections import Iterable
from itertools import chain
from numbers import Integral

import numpy as np

from import (
    Unknown, Variable, ContinuousVariable, DiscreteVariable, StringVariable
from Orange.util import deprecated, OrangeDeprecationWarning

__all__ = ["DomainConversion", "Domain"]

class DomainConversion:
    Indices and functions for conversion between domains.

    Every list contains indices (instances of int) of variables in the
    source domain, or the variable's compute_value function if the source
    domain does not contain the variable.

    .. attribute:: source

        The source domain. The destination is not stored since destination
        domain is the one which contains the instance of DomainConversion.

    .. attribute:: attributes

        Indices for attribute values.

    .. attribute:: class_vars

        Indices for class variables

    .. attribute:: variables

        Indices for attributes and class variables

    .. attribute:: metas

        Indices for meta attributes

    .. attribute:: sparse_X

        Flag whether the resulting X matrix should be sparse.

    .. attribute:: sparse_Y

        Flag whether the resulting Y matrix should be sparse.

    .. attribute:: sparse_metas

        Flag whether the resulting metas matrix should be sparse.

    def __init__(self, source, destination):
        Compute the conversion indices from the given `source` to `destination`
        self.source = source

        self.attributes = [
            source.index(var) if var in source
            else var.compute_value for var in destination.attributes]
        self.class_vars = [
            source.index(var) if var in source
            else var.compute_value for var in destination.class_vars]
        self.variables = self.attributes + self.class_vars
        self.metas = [
            source.index(var) if var in source
            else var.compute_value for var in destination.metas]

        def should_be_sparse(feats):
            For a matrix to be stored in sparse, more than 2/3 of columns
            should be marked as sparse and there should be no string columns
            since Scipy's sparse matrices don't support dtype=object.
            fraction_sparse = sum(f.sparse for f in feats) / max(len(feats), 1)
            contain_strings = any(f.is_string for f in feats)
            return fraction_sparse > 2/3 and not contain_strings

        # check whether X, Y or metas should be sparse
        self.sparse_X = should_be_sparse(destination.attributes)
        self.sparse_Y = should_be_sparse(destination.class_vars)
        self.sparse_metas = should_be_sparse(destination.metas)

def filter_visible(feats):
        feats (iterable): Features to be filtered.

    Returns: A filtered tuple of features that are visible (i.e. not hidden).
    return (f for f in feats if not f.attributes.get('hidden', False))

[docs]class Domain:
[docs] def __init__(self, attributes, class_vars=None, metas=None, source=None): """ Initialize a new domain descriptor. Arguments give the features and the class attribute(s). They can be described by descriptors (instances of :class:`Variable`), or by indices or names if the source domain is given. :param attributes: a list of attributes :type attributes: list of :class:`Variable` :param class_vars: target variable or a list of target variables :type class_vars: :class:`Variable` or list of :class:`Variable` :param metas: a list of meta attributes :type metas: list of :class:`Variable` :param source: the source domain for attributes :type source: :return: a new domain :rtype: :class:`Domain` """ if class_vars is None: class_vars = [] elif isinstance(class_vars, (Variable, Integral, str)): class_vars = [class_vars] elif isinstance(class_vars, Iterable): class_vars = list(class_vars) if not isinstance(attributes, list): attributes = list(attributes) metas = list(metas) if metas else [] # Replace str's and int's with descriptors if 'source' is given; # complain otherwise for lst in (attributes, class_vars, metas): for i, var in enumerate(lst): if not isinstance(var, Variable): if source is not None and isinstance(var, (str, int)): lst[i] = source[var] else: raise TypeError( "descriptors must be instances of Variable, " "not '%s'" % type(var).__name__) # Store everything self.attributes = tuple(attributes) self.class_vars = tuple(class_vars) self._variables = self.attributes + self.class_vars self._metas = tuple(metas) self.class_var = \ self.class_vars[0] if len(self.class_vars) == 1 else None if not all(var.is_primitive() for var in self._variables): raise TypeError("variables must be primitive") self._indices = dict(chain.from_iterable( ((var, idx), (, idx), (idx, idx)) for idx, var in enumerate(self._variables))) self._indices.update(chain.from_iterable( ((var, -1-idx), (, -1-idx), (-1-idx, -1-idx)) for idx, var in enumerate(self.metas))) self.anonymous = False self._known_domains = weakref.WeakKeyDictionary() self._last_conversion = None # Precompute hash, which is frequently used in domain conversions. self._hash = hash(self.attributes) ^ hash(self.class_vars) ^ hash(self.metas)
# noinspection PyPep8Naming
[docs] @classmethod def from_numpy(cls, X, Y=None, metas=None): """ Create a domain corresponding to the given numpy arrays. This method is usually invoked from :meth:``. All attributes are assumed to be continuous and are named "Feature <n>". Target variables are discrete if the only two values are 0 and 1; otherwise they are continuous. Discrete targets are named "Class <n>" and continuous are named "Target <n>". Domain is marked as :attr:`anonymous`, so data from any other domain of the same shape can be converted into this one and vice-versa. :param `numpy.ndarray` X: 2-dimensional array with data :param Y: 1- of 2- dimensional data for target :type Y: `numpy.ndarray` or None :param `numpy.ndarray` metas: meta attributes :type metas: `numpy.ndarray` or None :return: a new domain :rtype: :class:`Domain` """ def get_places(max_index): return 0 if max_index == 1 else int(log(max_index, 10)) + 1 def get_name(base, index, places): return base if not places \ else "{} {:0{}}".format(base, index + 1, places) if X.ndim != 2: raise ValueError('X must be a 2-dimensional array') n_attrs = X.shape[1] places = get_places(n_attrs) attr_vars = [ContinuousVariable(name=get_name("Feature", a, places)) for a in range(n_attrs)] class_vars = [] if Y is not None: if Y.ndim == 1: Y = Y.reshape(len(Y), 1) elif Y.ndim != 2: raise ValueError('Y has invalid shape') n_classes = Y.shape[1] places = get_places(n_classes) for i, values in enumerate(Y.T): if set(values) == {0, 1}: name = get_name('Class', i, places) values = ['v1', 'v2'] class_vars.append(DiscreteVariable(name, values)) else: name = get_name('Target', i + 1, places) class_vars.append(ContinuousVariable(name)) if metas is not None: n_metas = metas.shape[1] places = get_places(n_metas) meta_vars = [StringVariable(get_name("Meta", m, places)) for m in range(n_metas)] else: meta_vars = [] domain = cls(attr_vars, class_vars, meta_vars) domain.anonymous = True return domain
@property def variables(self): return self._variables @property def metas(self): return self._metas
[docs] def __len__(self): """The number of variables (features and class attributes).""" return len(self._variables)
def __bool__(self): warnings.warn( "Domain.__bool__ is ambiguous; use 'is None' or 'empty' instead", OrangeDeprecationWarning, stacklevel=2) return len(self) > 0 # Keep the obsolete behaviour def empty(self): """True if the domain has no variables of any kind""" return not self.variables and not self.metas
[docs] def __getitem__(self, idx): """ Return a variable descriptor from the given argument, which can be a descriptor, index or name. If `var` is a descriptor, the function returns this same object. :param idx: index, name or descriptor :type idx: int, str or :class:`Variable` :return: an instance of :class:`Variable` described by `var` :rtype: :class:`Variable` """ if isinstance(idx, slice): return self._variables[idx] idx = self._indices[idx] if idx >= 0: return self.variables[idx] else: return self.metas[-1-idx]
[docs] def __contains__(self, item): """ Return `True` if the item (`str`, `int`, :class:`Variable`) is in the domain. """ return item in self._indices
@deprecated("Domain.variables") def __iter__(self): """ Return an iterator through variables (features and class attributes). The current behaviour is confusing, as `x in domain` returns True for meta variables, but iter(domain) does not yield them. This will be consolidated eventually (in 3.12?), the code that currently iterates over domain should iterate over domain.variables instead. """ return iter(self._variables) def __str__(self): """ Return a list-like string with the domain's features, class attributes and meta attributes. """ s = "[" + ", ".join( for attr in self.attributes) if self.class_vars: s += " | " + ", ".join( for cls in self.class_vars) s += "]" if self._metas: s += " {" + ", ".join( for meta in self._metas) + "}" return s __repr__ = __str__ def __getstate__(self): state = self.__dict__.copy() state.pop("_known_domains", None) return state def __setstate__(self, state): self.__dict__.update(state) self._known_domains = weakref.WeakKeyDictionary()
[docs] def index(self, var): """ Return the index of the given variable or meta attribute, represented with an instance of :class:`Variable`, `int` or `str`. """ try: return self._indices[var] except KeyError: raise ValueError("'%s' is not in domain" % var)
[docs] def has_discrete_attributes(self, include_class=False, include_metas=False): """ Return `True` if domain has any discrete attributes. If `include_class` is set, the check includes the class attribute(s). If `include_metas` is set, the check includes the meta attributes. """ vars = self.variables if include_class else self.attributes vars += self.metas if include_metas else () return any(var.is_discrete for var in vars)
[docs] def has_continuous_attributes(self, include_class=False, include_metas=False): """ Return `True` if domain has any continuous attributes. If `include_class` is set, the check includes the class attribute(s). If `include_metas` is set, the check includes the meta attributes. """ vars = self.variables if include_class else self.attributes vars += self.metas if include_metas else () return any(var.is_continuous for var in vars)
def has_time_attributes(self, include_class=False, include_metas=False): """ Return `True` if domain has any time attributes. If `include_class` is set, the check includes the class attribute(s). If `include_metas` is set, the check includes the meta attributes. """ vars = self.variables if include_class else self.attributes vars += self.metas if include_metas else () return any(var.is_time for var in vars) @property def has_continuous_class(self): return bool(self.class_var and self.class_var.is_continuous) @property def has_discrete_class(self): return bool(self.class_var and self.class_var.is_discrete) @property def has_time_class(self): return bool(self.class_var and self.class_var.is_time) def get_conversion(self, source): """ Return an instance of :class:`DomainConversion` for conversion from the given source domain to this domain. Domain conversions are cached to speed-up the conversion in the common case in which the domain is based on another domain, for instance, when the domain contains discretized variables from another domain. :param source: the source domain :type source: """ # the method is thread-safe c = self._last_conversion if c and c.source is source: return c c = self._known_domains.get(source, None) if not c: c = DomainConversion(source, self) self._known_domains[source] = self._last_conversion = c return c # noinspection PyProtectedMember def convert(self, inst): """ Convert a data instance from another domain to this domain. :param inst: The data instance to be converted :return: The data instance in this domain """ from .instance import Instance if isinstance(inst, Instance): if inst.domain == self: return inst._x, inst._y, inst._metas c = self.get_conversion(inst.domain) l = len(inst.domain.attributes) values = [(inst._x[i] if 0 <= i < l else inst._y[i - l] if i >= l else inst._metas[-i - 1]) if isinstance(i, int) else (Unknown if not i else i(inst)) for i in c.variables] metas = [(inst._x[i] if 0 <= i < l else inst._y[i - l] if i >= l else inst._metas[-i - 1]) if isinstance(i, int) else (Unknown if not i else i(inst)) for i in c.metas] else: nvars = len(self._variables) nmetas = len(self._metas) if len(inst) != nvars and len(inst) != nvars + nmetas: raise ValueError("invalid data length for domain") values = [var.to_val(val) for var, val in zip(self._variables, inst)] if len(inst) == nvars + nmetas: metas = [var.to_val(val) for var, val in zip(self._metas, inst[nvars:])] else: metas = [var.Unknown for var in self._metas] nattrs = len(self.attributes) # Let np.array decide dtype for values return np.array(values[:nattrs]), np.array(values[nattrs:]),\ np.array(metas, dtype=object) def select_columns(self, col_idx): attributes, col_indices = self._compute_col_indices(col_idx) if attributes is not None: n_attrs = len(self.attributes) r_attrs = [attributes[i] for i, col in enumerate(col_indices) if 0 <= col < n_attrs] r_classes = [attributes[i] for i, col in enumerate(col_indices) if col >= n_attrs] r_metas = [attributes[i] for i, col in enumerate(col_indices) if col < 0] return Domain(r_attrs, r_classes, r_metas) else: return self def _compute_col_indices(self, col_idx): if col_idx is ...: return None, None if isinstance(col_idx, np.ndarray) and col_idx.dtype == bool: return ([attr for attr, c in zip(self, col_idx) if c], np.nonzero(col_idx)) elif isinstance(col_idx, slice): s = len(self.variables) start, end, stride = col_idx.indices(s) if col_idx.indices(s) == (0, s, 1): return None, None else: return (self[col_idx], np.arange(start, end, stride)) elif isinstance(col_idx, Iterable) and not isinstance(col_idx, str): attributes = [self[col] for col in col_idx] if attributes == self.attributes: return None, None return attributes, np.fromiter( (self.index(attr) for attr in attributes), int) elif isinstance(col_idx, Integral): attr = self[col_idx] else: attr = self[col_idx] col_idx = self.index(attr) return [attr], np.array([col_idx]) def checksum(self): return hash(self) def copy(self): """ Make a copy of the domain. New features are proxies of the old ones, hence the new domain can be used anywhere the old domain was used. Returns: Domain: a copy of the domain. """ return Domain( attributes=[a.make_proxy() for a in self.attributes], class_vars=[a.make_proxy() for a in self.class_vars], metas=[a.make_proxy() for a in self.metas], source=self, ) def __eq__(self, other): if not isinstance(other, Domain): return False return (self.attributes == other.attributes and self.class_vars == other.class_vars and self.metas == other.metas) def __hash__(self): return self._hash