Source code for Orange.preprocess.remove

from collections import namedtuple

import numpy as np

from import Domain, DiscreteVariable, Table
from Orange.preprocess.transformation import Lookup
from Orange.statistics.util import nanunique
from .preprocess import Preprocess

__all__ = ["Remove"]

[docs]class Remove(Preprocess): """ Construct a preprocessor for removing constant features/classes and unused values. Given a data table, preprocessor returns a new table and a list of results. In the new table, the constant features/classes and unused values are removed. The list of results consists of two dictionaries. The first one contains numbers of 'removed', 'reduced' and 'sorted' features. The second one contains numbers of 'removed', 'reduced' and 'sorted' features. Parameters ---------- attr_flags : int (default: 0) If SortValues, values of discrete attributes are sorted. If RemoveConstant, unused attributes are removed. If RemoveUnusedValues, unused values are removed from discrete attributes. It is possible to merge operations in one by summing several types. class_flags: int (default: 0) If SortValues, values of discrete class attributes are sorted. If RemoveConstant, unused class attributes are removed. If RemoveUnusedValues, unused values are removed from discrete class attributes. It is possible to merge operations in one by summing several types. Examples -------- >>> from import Table >>> from Orange.preprocess import Remove >>> data = Table("zoo")[:10] >>> flags = sum([Remove.SortValues, Remove.RemoveConstant, Remove.RemoveUnusedValues]) >>> remover = Remove(attr_flags=flags, class_flags=flags) >>> new_data = remover(data) >>> attr_results, class_results = remover.attr_results, remover.class_results """ SortValues, RemoveConstant, RemoveUnusedValues = 1, 2, 4 def __init__(self, attr_flags=0, class_flags=0, meta_flags=0): self.attr_flags = attr_flags self.class_flags = class_flags self.meta_flags = meta_flags self.attr_results = None self.class_results = None self.meta_results = None def __call__(self, data): """ Removes unused features or classes from the given data. Returns a new data table. Parameters ---------- data : A data table to remove features or classes from. Returns ------- data : New data table. """ if data is None: return None domain = data.domain attrs_state = [purge_var_M(var, data, self.attr_flags) for var in domain.attributes] class_state = [purge_var_M(var, data, self.class_flags) for var in domain.class_vars] metas_state = [purge_var_M(var, data, self.meta_flags) for var in domain.metas] att_vars, self.attr_results = self.get_vars_and_results(attrs_state) cls_vars, self.class_results = self.get_vars_and_results(class_state) meta_vars, self.meta_results = self.get_vars_and_results(metas_state) domain = Domain(att_vars, cls_vars, meta_vars) return data.transform(domain) def get_vars_and_results(self, state): removed, reduced, sorted = 0, 0, 0 vars = [] for st in state: removed += is_removed(st) reduced += not is_removed(st) and is_reduced(st) sorted += not is_removed(st) and is_sorted(st) if not is_removed(st): vars.append(merge_transforms(st).var) res = {'removed': removed, 'reduced': reduced, 'sorted': sorted} return vars, res
# Define a simple Purge expression 'language'. #: A input variable (leaf expression). Var = namedtuple("Var", ["var"]) #: Removed variable (can only ever be present as a root node). Removed = namedtuple("Removed", ["sub", "var"]) #: A reduced variable Reduced = namedtuple("Reduced", ["sub", "var"]) #: A sorted variable Sorted = namedtuple("Sorted", ["sub", "var"]) #: A general (lookup) transformed variable. #: (this node is returned as a result of `merge` which joins consecutive #: Removed/Reduced nodes into a single Transformed node) Transformed = namedtuple("Transformed", ["sub", "var"]) def is_var(exp): """Is `exp` a `Var` node.""" return isinstance(exp, Var) def is_removed(exp): """Is `exp` a `Removed` node.""" return isinstance(exp, Removed) def _contains(exp, cls): """Does `node` contain a sub node of type `cls`""" if isinstance(exp, cls): return True elif isinstance(exp, Var): return False else: return _contains(exp.sub, cls) def is_reduced(exp): """Does `exp` contain a `Reduced` node.""" return _contains(exp, Reduced) def is_sorted(exp): """Does `exp` contain a `Reduced` node.""" return _contains(exp, Sorted) def merge_transforms(exp): """ Merge consecutive Removed, Reduced or Transformed nodes. .. note:: Removed nodes are returned unchanged. """ if isinstance(exp, (Var, Removed)): return exp elif isinstance(exp, (Reduced, Sorted, Transformed)): prev = merge_transforms(exp.sub) if isinstance(prev, (Reduced, Sorted, Transformed)): B = exp.var.compute_value assert isinstance(B, Lookup) A = B.variable.compute_value assert isinstance(A, Lookup) new_var = DiscreteVariable(, values=exp.var.values, ordered=exp.var.ordered, compute_value=merge_lookup(A, B), sparse=exp.var.sparse, ) assert isinstance(prev.sub, Var) return Transformed(prev.sub, new_var) else: assert prev is exp.sub return exp else: raise TypeError def purge_var_M(var, data, flags): state = Var(var) if flags & Remove.RemoveConstant: var = remove_constant(state.var, data) if var is None: return Removed(state, state.var) if state.var.is_discrete: if flags & Remove.RemoveUnusedValues: newattr = remove_unused_values(state.var, data) if newattr is not state.var: state = Reduced(state, newattr) if flags & Remove.RemoveConstant and len(state.var.values) < 2: return Removed(state, state.var) if flags & Remove.SortValues: newattr = sort_var_values(state.var) if newattr is not state.var: state = Sorted(state, newattr) return state def has_at_least_two_values(data, var): ((dist, unknowns),) = data._compute_distributions([var]) # TODO this check is suboptimal for sparse since get_column_view # densifies the data. Should be irrelevant after Pandas. _, sparse = data.get_column_view(var) if var.is_continuous: dist = dist[1, :] min_size = 0 if sparse and unknowns else 1 return np.sum(dist > 0.0) > min_size def remove_constant(var, data): if var.is_continuous: if not has_at_least_two_values(data, var): return None else: return var elif var.is_discrete: if len(var.values) < 2: return None else: return var else: return var def remove_unused_values(var, data): column_data = Table.from_table( Domain([var]), data ) unique = nanunique(column_data.X).astype(int) if len(unique) == len(var.values): return var used_values = [var.values[i] for i in unique] translation_table = np.array([np.NaN] * len(var.values)) translation_table[unique] = range(len(used_values)) base_value = -1 if 0 >= var.base_value < len(var.values): base = translation_table[var.base_value] if np.isfinite(base): base_value = int(base) return DiscreteVariable("{}".format(, values=used_values, base_value=base_value, compute_value=Lookup(var, translation_table), sparse=var.sparse) def sort_var_values(var): newvalues = list(sorted(var.values)) if newvalues == list(var.values): return var translation_table = np.array( [float(newvalues.index(value)) for value in var.values] ) return DiscreteVariable(, values=newvalues, compute_value=Lookup(var, translation_table), sparse=var.sparse) def merge_lookup(A, B): """ Merge two consecutive Lookup transforms into one. """ lookup_table = np.array(A.lookup_table) mask = np.isfinite(lookup_table) indices = np.array(lookup_table[mask], dtype=int) lookup_table[mask] = B.lookup_table[indices] return Lookup(A.variable, lookup_table)