Source code for getml.pipeline.tables

# Copyright 2022 The SQLNet Company GmbH
# This file is licensed under the Elastic License 2.0 (ELv2).
# Refer to the LICENSE.txt file in the root of the repository
# for details.

Contains custom class for handling the tables of a pipeline.

from __future__ import annotations

import re
from typing import Sequence, Optional, Iterator, Union, Tuple, Callable, Any

import numpy as np
from numpy.typing import NDArray
import pandas as pd  # type: ignore

from getml.utilities.formatting import _Formatter

from .table import Table
from .columns import Columns

[docs]class Tables: """ This container holds a pipeline's tables. These tables are build from the columns for which importances can be calculated. The motivation behind this container is to determine which tables are more important than others. Tables can be accessed by name, index or with a NumPy array. The container supports slicing and can be sorted and filtered. Further, the container holds global methods to request tables' importances. Note: The container is an iterable. So, in addition to :meth:`~getml.pipeline.Tables.filter` you can also use python list comprehensions for filtering. Example: .. code-block:: python all_my_tables = my_pipeline.tables first_table = my_pipeline.tables[0] all_but_last_10_tables = my_pipeline.tables[:-10] important_tables = [table for table in my_pipeline.tables if table.importance > 0.1] names, importances = my_pipeline.tables.importances() """ def __init__( self, targets: Sequence[str], columns: Columns, data: Optional[Sequence[Table]] = None, ) -> None: self._targets = targets self._columns = columns if data is not None: = data else: self._load_tables() if not (targets and columns) and not data: raise ValueError( "Missing required arguments. Either provide `targets` & " "`columns` or else provide `data`." ) # ---------------------------------------------------------------- def __getitem__( self, key: Union[str, int, slice, Union[NDArray[np.int_], NDArray[np.bool_]]] ) -> Union[Table, Tables, list[Table]]: if not raise AttributeError("Tables container not fully initialized.") if isinstance(key, int): return[key] if isinstance(key, slice): tables_subset =[key] return self._make_tables(tables_subset) if isinstance(key, str): if key in self.names: return [table for table in if == key][0] raise AttributeError(f"No Table with name: {key}") if isinstance(key, np.ndarray): tables_subset = np.array([key].tolist() return self._make_tables(tables_subset) raise TypeError( "Columns can only be indexed by: int, slices, str or np.ndarray," f" not {type(key).__name__}" ) # ---------------------------------------------------------------- def __iter__(self) -> Iterator[Table]: yield from # ---------------------------------------------------------------- def __len__(self) -> int: return len( # ---------------------------------------------------------------- def __repr__(self) -> str: return self._format()._render_string() # ------------------------------------------------------------ def _repr_html_(self) -> str: return self._format()._render_html() # ---------------------------------------------------------------- def _format(self) -> _Formatter: headers = [["name", "importance", "target", "marker"]] rows = [ [, table.importance,, table.marker, ] for table in ] return _Formatter(headers, rows) # ---------------------------------------------------------------- def _load_tables(self) -> None: """ Gets tables data from columns """ tables = [] for table_target in self._targets: importances: dict[str, float] = { column.table: 0.0 for column in self._columns if == table_target } targets: dict[str, str] = {} markers: dict[str, str] = {} for column in self._columns: if == table_target: importances[column.table] += column.importance targets[column.table] = markers[column.table] = column.marker tables_zip = zip( importances.keys(), importances.values(), targets.values(), markers.values(), ) for name, importance, target, marker in tables_zip: tables.append( Table( name=name, importance=importance, target=target, marker=marker ) ) = tables # ---------------------------------------------------------------- def _make_tables(self, data: Sequence[Table]) -> Tables: """ A factory to construct a :class:`getml.pipeline.Tables` container from a list of :class:`getml.pipeline.Table`s. """ return Tables(self._targets, self._columns, data=data) # ----------------------------------------------------------------
[docs] def filter(self, conditional: Callable[[Table], bool]) -> Tables: """ Filters the tables container. Args: conditional (callable, optional): A callable that evaluates to a boolean for a given item. Return: :class:`getml.pipeline.Tables`: A container of filtered tables. Example: .. code-block:: python important_tables = my_pipeline.table.filter( lambda table: table.importance > 0.1) peripheral_tables = my_pipeline.tables.filter( lambda table: table.marker == "[PERIPHERAL]") """ tables_filtered = [table for table in if conditional(table)] return self._make_tables(tables_filtered)
# ----------------------------------------------------------------
[docs] def importances( self, target_num: int = 0, sort: bool = True ) -> Tuple[NDArray[np.str_], NDArray[np.float_]]: """ Returns the importances of tables. Table importances are calculated by summing up the importances of the columns belonging to the tables. Each column is assigned an importance value that measures its contribution to the predictive performance. For each target, the importances add up to 1. Args: target_num (int): Indicates for which target you want to view the importances. (Pipelines can have more than one target.) sort (bool): Whether you want the results to be sorted. Return: (:class:`numpy.ndarray`, :class:`numpy.ndarray`): - The first array contains the names of the tables. - The second array contains their importances. By definition, all importances add up to 1. """ target_name = self._targets[target_num] names = np.empty(0, dtype=str) importances = np.empty(0, dtype=float) for table in if == target_name: names = np.append(names, importances = np.append(importances, table.importance) if not sort: return names, importances indices = np.argsort(importances)[::-1] return (names[indices], importances[indices])
# ---------------------------------------------------------------- @property def names(self) -> list[str]: """ Holds the names of a :class:`~getml.Pipeline`\'s tables. Returns: :class:`list` containing the names. Note: The order corresponds to the current sorting of the container. """ return [ for table in] # ----------------------------------------------------------------
[docs] def sort( self, by: Optional[str] = None, key: Optional[Callable[[Table], Any]] = None, descending: Optional[bool] = None, ) -> Tables: """ Sorts the Tables container. If no arguments are provided the container is sorted by target and name. Args: by (str, optional): The name of field to sort by. Possible fields: - name(s) - importances(s) key (callable, optional): A callable that evaluates to a sort key for a given item. descending (bool, optional): Whether to sort in descending order. Return: :class:`getml.pipeline.Tables`: A container of sorted tables. Example: .. code-block:: python by_importance = my_pipeline.tables.sort(key=lambda table: table.importance) """ reverse = False if descending is None else descending if (by is not None) and (key is not None): raise ValueError("Only one of `by` and `key` can be provided.") if key is not None: tables_sorted = sorted(, key=key, reverse=reverse) return self._make_tables(tables_sorted) if by is None: tables_sorted = sorted(, key=lambda table:, reverse=reverse ) tables_sorted.sort(key=lambda table: return self._make_tables(tables_sorted) if re.match(pattern="names?$", string=by): tables_sorted = sorted(, key=lambda table:, reverse=reverse ) return self._make_tables(tables_sorted) if re.match(pattern="importances?$", string=by): reverse = True if descending is None else descending tables_sorted = sorted(, key=lambda table: table.importance, reverse=reverse ) return self._make_tables(tables_sorted) raise ValueError(f"Cannot sort by: {by}.")
# ---------------------------------------------------------------- @property def targets(self) -> list[str]: """ Holds the targets of a :class:`~getml.Pipeline`\'s tables. Returns: :class:`list` containing the names. Note: The order corresponds to the current sorting of the container. """ return [ for table in] # ----------------------------------------------------------------
[docs] def to_pandas(self) -> pd.DataFrame: """ Returns all information related to the tables in a pandas DataFrame. """ data_frame = pd.DataFrame() for i, table in enumerate( data_frame.loc[i, "name"] = data_frame.loc[i, "importance"] = table.importance data_frame.loc[i, "target"] = data_frame.loc[i, "marker"] = table.marker return data_frame