Source code for getml.pipeline.columns

# 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.

Custom class for handling the columns of a pipeline.

from __future__ import annotations

import json
import numbers
import re
from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union

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

import getml.communication as comm
from import Container, StarSchema, TimeSeries
from import _is_typed_list
from import Placeholder
from getml.utilities.formatting import _Formatter

from .column import Column
from .helpers import PERIPHERAL, POPULATION, _drop

[docs]class Columns: """ Container which holds a pipeline's columns. Columns can be accessed by name, index or with a numpy array. The container supports slicing and is sort- and filterable. Further, the container holds global methods to request columns' importances and apply a column selection to data frames provided to the pipeline. Note: The container is an iterable. So, in addition to :meth:`~getml.pipeline.Columns.filter` you can also use python list comprehensions for filtering. Example: .. code-block:: python all_my_columns = my_pipeline.columns first_column = my_pipeline.columns[0] all_but_last_10_columns = my_pipeline.columns[:-10] important_columns = [column for column in my_pipeline.columns if column.importance > 0.1] names, importances = my_pipeline.columns.importances() # Drops all categorical and numerical columns that are not # in the top 20%. new_container = container, share_selected_columns=0.2, ) """ # ---------------------------------------------------------------- def __init__( self, pipeline: str, targets: Sequence[str], peripheral: Sequence[Placeholder], data: Optional[Sequence[Column]] = None, ) -> None: if not isinstance(pipeline, str): raise ValueError("'pipeline' must be a str.") if not _is_typed_list(targets, str): raise TypeError("'targets' must be a list of str.") self.pipeline = pipeline self.targets = targets self.peripheral = peripheral self.peripheral_names = [ for p in self.peripheral] if data is not None: = data else: self._load_columns() # ---------------------------------------------------------------- def __getitem__( self, key: Union[str, int, slice, Union[NDArray[np.int_], NDArray[np.str_]]] ) -> Union[Column, Columns, List[Column]]: if not raise AttributeError("Columns container not fully initialised.") if isinstance(key, int): return[key] if isinstance(key, slice): columns_subset =[key] return self._make_columns(columns_subset) if isinstance(key, str): if key in self.names: return [column for column in if == key][0] raise AttributeError(f"No Column with name: {key}") if isinstance(key, np.ndarray): columns_subset = np.array([key].tolist() return list(columns_subset) raise TypeError( "Columns can only be indexed by: int, slices, or str," f" not {type(key).__name__}" ) # ---------------------------------------------------------------- def __repr__(self) -> str: return self._format()._render_string() # ------------------------------------------------------------ def _repr_html_(self) -> str: return self._format()._render_html() # ---------------------------------------------------------------- def _get_column_importances( self, target_num: int, sort: bool ) -> Tuple[NDArray[np.str_], NDArray[np.float_]]: cmd: Dict[str, Any] = {} cmd["type_"] = "Pipeline.column_importances" cmd["name_"] = self.pipeline cmd["target_num_"] = target_num with comm.send_and_get_socket(cmd) as sock: msg = comm.recv_string(sock) if msg != "Success!": comm.engine_exception_handler(msg) msg = comm.recv_string(sock) json_obj = json.loads(msg) descriptions = np.asarray(json_obj["column_descriptions_"]) importances = np.asarray(json_obj["column_importances_"]) if hasattr(self, "data"): indices = np.asarray( [ column.index for column in if == self.targets[target_num] and column.index < len(importances) ] ) descriptions = descriptions[indices] importances = importances[indices] if not sort: return descriptions, importances indices = np.argsort(importances)[::-1] return (descriptions[indices], importances[indices]) # ---------------------------------------------------------------- def _format(self) -> _Formatter: rows = [ [, column.marker, column.table, column.importance,, ] for column in ] headers = [ [ "name", "marker", "table", "importance", "target", ] ] return _Formatter(headers, rows) # ---------------------------------------------------------------- def _load_columns(self) -> None: """ Loads the actual column data from the engine. """ columns = [] for target_num, target in enumerate(self.targets): descriptions, importances = self._get_column_importances( target_num=target_num, sort=False ) columns.extend( [ Column( index=index, name=description.get("name_"), marker=description.get("marker_"), table=description.get("table_"), importance=importances[index], target=target, ) for index, description in enumerate(descriptions) ] ) = columns # ---------------------------------------------------------------- def _make_columns(self, data: Sequence[Column]) -> Columns: """ A factory to construct a :class:`getml.pipeline.Columns` container from a list of :class:`getml.pipeline.Column`s. """ return Columns(self.pipeline, self.targets, self.peripheral, data) # ---------------------------------------------------------------- def _pivot(self, field: str) -> Any: """ Pivots the data for a given field. Returns a list of values of the field's type. """ return [getattr(column, field) for column in] # ----------------------------------------------------------------
[docs] def filter(self, conditional: Callable[[Column], bool]) -> Columns: """ Filters the columns container. Args: conditional (callable, optional): A callable that evaluates to a boolean for a given item. Return: :class:`getml.pipeline.Column`: A container of filtered Columns. Example: .. code-block:: python important_columns = my_pipeline.columns.filter(lambda column: column.importance > 0.1) peripheral_columns = my_pipeline.columns.filter(lambda column: column.marker == "[PERIPHERAL]") """ columns_filtered = [column for column in if conditional(column)] return self._make_columns(columns_filtered)
# ----------------------------------------------------------------
[docs] def importances( self, target_num: int = 0, sort: bool = True ) -> Tuple[NDArray[np.str_], NDArray[np.float_]]: """ Returns the data for the column importances. Column importances extend the idea of column importances to the columns originally inserted into the pipeline. Each column is assigned an importance value that measures its contribution to the predictive performance. All columns 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 columns. - The second array contains their importances. By definition, all importances add up to 1. """ # ------------------------------------------------------------ descriptions, importances = self._get_column_importances( target_num=target_num, sort=sort ) # ------------------------------------------------------------ names = np.asarray( [d["marker_"] + " " + d["table_"] + "." + d["name_"] for d in descriptions] ) # ------------------------------------------------------------ return names, importances
# ---------------------------------------------------------------- @property def names(self) -> List[str]: """ Holds the names of a :class:`~getml.Pipeline`\'s columns. Returns: :class:`list` containing the names. Note: The order corresponds to the current sorting of the container. """ return [ for column in] # ----------------------------------------------------------------
[docs] def select( self, container: Container, share_selected_columns: float = 0.5 ) -> Container: """ Returns a new data container with all insufficiently important columns dropped. Args: container (:class:`` or :class:`` or :class:``): The container containing the data you want to use. share_selected_columns(numerical): The share of columns to keep. Must be between 0.0 and 1.0. """ # ------------------------------------------------------------ if isinstance(container, (StarSchema, TimeSeries)): data = container.container, share_selected_columns=share_selected_columns ) new_container = deepcopy(container) new_container._container = data return new_container # ------------------------------------------------------------ if not isinstance(container, Container): raise TypeError( "'container' must be a, " + "a or a" ) if not isinstance(share_selected_columns, numbers.Real): raise TypeError("'share_selected_columns' must be a real number!") if share_selected_columns < 0.0 or share_selected_columns > 1.0: raise ValueError("'share_selected_columns' must be between 0 and 1!") # ------------------------------------------------------------ descriptions, _ = self._get_column_importances(target_num=-1, sort=True) # ------------------------------------------------------------ num_keep = int(np.ceil(share_selected_columns * len(descriptions))) keep_columns = descriptions[:num_keep] # ------------------------------------------------------------ subsets = { k: _drop(v, keep_columns, k, POPULATION) for (k, v) in container.subsets.items() } peripheral = { k: _drop(v, keep_columns, k, PERIPHERAL) for (k, v) in container.peripheral.items() } # ------------------------------------------------------------ new_container = Container(**subsets) new_container.add(**peripheral) new_container.freeze() # ------------------------------------------------------------ return new_container
# ----------------------------------------------------------------
[docs] def sort( self, by: Optional[str] = None, key: Optional[Callable[[Column], Any]] = None, descending: bool = None, ) -> Columns: """ Sorts the Columns 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) - table(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.columns`: A container of sorted columns. Example: .. code-block:: python by_importance = my_pipeline.columns.sort(key=lambda column: column.importance) """ reverse = descending or False 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: columns_sorted = sorted(, key=key, reverse=reverse) return self._make_columns(columns_sorted) if by is None: columns_sorted = sorted(, key=lambda column:, reverse=reverse ) columns_sorted.sort(key=lambda column: return self._make_columns(columns_sorted) if re.match(by, "names?"): columns_sorted = sorted(, key=lambda column:, reverse=reverse ) return self._make_columns(columns_sorted) if re.match(by, "tables?"): columns_sorted = sorted(, key=lambda column: column.table, ) return self._make_columns(columns_sorted) if re.match(by, "importances?"): reverse = descending or True columns_sorted = sorted(, key=lambda column: column.importance, reverse=reverse ) return self._make_columns(columns_sorted) raise ValueError(f"Cannot sort by: {by}.")
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[docs] def to_pandas(self) -> pd.DataFrame: """Returns all information related to the columns in a pandas data frame.""" names, markers, tables, importances, targets = ( self._pivot(field) for field in ["name", "marker", "table", "importance", "target"] ) data_frame = pd.DataFrame(index=np.arange(len( data_frame["name"] = names data_frame["marker"] = markers data_frame["table"] = tables data_frame["importance"] = importances data_frame["target"] = targets return data_frame
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