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"""
Custom class for handling the columns of a pipeline.
"""
import json
import numbers
import re
import numpy as np
import pandas as pd
import getml.communication as comm
from getml.data.helpers import _is_typed_list
from getml.utilities.formatting import _Formatter
from .column import Column
from .helpers import _check_df_types, _set_unused, _transform_peripheral
[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()
# Sets all categorical and numerical columns that are not
# in the top 20% to unused.
my_pipeline.columns.select(
population_table,
peripheral_tables,
share_selected_columns=0.2
)
"""
# ----------------------------------------------------------------
def __init__(self, pipeline, targets, peripheral, data=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 = [p.name for p in self.peripheral]
if data is None:
self.data = self._load_columns()
else:
self.data = data
# ----------------------------------------------------------------
def __getitem__(self, key):
if isinstance(key, int):
return self.data[key]
if isinstance(key, slice):
columns_subset = self.data[key]
return self._make_columns(columns_subset)
if isinstance(key, str):
if key in self.names:
return [column for column in self.data if column.name == key][0]
raise AttributeError(f"No Column with name: {key}")
if isinstance(key, np.ndarray):
columns_subset = np.array(self.data)[key].tolist()
return columns_subset
raise TypeError(
f"Columns can only be indexed by: int, slices, or str, not {type(key).__name__}"
)
# ----------------------------------------------------------------
def __repr__(self):
return self._format()._render_string()
# ------------------------------------------------------------
def _repr_html_(self):
return self._format()._render_html()
# ----------------------------------------------------------------
def _get_column_importances(self, target_num, sort):
cmd = dict()
cmd["type_"] = "Pipeline.column_importances"
cmd["name_"] = self.pipeline
cmd["target_num_"] = target_num
# ------------------------------------------------------------
sock = comm.send_and_receive_socket(cmd)
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 self.data
if column.target == 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):
rows = [
[
column.name,
column.marker,
column.table,
column.importance,
column.target,
]
for column in self.data
]
headers = [
[
"name",
"marker",
"table",
"importance",
"target",
]
]
return _Formatter(headers, rows)
# ----------------------------------------------------------------
def _load_columns(self):
"""
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)
]
)
return columns
# ----------------------------------------------------------------
def _make_columns(self, data):
"""
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):
"""
Pivots the data for a given field. Returns a list of values of the field's type.
"""
return [getattr(column, field) for column in self.data]
# ----------------------------------------------------------------
[docs] def filter(self, conditional):
"""
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 self.data if conditional(column)]
return self._make_columns(columns_filtered)
# ----------------------------------------------------------------
[docs] def importances(self, target_num=0, sort=True):
"""
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):
"""
Holds the names of a :class:`~getml.pipeline.Pipeline`\'s columns.
Returns:
:class:`list` containing the names.
Note:
The order corresponds to the current sorting of the container.
"""
return [column.name for column in self.data]
# ----------------------------------------------------------------
[docs] def select(
self, population_table, peripheral_tables=None, share_selected_columns=0.5
):
"""
Sets all categorical or numerical columns that are not
sufficiently important to unused.
Args:
population_table (:class:`getml.data.DataFrame`):
Main table containing the target variable(s) and
corresponding to the ``population``
:class:`~getml.data.Placeholder` instance
variable.
peripheral_tables (List[:class:`getml.data.DataFrame`] or dict):
Additional tables corresponding to the ``peripheral``
:class:`~getml.data.Placeholder` instance
variable.
share_selected_columns(numerical): The share of columns
to keep. Must be between 0.0 and 1.0.
"""
# ------------------------------------------------------------
peripheral_tables = _transform_peripheral(peripheral_tables, self.peripheral)
_check_df_types(population_table, peripheral_tables)
# ------------------------------------------------------------
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!")
# ------------------------------------------------------------
if peripheral_tables and len(peripheral_tables) != len(self.peripheral_names):
raise ValueError(
"""There must be exactly
one peripheral table for
every peripheral placeholder!"""
)
# ------------------------------------------------------------
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]
# ------------------------------------------------------------
if peripheral_tables:
for data_frame, table in zip(peripheral_tables, self.peripheral_names):
_set_unused(data_frame, keep_columns, table, "[PERIPHERAL]")
_set_unused(population_table, keep_columns, None, "[POPULATION]")
# ----------------------------------------------------------------
[docs] def sort(self, by=None, key=None, descending=None):
"""
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(self.data, key=key, reverse=reverse)
return self._make_columns(columns_sorted)
if by is None:
columns_sorted = sorted(
self.data, key=lambda column: column.name, reverse=reverse
)
columns_sorted.sort(key=lambda column: column.target)
return self._make_columns(columns_sorted)
if re.match(by, "names?"):
columns_sorted = sorted(
self.data, key=lambda column: column.name, reverse=reverse
)
return self._make_columns(columns_sorted)
if re.match(by, "tables?"):
columns_sorted = sorted(
self.data,
key=lambda column: column.table,
)
return self._make_columns(columns_sorted)
if re.match(by, "importances?"):
reverse = descending or True
columns_sorted = sorted(
self.data, key=lambda column: column.importance, reverse=reverse
)
return self._make_columns(columns_sorted)
raise ValueError(f"Cannot sort by: {by}.")
# ----------------------------------------------------------------
[docs] def to_pandas(self):
"""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(self.data)))
data_frame["name"] = names
data_frame["marker"] = markers
data_frame["table"] = tables
data_frame["importance"] = importances
data_frame["target"] = targets
return data_frame
# ----------------------------------------------------------------