# 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 plots of a pipeline.
"""
import json
from typing import Any, Dict, Tuple
import numpy as np
import getml.communication as comm
[docs]class Plots:
"""
Custom class for handling the
plots generated by the pipeline.
Example:
.. code-block:: python
recall, precision = my_pipeline.plots.precision_recall_curve()
fpr, tpr = my_pipeline.plots.roc_curve()
"""
# ----------------------------------------------------------------
def __init__(self, name: str) -> None:
if not isinstance(name, str):
raise ValueError("'name' must be a str.")
self.name = name
# ------------------------------------------------------------
[docs] def lift_curve(self, target_num: int = 0) -> Tuple[np.ndarray, np.ndarray]:
"""
Returns the data for the lift curve, as displayed in the getML monitor.
This requires that you call
:meth:`~getml.Pipeline.score` first. The data used
for the curve will always be the data from the *last* time
you called :meth:`~getml.Pipeline.score`.
Args:
target_num (int):
Indicates for which target you want to plot the lift
curve. (Pipelines can have more than one target.)
Return:
(:class:`numpy.ndarray`, :class:`numpy.ndarray`):
- The first array is the proportion of samples, usually
displayed on the x-axis.
- The second array is the lift, usually
displayed on the y-axis.
"""
cmd: Dict[str, Any] = {}
cmd["type_"] = "Pipeline.lift_curve"
cmd["name_"] = self.name
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)
return (np.asarray(json_obj["proportion_"]), np.asarray(json_obj["lift_"]))
# ------------------------------------------------------------
[docs] def precision_recall_curve(
self, target_num: int = 0
) -> Tuple[np.ndarray, np.ndarray]:
"""
Returns the data for the precision-recall curve, as displayed in the getML
monitor.
This requires that you call
:meth:`~getml.Pipeline.score` first. The data used
for the curve will always be the data from the *last* time
you called :meth:`~getml.Pipeline.score`.
Args:
target_num (int):
Indicates for which target you want to plot the lift
curve. (Pipelines can have more than one target.)
Return:
(:class:`numpy.ndarray`, :class:`numpy.ndarray`):
- The first array is the recall (a.k.a. true postive rate),
usually displayed on the x-axis.
- The second array is the precision, usually
displayed on the y-axis.
"""
cmd: Dict[str, Any] = {}
cmd["type_"] = "Pipeline.precision_recall_curve"
cmd["name_"] = self.name
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)
return (np.asarray(json_obj["tpr_"]), np.asarray(json_obj["precision_"]))
# ------------------------------------------------------------
[docs] def roc_curve(self, target_num: int = 0) -> Tuple[np.ndarray, np.ndarray]:
"""
Returns the data for the ROC curve, as displayed in the getML monitor.
This requires that you call
:meth:`~getml.Pipeline.score` first. The data used
for the curve will always be the data from the *last* time
you called :meth:`~getml.Pipeline.score`.
Args:
target_num (int):
Indicates for which target you want to plot the lift
curve. (Pipelines can have more than one target.)
Return:
(:class:`numpy.ndarray`, :class:`numpy.ndarray`):
- The first array is the false positive rate, usually
displayed on the x-axis.
- The second array is the true positive rate, usually
displayed on the y-axis.
"""
cmd: Dict[str, Any] = {}
cmd["type_"] = "Pipeline.roc_curve"
cmd["name_"] = self.name
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)
return (np.asarray(json_obj["fpr_"]), np.asarray(json_obj["tpr_"]))