.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/recipies/_05_optimization_info.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_recipies__05_optimization_info.py: .. _opti_info: Optimization Info ================= Tpcp is focused on "running" pipelines and less on the "optimization" step. This is great for traditional algorithms and algorithms will complex return values, as you can easily store multiple parameters as attributes on the object during `run`. However, for optimization, you are limited to modifying input parameters. This works well in many cases, but sometimes, you need additional information from the optimization. For example, you might want to extract the loss decay of an iterative learning algorithms. This information is something that you wouldn't want to store in the input parameters (usually). For these cases tpcp provides the `self_optimize_with_info` method. This is basically identical to `self_optimize`, but is expected to provide two return values: the optimized instance AND an arbitrary additional object containing any information you like. Methods that get optimizable pipelines as input (e.g. :class:`~tpcp.optimize.Optimize` are aware of these method and will call `self_optimize_with_info` if available and store the additional info as result objects. The :class:`~tpcp.OptimizablePipeline` base-class is implemented in a way that you only need to worry about implementing either the `self_optimize_with_info` or the `self_optimize` method. The other will be available automatically (the additional info will be `NOTHING`, if the method is not implemented). If you are implementing a new Algorithm (instead of a pipeline), we don't provide this additional support, but it is relatively simple to implement yourself. In the following we will show how all of this works by expanding the QRS detection algorithm implemented in the other examples to return additional information from the optimization. .. GENERATED FROM PYTHON SOURCE LINES 32-46 .. code-block:: default import numpy as np import pandas as pd from sklearn.metrics import roc_curve from typing_extensions import Self from examples.algorithms.algorithms_qrs_detection_final import ( QRSDetector, match_events_with_reference, ) from examples.datasets.datasets_final_ecg import ECGExampleData from tpcp import HyperParameter, OptimizableParameter, OptimizablePipeline, Parameter, cf, make_optimize_safe from tpcp.optimize import Optimize .. GENERATED FROM PYTHON SOURCE LINES 47-53 In the algorithm class below, we basically reimplemented the `OptimizableQrsDetector` from the algorithm example. However, instead of the `self_optimize` method, we implemented the `self_optimize_with_info` method and added additional information from the threshold selection process to the output of the optimization. To ensure interface compatibility with other algorithms, we also provided a `self_optimize` method, that simply calls `self_optimize_with_info` under the hood. .. GENERATED FROM PYTHON SOURCE LINES 53-112 .. code-block:: default class OptimizableQrsDetectorWithInfo(QRSDetector): min_r_peak_height_over_baseline: OptimizableParameter[float] r_peak_match_tolerance_s: HyperParameter[float] def __init__( self, max_heart_rate_bpm: float = 200.0, min_r_peak_height_over_baseline: float = 1.0, r_peak_match_tolerance_s: float = 0.01, high_pass_filter_cutoff_hz: float = 1, ): self.r_peak_match_tolerance_s = r_peak_match_tolerance_s super().__init__( max_heart_rate_bpm=max_heart_rate_bpm, min_r_peak_height_over_baseline=min_r_peak_height_over_baseline, high_pass_filter_cutoff_hz=high_pass_filter_cutoff_hz, ) @make_optimize_safe def self_optimize_with_info( self, ecg_data: list[pd.Series], r_peaks: list[pd.Series], sampling_rate_hz: float ) -> tuple[Self, dict[str, np.ndarray]]: all_labels = [] all_peak_heights = [] for d, p in zip(ecg_data, r_peaks): filtered = self._filter(d.to_numpy().flatten(), sampling_rate_hz) # Find all potential peaks without the height threshold potential_peaks = self._search_strategy(filtered, sampling_rate_hz, use_height=False) # Determine the label for each peak, by matching them with our ground truth labels = np.zeros(potential_peaks.shape) matches = match_events_with_reference( events=np.atleast_2d(potential_peaks).T, reference=np.atleast_2d(p.to_numpy().astype(int)).T, tolerance=self.r_peak_match_tolerance_s * sampling_rate_hz, ) tp_matches = matches[(~np.isnan(matches)).all(axis=1), 0].astype(int) labels[tp_matches] = 1 labels = labels.astype(bool) all_labels.append(labels) all_peak_heights.append(filtered[potential_peaks]) all_labels = np.hstack(all_labels) all_peak_heights = np.hstack(all_peak_heights) # We "brute-force" a good cutoff by testing a bunch of thresholds and then calculating the Youden Index for # each. fpr, tpr, thresholds = roc_curve(all_labels, all_peak_heights) youden_index = tpr - fpr # The best Youden index gives us a balance between sensitivity and specificity. self.min_r_peak_height_over_baseline = thresholds[np.argmax(youden_index)] # Here we create the additional infor object: additional_info = {"all_youden_index": youden_index, "all_thresholds": thresholds} return self, additional_info def self_optimize(self, ecg_data: list[pd.Series], r_peaks: list[pd.Series], sampling_rate_hz: float) -> Self: return self.self_optimize_with_info(ecg_data=ecg_data, r_peaks=r_peaks, sampling_rate_hz=sampling_rate_hz)[0] .. GENERATED FROM PYTHON SOURCE LINES 113-121 To use this algorithm in an optimization, we need a pipeline to wrap it. Below we can find a reimplementation of the pipline from the "Optimizable Pipeline" example. However, instead of implementing `self_optimize` method, we implemented the `self_optimize_with_info` method and also called the `self_optimize_with_info` of our algorithm under the hood. Note, that for pipelines, we don't need to implement a dummy `self_optimize` method. Our baseclass already takes care of that. .. GENERATED FROM PYTHON SOURCE LINES 121-151 .. code-block:: default class MyPipeline(OptimizablePipeline[ECGExampleData]): algorithm: Parameter[OptimizableQrsDetectorWithInfo] algorithm__min_r_peak_height_over_baseline: OptimizableParameter[float] r_peak_positions_: pd.Series def __init__(self, algorithm: OptimizableQrsDetectorWithInfo = cf(OptimizableQrsDetectorWithInfo())): self.algorithm = algorithm @make_optimize_safe def self_optimize_with_info(self, dataset: ECGExampleData, **kwargs): ecg_data = [d.data["ecg"] for d in dataset] r_peaks = [d.r_peak_positions_["r_peak_position"] for d in dataset] # Note: We need to clone the algorithm instance, to make sure we don't leak any data between runs. algo = self.algorithm.clone() # Here we call the `self_optimize_with_info` method! self.algorithm, additional_data = algo.self_optimize_with_info(ecg_data, r_peaks, dataset.sampling_rate_hz) return self, additional_data def run(self, datapoint: ECGExampleData): # Note: We need to clone the algorithm instance, to make sure we don't leak any data between runs. algo = self.algorithm.clone() algo.detect(datapoint.data["ecg"], datapoint.sampling_rate_hz) self.r_peak_positions_ = algo.r_peak_positions_ return self .. GENERATED FROM PYTHON SOURCE LINES 152-155 Let's test this class! However, first we need some test data .. GENERATED FROM PYTHON SOURCE LINES 155-170 .. code-block:: default from pathlib import Path from sklearn.model_selection import train_test_split try: HERE = Path(__file__).parent except NameError: HERE = Path().resolve() data_path = HERE.parent.parent / "example_data/ecg_mit_bih_arrhythmia/data" example_data = ECGExampleData(data_path) train_set, test_set = train_test_split(example_data, train_size=0.7, random_state=0) # We only want a single dataset in the test set test_set = test_set[0] .. GENERATED FROM PYTHON SOURCE LINES 171-172 With the train data we can try out the optimization .. GENERATED FROM PYTHON SOURCE LINES 172-175 .. code-block:: default optimized_pipe, info = MyPipeline().self_optimize_with_info(train_set) info .. rst-class:: sphx-glr-script-out .. code-block:: none {'all_youden_index': array([0.00000000e+00, 6.19540301e-05, 1.52512466e-05, 8.86714683e-02, 8.86247656e-02, 1.01263388e-01, 1.01216685e-01, 1.07040364e-01, 1.06993661e-01, 1.07489293e-01, 1.07442590e-01, 1.07752361e-01, 1.07705658e-01, 1.23751752e-01, 1.23705049e-01, 1.24076773e-01, 1.24030070e-01, 1.29915703e-01, 1.29869000e-01, 1.30302678e-01, 1.30255976e-01, 1.31185286e-01, 1.31138583e-01, 1.36714446e-01, 1.36667743e-01, 1.36915559e-01, 1.36868857e-01, 1.39161156e-01, 1.39114453e-01, 1.40973074e-01, 1.40926371e-01, 1.42227406e-01, 1.42180703e-01, 1.43729554e-01, 1.43682851e-01, 1.57932278e-01, 1.57885575e-01, 2.48152597e-01, 2.48105894e-01, 2.48477618e-01, 2.48430916e-01, 2.49917812e-01, 2.49871109e-01, 2.50180880e-01, 2.50134177e-01, 2.51744982e-01, 2.51698279e-01, 2.52441727e-01, 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GENERATED FROM PYTHON SOURCE LINES 176-178 .. code-block:: default optimized_pipe .. rst-class:: sphx-glr-script-out .. code-block:: none MyPipeline(algorithm=OptimizableQrsDetectorWithInfo(high_pass_filter_cutoff_hz=1, max_heart_rate_bpm=200.0, min_r_peak_height_over_baseline=0.5816447455722318, r_peak_match_tolerance_s=0.01)) .. GENERATED FROM PYTHON SOURCE LINES 179-180 But we can also just call the auto-generated `self_optimize` method and don't get the info: .. GENERATED FROM PYTHON SOURCE LINES 180-183 .. code-block:: default optimized_pipe = MyPipeline().self_optimize(train_set) optimized_pipe .. rst-class:: sphx-glr-script-out .. code-block:: none MyPipeline(algorithm=OptimizableQrsDetectorWithInfo(high_pass_filter_cutoff_hz=1, max_heart_rate_bpm=200.0, min_r_peak_height_over_baseline=0.5816447455722318, r_peak_match_tolerance_s=0.01)) .. GENERATED FROM PYTHON SOURCE LINES 184-187 However, in most cases, we should just use the `Optimize` wrapper. It will call the `self_optimize_with_info` method if available (you can force it to use `self_optimize` using the `optimize_with_info` parameter) and then provide the additional info as attribute .. GENERATED FROM PYTHON SOURCE LINES 187-190 .. code-block:: default optimizer = Optimize(MyPipeline()).optimize(train_set) optimizer.optimization_info_ .. rst-class:: sphx-glr-script-out .. code-block:: none {'all_youden_index': array([0.00000000e+00, 6.19540301e-05, 1.52512466e-05, 8.86714683e-02, 8.86247656e-02, 1.01263388e-01, 1.01216685e-01, 1.07040364e-01, 1.06993661e-01, 1.07489293e-01, 1.07442590e-01, 1.07752361e-01, 1.07705658e-01, 1.23751752e-01, 1.23705049e-01, 1.24076773e-01, 1.24030070e-01, 1.29915703e-01, 1.29869000e-01, 1.30302678e-01, 1.30255976e-01, 1.31185286e-01, 1.31138583e-01, 1.36714446e-01, 1.36667743e-01, 1.36915559e-01, 1.36868857e-01, 1.39161156e-01, 1.39114453e-01, 1.40973074e-01, 1.40926371e-01, 1.42227406e-01, 1.42180703e-01, 1.43729554e-01, 1.43682851e-01, 1.57932278e-01, 1.57885575e-01, 2.48152597e-01, 2.48105894e-01, 2.48477618e-01, 2.48430916e-01, 2.49917812e-01, 2.49871109e-01, 2.50180880e-01, 2.50134177e-01, 2.51744982e-01, 2.51698279e-01, 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GENERATED FROM PYTHON SOURCE LINES 191-194 .. code-block:: default optimizer.optimized_pipeline_ .. rst-class:: sphx-glr-script-out .. code-block:: none MyPipeline(algorithm=OptimizableQrsDetectorWithInfo(high_pass_filter_cutoff_hz=1, max_heart_rate_bpm=200.0, min_r_peak_height_over_baseline=0.5816447455722318, r_peak_match_tolerance_s=0.01)) .. GENERATED FROM PYTHON SOURCE LINES 195-197 As `Optimize` is aware of this and stores the info as a result attribute, the information is also available in the output of a cross validation. .. GENERATED FROM PYTHON SOURCE LINES 199-204 Further Notes ------------- Sometimes it might be a good idea to provide separate implementation of `self_optimize` and `self_optimize_with_info`. This might be required, when collecting and calculating the additional info creates a relevant computational overhead. However, you should make sure, that the two methods return the same optimization result otherwise. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.259 seconds) **Estimated memory usage:** 14 MB .. _sphx_glr_download_auto_examples_recipies__05_optimization_info.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: _05_optimization_info.py <_05_optimization_info.py>` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: _05_optimization_info.ipynb <_05_optimization_info.ipynb>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_