Note
Go to the end to download the full example code
Cross Validation#
Whenever using some sort of trainable algorithm it is important to clearly separate the training and the testing data to get an unbiased result. Usually this is achieved by a train-test split. However, if you don’t have that much data, there is always a risk that one random train-test split, will provide better (or worse) results than another. In these cases it is a good idea to use cross-validation. In this procedure, you perform multiple train-test splits and average the results over all “folds”. For more information see our evaluation guide and the sklearn guide on cross validation.
In this example, we will learn how to use the cross_validate function implemented in
tcpc.
For this, we will redo the example on optimizable pipelines but we will perform the final
evaluation via cross-validation.
If you want to have more information on how the dataset and pipeline is built, head over to this example.
Here we will just copy the code over.
Dataset
from pathlib import Path
from examples.datasets.datasets_final_ecg import ECGExampleData
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)
Pipeline
import pandas as pd
from examples.algorithms.algorithms_qrs_detection_final import OptimizableQrsDetector
from tpcp import OptimizableParameter, OptimizablePipeline, Parameter, cf
class MyPipeline(OptimizablePipeline):
algorithm: Parameter[OptimizableQrsDetector]
algorithm__min_r_peak_height_over_baseline: OptimizableParameter[float]
r_peak_positions_: pd.Series
def __init__(self, algorithm: OptimizableQrsDetector = cf(OptimizableQrsDetector())):
self.algorithm = algorithm
def self_optimize(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()
self.algorithm = algo.self_optimize(ecg_data, r_peaks, dataset.sampling_rate_hz)
return self
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, datapoint.sampling_rate_hz)
self.r_peak_positions_ = algo.r_peak_positions_
return self
The Scorer#
The scorer is identical to the scoring function used in the other examples. The F1-score is still the most important parameter for our comparison.
from examples.algorithms.algorithms_qrs_detection_final import match_events_with_reference, precision_recall_f1_score
def score(pipeline: MyPipeline, datapoint: ECGExampleData):
# We use the `safe_run` wrapper instead of just run. This is always a good idea.
# We don't need to clone the pipeline here, as GridSearch will already clone the pipeline internally and `run`
# will clone it again.
pipeline = pipeline.safe_run(datapoint)
tolerance_s = 0.02 # We just use 20 ms for this example
matches = match_events_with_reference(
pipeline.r_peak_positions_.to_numpy(),
datapoint.r_peak_positions_.to_numpy(),
tolerance=tolerance_s * datapoint.sampling_rate_hz,
)
precision, recall, f1_score = precision_recall_f1_score(matches)
return {"precision": precision, "recall": recall, "f1_score": f1_score}
Data Splitting#
Before performing a cross validation, we need to decide on the number of folds and type of splits.
In tpcp we support all cross validation iterators provided in
sklearn.
To keep the runtime low for this example, we are going to use a 3-fold CV.
Cross Validation#
Now we have all the pieces for the final cross validation.
First we need to create instances of our data and pipeline.
Then we need to wrap our pipeline instance into an Optimize wrapper.
Finally, we can call tpcp.validate.cross_validate.
from tpcp.optimize import Optimize
from tpcp.validate import cross_validate
pipe = MyPipeline()
optimizable_pipe = Optimize(pipe)
results = cross_validate(
optimizable_pipe, example_data, scoring=score, cv=cv, return_optimizer=True, return_train_score=True
)
result_df = pd.DataFrame(results)
result_df
CV Folds: 0%| | 0/3 [00:00<?, ?it/s]
Datapoints: 0%| | 0/4 [00:00<?, ?it/s]
Datapoints: 50%|█████ | 2/4 [00:00<00:00, 14.31it/s]
Datapoints: 100%|██████████| 4/4 [00:00<00:00, 14.04it/s]
Datapoints: 100%|██████████| 4/4 [00:00<00:00, 14.06it/s]
Datapoints: 0%| | 0/8 [00:00<?, ?it/s]
Datapoints: 25%|██▌ | 2/8 [00:00<00:00, 14.17it/s]
Datapoints: 50%|█████ | 4/8 [00:00<00:00, 14.21it/s]
Datapoints: 75%|███████▌ | 6/8 [00:00<00:00, 14.35it/s]
Datapoints: 100%|██████████| 8/8 [00:00<00:00, 14.67it/s]
Datapoints: 100%|██████████| 8/8 [00:00<00:00, 14.50it/s]
CV Folds: 33%|███▎ | 1/3 [00:01<00:02, 1.34s/it]
Datapoints: 0%| | 0/4 [00:00<?, ?it/s]
Datapoints: 50%|█████ | 2/4 [00:00<00:00, 14.60it/s]
Datapoints: 100%|██████████| 4/4 [00:00<00:00, 14.68it/s]
Datapoints: 100%|██████████| 4/4 [00:00<00:00, 14.64it/s]
Datapoints: 0%| | 0/8 [00:00<?, ?it/s]
Datapoints: 25%|██▌ | 2/8 [00:00<00:00, 15.42it/s]
Datapoints: 50%|█████ | 4/8 [00:00<00:00, 15.13it/s]
Datapoints: 75%|███████▌ | 6/8 [00:00<00:00, 15.06it/s]
Datapoints: 100%|██████████| 8/8 [00:00<00:00, 14.95it/s]
Datapoints: 100%|██████████| 8/8 [00:00<00:00, 15.01it/s]
CV Folds: 67%|██████▋ | 2/3 [00:02<00:01, 1.35s/it]
Datapoints: 0%| | 0/4 [00:00<?, ?it/s]
Datapoints: 50%|█████ | 2/4 [00:00<00:00, 15.03it/s]
Datapoints: 100%|██████████| 4/4 [00:00<00:00, 14.93it/s]
Datapoints: 100%|██████████| 4/4 [00:00<00:00, 14.92it/s]
Datapoints: 0%| | 0/8 [00:00<?, ?it/s]
Datapoints: 25%|██▌ | 2/8 [00:00<00:00, 15.14it/s]
Datapoints: 50%|█████ | 4/8 [00:00<00:00, 14.69it/s]
Datapoints: 75%|███████▌ | 6/8 [00:00<00:00, 14.65it/s]
Datapoints: 100%|██████████| 8/8 [00:00<00:00, 14.87it/s]
Datapoints: 100%|██████████| 8/8 [00:00<00:00, 14.82it/s]
CV Folds: 100%|██████████| 3/3 [00:04<00:00, 1.36s/it]
CV Folds: 100%|██████████| 3/3 [00:04<00:00, 1.36s/it]
Understanding the Results#
The cross validation provides a lot of outputs (some of them can be disabled using the function parameters). To simplify things a little, we will split the output into four parts:
The main output are the test set performance values.
They are aggregated over all datapoints in each fold using the aggregation specified in the scoring function.
They all are prefixed with test__agg__ to easily filter them out within the results.
Each row corresponds to performance in respective fold.
performance = result_df.filter(like="test__agg__")
performance
The final generalization performance you would report is usually the average over all folds. The STD can also be interesting, as it tells you how stable your optimization is and if your splits provide comparable data distributions.
generalization_performance = performance.agg(["mean", "std"])
generalization_performance
If you need more insight into the results (e.g. when the std of your results is high), you can inspect the individual score for each data point. In this example this is only a list with a single element per score, as we only had a single datapoint per fold. In a real scenario, this will be a list of all datapoints. Inspecting this list can help to identify potential issues with certain parts of your dataset.
single_performance = result_df.filter(like="test__single__")
single_performance
To link the performance values to a specific datapoint, you can look at the test__data_labels field.
It is often quite handy to combine all the results into on df:
exploded_results = (
single_performance.explode(single_performance.columns.to_list())
.rename_axis("fold")
.set_index(result_df["test__data_labels"].explode(), append=True)
)
exploded_results
Even further insight is provided by the train results (if activated via return_train_score).
These are the performance results on the train set and can indicate if the training provided meaningful results and
can also indicate over-fitting, if the performance of the test set is much worse than the performance on the train
set.
train_performance = result_df.filter(like="train__")
train_performance
The final level of debug information is provided via the timings (note the long runtime in fold 0 can be explained
by the jit-compiler used in BarthDtw) …
timings = result_df.filter(like="debug__")
timings
… and the optimized pipeline object. This is the actual trained object generated in this fold. You can apply it to other data for testing or inspect the actual object for further debug information that might be stored on it.
optimized_pipeline = result_df["optimizer"][0]
optimized_pipeline
Optimize(optimize_with_info=True, pipeline=MyPipeline(algorithm=OptimizableQrsDetector(high_pass_filter_cutoff_hz=1, max_heart_rate_bpm=200.0, min_r_peak_height_over_baseline=1.0, r_peak_match_tolerance_s=0.01)), safe_optimize=True)
{'algorithm__high_pass_filter_cutoff_hz': 1, 'algorithm__max_heart_rate_bpm': 200.0, 'algorithm__min_r_peak_height_over_baseline': 0.6322168257130579, 'algorithm__r_peak_match_tolerance_s': 0.01, 'algorithm': OptimizableQrsDetector(high_pass_filter_cutoff_hz=1, max_heart_rate_bpm=200.0, min_r_peak_height_over_baseline=0.6322168257130579, r_peak_match_tolerance_s=0.01)}
Further Notes#
We also support grouped cross validation.
Check the dataset guide on how you can group the data before cross-validation or
generate data labels to be used with GroupedKFold.
Optimize is just an example of an optimizer that can be passed to cross validation.
You can pass any tpcp optimizer like GridSearch or GridSearchCV or custom optimizer that implement the
tpcp.optimize.BaseOptimize interface.
Total running time of the script: (0 minutes 6.637 seconds)
Estimated memory usage: 33 MB