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FIX: Overhaul GP estimation error analysis script #30

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FIX: Overhaul GP estimation error analysis script #30

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97d9edf
ENH: Fix return value type annotation in GP error analysis script func
jhlegarreta Dec 22, 2024
5c3e7d3
ENH: Use a boolean to tell whether to serialize the pandas df index
jhlegarreta Dec 22, 2024
d9dafbe
BUG: Provide missing position arg to local cross validation function
jhlegarreta Dec 22, 2024
6e8311f
ENH: Add type annotation for variable in GP estimation error analysis
jhlegarreta Dec 22, 2024
a19c66e
Merge remote-tracking branch 'upstream/main' into FixGPErrorAnalysisS…
oesteban Jan 20, 2025
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13 changes: 8 additions & 5 deletions scripts/dwi_gp_estimation_error_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@
import argparse
from collections import defaultdict
from pathlib import Path
from typing import DefaultDict, List

import numpy as np
import pandas as pd
Expand All @@ -49,7 +50,7 @@ def cross_validate(
cv: int,
n_repeats: int,
gpr: DiffusionGPR,
) -> dict[int, list[tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]]]:
) -> np.ndarray:
"""
Perform the experiment by estimating the dMRI signal using a Gaussian process model.

Expand All @@ -68,7 +69,7 @@ def cross_validate(

Returns
-------
:obj:`dict`
:obj:`~numpy.ndarray`
Data for the predicted signal and its error.

"""
Expand Down Expand Up @@ -202,12 +203,14 @@ def main() -> None:
# max_iter=2e5,
)

n_repeats = 10

if args.kfold:
# Use Scikit-learn cross validation
scores = defaultdict(list, {})
scores: DefaultDict[str, List[float | str]] = defaultdict(list)
for n in args.kfold:
for i in range(args.repeats):
cv_scores = -1.0 * cross_validate(X, y.T, n, gpr)
cv_scores = -1.0 * cross_validate(X, y.T, n, n_repeats, gpr)
scores["rmse"] += cv_scores.tolist()
scores["repeat"] += [i] * len(cv_scores)
scores["n_folds"] += [n] * len(cv_scores)
Expand All @@ -217,7 +220,7 @@ def main() -> None:
print(f"Finished {n}-fold cross-validation")

scores_df = pd.DataFrame(scores)
scores_df.to_csv(args.output_scores, sep="\t", index=None, na_rep="n/a")
scores_df.to_csv(args.output_scores, sep="\t", index=False, na_rep="n/a")

grouped = scores_df.groupby(["n_folds"])
print(grouped[["rmse"]].mean())
Expand Down
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