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36 | 36 | plot_store_univariates,
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37 | 37 | plot_store_bivariates,
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38 | 38 | )
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39 |
| -from mostlyai.qa.metrics import Metrics, Accuracy, Similarity, Distances |
| 39 | +from mostlyai.qa.metrics import ModelMetrics, Accuracy, Similarity, Distances |
40 | 40 | from mostlyai.qa._sampling import calculate_embeddings, pull_data_for_accuracy, pull_data_for_embeddings
|
41 | 41 | from mostlyai.qa._common import (
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42 | 42 | determine_data_size,
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@@ -73,7 +73,7 @@ def report(
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73 | 73 | max_sample_size_embeddings: int | None = None,
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74 | 74 | statistics_path: str | Path | None = None,
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75 | 75 | update_progress: ProgressCallback | None = None,
|
76 |
| -) -> tuple[Path, Metrics | None]: |
| 76 | +) -> tuple[Path, ModelMetrics | None]: |
77 | 77 | """
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78 | 78 | Generate HTML report and metrics for assessing synthetic data quality.
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79 | 79 |
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@@ -353,7 +353,7 @@ def _calculate_metrics(
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353 | 353 | sim_cosine_trn_syn: np.float64 | None = None,
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354 | 354 | sim_auc_trn_hol: np.float64 | None = None,
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355 | 355 | sim_auc_trn_syn: np.float64 | None = None,
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356 |
| -) -> Metrics: |
| 356 | +) -> ModelMetrics: |
357 | 357 | do_accuracy = acc_uni is not None and acc_biv is not None
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358 | 358 | do_distances = dcr_trn is not None
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359 | 359 | do_similarity = sim_cosine_trn_syn is not None
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@@ -412,7 +412,7 @@ def _calculate_metrics(
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412 | 412 | )
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413 | 413 | else:
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414 | 414 | distances = Distances()
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415 |
| - return Metrics( |
| 415 | + return ModelMetrics( |
416 | 416 | accuracy=accuracy,
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417 | 417 | similarity=similarity,
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418 | 418 | distances=distances,
|
|
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