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per-metric model selection in MBM #3032

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per-metric model selection in MBM #3032

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37 changes: 6 additions & 31 deletions ax/modelbridge/cross_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,17 +23,10 @@
from ax.modelbridge.base import ModelBridge, unwrap_observation_data
from ax.utils.common.logger import get_logger
from ax.utils.stats.model_fit_stats import (
_correlation_coefficient,
_fisher_exact_test_p,
_log_likelihood,
_mape,
_mean_prediction_ci,
_mse,
_rank_correlation,
_total_raw_effect,
_wmape,
coefficient_of_determination,
compute_model_fit_metrics,
DIAGNOSTIC_FNS,
FISHER_EXACT_TEST_P,
mean_of_the_standardized_error,
ModelFitMetricProtocol,
std_of_the_standardized_error,
Expand All @@ -44,16 +37,6 @@

CVDiagnostics = dict[str, dict[str, float]]

MEAN_PREDICTION_CI = "Mean prediction CI"
MAPE = "MAPE"
wMAPE = "wMAPE"
TOTAL_RAW_EFFECT = "Total raw effect"
CORRELATION_COEFFICIENT = "Correlation coefficient"
RANK_CORRELATION = "Rank correlation"
FISHER_EXACT_TEST_P = "Fisher exact test p"
LOG_LIKELIHOOD = "Log likelihood"
MSE = "MSE"


class CVResult(NamedTuple):
"""Container for cross validation results."""
Expand Down Expand Up @@ -312,19 +295,11 @@ def compute_diagnostics(result: list[CVResult]) -> CVDiagnostics:
y_pred = _arrayify_dict_values(y_pred)
se_pred = _arrayify_dict_values(se_pred)

diagnostic_fns = {
MEAN_PREDICTION_CI: _mean_prediction_ci,
MAPE: _mape,
wMAPE: _wmape,
TOTAL_RAW_EFFECT: _total_raw_effect,
CORRELATION_COEFFICIENT: _correlation_coefficient,
RANK_CORRELATION: _rank_correlation,
FISHER_EXACT_TEST_P: _fisher_exact_test_p,
LOG_LIKELIHOOD: _log_likelihood,
MSE: _mse,
}
diagnostics = compute_model_fit_metrics(
y_obs=y_obs, y_pred=y_pred, se_pred=se_pred, fit_metrics_dict=diagnostic_fns
y_obs=y_obs,
y_pred=y_pred,
se_pred=se_pred,
fit_metrics_dict=DIAGNOSTIC_FNS,
)
return diagnostics

Expand Down
6 changes: 4 additions & 2 deletions ax/models/torch/botorch_modular/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -186,9 +186,11 @@ def fit(
# If a surrogate has not been constructed, construct it.
if self._surrogate is None:
if self.surrogate_spec is not None:
self._surrogate = Surrogate(surrogate_spec=self.surrogate_spec)
self._surrogate = Surrogate(
surrogate_spec=self.surrogate_spec, refit_on_cv=self.refit_on_cv
)
else:
self._surrogate = Surrogate()
self._surrogate = Surrogate(refit_on_cv=self.refit_on_cv)

# Fit the surrogate.
for config in self.surrogate.surrogate_spec.model_configs:
Expand Down
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