# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, Mapping, Optional, Protocol
import numpy as np
from scipy.stats import fisher_exact, norm, pearsonr, spearmanr
"""
################################ Model Fit Metrics ###############################
"""
[docs]class ModelFitMetricProtocol(Protocol):
"""Structural type for model fit metrics."""
@staticmethod
def __call__(y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray) -> float:
...
[docs]def compute_model_fit_metrics(
y_obs: Mapping[str, np.ndarray],
y_pred: Mapping[str, np.ndarray],
se_pred: Mapping[str, np.ndarray],
fit_metrics_dict: Mapping[str, ModelFitMetricProtocol],
) -> Dict[str, Dict[str, float]]:
"""Computes the model fit metrics for each experimental metric in the input dicts.
Args:
y_obs: A dictionary mapping from experimental metric name to observed values.
y_pred: A dictionary mapping from experimental metric name to predicted values.
se_pred: A dictionary mapping from experimental metric name to predicted
standard errors.
fit_metrics_dict: A dictionary mapping from *model fit* metric name to a
ModelFitMetricProtocol function that evaluates a model fit metric.
Returns:
A nested dictionary mapping from *model fit* and *experimental* metric names
to their corresponding model fit metrics values.
"""
metric_names = list(y_obs.keys())
return {
fit_metric_name: {
exp_metric_name: fit_metric(
y_obs=y_obs[exp_metric_name],
y_pred=y_pred[exp_metric_name],
se_pred=se_pred[exp_metric_name],
)
for exp_metric_name in metric_names
}
for fit_metric_name, fit_metric in fit_metrics_dict.items()
}
[docs]def coefficient_of_determination(
y_obs: np.ndarray,
y_pred: np.ndarray,
se_pred: Optional[np.ndarray] = None,
eps: float = 1e-12,
) -> float:
"""Computes coefficient of determination, the proportion of variance in `y_obs`
accounted for by predictions `y_pred`.
Args:
y_obs: An array of observations for a single metric.
y_pred: An array of the predicted values corresponding to y_obs.
se_pred: Not used, kept for API compatibility.
eps: A small constant to add to the denominator for numerical stability.
Returns:
The scalar coefficient of determination, "R squared".
"""
ss_res = ((y_obs - y_pred) ** 2).sum()
ss_tot = ((y_obs - y_obs.mean()) ** 2).sum()
return 1 - (ss_res / (ss_tot + eps))
[docs]def mean_of_the_standardized_error( # i.e. standardized bias
y_obs: np.ndarray,
y_pred: np.ndarray,
se_pred: np.ndarray,
) -> float:
"""Computes the mean of the error standardized by the predictive standard deviation
of the model `se_pred`. If the model makes good predictions and its uncertainty is
quantified well, should be close to 0 and be normally distributed.
NOTE: This assumes that `se_pred` is the predictive standard deviation of the
*observations* of the objective `y`, not the predictive standard deviation of the
objective `f` itself. In practice, this will matter for very noisy observations.
Args:
y_obs: An array of observations for a single metric.
y_pred: An array of the predicted values corresponding to y_obs.
se_pred: An array of the standard errors of the predicted values.
Returns:
The scalar mean of the standardized error.
"""
return ((y_obs - y_pred) / se_pred).mean()
[docs]def std_of_the_standardized_error(
y_obs: np.ndarray,
y_pred: np.ndarray,
se_pred: np.ndarray,
) -> float:
"""Standard deviation of the error standardized by the predictive standard deviation
of the model `se_pred`. If the uncertainty is quantified well, should be close to 1.
NOTE: This assumes that `se_pred` is the predictive standard deviation of the
*observations* of the objective `y`, not the predictive standard deviation of the
objective `f` itself. In practice, this will matter for very noisy observations.
Args:
y_obs: An array of observations for a single metric.
y_pred: An array of the predicted values corresponding to y_obs.
se_pred: An array of the standard errors of the predicted values.
Returns:
The scalar standard deviation of the standardized error.
"""
return ((y_obs - y_pred) / se_pred).std()
def _mean_prediction_ci(
y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray
) -> float:
# Pyre does not allow float * np.ndarray.
return float(np.mean(1.96 * 2 * se_pred / np.abs(y_obs)))
def _log_likelihood(
y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray
) -> float:
return float(np.sum(norm.logpdf(y_obs, loc=y_pred, scale=se_pred)))
def _mape(y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray) -> float:
"""Mean absolute predictive error"""
return float(np.mean(np.abs((y_pred - y_obs) / y_obs)))
def _total_raw_effect(
y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray
) -> float:
min_y_obs = np.min(y_obs)
return float((np.max(y_obs) - min_y_obs) / min_y_obs)
def _correlation_coefficient(
y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray
) -> float:
with np.errstate(invalid="ignore"):
rho, _ = pearsonr(y_pred, y_obs)
return float(rho)
def _rank_correlation(
y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray
) -> float:
with np.errstate(invalid="ignore"):
rho, _ = spearmanr(y_pred, y_obs)
return float(rho)
def _fisher_exact_test_p(
y_obs: np.ndarray, y_pred: np.ndarray, se_pred: np.ndarray
) -> float:
n_half = len(y_obs) // 2
top_obs = y_obs.argsort(axis=0)[-n_half:]
top_est = y_pred.argsort(axis=0)[-n_half:]
# Construct contingency table
tp = len(set(top_est).intersection(top_obs))
fp = n_half - tp
fn = n_half - tp
tn = (len(y_obs) - n_half) - (n_half - tp)
table = np.array([[tp, fp], [fn, tn]])
# Compute the test statistic
_, p = fisher_exact(table, alternative="greater")
return float(p)
