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fix mse calculation and predict_params in easy networks #151

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Merged
cnellington merged 1 commit into from
Nov 28, 2022
Merged

fix mse calculation and predict_params in easy networks #151

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16 changes: 10 additions & 6 deletions contextualized/easy/ContextualizedNetworks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,9 +80,9 @@ def measure_mses(self, C, X, individual_preds=False):
mses = np.zeros((len(betas), len(C))) # n_bootstraps x n_samples
for i in range(X.shape[-1]):
for j in range(X.shape[-1]):
residuals = np.tile(X[:, i], len(betas)) - (
betas[:, :, i, j] * np.tile(X[:, j], len(betas)) + mus[:, :, i, j]
)
tiled_xi = np.array([X[:, i] for _ in range(len(betas))])
tiled_xj = np.array([X[:, j] for _ in range(len(betas))])
residuals = tiled_xi - betas[:, :, i, j]*tiled_xj + mus[:, :, i ,j]
mses += residuals**2 / (X.shape[-1] ** 2)
if not individual_preds:
mses = np.mean(mses, axis=0)
Expand Down Expand Up @@ -164,7 +164,7 @@ def __init__(self, **kwargs):
**kwargs,
)

def predict_params(self, C, individual_preds=False, **kwargs):
def predict_params(self, C, **kwargs):
"""

:param C:
Expand All @@ -173,8 +173,12 @@ def predict_params(self, C, individual_preds=False, **kwargs):
"""
# Returns betas
# TODO: No mus for NOTMAD at present.
return super().predict_params(C, individual_preds,
model_includes_mus=False, **kwargs)
return super().predict_params(
C,
individual_preds=kwargs.get("individual_preds", False),
model_includes_mus=False,
uses_y=False,
project_to_dag=kwargs.get("project_to_dag", True))

def predict_networks(self, C, with_offsets=False, project_to_dag=True, **kwargs):
"""
Expand Down