Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Save optimal projected frequencies for fixed model #2

Merged
merged 1 commit into from
Sep 4, 2024
Merged

Save optimal projected frequencies for fixed model #2

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 19 additions & 0 deletions src/popcast/fit.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -421,6 +421,11 @@ def _fit(self, coefficients, X, y, use_l1_penalty=True, calculate_optimal_distan
# Estimate target values.
y_hat = self.predict(X, coefficients)

# Save optimal frequencies by timepoint and strain, if calculating
# optimal distance to the future.
if calculate_optimal_distance:
optimal_frequency_records = []

# Calculate EMD for each timepoint in the estimated values and sum that
# distance across all timepoints.
error = 0.0
Expand Down Expand Up @@ -471,6 +476,12 @@ def _fit(self, coefficients, X, y, use_l1_penalty=True, calculate_optimal_distan
cost=distance_matrix
)

optimal_frequency_records.append(pd.DataFrame({
"strain": samples_a,
"timepoint": timepoint,
"optimal_projected_frequency": estimated_frequencies,
}))

# Estimate the distance between the model's estimated future and the
# observed future populations.
model_emd, _, self.model_flow = cv2.EMD(
Expand All @@ -490,6 +501,9 @@ def _fit(self, coefficients, X, y, use_l1_penalty=True, calculate_optimal_distan
else:
l1_penalty = 0.0

if calculate_optimal_distance:
self.optimal_frequencies = pd.concat(optimal_frequency_records)

return error + l1_penalty

def _fit_distance(self, coefficients, X, y, use_l1_penalty=True):
Expand Down Expand Up @@ -861,6 +875,11 @@ def test(model_class, model_kwargs, data, targets, timepoints, coefficients=None

# Get the estimated frequencies for test sets to export.
test_y_hat = model.predict(test_X)
test_y_hat = test_y_hat.merge(
model.optimal_frequencies,
on=["timepoint", "strain"],
validate="1:1",
)

# Convert timestamps to a serializable format.
for df in [test_X, test_y, test_y_hat]:
Expand Down
Loading