Skip to content

Refactor predict() to use grouped iteration instead of O(n²) dataframe filtering #75

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

Merged
KarinePistili merged 3 commits into from
Jun 9, 2026
Merged

Refactor predict() to use grouped iteration instead of O(n²) dataframe filtering #75

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
b1a0037
refactor for itreration to avoid o(n^2) loop
midaa1 Feb 18, 2026
aa83ce1
refactor for itreration to avoid o(n^2) loop
midaa1 Feb 18, 2026
161e4fa
Merge branch 'main' into prediction-refactor
KarinePistili Jun 9, 2026
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
37 changes: 18 additions & 19 deletions app/services/gaze_tracker.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Ridge

import time

# Model imports
from sklearn import linear_model
Expand Down Expand Up @@ -127,9 +127,12 @@ def train_and_predict(model_name, X_train, y_train, X_test, y_test, label):
"""
if model_name == "Linear Regression":
model = models[model_name]
start_time = time.time()
model.fit(X_train, y_train)
end_time = time.time()
y_pred = model.predict(X_test)
print(f"Score {label}: {r2_score(y_test, y_pred)}")
print(f"Time {label}: {end_time - start_time}")
return y_pred
else:
pipeline = models[model_name]
Expand All @@ -142,9 +145,12 @@ def train_and_predict(model_name, X_train, y_train, X_test, y_test, label):
refit="r2",
return_train_score=True,
)
start_time = time.time()
grid_search.fit(X_train, y_train)
end_time = time.time()
best_model = grid_search.best_estimator_
y_pred = best_model.predict(X_test)
print(f"Time {label}: {end_time - start_time}")
return y_pred


Expand Down Expand Up @@ -238,32 +244,25 @@ def predict(data, k, model_X, model_Y):

# Create a dictionary to store the data
data = {}
grouped = df_data.groupby("True XY")

# Iterate over the dataframe and store the data
for index, row in df_data.iterrows():
for (true_x, true_y), group in grouped:

# Get the outer and inner keys
outer_key = str(row["True X"]).split(".")[0]
inner_key = str(row["True Y"]).split(".")[0]
# keys
outer_key = str(true_x).split(".")[0]
inner_key = str(true_y).split(".")[0]

# If the outer key is not in the dictionary, add it
# create outer key if missing
if outer_key not in data:
data[outer_key] = {}

# Add the data to the dictionary
# fill data
data[outer_key][inner_key] = {
"predicted_x": df_data[
(df_data["True X"] == row["True X"])
& (df_data["True Y"] == row["True Y"])
]["Predicted X"].values.tolist(),
"predicted_y": df_data[
(df_data["True X"] == row["True X"])
& (df_data["True Y"] == row["True Y"])
]["Predicted Y"].values.tolist(),
"PrecisionSD": precision_xy[(row["True X"], row["True Y"])],
"Accuracy": accuracy_xy[(row["True X"], row["True Y"])],
"predicted_x": group["Predicted X"].tolist(),
"predicted_y": group["Predicted Y"].tolist(),
"PrecisionSD": precision_xy[(true_x, true_y)],
"Accuracy": accuracy_xy[(true_x, true_y)],
}

# Centroids of the clusters
data["centroids"] = model.cluster_centers_.tolist()

Expand Down
Loading