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Fix Model Selection in batch_predict Endpoint #54

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Fix Model Selection in batch_predict Endpoint #54

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fdc4ded
Create models_gaze_engineered dictionary with models compatible for 5…
sohampirale Feb 7, 2026
a5bd9ff
Fix : use requested model name for gaze estimation with predict_new_d…
sohampirale Feb 7, 2026
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8 changes: 4 additions & 4 deletions app/routes/session.py
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Original file line number Diff line number Diff line change
Expand Up @@ -153,8 +153,8 @@ def batch_predict():
iris_data = data["iris_tracking_data"]
screen_width = data.get("screen_width")
screen_height = data.get("screen_height")
model_X = data.get("model_X", "Linear Regression")
model_Y = data.get("model_Y", "Linear Regression")
model_name_X = data.get("model_name_X", "Linear Regression")
model_name_Y = data.get("model_name_Y", "Linear Regression")
calib_id = data.get("calib_id")

if not calib_id:
Expand Down Expand Up @@ -182,8 +182,8 @@ def batch_predict():
calib_csv_path=calib_csv_path,
predict_csv_path=predict_csv_path,
iris_data=iris_data,
# model_X="Random Forest Regressor",
# model_Y="Random Forest Regressor",
model_name_X=model_name_X,
model_name_Y=model_name_Y,
screen_width=screen_width,
screen_height=screen_height,
)
Expand Down
49 changes: 46 additions & 3 deletions app/services/gaze_tracker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,46 @@
)
)}

models_gaze_engineered = {
"Linear Regression": make_pipeline(
StandardScaler(),
linear_model.LinearRegression()
),
"Ridge Regression": make_pipeline(
StandardScaler(),
linear_model.Ridge()
),
"Lasso Regression": make_pipeline(
StandardScaler(),
linear_model.Lasso()
),
"Elastic Net": make_pipeline(
StandardScaler(),
linear_model.ElasticNet(alpha=1.0, l1_ratio=0.5)
),
"Bayesian Ridge": make_pipeline(
StandardScaler(),
linear_model.BayesianRidge()
),
"SGD Regressor": make_pipeline(
StandardScaler(),
linear_model.SGDRegressor()
),
"Support Vector Regressor": make_pipeline(
StandardScaler(),
SVR(kernel="linear")
),
"Random Forest Regressor": make_pipeline(
StandardScaler(),
RandomForestRegressor(
n_estimators=200,
max_depth=10,
min_samples_split=5,
random_state=42
)
)
}

# Set the scoring metrics for GridSearchCV to r2_score and mean_absolute_error
scoring = {
"r2": make_scorer(r2_score),
Expand Down Expand Up @@ -255,9 +295,11 @@ def predict_new_data_simple(
calib_csv_path,
predict_csv_path,
iris_data,
model_name_X="Linear Regression",
model_name_Y="Linear Regression",
screen_width=None,
screen_height=None,
):
):
# ============================
# CONFIG (WebGazer-inspired)
# ============================
Expand Down Expand Up @@ -328,8 +370,9 @@ def predict_new_data_simple(
# ============================
# MODELS
# ============================
model_x = make_pipeline(StandardScaler(), Ridge(alpha=1.0))
model_y = make_pipeline(StandardScaler(), Ridge(alpha=1.0))

model_x=models_gaze_engineered.get(model_name_X,models_gaze_engineered['Linear Regression'])
model_y=models.get(model_name_Y,models['Linear Regression'])

model_x.fit(X_train_x, y_train_x)
model_y.fit(X_train_y, y_train_y)
Expand Down