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Fix: Stratified Splitting to Prevent Calibration Crash #58

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Fix: Stratified Splitting to Prevent Calibration Crash #58

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28164b1
Fix: Stratified splitting for calibration data
thechillbasu Feb 7, 2026
ec12e79
fix: revert variable naming to original convention (X_train_x)
thechillbasu Feb 9, 2026
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44 changes: 26 additions & 18 deletions app/services/gaze_tracker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,21 +92,33 @@ def predict(data, k, model_X, model_Y):
dict: A dictionary containing the predicted gaze coordinates, precision, accuracy, and cluster centroids.
"""
# Inicialize standard scaler
sc = StandardScaler()
sc_x = StandardScaler()
sc_y = StandardScaler()

# Load data from csv file and drop unnecessary columns
df = pd.read_csv(data)
df = df.drop(["screen_height", "screen_width"], axis=1)

# Drop rows with NaN values to prevent sklearn errors
df = df.dropna()

# Data for X axis
X_x = df[["left_iris_x", "right_iris_x"]]
X_y = df["point_x"]

# Normalize data using standard scaler and split data into training and testing sets
X_x = sc.fit_transform(X_x)
X_train_x, X_test_x, y_train_x, y_test_x = train_test_split(
X_x, X_y, test_size=0.2, random_state=42
# Create a stratification key based on (point_x, point_y) to ensure all calibration
# points are represented in both training and test sets
df["stratify_key"] = df["point_x"].astype(str) + "_" + df["point_y"].astype(str)

# Perform a single stratified split to ensure all points are in both train and test sets
train_indices, test_indices = train_test_split(
df.index, test_size=0.2, random_state=42, stratify=df["stratify_key"]
)

df_train = df.loc[train_indices]
df_test = df.loc[test_indices]

# Data for X axis
X_train_x = sc_x.fit_transform(df_train[["left_iris_x", "right_iris_x"]])
X_test_x = sc_x.transform(df_test[["left_iris_x", "right_iris_x"]])
y_train_x = df_train["point_x"]
y_test_x = df_test["point_x"]

if (
model_X == "Linear Regression"
Expand Down Expand Up @@ -140,15 +152,11 @@ def predict(data, k, model_X, model_Y):
best_model_x = grid_search.best_estimator_
y_pred_x = best_model_x.predict(X_test_x)

# Data for Y axis
X_y = df[["left_iris_y", "right_iris_y"]]
y_y = df["point_y"]

# Normalize data using standard scaler and split data into training and testing sets
X_y = sc.fit_transform(X_y)
X_train_y, X_test_y, y_train_y, y_test_y = train_test_split(
X_y, y_y, test_size=0.2, random_state=42
)
# Data for Y axis (use same train/test split as X for consistency)
X_train_y = sc_y.fit_transform(df_train[["left_iris_y", "right_iris_y"]])
X_test_y = sc_y.transform(df_test[["left_iris_y", "right_iris_y"]])
y_train_y = df_train["point_y"]
y_test_y = df_test["point_y"]

if (
model_Y == "Linear Regression"
Expand Down