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optimized predict() loop using groupby #85

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optimized predict() loop using groupby #85

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26 changes: 8 additions & 18 deletions app/services/gaze_tracker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,29 +199,19 @@ def predict(data, k, model_X, model_Y):
# Create a dictionary to store the data
data = {}

# Iterate over the dataframe and store the data
for index, row in df_data.iterrows():
# Group once by (True X, True Y) — O(n) instead of O(n²)
for (true_x, true_y), group in df_data.groupby("True XY"):
outer_key = str(true_x).split(".")[0]
inner_key = str(true_y).split(".")[0]

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

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

# Add the data to the dictionary
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"].values.tolist(),
"predicted_y": group["Predicted Y"].values.tolist(),
"PrecisionSD": precision_xy[(true_x, true_y)],
"Accuracy": accuracy_xy[(true_x, true_y)],
}

# Centroids of the clusters
Expand Down