From 07f30dfde52866b507e74c662314a410ccec9b51 Mon Sep 17 00:00:00 2001
From: Aiden010200 <150222139+Aiden010200@users.noreply.github.com>
Date: Tue, 4 Feb 2025 00:09:56 +0800
Subject: [PATCH] Upload a SGD classifier predictor example (#3783)

This example uses aiplatform and scikit-learn library to provide a SGD classifier.
---
 .../sklearn/predictor_SGDClassifier.py        | 33 +++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 community-content/vertex_cpr_samples/sklearn/predictor_SGDClassifier.py

diff --git a/community-content/vertex_cpr_samples/sklearn/predictor_SGDClassifier.py b/community-content/vertex_cpr_samples/sklearn/predictor_SGDClassifier.py
new file mode 100644
index 000000000..0c137ccd0
--- /dev/null
+++ b/community-content/vertex_cpr_samples/sklearn/predictor_SGDClassifier.py
@@ -0,0 +1,33 @@
+import numpy as np
+import os
+import pickle
+
+from google.cloud.aiplatform.constants import prediction
+from google.cloud.aiplatform.utils import prediction_utils
+from google.cloud.aiplatform.prediction.predictor import Predictor
+from sklearn.linear_model import SGDClassifier
+
+class SGDClassifierPredictor(Predictor):
+
+    def __init__(self):
+        return
+
+    def load(self, artifacts_uri: str) -> None:
+        prediction_utils.download_model_artifacts(artifacts_uri)
+        if os.path.exists(prediction.MODEL_FILENAME_PKL):
+            self._model = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
+        else:
+            self._model = SGDClassifier(max_iter=5)
+        X = [[0., 0.], [1., 1.]]
+        y = [0, 1]
+        self._model.fit(X, y)
+
+    def preprocess(self, prediction_input: dict) -> np.ndarray:
+        instances = prediction_input["instances"]
+        return np.asarray(instances)
+
+    def predict(self, instances: np.ndarray) -> np.ndarray:
+        return self._model.predict(instances)
+
+    def postprocess(self, prediction_results: np.ndarray) -> dict:
+        return {"predictions": prediction_results.tolist()}