add sagemaker pipelines

Author: catwhiskers

sagemaker_pipelines/paddleocr/__init__.py

@@ -0,0 +1,59 @@
+"""Evaluation script for measuring mean squared error."""
+import json
+import logging
+import pathlib
+import pickle
+import tarfile
+
+import numpy as np
+import pandas as pd
+import xgboost
+
+from sklearn.metrics import mean_squared_error
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+logger.addHandler(logging.StreamHandler())
+
+
+
+if __name__ == "__main__":
+    logger.debug("Starting evaluation.")
+    model_path = "/opt/ml/processing/model/model.tar.gz"
+    with tarfile.open(model_path) as tar:
+        tar.extractall(path=".")
+
+    logger.debug("Loading xgboost model.")
+    model = pickle.load(open("xgboost-model", "rb"))
+
+    logger.debug("Reading test data.")
+    test_path = "/opt/ml/processing/test/test.csv"
+    df = pd.read_csv(test_path, header=None)
+
+    logger.debug("Reading test data.")
+    y_test = df.iloc[:, 0].to_numpy()
+    df.drop(df.columns[0], axis=1, inplace=True)
+    X_test = xgboost.DMatrix(df.values)
+
+    logger.info("Performing predictions against test data.")
+    predictions = model.predict(X_test)
+
+    logger.debug("Calculating mean squared error.")
+    mse = mean_squared_error(y_test, predictions)
+    std = np.std(y_test - predictions)
+    report_dict = {
+        "regression_metrics": {
+            "mse": {
+                "value": mse,
+                "standard_deviation": std
+            },
+        },
+    }
+
+    output_dir = "/opt/ml/processing/evaluation"
+    pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+    logger.info("Writing out evaluation report with mse: %f", mse)
+    evaluation_path = f"{output_dir}/evaluation.json"
+    with open(evaluation_path, "w") as f:
+        f.write(json.dumps(report_dict))