Updated lambda function with the required params

lambda/run_xgboost.py

@@ -15,34 +15,41 @@
 
 
 def run(event, context):
-    logger.info("Called run function")
+    logger.info('Called run function')
     input_body = json.loads(event['body'])
-    if 'params' not in input_body:
-        logger.error("Validation Failed")
-        raise Exception("Couldn't create the todo item.")
 
     xgb_params = input_body['params']
-    logger.info("Reading CSV")
-    df = pd.read_csv("./preprocessed.csv")
+    nfold = input_body['cv']
+    seed = input_body['seed']
+    num_boost_round = input_body['num_boost_round']
+
+    logger.info('Reading CSV')
+    df = pd.read_csv('./preprocessed.csv')
 
     X = df.loc[:, df.columns != 'RainTomorrow']
     y = df[['RainTomorrow']]
 
     data_dmatrix = xgb.DMatrix(data=X, label=y)
 
-    logger.info("Performing Cross Validation")
+    logger.info('Performing Cross Validation')
     # this returns a dataframe of mean error values, each row adds another boosting tree.
-    results = xgb.cv(dtrain=data_dmatrix, params=xgb_params, nfold=3, seed=123, metrics="error")
+    results = xgb.cv(
+        dtrain=data_dmatrix,
+        params=xgb_params,
+        nfold=nfold,
+        seed=seed,
+        num_boost_round=num_boost_round,
+        metrics='error')
 
     logger.info("Done")
     # select the iteration with the lowest error (with the optimal number of trees ensembled)
     best_mean_score = results['test-error-mean'].min()
 
     response = {
-        "statusCode": 200,
-        "body": json.dumps({"status": "OK", "evaluation_score": best_mean_score}),
+        'statusCode': 200,
+        'body': json.dumps({'status': 'OK', 'error': best_mean_score}),
     }
 
-    logger.info("Sending response", response)
+    logger.info('Sending response', response)
 
     return response