Parsing check

sklearn/model/evaluation.py

@@ -4,6 +4,7 @@
 import csv, sys
 import joblib
 import requests, os
+import argparse
 
 def log_metrics(key, value):
     url = "http://dkube-exporter.dkube:9401/mlflow-exporter"
@@ -20,11 +21,11 @@ def log_metrics(key, value):
 
 dates = []
 prices = []
-name = str(sys.argv[1]) if len(sys.argv) > 1 else 'SVM for stock Preiction'
-kernel = str(sys.argv[2]) if len(sys.argv) > 2 else 'rbf'
-C = float(sys.argv[3]) if len(sys.argv) > 3 else 1e3
-gamma = float(sys.argv[4]) if len(sys.argv) > 4 else 0.1
-degree= int(sys.argv[5]) if len(sys.argv) > 5 else 2
+name = args.name
+kernel = args.kernel
+C=args.C
+gamma = args.gamma
+degree= args.degree
 
 def eval_metrics(actual, pred):
     rmse = np.sqrt(mean_squared_error(actual, pred))
@@ -48,6 +49,14 @@ def get_data(filename):
 
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Argument parser')
+    parser.add_argument('name',metavar='name',type=str,help='name',default='SVM for stock Prediction')
+    parser.add_argument('kernel',metavar='kernel',type=str,help='kernel type',default='rbf')
+    parser.add_argument('C',metavar='C',type=float,help='penality parameter for the error term',default=1e3)
+    parser.add_argument('gamma',metavar='gamma',type=float,help='gamma parameter',default=0.1)
+    parser.add_argument('degree',metavar='degree',type=int,help='degree of polynomial kernel function',default=2)
+
+    args=parser.parse_args("")
 
     get_data(DATA_DIR +'goog.csv')
 

sklearn/model/train.py

@@ -8,14 +8,15 @@
 import cv2, os, json
 import joblib
 import requests
+import argparse
 
 dates = []
 prices = []
-name = str(sys.argv[1]) if len(sys.argv) > 1 else 'SVM for stock Preiction'
-kernel = str(sys.argv[2]) if len(sys.argv) > 2 else 'rbf'
-C = float(sys.argv[3]) if len(sys.argv) > 3 else 1e3
-gamma = float(sys.argv[4]) if len(sys.argv) > 4 else 0.1
-degree= int(sys.argv[5]) if len(sys.argv) > 5 else 2
+name = args.name
+kernel = args.kernel
+C=args.C
+gamma = args.gamma
+degree= args.degree
 
 def eval_metrics(actual, pred):
     rmse = np.sqrt(mean_squared_error(actual, pred))
@@ -54,6 +55,13 @@ def get_data(filename):
 configure(MODEL_DIR + "/logs/SVMrun", flush_secs=5)
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Argument parser')
+    parser.add_argument('name',metavar='name',type=str,help='name',default='SVM for stock Prediction')
+    parser.add_argument('kernel',metavar='kernel',type=str,help='kernel type',default='rbf')
+    parser.add_argument('C',metavar='C',type=float,help='penality parameter for the error term',default=1e3)
+    parser.add_argument('gamma',metavar='gamma',type=float,help='gamma parameter',default=0.1)
+    parser.add_argument('degree',metavar='degree',type=int,help='degree of polynomial kernel function',default=2)
+    args=parser.parse_args("")
 
     print ("MODEL_DIR:{}, DATA_DIR:{}".format(MODEL_DIR,DATA_DIR))
     get_data(DATA_DIR +'/goog.csv')

tensorflow/classification/mnist/digits/classifier/program-2.x/model.py

@@ -176,7 +176,7 @@ def main(unused_argv):
   parser.add_argument('--batch_size', type=int, default=int(hyperparams['batch_size']), help='Batch size for training.')
   parser.add_argument('--num_epochs', type=int, default=int(hyperparams['num_epochs']), help='Number of epochs to train for.')
   global FLAGS
-  FLAGS, unparsed = parser.parse_known_args()
+  FLAGS, unparsed = parser.parse_known_args("")
   data_format = None
   if data_format is None:
     data_format = ('channels_first'

tensorflow/classification/mnist/digits/classifier/program/model.py

@@ -181,7 +181,7 @@ def main(unused_argv):
   parser.add_argument('--batch_size', type=int, default=int(hyperparams['batch_size']), help='Batch size for training.')
   parser.add_argument('--num_epochs', type=int, default=int(hyperparams['num_epochs']), help='Number of epochs to train for.')
   global FLAGS
-  FLAGS, unparsed = parser.parse_known_args()
+  FLAGS, unparsed = parser.parse_known_args("")
   data_format = None
   if data_format is None:
     data_format = ('channels_first'

tensorflow/classification/resnet/catsdogs/classifier/program/model.py

@@ -18,7 +18,7 @@
 parser.add_argument("--epochs", dest = 'epochs', type = int, default = 10, help="no. of epochs")
 parser.add_argument("--learning_rate", dest = 'lr', type = float, default = 0.001, help="no. of epochs")
 parser.add_argument("--batch_size", dest = 'batch_size', type = int, default = 64, help="no. of epochs")
-args = parser.parse_args()
+args = parser.parse_args("")
 epochs = args.epochs
 lr = args.lr
 batch_size = args.batch_size