add command line arguments

digits.py

@@ -8,6 +8,7 @@
 from utils import preprocess_data, tune_hparams, split_train_dev_test,read_digits,predict_and_eval
 from joblib import load
 import pandas as pd
+import argparse, sys
 
 # The digits dataset consists of 8x8 pixel images of digits. The images attribute of the dataset stores 8x8 arrays of grayscale values for each image. We will use these arrays to visualize the first 4 images. The target attribute of the dataset stores the digit each image represents and this is included in the title of the 4 plots below.
 # Note: if we were working from image files (e.g., ‘png’ files), we would load them using matplotlib.pyplot.imread.
@@ -16,18 +17,37 @@
 
 x,y = read_digits()
 
+parser=argparse.ArgumentParser()
+
+parser.add_argument("--runs", help="number of runs")
+parser.add_argument("--test_sizes", help="comma sprated value of test sizes")
+parser.add_argument("--dev_sizes", help="comma sprated value of dev sizes")
+parser.add_argument("--models", help="comma sprated value of models")
+
+args=parser.parse_args()
+
+max_runs = int(args.runs)  
+test_sizes = args.test_sizes.split(',')
+test_sizes = [float(i) for i in test_sizes]
+dev_sizes = args.dev_sizes.split(',')
+dev_sizes = [float(i) for i in dev_sizes]
+models = args.models.split(',')
+models = [str(i) for i in models] 
+
+
+
 #print("Total number of samples : ", len(x))
 
 #print("(number of samples,length of image,height of image) is:",x.shape)
 
 # test_sizes = [0.1, 0.2, 0.3]
 # dev_sizes = [0.1, 0.2, 0.3]
 
-test_sizes = [0.2]
-dev_sizes = [0.2]
+# test_sizes = [0.2]
+# dev_sizes = [0.2]
 results = []
 
-for i in range(5):
+for i in range(max_runs):
     for test_size in test_sizes:
         for dev_size in dev_sizes:
         # 3. Data splitting
@@ -47,8 +67,6 @@
             max_depth = [5,10,15,20,50,100]
             classifer_hparam['tree'] = [{'max_depth': depth} for depth in max_depth]
 
-            models = ['svm','tree']
-
         # Predict the value of the digit on the test subset
         # 6.Predict and Evaluate 
             for model in models: