Add big test and feature selection notebook

report_score.py

@@ -51,6 +51,9 @@ def parse_arguments():
         type=int,
         required=True,
     )
+    ap.add_argument(
+        "-p", "--parameters", type=str, required=False, default="{}"
+    )
     args = ap.parse_args()
     return (
         args.set_of_files,
@@ -58,6 +61,7 @@ def parse_arguments():
         args.dataset,
         args.sql,
         bool(args.normalize),
+        args.parameters,
     )
 
 
@@ -72,7 +76,7 @@ def get_classifier(model, random_state, hyperparameters):
     return clf
 
 
-def process_dataset(dataset, verbose, model):
+def process_dataset(dataset, verbose, model, params):
     X, y = dt.load(dataset)
     scores = []
     times = []
@@ -87,7 +91,7 @@ def process_dataset(dataset, verbose, model):
         print(f"X.shape: {X.shape}")
         print(f"{X[:4]}")
         print(f"Random seeds: {random_seeds}")
-    hyperparameters = json.loads("{}")
+    hyperparameters = json.loads(params)
     if model == "stree":
         # Get the optimized parameters
         record = dbh.find_best(dataset, model, "gridsearch")
@@ -176,7 +180,7 @@ def store_string(
 
 random_seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
 standardize = False
-(set_of_files, model, dataset, sql, normalize) = parse_arguments()
+(set_of_files, model, dataset, sql, normalize, parameters) = parse_arguments()
 dbh = MySQL()
 if sql:
     sql_output = open(f"{model}.sql", "w")
@@ -216,7 +220,7 @@ if dataset == "all":
             end="",
         )
         scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
-            dataset[0], verbose=False, model=model
+            dataset[0], verbose=False, model=model, params=parameters
         )
         complexity = dict(
             nodes=float(np.mean(nodes)),
@@ -237,7 +241,7 @@ if dataset == "all":
             print(command, file=sql_output)
 else:
     scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
-        dataset, verbose=True, model=model
+        dataset, verbose=True, model=model, params=parameters
     )
     record = dbh.find_best(dataset, model, "crossval")
     accuracy = np.mean(scores)