Write main program script for training and predicting

Done via CLI

src/main.py

+from pathlib import Path
+from pandas import read_csv
+from autogluon.tabular import TabularDataset, TabularPredictor
+from datetime import datetime
 
 
+datasets = {
+        "a": "DataBinary",
+        "b": "DataMulti"
+    }
+
 def main():
     print("Training and predictor")
 
+    i = input("Which set would you like to work on?\n[A] DataBinary\n[B] DataMulti\n")
+    i = i.lower()
+
+    if i!="a" and i!="b":
+        print("I don't know that dataset!")
+        exit()
+
+    handleDataset(i)
+
+def handleDataset(ds):
+    i = input(f"What would you like to do to {ds}?\n[A] Predict\n[B] Train\n").lower()
+    if i=='b':
+        handleTrain(ds)
+    elif i =='a':
+        handlePredict(ds)
+
+def handlePredict(ds):
+    print(f"Predicting {ds} with model")
+
+    root = Path(__file__).parent.parent
+    path = root / "models" / datasets[ds]
+
+    files = path.glob('*')
+
+    print("Available predictors: ")
+    for i,val in enumerate(files):
+        print(f"[{i:2<}] {val}")
+    print("[q] Quit")
+
+    i = input()
+    if i == 'q':
+        exit()
+    try:
+        i = int(i)
+        predict(ds, list(path.glob('*'))[i])
+
+    except Exception:
+        print()
+
+def predict(ds, model):
+    root = Path(__file__).parent.parent
+    testing = root / "resources" / f"Testing{datasets[ds]}.csv"
+    testingData = read_csv(testing, header=None)
+    savePath = root / "output" / f"Testing{datasets[ds]}.csv"
+
+    predictor = TabularPredictor.load(path=model)
+
+    a = predictor.predict(data=testingData)
+    testingData[128] = a
+    print(a)
+
+    a = savePath.absolute().as_posix()
+    print(f"Saving to {a}")
+    testingData.to_csv(path_or_buf=a, header=False)
+
+
+def handleTrain(ds):
+    print("Starting training")
+
+    root = Path(__file__).parent.parent
+    path = root / "models" / datasets[ds]
+    name = datetime.now().isoformat()
+
+    file = path / name
+    print(f"Saving model to {file.absolute().as_posix()}")
+    train_data = read_csv(f'/home/andrei/cypersec-coursework2/resources/Training{datasets[ds]}.csv', header=None)
+    predictor = TabularPredictor(label=128,
+                                 problem_type="binary" if ds =="a" else "multiclass", 
+                                 eval_metric="accuracy",
+                                 path=file.absolute().as_posix()
+                                 )
+    predictor.fit(train_data)
+    print("Finished predictor!")
+
+
 if __name__ == "__main__":
     main()
+