Progress Bar

- added progress bar
- divided dataset into validation, test, learning
- added patience for learning

src/app.py

@@ -1,16 +1,17 @@
 from learning.data import Dataset
 from learning.supervised import LinearRegression
 from learning.ml import MLRegression
+from typing import Callable
 
-def auto_mpg() -> tuple[int, int, MLRegression]:
+def auto_mpg() -> tuple[int, MLRegression]:
     df = Dataset("datasets\\auto-mpg.csv", "MPG")
 
     df.to_numbers(["HP"])
     df.handle_na()
     df.regularize(excepts=["Cylinders","Year","Origin"])
-    return (5000, 1000, LinearRegression(df, learning_rate=0.0001))
+    return (1000, LinearRegression(df, learning_rate=0.0001))
 
-def automobile() -> tuple[int, int, MLRegression]:
+def automobile() -> tuple[int, MLRegression]:
     df = Dataset("datasets\\regression\\automobile.csv", "symboling")
 
     attributes_to_modify = ["fuel-system", "engine-type", "drive-wheels", "body-style", "make", "engine-location", "aspiration", "fuel-type", "num-of-cylinders", "num-of-doors"]
@@ -18,23 +19,27 @@ def automobile() -> tuple[int, int, MLRegression]:
     df.to_numbers(["normalized-losses", "bore", "stroke", "horsepower", "peak-rpm", "price"])
     df.handle_na()
     df.regularize(excepts=attributes_to_modify)
-    return (5000, 1000, LinearRegression(df, learning_rate=0.002))
+    return (1000, LinearRegression(df, learning_rate=0.004))
 
-def power_plant() -> tuple[int, int, MLRegression]:
+def power_plant() -> tuple[int, MLRegression]:
     df = Dataset("datasets\\regression\\power-plant.csv", "energy-output")
     df.regularize()
-    return (1000, 80, LinearRegression(df, learning_rate=0.1))
+    return (80, LinearRegression(df, learning_rate=0.1))
 
-epoch, skip, ml = automobile()
-ml.learn(epoch)
-ml.plot(skip=skip)
 
-"""
-for _ in range(0, epoch):
-    train_err = lr.learning_step()
-    test_err = lr.test_error()
 
-    plot.update("training", train_err)
-    plot.update("test", test_err)
-    plot.update_limits()
-"""
+def learn_dataset(function:Callable[..., tuple[int, MLRegression]], epochs:int=100000, verbose=True)-> None:
+    skip, ml = function()
+    ml.learn(epochs, verbose=verbose)
+
+    err_tests = ml.test_loss()
+    err_valid = ml.validation_loss()
+    err_learn = ml.learning_loss()
+    print(f"Loss value: tests={err_tests:1.5f}, valid={err_valid:1.5f}, learn={err_learn:1.5f}")
+
+    ml.plot(skip=skip)
+
+
+
+if __name__ == "__main__":
+    learn_dataset(auto_mpg)