Bugfixes

- fix bug recursion on init
- fix bug linux path
- added regularization

.vscode/launch.json

@@ -8,7 +8,7 @@
             "name": "Python Debugger: App",
             "type": "debugpy",
             "request": "launch",
-            "program": "src\\app.py",
+            "program": "src/app.py",
             "console": "integratedTerminal"
         }
     ]

src/app.py

@@ -3,8 +3,12 @@ from learning.supervised import LinearRegression, LogisticRegression, MultiLayer
 from learning.ml import MLAlgorithm
 from typing import Callable
 
+DATASET = "datasets/"
+REGRESSION = DATASET + "regression/"
+CLASSIFICATION = DATASET + "classification/"
+
 def auto_mpg() -> tuple[int, MLAlgorithm]:
-    ds = Dataset("datasets\\auto-mpg.csv", "MPG", TargetType.Regression)
+    ds = Dataset(REGRESSION + "auto-mpg.csv", "MPG", TargetType.Regression)
 
     ds.numbers(["HP"])
     ds.handle_na()
@@ -12,7 +16,7 @@ def auto_mpg() -> tuple[int, MLAlgorithm]:
     return (1000, LinearRegression(ds, learning_rate=0.0001))
 
 def automobile() -> tuple[int, MLAlgorithm]:
-    ds = Dataset("datasets\\regression\\automobile.csv", "symboling", TargetType.Regression)
+    ds = Dataset(REGRESSION + "automobile.csv", "symboling", TargetType.Regression)
 
     attributes_to_modify = ["fuel-system", "engine-type", "drive-wheels", "body-style", "make", "engine-location", "aspiration", "fuel-type", "num-of-cylinders", "num-of-doors"]
     ds.factorize(attributes_to_modify)
@@ -22,19 +26,19 @@ def automobile() -> tuple[int, MLAlgorithm]:
     return (1000, LinearRegression(ds, learning_rate=0.004))
 
 def power_plant() -> tuple[int, MLAlgorithm]:
-    ds = Dataset("datasets\\regression\\power-plant.csv", "energy-output", TargetType.Regression)
+    ds = Dataset(REGRESSION + "power-plant.csv", "energy-output", TargetType.Regression)
     ds.normalize()
     return (80, LinearRegression(ds, learning_rate=0.1))
 
 
 def electrical_grid() -> tuple[int, MLAlgorithm]:
-    ds = Dataset("datasets\\classification\\electrical_grid.csv", "stabf", TargetType.Classification)
+    ds = Dataset(CLASSIFICATION + "electrical_grid.csv", "stabf", TargetType.Classification)
     ds.factorize(["stabf"])
     ds.normalize()
     return (1000, LogisticRegression(ds, learning_rate=0.08))
 
 def frogs() -> tuple[int, MLAlgorithm]:
-    ds = Dataset("datasets\\classification\\frogs.csv", "Species", TargetType.MultiClassification)
+    ds = Dataset(CLASSIFICATION + "frogs.csv", "Species", TargetType.MultiClassification)
     ds.remove(["Family", "Genus", "RecordID"])
     ds.factorize(["Species"])
     return (1000, MultiLayerPerceptron(ds, learning_rate=0.08))
@@ -55,5 +59,5 @@ def learn_dataset(function:Callable[..., tuple[int, MLAlgorithm]], epochs:int=10
     return ml
 
 if __name__ == "__main__":
-    ml = learn_dataset(automobile)
+    ml = learn_dataset(electrical_grid)
     print(ml.accuracy(ml.testset))

src/learning/supervised.py

@@ -8,16 +8,21 @@ from learning.data import Dataset, Data
 class GradientDescent(MLAlgorithm):
     theta:np.ndarray
     alpha:float
+    lambd:float
 
-    def __init__(self, dataset:Dataset, learning_rate:float=0.1) -> None:
-        self.__init__(dataset)
+    def __init__(self, dataset:Dataset, learning_rate:float=0.1, regularization:float=0.01) -> None:
+        super().__init__(dataset)
         self.theta = np.random.rand(self.learnset.param)
         self.alpha = max(0, learning_rate)
+        self.lambd = max(0, regularization)
 
     def learning_step(self) -> float:
         x, y, m, _ = self.learnset.as_tuple()
 
-        self.theta -= self.alpha * (1/m) * np.sum((self._h0(x) - y) * x.T, axis=1)
+        regularization = (self.lambd / m) * self.theta
+        regularization[0] = 0
+        derivative = self.alpha * (1/m) * np.sum((self._h0(x) - y) * x.T, axis=1)
+        self.theta -= derivative + regularization
         return self._loss(x, y, m)
 
     def predict_loss(self, dataset:Data) -> float:
@@ -54,5 +59,16 @@ class MultiLayerPerceptron(MLAlgorithm):
     neurons: list[np.ndarray]
 
     def __init__(self, dataset:Dataset, layers:list[int]=[4,3]) -> None:
-        self.__init__(dataset)
+        super().__init__(dataset)
+
+    def _h0(self, x:np.ndarray) -> np.ndarray:
+        pass
+    def learning_step(self) -> float:
+        pass
+    def predict_loss(self, dataset:np.ndarray) -> float:
+        pass
+    def get_parameters(self):
+        pass
+    def set_parameters(self, parameters):
+        pass