Project struct

src/learning/data.py

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+import pandas as pd
+import numpy as np
+
+from typing_extensions import Self
+
+class Dataset:
+    def __init__(self, csv:str, target:str) -> None:
+        data = pd.read_csv(csv)
+
+        # move target to the start
+        col_target = data.pop(target)
+        data.insert(0, target, col_target)
+        data.insert(1, "Bias", 1.0)
+
+        self.original = data
+        self.data = data
+        self.target = target
+        self.classification = (data[target].dtype == object)
+
+    def regularize(self, excepts:list=[]) -> Self:
+        excepts.append(self.target)
+        excepts.append("Bias")
+        for col in self.data:
+            if col not in excepts:
+                dt = self.data[col]
+                self.data[col] = (dt - dt.mean()) / dt.std()
+        return self
+
+    def factorize(self, columns:list=[]) -> Self:
+        data = self.data
+        for col in columns:
+            data[col] = pd.factorize(data[col])[0]
+        return self
+
+    def to_numbers(self, columns:list=[]) -> Self:
+        data = self.data
+        for col in self.data.columns:
+            if data[col].dtype == object:
+                data[col] = pd.to_numeric(data[col], errors='coerce')
+        return self
+
+    def handle_na(self) -> Self:
+        self.data = self.data.dropna()
+        return self
+
+    def shuffle(self) -> Self:
+        self.data = self.data.sample(frac=1)
+        return self
+
+    def as_ndarray(self) -> np.ndarray:
+        return self.data.to_numpy()
+
+    def get_index(self, column:str) -> int:
+        return self.data.columns.get_loc(column)
+
+class PrincipalComponentAnalisys:
+    def __init__(self, data:np.ndarray) -> None:
+        self.data = data
+
+    def reduce(self, total:int=0, threshold:float=1) -> Self:
+        columns = self.data.shape[1]
+        if total > columns or total <= 0:
+            total = columns
+        if threshold <= 0 or threshold > 1:
+            threshold = 1
+