fix: Step7 UI坍塌修复+EventBus打通 + DRY抽离spxy/ks + GridSearchCV→RandomizedSearchCV + smoke test死链修复

src/core/utils/split_methods.py

@@ -0,0 +1,158 @@
+# -*- coding: utf-8 -*-
+"""
+数据集划分算法 —— SPXY / Kennard-Stone
+
+从 modeling_batch.py / inference_batch.py / sctter_batch.py 中抽离，
+消除三处完全相同的重复实现。
+"""
+
+import numpy as np
+import pandas as pd
+
+
+def spxy(data, label, test_size=0.2):
+    """
+    SPXY算法划分数据集（考虑X和Y空间的距离）
+
+    Args:
+        data: shape (n_samples, n_features) —— np.ndarray 或 pd.DataFrame
+        label: shape (n_samples, ) —— np.ndarray 或 pd.Series
+        test_size: 测试集比例，默认: 0.2
+
+    Returns:
+        X_train: (n_samples, n_features)
+        X_test: (n_samples, n_features)
+        y_train: (n_samples, )
+        y_test: (n_samples, )
+    """
+    data = data.to_numpy() if isinstance(data, pd.DataFrame) else data
+    label = label.to_numpy() if isinstance(label, pd.Series) else label
+
+    x_backup = data
+    y_backup = label
+
+    M = data.shape[0]
+    N = round((1 - test_size) * M)
+    samples = np.arange(M)
+
+    label = (label - np.mean(label)) / np.std(label)
+    D = np.zeros((M, M))
+    Dy = np.zeros((M, M))
+
+    for i in range(M - 1):
+        xa = data[i, :]
+        ya = label[i]
+        for j in range((i + 1), M):
+            xb = data[j, :]
+            yb = label[j]
+            D[i, j] = np.linalg.norm(xa - xb)
+            Dy[i, j] = np.linalg.norm(ya - yb)
+
+    Dmax = np.max(D)
+    Dymax = np.max(Dy)
+    D = D / Dmax + Dy / Dymax
+
+    maxD = D.max(axis=0)
+    index_row = D.argmax(axis=0)
+    index_column = maxD.argmax()
+
+    m = np.zeros(N, dtype=int)
+    m[0] = index_row[index_column]
+    m[1] = index_column
+
+    dminmax = np.zeros(N)
+    dminmax[1] = D[m[0], m[1]]
+
+    for i in range(2, N):
+        pool = np.delete(samples, m[:i])
+        dmin = np.zeros(M - i)
+        for j in range(M - i):
+            indexa = pool[j]
+            d = np.zeros(i)
+            for k in range(i):
+                indexb = m[k]
+                if indexa < indexb:
+                    d[k] = D[indexa, indexb]
+                else:
+                    d[k] = D[indexb, indexa]
+            dmin[j] = np.min(d)
+        dminmax[i] = np.max(dmin)
+        index = np.argmax(dmin)
+        m[i] = pool[index]
+
+    m_complement = np.delete(samples, m)
+
+    X_train = data[m, :]
+    y_train = y_backup[m]
+    X_test = data[m_complement, :]
+    y_test = y_backup[m_complement]
+
+    return X_train, X_test, y_train, y_test
+
+
+def ks(data, label, test_size=0.2):
+    """
+    Kennard-Stone算法划分数据集
+
+    Args:
+        data: shape (n_samples, n_features) —— np.ndarray 或 pd.DataFrame
+        label: shape (n_samples, ) —— np.ndarray 或 pd.Series
+        test_size: 测试集比例，默认: 0.2
+
+    Returns:
+        X_train: (n_samples, n_features)
+        X_test: (n_samples, n_features)
+        y_train: (n_samples, )
+        y_test: (n_samples, )
+    """
+    data = data.to_numpy() if isinstance(data, pd.DataFrame) else data
+    label = label.to_numpy() if isinstance(label, pd.Series) else label
+
+    M = data.shape[0]
+    N = round((1 - test_size) * M)
+    samples = np.arange(M)
+
+    D = np.zeros((M, M))
+
+    for i in range((M - 1)):
+        xa = data[i, :]
+        for j in range((i + 1), M):
+            xb = data[j, :]
+            D[i, j] = np.linalg.norm(xa - xb)
+
+    maxD = np.max(D, axis=0)
+    index_row = np.argmax(D, axis=0)
+    index_column = np.argmax(maxD)
+
+    m = np.zeros(N)
+    m[0] = np.array(index_row[index_column])
+    m[1] = np.array(index_column)
+    m = m.astype(int)
+    dminmax = np.zeros(N)
+    dminmax[1] = D[m[0], m[1]]
+
+    for i in range(2, N):
+        pool = np.delete(samples, m[:i])
+        dmin = np.zeros((M - i))
+        for j in range((M - i)):
+            indexa = pool[j]
+            d = np.zeros(i)
+            for k in range(i):
+                indexb = m[k]
+                if indexa < indexb:
+                    d[k] = D[indexa, indexb]
+                else:
+                    d[k] = D[indexb, indexa]
+            dmin[j] = np.min(d)
+        dminmax[i] = np.max(dmin)
+        index = np.argmax(dmin)
+        m[i] = pool[index]
+
+    m_complement = np.delete(np.arange(data.shape[0]), m)
+
+    X_train = data[m, :]
+    y_train = label[m]
+    X_test = data[m_complement, :]
+    y_test = label[m_complement]
+
+    return X_train, X_test, y_train, y_test