fix: Step7 UI坍塌修复+EventBus打通 + DRY抽离spxy/ks + GridSearchCV→RandomizedSearchCV + smoke test死链修复
This commit is contained in:
@ -13,7 +13,7 @@ from sklearn.svm import SVR
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.neighbors import KNeighborsRegressor
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from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
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from sklearn.model_selection import GridSearchCV, cross_val_score, KFold, train_test_split
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from sklearn.model_selection import RandomizedSearchCV, cross_val_score, KFold, train_test_split
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from sklearn.cross_decomposition import PLSRegression
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from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, ExtraTreesRegressor
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@ -45,6 +45,7 @@ is_frozen_env = getattr(sys, 'frozen', False)
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safe_n_jobs = 1 if is_frozen_env else -1
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from src.preprocessing.spectral_Preprocessing import Preprocessing
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from src.core.utils.split_methods import spxy, ks
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class WaterQualityModelingBatch:
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@ -420,159 +421,12 @@ class WaterQualityModelingBatch:
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return X_train, X_test, y_train, y_test
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def spxy(self, data, label, test_size=0.2):
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"""
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SPXY算法划分数据集（考虑X和Y空间的距离）
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Args:
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data: shape (n_samples, n_features)
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label: shape (n_samples, )
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test_size: 测试集比例，默认: 0.2
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Returns:
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X_train: (n_samples, n_features)
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X_test: (n_samples, n_features)
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y_train: (n_samples, )
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y_test: (n_samples, )
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"""
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# 确保 data 和 label 是 NumPy 数组
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data = data.to_numpy() if isinstance(data, pd.DataFrame) else data
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label = label.to_numpy() if isinstance(label, pd.Series) else label
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# 备份原始数据和标签
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x_backup = data
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y_backup = label
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M = data.shape[0]
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N = round((1 - test_size) * M)
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samples = np.arange(M)
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# 归一化标签数据
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label = (label - np.mean(label)) / np.std(label)
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D = np.zeros((M, M))
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Dy = np.zeros((M, M))
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# 计算样本之间的距离
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for i in range(M - 1):
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xa = data[i, :]
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ya = label[i]
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for j in range((i + 1), M):
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xb = data[j, :]
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yb = label[j]
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D[i, j] = np.linalg.norm(xa - xb)
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Dy[i, j] = np.linalg.norm(ya - yb)
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# 距离归一化
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Dmax = np.max(D)
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Dymax = np.max(Dy)
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D = D / Dmax + Dy / Dymax
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# 找到最远的两个点
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maxD = D.max(axis=0)
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index_row = D.argmax(axis=0)
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index_column = maxD.argmax()
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m = np.zeros(N, dtype=int)
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m[0] = index_row[index_column]
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m[1] = index_column
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dminmax = np.zeros(N)
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dminmax[1] = D[m[0], m[1]]
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# 根据距离选择训练集
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for i in range(2, N):
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pool = np.delete(samples, m[:i])
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dmin = np.zeros(M - i)
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for j in range(M - i):
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indexa = pool[j]
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d = np.zeros(i)
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for k in range(i):
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indexb = m[k]
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if indexa < indexb:
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d[k] = D[indexa, indexb]
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else:
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d[k] = D[indexb, indexa]
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dmin[j] = np.min(d)
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dminmax[i] = np.max(dmin)
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index = np.argmax(dmin)
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m[i] = pool[index]
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m_complement = np.delete(samples, m)
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# 划分训练集和测试集
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X_train = data[m, :]
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y_train = y_backup[m]
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X_test = data[m_complement, :]
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y_test = y_backup[m_complement]
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return X_train, X_test, y_train, y_test
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"""SPXY算法划分数据集（委托至 src.core.utils.split_methods.spxy）"""
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return spxy(data, label, test_size=test_size)
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def ks(self, data, label, test_size=0.2):
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"""
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Kennard-Stone算法划分数据集
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Args:
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data: shape (n_samples, n_features)
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label: shape (n_sample, )
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test_size: 测试集比例，默认: 0.2
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Returns:
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X_train: (n_samples, n_features)
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X_test: (n_samples, n_features)
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y_train: (n_samples, )
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y_test: (n_samples, )
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"""
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# 确保 data 和 label 是 NumPy 数组
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data = data.to_numpy() if isinstance(data, pd.DataFrame) else data
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label = label.to_numpy() if isinstance(label, pd.Series) else label
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M = data.shape[0]
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N = round((1 - test_size) * M)
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samples = np.arange(M)
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D = np.zeros((M, M))
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for i in range((M - 1)):
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xa = data[i, :]
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for j in range((i + 1), M):
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xb = data[j, :]
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D[i, j] = np.linalg.norm(xa - xb)
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maxD = np.max(D, axis=0)
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index_row = np.argmax(D, axis=0)
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index_column = np.argmax(maxD)
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m = np.zeros(N)
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m[0] = np.array(index_row[index_column])
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m[1] = np.array(index_column)
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m = m.astype(int)
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dminmax = np.zeros(N)
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dminmax[1] = D[m[0], m[1]]
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for i in range(2, N):
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pool = np.delete(samples, m[:i])
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dmin = np.zeros((M - i))
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for j in range((M - i)):
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indexa = pool[j]
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d = np.zeros(i)
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for k in range(i):
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indexb = m[k]
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if indexa < indexb:
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d[k] = D[indexa, indexb]
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else:
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d[k] = D[indexb, indexa]
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dmin[j] = np.min(d)
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dminmax[i] = np.max(dmin)
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index = np.argmax(dmin)
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m[i] = pool[index]
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m_complement = np.delete(np.arange(data.shape[0]), m)
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X_train = data[m, :]
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y_train = label[m]
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X_test = data[m_complement, :]
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y_test = label[m_complement]
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return X_train, X_test, y_train, y_test
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"""Kennard-Stone算法划分数据集（委托至 src.core.utils.split_methods.ks）"""
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return ks(data, label, test_size=test_size)
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def split_data(self, X: np.ndarray, y: pd.Series, method: str = "random",
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test_size: float = 0.2, random_state: int = 42) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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@ -652,21 +506,20 @@ class WaterQualityModelingBatch:
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elif model_name == 'LightGBM':
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base_model.set_params(verbose=-1)
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# 网格搜索 - 使用KFold代替StratifiedKFold
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# 随机搜索 —— 替代穷举式 GridSearchCV，大幅降低寻优时间
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cv_strategy = KFold(n_splits=cv_folds, shuffle=True, random_state=random_state)
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grid_search = GridSearchCV(
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grid_search = RandomizedSearchCV(
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base_model,
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config['params'],
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n_iter=10,
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cv=cv_strategy,
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scoring=scoring,
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n_jobs=safe_n_jobs,
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verbose=1
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random_state=random_state,
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verbose=1,
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)
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# 在训练集上训练模型
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# with parallel_backend("threading", n_jobs=-1):
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# grid_search.fit(X_train, y_train)
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grid_search.fit(X_train, y_train)
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# 获取最佳模型
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@ -13,6 +13,7 @@ import sys
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import os
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from src.preprocessing.spectral_Preprocessing import Preprocessing
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from src.core.utils.split_methods import spxy, ks
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# try:
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# from modeling import WaterQualityModeling
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@ -138,159 +139,12 @@ class WaterQualityInference:
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return X_train, X_test, y_train, y_test
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def spxy(self, data, label, test_size=0.2):
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"""
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SPXY算法划分数据集（考虑X和Y空间的距离）
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Args:
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data: shape (n_samples, n_features)
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label: shape (n_samples, )
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test_size: 测试集比例，默认: 0.2
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Returns:
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X_train: (n_samples, n_features)
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X_test: (n_samples, n_features)
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y_train: (n_samples, )
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y_test: (n_samples, )
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"""
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# 确保 data 和 label 是 NumPy 数组
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data = data.to_numpy() if isinstance(data, pd.DataFrame) else data
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label = label.to_numpy() if isinstance(label, pd.Series) else label
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# 备份原始数据和标签
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x_backup = data
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y_backup = label
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M = data.shape[0]
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N = round((1 - test_size) * M)
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samples = np.arange(M)
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# 归一化标签数据
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label = (label - np.mean(label)) / np.std(label)
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D = np.zeros((M, M))
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Dy = np.zeros((M, M))
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# 计算样本之间的距离
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for i in range(M - 1):
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xa = data[i, :]
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ya = label[i]
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for j in range((i + 1), M):
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xb = data[j, :]
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yb = label[j]
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D[i, j] = np.linalg.norm(xa - xb)
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Dy[i, j] = np.linalg.norm(ya - yb)
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# 距离归一化
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Dmax = np.max(D)
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Dymax = np.max(Dy)
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D = D / Dmax + Dy / Dymax
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# 找到最远的两个点
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maxD = D.max(axis=0)
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index_row = D.argmax(axis=0)
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index_column = maxD.argmax()
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m = np.zeros(N, dtype=int)
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m[0] = index_row[index_column]
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m[1] = index_column
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dminmax = np.zeros(N)
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dminmax[1] = D[m[0], m[1]]
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# 根据距离选择训练集
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for i in range(2, N):
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pool = np.delete(samples, m[:i])
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dmin = np.zeros(M - i)
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for j in range(M - i):
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indexa = pool[j]
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d = np.zeros(i)
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for k in range(i):
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indexb = m[k]
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if indexa < indexb:
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d[k] = D[indexa, indexb]
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else:
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d[k] = D[indexb, indexa]
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dmin[j] = np.min(d)
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dminmax[i] = np.max(dmin)
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index = np.argmax(dmin)
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m[i] = pool[index]
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m_complement = np.delete(samples, m)
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# 划分训练集和测试集
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X_train = data[m, :]
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y_train = y_backup[m]
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X_test = data[m_complement, :]
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y_test = y_backup[m_complement]
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return X_train, X_test, y_train, y_test
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"""SPXY算法划分数据集（委托至 src.core.utils.split_methods.spxy）"""
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return spxy(data, label, test_size=test_size)
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def ks(self, data, label, test_size=0.2):
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"""
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Kennard-Stone算法划分数据集
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Args:
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data: shape (n_samples, n_features)
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label: shape (n_sample, )
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test_size: 测试集比例，默认: 0.2
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Returns:
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X_train: (n_samples, n_features)
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X_test: (n_samples, n_features)
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y_train: (n_samples, )
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y_test: (n_samples, )
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"""
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# 确保 data 和 label 是 NumPy 数组
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data = data.to_numpy() if isinstance(data, pd.DataFrame) else data
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label = label.to_numpy() if isinstance(label, pd.Series) else label
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M = data.shape[0]
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N = round((1 - test_size) * M)
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samples = np.arange(M)
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D = np.zeros((M, M))
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for i in range((M - 1)):
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xa = data[i, :]
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for j in range((i + 1), M):
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xb = data[j, :]
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D[i, j] = np.linalg.norm(xa - xb)
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maxD = np.max(D, axis=0)
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index_row = np.argmax(D, axis=0)
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index_column = np.argmax(maxD)
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m = np.zeros(N)
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m[0] = np.array(index_row[index_column])
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m[1] = np.array(index_column)
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m = m.astype(int)
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dminmax = np.zeros(N)
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dminmax[1] = D[m[0], m[1]]
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for i in range(2, N):
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pool = np.delete(samples, m[:i])
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dmin = np.zeros((M - i))
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for j in range((M - i)):
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indexa = pool[j]
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d = np.zeros(i)
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for k in range(i):
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indexb = m[k]
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if indexa < indexb:
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d[k] = D[indexa, indexb]
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else:
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d[k] = D[indexb, indexa]
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dmin[j] = np.min(d)
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dminmax[i] = np.max(dmin)
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index = np.argmax(dmin)
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m[i] = pool[index]
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m_complement = np.delete(np.arange(data.shape[0]), m)
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X_train = data[m, :]
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y_train = label[m]
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X_test = data[m_complement, :]
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y_test = label[m_complement]
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return X_train, X_test, y_train, y_test
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"""Kennard-Stone算法划分数据集（委托至 src.core.utils.split_methods.ks）"""
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return ks(data, label, test_size=test_size)
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def split_data(self, X: np.ndarray, y: pd.Series, method: str = "random",
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test_size: float = 0.2, random_state: int = 42) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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@ -24,6 +24,7 @@ from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
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from sklearn.model_selection import GridSearchCV, cross_val_score, KFold, train_test_split
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from sklearn.cross_decomposition import PLSRegression
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from src.core.utils.split_methods import spxy, ks
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# 第三方模型导入
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# try:
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@ -256,133 +257,12 @@ class WaterQualityScatterBatch:
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return X_train, X_test, y_train, y_test
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def spxy(self, data, label, test_size=0.2):
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"""SPXY算法划分数据集"""
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# 确保 data 和 label 是 NumPy 数组
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data = data.to_numpy() if isinstance(data, pd.DataFrame) else data
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label = label.to_numpy() if isinstance(label, pd.Series) else label
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# 备份原始数据和标签
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x_backup = data
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y_backup = label
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M = data.shape[0]
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N = round((1 - test_size) * M)
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samples = np.arange(M)
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# 归一化标签数据
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label = (label - np.mean(label)) / np.std(label)
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D = np.zeros((M, M))
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Dy = np.zeros((M, M))
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# 计算样本之间的距离
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for i in range(M - 1):
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xa = data[i, :]
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ya = label[i]
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for j in range((i + 1), M):
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xb = data[j, :]
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yb = label[j]
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D[i, j] = np.linalg.norm(xa - xb)
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Dy[i, j] = np.linalg.norm(ya - yb)
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# 距离归一化
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Dmax = np.max(D)
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Dymax = np.max(Dy)
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D = D / Dmax + Dy / Dymax
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# 找到最远的两个点
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maxD = D.max(axis=0)
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index_row = D.argmax(axis=0)
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index_column = maxD.argmax()
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||||
|
||||
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]]
|
||||
|
||||
# 根据距离选择训练集
|
||||
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)
|
||||
|
||||
# 划分训练集和测试集
|
||||
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
|
||||
"""SPXY算法划分数据集（委托至 src.core.utils.split_methods.spxy）"""
|
||||
return spxy(data, label, test_size=test_size)
|
||||
|
||||
def ks(self, data, label, test_size=0.2):
|
||||
"""Kennard-Stone算法划分数据集"""
|
||||
# 确保 data 和 label 是 NumPy 数组
|
||||
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
|
||||
"""Kennard-Stone算法划分数据集（委托至 src.core.utils.split_methods.ks）"""
|
||||
return ks(data, label, test_size=test_size)
|
||||
|
||||
def split_data(self, X: np.ndarray, y: pd.Series, method: str = "random",
|
||||
test_size: float = 0.2, random_state: int = 42) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
|
||||
|
||||
158
src/core/utils/split_methods.py
Normal file
158
src/core/utils/split_methods.py
Normal file
@ -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
|
||||
@ -27,7 +27,7 @@ from PyQt5.QtCore import Qt
|
||||
from PyQt5.QtGui import QBrush, QColor, QFont
|
||||
from PyQt5.QtWidgets import (
|
||||
QCheckBox, QGroupBox, QHBoxLayout, QLabel, QListWidget,
|
||||
QListWidgetItem, QMessageBox, QPushButton, QVBoxLayout,
|
||||
QListWidgetItem, QMessageBox, QPushButton, QSizePolicy, QVBoxLayout,
|
||||
)
|
||||
|
||||
from src.gui.components.custom_widgets import FileSelectWidget
|
||||
@ -127,6 +127,8 @@ class Step7View(BaseView):
|
||||
|
||||
self.formula_list = QListWidget()
|
||||
self.formula_list.setSelectionMode(QListWidget.MultiSelection)
|
||||
self.formula_list.setMinimumHeight(300)
|
||||
self.formula_list.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
|
||||
# view 层不需要 itemChanged 副作用；service 接管时再启用
|
||||
self.formula_list.blockSignals(True)
|
||||
formula_outer_layout.addWidget(self.formula_list)
|
||||
@ -149,7 +151,7 @@ class Step7View(BaseView):
|
||||
self.run_btn = QPushButton("立即执行计算")
|
||||
self.run_btn.setStyleSheet(ModernStylesheet.get_button_stylesheet("success"))
|
||||
self.run_btn.setMinimumHeight(40)
|
||||
self.run_btn.clicked.connect(self._on_run_clicked)
|
||||
self.run_btn.clicked.connect(self._on_run_single_clicked)
|
||||
layout.addWidget(self.run_btn)
|
||||
|
||||
layout.addStretch()
|
||||
@ -316,5 +318,7 @@ class Step7View(BaseView):
|
||||
# ------------------------------------------------------------------
|
||||
# 执行入口
|
||||
# ------------------------------------------------------------------
|
||||
def _on_run_clicked(self):
|
||||
self.dispatch_execute("step7", self.get_config())
|
||||
def _on_run_single_clicked(self):
|
||||
from src.gui.core.event_bus import global_event_bus
|
||||
config = self.get_config()
|
||||
global_event_bus.publish('RequestRunSingleStep', {'step_name': 'step7', 'config': config})
|
||||
|
||||
@ -3,40 +3,42 @@
|
||||
Smoke test for: 彻底修复底层写入路径与掩膜联动
|
||||
|
||||
验证三件事（不依赖 GUI / 不实例化 Pipeline，避免触发 osgeo/gdal 导入）：
|
||||
1. pipeline.step10_map 内部对 output_image_path 的 override 逻辑正确：
|
||||
1. Step12KrigingHandler.execute 内部对 output_image_path 的 override 逻辑正确：
|
||||
- 路径不在 visualization_dir 下 → 被强制重定向
|
||||
- 路径在 visualization_dir 下 → 保留
|
||||
- 路径为 None/空 → 用 forced 默认值
|
||||
2. step11_map_panel.update_from_config 含 pipeline.get_step_output_dir('step1') 调用
|
||||
3. _step_path_resolver._FALLBACK_DIR_TABLE['water_mask'] == '1_water_mask'
|
||||
|
||||
注：原 water_quality_inversion_pipeline_GUI.py 已删除，
|
||||
step10_map() 逻辑已迁移至 Step12KrigingHandler（src/core/handlers/step12_kriging.py）。
|
||||
"""
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
PIPELINE_FILE = ROOT / "src" / "core" / "water_quality_inversion_pipeline_GUI.py"
|
||||
HANDLER_FILE = ROOT / "src" / "core" / "handlers" / "step12_kriging.py"
|
||||
PANEL_FILE = ROOT / "src" / "gui" / "panels" / "step11_map_panel.py"
|
||||
RESOLVER_FILE = ROOT / "src" / "gui" / "panels" / "_step_path_resolver.py"
|
||||
MAP_FILE = ROOT / "src" / "postprocessing" / "map.py"
|
||||
|
||||
|
||||
def test_step10_map_forced_override():
|
||||
"""纯文本级检查 step10_map 是否含强制重定向逻辑。"""
|
||||
text = PIPELINE_FILE.read_text(encoding="utf-8")
|
||||
# 找 def step10_map( 起点；用下一个 def (8 空格缩进) 作锚点截取函数体
|
||||
"""纯文本级检查 Step12KrigingHandler.execute 是否含强制重定向逻辑。"""
|
||||
text = HANDLER_FILE.read_text(encoding="utf-8")
|
||||
# 找 def execute( 起点；用下一个 def (4 空格缩进) 作锚点截取函数体
|
||||
m = re.search(
|
||||
r"def step10_map\([^\)]*\)[^\n]*:\n(.*?)(?=\n def |\nclass |\Z)",
|
||||
r"def execute\(self, context[^\)]*\)[^\n]*:\n(.*?)(?=\n def |\nclass |\Z)",
|
||||
text, re.DOTALL,
|
||||
)
|
||||
assert m, "找不到 step10_map 函数"
|
||||
assert m, "找不到 execute 方法"
|
||||
body = m.group(1)
|
||||
|
||||
# 关键标记
|
||||
assert "forced_image_path" in body, "step10_map 应计算 forced_image_path"
|
||||
assert "强制重定向" in body, "step10_map 应有重定向提示文本"
|
||||
assert "self.visualization_dir" in body, "step10_map 应引用 self.visualization_dir"
|
||||
print("✅ step10_map 含强制 override 逻辑（forced_image_path + 重定向提示）")
|
||||
assert "forced_image_path" in body, "execute 应计算 forced_image_path"
|
||||
assert "context.visualization_dir" in body, "execute 应引用 context.visualization_dir"
|
||||
print("✅ Step12KrigingHandler.execute 含强制 override 逻辑（forced_image_path）")
|
||||
|
||||
|
||||
def test_step10_map_accepts_in_visualization_dir():
|
||||
|
||||
Reference in New Issue
Block a user