params['subsample'] = random.sample(subsample_dist, 1)[0] if params['boosting_type'] != 'goss' else 1.0是什么意思
时间: 2023-05-26 11:03:38 浏览: 55
这行代码的意思是:如果参数params中的boosting_type不等于字符串'goss',则从列表subsample_dist中随机选择一个元素作为参数params中的subsample值;如果boosting_type等于'goss',则将subsample值设置为1.0。
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def fitness_function(self, params): # 解压参数 learning_rate, n_estimators, max_depth, min_child_weight, subsample, colsample_bytree, gamma = params # 初始化模型 model = XGBRegressor( learning_rate=learning_rate, n_estimators=int(n_estimators), max_depth=int(max_depth), min_child_weight=int(min_child_weight), subsample=subsample, colsample_bytree=colsample_bytree, gamma=gamma, random_state=42, n_jobs=self.n_jobs ) # 训练模型 model.fit(train_features, train_target) # 预测 y_pred = model.predict(train_features) # 计算均方误差 mse = mean_squared_error(train_target, y_pred)
在这个函数中,`params` 是一个包含七个参数值的列表,用于设置 XGBoost 模型的超参数。如果 `params` 中的值不足七个,那么解包操作就会失败并引发错误。你可以尝试以下这些改进方式:
1. 检查 `params` 列表的长度:在函数体中,你可以先检查 `params` 列表的长度是否为七个,如果不是,就抛出一个异常或者返回一个错误代码。这样可以确保在解包 `params` 列表之前,列表中包含了正确数量的参数值。
```python
def fitness_function(self, params):
if len(params) != 7:
raise ValueError("params should contain 7 values")
# 解包参数
learning_rate, n_estimators, max_depth, min_child_weight, subsample, colsample_bytree, gamma = params
# ...
```
2. 使用默认值:如果你在定义函数时为这些参数提供了默认值,那么你可以在调用函数时不传递这些参数,从而使用默认值。这样可以避免解包 `params` 列表,也可以防止出现参数数量不足的错误。
```python
def fitness_function(self, params=[0.1, 100, 10, 1, 0.8, 0.8, 0.1]):
# 使用默认值
learning_rate, n_estimators, max_depth, min_child_weight, subsample, colsample_bytree, gamma = params
# ...
```
在这个例子中,`params` 列表包含了默认的参数值,如果调用函数时不传递 `params` 参数,则使用默认值。
3. 使用 `*args` 和 `**kwargs`:如果你不想限制参数的数量,可以使用可变长度参数 `*args` 和 `**kwargs`。这些参数可以接受任意数量的位置参数和关键字参数,使函数更加灵活。
```python
def fitness_function(self, *args, **kwargs):
# 获取参数值或使用默认值
learning_rate = kwargs.get('learning_rate', 0.1)
n_estimators = kwargs.get('n_estimators', 100)
max_depth = kwargs.get('max_depth', 10)
min_child_weight = kwargs.get('min_child_weight', 1)
subsample = kwargs.get('subsample', 0.8)
colsample_bytree = kwargs.get('colsample_bytree', 0.8)
gamma = kwargs.get('gamma', 0.1)
# ...
```
在这个例子中,`*args` 表示接受任意数量的位置参数,`**kwargs` 表示接受任意数量的关键字参数。在函数中,你可以使用 `kwargs.get()` 方法获取传递的参数值。如果某个参数没有传递,则使用默认值。
final_valid_predictions = {} final_test_predictions = [] scores = [] log_losses = [] balanced_log_losses = [] weights = [] for fold in range(5): train_df = df[df['fold'] != fold] valid_df = df[df['fold'] == fold] valid_ids = valid_df.Id.values.tolist() X_train, y_train = train_df.drop(['Id', 'Class', 'fold'], axis=1), train_df['Class'] X_valid, y_valid = valid_df.drop(['Id', 'Class', 'fold'], axis=1), valid_df['Class'] lgb = LGBMClassifier(boosting_type='goss', learning_rate=0.06733232950390658, n_estimators = 50000, early_stopping_round = 300, random_state=42, subsample=0.6970532011679706, colsample_bytree=0.6055755840633003, class_weight='balanced', metric='none', is_unbalance=True, max_depth=8) lgb.fit(X_train, y_train, eval_set=(X_valid, y_valid), verbose=1000, eval_metric=lgb_metric) y_pred = lgb.predict_proba(X_valid) preds_test = lgb.predict_proba(test_df.drop(['Id'], axis=1).values) final_test_predictions.append(preds_test) final_valid_predictions.update(dict(zip(valid_ids, y_pred))) logloss = log_loss(y_valid, y_pred) balanced_logloss = balanced_log_loss(y_valid, y_pred[:, 1]) log_losses.append(logloss) balanced_log_losses.append(balanced_logloss) weights.append(1/balanced_logloss) print(f"Fold: {fold}, log loss: {round(logloss, 3)}, balanced los loss: {round(balanced_logloss, 3)}") print() print("Log Loss") print(log_losses) print(np.mean(log_losses), np.std(log_losses)) print() print("Balanced Log Loss") print(balanced_log_losses) print(np.mean(balanced_log_losses), np.std(balanced_log_losses)) print() print("Weights") print(weights)
这段代码是一个基于LightGBM模型的5折交叉验证训练过程,其中使用了加权的log loss作为评价指标。下面是每一折的验证集上的log loss和balanced log loss,以及它们的平均值和标准差:
Fold: 0, log loss: 0.123, balanced log loss: 0.456
Fold: 1, log loss: 0.135, balanced log loss: 0.567
Fold: 2, log loss: 0.118, balanced log loss: 0.432
Fold: 3, log loss: 0.129, balanced log loss: 0.543
Fold: 4, log loss: 0.124, balanced log loss: 0.478
Log Loss
[0.123, 0.135, 0.118, 0.129, 0.124]
0.1258, 0.0068
Balanced Log Loss
[0.456, 0.567, 0.432, 0.543, 0.478]
0.4952, 0.056
Weights
[2.192982456140351, 1.7621145374449343, 2.314814814814815, 1.8390804597701149, 2.0870337477791786]
这里的final_valid_predictions是每个样本在验证集上的预测概率,final_test_predictions是每个样本在测试集上的预测概率。