SELECT f.id AS firmId, f.name AS firmName,COUNT(1) AS num,up.phoneNum FROM ty_order o LEFT JOIN ty_firm f ON f.id = o.firmId LEFT JOIN user up ON up.firmId = o.id AND up.type = '档主' AND up.effectivStatus = '启用' WHERE o.status IN ('赊欠','已结清') AND ( f.name LIKE '%2%' OR f.id LIKE '%2%' OR up.phoneNum LIKE '%2%' ) GROUP BY f.id ORDER BY COUNT(1) DESC 中的like查询查询变慢

对于 LIKE 查询，如果在查询条件中使用了通配符 %，那么 MySQL 就无法使用索引进行优化，因为它无法确定哪些行匹配查询条件。因此，可以考虑以下几种优化方式：

1. 尽量避免在查询条件中使用通配符 %，可以考虑使用全文索引或者其他方式进行优化。

2. 对于类似于 '%2%' 这样的查询条件，可以使用字符集合索引来进行优化。例如，创建一个字符集合索引，包含所有可能出现的字符，然后在查询条件中使用 IN 运算符，将查询条件拆分成多个等值查询，这样就可以使用索引进行优化了。

3. 将 LIKE 查询改为全文索引查询，使用 MySQL 自带的全文索引功能进行优化。

4. 对于一些经常出现的查询条件，可以将这些条件提前进行处理，例如将查询条件中的 '%2%' 替换为一个固定的字符串，然后使用这个固定的字符串进行查询。

5. 优化查询语句本身，例如使用 EXPLAIN 命令查看查询计划，查找可能存在的性能瓶颈，进行适当的索引优化等。

相关问题

纠正代码：trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv') testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv') y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited'] X_train = trainsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited'] X_test = testsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42) rfc.fit(X_train, y_train_forced_turnover_nolimited) y_pred = rfc.predict_proba(X_test) # 计算AUC值 auc = roc_auc_score(y_test_forced_turnover_nolimited, y_pred) # 输出AUC值 print('测试集AUC值为：', auc)

trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv')
testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv')
y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited']
X_train = trainsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',
'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',
'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',
'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',
'm3_forced_turnover_5year','m3_forced_turnover_10year','CEOid','CEO_turnover_N','year',
'Firmid','appo_year'], axis=1)
y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited']
X_test = testsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',
'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',
'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',
'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',
'm3_forced_turnover_5year','m3_forced_turnover_10year','CEOid','CEO_turnover_N','year',
'Firmid','appo_year'], axis=1)

from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
rfc.fit(X_train, y_train_forced_turnover_nolimited)
y_pred = rfc.predict_proba(X_test)[:, 1] # 计算AUC值时需要使用预测结果的概率值而不是预测结果本身

from sklearn.metrics import roc_auc_score

auc = roc_auc_score(y_test_forced_turnover_nolimited, y_pred) # 计算AUC值
print('测试集AUC值为：', auc) # 输出AUC值

纠正这段代码：trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv') testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv') y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited'] X_train = trainsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited'] X_test = testsets.drop(['m3_P_perf_ind_all_1','m3_P_perf_ind_all_2','m3_P_perf_ind_all_3','m3_P_perf_ind_allind_1',\ 'm3_P_perf_ind_allind_2','m3_P_perf_ind_allind_3','m3_P_perf_ind_year_1','m3_P_perf_ind_year_2',\ 'm3_P_perf_ind_year_3','m3_forced_turnover_nolimited','m3_forced_turnover_3mon',\ 'm3_forced_turnover_6mon','m3_forced_turnover_1year','m3_forced_turnover_3year',\ 'm3_forced_turnover_5year','m3_forced_turnover_10year',\ 'CEOid','CEO_turnover_N','year','Firmid','appo_year'],axis=1) model = Sequential() model.add(Dense(64, activation='relu', input_dim=X_train.shape[1])) model.add(Dropout(0.5)) model.add(Dense(32, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1) model_checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True, verbose=1) history = model.fit(X_train, epochs=50, batch_size=32, validation_data=(y_train_forced_turnover_nolimited), callbacks=[early_stopping, model_checkpoint]) model.load_weights('model.h5') pred = model.predict(X_test) auc = roc_auc_score(test.iloc[:, -1], pred) print('Testing AUC:', auc)

纠正后的代码如下：

import pandas as pd
from sklearn.metrics import roc_auc_score
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

trainsets = pd.read_csv('/Users/zhangxinyu/Desktop/trainsets82.csv')
testsets = pd.read_csv('/Users/zhangxinyu/Desktop/testsets82.csv')

y_train_forced_turnover_nolimited = trainsets['m3_forced_turnover_nolimited']
X_train = trainsets.drop(['m3_P_perf_ind_all_1', 'm3_P_perf_ind_all_2', 'm3_P_perf_ind_all_3', 'm3_P_perf_ind_allind_1',
                          'm3_P_perf_ind_allind_2', 'm3_P_perf_ind_allind_3', 'm3_P_perf_ind_year_1', 'm3_P_perf_ind_year_2',
                          'm3_P_perf_ind_year_3', 'm3_forced_turnover_nolimited', 'm3_forced_turnover_3mon',
                          'm3_forced_turnover_6mon', 'm3_forced_turnover_1year', 'm3_forced_turnover_3year',
                          'm3_forced_turnover_5year', 'm3_forced_turnover_10year', 'CEOid', 'CEO_turnover_N', 'year',
                          'Firmid', 'appo_year'], axis=1)

y_test_forced_turnover_nolimited = testsets['m3_forced_turnover_nolimited']
X_test = testsets.drop(['m3_P_perf_ind_all_1', 'm3_P_perf_ind_all_2', 'm3_P_perf_ind_all_3', 'm3_P_perf_ind_allind_1',
                         'm3_P_perf_ind_allind_2', 'm3_P_perf_ind_allind_3', 'm3_P_perf_ind_year_1', 'm3_P_perf_ind_year_2',
                         'm3_P_perf_ind_year_3', 'm3_forced_turnover_nolimited', 'm3_forced_turnover_3mon',
                         'm3_forced_turnover_6mon', 'm3_forced_turnover_1year', 'm3_forced_turnover_3year',
                         'm3_forced_turnover_5year', 'm3_forced_turnover_10year', 'CEOid', 'CEO_turnover_N', 'year',
                         'Firmid', 'appo_year'], axis=1)

model = Sequential()
model.add(Dense(64, activation='relu', input_dim=X_train.shape[1]))
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1)
model_checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True, verbose=1)

history = model.fit(X_train, y_train_forced_turnover_nolimited, epochs=50, batch_size=32, validation_data=(X_test, y_test_forced_turnover_nolimited),
                    callbacks=[early_stopping, model_checkpoint])

model.load_weights('model.h5')
pred = model.predict(X_test)

auc = roc_auc_score(y_test_forced_turnover_nolimited, pred)
print('Testing AUC:', auc)

主要的改动有：

1. 加载了 roc_auc_score 函数，用于计算模型的 AUC 值。
2. 在 model.fit() 函数中添加了标签 y_train_forced_turnover_nolimited 和 validation_data 参数，用于训练和验证模型。
3. 将测试数据集的标签改为 y_test_forced_turnover_nolimited，用于计算模型的 AUC 值。
4. 在 print() 函数中输出了模型的 AUC 值。