Python使用Joblib模块实现加快任务处理速度

pip install joblib

import joblib

from joblib import delayed

# 定义一个耗时计算的函数
def compute(x):
    return x * x

# 创建延迟对象
delayed_compute = delayed(compute)

# 执行延迟对象
result = delayed_compute(5)
print(result)  # 输出：<function compute at 0x7f1476c485e0>

from joblib import Memory

# 创建内存缓存对象
memory = Memory(location='./cache')

# 定义一个耗时计算的函数，并使用内存缓存装饰它
@memory.cache
def compute(x):
    return x * x

# 第一次调用，会进行计算并将结果缓存起来
result1 = compute(5)
print(result1)  # 输出：25

# 第二次调用，直接从缓存中获取结果，而不是重新计算
result2 = compute(5)
print(result2)  # 输出：25

from joblib import Parallel, delayed

# 定义一个耗时计算的函数
def compute(x):
    return x * x

# 并行执行多个函数调用
results = Parallel(n_jobs=2, backend='threading')(delayed(compute)(i) for i in range(10))
print(results)  # 输出：[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

from joblib import parallel

# 定义一个耗时计算的函数，并使用parallel装饰它
@parallel(n_jobs=2, backend='threading')
def compute(x):
    return x * x

# 并行执行多个函数调用
results = compute(range(10))
print(results)  # 输出：[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

from joblib import Memory

# 创建内存映射对象
memory = Memory(location='./mmap')

# 定义一个耗时计算的函数，并使用内存映射装饰它
@memory.cache
def compute(x):
    return x * x

# 并行执行多个函数调用
results = Parallel(n_jobs=2, backend='threading')(delayed(compute)(i) for i in range(10))
print(results)  # 输出：[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

import numpy as np
from joblib import Parallel, delayed

# 定义一个耗时计算的函数
def compute(x):
    return np.sum(x)

# 并行执行多个函数调用
data = [np.random.rand(1000000) for _ in range(10)]
results = Parallel(n_jobs=2, backend='threading')(delayed(compute)(x) for x in data)
print(results)

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

# 定义参数网格
param_grid = {'n_estimators': [10, 20, 30], 'max_depth': [None, 5, 10]}

# 创建随机森林分类器
rf = RandomForestClassifier()

# 使用GridSearchCV进行参数搜索
grid_search = GridSearchCV(rf, param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train, y_train)

import pandas as pd
from joblib import Parallel, delayed

# 读取数据集
data = pd.read_csv('data.csv')

# 定义一个耗时计算的函数
def compute(row):
    return row['x'] * row['y']

# 并行执行多个函数调用
results = Parallel(n_jobs=2, backend='threading')(delayed(compute)(row) for _, row in data.iterrows())
print(results)