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本文约2900字，建议阅读5分钟
 
      
在进行数据科学时，可能会浪费大量时间编码并等待计算机运行某些东西。所以我选择了一些 Python 库，可以帮助你节省宝贵的时间。
 
     
 
    
 
   
 
  
 
   
   
 

1、Optuna

2、ITMO_FS

>>> from sklearn.linear_model import SGDClassifier
>>> from ITMO_FS.embedded import MOS

>>> X, y = make_classification(n_samples=300, n_features=10, random_state=0, n_informative=2)
>>> sel = MOS()
>>> trX = sel.fit_transform(X, y, smote=False)

>>> cl1 = SGDClassifier()
>>> cl1.fit(X, y)
>>> cl1.score(X, y)
0.9033333333333333

>>> cl2 = SGDClassifier()
>>> cl2.fit(trX, y)
>>> cl2.score(trX, y)
0.9433333333333334

3、shap-hypetune

 
   
   
 

  
    
    
  “SHAP（SHapley Additive exPlanations）是一种博弈论方法，用于解释任何机器学习模型的输出。”
 
   
   
 

4、PyCaret

# load dataset
from pycaret.datasets import get_data
diabetes = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data = diabetes, target = 'Class variable')

# compare models
best = compare_models()

from pycaret.datasets import get_data
juice = get_data('juice')
from pycaret.classification import *
exp_name = setup(data = juice, target = 'Purchase')
lr = create_model('lr')
create_app(lr)

from pycaret.datasets import get_data
juice = get_data('juice')
from pycaret.classification import *
exp_name = setup(data = juice, target = 'Purchase')
lr = create_model('lr')
create_api(lr, 'lr_api')
create_docker('lr_api')

5、floWeaver

在显示转化漏斗、营销旅程或预算分配的数据时，它们非常有用（上例）。入口数据应采用以下格式：“源 x 目标 x 值”，只需一行代码即可创建此类图（非常具体，但也非常直观）。

6、Gradio

7、Terality

8、torch-handle

from collections import OrderedDict
import torch
from torchhandle.workflow import BaseContext


class Net(torch.nn.Module):
  def __init__(self, ):
      super().__init__()
      self.layer = torch.nn.Sequential(OrderedDict([
          ('l1', torch.nn.Linear(10, 20)),
          ('a1', torch.nn.ReLU()),
          ('l2', torch.nn.Linear(20, 10)),
          ('a2', torch.nn.ReLU()),
          ('l3', torch.nn.Linear(10, 1))
      ]))

  def forward(self, x):
      x = self.layer(x)
      return x
   
num_samples, num_features = int(1e4), int(1e1)
X, Y = torch.rand(num_samples, num_features), torch.rand(num_samples)
dataset = torch.utils.data.TensorDataset(X, Y)
trn_loader = torch.utils.data.DataLoader(dataset, batch_size=64, num_workers=0, shuffle=True)
loaders = {"train": trn_loader, "valid": trn_loader}
device = 'cuda' if torch.cuda.is_available() else 'cpu'

model = {"fn": Net}
criterion = {"fn": torch.nn.MSELoss}
optimizer = {"fn": torch.optim.Adam,
            "args": {"lr": 0.1},
            "params": {"layer.l1.weight": {"lr": 0.01},
                      "layer.l1.bias": {"lr": 0.02}}
            }
scheduler = {"fn": torch.optim.lr_scheduler.StepLR,
            "args": {"step_size": 2, "gamma": 0.9}
            }

c = BaseContext(model=model,
              criterion=criterion,
              optimizer=optimizer,
              scheduler=scheduler,
              context_tag="ex01")
train = c.make_train_session(device, dataloader=loaders)
train.train(epochs=10)