如何在Python中动态构建模型？

发布于 2025-01-23 11:24:09 字数 524 浏览 3 评论 0原文

我有一个带有Alogitthm和超级参数的列的数据框，均以字符串格式，

看起来像这样。

id   Alg
------------
1    RandomForestClassifier(max_depth=2, random_state=0)
2    LinearRegression(n_jobs=-1)
3    RandomForestClassifier(n_estimators=750)
4    ExtraTreesClassifier(criterion='entropy')

有什么方法可以动态运行该算法？

所以我的代码将是这样的

for strCode in df["Alg"]:
   model = SomeFunction(strCode) # <---------------- strCode should run dynamically so model can be generated
   model.fit(X_train, y_train)

原文

I have a dataframe that has a column with an alogitthm and hyperparameters all in string format

it looks like this.

id   Alg
------------
1    RandomForestClassifier(max_depth=2, random_state=0)
2    LinearRegression(n_jobs=-1)
3    RandomForestClassifier(n_estimators=750)
4    ExtraTreesClassifier(criterion='entropy')

is there a way I can run the algorithm dynamically?

so my code will be something like this

for strCode in df["Alg"]:
   model = SomeFunction(strCode) # <---------------- strCode should run dynamically so model can be generated
   model.fit(X_train, y_train)

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缱倦旧时光 2025-01-30 11:24:09

您可能正在寻找 evary（） 函数将传递的字符串评估为python表达式。

from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier  # noqa
from sklearn.linear_model import LinearRegression  # noqa

algos = [
    "RandomForestClassifier(max_depth=2, random_state=0)",
    "LinearRegression(n_jobs=-1)",
    "RandomForestClassifier(n_estimators=750)",
    "ExtraTreesClassifier(criterion='entropy')",
]
for strCode in algos:
    model = eval(strCode)  # eval basically executes whatever string it gets
    model.fit(X_train, y_train)

You might be looking for the eval() function which evaluates the passed string as a python expression.

from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier  # noqa
from sklearn.linear_model import LinearRegression  # noqa

algos = [
    "RandomForestClassifier(max_depth=2, random_state=0)",
    "LinearRegression(n_jobs=-1)",
    "RandomForestClassifier(n_estimators=750)",
    "ExtraTreesClassifier(criterion='entropy')",
]
for strCode in algos:
    model = eval(strCode)  # eval basically executes whatever string it gets
    model.fit(X_train, y_train)

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