function to decide which classification method to use with optional parameters

My goal is to include a "mod_type" param that indicates the type of model to run, either a decision tree or knn, using kwargs to let the user pass in the optional keyword params "k" for knn and "max_depth" for decision tree. If the user passes these in, when initializing the model use them as appropriate. Return the model object.

For that, I'm using below function:

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier


def my_classification(x,y,mod_type,**kwargs):
    if mod_type == "dt":
        if max_d in kwargs.keys():
            dt = DecisionTreeClassifier(max_depth=max_d.values())
            dt.fit(x,y)
            return dt
        else:
            dt = DecisionTreeClassifier()
            dt.fit(x,y)     
            return dt       
    elif mod_type == "knn":
        if k in kwargs.keys():
            knn = KNeighborsClassifier(k.values())
            knn.fit(x,y)
            return knn
        else:
            knn = KNeighborsClassifier()
            knn.fit(x,y)
            return knn
    else:
        print("unavailable type")

iris = load_iris()
x = pd.DataFrame(iris.data)
y = iris.target
my_classification(x,y,"dt")

Understanding kwargs wasn't easy but I think I might have it now, error it's giving me is: NameError: name 'max_d' is not defined. I've tried creating them prior the function and then changing those within but it prints the model without any alteration.

Could someone please help?

CodePudding user response：

kwargs is a dictonary with the names of the arguments as keys, and their values as values.

This is how you can use it:

if mod_type == "dt":
    if "max_d" in kwargs:
        dt = DecisionTreeClassifier(max_depth=kwargs["max_d"])
        ...
elif mod_type == "knn":
    if "k" in kwargs:
        knn = KNeighborsClassifier(kwargs["k"])
        ...

CodePudding user response：

Keys in a dict object equate to strings.

if 'max_d' in kwargs.keys():

without that being a string, it's looking for an object named max_d in the kwargs.keys() list.