Invalid parameter n_neighbors for estimator Pipeline

Question

I'm pretty new to Python. I've run into an issue below that I really need help with:

df = pd.read_csv('train.csv') #titanic dataset from Kaggle
df = df.loc[df.Embarked.notna(), ['Survived', 'Pclass', 'Sex', 'SibSp', 'Embarked']]

X = df.drop('Survived', axis='columns')
y = df.Survived

column_trans = make_column_transformer(
               (OneHotEncoder(), ['Sex', 'Embarked']),
               remainder='passthrough')

column_trans.fit_transform(X)

train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

param_grid = dict(n_neighbors=k_range)

knn = KNeighborsClassifier()
pipe = make_pipeline(column_trans, knn)
grid = GridSearchCV(pipe, param_grid, cv=10, scoring='accuracy')

grid.fit(train_X, train_y) #this line gives me an error

The last line gives me an error of:

ValueError: Invalid parameter n_neighbors for estimator Pipeline(memory=None,
         steps=[('columntransformer',
                 ColumnTransformer(n_jobs=None, remainder='passthrough',
                                   sparse_threshold=0.3,
                                   transformer_weights=None,
                                   transformers=[('onehotencoder',
                                                  OneHotEncoder(categories='auto',
                                                                drop=None,
                                                                dtype=<class 'numpy.float64'>,
                                                                handle_unknown='error',
                                                                sparse=True),
                                                  ['Sex', 'Embarked'])],
                                   verbose=False)),
                ('kneighborsclassifier',
                 KNeighborsClassifier(algorithm='auto', leaf_size=30,
                                      metric='minkowski', metric_params=None,
                                      n_jobs=None, n_neighbors=5, p=2,
                                      weights='uniform'))],
         verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.

What am I doing wrong here? Is it just not possible to do oneHot encoding, knn and pipeline simultaneously?

Answer 1

Parameters of pipelines can be set using __ separated parameter names, also you need the way in which you have defined your pipeline needs a revision. Please refer to the modified code below:

import pandas as pd
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline

df = pd.read_csv("titanic.csv")
df = df.drop(["Name"], axis=1)
X = df.drop('Survived', axis='columns')
y = df.Survived

train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

column_trans = make_column_transformer(
               (OneHotEncoder(), ['Sex']),
               remainder='passthrough')

knn = KNeighborsClassifier()

pipe = Pipeline(steps=[('column_trans', column_trans), ('knn', knn)])
param_grid = {
    'knn__n_neighbors': [2,5,15, 30, 45, 64]
}

grid = GridSearchCV(pipe, param_grid, cv=10, scoring='accuracy')

grid.fit(train_X,train_y)

grid.best_params_
#{'knn__n_neighbors': 5}