How do you use GridSearchCV to search values for alpha in lasso and ridge regression models?-CodePudding

I'm doing an exercise on using sklearn Pipelines and GridSearchCV to find values for values for alpha in lasso and ridge regression models, where we also use SimpleImputer to take care of some missing values.

However, I keep getting the following traceback error:

ValueError: Invalid parameter alpha for estimator Pipeline

However, I'm certain that Sklearn's Ridge and Lasso both have the alpha parameter and have double checked on the sklearn docs.

Where am I going wrong? My code is below:

import numpy as np
import pandas as pd

from sklearn import svm, datasets
from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.model_selection import train_test_split, GridSearchCV

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn import metrics

columns = ["sex","length","diam","height","whole","shucked","viscera","shell","age"]
df = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data",names=columns)

y = df.age
X=df.drop(columns=['age'])
num_cols = X.select_dtypes(include=np.number).columns
cat_cols = X.select_dtypes(include=['object']).columns

#create some missing values
for i in range(1000):
    X.loc[np.random.choice(X.index),np.random.choice(X.columns)] = np.nan

x_train, x_test, y_train, y_test = train_test_split(X,y, random_state=0, test_size=0.25)

cat_vals = Pipeline([("imputer",SimpleImputer(strategy='most_frequent')), ("ohe",OneHotEncoder(sparse=False, drop='first'))])
num_vals = Pipeline([("imputer",SimpleImputer(strategy='mean')), ("scale",StandardScaler())])

preprocess = ColumnTransformer(
    transformers=[
        ("cat_process", cat_vals, cat_cols),
        ("num_process", num_vals, num_cols)
    ]
)

#Create a pipeline with preprocess and a linear regression model
pipeline = Pipeline([("preprocess",preprocess), 
                     ("regr",LinearRegression())])

#simple parameter grid, with and without the intercept
param_grid = {
    'regr': [LinearRegression()],
    "regr__fit_intercept": [True,False]
}

# 1. Update the dictionary to include values for alpha in lasso and ridge regression models. Use np.logspace(-4,2,10).
param_grid['alpha'] = np.logspace(-4,2,10)
param_grid['regr'] = [Lasso(), Ridge()]

# 2.  Fit the GridSearchCV on the training data and print the best estimator and score from the search.
gscv = GridSearchCV(pipeline, param_grid, scoring='neg_mean_squared_error', cv=5)
gscv.fit(x_train, y_train)
print(gscv.best_params_)
print(gscv.best_score_)
print(gscv.best_estimator_)

CodePudding user response：

The parameter name should be given a prefix according to the pipeline stage name, i.e. regr__alpha - just like you did for the fit_intercept.