The goal is to write an exhaustive search to find the best parameters combination maximizing the model performance
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import RandomizedSearchCV
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
# This line is currently required to import HistGradientBoostingClassifier
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
from scipy.stats import expon, uniform
from scipy.stats import randint
df = pd.read_csv(
"https://www.openml.org/data/get_csv/1595261/adult-census.csv")
# Or use the local copy:
# df = pd.read_csv('../datasets/adult-census.csv')
target_name = "class"
target = df[target_name].to_numpy()
data = df.drop(columns=target_name)
df_train, df_test, target_train, target_test = train_test_split(
data, target, random_state=42)
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder
categorical_columns = [
'workclass', 'education', 'marital-status', 'occupation',
'relationship', 'race', 'native-country', 'sex']
categories = [data[column].unique()
for column in data[categorical_columns]]
categorical_preprocessor = OrdinalEncoder(categories=categories)
preprocessor = ColumnTransformer(
[('cat-preprocessor', categorical_preprocessor, categorical_columns)],
remainder='passthrough', sparse_threshold=0)
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.pipeline import make_pipeline
model = make_pipeline(
preprocessor, HistGradientBoostingClassifier(random_state=42))
TODO: write your solution here
Use the previously defined model (called model) and using two nested for
loops, make a search of the best combinations of the learning_rate and
max_leaf_nodes parameters. In this regard, you will need to train and test
the model by setting the parameters. The evaluation of the model should be
performed using cross_val_score. We can propose to define the following
parameters search:
learning_rate for the values 0.01, 0.1, and 1;max_leaf_nodes for the values 5, 25, 45.from sklearn.model_selection import cross_val_score
learning_rate = [0.01, 0.1, 1, 10]
max_leaf_nodes = [5, 25, 45]
best_score = 0
best_params = {}
for lr in learning_rate:
for mln in max_leaf_nodes:
model.set_params(
histgradientboostingclassifier__learning_rate=lr,
histgradientboostingclassifier__max_leaf_nodes=mln
)
scores = cross_val_score(model, df_train, target_train, cv=3)
if scores.mean() > best_score:
best_score = scores.mean()
best_params = {'learning-rate': lr, 'max leaf nodes': mln}
print(f"The best accuracy obtained is {best_score:.3f}")
print(f"The best parameters found are:\n {best_params}")