Hyperparameter tuning by grid-search

In the previous notebook, we saw that hyperparameters can affect the generalization performance of a model. In this notebook, we show how to optimize hyperparameters using a grid-search approach.

Our predictive model

Let us reload the dataset as we did previously:

import pandas as pd

adult_census = pd.read_csv("../datasets/adult-census.csv")

We extract the column containing the target.

target_name = "class"
target = adult_census[target_name]
target

0         <=50K
1         <=50K
2          >50K
3          >50K
4         <=50K
          ...  
48837     <=50K
48838      >50K
48839     <=50K
48840     <=50K
48841      >50K
Name: class, Length: 48842, dtype: object

We drop from our data the target and the "education-num" column which duplicates the information from the "education" column.

data = adult_census.drop(columns=[target_name, "education-num"])
data

	age	workclass	education	marital-status	occupation	relationship	race	sex	capital-gain	capital-loss	hours-per-week	native-country
0	25	Private	11th	Never-married	Machine-op-inspct	Own-child	Black	Male	0	0	40	United-States
1	38	Private	HS-grad	Married-civ-spouse	Farming-fishing	Husband	White	Male	0	0	50	United-States
2	28	Local-gov	Assoc-acdm	Married-civ-spouse	Protective-serv	Husband	White	Male	0	0	40	United-States
3	44	Private	Some-college	Married-civ-spouse	Machine-op-inspct	Husband	Black	Male	7688	0	40	United-States
4	18	?	Some-college	Never-married	?	Own-child	White	Female	0	0	30	United-States
...	...	...	...	...	...	...	...	...	...	...	...	...
48837	27	Private	Assoc-acdm	Married-civ-spouse	Tech-support	Wife	White	Female	0	0	38	United-States
48838	40	Private	HS-grad	Married-civ-spouse	Machine-op-inspct	Husband	White	Male	0	0	40	United-States
48839	58	Private	HS-grad	Widowed	Adm-clerical	Unmarried	White	Female	0	0	40	United-States
48840	22	Private	HS-grad	Never-married	Adm-clerical	Own-child	White	Male	0	0	20	United-States
48841	52	Self-emp-inc	HS-grad	Married-civ-spouse	Exec-managerial	Wife	White	Female	15024	0	40	United-States

48842 rows × 12 columns

Once the dataset is loaded, we split it into a training and testing sets.

from sklearn.model_selection import train_test_split

data_train, data_test, target_train, target_test = train_test_split(
    data, target, random_state=42
)

We define a pipeline as seen in the first module, to handle both numerical and categorical features.

The first step is to select all the categorical columns.

from sklearn.compose import make_column_selector as selector

categorical_columns_selector = selector(dtype_include=object)
categorical_columns = categorical_columns_selector(data)

Here we use a tree-based model as a classifier (i.e. HistGradientBoostingClassifier). That means:

• Numerical variables don’t need scaling;

• Categorical variables can be dealt with an OrdinalEncoder even if the coding order is not meaningful;

• For tree-based models, the OrdinalEncoder avoids having high-dimensional representations.

We now build our OrdinalEncoder by passing it the known categories.

from sklearn.preprocessing import OrdinalEncoder

categorical_preprocessor = OrdinalEncoder(
    handle_unknown="use_encoded_value", unknown_value=-1
)

We then use make_column_transformer to select the categorical columns and apply the OrdinalEncoder to them.

from sklearn.compose import make_column_transformer

preprocessor = make_column_transformer(
    (categorical_preprocessor, categorical_columns),
    remainder="passthrough",
    # Silence a deprecation warning in scikit-learn v1.6 related to how the
    # ColumnTransformer stores an attribute that we do not use in this notebook
    force_int_remainder_cols=False,
)

Finally, we use a tree-based classifier (i.e. histogram gradient-boosting) to predict whether or not a person earns more than 50 k$ a year.

from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.pipeline import Pipeline

model = Pipeline(
    [
        ("preprocessor", preprocessor),
        (
            "classifier",
            HistGradientBoostingClassifier(random_state=42, max_leaf_nodes=4),
        ),
    ]
)
model

Pipeline(steps=[('preprocessor',
                 ColumnTransformer(force_int_remainder_cols=False,
                                   remainder='passthrough',
                                   transformers=[('ordinalencoder',
                                                  OrdinalEncoder(handle_unknown='use_encoded_value',
                                                                 unknown_value=-1),
                                                  ['workclass', 'education',
                                                   'marital-status',
                                                   'occupation', 'relationship',
                                                   'race', 'sex',
                                                   'native-country'])])),
                ('classifier',
                 HistGradientBoostingClassifier(max_leaf_nodes=4,
                                                random_state=42))])

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Tuning using a grid-search

In the previous exercise (M3.01) we used two nested for loops (one for each hyperparameter) to test different combinations over a fixed grid of hyperparameter values. In each iteration of the loop, we used cross_val_score to compute the mean score (as averaged across cross-validation splits), and compared those mean scores to select the best combination. GridSearchCV is a scikit-learn class that implements a very similar logic with less repetitive code. The suffix CV refers to the cross-validation it runs internally (instead of the cross_val_score we “hard” coded).

The GridSearchCV estimator takes a param_grid parameter which defines all hyperparameters and their associated values. The grid-search is in charge of creating all possible combinations and testing them.

The number of combinations is equal to the product of the number of values to explore for each parameter. Thus, adding new parameters with their associated values to be explored rapidly becomes computationally expensive. Because of that, here we only explore the combination learning-rate and the maximum number of nodes for a total of 4 x 3 = 12 combinations.

from sklearn.model_selection import GridSearchCV

param_grid = { “classifier__learning_rate”: (0.01, 0.1, 1, 10), # 4 possible values “classifier__max_leaf_nodes”: (3, 10, 30), # 3 possible values } # 12 unique combinations model_grid_search = GridSearchCV(model, param_grid=param_grid, n_jobs=2, cv=2) model_grid_search.fit(data_train, target_train)

You can access the best combination of hyperparameters found by the grid search using the best_params_ attribute.

print(f"The best set of parameters is: {model_grid_search.best_params_}")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[9], line 1
----> 1 print(f"The best set of parameters is: {model_grid_search.best_params_}")

NameError: name 'model_grid_search' is not defined

Once the grid-search is fitted, it can be used as any other estimator, i.e. it has predict and score methods. Internally, it uses the model with the best parameters found during fit.

Let’s get the predictions for the 5 first samples using the estimator with the best parameters:

model_grid_search.predict(data_test.iloc[0:5])

Finally, we check the accuracy of our model using the test set.

accuracy = model_grid_search.score(data_test, target_test)
print(
    f"The test accuracy score of the grid-search pipeline is: {accuracy:.2f}"
)

The accuracy and the best parameters of the grid-search pipeline are similar to the ones we found in the previous exercise, where we searched the best parameters “by hand” through a double for loop.

The need for a validation set

In the previous section, the selection of the best hyperparameters was done using the train set, coming from the initial train-test split. Then, we evaluated the generalization performance of our tuned model on the left out test set. This can be shown schematically as follows:

Note

This figure shows the particular case of K-fold cross-validation strategy using n_splits=5 to further split the train set coming from a train-test split. For each cross-validation split, the procedure trains a model on all the red samples, evaluates the score of a given set of hyperparameters on the green samples. The best combination of hyperparameters best_params is selected based on those intermediate scores.

Then a final model is refitted using best_params on the concatenation of the red and green samples and evaluated on the blue samples.

The green samples are sometimes referred as the validation set to differentiate them from the final test set in blue.

In addition, we can inspect all results which are stored in the attribute cv_results_ of the grid-search. We filter some specific columns from these results.

cv_results = pd.DataFrame(model_grid_search.cv_results_).sort_values(
    "mean_test_score", ascending=False
)
cv_results

Let us focus on the most interesting columns and shorten the parameter names to remove the "param_classifier__" prefix for readability:

# get the parameter names
column_results = [f"param_{name}" for name in param_grid.keys()]
column_results += ["mean_test_score", "std_test_score", "rank_test_score"]
cv_results = cv_results[column_results]

def shorten_param(param_name):
    if "__" in param_name:
        return param_name.rsplit("__", 1)[1]
    return param_name


cv_results = cv_results.rename(shorten_param, axis=1)
cv_results

Given that we are tuning only 2 parameters, we can visualize the results as a heatmap. To do so, we first need to reshape the cv_results into a dataframe where:

• the rows correspond to the learning-rate values;

• the columns correspond to the maximum number of leaf;

• the content of the dataframe is the mean test scores.

pivoted_cv_results = cv_results.pivot_table(
    values="mean_test_score",
    index=["learning_rate"],
    columns=["max_leaf_nodes"],
)

pivoted_cv_results

Now that we have the data in the right format, we can create the heatmap as follows:

import seaborn as sns

ax = sns.heatmap(
    pivoted_cv_results,
    annot=True,
    cmap="YlGnBu",
    vmin=0.7,
    vmax=0.9,
    cbar_kws={"label": "mean test accuracy"},
)
ax.invert_yaxis()

The heatmap above shows the mean test accuracy (i.e., the average over cross-validation splits) for each combination of hyperparameters, where darker colors indicate better performance. However, notice that using colors only allows us to visually compare the mean test score, but does not carry any information on the standard deviation over splits, making it difficult to say if different scores coming from different combinations lead to a significantly better model or not.

The above tables highlights the following things:

• for too high values of learning_rate, the generalization performance of the model is degraded and adjusting the value of max_leaf_nodes cannot fix that problem;

• outside of this pathological region, we observe that the optimal choice of max_leaf_nodes depends on the value of learning_rate;

• in particular, we observe a “diagonal” of good models with an accuracy close to the maximal of 0.87: when the value of max_leaf_nodes is increased, one should decrease the value of learning_rate accordingly to preserve a good accuracy.

The precise meaning of those two parameters will be explained later.

For now we note that, in general, there is no unique optimal parameter setting: 4 models out of the 12 parameter configurations reach the maximal accuracy (up to small random fluctuations caused by the sampling of the training set).

In this notebook we have seen:

• how to optimize the hyperparameters of a predictive model via a grid-search;

• that searching for more than two hyperparameters is too costly;

• that a grid-search does not necessarily find an optimal solution.