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# %% [markdown]
# # Hyperparameter tuning by randomized-search
#
# In the previous notebook, we showed how to use a grid-search approach to
# search for the best hyperparameters maximizing the generalization performance
# of a predictive model.
#
# However, a grid-search approach has limitations. It does not scale well when
# the number of parameters to tune increases. Also, the grid imposes a
# regularity during the search which might miss better parameter
# values between two consecutive values on the grid.
#
# In this notebook, we present a different method to tune hyperparameters called
# randomized search.
# %% [markdown]
# ## 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")
# %% [markdown]
# We extract the column containing the target.
# %%
target_name = "class"
target = adult_census[target_name]
target
# %% [markdown]
# We drop from our data the target and the `"education-num"` column which
# duplicates the information with `"education"` columns.
# %%
data = adult_census.drop(columns=[target_name, "education-num"])
data
# %% [markdown]
# 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
)
# %% [markdown]
# We create the same predictive pipeline as done for the grid-search section.
# %%
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.compose import make_column_selector as selector
categorical_columns_selector = selector(dtype_include=object)
categorical_columns = categorical_columns_selector(data)
categorical_preprocessor = OrdinalEncoder(
handle_unknown="use_encoded_value", unknown_value=-1
)
preprocessor = ColumnTransformer(
[("cat_preprocessor", categorical_preprocessor, categorical_columns)],
remainder="passthrough",
)
# %%
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.pipeline import Pipeline
model = Pipeline(
[
("preprocessor", preprocessor),
(
"classifier",
HistGradientBoostingClassifier(random_state=42, max_leaf_nodes=4),
),
]
)
model
# %% [markdown]
# ## Tuning using a randomized-search
#
# With the `GridSearchCV` estimator, the parameters need to be specified
# explicitly. We already mentioned that exploring a large number of values for
# different parameters quickly becomes untractable.
#
# Instead, we can randomly generate the parameter candidates. Indeed, such
# approach avoids the regularity of the grid. Hence, adding more evaluations can
# increase the resolution in each direction. This is the case in the frequent
# situation where the choice of some hyperparameters is not very important, as
# for the hyperparameter 2 in the figure below.
#
# ![Randomized vs grid search](../figures/grid_vs_random_search.svg)
#
# Indeed, the number of evaluation points needs to be divided across the two
# different hyperparameters. With a grid, the danger is that the region of good
# hyperparameters may fall between lines of the grid. In the figure such region
# is aligned with the grid given that hyperparameter 2 has a weak influence.
# Rather, stochastic search samples the hyperparameter 1 independently from the
# hyperparameter 2 and find the optimal region.
#
# The `RandomizedSearchCV` class allows for such stochastic search. It is used
# similarly to the `GridSearchCV` but the sampling distributions need to be
# specified instead of the parameter values. For instance, we can draw
# candidates using a log-uniform distribution because the parameters we are
# interested in take positive values with a natural log scaling (.1 is as close
# to 1 as 10 is).
#
# ```{note}
# Random search (with `RandomizedSearchCV`) is typically beneficial compared to
# grid search (with `GridSearchCV`) to optimize 3 or more hyperparameters.
# ```
#
# We now optimize 3 other parameters in addition to the ones we optimized in
# the notebook presenting the `GridSearchCV`:
#
# * `l2_regularization`: it corresponds to the strength of the regularization;
# * `min_samples_leaf`: it corresponds to the minimum number of samples required
# in a leaf;
# * `max_bins`: it corresponds to the maximum number of bins to construct the
# histograms.
#
# We recall the meaning of the 2 remaining parameters:
#
# * `learning_rate`: it corresponds to the speed at which the gradient-boosting
# corrects the residuals at each boosting iteration;
# * `max_leaf_nodes`: it corresponds to the maximum number of leaves for each
# tree in the ensemble.
#
# ```{note}
# `scipy.stats.loguniform` can be used to generate floating numbers. To generate
# random values for integer-valued parameters (e.g. `min_samples_leaf`) we can
# adapt it as follows:
# ```
# %%
from scipy.stats import loguniform
class loguniform_int:
"""Integer valued version of the log-uniform distribution"""
def __init__(self, a, b):
self._distribution = loguniform(a, b)
def rvs(self, *args, **kwargs):
"""Random variable sample"""
return self._distribution.rvs(*args, **kwargs).astype(int)
# %% [markdown]
# Now, we can define the randomized search using the different distributions.
# Executing 10 iterations of 5-fold cross-validation for random parametrizations
# of this model on this dataset can take from 10 seconds to several minutes,
# depending on the speed of the host computer and the number of available
# processors.
# %%
# %%time
from sklearn.model_selection import RandomizedSearchCV
param_distributions = {
"classifier__l2_regularization": loguniform(1e-6, 1e3),
"classifier__learning_rate": loguniform(0.001, 10),
"classifier__max_leaf_nodes": loguniform_int(2, 256),
"classifier__min_samples_leaf": loguniform_int(1, 100),
"classifier__max_bins": loguniform_int(2, 255),
}
model_random_search = RandomizedSearchCV(
model,
param_distributions=param_distributions,
n_iter=10,
cv=5,
verbose=1,
)
model_random_search.fit(data_train, target_train)
# %% [markdown]
# Then, we can compute the accuracy score on the test set.
# %%
accuracy = model_random_search.score(data_test, target_test)
print(f"The test accuracy score of the best model is {accuracy:.2f}")
# %%
from pprint import pprint
print("The best parameters are:")
pprint(model_random_search.best_params_)
# %% [markdown]
# We can inspect the results using the attributes `cv_results` as we did
# previously.
# %%
# get the parameter names
column_results = [f"param_{name}" for name in param_distributions.keys()]
column_results += ["mean_test_score", "std_test_score", "rank_test_score"]
cv_results = pd.DataFrame(model_random_search.cv_results_)
cv_results = cv_results[column_results].sort_values(
"mean_test_score", ascending=False
)
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
# %% [markdown]
# Keep in mind that tuning is limited by the number of different combinations of
# parameters that are scored by the randomized search. In fact, there might be
# other sets of parameters leading to similar or better generalization
# performances but that were not tested in the search. In practice, a randomized
# hyperparameter search is usually run with a large number of iterations. In
# order to avoid the computation cost and still make a decent analysis, we load
# the results obtained from a similar search with 500 iterations.
# %%
# model_random_search = RandomizedSearchCV(
# model, param_distributions=param_distributions, n_iter=500,
# n_jobs=2, cv=5)
# model_random_search.fit(data_train, target_train)
# cv_results = pd.DataFrame(model_random_search.cv_results_)
# cv_results.to_csv("../figures/randomized_search_results.csv")
# %%
cv_results = pd.read_csv(
"../figures/randomized_search_results.csv", index_col=0
)
(
cv_results[column_results]
.rename(shorten_param, axis=1)
.sort_values("mean_test_score", ascending=False)
)
# %% [markdown]
# In this case the top performing models have test scores with a high overlap
# between each other, meaning that indeed, the set of parameters leading to the
# best generalization performance is not unique.
# %% [markdown]
#
# In this notebook, we saw how a randomized search offers a valuable alternative
# to grid-search when the number of hyperparameters to tune is more than two. It
# also alleviates the regularity imposed by the grid that might be problematic
# sometimes.
#
# In the following, we will see how to use interactive plotting tools to explore
# the results of large hyperparameter search sessions and gain some insights on
# range of parameter values that lead to the highest performing models and how
# different hyperparameter are coupled or not.