# ---
# jupyter:
# kernelspec:
# display_name: Python 3
# name: python3
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# %% [markdown]
# # ðŸ“ƒ Solution for Exercise M1.05
#
# The goal of this exercise is to evaluate the impact of feature preprocessing
# on a pipeline that uses a decision-tree-based classifier instead of a logistic
# regression.
#
# - The first question is to empirically evaluate whether scaling numerical
# features is helpful or not;
# - The second question is to evaluate whether it is empirically better (both
# from a computational and a statistical perspective) to use integer coded or
# one-hot encoded categories.
# %%
import pandas as pd
adult_census = pd.read_csv("../datasets/adult-census.csv")
# %%
target_name = "class"
target = adult_census[target_name]
data = adult_census.drop(columns=[target_name, "education-num"])
# %% [markdown]
# As in the previous notebooks, we use the utility `make_column_selector` to
# select only columns with a specific data type. Besides, we list in advance all
# categories for the categorical columns.
# %%
from sklearn.compose import make_column_selector as selector
numerical_columns_selector = selector(dtype_exclude=object)
categorical_columns_selector = selector(dtype_include=object)
numerical_columns = numerical_columns_selector(data)
categorical_columns = categorical_columns_selector(data)
# %% [markdown]
# ## Reference pipeline (no numerical scaling and integer-coded categories)
#
# First let's time the pipeline we used in the main notebook to serve as a
# reference:
# %%
import time
from sklearn.model_selection import cross_validate
from sklearn.pipeline import make_pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.ensemble import HistGradientBoostingClassifier
categorical_preprocessor = OrdinalEncoder(
handle_unknown="use_encoded_value", unknown_value=-1
)
preprocessor = ColumnTransformer(
[("categorical", categorical_preprocessor, categorical_columns)],
remainder="passthrough",
)
model = make_pipeline(preprocessor, HistGradientBoostingClassifier())
start = time.time()
cv_results = cross_validate(model, data, target)
elapsed_time = time.time() - start
scores = cv_results["test_score"]
print(
"The mean cross-validation accuracy is: "
f"{scores.mean():.3f} Â± {scores.std():.3f} "
f"with a fitting time of {elapsed_time:.3f}"
)
# %% [markdown]
# ## Scaling numerical features
#
# Let's write a similar pipeline that also scales the numerical features using
# `StandardScaler` (or similar):
# %%
# solution
import time
from sklearn.preprocessing import StandardScaler
preprocessor = ColumnTransformer(
[
("numerical", StandardScaler(), numerical_columns),
(
"categorical",
OrdinalEncoder(
handle_unknown="use_encoded_value", unknown_value=-1
),
categorical_columns,
),
]
)
model = make_pipeline(preprocessor, HistGradientBoostingClassifier())
start = time.time()
cv_results = cross_validate(model, data, target)
elapsed_time = time.time() - start
scores = cv_results["test_score"]
print(
"The mean cross-validation accuracy is: "
f"{scores.mean():.3f} Â± {scores.std():.3f} "
f"with a fitting time of {elapsed_time:.3f}"
)
# %% [markdown] tags=["solution"]
# ### Analysis
#
# We can observe that both the accuracy and the training time are approximately
# the same as the reference pipeline (any time difference you might observe is
# not significant).
#
# Scaling numerical features is indeed useless for most decision tree models in
# general and for `HistGradientBoostingClassifier` in particular.
# %% [markdown]
# ## One-hot encoding of categorical variables
#
# We observed that integer coding of categorical variables can be very
# detrimental for linear models. However, it does not seem to be the case for
# `HistGradientBoostingClassifier` models, as the cross-validation score of the
# reference pipeline with `OrdinalEncoder` is reasonably good.
#
# Let's see if we can get an even better accuracy with `OneHotEncoder`.
#
# Hint: `HistGradientBoostingClassifier` does not yet support sparse input data.
# You might want to use `OneHotEncoder(handle_unknown="ignore",
# sparse_output=False)` to force the use of a dense representation as a
# workaround.
# %%
# solution
import time
from sklearn.preprocessing import OneHotEncoder
categorical_preprocessor = OneHotEncoder(
handle_unknown="ignore", sparse_output=False
)
preprocessor = ColumnTransformer(
[("one-hot-encoder", categorical_preprocessor, categorical_columns)],
remainder="passthrough",
)
model = make_pipeline(preprocessor, HistGradientBoostingClassifier())
start = time.time()
cv_results = cross_validate(model, data, target)
elapsed_time = time.time() - start
scores = cv_results["test_score"]
print(
"The mean cross-validation accuracy is: "
f"{scores.mean():.3f} Â± {scores.std():.3f} "
f"with a fitting time of {elapsed_time:.3f}"
)
# %% [markdown] tags=["solution"]
# ### Analysis
#
# From an accuracy point of view, the result is almost exactly the same. The
# reason is that `HistGradientBoostingClassifier` is expressive and robust
# enough to deal with misleading ordering of integer coded categories (which was
# not the case for linear models).
#
# However from a computation point of view, the training time is much longer:
# this is caused by the fact that `OneHotEncoder` generates approximately 10
# times more features than `OrdinalEncoder`.
#
# Note that the current implementation `HistGradientBoostingClassifier` is still
# incomplete, and once sparse representation are handled correctly, training
# time might improve with such kinds of encodings.
#
# The main take away message is that arbitrary integer coding of categories is
# perfectly fine for `HistGradientBoostingClassifier` and yields fast training
# times.
# %% [markdown] tags=["solution"]
# ```{important}
# Which encoder should I use?
#
# | | Meaningful order | Non-meaningful order |
# | ---------------- | ----------------------------- | -------------------- |
# | Tree-based model | `OrdinalEncoder` | `OrdinalEncoder` |
# | Linear model | `OrdinalEncoder` with caution | `OneHotEncoder` |
#
# - `OneHotEncoder`: always does something meaningful, but can be unnecessary
# slow with trees.
# - `OrdinalEncoder`: can be detrimental for linear models unless your category
# has a meaningful order and you make sure that `OrdinalEncoder` respects this
# order. Trees can deal with `OrdinalEncoder` fine as long as they are deep
# enough.
# ```