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.. "auto_examples/03_datetime_encoder.py"
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.. only:: html
.. note::
:class: sphx-glr-download-link-note
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.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_03_datetime_encoder.py:
.. _example_datetime_encoder :
===================================================
Handling datetime features with the DatetimeEncoder
===================================================
In this example, we illustrate how to better integrate datetime features
in machine learning models with the |DatetimeEncoder|.
This encoder breaks down passed datetime features into relevant numerical
features, such as the month, the day of the week, the hour of the day, etc.
It is used by default in the |TableVectorizer|.
.. |DatetimeEncoder| replace::
:class:`~skrub.DatetimeEncoder`
.. |TableVectorizer| replace::
:class:`~skrub.TableVectorizer`
.. |OneHotEncoder| replace::
:class:`~sklearn.preprocessing.OneHotEncoder`
.. |TimeSeriesSplit| replace::
:class:`~sklearn.model_selection.TimeSeriesSplit`
.. |ColumnTransformer| replace::
:class:`~sklearn.compose.ColumnTransformer`
.. |make_column_transformer| replace::
:class:`~sklearn.compose.make_column_transformer`
.. |HGBR| replace::
:class:`~sklearn.ensemble.HistGradientBoostingRegressor`
.. |ToDatetime| replace::
:class:`~skrub.ToDatetime`
.. GENERATED FROM PYTHON SOURCE LINES 43-49
A problem with relevant datetime features
-----------------------------------------
We will use a dataset of bike sharing demand in 2011 and 2012.
In this setting, we want to predict the number of bike rentals, based
on the date, time and weather conditions.
.. GENERATED FROM PYTHON SOURCE LINES 49-64
.. code-block:: Python
from pprint import pprint
import pandas as pd
from skrub import datasets
data = datasets.fetch_bike_sharing().bike_sharing
# Extract our input data (X) and the target column (y)
y = data["cnt"]
X = data[["date", "holiday", "temp", "hum", "windspeed", "weathersit"]]
X
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Downloading 'bike_sharing' from https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/bike_sharing.zip (attempt 1/3)
.. raw:: html
<div class="output_subarea output_html rendered_html output_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>date</th>
<th>holiday</th>
<th>temp</th>
<th>hum</th>
<th>windspeed</th>
<th>weathersit</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2011-01-01 00:00:00</td>
<td>0</td>
<td>0.24</td>
<td>0.81</td>
<td>0.0000</td>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>2011-01-01 01:00:00</td>
<td>0</td>
<td>0.22</td>
<td>0.80</td>
<td>0.0000</td>
<td>1</td>
</tr>
<tr>
<th>2</th>
<td>2011-01-01 02:00:00</td>
<td>0</td>
<td>0.22</td>
<td>0.80</td>
<td>0.0000</td>
<td>1</td>
</tr>
<tr>
<th>3</th>
<td>2011-01-01 03:00:00</td>
<td>0</td>
<td>0.24</td>
<td>0.75</td>
<td>0.0000</td>
<td>1</td>
</tr>
<tr>
<th>4</th>
<td>2011-01-01 04:00:00</td>
<td>0</td>
<td>0.24</td>
<td>0.75</td>
<td>0.0000</td>
<td>1</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>17374</th>
<td>2012-12-31 19:00:00</td>
<td>0</td>
<td>0.26</td>
<td>0.60</td>
<td>0.1642</td>
<td>2</td>
</tr>
<tr>
<th>17375</th>
<td>2012-12-31 20:00:00</td>
<td>0</td>
<td>0.26</td>
<td>0.60</td>
<td>0.1642</td>
<td>2</td>
</tr>
<tr>
<th>17376</th>
<td>2012-12-31 21:00:00</td>
<td>0</td>
<td>0.26</td>
<td>0.60</td>
<td>0.1642</td>
<td>1</td>
</tr>
<tr>
<th>17377</th>
<td>2012-12-31 22:00:00</td>
<td>0</td>
<td>0.26</td>
<td>0.56</td>
<td>0.1343</td>
<td>1</td>
</tr>
<tr>
<th>17378</th>
<td>2012-12-31 23:00:00</td>
<td>0</td>
<td>0.26</td>
<td>0.65</td>
<td>0.1343</td>
<td>1</td>
</tr>
</tbody>
</table>
<p>17379 rows × 6 columns</p>
</div>
</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 65-67
.. code-block:: Python
y
.. rst-class:: sphx-glr-script-out
.. code-block:: none
0 16
1 40
2 32
3 13
4 1
...
17374 119
17375 89
17376 90
17377 61
17378 49
Name: cnt, Length: 17379, dtype: int64
.. GENERATED FROM PYTHON SOURCE LINES 68-69
We convert the dataframe's ``"date"`` column using |ToDatetime|.
.. GENERATED FROM PYTHON SOURCE LINES 69-76
.. code-block:: Python
from skrub import ToDatetime
date = ToDatetime().fit_transform(X["date"])
print("original dtype:", X["date"].dtypes, "\n\nconverted dtype:", date.dtypes)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
original dtype: object
converted dtype: datetime64[ns]
.. GENERATED FROM PYTHON SOURCE LINES 77-86
Encoding the features
.....................
We now encode this column with a |DatetimeEncoder|.
During the instantiation of the |DatetimeEncoder|, we specify that we want
to extract the day of the week, and that we don't want to extract anything
finer than hours. This is because we don't want to extract minutes, seconds
and lower units, as they are unimportant here.
.. GENERATED FROM PYTHON SOURCE LINES 86-93
.. code-block:: Python
from skrub import DatetimeEncoder
date_enc = DatetimeEncoder().fit_transform(date)
print(date, "\n\nHas been encoded as:\n\n", date_enc)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
0 2011-01-01 00:00:00
1 2011-01-01 01:00:00
2 2011-01-01 02:00:00
3 2011-01-01 03:00:00
4 2011-01-01 04:00:00
...
17374 2012-12-31 19:00:00
17375 2012-12-31 20:00:00
17376 2012-12-31 21:00:00
17377 2012-12-31 22:00:00
17378 2012-12-31 23:00:00
Name: date, Length: 17379, dtype: datetime64[ns]
Has been encoded as:
date_year date_month date_day date_hour date_total_seconds
0 2011.0 1.0 1.0 0.0 1.293840e+09
1 2011.0 1.0 1.0 1.0 1.293844e+09
2 2011.0 1.0 1.0 2.0 1.293847e+09
3 2011.0 1.0 1.0 3.0 1.293851e+09
4 2011.0 1.0 1.0 4.0 1.293854e+09
... ... ... ... ... ...
17374 2012.0 12.0 31.0 19.0 1.356980e+09
17375 2012.0 12.0 31.0 20.0 1.356984e+09
17376 2012.0 12.0 31.0 21.0 1.356988e+09
17377 2012.0 12.0 31.0 22.0 1.356991e+09
17378 2012.0 12.0 31.0 23.0 1.356995e+09
[17379 rows x 5 columns]
.. GENERATED FROM PYTHON SOURCE LINES 94-97
We see that the encoder is working as expected: the column has
been replaced by features extracting the month, day, hour, day of the
week and total seconds since Epoch information.
.. GENERATED FROM PYTHON SOURCE LINES 99-106
One-liner with the TableVectorizer
..................................
As mentioned earlier, the |TableVectorizer| makes use of the
|DatetimeEncoder| by default. Note that ``X["date"]`` is still
a string, but will be automatically transformed into a datetime in the
|TableVectorizer|.
.. GENERATED FROM PYTHON SOURCE LINES 106-112
.. code-block:: Python
from skrub import TableVectorizer
table_vec = TableVectorizer().fit(X)
pprint(table_vec.get_feature_names_out())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
array(['date_year', 'date_month', 'date_day', 'date_hour',
'date_total_seconds', 'holiday', 'temp', 'hum', 'windspeed',
'weathersit'], dtype='<U18')
.. GENERATED FROM PYTHON SOURCE LINES 113-117
If we want to customize the |DatetimeEncoder| inside the |TableVectorizer|,
we can replace its default parameter with a new, custom instance.
Here, for example, we want it to extract the day of the week:
.. GENERATED FROM PYTHON SOURCE LINES 117-122
.. code-block:: Python
# use the ``datetime`` argument to customize how datetimes are handled
table_vec_weekday = TableVectorizer(datetime=DatetimeEncoder(add_weekday=True)).fit(X)
pprint(table_vec_weekday.get_feature_names_out())
.. rst-class:: sphx-glr-script-out
.. code-block:: none
array(['date_year', 'date_month', 'date_day', 'date_hour',
'date_total_seconds', 'date_weekday', 'holiday', 'temp', 'hum',
'windspeed', 'weathersit'], dtype='<U18')
.. GENERATED FROM PYTHON SOURCE LINES 123-129
.. note:
For more information on how to customize the |TableVectorizer|, see
:ref:`sphx_glr_auto_examples_01_dirty_categories.py`.
Inspecting the |TableVectorizer| further, we can check that the
|DatetimeEncoder| is used on the correct column(s).
.. GENERATED FROM PYTHON SOURCE LINES 129-131
.. code-block:: Python
pprint(table_vec_weekday.transformers_)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
{'date': DatetimeEncoder(add_weekday=True),
'holiday': PassThrough(),
'hum': PassThrough(),
'temp': PassThrough(),
'weathersit': PassThrough(),
'windspeed': PassThrough()}
.. GENERATED FROM PYTHON SOURCE LINES 132-140
Prediction with datetime features
---------------------------------
For prediction tasks, we recommend using the |TableVectorizer| inside a
pipeline, combined with a model that can use the features extracted by the
|DatetimeEncoder|.
Here we'll use a |HGBR| as our learner.
.. GENERATED FROM PYTHON SOURCE LINES 140-146
.. code-block:: Python
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.pipeline import make_pipeline
pipeline = make_pipeline(table_vec, HistGradientBoostingRegressor())
pipeline_weekday = make_pipeline(table_vec_weekday, HistGradientBoostingRegressor())
.. GENERATED FROM PYTHON SOURCE LINES 147-156
Evaluating the model
....................
When using date and time features, we often care about predicting the future.
In this case, we have to be careful when evaluating our model, because
the standard settings of the cross-validation do not respect time ordering.
Instead, we can use the |TimeSeriesSplit|,
which ensures that the test set is always in the future.
.. GENERATED FROM PYTHON SOURCE LINES 156-166
.. code-block:: Python
from sklearn.model_selection import TimeSeriesSplit, cross_val_score
cross_val_score(
pipeline,
X,
y,
scoring="neg_mean_squared_error",
cv=TimeSeriesSplit(n_splits=5),
)
.. rst-class:: sphx-glr-script-out
.. code-block:: none
array([ -6664.06809097, -6520.55512687, -21139.48809623, -12732.66488604,
-13952.10808106])
.. GENERATED FROM PYTHON SOURCE LINES 167-174
Plotting the prediction
.......................
The mean squared error is not obvious to interpret, so we visually
compare the prediction of our model with the actual values.
To do so, we will divide our dataset into a train and a test set:
we use 2011 data to predict what happened in 2012.
.. GENERATED FROM PYTHON SOURCE LINES 174-215
.. code-block:: Python
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
mask_train = X["date"] < "2012-01-01"
X_train, X_test = X.loc[mask_train], X.loc[~mask_train]
y_train, y_test = y.loc[mask_train], y.loc[~mask_train]
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
pipeline_weekday.fit(X_train, y_train)
y_pred_weekday = pipeline_weekday.predict(X_test)
fig, ax = plt.subplots(figsize=(12, 3))
fig.suptitle("Predictions with tree models")
ax.plot(
X.tail(96)["date"],
y.tail(96).values,
"x-",
alpha=0.2,
label="Actual demand",
color="black",
)
ax.plot(
X_test.tail(96)["date"],
y_pred[-96:],
"x-",
label="DatetimeEncoder() + HGBR prediction",
)
ax.plot(
X_test.tail(96)["date"],
y_pred_weekday[-96:],
"x-",
label="DatetimeEncoder(add_weekday=True) + HGBR prediction",
)
ax.tick_params(axis="x", labelsize=7, labelrotation=75)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d"))
_ = ax.legend()
plt.tight_layout()
plt.show()
.. image-sg:: /auto_examples/images/sphx_glr_03_datetime_encoder_001.png
:alt: Predictions with tree models
:srcset: /auto_examples/images/sphx_glr_03_datetime_encoder_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 216-217
As we can see, adding the weekday yields better predictions on our test set.
.. GENERATED FROM PYTHON SOURCE LINES 220-228
Feature importances
-------------------
Using the |DatetimeEncoder| allows us to better understand how the date
impacts the bike sharing demand. To this aim, we can compute the
importance of the features created by the |DatetimeEncoder|, using the
:func:`~sklearn.inspection.permutation_importance` function, which
basically shuffles a feature and sees how the model changes its prediction.
.. GENERATED FROM PYTHON SOURCE LINES 230-258
.. code-block:: Python
from sklearn.inspection import permutation_importance
# In this case, we don't use the whole pipeline, because we want to compute the
# importance of the features created by the DatetimeEncoder
X_test_transform = pipeline[:-1].transform(X_test)
result = permutation_importance(
pipeline[-1], X_test_transform, y_test, n_repeats=10, random_state=0
)
result = pd.DataFrame(
dict(
feature_names=X_test_transform.columns,
std=result.importances_std,
importances=result.importances_mean,
)
).sort_values("importances", ascending=True)
result.plot.barh(
y="importances",
x="feature_names",
title="Feature Importances",
xerr="std",
figsize=(12, 9),
)
plt.tight_layout()
plt.show()
.. image-sg:: /auto_examples/images/sphx_glr_03_datetime_encoder_002.png
:alt: Feature Importances
:srcset: /auto_examples/images/sphx_glr_03_datetime_encoder_002.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 259-269
We can see that the hour of the day, the temperature and the humidity
are the most important features, which seems reasonable.
Conclusion
----------
In this example, we saw how to use the |DatetimeEncoder| to create
features from a datetime column.
Also check out the |TableVectorizer|, which automatically recognizes
and transforms datetime columns by default.
.. rst-class:: sphx-glr-timing
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