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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.
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.
from pprint import pprint
import pandas as pd
data = pd.read_csv(
"https://raw.githubusercontent.com/skrub-data/datasets/master"
"/data/bike-sharing-dataset.csv"
)
# Extract our input data (X) and the target column (y)
y = data["cnt"]
X = data[["date", "holiday", "temp", "hum", "windspeed", "weathersit"]]
X
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
We convert the dataframe’s "date" column using ToDatetime.
from skrub import ToDatetime
date = ToDatetime().fit_transform(X["date"])
print("original dtype:", X["date"].dtypes, "\n\nconverted dtype:", date.dtypes)
original dtype: object
converted dtype: datetime64[ns]
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.
from skrub import DatetimeEncoder
date_enc = DatetimeEncoder().fit_transform(date)
print(date, "\n\nHas been encoded as:\n\n", date_enc)
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]
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.
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.
from skrub import TableVectorizer
table_vec = TableVectorizer().fit(X)
pprint(table_vec.get_feature_names_out())
array(['date_year', 'date_month', 'date_day', 'date_hour',
'date_total_seconds', 'holiday', 'temp', 'hum', 'windspeed',
'weathersit'], dtype='<U18')
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:
# 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())
array(['date_year', 'date_month', 'date_day', 'date_hour',
'date_total_seconds', 'date_weekday', 'holiday', 'temp', 'hum',
'windspeed', 'weathersit'], dtype='<U18')
Inspecting the TableVectorizer further, we can check that the
DatetimeEncoder is used on the correct column(s).
{'date': DatetimeEncoder(add_weekday=True),
'holiday': PassThrough(),
'hum': PassThrough(),
'temp': PassThrough(),
'weathersit': PassThrough(),
'windspeed': PassThrough()}
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 HistGradientBoostingRegressor as our learner.
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())
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.
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),
)
array([ -6664.06809097, -6520.55512687, -21139.48809623, -13282.31804904,
-13884.50150571])
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.
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()
As we can see, adding the weekday yields better predictions on our test set.
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
permutation_importance() function, which
basically shuffles a feature and sees how the model changes its prediction.
from sklearn.inspection import permutation_importance
# In this case, we don't use a 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()
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.
Total running time of the script: (0 minutes 5.970 seconds)
