Note
Go to the end to download the full example code.
Simplified experiment reporting#
This example shows how to leverage skore for reporting model evaluation and storing the results for further analysis.
We set some environment variables to avoid some spurious warnings related to parallelism.
import os
os.environ["POLARS_ALLOW_FORKING_THREAD"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "true"
Creating a skore project and loading some data#
Let’s open a skore project in which we will be able to store artifacts from our experiments.
import skore
my_project = skore.Project("my_project")
We use a skrub dataset that is non-trivial.
from skrub.datasets import fetch_employee_salaries
datasets = fetch_employee_salaries()
df, y = datasets.X, datasets.y
Let’s first have a condensed summary of the input data using a
skrub.TableReport.
from skrub import TableReport
table_report = TableReport(df)
table_report
From the table report, we can make a few observations:
The type of data is heterogeneous: we mainly have categorical and date-related features.
The year related to the
date_first_hiredcolumn is also present in thedatecolumn. Hence, we should beware of not creating twice the same feature during the feature engineering.By looking at the “Associations” tab of the table report, we observe that two features are holding the exact same information:
departmentanddepartment_name. Hence, during our feature engineering, we could potentially drop one of them if the final predictive model is sensitive to the collinearity.
When looking at the “Stats” tab, we observe that the “division” and “employee_position_title” are two features containing a large number of categories. It something that we should consider in our feature engineering.
We can store the report in the skore project so that we can easily retrieve it later without necessarily having to reload the dataset and recompute the report.
my_project.put("Input data summary", table_report)
In terms of target and thus the task that we want to solve, we are interested in predicting the salary of an employee given the previous features. We therefore have a regression task at end.
0 69222.18
1 97392.47
2 104717.28
3 52734.57
4 93396.00
...
9223 72094.53
9224 169543.85
9225 102736.52
9226 153747.50
9227 75484.08
Name: current_annual_salary, Length: 9228, dtype: float64
Modelling#
In a first attempt, we define a rather complex predictive model that uses a linear model as a base estimator.
import numpy as np
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, SplineTransformer
from sklearn.linear_model import RidgeCV
from skrub import DatetimeEncoder, ToDatetime, DropCols, GapEncoder
def periodic_spline_transformer(period, n_splines=None, degree=3):
if n_splines is None:
n_splines = period
n_knots = n_splines + 1 # periodic and include_bias is True
return SplineTransformer(
degree=degree,
n_knots=n_knots,
knots=np.linspace(0, period, n_knots).reshape(n_knots, 1),
extrapolation="periodic",
include_bias=True,
)
one_hot_features = ["gender", "department_name", "assignment_category"]
datetime_features = "date_first_hired"
date_encoder = make_pipeline(
ToDatetime(),
DatetimeEncoder(resolution="day", add_weekday=True, add_total_seconds=False),
DropCols("date_first_hired_year"),
)
date_engineering = make_column_transformer(
(periodic_spline_transformer(12, n_splines=6), ["date_first_hired_month"]),
(periodic_spline_transformer(31, n_splines=15), ["date_first_hired_day"]),
(periodic_spline_transformer(7, n_splines=3), ["date_first_hired_weekday"]),
)
feature_engineering_date = make_pipeline(date_encoder, date_engineering)
preprocessing = make_column_transformer(
(feature_engineering_date, datetime_features),
(OneHotEncoder(drop="if_binary", handle_unknown="ignore"), one_hot_features),
(GapEncoder(n_components=100), "division"),
(GapEncoder(n_components=100), "employee_position_title"),
)
model = make_pipeline(preprocessing, RidgeCV(alphas=np.logspace(-3, 3, 100)))
model
In the diagram above, we can see what how we performed our feature engineering:
In the diagram above, we can see what we intend to do as feature engineering. For categorical features, we use two approaches: if the number of categories is relatively small, we use a
OneHotEncoderand if the number of categories is large, we use aGapEncoderthat was designed to deal with high cardinality categorical features.Then, we have another transformation to encode the date features. We first split the date into multiple features (day, month, and year). Then, we apply a periodic spline transformation to each of the date features to capture the periodicity of the data.
Finally, we fit a
RidgeCVmodel.
Model evaluation using skore.CrossValidationReport#
First model#
Now, we want to evaluate this complex model via cross-validation (with 5 folds).
For that, we use skore’s CrossValidationReport to investigate the
performance of our model.
from skore import CrossValidationReport
report = CrossValidationReport(estimator=model, X=df, y=y, cv_splitter=5, n_jobs=4)
report.help()
╭──────────────────────── Tools to diagnose estimator RidgeCV ─────────────────────────╮
│ CrossValidationReport │
│ ├── .metrics │
│ │ ├── .prediction_error(...) - Plot the prediction error of a regression │
│ │ │ model. │
│ │ ├── .r2(...) (↗︎) - Compute the R² score. │
│ │ ├── .rmse(...) (↘︎) - Compute the root mean squared error. │
│ │ ├── .custom_metric(...) - Compute a custom metric. │
│ │ └── .report_metrics(...) - Report a set of metrics for our estimator. │
│ ├── .cache_predictions(...) - Cache the predictions for sub-estimators │
│ │ reports. │
│ ├── .clear_cache(...) - Clear the cache. │
│ └── Attributes │
│ ├── .X │
│ ├── .y │
│ ├── .estimator_ │
│ ├── .estimator_name_ │
│ ├── .estimator_reports_ │
│ └── .n_jobs │
│ │
│ │
│ Legend: │
│ (↗︎) higher is better (↘︎) lower is better │
╰──────────────────────────────────────────────────────────────────────────────────────╯
We observe that the cross-validation report detected that we have a regression task and provides us with some metrics and plots that make sense for our specific problem at hand.
To accelerate any future computation (e.g. of a metric), we cache once and for all the predictions of our model. Note that we don’t necessarily need to cache the predictions as the report will compute them on the fly (if not cached) and cache them for us.
import warnings
with warnings.catch_warnings():
# catch the warnings raised by the OneHotEncoder for seeing unknown categories
# at transform time
warnings.simplefilter(action="ignore", category=UserWarning)
report.cache_predictions(n_jobs=4)
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/sklearn/pipeline.py:62: FutureWarning: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using other methods such as transform, predict, etc. This will raise an error in 1.8 instead of the current warning.
warnings.warn(
To not lose this cross-validation report, let’s store it in our skore project.
my_project.put("Linear model report", report)
We can now have a look at the performance of the model with some standard metrics.
report.metrics.report_metrics(aggregate=["mean", "std"], indicator_favorability=True)
Second model#
Now that we have our first baseline model, we can try an out-of-the-box model:
skrub’s TableVectorizer that makes the feature engineering for us.
To deal with the high cardinality of the categorical features, we use a
TextEncoder that uses a language model and an embedding model to
encode the categorical features.
Finally, we use a HistGradientBoostingRegressor as a
base estimator that is a rather robust model.
from skrub import TableVectorizer, TextEncoder
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.pipeline import make_pipeline
model = make_pipeline(
TableVectorizer(high_cardinality=TextEncoder()),
HistGradientBoostingRegressor(),
)
model
Let’s compute the cross-validation report for this model.
report = CrossValidationReport(estimator=model, X=df, y=y, cv_splitter=5, n_jobs=4)
report.help()
/opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
warnings.warn(
╭───────────── Tools to diagnose estimator HistGradientBoostingRegressor ──────────────╮
│ CrossValidationReport │
│ ├── .metrics │
│ │ ├── .prediction_error(...) - Plot the prediction error of a regression │
│ │ │ model. │
│ │ ├── .r2(...) (↗︎) - Compute the R² score. │
│ │ ├── .rmse(...) (↘︎) - Compute the root mean squared error. │
│ │ ├── .custom_metric(...) - Compute a custom metric. │
│ │ └── .report_metrics(...) - Report a set of metrics for our estimator. │
│ ├── .cache_predictions(...) - Cache the predictions for sub-estimators │
│ │ reports. │
│ ├── .clear_cache(...) - Clear the cache. │
│ └── Attributes │
│ ├── .X │
│ ├── .y │
│ ├── .estimator_ │
│ ├── .estimator_name_ │
│ ├── .estimator_reports_ │
│ └── .n_jobs │
│ │
│ │
│ Legend: │
│ (↗︎) higher is better (↘︎) lower is better │
╰──────────────────────────────────────────────────────────────────────────────────────╯
We cache the predictions for later use.
report.cache_predictions(n_jobs=4)
We store the report in our skore project.
my_project.put("HGBDT model report", report)
We can now have a look at the performance of the model with some standard metrics.
report.metrics.report_metrics(aggregate=["mean", "std"])
Investigating the models#
At this stage, we might not been careful and have already overwritten the report and model from our first attempt. Hopefully, because we stored the reports in our skore project, we can easily retrieve them. So let’s retrieve the reports.
linear_model_report = my_project.get("Linear model report")
hgbdt_model_report = my_project.get("HGBDT model report")
Now that we retrieved the reports, we can make further comparison and build upon some usual pandas operations to concatenate the results.
import pandas as pd
results = pd.concat(
[
linear_model_report.metrics.report_metrics(aggregate=["mean", "std"]),
hgbdt_model_report.metrics.report_metrics(aggregate=["mean", "std"]),
]
)
results
In addition, if we forgot to compute a specific metric
(e.g. mean_absolute_error()),
we can easily add it to the report, without re-training the model and even
without re-computing the predictions since they are cached internally in the report.
This allows us to save some potentially huge computation time.
from sklearn.metrics import mean_absolute_error
scoring = ["r2", "rmse", mean_absolute_error]
scoring_kwargs = {"response_method": "predict"}
scoring_names = ["R2", "RMSE", "MAE"]
results = pd.concat(
[
linear_model_report.metrics.report_metrics(
scoring=scoring,
scoring_kwargs=scoring_kwargs,
scoring_names=scoring_names,
aggregate=["mean", "std"],
),
hgbdt_model_report.metrics.report_metrics(
scoring=scoring,
scoring_kwargs=scoring_kwargs,
scoring_names=scoring_names,
aggregate=["mean", "std"],
),
]
)
results
Note
We could have also used the skore.ComparisonReport to compare estimator
reports.
Finally, we can even get the individual EstimatorReport for each fold
from the cross-validation to make further analysis.
Here, we plot the actual vs predicted values for each fold.
from itertools import zip_longest
import matplotlib.pyplot as plt
fig, axs = plt.subplots(ncols=2, nrows=3, figsize=(12, 18))
for split_idx, (ax, estimator_report) in enumerate(
zip_longest(axs.flatten(), linear_model_report.estimator_reports_)
):
if estimator_report is None:
ax.axis("off")
continue
estimator_report.metrics.prediction_error().plot(kind="actual_vs_predicted", ax=ax)
ax.set_title(f"Split #{split_idx + 1}")
ax.legend(loc="lower right")
plt.tight_layout()
Total running time of the script: (1 minutes 46.622 seconds)