.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/use_cases/plot_employee_salaries.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_use_cases_plot_employee_salaries.py: .. _example_use_case_employee_salaries: =============================== Simplified experiment reporting =============================== This example shows how to leverage skore for reporting model evaluation and storing the results for further analysis. .. GENERATED FROM PYTHON SOURCE LINES 13-15 We set some environment variables to avoid some spurious warnings related to parallelism. .. GENERATED FROM PYTHON SOURCE LINES 16-21 .. code-block:: Python import os os.environ["POLARS_ALLOW_FORKING_THREAD"] = "1" os.environ["TOKENIZERS_PARALLELISM"] = "true" .. GENERATED FROM PYTHON SOURCE LINES 22-24 Creating a skore project and loading some data ============================================== .. GENERATED FROM PYTHON SOURCE LINES 26-28 Let's open a skore project in which we will be able to store artifacts from our experiments. .. GENERATED FROM PYTHON SOURCE LINES 29-33 .. code-block:: Python import skore my_project = skore.Project("my_project") .. GENERATED FROM PYTHON SOURCE LINES 43-44 We use a skrub dataset that is non-trivial. .. GENERATED FROM PYTHON SOURCE LINES 45-50 .. code-block:: Python from skrub.datasets import fetch_employee_salaries datasets = fetch_employee_salaries() df, y = datasets.X, datasets.y .. GENERATED FROM PYTHON SOURCE LINES 51-53 Let's first have a condensed summary of the input data using a :class:`skrub.TableReport`. .. GENERATED FROM PYTHON SOURCE LINES 54-59 .. code-block:: Python from skrub import TableReport table_report = TableReport(df) table_report .. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 60-82 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_hired`` column is also present in the ``date`` column. 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: ``department`` and ``department_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. .. GENERATED FROM PYTHON SOURCE LINES 82-84 .. code-block:: Python my_project.put("Input data summary", table_report) .. GENERATED FROM PYTHON SOURCE LINES 85-88 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. .. GENERATED FROM PYTHON SOURCE LINES 89-91 .. code-block:: Python y .. rst-class:: sphx-glr-script-out .. code-block:: none 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 .. GENERATED FROM PYTHON SOURCE LINES 92-94 Modelling ========= .. GENERATED FROM PYTHON SOURCE LINES 96-98 In a first attempt, we define a rather complex predictive model that uses a linear model as a base estimator. .. GENERATED FROM PYTHON SOURCE LINES 99-147 .. code-block:: Python 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 .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('pipeline',
                                                      Pipeline(steps=[('pipeline',
                                                                       Pipeline(steps=[('todatetime',
                                                                                        ToDatetime()),
                                                                                       ('datetimeencoder',
                                                                                        DatetimeEncoder(add_total_seconds=False,
                                                                                                        add_weekday=True,
                                                                                                        resolution='day')),
                                                                                       ('dropcols',
                                                                                        DropCols(cols='date_first_hired_year'))])),
                                                                      ('columntransformer',
                                                                       ColumnTransformer(transfor...
           4.03701726e+01, 4.64158883e+01, 5.33669923e+01, 6.13590727e+01,
           7.05480231e+01, 8.11130831e+01, 9.32603347e+01, 1.07226722e+02,
           1.23284674e+02, 1.41747416e+02, 1.62975083e+02, 1.87381742e+02,
           2.15443469e+02, 2.47707636e+02, 2.84803587e+02, 3.27454916e+02,
           3.76493581e+02, 4.32876128e+02, 4.97702356e+02, 5.72236766e+02,
           6.57933225e+02, 7.56463328e+02, 8.69749003e+02, 1.00000000e+03])))])
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 148-161 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 `OneHotEncoder` and if the number of categories is large, we use a `GapEncoder` that 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 :class:`~sklearn.linear_model.RidgeCV` model. .. GENERATED FROM PYTHON SOURCE LINES 163-172 Model evaluation using :class:`skore.CrossValidationReport` ============================================================ First model ^^^^^^^^^^^ Now, we want to evaluate this complex model via cross-validation (with 5 folds). For that, we use skore's :class:`~skore.CrossValidationReport` to investigate the performance of our model. .. GENERATED FROM PYTHON SOURCE LINES 172-177 .. code-block:: Python from skore import CrossValidationReport report = CrossValidationReport(estimator=model, X=df, y=y, cv_splitter=5, n_jobs=4) report.help() .. rst-class:: sphx-glr-script-out .. code-block:: none ╭──────────────────────── 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 │ ╰──────────────────────────────────────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 178-186 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. .. GENERATED FROM PYTHON SOURCE LINES 188-196 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none /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( .. GENERATED FROM PYTHON SOURCE LINES 197-198 To not lose this cross-validation report, let's store it in our skore project. .. GENERATED FROM PYTHON SOURCE LINES 198-200 .. code-block:: Python my_project.put("Linear model report", report) .. GENERATED FROM PYTHON SOURCE LINES 201-202 We can now have a look at the performance of the model with some standard metrics. .. GENERATED FROM PYTHON SOURCE LINES 202-204 .. code-block:: Python report.metrics.report_metrics(aggregate=["mean", "std"], indicator_favorability=True) .. raw:: html
RidgeCV Favorability
mean std
Metric
R² 0.763817 0.009138 (↗︎)
RMSE 14139.152526 622.821033 (↘︎)


.. GENERATED FROM PYTHON SOURCE LINES 205-216 Second model ^^^^^^^^^^^^ Now that we have our first baseline model, we can try an out-of-the-box model: skrub's :class:`~skrub.TableVectorizer` that makes the feature engineering for us. To deal with the high cardinality of the categorical features, we use a :class:`~skrub.TextEncoder` that uses a language model and an embedding model to encode the categorical features. Finally, we use a :class:`~sklearn.ensemble.HistGradientBoostingRegressor` as a base estimator that is a rather robust model. .. GENERATED FROM PYTHON SOURCE LINES 216-226 .. code-block:: Python 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 .. raw:: html 
Pipeline(steps=[('tablevectorizer',
                     TableVectorizer(high_cardinality=TextEncoder())),
                    ('histgradientboostingregressor',
                     HistGradientBoostingRegressor())])
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.. GENERATED FROM PYTHON SOURCE LINES 227-228 Let's compute the cross-validation report for this model. .. GENERATED FROM PYTHON SOURCE LINES 229-232 .. code-block:: Python report = CrossValidationReport(estimator=model, X=df, y=y, cv_splitter=5, n_jobs=4) report.help() .. rst-class:: sphx-glr-script-out .. code-block:: none /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 │ ╰──────────────────────────────────────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 233-234 We cache the predictions for later use. .. GENERATED FROM PYTHON SOURCE LINES 235-237 .. code-block:: Python report.cache_predictions(n_jobs=4) .. GENERATED FROM PYTHON SOURCE LINES 238-239 We store the report in our skore project. .. GENERATED FROM PYTHON SOURCE LINES 240-242 .. code-block:: Python my_project.put("HGBDT model report", report) .. GENERATED FROM PYTHON SOURCE LINES 243-244 We can now have a look at the performance of the model with some standard metrics. .. GENERATED FROM PYTHON SOURCE LINES 245-247 .. code-block:: Python report.metrics.report_metrics(aggregate=["mean", "std"]) .. raw:: html
HistGradientBoostingRegressor
mean std
Metric
R² 0.925649 0.015026
RMSE 7925.022613 1086.872675


.. GENERATED FROM PYTHON SOURCE LINES 248-254 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. .. GENERATED FROM PYTHON SOURCE LINES 254-257 .. code-block:: Python linear_model_report = my_project.get("Linear model report") hgbdt_model_report = my_project.get("HGBDT model report") .. GENERATED FROM PYTHON SOURCE LINES 258-260 Now that we retrieved the reports, we can make further comparison and build upon some usual pandas operations to concatenate the results. .. GENERATED FROM PYTHON SOURCE LINES 261-271 .. code-block:: Python 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 .. raw:: html
RidgeCV HistGradientBoostingRegressor
mean std mean std
Metric
R² 0.763817 0.009138 NaN NaN
RMSE 14139.152526 622.821033 NaN NaN
R² NaN NaN 0.925649 0.015026
RMSE NaN NaN 7925.022613 1086.872675


.. GENERATED FROM PYTHON SOURCE LINES 272-277 In addition, if we forgot to compute a specific metric (e.g. :func:`~sklearn.metrics.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. .. GENERATED FROM PYTHON SOURCE LINES 278-301 .. code-block:: Python 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 .. raw:: html
RidgeCV HistGradientBoostingRegressor
mean std mean std
Metric
R2 0.763817 0.009138 NaN NaN
RMSE 14139.152526 622.821033 NaN NaN
MAE 9870.457180 324.980658 NaN NaN
R2 NaN NaN 0.925649 0.015026
RMSE NaN NaN 7925.022613 1086.872675
MAE NaN NaN 4407.990704 185.681370


.. GENERATED FROM PYTHON SOURCE LINES 302-305 .. note:: We could have also used the :class:`skore.ComparisonReport` to compare estimator reports. .. GENERATED FROM PYTHON SOURCE LINES 307-310 Finally, we can even get the individual :class:`~skore.EstimatorReport` for each fold from the cross-validation to make further analysis. Here, we plot the actual vs predicted values for each fold. .. GENERATED FROM PYTHON SOURCE LINES 311-326 .. code-block:: Python 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() .. image-sg:: /auto_examples/use_cases/images/sphx_glr_plot_employee_salaries_001.png :alt: Split #1, Split #2, Split #3, Split #4, Split #5 :srcset: /auto_examples/use_cases/images/sphx_glr_plot_employee_salaries_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 46.622 seconds) .. _sphx_glr_download_auto_examples_use_cases_plot_employee_salaries.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_employee_salaries.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_employee_salaries.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_employee_salaries.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_