.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_tabular_regression.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_plot_tabular_regression.py: Tabular Regression with scikit-learn ------------------------------------- This example shows how you can create a Hugging Face Hub compatible repo for a tabular regression task using scikit-learn. We also show how you can generate a model card for the model and the task at hand. .. GENERATED FROM PYTHON SOURCE LINES 11-14 Imports ======= First we will import everything required for the rest of this document. .. GENERATED FROM PYTHON SOURCE LINES 14-30 .. code-block:: Python from pathlib import Path from tempfile import mkdtemp, mkstemp import matplotlib.pyplot as plt import sklearn from sklearn.datasets import load_diabetes from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler import skops.io as sio from skops import card, hub_utils .. GENERATED FROM PYTHON SOURCE LINES 31-34 Data ==== We will use diabetes dataset from sklearn. .. GENERATED FROM PYTHON SOURCE LINES 34-40 .. code-block:: Python X, y = load_diabetes(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) .. GENERATED FROM PYTHON SOURCE LINES 41-46 Train a Model ============= To train a model, we need to convert our data first to vectors. We will use StandardScalar in our pipeline. We will fit a Linear Regression model with the outputs of the scalar. .. GENERATED FROM PYTHON SOURCE LINES 46-55 .. code-block:: Python model = Pipeline( [ ("scaler", StandardScaler()), ("linear_regression", LinearRegression()), ] ) model.fit(X_train, y_train) .. raw:: html

Pipeline(steps=[('scaler', StandardScaler()),
                    ('linear_regression', LinearRegression())])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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.. GENERATED FROM PYTHON SOURCE LINES 56-59 Inference ========= Let's see if the model works. .. GENERATED FROM PYTHON SOURCE LINES 59-62 .. code-block:: Python y_pred = model.predict(X_test[:5]) print(y_pred) .. rst-class:: sphx-glr-script-out .. code-block:: none [139.5475584 179.51720835 134.03875572 291.41702925 123.78965872] .. GENERATED FROM PYTHON SOURCE LINES 63-66 Initialize a repository to save our files in ============================================ We will now initialize a repository and save our model .. GENERATED FROM PYTHON SOURCE LINES 66-85 .. code-block:: Python _, pkl_name = mkstemp(prefix="skops-", suffix=".pkl") with open(pkl_name, mode="bw") as f: sio.dump(model, file=f) local_repo = mkdtemp(prefix="skops-") hub_utils.init( model=pkl_name, requirements=[f"scikit-learn={sklearn.__version__}"], dst=local_repo, task="tabular-regression", data=X_test, ) if "__file__" in locals(): # __file__ not defined during docs built # Add this script itself to the files to be uploaded for reproducibility hub_utils.add_files(__file__, dst=local_repo) .. GENERATED FROM PYTHON SOURCE LINES 86-92 Create a model card =================== We now create a model card, and populate its metadata with information which is already provided in ``config.json``, which itself is created by the call to :func:`.hub_utils.init` above. We will see below how we can populate the model card with useful information. .. GENERATED FROM PYTHON SOURCE LINES 92-95 .. code-block:: Python model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo))) .. GENERATED FROM PYTHON SOURCE LINES 96-101 Add more information ==================== So far, the model card does not tell viewers a lot about the model. Therefore, we add more information about the model, like a description and what its license is. .. GENERATED FROM PYTHON SOURCE LINES 101-125 .. code-block:: Python model_card.metadata.license = "mit" limitations = ( "This model is made for educational purposes and is not ready to be used in" " production." ) model_description = ( "This is a Linear Regression model trained on diabetes dataset. This model could be" " used to predict the progression of diabetes. This model is pretty limited and" " should just be used as an example of how to user `skops` and Hugging Face Hub." ) model_card_authors = "skops_user, lazarust" citation_bibtex = "bibtex\n@inproceedings{...,year={2022}}" model_card.add( folded=False, **{ "Model Card Authors": model_card_authors, "Intended uses & limitations": limitations, "Citation": citation_bibtex, "Model description": model_description, "Model description/Intended uses & limitations": limitations, }, ) .. rst-class:: sphx-glr-script-out .. code-block:: none Card( model=Pipeline(steps=[('scaler', Standar..._regression', LinearRegression())]), metadata.library_name=sklearn, metadata.license=mit, metadata.tags=['sklearn', 'skops', 'tabular-regression'], metadata.model_format=pickle, metadata.model_file=skops-f_cwjjqe.pkl, metadata.widget=[{...}], Model description=This is a Linear Regression ...`skops` and Hugging Face Hub., Model description/Intended uses & limitations=This model is ... in production., Model description/Training Procedure/Hyperparameters=TableSection(13x2), Model description/Training Procedure/Model Plot=