Add tabular regression example

docs/changes.rst

@@ -11,6 +11,7 @@ skops Changelog
 
 v0.6
 ----
+- Added tabular regression example. :pr: `254` by `Thomas Lazarus`
 
 v0.5
 ----
@@ -102,4 +103,4 @@ Contributors
 :user:`Adrin Jalali <adrinjalali>`, :user:`Merve Noyan <merveenoyan>`,
 :user:`Benjamin Bossan <BenjaminBossan>`, :user:`Ayyuce Demirbas
 <ayyucedemirbas>`, :user:`Prajjwal Mishra <p-mishra1>`, :user:`Francesco Cariaggi <anferico>`,
-:user:`Erin Aho <E-Aho>`
+:user:`Erin Aho <E-Aho>`, :user:`Thomas Lazarus <lazarust>`

docs/examples.rst

@@ -0,0 +1,9 @@
+.. _examples:
+
+Examples of using skops
+=======================
+
+- Tabular Regression:
+    `Here <https://github.com/skops-dev/skops/blob/main/examples/plot_tabular_classification.py>`_ is an example of using skops to serialize a tabular regression model and create a model card and a Hugging Face Hub repository.
+- Text Classification:
+    `Here <https://github.com/skops-dev/skops/blob/main/examples/plot_text_classification.py>`_ is an example of using skops to serialize a text classification model and create a model card and a Hugging Face Hub repository.

docs/index.rst

@@ -40,6 +40,7 @@ User Guide / API Reference
    model_card
    persistence
    modules/classes
+   examples
 
 Community / About
 =================

examples/plot_tabular_regression.py

@@ -0,0 +1,153 @@
+"""
+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.
+"""
+
+# %%
+# Imports
+# =======
+# First we will import everything required for the rest of this document.
+
+from pathlib import Path
+from tempfile import mkdtemp, mkstemp
+
+import matplotlib.pyplot as plt
+import pandas as pd
+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
+
+# %%
+# Data
+# ====
+# We will use diabetes dataset from sklearn.
+
+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
+)
+
+# %%
+# 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.
+model = Pipeline(
+    [
+        ("scaler", StandardScaler()),
+        ("linear_regression", LinearRegression()),
+    ]
+)
+
+model.fit(X_train, y_train)
+
+# %%
+# Inference
+# =========
+# Let's see if the model works.
+y_pred = model.predict(X_test[:5])
+print(y_pred)
+
+# %%
+# Initialize a repository to save our files in
+# ============================================
+# We will now initialize a repository and save our model
+_, 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)
+
+# %%
+# 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.
+
+model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))
+
+# %%
+# 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.
+
+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(
+    **{
+        "Model Card Authors": model_card_authors,
+        "Intended uses & limitations": limitations,
+        "Citation": citation_bibtex,
+        "Model description": model_description,
+        "Model description/Intended uses & limitations": limitations,
+    }
+)
+
+# %%
+# Add plots, metrics, and tables to our model card
+# ================================================
+# We will now evaluate our model and add our findings to the model card.
+
+y_pred = model.predict(X_test)
+
+# plot the predicted values against the true values
+plt.scatter(y_test, y_pred)
+plt.xlabel("True values")
+plt.ylabel("Predicted values")
+plt.savefig(Path(local_repo) / "prediction_scatter.png")
+model_card.add_plot(**{"Prediction Scatter": "prediction_scatter.png"})
+
+mae = mean_absolute_error(y_test, y_pred)
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+model_card.add_metrics(
+    **{"Mean Absolute Error": mae, "Mean Squared Error": mse, "R-Squared Score": r2}
+)
+
+# %%
+# Save model card
+# ================
+# We can simply save our model card by providing a path to :meth:`.Card.save`.
+# The model hasn't been pushed to Hugging Face Hub yet, if you want to see how
+# to push your models please refer to
+# :ref:`this example <sphx_glr_auto_examples_plot_hf_hub.py>`.
+
+model_card.save(Path(local_repo) / "README.md")