Upload train.py

train.py

@@ -0,0 +1,91 @@
+# let's import the libraries first
+import sklearn
+from sklearn.datasets import load_breast_cancer
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from skops import card, hub_utils
+import pickle
+from sklearn.metrics import (ConfusionMatrixDisplay, confusion_matrix,
+                            accuracy_score, f1_score)
+import matplotlib.pyplot as plt
+from pathlib import Path
+
+# Load the data and split
+X, y = load_breast_cancer(as_frame=True, return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=42
+)
+
+# Train the model
+model = DecisionTreeClassifier().fit(X_train, y_train)
+
+# let's save the model
+model_path = "example.pkl"
+local_repo = "my-awesome-model"
+with open(model_path, mode="bw") as f:
+    pickle.dump(model, file=f)
+
+# we will now initialize a local repository
+hub_utils.init(
+    model=model_path, 
+    requirements=[f"scikit-learn={sklearn.__version__}"], 
+    dst=local_repo,
+    task="tabular-classification",
+    data=X_test,
+)
+
+
+# create the card 
+model_card = card.Card(model, metadata=card.metadata_from_config(Path(destination_folder)))
+
+limitations = "This model is not ready to be used in production."
+model_description = "This is a DecisionTreeClassifier model trained on breast cancer dataset."
+model_card_authors = "skops_user"
+get_started_code = "import pickle \nwith open(dtc_pkl_filename, 'rb') as file: \n    clf = pickle.load(file)"
+citation_bibtex = "bibtex\n@inproceedings{...,year={2020}}"
+
+# we can add the information using add
+model_card.add(
+    citation_bibtex=citation_bibtex,
+    get_started_code=get_started_code,
+    model_card_authors=model_card_authors,
+    limitations=limitations,
+    model_description=model_description,
+)
+
+# we can set the metadata part directly
+model_card.metadata.license = "mit"
+
+# let's make a prediction and evaluate the model
+y_pred = model.predict(X_test)
+
+# we can pass metrics using add_metrics and pass details with add
+model_card.add(eval_method="The model is evaluated using test split, on accuracy and F1 score with macro average.")
+model_card.add_metrics(accuracy=accuracy_score(y_test, y_pred))
+model_card.add_metrics(**{"f1 score": f1_score(y_test, y_pred, average="micro")})
+
+# we will create a confusion matrix
+cm = confusion_matrix(y_test, y_pred, labels=model.classes_)
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)
+disp.plot()
+
+# save the plot
+plt.savefig(Path(local_repo) / "confusion_matrix.png")
+
+# the plot will be written to the model card under the name confusion_matrix
+# we pass the path of the plot itself
+model_card.add_plot(confusion_matrix="confusion_matrix.png")
+
+# save the card
+model_card.save(Path(local_repo) / "README.md")
+
+# if the repository doesn't exist remotely on the Hugging Face Hub, it will be created when we set create_remote to True
+repo_id = "skops-user/my-awesome-model"
+hub_utils.push(
+    repo_id=repo_id,
+    source=local_repo,
+    token=token,
+    commit_message="pushing files to the repo from the example!",
+    create_remote=True,
+)
+