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| import os | |
| import modal | |
| LOCAL=True | |
| if LOCAL == False: | |
| stub = modal.Stub() | |
| image = modal.Image.debian_slim().apt_install(["libgomp1"]).pip_install(["hopsworks", "seaborn", "joblib", "scikit-learn"]) | |
| def f(): | |
| g() | |
| def g(): | |
| import hopsworks | |
| import pandas as pd | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.metrics import accuracy_score | |
| from sklearn.metrics import confusion_matrix | |
| from sklearn.metrics import classification_report | |
| import seaborn as sns | |
| from matplotlib import pyplot | |
| from hsml.schema import Schema | |
| from hsml.model_schema import ModelSchema | |
| import joblib | |
| # You have to set the environment variable 'HOPSWORKS_API_KEY' for login to succeed | |
| project = hopsworks.login() | |
| # fs is a reference to the Hopsworks Feature Store | |
| fs = project.get_feature_store() | |
| # The feature view is the input set of features for your model. The features can come from different feature groups. | |
| # You can select features from different feature groups and join them together to create a feature view | |
| try: | |
| feature_view = fs.get_feature_view(name="iris_modal", version=1) | |
| except: | |
| iris_fg = fs.get_feature_group(name="iris_modal", version=1) | |
| query = iris_fg.select_all() | |
| feature_view = fs.create_feature_view(name="iris_modal", | |
| version=1, | |
| description="Read from Iris flower dataset", | |
| labels=["variety"], | |
| query=query) | |
| # You can read training data, randomly split into train/test sets of features (X) and labels (y) | |
| X_train, X_test, y_train, y_test = feature_view.train_test_split(0.2) | |
| # Train our model with the Scikit-learn K-nearest-neighbors algorithm using our features (X_train) and labels (y_train) | |
| model = KNeighborsClassifier(n_neighbors=2) | |
| model.fit(X_train, y_train.values.ravel()) | |
| # Evaluate model performance using the features from the test set (X_test) | |
| y_pred = model.predict(X_test) | |
| # Compare predictions (y_pred) with the labels in the test set (y_test) | |
| metrics = classification_report(y_test, y_pred, output_dict=True) | |
| results = confusion_matrix(y_test, y_pred) | |
| # Create the confusion matrix as a figure, we will later store it as a PNG image file | |
| df_cm = pd.DataFrame(results, ['True Setosa', 'True Versicolor', 'True Virginica'], | |
| ['Pred Setosa', 'Pred Versicolor', 'Pred Virginica']) | |
| cm = sns.heatmap(df_cm, annot=True) | |
| fig = cm.get_figure() | |
| # We will now upload our model to the Hopsworks Model Registry. First get an object for the model registry. | |
| mr = project.get_model_registry() | |
| # The contents of the 'iris_model' directory will be saved to the model registry. Create the dir, first. | |
| model_dir="iris_model" | |
| if os.path.isdir(model_dir) == False: | |
| os.mkdir(model_dir) | |
| # Save both our model and the confusion matrix to 'model_dir', whose contents will be uploaded to the model registry | |
| joblib.dump(model, model_dir + "/iris_model.pkl") | |
| fig.savefig(model_dir + "/confusion_matrix.png") | |
| # Specify the schema of the model's input/output using the features (X_train) and labels (y_train) | |
| input_schema = Schema(X_train) | |
| output_schema = Schema(y_train) | |
| model_schema = ModelSchema(input_schema, output_schema) | |
| # Create an entry in the model registry that includes the model's name, desc, metrics | |
| iris_model = mr.python.create_model( | |
| name="iris_modal", | |
| metrics={"accuracy" : metrics['accuracy']}, | |
| model_schema=model_schema, | |
| description="Iris Flower Predictor" | |
| ) | |
| # Upload the model to the model registry, including all files in 'model_dir' | |
| iris_model.save(model_dir) | |
| if __name__ == "__main__": | |
| if LOCAL == True : | |
| g() | |
| else: | |
| with stub.run(): | |
| f() | |