Added new project for explainability

explainability-shap/.dockerignore

@@ -0,0 +1,4 @@
+!/materializers/**
+!/pipelines/**
+!/steps/**
+!/utils/**

explainability-shap/LICENSE

@@ -0,0 +1,15 @@
+Apache Software License 2.0
+
+Copyright (c) ZenML GmbH 2024. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

explainability-shap/README.md

@@ -0,0 +1,81 @@
+# 🌸 Iris Classification MLOps Pipeline with ZenML
+
+Welcome to the Iris Classification MLOps project! This project demonstrates how to build a production-ready machine learning pipeline using ZenML. It showcases various MLOps practices including data preparation, model training, evaluation, explainability, and data drift detection.
+
+## 🌟 Features
+
+- Data loading and splitting using scikit-learn's iris dataset
+- SVM model training with hyperparameter configuration
+- Model evaluation with accuracy metrics
+- Model explainability using SHAP (SHapley Additive exPlanations)
+- Data drift detection between training and test sets
+- Artifact and metadata logging for enhanced traceability
+
+<div align="center">
+  <br/>
+    <img alt="Iris Classification Pipeline" src=".assets/model.gif" width="70%">
+  <br/>
+</div>
+
+## 🏃 How to Run
+
+Before running the pipeline, set up your environment as follows:
+
+```bash
+# Set up a Python virtual environment
+python3 -m venv .venv
+source .venv/bin/activate
+
+# Install requirements
+pip install -r requirements.txt
+```
+
+To run the Iris Classification pipeline:
+
+```shell
+python run.py
+```
+
+## 🧩 Pipeline Steps
+
+1. **Load Data**: Loads the iris dataset and splits it into train and test sets.
+2. **Train Model**: Trains an SVM classifier on the training data.
+3. **Evaluate Model**: Evaluates the model on the test set and generates predictions.
+4. **Explain Model**: Generates SHAP values for model explainability.
+5. **Detect Data Drift**: Detects potential data drift between training and test sets.
+
+## 📊 Visualizations
+
+The pipeline generates a SHAP summary plot to explain feature importance:
+
+<div align="center">
+  <br/>
+    <img alt="SHAP Summary Plot" src=".assets/shap_visualization.png" width="70%">
+  <br/>
+</div>
+
+## 🛠️ Customization
+
+You can customize various aspects of the pipeline:
+
+- Adjust the `SVC` hyperparameters in the `train_model` step
+- Modify the train-test split ratio in the `load_data` step
+- Add or remove features from the iris dataset
+- Implement additional evaluation metrics in the `evaluate_model` step
+
+## 📜 Project Structure
+
+```
+.
+├── run.py                            # Main pipeline file
+├── requirements.txt                  # Python dependencies
+└── README.md                         # This file
+```
+
+## 🤝 Contributing
+
+Contributions to improve the pipeline are welcome! Please feel free to submit a Pull Request.
+
+## 📄 License
+
+This project is licensed under the Apache License 2.0. See the LICENSE file for details.

explainability-shap/requirements.txt

@@ -0,0 +1,7 @@
+scikit-learn
+shap
+matplotlib
+scipy
+zenml
+pyarrow
+fastparquet