Add project description to isolation-forest-onnx PyPI package

isolation-forest-onnx/MANIFEST.in

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 include version.txt
+include README.md

isolation-forest-onnx/README.md

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+# isolation-forest-onnx
+
+A converter for the LinkedIn Spark/Scala [isolation forest](https://github.com/linkedin/isolation-forest) model format to [ONNX](https://onnx.ai/) format for broad portability across platforms and languages.
+
+**Note:** ONNX conversion is supported for the standard `IsolationForestModel` only. The `ExtendedIsolationForestModel` uses hyperplane-based splits that are not compatible with the axis-aligned tree ensemble representation used by the ONNX converter.
+
+## Installation
+
+```bash
+pip install isolation-forest-onnx
+```
+
+It is recommended to use the same version of the converter as the version of the `isolation-forest` library used to train the model.
+
+## Converting a trained model to ONNX
+
+```python
+import os
+from isolationforestonnx.isolation_forest_converter import IsolationForestConverter
+
+# Path where the trained IsolationForestModel was saved in Scala
+path = '/user/testuser/isolationForestWriteTest'
+
+# Get model data path
+data_dir_path = path + '/data'
+avro_model_file = os.listdir(data_dir_path)
+model_file_path = data_dir_path + '/' + avro_model_file[0]
+
+# Get model metadata file path
+metadata_dir_path = path + '/metadata'
+metadata_file = os.listdir(metadata_dir_path)
+metadata_file_path = metadata_dir_path + '/' + metadata_file[0]
+
+# Convert the model to ONNX format (returns the ONNX model in memory)
+converter = IsolationForestConverter(model_file_path, metadata_file_path)
+onnx_model = converter.convert()
+
+# Convert and save the model in ONNX format
+onnx_model_path = '/user/testuser/isolationForestWriteTest.onnx'
+converter.convert_and_save(onnx_model_path)
+```
+
+## Using the ONNX model for inference
+
+```python
+import numpy as np
+import onnx
+from onnxruntime import InferenceSession
+
+onnx_model_path = '/user/testuser/isolationForestWriteTest.onnx'
+dataset_path = 'shuttle.csv'
+
+# Load data
+input_data = np.loadtxt(dataset_path, delimiter=',')
+num_features = input_data.shape[1] - 1
+last_col_index = num_features
+
+# The last column is the label column
+input_dict = {'features': np.delete(input_data, last_col_index, 1).astype(dtype=np.float32)}
+
+# Load the ONNX model and run inference
+onx = onnx.load(onnx_model_path)
+sess = InferenceSession(onx.SerializeToString())
+res = sess.run(None, input_dict)
+
+# Print scores
+outlier_scores = res[0]
+print(np.transpose(outlier_scores[:10])[0])
+```
+
+## License
+
+BSD 2-Clause License. See [LICENSE](https://github.com/linkedin/isolation-forest/blob/master/LICENSE) for details.

isolation-forest-onnx/setup.cfg

@@ -3,6 +3,8 @@ name = isolation-forest-onnx
 author = James Verbus
 author_email = james.verbus@gmail.com
 description = A converter for the LinkedIn Spark/Scala isolation forest model format to ONNX format.
+long_description = file: README.md
+long_description_content_type = text/markdown
 url = https://github.com/linkedin/isolation-forest
 license = BSD 2-Clause License
 keywords = isolation forest, ONNX