pymilo 1.5

PyMilo: Python for ML I/O

---

Overview

PyMilo is an open source Python package that provides a simple, efficient, and safe way for users to export pre-trained machine learning models in a transparent way. By this, the exported model can be used in other environments, transferred across different platforms, and shared with others. PyMilo allows the users to export the models that are trained using popular Python libraries like scikit-learn, and then use them in deployment environments, or share them without exposing the underlying code or dependencies. The transparency of the exported models ensures reliability and safety for the end users, as it eliminates the risks of binary or pickle formats.

PyPI Counter
Github Stars

Branch	main	dev
CI

Code Quality

Installation

PyPI

• Check Python Packaging User Guide
• Run pip install pymilo==1.5

Source code

• Download Version 1.5 or Latest Source
• Run pip install .

Conda

• Check Conda Managing Package
• Update Conda using conda update conda
• Run conda install -c openscilab pymilo

Usage

Import/Export

Imagine you want to train a LinearRegression model representing this equation: $y = x_0 + 2x_1 + 3$. You will create data points (X, y) and train your model as follows.

import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
# y = 1 * x_0 + 2 * x_1 + 3
model = LinearRegression().fit(X, y)
pred = model.predict(np.array([[3, 5]]))
# pred = [16.] (=1 * 3 + 2 * 5 + 3)

Using PyMilo Export class you can easily serialize and export your trained model into a JSON file.

from pymilo import Export
Export(model).save("model.json")

Export

The Export class facilitates exporting of machine learning models to JSON files.

Parameter	Description
model	The machine learning model to be exported

Property	Description
data	The serialized model data including all learned parameters
version	The scikit-learn version used to train the model
type	The type/class name of the exported model

Method	Description
save	Save the exported model to a JSON file
to_json	Return the model as a JSON string representation
batch_export	Export multiple models to individual JSON files in a directory

You can check out your model as a JSON file now.

{
    "data": {
        "fit_intercept": true,
        "copy_X": true,
        "n_jobs": null,
        "positive": false,
        "n_features_in_": 2,
        "coef_": {
            "pymiloed-ndarray-list": [
                1.0000000000000002,
                1.9999999999999991
            ],
            "pymiloed-ndarray-dtype": "float64",
            "pymiloed-ndarray-shape": [
                2
            ],
            "pymiloed-data-structure": "numpy.ndarray"
        },
        "rank_": 2,
        "singular_": {
            "pymiloed-ndarray-list": [
                1.618033988749895,
                0.6180339887498948
            ],
            "pymiloed-ndarray-dtype": "float64",
            "pymiloed-ndarray-shape": [
                2
            ],
            "pymiloed-data-structure": "numpy.ndarray"
        },
        "intercept_": {
            "value": 3.0000000000000018,
            "np-type": "numpy.float64"
        }
    },
    "sklearn_version": "1.4.2",
    "pymilo_version": "0.8",
    "model_type": "LinearRegression"
}

You can see all the learned parameters of the model in this file and change them if you want. This JSON representation is a transparent version of your model.

Now let's load it back. You can do it easily by using PyMilo Import class.

from pymilo import Import
model = Import("model.json").to_model()
pred = model.predict(np.array([[3, 5]]))
# pred = [16.] (=1 * 3 + 2 * 5 + 3)

Import

The Import class facilitates importing of serialized models from JSON files, JSON strings, or URLs.

Parameter	Description
file_adr	Path to the JSON file containing the serialized model
json_dump	JSON string representation of the serialized model
url	URL to download the serialized model from

Property	Description
data	The deserialized model data
version	The scikit-learn version of the original model
type	The type/class name of the imported model

Method	Description
to_model	Convert the imported data back to a scikit-learn model
batch_import	Import multiple models from JSON files in a directory

This loaded model is exactly the same as the original trained model.

ML streaming

You can easily serve your ML model from a remote server using ML streaming feature of PyMilo.

⚠️ ML streaming feature exists in versions >=1.0

⚠️ In order to use ML streaming feature, make sure you've installed the streaming mode of PyMilo

⚠️ The ML streaming feature is under construction and is not yet considered stable.

You can choose either REST or WebSocket as the communication medium protocol.

Server

Let's assume you are in the remote server and you want to import the exported JSON file and start serving your model through REST protocol!

from pymilo import Import
from pymilo.streaming import PymiloServer, CommunicationProtocol
my_model = Import("model.json").to_model()
communicator = PymiloServer(
    model=my_model,
    port=8000,
    communication_protocol=CommunicationProtocol["REST"],
    ).communicator
communicator.run()

PymiloServer

The PymiloServer class facilitates streaming machine learning models over a network.

Parameter	Description
port	Port number for the server to listen on (default: 8000)
host	Host address for the server (default: "127.0.0.1")
compressor	Compression method from Compression enum
communication_protocol	Communication protocol from CommunicationProtocol enum

The compressor parameter accepts values from the Compression enum including NULL (no compression), GZIP, ZLIB, LZMA, or BZ2. The communication_protocol parameter accepts values from the CommunicationProtocol enum including REST or WEBSOCKET.

Method	Description
init_client	Initialize a new client with the given client ID
remove_client	Remove an existing client by client ID
init_ml_model	Initialize a new ML model for a given client
set_ml_model	Set or update the ML model for a client
remove_ml_model	Remove an existing ML model for a client
get_ml_models	Get all ML model IDs for a client
execute_model	Execute model methods or access attributes
grant_access	Allow a client to access another client's model
revoke_access	Revoke access to a client's model
get_allowed_models	Get models a client is allowed to access

Now PymiloServer runs on port 8000 and exposes REST API to upload, download and retrieve attributes either data attributes like model._coef or method attributes like model.predict(x_test).

ℹ️ By default, PymiloServer listens on the loopback interface (127.0.0.1). To make it accessible over a local network (LAN), specify your machine’s LAN IP address in the host parameter of the PymiloServer constructor.

Client

By using PymiloClient you can easily connect to the remote PymiloServer and execute any functionalities that the given ML model has, let's say you want to run predict function on your remote ML model and get the result:

from pymilo.streaming import PymiloClient, CommunicationProtocol
pymilo_client = PymiloClient(
    mode=PymiloClient.Mode.LOCAL,
    server_url="SERVER_URL",
    communication_protocol=CommunicationProtocol["REST"],
    )
pymilo_client.toggle_mode(PymiloClient.Mode.DELEGATE)
result = pymilo_client.predict(x_test)

PymiloClient

The PymiloClient class facilitates working with remote PyMilo servers.

Parameter	Description
model	The local ML model to wrap around
mode	Operating mode (LOCAL or DELEGATE)
compressor	Compression method from Compression enum
server_url	URL of the PyMilo server
communication_protocol	Communication protocol from CommunicationProtocol enum

The mode parameter accepts two values LOCAL to execute operations on the local model, or DELEGATE to delegate operations to the remote server. The compressor parameter accepts values from the Compression enum including NULL (no compression), GZIP, ZLIB, LZMA, or BZ2. The communication_protocol parameter accepts values from the CommunicationProtocol enum including REST or WEBSOCKET.

Method	Description
toggle_mode	Switch between LOCAL and DELEGATE modes
register	Register the client with the remote server
deregister	Deregister the client from the server
register_ml_model	Register an ML model with the server
deregister_ml_model	Deregister an ML model from the server
upload	Upload the local model to the remote server
download	Download the remote model to local
get_ml_models	Get all registered ML models for this client
grant_access	Grant access to this client's model to another client
revoke_access	Revoke access previously granted to another client
get_allowance	Get clients who have access to this client's models
get_allowed_models	Get models this client is allowed to access from another client

ℹ️ If you've deployed PymiloServer locally (on port 8000 for instance), then SERVER_URL would be http://127.0.0.1:8000 or ws://127.0.0.1:8000 based on the selected protocol for the communication medium.

You can also download the remote ML model into your local and execute functions locally on your model.

Calling download function on PymiloClient will sync the local model that PymiloClient wraps upon with the remote ML model, and it doesn't save model directly to a file.

pymilo_client.download()

If you want to save the ML model to a file in your local, you can use Export class.

from pymilo import Export
Export(pymilo_client.model).save("model.json")

Now that you've synced the remote model with your local model, you can run functions.

pymilo_client.toggle_mode(mode=PymiloClient.Mode.LOCAL)
result = pymilo_client.predict(x_test)

PymiloClient wraps around the ML model, either to the local ML model or the remote ML model, and you can work with PymiloClient in the exact same way that you did with the ML model, you can run exact same functions with same signature.

ℹ️ Through the usage of toggle_mode function you can specify whether PymiloClient applies requests on the local ML model pymilo_client.toggle_mode(mode=Mode.LOCAL) or delegates it to the remote server pymilo_client.toggle_mode(mode=Mode.DELEGATE)

Supported ML models

scikit-learn	PyTorch
Linear Models ✅	-
Neural Networks ✅	-
Trees ✅	-
Clustering ✅	-
Naïve Bayes ✅	-
Support Vector Machines (SVMs) ✅	-
Nearest Neighbors ✅	-
Ensemble Models ✅	-
Pipeline Model ✅	-
Preprocessing Models ✅	-
Cross Decomposition Models ✅	-
Feature Extractor Models ✅	-
Composite Models ✅	-

Details are available in Supported Models.

Issues & bug reports

Just fill an issue and describe it. We'll check it ASAP! or send an email to [e-mail].

• Please complete the issue template

You can also join our discord server

Contributing

We welcome contributions! Please read our Contributing Guidelines before submitting any changes.

Acknowledgments

Python Software Foundation (PSF) grants PyMilo library partially for versions 1.0, 1.1. PSF is the organization behind Python. Their mission is to promote, protect, and advance the Python programming language and to support and facilitate the growth of a diverse and international community of Python programmers.

Trelis Research grants PyMilo library partially for version 1.0. Trelis Research provides tools and tutorials for businesses and developers looking to fine-tune and deploy large language models.

Cite

If you use PyMilo in your research, we would appreciate citations to the following paper:

Rostami, A., Haghighi, S., Sabouri, S. and Zolanvari, A., 2025. PyMilo: A Python Library for ML I/O. Journal of Open Source Software, 10(116), p.8858.

@article{Rostami2025,
  doi = {10.21105/joss.08858},
  url = {https://doi.org/10.21105/joss.08858},
  year = {2025},
  publisher = {The Open Journal},
  volume = {10},
  number = {116},
  pages = {8858},
  author = {Rostami, AmirHosein and Haghighi, Sepand and Sabouri, Sadra and Zolanvari, Alireza},
  title = {PyMilo: A Python Library for ML I/O},
  journal = {Journal of Open Source Software}
}

Download PyMilo.bib

Show your support

Star this repo

Give a ⭐️ if this project helped you!

Donate to our project

If you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do ;-) .

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

1.5 - 2026-01-26

Added

• _is_remainder_cols_list function in GeneralDataStructureTransporter
• ComposeTransporter Transporter
• Composite params initialized in pymilo_param.py
• get_transporter in chains/util.py
• deserialize_possible_ml_model in chains/util.py
• serialize_possible_ml_model in chains/util.py
• TransformedTargetRegressor model
• ColumnTransformer model
• Composite models test runner
• Composite models chain
• JOSS paper

Changed

• serialize function in FunctionTransporter
• serialize_spline function in PreprocessingTransporter
• deserialize_spline function in PreprocessingTransporter
• Ensemble models test runner
• get_deserialized_list function in GeneralDataStructureTransporter
• deserialize function in GeneralDataStructureTransporter
• serialize function in GeneralDataStructureTransporter
• get_deserialized_dict function in GeneralDataStructureTransporter
• serialize_dict function in GeneralDataStructureTransporter
• serialize_tuple function in GeneralDataStructureTransporter
• Test system modified
• README.md updated

Removed

• get_transporter in ensemble_chain.py
• deserialize_possible_ml_model in ensemble_chain.py
• serialize_possible_ml_model in ensemble_chain.py

1.4 - 2025-12-01

Added

• get_allowed_models function in PymiloClient
• get_allowance function in PymiloClient
• revoke_access function in PymiloClient
• grant_access function in PymiloClient
• get_ml_models function in PymiloClient
• deregister_ml_model function in PymiloClient
• register_ml_model function in PymiloClient
• deregister function in PymiloClient
• register function in PymiloClient
• REST_API_PREFIX function in streaming.param.py
• register_client function in RESTClientCommunicator
• remove_client function in RESTClientCommunicator
• register_model function in RESTClientCommunicator
• remove_model function in RESTClientCommunicator
• get_ml_models function in RESTClientCommunicator
• grant_access function in RESTClientCommunicator
• revoke_access function in RESTClientCommunicator
• get_allowance function in RESTClientCommunicator
• get_allowed_models function in RESTClientCommunicator
• _validate_id function in PymiloServer
• init_client function in PymiloServer
• remove_client function in PymiloServer
• grant_access function in PymiloServer
• revoke_access function in PymiloServer
• get_allowed_models function in PymiloServer
• get_clients_allowance function in PymiloServer
• get_clients function in PymiloServer
• init_ml_model function in PymiloServer
• set_ml_model function in PymiloServer
• remove_ml_model function in PymiloServer
• get_ml_models function in PymiloServer

Changed

• is_callable_attribute function in PymiloServer
• execute_model function in PymiloServer
• update_model function in PymiloServer
• export_model function in PymiloServer
• __getattr__ in PymiloClient
• upload function in PymiloClient
• download function in PymiloClient
• encrypt_compress function in PymiloClient
• ClientCommunicator interface
• handle_message function in WebSocketServerCommunicator
• _handle_download function in WebSocketServerCommunicator
• setup_routes function in RESTServerCommunicator
• __init__ function in RESTClientCommunicator
• download function in RESTClientCommunicator
• upload function in RESTClientCommunicator
• attribute_call function in RESTClientCommunicator
• attribute_type function in RESTClientCommunicator
• README.md updated
• __init__ function in PyMiloServer
• Test system modified
• Python 3.14 added to test.yml

Removed

• Python 3.6 support

1.3 - 2025-02-26

Added

• TfidfVectorizer feature extractor
• TfidfTransformer feature extractor
• HashingVectorizer feature extractor
• CountVectorizer feature extractor
• PatchExtractor feature extractor
• DictVectorizer feature extractor
• FeatureHasher feature extractor
• FeatureExtractorTransporter Transporter
• FeatureExtraction support added to Ensemble chain
• FeatureExtraction params initialized in pymilo_param.py
• Feature Extraction models test runner
• Zenodo badge to README.md

Changed

• get_deserialized_list in GeneralDataStructureTransporter
• get_deserialized_dict in GeneralDataStructureTransporter
• serialize in GeneralDataStructureTransporter
• serialize_tuple in GeneralDataStructureTransporter
• AttributeCallPayload in streaming.communicator.py
• get_deserialized_regular_primary_types in GeneralDataStructureTransporter
• Test system modified

1.2 - 2025-01-22

Added

• generate_dockerfile testcases
• generate_dockerfile function in streaming.util.py
• cite section in README.md
• CLI handler
• print_supported_ml_models function in pymilo_func.py
• pymilo_help function in pymilo_func.py
• SKLEARN_SUPPORTED_CATEGORIES in pymilo_param.py
• OVERVIEW in pymilo_param.py
• get_sklearn_class in utils.util.py

Changed

• ML Streaming testcases modified to use PyMilo CLI
• to_pymilo_issue function in PymiloException
• valid_url_valid_file testcase added in test_exceptions.py
• valid_url_valid_file function in import_exceptions.py
• StandardPayload in RESTServerCommunicator
• testcase for LogisticRegressionCV, LogisticRegression
• README.md updated
• AUTHORS.md updated

1.1 - 2024-11-25

Added

• is_socket_closed function in streaming.communicator.py
• validate_http_url function in streaming.util.py
• validate_websocket_url function in streaming.util.py
• ML Streaming WebSocket testcases
• CommunicationProtocol Enum in streaming.communicator.py
• WebSocketClientCommunicator class in streaming.communicator.py
• WebSocketServerCommunicator class in streaming.communicator.py
• batch operation testcases
• batch_export function in pymilo/pymilo_obj.py
• batch_import function in pymilo/pymilo_obj.py
• CCA model
• PLSCanonical model
• PLSRegression model
• Cross decomposition models test runner
• Cross decomposition chain
• PyMilo exception types added in pymilo/exceptions/__init__.py
• PyMilo exception types added in pymilo/__init__.py

Changed

• core and streaming tests divided in test.yml
• communication_protocol parameter added to PyMiloClient class
• communication_protocol parameter added to PyMiloServer class
• ML Streaming testcases updated to support protocol selection
• README.md updated
• Tests config modified
• Cross decomposition params initialized in pymilo_param
• Cross decomposition support added to pymilo_func.py
• SUPPORTED_MODELS.md updated
• README.md updated
• GitHub actions are limited to the dev and main branches
• Python 3.13 added to test.yml

1.0 - 2024-09-16

Added

• Compression method test in ML Streaming RESTful testcases
• CLI handler in tests/test_ml_streaming/run_server.py
• Compression Enum in streaming.compressor.py
• GZIPCompressor class in streaming.compressor.py
• ZLIBCompressor class in streaming.compressor.py
• LZMACompressor class in streaming.compressor.py
• BZ2Compressor class in streaming.compressor.py
• encrypt_compress function in PymiloClient
• parse function in RESTServerCommunicator
• is_callable_attribute function in PymiloServer
• streaming.param.py
• attribute_type function in RESTServerCommunicator
• AttributeTypePayload class in RESTServerCommunicator
• attribute_type function in RESTClientCommunicator
• Mode Enum in PymiloClient
• Import from url testcases
• download_model function in utils.util.py
• PymiloServer class in streaming.pymilo_server.py
• PymiloClient class in PymiloClient
• Communicator interface in streaming.interfaces.py
• RESTClientCommunicator class in streaming.communicator.py
• RESTServerCommunicator class in streaming.communicator.py
• Compressor interface in streaming.interfaces.py
• DummyCompressor class in streaming.compressor.py
• Encryptor interface in streaming.interfaces.py
• DummyEncryptor class in streaming.encryptor.py
• ML Streaming RESTful testcases
• streaming-requirements.txt

Changed

• README.md updated
• ML Streaming RESTful testcases
• attribute_call function in RESTServerCommunicator
• AttributeCallPayload class in RESTServerCommunicator
• upload function in RESTClientCommunicator
• download function in RESTClientCommunicator
• __init__ function in RESTClientCommunicator
• attribute_calls function in RESTClientCommunicator
• requests added to requirements.txt
• uvicorn, fastapi, requests and pydantic added to dev-requirements.txt
• ML Streaming RESTful testcases
• __init__ function in PymiloServer
• __getattr__ function in PymiloClient
• __init__ function in PymiloClient
• toggle_mode function in PymiloClient
• upload function in PymiloClient
• download function in PymiloClient
• __init__ function in PymiloServer
• serialize_cfnode function in transporters.cfnode_transporter.py
• __init__ function in Import class
• serialize function in transporters.tree_transporter.py
• deserialize function in transporters.tree_transporter.py
• serialize function in transporters.sgdoptimizer_transporter.py
• deserialize function in transporters.sgdoptimizer_transporter.py
• serialize function in transporters.randomstate_transporter.py
• deserialize function in transporters.randomstate_transporter.py
• serialize function in transporters.bunch_transporter.py
• deserialize function in transporters.bunch_transporter.py
• serialize function in transporters.adamoptimizer_transporter.py
• deserialize function in transporters.adamoptimizer_transporter.py
• serialize_linear_model function in chains.linear_model_chain.py
• serialize_ensemble function in chains.ensemble_chain.py
• serialize function in GeneralDataStructureTransporter Transporter refactored
• get_deserialized_list function in GeneralDataStructureTransporter Transporter refactored
• Export class call by reference bug fixed

0.9 - 2024-07-01

Added

• Anaconda workflow
• prefix_list function in utils.util.py
• KBinsDiscretizer preprocessing model
• PowerTransformer preprocessing model
• SplineTransformer preprocessing model
• TargetEncoder preprocessing model
• QuantileTransformer preprocessing model
• RobustScaler preprocessing model
• PolynomialFeatures preprocessing model
• OrdinalEncoder preprocessing model
• Normalizer preprocessing model
• MaxAbsScaler preprocessing model
• MultiLabelBinarizer preprocessing model
• KernelCenterer preprocessing model
• FunctionTransformer preprocessing model
• Binarizer preprocessing model
• Preprocessing models test runner

Changed

• Command enum class in transporter.py
• SerializationErrorTypes enum class in serialize_exception.py
• DeserializationErrorTypes enum class in deserialize_exception.py
• meta.yaml modified
• NaN type in pymilo_param
• NaN type transportation in GeneralDataStructureTransporter Transporter
• BSpline Transportation in PreprocessingTransporter Transporter
• one layer deeper transportation in PreprocessingTransporter Transporter
• dictating outer ndarray dtype in GeneralDataStructureTransporter Transporter
• preprocessing params fulfilled in pymilo_param
• SUPPORTED_MODELS.md updated
• README.md updated
• serialize_possible_ml_model in the Ensemble chain

0.8 - 2024-05-06

Added

• StandardScaler Transformer in pymilo_param.py
• PreprocessingTransporter Transporter
• ndarray shape config in GeneralDataStructure Transporter
• util.py in chains
• BinMapperTransporter Transporter
• BunchTransporter Transporter
• GeneratorTransporter Transporter
• TreePredictorTransporter Transporter
• AdaboostClassifier model
• AdaboostRegressor model
• BaggingClassifier model
• BaggingRegressor model
• ExtraTreesClassifier model
• ExtraTreesRegressor model
• GradientBoosterClassifier model
• GradientBoosterRegressor model
• HistGradientBoosterClassifier model
• HistGradientBoosterRegressor model
• RandomForestClassifier model
• RandomForestRegressor model
• IsolationForest model
• RandomTreesEmbedding model
• StackingClassifier model
• StackingRegressor model
• VotingClassifier model
• VotingRegressor model
• Pipeline model
• Ensemble models test runner
• Ensemble chain
• SECURITY.md

Changed

• Pipeline test updated
• LabelBinarizer,LabelEncoder and OneHotEncoder got embedded in PreprocessingTransporter
• Preprocessing support added to Ensemble chain
• Preprocessing params initialized in pymilo_param
• util.py in utils updated
• test_pymilo.py updated
• pymilo_func.py updated
• linear_model_chain.py updated
• neural_network_chain.py updated
• decision_tree_chain.py updated
• clustering_chain.py updated
• naive_bayes_chain.py updated
• neighbours_chain.py updated
• svm_chain.py updated
• GeneralDataStructure Transporter updated
• LossFunction Transporter updated
• AbstractTransporter updated
• Tests config modified
• Unequal sklearn version error added in pymilo_param.py
• Ensemble params initialized in pymilo_param
• Ensemble support added to pymilo_func.py
• SUPPORTED_MODELS.md updated
• README.md updated

0.7 - 2024-04-03

Added

• pymilo_nearest_neighbor_test function added to test_pymilo.py
• NeighborsTreeTransporter Transporter
• LocalOutlierFactor model
• RadiusNeighborsClassifier model
• RadiusNeighborsRegressor model
• NearestCentroid model
• NearestNeighbors model
• KNeighborsClassifier model
• KNeighborsRegressor model
• Neighbors models test runner
• Neighbors chain

Changed

• Tests config modified
• Neighbors params initialized in pymilo_param
• Neighbors support added to pymilo_func.py
• SUPPORTED_MODELS.md updated
• README.md updated

0.6 - 2024-03-27

Added

• deserialize_primitive_type function in GeneralDataStructureTransporter
• is_deserialized_ndarray function in GeneralDataStructureTransporter
• deep_deserialize_ndarray function in GeneralDataStructureTransporter
• deep_serialize_ndarray function in GeneralDataStructureTransporter
• SVR model
• SVC model
• One Class SVM model
• NuSVR model
• NuSVC model
• Linear SVR model
• Linear SVC model
• SVM models test runner
• SVM chain

Changed

• pymilo_param.py updated
• pymilo_obj.py updated to use predefined strings
• TreeTransporter updated
• get_homogeneous_type function in util.py updated
• GeneralDataStructureTransporter updated to use deep ndarray serializer & deserializer
• check_str_in_iterable updated
• Label Binarizer Transporter updated
• Function Transporter updated
• CFNode Transporter updated
• Bisecting Tree Transporter updated
• Tests config modified
• SVM params initialized in pymilo_param
• SVM support added to pymilo_func.py
• SUPPORTED_MODELS.md updated
• README.md updated

0.5 - 2024-01-31

Added

• reset function in the Transport interface
• reset function implementation in AbstractTransporter
• Gaussian Naive Bayes declared as GaussianNB model
• Multinomial Naive Bayes model declared as MultinomialNB model
• Complement Naive Bayes model declared as ComplementNB model
• Bernoulli Naive Bayes model declared as BernoulliNB model
• Categorical Naive Bayes model declared as CategoricalNB model
• Naive Bayes models test runner
• Naive Bayes chain

Changed

• Transport function of AbstractTransporter updated
• fix the order of CFNode fields serialization in CFNodeTransporter
• GeneralDataStructureTransporter support list of ndarray with different shapes
• Tests config modified
• Naive Bayes params initialized in pymilo_param
• Naive Bayes support added to pymilo_func.py
• SUPPORTED_MODELS.md updated
• README.md updated

0.4 - 2024-01-22

Added

• has_named_parameter method in util.py
• CFSubcluster Transporter(inside CFNode Transporter)
• CFNode Transporter
• Birch model
• SpectralBiclustering model
• SpectralCoclustering model
• MiniBatchKMeans model
• feature_request.yml template
• config.yml for issue template
• BayesianGaussianMixture model
• serialize_tuple method in GeneralDataStructureTransporter
• import_function method in util.py
• Function Transporter
• FeatureAgglomeration model
• HDBSCAN model
• GaussianMixture model
• OPTICS model
• DBSCAN model
• AgglomerativeClustering model
• SpectralClustering model
• MeanShift model
• AffinityPropagation model
• Kmeans model
• Clustering models test runner
• Clustering chain

Changed

• LossFunctionTransporter enhanced to handle scikit 1.4.0 _loss_function_ field
• Codacy Static Code Analyzer's suggestions applied
• Spectral Clustering test folder refactored
• Bug report template modified
• GeneralDataStructureTransporter updated
• Tests config modified
• Clustering data set preparation added to data_exporter.py
• Clustering params initialized in pymilo_param
• Clustering support added to pymilo_func.py
• Python 3.12 added to test.yml
• dev-requirements.txt updated
• Code quality badges added to README.md
• SUPPORTED_MODELS.md updated
• README.md updated

0.3 - 2023-09-27

Added

• scikit-learn decision tree models
• ExtraTreeClassifier model
• ExtraTreeRegressor model
• DecisionTreeClassifier model
• DecisionTreeRegressor model
• Tree Transporter
• Decision Tree chain

Changed

• Tests config modified
• DecisionTree params initialized in pymilo_param
• Decision Tree support added to pymilo_func.py

0.2 - 2023-08-02

Added

• scikit-learn neural network models
• MLP Regressor model
• MLP Classifier model
• BernoulliRBN model
• SGDOptimizer transporter
• RandomState(MT19937) transporter
• Adamoptimizer transporter
• Neural Network chain
• Neural Network exceptions
• ndarray_to_list method in GeneralDataStructureTransporter
• list_to_ndarray method in GeneralDataStructureTransporter
• neural_network_chain.py chain

Changed

• GeneralDataStructure Transporter updated
• LabelBinerizer Transporter updated
• linear model chain updated
• GeneralDataStructure transporter enhanced
• LabelBinerizer transporter updated
• transporters' chain router added to pymilo func
• NeuralNetwork params initialized in pymilo_param
• pymilo_test updated to support multiple models
• linear_model_chain refactored

0.1 - 2023-06-29

Added

• scikit-learn linear models support
• Export class
• Import class