sklearn-migrator 0.21.1

A utility to migrate scikit-learn models between versions.

sklearn-migrator 🧪

A Python library to serialize and migrate scikit-learn models across incompatible versions.

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🚀 Motivation

Machine learning teams frequently store trained scikit-learn models using pickle or joblib.
However:

❌ These serialized models break when scikit-learn versions change

• Internal attributes change
• APIs evolve (e.g., affinity → metric)
• Tree and boosting internals get reorganized
• New default parameters appear

❌ This creates real problems:

• Production services fail after dependency upgrades
• Research becomes non-reproducible
• Long-term model governance becomes impossible
• Models can't be migrated or audited reliably

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✅ What sklearn-migrator provides

✔ Serialize any supported model into a JSON-compatible dictionary

✔ Deserialize and reconstruct the model in a different scikit-learn version

✔ Remove dependency on pickle/joblib for long-term storage

✔ Enable reproducible ML pipelines across environments

This library has been validated across 900 version migration pairs (from → to), covering:

0.21.3 → 1.7.2

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💡 Supported Models (21 models)

sklearn-migrator supports 21 core models across classification, regression, clustering, and dimensionality reduction.

📘 Classification

Model	Supported
DecisionTreeClassifier	✅
RandomForestClassifier	✅
GradientBoostingClassifier	✅
LogisticRegression	✅
KNeighborsClassifier	✅
SVC (Support Vector Classifier)	✅
MLPClassifier	✅

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📗 Regression

Model	Supported
DecisionTreeRegressor	✅
RandomForestRegressor	✅
GradientBoostingRegressor	✅
LinearRegression	✅
Ridge	✅
Lasso	✅
KNeighborsRegressor	✅
SVR (Support Vector Regressor)	✅
AdaBoostRegressor	✅
MLPRegressor	✅

---

📙 Clustering

Model	Supported
KMeans	✅
MiniBatchKMeans	✅

---

📘 Dimensionality Reduction

Model	Supported
PCA	✅

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🔢 Version Compatibility Matrix

The library supports model migrations across the full matrix:

• 32 versions
• 1.024 migration pairs
• Fully tested using automated environments

versions = [
    '0.21.3', '0.22.0', '0.22.1', '0.23.0', '0.23.1', '0.23.2',
    '0.24.0', '0.24.1', '0.24.2', '1.0.0', '1.0.1', '1.0.2',
    '1.1.0', '1.1.1', '1.1.2', '1.1.3', '1.2.0', '1.2.1', '1.2.2',
    '1.3.0', '1.3.1', '1.3.2', '1.4.0', '1.4.2', '1.5.0', '1.5.1',
    '1.5.2', '1.6.0', '1.6.1', '1.7.0', '1.7.1', '1.7.2'
]

From \ To	0.21.3	0.22.0	...	1.7.2
0.21.3	✅	✅	...	✅
0.22.0	✅	✅	...	✅
...	...	...	...	...
1.7.2	✅	✅	...	✅

⚠️ All 1.024 combinations and 21 models were tested and validated in real environments.

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📂 Installation

pip install sklearn-migrator

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💥 Use Cases

• Long-term model storage: Store models in a future-proof format across teams and systems.
• Production model migration: Move models safely across major scikit-learn upgrades.
• Auditing and inspection: Read serialized models as JSON, inspect structure, hyperparameters, and internals.
• Cross-platform inference: Serialize in Python, serve elsewhere (e.g., microservices).

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1. Using two python environments

You can serialized the model from a environment with a scikit-learn version (for example 1.5.0) and then you can deserialized the model from another environment with a other version (for exmaple 1.7.0).

The deserialized model has the version of the environment where you deserialized it. In this case 1.7.0.

As you can see is very important understand what is the version of scikit learn from you want to migrate to create and environment with this version to deserialized the model. On the other side you have to understand to what version you want to migrate to again create and environment with this version of scikit-learn.

a. Serialize the model

import json
import sklearn
import numpy as np
import pandas as pd

from sklearn.ensemble import RandomForestRegressor

from sklearn_migrator.regression.random_forest_reg import serialize_random_forest_reg

version_sklearn_in = sklearn.__version__

model = RandomForestRegressor()
model.fit(X_train, y_train)

# If you want to compare output from this model and the new model with his new version
y_pred = pd.DataFrame(model.predict(X_test))
y_pred.to_csv('y_pred.csv', index = False)

all_data = serialize_random_forest_reg(model, version_sklearn_in)

# Save it

def convert(o):
    if isinstance(o, (np.integer, np.int64)):
        return int(o)
    elif isinstance(o, (np.floating, np.float64)):
        return float(o)
    elif isinstance(o, np.ndarray):
        return o.tolist()
    else:
        raise TypeError(f"Object of type {type(o).__name__} is not JSON serializable")

with open("/input/model.json", "w") as f:
    json.dump(all_data, f, default=convert)

b. Desarialize the model

import json
import sklearn
import numpy as np
import pandas as pd

from sklearn.ensemble import RandomForestRegressor

from sklearn_migrator.regression.random_forest_reg import deserialize_random_forest_reg

version_sklearn_out = sklearn.__version__

with open("/input/model.json", "r") as f:
    all_data = json.load(f)

new_model_reg_rfr = deserialize_random_forest_reg(all_data, version_sklearn_out)

# Now you have your model in this new version

# If you want to compare the outputs
y_pred_new = pd.DataFrame(new_model.predict(X_test))
y_pred_new.to_csv('y_pred_new.csv', index = False)

# Of course you compare "y_pred.csv" with "y_pred_new.csv"

2. Docker: Step by Step

You have a Random Forest Classifier saved in a .pkl format and it is called model.pkl. The version of this model is 1.5.0.

i. Create in your Desktop the next folder:

/test_github

And copy your model.pkl in this folder.

ii. Go to the repo: https://github.com/anvaldes/environments_scikit_learn and find out for the Dockerfile and requirements.txt for the corresponding input scikit-learn version.

You will have this:

/test_github/input/1.5.0/Dockerfile_input
/test_github/input/1.5.0/requirements_input.txt

iii. Go to the repo: https://github.com/anvaldes/environments_scikit_learn and find out for the Dockerfile and requirements.txt for the output corresponding scikit-learn version.

You will have this:

/test_github/output/1.7.0/Dockerfile_output
/test_github/output/1.7.0/requirements_output.txt

iv. Now you create your input.py:

import json
import joblib
import sklearn
import numpy as np
import pandas as pd
from joblib import load

from sklearn.ensemble import RandomForestClassifier

from sklearn_migrator.classification.random_forest_clf import serialize_random_forest_clf

version_sklearn_in = sklearn.__version__

model = load('model.pkl')

all_data = serialize_random_forest_clf(model, version_sklearn_in)

def convert(o):
    if isinstance(o, (np.integer, np.int64)):
        return int(o)
    elif isinstance(o, (np.floating, np.float64)):
        return float(o)
    elif isinstance(o, np.ndarray):
        return o.tolist()
    else:
        raise TypeError(f"Object of type {type(o).__name__} is not JSON serializable")

with open("input_model/all_data.json", "w") as f:
    json.dump(all_data, f, default=convert)

fake_row = np.array([[0.5, -1.2, 0.3, 1.1, -0.7, 0.9, 0.0, -0.3, 1.5, 0.2]])

y_pred = pd.DataFrame(model.predict_proba(fake_row))
y_pred.to_csv('input_model/y_pred.csv', index = False)

v. Now you create your output.py:

import json
import joblib
import sklearn
import numpy as np
import pandas as pd
from joblib import load

from sklearn.ensemble import RandomForestClassifier

from sklearn_migrator.classification.random_forest_clf import deserialize_random_forest_clf

version_sklearn_out = sklearn.__version__

with open("input_model/all_data.json", "r") as f:
    all_data = json.load(f)

new_model = deserialize_random_forest_clf(all_data, version_sklearn_out)

joblib.dump(new_model, 'output_model/new_model.pkl')

fake_row = np.array([[0.5, -1.2, 0.3, 1.1, -0.7, 0.9, 0.0, -0.3, 1.5, 0.2]])

y_pred_new = pd.DataFrame(new_model.predict_proba(fake_row))
y_pred_new.to_csv('output_model/y_pred_new.csv', index = False)

vi. Now you copy all the files:

cp input/1.5.0/* output/1.7.0/* .

vii. Now you create two folders: input_model/ and output_model/.

viii. Execute the next commands in your terminal (you should be in the root of test_github/ folder)

docker build -f Dockerfile_input -t image_input_1.5.0 .
docker build -f Dockerfile_output -t image_output_1.7.0 .

docker run --rm \
  -v "$(pwd)/input_model:/app/input_model" \
  -v "$(pwd)/model.pkl:/app/model.pkl" \
  image_input_1.5.0

docker run --rm \
  -v "$(pwd)/input_model:/app/input_model" \
  -v "$(pwd)/output_model:/app/output_model" \
  image_output_1.7.0

ix. Finally you can find your migrated model in the folder /output_model and its name is new_model.pkl. This model is a scikit-learn model of version 1.7.0.

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🧾 Function Signatures & Parameters

Each model in sklearn-migrator provides a pair of functions:

• serialize_<model_name>(model, version_in)
  Converts a trained scikit-learn model into a portable, version-agnostic dictionary.

• deserialize_<model_name>(data, version_out)
  Reconstructs a scikit-learn model in a specific target version using the serialized dictionary.

🧠 Classification Models

from sklearn_migrator.classification.decision_tree_clf import (
    serialize_decision_tree_clf,
    deserialize_decision_tree_clf,
)

from sklearn_migrator.classification.gradient_boosting_clf import (
    serialize_gradient_boosting_clf,
    deserialize_gradient_boosting_clf,
)

from sklearn_migrator.classification.knn_clf import (
    serialize_knn_clf,
    deserialize_knn_clf,
)

from sklearn_migrator.classification.logistic_regression_clf import (
    serialize_logistic_regression_clf,
    deserialize_logistic_regression_clf,
)

from sklearn_migrator.classification.mlp_clf import (
    serialize_mlp_clf,
    deserialize_mlp_clf,
)

from sklearn_migrator.classification.random_forest_clf import (
    serialize_random_forest_clf,
    deserialize_random_forest_clf,
)

from sklearn_migrator.classification.svm_clf import (
    serialize_svc,
    deserialize_svc,
)

🧮 Regression Models

from sklearn_migrator.regression.adaboost_reg import (
    serialize_adaboost_reg,
    deserialize_adaboost_reg,
)

from sklearn_migrator.regression.decision_tree_reg import (
    serialize_decision_tree_reg,
    deserialize_decision_tree_reg,
)

from sklearn_migrator.regression.gradient_boosting_reg import (
    serialize_gradient_boosting_reg,
    deserialize_gradient_boosting_reg,
)

from sklearn_migrator.regression.knn_reg import (
    serialize_knn_reg,
    deserialize_knn_reg,
)

from sklearn_migrator.regression.lasso_reg import (
    serialize_lasso_reg,
    deserialize_lasso_reg,
)

from sklearn_migrator.regression.linear_regression_reg import (
    serialize_linear_regression_reg,
    deserialize_linear_regression_reg,
)

from sklearn_migrator.regression.mlp_reg import (
    serialize_mlp_reg,
    deserialize_mlp_reg,
)

from sklearn_migrator.regression.random_forest_reg import (
    serialize_random_forest_reg,
    deserialize_random_forest_reg,
)

from sklearn_migrator.regression.ridge_reg import (
    serialize_ridge_reg,
    deserialize_ridge_reg,
)

from sklearn_migrator.regression.svm_reg import (
    serialize_svr,
    deserialize_svr,
)

🧩 Clustering Models

from sklearn_migrator.clustering.agglomerative import (
    serialize_agglomerative,
    deserialize_agglomerative,
)

from sklearn_migrator.clustering.k_means import (
    serialize_k_means,
    deserialize_k_means,
)

from sklearn_migrator.clustering.mini_batch_k_means import (
    serialize_mini_batch_kmeans,
    deserialize_mini_batch_kmeans,
)

📉 Dimensionality Reduction

from sklearn_migrator.dimension.pca import (
    serialize_pca,
    deserialize_pca,
)

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🔧 Development

Run tests

pytest tests/

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🤝 Contributing

1. Fork the repository
2. Create a new branch feature/my-feature
3. Open a pull request
4. Please ensure your pull request is tested for all combinations of functions; otherwise, it may be rejected.

We welcome bug reports, suggestions, and contributions of new models.

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📄 License

MIT License — see LICENSE for details.

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🔍 Author

Alberto Valdés

ML/AI Engineer | MLOps Engineer | Open Source Contributor

GitHub: @anvaldes

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