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license](https://img.shields.io/pypi/l/waveml.svg)](https://github.com/leffff/waveml/blob/main/LICENSE)\n[![PyPI pyversions](https://img.shields.io/pypi/pyversions/waveml.svg)](https://pypi.python.org/pypi/waveml/)\n\n# waveml\nOpen source machine learning library for performance of a weighted average  and linear transformations over stacked predictions\n\n### Pip\n```\npip install waveml\n```\n\n## Overview\n\nwaveml features four models: \u003c/br\u003e\n\u003e [WaveStackingTransformer](https://github.com/leffff/waveml#WaveStackingTransformer)\u003c/br\u003e\n\u003e [WaveRegressor](https://github.com/leffff/waveml#WaveRegressor)\u003c/br\u003e\n\u003e [WaveTransformer](https://github.com/leffff/waveml#WaveTransformer)\u003c/br\u003e\n\u003e [WaveEncoder](https://github.com/leffff/waveml#WaveEncoder)\u003c/br\u003e\n\n\n## WaveStackingTransformer\nPerforms Classical Stacking\n\nCan be used for following objectives:\u003c/br\u003e\n\u003e Regression\u003c/br\u003e\n\u003e Classification\u003c/br\u003e\n\u003e Probability Prediction\u003c/br\u003e\n\n### Usage example\n\n```python\nfrom waveml import WaveStackingTransformer\nfrom catboost import CatBoostRegressor\nfrom xgboost import XGBRegressor\nfrom lightgbm import LGBMRegressor\n\nwst = WaveStackingTransformer(\n    models=[\n      (\"CBR\", CatBoostRegressor()),\n      (\"XGBR\", XGBRegressor()),\n      (\"LGBMR\", LGBMRegressor())\n    ],\n    n_folds=5,\n    verbose=True,\n    regression=True,\n    random_state=42,\n    shuffle=True\n)\n\nfrom sklearn.datasets import load_boston\nform sklearn.model_selection import train_test_split\n\nX, y = load_boston(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, shuffle=True)\n\nSX_train = wst.fit_transform(X_train, y_train, prettified=True)\nSX_test = wst.transform(X_test, prettified=True)\n\nfrom sklearn.linear_model import LinearRegression\nlr = LinearRegression()\n\nlr.fit(SX_train, y_train)\nlr.predict(SX_test)\n```\n\n### Sklearn compatibility\n\n```python\nfrom sklearn.pipeline import Pipeline\n\npipeline = Pipeline(\n    steps=[\n        (\"Stack_L1\", wst),\n        (\"Final Estimator\", lr)\n    ]\n)\n\npipeline.fit(X_train, y_train)\npipeline.predict(X_test)\n```\n\n## WaveRegressor\nPerforms weighted average over stacked predictions\u003c/br\u003e\nAnalogue of Linear Regression without intercept\u003c/br\u003e\nLinear Regression: *y = b0 + b1x1 + b2x2 + ... + bnxn*\u003c/br\u003e\nWeihghted Average: *y = b1x1 + b2x2 + ... + bnxn*\u003c/br\u003e\n\n### Usage example\n\n```python\nfrom waveml import WaveRegressor\n\nwr = WaveRegressor()\nwr.fit(SX_train, y_train)\nwr.predict(SX_test)\n```\n\n### Sklearn compatebility\n\n```python\nfrom sklearn.pipeline import Pipeline\n\npipeline = Pipeline(\n    steps=[\n        (\"Stack_L1\", wst),\n        (\"Final Estimator\", WaveRegressor())\n    ]\n)\n\npipeline.fit(X_train, y_train)\npipeline.predict(X_test)\n```\n\n## WaveTransformer\nPerforms cross validated linear transformations over stacked predictions\n\n### Usage example\n\n```python\nfrom waveml import WaveTransformer\n\nwt = WaveTransformer()\nwt.fit(X_train, y_train)\nwt.transform(X_test)\n```\n\n### Sklearn compatebility\n\n```python\npipeline = Pipeline(\n    steps=[\n        (\"Stack_L1\", wst),\n        (\"LinearTransformations\", WaveTransformer()),\n        (\"Final Estimator\", WaveRegressor())\n    ]\n)\n```\n## WaveEncoder\nPerforms encoding of categorical features in the initial dataset\n\n```python\nfrom waveml import WaveEncoder\n\nwe = WaveEncoder(encodeing_type=\"label\")\nX_train = we.fit_transform(X_train)\nX_test = we.transform(X_test)\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleffff%2Fwaveml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fleffff%2Fwaveml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleffff%2Fwaveml/lists"}