waveml 0.2.1

Open source machine learning library with various models and tools

waveml

Open source machine learning library for performance of a weighted average and linear transformations over stacked predictions

Pip

pip install waveml

Overview

waveml features four models:

WaveStackingTransformer
WaveRegressor
WaveTransformer
WaveEncoder

WaveStackingTransformer

Performs Classical Stacking

Can be used for following objectives:

Regression
Classification
Probability Prediction

Usage example

from waveml import WaveStackingTransformer
from catboost import CatBoostRegressor
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor

wst = WaveStackingTransformer(
    models=[
      ("CBR", CatBoostRegressor()),
      ("XGBR", XGBRegressor()),
      ("LGBMR", LGBMRegressor())
    ],
    n_folds=5,
    verbose=True,
    regression=True,
    random_state=42,
    shuffle=True
)

from sklearn.datasets import load_boston
form sklearn.model_selection import train_test_split

X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, shuffle=True)

SX_train = wst.fit_transform(X_train, y_train, prettified=True)
SX_test = wst.transform(X_test, prettified=True)

from sklearn.linear_model import LinearRegression
lr = LinearRegression()

lr.fit(SX_train, y_train)
lr.predict(SX_test)

Sklearn compatebility

from sklearn.pipeline import Pipeline

pipeline = Pipeline(
    steps=[
        ("Stack_L1", wst),
        ("Final Estimator", lr)
    ]
)

pipeline.fit(X_train, y_train)
pipeline.predict(X_test)

WaveRegressor

Performs weighted average over stacked predictions
Analogue of Linear Regression without intercept
Linear Regression: y = b0 + b1x1 + b2x2 + ... + bnxn
Weihghted Average: y = b1x1 + b2x2 + ... + bnxn

Usage example

from waveml import WaveRegressor

wr = WaveRegressor()
wr.fit(SX_train, y_train)
wr.predict(SX_test)

Sklearn compatebility

from sklearn.pipeline import Pipeline

pipeline = Pipeline(
    steps=[
        ("Stack_L1", wst),
        ("Final Estimator", WaveRegressor())
    ]
)

pipeline.fit(X_train, y_train)
pipeline.predict(X_test)

WaveTransformer

Performs cross validated linear transformations over stacked predictions

Usage example

from waveml import WaveTransformer

wt = WaveTransformer()
wt.fit(X_train, y_train)
wt.transform(X_test)

Sklearn compatebility

pipeline = Pipeline(
    steps=[
        ("Stack_L1", wst),
        ("LinearTransformations", WaveTransformer()),
        ("Final Estimator", WaveRegressor())
    ]
)

WaveEncoder

Performs encoding of categorical features in the initial dataset

from waveml import WaveEncoder

we = WaveEncoder(encodeing_type="label")
X_train = we.fit_transform(X_train)
X_test = we.transform(X_test)