lazy_custom_classification.py

import os 
import nnetsauce as ns 
from sklearn.datasets import load_breast_cancer, load_iris, load_wine, load_digits
from sklearn.model_selection import train_test_split
from time import time

print(f"\n ----- Running: {os.path.basename(__file__)}... ----- \n")

load_models = [load_breast_cancer, load_iris, load_wine, load_digits]

for model in load_models: 

    data = model()
    X = data.data
    y= data.target

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3, random_state = 13)

    clf = ns.LazyClassifier(verbose=0, ignore_warnings=True, 
                            custom_metric=None, preprocess=False, 
                            predictions=True,
                            estimators=["RandomForestClassifier", 
                                        "RandomForestRegressor"],
                            n_jobs=-1)

    start = time()
    models, predictions = clf.fit(X_train, X_test, y_train, y_test)
    print(f"\nElapsed: {time() - start} seconds\n")

    print(models)
    print(predictions)

for model in load_models: 

    data = model()
    X = data.data
    y= data.target

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3, random_state = 13)

    clf = ns.LazyClassifier(verbose=0, ignore_warnings=True, 
                            predictions=True,
                            custom_metric=None, preprocess=False, n_jobs=-1)

    start = time()
    models, predictions = clf.fit(X_train, X_test, y_train, y_test)
    print(f"\nElapsed: {time() - start} seconds\n")

    print(models)
    print(predictions)