.. _sphx_glr_auto_examples_pipeline_plot_pipeline_classification.py: ========================= Pipeline Object ========================= An example of the Pipeline object working with transformers and resamplers. .. rst-class:: sphx-glr-script-out Out:: precision recall f1-score support 0 0.99 1.00 0.99 387 1 1.00 1.00 1.00 863 avg / total 1.00 1.00 1.00 1250 | .. code-block:: python # Authors: Christos Aridas # Guillaume Lemaitre # License: MIT from sklearn.cross_validation import train_test_split as tts from sklearn.datasets import make_classification from sklearn.decomposition import PCA from sklearn.metrics import classification_report from sklearn.neighbors import KNeighborsClassifier as KNN from imblearn.pipeline import make_pipeline from imblearn.under_sampling import (EditedNearestNeighbours, RepeatedEditedNearestNeighbours) print(__doc__) # Generate the dataset X, y = make_classification(n_classes=2, class_sep=1.25, weights=[0.3, 0.7], n_informative=3, n_redundant=1, flip_y=0, n_features=5, n_clusters_per_class=1, n_samples=5000, random_state=10) # Instanciate a PCA object for the sake of easy visualisation pca = PCA(n_components=2) # Create the samplers enn = EditedNearestNeighbours() renn = RepeatedEditedNearestNeighbours() # Create the classifier knn = KNN(1) # Make the splits X_train, X_test, y_train, y_test = tts(X, y, random_state=42) # Add one transformers and two samplers in the pipeline object pipeline = make_pipeline(pca, enn, renn, knn) pipeline.fit(X_train, y_train) y_hat = pipeline.predict(X_test) print(classification_report(y_test, y_hat)) **Total running time of the script:** ( 0 minutes 0.766 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: plot_pipeline_classification.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_pipeline_classification.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_