.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/feature_selection/plot_select_from_model_diabetes.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py: ============================================ Model-based and sequential feature selection ============================================ This example illustrates and compares two approaches for feature selection: :class:`~sklearn.feature_selection.SelectFromModel` which is based on feature importance, and :class:`~sklearn.feature_selection.SequentialFeatureSelector` which relies on a greedy approach. We use the Diabetes dataset, which consists of 10 features collected from 442 diabetes patients. Authors: `Manoj Kumar `_, `Maria Telenczuk `_, Nicolas Hug. License: BSD 3 clause .. GENERATED FROM PYTHON SOURCE LINES 23-28 Loading the data ---------------- We first load the diabetes dataset which is available from within scikit-learn, and print its description: .. GENERATED FROM PYTHON SOURCE LINES 28-34 .. code-block:: Python from sklearn.datasets import load_diabetes diabetes = load_diabetes() X, y = diabetes.data, diabetes.target print(diabetes.DESCR) .. rst-class:: sphx-glr-script-out .. code-block:: none .. _diabetes_dataset: Diabetes dataset ---------------- Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442 diabetes patients, as well as the response of interest, a quantitative measure of disease progression one year after baseline. **Data Set Characteristics:** :Number of Instances: 442 :Number of Attributes: First 10 columns are numeric predictive values :Target: Column 11 is a quantitative measure of disease progression one year after baseline :Attribute Information: - age age in years - sex - bmi body mass index - bp average blood pressure - s1 tc, total serum cholesterol - s2 ldl, low-density lipoproteins - s3 hdl, high-density lipoproteins - s4 tch, total cholesterol / HDL - s5 ltg, possibly log of serum triglycerides level - s6 glu, blood sugar level Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1). Source URL: https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html For more information see: Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "Least Angle Regression," Annals of Statistics (with discussion), 407-499. (https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf) .. GENERATED FROM PYTHON SOURCE LINES 35-47 Feature importance from coefficients ------------------------------------ To get an idea of the importance of the features, we are going to use the :class:`~sklearn.linear_model.RidgeCV` estimator. The features with the highest absolute `coef_` value are considered the most important. We can observe the coefficients directly without needing to scale them (or scale the data) because from the description above, we know that the features were already standardized. For a more complete example on the interpretations of the coefficients of linear models, you may refer to :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`. # noqa: E501 .. GENERATED FROM PYTHON SOURCE LINES 47-59 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import RidgeCV ridge = RidgeCV(alphas=np.logspace(-6, 6, num=5)).fit(X, y) importance = np.abs(ridge.coef_) feature_names = np.array(diabetes.feature_names) plt.bar(height=importance, x=feature_names) plt.title("Feature importances via coefficients") plt.show() .. image-sg:: /auto_examples/feature_selection/images/sphx_glr_plot_select_from_model_diabetes_001.png :alt: Feature importances via coefficients :srcset: /auto_examples/feature_selection/images/sphx_glr_plot_select_from_model_diabetes_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 60-71 Selecting features based on importance -------------------------------------- Now we want to select the two features which are the most important according to the coefficients. The :class:`~sklearn.feature_selection.SelectFromModel` is meant just for that. :class:`~sklearn.feature_selection.SelectFromModel` accepts a `threshold` parameter and will select the features whose importance (defined by the coefficients) are above this threshold. Since we want to select only 2 features, we will set this threshold slightly above the coefficient of third most important feature. .. GENERATED FROM PYTHON SOURCE LINES 71-83 .. code-block:: Python from time import time from sklearn.feature_selection import SelectFromModel threshold = np.sort(importance)[-3] + 0.01 tic = time() sfm = SelectFromModel(ridge, threshold=threshold).fit(X, y) toc = time() print(f"Features selected by SelectFromModel: {feature_names[sfm.get_support()]}") print(f"Done in {toc - tic:.3f}s") .. rst-class:: sphx-glr-script-out .. code-block:: none Features selected by SelectFromModel: ['s1' 's5'] Done in 0.002s .. GENERATED FROM PYTHON SOURCE LINES 84-98 Selecting features with Sequential Feature Selection ---------------------------------------------------- Another way of selecting features is to use :class:`~sklearn.feature_selection.SequentialFeatureSelector` (SFS). SFS is a greedy procedure where, at each iteration, we choose the best new feature to add to our selected features based a cross-validation score. That is, we start with 0 features and choose the best single feature with the highest score. The procedure is repeated until we reach the desired number of selected features. We can also go in the reverse direction (backward SFS), *i.e.* start with all the features and greedily choose features to remove one by one. We illustrate both approaches here. .. GENERATED FROM PYTHON SOURCE LINES 98-124 .. code-block:: Python from sklearn.feature_selection import SequentialFeatureSelector tic_fwd = time() sfs_forward = SequentialFeatureSelector( ridge, n_features_to_select=2, direction="forward" ).fit(X, y) toc_fwd = time() tic_bwd = time() sfs_backward = SequentialFeatureSelector( ridge, n_features_to_select=2, direction="backward" ).fit(X, y) toc_bwd = time() print( "Features selected by forward sequential selection: " f"{feature_names[sfs_forward.get_support()]}" ) print(f"Done in {toc_fwd - tic_fwd:.3f}s") print( "Features selected by backward sequential selection: " f"{feature_names[sfs_backward.get_support()]}" ) print(f"Done in {toc_bwd - tic_bwd:.3f}s") .. rst-class:: sphx-glr-script-out .. code-block:: none Features selected by forward sequential selection: ['bmi' 's5'] Done in 0.221s Features selected by backward sequential selection: ['bmi' 's5'] Done in 0.558s .. GENERATED FROM PYTHON SOURCE LINES 125-156 Interestingly, forward and backward selection have selected the same set of features. In general, this isn't the case and the two methods would lead to different results. We also note that the features selected by SFS differ from those selected by feature importance: SFS selects `bmi` instead of `s1`. This does sound reasonable though, since `bmi` corresponds to the third most important feature according to the coefficients. It is quite remarkable considering that SFS makes no use of the coefficients at all. To finish with, we should note that :class:`~sklearn.feature_selection.SelectFromModel` is significantly faster than SFS. Indeed, :class:`~sklearn.feature_selection.SelectFromModel` only needs to fit a model once, while SFS needs to cross-validate many different models for each of the iterations. SFS however works with any model, while :class:`~sklearn.feature_selection.SelectFromModel` requires the underlying estimator to expose a `coef_` attribute or a `feature_importances_` attribute. The forward SFS is faster than the backward SFS because it only needs to perform `n_features_to_select = 2` iterations, while the backward SFS needs to perform `n_features - n_features_to_select = 8` iterations. Using negative tolerance values ------------------------------- :class:`~sklearn.feature_selection.SequentialFeatureSelector` can be used to remove features present in the dataset and return a smaller subset of the original features with `direction="backward"` and a negative value of `tol`. We begin by loading the Breast Cancer dataset, consisting of 30 different features and 569 samples. .. GENERATED FROM PYTHON SOURCE LINES 156-165 .. code-block:: Python import numpy as np from sklearn.datasets import load_breast_cancer breast_cancer_data = load_breast_cancer() X, y = breast_cancer_data.data, breast_cancer_data.target feature_names = np.array(breast_cancer_data.feature_names) print(breast_cancer_data.DESCR) .. rst-class:: sphx-glr-script-out .. code-block:: none .. _breast_cancer_dataset: Breast cancer wisconsin (diagnostic) dataset -------------------------------------------- **Data Set Characteristics:** :Number of Instances: 569 :Number of Attributes: 30 numeric, predictive attributes and the class :Attribute Information: - radius (mean of distances from center to points on the perimeter) - texture (standard deviation of gray-scale values) - perimeter - area - smoothness (local variation in radius lengths) - compactness (perimeter^2 / area - 1.0) - concavity (severity of concave portions of the contour) - concave points (number of concave portions of the contour) - symmetry - fractal dimension ("coastline approximation" - 1) The mean, standard error, and "worst" or largest (mean of the three worst/largest values) of these features were computed for each image, resulting in 30 features. For instance, field 0 is Mean Radius, field 10 is Radius SE, field 20 is Worst Radius. - class: - WDBC-Malignant - WDBC-Benign :Summary Statistics: ===================================== ====== ====== Min Max ===================================== ====== ====== radius (mean): 6.981 28.11 texture (mean): 9.71 39.28 perimeter (mean): 43.79 188.5 area (mean): 143.5 2501.0 smoothness (mean): 0.053 0.163 compactness (mean): 0.019 0.345 concavity (mean): 0.0 0.427 concave points (mean): 0.0 0.201 symmetry (mean): 0.106 0.304 fractal dimension (mean): 0.05 0.097 radius (standard error): 0.112 2.873 texture (standard error): 0.36 4.885 perimeter (standard error): 0.757 21.98 area (standard error): 6.802 542.2 smoothness (standard error): 0.002 0.031 compactness (standard error): 0.002 0.135 concavity (standard error): 0.0 0.396 concave points (standard error): 0.0 0.053 symmetry (standard error): 0.008 0.079 fractal dimension (standard error): 0.001 0.03 radius (worst): 7.93 36.04 texture (worst): 12.02 49.54 perimeter (worst): 50.41 251.2 area (worst): 185.2 4254.0 smoothness (worst): 0.071 0.223 compactness (worst): 0.027 1.058 concavity (worst): 0.0 1.252 concave points (worst): 0.0 0.291 symmetry (worst): 0.156 0.664 fractal dimension (worst): 0.055 0.208 ===================================== ====== ====== :Missing Attribute Values: None :Class Distribution: 212 - Malignant, 357 - Benign :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian :Donor: Nick Street :Date: November, 1995 This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. https://goo.gl/U2Uwz2 Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes. The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34]. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/ |details-start| **References** |details-split| - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993. - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995. - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 163-171. |details-end| .. GENERATED FROM PYTHON SOURCE LINES 166-169 We will make use of the :class:`~sklearn.linear_model.LogisticRegression` estimator with :class:`~sklearn.feature_selection.SequentialFeatureSelector` to perform the feature selection. .. GENERATED FROM PYTHON SOURCE LINES 169-192 .. code-block:: Python from sklearn.linear_model import LogisticRegression from sklearn.metrics import roc_auc_score from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler for tol in [-1e-2, -1e-3, -1e-4]: start = time() feature_selector = SequentialFeatureSelector( LogisticRegression(), n_features_to_select="auto", direction="backward", scoring="roc_auc", tol=tol, n_jobs=2, ) model = make_pipeline(StandardScaler(), feature_selector, LogisticRegression()) model.fit(X, y) end = time() print(f"\ntol: {tol}") print(f"Features selected: {feature_names[model[1].get_support()]}") print(f"ROC AUC score: {roc_auc_score(y, model.predict_proba(X)[:, 1]):.3f}") print(f"Done in {end - start:.3f}s") .. rst-class:: sphx-glr-script-out .. code-block:: none tol: -0.01 Features selected: ['worst perimeter'] ROC AUC score: 0.975 Done in 15.860s tol: -0.001 Features selected: ['radius error' 'fractal dimension error' 'worst texture' 'worst perimeter' 'worst concave points'] ROC AUC score: 0.997 Done in 16.065s tol: -0.0001 Features selected: ['mean compactness' 'mean concavity' 'mean concave points' 'radius error' 'area error' 'concave points error' 'symmetry error' 'fractal dimension error' 'worst texture' 'worst perimeter' 'worst area' 'worst concave points' 'worst symmetry'] ROC AUC score: 0.998 Done in 14.765s .. GENERATED FROM PYTHON SOURCE LINES 193-196 We can see that the number of features selected tend to increase as negative values of `tol` approach to zero. The time taken for feature selection also decreases as the values of `tol` come closer to zero. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 47.568 seconds) .. _sphx_glr_download_auto_examples_feature_selection_plot_select_from_model_diabetes.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/feature_selection/plot_select_from_model_diabetes.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_select_from_model_diabetes.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_select_from_model_diabetes.py ` .. include:: plot_select_from_model_diabetes.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_