95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
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"""
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================================
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Nearest Neighbors Classification
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================================
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This example shows how to use :class:`~sklearn.neighbors.KNeighborsClassifier`.
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We train such a classifier on the iris dataset and observe the difference of the
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decision boundary obtained with regards to the parameter `weights`.
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"""
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# %%
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# Load the data
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# -------------
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#
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# In this example, we use the iris dataset. We split the data into a train and test
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# dataset.
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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iris = load_iris(as_frame=True)
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X = iris.data[["sepal length (cm)", "sepal width (cm)"]]
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y = iris.target
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X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)
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# %%
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# K-nearest neighbors classifier
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# ------------------------------
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#
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# We want to use a k-nearest neighbors classifier considering a neighborhood of 11 data
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# points. Since our k-nearest neighbors model uses euclidean distance to find the
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# nearest neighbors, it is therefore important to scale the data beforehand. Refer to
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# the example entitled
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# :ref:`sphx_glr_auto_examples_preprocessing_plot_scaling_importance.py` for more
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# detailed information.
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#
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# Thus, we use a :class:`~sklearn.pipeline.Pipeline` to chain a scaler before to use
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# our classifier.
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import StandardScaler
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clf = Pipeline(
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steps=[("scaler", StandardScaler()), ("knn", KNeighborsClassifier(n_neighbors=11))]
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)
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# %%
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# Decision boundary
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# -----------------
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#
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# Now, we fit two classifiers with different values of the parameter
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# `weights`. We plot the decision boundary of each classifier as well as the original
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# dataset to observe the difference.
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import matplotlib.pyplot as plt
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from sklearn.inspection import DecisionBoundaryDisplay
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_, axs = plt.subplots(ncols=2, figsize=(12, 5))
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for ax, weights in zip(axs, ("uniform", "distance")):
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clf.set_params(knn__weights=weights).fit(X_train, y_train)
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disp = DecisionBoundaryDisplay.from_estimator(
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clf,
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X_test,
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response_method="predict",
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plot_method="pcolormesh",
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xlabel=iris.feature_names[0],
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ylabel=iris.feature_names[1],
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shading="auto",
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alpha=0.5,
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ax=ax,
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)
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scatter = disp.ax_.scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, edgecolors="k")
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disp.ax_.legend(
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scatter.legend_elements()[0],
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iris.target_names,
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loc="lower left",
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title="Classes",
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)
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_ = disp.ax_.set_title(
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f"3-Class classification\n(k={clf[-1].n_neighbors}, weights={weights!r})"
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)
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plt.show()
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# %%
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# Conclusion
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# ----------
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#
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# We observe that the parameter `weights` has an impact on the decision boundary. When
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# `weights="unifom"` all nearest neighbors will have the same impact on the decision.
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# Whereas when `weights="distance"` the weight given to each neighbor is proportional
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# to the inverse of the distance from that neighbor to the query point.
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#
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# In some cases, taking the distance into account might improve the model.
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