90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
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"""
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=================================================
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Pixel importances with a parallel forest of trees
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=================================================
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This example shows the use of a forest of trees to evaluate the impurity
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based importance of the pixels in an image classification task on the faces
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dataset. The hotter the pixel, the more important it is.
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The code below also illustrates how the construction and the computation
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of the predictions can be parallelized within multiple jobs.
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"""
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# %%
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# Loading the data and model fitting
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# ----------------------------------
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# First, we load the olivetti faces dataset and limit the dataset to contain
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# only the first five classes. Then we train a random forest on the dataset
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# and evaluate the impurity-based feature importance. One drawback of this
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# method is that it cannot be evaluated on a separate test set. For this
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# example, we are interested in representing the information learned from
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# the full dataset. Also, we'll set the number of cores to use for the tasks.
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from sklearn.datasets import fetch_olivetti_faces
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# %%
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# We select the number of cores to use to perform parallel fitting of
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# the forest model. `-1` means use all available cores.
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n_jobs = -1
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# %%
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# Load the faces dataset
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data = fetch_olivetti_faces()
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X, y = data.data, data.target
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# %%
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# Limit the dataset to 5 classes.
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mask = y < 5
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X = X[mask]
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y = y[mask]
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# %%
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# A random forest classifier will be fitted to compute the feature importances.
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from sklearn.ensemble import RandomForestClassifier
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forest = RandomForestClassifier(n_estimators=750, n_jobs=n_jobs, random_state=42)
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forest.fit(X, y)
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# %%
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# Feature importance based on mean decrease in impurity (MDI)
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# -----------------------------------------------------------
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# Feature importances are provided by the fitted attribute
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# `feature_importances_` and they are computed as the mean and standard
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# deviation of accumulation of the impurity decrease within each tree.
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#
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# .. warning::
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# Impurity-based feature importances can be misleading for **high
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# cardinality** features (many unique values). See
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# :ref:`permutation_importance` as an alternative.
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import time
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import matplotlib.pyplot as plt
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start_time = time.time()
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img_shape = data.images[0].shape
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importances = forest.feature_importances_
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elapsed_time = time.time() - start_time
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print(f"Elapsed time to compute the importances: {elapsed_time:.3f} seconds")
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imp_reshaped = importances.reshape(img_shape)
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plt.matshow(imp_reshaped, cmap=plt.cm.hot)
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plt.title("Pixel importances using impurity values")
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plt.colorbar()
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plt.show()
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# %%
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# Can you still recognize a face?
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# %%
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# The limitations of MDI is not a problem for this dataset because:
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#
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# 1. All features are (ordered) numeric and will thus not suffer the
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# cardinality bias
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# 2. We are only interested to represent knowledge of the forest acquired
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# on the training set.
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#
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# If these two conditions are not met, it is recommended to instead use
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# the :func:`~sklearn.inspection.permutation_importance`.
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