resolve #4587 add inductive learning example #6478
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See #4587. (github doesn't create links from PR headers. |
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@jnothman this was your idea ;) |
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| """ | |||
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| Inductive Learning with Scikit Learn | |||
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I think you must mean 'inductive clustering'. "With scikit-learn" is redundant inside scikit-learn.
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| Clustering is expensive, especially when our dataset contains millions of | ||
| datapoints. Recomputing the clusters everytime we receive some new data | ||
| is thus in many cases, intractable. With more data, there is also the |
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"With more data, "... this comment only really makes sense if you say something more explicit about acquiring more data from a noisier source than was used to build a clustering... no?
| some unsupervised learning algorithm and then fit a classifier on the | ||
| inferred targets, treating it as a supervised problem. This is known as | ||
| Transductive learning. | ||
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| One solution to this problem, is to first infer the target classes using | ||
| some unsupervised learning algorithm and then fit a classifier on the | ||
| inferred targets, treating it as a supervised problem. This is known as | ||
| Transductive learning. |
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No, this is certainly not transductive. Whether we're using "inductive" correctly is another matter.
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| n_samples = 5000 | ||
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| colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk']) |
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this is just np.array(list('bgrcmyk' * 4))?
| plt.scatter(X[:, 0], X[:, 1], color="black", s=2) | ||
| plt.show() | ||
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| from sklearn import svm |
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| colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk']) | ||
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| blobs = datasets.make_blobs(n_samples=3*n_samples, random_state=8) |
| # Inferring class on a new random dataset | ||
| X_new = StandardScaler().fit_transform(np.random.rand(n_samples*2,2)) | ||
| y_pred = inductiveLearner.predict(X_new) | ||
| plt.scatter(X_new[:, 0], X_new[:, 1], color=colors[y_pred].tolist(), s=5) |
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This overlay doesn't really work if black is one of the plotted colours.
You need more clear titling/description of the plot.
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@jnothman what is the intension here? Do we need to provide just an example of inductive inference on cluster labels or to create a meta-estimator? |
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I don't mind examples containing new meta-estimators... |
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Closed by #10852 |
I added an example that uses a synthetic dataset (blobs + random), anduses dbscan to infer labels, and and then fits SVM over it, to infer labels on other data