57 lines
1.6 KiB
Python
Executable file
57 lines
1.6 KiB
Python
Executable file
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Mar 23 13:36:46 2022
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@author: tanu
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"""
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#https://umap-learn.readthedocs.io/en/latest/auto_examples/plot_feature_extraction_classification.html
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from sklearn.datasets import make_classification
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from sklearn.model_selection import train_test_split, GridSearchCV
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from sklearn.pipeline import Pipeline
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from sklearn.svm import LinearSVC
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from umap import UMAP
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# Make a toy dataset
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X, y = make_classification(
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n_samples=1000,
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n_features=300,
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n_informative=250,
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n_redundant=0,
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n_repeated=0,
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n_classes=2,
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random_state=1212,
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)
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# Split the dataset into a training set and a test set
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42
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)
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# Classification with a linear SVM
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svc = LinearSVC(dual=False, random_state=123)
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params_grid = {"C": [10 ** k for k in range(-3, 4)]}
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clf = GridSearchCV(svc, params_grid)
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clf.fit(X_train, y_train)
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print(
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"Accuracy on the test set with raw data: {:.3f}".format(clf.score(X_test, y_test))
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)
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# Transformation with UMAP followed by classification with a linear SVM
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umap = UMAP(random_state=456)
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pipeline = Pipeline([("umap", umap), ("svc", svc)])
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params_grid_pipeline = {
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"umap__n_neighbors": [5, 20],
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"umap__n_components": [15, 25, 50],
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"svc__C": [10 ** k for k in range(-3, 4)],
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}
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clf_pipeline = GridSearchCV(pipeline, params_grid_pipeline)
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clf_pipeline.fit(X_train, y_train)
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print(
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"Accuracy on the test set with UMAP transformation: {:.3f}".format(
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clf_pipeline.score(X_test, y_test)
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)
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)
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