Uploading source code for chapter 13 (ML)
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Chapter13/gcp/input.json
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Chapter13/gcp/input.json
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[5.6, 2.5, 3.9, 1.1]
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[3.2, 1.4, 3.0, 1.8]
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BIN
Chapter13/gcp/model.joblib
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Chapter13/gcp/model.joblib
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Chapter13/iris_eval_svc_model/iris.data
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Chapter13/iris_eval_svc_model/iris.data
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5.1,3.5,1.4,0.2,Iris-setosa
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4.9,3.0,1.4,0.2,Iris-setosa
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4.7,3.2,1.3,0.2,Iris-setosa
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4.6,3.1,1.5,0.2,Iris-setosa
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5.0,3.6,1.4,0.2,Iris-setosa
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5.4,3.9,1.7,0.4,Iris-setosa
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4.6,3.4,1.4,0.3,Iris-setosa
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5.0,3.4,1.5,0.2,Iris-setosa
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4.4,2.9,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.4,3.7,1.5,0.2,Iris-setosa
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4.8,3.4,1.6,0.2,Iris-setosa
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4.8,3.0,1.4,0.1,Iris-setosa
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4.3,3.0,1.1,0.1,Iris-setosa
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5.8,4.0,1.2,0.2,Iris-setosa
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5.7,4.4,1.5,0.4,Iris-setosa
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5.4,3.9,1.3,0.4,Iris-setosa
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5.1,3.5,1.4,0.3,Iris-setosa
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5.7,3.8,1.7,0.3,Iris-setosa
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5.1,3.8,1.5,0.3,Iris-setosa
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5.4,3.4,1.7,0.2,Iris-setosa
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5.1,3.7,1.5,0.4,Iris-setosa
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4.6,3.6,1.0,0.2,Iris-setosa
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5.1,3.3,1.7,0.5,Iris-setosa
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4.8,3.4,1.9,0.2,Iris-setosa
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5.0,3.0,1.6,0.2,Iris-setosa
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5.0,3.4,1.6,0.4,Iris-setosa
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5.2,3.5,1.5,0.2,Iris-setosa
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5.2,3.4,1.4,0.2,Iris-setosa
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4.7,3.2,1.6,0.2,Iris-setosa
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4.8,3.1,1.6,0.2,Iris-setosa
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5.4,3.4,1.5,0.4,Iris-setosa
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5.2,4.1,1.5,0.1,Iris-setosa
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5.5,4.2,1.4,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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5.0,3.2,1.2,0.2,Iris-setosa
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5.5,3.5,1.3,0.2,Iris-setosa
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4.9,3.1,1.5,0.1,Iris-setosa
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4.4,3.0,1.3,0.2,Iris-setosa
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5.1,3.4,1.5,0.2,Iris-setosa
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5.0,3.5,1.3,0.3,Iris-setosa
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4.5,2.3,1.3,0.3,Iris-setosa
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4.4,3.2,1.3,0.2,Iris-setosa
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5.0,3.5,1.6,0.6,Iris-setosa
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5.1,3.8,1.9,0.4,Iris-setosa
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4.8,3.0,1.4,0.3,Iris-setosa
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5.1,3.8,1.6,0.2,Iris-setosa
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4.6,3.2,1.4,0.2,Iris-setosa
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5.3,3.7,1.5,0.2,Iris-setosa
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5.0,3.3,1.4,0.2,Iris-setosa
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7.0,3.2,4.7,1.4,Iris-versicolor
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6.4,3.2,4.5,1.5,Iris-versicolor
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6.9,3.1,4.9,1.5,Iris-versicolor
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5.5,2.3,4.0,1.3,Iris-versicolor
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6.5,2.8,4.6,1.5,Iris-versicolor
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5.7,2.8,4.5,1.3,Iris-versicolor
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6.3,3.3,4.7,1.6,Iris-versicolor
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4.9,2.4,3.3,1.0,Iris-versicolor
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6.6,2.9,4.6,1.3,Iris-versicolor
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5.2,2.7,3.9,1.4,Iris-versicolor
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5.0,2.0,3.5,1.0,Iris-versicolor
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5.9,3.0,4.2,1.5,Iris-versicolor
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6.0,2.2,4.0,1.0,Iris-versicolor
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6.1,2.9,4.7,1.4,Iris-versicolor
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5.6,2.9,3.6,1.3,Iris-versicolor
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6.7,3.1,4.4,1.4,Iris-versicolor
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5.6,3.0,4.5,1.5,Iris-versicolor
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5.8,2.7,4.1,1.0,Iris-versicolor
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6.2,2.2,4.5,1.5,Iris-versicolor
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5.6,2.5,3.9,1.1,Iris-versicolor
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5.9,3.2,4.8,1.8,Iris-versicolor
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6.1,2.8,4.0,1.3,Iris-versicolor
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6.3,2.5,4.9,1.5,Iris-versicolor
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6.1,2.8,4.7,1.2,Iris-versicolor
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6.4,2.9,4.3,1.3,Iris-versicolor
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6.6,3.0,4.4,1.4,Iris-versicolor
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6.8,2.8,4.8,1.4,Iris-versicolor
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6.7,3.0,5.0,1.7,Iris-versicolor
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6.0,2.9,4.5,1.5,Iris-versicolor
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5.7,2.6,3.5,1.0,Iris-versicolor
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5.5,2.4,3.8,1.1,Iris-versicolor
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5.5,2.4,3.7,1.0,Iris-versicolor
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5.8,2.7,3.9,1.2,Iris-versicolor
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6.0,2.7,5.1,1.6,Iris-versicolor
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5.4,3.0,4.5,1.5,Iris-versicolor
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6.0,3.4,4.5,1.6,Iris-versicolor
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6.7,3.1,4.7,1.5,Iris-versicolor
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6.3,2.3,4.4,1.3,Iris-versicolor
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5.6,3.0,4.1,1.3,Iris-versicolor
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5.5,2.5,4.0,1.3,Iris-versicolor
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5.5,2.6,4.4,1.2,Iris-versicolor
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6.1,3.0,4.6,1.4,Iris-versicolor
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5.8,2.6,4.0,1.2,Iris-versicolor
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5.0,2.3,3.3,1.0,Iris-versicolor
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5.6,2.7,4.2,1.3,Iris-versicolor
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5.7,3.0,4.2,1.2,Iris-versicolor
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5.7,2.9,4.2,1.3,Iris-versicolor
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6.2,2.9,4.3,1.3,Iris-versicolor
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5.1,2.5,3.0,1.1,Iris-versicolor
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5.7,2.8,4.1,1.3,Iris-versicolor
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6.3,3.3,6.0,2.5,Iris-virginica
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5.8,2.7,5.1,1.9,Iris-virginica
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7.1,3.0,5.9,2.1,Iris-virginica
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6.3,2.9,5.6,1.8,Iris-virginica
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6.5,3.0,5.8,2.2,Iris-virginica
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7.6,3.0,6.6,2.1,Iris-virginica
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4.9,2.5,4.5,1.7,Iris-virginica
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7.3,2.9,6.3,1.8,Iris-virginica
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6.7,2.5,5.8,1.8,Iris-virginica
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7.2,3.6,6.1,2.5,Iris-virginica
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6.5,3.2,5.1,2.0,Iris-virginica
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6.4,2.7,5.3,1.9,Iris-virginica
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6.8,3.0,5.5,2.1,Iris-virginica
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5.7,2.5,5.0,2.0,Iris-virginica
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5.8,2.8,5.1,2.4,Iris-virginica
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6.4,3.2,5.3,2.3,Iris-virginica
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6.5,3.0,5.5,1.8,Iris-virginica
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7.7,3.8,6.7,2.2,Iris-virginica
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7.7,2.6,6.9,2.3,Iris-virginica
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6.0,2.2,5.0,1.5,Iris-virginica
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6.9,3.2,5.7,2.3,Iris-virginica
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5.6,2.8,4.9,2.0,Iris-virginica
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7.7,2.8,6.7,2.0,Iris-virginica
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6.3,2.7,4.9,1.8,Iris-virginica
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6.7,3.3,5.7,2.1,Iris-virginica
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7.2,3.2,6.0,1.8,Iris-virginica
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6.2,2.8,4.8,1.8,Iris-virginica
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6.1,3.0,4.9,1.8,Iris-virginica
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6.4,2.8,5.6,2.1,Iris-virginica
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7.2,3.0,5.8,1.6,Iris-virginica
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7.4,2.8,6.1,1.9,Iris-virginica
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7.9,3.8,6.4,2.0,Iris-virginica
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6.4,2.8,5.6,2.2,Iris-virginica
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6.3,2.8,5.1,1.5,Iris-virginica
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6.1,2.6,5.6,1.4,Iris-virginica
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7.7,3.0,6.1,2.3,Iris-virginica
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6.3,3.4,5.6,2.4,Iris-virginica
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6.4,3.1,5.5,1.8,Iris-virginica
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6.0,3.0,4.8,1.8,Iris-virginica
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6.9,3.1,5.4,2.1,Iris-virginica
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6.7,3.1,5.6,2.4,Iris-virginica
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6.9,3.1,5.1,2.3,Iris-virginica
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5.8,2.7,5.1,1.9,Iris-virginica
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6.8,3.2,5.9,2.3,Iris-virginica
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6.7,3.3,5.7,2.5,Iris-virginica
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6.7,3.0,5.2,2.3,Iris-virginica
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6.3,2.5,5.0,1.9,Iris-virginica
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6.5,3.0,5.2,2.0,Iris-virginica
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6.2,3.4,5.4,2.3,Iris-virginica
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5.9,3.0,5.1,1.8,Iris-virginica
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69
Chapter13/iris_eval_svc_model/iris.names
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Chapter13/iris_eval_svc_model/iris.names
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1. Title: Iris Plants Database
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Updated Sept 21 by C.Blake - Added discrepency information
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2. Sources:
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(a) Creator: R.A. Fisher
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(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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(c) Date: July, 1988
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3. Past Usage:
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- Publications: too many to mention!!! Here are a few.
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1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
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Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
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to Mathematical Statistics" (John Wiley, NY, 1950).
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2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
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(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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Structure and Classification Rule for Recognition in Partially Exposed
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Environments". IEEE Transactions on Pattern Analysis and Machine
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Intelligence, Vol. PAMI-2, No. 1, 67-71.
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-- Results:
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-- very low misclassification rates (0% for the setosa class)
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4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
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Transactions on Information Theory, May 1972, 431-433.
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-- Results:
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-- very low misclassification rates again
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5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
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conceptual clustering system finds 3 classes in the data.
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4. Relevant Information:
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--- This is perhaps the best known database to be found in the pattern
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recognition literature. Fisher's paper is a classic in the field
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and is referenced frequently to this day. (See Duda & Hart, for
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example.) The data set contains 3 classes of 50 instances each,
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where each class refers to a type of iris plant. One class is
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linearly separable from the other 2; the latter are NOT linearly
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separable from each other.
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--- Predicted attribute: class of iris plant.
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--- This is an exceedingly simple domain.
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--- This data differs from the data presented in Fishers article
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(identified by Steve Chadwick, spchadwick@espeedaz.net )
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The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
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where the error is in the fourth feature.
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The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
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where the errors are in the second and third features.
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5. Number of Instances: 150 (50 in each of three classes)
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6. Number of Attributes: 4 numeric, predictive attributes and the class
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7. Attribute Information:
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1. sepal length in cm
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2. sepal width in cm
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3. petal length in cm
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4. petal width in cm
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5. class:
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-- Iris Setosa
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-- Iris Versicolour
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-- Iris Virginica
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8. Missing Attribute Values: None
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Summary Statistics:
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Min Max Mean SD Class Correlation
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sepal length: 4.3 7.9 5.84 0.83 0.7826
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sepal width: 2.0 4.4 3.05 0.43 -0.4194
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petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
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petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
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9. Class Distribution: 33.3% for each of 3 classes.
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24
Chapter13/iris_eval_svc_model/iris_build_svm_model.py
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Chapter13/iris_eval_svc_model/iris_build_svm_model.py
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#iris_build_svm_model.py
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from pandas import read_csv
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import classification_report
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from sklearn.metrics import accuracy_score
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from sklearn.svm import SVC
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data_file = "iris/iris.data"
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iris_names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
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df = read_csv(data_file, names=iris_names)
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X = df.drop('class', axis =1)
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y = df['class']
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X_train, X_test, y_train, y_test = train_test_split(X, y,
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test_size=0.20, random_state=1, shuffle=True)
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# Make predictions
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model = SVC(gamma='auto')
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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print(accuracy_score(y_test, predictions))
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print(classification_report(y_test, predictions))
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25
Chapter13/iris_eval_svc_model/iris_data_analysis.py
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Chapter13/iris_eval_svc_model/iris_data_analysis.py
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#iris_data_analysis.py
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from pandas import read_csv
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from matplotlib import pyplot
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from pandas.plotting import scatter_matrix
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data_file = "iris/iris.data"
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iris_names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
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df = read_csv(data_file, names=iris_names)
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print(df.shape)
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print(df.head(20))
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print(df.describe())
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print(df.groupby('class').size())
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# box and whisker plots
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df.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
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pyplot.show()
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# check the histograms
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df.hist()
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pyplot.show()
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# scatter plot matrix
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scatter_matrix(df)
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pyplot.show()
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34
Chapter13/iris_eval_svc_model/iris_eval_svc_model.py
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34
Chapter13/iris_eval_svc_model/iris_eval_svc_model.py
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#iris_eval_svc_model
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from pandas import read_csv
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from sklearn.metrics import classification_report
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from sklearn.model_selection import train_test_split, GridSearchCV,RandomizedSearchCV
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from sklearn.datasets import load_iris
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from sklearn.svm import SVC
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data_file = "iris.data"
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iris_names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
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df = read_csv(data_file, names=iris_names)
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X = df.drop('class', axis =1)
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y = df['class']
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X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=1, shuffle=True)
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params = {"C":[0.001, 0.01, 1, 5, 10, 100],
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"gamma": [0.001, 0.01, 0.1, 1, 10, 100]}
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model1=SVC()
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rand_cv=RandomizedSearchCV(model1,params, n_iter = 5, cv=5)
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rand_cv.fit(X_train,y_train)
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print(f"RandomizedSearch - best parameter: {rand_cv.best_params_}")
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print(f"RandomizedSearch - accuracy: {rand_cv.best_score_}")
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print(classification_report(y_test,rand_cv.best_estimator_.predict(X_test)))
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model2=SVC()
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grid_cv=GridSearchCV(model2,params, cv=5)
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grid_cv.fit(X_train,y_train)
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print(f"GridSearch- best parameter: {grid_cv.best_params_}")
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print(f"GridSearch- accuracy: {grid_cv.best_score_}")
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print(classification_report(y_test,grid_cv.best_estimator_.predict(X_test)))
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import pickle
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from sklearn.metrics import accuracy_score, classification_report
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from sklearn.model_selection import train_test_split, GridSearchCV,RandomizedSearchCV
|
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|
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from sklearn.datasets import load_iris
|
||||
from sklearn.svm import SVC
|
||||
|
||||
iris= load_iris()
|
||||
|
||||
X = iris.data
|
||||
y = iris.target
|
||||
x_train, x_test, y_train, y_test=train_test_split(X,y,test_size=0.2)
|
||||
|
||||
#the numpy. logspace() function returns number spaces evenly w.r.t interval on a log scale
|
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params = {"C":[0.001, 0.01, 1, 5, 10, 100],
|
||||
"gamma": [0.001, 0.01, 0.1, 1, 10, 100]}
|
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model=SVC()
|
||||
grid_cv=GridSearchCV(model,params, cv=5)
|
||||
grid_cv.fit(x_train,y_train)
|
||||
|
||||
with open("svm_grid.pkl", 'wb') as file:
|
||||
pickle.dump(grid_cv, file)
|
||||
|
||||
with open("svm_grid.pkl", 'rb') as file:
|
||||
loaded_grid_cv = pickle.load(file)
|
||||
|
||||
print(f"GridSearch- best parameter: {loaded_grid_cv.best_params_}")
|
||||
print(f"GridSearch- accuracy: {loaded_grid_cv.best_score_}")
|
||||
|
||||
|
||||
with open("svm_grid_best.pkl", 'wb') as file:
|
||||
pickle.dump(grid_cv.best_estimator_, file)
|
||||
|
||||
with open("svm_grid_best.pkl", 'rb') as file:
|
||||
loaded_model = pickle.load(file)
|
||||
predictions = loaded_model.predict(x_test)
|
||||
|
||||
# Evaluate predictions
|
||||
print(accuracy_score(y_test, predictions))
|
||||
print(classification_report(y_test, predictions))
|
||||
@ -0,0 +1,31 @@
|
||||
#iris_save_load_predict_gridmodel.py
|
||||
|
||||
import joblib
|
||||
from pandas import read_csv
|
||||
from sklearn.metrics import accuracy_score, classification_report
|
||||
from sklearn.model_selection import train_test_split, GridSearchCV,RandomizedSearchCV
|
||||
|
||||
from sklearn.datasets import load_iris
|
||||
from sklearn.svm import SVC
|
||||
|
||||
data_file = "iris.data"
|
||||
iris_names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
|
||||
df = read_csv(data_file, names=iris_names)
|
||||
|
||||
X = df.drop('class', axis =1)
|
||||
y = df['class']
|
||||
x_train, x_test, y_train, y_test \
|
||||
= train_test_split(X, y, test_size=0.20,
|
||||
random_state=1, shuffle=True)
|
||||
|
||||
params = {"C":[0.001, 0.01, 1, 5, 10, 100],
|
||||
"gamma": [0.001, 0.01, 0.1, 1, 10, 100]}
|
||||
model=SVC()
|
||||
grid_cv=GridSearchCV(model,params, cv=5)
|
||||
grid_cv.fit(x_train,y_train)
|
||||
|
||||
joblib.dump(grid_cv.best_estimator_, "model.joblib")
|
||||
loaded_model = joblib.load("model.joblib")
|
||||
x_new = [[5.6, 2.5, 3.9, 1.1]]
|
||||
y_new = loaded_model.predict(x_new)
|
||||
print("X=%s, Predicted=%s" % (x_new[0], y_new[0]))
|
||||
@ -0,0 +1,27 @@
|
||||
#iris_save_load_predict_model.py
|
||||
from pandas import read_csv
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.svm import SVC
|
||||
import pickle
|
||||
|
||||
data_file = "iris.data"
|
||||
iris_names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
|
||||
df = read_csv(data_file, names=iris_names)
|
||||
|
||||
X = df.drop('class', axis =1)
|
||||
y = df['class']
|
||||
x_train, x_test, y_train, y_test \
|
||||
= train_test_split(X, y, test_size=0.20,
|
||||
random_state=1, shuffle=True)
|
||||
|
||||
model = SVC(gamma='auto')
|
||||
model.fit(x_train, y_train)
|
||||
|
||||
with open("model.pkl", 'wb') as file:
|
||||
pickle.dump(model, file)
|
||||
|
||||
with open("model.pkl", 'rb') as file:
|
||||
loaded_model = pickle.load(file)
|
||||
x_new = [[5.6, 2.5, 3.9, 1.1]]
|
||||
y_new = loaded_model.predict(x_new)
|
||||
print("X=%s, Predicted=%s" % (x_new[0], y_new[0]))
|
||||
Loading…
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Reference in New Issue
Block a user