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Auto dispatch create methods in python #5118

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Auto dispatch create methods in python #5118

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a616d71
Auto dispatch create methods in python
theartful Sep 2, 2020
87cba0b
bug fixes
theartful Sep 2, 2020
f54957b
Rearrange types from most derived to least
theartful Sep 2, 2020
3139635
Throw if class does not exist
theartful Sep 2, 2020
a3fefc4
Address comments and fix bugs
theartful Sep 2, 2020
e73f55f
Update notebooks
theartful Sep 2, 2020
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58 changes: 29 additions & 29 deletions doc/ipython-notebooks/classification/Classification.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -211,7 +211,7 @@
"c = 0.5\n",
"epsilon = 1e-3\n",
"\n",
"svm_linear = sg.create_machine(\"LibLinear\", C1=c, C2=c, \n",
"svm_linear = sg.create(\"LibLinear\", C1=c, C2=c, \n",
" labels=shogun_labels_linear, \n",
" epsilon=epsilon,\n",
" liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n",
Expand All @@ -224,7 +224,7 @@
"plt.title(\"Linear SVM - Linear Features\")\n",
"plot_model(plt,svm_linear,feats_linear,labels_linear)\n",
"\n",
"svm_non_linear = sg.create_machine(\"LibLinear\", C1=c, C2=c, \n",
"svm_non_linear = sg.create(\"LibLinear\", C1=c, C2=c, \n",
" labels=shogun_labels_non_linear,\n",
" epsilon=epsilon,\n",
" liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n",
Expand Down Expand Up @@ -266,14 +266,14 @@
"gaussian_c = 0.7\n",
"\n",
"gaussian_kernel_linear = sg.create_kernel(\"GaussianKernel\", width=20)\n",
"gaussian_svm_linear = sg.create_machine('LibSVM', C1=gaussian_c, C2=gaussian_c, \n",
"gaussian_svm_linear = sg.create('LibSVM', C1=gaussian_c, C2=gaussian_c, \n",
" kernel=gaussian_kernel_linear, labels=shogun_labels_linear)\n",
"gaussian_svm_linear.train(shogun_feats_linear)\n",
"classifiers_linear.append(gaussian_svm_linear)\n",
"fadings.append(True)\n",
"\n",
"gaussian_kernel_non_linear = sg.create_kernel(\"GaussianKernel\", width=10)\n",
"gaussian_svm_non_linear=sg.create_machine('LibSVM', C1=gaussian_c, C2=gaussian_c, \n",
"gaussian_svm_non_linear=sg.create('LibSVM', C1=gaussian_c, C2=gaussian_c, \n",
" kernel=gaussian_kernel_non_linear, labels=shogun_labels_non_linear)\n",
"gaussian_svm_non_linear.train(shogun_feats_non_linear)\n",
"classifiers_non_linear.append(gaussian_svm_non_linear)\n",
Expand Down Expand Up @@ -309,7 +309,7 @@
"\n",
"sigmoid_kernel_linear = sg.create_kernel(\"SigmoidKernel\", cache_size=200, gamma=1, coef0=0.5)\n",
"sigmoid_kernel_linear.init(shogun_feats_linear, shogun_feats_linear)\n",
"sigmoid_svm_linear = sg.create_machine('LibSVM', C1=sigmoid_c, C2=sigmoid_c, \n",
"sigmoid_svm_linear = sg.create('LibSVM', C1=sigmoid_c, C2=sigmoid_c, \n",
" kernel=sigmoid_kernel_linear, labels=shogun_labels_linear)\n",
"sigmoid_svm_linear.train()\n",
"classifiers_linear.append(sigmoid_svm_linear)\n",
Expand All @@ -323,7 +323,7 @@
"\n",
"sigmoid_kernel_non_linear = sg.create_kernel(\"SigmoidKernel\", cache_size=400, gamma=2.5, coef0=2)\n",
"sigmoid_kernel_non_linear.init(shogun_feats_non_linear, shogun_feats_non_linear)\n",
"sigmoid_svm_non_linear = sg.create_machine('LibSVM', C1=sigmoid_c, C2=sigmoid_c, \n",
"sigmoid_svm_non_linear = sg.create('LibSVM', C1=sigmoid_c, C2=sigmoid_c, \n",
" kernel=sigmoid_kernel_non_linear, labels=shogun_labels_non_linear)\n",
"sigmoid_svm_non_linear.train()\n",
"classifiers_non_linear.append(sigmoid_svm_non_linear)\n",
Expand Down Expand Up @@ -353,7 +353,7 @@
"\n",
"poly_kernel_linear = sg.create_kernel('PolyKernel', degree=degree, c=1.0)\n",
"poly_kernel_linear.init(shogun_feats_linear, shogun_feats_linear)\n",
"poly_svm_linear = sg.create_machine('LibSVM', C1=poly_c, C2=poly_c, \n",
"poly_svm_linear = sg.create('LibSVM', C1=poly_c, C2=poly_c, \n",
" kernel=poly_kernel_linear, labels=shogun_labels_linear)\n",
"poly_svm_linear.train()\n",
"classifiers_linear.append(poly_svm_linear)\n",
Expand All @@ -367,7 +367,7 @@
"\n",
"poly_kernel_non_linear = sg.create_kernel('PolyKernel', degree=degree, c=1.0)\n",
"poly_kernel_non_linear.init(shogun_feats_non_linear, shogun_feats_non_linear)\n",
"poly_svm_non_linear = sg.create_machine('LibSVM', C1=poly_c, C2=poly_c, \n",
"poly_svm_non_linear = sg.create('LibSVM', C1=poly_c, C2=poly_c, \n",
" kernel=poly_kernel_non_linear, labels=shogun_labels_non_linear)\n",
"poly_svm_non_linear.train()\n",
"classifiers_non_linear.append(poly_svm_non_linear)\n",
Expand Down Expand Up @@ -404,7 +404,7 @@
"shogun_multiclass_labels_linear = sg.MulticlassLabels(multiclass_labels_linear)\n",
"shogun_multiclass_labels_non_linear = sg.MulticlassLabels(multiclass_labels_non_linear)\n",
"\n",
"naive_bayes_linear = sg.create_machine(\"GaussianNaiveBayes\")\n",
"naive_bayes_linear = sg.create(\"GaussianNaiveBayes\")\n",
"naive_bayes_linear.put('features', shogun_feats_linear)\n",
"naive_bayes_linear.put('labels', shogun_multiclass_labels_linear)\n",
"naive_bayes_linear.train()\n",
Expand All @@ -417,7 +417,7 @@
"plt.title(\"Naive Bayes - Linear Features\")\n",
"plot_model(plt,naive_bayes_linear,feats_linear,labels_linear,fading=False)\n",
"\n",
"naive_bayes_non_linear = sg.create_machine(\"GaussianNaiveBayes\")\n",
"naive_bayes_non_linear = sg.create(\"GaussianNaiveBayes\")\n",
"naive_bayes_non_linear.put('features', shogun_feats_non_linear)\n",
"naive_bayes_non_linear.put('labels', shogun_multiclass_labels_non_linear)\n",
"naive_bayes_non_linear.train()\n",
Expand All @@ -443,9 +443,9 @@
"source": [
"number_of_neighbors = 10\n",
"\n",
"distances_linear = sg.create_distance('EuclideanDistance')\n",
"distances_linear = sg.create('EuclideanDistance')\n",
"distances_linear.init(shogun_feats_linear, shogun_feats_linear)\n",
"knn_linear = sg.create_machine(\"KNN\", k=number_of_neighbors, distance=distances_linear, \n",
"knn_linear = sg.create(\"KNN\", k=number_of_neighbors, distance=distances_linear, \n",
" labels=shogun_labels_linear)\n",
"knn_linear.train()\n",
"classifiers_linear.append(knn_linear)\n",
Expand All @@ -457,9 +457,9 @@
"plt.title(\"Nearest Neighbors - Linear Features\")\n",
"plot_model(plt,knn_linear,feats_linear,labels_linear,fading=False)\n",
"\n",
"distances_non_linear = sg.create_distance('EuclideanDistance')\n",
"distances_non_linear = sg.create('EuclideanDistance')\n",
"distances_non_linear.init(shogun_feats_non_linear, shogun_feats_non_linear)\n",
"knn_non_linear = sg.create_machine(\"KNN\", k=number_of_neighbors, distance=distances_non_linear, \n",
"knn_non_linear = sg.create(\"KNN\", k=number_of_neighbors, distance=distances_non_linear, \n",
" labels=shogun_labels_non_linear)\n",
"knn_non_linear.train()\n",
"classifiers_non_linear.append(knn_non_linear)\n",
Expand All @@ -484,7 +484,7 @@
"source": [
"gamma = 0.1\n",
"\n",
"lda_linear = sg.create_machine('LDA', gamma=gamma, labels=shogun_labels_linear)\n",
"lda_linear = sg.create('LDA', gamma=gamma, labels=shogun_labels_linear)\n",
"lda_linear.train(shogun_feats_linear)\n",
"classifiers_linear.append(lda_linear)\n",
"classifiers_names.append(\"LDA\")\n",
Expand All @@ -495,7 +495,7 @@
"plt.title(\"LDA - Linear Features\")\n",
"plot_model(plt,lda_linear,feats_linear,labels_linear)\n",
"\n",
"lda_non_linear = sg.create_machine('LDA', gamma=gamma, labels=shogun_labels_non_linear)\n",
"lda_non_linear = sg.create('LDA', gamma=gamma, labels=shogun_labels_non_linear)\n",
"lda_non_linear.train(shogun_feats_non_linear)\n",
"classifiers_non_linear.append(lda_non_linear)\n",
"\n",
Expand All @@ -517,7 +517,7 @@
"metadata": {},
"outputs": [],
"source": [
"qda_linear = sg.create_machine(\"QDA\", labels=shogun_multiclass_labels_linear)\n",
"qda_linear = sg.create(\"QDA\", labels=shogun_multiclass_labels_linear)\n",
"qda_linear.train(shogun_feats_linear)\n",
"classifiers_linear.append(qda_linear)\n",
"classifiers_names.append(\"QDA\")\n",
Expand All @@ -528,7 +528,7 @@
"plt.title(\"QDA - Linear Features\")\n",
"plot_model(plt,qda_linear,feats_linear,labels_linear,fading=False)\n",
"\n",
"qda_non_linear = sg.create_machine(\"QDA\", labels=shogun_multiclass_labels_non_linear)\n",
"qda_non_linear = sg.create(\"QDA\", labels=shogun_multiclass_labels_non_linear)\n",
"qda_non_linear.train(shogun_feats_non_linear)\n",
"classifiers_non_linear.append(qda_non_linear)\n",
"\n",
Expand Down Expand Up @@ -567,17 +567,17 @@
"# create Gaussian kernel with width = 5.0\n",
"kernel = sg.create_kernel(\"GaussianKernel\", width=5.0)\n",
"# create zero mean function\n",
"zero_mean = sg.create_gp_mean(\"ZeroMean\")\n",
"zero_mean = sg.create(\"ZeroMean\")\n",
"# create logit likelihood model\n",
"likelihood = sg.create_gp_likelihood(\"LogitLikelihood\")\n",
"likelihood = sg.create(\"LogitLikelihood\")\n",
"# specify EP approximation inference method\n",
"inference_model_linear = sg.create_gp_inference(\"EPInferenceMethod\",kernel=kernel, \n",
"inference_model_linear = sg.create(\"EPInferenceMethod\",kernel=kernel, \n",
" features=shogun_feats_linear, \n",
" mean_function=zero_mean, \n",
" labels=shogun_labels_linear, \n",
" likelihood_model=likelihood)\n",
"# create and train GP classifier, which uses Laplace approximation\n",
"gaussian_logit_linear = sg.create_gaussian_process(\"GaussianProcessClassification\", inference_method=inference_model_linear)\n",
"gaussian_logit_linear = sg.create(\"GaussianProcessClassification\", inference_method=inference_model_linear)\n",
"gaussian_logit_linear.train()\n",
"classifiers_linear.append(gaussian_logit_linear)\n",
"classifiers_names.append(\"Gaussian Process Logit\")\n",
Expand All @@ -588,12 +588,12 @@
"plt.title(\"Gaussian Process - Logit - Linear Features\")\n",
"plot_model(plt,gaussian_logit_linear,feats_linear,labels_linear)\n",
"\n",
"inference_model_non_linear = sg.create_gp_inference(\"EPInferenceMethod\", kernel=kernel, \n",
"inference_model_non_linear = sg.create(\"EPInferenceMethod\", kernel=kernel, \n",
" features=shogun_feats_non_linear, \n",
" mean_function=zero_mean, \n",
" labels=shogun_labels_non_linear, \n",
" likelihood_model=likelihood)\n",
"gaussian_logit_non_linear = sg.create_gaussian_process(\"GaussianProcessClassification\", \n",
"gaussian_logit_non_linear = sg.create(\"GaussianProcessClassification\", \n",
" inference_method=inference_model_non_linear)\n",
"gaussian_logit_non_linear.train()\n",
"classifiers_non_linear.append(gaussian_logit_non_linear)\n",
Expand Down Expand Up @@ -623,14 +623,14 @@
"metadata": {},
"outputs": [],
"source": [
"likelihood = sg.create_gp_likelihood(\"ProbitLikelihood\")\n",
"likelihood = sg.create(\"ProbitLikelihood\")\n",
"\n",
"inference_model_linear = sg.create_gp_inference(\"EPInferenceMethod\", kernel=kernel, \n",
"inference_model_linear = sg.create(\"EPInferenceMethod\", kernel=kernel, \n",
" features=shogun_feats_linear, \n",
" mean_function=zero_mean, \n",
" labels=shogun_labels_linear, \n",
" likelihood_model=likelihood)\n",
"gaussian_probit_linear = sg.create_gaussian_process(\"GaussianProcessClassification\", \n",
"gaussian_probit_linear = sg.create(\"GaussianProcessClassification\", \n",
" inference_method=inference_model_linear)\n",
"gaussian_probit_linear.train()\n",
"classifiers_linear.append(gaussian_probit_linear)\n",
Expand All @@ -642,12 +642,12 @@
"plt.title(\"Gaussian Process - Probit - Linear Features\")\n",
"plot_model(plt,gaussian_probit_linear,feats_linear,labels_linear)\n",
"\n",
"inference_model_non_linear = sg.create_gp_inference(\"EPInferenceMethod\", kernel=kernel, \n",
"inference_model_non_linear = sg.create(\"EPInferenceMethod\", kernel=kernel, \n",
" features=shogun_feats_non_linear, \n",
" mean_function=zero_mean, \n",
" labels=shogun_labels_non_linear, \n",
" likelihood_model=likelihood)\n",
"gaussian_probit_non_linear = sg.create_gaussian_process(\"GaussianProcessClassification\", \n",
"gaussian_probit_non_linear = sg.create(\"GaussianProcessClassification\", \n",
" inference_method=inference_model_non_linear)\n",
"gaussian_probit_non_linear.train()\n",
"classifiers_non_linear.append(gaussian_probit_non_linear)\n",
Expand Down
2 changes: 1 addition & 1 deletion doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -190,7 +190,7 @@
"source": [
"C = 0.1\n",
"epsilon = 0.01\n",
"svm = sg.create_machine(\"SVMOcas\", C1=C, C2=C, labels=labels, epsilon=epsilon)"
"svm = sg.create(\"SVMOcas\", C1=C, C2=C, labels=labels, epsilon=epsilon)"
]
},
{
Expand Down
26 changes: 13 additions & 13 deletions doc/ipython-notebooks/classification/MKL.ipynb
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -164,7 +164,7 @@
"\n",
"covs=np.array([[1.0,0.0],[0.0,1.0]])\n",
"\n",
"# gmm=sg.create_distribution(\"GMM\")\n",
"# gmm=sg.create(\"GMM\")\n",
"# gmm.set_pseudo_count(num_components)\n",
"gmm=sg.GMM(num_components)\n",
"[gmm.set_nth_mean(means[i], i) for i in range(num_components)]\n",
Expand Down Expand Up @@ -253,7 +253,7 @@
"kernel.add(\"kernel_array\", kernel1)\n",
"kernel.init(feats_train, feats_train)\n",
"\n",
"mkl = sg.create_machine(\"MKLClassification\", mkl_norm=1, C1=1, C2=1, kernel=kernel, labels=labels)\n",
"mkl = sg.create(\"MKLClassification\", mkl_norm=1, C1=1, C2=1, kernel=kernel, labels=labels)\n",
"\n",
"#train to get weights\n",
"mkl.train() \n",
Expand Down Expand Up @@ -387,22 +387,22 @@
"mkl.put(\"kernel\", kernelt)\n",
"out = mkl.apply()\n",
"\n",
"evaluator = sg.create_evaluation(\"ErrorRateMeasure\")\n",
"evaluator = sg.create(\"ErrorRateMeasure\")\n",
"print(\"Test error is %2.2f%% :MKL\" % (100*evaluator.evaluate(out,sg.BinaryLabels(testlab))))\n",
"\n",
"\n",
"comb_ker0t.init(feats_train, sg.create_features(testdata)) \n",
"mkl.put(\"kernel\", comb_ker0t)\n",
"out = mkl.apply()\n",
"\n",
"evaluator = sg.create_evaluation(\"ErrorRateMeasure\")\n",
"evaluator = sg.create(\"ErrorRateMeasure\")\n",
"print(\"Test error is %2.2f%% :Subkernel1\"% (100*evaluator.evaluate(out,sg.BinaryLabels(testlab))))\n",
"\n",
"comb_ker1t.init(feats_train, sg.create_features(testdata))\n",
"mkl.put(\"kernel\", comb_ker1t)\n",
"out = mkl.apply()\n",
"\n",
"evaluator = sg.create_evaluation(\"ErrorRateMeasure\")\n",
"evaluator = sg.create(\"ErrorRateMeasure\")\n",
"print(\"Test error is %2.2f%% :subkernel2\" % (100*evaluator.evaluate(out,sg.BinaryLabels(testlab))))\n"
]
},
Expand Down Expand Up @@ -490,7 +490,7 @@
" kernel.add(\"kernel_array\", kernel3)\n",
" \n",
" kernel.init(feats_tr, feats_tr)\n",
" mkl = sg.create_machine(\"MKLClassification\", mkl_norm=1, C1=1, C2=2, kernel=kernel, labels=lab)\n",
" mkl = sg.create(\"MKLClassification\", mkl_norm=1, C1=1, C2=2, kernel=kernel, labels=lab)\n",
" \n",
" mkl.train()\n",
" \n",
Expand Down Expand Up @@ -703,7 +703,7 @@
"\n",
"kernel.init(feats_train, feats_train)\n",
"\n",
"mkl = sg.create_machine(\"MKLMulticlass\", C=1.2, kernel=kernel, \n",
"mkl = sg.create(\"MKLMulticlass\", C=1.2, kernel=kernel, \n",
" labels=labels, mkl_eps=0.001, mkl_norm=1)\n",
"\n",
"# set epsilon of SVM\n",
Expand All @@ -715,7 +715,7 @@
"kernel.init(feats_train, feats_test) \n",
"\n",
"out = mkl.apply()\n",
"evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n",
"evaluator = sg.create(\"MulticlassAccuracy\")\n",
"accuracy = evaluator.evaluate(out, labels_rem)\n",
"print(\"Accuracy = %2.2f%%\" % (100*accuracy))\n",
"\n",
Expand Down Expand Up @@ -748,10 +748,10 @@
"\n",
"pk = sg.create_kernel('PolyKernel', degree=10, c=2) \n",
"\n",
"svm = sg.create_machine(\"GMNPSVM\", C=C, kernel=pk, labels=labels)\n",
"svm = sg.create(\"GMNPSVM\", C=C, kernel=pk, labels=labels)\n",
"_=svm.train(feats)\n",
"out=svm.apply(feats_rem)\n",
"evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n",
"evaluator = sg.create(\"MulticlassAccuracy\")\n",
"accuracy = evaluator.evaluate(out, labels_rem)\n",
"\n",
"print(\"Accuracy = %2.2f%%\" % (100*accuracy))\n",
Expand All @@ -776,10 +776,10 @@
"\n",
"gk=sg.create_kernel(\"GaussianKernel\", width=width)\n",
"\n",
"svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk, labels=labels)\n",
"svm=sg.create(\"GMNPSVM\", C=C, kernel=gk, labels=labels)\n",
"_=svm.train(feats)\n",
"out=svm.apply(feats_rem)\n",
"evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n",
"evaluator = sg.create(\"MulticlassAccuracy\")\n",
"accuracy = evaluator.evaluate(out, labels_rem)\n",
"\n",
"print(\"Accuracy = %2.2f%%\" % (100*accuracy))\n",
Expand Down Expand Up @@ -869,7 +869,7 @@
"\n",
"kernel.init(feats_train, feats_train)\n",
"\n",
"mkl = sg.create_machine(\"MKLOneClass\", kernel=kernel, labels=labels, interleaved_optimization=False,\n",
"mkl = sg.create(\"MKLOneClass\", kernel=kernel, labels=labels, interleaved_optimization=False,\n",
" mkl_norm=1)\n",
"\n",
"mkl.put(\"epsilon\", 1e-2)\n",
Expand Down
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