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Make Machine class stateless #5111

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Make Machine class stateless #5111

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a3adb93
Add NonParametricMachine class (#5055)
LiuYuHui Jun 15, 2020
5e91dd0
Refactor gaussian process machine (#5072)
LiuYuHui Jun 21, 2020
8e8149b
Refactor KernelMachine (#5075)
LiuYuHui Jul 1, 2020
a02018b
Refactor LinearMachine (#5089)
LiuYuHui Jul 12, 2020
bf39050
Refactor MulticlassMachine (#5101)
LiuYuHui Jul 28, 2020
314b50e
fix GPL
gf712 Jul 29, 2020
85bfb7f
Refactor NearestCentroid class (#5053)
LiuYuHui Jul 31, 2020
a668174
Refactor BaggingMachine (#5103)
LiuYuHui Aug 5, 2020
8ec52a4
Refactor all machine (#5104)
LiuYuHui Aug 10, 2020
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2 changes: 1 addition & 1 deletion data
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Submodule data updated 0 files
42 changes: 18 additions & 24 deletions doc/ipython-notebooks/classification/Classification.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -212,10 +212,9 @@
"epsilon = 1e-3\n",
"\n",
"svm_linear = sg.create_machine(\"LibLinear\", C1=c, C2=c, \n",
" labels=shogun_labels_linear, \n",
" epsilon=epsilon,\n",
" liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n",
"svm_linear.train(shogun_feats_linear)\n",
"svm_linear.train(shogun_feats_linear, shogun_labels_linear)\n",
"classifiers_linear.append(svm_linear)\n",
"classifiers_names.append(\"SVM Linear\")\n",
"fadings.append(True)\n",
Expand All @@ -224,11 +223,10 @@
"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",
" labels=shogun_labels_non_linear,\n",
"svm_non_linear = sg.create_machine(\"LibLinear\", C1=c, C2=c,\n",
" epsilon=epsilon,\n",
" liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n",
"svm_non_linear.train(shogun_feats_non_linear)\n",
"svm_non_linear.train(shogun_feats_non_linear, shogun_labels_non_linear)\n",
"classifiers_non_linear.append(svm_non_linear)\n",
"\n",
"plt.subplot(122)\n",
Expand Down Expand Up @@ -405,9 +403,7 @@
"shogun_multiclass_labels_non_linear = sg.MulticlassLabels(multiclass_labels_non_linear)\n",
"\n",
"naive_bayes_linear = sg.create_machine(\"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",
"naive_bayes_linear.train(shogun_feats_linear, shogun_multiclass_labels_linear)\n",
"classifiers_linear.append(naive_bayes_linear)\n",
"classifiers_names.append(\"Naive Bayes\")\n",
"fadings.append(False)\n",
Expand All @@ -418,9 +414,7 @@
"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.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",
"naive_bayes_non_linear.train(shogun_feats_non_linear, shogun_multiclass_labels_non_linear)\n",
"classifiers_non_linear.append(naive_bayes_non_linear)\n",
"\n",
"plt.subplot(122)\n",
Expand All @@ -447,7 +441,7 @@
"distances_linear.init(shogun_feats_linear, shogun_feats_linear)\n",
"knn_linear = sg.create_machine(\"KNN\", k=number_of_neighbors, distance=distances_linear, \n",
" labels=shogun_labels_linear)\n",
"knn_linear.train()\n",
"knn_linear.train(shogun_feats_linear)\n",
"classifiers_linear.append(knn_linear)\n",
"classifiers_names.append(\"Nearest Neighbors\")\n",
"fadings.append(False)\n",
Expand All @@ -461,7 +455,7 @@
"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",
" labels=shogun_labels_non_linear)\n",
"knn_non_linear.train()\n",
"knn_non_linear.train(shogun_feats_non_linear)\n",
"classifiers_non_linear.append(knn_non_linear)\n",
"\n",
"plt.subplot(122)\n",
Expand All @@ -484,8 +478,8 @@
"source": [
"gamma = 0.1\n",
"\n",
"lda_linear = sg.create_machine('LDA', gamma=gamma, labels=shogun_labels_linear)\n",
"lda_linear.train(shogun_feats_linear)\n",
"lda_linear = sg.create_machine('LDA', gamma=gamma)\n",
"lda_linear.train(shogun_feats_linear, shogun_labels_linear)\n",
"classifiers_linear.append(lda_linear)\n",
"classifiers_names.append(\"LDA\")\n",
"fadings.append(True)\n",
Expand All @@ -495,8 +489,8 @@
"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.train(shogun_feats_non_linear)\n",
"lda_non_linear = sg.create_machine('LDA', gamma=gamma)\n",
"lda_non_linear.train(shogun_feats_non_linear, shogun_labels_non_linear)\n",
"classifiers_non_linear.append(lda_non_linear)\n",
"\n",
"plt.subplot(122)\n",
Expand All @@ -517,8 +511,8 @@
"metadata": {},
"outputs": [],
"source": [
"qda_linear = sg.create_machine(\"QDA\", labels=shogun_multiclass_labels_linear)\n",
"qda_linear.train(shogun_feats_linear)\n",
"qda_linear = sg.create_machine(\"QDA\")\n",
"qda_linear.train(shogun_feats_linear, shogun_multiclass_labels_linear)\n",
"classifiers_linear.append(qda_linear)\n",
"classifiers_names.append(\"QDA\")\n",
"fadings.append(False)\n",
Expand All @@ -528,8 +522,8 @@
"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.train(shogun_feats_non_linear)\n",
"qda_non_linear = sg.create_machine(\"QDA\")\n",
"qda_non_linear.train(shogun_feats_non_linear, shogun_multiclass_labels_non_linear)\n",
"classifiers_non_linear.append(qda_non_linear)\n",
"\n",
"plt.subplot(122)\n",
Expand Down Expand Up @@ -682,8 +676,8 @@
"plot_binary_data(plt,feats_non_linear, labels_non_linear)\n",
"\n",
"for i in range(0,10):\n",
" plt.subplot(2,11,13+i)\n",
" plot_model(plt,classifiers_non_linear[i],feats_non_linear,labels_non_linear,fading=fadings[i])"
" plt.subplot(2,11,13+i)\n",
" plot_model(plt,classifiers_non_linear[i],feats_non_linear,labels_non_linear,fading=fadings[i])"
]
},
{
Expand All @@ -710,7 +704,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
"version": "3.6.9"
}
},
"nbformat": 4,
Expand Down
12 changes: 6 additions & 6 deletions doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb
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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_machine(\"SVMOcas\", C1=C, C2=C, epsilon=epsilon)"
]
},
{
Expand All @@ -207,7 +207,7 @@
"metadata": {},
"outputs": [],
"source": [
"_=svm.train(hashed_feats)"
"_=svm.train(hashed_feats, labels)"
]
},
{
Expand All @@ -224,7 +224,7 @@
"metadata": {},
"outputs": [],
"source": [
"predicted_labels = svm.apply()\n",
"predicted_labels = svm.apply(hashed_feats)\n",
"print(predicted_labels.get(\"labels\"))"
]
},
Expand Down Expand Up @@ -286,8 +286,8 @@
"metadata": {},
"outputs": [],
"source": [
"svm.train(hashed_feats_quad)\n",
"predicted_labels = svm.apply()\n",
"svm.train(hashed_feats_quad, labels)\n",
"predicted_labels = svm.apply(hashed_feats_quad)\n",
"print(predicted_labels.get(\"labels\"))"
]
},
Expand Down Expand Up @@ -454,4 +454,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
}
}
24 changes: 12 additions & 12 deletions doc/ipython-notebooks/classification/MKL.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -253,10 +253,10 @@
"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_machine(\"MKLClassification\", mkl_norm=1, C1=1, C2=1, kernel=kernel)\n",
"\n",
"#train to get weights\n",
"mkl.train() \n",
"mkl.train(feats_train, labels) \n",
"\n",
"w=kernel.get_subkernel_weights()\n",
"print(w)"
Expand Down Expand Up @@ -490,9 +490,9 @@
" 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_machine(\"MKLClassification\", mkl_norm=1, C1=1, C2=2, kernel=kernel)\n",
" \n",
" mkl.train()\n",
" mkl.train(feats_tr, lab)\n",
" \n",
" w=kernel.get_subkernel_weights()\n",
" return w, mkl\n",
Expand Down Expand Up @@ -704,17 +704,17 @@
"kernel.init(feats_train, feats_train)\n",
"\n",
"mkl = sg.create_machine(\"MKLMulticlass\", C=1.2, kernel=kernel, \n",
" labels=labels, mkl_eps=0.001, mkl_norm=1)\n",
" mkl_eps=0.001, mkl_norm=1)\n",
"\n",
"# set epsilon of SVM\n",
"mkl.get(\"machine\").put(\"epsilon\", 1e-2)\n",
"\n",
"mkl.train()\n",
"mkl.train(feats_train, labels)\n",
"\n",
"#initialize with test features\n",
"kernel.init(feats_train, feats_test) \n",
"\n",
"out = mkl.apply()\n",
"out = mkl.apply(feats_test)\n",
"evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n",
"accuracy = evaluator.evaluate(out, labels_rem)\n",
"print(\"Accuracy = %2.2f%%\" % (100*accuracy))\n",
Expand Down Expand Up @@ -748,8 +748,8 @@
"\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.train(feats)\n",
"svm = sg.create_machine(\"GMNPSVM\", C=C, kernel=pk)\n",
"_=svm.train(feats, labels)\n",
"out=svm.apply(feats_rem)\n",
"evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n",
"accuracy = evaluator.evaluate(out, labels_rem)\n",
Expand All @@ -776,8 +776,8 @@
"\n",
"gk=sg.create_kernel(\"GaussianKernel\", width=width)\n",
"\n",
"svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk, labels=labels)\n",
"_=svm.train(feats)\n",
"svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk)\n",
"_=svm.train(feats, labels)\n",
"out=svm.apply(feats_rem)\n",
"evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n",
"accuracy = evaluator.evaluate(out, labels_rem)\n",
Expand Down Expand Up @@ -984,7 +984,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
"version": "3.6.9"
}
},
"nbformat": 4,
Expand Down
11 changes: 5 additions & 6 deletions doc/ipython-notebooks/classification/SupportVectorMachines.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -164,8 +164,7 @@
"svm=sg.create_machine('LibLinear', C1=C, C2=C, liblinear_solver_type='L2R_L2LOSS_SVC', epsilon=epsilon)\n",
"\n",
"#train\n",
"svm.put('labels', labels)\n",
"svm.train(feats_train)\n",
"svm.train(feats_train, labels)\n",
"w=svm.get('w')\n",
"b=svm.get('bias')"
]
Expand Down Expand Up @@ -933,8 +932,8 @@
"metadata": {},
"outputs": [],
"source": [
"svm=sg.create_machine(\"GMNPSVM\", C=1, kernel=gaussian_kernel, labels=labels)\n",
"_=svm.train(feats_train)\n",
"svm=sg.create_machine(\"GMNPSVM\", C=1, kernel=gaussian_kernel)\n",
"_=svm.train(feats_train, labels)\n",
"\n",
"size=100\n",
"x1=np.linspace(-6, 6, size)\n",
Expand All @@ -948,7 +947,7 @@
" plt.subplot(1,len(kernels),i+1)\n",
" plt.title(kernels[i].get_name())\n",
" svm.put(\"kernel\", kernels[i])\n",
" svm.train(feats_train)\n",
" svm.train(feats_train, labels)\n",
" grid_out=svm.apply(grid)\n",
" z=grid_out.get(\"labels\").reshape((size, size))\n",
" plt.pcolor(x, y, z)\n",
Expand Down Expand Up @@ -1001,7 +1000,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
"version": "3.6.9"
}
},
"nbformat": 4,
Expand Down
10 changes: 5 additions & 5 deletions doc/ipython-notebooks/intro/Introduction.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -338,10 +338,10 @@
"#prameters to svm\n",
"C=0.9\n",
"\n",
"svm=sg.create_machine(\"LibLinear\", C1=C, C2=C, labels=labels, \n",
"svm=sg.create_machine(\"LibLinear\", C1=C, C2=C,\n",
" liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n",
"#train\n",
"svm.train(feats_train)\n",
"svm.train(feats_train, labels)\n",
"\n",
"size=100"
]
Expand Down Expand Up @@ -495,11 +495,11 @@
"label_e=trainlab[num_train:]\n",
"labels_true=sg.create_labels(label_e)\n",
"\n",
"svm=sg.create_machine(\"LibLinear\", C1=C, C2=C, labels=labels, \n",
"svm=sg.create_machine(\"LibLinear\", C1=C, C2=C,\n",
" liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n",
"\n",
"#train and evaluate\n",
"svm.train(feats_train)\n",
"svm.train(feats_train, labels)\n",
"output=svm.apply(feats_evaluate)\n",
"\n",
"#use AccuracyMeasure to get accuracy\n",
Expand Down Expand Up @@ -688,7 +688,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.6.9"
}
},
"nbformat": 4,
Expand Down
13 changes: 6 additions & 7 deletions doc/ipython-notebooks/multiclass/KNN.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -286,19 +286,18 @@
" labels.add_subset(idx_train)\n",
"\n",
" dist = sg.create_distance('EuclideanDistance')\n",
" dist.init(feats, feats)\n",
" knn = sg.create_machine(\"KNN\", k=k, distance=dist, labels=labels)\n",
" knn = sg.create_machine(\"KNN\", k=k, distance=dist)\n",
" #knn.set_store_model_features(True)\n",
" #FIXME: causes SEGFAULT\n",
" if use_cover_tree:\n",
" continue\n",
" # knn.put('knn_solver', \"KNN_COVER_TREE\")\n",
" else:\n",
" knn.put('knn_solver', \"KNN_BRUTE\")\n",
" knn.train()\n",
" knn.train(feats, labels)\n",
"\n",
" evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n",
" pred = knn.apply()\n",
" pred = knn.apply(feats)\n",
" acc_train[i, j] = evaluator.evaluate(pred, labels)\n",
"\n",
" feats.remove_subset()\n",
Expand Down Expand Up @@ -409,8 +408,8 @@
"\n",
"gk=sg.create_kernel(\"GaussianKernel\", width=width)\n",
"\n",
"svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk, labels=labels)\n",
"_=svm.train(feats)"
"svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk)\n",
"_=svm.train(feats, labels)"
]
},
{
Expand Down Expand Up @@ -490,7 +489,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.6.9"
}
},
"nbformat": 4,
Expand Down
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