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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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32 changes: 30 additions & 2 deletions examples/meta/generator/targets/python.json
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Expand Up @@ -2,7 +2,7 @@
"Program": "import numpy as np\n$dependencies\n\n$program",
"Dependencies": {
"IncludeAllClasses": false,
"IncludeInterfacedClasses": true,
"IncludeInterfacedClasses": true,
"IncludeEnums": true,
"IncludeGlobalFunctions": true,
"DependencyListElement": "import shogun as sg",
Expand Down Expand Up @@ -81,7 +81,35 @@
"get_string": "$object.get($arguments)"
},
"StaticCall": "sg.$typeName.$method($arguments)",
"GlobalCall": {"Default": "sg.$method($arguments$kwargs)"},
"GlobalCall": {
"Default": "sg.$method($arguments$kwargs)",
"create_svm": "sg.create($arguments$kwargs)",
"create_evaluation": "sg.create($arguments$kwargs)",
"create_multiclass_strategy": "sg.create($arguments$kwargs)",
"create_ecoc_encoder": "sg.create($arguments$kwargs)",
"create_ecoc_decoder": "sg.create($arguments$kwargs)",
"create_transformer": "sg.create($arguments$kwargs)",
"create_layer": "sg.create($arguments$kwargs)",
"create_splitting_strategy": "sg.create($arguments$kwargs)",
"create_machine_evaluation": "sg.create($arguments$kwargs)",
"create_gp_likelihood": "sg.create($arguments$kwargs)",
"create_gp_mean": "sg.create($arguments$kwargs)",
"create_gp_inference": "sg.create($arguments$kwargs)",
"create_differentiable": "sg.create($arguments$kwargs)",
"create_loss": "sg.create($arguments$kwargs)",
"create_parameter_observer": "sg.create($arguments$kwargs)",
"create_evaluation_result": "sg.create($arguments$kwargs)",
"create_distribution": "sg.create($arguments$kwargs)",
"create_combination_rule": "sg.create($arguments$kwargs)",
"create_distance": "sg.create($arguments$kwargs)",
"create_machine": "sg.create($arguments$kwargs)",
"create_structured_model": "sg.create($arguments$kwargs)",
"create_factor_type": "sg.create($arguments$kwargs)",
"create_gaussian_process": "sg.create($arguments$kwargs)",
"create_minimizer": "sg.create($arguments$kwargs)",
"create_lbfgs_minimizer": "sg.create($arguments$kwargs)",
"create_kernel_normalizer": "sg.create($arguments$kwargs)"
},
"Identifier": "$identifier",
"Enum":"sg.$value"
},
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2 changes: 1 addition & 1 deletion examples/undocumented/python/classifier_larank.py
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Expand Up @@ -24,7 +24,7 @@ def classifier_larank (num_vec,num_class,distance,C=0.9,num_threads=1,num_iter=5
feats_train=sg.create_features(fm_train)
feats_test=sg.create_features(fm_test)

kernel=sg.create_kernel("GaussianKernel", width=1)
kernel=sg.create("GaussianKernel", width=1)

epsilon=1e-5
labels=MulticlassLabels(label_train)
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4 changes: 2 additions & 2 deletions examples/undocumented/python/classifier_multiclassocas.py
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Expand Up @@ -6,7 +6,7 @@ def classifier_multiclassocas (num_vec=10,num_class=3,distance=15,width=2.1,C=1,
from shogun import MulticlassLabels
import shogun as sg
try:
sg.create_machine("MulticlassOCAS")
sg.create("MulticlassOCAS")
except ImportError:
print("MulticlassOCAS not available")
return
Expand All @@ -28,7 +28,7 @@ def classifier_multiclassocas (num_vec=10,num_class=3,distance=15,width=2.1,C=1,

labels=sg.create_labels(label_train)

classifier = sg.create_machine("MulticlassOCAS", labels=labels, C=C)
classifier = sg.create("MulticlassOCAS", labels=labels, C=C)
classifier.train(feats_train)

out = classifier.apply(feats_test).get("labels")
Expand Down
2 changes: 1 addition & 1 deletion examples/undocumented/python/classifier_multilabeloutputliblinear.py
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Expand Up @@ -14,7 +14,7 @@ def classifier_multilabeloutputliblinear (fm_train_real=traindat,fm_test_real=te

labels=MulticlassLabels(label_train_multiclass)

classifier = sg.create_machine("MulticlassLibLinear", C=C, labels=labels)
classifier = sg.create("MulticlassLibLinear", C=C, labels=labels)
classifier.train(feats_train)

# TODO: figure out the new style API for the below call, disabling for now
Expand Down
9 changes: 4 additions & 5 deletions examples/undocumented/python/converter_hasheddoc.py
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Expand Up @@ -20,7 +20,7 @@ def converter_hasheddoc(strings):
normalize=True

#create converter
converter = sg.create_transformer('HashedDocConverter', tokenizer=tokenizer, num_bits=num_bits, should_normalize=normalize)
converter = sg.create('HashedDocConverter', tokenizer=tokenizer, num_bits=num_bits, should_normalize=normalize)

converted_feats=converter.transform(f)

Expand All @@ -29,9 +29,9 @@ def converter_hasheddoc(strings):

#print('Self dot product of string 0 with converted feats:', converted_feats.dot(0, converted_feats, 0))

hashed_feats=sg.create_features("HashedDocDotFeatures", num_bits=num_bits,
doc_collection=f, tokenizer=tokenizer,
should_normalize=normalize)
hashed_feats = sg.create("HashedDocDotFeatures", num_bits=num_bits,
doc_collection=f, tokenizer=tokenizer,
should_normalize=normalize)

#print('Hashed features\' space dimensionality is', hashed_feats.get_dim_feature_space())

Expand All @@ -43,4 +43,3 @@ def converter_hasheddoc(strings):
print('HashedDocConverter')
converter_hasheddoc(*parameter_list[0])


2 changes: 1 addition & 1 deletion examples/undocumented/python/converter_locallylinearembedding.py
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Expand Up @@ -12,7 +12,7 @@ def converter_locallylinearembeeding (data, k):

features = sg.create_features(data)

converter = sg.create_transformer('LocallyLinearEmbedding', k=k)
converter = sg.create('LocallyLinearEmbedding', k=k)

converter.fit(features)
features = converter.transform(features)
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4 changes: 2 additions & 2 deletions examples/undocumented/python/distance_canberraword.py
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Expand Up @@ -12,15 +12,15 @@
def distance_canberraword (fm_train_dna=traindna,fm_test_dna=testdna,order=3,gap=0,reverse=False):
charfeat=sg.create_string_features(fm_train_dna, sg.DNA)
feats_train=sg.create_string_features(charfeat, order-1, order, gap, reverse)
preproc = sg.create_transformer("SortWordString")
preproc = sg.create("SortWordString")
preproc.fit(feats_train)
feats_train = preproc.transform(feats_train)

charfeat=sg.create_string_features(fm_test_dna, sg.DNA)
feats_test=sg.create_string_features(charfeat, order-1, order, gap, reverse)
feats_test = preproc.transform(feats_test)

distance = sg.create_distance("CanberraWordDistance")
distance = sg.create("CanberraWordDistance")
distance.init(feats_train, feats_train)

dm_train=distance.get_distance_matrix()
Expand Down
2 changes: 1 addition & 1 deletion examples/undocumented/python/distance_director_euclidean.py
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Expand Up @@ -29,7 +29,7 @@ def distance_function(self, idx_a, idx_b):
feats_train.get_global_parallel().set_num_threads(1)
feats_test=sg.create_features(fm_test_real)

distance=sg.create_distance("EuclideanDistance")
distance=sg.create("EuclideanDistance")
distance.init(feats_train, feats_test)

ddistance=DirectorEuclideanDistance()
Expand Down
4 changes: 2 additions & 2 deletions examples/undocumented/python/distance_hammingword.py
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Expand Up @@ -15,15 +15,15 @@ def distance_hammingword (fm_train_dna=traindna,fm_test_dna=testdna,

charfeat=sg.create_string_features(fm_train_dna, sg.DNA)
feats_train=sg.create_string_features(charfeat, order-1, order, gap, reverse)
preproc = sg.create_transformer("SortWordString")
preproc = sg.create("SortWordString")
preproc.fit(feats_train)
feats_train = preproc.transform(feats_train)

charfeat=sg.create_string_features(fm_test_dna, sg.DNA)
feats_test=sg.create_string_features(charfeat, order-1, order, gap, reverse)
feats_test = preproc.transform(feats_test)

distance = sg.create_distance("HammingWordDistance", use_sign=use_sign)
distance = sg.create("HammingWordDistance", use_sign=use_sign)
distance.init(feats_train, feats_train)

dm_train=distance.get_distance_matrix()
Expand Down
4 changes: 2 additions & 2 deletions examples/undocumented/python/distance_manhattanword.py
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Expand Up @@ -9,15 +9,15 @@ def distance_manhattenword (train_fname=traindna,test_fname=testdna,order=3,gap=

charfeat=sg.create_string_features(sg.read_csv(train_fname), sg.DNA)
feats_train=sg.create_string_features(charfeat, order-1, order, gap, reverse)
preproc = sg.create_transformer("SortWordString")
preproc = sg.create("SortWordString")
preproc.fit(feats_train)
feats_train = preproc.transform(feats_train)

charfeat=sg.create_string_features(sg.read_csv(test_fname), sg.DNA)
feats_test=sg.create_string_features(charfeat, order-1, order, gap, reverse)
feats_test = preproc.transform(feats_test)

distance = sg.create_distance('ManhattanWordDistance')
distance = sg.create('ManhattanWordDistance')
distance.init(feats_train, feats_train)

dm_train=distance.get_distance_matrix()
Expand Down
2 changes: 1 addition & 1 deletion examples/undocumented/python/distance_normsquared.py
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Expand Up @@ -10,7 +10,7 @@ def distance_normsquared (train_fname=traindat,test_fname=testdat):
feats_train=sg.create_features(sg.read_csv(train_fname))
feats_test=sg.create_features(sg.read_csv(test_fname))

distance = sg.create_distance('EuclideanDistance', disable_sqrt=True)
distance = sg.create('EuclideanDistance', disable_sqrt=True)
distance.init(feats_train, feats_train)

dm_train=distance.get_distance_matrix()
Expand Down
8 changes: 4 additions & 4 deletions examples/undocumented/python/evaluation_clustering_simple.py
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Expand Up @@ -7,9 +7,9 @@

def run_clustering(data, k):

distance = sg.create_distance('EuclideanDistance')
distance = sg.create('EuclideanDistance')
distance.init(data, data)
kmeans=sg.create_machine("KMeans", k=k, distance=distance, seed=1)
kmeans=sg.create("KMeans", k=k, distance=distance, seed=1)

#print("Running clustering...")
kmeans.train()
Expand All @@ -23,9 +23,9 @@ def assign_labels(data, centroids, ncenters):

labels = MulticlassLabels(arange(0.,ncenters))
fea_centroids = sg.create_features(centroids)
distance = sg.create_distance('EuclideanDistance')
distance = sg.create('EuclideanDistance')
distance.init(fea_centroids, fea_centroids)
knn = sg.create_machine("KNN", k=1, distance=distance, labels=labels)
knn = sg.create("KNN", k=1, distance=distance, labels=labels)
knn.train()
return knn.apply(data)

Expand Down
20 changes: 10 additions & 10 deletions examples/undocumented/python/evaluation_contingencytableevaluation.py
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Expand Up @@ -16,34 +16,34 @@ def evaluation_contingencytableevaluation (ground_truth, predicted):
ground_truth_labels = BinaryLabels(ground_truth)
predicted_labels = BinaryLabels(predicted)

base_evaluator = sg.create_evaluation("ContingencyTableEvaluation")
base_evaluator = sg.create("ContingencyTableEvaluation")
base_evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("AccuracyMeasure")
evaluator = sg.create("AccuracyMeasure")
accuracy = evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("ErrorRateMeasure")
evaluator = sg.create("ErrorRateMeasure")
errorrate = evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("BALMeasure")
evaluator = sg.create("BALMeasure")
bal = evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("WRACCMeasure")
evaluator = sg.create("WRACCMeasure")
wracc = evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("F1Measure")
evaluator = sg.create("F1Measure")
f1 = evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("CrossCorrelationMeasure")
evaluator = sg.create("CrossCorrelationMeasure")
crosscorrelation = evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("RecallMeasure")
evaluator = sg.create("RecallMeasure")
recall = evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("PrecisionMeasure")
evaluator = sg.create("PrecisionMeasure")
precision = evaluator.evaluate(predicted_labels,ground_truth_labels)

evaluator = sg.create_evaluation("SpecificityMeasure")
evaluator = sg.create("SpecificityMeasure")
specificity = evaluator.evaluate(predicted_labels,ground_truth_labels)

return accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity
Expand Down
22 changes: 11 additions & 11 deletions examples/undocumented/python/evaluation_cross_validation_mkl_weight_storage.py
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Expand Up @@ -23,40 +23,40 @@ def evaluation_cross_validation_mkl_weight_storage(traindat=traindat, label_trai

# training data, combined features all on same data
features=sg.create_features(traindat)
comb_features=sg.create_features("CombinedFeatures")
comb_features=sg.create("CombinedFeatures")
comb_features.add("feature_array", features)
comb_features.add("feature_array", features)
comb_features.add("feature_array", features)
labels=BinaryLabels(label_traindat)

# kernel, different Gaussians combined
kernel=sg.create_kernel("CombinedKernel")
kernel.add("kernel_array", sg.create_kernel("GaussianKernel", width=0.1))
kernel.add("kernel_array", sg.create_kernel("GaussianKernel", width=1))
kernel.add("kernel_array", sg.create_kernel("GaussianKernel", width=2))
kernel=sg.create("CombinedKernel")
kernel.add("kernel_array", sg.create("GaussianKernel", width=0.1))
kernel.add("kernel_array", sg.create("GaussianKernel", width=1))
kernel.add("kernel_array", sg.create("GaussianKernel", width=2))

# create mkl using libsvm, due to a mem-bug, interleaved is not possible
libsvm = sg.create_machine("LibSVM")
svm = sg.create_machine("MKLClassification", svm=sg.as_svm(libsvm),
libsvm = sg.create("LibSVM")
svm = sg.create("MKLClassification", svm=sg.as_svm(libsvm),
interleaved_optimization=False, kernel=kernel)

# splitting strategy for 5 fold cross-validation (for classification its better
# to use "StratifiedCrossValidation", but the standard
# "StratifiedCrossValidationSplitting" is also available
splitting_strategy = sg.create_splitting_strategy(
splitting_strategy = sg.create(
"StratifiedCrossValidationSplitting", labels=labels, num_subsets=5)

# evaluation method
evaluation_criterium=sg.create_evaluation("ContingencyTableEvaluation", type="ACCURACY")
evaluation_criterium=sg.create("ContingencyTableEvaluation", type="ACCURACY")

# cross-validation instance
cross_validation = sg.create_machine_evaluation(
cross_validation = sg.create(
"CrossValidation", machine=svm, features=comb_features,
labels=labels, splitting_strategy=splitting_strategy,
evaluation_criterion=evaluation_criterium, num_runs=3)

# append cross vlaidation output classes
mkl_storage=sg.create_parameter_observer("ParameterObserverCV")
mkl_storage=sg.create("ParameterObserverCV")
cross_validation.subscribe(mkl_storage)

# perform cross-validation
Expand Down
8 changes: 4 additions & 4 deletions examples/undocumented/python/evaluation_cross_validation_multiclass_storage.py
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Expand Up @@ -40,10 +40,10 @@ def evaluation_cross_validation_multiclass_storage (traindat=traindat, label_tra
labels=MulticlassLabels(label_traindat)

# kernel, different Gaussians combined
kernel=sg.create_kernel("CombinedKernel")
kernel.add("kernel_array", sg.create_kernel("GaussianKernel", width=0.1))
kernel.add("kernel_array", sg.create_kernel("GaussianKernel", width=1))
kernel.add("kernel_array", sg.create_kernel("GaussianKernel", width=2))
kernel=sg.create("CombinedKernel")
kernel.add("kernel_array", sg.create("GaussianKernel", width=0.1))
kernel.add("kernel_array", sg.create("GaussianKernel", width=1))
kernel.add("kernel_array", sg.create("GaussianKernel", width=2))

# create mkl using libsvm, due to a mem-bug, interleaved is not possible
svm=MKLMulticlass(1.0,kernel,labels);
Expand Down
2 changes: 1 addition & 1 deletion examples/undocumented/python/evaluation_meansquarederror.py
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Expand Up @@ -18,7 +18,7 @@ def evaluation_meansquarederror (ground_truth, predicted):
ground_truth_labels = RegressionLabels(ground_truth)
predicted_labels = RegressionLabels(predicted)

evaluator = sg.create_evaluation("MeanSquaredError")
evaluator = sg.create("MeanSquaredError")
mse = evaluator.evaluate(predicted_labels,ground_truth_labels)

return mse
Expand Down
2 changes: 1 addition & 1 deletion examples/undocumented/python/evaluation_meansquaredlogerror.py
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Expand Up @@ -18,7 +18,7 @@ def evaluation_meansquaredlogerror (ground_truth, predicted):
ground_truth_labels = RegressionLabels(ground_truth)
predicted_labels = RegressionLabels(predicted)

evaluator = sg.create_evaluation("MeanSquaredLogError")
evaluator = sg.create("MeanSquaredLogError")
mse = evaluator.evaluate(predicted_labels,ground_truth_labels)

return mse
Expand Down
2 changes: 1 addition & 1 deletion examples/undocumented/python/evaluation_prcevaluation.py
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Expand Up @@ -16,7 +16,7 @@ def evaluation_prcevaluation (ground_truth, predicted):
ground_truth_labels = BinaryLabels(ground_truth)
predicted_labels = BinaryLabels(predicted)

evaluator = sg.create_evaluation("PRCEvaluation")
evaluator = sg.create("PRCEvaluation")
evaluator.evaluate(predicted_labels,ground_truth_labels)

return evaluator.get("PRC"), evaluator.get("auPRC")
Expand Down
2 changes: 1 addition & 1 deletion examples/undocumented/python/evaluation_rocevaluation.py
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Expand Up @@ -16,7 +16,7 @@ def evaluation_rocevaluation (ground_truth, predicted):
ground_truth_labels = BinaryLabels(ground_truth)
predicted_labels = BinaryLabels(predicted)

evaluator = sg.create_evaluation("ROCEvaluation")
evaluator = sg.create("ROCEvaluation")
evaluator.evaluate(predicted_labels,ground_truth_labels)

return evaluator.get("ROC"), evaluator.get("auROC")
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2 changes: 1 addition & 1 deletion examples/undocumented/python/evaluation_thresholds.py
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Expand Up @@ -16,7 +16,7 @@ def evaluation_thresholds (index):
pred=BinaryLabels(output)
truth=BinaryLabels(label)

evaluator=sg.create_evaluation("ROCEvaluation")
evaluator=sg.create("ROCEvaluation")
evaluator.evaluate(pred, truth)

[fp,tp]=evaluator.get("ROC")
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6 changes: 3 additions & 3 deletions examples/undocumented/python/features_hasheddocdot.py
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Expand Up @@ -19,9 +19,9 @@ def features_hasheddocdot(strings):
normalize=True

#create HashedDocDot features
hddf = sg.create_features("HashedDocDotFeatures", num_bits=num_bits,
doc_collection=f, tokenizer=tokenizer,
should_normalize=normalize)
hddf = sg.create("HashedDocDotFeatures", num_bits=num_bits,
doc_collection=f, tokenizer=tokenizer,
should_normalize=normalize)
#should expect 32
#print('Feature space dimensionality is', hddf.get_dim_feature_space())

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2 changes: 1 addition & 1 deletion examples/undocumented/python/graphical/classifier_gaussian_process_binary_classification.py
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Expand Up @@ -43,7 +43,7 @@ def gaussian_process_binary_classification_laplace(X_train, y_train, n_test=50):
test_features = sg.create_features(X_test)

# create Gaussian kernel with width = 2.0
kernel = sg.create_kernel('GaussianKernel', width=2.0)
kernel = sg.create('GaussianKernel', width=2.0)

# create zero mean function
mean = sg.gp_mean("ZeroMean")
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