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remove common-primitive dependency, revise primitive_tests 

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master
lhenry15 5 years ago
parent
0d962845b5
commit
154cdfea8e
100 changed files with 59 additions and 3922 deletions
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  1. +0
    -70
      new_tests/build_ABOD_pipline.py
  2. +0
    -67
      new_tests/build_AutoEncoder.py
  3. +0
    -71
      new_tests/build_AutoRegODetect_pipeline.py
  4. +0
    -50
      new_tests/build_AxiswiseScale_pipline.py
  5. +0
    -44
      new_tests/build_BKFilter_pipline.py
  6. +0
    -51
      new_tests/build_CBLOF_pipline.py
  7. +0
    -48
      new_tests/build_CategoricalToBinary.py
  8. +0
    -49
      new_tests/build_ColumnFilter_pipeline.py
  9. +0
    -43
      new_tests/build_ContinuityValidation_pipline.py
  10. +0
    -49
      new_tests/build_DeepLog_pipeline.py
  11. +0
    -50
      new_tests/build_DiscreteCosineTransform.py
  12. +0
    -42
      new_tests/build_DuplicationValidation_pipline.py
  13. +0
    -48
      new_tests/build_FastFourierTransform.py
  14. +0
    -68
      new_tests/build_HBOS_pipline.py
  15. +0
    -71
      new_tests/build_HBOS_score_pipline.py
  16. +0
    -46
      new_tests/build_HPFilter_pipline.py
  17. +0
    -76
      new_tests/build_HoltSmoothing_pipline.py
  18. +0
    -76
      new_tests/build_HoltWintersExponentialSmoothing_pipline.py
  19. +0
    -59
      new_tests/build_IsolationForest_pipline.py
  20. +0
    -71
      new_tests/build_KDiscord_pipeline.py
  21. +0
    -51
      new_tests/build_KNN_pipline.py
  22. +0
    -51
      new_tests/build_LODA_pipline.py
  23. +0
    -51
      new_tests/build_LOF_pipline.py
  24. +0
    -70
      new_tests/build_LSTMOD_pipline.py
  25. +0
    -49
      new_tests/build_MatrixProfile_pipeline.py
  26. +0
    -77
      new_tests/build_MeanAverageTransform_pipline.py
  27. +0
    -50
      new_tests/build_NonNegativeMatrixFactorization.py
  28. +0
    -51
      new_tests/build_OCSVM_pipline.py
  29. +0
    -71
      new_tests/build_PCAODetect_pipeline.py
  30. +0
    -49
      new_tests/build_PowerTransform_pipline.py
  31. +0
    -51
      new_tests/build_PyodCOF.py
  32. +0
    -49
      new_tests/build_QuantileTransform_pipline.py
  33. +0
    -54
      new_tests/build_RuleBasedFilter_pipline.py
  34. +0
    -49
      new_tests/build_SOD_pipeline.py
  35. +0
    -76
      new_tests/build_SimpleExponentialSmoothing_pipline.py
  36. +0
    -49
      new_tests/build_Standardize_pipline.py
  37. +0
    -44
      new_tests/build_TRMF_pipline.py
  38. +0
    -48
      new_tests/build_Telemanom.py
  39. +0
    -86
      new_tests/build_TimeIntervalTransform_pipeline.py
  40. +0
    -44
      new_tests/build_TruncatedSVD_pipline.py
  41. +0
    -67
      new_tests/build_VariationalAutoEncoder.py
  42. +0
    -64
      new_tests/build_WaveletTransform_pipline.py
  43. +0
    -50
      new_tests/build_test_detection_algorithm_PyodMoGaal.py
  44. +0
    -50
      new_tests/build_test_detection_algorithm_PyodSoGaal.py
  45. +0
    -61
      new_tests/build_test_feature_analysis_spectral_residual_transform_pipeline.py
  46. +0
    -62
      new_tests/build_test_feature_analysis_statistical_abs_energy.py
  47. +0
    -62
      new_tests/build_test_feature_analysis_statistical_abs_sum.py
  48. +0
    -62
      new_tests/build_test_feature_analysis_statistical_gmean.py
  49. +0
    -62
      new_tests/build_test_feature_analysis_statistical_hmean.py
  50. +0
    -62
      new_tests/build_test_feature_analysis_statistical_kurtosis.py
  51. +0
    -62
      new_tests/build_test_feature_analysis_statistical_maximum.py
  52. +0
    -62
      new_tests/build_test_feature_analysis_statistical_mean.py
  53. +0
    -62
      new_tests/build_test_feature_analysis_statistical_mean_abs.py
  54. +0
    -62
      new_tests/build_test_feature_analysis_statistical_mean_abs_temporal_derivative.py
  55. +0
    -62
      new_tests/build_test_feature_analysis_statistical_mean_temporal_derivative.py
  56. +0
    -62
      new_tests/build_test_feature_analysis_statistical_median.py
  57. +0
    -63
      new_tests/build_test_feature_analysis_statistical_median_absolute_deviation.py
  58. +0
    -62
      new_tests/build_test_feature_analysis_statistical_minimum.py
  59. +0
    -62
      new_tests/build_test_feature_analysis_statistical_skew.py
  60. +0
    -62
      new_tests/build_test_feature_analysis_statistical_std.py
  61. +0
    -62
      new_tests/build_test_feature_analysis_statistical_var.py
  62. +0
    -62
      new_tests/build_test_feature_analysis_statistical_variation.py
  63. +0
    -62
      new_tests/build_test_feature_analysis_statistical_vec_sum.py
  64. +0
    -62
      new_tests/build_test_feature_analysis_statistical_willison_amplitude.py
  65. +0
    -62
      new_tests/build_test_feature_analysis_statistical_zero_crossing.py
  66. +0
    -61
      new_tests/build_test_time_series_seasonality_trend_decomposition.py
  67. +3
    -3
      primitive_tests/build_ABOD_pipline.py
  68. +3
    -3
      primitive_tests/build_AutoEncoder.py
  69. +2
    -2
      primitive_tests/build_AutoRegODetect_pipeline.py
  70. +1
    -1
      primitive_tests/build_AxiswiseScale_pipline.py
  71. +1
    -1
      primitive_tests/build_BKFilter_pipline.py
  72. +1
    -1
      primitive_tests/build_CBLOF_pipline.py
  73. +1
    -1
      primitive_tests/build_CategoricalToBinary.py
  74. +2
    -2
      primitive_tests/build_ColumnFilter_pipeline.py
  75. +1
    -1
      primitive_tests/build_ContinuityValidation_pipline.py
  76. +2
    -2
      primitive_tests/build_DeepLog_pipeline.py
  77. +1
    -1
      primitive_tests/build_DiscreteCosineTransform.py
  78. +1
    -1
      primitive_tests/build_DuplicationValidation_pipline.py
  79. +1
    -1
      primitive_tests/build_FastFourierTransform.py
  80. +3
    -3
      primitive_tests/build_HBOS_pipline.py
  81. +3
    -3
      primitive_tests/build_HBOS_score_pipline.py
  82. +2
    -2
      primitive_tests/build_HPFilter_pipline.py
  83. +3
    -3
      primitive_tests/build_HoltSmoothing_pipline.py
  84. +3
    -3
      primitive_tests/build_HoltWintersExponentialSmoothing_pipline.py
  85. +2
    -2
      primitive_tests/build_IsolationForest_pipline.py
  86. +3
    -3
      primitive_tests/build_KDiscord_pipeline.py
  87. +1
    -1
      primitive_tests/build_KNN_pipline.py
  88. +1
    -1
      primitive_tests/build_LODA_pipline.py
  89. +1
    -1
      primitive_tests/build_LOF_pipline.py
  90. +2
    -2
      primitive_tests/build_LSTMOD_pipline.py
  91. +1
    -1
      primitive_tests/build_MatrixProfile_pipeline.py
  92. +3
    -3
      primitive_tests/build_MeanAverageTransform_pipline.py
  93. +1
    -1
      primitive_tests/build_NonNegativeMatrixFactorization.py
  94. +1
    -1
      primitive_tests/build_OCSVM_pipline.py
  95. +3
    -3
      primitive_tests/build_PCAODetect_pipeline.py
  96. +1
    -1
      primitive_tests/build_PowerTransform_pipline.py
  97. +1
    -1
      primitive_tests/build_PyodCOF.py
  98. +1
    -1
      primitive_tests/build_QuantileTransform_pipline.py
  99. +2
    -2
      primitive_tests/build_RuleBasedFilter_pipline.py
  100. +1
    -1
      primitive_tests/build_SOD_pipeline.py

+ 0
- 70
new_tests/build_ABOD_pipline.py View File

@@ -1,70 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: ABOD
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_abod'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce')

step_5.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_5.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2, 4,))
step_5.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='replace')

step_5.add_output('produce')
pipeline_description.add_step(step_5)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 67
new_tests/build_AutoEncoder.py View File

@@ -1,67 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: auto encoder
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ae'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_5.add_output('produce')
pipeline_description.add_step(step_5)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 71
new_tests/build_AutoRegODetect_pipeline.py View File

@@ -1,71 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import numpy as np

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# # Step 3: Standardization
primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(1,2,3,4,5,))
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)

# # Step 4: test primitive
primitive_4 = index.get_primitive('d3m.primitives.tods.detection_algorithm.AutoRegODetector')
step_4 = PrimitiveStep(primitive=primitive_4)
step_4.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_4.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=10)
# step_4.add_hyperparameter(name='weights', argument_type=ArgumentType.VALUE, data=weights_ndarray)
step_4.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False)
# step_4.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) # There is sth wrong with multi-dimensional
step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_4.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True)
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce')
step_4.add_output('produce')
step_4.add_output('produce_score')
pipeline_description.add_step(step_4)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 50
new_tests/build_AxiswiseScale_pipline.py View File

@@ -1,50 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 44
new_tests/build_BKFilter_pipline.py View File

@@ -1,44 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)


# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: BKFilter
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.bk_filter'))
# step_2.add_hyperparameter(name = 'columns_using_method', argument_type=ArgumentType.VALUE, data = 'name')
step_2.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True)
step_2.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2,3))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 51
new_tests/build_CBLOF_pipline.py View File

@@ -1,51 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_cblof')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 48
new_tests/build_CategoricalToBinary.py View File

@@ -1,48 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')


# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: Categorical to Binary
primitive_2 = index.get_primitive('d3m.primitives.tods.data_processing.categorical_to_binary')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(3,))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 49
new_tests/build_ColumnFilter_pipeline.py View File

@@ -1,49 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

#Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.auto_correlation')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True)
step_2.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

primitive_3 = index.get_primitive('d3m.primitives.tods.data_processing.column_filter')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 43
new_tests/build_ContinuityValidation_pipline.py View File

@@ -1,43 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: ContinuityValidation
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.continuity_validation'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name = 'continuity_option', argument_type=ArgumentType.VALUE, data = 'imputation')
step_2.add_hyperparameter(name = 'interval', argument_type=ArgumentType.VALUE, data = 0.3)
# Or:
# step_2.add_hyperparameter(name = 'continuity_option', argument_type=ArgumentType.VALUE, data = 'ablation')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 49
new_tests/build_DeepLog_pipeline.py View File

@@ -1,49 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.deeplog')

step_2 = PrimitiveStep(primitive=primitive_2)
#step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# # Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 50
new_tests/build_DiscreteCosineTransform.py View File

@@ -1,50 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')


# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: Discrete Cosine Transform
primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.discrete_cosine_transform')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 42
new_tests/build_DuplicationValidation_pipline.py View File

@@ -1,42 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)


# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: DuplicationValidation
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.duplication_validation'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name = 'keep_option', argument_type=ArgumentType.VALUE, data = 'average') # Or: 'first'
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 48
new_tests/build_FastFourierTransform.py View File

@@ -1,48 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')


# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: Fast Fourier Transform
primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.fast_fourier_transform')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 68
new_tests/build_HBOS_pipline.py View File

@@ -1,68 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: HBOS
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_hbos'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce')

step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
# step_5.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')

step_5.add_output('produce')
pipeline_description.add_step(step_5)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 71
new_tests/build_HBOS_score_pipline.py View File

@@ -1,71 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: HBOS
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_hbos'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.4.produce')

step_5.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_5.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True)
# step_5.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')

step_5.add_output('produce_score')
step_5.add_output('produce')
pipeline_description.add_step(step_5)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')
# pipeline_description.add_output(name='output score', data_reference='steps.5.produce_score')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 46
new_tests/build_HPFilter_pipline.py View File

@@ -1,46 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)


# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: HPFilter
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.hp_filter'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')

step_2.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = [2,3,6])

step_2.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True)
step_2.add_hyperparameter(name = 'return_result', argument_type=ArgumentType.VALUE, data = 'append')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 76
new_tests/build_HoltSmoothing_pipline.py View File

@@ -1,76 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: holt smoothing
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.holt_smoothing'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_5.add_hyperparameter(name="exclude_columns", argument_type=ArgumentType.VALUE, data = (2, 3))
step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True)
step_5.add_output('produce')
pipeline_description.add_step(step_5)

# Step 6: isolation forest
#step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm'))
#step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')
#step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets)
#step_6.add_output('produce')
#pipeline_description.add_step(step_6)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 76
new_tests/build_HoltWintersExponentialSmoothing_pipline.py View File

@@ -1,76 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: holt winters exponential smoothing
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.holt_winters_exponential_smoothing'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_5.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3))
step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True)
step_5.add_output('produce')
pipeline_description.add_step(step_5)

# Step 6: isolation forest
#step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm'))
#step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')
#step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets)
#step_6.add_output('produce')
#pipeline_description.add_step(step_6)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 59
new_tests/build_IsolationForest_pipline.py View File

@@ -1,59 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# # Step 3: test primitive
primitive_3 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_iforest')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
# step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
# step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True)
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce_score')
step_3.add_output('produce')
pipeline_description.add_step(step_3)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce_score')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 71
new_tests/build_KDiscord_pipeline.py View File

@@ -1,71 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import numpy as np

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# # Step 3: Standardization
primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(1,2,3,4,5,))
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)

# # Step 4: test primitive
primitive_4 = index.get_primitive('d3m.primitives.tods.detection_algorithm.KDiscordODetector')
step_4 = PrimitiveStep(primitive=primitive_4)
step_4.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_4.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=10)
# step_4.add_hyperparameter(name='weights', argument_type=ArgumentType.VALUE, data=weights_ndarray)
step_4.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False)
# step_4.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) # There is sth wrong with multi-dimensional
step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_4.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True)
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce')
step_4.add_output('produce')
step_4.add_output('produce_score')
pipeline_description.add_step(step_4)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 51
new_tests/build_KNN_pipline.py View File

@@ -1,51 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_knn')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 51
new_tests/build_LODA_pipline.py View File

@@ -1,51 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_loda')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 51
new_tests/build_LOF_pipline.py View File

@@ -1,51 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_lof')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 70
new_tests/build_LSTMOD_pipline.py View File

@@ -1,70 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import numpy as np

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# # Step 2: Standardization
primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(1,2,3,4,5,))
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)

# # Step 3: test primitive
primitive_4 = index.get_primitive('d3m.primitives.tods.detection_algorithm.LSTMODetector')
step_4 = PrimitiveStep(primitive=primitive_4)
step_4.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_4.add_hyperparameter(name='diff_group_method', argument_type=ArgumentType.VALUE, data='average')
step_4.add_hyperparameter(name='feature_dim', argument_type=ArgumentType.VALUE, data=5)
step_4.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False)
# step_4.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) # There is sth wrong with multi-dimensional
step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_4.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True)
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce')
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 49
new_tests/build_MatrixProfile_pipeline.py View File

@@ -1,49 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.matrix_profile')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=3) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# # Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 77
new_tests/build_MeanAverageTransform_pipline.py View File

@@ -1,77 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: mean average transform
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.moving_average_transform'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_5.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (2, 3))
step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True)
step_5.add_output('produce')
pipeline_description.add_step(step_5)

# Step 6: isolation forest
#step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm'))
#step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')
#step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets)
#step_6.add_output('produce')
#pipeline_description.add_step(step_6)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 50
new_tests/build_NonNegativeMatrixFactorization.py View File

@@ -1,50 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')


# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: Non Negative Matrix Factorization
primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.non_negative_matrix_factorization')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_hyperparameter(name='rank', argument_type=ArgumentType.VALUE, data=5)
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 51
new_tests/build_OCSVM_pipline.py View File

@@ -1,51 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_ocsvm')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 71
new_tests/build_PCAODetect_pipeline.py View File

@@ -1,71 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import numpy as np

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# # Step 3: Standardization
primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(1,2,3,4,5,))
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)

# # Step 4: test primitive
primitive_4 = index.get_primitive('d3m.primitives.tods.detection_algorithm.PCAODetector')
step_4 = PrimitiveStep(primitive=primitive_4)
step_4.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_4.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=10)
# step_4.add_hyperparameter(name='weights', argument_type=ArgumentType.VALUE, data=weights_ndarray)
step_4.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False)
# step_4.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6)) # There is sth wrong with multi-dimensional
step_4.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_4.add_hyperparameter(name='return_subseq_inds', argument_type=ArgumentType.VALUE, data=True)
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce')
step_4.add_output('produce')
step_4.add_output('produce_score')
pipeline_description.add_step(step_4)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.4.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 49
new_tests/build_PowerTransform_pipline.py View File

@@ -1,49 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.power_transformer')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 51
new_tests/build_PyodCOF.py View File

@@ -1,51 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_cof')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 49
new_tests/build_QuantileTransform_pipline.py View File

@@ -1,49 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.quantile_transformer')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 54
new_tests/build_RuleBasedFilter_pipline.py View File

@@ -1,54 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)


step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.reinforcement.rule_filter'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')

step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2, 4,))
step_3.add_hyperparameter(name='rule', argument_type=ArgumentType.VALUE, data='#4# % 2 == 0 and #2# <= 0.3')

step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
pipeline_description.add_step(step_3)




# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 49
new_tests/build_SOD_pipeline.py View File

@@ -1,49 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_sod')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# # Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 76
new_tests/build_SimpleExponentialSmoothing_pipline.py View File

@@ -1,76 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: simple exponential smoothing
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.simple_exponential_smoothing'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_5.add_hyperparameter(name="use_columns", argument_type=ArgumentType.VALUE, data = (1,))
step_5.add_hyperparameter(name="use_semantic_types", argument_type=ArgumentType.VALUE, data = True)
step_5.add_output('produce')
pipeline_description.add_step(step_5)

# Step 6: isolation forest
#step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.anomaly_detection.isolation_forest.Algorithm'))
#step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')
#step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets)
#step_6.add_output('produce')
#pipeline_description.add_step(step_6)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 49
new_tests/build_Standardize_pipline.py View File

@@ -1,49 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 44
new_tests/build_TRMF_pipline.py View File

@@ -1,44 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: TRMF
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.trmf'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')

step_2.add_hyperparameter(name = 'lags', argument_type=ArgumentType.VALUE, data = [1,2,10,100])
# step_2.add_hyperparameter(name = 'K', argument_type=ArgumentType.VALUE, data = 3)
# step_2.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2, 3, 4, 5, 6))

pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 48
new_tests/build_Telemanom.py View File

@@ -1,48 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')


# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: Fast Fourier Transform
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.telemanom')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 86
new_tests/build_TimeIntervalTransform_pipeline.py View File

@@ -1,86 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: dataframe transformation
# primitive_1 = index.get_primitive('d3m.primitives.data_transformation.SKPowerTransformer')
# primitive_1 = index.get_primitive('d3m.primitives.data_transformation.SKStandardization')
# primitive_1 = index.get_primitive('d3m.primitives.data_transformation.SKQuantileTransformer')

#Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

primitive_2 = index.get_primitive('d3m.primitives.tods.data_processing.time_interval_transform')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name="time_interval", argument_type=ArgumentType.VALUE, data = '5T')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)
#
# # Step 2: column_parser
# step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
# step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
# step_2.add_output('produce')
# pipeline_description.add_step(step_2)
#
#
# # Step 3: extract_columns_by_semantic_types(attributes)
# step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
# step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
# step_3.add_output('produce')
# step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
# data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
# pipeline_description.add_step(step_3)
#
# # Step 4: extract_columns_by_semantic_types(targets)
# step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
# step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
# step_4.add_output('produce')
# step_4.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
# data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
# pipeline_description.add_step(step_4)
#
# attributes = 'steps.3.produce'
# targets = 'steps.4.produce'
#
# # Step 5: imputer
# step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_cleaning.imputer.SKlearn'))
# step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
# step_5.add_output('produce')
# pipeline_description.add_step(step_5)
#
# # Step 6: random_forest
# step_6 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.regression.random_forest.SKlearn'))
# step_6.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.5.produce')
# step_6.add_argument(name='outputs', argument_type=ArgumentType.CONTAINER, data_reference=targets)
# step_6.add_output('produce')
# pipeline_description.add_step(step_6)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.1.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 44
new_tests/build_TruncatedSVD_pipline.py View File

@@ -1,44 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep


# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: TruncatedSVD
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.truncated_svd'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name = 'n_components', argument_type=ArgumentType.VALUE, data = 3)
step_2.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2, 3, 4, 5, 6))
step_2.add_hyperparameter(name = 'return_result', argument_type=ArgumentType.VALUE, data = 'append')
step_2.add_hyperparameter(name = 'use_semantic_types', argument_type=ArgumentType.VALUE, data = True)
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 67
new_tests/build_VariationalAutoEncoder.py View File

@@ -1,67 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/Attribute'])
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
data=['https://metadata.datadrivendiscovery.org/types/TrueTarget'])
pipeline_description.add_step(step_3)

attributes = 'steps.2.produce'
targets = 'steps.3.produce'

# Step 4: imputer
step_4 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.impute_missing'))
step_4.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_4.add_output('produce')
pipeline_description.add_step(step_4)

# Step 5: variatinal auto encoder
step_5 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_vae'))
step_5.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference=attributes)
step_5.add_output('produce')
pipeline_description.add_step(step_5)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.5.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 64
new_tests/build_WaveletTransform_pipline.py View File

@@ -1,64 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test WaveletTransform
primitive_2 = index.get_primitive('d3m.primitives.tods.feature_analysis.wavelet_transform')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='wavelet', argument_type=ArgumentType.VALUE, data='db8')
step_2.add_hyperparameter(name='level', argument_type=ArgumentType.VALUE, data=2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 2: test inverse WaveletTransform
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.wavelet_transform')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='wavelet', argument_type=ArgumentType.VALUE, data='db8')
step_3.add_hyperparameter(name='level', argument_type=ArgumentType.VALUE, data=2)
step_3.add_hyperparameter(name='inverse', argument_type=ArgumentType.VALUE, data=1)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=False)
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='new')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)


# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 0
- 50
new_tests/build_test_detection_algorithm_PyodMoGaal.py View File

@@ -1,50 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_mogaal')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 50
new_tests/build_test_detection_algorithm_PyodSoGaal.py View File

@@ -1,50 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: test primitive
primitive_2 = index.get_primitive('d3m.primitives.tods.detection_algorithm.pyod_sogaal')

step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='contamination', argument_type=ArgumentType.VALUE, data=0.1)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,)) # There is sth wrong with multi-dimensional
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# Final Output
pipeline_description.add_output(name='output predictions', data_reference='steps.2.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)
print(yaml)

# Or you can output json
#data = pipline_description.to_json()

+ 0
- 61
new_tests/build_test_feature_analysis_spectral_residual_transform_pipeline.py View File

@@ -1,61 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.spectral_residual_transform')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='avg_filter_dimension', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_abs_energy.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_abs_energy')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_abs_sum.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_abs_sum')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_gmean.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_g_mean')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_hmean.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_h_mean')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_kurtosis.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_kurtosis')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_maximum.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_maximum')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_mean.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_mean_abs.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_abs')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_mean_abs_temporal_derivative.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_abs_temporal_derivative')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_mean_temporal_derivative.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_mean_temporal_derivative')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_median.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_median')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 63
new_tests/build_test_feature_analysis_statistical_median_absolute_deviation.py View File

@@ -1,63 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)


# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_median_abs_deviation')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_minimum.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_minimum')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_skew.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_skew')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_std.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_std')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_var.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_var')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_variation.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_variation')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_vec_sum.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_vec_sum')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_willison_amplitude.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_willison_amplitude')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='window_size', argument_type=ArgumentType.VALUE, data=4)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(5,6)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 62
new_tests/build_test_feature_analysis_statistical_zero_crossing.py View File

@@ -1,62 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)


# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.feature_analysis.statistical_zero_crossing')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(9,10)) # There is sth wrong with multi-dimensional
step_3.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)


# Or you can output json
#data = pipline_description.to_json()


+ 0
- 61
new_tests/build_test_time_series_seasonality_trend_decomposition.py View File

@@ -1,61 +0,0 @@
from d3m import index
from d3m.metadata.base import ArgumentType
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata import hyperparams
import copy

# -> dataset_to_dataframe -> column_parser -> extract_columns_by_semantic_types(attributes) -> imputer -> random_forest
# extract_columns_by_semantic_types(targets) -> ^

# Creating pipeline
pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# # Step 2: Standardization
primitive_2 = index.get_primitive('d3m.primitives.tods.timeseries_processing.transformation.standard_scaler')
step_2 = PrimitiveStep(primitive=primitive_2)
step_2.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_2.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(2,3,4,5,6))
step_2.add_hyperparameter(name='return_result', argument_type=ArgumentType.VALUE, data='append')
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
pipeline_description.add_step(step_2)

# # Step 3: test primitive
# primitive_3 = index.get_primitive('d3m.primitives.anomaly_detection.KNNPrimitive')
primitive_3 = index.get_primitive('d3m.primitives.tods.timeseries_processing.decomposition.time_series_seasonality_trend_decomposition')
step_3 = PrimitiveStep(primitive=primitive_3)
step_3.add_hyperparameter(name='period', argument_type=ArgumentType.VALUE, data=5)
step_3.add_hyperparameter(name='use_semantic_types', argument_type=ArgumentType.VALUE, data=True)
step_3.add_hyperparameter(name='use_columns', argument_type=ArgumentType.VALUE, data=(8,9,10,11,12)) # There is sth wrong with multi-dimensional
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.2.produce')
step_3.add_output('produce')
pipeline_description.add_step(step_3)



# Final Output
pipeline_description.add_output(name='output', data_reference='steps.3.produce')

# Output to YAML
yaml = pipeline_description.to_yaml()
with open('pipeline.yml', 'w') as f:
f.write(yaml)

# Or you can output json
#data = pipline_description.to_json()


+ 3
- 3
primitive_tests/build_ABOD_pipline.py View File

@@ -14,13 +14,13 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -28,7 +28,7 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,


+ 3
- 3
primitive_tests/build_AutoEncoder.py View File

@@ -16,13 +16,13 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -30,7 +30,7 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,


+ 2
- 2
primitive_tests/build_AutoRegODetect_pipeline.py View File

@@ -19,14 +19,14 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])


+ 1
- 1
primitive_tests/build_AxiswiseScale_pipline.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_BKFilter_pipline.py View File

@@ -15,7 +15,7 @@ pipeline_description.add_step(step_0)


# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


+ 1
- 1
primitive_tests/build_CBLOF_pipline.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_CategoricalToBinary.py View File

@@ -18,7 +18,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 2
- 2
primitive_tests/build_ColumnFilter_pipeline.py View File

@@ -10,14 +10,14 @@ pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

#Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


+ 1
- 1
primitive_tests/build_ContinuityValidation_pipline.py View File

@@ -13,7 +13,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


+ 2
- 2
primitive_tests/build_DeepLog_pipeline.py View File

@@ -11,14 +11,14 @@ pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_DiscreteCosineTransform.py View File

@@ -18,7 +18,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_DuplicationValidation_pipline.py View File

@@ -15,7 +15,7 @@ pipeline_description.add_step(step_0)


# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


+ 1
- 1
primitive_tests/build_FastFourierTransform.py View File

@@ -18,7 +18,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 3
- 3
primitive_tests/build_HBOS_pipline.py View File

@@ -14,13 +14,13 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -28,7 +28,7 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,


+ 3
- 3
primitive_tests/build_HBOS_score_pipline.py View File

@@ -14,13 +14,13 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -28,7 +28,7 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,


+ 2
- 2
primitive_tests/build_HPFilter_pipline.py View File

@@ -8,14 +8,14 @@ pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common'))
step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)


# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


+ 3
- 3
primitive_tests/build_HoltSmoothing_pipline.py View File

@@ -17,13 +17,13 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -31,7 +31,7 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,


+ 3
- 3
primitive_tests/build_HoltWintersExponentialSmoothing_pipline.py View File

@@ -17,13 +17,13 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -31,7 +31,7 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,


+ 2
- 2
primitive_tests/build_IsolationForest_pipline.py View File

@@ -19,14 +19,14 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])


+ 3
- 3
primitive_tests/build_KDiscord_pipeline.py View File

@@ -12,21 +12,21 @@ pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])


+ 1
- 1
primitive_tests/build_KNN_pipline.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_LODA_pipline.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_LOF_pipline.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 2
- 2
primitive_tests/build_LSTMOD_pipline.py View File

@@ -19,14 +19,14 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])


+ 1
- 1
primitive_tests/build_MatrixProfile_pipeline.py View File

@@ -18,7 +18,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 3
- 3
primitive_tests/build_MeanAverageTransform_pipline.py View File

@@ -17,14 +17,14 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)


# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -32,7 +32,7 @@ step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALU
pipeline_description.add_step(step_2)

# Step 3: extract_columns_by_semantic_types(targets)
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_3.add_output('produce')
step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,


+ 1
- 1
primitive_tests/build_NonNegativeMatrixFactorization.py View File

@@ -18,7 +18,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: Column Parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_OCSVM_pipline.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 3
- 3
primitive_tests/build_PCAODetect_pipeline.py View File

@@ -12,21 +12,21 @@ pipeline_description = Pipeline()
pipeline_description.add_input(name='inputs')

# Step 0: dataset_to_dataframe
primitive_0 = index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common')
primitive_0 = index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe')
step_0 = PrimitiveStep(primitive=primitive_0)
step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])


+ 1
- 1
primitive_tests/build_PowerTransform_pipline.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_PyodCOF.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 1
- 1
primitive_tests/build_QuantileTransform_pipline.py View File

@@ -19,7 +19,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


+ 2
- 2
primitive_tests/build_RuleBasedFilter_pipline.py View File

@@ -14,13 +14,13 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# Step 1: column_parser
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')
pipeline_description.add_step(step_1)

# Step 2: extract_columns_by_semantic_types(attributes)
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
step_2.add_output('produce')
step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE, data=['https://metadata.datadrivendiscovery.org/types/Attribute'])


+ 1
- 1
primitive_tests/build_SOD_pipeline.py View File

@@ -18,7 +18,7 @@ step_0.add_output('produce')
pipeline_description.add_step(step_0)

# # Step 1: column_parser
primitive_1 = index.get_primitive('d3m.primitives.data_transformation.column_parser.Common')
primitive_1 = index.get_primitive('d3m.primitives.tods.data_processing.column_parser')
step_1 = PrimitiveStep(primitive=primitive_1)
step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
step_1.add_output('produce')


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