modified metadata of feature_analysis module, fix bugs in primitive_tests

examples/axolotl_interface/example_pipelines/autoencoder_pipeline.json

@@ -1 +1 @@
-{"id": "5478db87-2eea-45c7-b0c1-f6a2fb180a61", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2021-02-04T06:35:44.477042Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.6.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "4b42ce1e-9b98-4a25-b68e-fad13311eb65", "version": "0.3.0", "python_path": "d3m.primitives.tods.data_processing.dataset_to_dataframe", "name": "Extract a DataFrame from a Dataset", "digest": "f817673b67b0bf1fad5efb694e44a59b9c15931520b419f3a852f79152f58bb7"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "d510cb7a-1782-4f51-b44c-58f0236e47c7", "version": "0.6.0", "python_path": "d3m.primitives.tods.data_processing.column_parser", "name": "Parses strings into their types", "digest": "6aea6b48ddcd847030b423ebf2cac62e67b2935d93f75d10fa0712ea019b2739"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1", "version": "0.4.0", "python_path": "d3m.primitives.tods.data_processing.extract_columns_by_semantic_types", "name": "Extracts columns by semantic type", "digest": "e165d114bca8e1e47f349fffbbf2cf8872f0222328287e24acd8ac8c1cf2511c"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"semantic_types": {"type": "VALUE", "data": ["https://metadata.datadrivendiscovery.org/types/Attribute"]}}}, {"type": "PRIMITIVE", "primitive": {"id": "4503a4c6-42f7-45a1-a1d4-ed69699cf5e1", "version": "0.4.0", "python_path": "d3m.primitives.tods.data_processing.extract_columns_by_semantic_types", "name": "Extracts columns by semantic type", "digest": "e165d114bca8e1e47f349fffbbf2cf8872f0222328287e24acd8ac8c1cf2511c"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"semantic_types": {"type": "VALUE", "data": ["https://metadata.datadrivendiscovery.org/types/TrueTarget"]}}}, {"type": "PRIMITIVE", "primitive": {"id": "642de2e7-5590-3cab-9266-2a53c326c461", "version": "0.0.1", "python_path": "d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler", "name": "Axis_wise_scale"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "67e7fcdf-d645-3417-9aa4-85cd369487d9", "version": "0.0.1", "python_path": "d3m.primitives.tods.detection_algorithm.pyod_ae", "name": "TODS.anomaly_detection_primitives.AutoEncoder"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "8d38b340-f83f-4877-baaa-162f8e551736", "version": "0.3.0", "python_path": "d3m.primitives.tods.data_processing.construct_predictions", "name": "Construct pipeline predictions output", "digest": "c8a5cb30cc5b6742dbf024cbd54d5283937864c5d5dd9f3d0fb242fa406b1736"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.5.produce"}, "reference": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "aa2c6d9f9a56d76f5f2e8bb29ad5b21b43970a2acb7a7a7695e057bfcd1fdabd"}
+{"id": "bfd8aedf-36be-4dad-af8a-c324a03db5f9", "schema": "https://metadata.datadrivendiscovery.org/schemas/v0/pipeline.json", "created": "2021-02-13T17:02:35.500457Z", "inputs": [{"name": "inputs"}], "outputs": [{"data": "steps.6.produce", "name": "output predictions"}], "steps": [{"type": "PRIMITIVE", "primitive": {"id": "c78138d9-9377-31dc-aee8-83d9df049c60", "version": "0.3.0", "python_path": "d3m.primitives.tods.data_processing.dataset_to_dataframe", "name": "Extract a DataFrame from a Dataset"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "inputs.0"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "81235c29-aeb9-3828-911a-1b25319b6998", "version": "0.6.0", "python_path": "d3m.primitives.tods.data_processing.column_parser", "name": "Parses strings into their types"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "a996cd89-ddf0-367f-8e7f-8c013cbc2891", "version": "0.4.0", "python_path": "d3m.primitives.tods.data_processing.extract_columns_by_semantic_types", "name": "Extracts columns by semantic type"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"semantic_types": {"type": "VALUE", "data": ["https://metadata.datadrivendiscovery.org/types/Attribute"]}}}, {"type": "PRIMITIVE", "primitive": {"id": "a996cd89-ddf0-367f-8e7f-8c013cbc2891", "version": "0.4.0", "python_path": "d3m.primitives.tods.data_processing.extract_columns_by_semantic_types", "name": "Extracts columns by semantic type"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.0.produce"}}, "outputs": [{"id": "produce"}], "hyperparams": {"semantic_types": {"type": "VALUE", "data": ["https://metadata.datadrivendiscovery.org/types/TrueTarget"]}}}, {"type": "PRIMITIVE", "primitive": {"id": "642de2e7-5590-3cab-9266-2a53c326c461", "version": "0.0.1", "python_path": "d3m.primitives.tods.timeseries_processing.transformation.axiswise_scaler", "name": "Axis_wise_scale"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.2.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "67e7fcdf-d645-3417-9aa4-85cd369487d9", "version": "0.0.1", "python_path": "d3m.primitives.tods.detection_algorithm.pyod_ae", "name": "TODS.anomaly_detection_primitives.AutoEncoder"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.4.produce"}}, "outputs": [{"id": "produce"}]}, {"type": "PRIMITIVE", "primitive": {"id": "2530840a-07d4-3874-b7d8-9eb5e4ae2bf3", "version": "0.3.0", "python_path": "d3m.primitives.tods.data_processing.construct_predictions", "name": "Construct pipeline predictions output"}, "arguments": {"inputs": {"type": "CONTAINER", "data": "steps.5.produce"}, "reference": {"type": "CONTAINER", "data": "steps.1.produce"}}, "outputs": [{"id": "produce"}]}], "digest": "01ad8ccf817150186ca15157a4f02ee1f738582137321a8a5a4a3252832ce555"}

primitive_tests/feature_analysis/TruncatedSVD_pipeline.py

@@ -29,12 +29,12 @@ pipeline_description.add_step(step_2)
 
 # Step 3: TruncatedSVD
 step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.feature_analysis.truncated_svd'))
-step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.3.produce')
-step_3.add_output('produce')
 step_3.add_hyperparameter(name = 'n_components', argument_type=ArgumentType.VALUE, data = 3)
 step_3.add_hyperparameter(name = 'use_columns', argument_type=ArgumentType.VALUE, data = (2, 3, 4, 5))
 step_3.add_hyperparameter(name = 'return_result', argument_type=ArgumentType.VALUE, data = 'append')
 step_3.add_hyperparameter(name = 'use_semantic_types', 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')
 pipeline_description.add_step(step_3)
 
 # Final Output
@@ -44,4 +44,4 @@ pipeline_description.add_output(name='output predictions', data_reference='steps
 data = pipeline_description.to_json()
 with open('example_pipeline.json', 'w') as f:
     f.write(data)
-    print(data)
+    print(data)

primitive_tests/test.sh

@@ -1,12 +1,13 @@
 #!/bin/bash
 
 #modules="data_processing timeseries_processing feature_analysis detection_algorithms reinforcement"
-modules="data_processing timeseries_processing"
+#modules="data_processing timeseries_processing"
+modules="feature_analysis"
 #test_scripts=$(ls primitive_tests | grep -v -f tested_file.txt)
 
 for module in $modules
 do
-	test_scripts=$(ls $module)
+	test_scripts=$(ls $module | grep -v -f tested_file.txt)
 
 	for file in $test_scripts
 	do

tods/feature_analysis/AutoCorrelation.py

@@ -220,13 +220,17 @@ class AutoCorrelationPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outp
 		'__author__': "DATA Lab @Texas A&M University",
 		'name': "AutoCorrelation of values",
 		'python_path': 'd3m.primitives.tods.feature_analysis.auto_correlation',
-		'source': {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu',
-				   'uris': ['https://gitlab.com/lhenry15/tods/-/blob/Yile/anomaly-primitives/anomaly_primitives/AutoCorrelation.py']},
-		'algorithm_types': [metadata_base.PrimitiveAlgorithmType.AUTOCORRELATION,], 
+		'source': {
+                    'name': "DATALAB @Taxes A&M University", 
+                    'contact': 'mailto:khlai037@tamu.edu',		   
+                },
+		'version': '0.0.2',		
+		'hyperparams_to_tune': ['unbiased', 'nlags', 'qstat', 'fft', 'alpha', 'missing'],
+		'algorithm_types': [
+                    metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+                ], 
 		'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
 		'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'AutocorrelationPrimitive')),
-		'hyperparams_to_tune': ['unbiased', 'nlags', 'qstat', 'fft', 'alpha', 'missing'],
-		'version': '0.0.2',		
 		})
 
 	def __init__(self, *,

tods/feature_analysis/BKFilter.py

@@ -7,6 +7,7 @@ import sklearn
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -154,17 +155,21 @@ class BKFilterPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hy
         Decides what semantic type to attach to generated attributes'
     """
 
-    __author__: "DATA Lab at Texas A&M University"
     metadata = metadata_base.PrimitiveMetadata({ 
+         "__author__": "DATA Lab at Texas A&M University",
          "name": "Baxter-King Filter Primitive",
          "python_path": "d3m.primitives.tods.feature_analysis.bk_filter",
-         "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 
-         'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/Junjie/anomaly-primitives/anomaly_primitives/DuplicationValidation.py']},
-         "algorithm_types": [metadata_base.PrimitiveAlgorithmType.BK_FILTER,],
-         "primitive_family": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-         "id": "b2bfadc5-dbca-482c-b188-8585e5f245c4",
+         "source": {
+             'name': 'DATA Lab at Texas A&M University', 
+             'contact': 'mailto:khlai037@tamu.edu', 
+         },
          "hyperparams_to_tune": ['low', 'high', 'K'],
          "version": "0.0.1",
+         "algorithm_types": [
+             metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+         ],
+         "primitive_family": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'BKFilterPrimitive')),
     })
 
 

tods/feature_analysis/DiscreteCosineTransform.py

@@ -7,6 +7,7 @@ from d3m import container, utils
 from d3m.base import utils as base_utils
 from d3m.metadata import base as metadata_base, hyperparams
 from d3m.primitive_interfaces import base, transformer
+import uuid
 
 import logging
 import math
@@ -212,27 +213,21 @@ class DiscreteCosineTransformPrimitive(transformer.TransformerPrimitiveBase[Inpu
 
     """
 
-    __author__ = "Data Lab"
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            "__author__ " : "DATA Lab at Texas A&M University",
+    metadata = metadata_base.PrimitiveMetadata({
+            "__author__ " : "DATA Lab @ Texas A&M University",
             'name': "Discrete Cosine Transform",
             'python_path': 'd3m.primitives.tods.feature_analysis.discrete_cosine_transform',
             'source': {
                 'name': 'DATA Lab at Texas A&M University',
                 'contact': 'mailto:khlai037@tamu.edu',
-                'uris': [
-                    'https://gitlab.com/lhenry15/tods.git',
-                    'https://gitlab.com/lhenry15/tods/-/blob/purav/anomaly-primitives/anomaly_primitives/DiscreteCosineTransform.py',
-                ],
             },
+            'hyperparameters_to_tune':['n','norm','axis','type_'],
+            'version': '0.0.1',
             'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DISCRETE_COSINE_TRANSFORM,
+                metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
             ],
             'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-            'id': '584fa7d5-39cc-4cf8-8d5b-5f3a2648f767',
-            'hyperparameters_to_tune':['n','norm','axis','type_'],
-            'version': '0.0.1',
+	    'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'DiscreteCosineTransformPrimitive')),
         },
     )
 

tods/feature_analysis/FastFourierTransform.py

@@ -7,6 +7,7 @@ from d3m import container, utils
 from d3m.base import utils as base_utils
 from d3m.metadata import base as metadata_base, hyperparams
 from d3m.primitive_interfaces import base, transformer
+import uuid
 
 import logging
 from cmath import polar
@@ -204,29 +205,22 @@ class FastFourierTransformPrimitive(transformer.TransformerPrimitiveBase[Inputs,
         Decides what semantic type to attach to generated attributes'
     """
     
-    __author__ = "Data Lab"
-    metadata = metadata_base.PrimitiveMetadata(
-    {
-        '__author__' : "DATA Lab at Texas A&M University",
+    metadata = metadata_base.PrimitiveMetadata({
+        '__author__' : "DATA Lab @ Texas A&M University",
         'name': "Fast Fourier Transform",
         'python_path': 'd3m.primitives.tods.feature_analysis.fast_fourier_transform',
         'source': {
-            'name': 'DATA Lab at Texas A&M University',
+            'name': 'DATA Lab @ Texas A&M University',
             'contact': 'mailto:khlai037@tamu.edu',
-            'uris': [
-                'https://gitlab.com/lhenry15/tods.git',
-                'https://gitlab.com/lhenry15/tods/-/blob/purav/anomaly-primitives/anomaly_primitives/FastFourierTransform.py',
-            ],
         },
+        'hyperparameters_to_tune':['n','norm','axis'],
+        'version': '0.0.1',
         'algorithm_types': [
-            metadata_base.PrimitiveAlgorithmType.FAST_FOURIER_TRANSFORM,
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
         ],
         'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-        'id': '7bd269bc-de7e-47b8-8d6c-0bd46594d3cb',
-        'hyperparameters_to_tune':['n','norm','axis'],
-        'version': '0.0.1',
-    },
-    )
+	 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'FastFourierTransformPrimitive')),
+    })
 
     def __init__(self, *, hyperparams: Hyperparams) -> None:
         super().__init__(hyperparams=hyperparams)

tods/feature_analysis/HPFilter.py

@@ -7,6 +7,7 @@ import sklearn
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -131,17 +132,21 @@ class HPFilterPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hy
         Decides what semantic type to attach to generated attributes'
     """
 
-    __author__: "DATA Lab at Texas A&M University"
     metadata = metadata_base.PrimitiveMetadata({
+         "__author__": "DATA Lab at Texas A&M University",
          "name": "Hodrick-Prescott filter Primitive",
          "python_path": "d3m.primitives.tods.feature_analysis.hp_filter",
-         "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 
-         'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/Junjie/anomaly-primitives/anomaly_primitives/DuplicationValidation.py']},
-         "algorithm_types": [metadata_base.PrimitiveAlgorithmType.HP_FILTER,],
-         "primitive_family": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-         "id": "3af1be06-e45e-4ead-8523-4373264598e4",
+         "source": {
+             'name': 'DATA Lab at Texas A&M University', 
+             'contact': 'mailto:khlai037@tamu.edu', 
+         },
          "hyperparams_to_tune": ['lamb'],
          "version": "0.0.1",
+         "algorithm_types": [
+             metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+         ],
+         "primitive_family": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'HPFilterPrimitive')),
     })
 
 

tods/feature_analysis/NonNegativeMatrixFactorization.py

@@ -7,6 +7,7 @@ from d3m.primitive_interfaces.base import CallResult, DockerContainer
 from typing import cast, Dict, List, Union, Sequence, Optional, Tuple
 from collections import OrderedDict
 from scipy import sparse
+import uuid
 
 import nimfa
 import pandas as pd 
@@ -266,29 +267,22 @@ class NonNegativeMatrixFactorizationPrimitive(transformer.TransformerPrimitiveBa
 
 
 
-	__author__ = "Data Lab"
-	metadata = metadata_base.PrimitiveMetadata(
-	{
-		'__author__' : "DATA Lab at Texas A&M University",
-		'name': "Fast Fourier Transform",
-		'python_path': 'd3m.primitives.tods.feature_analysis.non_negative_matrix_factorization',
-		'source': {
-		'name': 'DATA Lab at Texas A&M University',
+	metadata = metadata_base.PrimitiveMetadata({
+	    '__author__' : "DATA Lab @ Texas A&M University",
+	    'name': "Fast Fourier Transform",
+	    'python_path': 'd3m.primitives.tods.feature_analysis.non_negative_matrix_factorization',
+	    'source': {
+		'name': 'DATA Lab @ Texas A&M University',
 		'contact': 'mailto:khlai037@tamu.edu',
-		'uris': [
-			'https://gitlab.com/lhenry15/tods.git',
-			'https://gitlab.com/lhenry15/tods/-/blob/purav/anomaly-primitives/anomaly_primitives/NonNegativeMatrixFactorization.py',
-		],
-		},
-		'algorithm_types': [
-			metadata_base.PrimitiveAlgorithmType.NON_NEGATIVE_MATRIX_FACTORIZATION,
-		],
-		'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-		'id': 'c7259da6-7ce6-42ad-83c6-15238679f5fa',
-		'hyperparameters_to_tune':['rank','update','objective','max_iter','learning_rate'],
-		'version': '0.0.1',
-	},
-	)
+	    },
+	    'hyperparameters_to_tune':['rank','update','objective','max_iter','learning_rate'],
+	    'version': '0.0.1',
+	    'algorithm_types': [
+		metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+	    ],
+	    'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	    'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'NonNegativeMatrixFactorizationPrimitive')),
+	})
 
 	def __init__(self, *, hyperparams: Hyperparams) -> None:
 		super().__init__(hyperparams=hyperparams)

tods/feature_analysis/SKTruncatedSVD.py

@@ -7,6 +7,7 @@ import sklearn
 import numpy
 import typing
 import time
+import uuid
 
 # Custom import commands if any
 from sklearn.decomposition.truncated_svd import TruncatedSVD
@@ -156,17 +157,21 @@ class SKTruncatedSVDPrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Outputs,
         Decides what semantic type to attach to generated attributes'
     """    
 
-    __author__: "DATA Lab at Texas A&M University"
     metadata = metadata_base.PrimitiveMetadata({
-         "name": "Truncated SVD",
-         "python_path": "d3m.primitives.tods.feature_analysis.truncated_svd",
-         "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 
-         'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/Junjie/anomaly-primitives/anomaly_primitives/SKTruncatedSVD.py']},
-         "algorithm_types": [metadata_base.PrimitiveAlgorithmType.SINGULAR_VALUE_DECOMPOSITION, ],
-         "primitive_family": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-         "id": "9231fde3-7322-3c41-b4cf-d00a93558c44",
-         "hyperparams_to_tune": ['n_components', 'algorithm', 'use_columns', 'exclude_columns', 'return_result', 'use_semantic_types', 'add_index_columns', 'error_on_no_input', 'return_semantic_type'],
-         "version": "0.0.1",
+        "__author__": "DATA Lab at Texas A&M University",
+        "name": "Truncated SVD",
+        "python_path": "d3m.primitives.tods.feature_analysis.truncated_svd",
+        "source": {
+            'name': 'DATA Lab at Texas A&M University', 
+            'contact': 'mailto:khlai037@tamu.edu', 
+        },
+        "hyperparams_to_tune": ['n_components', 'algorithm', 'use_columns', 'exclude_columns', 'return_result', 'use_semantic_types', 'add_index_columns', 'error_on_no_input', 'return_semantic_type'],
+        "version": "0.0.1",
+        "algorithm_types": [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        "primitive_family": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SKTruncatedSVDPrimitive')),
     })
 
     def __init__(self, *,

tods/feature_analysis/SpectralResidualTransform.py

@@ -9,6 +9,7 @@ from numpy import ndarray
 from collections import OrderedDict
 from scipy import sparse
 import os
+import uuid
 
 import numpy
 import typing
@@ -90,36 +91,24 @@ class SpectralResidualTransformPrimitive(transformer.TransformerPrimitiveBase[In
     """
     Primitive to find Spectral Residual Transform of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '88dda04b-090b-49a5-8035-279eb3be9cd9',
-            'version': '0.1.0',
-            'name': 'Time Series Spectral Residual',
-            'python_path': 'd3m.primitives.tods.feature_analysis.spectral_residual_transform',
-            'keywords': ['Time Series','FFT'],
-            "hyperparams_to_tune": ['avg_filter_dimension'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/SpectralResidualTransform.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Spectral Residual',
+        'python_path': 'd3m.primitives.tods.feature_analysis.spectral_residual_transform',
+        'keywords': ['Time Series','FFT'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+
+        },
+        "hyperparams_to_tune": ['avg_filter_dimension'],
+        'version': '0.1.0',
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'SpectralResidualTransformPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalAbsEnergy.py

@@ -9,6 +9,7 @@ from numpy import ndarray
 from collections import OrderedDict
 from scipy import sparse
 import os
+import uuid
 
 import numpy
 import typing
@@ -92,35 +93,24 @@ class StatisticalAbsEnergyPrimitive(transformer.TransformerPrimitiveBase[Inputs,
     """
 
     __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '73299ffe-d8bb-43c6-a6cc-9261f5e17a5e',
-            'version': '0.1.0',
-            'name': 'Time Series Statistical Abs Energy',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_abs_energy',
-            'keywords': ['Time Series','AbsEnergy'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalAbsEnergy.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Statistical Abs Energy',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_abs_energy',
+        'keywords': ['Time Series','AbsEnergy'],
+        'source': {
+            'name': 'DATA Lab at Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        "hyperparams_to_tune": ['window_size'],
+        'version': '0.1.0',
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalAbsEnergyPrimitive')),
+
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalAbsSum.py

@@ -9,6 +9,7 @@ from numpy import ndarray
 from collections import OrderedDict
 from scipy import sparse
 import os
+import uuid
 
 import numpy
 import typing
@@ -90,36 +91,23 @@ class StatisticalAbsSumPrimitive(transformer.TransformerPrimitiveBase[Inputs, Ou
     """
     Primitive to find abs_sum of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'fbc10e6f-d75b-4815-b4c8-5ad4f2f577db',
-            'version': '0.1.0',
-            'name': 'Time Series Absolute Sum',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_abs_sum',
-            'keywords': ['Time Series','AbsSum'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalAbsSum.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Absolute Sum',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_abs_sum',
+        'keywords': ['Time Series','AbsSum'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        "hyperparams_to_tune": ['window_size'],
+        'version': '0.1.0',
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalAbsSumPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalGmean.py

@@ -10,7 +10,7 @@ from collections import OrderedDict
 from scipy import sparse
 import os
 from scipy import stats
-
+import uuid
 import numpy
 import typing
 import time
@@ -93,36 +93,23 @@ class StatisticalGmeanPrimitive(transformer.TransformerPrimitiveBase[Inputs, Out
     Primitive to find gmean of time series .
     Will only take positive values as inputs .
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '6be88a7d-e72d-45c6-bd3b-3191d4eff623',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_g_mean',
-            'keywords': ['Time Series','Gmean'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalGmean.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_g_mean',
+        'keywords': ['Time Series','Gmean'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        "hyperparams_to_tune": ['window_size'],
+        'version': '0.1.0',
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalGmeanPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalHmean.py

@@ -10,6 +10,7 @@ from collections import OrderedDict
 from scipy import sparse
 import os
 from scipy import stats
+import uuid
 
 import numpy
 import typing
@@ -93,35 +94,24 @@ class StatisticalHmeanPrimitive(transformer.TransformerPrimitiveBase[Inputs, Out
      Harmonic mean only defined if all elements greater than or equal to zero
     """
     __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '7c4bf669-26f4-4756-8e00-c3e5e89fa43c',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_h_mean',
-            'keywords': ['Time Series','Hmean'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalHmean.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_h_mean',
+        'keywords': ['Time Series','Hmean'],
+        'source': {
+            'name': 'DATA Lab at Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalHmeanPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalKurtosis.py

@@ -10,6 +10,7 @@ from collections import OrderedDict
 from scipy import sparse
 import os
 from scipy import stats
+import uuid
 
 import numpy
 import typing
@@ -91,36 +92,23 @@ class StatisticalKurtosisPrimitive(transformer.TransformerPrimitiveBase[Inputs,
     """
     Primitive to find kurtosis of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'c86af521-05b6-4f7c-a7b9-929318d944fc',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_kurtosis',
-            'keywords': ['Time Series','Kurtosis'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalKurtosis.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_kurtosis',
+        'keywords': ['Time Series','Kurtosis'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalKurtosisPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalMaximum.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -91,35 +92,23 @@ class StatisticalMaximumPrimitive(transformer.TransformerPrimitiveBase[Inputs, O
     Primitive to find maximum of time series
     """
     __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '3b448057-ac26-4f1b-96b6-141782f16a54',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_maximum',
-            'keywords': ['Time Series','Maximum'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalMaximum.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_maximum',
+        'keywords': ['Time Series','Maximum'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        "hyperparams_to_tune": ['window_size'],
+        'version': '0.1.0',
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalMaximumPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalMean.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -90,36 +91,24 @@ class StatisticalMeanPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outp
     """
     Primitive to find mean of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'eaff2f35-978c-4530-a12e-061a5f0beacd',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_mean',
-            'keywords': ['Time Series','Mean'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalMean.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_mean',
+        'keywords': ['Time Series','Mean'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalMeanPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalMeanAbs.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -90,36 +91,23 @@ class StatisticalMeanAbsPrimitive(transformer.TransformerPrimitiveBase[Inputs, O
     """
     Primitive to find mean_abs of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'c397f0b2-45da-4263-8cca-b4e1a9502918',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_mean_abs',
-            'keywords': ['Time Series','MeanAbs'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalMeanAbs.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_mean_abs',
+        'keywords': ['Time Series','MeanAbs'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalMeanAbsPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalMeanAbsTemporalDerivative.py

@@ -9,6 +9,7 @@ from numpy import ndarray
 from collections import OrderedDict
 from scipy import sparse
 import os
+import uuid
 
 import numpy
 import typing
@@ -90,36 +91,24 @@ class StatisticalMeanAbsTemporalDerivativePrimitive(transformer.TransformerPrimi
     """
     Primitive to find mean_abs_temporal_derivative of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'eb571238-6229-4fe4-94b3-684f043e4dbf',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_mean_abs_temporal_derivative',
-            'keywords': ['Time Series','MeanAbsTemporalDerivative'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalMeanAbsTemporalDerivative.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_mean_abs_temporal_derivative',
+        'keywords': ['Time Series','MeanAbsTemporalDerivative'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalMeanAbsTemporalDerivativePrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalMeanTemporalDerivative.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -90,36 +91,24 @@ class StatisticalMeanTemporalDerivativePrimitive(transformer.TransformerPrimitiv
     """
     Primitive to find mean_temporal_derivative of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'bc051fbb-836b-414e-ad3e-5bf29c9f78f1',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_mean_temporal_derivative',
-            'keywords': ['Time Series','MeanTemporalDerivative'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalMeanTemporalDerivative.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_mean_temporal_derivative',
+        'keywords': ['Time Series','MeanTemporalDerivative'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalMeanTemporalDerivativePrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalMedian.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -90,36 +91,24 @@ class StatisticalMedianPrimitive(transformer.TransformerPrimitiveBase[Inputs, Ou
     """
     Primitive to find median of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '9f05a450-c1f0-49f6-971b-dcc3789174d0',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_median',
-            'keywords': ['Time Series','Median'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalMedian.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_median',
+        'keywords': ['Time Series','Median'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        "hyperparams_to_tune": ['window_size'],
+        'version': '0.1.0',
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalMedianPrimitive')),
+
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalMedianAbsoluteDeviation.py

@@ -14,6 +14,7 @@ from scipy import stats
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -91,36 +92,24 @@ class StatisticalMedianAbsoluteDeviationPrimitive(transformer.TransformerPrimiti
     """
     Primitive to find median_absolute_deviation of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '36e7d739-72c3-4e6e-91b8-b2b64cbe4e12',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_median_abs_deviation',
-            'keywords': ['Time Series','MedianAbsoluteDeviation'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalMedianAbsoluteDeviation.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_median_abs_deviation',
+        'keywords': ['Time Series','MedianAbsoluteDeviation'],
+        'source': {
+            'name': 'DATA Lab at Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalMedianAbsoluteDeviationPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalMinimum.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -90,36 +91,24 @@ class StatisticalMinimumPrimitive(transformer.TransformerPrimitiveBase[Inputs, O
     """
     Primitive to find minimum of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '255955d0-1d64-433b-b9f0-e2a1b679be45',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_minimum',
-            'keywords': ['Time Series','Minimum'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalMinimum.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_minimum',
+        'keywords': ['Time Series','Minimum'],
+        'source': {
+            'name': 'DATA Lab at Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalMinimumPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalSkew.py

@@ -14,6 +14,7 @@ from scipy import stats
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -91,36 +92,24 @@ class StatisticalSkewPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outp
     """
     Primitive to find skew of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'cd154af5-8f98-480a-8a72-6a22365c3c6f',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_skew',
-            'keywords': ['Time Series','Skew'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalSkew.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_skew',
+        'keywords': ['Time Series','Skew'],
+        'source': {
+            'name': 'DATA Lab at Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalSkewPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalStd.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -90,36 +91,24 @@ class StatisticalStdPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outpu
     """
     Primitive to find std of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '652fc98a-8bd9-45a2-8005-dc781bf0c136',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_std',
-            'keywords': ['Time Series','Std'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalStd.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_std',
+        'keywords': ['Time Series','Std'],
+        'source': {
+            'name': 'DATA Lab at Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalStdPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalVar.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -90,36 +91,23 @@ class StatisticalVarPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outpu
     """
     Primitive to find var of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '9b237f3f-c638-44f4-adb1-f3f24a173711',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_var',
-            'keywords': ['Time Series','Var'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalVar.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_var',
+        'keywords': ['Time Series','Var'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalVarPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalVariation.py

@@ -14,6 +14,7 @@ from scipy import stats
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -91,36 +92,25 @@ class StatisticalVariationPrimitive(transformer.TransformerPrimitiveBase[Inputs,
     """
     Primitive to find variation of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'ea6e852f-164b-4245-b5e6-02fde55c5491',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_variation',
-            'keywords': ['Time Series','Variation'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalVariation.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_variation',
+        'keywords': ['Time Series','Variation'],
+        'source': {
+            'name': 'DATA Lab at Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE,
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalVariationPrimitive')),
+
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalVecSum.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -90,36 +91,24 @@ class StatisticalVecSumPrimitive(transformer.TransformerPrimitiveBase[Inputs, Ou
     """
     Primitive to find vec_sum of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'a5ff2fc8-657e-4c4f-8a4e-6949dd37bf9c',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_vec_sum',
-            'keywords': ['Time Series','VecSum'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalVecSum.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_vec_sum',
+        'keywords': ['Time Series','VecSum'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalVecSumPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalWillisonAmplitude.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -94,36 +95,24 @@ class StatisticalWillisonAmplitudePrimitive(transformer.TransformerPrimitiveBase
     """
     Primitive to find willison amplitude of time series
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': 'f1dee9fb-7e3b-499d-a559-7979fa4a2e1c',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_willison_amplitude',
-            'keywords': ['Time Series','WillisonAmplitude'],
-            "hyperparams_to_tune": ['window_size'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalWillisonAmplitude.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_willison_amplitude',
+        'keywords': ['Time Series','WillisonAmplitude'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        "hyperparams_to_tune": ['window_size'],
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalWillisonAmplitudePrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/StatisticalZeroCrossing.py

@@ -13,6 +13,7 @@ import os
 import numpy
 import typing
 import time
+import uuid
 
 from d3m import container
 from d3m.primitive_interfaces import base, transformer
@@ -86,35 +87,23 @@ class StatisticalZeroCrossingPrimitive(transformer.TransformerPrimitiveBase[Inpu
     """
     Primitive to find zero_crossing of time series. A column indicating zero crossing on ith row . 1 indicates crossing 0 is for normal
     """
-    __author__ = "DATA Lab at Texas A&M University",
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            'id': '1064c78f-37e2-45a1-94a3-401a6726c220',
-            'version': '0.1.0',
-            'name': 'Time Series Decompostional',
-            'python_path': 'd3m.primitives.tods.feature_analysis.statistical_zero_crossing',
-            'keywords': ['Time Series','ZeroCrossing'],
-            'source': {
-                'name': 'DATA Lab at Texas A&M University',
-                'uris': ['https://gitlab.com/lhenry15/tods.git','https://gitlab.com/lhenry15/tods/-/blob/devesh/tods/feature_analysis/StatisticalZeroCrossing.py'],
-                'contact': 'mailto:khlai037@tamu.edu'
-
-            },
-            'installation': [
-                {'type': metadata_base.PrimitiveInstallationType.PIP,
-                 'package_uri': 'git+https://gitlab.com/lhenry15/tods.git@{git_commit}#egg=TODS'.format(
-                     git_commit=d3m_utils.current_git_commit(os.path.dirname(__file__)),
-                 ),
-                 }
-
-            ],
-            'algorithm_types': [
-                metadata_base.PrimitiveAlgorithmType.DATA_PROFILING,
-            ],
-            'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-
-        }
-    )
+
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        'name': 'Time Series Decompostional',
+        'python_path': 'd3m.primitives.tods.feature_analysis.statistical_zero_crossing',
+        'keywords': ['Time Series','ZeroCrossing'],
+        'source': {
+            'name': 'DATA Lab @ Texas A&M University',
+            'contact': 'mailto:khlai037@tamu.edu'
+        },
+        'version': '0.1.0',
+        'algorithm_types': [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'StatisticalZeroCrossingPrimitive')),
+    })
 
     def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:
         """

tods/feature_analysis/TRMF.py

@@ -7,6 +7,7 @@ import sklearn
 import numpy as np
 import typing
 import time
+import uuid
 
 # Custom import commands if any
 from sklearn.decomposition.truncated_svd import TruncatedSVD
@@ -222,17 +223,21 @@ class TRMFPrimitive(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hyperp
     Which can be found there: http://www.cs.utexas.edu/~rofuyu/papers/tr-mf-nips.pdf
     """
 
-    __author__: "DATA Lab at Texas A&M University"
     metadata = metadata_base.PrimitiveMetadata({
-         "name": "Temporal Regularized Matrix Factorization Primitive",
-         "python_path": "d3m.primitives.tods.feature_analysis.trmf",
-         "source": {'name': 'DATA Lab at Texas A&M University', 'contact': 'mailto:khlai037@tamu.edu', 
-         'uris': ['https://gitlab.com/lhenry15/tods.git', 'https://gitlab.com/lhenry15/tods/-/blob/Junjie/anomaly-primitives/anomaly_primitives/TRMF.py']},
-         "algorithm_types": [metadata_base.PrimitiveAlgorithmType.TEMPORAL_REGULARIZED_MATRIX_FACTORIZATION, ],
-         "primitive_family": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
-         "id": "d6be6941-61d0-4cbd-85ef-a10c86aa40b1",
-         "hyperparams_to_tune": ['lags', 'K', 'lambda_f', 'lambda_x', 'lambda_w', 'alpha', 'eta', 'max_iter', 'F_step', 'X_step', 'W_step'],
-         "version": "0.0.1",
+        "__author__": "DATA Lab @ Texas A&M University",
+        "name": "Temporal Regularized Matrix Factorization Primitive",
+        "python_path": "d3m.primitives.tods.feature_analysis.trmf",
+        "source": {
+            'name': 'DATA Lab @ Texas A&M University', 
+            'contact': 'mailto:khlai037@tamu.edu', 
+        },
+        "version": "0.0.1",
+        "hyperparams_to_tune": ['lags', 'K', 'lambda_f', 'lambda_x', 'lambda_w', 'alpha', 'eta', 'max_iter', 'F_step', 'X_step', 'W_step'],
+        "algorithm_types": [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        "primitive_family": metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'TRMFPrimitive')),
     })
 
         

tods/feature_analysis/WaveletTransform.py

@@ -174,20 +174,22 @@ class WaveletTransformPrimitive(transformer.TransformerPrimitiveBase[Inputs, Out
         None
     """
 
-    __author__ = "DATALAB @Taxes A&M University"
-    metadata = metadata_base.PrimitiveMetadata(
-        {
-            "name": "Wavelet_transformation",
-            "python_path": "d3m.primitives.tods.feature_analysis.wavelet_transform",
-            "source": {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu',
-                       'uris': ['https://gitlab.com/lhenry15/tods.git']},
-            "algorithm_types": [metadata_base.PrimitiveAlgorithmType.FREQUENCY_TRANSFORM, ],
-            "primitive_family": metadata_base.PrimitiveFamily.FEATURE_EXTRACTION,
-            "version": "0.0.1",
-            "hyperparams_to_tune": ['wavelet', 'mode', 'axis', 'level'],
-            "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'WaveletTransformer')),
+    metadata = metadata_base.PrimitiveMetadata({
+        "__author__": "DATA Lab @ Texas A&M University",
+        "name": "Wavelet_transformation",
+        "python_path": "d3m.primitives.tods.feature_analysis.wavelet_transform",
+        "source": {
+            'name': "DATA Lab @ Taxes A&M University", 
+            'contact': 'mailto:khlai037@tamu.edu',
         },
-    )
+        "version": "0.0.1",
+        "hyperparams_to_tune": ['wavelet', 'mode', 'axis', 'level'],
+        "algorithm_types": [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        "primitive_family": metadata_base.PrimitiveFamily.FEATURE_EXTRACTION,
+        "id": str(uuid.uuid3(uuid.NAMESPACE_DNS, 'WaveletTransformer')),
+    })
 
     def __init__(self, *, hyperparams: Hyperparams) -> None:
         super().__init__(hyperparams=hyperparams) # , random_seed=random_seed, docker_containers=docker_containers)

tods/timeseries_processing/HoltSmoothing.py

@@ -112,20 +112,20 @@ class HoltSmoothingPrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Outputs, P
     """
     
     metadata = metadata_base.PrimitiveMetadata({ 
-         "__author__": "DATA Lab @ Texas A&M University",
-         "name": "statsmodels.preprocessing.HoltSmoothing",
-         "python_path": "d3m.primitives.tods.timeseries_processing.transformation.holt_smoothing",
-         "source": {
-             'name': 'DATA Lab @ Texas A&M University', 
-             'contact': 'mailto:khlai037@tamu.edu', 
-         },
-         "version": "0.0.1",
-         "hyperparams_to_tune": ['endog','use_columns'],
-         "algorithm_types": [
-             metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
-         ],
-         "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING,
-	 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'HoltSmoothingPrimitive')),
+        "__author__": "DATA Lab @ Texas A&M University",
+        "name": "statsmodels.preprocessing.HoltSmoothing",
+        "python_path": "d3m.primitives.tods.timeseries_processing.transformation.holt_smoothing",
+        "source": {
+            'name': 'DATA Lab @ Texas A&M University', 
+            'contact': 'mailto:khlai037@tamu.edu', 
+        },
+        "version": "0.0.1",
+        "hyperparams_to_tune": ['endog','use_columns'],
+        "algorithm_types": [
+            metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
+        ],
+        "primitive_family": metadata_base.PrimitiveFamily.DATA_PREPROCESSING,
+	'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'HoltSmoothingPrimitive')),
      })
 
     def __init__(self, *,