added sk interface for data_processing and timeseries_processing

tods/sk_interface/data_processing/CategoricalToBinary_skinterface.py

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+import numpy as np 
+from ..base import BaseSKI
+from tods.data_processing.CategoricalToBinary import CategoricalToBinaryPrimitive
+
+class CategoricalToBinarySKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=CategoricalToBinaryPrimitive, **hyperparams)
+		self.fit_available = False
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/data_processing/ContinuityValidation_skinterface.py

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+import numpy as np 
+from ..base import BaseSKI
+from tods.data_processing.ContinuityValidation import ContinuityValidationPrimitive
+
+class ContinuityValidationSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=ContinuityValidationPrimitive, **hyperparams)
+		self.fit_available = False
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/data_processing/DuplicationValidation_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.data_processing.DuplicationValidation import DuplicationValidationPrimitive
+
+class DuplicationValidationSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=DuplicationValidationPrimitive, **hyperparams)
+		self.fit_available = False
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/data_processing/SKImputer_skinterface.py

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+import numpy as np 
+from ..base import BaseSKI
+from tods.data_processing.SKImputer import SKImputerPrimitive
+
+class SKImputerSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=SKImputerPrimitive, **hyperparams)
+		self.fit_available = True
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/data_processing/TimeIntervalTransform_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.data_processing.TimeIntervalTransform import TimeIntervalTransformPrimitive
+
+class TimeIntervalTransformSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=TimeIntervalTransformPrimitive, **hyperparams)
+		self.fit_available = False
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/data_processing/TimeStampValidation_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.data_processing.TimeStampValidation import TimeStampValidationPrimitive
+
+class TimeStampValidationSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=TimeStampValidationPrimitive, **hyperparams)
+		self.fit_available = False
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/script/data_processing_skinterface_generation.py

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+
+import re
+import os
+
+with open('../utils/entry_points/entry_points_data_processing.txt','r',encoding='utf-8') as f:
+    entry_file = f.read()
+
+output_dir = '../data_processing'
+
+fit_available_primitives = ['SKImputerPrimitive']
+
+primitive_folder_start_loc_buf = [i.start()+2 for i in re.finditer('=', entry_file)]
+primitive_start_loc_buf = [i.start()+1 for i in re.finditer(':', entry_file)]
+primitive_end_loc_buf = [i.start() for i in re.finditer('\n', entry_file)]
+
+for primitive_index, primitive_start_loc in enumerate(primitive_start_loc_buf):
+
+    primitive_folder_start_loc = primitive_folder_start_loc_buf[primitive_index]
+    primitive_end_loc = primitive_end_loc_buf[primitive_index]
+
+    primitive_folder = entry_file[primitive_folder_start_loc:primitive_start_loc-1]
+    primitive_name = entry_file[primitive_start_loc:primitive_end_loc]
+    # print(entry_file[primitive_folder_start_loc:primitive_start_loc-1])
+    # print(entry_file[primitive_start_loc:primitive_end_loc])
+
+    import_line1 = 'import numpy as np \nfrom ..base import BaseSKI\n'
+    import_line2 = 'from ' + primitive_folder + ' import ' + primitive_name + '\n\n'
+    # print(import_line)
+
+    class_name = primitive_name.replace('Primitive', 'SKI')
+    class_line1 = 'class ' + class_name + '(BaseSKI):\n\tdef __init__(self, **hyperparams):\n\t\tsuper().__init__(primitive='
+    class_line2 = primitive_name + ', **hyperparams)\n'
+
+    if primitive_name in fit_available_primitives:
+        class_line3 = '\t\tself.fit_available = True\n\t\tself.predict_available = False\n\t\tself.produce_available = True\n'
+    else:
+        class_line3 = '\t\tself.fit_available = False\n\t\tself.predict_available = False\n\t\tself.produce_available = True\n'
+
+    python_content = import_line1 + import_line2 + class_line1 + class_line2 + class_line3
+    python_name = primitive_name.replace('Primitive', '_skinterface.py')
+    with open(os.path.join(output_dir, python_name), 'w', encoding='utf-8') as f:
+        f.write(python_content)
+    print(os.path.join(output_dir, python_name))
+    print(python_content)
+
+

tods/sk_interface/script/timeseries_processing_skinterface_generation.py

@@ -0,0 +1,46 @@
+
+import re
+import os
+
+with open('../utils/entry_points/entry_points_timeseries_processing.txt','r',encoding='utf-8') as f:
+    entry_file = f.read()
+
+output_dir = '../timeseries_processing'
+
+fit_unavailable_primitives = ['SubsequenceSegmentationPrimitive', 'TimeSeriesSeasonalityTrendDecompositionPrimitive', 'SKAxiswiseScalerPrimitive']
+
+primitive_folder_start_loc_buf = [i.start()+2 for i in re.finditer('=', entry_file)]
+primitive_start_loc_buf = [i.start()+1 for i in re.finditer(':', entry_file)]
+primitive_end_loc_buf = [i.start() for i in re.finditer('\n', entry_file)]
+
+for primitive_index, primitive_start_loc in enumerate(primitive_start_loc_buf):
+
+    primitive_folder_start_loc = primitive_folder_start_loc_buf[primitive_index]
+    primitive_end_loc = primitive_end_loc_buf[primitive_index]
+
+    primitive_folder = entry_file[primitive_folder_start_loc:primitive_start_loc-1]
+    primitive_name = entry_file[primitive_start_loc:primitive_end_loc]
+    # print(entry_file[primitive_folder_start_loc:primitive_start_loc-1])
+    # print(entry_file[primitive_start_loc:primitive_end_loc])
+
+    import_line1 = 'import numpy as np \nfrom ..base import BaseSKI\n'
+    import_line2 = 'from ' + primitive_folder + ' import ' + primitive_name + '\n\n'
+    # print(import_line)
+
+    class_name = primitive_name.replace('Primitive', 'SKI')
+    class_line1 = 'class ' + class_name + '(BaseSKI):\n\tdef __init__(self, **hyperparams):\n\t\tsuper().__init__(primitive='
+    class_line2 = primitive_name + ', **hyperparams)\n'
+
+    if primitive_name in fit_unavailable_primitives:
+        class_line3 = '\t\tself.fit_available = False\n\t\tself.predict_available = False\n\t\tself.produce_available = True\n'
+    else:
+        class_line3 = '\t\tself.fit_available = True\n\t\tself.predict_available = False\n\t\tself.produce_available = True\n'
+
+    python_content = import_line1 + import_line2 + class_line1 + class_line2 + class_line3
+    python_name = primitive_name.replace('Primitive', '_skinterface.py')
+    with open(os.path.join(output_dir, python_name), 'w', encoding='utf-8') as f:
+        f.write(python_content)
+    print(os.path.join(output_dir, python_name))
+    print(python_content)
+
+

tods/sk_interface/timeseries_processing/HoltSmoothing_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.HoltSmoothing import HoltSmoothingPrimitive
+
+class HoltSmoothingSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=HoltSmoothingPrimitive, **hyperparams)
+		self.fit_available = True
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/HoltWintersExponentialSmoothing_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.HoltWintersExponentialSmoothing import HoltWintersExponentialSmoothingPrimitive
+
+class HoltWintersExponentialSmoothingSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=HoltWintersExponentialSmoothingPrimitive, **hyperparams)
+		self.fit_available = True
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/MovingAverageTransformer_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.MovingAverageTransformer import MovingAverageTransformerPrimitive
+
+class MovingAverageTransformerSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=MovingAverageTransformerPrimitive, **hyperparams)
+		self.fit_available = True
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/SKAxiswiseScaler_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.SKAxiswiseScaler import SKAxiswiseScalerPrimitive
+
+class SKAxiswiseScalerSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=SKAxiswiseScalerPrimitive, **hyperparams)
+		self.fit_available = False
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/SKPowerTransformer_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.SKPowerTransformer import SKPowerTransformerPrimitive
+
+class SKPowerTransformerSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=SKPowerTransformerPrimitive, **hyperparams)
+		self.fit_available = True
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/SKQuantileTransformer_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.SKQuantileTransformer import SKQuantileTransformerPrimitive
+
+class SKQuantileTransformerSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=SKQuantileTransformerPrimitive, **hyperparams)
+		self.fit_available = True
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/SKStandardScaler_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.SKStandardScaler import SKStandardScalerPrimitive
+
+class SKStandardScalerSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=SKStandardScalerPrimitive, **hyperparams)
+		self.fit_available = True
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/SimpleExponentialSmoothing_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.SimpleExponentialSmoothing import SimpleExponentialSmoothingPrimitive
+
+class SimpleExponentialSmoothingSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=SimpleExponentialSmoothingPrimitive, **hyperparams)
+		self.fit_available = True
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/SubsequenceSegmentation_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.SubsequenceSegmentation import SubsequenceSegmentationPrimitive
+
+class SubsequenceSegmentationSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=SubsequenceSegmentationPrimitive, **hyperparams)
+		self.fit_available = False
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/timeseries_processing/TimeSeriesSeasonalityTrendDecomposition_skinterface.py

@@ -0,0 +1,10 @@
+import numpy as np 
+from ..base import BaseSKI
+from tods.timeseries_processing.TimeSeriesSeasonalityTrendDecomposition import TimeSeriesSeasonalityTrendDecompositionPrimitive
+
+class TimeSeriesSeasonalityTrendDecompositionSKI(BaseSKI):
+	def __init__(self, **hyperparams):
+		super().__init__(primitive=TimeSeriesSeasonalityTrendDecompositionPrimitive, **hyperparams)
+		self.fit_available = False
+		self.predict_available = False
+		self.produce_available = True

tods/sk_interface/utils/entry_points/entry_points_data_processing.txt

@@ -0,0 +1,6 @@
+tods.data_processing.time_interval_transform = tods.data_processing.TimeIntervalTransform:TimeIntervalTransformPrimitive
+tods.data_processing.categorical_to_binary = tods.data_processing.CategoricalToBinary:CategoricalToBinaryPrimitive
+tods.data_processing.timestamp_validation = tods.data_processing.TimeStampValidation:TimeStampValidationPrimitive
+tods.data_processing.duplication_validation = tods.data_processing.DuplicationValidation:DuplicationValidationPrimitive
+tods.data_processing.continuity_validation = tods.data_processing.ContinuityValidation:ContinuityValidationPrimitive
+tods.data_processing.impute_missing = tods.data_processing.SKImputer:SKImputerPrimitive

tods/sk_interface/utils/entry_points/entry_points_timeseries_processing.txt

@@ -0,0 +1,10 @@
+tods.timeseries_processing.transformation.axiswise_scaler = tods.timeseries_processing.SKAxiswiseScaler:SKAxiswiseScalerPrimitive
+tods.timeseries_processing.transformation.standard_scaler = tods.timeseries_processing.SKStandardScaler:SKStandardScalerPrimitive
+tods.timeseries_processing.transformation.power_transformer = tods.timeseries_processing.SKPowerTransformer:SKPowerTransformerPrimitive
+tods.timeseries_processing.transformation.quantile_transformer = tods.timeseries_processing.SKQuantileTransformer:SKQuantileTransformerPrimitive
+tods.timeseries_processing.transformation.moving_average_transform = tods.timeseries_processing.MovingAverageTransformer:MovingAverageTransformerPrimitive
+tods.timeseries_processing.transformation.simple_exponential_smoothing = tods.timeseries_processing.SimpleExponentialSmoothing:SimpleExponentialSmoothingPrimitive
+tods.timeseries_processing.transformation.holt_smoothing = tods.timeseries_processing.HoltSmoothing:HoltSmoothingPrimitive
+tods.timeseries_processing.transformation.subsequence_segmentation = tods.timeseries_processing.SubsequenceSegmentation:SubsequenceSegmentationPrimitive
+tods.timeseries_processing.transformation.holt_winters_exponential_smoothing = tods.timeseries_processing.HoltWintersExponentialSmoothing:HoltWintersExponentialSmoothingPrimitive
+tods.timeseries_processing.decomposition.time_series_seasonality_trend_decomposition = tods.timeseries_processing.TimeSeriesSeasonalityTrendDecomposition:TimeSeriesSeasonalityTrendDecompositionPrimitive