def build_ui(cls): #define arguments that behave as function inputs inputs = [] inputs.append( ui.UIMultiItem(name='features', datatype=float, description="Data items to use as features")) inputs.append( ui.UIMultiItem(name='targets', datatype=float, description="Data items to use as targets")) inputs.append(ui.UISingle(name='threshold', datatype=float)) outputs = [] outputs.append( ui.UIFunctionOutMulti(name='predictions', datatype=float, cardinality_from='targets', description='Output predictions')) outputs.append( ui.UIFunctionOutMulti(name='alerts', datatype=bool, cardinality_from='targets', description='Alert outputs')) return (inputs, outputs)
def build_ui(cls): #define arguments that behave as function inputs inputs = [] inputs.append( ui.UIMultiItem(name='input_items', datatype=float, description="Data items adjust", output_item='output_items', is_output_datatype_derived=True)) inputs.append(ui.UIMultiItem(name='input_items_str', datatype=str)) outputs = [] return (inputs, outputs)
def build_ui(cls): """ Describe the sklearn model and target column """ # define arguments that behave as function inputs inputs = [ ui.UISingle(required=True, datatype=str, name='model_path', description='Path of sklearn pickle file'), ui.UIMultiItem( required=True, datatype=str, name='dependent_variables', description= "Columns to provide to the model's predict() function") ] # define arguments that behave as function outputs outputs = [ ui.UISingle(required=True, datatype=str, name='predicted_value', description='Name of the predicted column') ] return (inputs, outputs)
def build_ui(cls): #define arguments that behave as function inputs inputs = [] inputs.append( ui.UIMultiItem( name='input_items', datatype=float, description="Data items adjust", # output_item = 'output_item', is_output_datatype_derived=True)) inputs.append( ui.UISingle( name='wml_endpoint', datatype=str, description='Endpoint to WML service where model is hosted', tags=['TEXT'], required=True)) inputs.append( ui.UISingle(name='deployment_id', datatype=str, description='Deployment ID for WML model', tags=['TEXT'], required=True)) inputs.append( ui.UISingle(name='apikey', datatype=str, description='IBM Cloud API Key', tags=['TEXT'], required=True)) outputs = [] outputs.append(ui.UISingle(name='output_item', datatype=float)) return (inputs, outputs)
def build_ui(cls): # define arguments that behave as function inputs inputs = [] inputs.append( ui.UIMultiItem( name='input_items', datatype=float, description='Choose columns to multiply', required=True, )) outputs = [ui.UIFunctionOutSingle(name='output_item', datatype=float)] return (inputs, outputs)
def build_ui(cls): from iotfunctions import ui import datetime # define arguments that behave as function inputs inputs = [ ui.UIMultiItem(name='drop_if_NaN', datatype=float), ui.UISingleItem(name='timestamp_column', datatype=datetime.datetime), ui.UIMulti(name='keep_timestamp', datatype=str, values=['min', 'mean', 'max']) ] return (inputs, [ui.UIStatusFlag('filter_set')])
def build_ui(cls): #define arguments that behave as function inputs inputs = [] inputs.append( ui.UIMultiItem( name='input_items', datatype=float, description="Data items adjust", # output_item = 'output_item', is_output_datatype_derived=True)) # inputs.append(ui.UISingle(name='input_columns', # datatype=str, # description='Features to load from entity rows. Provide as list of comma seperated values like so - torque,speed,pressure', # tags=['TEXT'], # required=True # )) inputs.append( ui.UISingle( name='wml_endpoint', datatype=str, description='Endpoint to WML service where model is hosted', tags=['TEXT'], required=True)) inputs.append( ui.UISingle(name='instance_id', datatype=str, description='Instance ID for WML model', tags=['TEXT'], required=True)) inputs.append( ui.UISingle(name='deployment_id', datatype=str, description='Deployment ID for WML model', tags=['TEXT'], required=True)) inputs.append( ui.UISingle(name='apikey', datatype=str, description='IBM Cloud API Key', tags=['TEXT'], required=True)) # define arguments that behave as function outputs outputs = [] outputs.append(ui.UISingle(name='output_items', datatype=float)) return (inputs, outputs) outputs = [] return (inputs, outputs)
def build_ui(cls): inputs = [] inputs.append( ui.UIMultiItem(name='source', datatype=None, description=('Choose the data items' ' that you would like to' ' aggregate'), output_item='name', is_output_datatype_derived=True)) inputs.append( ui.UIExpression( name='expression', description='Use ${GROUP} to reference the current grain.' 'All Pandas Series methods can be used on the grain.' 'For example, ${GROUP}.max() - ${GROUP}.min().')) return (inputs, [])
def build_ui(cls): # define arguments that behave as function inputs inputs = [ ui.UIMultiItem(name='features', datatype=float, description='Predictive features'), ui.UISingle( name='saved_model_name', datatype=str, description='Name of the model to use with ' 'this forecaster. This model will be retrieved from model store' ) ] # define arguments that behave as function outputs outputs = [ ui.UIFunctionOutSingle(name='target', datatype=float, description='Predicted output') ] return inputs, outputs
def build_ui(cls): # Your function will UI built automatically for configuring it # This method describes the contents of the dialog that will be built # Account for each argument - specifying it as a ui object in the "inputs" or "outputs" list inputs = [] '''inputs.append(ui.UISingle(name='BPT1', datatype=float, description='BPT1'))''' inputs.append( ui.UIMultiItem(name='BPT1', datatype=float, description='BPT1')) inputs.append( ui.UIMultiItem(name='BPT2', datatype=float, description='BPT2')) inputs.append( ui.UIMultiItem(name='BPT3', datatype=float, description='BPT3')) inputs.append( ui.UIMultiItem(name='BPT4', datatype=float, description='BPT4')) inputs.append( ui.UIMultiItem(name='BPT5', datatype=float, description='BPT5')) inputs.append( ui.UIMultiItem(name='BPT6', datatype=float, description='BPT6')) inputs.append( ui.UIMultiItem(name='BPT7', datatype=float, description='BPT7')) inputs.append( ui.UIMultiItem(name='BPT8', datatype=float, description='BPT8')) inputs.append( ui.UIMultiItem(name='Powerup_Steam_Flow_Rate', datatype=float, description='Powerup Steam Flow Rate')) inputs.append( ui.UIMultiItem(name='Ratio_outlet_inlet_temp', datatype=float, description='Ratio of outlet inlet temp')) inputs.append( ui.UIMultiItem(name='Turbine_Inlet_Temperature', datatype=float, description='Turbine Inlet Temperature')) inputs.append( ui.UIMultiItem(name='Turbine_Outlet_Temperature', datatype=float, description='Turbine Outlet Temperature')) inputs.append( ui.UIMultiItem(name='Vibration', datatype=float, description='Vibration')) outputs = [ ui.UIFunctionOutSingle( name='prediction', datatype=float, description='Output item produced by function') ] return inputs, outputs