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
