Seeq Interactions
- class seeq.addons.azureml.backend._run_investigation.RunInvestigation(input_signals: dict, result_name: str, az_model_name: str, az_model_version: str, start: Union[pandas._libs.tslibs.timedeltas.Timedelta, datetime.datetime], end: Union[pandas._libs.tslibs.timedeltas.Timedelta, datetime.datetime], grid: Optional[str] = '5 min', workbook: Optional[str] = 'Data Lab >> Azure ML Integration', worksheet: Optional[str] = 'From Azure ML Integration', datasource: Optional[str] = 'Azure ML', endpoint_uri: Optional[str] = None, aml_primary_key: Optional[str] = None, self_signed_certificate=True, quiet=True)[source]
Bases:
objectTakes the input parameters supplied by the user (typically, via Azure ML Integration UI), posts a request to the Azure ML model, gets a result signal back from Azure ML and pushes the result back to Seeq.
This class assumes that the Azure ML model returns only ONE signal.
- input_signals
A dictionary whose keys are the names of the input signals and whose values are the Seeq IDs of the input signals.
- Type
dict
- result_name
The name of the result signal that will be pushed to Seeq.
- Type
str
- az_model_name
Name of the Azure ML model used to compute the result signal.
- Type
str
- az_model_version
The version of the Azure ML model used to compute the result signal.
- Type
str
- start
The starting time for which to pull data with spy.pull.
- Type
str
- end
The end time for which to pull data with spy.pull.
- Type
str
- grid
A period to use for interpolation in the spy.pull call, such that all returned samples have the same timestamps.
- Type
str
- workbook
The ID of the Seeq workbook that all pushed items will be ‘scoped to’.
- Type
str
- worksheet
The name of a worksheet within the workbook to create/update that will render the result signal that has been pushed.
- Type
str
- datasource
The name of the datasource within which to contain all the pushed items.
- Type
str
- endpoint_uri
The endpoint identifier of the AzureML model used to compute the result signal.
- Type
str
- aml_primary_key
The primary key of the Azure ML endpoint
- Type
str
- quiet
If True, suppresses progress output. Note that when status is provided, the quiet setting of the Status object that is passed in takes precedence.
- Type
bool
- data
A DataFrame with timestamps as Index and input signals data as columns. This dataset is passed in the request to the endpoint_uri to compute the resulting signal.
- Type
pd.DataFrame
- result_signal
A DataFrame with timestamps as Index and one column with the data of the result signal
- Type
pd.DataFrame
- pushed_df
A DataFrame with the metadata for the result signal pushed, along with any errors and statistics about the operation.
- Type
pd.DataFrame
- error_info
Information on the most recent error that has occurred.
- Type
str
- run()[source]
Posts a request to the Azure ML endpoint_uri with the input data and, if successful, retrieves the serialized result signal
- static allow_self_signed_https(allowed)[source]
Checks whether to allow self-signed https certificates
- Parameters
allowed (bool) – If True, allows self-signed https certificates
- Returns
-
- Return type
None
- get_seeq_data()[source]
Pulls the input signals required for the Azure ML model from Seeq
- Returns
-
- Return type
None
- push_to_seeq()[source]
Pushes the result signal from Azure ML model to Seeq.
- Returns
-
- Return type
None