Project ml_suite¶
Invoke with: import icecube.ml_suite
Python I3Modules¶
- EventFeatureExtractorModule¶
EventFeatureExtractorModule
(Python I3Module)Builds an EventFeatureExtractor from a config file and applies to P frames.
- Param cfg_file:
Default =
None
,- Param IcePickServiceKey:
Default =
''
, Key for an IcePick in the context that this module should check before processing physics frames.- Param If:
Default =
None
, A python function… if this returns something that evaluates to True, Module runs, else it doesn’t- Param output_key:
Default =
'ml_suite_features'
,- Param plot_results:
Default =
False
, Plot results if true.
- ModelWrapper¶
ModelWrapper
(Python I3Module)General Model Wrapper.
- Param batch_size:
Default =
64
, The number of events to accumulate and pass through the NN in parallel. A higher batch size than 1 can usually improve reconstruction runtime, but will also increase the memory footprint.- Param data_transformer:
Default =
None
, Optionally, a data transformer may be provided. This must be a python callable that takes the feature tensor obtained from the event_feature_extractor as input and returns the transformed output. The data_transformer may, for instance, be used to transform IceCube data on an hexagonal grid, or to normalize the input data prior to passing to the neural network. The data_transformer may return a single numpy array, or a tuple or list of numpy arrays. There is no constraint on the number of tensors the data_transformer may return. However, the first axis of each tensor must always correspond to the batch dimension.- Param event_feature_extractor:
Default =
<Unprintable>
, The EventFeatureExtractor object or file path to a yaml configuration file defining the extractor. The EventFeatureExtractor will be used to compute. the input data to the NN.- Param IcePickServiceKey:
Default =
''
, Key for an IcePick in the context that this module should check before processing physics frames.- Param If:
Default =
None
, A python function… if this returns something that evaluates to True, Module runs, else it doesn’t- Param nn_model:
Default =
None
, The callable ML model. It can be a function or other callable with signature nn_model(input) -> prediction.- Param output_key:
Default =
'TFModelWrapperOutput'
, Frame key to which the result will be written.- Param output_names:
Default =
None
, If provided the predictions will named according to providedlist. Otherwise names will be: prediction_{:04d}- Param sub_event_stream:
Default =
None
, If provided, only process events from this sub event stream- Param write_runtime_info:
Default =
True
, Whether or not to write runtime estimates to the frame.
- TFModelWrapper¶
TFModelWrapper
(Python I3Module)Tensorflow Model Wrapper.
- Param batch_size:
Default =
64
, The number of events to accumulate and pass through the NN in parallel. A higher batch size than 1 can usually improve reconstruction runtime, but will also increase the memory footprint.- Param data_transformer:
Default =
None
, Optionally, a data transformer may be provided. This must be a python callable that takes the feature tensor obtained from the event_feature_extractor as input and returns the transformed output. The data_transformer may, for instance, be used to transform IceCube data on an hexagonal grid, or to normalize the input data prior to passing to the neural network. The data_transformer may return a single numpy array, or a tuple or list of numpy arrays. There is no constraint on the number of tensors the data_transformer may return. However, the first axis of each tensor must always correspond to the batch dimension.- Param event_feature_extractor:
Default =
<Unprintable>
, The EventFeatureExtractor object or file path to a yaml configuration file defining the extractor. The EventFeatureExtractor will be used to compute. the input data to the NN.- Param IcePickServiceKey:
Default =
''
, Key for an IcePick in the context that this module should check before processing physics frames.- Param If:
Default =
None
, A python function… if this returns something that evaluates to True, Module runs, else it doesn’t- Param nn_model:
Default =
None
, The callable ML model. It can be a function or other callable with signature nn_model(input) -> prediction.- Param output_key:
Default =
'TFModelWrapperOutput'
, Frame key to which the result will be written.- Param output_names:
Default =
None
, If provided the predictions will named according to providedlist. Otherwise names will be: prediction_{:04d}- Param sub_event_stream:
Default =
None
, If provided, only process events from this sub event stream- Param write_runtime_info:
Default =
True
, Whether or not to write runtime estimates to the frame.