Fusion subcommand

The bigmler fusion subcommand generates all the resources needed to build a fusion model and use it to predict. The fusion model is a supervised learning method for solving both regression and classification problems. It’s a model composed of different supervised models, ensembles, deepnets, logistic regressions, linear regressions or fusions. The prediction obtained from a fusion will be an aggregation of the predictions of its component models. The aggregation will take into account the weight associated to each of the models in the fusion object. If no specific weight is given on creation, each model in the fusion will be assigned the same weight.

The simplest call to build a fusion is:

bigmler fusion  \
    --fusion-models deepnet/53b1f71437203f5ac30004ed,model/53b1f71437203f5ac32004e2 \
    --output-dir my_fusion

that creates the fusion object for the deepnet and model described in --fusiion-models. The fusion ID is stored in a fusions file in the directory specified in --output-dir.

As explained, different weights can be applied to the predictions of each model to generate the final prediction. To set these weights, you can use a --fusion-models-file option to point to the JSON file describing the models and their weights as explained in the API developers docs.

bigmler fusion --fusion-models-file components.json \
                --output-dir my_fusion

An existing fusion can also be used to predict.

bigmler fusion --fusion fusion/53b1f71437203f5ac30004cd \
               --test my_test_data.csv \
               --output my_predictions.csv

with this code, the my_test_data file contents are run through the fusion and a new prediction is asociated to each line in the CSV file. The results are stored in the my_predictions.csv file. The fusion information is downloaded to your local computer and the fusion predictions are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the predictions remotely, you can do so too

bigmler fusion --fusion fusion/53b1f71437203f5ac30004cd \
               --test my_test_data.csv \
               --output my_predictions.csv --remote

would create a remote source and dataset from the test file data, generate a batch prediction also remotely and finally download the result to your computer. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output. Other output configurations can be set by using the --batch-prediction-attributes option pointing to a JSON file that contains the desired attributes, like:

{"probabilities": true,
 "all_fields": true}

Fusion Subcommand Options

--fusion FUSION	BigML fusion Id
--fusions PATH	Path to a file containing fusion/ids. One fusion per line (e.g., fusion/4f824203ce80051)
--fusion-models MODELS	Comma-separated list of models to be aggregated in a fusion
--fusion-models-file PATH	Path to the JSON file that contains the models to be aggregated in a fusion and its associated weights
--no-fusion	No fusion will be generated
--fusion-attributes PATH	Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the fusion creation call
--fusion-file PATH	Path to a JSON file containing the fusion regression info