rail.interactive.estimation.algos.random_forest module

rail.interactive.estimation.algos.random_forest.random_forest_classifier(**kwargs)

Classifier that assigns tomographic bins based on random forest method

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The main run method for the classifier, should be implemented in the specific subclass.

This will attach the input_data to this CatClassifier (for introspection and provenance tracking).

Then it will call the run() and finalize() methods, which need to be implemented by the sub-classes.

The run() method will need to register the data that it creates to this Classifier by using self.add_data(‘output’, output_data).

Finally, this will return a TableHandle providing access to that output data.

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This function was generated from the function rail.estimation.algos.random_forest.RandomForestClassifier.classify

Parameters:

  • input_data (TableLike, required) – A dictionary of all input data

  • model (numpy.ndarray, required)

  • chunk_size (int, optional) – Number of objects per chunk for parallel processing or to evalute per loop in single node processing Default: 10000

  • hdf5_groupname (str, optional) – name of hdf5 group for data, if None, then set to ‘’ Default: photometry

  • id_name (str, optional) – Column name for the object ID in the input data, if empty the row index is used as the ID. Default:

  • class_bands (list, optional) – Which bands to use for classification Default: [‘r’, ‘i’, ‘z’]

  • band_map (dict, optional) – column names for the the bands Default: {‘r’: ‘mag_r_lsst’, ‘i’: ‘mag_i_lsst’, ‘z’: ‘mag_z_lsst’}

Returns:

Class assignment for each galaxy.

Return type:

A tablesio-compatible table

rail.interactive.estimation.algos.random_forest.random_forest_informer(**kwargs)

Train the random forest classifier

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The main interface method for Informers

This will attach the input_data to this Informer (for introspection and provenance tracking).

Then it will call the run(), validate() and finalize() methods, which need to be implemented by the sub-classes.

The run() method will need to register the model that it creates to this Estimator by using self.add_data(‘model’, model).

Finally, this will return a ModelHandle providing access to the trained model.

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This function was generated from the function rail.estimation.algos.random_forest.RandomForestInformer.inform

Parameters:

  • training_data (TableLike, required) – dictionary of all input data, or a TableHandle providing access to it

  • seed (int, required) – random seed

  • hdf5_groupname (str, optional) – name of hdf5 group for data, if None, then set to ‘’ Default: photometry

  • class_bands (list, optional) – Which bands to use for classification Default: [‘r’, ‘i’, ‘z’]

  • band_map (dict, optional) – column names for the the bands Default: {‘r’: ‘mag_r_lsst’, ‘i’: ‘mag_i_lsst’, ‘z’: ‘mag_z_lsst’}

  • redshift_col (str, optional) – Redshift column names Default: sz

  • bin_edges (list, optional) – Binning for training data Default: [0, 0.5, 1.0]

  • no_assign (int, optional) – Value for no assignment flag Default: -99

Returns:

Handle providing access to trained model

Return type:

numpy.ndarray