Merge pull request #76 from LSSTDESC/documentation_fix

documentation fix
LSSTDESC · Nov 8, 2024 · c027925 · c027925
2 parents 7c56dc6 + 77c2dbf
commit c027925
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Showing 2 changed files with 5 additions and 4 deletions.
diff --git a/docs/learn_loop.rst b/docs/learn_loop.rst
@@ -82,6 +82,7 @@ In interactive mode, you must define the required variables and use the :py:mod:
    :linenos:
 
    >>> from resspect.learn_loop import  learn_loop
+   >>> from resspect import LoopConfiguration
 
    >>> nloops = 1000                                  # number of iterations
    >>> method = 'Bazin'                               # only option in v1.0
@@ -93,9 +94,9 @@ In interactive mode, you must define the required variables and use the :py:mod:
    >>> train = 'original'                             # initial training
    >>> batch = 1                                      # size of batch
 
-   >>> learn_loop(nloops=nloops, features_method=method, classifier=ml,
+   >>> learn_loop(LoopConfiguration(nloops=nloops, features_method=method, classifier=ml,
    >>>            strategy=strategy, path_to_features=input_file, output_metrics_file=metric, 
-   >>>            output_queried_file=queried, training=train, batch=batch)
+   >>>            output_queried_file=queried, training=train, batch=batch))
 
 Alternatively you can also run everything from the command line:
 

diff --git a/docs/pre_processing.rst b/docs/pre_processing.rst
@@ -250,7 +250,7 @@ For SNPCC using Bazin features:
    >>> features_file = 'results/Bazin.csv'                     # output file
    >>> feature_extractor = 'Bazin'
 
-   >>> fit_snpcc(path_to_data_dir=path_to_data_dir, features_file=features_file)
+   >>> fit_snpcc(path_to_data_dir=path_to_data_dir, features_file=features_file, feature_extractor=feature_extractor)
 
 For SNPCC using Malanchev features:
 ^^^^^^^^^^
@@ -265,7 +265,7 @@ For SNPCC using Malanchev features:
    >>> features_file = 'results/Malanchev.csv'                 # output file
    >>> feature_extractor = 'Malanchev'
 
-   >>> fit_snpcc(path_to_data_dir=path_to_data_dir, features_file=features_file)
+   >>> fit_snpcc(path_to_data_dir=path_to_data_dir, features_file=features_file, feature_extractor=feature_extractor)
 
 
 For PLAsTiCC: