From 80dddc5018fd0e7ab5a3d2703d7a6c7e34a14bf3 Mon Sep 17 00:00:00 2001 From: AmandaWasserman <65247678+AmandaWasserman@users.noreply.github.com> Date: Fri, 8 Nov 2024 15:03:59 -0600 Subject: [PATCH 1/2] Update pre_processing.rst --- docs/pre_processing.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/pre_processing.rst b/docs/pre_processing.rst index 89270490..95965083 100644 --- 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: From 77c2dbff3de0b8026567ae9c3666a1a9d01fa019 Mon Sep 17 00:00:00 2001 From: AmandaWasserman <65247678+AmandaWasserman@users.noreply.github.com> Date: Fri, 8 Nov 2024 15:21:35 -0600 Subject: [PATCH 2/2] Update learn_loop.rst --- docs/learn_loop.rst | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/learn_loop.rst b/docs/learn_loop.rst index dc84a8ca..1c75fa4c 100644 --- 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: