diff options
author | Christoph Helma <helma@in-silico.ch> | 2017-01-11 08:24:23 +0100 |
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committer | Christoph Helma <helma@in-silico.ch> | 2017-01-11 08:24:23 +0100 |
commit | ed0d7edee4ac9831b58a01555de8bdba3534495e (patch) | |
tree | 6eae2f15554e6780576eac4bdd79a6a8422977ba | |
parent | b5d6446f058916d018139948002b6e9d1162d4fe (diff) |
model documentation updated
-rw-r--r-- | README.md | 2 | ||||
-rw-r--r-- | lib/model.rb | 12 |
2 files changed, 9 insertions, 5 deletions
@@ -87,7 +87,7 @@ Execute the following commands either from an interactive Ruby shell or a Ruby s #### Experiment with other datasets, endpoints and algorithms - You can pass training_dataset, prediction_feature and algorithms parameters to the `Model::Validation.create_from_enanomapper` command. The [API documentation](http://rdoc.info/gems/lazar) provides detailed instructions. + You can pass training_dataset, prediction_feature and algorithms parameters to the `Model::Validation.create_from_enanomapper` command. The [API documentation](http://rdoc.info/gems/lazar) provides detailed instructions. Detailed documentation and validation results can be found in this [publication](https://github.com/enanomapper/nano-lazar-paper/blob/master/nano-lazar.pdf). Documentation ------------- diff --git a/lib/model.rb b/lib/model.rb index 321636d..64edb76 100644 --- a/lib/model.rb +++ b/lib/model.rb @@ -28,11 +28,14 @@ module OpenTox field :version, type: Hash, default:{} # Create a lazar model - # @param [OpenTox::Dataset, nil] training_dataset + # @param [OpenTox::Dataset] training_dataset # @param [OpenTox::Feature, nil] prediction_feature - # @param [Hash] algorithms + # By default the first feature of the training dataset will be predicted, specify a prediction_feature if you want to predict another feature + # @param [Hash, nil] algorithms + # Default algorithms will be used, if no algorithms parameter is provided. The algorithms hash has the following keys: :descriptors (specifies the descriptors to be used for similarity calculations and local QSAR models), :similarity (similarity algorithm and threshold), :feature_selection (feature selection algorithm), :prediction (local QSAR algorithm). Default parameters are used for unspecified keys. + # # @return [OpenTox::Model::Lazar] - def self.create prediction_feature:nil, training_dataset:nil, algorithms:{} + def self.create prediction_feature:nil, training_dataset:, algorithms:{} bad_request_error "Please provide a prediction_feature and/or a training_dataset." unless prediction_feature or training_dataset prediction_feature = training_dataset.features.first unless prediction_feature # TODO: prediction_feature without training_dataset: use all available data @@ -185,7 +188,7 @@ module OpenTox model end - # Predict a substance + # Predict a substance (compound or nanoparticle) # @param [OpenTox::Substance] # @return [Hash] def predict_substance substance @@ -449,6 +452,7 @@ module OpenTox end # Create and validate a nano-lazar model, import data from eNanoMapper if necessary + # nano-lazar methods are described in detail in https://github.com/enanomapper/nano-lazar-paper/blob/master/nano-lazar.pdf # @param [OpenTox::Dataset, nil] training_dataset # @param [OpenTox::Feature, nil] prediction_feature # @param [Hash, nil] algorithms |