Mercurial > repos > recetox > ms2deepscore_training
diff macros.xml @ 0:0a0529822d91 draft default tip
planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/ms2deepscore commit 4bd610e0cbbcbed51a6bfb880179777fc8034fd6
| author | recetox |
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| date | Mon, 02 Sep 2024 12:12:30 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Mon Sep 02 12:12:30 2024 +0000 @@ -0,0 +1,150 @@ +<macros> + <token name="@TOOL_VERSION@">2.0.0</token> + <token name="@ONNX_VERSION@">1.16.2</token> + + <xml name="creator"> + <creator> + <person + givenName="Zargham" + familyName="Ahmad" + url="https://github.com/zargham-ahmad" + identifier="0000-0002-6096-224X" /> + <organization + url="https://www.recetox.muni.cz/" + email="GalaxyToolsDevelopmentandDeployment@space.muni.cz" + name="RECETOX MUNI" /> + </creator> + </xml> + + <xml name="edam"> + <xrefs> + <xref type="bio.tools">ms2deepscore</xref> + </xrefs> + </xml> + + <xml name="input_param"> + <conditional name="scores"> + <param name="use_scores" label="Use Scores Object" type="select"> + <option value="False" selected="true">FALSE</option> + <option value="True">TRUE</option> + </param> + <when value="True"> + <param label="Scores object" name="scores_in" type="data" format="json" + help="Scores objects calculated previously using one of the matchms similarity tools." /> + </when> + <when value="False"> + <param label="Queries spectra" name="queries" type="data" format="msp" + help="Query mass spectra to match against references."/> + <param label="Reference spectra" name="references" type="data" format="msp" + help="Reference mass spectra to match against as library."/> + </when> + </conditional> + <param name="model" type="data" format="onnx" label="Model" + help="Select the trained MS2DeepScore model file (onnx format) in the ONNX format as created by the 'MS2DeepScore Training' tool."/> + <param name="model_param" type="data" format="json" label="Configuration" + help="Select the MS2DeepScore model configurations in JSON format. Can be created using the 'MS2DeepScore Config Generator' tool."/> + </xml> + + <xml name="training_param"> + <param label="Training Dataset" name="spectra" type="data" format="msp,mgf" + help="Spectra file that should be used for training. (it will be split in training, validation and test sets)."/> + <param name="model_param" type="data" format="json" label="Model Settings" help="json file with the MS2Deepscore model settings."/> + <param name="validation_split_fraction" type="integer" min="0" max="100" value="20" label="Validation split fraction [%]" + help="The fraction of the inchikeys that will be used for validation and test"/> + </xml> + + <xml name="config_generator"> + <section name="model_structure" title="Model Structure" expanded="true"> + <repeat name="layers" title="Layer" min="1" default="1" > + <param name="dims" type="integer" label="Dimensions" min = "0" value="2000" help="Size of the in-between layer to add." /> + </repeat> + <param name="embedding_dim" type="integer" label="Embedding Dimension" value="400" help="The dimension of the final embedding layer." /> + <param name="ionisation_mode" type="select" label="Ionisation Mode"> + <option value="positive" selected="true">Positive</option> + <option value="negative">Negative</option> + <option value="both">Both</option> + </param> + </section> + + <section name="tensorization_settings" title="Tensorization Settings" expanded="true"> + <param name="min_mz" type="integer" label="Min m/z" value="10" /> + <param name="max_mz" type="integer" label="Max m/z" value="1000" /> + <param name="mz_bin_width" type="float" label="m/z Bin Width" value="0.1" /> + <param name="intensity_scaling" type="float" label="Intensity Scaling" value="0.5" /> + <param name="fingerprint_type" type="text" value="daylight" label="Fingerprint Type" help="The fingerprint type that should be used for tanimoto score calculations." /> + <param name="fingerprint_nbits" type="integer" label="Fingerprint Number of Bits" value="2048" help="The number of bits to use for the fingerprint." /> + </section> + + + <section name="training_settings" title="Training Settings" expanded="false"> + <param name="dropout_rate" type="float" label="Dropout Rate" value="0.0" /> + <param name="learning_rate" type="float" label="Learning Rate" value="0.00025" /> + <param name="epochs" type="integer" label="Epochs" value="250" /> + <param name="patience" type="integer" label="Patience" value="20" help="How long the model should keep training if validation does not improve" /> + <param name="loss_function" type="select" label="Loss Function"> + <option value="mse" selected="true">Mean Squared Error (mse)</option> + <option value="mae">Mean Absolute Error (mae)</option> + <option value="rmse">Root Mean Squared Error (rmse)</option> + <option value="risk_mae">Risk Aware MAE (risk_aware_mae)</option> + <option value="risk_mse">Risk Aware MSE (risk_aware_mse)</option> + </param> + <param name="weighting_factor" type="integer" label="Weighting Factor" value="0" /> + <param name="batch_size" type="integer" value="32" label="Batch Size" help="Number of pairs per batch" /> + <param name="average_pairs_per_bin" type="integer" value="20" label="Average pairs per bin" help="The aimed average number of pairs of spectra per spectrum in each bin." /> + <param name="random_seed" type="text" label="Random seed" value="None" help="Specify random seed for reproducible random number generation." /> + </section> + </xml> + + <xml name="citations"> + <citations> + <citation type="doi">https://doi.org/10.1186/s13321-021-00558-4</citation> + <citation type="doi">https://doi.org/10.1101/2024.03.25.586580</citation> + </citations> + </xml> + + +<token name="@HELP@"> + ms2deepscore provides a Siamese neural network that is trained to predict molecular structural similarities (Tanimoto scores) from pairs of mass spectrometry spectra. + The library provides an intuitive classes to prepare data, train a siamese model, and compute similarities between pairs of spectra. + In addition to the prediction of a structural similarity, MS2DeepScore can also make use of Monte-Carlo dropout to assess the model uncertainty. + MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. + Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. + MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. +</token> + + +<token name="@init_scores@"> +from matchms.importing import load_from_msp, scores_from_json +from matchms import Scores +#if $scores.use_scores == "True" +scores = scores_from_json("${scores_in}") +#else +scores = Scores(references=list(load_from_msp("$references")), queries=list(load_from_msp("$queries")), is_symmetric=False) +#end if +</token> + +<token name="@init_logger@"> +from matchms import set_matchms_logger_level +set_matchms_logger_level("WARNING") +</token> + +<token name="@json_load@"> +import numpy as np +import json + +with open("$model_param", 'r') as json_file: + model_params = json.load(json_file) + +# Conditionally convert specific keys if they are present +if 'base_dims' in model_params: + model_params['base_dims'] = tuple(model_params['base_dims']) + +if 'same_prob_bins' in model_params: + model_params['same_prob_bins'] = np.array(model_params['same_prob_bins']) + +if 'additional_metadata' in model_params: + model_params['additional_metadata'] = [ + (entry[0], entry[1]) for entry in model_params['additional_metadata'] + ] +</token> +</macros>