Merge pull request #152 from iomega/workflow_k_fold_validation

Added k fold cross validation script

.gitignore

@@ -29,7 +29,7 @@ docs/apidocs
 config.py
 output/
 /data/
-/ms2query/use_case_python_files
+/experiment_python_files
 models_trained/
 computed_results/
 notebooks/.ipynb_checkpoints/

CHANGELOG.md

@@ -7,6 +7,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.5.0]
+### Added
+- Training models is now fully automatic (no need for notebooks)
+- Functions for creating benchmarking results
+- Functions for doing k_fold_cross_validation
+- Functions for visualizing benchmarking results
+### Changed
+- Method for creating new library files
+- Cleaning spectra functions for running are now combined with cleaning spectra functions for training
+
 ## [0.4.3]
 - Do not store MS2Deepscores in results table, to prevent memory issues
 
@@ -114,7 +124,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - First test workflow and basic batches.
 - Licence.
 
-[Unreleased]: https://github.com/iomega/ms2query/compare/0.4.2...HEAD
+[Unreleased]: https://github.com/iomega/ms2query/compare/0.5.0...HEAD
+[0.4.3]: https://github.com/iomega/ms2query/compare/0.4.3...0.5.0
 [0.4.3]: https://github.com/iomega/ms2query/compare/0.4.1...0.4.3
 [0.4.1]: https://github.com/iomega/ms2query/compare/0.4.0...0.4.1
 [0.4.0]: https://github.com/iomega/ms2query/compare/0.3.3...0.4.0

README.md

@@ -77,7 +77,10 @@ run_complete_folder(ms2library, ms2_spectra_directory)
 
 ```
 
-## Create your own library
+## Create your own library (without training new models)
+The code below creates all required library files for your own in house library. 
+No new models for MS2deepscore, Spec2Vec and MS2Query will be trained, to do this see the next section.
+
 To create your own library you also need to install RDKit, by running the following in your command line (while in the ms2query conda environment):
 ```
 conda install -c conda-forge rdkit
@@ -87,23 +90,22 @@ It is important that the library spectra are annotated with smiles, inchi's or i
 are not included in the library. 
 
 Fill in the blank spots with the file locations.
-The models for spec2vec, ms2deepscore and ms2query can be downloaded from the zenodo links (see above). 
+The models for spec2vec, ms2deepscore and ms2query can be downloaded from the zenodo links (see above).
 
 ```python
-from ms2query.library_files_creator import LibraryFilesCreator
+from ms2query.create_new_library.library_files_creator import LibraryFilesCreator
+from ms2query.clean_and_filter_spectra import clean_normalize_and_split_annotated_spectra
 from ms2query.utils import load_matchms_spectrum_objects_from_file
 
 spectrum_file_location =  # The file location of the library spectra
 library_spectra = load_matchms_spectrum_objects_from_file(spectrum_file_location)
 # Fill in the missing values:
-library_creator = LibraryFilesCreator(library_spectra,
-                                      output_directory=,  # For instance "data/library_data/all_GNPS_positive_mode_"
-                                      ion_mode="positive",
-                                      ms2ds_model_file_name=,  # The file location of the ms2ds model
-                                      s2v_model_file_name=, )  # The file location of the s2v model
-library_creator.clean_up_smiles_inchi_and_inchikeys(do_pubchem_lookup=True)
-library_creator.clean_peaks_and_normalise_intensities_spectra()
-library_creator.remove_not_fully_annotated_spectra()
+cleaned_library_spectra = clean_normalize_and_split_annotated_spectra(library_spectra, ion_mode_to_keep="")[
+    0]  # fill in "positive" or "negative"
+library_creator = LibraryFilesCreator(cleaned_library_spectra,
+                                      output_directory="",  # For instance "data/library_data/all_GNPS_positive_mode_"
+                                      ms2ds_model_file_name="",  # The file location of the ms2ds model
+                                      s2v_model_file_name="", )  # The file location of the s2v model
 library_creator.create_all_library_files()
 ```
 
@@ -143,6 +145,46 @@ ms2library = MS2Library(sqlite_file_name= ,
                         )
 ```
 
+# Create your own library and train new models
+The code trains new MS2Deepscore, Spec2Vec and MS2Query models for your in house library, 
+and creates all needed files for running MS2Query. 
+
+It is important that the library spectra are annotated with smiles, inchi's or inchikeys in the metadata otherwise they
+are not included in the library and training. 
+
+Fill in the blank spots below and run the code (can take several days). 
+The models will be stored in the specified output_folder. MS2Query can be run
+
+```python
+from ms2query.create_new_library.train_models import clean_and_train_models
+clean_and_train_models(spectrum_file=, #Fill in the location of the file containing the library spectra
+                       # Accepted formats are: "mzML", "json", "mgf", "msp", "mzxml", "usi" or a pickled matchms object. 
+                       ion_mode=, # Fill in the ion mode, choose from "positive" or "negative"
+                       output_folder= # The output folder in which all the models are stored. 
+                       )
+```
+
+To run MS2Query on your own created library run the code below (again fill in the blanks).
+
+```python
+from ms2query.run_ms2query import run_complete_folder
+from ms2query.ms2library import create_library_object_from_one_dir
+
+# Define the folder in which your query spectra are stored.
+# Accepted formats are: "mzML", "json", "mgf", "msp", "mzxml", "usi" or a pickled matchms object. 
+ms2_spectra_directory = # Specify the folder containing the query spectra you want to run against the library
+ms2_library_directory = # Specify the directory containing all the library and model files
+
+# Create a MS2Library object from one directory
+# If this does not work (because files have unexpected names or are not in one dir) see below.
+ms2library = create_library_object_from_one_dir(ms2_library_directory)
+
+# Run library search and analog search on your files.
+run_complete_folder(ms2library, ms2_spectra_directory)
+```
+
+After running the model can be loaded 
+
 ## Documentation for developers
 ### Prepare environmnent
 We recommend to create an Anaconda environment with

ms2query/__init__.py

@@ -1,6 +1,6 @@
 import logging
 from .__version__ import __version__
-from .library_files_creator import LibraryFilesCreator
+from ms2query.create_new_library.library_files_creator import LibraryFilesCreator
 from .ms2library import MS2Library
 from .results_table import ResultsTable
 

ms2query/__version__.py

@@ -1 +1 @@
-__version__ = '0.4.3'
+__version__ = '0.5.0'