Use dummy spectra in test_train-all_models and optimize ms2deepscore …

…settings for running fast

tests/test_train_models.py

@@ -2,30 +2,52 @@
 import numpy as np
 import pytest
 from ms2deepscore import SettingsMS2Deepscore
-
-from ms2query.create_new_library.train_models import clean_and_train_models
+import string
+from matchms.Spectrum import Spectrum
+from ms2query.create_new_library.train_models import train_all_models, SettingsTrainingModels
 from ms2query.ms2library import MS2Library, create_library_object_from_one_dir
 
 
+def create_test_spectra(num_of_unique_inchikeys):
+    # Define other parameters
+    mz, intens = 100.0, 0.1
+    spectrums = []
+    letters = list(string.ascii_uppercase[:num_of_unique_inchikeys])
+    letters += letters
+
+    # Create fake spectra
+    fake_inchikeys = []
+    for i, letter in enumerate(letters):
+        dummy_inchikey = f"{14 * letter}-{10 * letter}-N"
+        # fingerprint = generate_binary_vector(i)
+        fake_inchikeys.append(dummy_inchikey)
+        spectrums.append(
+            Spectrum(mz=np.array([mz + (i + 1) * 1.0, mz + 100 + (i + 1) * 1.0, mz + 200 + (i + 1) * 1.0]),
+                     intensities=np.array([intens, intens, intens]),
+                     metadata={"precursor_mz": 111.1,
+                               "inchikey": dummy_inchikey,
+                               "smiles": "C"*(i+1)
+                               }))
+    return spectrums
+
+
 @pytest.mark.integration
-def test_train_all_models(path_to_general_test_files, tmp_path):
-    path_to_test_spectra = os.path.join(path_to_general_test_files, "2000_negative_test_spectra.mgf")
+def test_train_all_models(tmp_path):
+    test_spectra = create_test_spectra(11)
+
     models_folder = os.path.join(tmp_path, "models")
-    clean_and_train_models(path_to_test_spectra,
-                           "negative",
-                           models_folder,
-                           {"ms2ds_fraction_validation_spectra": 2,
-                            "ms2ds_training_settings": SettingsMS2Deepscore(
-                                mz_bin_width=1.0,
-                                epochs=2,
-                                base_dims=(100, 100),
-                                embedding_dim=50,
-                                same_prob_bins=np.array([(0, 0.5), (0.5, 1.0)]),
-                                average_pairs_per_bin=2,
-                                batch_size=2),
-                            "spec2vec_iterations": 2,
-                            "ms2query_fraction_for_making_pairs": 400,
-                            "add_compound_classes": False}
-                           )
+    train_all_models(test_spectra, test_spectra, output_folder=models_folder,
+                     settings=SettingsTrainingModels({"ms2ds_fraction_validation_spectra": 2,
+                                                      "ms2ds_training_settings": SettingsMS2Deepscore(
+                                                          mz_bin_width=1.0,
+                                                          epochs=2,
+                                                          base_dims=(100, 100),
+                                                          embedding_dim=50,
+                                                          same_prob_bins=np.array([(0, 1.0)]),
+                                                          average_pairs_per_bin=2,
+                                                          batch_size=2),
+                                                      "spec2vec_iterations": 2,
+                                                      "ms2query_fraction_for_making_pairs": 10,
+                                                      "add_compound_classes": False}))
     ms2library = create_library_object_from_one_dir(models_folder)
     assert isinstance(ms2library, MS2Library)