Simplify config

README.md

@@ -73,7 +73,7 @@ Not all releases might have a model file included on the [Releases page](https:/
 - To run _de novo_ sequencing:
 
 ```
-casanovo --mode=denovo --peak_path=path/to/predict/spectra.mgf --config=path/to/config.yaml --output=path/to/output
+casanovo --mode=denovo --peak_path=path/to/predict/spectra.mgf --output=path/to/output
 ```
 
 Casanovo can predict peptide sequences for MS/MS data in mzML, mzXML, and MGF files.
@@ -82,15 +82,15 @@ This will write peptide predictions for the given MS/MS spectra to the specified
 - To evaluate _de novo_ sequencing performance based on known spectrum annotations:
 
 ```
-casanovo --mode=eval --peak_path=path/to/test/annotated_spectra.mgf --config=path/to/config.yaml
+casanovo --mode=eval --peak_path=path/to/test/annotated_spectra.mgf
 ```
 
 To evaluate the peptide predictions, ground truth peptide labels need to be provided as an annotated MGF file.
 
 - To train a model from scratch:
 
 ```
-casanovo --mode=train --peak_path=path/to/train/annotated_spectra.mgf --peak_path_val=path/to/validation/annotated_spectra.mgf --config=path/to/config.yaml
+casanovo --mode=train --peak_path=path/to/train/annotated_spectra.mgf --peak_path_val=path/to/validation/annotated_spectra.mgf
 ```
 
 Training and validation MS/MS data need to be provided as annotated MGF files.
@@ -103,11 +103,10 @@ We will demonstrate how to use Casanovo using a small walkthrough example on a s
 The example MGF file is available at [`sample_data/sample_preprocessed_spectra.mgf`](https://github.com/Noble-Lab/casanovo/blob/main/sample_data/sample_preprocessed_spectra.mgf`).
 
 1. Install Casanovo (see above for details).
-2. Copy the example `config.yaml` file to a location you can easily access. 
-3. Ensure you are in the proper anaconda environment by typing `conda activate casanovo_env`. (If you named your environment differently, type in that name instead.)
-4. Run this command:
+2. Ensure you are in the proper anaconda environment by typing `conda activate casanovo_env`. (If you named your environment differently, type in that name instead.)
+3. Run this command:
 ```
-casanovo --mode=denovo --peak_path=[PATH_TO]/sample_preprocessed_spectra.mgf --config=[PATH_TO]/config.yaml
+casanovo --mode=denovo --peak_path=[PATH_TO]/sample_preprocessed_spectra.mgf
 ```
 
 This job will take very little time to run (< 1 minute).
@@ -139,6 +138,9 @@ Run the following command in your command prompt to see all possible command-lin
 casanovo --help
 ```
 
+Additionally, you can use a configuration file to fully customize Casanovo.
+You can find the `config.yaml` configuration file that is used by default [here](https://github.com/Noble-Lab/casanovo/blob/main/casanovo/config.yaml).
+
 **I get a "CUDA out of memory" error when trying to run Casanovo. Help!**
 
 This means that there was not enough (free) memory available on your GPU to run Casanovo, which is especially likely to happen when you are using a smaller, consumer-grade GPU.

casanovo/config.yaml

@@ -8,7 +8,7 @@ random_seed: 454
 
 # Spectrum processing options.
 n_peaks: 150
-min_mz: 50.52564895  # 1.0005079 * 50.5
+min_mz: 50.0
 max_mz: 2500.0
 min_intensity: 0.01
 remove_precursor_tol: 2.0  # Da
@@ -18,7 +18,7 @@ dim_model: 512
 n_head: 8
 dim_feedforward: 1024
 n_layers: 9
-dropout: 0
+dropout: 0.0
 dim_intensity:
 custom_encoder:
 max_length: 100

casanovo/denovo/dataloaders.py

@@ -54,7 +54,7 @@ def __init__(
         test_index: Optional[AnnotatedSpectrumIndex] = None,
         batch_size: int = 128,
         n_peaks: Optional[int] = 150,
-        min_mz: float = 140.0,
+        min_mz: float = 50.0,
         max_mz: float = 2500.0,
         min_intensity: float = 0.01,
         remove_precursor_tol: float = 2.0,

casanovo/denovo/model.py

@@ -76,18 +76,18 @@ class Spec2Pep(pl.LightningModule, ModelMixin):
 
     def __init__(
         self,
-        dim_model: int = 128,
+        dim_model: int = 512,
         n_head: int = 8,
         dim_feedforward: int = 1024,
-        n_layers: int = 1,
+        n_layers: int = 9,
         dropout: float = 0.0,
         dim_intensity: Optional[int] = None,
         custom_encoder: Optional[
             Union[SpectrumEncoder, PairedSpectrumEncoder]
         ] = None,
         max_length: int = 100,
         residues: Union[Dict[str, float], str] = "canonical",
-        max_charge: int = 5,
+        max_charge: int = 10,
         precursor_mass_tol=50,
         n_log: int = 10,
         tb_summarywriter: Optional[