Repository: Noble-Lab/casanovo (language: python)

Issue:
Write unit tests
At least adapt the tests from [Depthcharge](https://github.com/wfondrie/depthcharge/tree/main/tests/unit_tests). Other tests to be written TBD.
Use types for our command line arguments and improve --help doc
Right now, all of them say TEXT - For example, the --mode option looks like it takes two choices, but I don’t know what those are purely from looking at the help (in general, if an option has two choices, it would be better served by a flag instead). Try to be more descriptive with those options and improve documentation for --help.

Another format for config file
A .py file is not a proper config file format - Choose a standard such as .ini (https://docs.python.org/3/library/configparser.html), TOML, or YAML. 
Add citation information and link to paper

Add simple tutorial with sample data
Step by step guide to de novo sequencing with Casanovo with a sample mgf file.


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index fdea8051..dbf68368 100644
--- a/README.md
+++ b/README.md
@@ -5,14 +5,43 @@
 
 Data and pre-trained model weights are available [here](https://zenodo.org/record/5976003).
 
-## How to get started with Casanovo?
+A link to the preprint of the paper where we discuss our methods and tests can be found [here](https://www.biorxiv.org/content/10.1101/2022.02.07.479481v1).
 
-Install `casanovo` as a Python package from this repo (requires Python 3.7+, dependencies will be installed automatically as needed):
+# How to get started with Casanovo?
+## Our recommendation:
+
+Install **Anaconda**! It helps keep your environment for casanovo and its dependencies separate from your other Python environments. **This is especially helpful because casanovo works within a specific range of Python versions (3.7 > Python version > 3.10).**
+
+- Check out the [Windows](https://docs.anaconda.com/anaconda/install/windows/#), [MacOS](https://docs.anaconda.com/anaconda/install/mac-os/), and [Linux](https://docs.anaconda.com/anaconda/install/linux/) installation instructions.
+
+Once you have Anaconda installed, you can use this helpful [cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf) to see common commands and what they do.
+
+## Environment creation:
+
+Open up the anaconda prompt and run this command:
+```
+conda create --name casanovo_env python=3.7
+```
+This will create an anaconda environment called `casanovo_env` that has python 3.7 installed (You can check if it was created by typing `conda env list`). 
+
+You can activate this environment by typing:
+```
+conda activate casanovo_env
+```
+To the left of your anaconda prompt line it should now say **(casanovo_env)** instead of **(base)**. If this is the case, then you have set up anaconda and the environment properly.
+
+**Be sure to retype in the activation command into your terminal when you reopen anaconda and want to use casanovo.** The base environment most likely will not work.
+
+## Installation:
+
+Install `casanovo` as a Python package from this repo (requires 3.7 > [Python version] > 3.10 , dependencies will be installed automatically as needed):
 ```
 pip install git+https://github.com/Noble-Lab/casanovo.git#egg=casanovo
 ```
 
-Once installed, Casanovo can be used with a simple command line interface. Run `casanovo --help` for more details. All auxiliary data, model and training-related variables can be specified in a user created `.py` file, see `casanovo/config.py` for the default configuration that was used to obtain the reported results.
+Once installed, Casanovo can be used with a simple command line interface. **Run `casanovo --help` for more details.** All auxiliary data, model and training-related variables can be specified in a user created `.yaml` file, see `casanovo/config.yaml` for the default configuration that was used to obtain the reported results.
+
+# Example Commands:
 
 - To evaluate _de novo_ sequencing performance of a pre-trained model (peptide annotations are needed for spectra):
 ```
@@ -28,5 +57,43 @@ casanovo --mode=denovo --model_path='path/to/pretrained' --test_data_path='path/
 ```
 casanovo train --mode=train --model_path='path/to/pretrained' --train_data_path='path/to/train/mgf/files/dir'  --val_data_path='path/to/validation/mgf/files/dir' --config_path='path/to/config'
 ```
+# Example Job:
+## A small walkthrough on how to use casanovo with a very small spectra (~100) set
 
+### The spectra file (.mgf) that we will be running this job on can be seen in the sample_data folder.
+
+- Step 1: Install casanovo (see above for details)
+- Step 2: Download the casanovo_pretrained_model_weights.zip from [here](https://zenodo.org/record/5976003). Place these models in a location that you can easly access and know the path of.
+    - We will be using pretrained_excl_mouse.ckpt for this job.
+- Step 3: Ensure you are in the proper anaconda environment by typing ```conda activate casanovo_env```. (If you named it differently, type in that name instead)
+- Step 4: Run this command:
+```
+casanovo --mode=denovo --model_path='[PATH_TO]/pretrained_excl_mouse.ckpt' --test_data_path='sample_data' --preprocess_spec=False
+```
+Make sure you have the proper filepath to the pretrained_excl_mouse.ckpt file.
+ - Note: If you want to get the ouput csv in a place OTHER than where you ran this command, specify where you would like the output to be placed by specifying a directory in the --output_path CLI field
+    - It would look like ```--output_path='path/to/output/location'``` appended onto the end of the above command. Be sure to provide a directory, not a file!
+
+This job should take very little time to run (< 1 minute), and the result should be a file named ```casanovo_output.csv``` wherever you specified.
+
+If the first few lines look like:
+```
+spectrum_id,denovo_seq,peptide_score,aa_scores
+0,LAHYNKR,0.9912219984190804,"[1.0, 1.0, 1.0, 0.99948...
+```
+Congratulations! You got casanovo to work!
+
+# Common Troubleshooting/FAQ
+
+## Installed casanovo and it worked before, but I reopened Anaconda again and now it says casanovo is not installed
+Make sure you are in the `casanovo_env` environment. You can make sure you are in it by typing
+```
+conda activate casanovo_env
+```
+## What CLI Prompts can I use?
+Run the following command in your command prompt:
+```
+casanovo --help
+```
+It should give you a comprehensive list of all CLI options you can tag onto a casanovo job and how/why to use them.
 
diff --git a/casanovo/casanovo.py b/casanovo/casanovo.py
index 314e9363..61617f10 100644
--- a/casanovo/casanovo.py
+++ b/casanovo/casanovo.py
@@ -1,45 +1,86 @@
 """The command line entry point for casanovo"""
+from email.policy import default
 import click, logging
-from casanovo.denovo import train, test_evaluate, test_denovo
-
+import yaml
+import os
+from casanovo.denovo import train, evaluate, denovo
 
+#Required options
 @click.command()
-@click.option("--mode", default='eval', help="Choose to train a model or test denovo predictions")
-@click.option("--model_path", help="Specify path to pre-trained model weights for testing or to continue to train")
-@click.option("--train_data_path", help="Specify path to mgf files to be used as training data")
-@click.option("--val_data_path", help="Specify path to mgf files to be used as validation data")
-@click.option("--test_data_path", help="Specify path to mgf files to be used as test data")
-@click.option("--config_path", help="Specify path to config file which includes data and model related options")
-@click.option("--output_path", help="Specify path to output de novo sequences")
+@click.option("--mode", required=True, default='eval', help="Choose on a high level what the program will do. \"train\" will train a model from scratch or continue training a pre-trained model. \"eval\" will evaluate de novo sequencing performance of a pre-trained model (peptide annotations are needed for spectra). \"denovo\" will run de novo sequencing without evaluation (specificy directory path for output csv file with de novo sequences).", type=click.Choice(['train', 'eval', 'denovo']))
+@click.option("--model_path", required=True, help="Specify path to pre-trained model weights (.ckpt file) for testing or to continue to train.", type=click.Path(exists=True, dir_okay=False, file_okay=True))
+#Base options
+@click.option("--train_data_path", help="Specify path to .mgf files to be used as training data", type=click.Path(exists=True, dir_okay=True, file_okay=False))
+@click.option("--val_data_path", help="Specify path to .mgf files to be used as validation data", type=click.Path(exists=True, dir_okay=True, file_okay=False))
+@click.option("--test_data_path", help="Specify path to .mgf files to be used as test data", type=click.Path(exists=True, dir_okay=True, file_okay=False))
+@click.option("--config_path", help="Specify path to custom config file which includes data and model related options. If not included, the default config.yaml will be used.", type=click.Path(exists=True, dir_okay=False, file_okay=True))
+@click.option("--output_path", help="Specify path to output de novo sequences. Output format is .csv", type=click.Path(exists=True, dir_okay=True, file_okay=False))
+#De Novo sequencing options
+@click.option("--preprocess_spec", default=None, help="True if spectra data should be preprocessed, False if using preprocessed data.", type=click.BOOL)
+@click.option("--num_workers", default=None, help="Number of workers to use for spectra reading.", type=click.INT)
+@click.option("--gpus", default=(), help="Specify gpus for usage. For multiple gpus, use format: --gpus=0 --gpus=1 --gpus=2... etc. etc.", type=click.INT, multiple=True)
 
 def main(
+    #Req + base vars
     mode,
     model_path,
     train_data_path,
     val_data_path,
     test_data_path,
     config_path,
-    output_path
+    output_path,
+    #De Novo vars
+    preprocess_spec,
+    num_workers,
+    gpus
 ):
-    """The command line function"""
+    """
+    The command line function for casanovo. De Novo Mass Spectrometry Peptide Sequencing with a Transformer Model.
+    
+    \b
+    Training option requirements:
+    mode, model_path, train_data_path, val_data_path, config_path
+    
+    \b
+    Evaluation option requirements:
+    mode, model_path, test_data_path, config_path
+    
+    \b
+    De Novo option requirements:
+    mode, model_path, test_data_path, config_path, output_path
+    """
     logging.basicConfig(
     level=logging.INFO, 
     format="%(levelname)s: %(message)s",
 )
+    if config_path == None:
+        abs_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml')
+        with open(abs_path) as f:
+            config = yaml.safe_load(f)
+    else:
+        with open(config_path) as f:
+            config = yaml.safe_load(f)
+
+    if(preprocess_spec != None): 
+        config['preprocess_spec'] = preprocess_spec
+    if(num_workers != None): 
+            config['num_workers'] = num_workers
+    if(gpus != ()):
+            config['gpus'] = gpus
     if mode == 'train':
-        
+
         logging.info('Training Casanovo...')
-        train(train_data_path, val_data_path, model_path, config_path)
+        train(train_data_path, val_data_path, model_path, config)
         
     elif mode == 'eval':
         
         logging.info('Evaluating Casanovo...')
-        test_evaluate(test_data_path, model_path, config_path)
+        evaluate(test_data_path, model_path, config)
 
     elif mode == 'denovo':
         
         logging.info('De novo sequencing with Casanovo...')
-        test_denovo(test_data_path, model_path, config_path, output_path)        
+        denovo(test_data_path, model_path, config, output_path)  
         
     pass
 
diff --git a/casanovo/config.py b/casanovo/config.py
deleted file mode 100644
index 24885c05..00000000
--- a/casanovo/config.py
+++ /dev/null
@@ -1,92 +0,0 @@
-"""
-Data, model and training/testing related options can be specified here.
-"""
-import os
-
-#Random seed to be used across the pipeline
-random_seed = 454
-
-#Train data options
-train_annot_spec_idx_path = os.path.join(os.getcwd(),'casanovo_train.hdf5') #path to write the training data index file
-train_spec_idx_overwrite = True
-
-#Validation data options
-val_annot_spec_idx_path = os.path.join(os.getcwd(),'casanovo_val.hdf5') #path to write the validation data index file
-val_spec_idx_overwrite = True
-
-#Test data options
-test_annot_spec_idx_path = os.path.join(os.getcwd(),'casanovo_test.hdf5') #path to write the test data index file
-test_spec_idx_overwrite = True
-
-#Preprocessing parameters
-preprocess_spec = True #Should be True when using raw mass spec data. Use False when reproducing Casanovo results with the provided benchmark data set which is pre-processed
-n_peaks = 150
-min_mz = 1.0005079* 50.5
-max_mz = 2500
-min_intensity = 0.01
-fragment_tol_mass = 2  # Da
-
-#Hardware options
-num_workers = 0
-gpus = None #None for CPU, int list to specify GPUs
-
-#Model options
-max_charge = 10
-dim_model = 512
-n_head = 8
-dim_feedforward = 1024
-n_layers = 9
-dropout = 0
-dim_intensity = None
-custom_encoder = None
-max_length = 100
-residues = {
-        "G": 57.021463735,
-        "A": 71.037113805,
-        "S": 87.032028435,
-        "P": 97.052763875,
-        "V": 99.068413945,
-        "T": 101.047678505,
-        "C+57.021": 103.009184505 + 57.02146,
-        "L": 113.084064015,
-        "I": 113.084064015,
-        "N": 114.042927470,
-        "D": 115.026943065,
-        "Q": 128.058577540,
-        "K": 128.094963050,
-        "E": 129.042593135,
-        "M": 131.040484645,
-        "H": 137.058911875,
-        "F": 147.068413945,
-        "R": 156.101111050,
-        "Y": 163.063328575,
-        "W": 186.079312980,
-        # AA mods:
-        "M+15.995": 131.040484645 + 15.994915,  # Met Oxidation
-        "N+0.984": 114.042927470 + 0.984016,  # Asn Deamidation
-        "Q+0.984": 128.058577540 + 0.984016,  # Gln Deamidation
-    }
-max_charge = 10
-n_log = 1
-tb_summarywriter = None
-warmup_iters = 100000
-max_iters = 600000
-learning_rate = 5e-4
-weight_decay = 1e-5
-
-#Training/inference options
-train_batch_size = 32
-val_batch_size = 1024
-test_batch_size = 1024
-
-accelerator="ddp"
-logger = None
-max_epochs = 30
-num_sanity_val_steps = 0
-
-train_from_scratch = True
-
-save_model = False
-model_save_folder_path = ''
-save_weights_only = True
-every_n_epochs = 1
diff --git a/casanovo/config.yaml b/casanovo/config.yaml
new file mode 100644
index 00000000..80b91a01
--- /dev/null
+++ b/casanovo/config.yaml
@@ -0,0 +1,90 @@
+###
+# Config file for casanovo. Edit this to change parameters for train, eval, and denovo.
+# Note that blank entries are interpreted as "None"
+###
+
+#Random seed to be used across the pipeline
+random_seed: 454
+
+#Train data options
+train_annot_spec_idx_path: casanovo_train.hdf5 #path to write the training data index file, starts at os.getcwd()
+train_spec_idx_overwrite: True
+
+#Validation data options
+val_annot_spec_idx_path: casanovo_val.hdf5 #path to write the validation data index file, starts at os.getcwd()
+val_spec_idx_overwrite: True
+
+#Test data options
+test_annot_spec_idx_path: casanovo_test.hdf5 #path to write the test data index file
+test_spec_idx_overwrite: True
+
+#Preprocessing parameters
+preprocess_spec: True #Should be True when using raw mass spec data. Use False when reproducing Casanovo results with the provided benchmark data set which is pre-processed
+n_peaks: 150
+min_mz: 50.52564895 #1.0005079 * 50.5 
+max_mz: 2500
+min_intensity: 0.01
+fragment_tol_mass: 2  # Da
+
+#Hardware options
+num_workers: 8
+gpus: [0] #None for CPU, int list to specify GPUs
+
+#Model options
+dim_model: 512
+n_head: 8
+dim_feedforward: 1024
+n_layers: 9
+dropout: 0
+dim_intensity: 
+custom_encoder: 
+max_length: 100
+residues:
+  G: 57.021463735
+  A: 71.037113805
+  S: 87.032028435
+  P: 97.052763875
+  V: 99.068413945
+  T: 101.047678505
+  C+57.021: 160.030644505 #103.009184505 + 57.02146
+  L: 113.084064015
+  I: 113.084064015
+  N: 114.042927470
+  D: 115.026943065
+  Q: 128.058577540
+  K: 128.094963050
+  E: 129.042593135
+  M: 131.040484645
+  H: 137.058911875
+  F: 147.068413945
+  R: 156.101111050
+  Y: 163.063328575
+  W: 186.079312980
+  # AA mods:
+  M+15.995: 147.035399645 #131.040484645 + 15.994915  # Met Oxidation
+  N+0.984: 115.02694347 #114.042927470 + 0.984016,  # Asn Deamidation
+  Q+0.984: 129.04259354 #128.058577540 + 0.984016,  # Gln Deamidation
+max_charge: 10
+n_log: 1
+tb_summarywriter: 
+warmup_iters: 100000
+max_iters: 600000
+learning_rate: 5e-4
+weight_decay: 1e-5
+
+#Training/inference options
+train_batch_size: 32
+val_batch_size: 1024
+test_batch_size: 1024
+
+accelerator: "ddp"
+logger: 
+max_epochs: 30
+num_sanity_val_steps: 0
+
+train_from_scratch: True
+
+save_model: False
+model_save_folder_path: ''
+save_weights_only: True
+every_n_epochs: 1
diff --git a/casanovo/denovo/__init__.py b/casanovo/denovo/__init__.py
index fd80e64f..c845d2a5 100644
--- a/casanovo/denovo/__init__.py
+++ b/casanovo/denovo/__init__.py
@@ -1,5 +1,5 @@
 """The de novo sequencing model"""
 from .model import Spec2Pep
 from .dataloaders import DeNovoDataModule
-from .train_test import train, test_denovo, test_evaluate
+from .train_test import train, denovo, evaluate
 
diff --git a/casanovo/denovo/model.py b/casanovo/denovo/model.py
index 3ddab446..ebccd9f3 100644
--- a/casanovo/denovo/model.py
+++ b/casanovo/denovo/model.py
@@ -81,7 +81,7 @@ def __init__(
         tb_summarywriter=None,
         warmup_iters=100000,
         max_iters=600000,
-        output_path=None,
+        output_path='',
         **kwargs,
     ):
         """Initialize a Spec2Pep model"""
@@ -432,7 +432,7 @@ def on_test_epoch_end(self):
 
         This is a pytorch-lightning hook.
         """
-        with open(os.path.join(self.output_path,'casanovo_output.csv'), 'w') as f: 
+        with open(os.path.join(str(self.output_path),'casanovo_output.csv'), 'w') as f: 
             writer = csv.writer(f)
             writer.writerow(['spectrum_id','denovo_seq','peptide_score','aa_scores'])
             
diff --git a/sample_data/sample_preprocessed_spectra.mgf b/sample_data/sample_preprocessed_spectra.mgf
new file mode 100644
index 00000000..b9e2638f
--- /dev/null
+++ b/sample_data/sample_preprocessed_spectra.mgf
@@ -0,0 +1,8081 @@
+BEGIN IONS
+TITLE=0
+PEPMASS=451.25348
+CHARGE=2+
+SCANS=F1:2478
+RTINSECONDS=824.574
+SEQ=IAHYNKR
+63.994834899902344 0.0611930787563324 
+70.06543731689453 0.06860413402318954 
+84.081298828125 0.22455614805221558 
+85.08439636230469 0.06763620674610138 
+86.09666442871094 0.22344912588596344 
+110.07109069824219 0.3034861385822296 
+129.1020050048828 0.0932231917977333 
+138.06597900390625 0.07667151838541031 
+157.13291931152344 0.14716865122318268 
+175.1185302734375 0.19198034703731537 
+185.1283721923828 0.09717456996440887 
+209.10263061523438 0.13139843940734863 
+273.1337890625 0.09324286878108978 
+301.1282958984375 0.08515828102827072 
+303.21221923828125 0.07235292345285416 
+304.17529296875 0.07120858132839203 
+322.1859130859375 0.15834060311317444 
+350.6787414550781 0.07397215068340302 
+417.2552185058594 0.14982180297374725 
+580.3185424804688 0.31572264432907104 
+630.36572265625 0.06255878508090973 
+717.376708984375 0.5990896821022034 
+753.3748779296875 0.09976936876773834 
+788.4207763671875 0.35858696699142456 
+866.4544677734375 0.12016354501247406 
+END IONS
+
+BEGIN IONS
+TITLE=1
+PEPMASS=626.79913
+CHARGE=2+
+SCANS=F1:2484
+RTINSECONDS=825.468
+SEQ=VKEDPDGEHAR
+107.2381362915039 0.051370881497859955 
+110.07106018066406 0.09514826536178589 
+127.08580780029297 0.051932577043771744 
+129.10214233398438 0.4483136534690857 
+133.4121551513672 0.048764534294605255 
+151.06536865234375 0.05164254084229469 
+175.11810302734375 0.12925496697425842 
+183.14926147460938 0.08551527559757233 
+225.36390686035156 0.052010536193847656 
+228.1688232421875 0.1843532919883728 
+239.09396362304688 0.05292532965540886 
+240.13259887695312 0.057184796780347824 
+255.14370727539062 0.058511145412921906 
+286.1373291015625 0.11974132806062698 
+339.2006530761719 0.07786702364683151 
+357.2111511230469 0.14482076466083527 
+373.1705627441406 0.11663464456796646 
+383.2125244140625 0.10688323527574539 
+391.1812744140625 0.12372428178787231 
+399.9803466796875 0.05831653252243996 
+401.16644287109375 0.12899114191532135 
+418.63897705078125 0.06138816103339195 
+472.23931884765625 0.19473803043365479 
+511.2763977050781 0.0620708130300045 
+568.749755859375 0.07995174080133438 
+569.2789306640625 0.208326056599617 
+590.31884765625 0.049487706273794174 
+618.283935546875 0.08301079273223877 
+621.3023681640625 0.06632130593061447 
+684.3104858398438 0.08932632207870483 
+764.3204345703125 0.07453980296850204 
+781.221923828125 0.07784717530012131 
+781.3588256835938 0.45641443133354187 
+896.3796997070312 0.22031182050704956 
+1007.4012451171875 0.09667406231164932 
+1025.42626953125 0.3063616454601288 
+1128.49658203125 0.06959544122219086 
+1153.51806640625 0.3152834177017212 
+1178.6268310546875 0.12105284631252289 
+END IONS
+
+BEGIN IONS
+TITLE=2
+PEPMASS=598.80054
+CHARGE=2+
+SCANS=F1:2485
+RTINSECONDS=825.618
+SEQ=C+57.021GHTNNIRPK
+83.77787780761719 0.03790455684065819 
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diff --git a/setup.cfg b/setup.cfg
index 41b58d48..0da00d24 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -26,7 +26,7 @@ install_requires =
     pandas
     scikit-learn
     torch>=1.9 
-    pytorch-lightning
+    pytorch-lightning==1.5.10
     spectrum_utils
     click
 


Test patch:
diff --git a/casanovo/denovo/train_test.py b/casanovo/denovo/train_test.py
index 741ce84d..5d867df2 100644
--- a/casanovo/denovo/train_test.py
+++ b/casanovo/denovo/train_test.py
@@ -1,22 +1,17 @@
 """Training and testing functionality for the de novo peptide sequencing model"""
+import pytest
 import logging, importlib, os
 from pathlib import Path
 import pytorch_lightning as pl
 from depthcharge.data import AnnotatedSpectrumIndex, SpectrumIndex
 from casanovo.denovo import DeNovoDataModule, Spec2Pep
+import yaml
 
-def train(train_data_path, val_data_path, model_path, config_path):
+def train(train_data_path, val_data_path, model_path, config):
     """Train a Casanovo model with options specified in config.py.""" 
-    
-    #Use custom config file if specified    
-    if config_path == None:
-        from casanovo import config
-    else:
-        importlib.machinery.SourceFileLoader('config', config_path).load_module()
-        import config
         
     #Set random seed across PyTorch, numpy and python.random
-    pl.utilities.seed.seed_everything(seed=config.random_seed, workers=True)
+    pl.utilities.seed.seed_everything(seed=config['random_seed'], workers=True)
 
     #Index training and validation data
     train_data_path = Path(train_data_path)
@@ -28,56 +23,56 @@ def train(train_data_path, val_data_path, model_path, config_path):
         raise FileNotFoundError(f'val_data_path expects directory path but file path was provided instead')        
     val_mgf_files = [val_data_path/f for f in os.listdir(val_data_path) if (val_data_path/f).suffix.lower() == ".mgf"]   
     
-    train_index = AnnotatedSpectrumIndex(config.train_annot_spec_idx_path, train_mgf_files, overwrite=config.train_spec_idx_overwrite)
-    val_index = AnnotatedSpectrumIndex(config.val_annot_spec_idx_path, val_mgf_files, overwrite=config.val_spec_idx_overwrite)
+    train_index = AnnotatedSpectrumIndex(os.path.join(os.getcwd(),config['train_annot_spec_idx_path']), train_mgf_files, overwrite=config['train_spec_idx_overwrite'])
+    val_index = AnnotatedSpectrumIndex(os.path.join(os.getcwd(),config['val_annot_spec_idx_path']), val_mgf_files, overwrite=config['val_spec_idx_overwrite'])
 
     #Initialize data loaders
     train_loader = DeNovoDataModule(
         train_index=train_index,
-        n_peaks=config.n_peaks,
-        min_mz=config.min_mz,
-        max_mz=config.max_mz,
-        min_intensity=config.min_intensity,
-        fragment_tol_mass=config.fragment_tol_mass,        
-        preprocess_spec=config.preprocess_spec,
-        num_workers=config.num_workers,
-        batch_size=config.train_batch_size
+        n_peaks=config['n_peaks'],
+        min_mz=config['min_mz'],
+        max_mz=config['max_mz'],
+        min_intensity=config['min_intensity'],
+        fragment_tol_mass=config['fragment_tol_mass'],        
+        preprocess_spec=config['preprocess_spec'],
+        num_workers=config['num_workers'],
+        batch_size=config['train_batch_size']
     )
 
     val_loader = DeNovoDataModule(
         valid_index=val_index,
-        n_peaks=config.n_peaks,
-        min_mz=config.min_mz,
-        max_mz=config.max_mz,
-        min_intensity=config.min_intensity,
-        fragment_tol_mass=config.fragment_tol_mass,       
-        preprocess_spec=config.preprocess_spec,
-        num_workers=config.num_workers,
-        batch_size=config.val_batch_size
+        n_peaks=config['n_peaks'],
+        min_mz=config['min_mz'],
+        max_mz=config['max_mz'],
+        min_intensity=config['min_intensity'],
+        fragment_tol_mass=config['fragment_tol_mass'],       
+        preprocess_spec=config['preprocess_spec'],
+        num_workers=config['num_workers'],
+        batch_size=config['val_batch_size']
     )
 
     train_loader.setup()
     val_loader.setup()
 
     # Initialize the model
-    if config.train_from_scratch == True:
+    if config['train_from_scratch'] == True:
         model = Spec2Pep(
-            dim_model=config.dim_model,
-            n_head=config.n_head,
-            dim_feedforward=config.dim_feedforward,
-            n_layers=config.n_layers,
-            dropout=config.dropout,
-            dim_intensity=config.dim_intensity,
-            custom_encoder=config.custom_encoder,
-            max_length=config.max_length,
-            residues=config.residues,
-            max_charge=config.max_charge,        
-            n_log=config.n_log,
-            tb_summarywriter=config.tb_summarywriter,
-            warmup_iters = config.warmup_iters,
-            max_iters = config.max_iters,
-            lr=config.learning_rate,
-            weight_decay=config.weight_decay
+            dim_model=config['dim_model'],
+            n_head=config['n_head'],
+            dim_feedforward=config['dim_feedforward'],
+            n_layers=config['n_layers'],
+            dropout=config['dropout'],
+            dim_intensity=config['dim_intensity'],
+            custom_encoder=config['custom_encoder'],
+            max_length=config['max_length'],
+            residues=config['residues'],
+            max_charge=config['max_charge'],        
+            n_log=config['n_log'],
+            tb_summarywriter=config['tb_summarywriter'],
+            warmup_iters = config['warmup_iters'],
+            max_iters = config['max_iters'],
+            lr=config['learning_rate'],
+            weight_decay=config['weight_decay']
         ) 
 
     else:
@@ -86,148 +81,134 @@ def train(train_data_path, val_data_path, model_path, config_path):
             raise FileNotFoundError(f'model_path expects file path but directory path was provided instead')        
         model = Spec2Pep().load_from_checkpoint(
         model_path,
-        dim_model=config.dim_model,
-        n_head=config.n_head,
-        dim_feedforward=config.dim_feedforward,
-        n_layers=config.n_layers,
-        dropout=config.dropout,
-        dim_intensity=config.dim_intensity,
-        custom_encoder=config.custom_encoder,
-        max_length=config.max_length,
-        residues=config.residues,
-        max_charge=config.max_charge,        
-        n_log=config.n_log,
-        tb_summarywriter=config.tb_summarywriter,
-        warmup_iters = config.warmup_iters,
-        max_iters = config.max_iters,
-        lr=config.learning_rate,
-        weight_decay=config.weight_decay
+        dim_model=config['dim_model'],
+        n_head=config['n_head'],
+        dim_feedforward=config['dim_feedforward'],
+        n_layers=config['n_layers'],
+        dropout=config['dropout'],
+        dim_intensity=config['dim_intensity'],
+        custom_encoder=config['custom_encoder'],
+        max_length=config['max_length'],
+        residues=config['residues'],
+        max_charge=config['max_charge'],        
+        n_log=config['n_log'],
+        tb_summarywriter=config['tb_summarywriter'],
+        warmup_iters = config['warmup_iters'],
+        max_iters = config['max_iters'],
+        lr=config['learning_rate'],
+        weight_decay=config['weight_decay']
     )
 
 
 
     #Create Trainer object and checkpoint callback to save model
-    if config.save_model == True:
+    if config['save_model'] == True:
 
         checkpoint_callback = pl.callbacks.ModelCheckpoint(
-            dirpath=config.model_save_folder_path,
-            save_weights_only=config.save_weights_only,
+            dirpath=config['model_save_folder_path'],
+            save_weights_only=config['save_weights_only'],
             filename='{epoch}',
-            every_n_epochs=config.every_n_epochs,
+            every_n_epochs=config['every_n_epochs'],
             save_top_k=-1
         )
 
         trainer = pl.Trainer(
-            accelerator=config.accelerator,
-            logger=config.logger,
-            gpus=config.gpus,
-            max_epochs=config.max_epochs,
-            num_sanity_val_steps=config.num_sanity_val_steps,
+            accelerator=config['accelerator'],
+            logger=config['logger'],
+            gpus=config['gpus'],
+            max_epochs=config['max_epochs'],
+            num_sanity_val_steps=config['num_sanity_val_steps'],
             callbacks=[checkpoint_callback]
         )    
 
     else:
 
         trainer = pl.Trainer(
-            accelerator=config.accelerator,
-            logger=config.logger,
-            gpus=config.gpus,
-            max_epochs=config.max_epochs,
-            num_sanity_val_steps=config.num_sanity_val_steps
+            accelerator=config['accelerator'],
+            logger=config['logger'],
+            gpus=config['gpus'],
+            max_epochs=config['max_epochs'],
+            num_sanity_val_steps=config['num_sanity_val_steps']
         )
         
     #Train the model
     trainer.fit(model, train_loader.train_dataloader(), val_loader.val_dataloader())
 
-def test_evaluate(test_data_path, model_path, config_path):
+def evaluate(test_data_path, model_path, config):
     """Run inference a pre-trained Casanovo model with evaluation and using options specified in config.py."""
-    
-    #Use custom config file if specified
-    if config_path == None:
-        from casanovo import config
-    else:
-        importlib.machinery.SourceFileLoader('config', config_path).load_module()
-        import config
         
     # Initialize the pre-trained model
     model_path = Path(model_path)
     if model_path.is_dir():
-        raise FileNotFoundError(f'model_path expects file path but directory path was provided instead')    
+        raise FileNotFoundError(f'model_path expects file path but directory path was provided instead')
     model_trained = Spec2Pep().load_from_checkpoint(
     model_path,
-    dim_model=config.dim_model,
-    n_head=config.n_head,
-    dim_feedforward=config.dim_feedforward,
-    n_layers=config.n_layers,
-    dropout=config.dropout,
-    dim_intensity=config.dim_intensity,
-    custom_encoder=config.custom_encoder,
-    max_length=config.max_length,
-    residues=config.residues,
-    max_charge=config.max_charge,       
-    n_log=config.n_log,       
+    dim_model=config['dim_model'],
+    n_head=config['n_head'],
+    dim_feedforward=config['dim_feedforward'],
+    n_layers=config['n_layers'],
+    dropout=config['dropout'],
+    dim_intensity=config['dim_intensity'],
+    custom_encoder=config['custom_encoder'],
+    max_length=config['max_length'],
+    residues=config['residues'],
+    max_charge=config['max_charge'],       
+    n_log=config['n_log'],      
 )
     #Index test data
     test_data_path = Path(test_data_path)
     if test_data_path.is_file():
         raise FileNotFoundError(f'test_data_path expects directory path but file path was provided instead')        
     mgf_files = [test_data_path/f for f in os.listdir(test_data_path) if (test_data_path/f).suffix.lower() == ".mgf"]
-    index = AnnotatedSpectrumIndex(config.test_annot_spec_idx_path, mgf_files, overwrite=config.test_spec_idx_overwrite)
+    index = AnnotatedSpectrumIndex(os.path.join(os.getcwd(),config['test_annot_spec_idx_path']), mgf_files, overwrite=config['test_spec_idx_overwrite'])
     
     #Initialize the data loader
     loaders=DeNovoDataModule(
         test_index=index,
-        n_peaks=config.n_peaks,
-        min_mz=config.min_mz,
-        max_mz=config.max_mz,
-        min_intensity=config.min_intensity,
-        fragment_tol_mass=config.fragment_tol_mass,        
-        preprocess_spec=config.preprocess_spec,
-        num_workers=config.num_workers,
-        batch_size=config.test_batch_size
+        n_peaks=config['n_peaks'],
+        min_mz=config['min_mz'],
+        max_mz=config['max_mz'],
+        min_intensity=config['min_intensity'],
+        fragment_tol_mass=config['fragment_tol_mass'],
+        preprocess_spec=config['preprocess_spec'],
+        num_workers=config['num_workers'],
+        batch_size=config['test_batch_size']
     )
     
     loaders.setup(stage='test', annotated=True)
 
     #Create Trainer object
     trainer = pl.Trainer(
-        accelerator=config.accelerator,
-        logger=config.logger,
-        gpus=config.gpus,
-        max_epochs=config.max_epochs,
-        num_sanity_val_steps=config.num_sanity_val_steps
+        accelerator=config['accelerator'],
+        logger=config['logger'],
+        gpus=config['gpus'],
+        max_epochs=config['max_epochs'],
+        num_sanity_val_steps=config['num_sanity_val_steps']
     )
     
     #Run test
     trainer.validate(model_trained, loaders.test_dataloader())
-
-def test_denovo(test_data_path, model_path, config_path, output_path):
-    """Run inference with a pre-trained Casanovo model without evaluation and using options specified in config.py."""
     
-    #Use custom config file if specified
-    if config_path == None:
-        from casanovo import config
-    else:
-        importlib.machinery.SourceFileLoader('config', config_path).load_module()
-        import config
-        
+def denovo(test_data_path, model_path, config, output_path):
+    """Run inference with a pre-trained Casanovo model without evaluation and using options specified in config.py."""
+
     # Initialize the pre-trained model
     model_path = Path(model_path)
     if model_path.is_dir():
         raise FileNotFoundError(f'model_path expects file path but directory path was provided instead')
     model_trained = Spec2Pep().load_from_checkpoint(
     model_path,
-    dim_model=config.dim_model,
-    n_head=config.n_head,
-    dim_feedforward=config.dim_feedforward,
-    n_layers=config.n_layers,
-    dropout=config.dropout,
-    dim_intensity=config.dim_intensity,
-    custom_encoder=config.custom_encoder,
-    max_length=config.max_length,
-    residues=config.residues,
-    max_charge=config.max_charge,       
-    n_log=config.n_log,
+    dim_model=config['dim_model'],
+    n_head=config['n_head'],
+    dim_feedforward=config['dim_feedforward'],
+    n_layers=config['n_layers'],
+    dropout=config['dropout'],
+    dim_intensity=config['dim_intensity'],
+    custom_encoder=config['custom_encoder'],
+    max_length=config['max_length'],
+    residues=config['residues'],
+    max_charge=config['max_charge'],       
+    n_log=config['n_log'],
     output_path=output_path        
 )
     #Index test data
@@ -235,31 +216,31 @@ def test_denovo(test_data_path, model_path, config_path, output_path):
     if test_data_path.is_file():
         raise FileNotFoundError(f'test_data_path expects directory path but file path was provided instead')    
     mgf_files = [test_data_path/f for f in os.listdir(test_data_path) if (test_data_path/f).suffix.lower() == ".mgf"]
-    index = SpectrumIndex(config.test_annot_spec_idx_path, mgf_files, overwrite=config.test_spec_idx_overwrite)
-    
+    index = SpectrumIndex(os.path.join(os.getcwd(),config['test_annot_spec_idx_path']), mgf_files, overwrite=config['test_spec_idx_overwrite'])
+
     #Initialize the data loader
     loaders=DeNovoDataModule(
         test_index=index,
-        n_peaks=config.n_peaks,
-        min_mz=config.min_mz,
-        max_mz=config.max_mz,
-        min_intensity=config.min_intensity,
-        fragment_tol_mass=config.fragment_tol_mass,
-        preprocess_spec=config.preprocess_spec,
-        num_workers=config.num_workers,
-        batch_size=config.test_batch_size
+        n_peaks=config['n_peaks'],
+        min_mz=config['min_mz'],
+        max_mz=config['max_mz'],
+        min_intensity=config['min_intensity'],
+        fragment_tol_mass=config['fragment_tol_mass'],
+        preprocess_spec=config['preprocess_spec'],
+        num_workers=config['num_workers'],
+        batch_size=config['test_batch_size']
     )
     
     loaders.setup(stage='test', annotated=False)
 
     #Create Trainer object
     trainer = pl.Trainer(
-        accelerator=config.accelerator,
-        logger=config.logger,
-        gpus=config.gpus,
-        max_epochs=config.max_epochs,
-        num_sanity_val_steps=config.num_sanity_val_steps
+        accelerator=config['accelerator'],
+        logger=config['logger'],
+        gpus=config['gpus'],
+        max_epochs=config['max_epochs'],
+        num_sanity_val_steps=config['num_sanity_val_steps']
     )
     
     #Run model without evaluation
-    trainer.test(model_trained, loaders.test_dataloader())
\ No newline at end of file
+    trainer.test(model_trained, loaders.test_dataloader())
diff --git a/tests/conftest.py b/tests/conftest.py
index 09223aa3..2145e977 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -13,9 +13,81 @@
 
 import pytest
 import requests
-
 import ppx
 
+from pyteomics.mass import calculate_mass
+import numpy as np
+
+# Pytest fixtures added here can be used as parameters for testing methods
+
+@pytest.fixture
+def mgf_small(tmp_path):
+    """An MGF file with 2 annotated spectra."""
+    peptides = ["LESLIEK", "PEPTIDEK"]
+    mgf_file = tmp_path / "small.mgf"
+    return _create_mgf(peptides, mgf_file)
+
+#Utility Funcs
+def _create_mgf(peptides, mgf_file, random_state=42):
+    """Create a fake MGF file from one or more peptides.
+    Parameters
+    ----------
+    peptides : str or list of str
+        The peptides for which to create spectra.
+    mgf_file : Path
+        The MGF file to create.
+    random_state : int or numpy.random.Generator, optional
+        The random seed. The charge states are chosen to be
+        2 or 3 randomly.
+    Returns
+    -------
+    """
+    rng = np.random.default_rng(random_state)
+    entries = [_create_mgf_entry(p, rng.choice([2, 3])) for p in peptides]
+    with mgf_file.open("w+") as mgf_ref:
+        mgf_ref.write("\n".join(entries))
+
+    return mgf_file
+
+def _create_mgf_entry(peptide, charge=2):
+    """Create a MassIVE-KB style MGF entry for a single PSM.
+    Parameters
+    ----------
+    peptide : str
+        A peptide sequence.
+    charge : int, optional
+        The peptide charge state.
+    Returns
+    -------
+    str
+        The PSM entry in an MGF file format.
+    """
+    mz = calculate_mass(peptide, charge=int(charge))
+    frags = []
+    for idx in range(len(peptide)):
+        for zstate in range(1, charge):
+            b_pep = peptide[: idx + 1]
+            frags.append(
+                str(calculate_mass(b_pep, charge=zstate, ion_type="b"))
+            )
+
+            y_pep = peptide[idx:]
+            frags.append(
+                str(calculate_mass(y_pep, charge=zstate, ion_type="y"))
+            )
+
+    frag_string = " 1\n".join(frags) + " 1"
+
+    mgf = [
+        "BEGIN IONS",
+        f"SEQ={peptide}",
+        f"PEPMASS={mz}",
+        f"CHARGE={charge}+",
+        f"{frag_string}",
+        "END IONS",
+    ]
+    return "\n".join(mgf)
+
 # Set the PPX_DATA_DIRECTORY --------------------------------------------------
 @pytest.fixture(autouse=True)
 def ppx_data_dir(monkeypatch, tmp_path):
diff --git a/tests/system_tests/test_program.py b/tests/system_tests/test_program.py
new file mode 100644
index 00000000..139a3564
--- /dev/null
+++ b/tests/system_tests/test_program.py
@@ -0,0 +1,74 @@
+import pytest
+from casanovo.denovo import train, evaluate, denovo, Spec2Pep, DeNovoDataModule
+import os
+import yaml
+from depthcharge.data import AnnotatedSpectrumIndex, SpectrumIndex
+import pytorch_lightning as pl
+from os.path import isfile
+
+def test_denovo_casanovo(mgf_small):
+
+    """Load up a basic config"""
+    abs_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../casanovo/config.yaml')
+    with open(abs_path) as f:
+        config = yaml.safe_load(f)
+
+    """Ensure that the test doesn't raise any system overdraw issues in terms of resources"""
+    config['gpus'] = []
+    config['num_workers'] = 0
+    output_path=os.path.dirname(os.path.abspath(__file__))
+
+    """Running a small mgf through an untrained model just to test that the model output is valid in terms of format"""
+    tabula_rasa_model = Spec2Pep(
+            dim_model=config['dim_model'],
+            n_head=config['n_head'],
+            dim_feedforward=config['dim_feedforward'],
+            n_layers=config['n_layers'],
+            dropout=config['dropout'],
+            dim_intensity=config['dim_intensity'],
+            custom_encoder=config['custom_encoder'],
+            max_length=config['max_length'],
+            residues=config['residues'],
+            max_charge=config['max_charge'],        
+            n_log=config['n_log'],
+            tb_summarywriter=config['tb_summarywriter'],
+            warmup_iters = config['warmup_iters'],
+            max_iters = config['max_iters'],
+            lr=config['learning_rate'],
+            weight_decay=config['weight_decay'],
+            output_path=output_path
+        )
+    from pathlib import Path
+    mgf_small = Path(mgf_small).parents[0]
+    mgf_files = [mgf_small/f for f in os.listdir(mgf_small) if (mgf_small/f).suffix.lower() == ".mgf"]
+    index = SpectrumIndex(os.path.join(os.getcwd(),config['test_annot_spec_idx_path']), mgf_files, overwrite=config['test_spec_idx_overwrite'])
+
+    loaders=DeNovoDataModule(
+        test_index=index,
+        n_peaks=config['n_peaks'],
+        min_mz=config['min_mz'],
+        max_mz=config['max_mz'],
+        min_intensity=config['min_intensity'],
+        fragment_tol_mass=config['fragment_tol_mass'],
+        preprocess_spec=config['preprocess_spec'],
+        num_workers=config['num_workers'],
+        batch_size=config['test_batch_size']
+    )
+    
+    loaders.setup(stage='test', annotated=False)
+
+    #Create Trainer object
+    trainer = pl.Trainer(
+        accelerator=config['accelerator'],
+        logger=config['logger'],
+        gpus=config['gpus'],
+        max_epochs=config['max_epochs'],
+        num_sanity_val_steps=config['num_sanity_val_steps']
+    )
+
+    trainer.test(tabula_rasa_model, loaders.test_dataloader())
+
+    """If the output exists then the test has passed"""
+    if(isfile(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'casanovo_output.csv'))):
+        os.remove(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'casanovo_output.csv'))
+        assert True
\ No newline at end of file
diff --git a/tests/unit_tests/test_transformer.py b/tests/unit_tests/test_transformer.py
new file mode 100644
index 00000000..9007a281
--- /dev/null
+++ b/tests/unit_tests/test_transformer.py
@@ -0,0 +1,80 @@
+import pytest
+import torch
+from depthcharge.components.transformers import (
+    SpectrumEncoder,
+    PeptideEncoder,
+    PeptideDecoder,
+)
+from depthcharge.masses import PeptideMass
+
+def test_spectrum_encoder():
+    spectra = torch.tensor(
+        [
+            [[100.1, 0.1], [200.2, 0.2], [300.3, 0.3]],
+            [[400.4, 0.4], [500, 0.5], [0, 0]],
+        ]
+    )
+    model = SpectrumEncoder(
+        dim_model=128, 
+        n_head=8, 
+        dim_feedforward=1024,
+        n_layers=1, 
+        dropout=0, 
+        dim_intensity=None
+    )
+    emb, mask = model(spectra)
+    assert emb.shape == (2, 4, 128)
+    assert mask.sum() == 1
+
+    model = SpectrumEncoder(
+        dim_model=128, 
+        n_head=8, 
+        dim_feedforward=1024,
+        n_layers=1, 
+        dropout=0, 
+        dim_intensity=4
+    )
+    emb, mask = model(spectra)
+    assert emb.shape == (2, 4, 128)
+    assert mask.sum() == 1
+
+    model = SpectrumEncoder(8, 1, 12, dim_intensity=None)
+    emb, mask = model(spectra)
+    assert emb.shape == (2, 4, 8)
+    assert mask.sum() == 1
+
+    model = SpectrumEncoder(8, 1, 12, dim_intensity=4)
+    emb, mask = model(spectra)
+    assert emb.shape == (2, 4, 8)
+    assert mask.sum() == 1
+
+def test_peptide_decoder():
+    spectra = torch.tensor(
+        [
+            [[100.1, 0.1], [200.2, 0.2], [300.3, 0.3]],
+            [[400.4, 0.4], [500, 0.5], [0, 0]],
+        ]
+    )
+    peptides = ["LESLIEK", "PEPTIDER"]
+    precursors = torch.tensor([[100.0, 2], [200.0, 3]])
+    dec_model = PeptideDecoder(
+        dim_model=128,
+        n_head=8,
+        dim_feedforward=1024,
+        n_layers=1,
+        dropout=0,
+        residues="canonical",
+        max_charge=5,
+    )
+    enc_model = SpectrumEncoder(
+        dim_model=128, 
+        n_head=8, 
+        dim_feedforward=1024,
+        n_layers=1, 
+        dropout=0, 
+        dim_intensity=None
+    )
+    memory, mem_mask = enc_model(spectra)
+    scores, tokens = dec_model(peptides, precursors, memory, mem_mask)
+    assert scores.shape == (2, 10, dec_model.vocab_size + 1)
+    assert tokens.shape == (2, 9)
\ No newline at end of file