ms2deepscore

R/ms2deepscore.R

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+library(reticulate)
+
+# Specify Python 3.10
+use_python("/your/path/to/r-miniconda-arm64/bin/python3.10", required = TRUE)
+
+py_config()
+
+system("pip install ms2deepscore matchms numpy torch", intern = TRUE)
+
+# Step 1: Initialize Python Environment and Import Required Libraries
+initialize_ms2deepscore <- function(python_env = NULL) {
+  if (!is.null(python_env)) {
+    use_virtualenv(python_env, required = TRUE)
+  }
+
+  # Import Python modules
+  torch <- import("torch", convert = FALSE)
+  ms2ds <- import("ms2deepscore", convert = FALSE)
+  matchms <- import("matchms", convert = FALSE)
+
+  # Step 2: Patch PyTorch to Allow Full Model Loading
+  cat("Patching PyTorch to allow full model loading...\n")
+  py_run_string("
+import torch
+
+# Patch `torch.load()` globally to set `weights_only=False`
+original_load = torch.load
+def patched_load(*args, **kwargs):
+    if 'weights_only' not in kwargs:
+        kwargs['weights_only'] = False  # Force weights_only=False
+    return original_load(*args, **kwargs)
+
+torch.load = patched_load
+  ")
+  py_run_string("
+from matchms.Pipeline import Pipeline, create_workflow
+from matchms.filtering.default_pipelines import DEFAULT_FILTERS
+from ms2deepscore import MS2DeepScore
+")
+
+  return(list(ms2ds = ms2ds, matchms = matchms))#, create_workflow = create_workflow))
+}
+
+# Step 3: Load MS2DeepScore Model
+load_ms2deepscore_model <- function(model_file, env) {
+  ms2ds <- env$ms2ds
+  cat("Loading MS2DeepScore model...\n")
+
+  model <- ms2ds$models$load_model(model_file)
+
+  if (is.null(model)) {
+    stop("ERROR: Model failed to load.")
+  } else {
+    cat("Model loaded successfully!\n")
+  }
+
+  return(model)
+}
+
+# Step 3: Run MS2DeepScore Pipeline
+run_ms2deepscore_pipeline <- function(spectrum_path, model, env) {
+  matchms <- env$matchms
+  create_workflow <- env$create_workflow
+  ms2ds <- env$ms2ds
+
+  cat("Setting up MS2DeepScore pipeline...\n")
+
+  # Create the processing pipeline using `create_workflow`
+  # Create the processing pipeline using `create_workflow`
+  py_run_string("
+pipeline = Pipeline(create_workflow(
+    query_filters=DEFAULT_FILTERS,
+    score_computations=[[MS2DeepScore, {'model': r.model}]]
+))
+report = pipeline.run(r.spectrum_path)
+similarity_matrix = pipeline.scores.to_array()
+")
+
+  # Run the pipeline on the spectrum file
+  cat("Running pipeline...\n")
+  pipeline <- py$pipeline
+  #report <- py$report
+  # Extract similarity matrix
+  similarity_matrix <- py$similarity_matrix #pipeline$score_computations
+
+  cat("Pipeline run successfully!\n")
+  return(similarity_matrix)
+}
+
+# Step 5: Execute the Pipeline in R
+model_path <- "/your/path/to/ms2deepscore_model.pt"  # Replace with the actual path
+spectrum_path <- "/your/path/to/R_python_hackathon/pesticides.mgf"  # Replace with the actual path
+
+# Initialize the Python environment and import libraries
+env <- initialize_ms2deepscore()
+
+# Load MS2DeepScore model
+model <- load_ms2deepscore_model(model_path, env)
+
+# Run the pipeline and get similarity scores
+similarity_scores <- run_ms2deepscore_pipeline(spectrum_path, model, env)
+