make bar plots when run_features is called

src/br/analysis/analysis_utils.py

@@ -55,7 +55,14 @@ def check_mig():
 def get_mig_ids(gpu_uuid):
     try:
         # Get the list of GPUs
-        output = subprocess.check_output(['nvidia-smi','--query-gpu=,index,uuid' ,'--format=csv,noheader']).decode('utf-8').strip().split('\n')
+        output = (
+            subprocess.check_output(
+                ["nvidia-smi", "--query-gpu=,index,uuid", "--format=csv,noheader"]
+            )
+            .decode("utf-8")
+            .strip()
+            .split("\n")
+        )
 
         # Find the index of the specified GPU UUID
         gpu_index = -1
@@ -71,7 +78,9 @@ def get_mig_ids(gpu_uuid):
         # Now we need to get the MIG IDs for this GPU
         mig_ids = []
         # Run nvidia-smi command to get detailed information including MIG IDs
-        detailed_output = subprocess.check_output(['nvidia-smi', '-L']).decode('utf-8').strip().split('\n')
+        detailed_output = (
+            subprocess.check_output(["nvidia-smi", "-L"]).decode("utf-8").strip().split("\n")
+        )
 
         # Flag to determine if we are in the right GPU section
         in_gpu_section = False
@@ -82,11 +91,13 @@ def get_mig_ids(gpu_uuid):
                 break
 
             # print(line)
-            
+
             if in_gpu_section:
                 # Check for MIG devices
                 if "MIG" in line:
-                    mig_id = line.split('(')[1].split(')')[0].split(' ')[-1]  # Assuming format is '.... MIG (UUID) ...'
+                    mig_id = (
+                        line.split("(")[1].split(")")[0].split(" ")[-1]
+                    )  # Assuming format is '.... MIG (UUID) ...'
                     mig_ids.append(mig_id.strip())
 
         return mig_ids
@@ -105,14 +116,14 @@ def config_gpu():
 
     for line in lines:
         index, uuid, name, mem_used, mem_total = map(str.strip, line.split(","))
-        utilization = float(mem_used)*100/float(mem_total)
-      
+        utilization = float(mem_used) * 100 / float(mem_total)
+
         # Check if GPU utilization is under 20% (indicating it's idle)
         if utilization < 20:
             # print(uuid, utilization)
             if is_mig:
                 mig_ids = get_mig_ids(uuid)
-             
+
                 if mig_ids:
                     selected_gpu_id_or_uuid = mig_ids[0]  # Select the first MIG ID
                     break  # Exit the loop after finding the first MIG ID

src/br/analysis/run_drugdata_analysis.py

@@ -1,19 +1,20 @@
+import argparse
 import os
+import sys
 from pathlib import Path
+
+from br.chandrasekaran_et_al.utils import _plot, perturbation_detection
 from br.models.compute_features import get_embeddings
 from br.models.utils import get_all_configs_per_dataset
-from br.chandrasekaran_et_al.utils import perturbation_detection, _plot
-import sys
-import argparse
 
 
 def _get_featurecols(df):
-    """returna  list of featuredata columns"""
+    """returna  list of featuredata columns."""
     return [c for c in df.columns if "mu" in c]
 
 
 def _get_featuredata(df):
-    """return dataframe of just featuredata columns"""
+    """return dataframe of just featuredata columns."""
     return df[_get_featurecols(df)]
 
 
@@ -25,11 +26,9 @@ def main(args):
     dataset_name = args.dataset_name
     DATASET_INFO = get_all_configs_per_dataset(results_path)
     dataset = DATASET_INFO[dataset_name]
-    run_names = dataset['names']
+    run_names = dataset["names"]
 
-    all_ret, df = get_embeddings(
-        run_names, args.dataset_name, DATASET_INFO, args.embeddings_path
-    )
+    all_ret, df = get_embeddings(run_names, args.dataset_name, DATASET_INFO, args.embeddings_path)
     all_ret["well_position"] = "A0"  # dummy
     all_ret["Assay_Plate_Barcode"] = "Plate0"  # dummy
 
@@ -41,10 +40,10 @@ def main(args):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Script for computing perturbation detection metrics")
-    parser.add_argument(
-        "--save_path", type=str, required=True, help="Path to save the results."
+    parser = argparse.ArgumentParser(
+        description="Script for computing perturbation detection metrics"
     )
+    parser.add_argument("--save_path", type=str, required=True, help="Path to save the results.")
     parser.add_argument(
         "--embeddings_path", type=str, required=True, help="Path to the saved embeddings."
     )
@@ -63,4 +62,4 @@ def main(args):
 
     cellpack dataset
     python src/br/analysis/run_drugdata_analysis.py --save_path "./outputs_npm1_perturb/" --embeddings_path "./morphology_appropriate_representation_learning/model_embeddings/npm1_perturb/" --dataset_name "npm1_perturb"
-    """
+    """

src/br/analysis/run_embeddings.py

@@ -3,6 +3,7 @@
 import os
 import sys
 from pathlib import Path
+
 from br.analysis.analysis_utils import setup_evaluation_params, setup_gpu, str2bool
 from br.models.load_models import get_data_and_models
 from br.models.save_embeddings import save_embeddings
@@ -31,7 +32,7 @@ def main(args):
         skew_scale,
     ) = setup_evaluation_params(manifest, run_names)
 
-    # make save path directory 
+    # make save path directory
     Path(args.save_path).mkdir(parents=True, exist_ok=True)
 
     # save embeddings for each model

src/br/analysis/run_features.py

@@ -3,7 +3,9 @@
 import os
 import sys
 from pathlib import Path
+
 import pandas as pd
+
 from br.analysis.analysis_utils import (
     get_feature_params,
     setup_evaluation_params,
@@ -39,7 +41,7 @@ def main(args):
     ) = get_data_and_models(args.dataset_name, batch_size, config_path + "/results/", args.debug)
     max_embed_dim = min(latent_dims)
 
-    # make save path directory 
+    # make save path directory
     Path(args.save_path).mkdir(parents=True, exist_ok=True)
 
     # Save model sizes to CSV
@@ -81,9 +83,7 @@ def main(args):
         classification_params,
         evolve_params,
         regression_params,
-    ) = get_feature_params(
-        config_path + "/results/", args.dataset_name, manifest, keys, run_names
-    )
+    ) = get_feature_params(config_path + "/results/", args.dataset_name, manifest, keys, run_names)
 
     metric_list = [
         "Rotation Invariance Error",
@@ -95,7 +95,6 @@ def main(args):
     if regression_params["target_cols"]:
         metric_list.append("Regression")
 
-
     # Compute multi-metric benchmarking features
     compute_features(
         dataset=args.dataset_name,
@@ -126,12 +125,12 @@ def main(args):
     csvs = [i.split(".")[0] for i in csvs]
     # Remove non metric related csvs
     csvs = [i for i in csvs if i not in run_names and i not in keys]
-    csvs = [i for i in csvs if i not in ['image', 'pcloud']]
+    csvs = [i for i in csvs if i not in ["image", "pcloud"]]
     # classification and regression metrics are unique to each dataset
     unique_metrics = [i for i in csvs if "classification" in i or "regression" in i]
     # Collect dataframe and make plots
     df, df_non_agg = collect_outputs(args.save_path, "std", run_names, csvs)
-    plot(args.save_path, df, run_names, args.dataset_name, "std", unique_metrics)
+    plot(args.save_path, df, run_names, args.dataset_name, "std", unique_metrics, df_non_agg)
 
 
 if __name__ == "__main__":

src/br/chandrasekaran_et_al/utils.py

@@ -2,10 +2,13 @@
 import itertools
 import os
 from pathlib import Path
-import seaborn as sns
+
 import copairs.compute_np as backend
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import pycytominer
+import seaborn as sns
 from copairs.compute import cosine_indexed
 from copairs.map import (
     aggregate,
@@ -18,8 +21,7 @@
 from sklearn.metrics import average_precision_score
 from sklearn.metrics.pairwise import cosine_similarity
 from tqdm import tqdm
-import matplotlib.pyplot as plt
-import pycytominer
+
 from br.chandrasekaran_et_al import utils
 
 
@@ -172,7 +174,12 @@ def _plot(all_rep, save_path):
         .sort_values(by="q_value")["Metadata_broad_sample"]
         .values
     )
-    ordered_drugs = all_rep.groupby(['Metadata_broad_sample']).mean().sort_values(by='q_value').reset_index()['Metadata_broad_sample']
+    ordered_drugs = (
+        all_rep.groupby(["Metadata_broad_sample"])
+        .mean()
+        .sort_values(by="q_value")
+        .reset_index()["Metadata_broad_sample"]
+    )
     x_order = ordered_drugs
 
     g = sns.catplot(

src/br/features/plot.py

@@ -1,5 +1,6 @@
 from pathlib import Path
 
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 import mitsuba as mi
 
@@ -159,8 +160,8 @@ def plot(
     title,
     norm="std",
     unique_expressivity_metrics=None,
+    df_non_agg=None,
 ):
-    import matplotlib as mpl
 
     mpl.rcParams["pdf.fonttype"] = 42
     df = df.dropna()
@@ -248,8 +249,50 @@ def plot(
 
     fig.write_image(path / f"{title}.png", scale=3)
     fig.write_image(path / f"{title}.pdf", scale=3)
-    # fig.write_image(path / f"{title}.eps", scale=2)
-    # fig.write_image(path / f"{title}.pdf")
+
+    if df_non_agg is not None:
+        sns.set(font_scale=1.1)
+        sns.set_theme(style="white")
+        for var in df_non_agg["variable"].unique():
+            this_df = df_non_agg.loc[df_non_agg["variable"] == var].reset_index(drop=True)
+            g = sns.catplot(
+                data=this_df,
+                y="model",
+                x="value",
+                kind="bar",
+                aspect=1.1,
+                height=2.6,
+                log=False,
+                errorbar="sd",
+                hue="model",
+                legend=False,
+                dodge=False,
+                palette=colors,
+            )
+
+            g.map(
+                sns.stripplot,
+                "value",
+                "model",
+                color="k",
+                dodge=True,
+                alpha=0.6,
+                ec="k",
+                linewidth=1,
+                s=1,
+            )
+
+            g.set(
+                xlim=[
+                    np.quantile(this_df["value"].values, 0.05),
+                    np.quantile(this_df["value"].values, 0.95),
+                ]
+            )
+
+            g.set(yticklabels=[])
+
+            g.savefig(path / f"{var}.png", bbox_inches="tight", dpi=300)
+            g.savefig(path / f"{var}.pdf", bbox_inches="tight", dpi=300)
 
 
 def plot_pc_saved(