add test_hist_classified_stable_as_func_of_hull_dist()

refactor plot funcs to use e_above_hull_pred and e_above_hull_true as main inputs
 rewrite plot scripts to match new signature

.gitignore

@@ -15,6 +15,7 @@ pretrained/
 
 # Weights and Biases logs
 wandb/
+job-logs/
 
 # slurm logs
 slurm-*out

mb_discovery/m3gnet/join_and_plot_m3gnet_relax_results.py

@@ -4,9 +4,9 @@
 import gzip
 import io
 import pickle
+import urllib.request
 from datetime import datetime
 from glob import glob
-from urllib.request import urlopen
 
 import pandas as pd
 from pymatgen.analysis.phase_diagram import PatchedPhaseDiagram, PDEntry
@@ -15,7 +15,7 @@
 
 from mb_discovery import ROOT, as_dict_handler
 from mb_discovery.plot_scripts.plot_funcs import (
-    hist_classify_stable_as_func_of_hull_dist,
+    hist_classified_stable_as_func_of_hull_dist,
 )
 
 
@@ -25,7 +25,7 @@
 # %%
 task_type = "RS3RE"
 date = "2022-08-19"
-glob_pattern = f"{date}-m3gnet-relax-wbm-{task_type}/*.json.gz"
+glob_pattern = f"{date}-m3gnet-wbm-relax-{task_type}/*.json.gz"
 file_paths = sorted(glob(f"{ROOT}/data/{glob_pattern}"))
 print(f"Found {len(file_paths):,} files for {glob_pattern = }")
 
@@ -68,7 +68,7 @@
 # %%
 # 2022-01-25-ppd-mp+wbm.pkl.gz (235 MB)
 ppd_pickle_url = "https://figshare.com/ndownloader/files/36669624"
-zipped_file = urlopen(ppd_pickle_url)
+zipped_file = urllib.request.urlopen(ppd_pickle_url)
 
 ppd_mp_wbm: PatchedPhaseDiagram = pickle.load(
     io.BytesIO(gzip.decompress(zipped_file.read()))
@@ -114,15 +114,21 @@
 
 
 # %%
-out_path = f"{ROOT}/data/{today}-m3gnet-relax-wbm-{task_type}.json.gz"
+out_path = f"{ROOT}/data/{today}-m3gnet-wbm-relax-{task_type}.json.gz"
 df_m3gnet.reset_index().to_json(out_path, default_handler=as_dict_handler)
 
+out_path = f"{ROOT}/data/2022-08-16-m3gnet-wbm-relax-results-IS2RE.json.gz"
+df_m3gnet = pd.read_json(out_path)
+
 
 # %%
-ax_hull_dist_hist = hist_classify_stable_as_func_of_hull_dist(
-    formation_energy_targets=df_m3gnet.e_form_ppd_2022_01_25,
-    formation_energy_preds=df_m3gnet.e_form_m3gnet_from_ppd,
-    e_above_hull_vals=df_m3gnet.e_above_mp_hull,
+df_m3gnet["e_above_hull_pred"] = (  # TODO fix this incorrect e_above_hull_pred
+    df_m3gnet["e_form_m3gnet_from_ppd"] - df_m3gnet["e_above_mp_hull"]
+)
+
+ax_hull_dist_hist = hist_classified_stable_as_func_of_hull_dist(
+    e_above_hull_pred=df_m3gnet.e_above_hull_pred,
+    e_above_hull_true=df_m3gnet.e_above_mp_hull,
 )
 
 # ax_hull_dist_hist.figure.savefig(f"{ROOT}/plots/{today}-m3gnet-wbm-hull-dist-hist.pdf")

mb_discovery/m3gnet/slurm_array_m3gnet_relax_wbm.py

@@ -9,9 +9,9 @@
 import m3gnet
 import numpy as np
 import pandas as pd
+import wandb
 from m3gnet.models import Relaxer
 
-import wandb
 from mb_discovery import ROOT, as_dict_handler
 
 
@@ -20,7 +20,7 @@
 
 ```sh
 sbatch --partition icelake-himem --account LEE-SL3-CPU --array 1-101 \
-    --time 3:0:0 --job-name m3gnet-relax-wbm-RS2RE --mem 12000 \
+    --time 3:0:0 --job-name m3gnet-wbm-relax-RS2RE --mem 12000 \
     --output mb_discovery/m3gnet/slurm_logs/slurm-%A-%a.out \
     --wrap "python mb_discovery/m3gnet/slurm_array_m3gnet_relax_wbm.py"
 ```
@@ -48,7 +48,7 @@
 print(f"{job_array_id=}")
 
 today = f"{datetime.now():%Y-%m-%d}"
-out_dir = f"{ROOT}/data/{today}-m3gnet-relax-wbm-{task_type}"
+out_dir = f"{ROOT}/data/{today}-m3gnet-wbm-relax-{task_type}"
 os.makedirs(out_dir, exist_ok=True)
 json_out_path = f"{out_dir}/{job_array_id}.json.gz"
 
@@ -77,7 +77,7 @@
     wandb.login()
 wandb.init(
     project="m3gnet",  # run will be added to this project
-    name=f"m3gnet-relax-wbm-{task_type}-{job_id}-{job_array_id}",
+    name=f"m3gnet-wbm-relax-{task_type}-{job_id}-{job_array_id}",
     config=run_params,
 )
 

mb_discovery/plot_scripts/hist_classified_stable_as_func_of_hull_dist.py

@@ -1,12 +1,13 @@
 # %%
 from datetime import datetime
-from typing import Literal
 
 import matplotlib.pyplot as plt
 import pandas as pd
 
 from mb_discovery import ROOT
 from mb_discovery.plot_scripts.plot_funcs import (
+    StabilityCriterion,
+    WhichEnergy,
     hist_classified_stable_as_func_of_hull_dist,
 )
 
@@ -55,27 +56,34 @@
 assert all(nan_counts == 0), f"df should not have missing values: {nan_counts}"
 
 target_col = "e_form_target"
-stability_crit: Literal["energy", "energy+std", "energy-std"] = "energy"
-energy_type: Literal["true", "pred"] = "true"
-
+stability_crit: StabilityCriterion = "energy"
+which_energy: WhichEnergy = "true"
+
+if "std" in stability_crit:
+    # TODO column names to compute standard deviation from are currently hardcoded
+    # needs to be updated when adding non-aviary models with uncertainty estimation
+    var_aleatoric = (df.filter(like="_ale_") ** 2).mean(axis=1)
+    var_epistemic = df.filter(regex=r"_pred_\d").var(axis=1, ddof=0)
+    std_total = (var_epistemic + var_aleatoric) ** 0.5
+else:
+    std_total = None
 
 # make sure we average the expected number of ensemble member predictions
 pred_cols = df.filter(regex=r"_pred_\d").columns
 assert len(pred_cols) == 10
 
 ax = hist_classified_stable_as_func_of_hull_dist(
-    df,
-    target_col,
-    pred_cols,
-    e_above_hull_col="e_above_mp_hull",
-    energy_type=energy_type,
+    e_above_hull_pred=df[pred_cols].mean(axis=1) - df[target_col],
+    e_above_hull_true=df.e_above_mp_hull,
+    which_energy=which_energy,
     stability_crit=stability_crit,
+    std_pred=std_total,
 )
 
 ax.figure.set_size_inches(10, 9)
 
 ax.legend(loc="upper left", frameon=False)
 
-fig_name = f"wren-wbm-hull-dist-hist-{energy_type=}-{stability_crit=}"
+fig_name = f"wren-wbm-hull-dist-hist-{which_energy=}-{stability_crit=}"
 img_path = f"{ROOT}/figures/{today}-{fig_name}.pdf"
 # plt.savefig(img_path)

mb_discovery/plot_scripts/hist_classified_stable_as_func_of_hull_dist_batches.py

@@ -6,6 +6,8 @@
 
 from mb_discovery import ROOT
 from mb_discovery.plot_scripts.plot_funcs import (
+    StabilityCriterion,
+    WhichEnergy,
     hist_classified_stable_as_func_of_hull_dist,
 )
 
@@ -50,34 +52,39 @@
 
 
 # %%
-energy_type = "true"
-stability_crit = "energy"
+which_energy: WhichEnergy = "true"
+stability_crit: StabilityCriterion = "energy"
 df["wbm_batch"] = df.index.str.split("-").str[2]
 fig, axs = plt.subplots(2, 3, figsize=(18, 9))
 
 # make sure we average the expected number of ensemble member predictions
 pred_cols = df.filter(regex=r"_pred_\d").columns
 assert len(pred_cols) == 10
 
-common_kwargs = dict(
-    target_col="e_form_target",
-    pred_cols=pred_cols,
-    energy_type=energy_type,
-    stability_crit=stability_crit,
-    e_above_hull_col="e_above_mp_hull",
-)
 
 for (batch_idx, batch_df), ax in zip(df.groupby("wbm_batch"), axs.flat):
-    hist_classified_stable_as_func_of_hull_dist(batch_df, ax=ax, **common_kwargs)
+    hist_classified_stable_as_func_of_hull_dist(
+        e_above_hull_pred=batch_df[pred_cols].mean(axis=1) - batch_df.e_form_target,
+        e_above_hull_true=batch_df.e_above_mp_hull,
+        which_energy=which_energy,
+        stability_crit=stability_crit,
+        ax=ax,
+    )
 
     title = f"Batch {batch_idx} ({len(df):,})"
     ax.set(title=title)
 
 
-hist_classified_stable_as_func_of_hull_dist(df, ax=axs.flat[-1], **common_kwargs)
+hist_classified_stable_as_func_of_hull_dist(
+    e_above_hull_pred=df[pred_cols].mean(axis=1),
+    e_above_hull_true=df.e_above_mp_hull,
+    which_energy=which_energy,
+    stability_crit=stability_crit,
+    ax=axs.flat[-1],
+)
 
 axs.flat[-1].set(title=f"Combined {batch_idx} ({len(df):,})")
 axs.flat[0].legend(frameon=False, loc="upper left")
 
-img_name = f"{today}-wren-wbm-hull-dist-hist-{energy_type=}-{stability_crit=}.pdf"
+img_name = f"{today}-wren-wbm-hull-dist-hist-{which_energy=}-{stability_crit=}.pdf"
 # plt.savefig(f"{ROOT}/figures/{img_name}")