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Merge pull request #153 from sparks-baird/gridrdf
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gridrdf functionality
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sgbaird authored Jun 23, 2023
2 parents 6f169b3 + 9f45169 commit 5b55807
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Showing 6 changed files with 9 additions and 7 deletions.
2 changes: 1 addition & 1 deletion .pre-commit-config.yaml
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Expand Up @@ -6,6 +6,6 @@ repos:
# - id: end-of-file-fixer
# - id: trailing-whitespace
- repo: https://github.com/psf/black
rev: 19.3b0
rev: 23.3.0
hooks:
- id: black
3 changes: 2 additions & 1 deletion examples/crabnet_performance.py
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Expand Up @@ -4,6 +4,7 @@
# TODO: incorporate CrabNet uncertainty into search
"""
from crabnet.crabnet_ import CrabNet
import pandas as pd

# %% imports
from tqdm import tqdm
Expand Down Expand Up @@ -82,7 +83,7 @@
idx = perf_val_df.pred.idxmax()
# idx = np.where(val_pred == max(val_pred))[0][0]
move_row = perf_val_df.loc[idx]
perf_train_df.append(move_row)
perf_train_df = pd.concat((perf_train_df, move_row))
perf_val_df = perf_val_df.drop(index=idx)
next_experiments.append(move_row.to_dict())
experiment = ad_experiments_metrics(
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2 changes: 1 addition & 1 deletion mat_discover/adaptive_design.py
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Expand Up @@ -150,7 +150,7 @@ def suggest_next_experiment(
# append compound to train, remove from val, and reset indices
# https://stackoverflow.com/a/12204428/13697228
move_row = self.val_df[self.val_df.index == next_index]
self.train_df = self.train_df.append(move_row)
self.train_df = pd.concat((self.train_df, move_row))
self.val_df = self.val_df[self.val_df.index != next_index]
# self.val_df = self.val_df.drop(index=next_index)

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4 changes: 2 additions & 2 deletions mat_discover/mat_discover_.py
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Expand Up @@ -813,8 +813,8 @@ def predict(
f"self.val_rad_neigh_avg` and `self.val_k_neigh_avg` are being assigned the same values as `val_dens` for compatibility reasons since a non-DiSCoVeR novelty learner was specified: {self.novelty_learner}."
)
# composition-based featurization
X_train: Union[pd.DataFrame, np.ndarray, List] = []
X_val: Union[pd.DataFrame, np.ndarray, List] = []
X_train: Union[np.ndarray, List] = []
X_val: Union[np.ndarray, List] = []
assert self.train_inputs is not None
if self.novelty_prop == "mod_petti":
assert isinstance(X_train, list)
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4 changes: 2 additions & 2 deletions mat_discover/utils/extraordinary.py
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Expand Up @@ -17,7 +17,7 @@ def extraordinary_split(
):
# set aside high-performing candidates
if val_df is not None:
train_val_df = train_df.append(val_df)
train_val_df = pd.concat((train_df, val_df))
else:
train_val_df = train_df

Expand All @@ -31,7 +31,7 @@ def extraordinary_split(
train_df, val_df = train_test_split(
train_val_df, train_size=train_size, random_state=random_state
)
val_df = val_df.append(extraordinary_df)
val_df = pd.concat((val_df, extraordinary_df))

return train_df, val_df, extraordinary_thresh

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1 change: 1 addition & 0 deletions pyproject.toml
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Expand Up @@ -33,6 +33,7 @@ dependencies = [
"umap-learn",
"dill",
"crabnet >=2.0.5,<3.0.0",
"torch <2",
"chem_wasserstein >=1.0.8,<2.0.0",
"composition_based_feature_vector",
"matbench_genmetrics >= 0.6.1",
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