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new_df_dict["composition"].append(c)
new_df_dict["gfa"].append(gfa)
df_new = pd.DataFrame(new_df_dict)
df_new = df_new.sort_values(by="composition")
df_new = df_new.reset_index(drop=True)
# convert to bools
df_new["gfa"] = df_new["gfa"] == 1
print(df_new)
print(df_new["gfa"].value_counts())
print(f"Problem compositions: {problem_compositions}")
store_dataframe_as_json(df_new, "glass.json.gz", compression="gz")
elif not all_metals and not any_metals:
print(f"No metals: {c}")
is_metal = 0
elif all_metals and not any_metals:
raise ValueError("Impossible combination of metals.")
new_df_dict["composition"].append(c)
new_df_dict["is_metal"].append(is_metal)
df_new = pd.DataFrame(new_df_dict)
df_new = df_new.sort_values(by="composition")
df_new = df_new.reset_index(drop=True)
df_new["is_metal"] = df_new["is_metal"] == 1
store_dataframe_as_json(df_new, "expt_is_metal.json.gz", compression="gz")
print(df_new)
print(df_new["is_metal"].value_counts())
print(f"Problem compositions: {problem_compositions}")
if self.functionalize:
ff = FunctionFeaturizer()
ff.set_n_jobs(self.n_jobs)
cols = df.columns.tolist()
for ft in self.featurizers.keys():
if ft in cols:
cols.pop(ft)
df = ff.fit_featurize_dataframe(
df,
cols,
ignore_errors=self.ignore_errors,
multiindex=self.multiindex,
inplace=False,
)
if self.cache_src and not os.path.exists(self.cache_src):
store_dataframe_as_json(df, self.cache_src)
return df
def transfer_data(df, worker, now):
this_dir = os.path.dirname(os.path.abspath(__file__))
user_folder = os.path.join(this_dir, "user_dfs")
if not os.path.exists(user_folder):
os.makedirs(user_folder)
filename = "user_df_" + now + ".json"
filepath = os.path.join(user_folder, filename)
store_dataframe_as_json(df, filepath)
if worker != "local":
if worker == "cori":
o = subprocess.check_output(
['bash', '-c', '. ~/.bash_profile; cori_get_password']
)
user = os.environ["CORI_USER"]
host = "lrc-login.lbl.gov"
elif worker == "lrc":
o = subprocess.check_output(
['bash', '-c', '. ~/.bash_profile; lrc_get_password']
)
user = os.environ["LRC_USER"]
host = "lrc-login.lbl.gov"
else:
raise ValueError(f"Worker {worker} not valid!")
gap_diffs = per_comp_gaps - mean_gap
min_gap_diff = gap_diffs.min()
min_gap_diff_index = gap_diffs.tolist().index(min_gap_diff)
actual_gap_diff = per_comp_gaps.tolist()[min_gap_diff_index]
# if len(per_comp_gaps) > 1:
# print(f"{c} decided on {actual_gap_diff} from \n {per_comp_gaps} \n\n")
new_df_dict["composition"].append(c)
new_df_dict["gap expt"].append(actual_gap_diff)
df_new = pd.DataFrame(new_df_dict)
df_new = df_new.sort_values(by="composition")
df_new = df_new.reset_index(drop=True)
store_dataframe_as_json(df_new, "expt_gap.json.gz", compression="gz")
print(df_new)