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Address some pandas FuturesWarnings #653

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merged 4 commits into from
Jan 27, 2024
Merged

Address some pandas FuturesWarnings #653

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bee4b0f
address futures warnings
bpkroth Jan 26, 2024
698b776
Merge branch 'main' into mlos-bench/address-futures-warnings
motus Jan 26, 2024
0f8e813
Pandas address a FutureWarning
bpkroth Jan 26, 2024
83a63b0
Merge branch 'main' into mlos-bench/address-futures-warnings
motus Jan 26, 2024
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2 changes: 1 addition & 1 deletion mlos_bench/mlos_bench/environments/local/local_env.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,7 +193,7 @@ def run(self) -> Tuple[Status, Optional[Dict[str, TunableValue]]]:
_LOG.info("Local results have (metric,value) header and %d rows: assume long format", len(data))
data = pandas.DataFrame([data.value.to_list()], columns=data.metric.to_list())
# Try to convert string metrics to numbers.
data = data.apply(pandas.to_numeric, errors="ignore") # type: ignore[assignment] # (false positive)
data = data.apply(pandas.to_numeric, errors='coerce').fillna(data) # type: ignore[assignment] # (false positive)
elif len(data) == 1:
_LOG.info("Local results have 1 row: assume wide format")
else:
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6 changes: 4 additions & 2 deletions mlos_bench/mlos_bench/storage/sql/common.py
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Expand Up @@ -138,7 +138,8 @@ def get_results_df(
columns=['trial_id', 'tunable_config_id', 'param', 'value']
).pivot(
index=["trial_id", "tunable_config_id"], columns="param", values="value",
).apply(pandas.to_numeric, errors='ignore')
)
configs_df = configs_df.apply(pandas.to_numeric, errors='coerce').fillna(configs_df) # type: ignore[assignment] # (fp)

# Get each trial's results in wide format.
results_stmt = schema.trial_result.select().with_only_columns(
Expand All @@ -164,7 +165,8 @@ def get_results_df(
columns=['trial_id', 'metric', 'value']
).pivot(
index="trial_id", columns="metric", values="value",
).apply(pandas.to_numeric, errors='ignore')
)
results_df = results_df.apply(pandas.to_numeric, errors='coerce').fillna(results_df) # type: ignore[assignment] # (fp)

# Concat the trials, configs, and results.
return trials_df.merge(configs_df, on=["trial_id", "tunable_config_id"], how="left") \
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