handle command names

src/imitation/scripts/analyze.py

@@ -152,16 +152,20 @@ def _get_exp_command(sd: sacred_util.SacredDicts) -> str:
 def _get_algo_name(sd: sacred_util.SacredDicts) -> str:
     exp_command = _get_exp_command(sd)
 
-    if exp_command == "gail":
-        return "GAIL"
-    elif exp_command == "airl":
-        return "AIRL"
-    elif exp_command == "train_bc":
-        return "BC"
-    elif exp_command == "train_dagger":
-        return "DAgger"
+    COMMAND_TO_ALGO = {
+        "train_bc": "BC",
+        "bc": "BC",
+        "train_dagger": "DAgger",
+        "dagger": "DAgger",
+        "gail": "GAIL",
+        "airl": "AIRL",
+        "preference_comparisons": "Preference Comparisons",
+    }
+
+    if exp_command.lower() in COMMAND_TO_ALGO.keys():
+        return COMMAND_TO_ALGO[exp_command.lower()]
     else:
-        return f"??exp_command={exp_command}"
+        raise ValueError(f"Unknown command: {exp_command}")
 
 
 def _return_summaries(sd: sacred_util.SacredDicts) -> dict:

src/imitation/scripts/compare_to_baseline.py

@@ -15,66 +15,26 @@
       experiment returns, as reported by `imitation.scripts.analyze`.
 """
 
-import numpy as np
 import pandas as pd
 import scipy
 
 from imitation.data import types
 
 
-def compare_results_to_baseline(results_file: types.AnyPath) -> pd.DataFrame:
+def compare_results_to_baseline(results_filename: types.AnyPath) -> pd.DataFrame:
     """Compare benchmark results to baseline results.
 
     Args:
-        results_file: Path to a CSV file containing experiment results.
+        results_filename: Path to a CSV file containing experiment results.
 
     Returns:
         A string containing a table of p-values comparing the experiment results to
         the baseline results.
     """
-    data = pd.read_csv(results_file)
-    data["imit_return"] = data["imit_return_summary"].apply(
-        lambda x: float(x.split(" ")[0]),
-    )
-    summary = (
-        data[["algo", "env_name", "imit_return"]]
-        .groupby(["algo", "env_name"])
-        .describe()
-    )
-    summary.columns = summary.columns.get_level_values(1)
-    summary = summary.reset_index()
-
-    # Table 2 (https://arxiv.org/pdf/2211.11972.pdf)
-    # todo: store results in this repo outside this file
-    baseline = pd.DataFrame.from_records(
-        [
-            {
-                "algo": "??exp_command=bc",
-                "env_name": "seals/Ant-v0",
-                "mean": 1953,
-                "margin": 123,
-            },
-            {
-                "algo": "??exp_command=bc",
-                "env_name": "seals/HalfCheetah-v0",
-                "mean": 3446,
-                "margin": 130,
-            },
-        ],
-    )
-    baseline["count"] = 5
-    baseline["confidence_level"] = 0.95
-    # Back out the standard deviation from the margin of error.
+    results_summary = load_and_summarize_csv(results_filename)
+    baseline_summary = load_and_summarize_csv("baseline.csv")
 
-    t_score = scipy.stats.t.ppf(
-        1 - ((1 - baseline["confidence_level"]) / 2),
-        baseline["count"] - 1,
-    )
-    std_err = baseline["margin"] / t_score
-
-    baseline["std"] = std_err * np.sqrt(baseline["count"])
-
-    comparison = pd.merge(summary, baseline, on=["algo", "env_name"])
+    comparison = pd.merge(results_summary, baseline_summary, on=["algo", "env_name"])
 
     comparison["pvalue"] = scipy.stats.ttest_ind_from_stats(
         comparison["mean_x"],
@@ -88,6 +48,30 @@ def compare_results_to_baseline(results_file: types.AnyPath) -> pd.DataFrame:
     return comparison[["algo", "env_name", "pvalue"]]
 
 
+def load_and_summarize_csv(results_filename: types.AnyPath) -> pd.DataFrame:
+    """Load a results CSV file and summarize the statistics.
+
+    Args:
+        results_filename: Path to a CSV file containing experiment results.
+
+    Returns:
+        A DataFrame containing the mean and standard deviation of the experiment
+        returns, grouped by algorithm and environment.
+    """
+    data = pd.read_csv(results_filename)
+    data["imit_return"] = data["imit_return_summary"].apply(
+        lambda x: float(x.split(" ")[0]),
+    )
+    summary = (
+        data[["algo", "env_name", "imit_return"]]
+        .groupby(["algo", "env_name"])
+        .describe()
+    )
+    summary.columns = summary.columns.get_level_values(1)
+    summary = summary.reset_index()
+    return summary
+
+
 def main() -> None:  # pragma: no cover
     """Run the script."""
     import sys

tests/scripts/test_scripts.py

@@ -1110,8 +1110,8 @@ def test_compare_to_baseline_p_values(
     comparison.to_csv(tmpfile)
 
     assert (
-        compare_to_baseline.compare_results_to_baseline(results_file=tmpfile)["pvalue"][
-            0
-        ]
+        compare_to_baseline.compare_results_to_baseline(results_filename=tmpfile)[
+            "pvalue"
+        ][0]
         < p_value
     )