diff --git a/rsmtool/utils/prmse.py b/rsmtool/utils/prmse.py
index c95e969c..5c69d415 100644
--- a/rsmtool/utils/prmse.py
+++ b/rsmtool/utils/prmse.py
@@ -1,4 +1,6 @@
 """
+PRMSE utilities.
+
 Utility classes and functions related to computing test
 theory based evaluations.
 
@@ -13,6 +15,8 @@ The derivations and formulas were provided by Matt Johnson.
 import pandas as pd
 import numpy as np
 
+import warnings
+
 
 def get_n_human_scores(human_scores):
     """
@@ -46,7 +50,6 @@ def variance_of_errors(human_scores):
     variance_of_errors : float
         Estimated variance of errors in human scores.
     """
-
     # we first compute the total number of scores
     # available for each response
 
@@ -56,35 +59,35 @@ def variance_of_errors(human_scores):
     # than one score
     multiple_mask = n_scores > 1
 
-    # raise an error if we don't have any such responses
+    # show a warning and return None
+    # if we don't have valid human scores
     if multiple_mask.sum() == 0:
-        raise ValueError("Variance of human errors "
-                         "necessary for true score "
-                         "evaluations requires "
-                         "at least a subset of responses "
-                         "to be scored by 2 or more "
-                         "raters.")
+        warnings.warn("True score evaluations cannot be "
+                      "computed because none of the responses in the "
+                      "evaluation set has valid "
+                      "system scores and 2 human scores.")
+        return None
 
-    # only select the responses with multiple scores
-    multiple_scores = human_scores[multiple_mask]
+    else:
+        # only select the responses with multiple scores
+        multiple_scores = human_scores[multiple_mask]
 
-    n_scores = n_scores[multiple_mask]
+        n_scores = n_scores[multiple_mask]
 
-    # now let's compute the rater error variance for each
-    # response
-    response_variances = np.nanvar(multiple_scores, ddof=1, axis=1)
+        # now let's compute the rater error variance for each
+        # response
+        response_variances = np.nanvar(multiple_scores, ddof=1, axis=1)
 
-    # finally, let's compute the variance of errors as a weighted average
-    # of response variances
+        # finally, let's compute the variance of errors as a weighted average
+        # of response variances
 
-    variance_of_errors = np.average(response_variances, weights=n_scores - 1)
+        variance_of_errors = np.average(response_variances, weights=n_scores - 1)
 
-    return variance_of_errors
+        return variance_of_errors
 
 
 def true_score_variance(human_scores,
                         variance_errors_human=None):
-
     """
     Compute variance of true scores for multiple raters.
 
@@ -106,57 +109,62 @@ def true_score_variance(human_scores,
     variance_true_scores : float
         Variance of true scores.
     """
-
     # if we don't have variance of errors, compute it
     # from the data
 
     if variance_errors_human is None:
         variance_errors_human = variance_of_errors(human_scores)
 
-    # compute mean human score and total number of scores
-    # for each response
-    mean_scores = np.nanmean(human_scores, axis=1)
-    n_scores = get_n_human_scores(human_scores)
+    # if it's still None, return None
+    if variance_errors_human is None:
+        return None
+
+    else:
+        # compute mean human score and total number of scores
+        # for each response
+        mean_scores = np.nanmean(human_scores, axis=1)
+        n_scores = get_n_human_scores(human_scores)
 
-    # compute overall mean
-    mean_human_score = np.nanmean(human_scores)
+        # compute overall mean
+        mean_human_score = np.nanmean(human_scores)
 
-    # let N be total number of responses
-    N = len(human_scores)
+        # let N be total number of responses
+        N = len(human_scores)
 
-    # let M be total number of human ratings
-    M = n_scores.sum()
+        # let M be total number of human ratings
+        M = n_scores.sum()
 
-    # compute squared deviations
-    squared_devs = (mean_scores - mean_human_score)**2
+        # compute squared deviations
+        squared_devs = (mean_scores - mean_human_score)**2
 
-    # adjust them by the number of human scores available
-    # for each responses: deviations with higher number of
-    # human scores are assigned a greater weight
-    adjusted_squared_devs = n_scores * squared_devs
+        # adjust them by the number of human scores available
+        # for each responses: deviations with higher number of
+        # human scores are assigned a greater weight
+        adjusted_squared_devs = n_scores * squared_devs
 
-    # compute sum of squares
-    sum_of_squares = adjusted_squared_devs.sum()
+        # compute sum of squares
+        sum_of_squares = adjusted_squared_devs.sum()
 
-    # now compute the numerator as sum of squares
-    # adjusted for the variance of human errors
-    numerator = sum_of_squares - (N-1) * variance_errors_human
+        # now compute the numerator as sum of squares
+        # adjusted for the variance of human errors
+        numerator = sum_of_squares - (N - 1) * variance_errors_human
 
-    # compute the denominator as the adjusted total number of scores
-    denominator = M - ((n_scores**2).sum() / M)
+        # compute the denominator as the adjusted total number of scores
+        denominator = M - ((n_scores**2).sum() / M)
 
-    # finally compute variance of true scores
-    variance_true_scores = numerator / denominator
+        # finally compute variance of true scores
+        variance_true_scores = numerator / denominator
 
-    return variance_true_scores
+        return variance_true_scores
 
 
 def mse_true(system,
              human_scores,
              variance_errors_human=None):
-
     """
-    Compute mean squared error (MSE) when predicting true score
+    Mean squared error (MSE).
+
+    Compute MSE when predicting true score
     from system score.
 
     Parameters
@@ -178,24 +186,28 @@ def mse_true(system,
     variance_true_scores : float
         Variance of true scores.
     """
-
     # if we don't have variance of errors, compute it
     # from the data
 
     if variance_errors_human is None:
         variance_errors_human = variance_of_errors(human_scores)
 
+    # if it's still None, return None
+    if variance_errors_human is None:
+        return None
+
+    else:
 
-    # get total number of scores for each response
-    n_scores = get_n_human_scores(human_scores)
-    mean_scores = np.nanmean(human_scores, axis=1)
+        # get total number of scores for each response
+        n_scores = get_n_human_scores(human_scores)
+        mean_scores = np.nanmean(human_scores, axis=1)
 
-    N = len(system)
+        N = len(system)
 
-    se = ((mean_scores - system)**2) * n_scores
+        se = ((mean_scores - system)**2) * n_scores
 
-    # Compute mean squared error when predicting true score
-    mse = (se.sum() - N * variance_errors_human) / n_scores.sum()
+        # Compute mean squared error when predicting true score
+        mse = (se.sum() - N * variance_errors_human) / n_scores.sum()
     return mse
 
 
@@ -203,6 +215,8 @@ def prmse_true(system,
                human_scores,
                variance_errors_human=None):
     """
+    PRMSE.
+
     Compute Proportional Reduction in Mean Squared Error (PRMSE)
     when predicting true score from system scores.
     The formula to compute PRMSE implemented in RSMTool
@@ -228,7 +242,6 @@ def prmse_true(system,
     prmse : float
         Proportional reduction in mean squared error
     """
-
     # check that human_scors is a two dimensional array
     # and reshape if necessary
     if len(human_scores.shape) == 1:
@@ -240,17 +253,22 @@ def prmse_true(system,
         except AttributeError:
             human_scores = human_scores.reshape(current_length, 1)
 
-
     if variance_errors_human is None:
         variance_errors_human = variance_of_errors(human_scores)
 
-    variance_true = true_score_variance(human_scores, variance_errors_human)
+    # if it's still None, return None
+    if variance_errors_human is None:
+        return None
+
+    else:
+
+        variance_true = true_score_variance(human_scores, variance_errors_human)
 
-    mse = mse_true(system, human_scores, variance_errors_human)
+        mse = mse_true(system, human_scores, variance_errors_human)
 
-    prmse = 1 - (mse / variance_true)
+        prmse = 1 - (mse / variance_true)
 
-    return prmse
+        return prmse
 
 
 def get_true_score_evaluations(df,
@@ -258,7 +276,7 @@ def get_true_score_evaluations(df,
                                human_score_columns,
                                variance_errors_human=None):
     """
-    Get true score evaluations for reporting
+    Get true score evaluations for reporting.
 
     Parameters
     ----------
@@ -281,8 +299,6 @@ def get_true_score_evaluations(df,
         some responses must have more than one
         human rating.
 
-
-
     Returns
     -------
     prmse_metrics: pandas DataFrame
@@ -297,17 +313,16 @@ def get_true_score_evaluations(df,
         - ``mse_true``: mean squared error when predicting true score from machine score
         - ``prmse``: proportional reduction in mean squared error when predicting true score
     """
-
     # check that if we only have one human column, we were also given
     # variance of errors
     if isinstance(human_score_columns, str):
         if variance_errors_human is None:
             raise(ValueError("True score evaluations require estimating "
-                            "variance of human errors, "
-                            "which can only be computed when a subset "
-                            "of responses has two or more human ratings. "
-                            "If a single human_score_column "
-                            "is supplied, one must also specify variance_errors_human"))
+                             "variance of human errors, "
+                             "which can only be computed when a subset "
+                             "of responses has two or more human ratings. "
+                             "If a single human_score_column "
+                             "is supplied, one must also specify variance_errors_human"))
 
     if isinstance(system_score_columns, str):
         system_score_columns = [system_score_columns]
@@ -329,7 +344,6 @@ def get_true_score_evaluations(df,
 
     score_counts = get_n_human_scores(df[human_score_columns])
 
-
     # compute values that are the same for all scores
     df_prmse.insert(0, 'N', len(df))
     df_prmse.insert(1, 'N raters', score_counts.max())

Skip PRMSE if there are no double-scored responses
We need to change PRMSE code to deal with an edge case where the second_human_score column is present and populated, but no valid second human scores are left after filtering out non-numeric features (rsmtool) or non-numeric system scores (rsmeval). A particular use case is a situation where unbeknownst to the user each responses was either scored by the system or by a second human but not by both. 

Currently if the user requests consistency etc. but there are no responses with valid system score and valid system score, we return N double-scored responses (0) and `nan` for other values. 

 For PRMSE we raise an error which doesn't allow the rest of the evaluation to proceed:

``Variance of human errors necessary for true score evaluations requires at least a subset of responses to be scored by 2 or more raters.``

It would be best to skip the evaluation as we do for consistency since in some context it is useful to have the same master config file.
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/features.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/features.csv
new file mode 100644
index 00000000..b5e365ae
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/features.csv
@@ -0,0 +1,9 @@
+feature,sign,transform
+FEATURE1,1,raw
+FEATURE2,1,raw
+FEATURE3,1,raw
+FEATURE4,1,raw
+FEATURE5,1,raw
+FEATURE6,1,raw
+FEATURE7,1,raw
+FEATURE8,1,raw
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/lr_subgroups_with_h2_but_only_for_nonscoreable.json b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/lr_subgroups_with_h2_but_only_for_nonscoreable.json
new file mode 100644
index 00000000..4eceb732
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/lr_subgroups_with_h2_but_only_for_nonscoreable.json
@@ -0,0 +1,19 @@
+{
+    "train_file": "../../files/train.csv",
+    "id_column": "ID",
+    "use_scaled_predictions": true,
+    "test_label_column": "score",
+    "train_label_column": "score",
+    "test_file": "../../files/test_with_no_double_scored_responses_for_valid_system_scores.csv",
+    "second_human_score_column": "score2",
+    "subgroups": [
+        "QUESTION",
+        "L1"
+    ],
+    "trim_max": 6,
+    "features": "features.csv",
+    "trim_min": 1,
+    "model": "LinearRegression",
+    "experiment_id": "lr_subgroups_with_h2_but_only_for_nonscoreable",
+    "description": "Using all features with an LinearRegression model."
+}
\ No newline at end of file
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_betas.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_betas.csv
new file mode 100644
index 00000000..42254410
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_betas.csv
@@ -0,0 +1,9 @@
+feature,standardized,relative
+FEATURE1,0.4121353697173701,0.36173324876232976
+FEATURE2,0.042308527446622196,0.037134403422138064
+FEATURE3,0.2087431643498745,0.18321490593974268
+FEATURE4,0.1465987367652527,0.1286704350342815
+FEATURE5,0.04932715247603867,0.0432946852622444
+FEATURE6,0.16537948164673932,0.14515438753953605
+FEATURE7,-0.05562887738427005,-0.04882574032656531
+FEATURE8,-0.05921374201648318,-0.05197219371316232
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_coefficients.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_coefficients.csv
new file mode 100644
index 00000000..614ac068
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_coefficients.csv
@@ -0,0 +1,10 @@
+feature,coefficient
+Intercept,3.4200000000000026
+FEATURE1,0.3360684984636124
+FEATURE2,0.03449974046377896
+FEATURE3,0.17021592166601351
+FEATURE4,0.11954134724022067
+FEATURE5,0.040222954116935355
+FEATURE6,0.13485577350913724
+FEATURE7,-0.045361584244925625
+FEATURE8,-0.04828479870955494
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_coefficients_scaled.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_coefficients_scaled.csv
new file mode 100644
index 00000000..c8477266
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_coefficients_scaled.csv
@@ -0,0 +1,10 @@
+feature,coefficient
+Intercept,3.4200000000000026
+FEATURE1,0.4766778562253703
+FEATURE2,0.0489342571523002
+FEATURE3,0.24143340124443724
+FEATURE4,0.16955684151673245
+FEATURE5,0.057052034413121774
+FEATURE6,0.19127874617772242
+FEATURE7,-0.0643406413624301
+FEATURE8,-0.06848691395464501
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_confMatrix.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_confMatrix.csv
new file mode 100644
index 00000000..b61b3f8f
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_confMatrix.csv
@@ -0,0 +1,6 @@
+,1,2,3,4,5
+1,2,5,0,0,0
+2,2,9,0,0,0
+3,0,2,31,16,0
+4,0,1,12,48,6
+5,0,0,0,8,9
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency.csv
new file mode 100644
index 00000000..b0807b9e
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency.csv
@@ -0,0 +1,2 @@
+,N,h1_mean,h1_sd,h1_min,h1_max,h2_mean,h2_sd,h2_min,h2_max,corr,wtkappa,kappa,exact_agr,adj_agr,SMD
+,0,,,,,,,,,,,,,,
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency_by_L1.csv
new file mode 100644
index 00000000..e7dd06bc
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency_by_L1.csv
@@ -0,0 +1,6 @@
+,N,h1_mean,h1_sd,h1_min,h1_max,h2_mean,h2_sd,h2_min,h2_max,corr,wtkappa,kappa,exact_agr,adj_agr,DSM
+All data,0,,,,,,,,,,,,,,
+Esperanto,0,,,,,,,,,,,,,,
+Klingon,0,,,,,,,,,,,,,,
+Navi,0,,,,,,,,,,,,,,
+Vulcan,0,,,,,,,,,,,,,,
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency_by_QUESTION.csv
new file mode 100644
index 00000000..878f8d27
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_consistency_by_QUESTION.csv
@@ -0,0 +1,6 @@
+,N,h1_mean,h1_sd,h1_min,h1_max,h2_mean,h2_sd,h2_min,h2_max,corr,wtkappa,kappa,exact_agr,adj_agr,DSM
+All data,0,,,,,,,,,,,,,,
+QUESTION_2,0,,,,,,,,,,,,,,
+QUESTION_3,0,,,,,,,,,,,,,,
+QUESTION_4,0,,,,,,,,,,,,,,
+QUESTION_5,0,,,,,,,,,,,,,,
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_cors_orig.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_cors_orig.csv
new file mode 100644
index 00000000..fc20eab5
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_cors_orig.csv
@@ -0,0 +1,10 @@
+,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8,sc1
+FEATURE1,1.0,0.25046600963484905,0.40799026832024865,0.464160236171879,0.21761011536933708,-0.07898604351813233,-0.04283682647661093,-0.02006754059967186,0.5763971598016531
+FEATURE2,0.25046600963484905,1.0,0.358482299657574,0.32486005717920496,0.4019871198703128,0.2365570243500153,-0.007175576295387261,-0.07892924727661298,0.33756930655835915
+FEATURE3,0.40799026832024865,0.358482299657574,1.0,0.34108689219085514,0.36679572765697566,0.2866651664877243,0.02484802374346811,-0.04718278554012104,0.509507228821961
+FEATURE4,0.464160236171879,0.32486005717920496,0.34108689219085514,1.0,0.3252672146756019,0.2590527982381009,-0.015737371226134094,-0.09071074511789955,0.48752876987183547
+FEATURE5,0.21761011536933708,0.4019871198703128,0.36679572765697566,0.3252672146756019,1.0,0.2679408186139726,-0.006374590888293129,-0.0829554096784736,0.32954217319350565
+FEATURE6,-0.07898604351813233,0.2365570243500153,0.2866651664877243,0.2590527982381009,0.2679408186139726,1.0,0.02318904560344916,-0.0337084764523672,0.25329384574569086
+FEATURE7,-0.04283682647661093,-0.007175576295387261,0.02484802374346811,-0.015737371226134094,-0.006374590888293129,0.02318904560344916,1.0,0.035118707729159815,-0.06912827010818752
+FEATURE8,-0.02006754059967186,-0.07892924727661298,-0.04718278554012104,-0.09071074511789955,-0.0829554096784736,-0.0337084764523672,0.035118707729159815,1.0,-0.10548661845698235
+sc1,0.5763971598016531,0.33756930655835915,0.509507228821961,0.48752876987183547,0.32954217319350565,0.25329384574569086,-0.06912827010818752,-0.10548661845698235,1.0
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_cors_processed.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_cors_processed.csv
new file mode 100644
index 00000000..0cfc3b42
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_cors_processed.csv
@@ -0,0 +1,10 @@
+,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8,sc1
+FEATURE1,1.0,0.23622837268512017,0.40799026832024815,0.4641602361718795,0.21761011536933705,-0.08010966273761898,-0.04283682647661097,-0.02006754059967183,0.5763971598016532
+FEATURE2,0.23622837268512017,1.0,0.37036963393261174,0.31632897208655153,0.39677965237478424,0.19993155869320842,-0.005106179228407075,-0.07683256610594263,0.3208223349569226
+FEATURE3,0.40799026832024815,0.37036963393261174,1.0,0.3410868921908552,0.36679572765697493,0.286852710924245,0.024848023743467965,-0.047182785540121036,0.5095072288219606
+FEATURE4,0.4641602361718795,0.31632897208655153,0.3410868921908552,1.0,0.325267214675602,0.258550404793668,-0.01573737122613413,-0.09071074511789955,0.48752876987183597
+FEATURE5,0.21761011536933705,0.39677965237478424,0.36679572765697493,0.325267214675602,1.0,0.2674240897731013,-0.00637459088829308,-0.08295540967847372,0.3295421731935059
+FEATURE6,-0.08010966273761898,0.19993155869320842,0.286852710924245,0.258550404793668,0.2674240897731013,1.0,0.023493771009162837,-0.03361316264271769,0.25247866921474205
+FEATURE7,-0.04283682647661097,-0.005106179228407075,0.024848023743467965,-0.01573737122613413,-0.00637459088829308,0.023493771009162837,1.0,0.03511870772915975,-0.0691282701081875
+FEATURE8,-0.02006754059967183,-0.07683256610594263,-0.047182785540121036,-0.09071074511789955,-0.08295540967847372,-0.03361316264271769,0.03511870772915975,1.0,-0.10548661845698222
+sc1,0.5763971598016532,0.3208223349569226,0.5095072288219606,0.48752876987183597,0.3295421731935059,0.25247866921474205,-0.0691282701081875,-0.10548661845698222,1.0
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition.csv
new file mode 100644
index 00000000..4ef954a6
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition.csv
@@ -0,0 +1,5 @@
+partition,responses,QUESTION,L1
+Training,500,5,4
+Evaluation,151,4,4
+Overlapping,151,4,4
+Total,500,5,4
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition_by_L1.csv
new file mode 100644
index 00000000..246fd2d8
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition_by_L1.csv
@@ -0,0 +1,5 @@
+L1,Training set,Evaluation set
+Esperanto,127,30
+Klingon,134,35
+Navi,112,39
+Vulcan,127,47
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition_by_QUESTION.csv
new file mode 100644
index 00000000..e78548c6
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_data_composition_by_QUESTION.csv
@@ -0,0 +1,6 @@
+QUESTION,Training set,Evaluation set
+QUESTION_1,100,0
+QUESTION_2,100,31
+QUESTION_3,100,40
+QUESTION_4,100,40
+QUESTION_5,100,40
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_degradation.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_degradation.csv
new file mode 100644
index 00000000..f184e71a
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_degradation.csv
@@ -0,0 +1,7 @@
+,corr,kappa,wtkappa,exact_agr,adj_agr,SMD
+raw,,,,,,
+raw_trim,,,,,,
+raw_trim_round,,,,,,
+scale,,,,,,
+scale_trim,,,,,,
+scale_trim_round,,,,,,
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations.csv
new file mode 100644
index 00000000..6888ba36
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations.csv
@@ -0,0 +1,7 @@
+,corr_HM,corr_HH,sqrt_HH,corr_disattenuated
+raw,0.7855367907853162,,,
+raw_trim,0.7855367907853162,,,
+raw_trim_round,0.7297482079283752,,,
+scale,0.7855367907853162,,,
+scale_trim,0.7855367907853162,,,
+scale_trim_round,0.7902666096661248,,,
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations_by_L1.csv
new file mode 100644
index 00000000..6b798c9b
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations_by_L1.csv
@@ -0,0 +1,6 @@
+,corr_HM,corr_HH,sqrt_HH,corr_disattenuated
+All data,0.7855367907853162,,,
+Esperanto,0.7830874665509566,,,
+Klingon,0.8055397820559698,,,
+Navi,0.7524215273690428,,,
+Vulcan,0.7977195960430451,,,
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations_by_QUESTION.csv
new file mode 100644
index 00000000..ea952808
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_disattenuated_correlations_by_QUESTION.csv
@@ -0,0 +1,6 @@
+,corr_HM,corr_HH,sqrt_HH,corr_disattenuated
+All data,0.7855367907853162,,,
+QUESTION_2,0.7871441713835935,,,
+QUESTION_3,0.7759113437845991,,,
+QUESTION_4,0.8050680429662136,,,
+QUESTION_5,0.7742921255506653,,,
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_csd_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_csd_by_L1.csv
new file mode 100644
index 00000000..13546aee
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_csd_by_L1.csv
@@ -0,0 +1,5 @@
+,estimate,P>[t],[0.025,0.975]
+Intercept (Vulcan),-0.11782547771087441,0.21239269893871887,-0.303761187082722,0.06811023166097314
+Navi,0.17241677936045852,0.17041492916489887,-0.07494346383096209,0.4197770225518791
+Klingon,0.07109374750250153,0.5808678146812607,-0.18285582493159536,0.3250433199365984
+Esperanto,0.04753495837692035,0.7220666151511104,-0.21610724666845132,0.311177163422292
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_csd_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_csd_by_QUESTION.csv
new file mode 100644
index 00000000..06ad3659
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_csd_by_QUESTION.csv
@@ -0,0 +1,5 @@
+,estimate,P>[t],[0.025,0.975]
+Intercept (QUESTION_3),-0.04126287811284367,0.6874470992414771,-0.24358450909618545,0.1610587528704981
+QUESTION_4,-0.0058295179597003125,0.9635352589662806,-0.2574387157980291,0.24577967987862848
+QUESTION_5,-0.004033374483862456,0.9747658480900421,-0.25564257232219134,0.2475758233544664
+QUESTION_2,-0.0326476370885256,0.8113814410163166,-0.30256895713535553,0.23727368295830437
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osa_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osa_by_L1.csv
new file mode 100644
index 00000000..816c7742
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osa_by_L1.csv
@@ -0,0 +1,5 @@
+,estimate,P>[t],[0.025,0.975]
+Intercept (Vulcan),0.3184560572014623,4.641804386392916e-05,0.16854443492735674,0.4683676794755679
+Navi,0.04687247074222539,0.6779394033620519,-0.1757414025316251,0.26948634401607585
+Klingon,0.07027699097222027,0.5459384183937674,-0.15918365292460118,0.2997376348690417
+Esperanto,0.11350788098096877,0.3518385244645096,-0.12666282747101015,0.3536785894329477
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osa_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osa_by_QUESTION.csv
new file mode 100644
index 00000000..b43dd28f
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osa_by_QUESTION.csv
@@ -0,0 +1,5 @@
+,estimate,P>[t],[0.025,0.975]
+Intercept (QUESTION_3),0.38577086948011613,6.414863505635027e-06,0.22292274625127292,0.5486189927089593
+QUESTION_4,-0.04202111281207471,0.7189271054770814,-0.2723231372893098,0.18828091166516037
+QUESTION_5,0.010725512806853462,0.9267946097337174,-0.21957651167038164,0.24102753728408857
+QUESTION_2,-0.039346941337558575,0.7528191966905029,-0.28579822540545147,0.20710434273033435
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osd_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osd_by_L1.csv
new file mode 100644
index 00000000..7b61de16
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osd_by_L1.csv
@@ -0,0 +1,5 @@
+,estimate,P>[t],[0.025,0.975]
+Intercept (Vulcan),-0.04289308534932929,0.6317314970443954,-0.2193819469896938,0.13359577629103522
+Navi,0.14572714725226016,0.27362331108081167,-0.11635306029573494,0.40780735480025526
+Klingon,0.10772919795849348,0.43190711841126384,-0.1624116171316666,0.37787001304865353
+Esperanto,0.010726983019053764,0.9403369077008504,-0.27202264166679196,0.29347660770489953
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osd_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osd_by_QUESTION.csv
new file mode 100644
index 00000000..1261846e
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_estimates_osd_by_QUESTION.csv
@@ -0,0 +1,5 @@
+,estimate,P>[t],[0.025,0.975]
+Intercept (QUESTION_3),0.0265135062265256,0.7856976801756158,-0.16584006853534955,0.21886708098840074
+QUESTION_4,-0.005829517959700303,0.9662769859598577,-0.2778585521588912,0.26619951623949056
+QUESTION_5,-0.004033374483862519,0.9766637855298826,-0.2760624086830535,0.2679956597153284
+QUESTION_2,-0.010006111879178926,0.9459344918552414,-0.3011103908550952,0.28109816709673735
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval.csv
new file mode 100644
index 00000000..c0c4d3c1
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval.csv
@@ -0,0 +1,7 @@
+,N,h_mean,h_sd,h_min,h_max,sys_mean,sys_sd,sys_min,sys_max,corr,wtkappa,R2,kappa,exact_agr,adj_agr,SMD,RMSE
+raw,151,3.5099337748344372,0.9155511976863931,1.0,5.0,3.4988076413900435,0.6653260739348766,1.5955568495014611,4.7011482878986826,0.7855367907853162,0.7470654933497711,0.6134569763749168,0.3878238163952451,0.0,90.06622516556291,-0.012152388061431818,0.5673338685197856
+raw_trim,151,3.5099337748344372,0.9155511976863931,1.0,5.0,3.4988076413900435,0.6653260739348766,1.5955568495014611,4.7011482878986826,0.7855367907853162,0.7470654933497711,0.6134569763749168,0.3878238163952451,0.0,90.06622516556291,-0.012152388061431818,0.5673338685197856
+raw_trim_round,151,3.5099337748344372,0.9155511976863931,1.0,5.0,3.4900662251655628,0.7011186268516351,2.0,5.0,0.7297482079283752,0.7043048225961689,0.5307595070051617,0.3878238163952451,60.9271523178808,100.0,-0.021700096858679217,0.6250827759754639
+scale,151,3.5099337748344372,0.9155511976863931,1.0,5.0,3.531780359431843,0.9436951337718372,0.8322190750976661,5.237174243269542,0.7855367907853162,0.7849587311691687,0.5563703182037567,0.4901298701298701,0.0,85.43046357615894,0.02386167442368287,0.6077851556506627
+scale_trim,151,3.5099337748344372,0.9155511976863931,1.0,5.0,3.531780359431843,0.9436951337718372,0.8322190750976661,5.237174243269542,0.7855367907853162,0.7849587311691687,0.5563703182037567,0.4901298701298701,0.0,85.43046357615894,0.02386167442368287,0.6077851556506627
+scale_trim_round,151,3.5099337748344372,0.9155511976863931,1.0,5.0,3.5033112582781456,0.9513090783086252,1.0,5.0,0.7902666096661248,0.7896669621375187,0.5625724217844728,0.4901298701298701,65.56291390728477,99.33774834437085,-0.007233365619559901,0.6035216736754584
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_by_L1.csv
new file mode 100644
index 00000000..0c109a3e
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_by_L1.csv
@@ -0,0 +1,6 @@
+,N,h_mean,h_sd,sys_mean.scale_trim,sys_sd.scale_trim,wtkappa.scale_trim,corr.scale_trim,DSM.scale_trim,RMSE.scale_trim,R2.scale_trim,sys_mean.scale_trim_round,sys_sd.scale_trim_round,kappa.scale_trim_round,exact_agr.scale_trim_round,adj_agr.scale_trim_round,DSM.scale_trim_round
+All data,151.0,3.5099337748344372,0.9155511976863931,3.531780359431843,0.9436951337718372,0.7849587311691687,0.7855367907853162,4.3820723356065783e-16,0.6077851556506627,0.5563703182037567,3.5033112582781456,0.9513090783086252,0.4901298701298701,65.56291390728477,99.33774834437085,2.9262832039788896e-16
+Esperanto,30.0,3.533333333333333,1.0080138659874616,3.5011672310030577,1.0191258172789983,0.7826327199709348,0.7830874665509566,-0.057997529757085255,0.6572396352795766,0.5602177099952625,3.433333333333333,0.9714309861845772,0.5566502463054187,70.0,100.0,-0.09911750201867935
+Klingon,35.0,3.4,0.9761870601839528,3.4648361126091647,1.033856951101071,0.8024970640981379,0.8055397820559698,0.049135443910903735,0.6234846013925944,0.5800723245037382,3.4571428571428573,1.0100336960848835,0.465962441314554,62.857142857142854,97.14285714285714,0.07154242751880943
+Navi,39.0,3.6666666666666665,0.898341551894183,3.7695007285695974,0.7940635938415452,0.7411347331688345,0.7524215273690428,0.0807141246439079,0.6044241291871857,0.5353974155498754,3.717948717948718,0.8568214807837884,0.5301204819277109,69.23076923076923,100.0,0.054433611371405774
+Vulcan,47.0,3.4468085106382977,0.8290515963986873,3.4039154252889694,0.9325653928487126,0.7912744661483543,0.7977195960430451,-0.06654607458045564,0.5643191093711626,0.5266019984131696,3.404255319148936,0.9704229124702171,0.42054794520547945,61.702127659574465,100.0,-0.035178100980483636
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_by_QUESTION.csv
new file mode 100644
index 00000000..5635214d
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_by_QUESTION.csv
@@ -0,0 +1,6 @@
+,N,h_mean,h_sd,sys_mean.scale_trim,sys_sd.scale_trim,wtkappa.scale_trim,corr.scale_trim,DSM.scale_trim,RMSE.scale_trim,R2.scale_trim,sys_mean.scale_trim_round,sys_sd.scale_trim_round,kappa.scale_trim_round,exact_agr.scale_trim_round,adj_agr.scale_trim_round,DSM.scale_trim_round
+All data,151.0,3.5099337748344372,0.9155511976863931,3.531780359431843,0.9436951337718372,0.7849587311691687,0.7855367907853162,4.3820723356065783e-16,0.6077851556506627,0.5563703182037567,3.5033112582781456,0.9513090783086252,0.4901298701298701,65.56291390728477,99.33774834437085,2.9262832039788896e-16
+QUESTION_2,31.0,3.5483870967741935,0.8883959206502229,3.56489449112154,0.938997440966451,0.7858055673472591,0.7871441713835935,-0.006910325812195263,0.5885778862160532,0.5464395164237091,3.5483870967741935,0.9251561914047939,0.5148669796557122,67.74193548387096,100.0,0.005382770945263332
+QUESTION_3,40.0,3.5,0.9336995618478525,3.526513506226526,0.9435584035022332,0.7755512153551539,0.7759113437845991,0.005268952356218881,0.6211045559969076,0.5461519182586874,3.45,0.95943359359198,0.5596330275229358,70.0,100.0,-0.04518984385564737
+QUESTION_4,40.0,3.5,0.9336995618478525,3.520683988266825,0.9646953506230457,0.8044430830984088,0.8050680429662136,-0.000908379444148244,0.5863017624637006,0.59558852156701,3.45,1.0114726506816303,0.4843462246777165,65.0,100.0,-0.045189843855647366
+QUESTION_5,40.0,3.5,0.9336995618478525,3.5224801317426633,0.9617353294685578,0.7737301978237484,0.7742921255506653,0.0009949295923823431,0.6296795870019686,0.5335336678976828,3.575,0.9306049865682223,0.4057567316620241,60.0,97.5,0.08620804022871673
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_short.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_short.csv
new file mode 100644
index 00000000..44f51122
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_eval_short.csv
@@ -0,0 +1,2 @@
+,N,h_mean,h_sd,sys_mean.scale_trim,sys_sd.scale_trim,wtkappa.scale_trim,corr.scale_trim,SMD.scale_trim,RMSE.scale_trim,R2.scale_trim,sys_mean.scale_trim_round,sys_sd.scale_trim_round,kappa.scale_trim_round,exact_agr.scale_trim_round,adj_agr.scale_trim_round,SMD.scale_trim_round
+0,151,3.5099337748344372,0.9155511976863931,3.531780359431843,0.9436951337718372,0.7849587311691687,0.7855367907853162,0.02386167442368287,0.6077851556506627,0.5563703182037567,3.5033112582781456,0.9513090783086252,0.4901298701298701,65.56291390728477,99.33774834437085,-0.007233365619559901
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_fairness_metrics_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_fairness_metrics_by_L1.csv
new file mode 100644
index 00000000..623af901
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_fairness_metrics_by_L1.csv
@@ -0,0 +1,3 @@
+,Overall score accuracy,Overall score difference,Conditional score difference,base_category
+R2,-0.013952232906348838,-0.009326423581024645,-0.006086070936839105,Vulcan
+sig,0.8166942617182975,0.6569872004720319,0.5804405650448945,Vulcan
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_fairness_metrics_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_fairness_metrics_by_QUESTION.csv
new file mode 100644
index 00000000..a7d55784
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_fairness_metrics_by_QUESTION.csv
@@ -0,0 +1,3 @@
+,Overall score accuracy,Overall score difference,Conditional score difference,base_category
+R2,-0.018297170836130805,-0.02037473366261211,-0.017526631405173188,QUESTION_3
+sig,0.9589989342594288,0.9999107891458927,0.9954293989072885,QUESTION_3
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature.csv
new file mode 100644
index 00000000..e3fd2864
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature.csv
@@ -0,0 +1,9 @@
+feature,transform,sign,train_mean,train_sd,train_transformed_mean,train_transformed_sd
+FEATURE1,raw,1.0,5.218315541739041,0.5232330811206761,5.218315541739041,0.5232330811206761
+FEATURE2,raw,1.0,-0.09905531550709007,0.029082144098777594,-0.0987216357919914,0.027507362950694694
+FEATURE3,raw,1.0,-0.18242617536302583,0.0750396646084921,-0.18242617536302583,0.0750396646084921
+FEATURE4,raw,1.0,-0.19067515567207455,0.15152973701964464,-0.19067515567207455,0.15152973701964464
+FEATURE5,raw,1.0,-0.09655149330236194,0.041429195234732256,-0.09655149330236194,0.041429195234732256
+FEATURE6,raw,1.0,4.448370880142472,0.30002006018773947,4.448496342999794,0.29951102956014236
+FEATURE7,raw,1.0,10.668,5.733718431708014,10.668,5.733718431708014
+FEATURE8,raw,1.0,-8484.539584319547,2530.851135611023,-8484.539584319547,2530.851135611023
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_descriptives.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_descriptives.csv
new file mode 100644
index 00000000..259e7af8
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_descriptives.csv
@@ -0,0 +1,9 @@
+,mean,std. dev.,min,max,skewness,kurtosis,Correlation,p,N
+FEATURE1,5.218315541739036,0.5232330811206759,3.1716191588351887,6.621917167022832,-0.43947150562671017,4.61993794843334,0.5763971598016536,1.3248268450475596e-45,500
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+FEATURE3,-0.1824261753630257,0.07503966460849208,-0.412356692688007,-0.05585430033547443,-0.42111137909964513,3.022586533916796,0.5095072288219618,2.1927958149089833e-34,500
+FEATURE4,-0.19067515567207446,0.15152973701964467,-0.6293019740034831,0.0021233446269846613,-0.2799449283677885,2.4269444811312484,0.4875287698718363,3.2654680548063517e-31,500
+FEATURE5,-0.09655149330236196,0.041429195234732256,-0.1951925975152189,0.002238968062144306,0.2077127987045296,3.187532325191205,0.32954217319350637,3.9505636686760996e-14,500
+FEATURE6,4.4483708801424715,0.3000200601877394,3.2125821261868217,5.1439924346485935,-0.8041265479251687,4.800149080423386,0.25329384574569097,9.270001490892614e-09,500
+FEATURE7,10.668,5.733718431708014,1.0,20.0,-0.01200231832859562,1.8450916126654766,-0.0691282701081876,0.12265292578751898,500
+FEATURE8,-8484.539584319544,2530.8511356110225,-13950.786157620996,-2680.4031019342965,0.0359165803828767,2.070729442950805,-0.10548661845698235,0.018302788849419712,500
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_descriptivesExtra.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_descriptivesExtra.csv
new file mode 100644
index 00000000..1ba85fb7
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_descriptivesExtra.csv
@@ -0,0 +1,9 @@
+,1%,5%,25%,50%,75%,95%,99%,IQR,Mild outliers,Extreme outliers
+FEATURE1,3.2448836842918776,4.4419549177082525,4.948916020597414,5.233756752096073,5.4997815243424855,6.08335981181224,6.456597232154773,0.5508655037450714,18,5
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+FEATURE3,-0.40824829046386296,-0.31556646178034703,-0.2316645725420893,-0.1890905455114685,-0.12136285530957602,-0.0700180342218257,-0.059532805140822535,0.11030171723251328,5,0
+FEATURE4,-0.6220681139791858,-0.4520400725233725,-0.30207249876998904,-0.20083084794271694,-0.001375991250069592,0.0006576004467528831,0.0016974899259111177,0.30069650751991944,0,0
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+FEATURE6,3.2881456701417573,3.8963021311648736,4.280752782612604,4.489489489489491,4.631338477784556,4.880731185927004,5.0607183242746885,0.3505856951719526,8,2
+FEATURE7,1.0,2.0,6.0,10.0,16.0,19.0,20.0,10.0,0,0
+FEATURE8,-13385.395462038694,-12497.212282424329,-10498.89177526062,-8566.848384984332,-6375.794678656474,-4419.711761559004,-3711.2250926084307,4123.097096604145,0,0
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_outliers.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_outliers.csv
new file mode 100644
index 00000000..76804969
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_feature_outliers.csv
@@ -0,0 +1,9 @@
+,lower,upper,both,lowerperc,upperperc,bothperc
+FEATURE1,0,0,0,0.0,0.0,0.0
+FEATURE2,5,0,5,1.0,0.0,1.0
+FEATURE3,0,0,0,0.0,0.0,0.0
+FEATURE4,0,0,0,0.0,0.0,0.0
+FEATURE5,0,0,0,0.0,0.0,0.0
+FEATURE6,3,0,3,0.6,0.0,0.6
+FEATURE7,0,0,0,0.0,0.0,0.0
+FEATURE8,0,0,0,0.0,0.0,0.0
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_all_data.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_all_data.csv
new file mode 100644
index 00000000..3bf34721
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_all_data.csv
@@ -0,0 +1,2 @@
+,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8
+All data,0.5763971598016536,0.3208223349569232,0.5095072288219618,0.4875287698718363,0.32954217319350637,0.2524786692147425,-0.0691282701081876,-0.10548661845698232
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_by_L1.csv
new file mode 100644
index 00000000..7841a83c
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_by_L1.csv
@@ -0,0 +1,6 @@
+,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8
+All data,0.5763971598016536,0.3208223349569232,0.5095072288219618,0.4875287698718363,0.32954217319350637,0.2524786692147425,-0.0691282701081876,-0.10548661845698232
+Esperanto,0.5471477767347491,0.40499983046815685,0.5053425978670631,0.4743820195312597,0.37692834409930637,0.29413325304791177,-0.1827091799904048,-0.048396550486959386
+Klingon,0.5158160108169385,0.16014710149545744,0.41414417337450127,0.3955802669879509,0.1838794489065699,0.15240964155192577,-0.0032814760832175966,-0.021608159570350692
+Navi,0.6385457641398873,0.24155076171898784,0.581613538329768,0.5284179660502356,0.3471470789972487,0.084484209035214,0.0118381154813876,-0.07416392279738226
+Vulcan,0.5974354458596477,0.3802035699981305,0.5292892681606335,0.5429669598553168,0.37995766670464315,0.40095880020172603,-0.08301767694673019,-0.24509449161886215
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_by_QUESTION.csv
new file mode 100644
index 00000000..3d0ba582
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_margcor_score_by_QUESTION.csv
@@ -0,0 +1,7 @@
+,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8
+All data,0.5763971598016536,0.3208223349569232,0.5095072288219618,0.4875287698718363,0.32954217319350637,0.2524786692147425,-0.0691282701081876,-0.10548661845698232
+QUESTION_1,0.5825559081187357,0.3217713193320128,0.5105453312561148,0.48730173765846074,0.32880799227759827,0.25528538884614754,-0.09543148880744699,-0.06295014728474597
+QUESTION_2,0.5814398521967387,0.3184543612457427,0.5116676088301848,0.48949922207947555,0.33021597482552384,0.2593793842220776,0.017972289446566203,-0.1090174166684455
+QUESTION_3,0.5787341708841691,0.3205769441240706,0.5049951988958794,0.48727414133842034,0.33117580518781675,0.25255170972442675,0.03943551019941079,-0.08340002234370858
+QUESTION_4,0.5740853436832722,0.32112349432240583,0.5090739798661875,0.48580427413076177,0.32699639906173644,0.2389678915189089,-0.09408639444492292,-0.15643426675388586
+QUESTION_5,0.5655755427116818,0.3221875040517872,0.5112898594016663,0.4877743769267795,0.33051344709561264,0.2561431654327727,-0.21154479938542084,-0.11680472372503498
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_model_fit.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_model_fit.csv
new file mode 100644
index 00000000..507ec0f2
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_model_fit.csv
@@ -0,0 +1,2 @@
+N responses,N features,R2,R2_adjusted
+500,8,0.49705638406540364,0.48886178339844477
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pca.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pca.csv
new file mode 100644
index 00000000..f503a9f4
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pca.csv
@@ -0,0 +1,9 @@
+,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8
+FEATURE1,-0.3754016470124534,0.6179108824663035,-0.16857723263790253,0.10248353643660234,-0.1599804811631134,-0.07800192063962152,0.0628864466071843,-0.6345910706920763
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+FEATURE4,-0.4504649189818107,0.16984646887547672,-0.020101142919054328,0.04021678358414059,-0.4651458522506059,0.5500377975582984,-0.12728932218726147,0.4807361792055153
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+FEATURE7,0.008858896347248041,-0.3146095281749996,-0.6582794207810682,0.6795508179947767,0.0040440337779723175,0.06695016035695074,0.008408075482846535,-0.03538192704531955
+FEATURE8,0.09709982047773874,0.054819716515948316,-0.7145564971375661,-0.6767878331449505,0.07273347898791527,0.10791998462528,-0.0023920710565541303,0.04462130431319466
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcavar.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcavar.csv
new file mode 100644
index 00000000..3ec0c6d8
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcavar.csv
@@ -0,0 +1,4 @@
+,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8
+Eigenvalues,2.5167996426675403,1.1298723288277805,1.0302110597675893,0.9488517345913492,0.7708703402012553,0.63780152964887,0.5963294457802476,0.3692639185153687
+Percentage of variance,0.31459995533344254,0.14123404110347257,0.12877638247094866,0.11860646682391865,0.09635879252515692,0.07972519120610876,0.07454118072253095,0.04615798981442109
+Cumulative percentage of variance,0.31459995533344254,0.45583399643691513,0.5846103789078638,0.7032168457317824,0.7995756382569393,0.8793008294630481,0.953842010185579,1.0
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_all_data.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_all_data.csv
new file mode 100644
index 00000000..7605a9b0
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_all_data.csv
@@ -0,0 +1,2 @@
+,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8
+All data,0.4204719074102604,0.051985360238131516,0.23322156251921694,0.16555506031448075,0.059981686955074956,0.20126615832718325,-0.0779875721855451,-0.08258167482300094
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_by_L1.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_by_L1.csv
new file mode 100644
index 00000000..d9b7db53
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_by_L1.csv
@@ -0,0 +1,6 @@
+,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8
+All data,0.4204719074102604,0.051985360238131516,0.23322156251921694,0.16555506031448075,0.059981686955074956,0.20126615832718325,-0.0779875721855451,-0.08258167482300094
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+Klingon,0.415755176225006,-0.03571431108704137,0.21385198134396324,0.1384473756930803,0.006573969759310449,0.17056841968370035,-0.028585911131574417,-0.056732342468637384
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_by_QUESTION.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_by_QUESTION.csv
new file mode 100644
index 00000000..0face12c
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pcor_score_by_QUESTION.csv
@@ -0,0 +1,7 @@
+,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8
+All data,0.4204719074102604,0.051985360238131516,0.23322156251921694,0.16555506031448075,0.059981686955074956,0.20126615832718325,-0.0779875721855451,-0.08258167482300094
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+QUESTION_2,0.4170688519547416,0.06132896509260619,0.22376622704434276,0.16392765768398748,0.0571786849263618,0.20797355707522985,-0.019088520565134656,-0.02910598670261216
+QUESTION_3,0.43106774071596277,0.040230969110836214,0.2377324012932808,0.16506258114926015,0.07455314303405998,0.18559652482281855,-0.10668162617732235,-0.14198218597275725
+QUESTION_4,0.43871126736009486,0.006167288767784545,0.24563021473691668,0.13713133684715892,0.11145793299625333,0.21305836679353535,-0.19688489343540383,-0.1669639593123486
+QUESTION_5,0.4103370521711889,0.05675124209461061,0.2154320006349744,0.18684381696110203,0.03180839576980149,0.22040350508012937,-0.11870450570420857,-0.05977674327423543
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_postprocessing_params.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_postprocessing_params.csv
new file mode 100644
index 00000000..bdcb4e27
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_postprocessing_params.csv
@@ -0,0 +1,2 @@
+trim_min,trim_max,trim_tolerance,h1_mean,h1_sd,train_predictions_mean,train_predictions_sd
+1.0,6.0,0.4998,3.42,0.8154323146156515,3.420000000000003,0.5748979315750407
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pred_processed.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pred_processed.csv
new file mode 100644
index 00000000..d4248f38
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pred_processed.csv
@@ -0,0 +1,152 @@
+spkitemid,raw,sc1,scale,raw_trim,raw_trim_round,scale_trim,scale_trim_round
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pred_train.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pred_train.csv
new file mode 100644
index 00000000..9b20bc19
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_pred_train.csv
@@ -0,0 +1,501 @@
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_score_dist.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_score_dist.csv
new file mode 100644
index 00000000..70e7805e
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_score_dist.csv
@@ -0,0 +1,6 @@
+,score,human,sys_scale,difference
+1.0,1.0,2.6490066225165565,4.635761589403973,1.9867549668874167
+2.0,2.0,11.258278145695364,7.28476821192053,-3.9735099337748343
+3.0,3.0,28.47682119205298,32.450331125827816,3.973509933774835
+4.0,4.0,47.682119205298015,44.370860927152314,-3.3112582781457007
+5.0,5.0,9.933774834437086,11.258278145695364,1.3245033112582778
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_excluded_composition.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_excluded_composition.csv
new file mode 100644
index 00000000..f9cfc254
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_excluded_composition.csv
@@ -0,0 +1,4 @@
+Score/Features,all features numeric,non-numeric feature values
+numeric non-zero human score,-,49
+zero human score,0,0
+non-numeric human score,0,0
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_excluded_responses.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_excluded_responses.csv
new file mode 100644
index 00000000..2e0e062f
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_excluded_responses.csv
@@ -0,0 +1,50 @@
+spkitemid,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8,L1,LENGTH,QUESTION,sc1,sc2
+RESPONSE_1,,-0.11272246300000001,-0.195538472,0.0,-0.127186755,4.611764706000001,12.0,-6480.182775,Vulcan,680,QUESTION_1,4.0,4.0
+RESPONSE_13,,-0.08793134400000001,-0.08247861,-0.10432810599999999,-0.063887656,4.40952381,18.0,-8286.345698000001,Esperanto,735,QUESTION_1,4.0,3.0
+RESPONSE_14,,-0.077920538,-0.151686682,0.0,-0.10305080800000001,4.591150442,6.0,-9360.986354,Navi,565,QUESTION_1,4.0,4.0
+RESPONSE_15,,-0.11571934800000001,-0.17568209199999998,-0.19245009,0.0,4.413580247,16.0,-11647.41617,Vulcan,324,QUESTION_1,3.0,4.0
+RESPONSE_16,,-0.11951218400000001,-0.11516336,-0.199468791,-0.12615514,4.74005305,4.0,-11310.07353,Vulcan,377,QUESTION_1,4.0,4.0
+RESPONSE_17,,-0.149747126,-0.185987083,-0.441552436,-0.112154431,4.2547169810000005,14.0,-9880.144983,Navi,318,QUESTION_1,4.0,4.0
+RESPONSE_18,,-0.102489201,-0.125821837,-0.170363572,-0.10273309900000001,4.335092348,1.0,-3574.403625,Esperanto,379,QUESTION_1,3.0,3.0
+RESPONSE_19,,-0.15701216699999998,-0.332717759,-0.309743377,-0.12149134800000001,4.121771218,3.0,-8801.023452,Klingon,271,QUESTION_1,2.0,3.0
+RESPONSE_20,,-0.10299769,-0.09552009,-0.042717883,-0.073989544,4.74270073,14.0,-8849.349327,Navi,548,QUESTION_1,4.0,4.0
+RESPONSE_2,5.785362462,,-0.103695169,0.0,-0.067884423,4.533026114,20.0,-4929.680684,Esperanto,651,QUESTION_1,4.0,4.0
+RESPONSE_12,5.566434284,,-0.131180841,0.0,-0.043726947,4.9521988530000005,13.0,-7714.666969,Esperanto,523,QUESTION_1,5.0,5.0
+RESPONSE_21,6.084499413,,-0.082666275,-0.190909584,-0.082666275,4.355353075,10.0,-6208.381874000001,Vulcan,439,QUESTION_1,4.0,3.0
+RESPONSE_22,5.705447754,,-0.122372552,-0.040790851,-0.070651826,4.875207987,13.0,-4647.680689,Klingon,601,QUESTION_1,5.0,5.0
+RESPONSE_23,4.973279508,,-0.117647059,-0.249567099,-0.083189033,4.415224913,18.0,-8493.435016,Esperanto,289,QUESTION_1,3.0,3.0
+RESPONSE_24,5.084505143,,-0.22256595399999998,-0.347480984,-0.136293255,4.681114551,8.0,-6454.317071,Klingon,323,QUESTION_1,3.0,3.0
+RESPONSE_25,5.932245187,,-0.158741737,-0.218507112,-0.081432793,4.133952255,14.0,-4766.85639,Navi,754,QUESTION_1,4.0,4.0
+RESPONSE_26,5.957131867999999,,-0.071935,-0.148297802,-0.062297538,4.150064683,1.0,-7258.168156999999,Klingon,773,QUESTION_1,4.0,4.0
+RESPONSE_3,4.677490848,-0.113823323,,-0.272797736,-0.096448564,4.646511628,4.0,-5075.591347,Vulcan,215,QUESTION_1,4.0,3.0
+RESPONSE_11,5.209486153,-0.142623901,,-0.165294901,-0.11688114599999999,4.259562842,15.0,-9543.875498000001,Klingon,366,QUESTION_1,4.0,4.0
+RESPONSE_27,5.736572297,-0.066298694,,-0.160643866,-0.056796183,4.201612903,16.0,-8511.089076,Navi,620,QUESTION_1,4.0,4.0
+RESPONSE_28,5.82008293,-0.136357797,,-0.24361276899999998,-0.09435082,4.28189911,11.0,-10726.06197,Vulcan,337,QUESTION_1,4.0,4.0
+RESPONSE_29,5.017279837,-0.09261460199999999,,-0.293415602,-0.08137884599999999,4.350993377,13.0,-8053.8467709999995,Esperanto,302,QUESTION_1,3.0,3.0
+RESPONSE_30,5.1984970310000005,-0.113666216,,-0.166205624,-0.105117666,4.309392265,3.0,-8420.586662,Navi,362,QUESTION_1,3.0,3.0
+RESPONSE_4,5.141663557,-0.085858252,-0.12091270800000001,,-0.07647191099999999,4.684210526,1.0,-12275.30154,Navi,342,QUESTION_1,3.0,4.0
+RESPONSE_10,5.173320876,-0.10523566,-0.184375525,,,,17.0,-11592.315359999999,Esperanto,353,QUESTION_1,3.0,3.0
+RESPONSE_31,4.795790546,-0.11462360099999999,-0.090909091,,-0.10497277599999999,4.303030303,19.0,-7281.693413,Vulcan,363,QUESTION_1,3.0,3.0
+RESPONSE_32,5.305789381,-0.105524466,-0.211340986,,-0.179605302,4.163771712,19.0,-8406.190106999999,Klingon,403,QUESTION_1,3.0,3.0
+RESPONSE_33,4.262679877,-0.089851123,-0.37529331299999996,,-0.118678166,4.352112676,17.0,-7727.415028,Vulcan,71,QUESTION_1,1.0,1.0
+RESPONSE_34,5.605802066,-0.129003166,-0.095870624,,-0.060633905999999994,4.511029412,16.0,-5082.828838,Vulcan,544,QUESTION_1,4.0,4.0
+RESPONSE_5,5.300814247,-0.045987852,-0.149812851,-0.303758665,,4.249376559,7.0,-10578.23476,Vulcan,401,QUESTION_1,2.0,3.0
+RESPONSE_35,4.9452074889999995,-0.083284577,-0.17896499600000002,0.0,,4.37366548,9.0,-9307.460306,Vulcan,281,QUESTION_1,4.0,3.0
+RESPONSE_36,4.527208645,-0.073499388,-0.254685816,-0.24384310399999998,,4.756756757,15.0,-11879.05033,Navi,185,QUESTION_1,3.0,3.0
+RESPONSE_37,5.5606816310000005,-0.10523696199999999,-0.169841555,0.0,,4.211538462,12.0,-7221.795746,Klingon,520,QUESTION_1,4.0,4.0
+RESPONSE_38,6.398594935,-0.08806854300000001,-0.168184986,-0.081581702,,4.780366057,8.0,-6916.770473999999,Vulcan,601,QUESTION_1,5.0,5.0
+RESPONSE_39,3.7841896339999996,-0.209372633,-0.23836564699999999,-0.533001791,,3.9886363639999995,4.0,-4706.097845,Vulcan,88,QUESTION_1,2.0,1.0
+RESPONSE_40,5.117993812,-0.094795764,-0.189547207,-0.197286988,,4.763473053999999,19.0,-10728.05567,Vulcan,334,QUESTION_1,4.0,3.0
+RESPONSE_6,4.26969745,-0.14984186900000002,-0.277350098,-0.409673245,-0.083624201,,17.0,-7035.443198000001,Klingon,143,QUESTION_1,2.0,2.0
+RESPONSE_41,5.804007866,-0.11227845800000001,-0.19754756899999998,0.00138458,-0.12744134699999998,,6.0,-5034.432954,Navi,680,QUESTION_2,4.0,4.0
+RESPONSE_42,5.872060165,-0.086904381,-0.104013139,-0.000286425,-0.068211158,,1.0,-9749.746134,Esperanto,651,QUESTION_2,4.0,4.0
+RESPONSE_43,4.6606445569999995,-0.11344485300000001,-0.177901455,-0.271433557,-0.09479791900000001,,10.0,-7165.506632,Esperanto,215,QUESTION_2,4.0,3.0
+RESPONSE_44,5.118102377,-0.085404109,-0.122423751,-0.23710715300000001,-0.075374243,,7.0,-12418.6903,Klingon,342,QUESTION_2,3.0,4.0
+RESPONSE_45,5.229891027,-0.046503155,-0.152150154,-0.30460423,-0.123749811,,11.0,-6162.042864999999,Vulcan,401,QUESTION_2,2.0,3.0
+RESPONSE_7,6.29710932,-0.105956834,-0.281406622,0.0,-0.121379426,4.248618785,,-9650.86543,Klingon,543,QUESTION_1,4.0,4.0
+RESPONSE_9,5.629418059,-0.09555372599999999,-0.152772111,-0.22016804399999998,-0.094745241,4.398563734,,-11808.50949,Esperanto,557,QUESTION_1,5.0,4.0
+RESPONSE_46,4.26793037,-0.149769549,-0.273030654,-0.40952254200000004,-0.083590046,3.909992575,,-5232.625456000001,Esperanto,143,QUESTION_2,2.0,2.0
+RESPONSE_47,6.348811607000001,-0.105891153,-0.283076366,0.000445853,-0.12094001,4.175370463999999,,-10385.8751,Klingon,543,QUESTION_2,4.0,4.0
+RESPONSE_48,4.701777482,-0.145331625,-0.33222726199999997,-0.189721394,-0.12177320800000001,4.1687444010000005,,-6864.614908,Esperanto,200,QUESTION_2,2.0,2.0
+RESPONSE_49,5.701639735,-0.09578146400000001,-0.150216803,-0.219177285,-0.09369881699999999,4.41275964,,-6029.567409,Navi,557,QUESTION_2,5.0,4.0
+RESPONSE_8,4.6051701860000005,-0.143853507,-0.331662479,-0.18708286899999998,-0.12247448699999999,4.165,19.0,,Navi,200,QUESTION_1,2.0,2.0
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_features.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_features.csv
new file mode 100644
index 00000000..a946db87
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_features.csv
@@ -0,0 +1,152 @@
+spkitemid,sc1,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8
+RESPONSE_50,3.0,5.187306325,-0.10320232,-0.185372628,-0.19812175899999998,-0.141418561,4.182466418,10.0,-8146.931922
+RESPONSE_51,4.0,5.261744611,-0.142947133,-0.229402038,-0.167202543,-0.11599788800000001,4.259553432,11.0,-8986.084993999999
+RESPONSE_52,5.0,5.626477938,-0.063151018,-0.130393806,-0.001380173,-0.043722450999999996,5.004202571,10.0,-6922.795076
+RESPONSE_53,4.0,6.622359735,-0.08761845,-0.082657753,-0.104845399,-0.063744174,4.402084982,15.0,-5094.247437
+RESPONSE_54,4.0,5.2278378530000005,-0.078910178,-0.151183801,-0.000413279,-0.104220134,4.627782153,18.0,-12490.377129999999
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+RESPONSE_56,4.0,6.014421903,-0.12006602599999999,-0.11606372599999999,-0.199527755,-0.12631552199999999,4.685395818,18.0,-9357.455226
+RESPONSE_57,4.0,5.010378585,-0.148265506,-0.185491916,-0.444934903,-0.112121968,4.324560381,13.0,-6909.954620999999
+RESPONSE_58,3.0,5.17788655,-0.101065987,-0.125470255,-0.170193409,-0.100571515,4.27271133,2.0,-4797.839374
+RESPONSE_59,2.0,4.938811423,-0.155304324,-0.33677567,-0.311951921,-0.11958695400000001,4.205350001,13.0,-4675.735681
+RESPONSE_60,4.0,5.552500907000001,-0.102545735,-0.09621160699999999,-0.043668833,-0.074451408,4.677947815,1.0,-8120.792963
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+RESPONSE_63,3.0,4.90183467,-0.116544056,-0.11753045599999999,-0.250974384,-0.08394036199999999,4.480974961,3.0,-10540.41014
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_human_scores.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_human_scores.csv
new file mode 100644
index 00000000..6f70d5b3
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_test_human_scores.csv
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index 00000000..2e194c91
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_excluded_composition.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_excluded_composition.csv
new file mode 100644
index 00000000..1dd00c72
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_excluded_composition.csv
@@ -0,0 +1,4 @@
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_features.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_features.csv
new file mode 100644
index 00000000..5c8b7e2f
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_features.csv
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_metadata.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_metadata.csv
new file mode 100644
index 00000000..f803d725
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_metadata.csv
@@ -0,0 +1,501 @@
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_other_columns.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_other_columns.csv
new file mode 100644
index 00000000..6ffa6558
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_other_columns.csv
@@ -0,0 +1,501 @@
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diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_preprocessed_features.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_preprocessed_features.csv
new file mode 100644
index 00000000..1bd2083a
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_train_preprocessed_features.csv
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+RESPONSE_452,3.0,-0.8533639018084399,0.6993561034409693,0.5823051822262858,0.211369108045735,2.350174986897798,0.26505318646411635,-0.9885382527079486,-1.936445731145674
+RESPONSE_453,5.0,0.9887501424030034,0.6508408989964428,1.3869643766291102,1.2667566694710808,1.0208767483425554,0.42379572269795796,-0.2909106925753101,-1.783462921957649
+RESPONSE_454,4.0,0.42453914216847166,-1.1965807239638386,-0.0764360167632395,1.2601231146781497,0.029566517771051713,0.039490109248172985,-0.2909106925753101,-1.380028089627337
+RESPONSE_455,3.0,0.679154831797524,-0.8154583871340393,-0.8570814735345661,-0.3811749200902036,2.341783162718009,1.3560375492989647,-1.686165812840587,0.0820379862690258
+RESPONSE_456,4.0,0.15576987625775526,0.4959810138081703,0.5795618131922198,1.2518105603991616,0.7070232522813171,1.2743365895809267,-1.686165812840587,1.0234274544826523
+RESPONSE_457,3.0,-1.0896995639435556,0.3799240575846346,0.8469323243317181,0.2393278078506748,-0.011888189361893089,1.9764641562772658,-0.6397244726416293,-1.57266647651203
+RESPONSE_458,4.0,0.41820201214567876,1.3549290316021736,0.6997409715436692,1.2507995829444827,0.7472830513323664,0.1011423488900603,-1.1629451427411082,-0.13991947849397537
+RESPONSE_459,3.0,0.13879870330423852,-0.3001164395527587,0.22618919576794302,-1.7248424104201676,-1.3858356791872697,-1.387095760404091,0.40671686755732833,0.06322829205060977
+RESPONSE_460,4.0,-0.24608151955342855,0.07106427004441748,0.6277595365295378,-0.8670052231834378,0.03987099946871599,0.4851722879131462,-0.9885382527079486,-0.2510865666119937
+RESPONSE_461,3.0,-0.3997799350750213,-0.6101625846499605,-0.40135493289142365,-0.7194516989736655,-1.366783723663481,0.33207821271537313,-1.686165812840587,-0.9711579027452223
+RESPONSE_462,3.0,0.20416224455448292,0.059646127048031426,-0.6466446064152086,-0.49744427003152913,1.1368367877294476,-0.9423002412295838,-0.6397244726416293,-0.7303513449833055
+RESPONSE_463,6.0,2.418743268894747,0.3585139038188497,1.1908675611225485,0.4283096189464702,0.4928717707571282,0.46484489925479,-0.4653175826084697,-0.042292950819678404
+RESPONSE_464,4.0,1.4358686015100663,0.6420968495615239,1.599263291992577,1.2615073537125778,0.8449513660984609,0.6229798000120358,-0.8141313626747889,1.2389960622351006
+RESPONSE_465,3.0,-2.3563490427296534,-0.865604273996166,-1.412871663586643,1.2619366995803598,0.014079304721908081,1.816677483527052,1.1043444276899668,0.647970590862939
+RESPONSE_466,4.0,1.3977012645287286,0.4481528473668579,-1.0255937862510827,-0.7802972858585115,-0.20308639203915485,0.11307812257869093,-1.686165812840587,-0.1526668921264724
+RESPONSE_467,4.0,0.1055471720920987,-0.04138116019847073,1.0082566890644171,1.2600468028194145,0.5012063007480861,0.6010235588000463,1.2787513177231264,-0.3633197624697063
+RESPONSE_468,3.0,1.1949773658156673,0.9925743891085173,1.386141110624613,1.2458079533536683,0.2442001465456441,-0.35362983207183823,-1.3373520327742678,-0.3498902761612077
+RESPONSE_469,4.0,1.1726578492551947,0.9508292191305656,0.6975573489800457,-0.03829448890505734,-0.16714060271339473,-0.014826917179386043,-0.2909106925753101,-0.8848523152173101
+RESPONSE_470,4.0,-0.37315386172860454,-0.18517537843491658,-1.4101613841619338,1.2610456279134872,0.037162852140188954,-0.3382556685125261,-1.686165812840587,-1.6433273456947248
+RESPONSE_471,2.0,-1.8497411804021688,0.08217470537561487,-0.31794473957769104,-1.544177849022828,-2.255173524198963,0.310393960621982,-0.6397244726416293,0.7762816874360373
+RESPONSE_472,4.0,-0.2093539164448694,-1.6034443176863935,0.014114886946758984,-0.7715968862359323,-0.28364965029313205,0.6619845800220069,0.23230997752416874,-0.3821178418528351
+RESPONSE_473,3.0,-0.32137522288711157,-1.2142733618151826,-0.6764145608959613,-1.2292428316290107,0.4498765658486946,-0.6391776831686738,1.6275650977894456,0.17907285817121288
+RESPONSE_474,2.0,-0.7390865641676524,-1.6237669185470298,-1.5263605608434963,-2.095221439918956,-2.06076344720947,1.3256205203478961,0.9299375376568072,0.5820636045960251
+RESPONSE_475,3.0,-0.33515368184215644,0.18591942030462819,-0.22029567313976425,-0.9132462924318674,-0.7891307258746576,-1.3082942867816347,-0.1165038025421505,-0.3229045993247593
+RESPONSE_476,4.0,0.10175984695169862,1.1845287085727048,-0.9700812535368788,1.2637400467715956,0.20532519024383028,0.4133643226291596,-0.1165038025421505,-1.2304953810853985
+RESPONSE_477,4.0,1.2269116930290795,0.8513389022026604,1.5791648310711814,0.9149018586605703,0.14014227222448716,0.8272093875070204,-1.5117589228074275,-2.129052570044533
+RESPONSE_478,3.0,0.21463568033447797,-1.0847196531914851,-0.08550133921383271,-0.21518649739911172,-1.546243030791357,0.6047349898416565,1.1043444276899668,-1.1240534538916356
+RESPONSE_479,4.0,-0.5938212823276847,0.3753384795671694,0.07143835728743356,-0.4737894146947003,-0.2438493835641325,-0.13586126008483349,-0.4653175826084697,1.6628437872922777
+RESPONSE_480,4.0,-1.1435110072802466,-0.9143404040984385,1.686732952338216,-0.8353423099285224,0.37783655246674147,0.3142187002406341,-1.5117589228074275,-0.6570219939881757
+RESPONSE_481,1.0,-2.354217477564537,-4.241128323326346,-0.710222747046501,-2.1959373858813325,-2.2944038842970667,-3.9686836090557103,-0.4653175826084697,0.8892199055712964
+RESPONSE_482,4.0,0.12561732402453374,0.9175834083905419,1.2150655078469594,0.18343892526738495,1.0760348294922755,0.8673466604380278,-1.5117589228074275,-0.07014965013666977
+RESPONSE_483,4.0,-0.13776266044544613,0.5131879915779308,-0.07466182800656465,0.0953742726198418,-0.7590033024672461,-0.5784529655805246,-0.8141313626747889,-0.7386472822206502
+RESPONSE_484,4.0,0.3075135802839738,-0.17406060895456196,-0.24064630890857389,1.2601348336349716,0.05884548524061662,-0.6944574620706582,1.1043444276899668,1.412107417456681
+RESPONSE_485,4.0,-0.05365037346553515,0.1096574570687338,0.13071290827025436,0.9138766133760313,0.5474514288838195,0.4774409029602787,-0.1165038025421505,0.05815418426924079
+RESPONSE_486,4.0,2.310179090300348,0.31656818148668875,0.3895423229065775,-0.5440165557184136,1.284533714544959,-0.39169284746547167,0.9299375376568072,-1.5143973586460848
+RESPONSE_487,3.0,0.07798954458754546,1.0170148763486382,-0.3873524051084962,-0.0881513661463542,0.18424398972169861,-0.5353877012570849,-0.2909106925753101,0.8414603831007598
+RESPONSE_488,5.0,1.7001226888012624,-0.5407855574319225,0.47289878987824085,0.6367635586414272,0.034930270100658495,1.0885434709993322,0.581123757590488,1.1215862250763056
+RESPONSE_489,3.0,-0.213381051738411,0.9741845790842304,-0.7886365799580932,0.10994479589696399,0.9819136510122557,0.29823029818955243,0.40671686755732833,1.4626891636838613
+RESPONSE_490,3.0,0.045801558269584326,-0.9181572067568676,-0.5771262843053614,-1.2089984248876398,-1.2399826138500794,-0.24093590565068906,0.40671686755732833,-0.3421055440012064
+RESPONSE_491,4.0,0.7750793495105092,-0.3214826216978645,1.6337807742239345,1.2679399875705593,0.8624117770161819,0.6126971679019836,-0.4653175826084697,1.2179460833607871
+RESPONSE_492,4.0,0.3100875062428003,-0.1422013721948301,0.8137472411695639,-0.5570141108501037,0.6186495434895123,-0.0019418072483961618,-0.6397244726416293,-1.4248413128800241
+RESPONSE_493,2.0,0.33975242152109814,-0.8551956466230052,-0.1284977565186615,-0.644846246080041,-0.46487767219121384,-0.9191881924773284,-0.8141313626747889,0.7550645343044803
+RESPONSE_494,4.0,0.9812738522725979,1.123283863437016,1.1265004330700907,-0.3965273111341004,0.16403070223751928,-0.42672481604167967,-0.6397244726416293,0.6157087747339088
+RESPONSE_495,1.0,-1.4235487182199142,-1.3839752308408775,-0.5759630559678318,-1.1340436636946827,-1.3062707079411544,-1.4586295560313485,1.4531582077562861,1.059760120048978
+RESPONSE_496,3.0,-0.0610361507236436,-0.7399889115538639,-1.87526414763041,-0.8013574254616849,-1.4356532284410255,-1.6902191382312095,-0.1165038025421505,-0.34921391608464114
+RESPONSE_497,3.0,-0.10763796159820847,0.24285018279435025,-0.0037664142672311617,-0.5823664184673659,-0.6745740814609754,0.6100752988847478,0.05790308749100912,-0.8284271033183402
+RESPONSE_498,4.0,-0.0034714717950806084,-0.22732034424621997,-1.1000802247478896,-1.120193975342974,0.5357285373385723,-2.0656757795309373,-1.686165812840587,-1.2144220039774765
+RESPONSE_499,3.0,0.443586395742481,0.20981086254440984,1.4386643271982702,-0.22995769462466387,-0.027686658877686027,-1.2970914496505783,-1.3373520327742678,0.37419018141568633
+RESPONSE_500,4.0,-1.3701944665637884,-0.5541033523008351,-0.26277289093053513,-0.0637389349660351,-0.14602835530209926,1.3514845868464627,0.05790308749100912,-1.8262863232083808
diff --git a/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_true_score_eval.csv b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_true_score_eval.csv
new file mode 100644
index 00000000..a2d37f18
--- /dev/null
+++ b/tests/data/experiments/lr-subgroups-with-h2-but-only-for-nonscoreable/output/lr_subgroups_with_h2_but_only_for_nonscoreable_true_score_eval.csv
@@ -0,0 +1,7 @@
+,N,N raters,N single,N multiple,Variance of errors,True score var,MSE true,PRMSE true
+raw,151,1,151,0,,,,
+scale,151,1,151,0,,,,
+raw_trim,151,1,151,0,,,,
+raw_trim_round,151,1,151,0,,,,
+scale_trim,151,1,151,0,,,,
+scale_trim_round,151,1,151,0,,,,
diff --git a/tests/data/files/test_with_no_double_scored_responses_for_valid_system_scores.csv b/tests/data/files/test_with_no_double_scored_responses_for_valid_system_scores.csv
new file mode 100644
index 00000000..b7bdd3ab
--- /dev/null
+++ b/tests/data/files/test_with_no_double_scored_responses_for_valid_system_scores.csv
@@ -0,0 +1,201 @@
+ID,FEATURE1,FEATURE2,FEATURE3,FEATURE4,FEATURE5,FEATURE6,FEATURE7,FEATURE8,LENGTH,QUESTION,L1,score,score2
+RESPONSE_1,,-0.112722463,-0.195538472,0,-0.127186755,4.611764706,12,-6480.182775,680,QUESTION_1,Vulcan,4,4
+RESPONSE_2,5.785362462,,-0.103695169,0,-0.067884423,4.533026114,20,-4929.680684,651,QUESTION_1,Esperanto,4,4
+RESPONSE_3,4.677490848,-0.113823323,,-0.272797736,-0.096448564,4.646511628,4,-5075.591347,215,QUESTION_1,Vulcan,4,3
+RESPONSE_4,5.141663557,-0.085858252,-0.120912708,,-0.076471911,4.684210526,1,-12275.30154,342,QUESTION_1,Navi,3,4
+RESPONSE_5,5.300814247,-0.045987852,-0.149812851,-0.303758665,,4.249376559,7,-10578.23476,401,QUESTION_1,Vulcan,2,3
+RESPONSE_6,4.26969745,-0.149841869,-0.277350098,-0.409673245,-0.083624201,,17,-7035.443198,143,QUESTION_1,Klingon,2,2
+RESPONSE_7,6.29710932,-0.105956834,-0.281406622,0,-0.121379426,4.248618785,,-9650.86543,543,QUESTION_1,Klingon,4,4
+RESPONSE_8,4.605170186,-0.143853507,-0.331662479,-0.187082869,-0.122474487,4.165,19,,200,QUESTION_1,Navi,2,2
+RESPONSE_9,5.629418059,-0.095553726,-0.152772111,-0.220168044,-0.094745241,4.398563734,,-11808.50949,557,QUESTION_1,Esperanto,5,4
+RESPONSE_10,5.173320876,-0.10523566,-0.184375525,,,,17,-11592.31536,353,QUESTION_1,Esperanto,3,3
+RESPONSE_11,5.209486153,-0.142623901,,-0.165294901,-0.116881146,4.259562842,15,-9543.875498,366,QUESTION_1,Klingon,4,4
+RESPONSE_12,5.566434284,,-0.131180841,0,-0.043726947,4.952198853,13,-7714.666969,523,QUESTION_1,Esperanto,5,5
+RESPONSE_13,,-0.087931344,-0.08247861,-0.104328106,-0.063887656,4.40952381,18,-8286.345698,735,QUESTION_1,Esperanto,4,3
+RESPONSE_14,,-0.077920538,-0.151686682,0,-0.103050808,4.591150442,6,-9360.986354,565,QUESTION_1,Navi,4,4
+RESPONSE_15,,-0.115719348,-0.175682092,-0.19245009,0,4.413580247,16,-11647.41617,324,QUESTION_1,Vulcan,3,4
+RESPONSE_16,,-0.119512184,-0.11516336,-0.199468791,-0.12615514,4.74005305,4,-11310.07353,377,QUESTION_1,Vulcan,4,4
+RESPONSE_17,,-0.149747126,-0.185987083,-0.441552436,-0.112154431,4.254716981,14,-9880.144983,318,QUESTION_1,Navi,4,4
+RESPONSE_18,,-0.102489201,-0.125821837,-0.170363572,-0.102733099,4.335092348,1,-3574.403625,379,QUESTION_1,Esperanto,3,3
+RESPONSE_19,,-0.157012167,-0.332717759,-0.309743377,-0.121491348,4.121771218,3,-8801.023452,271,QUESTION_1,Klingon,2,3
+RESPONSE_20,,-0.10299769,-0.09552009,-0.042717883,-0.073989544,4.74270073,14,-8849.349327,548,QUESTION_1,Navi,4,4
+RESPONSE_21,6.084499413,,-0.082666275,-0.190909584,-0.082666275,4.355353075,10,-6208.381874,439,QUESTION_1,Vulcan,4,3
+RESPONSE_22,5.705447754,,-0.122372552,-0.040790851,-0.070651826,4.875207987,13,-4647.680689,601,QUESTION_1,Klingon,5,5
+RESPONSE_23,4.973279508,,-0.117647059,-0.249567099,-0.083189033,4.415224913,18,-8493.435016,289,QUESTION_1,Esperanto,3,3
+RESPONSE_24,5.084505143,,-0.222565954,-0.347480984,-0.136293255,4.681114551,8,-6454.317071,323,QUESTION_1,Klingon,3,3
+RESPONSE_25,5.932245187,,-0.158741737,-0.218507112,-0.081432793,4.133952255,14,-4766.85639,754,QUESTION_1,Navi,4,4
+RESPONSE_26,5.957131868,,-0.071935,-0.148297802,-0.062297538,4.150064683,1,-7258.168157,773,QUESTION_1,Klingon,4,4
+RESPONSE_27,5.736572297,-0.066298694,,-0.160643866,-0.056796183,4.201612903,16,-8511.089076,620,QUESTION_1,Navi,4,4
+RESPONSE_28,5.82008293,-0.136357797,,-0.243612769,-0.09435082,4.28189911,11,-10726.06197,337,QUESTION_1,Vulcan,4,4
+RESPONSE_29,5.017279837,-0.092614602,,-0.293415602,-0.081378846,4.350993377,13,-8053.846771,302,QUESTION_1,Esperanto,3,3
+RESPONSE_30,5.198497031,-0.113666216,,-0.166205624,-0.105117666,4.309392265,3,-8420.586662,362,QUESTION_1,Navi,3,3
+RESPONSE_31,4.795790546,-0.114623601,-0.090909091,,-0.104972776,4.303030303,19,-7281.693413,363,QUESTION_1,Vulcan,3,3
+RESPONSE_32,5.305789381,-0.105524466,-0.211340986,,-0.179605302,4.163771712,19,-8406.190107,403,QUESTION_1,Klingon,3,3
+RESPONSE_33,4.262679877,-0.089851123,-0.375293313,,-0.118678166,4.352112676,17,-7727.415028,71,QUESTION_1,Vulcan,1,1
+RESPONSE_34,5.605802066,-0.129003166,-0.095870624,,-0.060633906,4.511029412,16,-5082.828838,544,QUESTION_1,Vulcan,4,4
+RESPONSE_35,4.945207489,-0.083284577,-0.178964996,0,,4.37366548,9,-9307.460306,281,QUESTION_1,Vulcan,4,3
+RESPONSE_36,4.527208645,-0.073499388,-0.254685816,-0.243843104,,4.756756757,15,-11879.05033,185,QUESTION_1,Navi,3,3
+RESPONSE_37,5.560681631,-0.105236962,-0.169841555,0,,4.211538462,12,-7221.795746,520,QUESTION_1,Klingon,4,4
+RESPONSE_38,6.398594935,-0.088068543,-0.168184986,-0.081581702,,4.780366057,8,-6916.770474,601,QUESTION_1,Vulcan,5,5
+RESPONSE_39,3.784189634,-0.209372633,-0.238365647,-0.533001791,,3.988636364,4,-4706.097845,88,QUESTION_1,Vulcan,2,1
+RESPONSE_40,5.117993812,-0.094795764,-0.189547207,-0.197286988,,4.763473054,19,-10728.05567,334,QUESTION_1,Vulcan,4,3
+RESPONSE_41,5.804007866,-0.112278458,-0.197547569,0.00138458,-0.127441347,,6,-5034.432954,680,QUESTION_2,Navi,4,4
+RESPONSE_42,5.872060165,-0.086904381,-0.104013139,-0.000286425,-0.068211158,,1,-9749.746134,651,QUESTION_2,Esperanto,4,4
+RESPONSE_43,4.660644557,-0.113444853,-0.177901455,-0.271433557,-0.094797919,,10,-7165.506632,215,QUESTION_2,Esperanto,4,3
+RESPONSE_44,5.118102377,-0.085404109,-0.122423751,-0.237107153,-0.075374243,,7,-12418.6903,342,QUESTION_2,Klingon,3,4
+RESPONSE_45,5.229891027,-0.046503155,-0.152150154,-0.30460423,-0.123749811,,11,-6162.042865,401,QUESTION_2,Vulcan,2,3
+RESPONSE_46,4.26793037,-0.149769549,-0.273030654,-0.409522542,-0.083590046,3.909992575,,-5232.625456,143,QUESTION_2,Esperanto,2,2
+RESPONSE_47,6.348811607,-0.105891153,-0.283076366,0.000445853,-0.12094001,4.175370464,,-10385.8751,543,QUESTION_2,Klingon,4,4
+RESPONSE_48,4.701777482,-0.145331625,-0.332227262,-0.189721394,-0.121773208,4.168744401,,-6864.614908,200,QUESTION_2,Esperanto,2,2
+RESPONSE_49,5.701639735,-0.095781464,-0.150216803,-0.219177285,-0.093698817,4.41275964,,-6029.567409,557,QUESTION_2,Navi,5,4
+RESPONSE_50,5.187306325,-0.10320232,-0.185372628,-0.198121759,-0.141418561,4.182466418,10,-8146.931922,353,QUESTION_2,Vulcan,3,
+RESPONSE_51,5.261744611,-0.142947133,-0.229402038,-0.167202543,-0.115997888,4.259553432,11,-8986.084994,366,QUESTION_2,Klingon,4,
+RESPONSE_52,5.626477938,-0.063151018,-0.130393806,-0.001380173,-0.043722451,5.004202571,10,-6922.795076,523,QUESTION_2,Klingon,5,
+RESPONSE_53,6.622359735,-0.08761845,-0.082657753,-0.104845399,-0.063744174,4.402084982,15,-5094.247437,735,QUESTION_2,Klingon,4,
+RESPONSE_54,5.227837853,-0.078910178,-0.151183801,-0.000413279,-0.104220134,4.627782153,18,-12490.37713,565,QUESTION_2,Vulcan,4,
+RESPONSE_55,5.924190263,-0.115945547,-0.176355595,-0.195758916,-0.000951521,4.437966351,2,-9924.560568,324,QUESTION_2,Klingon,3,
+RESPONSE_56,6.014421903,-0.120066026,-0.116063726,-0.199527755,-0.126315522,4.685395818,18,-9357.455226,377,QUESTION_2,Vulcan,4,
+RESPONSE_57,5.010378585,-0.148265506,-0.185491916,-0.444934903,-0.112121968,4.324560381,13,-6909.954621,318,QUESTION_2,Esperanto,4,
+RESPONSE_58,5.17788655,-0.101065987,-0.125470255,-0.170193409,-0.100571515,4.27271133,2,-4797.839374,379,QUESTION_2,Esperanto,3,
+RESPONSE_59,4.938811423,-0.155304324,-0.33677567,-0.311951921,-0.119586954,4.205350001,13,-4675.735681,271,QUESTION_2,Vulcan,2,
+RESPONSE_60,5.552500907,-0.102545735,-0.096211607,-0.043668833,-0.074451408,4.677947815,1,-8120.792963,548,QUESTION_2,Navi,4,
+RESPONSE_61,6.047305983,-0.108251744,-0.084100351,-0.189524126,-0.082997472,4.358495413,14,-12006.73146,439,QUESTION_2,Esperanto,4,
+RESPONSE_62,5.711269314,-0.101794787,-0.122040114,-0.040416808,-0.071455181,4.905100406,13,-9964.150648,601,QUESTION_2,Vulcan,5,
+RESPONSE_63,4.90183467,-0.116544056,-0.117530456,-0.250974384,-0.083940362,4.480974961,3,-10540.41014,289,QUESTION_2,Klingon,3,
+RESPONSE_64,5.095219062,-0.115556879,-0.223374068,-0.352770268,-0.137788771,4.691473697,18,-5665.940964,323,QUESTION_2,Klingon,3,
+RESPONSE_65,5.879038633,-0.111179523,-0.159675498,-0.218538312,-0.081490862,4.070665906,9,-12184.11202,754,QUESTION_2,Klingon,4,
+RESPONSE_66,5.854407498,-0.091884116,-0.071916339,-0.148663683,-0.063119453,4.183343563,18,-8217.19558,773,QUESTION_2,Navi,4,
+RESPONSE_67,5.839391131,-0.065910449,-0.069958933,-0.160909457,-0.057114295,4.183778455,10,-7010.888033,620,QUESTION_2,Vulcan,4,
+RESPONSE_68,5.743681962,-0.135876726,-0.228556228,-0.243322592,-0.094195184,4.329807351,18,-4563.021383,337,QUESTION_2,Klingon,4,
+RESPONSE_69,5.045169763,-0.094296074,-0.082361915,-0.299503918,-0.081892823,4.41928431,2,-8337.573446,302,QUESTION_2,Navi,3,
+RESPONSE_70,5.163049452,-0.114700719,-0.159788893,-0.167872259,-0.10602911,4.370918828,8,-9313.097349,362,QUESTION_2,Esperanto,3,
+RESPONSE_71,4.871215994,-0.11548159,-0.089022559,-0.052211598,-0.106025194,4.243999704,2,-12074.28665,363,QUESTION_2,Vulcan,3,
+RESPONSE_72,5.300236789,-0.105638565,-0.214488719,-0.220942841,-0.177436841,4.152678679,18,-3335.986984,403,QUESTION_2,Navi,3,
+RESPONSE_73,4.341556412,-0.089604231,-0.379612762,-0.518075582,-0.116646411,4.386024972,7,-11531.04041,71,QUESTION_2,Klingon,1,
+RESPONSE_74,5.587115101,-0.128696977,-0.095468868,-0.148589517,-0.061012619,4.506391429,5,-9377.278523,544,QUESTION_2,Vulcan,4,
+RESPONSE_75,4.954774697,-0.083500987,-0.178225145,-0.000704493,-0.119563108,4.34801548,6,-5381.200481,281,QUESTION_2,Navi,4,
+RESPONSE_76,4.504719965,-0.073729031,-0.252858249,-0.239623849,-0.128862827,4.768184991,17,-6413.573846,185,QUESTION_2,Klingon,3,
+RESPONSE_77,5.618108081,-0.104764964,-0.169397038,0.000228927,-0.130693591,4.169504964,18,-6806.096875,520,QUESTION_2,Navi,4,
+RESPONSE_78,6.287271937,-0.088120972,-0.168593291,-0.080690225,-0.040709237,4.789504678,5,-6086.896497,601,QUESTION_2,Klingon,5,
+RESPONSE_79,3.755586376,-0.210356715,-0.239560498,-0.534886333,-0.210874143,3.950632516,6,-8078.370253,88,QUESTION_2,Esperanto,2,
+RESPONSE_80,5.072898568,-0.09446439,-0.190870491,-0.197347474,-0.054985146,4.83354358,8,-7413.623263,334,QUESTION_2,Vulcan,4,
+RESPONSE_81,5.870850195,-0.112528621,-0.194880507,0.001454343,-0.129489382,4.633829846,8,-11250.06615,680,QUESTION_3,Navi,4,
+RESPONSE_82,5.775726386,-0.086809792,-0.102599002,0.000704308,-0.068497629,4.534367027,20,-8954.039329,651,QUESTION_3,Vulcan,4,
+RESPONSE_83,4.713255471,-0.112838753,-0.178275861,-0.268797562,-0.095431953,4.63803444,4,-7473.019789,215,QUESTION_3,Vulcan,4,
+RESPONSE_84,5.080506375,-0.086034142,-0.124049975,-0.240970268,-0.077222007,4.685028702,2,-5695.647711,342,QUESTION_3,Vulcan,3,
+RESPONSE_85,5.272462457,-0.046037092,-0.150883631,-0.307056037,-0.121891012,4.336671056,5,-8108.209641,401,QUESTION_3,Navi,2,
+RESPONSE_86,4.268775287,-0.150204646,-0.275119244,-0.408185272,-0.083067279,3.78285348,6,-7932.758131,143,QUESTION_3,Esperanto,2,
+RESPONSE_87,6.227514197,-0.106913077,-0.278053225,-0.001813277,-0.120086126,4.266625055,5,-4378.666713,543,QUESTION_3,Esperanto,4,
+RESPONSE_88,4.55654206,-0.142705742,-0.332084902,-0.187453126,-0.12275426,4.125195782,16,-9001.053165,200,QUESTION_3,Vulcan,2,
+RESPONSE_89,5.686914439,-0.095829968,-0.154414373,-0.219214993,-0.09439991,4.392683989,9,-3806.313999,557,QUESTION_3,Esperanto,5,
+RESPONSE_90,5.261766872,-0.104523238,-0.181705271,-0.200224856,-0.142952327,4.153067656,10,-6812.741366,353,QUESTION_3,Vulcan,3,
+RESPONSE_91,5.211026179,-0.143113363,-0.228477509,-0.164806239,-0.116969112,4.312803224,9,-5691.292243,366,QUESTION_3,Vulcan,4,
+RESPONSE_92,5.573327868,-0.062268088,-0.132540948,0.002079844,-0.043856841,4.980483275,19,-10463.76067,523,QUESTION_3,Klingon,5,
+RESPONSE_93,6.618972508,-0.087258084,-0.082312456,-0.106250282,-0.064352385,4.422622231,2,-7711.792789,735,QUESTION_3,Vulcan,4,
+RESPONSE_94,5.222420635,-0.079635229,-0.150925585,-0.000511166,-0.103304185,4.631179086,9,-11687.75948,565,QUESTION_3,Vulcan,4,
+RESPONSE_95,5.804700941,-0.118723494,-0.172847752,-0.1935681,-0.000127949,4.406354722,4,-9697.206628,324,QUESTION_3,Navi,3,
+RESPONSE_96,5.988593087,-0.118943694,-0.117348827,-0.197348963,-0.126621044,4.772736816,9,-12143.25223,377,QUESTION_3,Vulcan,4,
+RESPONSE_97,5.095356205,-0.152135421,-0.183570957,-0.441472713,-0.115096246,4.339155665,12,-7152.312446,318,QUESTION_3,Vulcan,4,
+RESPONSE_98,5.141113218,-0.103626909,-0.12512581,-0.170324732,-0.10055942,4.329964719,8,-5462.037555,379,QUESTION_3,Klingon,3,
+RESPONSE_99,4.933654442,-0.158579786,-0.3333844,-0.31494642,-0.12065845,4.161139488,17,-11295.77389,271,QUESTION_3,Esperanto,2,
+RESPONSE_100,5.593233921,-0.105341718,-0.094998866,-0.042370833,-0.074106036,4.68671064,9,-9070.158477,548,QUESTION_3,Vulcan,4,
+RESPONSE_101,6.05730228,-0.105555776,-0.082449452,-0.192104465,-0.081524416,4.419397407,3,-3868.769541,439,QUESTION_3,Navi,4,
+RESPONSE_102,5.641719136,-0.104263429,-0.122104471,-0.041300847,-0.070603481,4.874156995,15,-5564.043152,601,QUESTION_3,Navi,5,
+RESPONSE_103,4.934076817,-0.116912317,-0.116550173,-0.248031924,-0.082929037,4.446611046,10,-6626.416298,289,QUESTION_3,Esperanto,3,
+RESPONSE_104,5.138184916,-0.113696187,-0.218686512,-0.347902807,-0.135674139,4.643111622,13,-12563.16,323,QUESTION_3,Navi,3,
+RESPONSE_105,5.854856604,-0.112153141,-0.158939694,-0.221305959,-0.082074867,4.040888181,14,-7278.237132,754,QUESTION_3,Esperanto,4,
+RESPONSE_106,5.989284608,-0.092096683,-0.071248717,-0.147816413,-0.062338554,4.103330797,5,-10971.47484,773,QUESTION_3,Klingon,4,
+RESPONSE_107,5.756477701,-0.066423896,-0.069065526,-0.159566637,-0.057601826,4.188677534,10,-3994.846505,620,QUESTION_3,Klingon,4,
+RESPONSE_108,5.870961682,-0.136069909,-0.232450278,-0.243249393,-0.094482678,4.240712184,1,-7692.412743,337,QUESTION_3,Klingon,4,
+RESPONSE_109,4.93824868,-0.091121912,-0.081998806,-0.292455945,-0.081830787,4.380180971,8,-6024.372806,302,QUESTION_3,Navi,3,
+RESPONSE_110,5.190180286,-0.113022035,-0.155056701,-0.166792266,-0.105645271,4.312123706,1,-8721.816077,362,QUESTION_3,Vulcan,3,
+RESPONSE_111,4.764937631,-0.115358541,-0.088593008,-0.052961667,-0.10424519,4.274768177,6,-4906.353652,363,QUESTION_3,Klingon,3,
+RESPONSE_112,5.267833309,-0.103900683,-0.211423224,-0.219038872,-0.180045982,4.151764928,16,-10849.37199,403,QUESTION_3,Vulcan,3,
+RESPONSE_113,4.335122371,-0.089572886,-0.376630892,-0.513783298,-0.120184704,4.342547015,15,-6797.613304,71,QUESTION_3,Navi,1,
+RESPONSE_114,5.677715382,-0.129582195,-0.09765628,-0.149567308,-0.060792963,4.520062656,19,-9253.50872,544,QUESTION_3,Navi,4,
+RESPONSE_115,4.922827573,-0.081811189,-0.174218846,0.00010208,-0.116521009,4.336686128,13,-12904.30022,281,QUESTION_3,Klingon,4,
+RESPONSE_116,4.545036798,-0.073302262,-0.255032775,-0.240929042,-0.12833837,4.88247416,13,-10339.63382,185,QUESTION_3,Klingon,3,
+RESPONSE_117,5.584355711,-0.106775763,-0.169665346,-0.000277959,-0.131453325,4.197482672,16,-5511.344372,520,QUESTION_3,Vulcan,4,
+RESPONSE_118,6.390273726,-0.086438561,-0.167990847,-0.081624259,-0.041337208,4.773410224,17,-6332.60168,601,QUESTION_3,Navi,5,
+RESPONSE_119,3.769290024,-0.205811687,-0.237749066,-0.531116651,-0.212319236,4.024269593,4,-5243.913779,88,QUESTION_3,Klingon,2,
+RESPONSE_120,5.085184413,-0.094460765,-0.186727231,-0.198092532,-0.054605694,4.824853012,10,-12561.99396,334,QUESTION_3,Navi,4,
+RESPONSE_121,5.84825098,-0.112996027,-0.195410515,-0.000231024,-0.126602792,4.531326605,13,-12396.34128,680,QUESTION_4,Esperanto,4,
+RESPONSE_122,5.781002156,-0.085133602,-0.104132214,-0.000338929,-0.06743894,4.535529206,17,-12762.75656,651,QUESTION_4,Vulcan,4,
+RESPONSE_123,4.674630672,-0.113863765,-0.181054429,-0.271928559,-0.09572375,4.66665302,4,-6943.068522,215,QUESTION_4,Klingon,4,
+RESPONSE_124,5.237095756,-0.086682418,-0.119326289,-0.238447022,-0.077903606,4.681865648,20,-7907.632934,342,QUESTION_4,Vulcan,3,
+RESPONSE_125,5.260896927,-0.046277093,-0.152543838,-0.306431174,-0.123002132,4.221733835,13,-8428.159804,401,QUESTION_4,Esperanto,2,
+RESPONSE_126,4.255170834,-0.149439522,-0.276752753,-0.409215147,-0.083396606,3.915944705,10,-4570.266127,143,QUESTION_4,Vulcan,2,
+RESPONSE_127,6.262412497,-0.105439383,-0.283294054,0.000431568,-0.120248721,4.190359144,8,-9831.679259,543,QUESTION_4,Vulcan,4,
+RESPONSE_128,4.577353239,-0.143009319,-0.330801094,-0.184063754,-0.122073344,4.175535721,3,-5784.813818,200,QUESTION_4,Navi,2,
+RESPONSE_129,5.748907522,-0.09572132,-0.15205477,-0.22082279,-0.094043355,4.377098236,14,-7232.756749,557,QUESTION_4,Navi,5,
+RESPONSE_130,5.174371346,-0.105691779,-0.183467968,-0.195884362,-0.141948369,4.225058995,10,-5978.798342,353,QUESTION_4,Navi,3,
+RESPONSE_131,5.164358455,-0.142561614,-0.226676351,-0.165073246,-0.118730448,4.179078216,4,-10250.58473,366,QUESTION_4,Vulcan,4,
+RESPONSE_132,5.54662399,-0.064055236,-0.132678953,0.000172037,-0.044435407,4.866929348,5,-4782.459058,523,QUESTION_4,Esperanto,5,
+RESPONSE_133,6.536885143,-0.089331845,-0.082573373,-0.105752203,-0.063857909,4.387906382,9,-8658.597351,735,QUESTION_4,Navi,4,
+RESPONSE_134,5.32157773,-0.0798231,-0.149766367,-0.000421266,-0.104781885,4.63137951,11,-12064.29333,565,QUESTION_4,Navi,4,
+RESPONSE_135,5.780777468,-0.116023382,-0.177314583,-0.190755297,0.001395892,4.391573846,20,-5505.705263,324,QUESTION_4,Navi,3,
+RESPONSE_136,5.904717687,-0.120495313,-0.114943495,-0.202406101,-0.127221115,4.772456187,19,-10299.51321,377,QUESTION_4,Esperanto,4,
+RESPONSE_137,4.991668762,-0.149810422,-0.184124319,-0.43820675,-0.111241486,4.270362613,13,-10558.49481,318,QUESTION_4,Esperanto,4,
+RESPONSE_138,5.252511029,-0.10289021,-0.125944414,-0.171448115,-0.102784201,4.38157749,12,-9244.440511,379,QUESTION_4,Vulcan,3,
+RESPONSE_139,4.867704402,-0.156650127,-0.339770034,-0.309792434,-0.120842284,4.123278963,15,-11869.5378,271,QUESTION_4,Vulcan,2,
+RESPONSE_140,5.613727913,-0.102659277,-0.097618592,-0.042883549,-0.074586646,4.750299687,9,-6450.177765,548,QUESTION_4,Esperanto,4,
+RESPONSE_141,6.051415532,-0.106322146,-0.081738538,-0.189211131,-0.081970522,4.412211657,10,-7048.94852,439,QUESTION_4,Klingon,4,
+RESPONSE_142,5.743985506,-0.10401458,-0.11991061,-0.040328218,-0.071046185,4.937713189,3,-9285.920858,601,QUESTION_4,Navi,5,
+RESPONSE_143,4.924872479,-0.117182046,-0.117291752,-0.24524552,-0.083712423,4.356748583,8,-12188.86817,289,QUESTION_4,Vulcan,3,
+RESPONSE_144,5.126114094,-0.117121964,-0.223655774,-0.349932531,-0.134688174,4.691437462,10,-11428.17852,323,QUESTION_4,Navi,3,
+RESPONSE_145,5.925362758,-0.110282599,-0.159556954,-0.221316699,-0.082120966,4.140754969,10,-10236.69007,754,QUESTION_4,Klingon,4,
+RESPONSE_146,5.915976533,-0.092203842,-0.072707525,-0.149244253,-0.062472867,4.21893363,8,-10220.64897,773,QUESTION_4,Esperanto,4,
+RESPONSE_147,5.796172729,-0.066951723,-0.068028051,-0.159518098,-0.055819313,4.160737548,9,-5133.191839,620,QUESTION_4,Navi,4,
+RESPONSE_148,5.877193766,-0.137770706,-0.231801991,-0.241466198,-0.095367449,4.249831766,8,-7012.674331,337,QUESTION_4,Navi,4,
+RESPONSE_149,5.048217428,-0.090725555,-0.080283407,-0.295332203,-0.082500476,4.384270173,17,-6060.621972,302,QUESTION_4,Vulcan,3,
+RESPONSE_150,5.157559541,-0.114415,-0.156688151,-0.168399481,-0.105427915,4.309677566,14,-7575.220061,362,QUESTION_4,Navi,3,
+RESPONSE_151,4.796790791,-0.115320493,-0.090968919,-0.052916101,-0.103262921,4.311279225,19,-11990.70509,363,QUESTION_4,Esperanto,3,
+RESPONSE_152,5.337589094,-0.104610147,-0.215237829,-0.222410503,-0.1802832,4.164979895,17,-11787.27294,403,QUESTION_4,Klingon,3,
+RESPONSE_153,4.246558624,-0.090517341,-0.374880593,-0.513841422,-0.11864641,4.269891572,19,-5337.275428,71,QUESTION_4,Esperanto,1,
+RESPONSE_154,5.602692597,-0.130900236,-0.093946921,-0.149374768,-0.061193095,4.439476602,10,-13900.90628,544,QUESTION_4,Klingon,4,
+RESPONSE_155,5.043295089,-0.082694528,-0.180321283,0.000743687,-0.121000331,4.406828784,11,-7102.893518,281,QUESTION_4,Vulcan,4,
+RESPONSE_156,4.530734798,-0.073593843,-0.252098214,-0.242339293,-0.126243266,4.768078919,12,-11105.77005,185,QUESTION_4,Esperanto,3,
+RESPONSE_157,5.583179282,-0.105406015,-0.17254627,0.000971867,-0.131827616,4.215237578,6,-5528.750941,520,QUESTION_4,Navi,4,
+RESPONSE_158,6.344270255,-0.087119895,-0.16769327,-0.082162485,-0.041651473,4.74449767,11,-8691.165306,601,QUESTION_4,Navi,5,
+RESPONSE_159,3.781234568,-0.210656919,-0.235178622,-0.536923047,-0.214322331,4.007785941,13,-6545.65578,88,QUESTION_4,Klingon,2,
+RESPONSE_160,5.091006005,-0.095470486,-0.190008288,-0.199187777,-0.054688907,4.727331969,12,-9024.2415,334,QUESTION_4,Vulcan,4,
+RESPONSE_161,5.808565233,-0.113044468,-0.194642238,-0.000996172,-0.126216421,4.690506611,18,-9851.950131,680,QUESTION_5,Vulcan,4,
+RESPONSE_162,5.786932313,-0.085871346,-0.10310622,-0.001439031,-0.067534737,4.505330367,16,-11917.66694,651,QUESTION_5,Esperanto,4,
+RESPONSE_163,4.6490814,-0.114283497,-0.18244279,-0.274626348,-0.093962217,4.614375031,19,-5608.074012,215,QUESTION_5,Navi,4,
+RESPONSE_164,5.14410479,-0.086713917,-0.121437765,-0.240489731,-0.077285228,4.658488273,17,-9630.90999,342,QUESTION_5,Navi,3,
+RESPONSE_165,5.312822573,-0.046360537,-0.148561423,-0.30375047,-0.122096961,4.279402693,3,-8293.271034,401,QUESTION_5,Klingon,2,
+RESPONSE_166,4.259940105,-0.150906367,-0.282643389,-0.414324429,-0.083875264,3.846466518,16,-11862.86083,143,QUESTION_5,Vulcan,2,
+RESPONSE_167,6.256850294,-0.106860179,-0.280147401,6.05E-05,-0.122414997,4.299990356,12,-12332.965,543,QUESTION_5,Klingon,4,
+RESPONSE_168,4.649831042,-0.139954556,-0.333508847,-0.191049852,-0.121102236,4.187482065,9,-8212.823655,200,QUESTION_5,Vulcan,2,
+RESPONSE_169,5.568145105,-0.095524151,-0.152320489,-0.224994774,-0.094631251,4.377692974,16,-8011.329355,557,QUESTION_5,Esperanto,5,
+RESPONSE_170,5.060598376,-0.106188812,-0.188043756,-0.19937058,-0.139125659,4.214332116,18,-7851.892259,353,QUESTION_5,Navi,3,
+RESPONSE_171,5.212287642,-0.142755157,-0.22583934,-0.166086124,-0.117360252,4.250575021,6,-9403.339129,366,QUESTION_5,Navi,4,
+RESPONSE_172,5.554225004,-0.063456108,-0.13078852,-0.000418925,-0.043537085,4.934478051,8,-6088.041676,523,QUESTION_5,Navi,5,
+RESPONSE_173,6.559851041,-0.088176925,-0.082585034,-0.106188042,-0.06350601,4.451859904,5,-8544.917479,735,QUESTION_5,Navi,4,
+RESPONSE_174,5.274603591,-0.077251317,-0.151628807,0.002020994,-0.10471137,4.679753926,16,-11004.5879,565,QUESTION_5,Esperanto,4,
+RESPONSE_175,5.770949398,-0.114574398,-0.174477883,-0.191194588,-0.00040575,4.408403865,16,-5024.312003,324,QUESTION_5,Esperanto,3,
+RESPONSE_176,5.782108487,-0.119774567,-0.114762789,-0.201180013,-0.125669334,4.69057035,12,-6222.754641,377,QUESTION_5,Klingon,4,
+RESPONSE_177,4.962609977,-0.149314373,-0.187538444,-0.433275157,-0.11441956,4.352782453,14,-13125.97895,318,QUESTION_5,Vulcan,4,
+RESPONSE_178,5.245631425,-0.100883878,-0.124708219,-0.167649772,-0.10367033,4.367178134,12,-11458.40072,379,QUESTION_5,Klingon,3,
+RESPONSE_179,4.922848507,-0.15317391,-0.336271509,-0.302631781,-0.12309408,4.110182001,19,-10133.77634,271,QUESTION_5,Vulcan,2,
+RESPONSE_180,5.568663568,-0.104323186,-0.095499527,-0.043234826,-0.073952126,4.794746064,2,-11260.87816,548,QUESTION_5,Klingon,4,
+RESPONSE_181,6.06229373,-0.105957106,-0.081367041,-0.188126102,-0.080578514,4.393466256,19,-11261.80244,439,QUESTION_5,Vulcan,4,
+RESPONSE_182,5.681639801,-0.103450137,-0.121290833,-0.040856401,-0.070243407,4.835256147,10,-6988.860776,601,QUESTION_5,Vulcan,5,
+RESPONSE_183,4.957106082,-0.114939925,-0.117844818,-0.249065237,-0.084126248,4.443031924,14,-8152.894003,289,QUESTION_5,Navi,3,
+RESPONSE_184,5.100539583,-0.115327301,-0.224212766,-0.346139093,-0.136531805,4.710984677,18,-5231.525599,323,QUESTION_5,Klingon,3,
+RESPONSE_185,5.933549964,-0.108854353,-0.156529253,-0.217672788,-0.081505664,4.128676142,14,-6569.936538,754,QUESTION_5,Esperanto,4,
+RESPONSE_186,6.023146425,-0.090526277,-0.071890964,-0.149264584,-0.061984825,4.173831731,9,-8598.629984,773,QUESTION_5,Esperanto,4,
+RESPONSE_187,5.756246984,-0.065589887,-0.069605244,-0.163033701,-0.056592974,4.21594904,6,-6539.265608,620,QUESTION_5,Navi,4,
+RESPONSE_188,5.805889146,-0.137844201,-0.233265558,-0.239917849,-0.095177972,4.372066644,7,-5044.157533,337,QUESTION_5,Vulcan,4,
+RESPONSE_189,5.10967357,-0.092566926,-0.080327335,-0.294087871,-0.082615855,4.273942856,19,-4980.733597,302,QUESTION_5,Klingon,3,
+RESPONSE_190,5.174673295,-0.114678516,-0.15386366,-0.165933179,-0.104544974,4.241833201,6,-6040.11956,362,QUESTION_5,Klingon,3,
+RESPONSE_191,4.871151406,-0.113477832,-0.090595584,-0.051700914,-0.104035832,4.347811894,7,-11006.78805,363,QUESTION_5,Vulcan,3,
+RESPONSE_192,5.231194913,-0.104702728,-0.213992626,-0.224107928,-0.180721537,4.147550981,9,-4338.532726,403,QUESTION_5,Esperanto,3,
+RESPONSE_193,4.262989546,-0.090965301,-0.366327878,-0.521238534,-0.118771636,4.459987654,9,-6493.360572,71,QUESTION_5,Klingon,1,
+RESPONSE_194,5.708388929,-0.130004887,-0.095229609,-0.148626479,-0.060729454,4.472606926,5,-8798.690666,544,QUESTION_5,Navi,4,
+RESPONSE_195,4.977245221,-0.08403975,-0.178548369,-0.000144263,-0.11908346,4.381323391,11,-10245.03897,281,QUESTION_5,Vulcan,4,
+RESPONSE_196,4.550143162,-0.07318163,-0.255866296,-0.243372248,-0.12629984,4.697718843,5,-7610.593936,185,QUESTION_5,Vulcan,3,
+RESPONSE_197,5.651357379,-0.105676429,-0.170157403,-3.67E-06,-0.132444955,4.237412367,1,-6182.833972,520,QUESTION_5,Esperanto,4,
+RESPONSE_198,6.541219953,-0.088090933,-0.168581618,-0.080957842,-0.040308777,4.929782858,9,-4285.62848,601,QUESTION_5,Esperanto,5,
+RESPONSE_199,3.770318292,-0.210917177,-0.238927309,-0.538483987,-0.216422646,4.014736799,14,-9203.66106,88,QUESTION_5,Vulcan,2,
+RESPONSE_200,5.121469472,-0.094178735,-0.186720047,-0.198124662,-0.055504321,4.863284226,13,-9905.564035,334,QUESTION_5,Vulcan,4,
\ No newline at end of file
diff --git a/tests/test_experiment_rsmtool_3.py b/tests/test_experiment_rsmtool_3.py
index 675d32ec..022b17ad 100644
--- a/tests/test_experiment_rsmtool_3.py
+++ b/tests/test_experiment_rsmtool_3.py
@@ -49,7 +49,12 @@ else:
           'lr_subgroups_with_numeric_threshold_and_empty_group',
           subgroups=['L1', 'QUESTION']),
     param('lr-subgroups-h2-long-feature-names',
-          'lr_subgroups_h2_long_feature_names', subgroups=['L1', 'QUESTION'], consistency=True)
+          'lr_subgroups_h2_long_feature_names', subgroups=['L1', 'QUESTION'], consistency=True),
+    param('lr-subgroups-with-h2-but-only-for-nonscoreable',
+          'lr_subgroups_with_h2_but_only_for_nonscoreable',
+          subgroups=['L1', 'QUESTION'],
+          consistency=True,
+          suppress_warnings_for=[UserWarning])
 ])
 def test_run_experiment_parameterized(*args, **kwargs):
     if TEST_DIR:
diff --git a/tests/test_utils_prmse.py b/tests/test_utils_prmse.py
index 5951845d..52880f3d 100644
--- a/tests/test_utils_prmse.py
+++ b/tests/test_utils_prmse.py
@@ -2,12 +2,14 @@ import os
 
 import numpy as np
 import pandas as pd
+import warnings
 
 from numpy.testing import assert_array_equal
 
 from pandas.testing import assert_frame_equal
 
 from nose.tools import (eq_,
+                        ok_,
                         raises,
                         assert_almost_equal)
 
@@ -72,13 +74,30 @@ def test_prmse_single_human_ve_array_as_input():
     eq_(prmse, 0.9090909090909091)
 
 
-@raises(ValueError)
 def test_variance_of_errors_all_single_scored():
+    # this test should raise a UserWarning
     sc1 = [1, 2, 3, None, None]
     sc2 = [None, None, None, 2, 3]
     df = pd.DataFrame({'sc1': sc1,
                        'sc2': sc2})
-    variance_of_errors(df)
+    with warnings.catch_warnings(record=True) as warning_list:
+        variance_of_errors_human = variance_of_errors(df)
+    ok_(variance_of_errors_human is None)
+    assert issubclass(warning_list[-1].category, UserWarning)
+
+
+def test_prmse_all_single_scored():
+    # this test should raise a UserWarning
+    system_scores = [1, 2, 3, 4, 5]
+    sc1 = [1, 2, 3, None, None]
+    sc2 = [None, None, None, 2, 3]
+    df = pd.DataFrame({'sc1': sc1,
+                       'sc2': sc2,
+                       'system': system_scores})
+    with warnings.catch_warnings(record=True) as warning_list:
+        prmse = prmse_true(df['system'], df[['sc1', 'sc2']])
+    ok_(prmse is None)
+    assert issubclass(warning_list[-1].category, UserWarning)
 
 
 @raises(ValueError)
@@ -89,14 +108,12 @@ def test_get_true_score_evaluations_single_human_no_ve():
 
 
 class TestPrmseJohnsonData():
-
-    '''
+    """
     This class tests the PRMSE functions against the benchmarks
     provided by Matt Johnson who did the original derivation and
     implemented the function in R. This test ensures that Python
     implementation results in the same values
-    '''
-
+    """
     def setUp(self):
         full_matrix_file = Path(rsmtool_test_dir) / 'data' / 'files' / 'prmse_data.csv'
         sparse_matrix_file = Path(rsmtool_test_dir) / 'data' / 'files' / 'prmse_data_sparse_matrix.csv'
@@ -197,7 +214,6 @@ class TestPrmseJohnsonData():
                            variance_errors_human)
         assert_almost_equal(prmse, expected_prmse_true, 7)
 
-
     def test_prmse_full_matrix_computed_ve(self):
         human_scores = self.human_score_columns
         df_humans = self.data_full[human_scores]
@@ -216,7 +232,6 @@ class TestPrmseJohnsonData():
                            df_humans)
         assert_almost_equal(prmse, expected_prmse_true, 7)
 
-
     def test_prmse_sparse_matrix_array_as_input(self):
         human_scores = self.human_score_columns
         df_humans = self.data_sparse[human_scores].to_numpy()
@@ -241,7 +256,6 @@ class TestPrmseJohnsonData():
                                               self.human_score_columns)
         assert_frame_equal(df_prmse, expected_df, check_dtype=False)
 
-
     def test_compute_true_score_evaluations_sparse(self):
         expected_df = pd.DataFrame({'N': 10000,
                                     "N raters": 4,
@@ -257,7 +271,6 @@ class TestPrmseJohnsonData():
                                               self.human_score_columns)
         assert_frame_equal(df_prmse, expected_df, check_dtype=False)
 
-
     def test_compute_true_score_evaluations_given_ve(self):
         expected_df = pd.DataFrame({'N': 10000,
                                     "N raters": 4,

pip install -e .
parse_log_pytest
requirements.txt
nose nose-cov pytest
3.7
requirements.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
aiofiles==22.1.0
aiosqlite==0.19.0
alabaster==0.7.13
anyio==3.7.1
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
attrs==24.2.0
Babel==2.14.0
backcall==0.2.0
beautifulsoup4==4.13.3
bleach==6.0.0
cached-property==1.5.2
certifi @ file:///croot/certifi_1671487769961/work/certifi
cffi==1.15.1
charset-normalizer==3.4.1
click==8.1.8
colorama==0.4.6
comm==0.1.4
cov-core==1.15.0
coverage==6.5.0
coveralls==3.3.1
cycler==0.11.0
debugpy==1.7.0
decorator==5.1.1
defusedxml==0.7.1
doc2dash==2.3.0
docopt==0.6.2
docutils==0.19
entrypoints==0.4
et-xmlfile==1.1.0
exceptiongroup==1.2.2
fastjsonschema==2.21.1
fonttools==4.38.0
fqdn==1.5.1
idna==3.10
imagesize==1.4.1
importlib-metadata==6.7.0
importlib-resources==5.12.0
iniconfig==2.0.0
ipykernel==6.16.2
ipython==7.34.0
ipython-genutils==0.2.0
ipywidgets==8.1.5
isoduration==20.11.0
jedi==0.19.2
Jinja2==3.1.6
joblib==1.3.2
json5==0.9.16
jsonpointer==3.0.0
jsonschema==4.17.3
jupyter==1.1.1
jupyter-console==6.6.3
jupyter-events==0.6.3
jupyter-server==1.24.0
jupyter-ydoc==0.2.5
jupyter_client==7.4.9
jupyter_core==4.12.0
jupyter_server_fileid==0.9.3
jupyter_server_ydoc==0.8.0
jupyterlab==3.6.8
jupyterlab-pygments==0.2.2
jupyterlab_server==2.24.0
jupyterlab_widgets==3.0.13
kiwisolver==1.4.5
MarkupSafe==2.1.5
matplotlib==3.5.3
matplotlib-inline==0.1.6
mistune==3.0.2
nbclassic==1.2.0
nbclient==0.7.4
nbconvert==7.6.0
nbformat==5.8.0
nest-asyncio==1.6.0
nose==1.3.7
nose-cov==1.6
notebook==6.5.7
notebook_shim==0.2.4
numpy==1.21.6
openpyxl==3.1.3
packaging==24.0
pandas==1.3.5
pandocfilters==1.5.1
parameterized==0.9.0
parso==0.8.4
patsy==1.0.1
pexpect==4.9.0
pickleshare==0.7.5
Pillow==9.5.0
pkgutil_resolve_name==1.3.10
pluggy==1.2.0
prometheus-client==0.17.1
prompt_toolkit==3.0.48
psutil==7.0.0
ptyprocess==0.7.0
pycparser==2.21
Pygments==2.17.2
pyparsing==3.1.4
pyrsistent==0.19.3
pytest==7.4.4
python-dateutil==2.9.0.post0
python-json-logger==3.0.1
pytz==2025.2
PyYAML==6.0.1
pyzmq==26.2.1
requests==2.31.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
-e git+https://github.com/EducationalTestingService/rsmtool.git@e1ee0b754cf9a86bfe3b81e43782ce7a89c1c5df#egg=rsmtool
ruamel.yaml==0.18.10
ruamel.yaml.clib==0.2.8
scikit-learn==0.22.2.post1
scipy==1.7.3
seaborn==0.12.2
Send2Trash==1.8.3
six==1.17.0
skll==2.1
sniffio==1.3.1
snowballstemmer==2.2.0
soupsieve==2.4.1
Sphinx==5.3.0
sphinx-rtd-theme==2.0.0
sphinxcontrib-applehelp==1.0.2
sphinxcontrib-devhelp==1.0.2
sphinxcontrib-htmlhelp==2.0.0
sphinxcontrib-jquery==4.1
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.3
sphinxcontrib-serializinghtml==1.1.5
statsmodels==0.13.5
tabulate==0.9.0
terminado==0.17.1
tinycss2==1.2.1
tomli==2.0.1
tornado==6.2
traitlets==5.9.0
typing_extensions==4.7.1
uri-template==1.3.0
urllib3==2.0.7
wcwidth==0.2.13
webcolors==1.13
webencodings==0.5.1
websocket-client==1.6.1
widgetsnbextension==4.0.13
xlrd==2.0.1
xlwt==1.3.0
y-py==0.6.2
ypy-websocket==0.8.4
zipp==3.15.0
zope.interface==6.4.post2

name: rsmtool
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2022.12.7=py37h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - pip=22.3.1=py37h06a4308_0
  - python=3.7.16=h7a1cb2a_0
  - readline=8.2=h5eee18b_0
  - setuptools=65.6.3=py37h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.38.4=py37h06a4308_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - aiofiles==22.1.0
      - aiosqlite==0.19.0
      - alabaster==0.7.13
      - anyio==3.7.1
      - argon2-cffi==23.1.0
      - argon2-cffi-bindings==21.2.0
      - arrow==1.2.3
      - attrs==24.2.0
      - babel==2.14.0
      - backcall==0.2.0
      - beautifulsoup4==4.13.3
      - bleach==6.0.0
      - cached-property==1.5.2
      - cffi==1.15.1
      - charset-normalizer==3.4.1
      - click==8.1.8
      - colorama==0.4.6
      - comm==0.1.4
      - cov-core==1.15.0
      - coverage==6.5.0
      - coveralls==3.3.1
      - cycler==0.11.0
      - debugpy==1.7.0
      - decorator==5.1.1
      - defusedxml==0.7.1
      - doc2dash==2.3.0
      - docopt==0.6.2
      - docutils==0.19
      - entrypoints==0.4
      - et-xmlfile==1.1.0
      - exceptiongroup==1.2.2
      - fastjsonschema==2.21.1
      - fonttools==4.38.0
      - fqdn==1.5.1
      - idna==3.10
      - imagesize==1.4.1
      - importlib-metadata==6.7.0
      - importlib-resources==5.12.0
      - iniconfig==2.0.0
      - ipykernel==6.16.2
      - ipython==7.34.0
      - ipython-genutils==0.2.0
      - ipywidgets==8.1.5
      - isoduration==20.11.0
      - jedi==0.19.2
      - jinja2==3.1.6
      - joblib==1.3.2
      - json5==0.9.16
      - jsonpointer==3.0.0
      - jsonschema==4.17.3
      - jupyter==1.1.1
      - jupyter-client==7.4.9
      - jupyter-console==6.6.3
      - jupyter-core==4.12.0
      - jupyter-events==0.6.3
      - jupyter-server==1.24.0
      - jupyter-server-fileid==0.9.3
      - jupyter-server-ydoc==0.8.0
      - jupyter-ydoc==0.2.5
      - jupyterlab==3.6.8
      - jupyterlab-pygments==0.2.2
      - jupyterlab-server==2.24.0
      - jupyterlab-widgets==3.0.13
      - kiwisolver==1.4.5
      - markupsafe==2.1.5
      - matplotlib==3.5.3
      - matplotlib-inline==0.1.6
      - mistune==3.0.2
      - nbclassic==1.2.0
      - nbclient==0.7.4
      - nbconvert==7.6.0
      - nbformat==5.8.0
      - nest-asyncio==1.6.0
      - nose==1.3.7
      - nose-cov==1.6
      - notebook==6.5.7
      - notebook-shim==0.2.4
      - numpy==1.21.6
      - openpyxl==3.1.3
      - packaging==24.0
      - pandas==1.3.5
      - pandocfilters==1.5.1
      - parameterized==0.9.0
      - parso==0.8.4
      - patsy==1.0.1
      - pexpect==4.9.0
      - pickleshare==0.7.5
      - pillow==9.5.0
      - pkgutil-resolve-name==1.3.10
      - pluggy==1.2.0
      - prometheus-client==0.17.1
      - prompt-toolkit==3.0.48
      - psutil==7.0.0
      - ptyprocess==0.7.0
      - pycparser==2.21
      - pygments==2.17.2
      - pyparsing==3.1.4
      - pyrsistent==0.19.3
      - pytest==7.4.4
      - python-dateutil==2.9.0.post0
      - python-json-logger==3.0.1
      - pytz==2025.2
      - pyyaml==6.0.1
      - pyzmq==26.2.1
      - requests==2.31.0
      - rfc3339-validator==0.1.4
      - rfc3986-validator==0.1.1
      - ruamel-yaml==0.18.10
      - ruamel-yaml-clib==0.2.8
      - scikit-learn==0.22.2.post1
      - scipy==1.7.3
      - seaborn==0.12.2
      - send2trash==1.8.3
      - six==1.17.0
      - skll==2.1
      - sniffio==1.3.1
      - snowballstemmer==2.2.0
      - soupsieve==2.4.1
      - sphinx==5.3.0
      - sphinx-rtd-theme==2.0.0
      - sphinxcontrib-applehelp==1.0.2
      - sphinxcontrib-devhelp==1.0.2
      - sphinxcontrib-htmlhelp==2.0.0
      - sphinxcontrib-jquery==4.1
      - sphinxcontrib-jsmath==1.0.1
      - sphinxcontrib-qthelp==1.0.3
      - sphinxcontrib-serializinghtml==1.1.5
      - statsmodels==0.13.5
      - tabulate==0.9.0
      - terminado==0.17.1
      - tinycss2==1.2.1
      - tomli==2.0.1
      - tornado==6.2
      - traitlets==5.9.0
      - typing-extensions==4.7.1
      - uri-template==1.3.0
      - urllib3==2.0.7
      - wcwidth==0.2.13
      - webcolors==1.13
      - webencodings==0.5.1
      - websocket-client==1.6.1
      - widgetsnbextension==4.0.13
      - xlrd==2.0.1
      - xlwt==1.3.0
      - y-py==0.6.2
      - ypy-websocket==0.8.4
      - zipp==3.15.0
      - zope-interface==6.4.post2
prefix: /opt/conda/envs/rsmtool

tests/test_utils_prmse.py::test_variance_of_errors_all_single_scored tests/test_utils_prmse.py::test_prmse_all_single_scored
tests/test_experiment_rsmtool_3.py::test_run_experiment_lr_with_object_and_configdir tests/test_experiment_rsmtool_3.py::test_run_experiment_lr_with_object_no_configdir tests/test_experiment_rsmtool_3.py::test_run_experiment_lr_with_dictionary tests/test_utils_prmse.py::TestPrmseJohnsonData::test_variance_of_errors_full_matrix tests/test_utils_prmse.py::TestPrmseJohnsonData::test_variance_of_errors_sparse_matrix tests/test_utils_prmse.py::TestPrmseJohnsonData::test_variance_of_true_scores_full_matrix tests/test_utils_prmse.py::TestPrmseJohnsonData::test_variance_of_true_scores_sparse_matrix tests/test_utils_prmse.py::TestPrmseJohnsonData::test_variance_of_true_scores_sparse_matrix_computed_ve tests/test_utils_prmse.py::TestPrmseJohnsonData::test_mse_full_matrix tests/test_utils_prmse.py::TestPrmseJohnsonData::test_mse_sparse_matrix tests/test_utils_prmse.py::TestPrmseJohnsonData::test_mse_sparse_matrix_computed_ve tests/test_utils_prmse.py::TestPrmseJohnsonData::test_prmse_full_matrix_given_ve tests/test_utils_prmse.py::TestPrmseJohnsonData::test_prmse_sparse_matrix_given_ve tests/test_utils_prmse.py::TestPrmseJohnsonData::test_prmse_full_matrix_computed_ve tests/test_utils_prmse.py::TestPrmseJohnsonData::test_prmse_sparse_matrix_computed_ve tests/test_utils_prmse.py::TestPrmseJohnsonData::test_prmse_sparse_matrix_array_as_input tests/test_utils_prmse.py::TestPrmseJohnsonData::test_compute_true_score_evaluations_full tests/test_utils_prmse.py::TestPrmseJohnsonData::test_compute_true_score_evaluations_sparse tests/test_utils_prmse.py::TestPrmseJohnsonData::test_compute_true_score_evaluations_given_ve
tests/test_experiment_rsmtool_3.py::test_run_experiment_lr_with_object_and_filepath tests/test_experiment_rsmtool_3.py::test_run_experiment_wrong_input_format tests/test_experiment_rsmtool_3.py::test_run_experiment_duplicate_feature_names tests/test_experiment_rsmtool_3.py::test_run_experiment_lr_feature_json tests/test_experiment_rsmtool_3.py::test_run_experiment_wrong_train_file_path tests/test_experiment_rsmtool_3.py::test_run_experiment_wrong_feature_file_path tests/test_experiment_rsmtool_3.py::test_run_experiment_lr_length_column_and_feature_list tests/test_utils_prmse.py::test_compute_n_human_scores tests/test_utils_prmse.py::test_compute_n_human_scores_zeros tests/test_utils_prmse.py::test_compute_n_human_scores_array tests/test_utils_prmse.py::test_prmse_single_human_ve tests/test_utils_prmse.py::test_prmse_single_human_ve_array_as_input tests/test_utils_prmse.py::test_get_true_score_evaluations_single_human_no_ve
rsmtool/utils/prmse.py
rsmtool/utils/prmse.py