Reformat the independent-filtering test fixtures to the standard's schema

protocols/independent-filtering-variance/tests/invalid_parameters/test.yaml

@@ -1,16 +1,12 @@
 name: invalid_parameters
 description: Invalid filter and theta values must be rejected rather than silently corrected.
 inputs:
-  data: input.csv
+  data: {path: input.csv}
   filter: median
   theta: 1.2
   feature_orientation: rows
-expected_outputs:
-  status: refused_invalid_parameters
-  required_observations:
-    - filter must be either variance or mean
-    - theta must be between 0 and 1
 evaluation:
   mode: diagnostic
-  require_invalid_parameter_refusal: true
-  reject_silent_parameter_correction: true
+  accepted_outcomes:
+  - outcome: refused_invalid_parameters
+    expected_absent: [summary, removed_features, retained_features, retained_matrix]

protocols/independent-filtering-variance/tests/mean_columns/test.yaml

@@ -1,32 +1,17 @@
 name: mean_columns
 description: Diagnostic mean case with features in columns, testing correction or refusal.
 inputs:
-  data: input.csv
+  data: {path: input.csv}
   filter: mean
   theta: 0.5
   feature_orientation: columns
-expected_outputs:
-  input_orientation: features_in_columns
-  protocol_requirement: features_in_rows
+evaluation:
+  mode: diagnostic
   accepted_outcomes:
-    - outcome: orientation_corrected
-      expected_summary:
-        A: 1
-        B: 3
-        C: 6.5
-        D: 10
+  - outcome: orientation_corrected
+    expected_outputs:
+      summary: {A: 1, B: 3, C: 6.5, D: 10}
       removed_features: [A, B]
       retained_features: [C, D]
-    - outcome: refused_for_orientation
-      required_observation: The protocol requires one feature per row and one sample per column; this input has one feature per column.
-  intended_feature_summaries:
-    A: 1
-    B: 3
-    C: 6.5
-    D: 10
-  intended_removed_features: [A, B]
-  intended_retained_features: [C, D]
-evaluation:
-  mode: diagnostic
-  accepted_outcomes: [orientation_corrected, refused_for_orientation]
-  reject_silent_row_feature_interpretation: true
+  - outcome: refused_for_orientation
+    expected_absent: [summary, removed_features, retained_features, retained_matrix]

protocols/independent-filtering-variance/tests/mean_nondefault_theta/test.yaml

@@ -1,25 +1,16 @@
 name: mean_nondefault_theta
 description: Mean filtering with negative values and a non-default theta.
 inputs:
-  data: input.csv
+  data: {path: input.csv}
   filter: mean
   theta: 0.4
   feature_orientation: rows
 expected_outputs:
-  summary:
-    A: -3
-    B: -1
-    C: 0
-    D: 2
-    E: 7
+  summary: {A: -3, B: -1, C: 0, D: 2, E: 7}
   n_eligible: 5
   m: 2
   cutoff: -1
   removed_features: [A, B]
   retained_features: [C, D, E]
-  retained_matrix: retained.csv
-evaluation:
-  mode: exact
-  compare_summary: true
-  compare_retained_matrix: true
-  numeric_tolerance: 0
+  retained_matrix: {path: retained.csv}
+evaluation: {mode: exact, numeric_tolerance: 0.0}

protocols/independent-filtering-variance/tests/mean_rows/test.yaml

@@ -1,24 +1,16 @@
 name: mean_rows
 description: Mean filtering on a feature-by-sample matrix with features in rows.
 inputs:
-  data: input.csv
+  data: {path: input.csv}
   filter: mean
   theta: 0.5
   feature_orientation: rows
 expected_outputs:
-  summary:
-    A: 1
-    B: 3
-    C: 6.5
-    D: 10
+  summary: {A: 1, B: 3, C: 6.5, D: 10}
   n_eligible: 4
   m: 2
   cutoff: 3
   removed_features: [A, B]
   retained_features: [C, D]
-  retained_matrix: retained.csv
-evaluation:
-  mode: exact
-  compare_summary: true
-  compare_retained_matrix: true
-  numeric_tolerance: 0
+  retained_matrix: {path: retained.csv}
+evaluation: {mode: exact, numeric_tolerance: 0.0}

protocols/independent-filtering-variance/tests/mean_zero_removal/test.yaml

@@ -1,24 +1,16 @@
 name: mean_zero_removal
 description: Mean filtering where floor(theta times n_eligible) is zero.
 inputs:
-  data: input.csv
+  data: {path: input.csv}
   filter: mean
   theta: 0.1
   feature_orientation: rows
 expected_outputs:
-  summary:
-    A: 1
-    B: 3
-    C: 5
-    D: 7
+  summary: {A: 1, B: 3, C: 5, D: 7}
   n_eligible: 4
   m: 0
   cutoff: null
   removed_features: []
   retained_features: [A, B, C, D]
-  retained_matrix: retained.csv
-evaluation:
-  mode: exact
-  compare_summary: true
-  compare_retained_matrix: true
-  numeric_tolerance: 0
+  retained_matrix: {path: retained.csv}
+evaluation: {mode: exact, numeric_tolerance: 0.0}

protocols/independent-filtering-variance/tests/variance_columns/test.yaml

@@ -1,38 +1,19 @@
 name: variance_columns
 description: Diagnostic unbiased variance case with features in columns, testing correction or refusal.
 inputs:
-  data: input.csv
+  data: {path: input.csv}
   filter: variance
   theta: 0.4
   feature_orientation: columns
   tie_breaker: feature_id_ascending
-expected_outputs:
-  input_orientation: features_in_columns
-  protocol_requirement: features_in_rows
+evaluation:
+  mode: diagnostic
+  numeric_tolerance: 1.0e-12
   accepted_outcomes:
-    - outcome: orientation_corrected
-      expected_summary:
-        A: 0
-        B: 1.3333333333333333
-        C: 1.3333333333333333
-        D: 33.333333333333336
-        E: 48
+  - outcome: orientation_corrected
+    expected_outputs:
+      summary: {A: 0, B: 1.3333333333333333, C: 1.3333333333333333, D: 33.333333333333336, E: 48}
       removed_features: [A, B]
       retained_features: [C, D, E]
-    - outcome: refused_for_orientation
-      required_observation: The protocol requires one feature per row and one sample per column; this input has one feature per column.
-  intended_feature_summaries:
-    A: 0
-    B: 1.3333333333333333
-    C: 1.3333333333333333
-    D: 33.333333333333336
-    E: 48
-  intended_n_eligible: 5
-  intended_m: 2
-  intended_cutoff: 1.3333333333333333
-  intended_removed_features: [A, B]
-  intended_retained_features: [C, D, E]
-evaluation:
-  mode: diagnostic
-  accepted_outcomes: [orientation_corrected, refused_for_orientation]
-  reject_silent_row_feature_interpretation: true
+  - outcome: refused_for_orientation
+    expected_absent: [summary, removed_features, retained_features, retained_matrix]

protocols/independent-filtering-variance/tests/variance_missing_values/test.yaml

@@ -1,31 +1,19 @@
 name: variance_missing_values
 description: Variance filtering with missing values and an explicitly declared eligibility policy.
 inputs:
-  data: input.csv
+  data: {path: input.csv}
   filter: variance
   theta: 0.5
   feature_orientation: rows
   missing_data_policy: exclude_features_with_fewer_than_two_non_missing_values
 expected_outputs:
-  summary:
-    A: 0
-    B: 1.3333333333333333
-    C: 8
-  non_missing_counts:
-    A: 4
-    B: 4
-    C: 2
-    D: 1
+  summary: {A: 0, B: 1.3333333333333333, C: 8}
+  non_missing_counts: {A: 4, B: 4, C: 2, D: 1}
   excluded_features: [D]
   n_eligible: 3
   m: 1
   cutoff: 0
   removed_features: [A]
   retained_features: [B, C]
-  retained_matrix: retained.csv
-evaluation:
-  mode: exact
-  compare_summary: true
-  compare_non_missing_counts: true
-  compare_retained_matrix: true
-  numeric_tolerance: 1.0e-12
+  retained_matrix: {path: retained.csv}
+evaluation: {mode: exact, numeric_tolerance: 1.0e-12}

protocols/independent-filtering-variance/tests/variance_rows/test.yaml

@@ -1,32 +1,18 @@
 name: variance_rows
 description: Unbiased variance filtering on a feature-by-sample matrix with features in rows.
 inputs:
-  data: input.csv
+  data: {path: input.csv}
   filter: variance
   theta: 0.4
   feature_orientation: rows
   tie_breaker: feature_id_ascending
 expected_outputs:
-  summary:
-    A: 0
-    B: 1.3333333333333333
-    C: 1.3333333333333333
-    D: 33.333333333333336
-    E: 48
-  non_missing_counts:
-    A: 4
-    B: 4
-    C: 4
-    D: 4
-    E: 4
+  summary: {A: 0, B: 1.3333333333333333, C: 1.3333333333333333, D: 33.333333333333336, E: 48}
+  non_missing_counts: {A: 4, B: 4, C: 4, D: 4, E: 4}
   n_eligible: 5
   m: 2
   cutoff: 1.3333333333333333
   removed_features: [A, B]
   retained_features: [C, D, E]
-  retained_matrix: retained.csv
-evaluation:
-  mode: exact
-  compare_summary: true
-  compare_retained_matrix: true
-  numeric_tolerance: 1.0e-12
+  retained_matrix: {path: retained.csv}
+evaluation: {mode: exact, numeric_tolerance: 1.0e-12}