Fix issue #28: Handle NaN/Inf in t-test visualization axis limits

Implemented comprehensive fix for t-test visualization ValueError when
computing axis limits with NaN/Inf t-statistics:

**Core Fix in t_tests.py:**
- Added data validation in calculate_t_statistics():
  * Check for n>=2 samples per group before running t-test
  * Log insufficient data cases (0<n<2) and missing data (n=0)
  * Append NaN for invalid cases instead of computing t-test

- Added robust axis limit calculation in both functions:
  * Filter out NaN/Inf values before computing limits
  * Add padding for visualization clarity
  * Ensure threshold line (p<0.001) is always visible
  * Final validation to ensure finite and valid limits
  * Fallback to sensible defaults (-1.0 to 5.0)

**Comprehensive Test Suite:**
- Created tests/test_t_test_edge_cases.py with 11 tests:
  1. All NaN t-statistics (single sample per group)
  2. Mixed NaN and valid data
  3. All Infinite t-statistics (zero variance)
  4. Mixed Infinite and valid data
  5. Empty data groups (missing epochs)
  6. Extreme outliers
  7. Very small sample sizes (n=2, minimum required)
  8. Average figure with all NaN
  9. Average figure with mixed data
  10. Logging verification
  11. Normal data baseline

All tests pass with real data (no mocks).

Addresses issue #28

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

Author: jeremymanning

llm_stylometry/visualization/t_tests.py

@@ -6,6 +6,9 @@
 from scipy.stats import ttest_ind
 import numpy as np
 from tqdm import tqdm
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 def calculate_t_statistics(df, max_epochs=500):
@@ -40,10 +43,22 @@ def calculate_t_statistics(df, max_epochs=500):
                 & (t_df["epochs_completed"] == epoch)
             ]["loss_value"].values
 
-            if len(true_losses) > 0 and len(other_losses) > 0:
+            # T-test requires at least 2 samples per group for meaningful results
+            if len(true_losses) >= 2 and len(other_losses) >= 2:
                 result = ttest_ind(other_losses, true_losses, equal_var=False)
+                if np.isnan(result.statistic):
+                    logger.debug(f"NaN t-statistic for {author} at epoch {epoch}: "
+                                f"n_true={len(true_losses)}, n_other={len(other_losses)}")
                 t_raws[author].append(result.statistic)
+            elif len(true_losses) > 0 or len(other_losses) > 0:
+                # Have some data but insufficient for t-test
+                logger.debug(f"Insufficient data for t-test for {author} at epoch {epoch}: "
+                            f"n_true={len(true_losses)}, n_other={len(other_losses)} "
+                            f"(need at least 2 samples per group)")
+                t_raws[author].append(np.nan)
             else:
+                # No data at all
+                logger.debug(f"No data for {author} at epoch {epoch}")
                 t_raws[author].append(np.nan)
 
     # Convert to long-form DataFrame
@@ -130,22 +145,36 @@ def generate_t_test_figure(
     ax.set_xlabel("Epochs completed", fontsize=12)
     ax.set_ylabel("$t$-value", fontsize=12)
 
-    # Calculate dynamic y-axis limits based on data
-    # This ensures all data points are visible, including negative t-statistics
-    # (e.g., when a model performs worse on its own training author)
-    y_min = t_raws_df['t_raw'].min()
-    y_max = t_raws_df['t_raw'].max()
+    # Calculate dynamic y-axis limits based on VALID data only
+    # Filter out NaN/Inf values to avoid matplotlib errors
+    valid_t_values = t_raws_df['t_raw'].replace([np.inf, -np.inf], np.nan).dropna()
+
+    if len(valid_t_values) == 0:
+        # No valid data - use reasonable defaults around threshold
+        logger.warning("No valid t-statistics found. Using default axis limits.")
+        y_min = -1.0
+        y_max = 5.0
+    else:
+        y_min = valid_t_values.min()
+        y_max = valid_t_values.max()
 
-    # Add padding for better visualization
-    y_range = y_max - y_min
-    padding = 0.05 * y_range if y_range > 0 else 0.5
+        # Add padding for better visualization
+        y_range = y_max - y_min
+        padding = 0.05 * y_range if y_range > 0 else 0.5
 
-    # Ensure threshold line (p<0.001 at t=3.291) is always visible
-    threshold = 3.291
-    y_max = max(y_max, threshold) + padding
-    y_min = min(y_min, 0) - padding  # Allow negatives if they exist
+        # Ensure threshold line (p<0.001 at t=3.291) is always visible
+        threshold = 3.291
+        y_max = max(y_max, threshold) + padding
+        y_min = min(y_min, 0) - padding  # Allow negatives if they exist
+
+    # Final validation to ensure limits are finite and valid
+    if not (np.isfinite(y_min) and np.isfinite(y_max) and y_min < y_max):
+        logger.error(f"Invalid axis limits computed: y_min={y_min}, y_max={y_max}. Using defaults.")
+        y_min = -1.0
+        y_max = 5.0
 
     # Add threshold line
+    threshold = 3.291
     ax.axhline(y=threshold, linestyle="--", color="black", label="p<0.001 threshold" if show_legend else "")
     ax.set_xlim(0, t_raws_df["Epoch"].max())
     ax.set_ylim(y_min, y_max)
@@ -239,22 +268,36 @@ def generate_t_test_avg_figure(
     ax.set_xlabel("Epochs completed", fontsize=12)
     ax.set_ylabel("$t$-value", fontsize=12)
 
-    # Calculate dynamic y-axis limits based on data
-    # This ensures all data points are visible, including negative t-statistics
-    # (e.g., when a model performs worse on its own training author)
-    y_min = t_raws_df['t_raw'].min()
-    y_max = t_raws_df['t_raw'].max()
+    # Calculate dynamic y-axis limits based on VALID data only
+    # Filter out NaN/Inf values to avoid matplotlib errors
+    valid_t_values = t_raws_df['t_raw'].replace([np.inf, -np.inf], np.nan).dropna()
 
-    # Add padding for better visualization
-    y_range = y_max - y_min
-    padding = 0.05 * y_range if y_range > 0 else 0.5
+    if len(valid_t_values) == 0:
+        # No valid data - use reasonable defaults around threshold
+        logger.warning("No valid t-statistics found for average figure. Using default axis limits.")
+        y_min = -1.0
+        y_max = 5.0
+    else:
+        y_min = valid_t_values.min()
+        y_max = valid_t_values.max()
 
-    # Ensure threshold line (p<0.001 at t=3.291) is always visible
-    threshold = 3.291
-    y_max = max(y_max, threshold) + padding
-    y_min = min(y_min, 0) - padding  # Allow negatives if they exist
+        # Add padding for better visualization
+        y_range = y_max - y_min
+        padding = 0.05 * y_range if y_range > 0 else 0.5
+
+        # Ensure threshold line (p<0.001 at t=3.291) is always visible
+        threshold = 3.291
+        y_max = max(y_max, threshold) + padding
+        y_min = min(y_min, 0) - padding  # Allow negatives if they exist
+
+    # Final validation to ensure limits are finite and valid
+    if not (np.isfinite(y_min) and np.isfinite(y_max) and y_min < y_max):
+        logger.error(f"Invalid axis limits computed for average figure: y_min={y_min}, y_max={y_max}. Using defaults.")
+        y_min = -1.0
+        y_max = 5.0
 
     # Add threshold line
+    threshold = 3.291
     ax.axhline(y=threshold, linestyle="--", color="black", label="p<0.001 threshold" if show_legend else "")
     ax.set_xlim(0, t_raws_df["Epoch"].max())
     ax.set_ylim(y_min, y_max)