Fixes #12 - Add more descriptive email body

.gitignore

@@ -3,3 +3,4 @@ __pycache__/
 tmp/
 common/common_constants.py
 home/
+.claude/*

common/common_constants.py

@@ -1,5 +1,4 @@
 CAUSAL_ANALYSIS_FAILED = "Causal analysis failed. Please try again later."
-CAUSAL_ANALYSIS_EMAIL_BODY = "Please find the causal analysis results in the pdf document attached to this email."
 EMAIL = "admin@causalbench.org"
 REPLY_TO_ADDRESS = "contact@causalbench.org"
 EMAIL_PASSWORD = ""

helper_services/causal_analysis_helper.py

@@ -116,7 +116,11 @@ def run_causal_analysis(download_dir,
     }
 
     encode = []
-    try:    
+    raw_df = pd.DataFrame()
+    hyperparameters = []
+    experiment_count = 0
+    load_error = None
+    try:
         if download_dir:
             print(f"Processing ZIP files from {download_dir}")
             raw_df = process_yaml_data(download_dir, headers)
@@ -164,6 +168,7 @@ def run_causal_analysis(download_dir,
 
     except Exception as e:
         print(f"Error loading data: {e}")
+        load_error = str(e)
 
     if group_by_metric:
         df_columns = ['dataset', 'model', 'metric'] + hyperparameters + ['outcome']
@@ -206,7 +211,8 @@ def run_causal_analysis(download_dir,
             print(label_encoder.classes_)
 
     df = df.dropna()
-    print(f"After cleaning: {len(df)} experiments remain")
+    experiment_count = len(df)
+    print(f"After cleaning: {experiment_count} experiments remain")
 
     df = df.sort_values(['dataset'] + [col for col in sorted(df.columns) if col != 'dataset']).reset_index(drop=True)
 
@@ -286,4 +292,36 @@ def run_causal_analysis(download_dir,
         except Exception as e:
             print(f"Error in causal analysis: {e}")
 
+    insufficient_data = False
+    insufficient_data_reason = None
+
+    if load_error is not None:
+        insufficient_data = True
+        insufficient_data_reason = f"Error loading data: {load_error}"
+    elif raw_df.empty:
+        insufficient_data = True
+        insufficient_data_reason = "No data files could be downloaded from provided URLs"
+    elif len(hyperparameters) == 0:
+        insufficient_data = True
+        insufficient_data_reason = "No hyperparameters with sufficient variation found"
+    elif experiment_count < 2:
+        insufficient_data = True
+        insufficient_data_reason = "Too few experiments after data cleaning (need ≥ 2)"
+    elif group_results:
+        all_nan = all(
+            all(np.isnan(v) for v in group_data.get("effects", {}).values())
+            for group_data in group_results.values()
+            if "effects" in group_data
+        )
+        if all_nan:
+            insufficient_data = True
+            insufficient_data_reason = "Causal effects could not be estimated (insufficient variation in data)"
+
+    group_results["_metadata"] = {
+        "experiment_count": experiment_count,
+        "hyperparameter_count": len(hyperparameters),
+        "insufficient_data": insufficient_data,
+        "insufficient_data_reason": insufficient_data_reason,
+    }
+
     return group_results, download_dir

helper_services/report_helper.py

@@ -169,14 +169,16 @@ def generate_report(outcome_column, causal_analysis_results, unique_id, run_ids,
 
     elements.append(separator)
 
-    if len(causal_analysis_results) == 0:
+    analysis_groups = {k: v for k, v in causal_analysis_results.items() if k != "_metadata"}
+
+    if len(analysis_groups) == 0:
         elements.append(Paragraph(f"<b>Analysis:</b> Effects on {outcome_column} (0 experiments)", header_style))
         elements.append(Paragraph("Insufficient data to perform causal analysis.", body_style))
         elements.append(separator)
-    
+
     else:
         # Process data
-        for group, group_data in causal_analysis_results.items():
+        for group, group_data in analysis_groups.items():
             table_data = [["Variable", "Effect", "Strength"]]
 
             if group_data['experiments'] == 1:

lambda_function.py

@@ -12,7 +12,56 @@
 from helper_services.hp_dtype_helper import get_hp_dtypes
 from helper_services.mail_helper import send_email
 import numpy as np
-from common.common_constants import CAUSAL_ANALYSIS_EMAIL_BODY, TEMP_DIR
+from common.common_constants import TEMP_DIR
+
+
+def build_email_body(causal_analysis_results, event):
+    outcome_column = event.get('outcome_column', 'Time.Duration')
+    filters = event.get('filters', None)
+    metadata = causal_analysis_results.get('_metadata', {})
+
+    experiment_count = metadata.get('experiment_count', 0)
+    insufficient_data = metadata.get('insufficient_data', False)
+    insufficient_data_reason = metadata.get('insufficient_data_reason', None)
+
+    lines = ["CausalBench+ Causal Analysis Report", ""]
+    lines.append(f"Outcome metric: {outcome_column}")
+    lines.append(f"Experiments: Effects on {outcome_column} ({experiment_count} experiments)")
+
+    if filters:
+        filter_str = ", ".join(f"{k}={v}" for k, v in filters.items()) if isinstance(filters, dict) else str(filters)
+        lines.append(f"Filters applied: {filter_str}")
+
+    lines.append("")
+
+    if insufficient_data:
+        lines.append("INSUFFICIENT DATA: Causal effects could not be computed.")
+        if insufficient_data_reason:
+            lines.append(f"Reason: {insufficient_data_reason}")
+        lines.append("")
+        lines.append("To get results, run more experiments with varied hyperparameter configurations.")
+        lines.append("Minimum requirements: ≥ 2 data points per variable, ≥ 2 unique values per hyperparameter.")
+    else:
+        all_effects = {}
+        for group, group_data in causal_analysis_results.items():
+            if group == "_metadata":
+                continue
+            for k, v in group_data.get("effects", {}).items():
+                if isinstance(v, (int, float)) and math.isfinite(v):
+                    all_effects[k] = v
+
+        if all_effects:
+            sorted_effects = sorted(all_effects.items(), key=lambda x: abs(x[1]), reverse=True)[:3]
+            lines.append("Top causal effects:")
+            for hp, effect in sorted_effects:
+                hp_name = hp.split(".", 1)[1] if "." in hp else hp
+                sign = "+" if effect >= 0 else ""
+                lines.append(f"  {hp_name}: {sign}{effect:.4f}")
+
+    lines.append("")
+    lines.append("Full results are in the attached PDF report.")
+
+    return "\n".join(lines)
 
 
 def handler(event, context):
@@ -47,11 +96,13 @@ def handler(event, context):
 
     # find all causal recommendations
     for group, group_data in causal_analysis_results.items():
+        if group == "_metadata":
+            continue
         effects = group_data["effects"]
         dimensions = defaultdict(dict)
         for k, v in effects.items():
             k = k.split(".")[1]  # Remove 'HP.' prefix
-            if k in list(event.get('hyperparameter_limits', {}).keys()) and v != 0:
+            if k in list(event.get('hyperparameter_limits', {}).keys()) and math.isfinite(v) and v != 0:
                 dimensions[k]['strength'] = v
                 dimensions[k]['min_val'] = event.get('hyperparameter_limits', {})[k]['min']
                 dimensions[k]['max_val'] = event.get('hyperparameter_limits', {})[k]['max']
@@ -81,7 +132,7 @@ def handler(event, context):
         attachments.append(xlsx_filepath)
 
     try:
-        send_email(event.get('user_email'), "[CausalBench] Causal Analysis Results", CAUSAL_ANALYSIS_EMAIL_BODY, attachments=attachments)
+        send_email(event.get('user_email'), "[CausalBench] Causal Analysis Results", build_email_body(causal_analysis_results, event), attachments=attachments)
     except Exception as e:
         print(f"Error sending email: {e}")