feat(api): add average emotion per user into per user analysis

server/analysis/interactional.py

@@ -3,6 +3,7 @@ import re
 
 from collections import Counter
 
+
 class InteractionAnalysis:
     def __init__(self, df: pd.DataFrame, word_exclusions: set[str]):
         self.df = df
@@ -12,7 +13,9 @@ class InteractionAnalysis:
         tokens = re.findall(r"\b[a-z]{3,}\b", text)
         return [t for t in tokens if t not in self.word_exclusions]
 
-    def _vocab_richness_per_user(self, min_words: int = 20, top_most_used_words: int = 100) -> list:
+    def _vocab_richness_per_user(
+        self, min_words: int = 20, top_most_used_words: int = 100
+    ) -> list:
         df = self.df.copy()
         df["content"] = df["content"].fillna("").astype(str).str.lower()
         df["tokens"] = df["content"].apply(self._tokenize)
@@ -39,15 +42,17 @@ class InteractionAnalysis:
                 for w, c in counts.most_common(top_most_used_words)
             ]
 
-            rows.append({
+            rows.append(
+                {
                     "author": author,
                     "events": int(events),
                     "total_words": int(total_words),
                     "unique_words": int(unique_words),
                     "vocab_richness": round(vocab_richness, 3),
                     "avg_words_per_event": round(avg_words, 2),
-                "top_words": top_words
+                    "top_words": top_words,
-            })
+                }
+            )
 
         rows = sorted(rows, key=lambda x: x["vocab_richness"], reverse=True)
 
@@ -55,9 +60,7 @@ class InteractionAnalysis:
 
     def top_users(self) -> list:
         counts = (
-            self.df.groupby(["author", "source"])
+            self.df.groupby(["author", "source"]).size().sort_values(ascending=False)
-            .size()
-            .sort_values(ascending=False)
         )
 
         top_users = [
@@ -68,19 +71,29 @@ class InteractionAnalysis:
         return top_users
 
     def per_user_analysis(self) -> dict:
-        per_user = (
+        per_user = self.df.groupby(["author", "type"]).size().unstack(fill_value=0)
-            self.df.groupby(["author", "type"])
+
-            .size()
+        emotion_cols = [col for col in self.df.columns if col.startswith("emotion_")]
-            .unstack(fill_value=0)
+
-        )
+        avg_emotions_by_author = {}
+        if emotion_cols:
+            avg_emotions = self.df.groupby("author")[emotion_cols].mean().fillna(0.0)
+            avg_emotions_by_author = {
+                author: {emotion: float(score) for emotion, score in row.items()}
+                for author, row in avg_emotions.iterrows()
+            }
 
         # ensure columns always exist
         for col in ("post", "comment"):
             if col not in per_user.columns:
                 per_user[col] = 0
 
-        per_user["comment_post_ratio"] = per_user["comment"] / per_user["post"].replace(0, 1)
+        per_user["comment_post_ratio"] = per_user["comment"] / per_user["post"].replace(
-        per_user["comment_share"] = per_user["comment"] / (per_user["post"] + per_user["comment"]).replace(0, 1)
+            0, 1
+        )
+        per_user["comment_share"] = per_user["comment"] / (
+            per_user["post"] + per_user["comment"]
+        ).replace(0, 1)
         per_user = per_user.sort_values("comment_post_ratio", ascending=True)
         per_user_records = per_user.reset_index().to_dict(orient="records")
 
@@ -91,14 +104,17 @@ class InteractionAnalysis:
         merged_users = []
         for row in per_user_records:
             author = row["author"]
-            merged_users.append({
+            merged_users.append(
+                {
                     "author": author,
                     "post": int(row.get("post", 0)),
                     "comment": int(row.get("comment", 0)),
                     "comment_post_ratio": float(row.get("comment_post_ratio", 0)),
                     "comment_share": float(row.get("comment_share", 0)),
-                "vocab": vocab_by_author.get(author)
+                    "avg_emotions": avg_emotions_by_author.get(author, {}),
-            })
+                    "vocab": vocab_by_author.get(author, {"vocab_richness": 0, "avg_words_per_event": 0, "top_words": []}),
+                }
+            )
 
         merged_users.sort(key=lambda u: u["comment_post_ratio"])
 
@@ -151,7 +167,8 @@ class InteractionAnalysis:
         emotion_exclusions = {"emotion_neutral", "emotion_surprise"}
 
         emotion_cols = [
-            c for c in self.df.columns
+            c
+            for c in self.df.columns
             if c.startswith("emotion_") and c not in emotion_exclusions
         ]
 
@@ -174,14 +191,18 @@ class InteractionAnalysis:
 
                 reply_to = id_to_reply.get(current)
 
-                if reply_to is None or (isinstance(reply_to, float) and pd.isna(reply_to)) or reply_to == "":
+                if (
+                    reply_to is None
+                    or (isinstance(reply_to, float) and pd.isna(reply_to))
+                    or reply_to == ""
+                ):
                     break
 
                 length += 1
                 current = reply_to
 
                 if current in length_cache:
-                    length += (length_cache[current] - 1)
+                    length += length_cache[current] - 1
                     break
 
             length_cache[start_id] = length