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"""Pure‑function helpers for daily aggregation."""
from __future__ import annotations
import pandas as pd
import numpy as np
def summary_from_df(df: pd.DataFrame, gamma_post: float = 0.3) -> pd.DataFrame:
"""
Return a DataFrame with daily & subreddit aggregates.
Expects columns:
retrieved_at - UTC timestamp or ISO-date string
subreddit - subreddit name
sentiment - numeric score (e.g. −1 … 1)
score - numeric weight / post score
Output columns:
date (datetime.date)
subreddit (string)
mean_sentiment
community_weighted_sentiment
count
"""
# Normalize retrieved_at to datetime and extract calendar day
df = df.copy()
df["date"] = pd.to_datetime(df["retrieved_at"]).dt.date
# Group by date and subreddit
grouped = df.groupby(["date", "subreddit"])
# Aggregate metrics
result = grouped.agg(
# First calculate raw mean_sentiment
raw_mean_sentiment=("sentiment", "mean"),
count=("sentiment", "count"),
).reset_index()
# Apply transformation to raw_mean_sentiment to get values in range [-1, 1] instead of [0, 1]
result["mean_sentiment"] = 2 * result["raw_mean_sentiment"] - 1
# Remove the raw mean column
result = result.drop(columns="raw_mean_sentiment")
# Calculate engagement-adjusted sentiment (EAS) for each group
# 1. Ensure 'score' is numeric
df["score_num"] = pd.to_numeric(df["score"], errors="coerce").fillna(0)
# 2. Compute base weights (1 + log1p(score))
weights_base = 1 + np.log1p(df["score_num"].clip(lower=0))
# 3. Apply post weight multiplier
weights = weights_base * np.where(df.get("type", None) == "post", gamma_post, 1.0)
df["weight"] = weights
# 4. Compute EAS per group: weighted average of sentiment
community_weighted_sentiments = []
for (date, subreddit), group in grouped:
w = group["weight"]
s = group["sentiment"]
eas = (w * s).sum() / w.sum() if w.sum() > 0 else 0
community_weighted_sentiments.append(eas)
result["community_weighted_sentiment"] = community_weighted_sentiments
# Normalize community_weighted_sentiment to range [-1,1]
result["community_weighted_sentiment"] = 2 * result["community_weighted_sentiment"] - 1
# Ensure consistent column order
result = result[["date", "subreddit", "mean_sentiment", "community_weighted_sentiment", "count"]]
return result
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