Initial commit - ACME demo version
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sujucu70
318
backend/beyond_metrics/dimensions/SatisfactionExperience.py
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318
backend/beyond_metrics/dimensions/SatisfactionExperience.py
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, List, Any
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from matplotlib.axes import Axes
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# Solo columnas del dataset “core”
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REQUIRED_COLUMNS_SAT: List[str] = [
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"interaction_id",
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"datetime_start",
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"queue_skill",
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"channel",
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"duration_talk",
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"hold_time",
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"wrap_up_time",
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]
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@dataclass
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class SatisfactionExperienceMetrics:
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"""
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Dimensión 3: SATISFACCIÓN y EXPERIENCIA
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Todas las columnas de satisfacción (csat/nps/ces/aht) son OPCIONALES.
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Si no están, las métricas que las usan devuelven vacío/NaN pero
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nunca rompen el pipeline.
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"""
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df: pd.DataFrame
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def __post_init__(self) -> None:
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self._validate_columns()
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self._prepare_data()
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# ------------------------------------------------------------------ #
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# Helpers
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# ------------------------------------------------------------------ #
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def _validate_columns(self) -> None:
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missing = [c for c in REQUIRED_COLUMNS_SAT if c not in self.df.columns]
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if missing:
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raise ValueError(
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f"Faltan columnas obligatorias para SatisfactionExperienceMetrics: {missing}"
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)
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def _prepare_data(self) -> None:
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df = self.df.copy()
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df["datetime_start"] = pd.to_datetime(df["datetime_start"], errors="coerce")
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# Duraciones base siempre existen
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for col in ["duration_talk", "hold_time", "wrap_up_time"]:
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df[col] = pd.to_numeric(df[col], errors="coerce")
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# Handle time
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df["handle_time"] = (
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df["duration_talk"].fillna(0)
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+ df["hold_time"].fillna(0)
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+ df["wrap_up_time"].fillna(0)
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)
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# csat_score opcional
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df["csat_score"] = pd.to_numeric(df.get("csat_score", np.nan), errors="coerce")
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# aht opcional: si existe columna explícita la usamos, si no usamos handle_time
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if "aht" in df.columns:
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df["aht"] = pd.to_numeric(df["aht"], errors="coerce")
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else:
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df["aht"] = df["handle_time"]
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# NPS / CES opcionales
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df["nps_score"] = pd.to_numeric(df.get("nps_score", np.nan), errors="coerce")
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df["ces_score"] = pd.to_numeric(df.get("ces_score", np.nan), errors="coerce")
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df["queue_skill"] = df["queue_skill"].astype(str).str.strip()
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df["channel"] = df["channel"].astype(str).str.strip()
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self.df = df
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@property
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def is_empty(self) -> bool:
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return self.df.empty
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# ------------------------------------------------------------------ #
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# KPIs
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# ------------------------------------------------------------------ #
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def csat_avg_by_skill_channel(self) -> pd.DataFrame:
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"""
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CSAT promedio por skill/canal.
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Si no hay csat_score, devuelve DataFrame vacío.
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"""
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df = self.df
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if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
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return pd.DataFrame()
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df = df.dropna(subset=["csat_score"])
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if df.empty:
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return pd.DataFrame()
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pivot = (
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df.pivot_table(
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index="queue_skill",
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columns="channel",
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values="csat_score",
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aggfunc="mean",
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)
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.sort_index()
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.round(2)
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)
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return pivot
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def nps_avg_by_skill_channel(self) -> pd.DataFrame:
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"""
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NPS medio por skill/canal, si existe nps_score.
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"""
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df = self.df
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if "nps_score" not in df.columns or df["nps_score"].notna().sum() == 0:
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return pd.DataFrame()
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df = df.dropna(subset=["nps_score"])
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if df.empty:
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return pd.DataFrame()
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pivot = (
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df.pivot_table(
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index="queue_skill",
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columns="channel",
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values="nps_score",
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aggfunc="mean",
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)
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.sort_index()
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.round(2)
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)
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return pivot
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def ces_avg_by_skill_channel(self) -> pd.DataFrame:
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"""
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CES medio por skill/canal, si existe ces_score.
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"""
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df = self.df
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if "ces_score" not in df.columns or df["ces_score"].notna().sum() == 0:
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return pd.DataFrame()
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df = df.dropna(subset=["ces_score"])
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if df.empty:
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return pd.DataFrame()
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pivot = (
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df.pivot_table(
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index="queue_skill",
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columns="channel",
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values="ces_score",
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aggfunc="mean",
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)
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.sort_index()
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.round(2)
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)
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return pivot
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def csat_global(self) -> float:
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"""
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CSAT medio global (todas las interacciones).
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Usa la columna opcional `csat_score`:
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- Si no existe, devuelve NaN.
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- Si todos los valores son NaN / vacíos, devuelve NaN.
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"""
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df = self.df
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if "csat_score" not in df.columns:
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return float("nan")
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series = pd.to_numeric(df["csat_score"], errors="coerce").dropna()
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if series.empty:
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return float("nan")
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mean = series.mean()
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return float(round(mean, 2))
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def csat_aht_correlation(self) -> Dict[str, Any]:
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"""
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Correlación Pearson CSAT vs AHT.
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Si falta csat o aht, o no hay varianza, devuelve NaN y código adecuado.
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"""
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df = self.df
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if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
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return {"r": float("nan"), "n": 0.0, "interpretation_code": "sin_datos"}
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if "aht" not in df.columns or df["aht"].notna().sum() == 0:
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return {"r": float("nan"), "n": 0.0, "interpretation_code": "sin_datos"}
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df = df.dropna(subset=["csat_score", "aht"]).copy()
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n = len(df)
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if n < 2:
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return {"r": float("nan"), "n": float(n), "interpretation_code": "insuficiente"}
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x = df["aht"].astype(float)
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y = df["csat_score"].astype(float)
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if x.std(ddof=1) == 0 or y.std(ddof=1) == 0:
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return {"r": float("nan"), "n": float(n), "interpretation_code": "sin_varianza"}
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r = float(np.corrcoef(x, y)[0, 1])
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if r < -0.3:
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interpretation = "negativo"
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elif r > 0.3:
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interpretation = "positivo"
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else:
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interpretation = "neutral"
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return {"r": round(r, 3), "n": float(n), "interpretation_code": interpretation}
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def csat_aht_skill_summary(self) -> pd.DataFrame:
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"""
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Resumen por skill con clasificación del "sweet spot".
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Si falta csat o aht, devuelve DataFrame vacío.
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"""
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df = self.df
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if df["csat_score"].notna().sum() == 0 or df["aht"].notna().sum() == 0:
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return pd.DataFrame(columns=["csat_avg", "aht_avg", "classification"])
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df = df.dropna(subset=["csat_score", "aht"]).copy()
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if df.empty:
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return pd.DataFrame(columns=["csat_avg", "aht_avg", "classification"])
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grouped = df.groupby("queue_skill").agg(
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csat_avg=("csat_score", "mean"),
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aht_avg=("aht", "mean"),
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)
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aht_all = df["aht"].astype(float)
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csat_all = df["csat_score"].astype(float)
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aht_p40 = float(np.percentile(aht_all, 40))
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aht_p60 = float(np.percentile(aht_all, 60))
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csat_p40 = float(np.percentile(csat_all, 40))
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csat_p60 = float(np.percentile(csat_all, 60))
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def classify(row) -> str:
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csat = row["csat_avg"]
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aht = row["aht_avg"]
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if aht <= aht_p40 and csat >= csat_p60:
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return "ideal_automatizar"
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if aht >= aht_p60 and csat >= csat_p40:
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return "requiere_humano"
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return "neutral"
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grouped["classification"] = grouped.apply(classify, axis=1)
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return grouped.round({"csat_avg": 2, "aht_avg": 2})
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# ------------------------------------------------------------------ #
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# Plots
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# ------------------------------------------------------------------ #
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def plot_csat_vs_aht_scatter(self) -> Axes:
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"""
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Scatter CSAT vs AHT por skill.
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Si no hay datos suficientes, devuelve un Axes con mensaje.
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"""
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df = self.df
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if df["csat_score"].notna().sum() == 0 or df["aht"].notna().sum() == 0:
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fig, ax = plt.subplots()
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ax.text(0.5, 0.5, "Sin datos de CSAT/AHT", ha="center", va="center")
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ax.set_axis_off()
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return ax
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df = df.dropna(subset=["csat_score", "aht"]).copy()
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if df.empty:
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fig, ax = plt.subplots()
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ax.text(0.5, 0.5, "Sin datos de CSAT/AHT", ha="center", va="center")
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ax.set_axis_off()
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return ax
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fig, ax = plt.subplots(figsize=(8, 5))
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for skill, sub in df.groupby("queue_skill"):
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ax.scatter(sub["aht"], sub["csat_score"], label=skill, alpha=0.7)
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ax.set_xlabel("AHT (segundos)")
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ax.set_ylabel("CSAT")
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ax.set_title("CSAT vs AHT por skill")
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ax.grid(alpha=0.3)
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ax.legend(title="Skill", bbox_to_anchor=(1.05, 1), loc="upper left")
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plt.tight_layout()
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return ax
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def plot_csat_distribution(self) -> Axes:
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"""
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Histograma de CSAT.
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Si no hay csat_score, devuelve un Axes con mensaje.
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"""
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df = self.df
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if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
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fig, ax = plt.subplots()
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ax.text(0.5, 0.5, "Sin datos de CSAT", ha="center", va="center")
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ax.set_axis_off()
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return ax
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df = df.dropna(subset=["csat_score"]).copy()
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if df.empty:
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fig, ax = plt.subplots()
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ax.text(0.5, 0.5, "Sin datos de CSAT", ha="center", va="center")
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ax.set_axis_off()
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return ax
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fig, ax = plt.subplots(figsize=(6, 4))
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ax.hist(df["csat_score"], bins=10, alpha=0.7)
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ax.set_xlabel("CSAT")
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ax.set_ylabel("Frecuencia")
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ax.set_title("Distribución de CSAT")
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ax.grid(axis="y", alpha=0.3)
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return ax
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