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Translate Phase 2 medium-priority files (frontend utils + backend dimensions)

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Phase 2 of Spanish-to-English translation for medium-priority files:

Frontend utils (2 files):
- dataTransformation.ts: Translated ~72 occurrences (comments, docs, console logs)
- segmentClassifier.ts: Translated ~20 occurrences (JSDoc, inline comments, UI strings)

Backend dimensions (3 files):
- OperationalPerformance.py: Translated ~117 lines (docstrings, comments)
- SatisfactionExperience.py: Translated ~33 lines (docstrings, comments)
- EconomyCost.py: Translated ~79 lines (docstrings, comments)

All function names and variable names preserved for API compatibility.
Frontend and backend compilation tested and verified successful.

Related to TRANSLATION_STATUS.md Phase 2 objectives.

https://claude.ai/code/session_01GNbnkFoESkRcnPr3bLCYDg
This commit is contained in:
Claude
2026-02-07 11:03:00 +00:00
parent 94178eaaae
commit 8c7f5fa827
5 changed files with 325 additions and 335 deletions
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65
backend/beyond_metrics/dimensions/SatisfactionExperience.py
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@@ -24,11 +24,10 @@ REQUIRED_COLUMNS_SAT: List[str] = [
@dataclass
class SatisfactionExperienceMetrics:
"""
Dimensión 3: SATISFACCIÓN y EXPERIENCIA
Dimension 3: SATISFACTION and EXPERIENCE
Todas las columnas de satisfacción (csat/nps/ces/aht) son OPCIONALES.
Si no están, las métricas que las usan devuelven vacío/NaN pero
nunca rompen el pipeline.
All satisfaction columns (csat/nps/ces/aht) are OPTIONAL.
If they are not present, the metrics that use them return empty/NaN but never break the pipeline.
"""
df: pd.DataFrame
@@ -44,7 +43,7 @@ class SatisfactionExperienceMetrics:
missing = [c for c in REQUIRED_COLUMNS_SAT if c not in self.df.columns]
if missing:
raise ValueError(
f"Faltan columnas obligatorias para SatisfactionExperienceMetrics: {missing}"
f"Missing required columns for SatisfactionExperienceMetrics: {missing}"
)
def _prepare_data(self) -> None:
@@ -52,7 +51,7 @@ class SatisfactionExperienceMetrics:
df["datetime_start"] = pd.to_datetime(df["datetime_start"], errors="coerce")
# Duraciones base siempre existen
# Base durations always exist
for col in ["duration_talk", "hold_time", "wrap_up_time"]:
df[col] = pd.to_numeric(df[col], errors="coerce")
@@ -63,16 +62,16 @@ class SatisfactionExperienceMetrics:
+ df["wrap_up_time"].fillna(0)
)
# csat_score opcional
# csat_score optional
df["csat_score"] = pd.to_numeric(df.get("csat_score", np.nan), errors="coerce")
# aht opcional: si existe columna explícita la usamos, si no usamos handle_time
# aht optional: if explicit column exists we use it, otherwise we use handle_time
if "aht" in df.columns:
df["aht"] = pd.to_numeric(df["aht"], errors="coerce")
else:
df["aht"] = df["handle_time"]
# NPS / CES opcionales
# NPS / CES optional
df["nps_score"] = pd.to_numeric(df.get("nps_score", np.nan), errors="coerce")
df["ces_score"] = pd.to_numeric(df.get("ces_score", np.nan), errors="coerce")
@@ -90,8 +89,8 @@ class SatisfactionExperienceMetrics:
# ------------------------------------------------------------------ #
def csat_avg_by_skill_channel(self) -> pd.DataFrame:
"""
CSAT promedio por skill/canal.
Si no hay csat_score, devuelve DataFrame vacío.
Average CSAT by skill/channel.
If there is no csat_score, returns empty DataFrame.
"""
df = self.df
if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
@@ -115,7 +114,7 @@ class SatisfactionExperienceMetrics:
def nps_avg_by_skill_channel(self) -> pd.DataFrame:
"""
NPS medio por skill/canal, si existe nps_score.
Average NPS by skill/channel, if nps_score exists.
"""
df = self.df
if "nps_score" not in df.columns or df["nps_score"].notna().sum() == 0:
@@ -139,7 +138,7 @@ class SatisfactionExperienceMetrics:
def ces_avg_by_skill_channel(self) -> pd.DataFrame:
"""
CES medio por skill/canal, si existe ces_score.
Average CES by skill/channel, if ces_score exists.
"""
df = self.df
if "ces_score" not in df.columns or df["ces_score"].notna().sum() == 0:
@@ -163,11 +162,11 @@ class SatisfactionExperienceMetrics:
def csat_global(self) -> float:
"""
CSAT medio global (todas las interacciones).
Global average CSAT (all interactions).
Usa la columna opcional `csat_score`:
- Si no existe, devuelve NaN.
- Si todos los valores son NaN / vacíos, devuelve NaN.
Uses the optional `csat_score` column:
- If it does not exist, returns NaN.
- If all values are NaN / empty, returns NaN.
"""
df = self.df
if "csat_score" not in df.columns:
@@ -183,8 +182,8 @@ class SatisfactionExperienceMetrics:
def csat_aht_correlation(self) -> Dict[str, Any]:
"""
Correlación Pearson CSAT vs AHT.
Si falta csat o aht, o no hay varianza, devuelve NaN y código adecuado.
Pearson correlation CSAT vs AHT.
If csat or aht is missing, or there is no variance, returns NaN and appropriate code.
"""
df = self.df
if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
@@ -216,8 +215,8 @@ class SatisfactionExperienceMetrics:
def csat_aht_skill_summary(self) -> pd.DataFrame:
"""
Resumen por skill con clasificación del "sweet spot".
Si falta csat o aht, devuelve DataFrame vacío.
Summary by skill with "sweet spot" classification.
If csat or aht is missing, returns empty DataFrame.
"""
df = self.df
if df["csat_score"].notna().sum() == 0 or df["aht"].notna().sum() == 0:
@@ -258,20 +257,20 @@ class SatisfactionExperienceMetrics:
# ------------------------------------------------------------------ #
def plot_csat_vs_aht_scatter(self) -> Axes:
"""
Scatter CSAT vs AHT por skill.
Si no hay datos suficientes, devuelve un Axes con mensaje.
Scatter CSAT vs AHT by skill.
If there is insufficient data, returns an Axes with message.
"""
df = self.df
if df["csat_score"].notna().sum() == 0 or df["aht"].notna().sum() == 0:
fig, ax = plt.subplots()
ax.text(0.5, 0.5, "Sin datos de CSAT/AHT", ha="center", va="center")
ax.text(0.5, 0.5, "No CSAT/AHT data", ha="center", va="center")
ax.set_axis_off()
return ax
df = df.dropna(subset=["csat_score", "aht"]).copy()
if df.empty:
fig, ax = plt.subplots()
ax.text(0.5, 0.5, "Sin datos de CSAT/AHT", ha="center", va="center")
ax.text(0.5, 0.5, "No CSAT/AHT data", ha="center", va="center")
ax.set_axis_off()
return ax
@@ -280,9 +279,9 @@ class SatisfactionExperienceMetrics:
for skill, sub in df.groupby("queue_skill"):
ax.scatter(sub["aht"], sub["csat_score"], label=skill, alpha=0.7)
ax.set_xlabel("AHT (segundos)")
ax.set_xlabel("AHT (seconds)")
ax.set_ylabel("CSAT")
ax.set_title("CSAT vs AHT por skill")
ax.set_title("CSAT vs AHT by skill")
ax.grid(alpha=0.3)
ax.legend(title="Skill", bbox_to_anchor=(1.05, 1), loc="upper left")
@@ -291,28 +290,28 @@ class SatisfactionExperienceMetrics:
def plot_csat_distribution(self) -> Axes:
"""
Histograma de CSAT.
Si no hay csat_score, devuelve un Axes con mensaje.
CSAT histogram.
If there is no csat_score, returns an Axes with message.
"""
df = self.df
if "csat_score" not in df.columns or df["csat_score"].notna().sum() == 0:
fig, ax = plt.subplots()
ax.text(0.5, 0.5, "Sin datos de CSAT", ha="center", va="center")
ax.text(0.5, 0.5, "No CSAT data", ha="center", va="center")
ax.set_axis_off()
return ax
df = df.dropna(subset=["csat_score"]).copy()
if df.empty:
fig, ax = plt.subplots()
ax.text(0.5, 0.5, "Sin datos de CSAT", ha="center", va="center")
ax.text(0.5, 0.5, "No CSAT data", ha="center", va="center")
ax.set_axis_off()
return ax
fig, ax = plt.subplots(figsize=(6, 4))
ax.hist(df["csat_score"], bins=10, alpha=0.7)
ax.set_xlabel("CSAT")
ax.set_ylabel("Frecuencia")
ax.set_title("Distribución de CSAT")
ax.set_ylabel("Frequency")
ax.set_title("CSAT distribution")
ax.grid(axis="y", alpha=0.3)
return ax