Initial commit: frontend + backend integration

backend/tests/test_satisfaction_experience.py

@@ -0,0 +1,200 @@
+import math
+from datetime import datetime, timedelta
+import pytest
+
+import matplotlib
+import numpy as np
+import pandas as pd
+
+from beyond_metrics.dimensions.SatisfactionExperience import SatisfactionExperienceMetrics
+
+matplotlib.use("Agg")
+
+
+def _sample_df_negative_corr() -> pd.DataFrame:
+    """
+    Dataset sintético donde CSAT decrece claramente cuando AHT aumenta,
+    para que la correlación sea negativa (< -0.3).
+    """
+    base = datetime(2024, 1, 1, 10, 0, 0)
+
+    rows = []
+    # AHT crece, CSAT baja
+    aht_values = [200, 300, 400, 500, 600, 700, 800, 900]
+    csat_values = [5.0, 4.7, 4.3, 3.8, 3.3, 2.8, 2.3, 2.0]
+
+    skills = ["ventas", "retencion"]
+    channels = ["voz", "chat"]
+
+    for i, (aht, csat) in enumerate(zip(aht_values, csat_values), start=1):
+        rows.append(
+            {
+                "interaction_id": f"id{i}",
+                "datetime_start": base + timedelta(minutes=5 * i),
+                "queue_skill": skills[i % len(skills)],
+                "channel": channels[i % len(channels)],
+                "csat_score": csat,
+                "duration_talk": aht * 0.7,
+                "hold_time": aht * 0.2,
+                "wrap_up_time": aht * 0.1,
+            }
+        )
+
+    return pd.DataFrame(rows)
+
+
+def _sample_df_full() -> pd.DataFrame:
+    """
+    Dataset más completo con NPS y CES para otras pruebas.
+    """
+    base = datetime(2024, 1, 1, 10, 0, 0)
+    rows = []
+
+    for i in range(1, 11):
+        aht = 300 + 30 * i
+        csat = 3.0 + 0.1 * i  # ligero incremento
+        nps = -20 + 5 * i
+        ces = 4.0 - 0.05 * i
+
+        rows.append(
+            {
+                "interaction_id": f"id{i}",
+                "datetime_start": base + timedelta(minutes=10 * i),
+                "queue_skill": "ventas" if i <= 5 else "retencion",
+                "channel": "voz" if i % 2 == 0 else "chat",
+                "csat_score": csat,
+                "duration_talk": aht * 0.7,
+                "hold_time": aht * 0.2,
+                "wrap_up_time": aht * 0.1,
+                "nps_score": nps,
+                "ces_score": ces,
+            }
+        )
+
+    return pd.DataFrame(rows)
+
+
+# ----------------------------------------------------------------------
+# Inicialización y validación
+# ----------------------------------------------------------------------
+
+
+def test_init_and_required_columns():
+    df = _sample_df_negative_corr()
+    sm = SatisfactionExperienceMetrics(df)
+    assert not sm.is_empty
+
+    # Quitar una columna REALMENTE obligatoria -> debe lanzar ValueError
+    df_missing = df.drop(columns=["duration_talk"])
+    with pytest.raises(ValueError):
+        SatisfactionExperienceMetrics(df_missing)
+
+    # Quitar csat_score ya NO debe romper: es opcional
+    df_no_csat = df.drop(columns=["csat_score"])
+    sm2 = SatisfactionExperienceMetrics(df_no_csat)
+    # simplemente no tendrá métricas de csat
+    assert sm2.is_empty is False
+
+
+# ----------------------------------------------------------------------
+# CSAT promedio y tablas
+# ----------------------------------------------------------------------
+
+
+def test_csat_avg_by_skill_channel():
+    df = _sample_df_full()
+    sm = SatisfactionExperienceMetrics(df)
+
+    table = sm.csat_avg_by_skill_channel()
+    # Debe tener al menos 2 skills y 2 canales
+    assert "ventas" in table.index
+    assert "retencion" in table.index
+    # Algún canal
+    assert any(col in table.columns for col in ["voz", "chat"])
+
+
+def test_nps_and_ces_tables():
+    df = _sample_df_full()
+    sm = SatisfactionExperienceMetrics(df)
+
+    nps = sm.nps_avg_by_skill_channel()
+    ces = sm.ces_avg_by_skill_channel()
+
+    # Deben devolver DataFrame no vacío
+    assert not nps.empty
+    assert not ces.empty
+    assert "ventas" in nps.index
+    assert "ventas" in ces.index
+
+
+# ----------------------------------------------------------------------
+# Correlación CSAT vs AHT
+# ----------------------------------------------------------------------
+
+
+def test_csat_aht_correlation_negative():
+    df = _sample_df_negative_corr()
+    sm = SatisfactionExperienceMetrics(df)
+
+    corr = sm.csat_aht_correlation()
+    r = corr["r"]
+    code = corr["interpretation_code"]
+
+    assert r < -0.3
+    assert code == "negativo"
+
+
+# ----------------------------------------------------------------------
+# Clasificación por skill (sweet spot)
+# ----------------------------------------------------------------------
+
+
+def test_csat_aht_skill_summary_structure():
+    df = _sample_df_full()
+    sm = SatisfactionExperienceMetrics(df)
+
+    summary = sm.csat_aht_skill_summary()
+    assert "csat_avg" in summary.columns
+    assert "aht_avg" in summary.columns
+    assert "classification" in summary.columns
+    assert set(summary.index) == {"ventas", "retencion"}
+
+
+# ----------------------------------------------------------------------
+# Plots
+# ----------------------------------------------------------------------
+
+
+def test_plot_methods_return_axes():
+    df = _sample_df_full()
+    sm = SatisfactionExperienceMetrics(df)
+
+    ax1 = sm.plot_csat_vs_aht_scatter()
+    ax2 = sm.plot_csat_distribution()
+
+    from matplotlib.axes import Axes
+
+    assert isinstance(ax1, Axes)
+    assert isinstance(ax2, Axes)
+
+
+def test_dataset_without_csat_does_not_break():
+    # Dataset “core” sin csat/nps/ces
+    df = pd.DataFrame(
+        {
+            "interaction_id": ["id1", "id2"],
+            "datetime_start": [datetime(2024, 1, 1, 10), datetime(2024, 1, 1, 11)],
+            "queue_skill": ["ventas", "soporte"],
+            "channel": ["voz", "chat"],
+            "duration_talk": [300, 400],
+            "hold_time": [30, 20],
+            "wrap_up_time": [20, 30],
+        }
+    )
+
+    sm = SatisfactionExperienceMetrics(df)
+
+    # No debe petar, simplemente devolver vacío/NaN
+    assert sm.csat_avg_by_skill_channel().empty
+    corr = sm.csat_aht_correlation()
+    assert math.isnan(corr["r"])