Initial commit - ACME demo version

backend/tests/test_operational_performance.py

@@ -0,0 +1,238 @@
+import math
+from datetime import datetime, timedelta
+
+import matplotlib
+import numpy as np
+import pandas as pd
+
+from beyond_metrics.dimensions.OperationalPerformance import OperationalPerformanceMetrics
+
+matplotlib.use("Agg")
+
+
+def _sample_df() -> pd.DataFrame:
+    """
+    Dataset sintético pequeño para probar la dimensión de rendimiento operacional.
+
+    Incluye:
+    - varios skills
+    - FCR, abandonos, transferencias
+    - reincidencia <7 días
+    - logged_time para occupancy
+    """
+    base = datetime(2024, 1, 1, 10, 0, 0)
+
+    rows = [
+        # cliente C1, resolved, no abandon, voz, ventas
+        {
+            "interaction_id": "id1",
+            "datetime_start": base,
+            "queue_skill": "ventas",
+            "channel": "voz",
+            "duration_talk": 600,
+            "hold_time": 60,
+            "wrap_up_time": 30,
+            "agent_id": "A1",
+            "transfer_flag": 0,
+            "is_resolved": 1,
+            "abandoned_flag": 0,
+            "customer_id": "C1",
+            "logged_time": 900,
+        },
+        # C1 vuelve en 3 días mismo canal/skill
+        {
+            "interaction_id": "id2",
+            "datetime_start": base + timedelta(days=3),
+            "queue_skill": "ventas",
+            "channel": "voz",
+            "duration_talk": 700,
+            "hold_time": 30,
+            "wrap_up_time": 40,
+            "agent_id": "A1",
+            "transfer_flag": 1,
+            "is_resolved": 1,
+            "abandoned_flag": 0,
+            "customer_id": "C1",
+            "logged_time": 900,
+        },
+        # cliente C2, soporte, chat, no resuelto, transferido
+        {
+            "interaction_id": "id3",
+            "datetime_start": base + timedelta(hours=1),
+            "queue_skill": "soporte",
+            "channel": "chat",
+            "duration_talk": 400,
+            "hold_time": 20,
+            "wrap_up_time": 30,
+            "agent_id": "A2",
+            "transfer_flag": 1,
+            "is_resolved": 0,
+            "abandoned_flag": 0,
+            "customer_id": "C2",
+            "logged_time": 800,
+        },
+        # cliente C3, abandonado
+        {
+            "interaction_id": "id4",
+            "datetime_start": base + timedelta(hours=2),
+            "queue_skill": "soporte",
+            "channel": "voz",
+            "duration_talk": 100,
+            "hold_time": 50,
+            "wrap_up_time": 10,
+            "agent_id": "A2",
+            "transfer_flag": 0,
+            "is_resolved": 0,
+            "abandoned_flag": 1,
+            "customer_id": "C3",
+            "logged_time": 600,
+        },
+        # cliente C4, una sola interacción, email
+        {
+            "interaction_id": "id5",
+            "datetime_start": base + timedelta(days=10),
+            "queue_skill": "ventas",
+            "channel": "email",
+            "duration_talk": 300,
+            "hold_time": 0,
+            "wrap_up_time": 20,
+            "agent_id": "A1",
+            "transfer_flag": 0,
+            "is_resolved": 1,
+            "abandoned_flag": 0,
+            "customer_id": "C4",
+            "logged_time": 700,
+        },
+    ]
+
+    return pd.DataFrame(rows)
+
+
+# ----------------------------------------------------------------------
+# Inicialización y validación básica
+# ----------------------------------------------------------------------
+
+
+def test_init_and_required_columns():
+    df = _sample_df()
+    op = OperationalPerformanceMetrics(df)
+    assert not op.is_empty
+
+    # Falta columna obligatoria -> ValueError
+    df_missing = df.drop(columns=["duration_talk"])
+    try:
+        OperationalPerformanceMetrics(df_missing)
+        assert False, "Debería lanzar ValueError si falta duration_talk"
+    except ValueError:
+        pass
+
+
+# ----------------------------------------------------------------------
+# AHT y distribución
+# ----------------------------------------------------------------------
+
+
+def test_aht_distribution_basic():
+    df = _sample_df()
+    op = OperationalPerformanceMetrics(df)
+
+    dist = op.aht_distribution()
+    assert "p10" in dist and "p50" in dist and "p90" in dist and "p90_p50_ratio" in dist
+
+    # Comprobamos que el ratio P90/P50 es razonable (>1)
+    assert dist["p90_p50_ratio"] >= 1.0
+
+
+# ----------------------------------------------------------------------
+# FCR, escalación, abandono
+# ----------------------------------------------------------------------
+
+
+def test_fcr_escalation_abandonment_rates():
+    df = _sample_df()
+    op = OperationalPerformanceMetrics(df)
+
+    fcr = op.fcr_rate()
+    esc = op.escalation_rate()
+    aband = op.abandonment_rate()
+
+    # FCR: interacciones resueltas / total
+    # is_resolved=1 en id1, id2, id5 -> 3 de 5
+    assert math.isclose(fcr, 60.0, rel_tol=1e-6)
+
+    # Escalación: transfer_flag=1 en id2, id3 -> 2 de 5
+    assert math.isclose(esc, 40.0, rel_tol=1e-6)
+
+    # Abandono: abandoned_flag=1 en id4 -> 1 de 5
+    assert math.isclose(aband, 20.0, rel_tol=1e-6)
+
+
+# ----------------------------------------------------------------------
+# Reincidencia y repetición de canal
+# ----------------------------------------------------------------------
+
+
+def test_recurrence_and_repeat_channel():
+    df = _sample_df()
+    op = OperationalPerformanceMetrics(df)
+
+    rec = op.recurrence_rate_7d()
+    rep = op.repeat_channel_rate()
+
+    # Clientes: C1, C2, C3, C4 -> 4 clientes
+    # Recurrente: C1 (tiene 2 contactos en 3 días). Solo 1 de 4 -> 25%
+    assert math.isclose(rec, 25.0, rel_tol=1e-6)
+
+    # Reincidencias (<7d):
+    # Solo el par de C1: voz -> voz, mismo canal => 100%
+    assert math.isclose(rep, 100.0, rel_tol=1e-6)
+
+
+# ----------------------------------------------------------------------
+# Occupancy
+# ----------------------------------------------------------------------
+
+
+def test_occupancy_rate():
+    df = _sample_df()
+    op = OperationalPerformanceMetrics(df)
+
+    occ = op.occupancy_rate()
+
+    # handle_time = (600+60+30) + (700+30+40) + (400+20+30) + (100+50+10) + (300+0+20)
+    #             = 690 + 770 + 450 + 160 + 320 = 2390
+    # logged_time total = 900 + 900 + 800 + 600 + 700 = 3900
+    expected_occ = 2390 / 3900 * 100
+    assert math.isclose(occ, round(expected_occ, 2), rel_tol=1e-6)
+
+
+# ----------------------------------------------------------------------
+# Performance Score
+# ----------------------------------------------------------------------
+
+
+def test_performance_score_structure_and_range():
+    df = _sample_df()
+    op = OperationalPerformanceMetrics(df)
+
+    score_info = op.performance_score()
+    assert "score" in score_info
+    assert 0.0 <= score_info["score"] <= 10.0
+
+
+# ----------------------------------------------------------------------
+# Plots
+# ----------------------------------------------------------------------
+
+
+def test_plot_methods_return_axes():
+    df = _sample_df()
+    op = OperationalPerformanceMetrics(df)
+
+    ax1 = op.plot_aht_boxplot_by_skill()
+    ax2 = op.plot_resolution_funnel_by_skill()
+
+    from matplotlib.axes import Axes
+
+    assert isinstance(ax1, Axes)
+    assert isinstance(ax2, Axes)