Initial commit: frontend + backend integration

backend/tests/test_economy_cost.py

@@ -0,0 +1,128 @@
+import math
+from datetime import datetime
+
+import matplotlib
+import pandas as pd
+
+from beyond_metrics.dimensions.EconomyCost import EconomyCostMetrics, EconomyConfig
+
+matplotlib.use("Agg")
+
+
+def _sample_df() -> pd.DataFrame:
+    data = [
+        {
+            "interaction_id": "id1",
+            "datetime_start": datetime(2024, 1, 1, 10, 0),
+            "queue_skill": "ventas",
+            "channel": "voz",
+            "duration_talk": 600,
+            "hold_time": 60,
+            "wrap_up_time": 30,
+        },
+        {
+            "interaction_id": "id2",
+            "datetime_start": datetime(2024, 1, 1, 10, 5),
+            "queue_skill": "ventas",
+            "channel": "voz",
+            "duration_talk": 300,
+            "hold_time": 30,
+            "wrap_up_time": 20,
+        },
+        {
+            "interaction_id": "id3",
+            "datetime_start": datetime(2024, 1, 1, 11, 0),
+            "queue_skill": "soporte",
+            "channel": "chat",
+            "duration_talk": 400,
+            "hold_time": 20,
+            "wrap_up_time": 30,
+        },
+    ]
+    return pd.DataFrame(data)
+
+
+def test_init_and_required_columns():
+    df = _sample_df()
+    cfg = EconomyConfig(labor_cost_per_hour=20.0, overhead_rate=0.1, tech_costs_annual=10000.0)
+    em = EconomyCostMetrics(df, cfg)
+    assert not em.is_empty
+
+    # Falta de columna obligatoria -> ValueError
+    df_missing = df.drop(columns=["duration_talk"])
+    import pytest
+    with pytest.raises(ValueError):
+        EconomyCostMetrics(df_missing, cfg)
+
+
+def test_metrics_without_config_do_not_crash():
+    df = _sample_df()
+    em = EconomyCostMetrics(df, None)
+
+    assert em.cpi_by_skill_channel().empty
+    assert em.annual_cost_by_skill_channel().empty
+    assert em.cost_breakdown() == {}
+    assert em.inefficiency_cost_by_skill_channel().empty
+    assert em.potential_savings() == {}
+
+
+def test_basic_cpi_and_annual_cost():
+    df = _sample_df()
+    cfg = EconomyConfig(labor_cost_per_hour=20.0, overhead_rate=0.1)
+    em = EconomyCostMetrics(df, cfg)
+
+    cpi = em.cpi_by_skill_channel()
+    assert not cpi.empty
+    # Debe haber filas para ventas/voz y soporte/chat
+    assert ("ventas", "voz") in cpi.index
+    assert ("soporte", "chat") in cpi.index
+
+    annual = em.annual_cost_by_skill_channel()
+    assert "annual_cost" in annual.columns
+    # costes positivos
+    assert (annual["annual_cost"] > 0).any()
+
+
+def test_cost_breakdown_and_potential_savings():
+    df = _sample_df()
+    cfg = EconomyConfig(
+        labor_cost_per_hour=20.0,
+        overhead_rate=0.1,
+        tech_costs_annual=5000.0,
+        automation_cpi=0.2,
+        automation_volume_share=0.5,
+        automation_success_rate=0.8,
+    )
+    em = EconomyCostMetrics(df, cfg)
+
+    breakdown = em.cost_breakdown()
+    assert "labor_pct" in breakdown
+    assert "overhead_pct" in breakdown
+    assert "tech_pct" in breakdown
+
+    total_pct = (
+        breakdown["labor_pct"]
+        + breakdown["overhead_pct"]
+        + breakdown["tech_pct"]
+    )
+
+    # Permitimos pequeño error por redondeo a 2 decimales
+    assert abs(total_pct - 100.0) < 0.2
+
+    savings = em.potential_savings()
+    assert "annual_savings" in savings
+    assert savings["annual_savings"] >= 0.0
+
+
+def test_plot_methods_return_axes():
+    from matplotlib.axes import Axes
+
+    df = _sample_df()
+    cfg = EconomyConfig(labor_cost_per_hour=20.0, overhead_rate=0.1)
+    em = EconomyCostMetrics(df, cfg)
+
+    ax1 = em.plot_cost_waterfall()
+    ax2 = em.plot_cpi_by_channel()
+
+    assert isinstance(ax1, Axes)
+    assert isinstance(ax2, Axes)