Initial commit - ACME demo version

backend/tests/test_volumetria.py

@@ -0,0 +1,221 @@
+import math
+from datetime import datetime
+
+import matplotlib
+import pandas as pd
+
+from beyond_metrics.dimensions.Volumetria import VolumetriaMetrics
+
+# Usamos backend "Agg" para que matplotlib no intente abrir ventanas
+matplotlib.use("Agg")
+
+
+def _sample_df() -> pd.DataFrame:
+    """
+    DataFrame de prueba con el nuevo esquema de columnas:
+
+    Campos usados por VolumetriaMetrics:
+    - interaction_id
+    - datetime_start
+    - queue_skill
+    - channel
+
+    5 interacciones:
+    - 3 por canal "voz", 2 por canal "chat"
+    - 3 en skill "ventas", 2 en skill "soporte"
+    - 3 en enero, 2 en febrero
+    """
+    data = [
+        {
+            "interaction_id": "id1",
+            "datetime_start": datetime(2024, 1, 1, 9, 0),
+            "queue_skill": "ventas",
+            "channel": "voz",
+        },
+        {
+            "interaction_id": "id2",
+            "datetime_start": datetime(2024, 1, 1, 9, 30),
+            "queue_skill": "ventas",
+            "channel": "voz",
+        },
+        {
+            "interaction_id": "id3",
+            "datetime_start": datetime(2024, 1, 1, 10, 0),
+            "queue_skill": "soporte",
+            "channel": "voz",
+        },
+        {
+            "interaction_id": "id4",
+            "datetime_start": datetime(2024, 2, 1, 10, 0),
+            "queue_skill": "ventas",
+            "channel": "chat",
+        },
+        {
+            "interaction_id": "id5",
+            "datetime_start": datetime(2024, 2, 2, 11, 0),
+            "queue_skill": "soporte",
+            "channel": "chat",
+        },
+    ]
+    return pd.DataFrame(data)
+
+
+# ----------------------------------------------------------------------
+# VALIDACIÓN BÁSICA
+# ----------------------------------------------------------------------
+
+
+def test_init_validates_required_columns():
+    df = _sample_df()
+
+    # No debe lanzar error con las columnas por defecto
+    vm = VolumetriaMetrics(df)
+    assert not vm.is_empty
+
+    # Si falta alguna columna requerida, debe lanzar ValueError
+    for col in ["interaction_id", "datetime_start", "queue_skill", "channel"]:
+        df_missing = df.drop(columns=[col])
+        try:
+            VolumetriaMetrics(df_missing)
+            assert False, f"Debería fallar al faltar la columna: {col}"
+        except ValueError:
+            pass
+
+
+# ----------------------------------------------------------------------
+# VOLUMEN Y DISTRIBUCIONES
+# ----------------------------------------------------------------------
+
+
+def test_volume_by_channel_and_skill():
+    df = _sample_df()
+    vm = VolumetriaMetrics(df)
+
+    vol_channel = vm.volume_by_channel()
+    vol_skill = vm.volume_by_skill()
+
+    # Canales
+    assert vol_channel.sum() == len(df)
+    assert vol_channel["voz"] == 3
+    assert vol_channel["chat"] == 2
+
+    # Skills
+    assert vol_skill.sum() == len(df)
+    assert vol_skill["ventas"] == 3
+    assert vol_skill["soporte"] == 2
+
+
+def test_channel_and_skill_distribution_pct():
+    df = _sample_df()
+    vm = VolumetriaMetrics(df)
+
+    dist_channel = vm.channel_distribution_pct()
+    dist_skill = vm.skill_distribution_pct()
+
+    # 3/5 = 60%, 2/5 = 40%
+    assert math.isclose(dist_channel["voz"], 60.0, rel_tol=1e-6)
+    assert math.isclose(dist_channel["chat"], 40.0, rel_tol=1e-6)
+
+    assert math.isclose(dist_skill["ventas"], 60.0, rel_tol=1e-6)
+    assert math.isclose(dist_skill["soporte"], 40.0, rel_tol=1e-6)
+
+
+# ----------------------------------------------------------------------
+# HEATMAP Y SAZONALIDAD
+# ----------------------------------------------------------------------
+
+
+def test_heatmap_24x7_shape_and_values():
+    df = _sample_df()
+    vm = VolumetriaMetrics(df)
+
+    heatmap = vm.heatmap_24x7()
+
+    # 7 días x 24 horas
+    assert heatmap.shape == (7, 24)
+
+    # Comprobamos algunas celdas concretas
+    # 2024-01-01 es lunes (dayofweek=0), llamadas a las 9h (2) y 10h (1)
+    assert heatmap.loc[0, 9] == 2
+    assert heatmap.loc[0, 10] == 1
+
+    # 2024-02-01 es jueves (dayofweek=3), 10h
+    assert heatmap.loc[3, 10] == 1
+
+    # 2024-02-02 es viernes (dayofweek=4), 11h
+    assert heatmap.loc[4, 11] == 1
+
+
+def test_monthly_seasonality_cv():
+    df = _sample_df()
+    vm = VolumetriaMetrics(df)
+
+    cv = vm.monthly_seasonality_cv()
+
+    # Volumen mensual: [3, 2]
+    # mean = 2.5, std (ddof=1) ≈ 0.7071 -> CV ≈ 28.28%
+    assert math.isclose(cv, 28.28, rel_tol=1e-2)
+
+
+def test_peak_offpeak_ratio():
+    df = _sample_df()
+    vm = VolumetriaMetrics(df)
+
+    ratio = vm.peak_offpeak_ratio()
+
+    # Horas pico definidas en la clase: 10-19
+    # Pico: 10h,10h,11h -> 3 interacciones
+    # Valle: 9h,9h      -> 2 interacciones
+    # Ratio = 3/2 = 1.5
+    assert math.isclose(ratio, 1.5, rel_tol=1e-6)
+
+
+def test_concentration_top20_skills_pct():
+    df = _sample_df()
+    vm = VolumetriaMetrics(df)
+
+    conc = vm.concentration_top20_skills_pct()
+
+    # Skills: ventas=3, soporte=2, total=5
+    # Top 20% de skills (ceil(0.2 * 2) = 1 skill) -> ventas=3
+    # 3/5 = 60%
+    assert math.isclose(conc, 60.0, rel_tol=1e-6)
+
+
+# ----------------------------------------------------------------------
+# CASO DATAFRAME VACÍO
+# ----------------------------------------------------------------------
+
+
+def test_empty_dataframe_behaviour():
+    df_empty = pd.DataFrame(
+        columns=["interaction_id", "datetime_start", "queue_skill", "channel"]
+    )
+    vm = VolumetriaMetrics(df_empty)
+
+    assert vm.is_empty
+    assert vm.volume_by_channel().empty
+    assert vm.volume_by_skill().empty
+    assert math.isnan(vm.monthly_seasonality_cv())
+    assert math.isnan(vm.peak_offpeak_ratio())
+    assert math.isnan(vm.concentration_top20_skills_pct())
+
+
+# ----------------------------------------------------------------------
+# PLOTS
+# ----------------------------------------------------------------------
+
+
+def test_plot_methods_return_axes():
+    df = _sample_df()
+    vm = VolumetriaMetrics(df)
+
+    ax1 = vm.plot_heatmap_24x7()
+    ax2 = vm.plot_channel_distribution()
+    ax3 = vm.plot_skill_pareto()
+
+    from matplotlib.axes import Axes
+
+    assert isinstance(ax1, Axes)
+    assert isinstance(ax2, Axes)
+    assert isinstance(ax3, Axes)