Add example of multiple geo lift test analysis

causalpy/data/datasets.py

@@ -33,6 +33,7 @@
     "sc": {"filename": "synthetic_control.csv"},
     "anova1": {"filename": "ancova_generated.csv"},
     "geolift1": {"filename": "geolift1.csv"},
+    "geolift_multi_cell": {"filename": "geolift_multi_cell.csv"},
     "risk": {"filename": "AJR2001.csv"},
     "nhefs": {"filename": "nhefs.csv"},
     "schoolReturns": {"filename": "schoolingReturns.csv"},

causalpy/data/simulate_data.py

@@ -58,7 +58,7 @@ def generate_synthetic_control_data(
     Generates data for synthetic control example.
 
     :param N:
-        Number fo data points
+        Number of data points
     :param treatment_time:
         Index where treatment begins in the generated dataframe
     :param grw_mu:
@@ -324,15 +324,6 @@ def generate_geolift_data():
     treatment_time = pd.to_datetime("2022-01-01")
     causal_impact = 0.2
 
-    def create_series(n=52, amplitude=1, length_scale=2):
-        """
-        Returns numpy tile with generated seasonality data repeated over
-        multiple years
-        """
-        return np.tile(
-            generate_seasonality(n=n, amplitude=amplitude, length_scale=2) + 3, n_years
-        )
-
     time = pd.date_range(start="2019-01-01", periods=52 * n_years, freq="W")
 
     untreated = [
@@ -345,7 +336,12 @@ def create_series(n=52, amplitude=1, length_scale=2):
     ]
 
     df = (
-        pd.DataFrame({country: create_series() for country in untreated})
+        pd.DataFrame(
+            {
+                country: create_series(n_years=n_years, intercept=3)
+                for country in untreated
+            }
+        )
         .assign(time=time)
         .set_index("time")
     )
@@ -360,6 +356,67 @@ def create_series(n=52, amplitude=1, length_scale=2):
 
     # add treatment effect
     df["Denmark"] += np.where(df.index < treatment_time, 0, causal_impact)
+
+    # ensure we never see any negative sales
+    df = df.clip(lower=0)
+
+    return df
+
+
+def generate_multicell_geolift_data():
+    """Generate synthetic data for a geolift example. This will consists of 6 untreated
+    countries. The treated unit `Denmark` is a weighted combination of the untreated
+    units. We additionally specify a treatment effect which takes effect after the
+    `treatment_time`. The timeseries data is observed at weekly resolution and has
+    annual seasonality, with this seasonality being a drawn from a Gaussian Process with
+    a periodic kernel."""
+    n_years = 4
+    treatment_time = pd.to_datetime("2022-01-01")
+    causal_impact = 0.2
+    time = pd.date_range(start="2019-01-01", periods=52 * n_years, freq="W")
+
+    untreated = [
+        "u1",
+        "u2",
+        "u3",
+        "u4",
+        "u5",
+        "u6",
+        "u7",
+        "u8",
+        "u9",
+        "u10",
+        "u11",
+        "u12",
+    ]
+
+    df = (
+        pd.DataFrame(
+            {
+                country: create_series(n_years=n_years, intercept=3)
+                for country in untreated
+            }
+        )
+        .assign(time=time)
+        .set_index("time")
+    )
+
+    treated = ["t1", "t2", "t3", "t4"]
+
+    for treated_geo in treated:
+        # create treated unit as a weighted sum of the untreated units
+        weights = np.random.dirichlet(np.ones(len(untreated)), size=1)[0]
+        df[treated_geo] = np.dot(df[untreated].values, weights)
+        # add treatment effect
+        df[treated_geo] += np.where(df.index < treatment_time, 0, causal_impact)
+
+    # add observation noise to all geos
+    for col in untreated + treated:
+        df[col] += np.random.normal(size=len(df), scale=0.1)
+
+    # ensure we never see any negative sales
+    df = df.clip(lower=0)
+
     return df
 
 
@@ -387,3 +444,14 @@ def periodic_kernel(x1, x2, period=1, length_scale=1, amplitude=1):
     return amplitude**2 * np.exp(
         -2 * np.sin(np.pi * np.abs(x1 - x2) / period) ** 2 / length_scale**2
     )
+
+
+def create_series(n=52, amplitude=1, length_scale=2, n_years=4, intercept=3):
+    """
+    Returns numpy tile with generated seasonality data repeated over
+    multiple years
+    """
+    return np.tile(
+        generate_seasonality(n=n, amplitude=amplitude, length_scale=2) + intercept,
+        n_years,
+    )

causalpy/tests/test_synthetic_data.py

@@ -0,0 +1,41 @@
+#   Copyright 2024 The PyMC Labs Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+"""
+Tests for the simulated data functions
+"""
+
+import numpy as np
+import pandas as pd
+
+
+def test_generate_multicell_geolift_data():
+    """
+    Test the generate_multicell_geolift_data function.
+    """
+    from causalpy.data.simulate_data import generate_multicell_geolift_data
+
+    df = generate_multicell_geolift_data()
+    assert type(df) == pd.DataFrame
+    assert np.all(df >= 0), "Found negative values in dataset"
+
+
+def test_generate_geolift_data():
+    """
+    Test the generate_geolift_data function.
+    """
+    from causalpy.data.simulate_data import generate_geolift_data
+
+    df = generate_geolift_data()
+    assert type(df) == pd.DataFrame
+    assert np.all(df >= 0), "Found negative values in dataset"

docs/source/examples.rst

@@ -16,7 +16,15 @@ Synthetic Control
    notebooks/sc_pymc.ipynb
    notebooks/sc_skl.ipynb
    notebooks/sc_pymc_brexit.ipynb
+
+Geographical lift testing
+=========================
+
+.. toctree::
+   :titlesonly:
+
    notebooks/geolift1.ipynb
+   notebooks/multi_cell_geolift.ipynb
 
 
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