Namings mix

Author: cetagostini

causalpy/__init__.py

@@ -28,7 +28,7 @@
 from .experiments.prepostnegd import PrePostNEGD
 from .experiments.regression_discontinuity import RegressionDiscontinuity
 from .experiments.regression_kink import RegressionKink
-from .experiments.structural_time_series import BasisExpansionTimeSeries
+from .experiments.structural_time_series import StructuralTimeSeries
 from .experiments.synthetic_control import SyntheticControl
 
 az.style.use("arviz-darkgrid")
@@ -39,7 +39,7 @@
     "create_causalpy_compatible_class",
     "InstrumentalVariable",
     "InterruptedTimeSeries",
-    "BasisExpansionTimeSeries",
+    "StructuralTimeSeries",
     "InversePropensityWeighting",
     "load_data",
     "PrePostNEGD",

causalpy/experiments/__init__.py

@@ -20,7 +20,7 @@
 from .prepostnegd import PrePostNEGD
 from .regression_discontinuity import RegressionDiscontinuity
 from .regression_kink import RegressionKink
-from .structural_time_series import BasisExpansionTimeSeries
+from .structural_time_series import StructuralTimeSeries
 from .synthetic_control import SyntheticControl
 
 __all__ = [
@@ -31,7 +31,7 @@
     "RegressionDiscontinuity",
     "RegressionKink",
     "SyntheticControl",
-    "BasisExpansionTimeSeries",
+    "StructuralTimeSeries",
     "InterruptedTimeSeries",  # For backward compatibility
     "IVRegression",
 ]

causalpy/experiments/interrupted_time_series.py

@@ -17,19 +17,19 @@
 
 import warnings
 
-from .structural_time_series import BasisExpansionTimeSeries
+from .structural_time_series import StructuralTimeSeries
 
 
-class InterruptedTimeSeries(BasisExpansionTimeSeries):
+class InterruptedTimeSeries(StructuralTimeSeries):
     """
     DEPRECATED: This class is deprecated and will be removed in a future version.
-    Please use BasisExpansionTimeSeries instead.
+    Please use StructuralTimeSeries instead.
     """
 
     def __init__(self, *args, **kwargs):
         warnings.warn(
             "The InterruptedTimeSeries class is deprecated and will be removed in a "
-            "future version. Please use BasisExpansionTimeSeries instead.",
+            "future version. Please use StructuralTimeSeries instead.",
             FutureWarning,
         )
         super().__init__(*args, **kwargs)

causalpy/experiments/structural_time_series.py

@@ -34,7 +34,7 @@
 LEGEND_FONT_SIZE = 12
 
 
-class BasisExpansionTimeSeries(BaseExperiment):
+class StructuralTimeSeries(BaseExperiment):
     """
     The class for basis expansion time series analysis.
 
@@ -60,7 +60,7 @@ class BasisExpansionTimeSeries(BaseExperiment):
     ... )
     >>> treatment_time = pd.to_datetime("2017-01-01")
     >>> seed = 42
-    >>> result = cp.BasisExpansionTimeSeries(
+    >>> result = cp.StructuralTimeSeries(
     ...     df,
     ...     treatment_time,
     ...     formula="y ~ 1 + t + C(month)",

causalpy/pymc_experiments.py

@@ -39,7 +39,7 @@
 )
 from .experiments.regression_kink import RegressionKink as NewRegressionKink
 from .experiments.structural_time_series import (
-    BasisExpansionTimeSeries as NewBasisExpansionTimeSeries,
+    StructuralTimeSeries as NewBasisExpansionTimeSeries,
 )
 from .experiments.synthetic_control import (
     SyntheticControl as NewSyntheticControl,
@@ -140,11 +140,11 @@ def InstrumentalVariable(*args, **kwargs):
     return NewInstrumentalVariable(*args, **kwargs)
 
 
-def BasisExpansionTimeSeries(*args, **kwargs):
+def StructuralTimeSeries(*args, **kwargs):
     warnings.warn(
-        f"""{RED}cp.pymc_experiments.BasisExpansionTimeSeries is deprecated and will be removed in a future release. Please use:
+        f"""{RED}cp.pymc_experiments.StructuralTimeSeries is deprecated and will be removed in a future release. Please use:
         import causalpy as cp
-        cp.BasisExpansionTimeSeries(...){RESET}""",
+        cp.StructuralTimeSeries(...){RESET}""",
         DeprecationWarning,
         stacklevel=2,
     )

causalpy/skl_experiments.py

@@ -64,7 +64,7 @@ def DifferenceInDifferences(*args, **kwargs):
 
 
 def InterruptedTimeSeries(*args, **kwargs):
-    """DEPRECATED: Please use cp.BasisExpansionTimeSeries(...)"""
+    """DEPRECATED: Please use cp.StructuralTimeSeries(...)"""
     warnings.warn(
         f"""{RED}cp.skl_experiments.InterruptedTimeSeries is deprecated and will be removed in a future release. Please use:
         {BLUE}cp.InterruptedTimeSeries(...){RESET}""",

causalpy/tests/test_integration_pymc_examples.py

@@ -357,7 +357,7 @@ def test_its():
 
     Loads data and checks:
     1. data is a dataframe
-    2. causalpy.BasisExpansionTimeSeries returns correct type
+    2. causalpy.StructuralTimeSeries returns correct type
     3. the correct number of MCMC chains exists in the posterior inference data
     4. the correct number of MCMC draws exists in the posterior inference data
     5. the method get_plot_data returns a DataFrame with expected columns
@@ -368,16 +368,16 @@ def test_its():
         .set_index("date")
     )
     treatment_time = pd.to_datetime("2017-01-01")
-    result = cp.BasisExpansionTimeSeries(
+    result = cp.StructuralTimeSeries(
         df,
         treatment_time,
         formula="y ~ 1 + t + C(month)",
         model=cp.pymc_models.LinearRegression(sample_kwargs=sample_kwargs),
     )
     # Test 1. plot method runs
     result.plot()
-    # 2. causalpy.BasisExpansionTimeSeries returns correct type
-    assert isinstance(result, cp.BasisExpansionTimeSeries)
+    # 2. causalpy.StructuralTimeSeries returns correct type
+    assert isinstance(result, cp.StructuralTimeSeries)
     assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"]
     assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"]
     result.summary()
@@ -412,7 +412,7 @@ def test_its_covid():
 
     Loads data and checks:
     1. data is a dataframe
-    2. causalpy.BasisExpansionTimeSeries returns correct type
+    2. causalpy.StructuralTimeSeries returns correct type
     3. the correct number of MCMC chains exists in the posterior inference data
     4. the correct number of MCMC draws exists in the posterior inference data
     5. the method get_plot_data returns a DataFrame with expected columns
@@ -424,16 +424,16 @@ def test_its_covid():
         .set_index("date")
     )
     treatment_time = pd.to_datetime("2020-01-01")
-    result = cp.BasisExpansionTimeSeries(
+    result = cp.StructuralTimeSeries(
         df,
         treatment_time,
         formula="standardize(deaths) ~ 0 + standardize(t) + C(month) + standardize(temp)",  # noqa E501
         model=cp.pymc_models.LinearRegression(sample_kwargs=sample_kwargs),
     )
     # Test 1. plot method runs
     result.plot()
-    # 2. causalpy.BasisExpansionTimeSeries returns correct type
-    assert isinstance(result, cp.BasisExpansionTimeSeries)
+    # 2. causalpy.StructuralTimeSeries returns correct type
+    assert isinstance(result, cp.StructuralTimeSeries)
     assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"]
     assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"]
     result.summary()