Revert "Rmspe test stat (#22)"

This reverts commit 9b7b5aa.

Author: thomaspinder

README.md

@@ -76,4 +76,4 @@ in your terminal.
 1. Follow steps 1-3 from `For Users`
 2. Create a hatch environment `hatch env create`
 3. Open a hatch shell `hatch shell`
-4. Validate your installation by running `hatch run dev:test`
+4. Validate your installation by running `hatch run tests:test`

src/causal_validation/__about__.py

@@ -1,3 +1,3 @@
-__version__ = "0.0.8"
+__version__ = "0.0.7"
 
 __all__ = ["__version__"]

src/causal_validation/data.py

@@ -27,7 +27,6 @@ class Dataset:
     yte: Float[np.ndarray, "M 1"]
     _start_date: dt.date
     counterfactual: tp.Optional[Float[np.ndarray, "M 1"]] = None
-    synthetic: tp.Optional[Float[np.ndarray, "M 1"]] = None
     _name: str = None
 
     def to_df(
@@ -152,13 +151,7 @@ def drop_unit(self, idx: int) -> Dataset:
         Xtr = np.delete(self.Xtr, [idx], axis=1)
         Xte = np.delete(self.Xte, [idx], axis=1)
         return Dataset(
-            Xtr,
-            Xte,
-            self.ytr,
-            self.yte,
-            self._start_date,
-            self.counterfactual,
-            self.synthetic,
+            Xtr, Xte, self.ytr, self.yte, self._start_date, self.counterfactual
         )
 
     def to_placebo_data(self, to_treat_idx: int) -> Dataset:
@@ -211,6 +204,4 @@ def reassign_treatment(
 ) -> Dataset:
     Xtr = data.Xtr
     Xte = data.Xte
-    return Dataset(
-        Xtr, Xte, ytr, yte, data._start_date, data.counterfactual, data.synthetic
-    )
+    return Dataset(Xtr, Xte, ytr, yte, data._start_date, data.counterfactual)

src/causal_validation/models.py

@@ -15,15 +15,14 @@
 class Result:
     effect: Effect
     counterfactual: Float[NPArray, "N 1"]
-    synthetic: Float[NPArray, "N 1"]
     observed: Float[NPArray, "N 1"]
 
 
 @dataclass
 class AZCausalWrapper:
     model: Estimator
     error_estimator: tp.Optional[Error] = None
-    _az_result: _Result = None
+    _az_result: Result = None
 
     def __post_init__(self):
         self._model_name = self.model.__class__.__name__
@@ -38,7 +37,6 @@ def __call__(self, data: Dataset, **kwargs) -> Result:
         res = Result(
             effect=result.effect,
             counterfactual=self.counterfactual,
-            synthetic=self.synthetic,
             observed=self.observed,
         )
         return res
@@ -49,12 +47,6 @@ def counterfactual(self) -> Float[NPArray, "N 1"]:
         c_factual = df.loc[:, "CF"].values.reshape(-1, 1)
         return c_factual
 
-    @property
-    def synthetic(self) -> Float[NPArray, "N 1"]:
-        df = self._az_result.effect.by_time
-        synth_control = df.loc[:, "C"].values.reshape(-1, 1)
-        return synth_control
-
     @property
     def observed(self) -> Float[NPArray, "N 1"]:
         df = self._az_result.effect.by_time

src/causal_validation/transforms/__init__.py

@@ -1,5 +1,4 @@
-from causal_validation.transforms.noise import Noise
 from causal_validation.transforms.periodic import Periodic
 from causal_validation.transforms.trends import Trend
 
-__all__ = ["Trend", "Periodic", "Noise"]
+__all__ = ["Trend", "Periodic"]

src/causal_validation/transforms/base.py

@@ -74,9 +74,7 @@ def apply_values(
         ytr = ytr + pre_intervention_vals[:, :1]
         Xte = Xte + post_intervention_vals[:, 1:]
         yte = yte + post_intervention_vals[:, :1]
-        return Dataset(
-            Xtr, Xte, ytr, yte, data._start_date, data.counterfactual, data.synthetic
-        )
+        return Dataset(Xtr, Xte, ytr, yte, data._start_date, data.counterfactual)
 
 
 @dataclass(kw_only=True)
@@ -93,6 +91,4 @@ def apply_values(
         ytr = ytr * pre_intervention_vals
         Xte = Xte * post_intervention_vals
         yte = yte * post_intervention_vals
-        return Dataset(
-            Xtr, Xte, ytr, yte, data._start_date, data.counterfactual, data.synthetic
-        )
+        return Dataset(Xtr, Xte, ytr, yte, data._start_date, data.counterfactual)

src/causal_validation/transforms/noise.py

@@ -9,11 +9,13 @@
     Column,
     DataFrameSchema,
 )
+from rich import box
 from rich.progress import (
     Progress,
     ProgressBar,
     track,
 )
+from rich.table import Table
 from scipy.stats import ttest_1samp
 from tqdm import (
     tqdm,
@@ -28,7 +30,6 @@
     AZCausalWrapper,
     Result,
 )
-from causal_validation.validation.testing import TestResultFrame
 
 PlaceboSchema = DataFrameSchema(
     {
@@ -45,12 +46,13 @@
 
 
 @dataclass
-class PlaceboTestResult(TestResultFrame):
+class PlaceboTestResult:
     effects: tp.Dict[tp.Tuple[str, str], tp.List[Result]]
 
     def _model_to_df(
         self, model_name: str, dataset_name: str, effects: tp.List[Result]
     ) -> pd.DataFrame:
+        breakpoint()
         _effects = [e.effect.percentage().value for e in effects]
         _n_effects = len(_effects)
         expected_effect = np.mean(_effects)
@@ -77,6 +79,21 @@ def to_df(self) -> pd.DataFrame:
         PlaceboSchema.validate(df)
         return df
 
+    def summary(self, precision: int = 4) -> Table:
+        table = Table(show_header=True, box=box.MARKDOWN)
+        df = self.to_df()
+        numeric_cols = df.select_dtypes(include=[np.number])
+        df.loc[:, numeric_cols.columns] = np.round(numeric_cols, decimals=precision)
+
+        for column in df.columns:
+            table.add_column(str(column), style="magenta")
+
+        for _, value_list in enumerate(df.values.tolist()):
+            row = [str(x) for x in value_list]
+            table.add_row(*row)
+
+        return table
+
 
 @dataclass
 class PlaceboTest: