Merge pull request #355 from pymc-labs/summary

Enable `summary` method for all currently implemented frequentist experiments

causalpy/pymc_experiments.py

@@ -202,11 +202,9 @@ def __init__(
         self.post_X = np.asarray(new_x)
         self.post_y = np.asarray(new_y)
 
-        # DEVIATION FROM SKL EXPERIMENT CODE =============================
         # fit the model to the observed (pre-intervention) data
         COORDS = {"coeffs": self.labels, "obs_indx": np.arange(self.pre_X.shape[0])}
         self.model.fit(X=self.pre_X, y=self.pre_y, coords=COORDS)
-        # ================================================================
 
         # score the goodness of fit to the pre-intervention data
         self.score = self.model.score(X=self.pre_X, y=self.pre_y)
@@ -347,7 +345,6 @@ def summary(self, round_to=None) -> None:
 
         print(f"{self.expt_type:=^80}")
         print(f"Formula: {self.formula}")
-        # TODO: extra experiment specific outputs here
         self.print_coefficients(round_to)
 
 

causalpy/skl_experiments.py

@@ -45,6 +45,7 @@ class ExperimentalDesign:
     """Base class for experiment designs"""
 
     model = None
+    expt_type = None
     outcome_variable_name = None
 
     def __init__(self, model=None, **kwargs):
@@ -53,6 +54,24 @@ def __init__(self, model=None, **kwargs):
         if self.model is None:
             raise ValueError("fitting_model not set or passed.")
 
+    def print_coefficients(self, round_to=None) -> None:
+        """
+        Prints the model coefficients
+
+        :param round_to:
+            Number of decimals used to round results. Defaults to 2. Use "None" to return raw numbers.
+        """
+        print("Model coefficients:")
+        # Determine the width of the longest label
+        max_label_length = max(len(name) for name in self.labels)
+        # Print each coefficient with formatted alignment
+        for name, val in zip(self.labels, self.model.coef_[0]):
+            # Left-align the name
+            formatted_name = f"{name:<{max_label_length}}"
+            # Right-align the value with width 10
+            formatted_val = f"{round_num(val, round_to):>10}"
+            print(f"  {formatted_name}\t{formatted_val}")
+
 
 class PrePostFit(ExperimentalDesign, PrePostFitDataValidator):
     """
@@ -95,6 +114,8 @@ def __init__(
         super().__init__(model=model, **kwargs)
         self._input_validation(data, treatment_time)
         self.treatment_time = treatment_time
+        # set experiment type - usually done in subclasses
+        self.expt_type = "Pre-Post Fit"
         # split data in to pre and post intervention
         self.datapre = data[data.index < self.treatment_time]
         self.datapost = data[data.index >= self.treatment_time]
@@ -103,10 +124,10 @@ def __init__(
 
         # set things up with pre-intervention data
         y, X = dmatrices(formula, self.datapre)
+        self.outcome_variable_name = y.design_info.column_names[0]
         self._y_design_info = y.design_info
         self._x_design_info = X.design_info
         self.labels = X.design_info.column_names
-        self.outcome_variable_name = y.design_info.column_names[0]
         self.pre_y, self.pre_X = np.asarray(y), np.asarray(X)
         # process post-intervention data
         (new_y, new_x) = build_design_matrices(
@@ -222,6 +243,18 @@ def plot_coeffs(self):
             palette=sns.color_palette("husl"),
         )
 
+    def summary(self, round_to=None) -> None:
+        """
+        Print text output summarising the results
+
+        :param round_to:
+            Number of decimals used to round results. Defaults to 2. Use "None" to return raw numbers.
+        """
+
+        print(f"{self.expt_type:=^80}")
+        print(f"Formula: {self.formula}")
+        self.print_coefficients(round_to)
+
 
 class InterruptedTimeSeries(PrePostFit):
     """
@@ -253,7 +286,6 @@ class InterruptedTimeSeries(PrePostFit):
     ...     formula="y ~ 1 + t + C(month)",
     ...     model = LinearRegression()
     ... )
-
     """
 
     expt_type = "Interrupted Time Series"
@@ -351,6 +383,7 @@ def __init__(
     ):
         super().__init__(model=model, **kwargs)
         self.data = data
+        self.expt_type = "Difference in Differences"
         self.formula = formula
         self.time_variable_name = time_variable_name
         self.group_variable_name = group_variable_name
@@ -509,6 +542,20 @@ def plot(self, round_to=None):
         ax.legend(fontsize=LEGEND_FONT_SIZE)
         return (fig, ax)
 
+    def summary(self, round_to=None) -> None:
+        """
+        Print text output summarising the results.
+
+        :param round_to:
+            Number of decimals used to round results. Defaults to 2. Use "None" to return raw numbers.
+        """
+
+        print(f"{self.expt_type:=^80}")
+        print(f"Formula: {self.formula}")
+        print("\nResults:")
+        print(f"Causal impact = {round_num(self.causal_impact[0], round_to)}")
+        self.print_coefficients(round_to)
+
 
 class RegressionDiscontinuity(ExperimentalDesign, RDDataValidator):
     """
@@ -542,17 +589,6 @@ class RegressionDiscontinuity(ExperimentalDesign, RDDataValidator):
     ...     model=LinearRegression(),
     ...     treatment_threshold=0.5,
     ... )
-    >>> result.summary() # doctest: +NORMALIZE_WHITESPACE,+NUMBER
-    Difference in Differences experiment
-    Formula: y ~ 1 + x + treated
-    Running variable: x
-    Threshold on running variable: 0.5
-    Results:
-    Discontinuity at threshold = 0.19
-    Model coefficients:
-        Intercept		0.0
-        treated[T.True]		0.19
-        x		1.23
     """
 
     def __init__(
@@ -687,16 +723,18 @@ def plot(self, round_to=None):
         ax.legend(fontsize=LEGEND_FONT_SIZE)
         return (fig, ax)
 
-    def summary(self):
+    def summary(self, round_to=None) -> None:
         """
         Print text output summarising the results
+
+        :param round_to:
+            Number of decimals used to round results. Defaults to 2. Use "None" to return raw numbers.
         """
         print("Difference in Differences experiment")
         print(f"Formula: {self.formula}")
         print(f"Running variable: {self.running_variable_name}")
         print(f"Threshold on running variable: {self.treatment_threshold}")
         print("\nResults:")
         print(f"Discontinuity at threshold = {self.discontinuity_at_threshold:.2f}")
-        print("Model coefficients:")
-        for name, val in zip(self.labels, self.model.coef_[0]):
-            print(f"\t{name}\t\t{val}")
+        print("\n")
+        self.print_coefficients(round_to)

causalpy/tests/test_integration_skl_examples.py

@@ -42,6 +42,7 @@ def test_did():
     )
     assert isinstance(data, pd.DataFrame)
     assert isinstance(result, cp.skl_experiments.DifferenceInDifferences)
+    result.summary()
 
 
 @pytest.mark.integration
@@ -68,6 +69,7 @@ def test_rd_drinking():
     )
     assert isinstance(df, pd.DataFrame)
     assert isinstance(result, cp.skl_experiments.RegressionDiscontinuity)
+    result.summary()
 
 
 @pytest.mark.integration
@@ -94,6 +96,7 @@ def test_its():
     )
     assert isinstance(df, pd.DataFrame)
     assert isinstance(result, cp.skl_experiments.SyntheticControl)
+    result.summary()
 
 
 @pytest.mark.integration
@@ -115,6 +118,7 @@ def test_sc():
     )
     assert isinstance(df, pd.DataFrame)
     assert isinstance(result, cp.skl_experiments.SyntheticControl)
+    result.summary()
 
 
 @pytest.mark.integration
@@ -136,6 +140,7 @@ def test_rd_linear_main_effects():
     )
     assert isinstance(data, pd.DataFrame)
     assert isinstance(result, cp.skl_experiments.RegressionDiscontinuity)
+    result.summary()
 
 
 @pytest.mark.integration
@@ -159,6 +164,7 @@ def test_rd_linear_main_effects_bandwidth():
     )
     assert isinstance(data, pd.DataFrame)
     assert isinstance(result, cp.skl_experiments.RegressionDiscontinuity)
+    result.summary()
 
 
 @pytest.mark.integration
@@ -180,6 +186,7 @@ def test_rd_linear_with_interaction():
     )
     assert isinstance(data, pd.DataFrame)
     assert isinstance(result, cp.skl_experiments.RegressionDiscontinuity)
+    result.summary()
 
 
 @pytest.mark.integration