Add weighted to plot (#72)

* Add weighted to plot

* bump version

Author: TomeHirata

CITATION.cff

@@ -2,7 +2,7 @@ cff-version: 1.2.0
 message: "If you use this software, please cite it as below."
 type: software
 title: "dte_adj: A Python Package for Estimating Distribution Treatment Effects"
-version: 0.1.7
+version: 0.1.8
 date-released: 2024-12-01
 url: "https://github.com/CyberAgentAILab/python-dte-adjustment"
 repository-code: "https://github.com/CyberAgentAILab/python-dte-adjustment"

docs/source/conf.py

@@ -13,7 +13,7 @@
 project = "dte_adj"
 copyright = "2024, CyberAgent, Inc."
 author = "CyberAgent, Inc"
-release = "0.1.7"
+release = "0.1.8"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

dte_adj/plot/__init__.py

@@ -15,6 +15,7 @@ def plot(
     title: Optional[str] = None,
     xlabel: Optional[str] = None,
     ylabel: Optional[str] = None,
+    weighted: bool = False,
 ):
     """Visualize distributional parameters and their confidence intervals.
 
@@ -29,12 +30,18 @@ def plot(
         title (str, optional): Axes title.
         xlabel (str, optional): X-axis title label.
         ylabel (str, optional): Y-axis title label.
+        weighted (bool, optional): If True, multiply treatment effects by X values to show value-weighted effects. Defaults to False.
 
     Returns:
         matplotlib.axes.Axes: The axes with the plot.
     """
     if ax is None:
-        fig, ax = plt.subplots()
+        _, ax = plt.subplots()
+
+    if weighted:
+        means = means * X
+        lower_bounds = lower_bounds * X
+        upper_bounds = upper_bounds * X
 
     if chart_type == "line":
         ax.plot(X, means, label="Values", color=color)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "dte_adj"
-version = "0.1.7"
+version = "0.1.8"
 description = "This is a Python library for estimating distributional treatment effects"
 readme = "README.md"
 requires-python = ">=3.10"

tests/test_plot.py

@@ -72,6 +72,44 @@ def test_plot_fail_unknown_chart_type(self):
             "Chart type other is not supported",
         )
 
+    @patch("dte_adj.plot.plt")
+    def test_plot_weighted(self, mock_plt):
+        # Arrange
+        x_values = np.array([1, 2, 3, 4, 5])
+        means = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
+        upper_bands = np.array([0.2, 0.3, 0.4, 0.5, 0.6])
+        lower_bands = np.array([0.0, 0.1, 0.2, 0.3, 0.4])
+        mock_ax = MagicMock()
+        mock_plt.subplots.return_value = (MagicMock(), mock_ax)
+
+        # Act
+        result_ax = plot(
+            x_values,
+            means,
+            lower_bands,
+            upper_bands,
+            chart_type="line",
+            weighted=True,
+        )
 
-if __name__ == "__main__":
-    unittest.main()
+        # Assert
+        self.assertEqual(result_ax, mock_ax)
+        mock_plt.subplots.assert_called_once()
+        plot_call = mock_ax.plot.call_args
+        fill_between_call = mock_ax.fill_between.call_args
+
+        # Check that values are weighted (multiplied by x_values)
+        plot_args, plot_kwargs = plot_call
+        x_values_arg, y_values_arg = plot_args
+        expected_weighted_means = means * x_values
+        self.assertTrue(np.array_equal(x_values_arg, x_values))
+        self.assertTrue(np.array_equal(y_values_arg, expected_weighted_means))
+
+        # Check that confidence intervals are also weighted
+        fill_between_args, fill_between_kwargs = fill_between_call
+        x_fill, lower_fill, upper_fill = fill_between_args
+        expected_weighted_lower = lower_bands * x_values
+        expected_weighted_upper = upper_bands * x_values
+        self.assertTrue(np.array_equal(x_fill, x_values_arg))
+        self.assertTrue(np.array_equal(lower_fill, expected_weighted_lower))
+        self.assertTrue(np.array_equal(upper_fill, expected_weighted_upper))