Merge pull request #6 from wd60622/minor-ticks

bounds for all plots

conjugate/distributions.py

@@ -165,6 +165,11 @@ class Exponential(ContinuousPlotDistMixin, SliceMixin):
     def dist(self):
         return stats.expon(scale=self.lam)
 
+    def __mul__(self, other):
+        return Gamma(alpha=other, beta=1 / self.lam)
+
+    __rmul__ = __mul__
+
 
 @dataclass
 class Gamma(ContinuousPlotDistMixin, SliceMixin):

conjugate/plot.py

@@ -35,7 +35,7 @@ def resolve_label(label: LABEL_INPUT, yy: np.ndarray):
             return [f"{label} {i}" for i in range(1, ncols + 1)]
 
         if callable(label):
-            return [label(i) for i in range(1, ncols + 1)]
+            return [label(i) for i in range(ncols)]
 
         if isinstance(label, Iterable):
             return label
@@ -68,6 +68,27 @@ def set_max_value(self, value: float) -> "PlotDistMixin":
 
         return self
 
+    @property
+    def min_value(self) -> float:
+        if not hasattr(self, "_min_value"):
+            self._min_value = 0.0
+
+        return self._min_value
+
+    @min_value.setter
+    def min_value(self, value: float) -> None:
+        self._min_value = value
+
+    def set_min_value(self, value: float) -> "PlotDistMixin":
+        """Set the minimum value for plotting."""
+        self.min_value = value
+
+        return self
+
+    def set_bounds(self, lower: float, upper: float) -> "PlotDistMixin":
+        """Set both the min and max values for plotting."""
+        return self.set_min_value(lower).set_max_value(upper)
+
     def _reshape_x_values(self, x: np.ndarray) -> np.ndarray:
         """Make sure that the values are ready for plotting."""
         for value in asdict(self).values():
@@ -101,27 +122,6 @@ def plot_pdf(self, ax: Optional[plt.Axes] = None, **kwargs) -> plt.Axes:
 
         return self._create_plot_on_axis(x, ax, **kwargs)
 
-    @property
-    def min_value(self) -> float:
-        if not hasattr(self, "_min_value"):
-            self._min_value = 0.0
-
-        return self._min_value
-
-    @min_value.setter
-    def min_value(self, value: float) -> None:
-        self._min_value = value
-
-    def set_min_value(self, value: float) -> "ContinuousPlotDistMixin":
-        """Set the minimum value for plotting."""
-        self.min_value = value
-
-        return self
-
-    def set_bounds(self, lower: float, upper: float) -> "ContinuousPlotDistMixin":
-        """Set both the min and max values for plotting."""
-        return self.set_min_value(lower).set_max_value(upper)
-
     def _create_x_values(self) -> np.ndarray:
         return np.linspace(self.min_value, self.max_value, 100)
 
@@ -180,21 +180,33 @@ def plot_pmf(
         return self._create_plot_on_axis(x, ax, mark, **kwargs)
 
     def _create_x_values(self) -> np.ndarray:
-        return np.arange(0, self.max_value + 1, 1)
+        return np.arange(self.min_value, self.max_value + 1, 1)
 
-    def _create_plot_on_axis(self, x, ax, mark, **kwargs) -> plt.Axes:
+    def _create_plot_on_axis(
+        self, x, ax, mark, conditional: bool = False, **kwargs
+    ) -> plt.Axes:
         yy = self.dist.pmf(x)
+        if conditional:
+            yy = yy / np.sum(yy)
+            ylabel = f"Conditional Probability $f(x|{self.min_value} \\leq x \\leq {self.max_value})$"
+        else:
+            ylabel = "Probability $f(x)$"
+
         if "label" in kwargs:
             label = kwargs.pop("label")
             label = resolve_label(label, yy)
         else:
             label = None
 
         ax.plot(x, yy, mark, label=label, **kwargs)
-        if self.max_value <= 15:
+
+        if self.max_value - self.min_value < 15:
             ax.set_xticks(x.ravel())
+        else:
+            ax.set_xticks(x.ravel(), minor=True)
+            ax.set_xticks(x[::5].ravel())
 
         ax.set_xlabel("Domain")
-        ax.set_ylabel("Probability $f(x)$")
+        ax.set_ylabel(ylabel)
         ax.set_ylim(0, None)
         return ax

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "conjugate-models"
-version = "0.1.2"
+version = "0.1.3"
 description = "Bayesian Conjugate Models in Python"
 authors = ["Will Dean <wd60622@gmail.com>"]
 license = "MIT"

tests/test_distributions.py

@@ -8,6 +8,7 @@
     Beta,
     Dirichlet,
     Gamma,
+    Exponential,
     NegativeBinomial,
     Poisson,
 )
@@ -76,14 +77,15 @@ def test_slicing(alpha, beta, result_alpha, result_beta):
 
 
 @pytest.mark.parametrize(
-    "dist, result_int",
+    "dist, result_dist",
     [
         (Poisson(1), Poisson(2)),
         (Gamma(1, 1), Gamma(2, 1)),
         (NegativeBinomial(1, 1), NegativeBinomial(2, 1)),
+        (Exponential(1), Gamma(2, 1)),
     ],
 )
-def test_distribution(dist, result_int) -> None:
+def test_distribution(dist, result_dist) -> None:
     if hasattr(dist, "plot_pdf"):
         with pytest.raises(ValueError):
             dist.plot_pdf()
@@ -100,8 +102,19 @@ def test_distribution(dist, result_int) -> None:
         ax = dist.plot_pmf()
     assert isinstance(ax, plt.Axes)
 
-    other = 2 * dist
-    assert other == result_int
+    if result_dist is not None:
+        other = 2 * dist
+        assert other == result_dist
+
+    lower, upper = -20, 20
+
+    assert dist.min_value != lower
+    assert dist.max_value != upper
+
+    dist.set_bounds(lower, upper)
+
+    assert dist.min_value == lower
+    assert dist.max_value == upper
 
 
 @pytest.mark.parametrize(

tests/test_plot.py

@@ -14,7 +14,7 @@
         ("label", np.ones(shape=(3, 2)), ["label 1", "label 2"]),
         ("label", np.ones(shape=(2, 3)), ["label 1", "label 2", "label 3"]),
         (
-            lambda i: f"another {i} label",
+            lambda i: f"another {i + 1} label",
             np.ones(shape=(2, 3)),
             ["another 1 label", "another 2 label", "another 3 label"],
         ),