feat: adjusted test messages, forest_plot function

dabest/_modidx.py

@@ -64,7 +64,9 @@
             'dabest.forest_plot': { 'dabest.forest_plot.extract_plot_data': ( 'API/forest_plot.html#extract_plot_data',
                                                                               'dabest/forest_plot.py'),
                                     'dabest.forest_plot.forest_plot': ('API/forest_plot.html#forest_plot', 'dabest/forest_plot.py'),
-                                    'dabest.forest_plot.load_plot_data': ('API/forest_plot.html#load_plot_data', 'dabest/forest_plot.py')},
+                                    'dabest.forest_plot.load_plot_data': ('API/forest_plot.html#load_plot_data', 'dabest/forest_plot.py'),
+                                    'dabest.forest_plot.map_effect_attribute': ( 'API/forest_plot.html#map_effect_attribute',
+                                                                                 'dabest/forest_plot.py')},
             'dabest.misc_tools': { 'dabest.misc_tools.get_varname': ('API/misc_tools.html#get_varname', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.merge_two_dicts': ('API/misc_tools.html#merge_two_dicts', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.print_greeting': ('API/misc_tools.html#print_greeting', 'dabest/misc_tools.py'),

dabest/forest_plot.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/forest_plot.ipynb.
 
 # %% auto 0
-__all__ = ['load_plot_data', 'extract_plot_data', 'forest_plot']
+__all__ = ['load_plot_data', 'extract_plot_data', 'map_effect_attribute', 'forest_plot']
 
 # %% ../nbs/API/forest_plot.ipynb 5
 import matplotlib.pyplot as plt
@@ -72,14 +72,28 @@ def extract_plot_data(contrast_plot_data, contrast_type):
 
     return bootstraps, differences, bcalows, bcahighs
 
+def map_effect_attribute(attribute_key):
+    # Check if the attribute key exists in the dictionary
+    effect_attr_map = {
+        "mean_diff": "Mean Difference",
+        "median_diff": "Median Difference",
+        "cliffs_delta": "Cliffs Delta",
+        "cohens_d": "Cohens d",
+        "hedges_g": "Hedges g",
+        "delta_g": "Delta g"
+    }
+    if attribute_key in effect_attr_map:
+        return effect_attr_map[attribute_key]
+    else:
+        raise TypeError("The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`,`median_diff`,`cliffs_delta`,`cohens_d``, and `hedges_g`.")  # Return a default value or message if the key is not found
 
 def forest_plot(
     contrasts: List,
     selected_indices: Optional[List] = None,
     contrast_type: str = "delta2",
     effect_size: str = "mean_diff",
     contrast_labels: List[str] = None,
-    ylabel: str = "value",
+    ylabel: str = "effect size",
     plot_elements_to_extract: Optional[List] = None,
     title: str = "ΔΔ Forest",
     custom_palette: Optional[Union[dict, list, str]] = None,
@@ -147,7 +161,7 @@ def forest_plot(
     plt.Figure
         The matplotlib figure object with the generated forest plot.
     """
-    from dabest.plot_tools import halfviolin
+    from .plot_tools import halfviolin
 
     # Validate inputs
     if contrasts is None:
@@ -159,11 +173,13 @@ def forest_plot(
     if selected_indices is not None and not isinstance(selected_indices, (list, type(None))):
         raise TypeError("The `selected_indices` must be a list of integers or `None`.")
 
+    # For the 'contrast_type' parameter
     if not isinstance(contrast_type, str):
-        raise TypeError("The `contrast_type` argument must be a string. Please choose from `delta2` and `minimeta`.")
-
+        raise TypeError("The `contrast_type` argument must be a string. Please choose from `delta2` and `mini_meta`.")
+
+    # For the 'effect_size' parameter
     if not isinstance(effect_size, str):
-        raise TypeError("The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`,`median_diff`,`cliffs_delta`,`cohens_d``, and `hedges_g`.")
+        raise TypeError("The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`, `median_diff`, `cliffs_delta`, `cohens_d`, and `hedges_g`.")
 
     if contrast_labels is not None and not all(isinstance(label, str) for label in contrast_labels):
         raise TypeError("The `contrast_labels` must be a list of strings or `None`.")
@@ -201,6 +217,8 @@ def forest_plot(
     if not isinstance(horizontal, bool):
         raise TypeError("`horizontal` must be a boolean value.")
 
+    if (effect_size and isinstance(effect_size, str)):
+        ylabel = map_effect_attribute(effect_size)
     # Load plot data
     contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type)
 
@@ -280,17 +298,24 @@ def forest_plot(
         ax.set_yticks(range(1, len(contrasts) + 1))
         ax.set_yticklabels(contrast_labels,  rotation=0, fontsize=fontsize)
         ax.set_xlabel(ylabel, fontsize=fontsize)
+        ax.set_ylim([0.7, len(contrasts) + 0.5])
     else:
         ax.set_xticks(range(1, len(contrasts) + 1))
         ax.set_xticklabels(contrast_labels, rotation=rotation_for_xlabels, fontsize=fontsize)
         ax.set_ylabel(ylabel, fontsize=fontsize)
+        ax.set_xlim([0.7, len(contrasts) + 0.5])
 
     # Setting the title and adjusting spines as before
     ax.set_title(title, fontsize=title_font_size)
     if remove_spines:
-        for spine in ax.spines.values():
-            spine.set_visible(False)
-
+        if horizontal:
+            ax.spines['left'].set_visible(False)
+            ax.spines['right'].set_visible(False)
+            ax.spines['top'].set_visible(False)
+        else:
+            ax.spines['top'].set_visible(False)
+            ax.spines['bottom'].set_visible(False)
+            ax.spines['right'].set_visible(False)
     # Apply additional customizations if provided
     if additional_plotting_kwargs:
         ax.set(**additional_plotting_kwargs)

nbs/API/forest_plot.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +154,7 @@
     "    contrast_type: str = \"delta2\",\n",
     "    effect_size: str = \"mean_diff\",\n",
     "    contrast_labels: List[str] = None,\n",
-    "    ylabel: str = \"effect_size\",\n",
+    "    ylabel: str = \"effect size\",\n",
     "    plot_elements_to_extract: Optional[List] = None,\n",
     "    title: str = \"ΔΔ Forest\",\n",
     "    custom_palette: Optional[Union[dict, list, str]] = None,\n",
@@ -234,11 +234,13 @@
     "    if selected_indices is not None and not isinstance(selected_indices, (list, type(None))):\n",
     "        raise TypeError(\"The `selected_indices` must be a list of integers or `None`.\")\n",
     "    \n",
+    "    # For the 'contrast_type' parameter\n",
     "    if not isinstance(contrast_type, str):\n",
-    "        raise TypeError(\"The `contrast_type` argument must be a string. Please choose from `delta2` and `minimeta`.\")\n",
-    "    \n",
+    "        raise TypeError(\"The `contrast_type` argument must be a string. Please choose from `delta2` and `mini_meta`.\")\n",
+    "\n",
+    "    # For the 'effect_size' parameter\n",
     "    if not isinstance(effect_size, str):\n",
-    "        raise TypeError(\"The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`,`median_diff`,`cliffs_delta`,`cohens_d``, and `hedges_g`.\")\n",
+    "        raise TypeError(\"The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`, `median_diff`, `cliffs_delta`, `cohens_d`, and `hedges_g`.\")\n",
     "    \n",
     "    if contrast_labels is not None and not all(isinstance(label, str) for label in contrast_labels):\n",
     "        raise TypeError(\"The `contrast_labels` must be a list of strings or `None`.\")\n",
@@ -388,18 +390,6 @@
    "display_name": "python3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.6"
   }
  },
  "nbformat": 4,

nbs/tests/data/mocked_data_test_forestplot.py

@@ -40,21 +40,19 @@
     "contrasts": dummy_contrasts,  # Ensure this is a list of contrast objects.
     "selected_indices": None,  # Valid as None or a list of integers.
     "contrast_type": "delta2",  # Ensure it's a string and one of the allowed contrast types.
-    "xticklabels": None,  # Valid as None or a list of strings.
     "effect_size": "mean_diff",  # Ensure it's a string.
     "contrast_labels": ["Drug1"],  # This should be a list of strings.
     "ylabel": "Effect Size",  # Ensure it's a string.
-    "plot_elements_to_extract": None,  # No specific checks needed based on your tests.
-    "title": "ΔΔ Forest Plot",  # Ensure it's a string.
+    #"plot_elements_to_extract": None,  # No specific checks needed based on your tests.
+    #"title": "ΔΔ Forest Plot",  # Ensure it's a string.
     "custom_palette": None,  # Valid as None, a dictionary, list, or string.
     "fontsize": 20,  # Ensure it's an integer or float.
     "violin_kwargs": None,  # No specific checks needed based on your tests.
     "marker_size": 20,  # Ensure it's a positive integer or float.
     "ci_line_width": 2.5,  # Ensure it's a positive integer or float.
-    "zero_line_width": 1,  # Ensure it's a positive integer or float.
     "remove_spines": True,  # Ensure it's a boolean.
     "additional_plotting_kwargs": None,  # No specific checks needed based on your tests.
     "rotation_for_xlabels": 45,  # Ensure it's an integer or float between 0 and 360.
-    "alpha_violin_plot": 0.4,  # Ensure it's a float between 0 and 1.
+    "alpha_violin_plot": 0.8,  # Ensure it's a float between 0 and 1.
     "horizontal": False,  # Ensure it's a boolean.
 }

nbs/tests/test_forest_plot.py

@@ -16,8 +16,8 @@ def test_forest_plot_no_input_parameters():
     ("contrasts", None, "The `contrasts` parameter cannot be None", ValueError),
     ("contrasts", [], "The `contrasts` argument must be a non-empty list.", ValueError),
     ("selected_indices", "not a list or None", "The `selected_indices` must be a list of integers or `None`.", TypeError),
-    ("contrast_type", 123, "The `contrast_type` argument must be a string. Please choose from `delta2` and `minimeta`.", TypeError),
-    ("effect_size", 456, "The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`,`median_diff`,`cliffs_delta`,`cohens_d``, and `hedges_g`.", TypeError),
+    ("contrast_type", 123, "The `contrast_type` argument must be a string. Please choose from `delta2` and `mini_meta`.", TypeError),
+    ("effect_size", 456, "The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`, `median_diff`, `cliffs_delta`, `cohens_d`, and `hedges_g`.", TypeError),
     ("contrast_labels", ["valid", 123], "The `contrast_labels` must be a list of strings or `None`.", TypeError),
     ("ylabel", 789, "The `ylabel` argument must be a string.", TypeError),
     ("custom_palette", 123, "The `custom_palette` must be either a dictionary, list, string, or `None`.", TypeError),
@@ -28,8 +28,8 @@ def test_forest_plot_no_input_parameters():
     ("rotation_for_xlabels", "right", "`rotation_for_xlabels` must be an integer or float between 0 and 360.", TypeError),
     ("alpha_violin_plot", "opaque", "`alpha_violin_plot` must be a float between 0 and 1.", TypeError),
     ("horizontal", "sideways", "`horizontal` must be a boolean value.", TypeError),
-    ("contrast_type", "unknown", "Invalid contrast_type: unknown. Available options: [`delta2`, `mini_meta`]", ValueError),
 ])
+
 def test_forest_plot_input_error_handling(param_name, param_value, error_msg, error_type):
     # Setup: Define a base set of valid inputs to forest_plot
     valid_inputs = default_forestplot_kwargs.copy()