Add unit tests, test data for forestplot

Author: Lucas1213WZY

dabest/forest_plot.py

@@ -45,7 +45,9 @@ def load_plot_data(
     contrast_attr = contrast_attr_map.get(contrast_type)
 
     if not effect_attr:
-        raise ValueError(f"Invalid effect_size: {effect_size}")
+        raise ValueError(f"Invalid effect_size: {effect_size}") 
+    if not contrast_attr:
+        raise ValueError(f"Invalid contrast_type: {contrast_type}. Available options: [`delta2`, `mini_meta`]")
 
     return [
         getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in contrasts
@@ -150,12 +152,63 @@ def forest_plot(
     from .plot_tools import halfviolin
 
     # Validate inputs
-    if not contrasts:
-        raise ValueError("The `contrasts` list cannot be empty.")
+    if contrasts is None:
+        raise ValueError("The `contrasts` parameter cannot be None")
+    
+    if not isinstance(contrasts, list) or not contrasts:
+        raise ValueError("The `contrasts` argument must be a non-empty list.")
+    
+    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`.")
+    
+    if not isinstance(contrast_type, str):
+        raise TypeError("The `contrast_type` argument must be a string.")
+    
+    if xticklabels is not None and not all(isinstance(label, str) for label in xticklabels):
+        raise TypeError("The `xticklabels` must be a list of strings or `None`.")
+    
+    if not isinstance(effect_size, str):
+        raise TypeError("The `effect_size` argument must be a string.")
+    
+    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`.")
 
     if contrast_labels is not None and len(contrast_labels) != len(contrasts):
         raise ValueError("`contrast_labels` must match the number of `contrasts` if provided.")
 
+    if not isinstance(ylabel, str):
+        raise TypeError("The `ylabel` argument must be a string.")
+    
+    if custom_palette is not None and not isinstance(custom_palette, (dict, list, str, type(None))):
+        raise TypeError("The `custom_palette` must be either a dictionary, list, string, or `None`.")
+    
+    if not isinstance(fontsize, (int, float)):
+        raise TypeError("`fontsize` must be an integer or float.")
+    
+    if not isinstance(marker_size, (int, float)) or marker_size <= 0:
+        raise TypeError("`marker_size` must be a positive integer or float.")
+    
+    if not isinstance(ci_line_width, (int, float)) or ci_line_width <= 0:
+        raise TypeError("`ci_line_width` must be a positive integer or float.")
+    
+    if not isinstance(zero_line_width, (int, float)) or zero_line_width <= 0:
+        raise TypeError("`zero_line_width` must be a positive integer or float.")
+    
+    if not isinstance(remove_spines, bool):
+        raise TypeError("`remove_spines` must be a boolean value.")
+    
+    if ax is not None and not isinstance(ax, plt.Axes):
+        raise TypeError("`ax` must be a `matplotlib.axes.Axes` instance or `None`.")
+    
+    if not isinstance(rotation_for_xlabels, (int, float)) or not 0 <= rotation_for_xlabels <= 360:
+        raise TypeError("`rotation_for_xlabels` must be an integer or float between 0 and 360.")
+    
+    if not isinstance(alpha_violin_plot, float) or not 0 <= alpha_violin_plot <= 1:
+        raise TypeError("`alpha_violin_plot` must be a float between 0 and 1.")
+    
+    if not isinstance(horizontal, bool):
+        raise TypeError("`horizontal` must be a boolean value.")
+
     # Load plot data
     contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type)
 

nbs/API/forest_plot.ipynb

@@ -106,7 +106,9 @@
     "    contrast_attr = contrast_attr_map.get(contrast_type)\n",
     "\n",
     "    if not effect_attr:\n",
-    "        raise ValueError(f\"Invalid effect_size: {effect_size}\")\n",
+    "        raise ValueError(f\"Invalid effect_size: {effect_size}\") \n",
+    "    if not contrast_attr:\n",
+    "        raise ValueError(f\"Invalid contrast_type: {contrast_type}. Available options: [`delta2`, `mini_meta`]\")\n",
     "\n",
     "    return [\n",
     "        getattr(getattr(contrast, effect_attr), contrast_attr) for contrast in contrasts\n",
@@ -211,12 +213,63 @@
     "    from .plot_tools import halfviolin\n",
     "\n",
     "    # Validate inputs\n",
-    "    if not contrasts:\n",
-    "        raise ValueError(\"The `contrasts` list cannot be empty.\")\n",
+    "    if contrasts is None:\n",
+    "        raise ValueError(\"The `contrasts` parameter cannot be None\")\n",
+    "    \n",
+    "    if not isinstance(contrasts, list) or not contrasts:\n",
+    "        raise ValueError(\"The `contrasts` argument must be a non-empty list.\")\n",
+    "    \n",
+    "    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",
+    "    if not isinstance(contrast_type, str):\n",
+    "        raise TypeError(\"The `contrast_type` argument must be a string.\")\n",
+    "    \n",
+    "    if xticklabels is not None and not all(isinstance(label, str) for label in xticklabels):\n",
+    "        raise TypeError(\"The `xticklabels` must be a list of strings or `None`.\")\n",
+    "    \n",
+    "    if not isinstance(effect_size, str):\n",
+    "        raise TypeError(\"The `effect_size` argument must be a string.\")\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",
     "    \n",
     "    if contrast_labels is not None and len(contrast_labels) != len(contrasts):\n",
     "        raise ValueError(\"`contrast_labels` must match the number of `contrasts` if provided.\")\n",
     "    \n",
+    "    if not isinstance(ylabel, str):\n",
+    "        raise TypeError(\"The `ylabel` argument must be a string.\")\n",
+    "    \n",
+    "    if custom_palette is not None and not isinstance(custom_palette, (dict, list, str, type(None))):\n",
+    "        raise TypeError(\"The `custom_palette` must be either a dictionary, list, string, or `None`.\")\n",
+    "    \n",
+    "    if not isinstance(fontsize, (int, float)):\n",
+    "        raise TypeError(\"`fontsize` must be an integer or float.\")\n",
+    "    \n",
+    "    if not isinstance(marker_size, (int, float)) or marker_size <= 0:\n",
+    "        raise TypeError(\"`marker_size` must be a positive integer or float.\")\n",
+    "    \n",
+    "    if not isinstance(ci_line_width, (int, float)) or ci_line_width <= 0:\n",
+    "        raise TypeError(\"`ci_line_width` must be a positive integer or float.\")\n",
+    "    \n",
+    "    if not isinstance(zero_line_width, (int, float)) or zero_line_width <= 0:\n",
+    "        raise TypeError(\"`zero_line_width` must be a positive integer or float.\")\n",
+    "    \n",
+    "    if not isinstance(remove_spines, bool):\n",
+    "        raise TypeError(\"`remove_spines` must be a boolean value.\")\n",
+    "    \n",
+    "    if ax is not None and not isinstance(ax, plt.Axes):\n",
+    "        raise TypeError(\"`ax` must be a `matplotlib.axes.Axes` instance or `None`.\")\n",
+    "    \n",
+    "    if not isinstance(rotation_for_xlabels, (int, float)) or not 0 <= rotation_for_xlabels <= 360:\n",
+    "        raise TypeError(\"`rotation_for_xlabels` must be an integer or float between 0 and 360.\")\n",
+    "    \n",
+    "    if not isinstance(alpha_violin_plot, float) or not 0 <= alpha_violin_plot <= 1:\n",
+    "        raise TypeError(\"`alpha_violin_plot` must be a float between 0 and 1.\")\n",
+    "    \n",
+    "    if not isinstance(horizontal, bool):\n",
+    "        raise TypeError(\"`horizontal` must be a boolean value.\")\n",
+    "\n",
     "    # Load plot data\n",
     "    contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type)\n",
     "\n",
@@ -307,6 +360,26 @@
     "\n",
     "    return fig"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not []"
+   ]
   }
  ],
  "metadata": {

nbs/tests/data/mocked_data_test_forestplot.py

@@ -0,0 +1,60 @@
+import pandas as pd
+import scipy as sp
+import numpy as np
+import matplotlib.pyplot as plt
+from numpy import random
+from scipy.stats import norm
+import dabest 
+
+np.random.seed(9999)  # Set the seed for reproducibility
+N=20
+# Create samples
+y = norm.rvs(loc=3, scale=0.4, size=N*4)
+y[N:2*N] += 1
+y[2*N:3*N] -= 0.5
+
+# Treatment, Rep, Genotype, and ID columns
+treatment = np.repeat(['Placebo', 'Drug'], N*2).tolist()
+rep = ['Rep1', 'Rep2'] * (N*2)
+genotype = np.repeat(['W', 'M', 'W', 'M'], N).tolist()
+id_col = list(range(0, N*2)) * 2
+
+    # Combine all columns into a DataFrame
+dummy_df = pd.DataFrame({
+    'ID': id_col,
+    'Rep': rep,
+    'Genotype': genotype,
+    'Treatment': treatment,
+    'Y': y
+})
+
+unpaired_delta_01 = dabest.load(data = dummy_df, 
+                                x = ["Genotype", "Genotype"], 
+                                y = "Y", delta2 = True, 
+                                experiment = "Treatment")
+
+dummy_contrasts = [unpaired_delta_01]
+
+# Default forestplot params for unit testing
+default_forestplot_kwargs = {
+    "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.
+    "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.
+    "horizontal": False,  # Ensure it's a boolean.
+}

nbs/tests/test_forest_plot.py

@@ -0,0 +1,47 @@
+import pytest
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from dabest.forest_plot import load_plot_data, extract_plot_data, forest_plot
+from data.mocked_data_test_forestplot import dummy_contrasts, default_forestplot_kwargs
+
+def test_forest_plot_no_input_parameters():
+    error_msg = "The `contrasts` parameter cannot be None"
+    with pytest.raises(ValueError) as excinfo:
+        forest_plot(contrasts = None)
+    
+    assert error_msg in str(excinfo.value)
+
+@pytest.mark.parametrize("param_name, param_value, error_msg, error_type", [
+    ("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.", TypeError),
+    ("xticklabels", [123, 456], "The `xticklabels` must be a list of strings or `None`.", TypeError),
+    ("effect_size", 456, "The `effect_size` argument must be a string.", 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),
+    ("fontsize", "big", "`fontsize` must be an integer or float.", TypeError),
+    ("marker_size", "large", "`marker_size` must be a positive integer or float.", TypeError),
+    ("ci_line_width", "thick", "`ci_line_width` must be a positive integer or float.", TypeError),
+    ("zero_line_width", "thin", "`zero_line_width` must be a positive integer or float.", TypeError),
+    ("remove_spines", "yes", "`remove_spines` must be a boolean value.", TypeError),
+    ("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()
+
+    # Replace the tested parameter with the invalid value
+    valid_inputs[param_name] = param_value
+
+    # Perform the test
+    with pytest.raises(error_type) as excinfo:
+        forest_plot(**valid_inputs)
+    
+    # Check the error message
+    assert error_msg in str(excinfo.value)