Add new tests, modify warnings

Author: Jacobluke-

dabest/_dabest_object.py

@@ -7,6 +7,7 @@
 
 # %% ../nbs/API/dabest_object.ipynb 5
 # Import standard data science libraries
+import warnings
 from numpy import array, repeat, random, issubdtype, number
 import numpy as np
 import pandas as pd
@@ -62,7 +63,6 @@ def __init__(
 
         # Check if there is NaN under any of the paired settings
         if self.__is_paired and self.__output_data.isnull().values.any():
-            import warnings
             warn1 = f"NaN values detected under paired setting and removed,"
             warn2 = f" please check your data."
             warnings.warn(warn1 + warn2)
@@ -500,7 +500,10 @@ def _check_errors(self, x, y, idx, experiment, experiment_label, x1_level):
             if x is None:
                 error_msg = "If `delta2` is True. `x` parameter cannot be None. String or list expected"
                 raise ValueError(error_msg)
-                
+            
+            if self.__proportional:
+                mes1 = "Only mean_diff is supported for proportional data when `delta2` is True"
+                warnings.warn(message=mes1, category=UserWarning)
 
             # idx should not be specified
             if idx:
@@ -578,8 +581,6 @@ def _get_plot_data(self, x, y, all_plot_groups):
         """
         # Check if there is NaN under any of the paired settings
         if self.__is_paired is not None and self.__output_data.isnull().values.any():
-            print("Nan")
-            import warnings
             warn1 = f"NaN values detected under paired setting and removed,"
             warn2 = f" please check your data."
             warnings.warn(warn1 + warn2)
@@ -631,7 +632,6 @@ def _get_plot_data(self, x, y, all_plot_groups):
 
             # Check if there is NaN under any of the paired settings
             if self.__is_paired is not None and self.__output_data.isnull().values.any():
-                import warnings
                 warn1 = f"NaN values detected under paired setting and removed,"
                 warn2 = f" please check your data."
                 warnings.warn(warn1 + warn2)

dabest/_effsize_objects.py

@@ -257,7 +257,8 @@ def _check_errors(self, control, test):
             raise ValueError(err1)
 
         if self.__proportional and self.__effect_size not in ["mean_diff", "cohens_h"]:
-            err1 = "`proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined."
+            err1 = "`proportional` is True; therefore effect size other than mean_diff and cohens_h is not defined." + \
+                    "If you are calculating deltas' g, it's the same as delta-delta when `proportional` is True"
             raise ValueError(err1)
 
         if self.__proportional and (

nbs/tests/mpl_image_tests/test_07_delta-delta_plots.py

@@ -26,6 +26,9 @@ def create_demo_dataset_delta(seed=9999, N=20):
     y = norm.rvs(loc=3, scale=0.4, size=N*4)
     y[N:2*N] = y[N:2*N]+1
     y[2*N:3*N] = y[2*N:3*N]-0.5
+    ind = np.random.binomial(1, 0.5, size=N*4)
+    ind[N:2*N] = np.random.binomial(1, 0.2, size=N)
+    ind[2*N:3*N] = np.random.binomial(1, 0.7, size=N)
 
     # Add drug column
     t1 = np.repeat('Placebo', N*2).tolist()
@@ -54,10 +57,11 @@ def create_demo_dataset_delta(seed=9999, N=20):
 
     # Combine all columns into a DataFrame.
     df = pd.DataFrame({'ID'        : id_col,
-                      'Rep'      : rep,
+                      'Rep'        : rep,
                        'Genotype'  : genotype, 
-                       'Treatment': treatment,
-                       'Y'         : y
+                       'Treatment' : treatment,
+                       'Y'         : y,
+                       'Cat'       :ind
                     })
     return df
 
@@ -81,6 +85,34 @@ def create_demo_dataset_delta(seed=9999, N=20):
                 experiment = "Genotype",
                 paired="sequential", id_col="ID")
 
+unpaired_prop = load(data = df, proportional=True,
+                            # id_col="index", paired='baseline', 
+                            x = ["Genotype", "Genotype"], 
+                            y = "Cat", delta2=True,
+                            experiment="Treatment",)
+
+unpaired_specified_prop = load(data = df, proportional=True,
+                            # id_col="index", paired='baseline', 
+                            x = ["Genotype", "Genotype"], 
+                            y = "Cat", delta2=True,
+                            experiment="Treatment",
+                            experiment_label = ["Drug", "Placebo"],
+                            x1_level = ["M", "W"])
+
+paired_prop = load(data = df, proportional=True,
+                            id_col="ID", paired='baseline', 
+                            x = ["Genotype", "Genotype"], 
+                            y = "Cat", delta2=True,
+                            experiment="Treatment",)
+
+paired_specified_prop = load(data = df, proportional=True,
+                            id_col="ID", paired='baseline', 
+                            x = ["Genotype", "Genotype"], 
+                            y = "Cat", delta2=True,
+                            experiment="Treatment",
+                            experiment_label = ["Drug", "Placebo"],
+                            x1_level = ["M", "W"])
+
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_47_cummings_unpaired_delta_delta_meandiff():
@@ -164,4 +196,20 @@ def test_72_sequential_delta_g():
 
 @pytest.mark.mpl_image_compare(tolerance=8)
 def test_73_baseline_delta_g():
-    return baseline.mean_diff.plot();
+    return baseline.mean_diff.plot();
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_74_unpaired_prop_delta2():
+    return unpaired_prop.mean_diff.plot()
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_75_unpaired_specified_prop_delta2():
+    return unpaired_specified_prop.mean_diff.plot()
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_76_paired_prop_delta2():
+    return paired_prop.mean_diff.plot()
+
+@pytest.mark.mpl_image_compare(tolerance=8)
+def test_77_paired_specified_prop_delta2():
+    return paired_specified_prop.mean_diff.plot()

nbs/tests/test_load_errors.py

@@ -35,18 +35,6 @@ def test_wrong_params_combinations():
 
     assert error_msg in str(excinfo.value)
 
-    error_msg = "`proportional` and `delta2` cannot be True at the same time."
-    with pytest.raises(ValueError) as excinfo:
-        my_data = load(
-            dummy_df,
-            x=["Control 1", "Control 1"],
-            y="Test 1",
-            delta2=True,
-            proportional=True
-        )
-
-    assert error_msg in str(excinfo.value)
-
     error_msg = "`idx` should not be specified when `delta2` is True.".format(N)
     with pytest.raises(ValueError) as excinfo:
         my_data = load(