Adjust delta-delta plot for paired data

- support plots for paired data
- add a slight change to the test cases
- restrict the delta-delta plots for only mean_diff

Author: LI-Yixuan

dabest/_api.py

@@ -6,7 +6,8 @@
 
 def load(data, idx=None, x=None, y=None, paired=None, id_col=None,
         ci=95, resamples=5000, random_seed=12345, proportional=False, 
-        delta2 = False, experiment = None):
+        delta2 = False, experiment = None, experiment_label = None,
+        x1_level = None):
     '''
     Loads data in preparation for estimation statistics.
 
@@ -82,4 +83,4 @@ def load(data, idx=None, x=None, y=None, paired=None, id_col=None,
     '''
     from ._classes import Dabest
 
-    return Dabest(data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment)
+    return Dabest(data, idx, x, y, paired, id_col, ci, resamples, random_seed, proportional, delta2, experiment, experiment_label, x1_level)

dabest/_classes.py

@@ -9,8 +9,9 @@ class Dabest(object):
     Class for estimation statistics and plots.
     """
 
-    def __init__(self, data, idx, x, y, paired, id_col, ci, resamples,
-                random_seed, proportional, delta2, experiment):
+    def __init__(self, data, idx, x, y, paired, id_col, ci, 
+                resamples, random_seed, proportional, delta2, 
+                experiment, experiment_label, x1_level):
 
         """
         Parses and stores pandas DataFrames in preparation for estimation
@@ -40,21 +41,65 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples,
 
 
 
-        # check if this is a 2x2 ANOVA case and x & y are valid columns:
+        # check if this is a 2x2 ANOVA case and x & y are valid columns
+        # create experiment_label and x1_level
         if delta2:
+            # idx should not be specified
+            if idx:
+                err0 = '`idx` should not be specified when `delta2` is True.'.format(len(x))
+                raise ValueError(err0)
+
+            # check if x is valid
             if len(x) != 2:
                 err0 = '`delta2` is True but the number of variables indicated by `x` is {}.'.format(len(x))
                 raise ValueError(err0)
-            if any(i not in data_in.columns for i in x):
-                err = 'Not all of {0} is a column in `data`. Please check.'.format(x)
-                raise IndexError(err)
-            if y not in data_in.columns:
+            else:
+                for i in x:
+                    if i not in data_in.columns:
+                        err = '{0} is a column in `data`. Please check.'.format(i)
+                        raise IndexError(err)
+
+            # check if y is valid
+            if not y:
+                err0 = '`delta2` is True but `y` is not indicated.'
+                raise ValueError(err0)
+            elif y not in data_in.columns:
                 err = '{0} is not a column in `data`. Please check.'.format(y)
                 raise IndexError(err)
+
+            # check if experiment is valid
             if experiment not in data_in.columns:
                 err = '{0} is not a column in `data`. Please check.'.format(experiment)
                 raise IndexError(err)
 
+            # check if experiment_label is valid and create experiment when needed
+            if experiment_label:
+                if len(experiment_label) != 2:
+                    err0 = '`experiment_label` does not have a length of 2.'
+                    raise ValueError(err0)
+                else: 
+                    for i in experiment_label:
+                        if i not in data_in[experiment].unique():
+                            err = '{0} is an element in the column `{1}` of `data`. Please check.'.format(i, experiment)
+                            raise IndexError(err)
+            else:
+                experiment_label = data_in[experiment].unique()
+
+            # check if x1_level is valid
+            if x1_level:
+                if len(x1_level) != 2:
+                    err0 = '`x1_level` does not have a length of 2.'
+                    raise ValueError(err0)
+                else: 
+                    for i in x1_level:
+                        if i not in data_in[x[0]].unique():
+                            err = '{0} is an element in the column `{1}` of `data`. Please check.'.format(i, experiment)
+                            raise IndexError(err)
+
+            else:
+                x1_level = data_in[x[0]].unique()    
+        self.__experiment_label = experiment_label
+        self.__x1_level         = x1_level
 
 
         # check if idx is specified
@@ -72,26 +117,21 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples,
             data_in[new_col_name] = data_in[x[0]].apply(lambda x: str(x)) + " " + data_in[experiment].apply(lambda x: str(x))
 
             #create idx            
-            experiment = data_in[experiment].unique()
-            x1_level = data_in[x[0]].unique()
             idx = []
-            for i in experiment:
+            for i in experiment_label:
                 temp = []
                 for j in x1_level:
                     temp.append(j + " " + i)
                 idx.append(temp)         
-            self.__idx        = idx
-            self.__first      = x1_level
-            self.__experiment = experiment
+            self.__idx = idx
+            self.__x1  = x[0]
+            self.__x2  = x[1]
             # record the second variable and create idx
-            self.__second     = x[1]
-            x                 = new_col_name
-
+            x = new_col_name
         else:
-            self.__second     = None
-            self.__idx        = idx
-            self.__first      = None
-            self.__experiment = None
+            self.__idx = idx
+            self.__x1  = None
+            self.__x2  = None
 
         # Determine the kind of estimation plot we need to produce.
         if all([isinstance(i, str) for i in idx]):
@@ -256,9 +296,9 @@ def __init__(self, data, idx, x, y, paired, id_col, ci, resamples,
                                            resamples=resamples,
                                            proportional=proportional, 
                                            delta2=delta2, 
-                                           experiment = self.__experiment,
-                                           first = self.__first,
-                                           second=self.__second)
+                                           experiment_label=self.__experiment_label,
+                                           x1_level=self.__x1_level,
+                                           x2=self.__x2)
 
         self.__mean_diff    = EffectSizeDataFrame(self, "mean_diff",
                                                 **EffectSizeDataFrame_kwargs)
@@ -328,7 +368,7 @@ def __repr__(self):
                     comparisons.append("{} minus {}".format(test_name, control_name))
 
         if self.__delta2:
-            comparison.append("{} minus {}".format(self.__experiment[1], self.__experiment[0]))
+            comparison.append("{} minus {}".format(self.__experiment_label[1], self.__experiment_label[0]))
 
         for j, g in enumerate(comparisons):
             out.append("{}. {}".format(j+1, g))
@@ -573,20 +613,30 @@ def idx(self):
 
 
     @property
-    def first(self):
-        return self.__first
+    def x1(self):
+        return self.__x1
 
 
     @property
-    def second(self):
-        return self.__second
+    def x1_level(self):
+        return self.__x1_level
+
+
+    @property
+    def x2(self):
+        return self.__x2
 
 
     @property
     def experiment(self):
         return self.__experiment
 
 
+    @property
+    def experiment_label(self):
+        return self.__experiment_label
+
+
     @property
     def delta2(self):
         return self.__delta2
@@ -705,7 +755,7 @@ def __init__(self, control, test, effect_size,
                  resamples=5000, 
                  permutation_count=5000, 
                  random_seed=12345,
-                 first=None, delta2=False):
+                 delta2=False):
 
         """
         Compute the effect size between two groups.
@@ -870,7 +920,6 @@ def __init__(self, control, test, effect_size,
         self.__ci                = ci
         self.__alpha             = ci2g._compute_alpha_from_ci(ci)
         self.__delta2              = delta2
-        self.__first             = first
 
         self.__difference = es.two_group_difference(
                                 control, test, is_paired, effect_size)
@@ -1485,8 +1534,8 @@ def __init__(self, dabest, effect_size,
                  resamples=5000, 
                  permutation_count=5000,
                  random_seed=12345, 
-                 first=None, second=None, 
-                 delta2=False, experiment=None):
+                 x1_level=None, x2=None, 
+                 delta2=False, experiment_label=None):
         """
         Parses the data from a Dabest object, enabling plotting and printing
         capability for the effect size of interest.
@@ -1500,10 +1549,10 @@ def __init__(self, dabest, effect_size,
         self.__permutation_count = permutation_count
         self.__random_seed       = random_seed
         self.__proportional      = proportional
-        self.__first             = first
-        self.__experiment        = experiment 
-        self.__second            = second
-        self.__delta2              = delta2 
+        self.__x1_level          = x1_level
+        self.__experiment_label  = experiment_label 
+        self.__x2                = x2
+        self.__delta2            = delta2 
 
 
     def __pre_calc(self):
@@ -1558,7 +1607,7 @@ def __pre_calc(self):
 
                 reprs.append(text_repr)
 
-        if self.__delta2:
+        if self.__delta2 and self.__effect_size == "mean_diff":
             delta = TwoGroupsEffectSize(out[0]["bootstraps"], 
                                     out[1]["bootstraps"],
                                     self.__effect_size,
@@ -1567,19 +1616,20 @@ def __pre_calc(self):
                                     self.__resamples,
                                     self.__permutation_count,
                                     self.__random_seed,
-                                    self.__first,
                                     self.__delta2
                                     )
             r_dict = delta.to_dict()
-            r_dict["control"]   = self.__experiment[1]
-            r_dict["test"]      = self.__experiment[0]
+            r_dict["control"]   = self.__experiment_label[0]
+            r_dict["test"]      = self.__experiment_label[1]
             r_dict["control_N"] = self.__resamples
             r_dict["test_N"]    = self.__resamples
             out.append(r_dict)
-            to_replace = "between {} and {} is".format(self.__experiment[0], self.__experiment[1])
+            to_replace = "between {} and {} is".format(self.__experiment_label[0], self.__experiment_label[1])
             text_repr = text_repr.replace("is", to_replace, 1)
             reprs.append(text_repr)
-
+        else:
+            err0 = 'The calculation of delta-delta is not supported for {}.'.format(self.__effect_size)
+            raise ValueError(err0)
         varname = get_varname(self.__dabest_obj)
         lastline = "To get the results of all valid statistical tests, " +\
         "use `{}.{}.statistical_tests`".format(varname, self.__effect_size)
@@ -1626,7 +1676,6 @@ def __pre_calc(self):
 
         self.__results   = out_.reindex(columns=columns_in_order)
         self.__results.dropna(axis="columns", how="all", inplace=True)
-        
 
 
 
@@ -1906,7 +1955,7 @@ def plot(self, color_col=None,
             self.__pre_calc()
 
         if self.__delta2:
-            color_col = self.__second
+            color_col = self.__x2
 
         if self.__proportional:
             raw_marker_size = 0.01
@@ -1991,18 +2040,18 @@ def ci(self):
         return self.__ci
 
     @property
-    def first(self):
-        return self.__first
+    def x1_level(self):
+        return self.__x1_level
 
 
     @property
-    def second(self):
-        return self.__second
+    def x2(self):
+        return self.__x2
 
 
     @property
-    def experiment(self):
-        return self.__experiment
+    def experiment_label(self):
+        return self.__experiment_label
 
 
     @property

dabest/plotter.py

@@ -65,8 +65,6 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
     yvar       = EffectSizeDataFrame.yvar
     is_paired  = EffectSizeDataFrame.is_paired
     delta2     = EffectSizeDataFrame.delta2
-    first      = EffectSizeDataFrame.first
-    experiment = EffectSizeDataFrame.experiment
 
     all_plot_groups = dabest_obj._all_plot_groups
     idx             = dabest_obj.idx
@@ -363,20 +361,20 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
                     current_pair = pivoted_plot_data
                 else:
                     current_pair = pivoted_plot_data[yvar]
-                    
             grp_count = len(current_tuple)
             # Iterate through the data for the current tuple.
             for ID, observation in current_pair.iterrows():
                 x_points = [t for t in range(x_start,x_start+grp_count)]
                 y_points = observation.tolist()
-    
+
                 if color_col is None:
                     slopegraph_kwargs['color'] = ytick_color
                 else:
                     color_key = pivoted_plot_data[color_col,
                                                   current_tuple[0]].loc[ID]
-                    slopegraph_kwargs['color']  = plot_palette_raw[color_key]
-                    slopegraph_kwargs['label']  = color_key
+                    if not pd.isna(color_key):
+                        slopegraph_kwargs['color']  = plot_palette_raw[color_key]
+                        slopegraph_kwargs['label']  = color_key
 
                 rawdata_axes.plot(x_points, y_points, **slopegraph_kwargs)
             x_start  = x_start + grp_count
@@ -454,7 +452,8 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
         ticks_to_plot = np.arange(1, len(temp_all_plot_groups), 2).tolist() 
         ticks_to_skip_contrast = np.cumsum([(len(t)-1)*2 for t in idx])[:-1].tolist()
         ticks_to_skip_contrast.insert(0, 0)
-
+        if delta2:
+            ticks_to_skip_contrast.append(max(ticks_to_skip_contrast)+2)
     else:
         ticks_to_skip   = np.cumsum([len(t) for t in idx])[:-1].tolist()
         ticks_to_skip.insert(0, 0)
@@ -529,7 +528,7 @@ def EffectSizeDataFramePlotter(EffectSizeDataFrame, **plot_kwargs):
 
     if float_contrast is True:
         contrast_axes.set_xlim(0.5, 1.5)
-    elif delta2:
+    elif not is_paired and delta2:
         temp = rawdata_axes.get_xlim()
         contrast_axes.set_xlim(temp[0], temp[1]+2)
         rawdata_axes.set_xlim(temp[0], temp[1]+2)

dabest/tests/test_04_repeated_measures_effsizes_pvals copy.py

@@ -37,7 +37,9 @@
 
 # kwargs for Dabest class init.
 dabest_default_kwargs = dict(x=None, y=None, ci=95, 
-                            resamples=5000, random_seed=12345, proportional=False)
+                            resamples=5000, random_seed=12345, proportional=False,
+                            delta2 = False, experiment=None, 
+                            experiment_label=None, x1_level=None)
 
 # example of sequential repeated measures
 sequential = Dabest(df, id_col = "ID",