Merge pull request #212 from sangyu/feat-multicontrast_whorlmap_forest_plot_integration

Feature: Multicontrast object and new whorlmap

Author: sangyu

README.md

@@ -1,6 +1,5 @@
 # DABEST-Python
 
-
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
 [![minimal Python

dabest/_delta_objects.py

@@ -435,17 +435,19 @@ def __init__(self, effectsizedataframe, permutation_count,
                                                  self.__control_N,
                                                  self.__test_var, 
                                                  self.__test_N)
+        
+        self.__bootstraps_variance = ci2g.calculate_bootstraps_var(self.__bootstraps)
 
         # Compute the weighted average mean differences of the bootstrap data
         # using the pooled group variances of the raw data as the inverse of 
         # weights
         self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta(
-                                                          self.__group_var, 
+                                                          self.__bootstraps_variance, 
                                                           self.__bootstraps)
 
         # Compute the weighted average mean difference based on the raw data
         self.__difference = es.weighted_delta(np.array(self.__effsizedf["difference"]),
-                                                   self.__group_var)
+                                                   self.__bootstraps_variance)
 
         sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)
 
@@ -753,6 +755,14 @@ def group_var(self):
         in order. 
         '''
         return self.__group_var
+    
+    @property
+    def bootstraps_var(self):
+        '''
+        Return the variances of each bootstrapped mean difference distribution
+        in order. 
+        '''
+        return self.__bootstraps_variance
 
 
     @property

dabest/_effsize_objects.py

@@ -1143,7 +1143,7 @@ def plot(
         face_color=None,
 
         raw_desat=0.5, # swarm_desat=0.5, OLD # bar_desat=0.5, OLD
-        contrast_desat=1, # halfviolin_desat=1, OLD
+        contrast_desat=1.0, # halfviolin_desat=1, OLD
 
         raw_alpha=None, # NEW
         contrast_alpha=0.8, # halfviolin_alpha=0.8, OLD
@@ -1478,7 +1478,8 @@ def plot(
 
         if raw_alpha is None:
             raw_alpha = (0.4 if self.is_proportional and self.is_paired 
-                         else 0.5 if self.is_paired
+                         else 0.5 if self.is_paired and (color_col is not None or self.__delta2)
+                         else 0.2 if self.is_paired and color_col is None
                          else 1.0
             )
 

dabest/_modidx.py

@@ -27,6 +27,8 @@
                                                                                                                                           'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.bootstrap_indices': ( 'API/confint_2group_diff.html#bootstrap_indices',
                                                                                                                         'dabest/_stats_tools/confint_2group_diff.py'),
+                                                         'dabest._stats_tools.confint_2group_diff.calculate_bootstraps_var': ( 'API/confint_2group_diff.html#calculate_bootstraps_var',
+                                                                                                                               'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.calculate_group_var': ( 'API/confint_2group_diff.html#calculate_group_var',
                                                                                                                           'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.calculate_weighted_delta': ( 'API/confint_2group_diff.html#calculate_weighted_delta',
@@ -107,6 +109,36 @@
                                                                                    'dabest/misc_tools.py'),
                                    'dabest.misc_tools.show_legend': ('API/misc_tools.html#show_legend', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.unpack_and_add': ('API/misc_tools.html#unpack_and_add', 'dabest/misc_tools.py')},
+            'dabest.multi': { 'dabest.multi.MultiContrast': ('API/multi.html#multicontrast', 'dabest/multi.py'),
+                              'dabest.multi.MultiContrast.__init__': ('API/multi.html#multicontrast.__init__', 'dabest/multi.py'),
+                              'dabest.multi.MultiContrast.__repr__': ('API/multi.html#multicontrast.__repr__', 'dabest/multi.py'),
+                              'dabest.multi.MultiContrast._extract_data': ('API/multi.html#multicontrast._extract_data', 'dabest/multi.py'),
+                              'dabest.multi.MultiContrast._extract_single_contrast': ( 'API/multi.html#multicontrast._extract_single_contrast',
+                                                                                       'dabest/multi.py'),
+                              'dabest.multi.MultiContrast._validate_and_parse_structure': ( 'API/multi.html#multicontrast._validate_and_parse_structure',
+                                                                                            'dabest/multi.py'),
+                              'dabest.multi.MultiContrast._validate_ci_type': ( 'API/multi.html#multicontrast._validate_ci_type',
+                                                                                'dabest/multi.py'),
+                              'dabest.multi.MultiContrast._validate_contrast_consistency': ( 'API/multi.html#multicontrast._validate_contrast_consistency',
+                                                                                             'dabest/multi.py'),
+                              'dabest.multi.MultiContrast._validate_effect_size': ( 'API/multi.html#multicontrast._validate_effect_size',
+                                                                                    'dabest/multi.py'),
+                              'dabest.multi.MultiContrast._validate_effect_size_compatibility': ( 'API/multi.html#multicontrast._validate_effect_size_compatibility',
+                                                                                                  'dabest/multi.py'),
+                              'dabest.multi.MultiContrast._validate_individual_dabest_obj': ( 'API/multi.html#multicontrast._validate_individual_dabest_obj',
+                                                                                              'dabest/multi.py'),
+                              'dabest.multi.MultiContrast.bootstraps': ('API/multi.html#multicontrast.bootstraps', 'dabest/multi.py'),
+                              'dabest.multi.MultiContrast.confidence_intervals': ( 'API/multi.html#multicontrast.confidence_intervals',
+                                                                                   'dabest/multi.py'),
+                              'dabest.multi.MultiContrast.effect_sizes': ('API/multi.html#multicontrast.effect_sizes', 'dabest/multi.py'),
+                              'dabest.multi.MultiContrast.forest_plot': ('API/multi.html#multicontrast.forest_plot', 'dabest/multi.py'),
+                              'dabest.multi.MultiContrast.get_bootstrap_by_position': ( 'API/multi.html#multicontrast.get_bootstrap_by_position',
+                                                                                        'dabest/multi.py'),
+                              'dabest.multi.MultiContrast.whorlmap': ('API/multi.html#multicontrast.whorlmap', 'dabest/multi.py'),
+                              'dabest.multi._sample_bootstrap': ('API/multi.html#_sample_bootstrap', 'dabest/multi.py'),
+                              'dabest.multi._spiralize': ('API/multi.html#_spiralize', 'dabest/multi.py'),
+                              'dabest.multi.combine': ('API/multi.html#combine', 'dabest/multi.py'),
+                              'dabest.multi.whorlmap': ('API/multi.html#whorlmap', 'dabest/multi.py')},
             'dabest.plot_tools': { 'dabest.plot_tools.SwarmPlot': ('API/plot_tools.html#swarmplot', 'dabest/plot_tools.py'),
                                    'dabest.plot_tools.SwarmPlot.__init__': ( 'API/plot_tools.html#swarmplot.__init__',
                                                                              'dabest/plot_tools.py'),

dabest/_stats_tools/confint_2group_diff.py

@@ -6,7 +6,7 @@
 __all__ = ['create_jackknife_indexes', 'create_repeated_indexes', 'compute_meandiff_jackknife', 'bootstrap_indices',
            'compute_bootstrapped_diff', 'delta2_bootstrap_loop', 'compute_delta2_bootstrapped_diff',
            'compute_meandiff_bias_correction', 'compute_interval_limits', 'calculate_group_var',
-           'calculate_weighted_delta']
+           'calculate_bootstraps_var', 'calculate_weighted_delta']
 
 # %% ../../nbs/API/confint_2group_diff.ipynb 4
 import numpy as np
@@ -319,15 +319,23 @@ def calculate_group_var(control_var, control_N, test_var, test_N):
 
     return pooled_var
 
+def calculate_bootstraps_var(bootstraps):
 
-def calculate_weighted_delta(group_var, differences):
+    bootstraps_var_list = [np.var(x, ddof=1) for x in bootstraps]
+    bootstraps_var_array = np.array(bootstraps_var_list)
+    
+    return bootstraps_var_array
+    
+
+
+def calculate_weighted_delta(bootstrap_dist_var, differences):
     """
     Compute the weighted deltas.
     """
 
-    weight = 1 / group_var
+    weight = np.true_divide(1, bootstrap_dist_var)
     denom = np.sum(weight)
     num = 0.0
     for i in range(len(weight)):
         num += weight[i] * differences[i]
-    return num / denom
+    return np.true_divide(num, denom)

dabest/_stats_tools/effsize.py

@@ -392,11 +392,11 @@ def _compute_hedges_correction_factor(n1,
 
 # %% ../../nbs/API/effsize.ipynb 13
 @njit(cache=True)
-def weighted_delta(difference, group_var):
+def weighted_delta(difference, bootstrap_dist_var):
     '''
     Compute the weighted deltas where the weight is the inverse of the
     pooled group difference.
     '''
 
-    weight = np.true_divide(1, group_var)
+    weight = np.true_divide(1, bootstrap_dist_var)
     return np.sum(difference*weight)/np.sum(weight)

dabest/misc_tools.py

@@ -203,7 +203,8 @@ def get_params(
 
 def get_kwargs(
         plot_kwargs: dict, 
-        ytick_color
+        ytick_color,
+        is_paired: bool = False
     ):
     """
     Extracts the kwargs from the `plot_kwargs` object for use in the plotter function.
@@ -214,6 +215,8 @@ def get_kwargs(
         Kwargs passed to the plot function.
     ytick_color : str or color list
         Color of the yticks.
+    is_paired : bool, optional
+        A boolean flag to determine if the plot is for paired data. Default is False.
     """
     from .misc_tools import merge_two_dicts
 
@@ -334,7 +337,7 @@ def get_kwargs(
     default_group_summaries_kwargs = {
                         "zorder": 3, 
                         "lw": 2, 
-                        "alpha": 1,
+                        "alpha": 1 if not is_paired else 0.6,
                         'gap_width_percent': 1.5,
                         'offset': 0.1,
                         'color': None
@@ -513,7 +516,7 @@ def get_color_palette(
         idx: list, 
         all_plot_groups: list,
         delta2: bool,
-        sankey: bool
+        proportional: bool
     ):
     """
     Create the color palette to be used in the plotter function.
@@ -534,9 +537,11 @@ def get_color_palette(
         A list of all the group names.
     delta2 : bool
         A boolean flag to determine if the plot will have a delta-delta effect size.
-    sankey : bool
-        A boolean flag to determine if the plot is for a Sankey diagram.
+    proportional : bool
+        A boolean flag to determine if the plot is for a proportional plot.
     """
+    sankey = True if proportional and show_pairs else False
+
     # Create color palette that will be shared across subplots.
     color_col = plot_kwargs["color_col"]
     if color_col is None:
@@ -548,7 +553,13 @@ def get_color_palette(
         color_groups = pd.unique(plot_data[color_col])
         bootstraps_color_by_group = False
     if show_pairs:
-        bootstraps_color_by_group = False
+        if plot_kwargs["custom_palette"] is not None:
+            if delta2 or sankey:
+                bootstraps_color_by_group = False
+            else:
+                bootstraps_color_by_group = True
+        else:
+            bootstraps_color_by_group = False
 
     # Handle the color palette.
     filled = True
@@ -599,6 +610,17 @@ def get_color_palette(
                 groups_in_palette = {
                     k: custom_pal[k] for k in color_groups
                 }
+            elif proportional and not sankey: # barplots (unpaired proportional data)
+                keys = list(custom_pal.keys())
+                if all(k in keys for k in [1, 0]) and len(keys) == 2:
+                    groups_in_palette = {
+                        k: custom_pal[k] for k in [1, 0]
+                    }
+                    bootstraps_color_by_group = False
+                else:
+                    groups_in_palette = {
+                        k: custom_pal[k] for k in all_plot_groups if k in color_groups
+                    }
             elif sankey:
                 groups_in_palette = {
                     k: custom_pal[k] for k in [1, 0]
@@ -915,7 +937,7 @@ def initialize_fig(
     raw_label = plot_kwargs["raw_label"]
     if raw_label is None:
         if proportional:
-            raw_label = "Proportion of Success" if effect_size_type != "cohens_h" else "Value"
+            raw_label = "Proportion of success" if effect_size_type != "cohens_h" else "Value"
         else:
             raw_label = yvar 
 
@@ -929,16 +951,16 @@ def initialize_fig(
 
     # Set contrast axes y-label.
     contrast_label_dict = {
-        "mean_diff": "Mean Difference",
-        "median_diff": "Median Difference",
+        "mean_diff": "Mean difference",
+        "median_diff": "Median difference",
         "cohens_d": "Cohen's d",
         "hedges_g": "Hedges' g",
         "cliffs_delta": "Cliff's delta",
         "cohens_h": "Cohen's h",
     }
 
     if proportional and effect_size_type != "cohens_h":
-        default_contrast_label = "Proportion Difference"
+        default_contrast_label = "Proportion difference"
     else:
         default_contrast_label = contrast_label_dict[effect_size_type]
 
@@ -1856,13 +1878,15 @@ def color_picker(color_type: str,
                  elements: list, 
                  color_col: str, 
                  show_pairs: bool, 
-                 color_palette: dict) -> list:
+                 color_palette: dict,
+                 bootstraps_color_by_group: bool) -> list:
     num_of_elements = len(elements)
     colors = (
         [kwargs.pop('color')] * num_of_elements
         if kwargs.get('color', None) is not None
         else ['black'] * num_of_elements
-        if color_col is not None or show_pairs 
+        # if color_col is not None or show_pairs
+        if color_col is not None or not bootstraps_color_by_group
         else list(color_palette.values())
     )
     if color_type in ['contrast', 'summary', 'delta_text']:
@@ -1877,7 +1901,7 @@ def color_picker(color_type: str,
     return final_colors
 
 
-def prepare_bars_for_plot(bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs,
+def prepare_bars_for_plot(bar_type, bar_kwargs, horizontal, plot_palette_raw, color_col, show_pairs, bootstraps_color_by_group,
                           plot_data = None, xvar = None, yvar = None,  # Raw data
                           results = None, ticks_to_plot = None, extra_delta = None, # Contrast data
                           reference_band = None, summary_axes = None, ci_type = None  # Summary data
@@ -1951,7 +1975,8 @@ def prepare_bars_for_plot(bar_type, bar_kwargs, horizontal, plot_palette_raw, co
                 elements = ticks_to_plot if bar_type=='contrast' else ticks, 
                 color_col = color_col, 
                 show_pairs = show_pairs, 
-                color_palette = plot_palette_raw
+                color_palette = plot_palette_raw,
+                bootstraps_color_by_group = bootstraps_color_by_group
             )
     if bar_type == 'contrast' and extra_delta is not None:
         colors.append('black')