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# Copyright 2017-2020 Spotify AB 

# 

# Licensed under the Apache License, Version 2.0 (the "License"); 

# you may not use this file except in compliance with the License. 

# You may obtain a copy of the License at 

# 

# http://www.apache.org/licenses/LICENSE-2.0 

# 

# Unless required by applicable law or agreed to in writing, software 

# distributed under the License is distributed on an "AS IS" BASIS, 

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

# See the License for the specific language governing permissions and 

# limitations under the License. 

 

from typing import Union, Iterable, Tuple 

 

import numpy as np 

from bokeh.models import tools 

from chartify import Chart 

from pandas import DataFrame 

 

from ..abstract_base_classes.confidence_grapher_abc import ConfidenceGrapherABC 

from ..confidence_utils import (axis_format_precision, 

add_color_column, 

get_remaning_groups, get_all_group_columns, 

listify, level2str, to_finite) 

from ..constants import (POINT_ESTIMATE, DIFFERENCE, 

CI_LOWER, CI_UPPER, P_VALUE, 

ADJUSTED_LOWER, ADJUSTED_UPPER, ADJUSTED_P, 

NULL_HYPOTHESIS, NIM, NIM_TYPE) 

from ...chartgrid import ChartGrid 

 

 

class ChartifyGrapher(ConfidenceGrapherABC): 

def __init__(self, 

data_frame: DataFrame, 

numerator_column: str, 

denominator_column: str, 

categorical_group_columns: str, 

ordinal_group_column: str): 

 

self._df = data_frame 

self._numerator = numerator_column 

self._denominator = denominator_column 

self._categorical_group_columns = categorical_group_columns 

self._ordinal_group_column = ordinal_group_column 

self._all_group_columns = get_all_group_columns( 

self._categorical_group_columns, self._ordinal_group_column) 

 

def plot_summary(self, 

summary_df: DataFrame, 

groupby: Union[str, Iterable]) -> ChartGrid: 

 

ch = ChartGrid() 

if groupby is None: 

ch.charts.append( 

self._summary_plot(level_name=None, 

level_df=summary_df, 

groupby=groupby)) 

else: 

for level_name, level_df in summary_df.groupby(groupby): 

ch.charts.append( 

self._summary_plot(level_name=level_name, 

level_df=level_df, 

groupby=groupby)) 

return ch 

 

def plot_difference(self, 

difference_df, 

absolute, 

groupby, 

nims: NIM_TYPE, 

use_adjusted_intervals: bool 

) -> ChartGrid: 

if self._ordinal_group_column in listify(groupby): 

ch = self._ordinal_difference_plot(difference_df, 

absolute, 

groupby, 

use_adjusted_intervals) 

chart_grid = ChartGrid([ch]) 

else: 

chart_grid = self._categorical_difference_plot( 

difference_df, 

absolute, 

groupby, 

use_adjusted_intervals) 

return chart_grid 

 

def plot_multiple_difference(self, 

difference_df, 

absolute, 

groupby, 

level_as_reference, 

nims: NIM_TYPE, 

use_adjusted_intervals: bool 

) -> ChartGrid: 

if self._ordinal_group_column in listify(groupby): 

ch = self._ordinal_multiple_difference_plot( 

difference_df, 

absolute, 

groupby, 

level_as_reference, 

use_adjusted_intervals) 

chart_grid = ChartGrid([ch]) 

else: 

chart_grid = self._categorical_multiple_difference_plot( 

difference_df, 

absolute, 

groupby, 

level_as_reference, 

use_adjusted_intervals) 

return chart_grid 

 

def _ordinal_difference_plot(self, 

difference_df: DataFrame, 

absolute: bool, 

groupby: Union[str, Iterable], 

use_adjusted_intervals: bool) -> Chart: 

remaining_groups = get_remaning_groups(groupby, 

self._ordinal_group_column) 

title = "Change from {} to {}".format(difference_df['level_1'].any(), 

difference_df['level_2'].any()) 

y_axis_label = self._get_difference_plot_label(absolute) 

ch = self._ordinal_plot('difference', difference_df, groupby=None, 

level_name="", 

remaining_groups=remaining_groups, 

absolute=absolute, 

title=title, y_axis_label=y_axis_label, 

use_adjusted_intervals=use_adjusted_intervals) 

ch.callout.line(0) 

 

return ch 

 

def _get_difference_plot_label(self, absolute): 

change_type = 'Absolute' if absolute else "Relative" 

return change_type + " change in {} / {}".format( 

self._numerator, self._denominator) 

 

def _categorical_difference_plot(self, 

difference_df: DataFrame, 

absolute: bool, 

groupby: Union[str, Iterable], 

use_adjusted_intervals: bool) -> ChartGrid: 

if groupby is None: 

groupby = 'dummy_groupby' 

difference_df[groupby] = 'Difference' 

 

title = "Change from {} to {}".format(difference_df['level_1'].any(), 

difference_df['level_2'].any()) 

x_label = "" if groupby is None else "{}".format(groupby) 

 

chart_grid = self._categorical_difference_chart(absolute, 

difference_df, groupby, 

title, x_label, 

use_adjusted_intervals) 

 

return chart_grid 

 

def _categorical_difference_chart(self, 

absolute: bool, 

difference_df: DataFrame, 

groupby_columns: Union[str, Iterable], 

title: str, 

x_label: str, 

use_adjusted_intervals: bool 

) -> ChartGrid: 

LOWER, UPPER = ((ADJUSTED_LOWER, ADJUSTED_UPPER) 

if use_adjusted_intervals 

else (CI_LOWER, CI_UPPER)) 

axis_format, y_min, y_max = axis_format_precision( 

numbers=(difference_df[LOWER] 

.append(difference_df[DIFFERENCE]) 

.append(difference_df[UPPER]) 

.append(difference_df[NULL_HYPOTHESIS] 

if NULL_HYPOTHESIS in difference_df.columns 

else None) 

), 

absolute=absolute) 

 

df = ( 

difference_df 

.assign(**{LOWER: to_finite(difference_df[LOWER], y_min)}) 

.assign(**{UPPER: to_finite(difference_df[UPPER], y_max)}) 

.assign(level_1=difference_df.level_1.map(level2str)) 

.assign(level_2=difference_df.level_2.map(level2str)) 

.set_index(groupby_columns) 

.assign(categorical_x=lambda df: df.index) 

.reset_index() 

) 

 

ch = Chart(x_axis_type='categorical') 

ch.plot.interval( 

data_frame=df.sort_values(groupby_columns), 

categorical_columns=groupby_columns, 

lower_bound_column=LOWER, 

upper_bound_column=UPPER, 

middle_column=DIFFERENCE, 

categorical_order_by='labels', 

categorical_order_ascending=False) 

# Also plot transparent circles, just to be able to show hover box 

ch.style.color_palette.reset_palette_order() 

ch.figure.circle(source=df, 

x='categorical_x', 

y=DIFFERENCE, 

size=20, 

name='center', 

line_alpha=0, 

fill_alpha=0) 

if NULL_HYPOTHESIS in df.columns: 

ch.style.color_palette.reset_palette_order() 

dash_source = ( 

df[~df[NIM].isna()] 

.assign(color_column=lambda df: df.apply( 

lambda row: 'red' if row[LOWER] < row[NULL_HYPOTHESIS] 

and row[NULL_HYPOTHESIS] < row[UPPER] 

else 'green', axis=1)) 

.sort_values(groupby_columns)) 

ch.figure.dash(source=dash_source, 

x='categorical_x', 

y=NULL_HYPOTHESIS, 

size=320/len(df), 

line_width=3, 

name='nim', 

line_color='color_column') 

ch.axes.set_yaxis_label(self._get_difference_plot_label(absolute)) 

ch.set_source_label("") 

ch.callout.line(0) 

ch.axes.set_yaxis_range(y_min - 0.05 * (y_max - y_min), 

y_max + 0.05 * (y_max - y_min)) 

ch.axes.set_yaxis_tick_format(axis_format) 

ch.set_title(title) 

ch.axes.set_xaxis_label(x_label) 

ch.set_subtitle("") 

 

self.add_tools(chart=ch, 

df=(difference_df.set_index(groupby_columns) 

.assign(categorical_x=lambda df: df.index) 

.reset_index()), 

center_name=DIFFERENCE, 

absolute=absolute, 

ordinal=False, 

use_adjusted_intervals=use_adjusted_intervals) 

 

chart_grid = ChartGrid() 

chart_grid.charts.append(ch) 

 

return chart_grid 

 

def _summary_plot(self, 

level_name: Union[str, Tuple], 

level_df: DataFrame, 

groupby: Union[str, Iterable]): 

remaining_groups = get_remaning_groups(self._all_group_columns, groupby) 

if (self._ordinal_group_column is not None 

and self._ordinal_group_column in remaining_groups): 

 

ch = self._ordinal_summary_plot(level_name, level_df, 

remaining_groups, groupby) 

else: 

ch = self._categorical_summary_plot(level_name, level_df, 

remaining_groups, groupby) 

return ch 

 

def _ordinal_summary_plot(self, 

level_name: Union[str, Tuple], 

level_df: DataFrame, 

remaining_groups: Union[str, Iterable], 

groupby: Union[str, Iterable]): 

remaining_groups = get_remaning_groups(remaining_groups, 

self._ordinal_group_column) 

title = "Estimate of {} / {}".format(self._numerator, 

self._denominator) 

y_axis_label = "{} / {}".format(self._numerator, 

self._denominator) 

return self._ordinal_plot(POINT_ESTIMATE, level_df, 

groupby, level_name, 

remaining_groups, absolute=True, 

title=title, y_axis_label=y_axis_label, 

use_adjusted_intervals=False) 

 

def _ordinal_plot(self, 

center_name: str, 

level_df: DataFrame, 

groupby: Union[str, Iterable], 

level_name: Union[str, Tuple], 

remaining_groups: Union[str, Iterable], 

absolute: bool, 

title: str, 

y_axis_label: str, 

use_adjusted_intervals: bool): 

LOWER, UPPER = ((ADJUSTED_LOWER, ADJUSTED_UPPER) 

if use_adjusted_intervals 

else (CI_LOWER, CI_UPPER)) 

df = add_color_column(level_df, remaining_groups) 

colors = 'color' if remaining_groups else None 

axis_format, y_min, y_max = axis_format_precision( 

numbers=(df[LOWER] 

.append(df[center_name]) 

.append(df[UPPER]) 

.append(df[NULL_HYPOTHESIS] 

if NULL_HYPOTHESIS in df.columns 

else None) 

), 

absolute=absolute) 

ch = Chart(x_axis_type=self._ordinal_type()) 

ch.plot.line( 

data_frame=df.sort_values(self._ordinal_group_column), 

x_column=self._ordinal_group_column, 

y_column=center_name, 

color_column=colors) 

ch.style.color_palette.reset_palette_order() 

ch.plot.area( 

data_frame=(df.assign(**{LOWER: to_finite(df[LOWER], y_min)}) 

.assign(**{UPPER: to_finite(df[UPPER], y_max)}) 

.sort_values(self._ordinal_group_column) 

), 

x_column=self._ordinal_group_column, 

y_column=LOWER, 

second_y_column=UPPER, 

color_column=colors) 

if NULL_HYPOTHESIS in df.columns: 

ch.style.color_palette.reset_palette_order() 

ch.plot.line(data_frame=df.sort_values(self._ordinal_group_column), 

x_column=self._ordinal_group_column, 

y_column=NULL_HYPOTHESIS, 

color_column=colors, 

line_dash='dashed', 

line_width=1) 

ch.axes.set_yaxis_label(y_axis_label) 

ch.axes.set_xaxis_label(self._ordinal_group_column) 

ch.set_source_label("") 

ch.axes.set_yaxis_range(y_min - 0.05 * (y_max-y_min), 

y_max + 0.05 * (y_max-y_min)) 

ch.axes.set_yaxis_tick_format(axis_format) 

subtitle = "" if not groupby else "{}: {}".format(groupby, level_name) 

ch.set_subtitle(subtitle) 

ch.set_title(title) 

if colors: 

ch.set_legend_location('outside_bottom') 

self.add_tools(chart=ch, 

df=df, 

center_name=center_name, 

absolute=absolute, 

ordinal=True, 

use_adjusted_intervals=use_adjusted_intervals) 

return ch 

 

def _categorical_summary_plot(self, 

level_name, 

summary_df, 

remaining_groups, 

groupby): 

if not remaining_groups: 

remaining_groups = listify(groupby) 

df = ( 

summary_df 

.set_index(remaining_groups) 

.assign(categorical_x=lambda df: df.index) 

.reset_index() 

) 

 

axis_format, y_min, y_max = axis_format_precision( 

numbers=(df[CI_LOWER] 

.append(df[POINT_ESTIMATE]) 

.append(df[CI_UPPER]) 

), 

absolute=True) 

 

ch = Chart(x_axis_type='categorical') 

ch.plot.interval( 

(df 

.assign(**{CI_LOWER: to_finite(df[CI_LOWER], y_min)}) 

.assign(**{CI_UPPER: to_finite(df[CI_UPPER], y_max)})), 

categorical_columns=remaining_groups, 

lower_bound_column=CI_LOWER, 

upper_bound_column=CI_UPPER, 

middle_column=POINT_ESTIMATE, 

categorical_order_by='labels', 

categorical_order_ascending=True) 

# Also plot transparent circles, just to be able to show hover box 

ch.style.color_palette.reset_palette_order() 

ch.figure.circle(source=df, 

x='categorical_x', 

y=POINT_ESTIMATE, 

size=20, 

name='center', 

line_alpha=0, 

fill_alpha=0) 

ch.set_title("Estimate of {} / {}".format(self._numerator, 

self._denominator)) 

if groupby: 

ch.set_subtitle("{}: {}".format(groupby, level_name)) 

else: 

ch.set_subtitle("") 

ch.axes.set_xaxis_label("{}".format(', '.join(remaining_groups))) 

ch.axes.set_yaxis_label("{} / {}".format(self._numerator, 

self._denominator)) 

ch.set_source_label("") 

ch.axes.set_yaxis_tick_format(axis_format) 

self.add_tools(chart=ch, 

df=df, 

center_name=POINT_ESTIMATE, 

absolute=True, 

ordinal=False, 

use_adjusted_intervals=False) 

return ch 

 

def _ordinal_type(self): 

ordinal_column_type = \ 

self._df[self._ordinal_group_column].dtype.type 

axis_type = 'datetime' if issubclass(ordinal_column_type, 

np.datetime64) else 'linear' 

return axis_type 

 

def _ordinal_multiple_difference_plot(self, 

difference_df: DataFrame, 

absolute: bool, 

groupby: Union[str, Iterable], 

level_as_reference: bool, 

use_adjusted_intervals: bool): 

remaining_groups = get_remaning_groups(groupby, 

self._ordinal_group_column) 

groupby_columns = self._add_level_column(remaining_groups, 

level_as_reference) 

title = self._get_multiple_difference_title(difference_df, 

level_as_reference) 

y_axis_label = self._get_difference_plot_label(absolute) 

ch = self._ordinal_plot(DIFFERENCE, difference_df, groupby=None, 

level_name="", remaining_groups=groupby_columns, 

absolute=absolute, 

title=title, y_axis_label=y_axis_label, 

use_adjusted_intervals=use_adjusted_intervals) 

ch.callout.line(0) 

return ch 

 

def _categorical_multiple_difference_plot(self, 

difference_df: DataFrame, 

absolute: bool, 

groupby: Union[str, Iterable], 

level_as_reference: bool, 

use_adjusted_intervals: bool): 

groupby_columns = self._add_level_column(groupby, 

level_as_reference) 

title = self._get_multiple_difference_title(difference_df, 

level_as_reference) 

x_label = "" if groupby is None else "{}".format(groupby) 

chart_grid = self._categorical_difference_chart(absolute, 

difference_df, 

groupby_columns, 

title, 

x_label, 

use_adjusted_intervals) 

 

return chart_grid 

 

def _get_multiple_difference_title(self, difference_df, level_as_reference): 

reference_level = 'level_1' if level_as_reference else 'level_2' 

title = "Comparison to {}".format(difference_df[reference_level].any()) 

return title 

 

def _add_level_column(self, groupby, level_as_reference): 

level_column = 'level_2' if level_as_reference else 'level_1' 

if groupby is None: 

groupby_columns = level_column 

else: 

if isinstance(groupby, str): 

groupby_columns = [groupby, level_column] 

else: 

groupby_columns = groupby + [level_column] 

return groupby_columns 

 

def add_ci_to_chart_datasources(self, 

chart: Chart, 

df: DataFrame, 

center_name: str, 

ordinal: bool, 

use_adjusted_intervals: bool): 

LOWER, UPPER = ((ADJUSTED_LOWER, ADJUSTED_UPPER) 

if use_adjusted_intervals 

else (CI_LOWER, CI_UPPER)) 

group_col = ('color' if ordinal and 'color' in df.columns 

else 'categorical_x') 

for data in chart.data: 

if center_name in data.keys() or NULL_HYPOTHESIS in data.keys(): 

index = data['index'] 

data[LOWER] = np.array(df[LOWER][index]) 

data[UPPER] = np.array(df[UPPER][index]) 

data['color'] = np.array(df[group_col][index]) 

if DIFFERENCE in data.keys() or NULL_HYPOTHESIS in data.keys(): 

index = data['index'] 

data[DIFFERENCE] = np.array(df[DIFFERENCE][index]) 

data['p_value'] = np.array(df[P_VALUE][index]) 

data['adjusted_p'] = np.array(df[ADJUSTED_P][index]) 

if NULL_HYPOTHESIS in df.columns: 

data['null_hyp'] = np.array(df[NULL_HYPOTHESIS][index]) 

 

def add_tools(self, 

chart: Chart, 

df: DataFrame, 

center_name: str, 

absolute: bool, 

ordinal: bool, 

use_adjusted_intervals: bool): 

self.add_ci_to_chart_datasources(chart, 

df, 

center_name, 

ordinal, 

use_adjusted_intervals) 

LOWER, UPPER = ((ADJUSTED_LOWER, ADJUSTED_UPPER) 

if use_adjusted_intervals 

else (CI_LOWER, CI_UPPER)) 

 

if len(chart.figure.legend) > 0: 

chart.figure.legend.click_policy = "hide" 

axis_format, y_min, y_max = axis_format_precision( 

numbers=(df[LOWER] 

.append(df[center_name]) 

.append(df[UPPER]) 

.append(df[NULL_HYPOTHESIS] 

if NULL_HYPOTHESIS in df.columns 

else None) 

), 

absolute=absolute, 

extra_zeros=2) 

ordinal_tool_tip = ( 

[] if not ordinal else 

[(self._ordinal_group_column, f"@{self._ordinal_group_column}")] 

) 

p_value_tool_tip = ( 

([("p-value", "@p_value{0.0000}")] + 

([("adjusted p-value", "@adjusted_p{0.0000}")] 

if len(df) > 1 else [])) 

if center_name == DIFFERENCE else []) 

nim_tool_tip = ( 

[("null hypothesis", f"@null_hyp{{{axis_format}}}")] 

if NULL_HYPOTHESIS in df.columns else []) 

tooltips = ( 

[("group", "@color")] + 

ordinal_tool_tip + 

[(f"{center_name}", f"@{center_name}{{{axis_format}}}")] + 

[(("adjusted " if use_adjusted_intervals else "") + 

"confidence interval", 

f"(@{{{LOWER}}}{{{axis_format}}}," 

f" @{{{UPPER}}}{{{axis_format}}})")] + 

p_value_tool_tip + 

nim_tool_tip 

) 

lines_with_hover = [] if ordinal else ['center', 'nim'] 

hover = tools.HoverTool(tooltips=tooltips, names=lines_with_hover) 

box_zoom = tools.BoxZoomTool() 

 

chart.figure.add_tools(hover, 

tools.ZoomInTool(), 

tools.ZoomOutTool(), 

box_zoom, 

tools.PanTool(), 

tools.ResetTool()) 

chart.figure.toolbar.active_drag = box_zoom