Source code for ax.plot.table_view

#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import math
from typing import Tuple

import pandas as pd
import plotly.graph_objs as go
from ax.core.data import Data
from ax.core.experiment import Experiment
from ax.modelbridge.factory import get_empirical_bayes_thompson, get_thompson
from ax.plot.base import AxPlotConfig, AxPlotTypes, PlotMetric, Z
from ax.plot.helper import get_plot_data
from ax.plot.scatter import _error_scatter_data
from pandas.core.frame import DataFrame


COLOR_SCALE = ["#ff3333", "#ff6666", "#ffffff", "#99ff99", "#33ff33"]


[docs]def get_color(x: float, ci: float, rel: bool, reverse: bool) -> str: """Determine the color of the table cell.""" if not rel: # Color coding is meant to be relative to the status quo, # and thus doesn't make sense if rel = False return "#ffffff" r = min(math.floor(abs(x) / ci), 2) if ci > 0 else 2 index = int(2 + r * math.copysign(1, x)) color_scale = list(COLOR_SCALE) if reverse: color_scale = list(reversed(color_scale)) return color_scale[index]
[docs]def table_view_plot( experiment: Experiment, data: Data, use_empirical_bayes: bool = True, only_data_frame: bool = False, arm_noun: str = "arm", ) -> Tuple[DataFrame]: """Table of means and confidence intervals. Table is of the form: +-------+------------+-----------+ | arm | metric_1 | metric_2 | +=======+============+===========+ | 0_0 | mean +- CI | ... | +-------+------------+-----------+ | 0_1 | ... | ... | +-------+------------+-----------+ """ model_func = get_empirical_bayes_thompson if use_empirical_bayes else get_thompson model = model_func(experiment=experiment, data=data) # We don't want to include metrics from a collection, # or the chart will be too big to read easily. # Example: # experiment.metrics = { # 'regular_metric': Metric(), # 'collection_metric: CollectionMetric()', # collection_metric =[metric1, metric2] # } # model.metric_names = [regular_metric, metric1, metric2] # "exploded" out # We want to filter model.metric_names and get rid of metric1, metric2 metric_names = [ metric_name for metric_name in model.metric_names if metric_name in experiment.metrics ] metric_name_to_lower_is_better = { metric_name: experiment.metrics[metric_name].lower_is_better for metric_name in metric_names } plot_data, _, _ = get_plot_data( model=model, generator_runs_dict={}, # pyre-fixme[6]: Expected `Optional[typing.Set[str]]` for 3rd param but got # `List[str]`. metric_names=metric_names, ) if plot_data.status_quo_name: status_quo_arm = plot_data.in_sample.get(plot_data.status_quo_name) rel = True else: status_quo_arm = None rel = False records = [] colors = [] records_with_mean = [] records_with_ci = [] for metric_name in metric_names: arm_names, _, ys, ys_se = _error_scatter_data( arms=list(plot_data.in_sample.values()), y_axis_var=PlotMetric(metric_name, pred=True, rel=rel), x_axis_var=None, status_quo_arm=status_quo_arm, ) results_by_arm = list(zip(arm_names, ys, ys_se)) colors.append( [ get_color( x=y, ci=Z * y_se, rel=rel, # pyre-fixme[6]: Expected `bool` for 4th param but got # `Optional[bool]`. reverse=metric_name_to_lower_is_better[metric_name], ) for (_, y, y_se) in results_by_arm ] ) records.append( [ "{:.3f} &plusmn; {:.3f}".format(y, Z * y_se) for (_, y, y_se) in results_by_arm ] ) records_with_mean.append({arm_name: y for (arm_name, y, _) in results_by_arm}) records_with_ci.append( {arm_name: Z * y_se for (arm_name, _, y_se) in results_by_arm} ) if only_data_frame: return tuple( pd.DataFrame.from_records(records, index=metric_names) for records in [records_with_mean, records_with_ci] ) # pyre-fixme[3]: Return type must be annotated. # pyre-fixme[2]: Parameter must be annotated. def transpose(m): return [[m[j][i] for j in range(len(m))] for i in range(len(m[0]))] records = [[name.replace(":", " : ") for name in metric_names]] + transpose(records) colors = [["#ffffff"] * len(metric_names)] + transpose(colors) # pyre-fixme[61]: `arm_names` may not be initialized here. header = [f"<b>{x}</b>" for x in [f"{arm_noun}s"] + arm_names] # pyre-fixme[61]: `arm_names` may not be initialized here. column_widths = [300] + [150] * len(arm_names) trace = go.Table( header={"values": header, "align": ["left"]}, cells={"values": records, "align": ["left"], "fill": {"color": colors}}, columnwidth=column_widths, ) layout = go.Layout( width=sum(column_widths), margin=go.layout.Margin(l=0, r=20, b=20, t=20, pad=4), # noqa E741 ) fig = go.Figure(data=[trace], layout=layout) # pyre-fixme[7]: Expected `Tuple[DataFrame]` but got `AxPlotConfig`. return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)