#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import Counter
from typing import Any, Dict, List, Optional, Set, Tuple, Union
import numpy as np
from ax.core.generator_run import GeneratorRun
from ax.core.observation import ObservationFeatures
from ax.core.parameter import ChoiceParameter, FixedParameter, RangeParameter
from ax.core.types import TParameterization
from ax.modelbridge.base import ModelBridge
from ax.modelbridge.transforms.ivw import IVW
from ax.plot.base import DECIMALS, PlotData, PlotInSampleArm, PlotOutOfSampleArm, Z
from ax.utils.common.logger import get_logger
logger = get_logger(name="PlotHelper")
# Typing alias
RawData = List[Dict[str, Union[str, float]]]
TNullableGeneratorRunsDict = Optional[Dict[str, GeneratorRun]]
def _format_dict(param_dict: TParameterization, name: str = "Parameterization") -> str:
"""Format a dictionary for labels.
Args:
param_dict: Dictionary to be formatted
name: String name of the thing being formatted.
Returns: stringified blob.
"""
if len(param_dict) >= 10:
blob = "{} has too many items to render on hover ({}).".format(
name, len(param_dict)
)
else:
blob = "<br><em>{}:</em><br>{}".format(
name, "<br>".join("{}: {}".format(n, v) for n, v in param_dict.items())
)
return blob
def _wrap_metric(metric_name: str) -> str:
"""Put a newline on "::" for metric names.
Args:
metric_name: metric name.
Returns: wrapped metric name.
"""
if "::" in metric_name:
return "<br>".join(metric_name.split("::"))
else:
return metric_name
def _format_CI(estimate: float, sd: float, relative: bool, zval: float = Z) -> str:
"""Format confidence intervals given estimate and standard deviation.
Args:
estimate: point estimate.
sd: standard deviation of point estimate.
relative: if True, '%' is appended.
zval: z-value associated with desired CI (e.g. 1.96 for 95% CIs)
Returns: formatted confidence interval.
"""
return "[{lb:.{digits}f}{perc}, {ub:.{digits}f}{perc}]".format(
lb=estimate - zval * sd,
ub=estimate + zval * sd,
digits=DECIMALS,
perc="%" if relative else "",
)
[docs]def arm_name_to_tuple(arm_name: str) -> Union[Tuple[int, int], Tuple[int]]:
tup = arm_name.split("_")
if len(tup) == 2:
try:
return (int(tup[0]), int(tup[1]))
except ValueError:
return (0,)
return (0,)
[docs]def resize_subtitles(figure: Dict[str, Any], size: int):
for ant in figure["layout"]["annotations"]:
ant["font"].update(size=size)
return figure
def _filter_dict(
param_dict: TParameterization, subset_keys: List[str]
) -> TParameterization:
"""Filter a dictionary to keys present in a given list."""
return {k: v for k, v in param_dict.items() if k in subset_keys}
def _get_in_sample_arms(
model: ModelBridge, metric_names: Set[str]
) -> Tuple[Dict[str, PlotInSampleArm], RawData, Dict[str, TParameterization]]:
"""Get in-sample arms from a model with observed and predicted values
for specified metrics.
Returns a PlotInSampleArm object in which repeated observations are merged
with IVW, and a RawData object in which every observation is listed.
Args:
model: An instance of the model bridge.
metric_names: Restrict predictions to these metrics. If None, uses all
metrics in the model.
Returns:
A tuple containing
- Map from arm name to PlotInSampleArm.
- List of the data for each observation like::
{'metric_name': 'likes', 'arm_name': '0_0', 'mean': 1., 'sem': 0.1}
- Map from arm name to parameters
"""
observations = model.get_training_data()
# Calculate raw data
raw_data = []
cond_name_to_parameters = {}
for obs in observations:
cond_name_to_parameters[obs.arm_name] = obs.features.parameters
for j, metric_name in enumerate(obs.data.metric_names):
if metric_name in metric_names:
raw_data.append(
{
"metric_name": metric_name,
"arm_name": obs.arm_name,
"mean": obs.data.means[j],
"sem": np.sqrt(obs.data.covariance[j, j]),
}
)
# Check that we have one ObservationFeatures per arm name since we
# key by arm name.
if len(cond_name_to_parameters) != len(observations):
logger.error(
"Have observations of arms with different features but same"
" name. Arbitrary one will be plotted."
)
# Merge multiple measurements within each Observation with IVW to get
# un-modeled prediction
t = IVW(None, [], [])
obs_data = t.transform_observation_data([obs.data for obs in observations], [])
# Start filling in plot data
in_sample_plot: Dict[str, PlotInSampleArm] = {}
for i, obs in enumerate(observations):
if obs.arm_name is None:
raise ValueError("Observation must have arm name for plotting.")
# Extract raw measurement
obs_y = {}
obs_se = {}
# Use the IVW data, not obs.data
for j, metric_name in enumerate(obs_data[i].metric_names):
if metric_name in metric_names:
obs_y[metric_name] = obs_data[i].means[j]
obs_se[metric_name] = np.sqrt(obs_data[i].covariance[j, j])
# Make a prediction.
if model.training_in_design[i]:
pred_y, pred_se = _predict_at_point(model, obs.features, metric_names)
else:
# Use raw data for out-of-design points
pred_y = obs_y
pred_se = obs_se
in_sample_plot[obs.arm_name] = PlotInSampleArm(
name=obs.arm_name,
y=obs_y,
se=obs_se,
parameters=obs.features.parameters,
y_hat=pred_y,
se_hat=pred_se,
context_stratum=None,
)
return in_sample_plot, raw_data, cond_name_to_parameters
def _predict_at_point(
model: ModelBridge, obsf: ObservationFeatures, metric_names: Set[str]
) -> Tuple[Dict[str, float], Dict[str, float]]:
"""Make a prediction at a point.
Returns mean and standard deviation in format expected by plotting.
Args:
model: ModelBridge
obsf: ObservationFeatures for which to predict
metric_names: Limit predictions to these metrics.
Returns:
A tuple containing
- Map from metric name to prediction.
- Map from metric name to standard error.
"""
y_hat = {}
se_hat = {}
f_pred, cov_pred = model.predict([obsf])
for metric_name in f_pred:
if metric_name in metric_names:
y_hat[metric_name] = f_pred[metric_name][0]
se_hat[metric_name] = np.sqrt(cov_pred[metric_name][metric_name][0])
return y_hat, se_hat
def _get_out_of_sample_arms(
model: ModelBridge,
generator_runs_dict: Dict[str, GeneratorRun],
metric_names: Set[str],
) -> Dict[str, Dict[str, PlotOutOfSampleArm]]:
"""Get out-of-sample predictions from a model given a dict of generator runs.
Args:
model: The model.
generator_runs_dict: a mapping from generator run name to generator run.
metric_names: metrics to include in the plot.
Returns:
A mapping from name to a mapping from arm name to plot.
"""
out_of_sample_plot: Dict[str, Dict[str, PlotOutOfSampleArm]] = {}
for generator_run_name, generator_run in generator_runs_dict.items():
out_of_sample_plot[generator_run_name] = {}
for arm in generator_run.arms:
# This assumes context is None
obsf = ObservationFeatures.from_arm(arm)
# Make a prediction
try:
pred_y, pred_se = _predict_at_point(model, obsf, metric_names)
except Exception:
# Check if it is an out-of-design arm.
if not model.model_space.check_membership(obsf.parameters):
# Skip this point
continue
else:
# It should have worked
raise
arm_name = arm.name_or_short_signature
out_of_sample_plot[generator_run_name][arm_name] = PlotOutOfSampleArm(
name=arm_name,
parameters=obsf.parameters,
y_hat=pred_y,
se_hat=pred_se,
context_stratum=None,
)
return out_of_sample_plot
[docs]def get_plot_data(
model: ModelBridge,
generator_runs_dict: Dict[str, GeneratorRun],
metric_names: Optional[Set[str]] = None,
) -> Tuple[PlotData, RawData, Dict[str, TParameterization]]:
"""Format data object with metrics for in-sample and out-of-sample
arms.
Calculate both observed and predicted metrics for in-sample arms.
Calculate predicted metrics for out-of-sample arms passed via the
`generator_runs_dict` argument.
In PlotData, in-sample observations are merged with IVW. In RawData, they
are left un-merged and given as a list of dictionaries, one for each
observation and having keys 'arm_name', 'mean', and 'sem'.
Args:
model: The model.
generator_runs_dict: a mapping from generator run name to generator run.
metric_names: Restrict predictions to this set. If None, all metrics
in the model will be returned.
Returns:
A tuple containing
- PlotData object with in-sample and out-of-sample predictions.
- List of observations like::
{'metric_name': 'likes', 'arm_name': '0_1', 'mean': 1., 'sem': 0.1}.
- Mapping from arm name to parameters.
"""
metrics_plot = model.metric_names if metric_names is None else metric_names
in_sample_plot, raw_data, cond_name_to_parameters = _get_in_sample_arms(
model=model, metric_names=metrics_plot
)
out_of_sample_plot = _get_out_of_sample_arms(
model=model, generator_runs_dict=generator_runs_dict, metric_names=metrics_plot
)
status_quo_name = None if model.status_quo is None else model.status_quo.arm_name
plot_data = PlotData(
metrics=list(metrics_plot),
in_sample=in_sample_plot,
out_of_sample=out_of_sample_plot,
status_quo_name=status_quo_name,
)
return plot_data, raw_data, cond_name_to_parameters
[docs]def get_range_parameter(model: ModelBridge, param_name: str) -> RangeParameter:
"""
Get the range parameter with the given name from the model.
Throws if parameter doesn't exist or is not a range parameter.
Args:
model: The model.
param_name: The name of the RangeParameter to be found.
Returns: The RangeParameter named `param_name`.
"""
range_param = model.model_space.parameters.get(param_name)
if range_param is None:
raise ValueError(f"Parameter `{param_name}` does not exist.")
if not isinstance(range_param, RangeParameter):
raise ValueError(f"{param_name} is not a RangeParameter")
return range_param
[docs]def get_range_parameters(model: ModelBridge) -> List[RangeParameter]:
"""
Get a list of range parameters from a model.
Args:
model: The model.
Returns: List of RangeParameters.
"""
return [
parameter
for parameter in model.model_space.parameters.values()
if isinstance(parameter, RangeParameter)
]
[docs]def get_grid_for_parameter(parameter: RangeParameter, density: int) -> np.ndarray:
"""Get a grid of points along the range of the parameter.
Will be a log-scale grid if parameter is log scale.
Args:
parameter: Parameter for which to generate grid.
density: Number of points in the grid.
"""
is_log = parameter.log_scale
if is_log:
grid = np.linspace(
np.log10(parameter.lower), np.log10(parameter.upper), density
)
grid = 10 ** grid
else:
grid = np.linspace(parameter.lower, parameter.upper, density)
return grid
[docs]def get_fixed_values(
model: ModelBridge, slice_values: Optional[Dict[str, Any]] = None
) -> TParameterization:
"""Get fixed values for parameters in a slice plot.
If there is an in-design status quo, those values will be used. Otherwise,
the mean of RangeParameters or the mode of ChoiceParameters is used.
Any value in slice_values will override the above.
Args:
model: ModelBridge being used for plotting
slice_values: Map from parameter name to value at which is should be
fixed.
Returns: Map from parameter name to fixed value.
"""
# Check if status_quo is in design
if model.status_quo is not None and model.model_space.check_membership(
model.status_quo.features.parameters
):
setx = model.status_quo.features.parameters
else:
observations = model.get_training_data()
setx = {}
for p_name, parameter in model.model_space.parameters.items():
# Exclude out of design status quo (no parameters)
vals = [
obs.features.parameters[p_name]
for obs in observations
if (
len(obs.features.parameters) > 0
and parameter.validate(obs.features.parameters[p_name])
)
]
if isinstance(parameter, FixedParameter):
setx[p_name] = parameter.value
elif isinstance(parameter, ChoiceParameter):
setx[p_name] = Counter(vals).most_common(1)[0][1]
elif isinstance(parameter, RangeParameter):
setx[p_name] = parameter._cast(np.mean(vals))
if slice_values is not None:
# slice_values has type Dictionary[str, Any]
setx.update(slice_values)
return setx
