#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict
import pandas as pd
import plotly.graph_objs as go
from ax.exceptions.core import NoDataError
from ax.modelbridge import ModelBridge
from ax.plot.base import AxPlotConfig, AxPlotTypes
from ax.utils.common.logger import get_logger
from plotly import subplots
logger = get_logger(__name__)
[docs]def plot_feature_importance(df: pd.DataFrame, title: str) -> AxPlotConfig:
if df.empty:
raise NoDataError("No Data on Feature Importances found.")
df.set_index(df.columns[0], inplace=True)
data = [
go.Bar(y=df.index, x=df[column_name], name=column_name, orientation="h")
for column_name in df.columns
]
fig = subplots.make_subplots(
rows=len(df.columns),
cols=1,
subplot_titles=df.columns,
print_grid=False,
shared_xaxes=True,
)
for idx, item in enumerate(data):
fig.append_trace(item, idx + 1, 1)
fig.layout.showlegend = False
fig.layout.margin = go.layout.Margin(
l=8 * min(max(len(idx) for idx in df.index), 75) # noqa E741
)
fig.layout.title = title
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
[docs]def plot_feature_importance_by_metric(model: ModelBridge) -> AxPlotConfig:
"""One plot per feature, showing importances by metric."""
importances = []
for metric_name in sorted(model.metric_names):
try:
vals: Dict[str, Any] = model.feature_importances(metric_name)
vals["index"] = metric_name
importances.append(vals)
except NotImplementedError:
logger.warning(
f"Model for {metric_name} does not support feature importances."
)
if not importances:
raise NotImplementedError(
"Feature importances could not be calculated for any metric"
)
df = pd.DataFrame(importances)
# plot_feature_importance expects index in first column
df = df.reindex(columns=(["index"] + [a for a in df.columns if a != "index"]))
plot_fi = plot_feature_importance(df, "Absolute Feature Importances by Metric")
num_subplots = len(df.columns) - 1 # one column is the index
num_features = len(df)
plot_fi.data["layout"]["height"] = num_subplots * num_features * 50
return plot_fi
[docs]def plot_feature_importance_by_feature(
model: ModelBridge, relative: bool = True
) -> AxPlotConfig:
"""One plot per metric, showing importances by feature."""
traces = []
dropdown = []
for i, metric_name in enumerate(sorted(model.metric_names)):
try:
importances = model.feature_importances(metric_name)
except NotImplementedError:
logger.warning(
f"Model for {metric_name} does not support feature importances."
)
continue
df = pd.DataFrame(
[
{"Factor": factor, "Importance": importance}
for factor, importance in importances.items()
]
)
if relative:
df["Importance"] = df["Importance"].div(df["Importance"].sum())
df = df.sort_values("Importance")
traces.append(
go.Bar(
name="Importance",
orientation="h",
visible=i == 0,
x=df["Importance"],
y=df["Factor"],
)
)
is_visible = [False] * len(sorted(model.metric_names))
is_visible[i] = True
dropdown.append(
{"args": ["visible", is_visible], "label": metric_name, "method": "restyle"}
)
if not traces:
raise NotImplementedError("No traces found for metric")
updatemenus = [
{
"x": 0,
"y": 1,
"yanchor": "top",
"xanchor": "left",
"buttons": dropdown,
"pad": {
"t": -40
}, # hack to put dropdown below title regardless of number of features
}
]
features = traces[0].y
title = (
"Relative Feature Importances" if relative else "Absolute Feature Importances"
)
layout = go.Layout(
height=200 + len(features) * 20,
hovermode="closest",
margin=go.layout.Margin(
l=8 * min(max(len(idx) for idx in features), 75) # noqa E741
),
showlegend=False,
title=title,
updatemenus=updatemenus,
)
if relative:
layout.update({"xaxis": {"tickformat": ".0%"}})
fig = go.Figure(data=traces, layout=layout)
plot_fi = AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
return plot_fi
[docs]def plot_relative_feature_importance(model: ModelBridge) -> AxPlotConfig:
"""Create a stacked bar chart of feature importances per metric"""
importances = []
for metric_name in sorted(model.metric_names):
try:
vals: Dict[str, Any] = model.feature_importances(metric_name)
vals["index"] = metric_name
importances.append(vals)
except Exception:
logger.warning(
"Model for {} does not support feature importances.".format(metric_name)
)
df = pd.DataFrame(importances)
df.set_index("index", inplace=True)
df = df.div(df.sum(axis=1), axis=0)
data = [
go.Bar(y=df.index, x=df[column_name], name=column_name, orientation="h")
for column_name in df.columns
]
layout = go.Layout(
margin=go.layout.Margin(l=250), # noqa E741
barmode="grouped",
yaxis={"title": ""},
xaxis={"title": "Relative Feature importance"},
showlegend=False,
title="Relative Feature Importance per Metric",
)
fig = go.Figure(data=data, layout=layout)
return AxPlotConfig(data=fig, plot_type=AxPlotTypes.GENERIC)
