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# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2022
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hundreds of individual components, each of which was
# individually copyrighted. Each Cloudera open source product is a
# collective work under U.S. Copyright Law. Your license to use the
# collective work is as provided in your written agreement with
# Cloudera. Used apart from the collective work, this file is
# licensed for your use pursuant to the open source license
# identified above.
#
# This code is provided to you pursuant a written agreement with
# (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
# this code. If you do not have a written agreement with Cloudera nor
# with an authorized and properly licensed third party, you do not
# have any rights to access nor to use this code.
#
# Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the
# contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
# KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
# WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
# IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
# AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
# ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
# OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
# CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
# RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
# BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
# DATA.
#
# ###########################################################################
from typing import Iterable
import altair as alt
from captum.attr._utils.visualization import (
VisualizationDataRecord,
format_word_importances,
_get_color,
)
try:
from IPython.display import display, HTML
HAS_IPYTHON = True
except ImportError:
HAS_IPYTHON = False
def format_classname(classname):
return f'<td>{classname}</td>'
def visualize_text(
datarecords: Iterable[VisualizationDataRecord], legend: bool = True
) -> "HTML": # In quotes because this type doesn't exist in standalone mode
assert HAS_IPYTHON, (
"IPython must be available to visualize text. "
"Please run 'pip install ipython'."
)
dom = []
dom.append(
'<head><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.0.0/dist/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous"></head>'
)
dom.append("""<table width:100; class="table">""")
rows = [
"<thead>"
"<tr>"
"<th scope='col'><span class='text-nowrap'>Predicted Label</span></th>"
"<th scope='col'><span class='text-nowrap'>Attribution Score</span></th>"
"<th scope='col'><span class='text-nowrap'>Feature Importance</span></th>"
"</tr>"
"</thead>"
]
for datarecord in datarecords:
rows.append(
"".join(
[
"<tbody>",
"<tr>",
format_classname(
f"{datarecord.pred_class.capitalize()}"
),
format_classname(f"{round(datarecord.attr_score.item(), 2)}"),
format_word_importances(
datarecord.raw_input_ids, datarecord.word_attributions
),
"<tr>",
"</tbody>",
]
)
)
dom.append("".join(rows))
dom.append("</table>")
if legend:
dom.append("<div class='row'>")
dom.append("<div class='col-6'>")
dom.append("<b>Legend: </b>")
for value, label in zip([-1, 0, 1], ["Negative", "Neutral", "Positive"]):
dom.append(
'<span style="display: inline-block; width: 10px; height: 10px; \
border: 1px solid; background-color: \
{value}"></span> {label} '.format(
value=_get_color(value), label=label
)
)
dom.append("</div>")
dom.append("<div class='col-6'></div>")
dom.append("</div>")
html = HTML("".join(dom))
display(html)
return html
def build_altair_classification_plot(format_cls_result):
"""
Builds Altair bar chart for classification results.
Args:
format_cls_result (List): Output from `format_classification_results()`
"""
source = alt.pd.DataFrame(format_cls_result)
color_scale = alt.Scale(
domain=[record["type"] for record in format_cls_result],
range=["#00A3AF", "#F96702"],
)
c = (
alt.Chart(source)
.mark_bar(size=50)
.encode(
x=alt.X(
"percentage_start:Q", axis=alt.Axis(title="Style Distribution (%)")
),
x2=alt.X2("percentage_end:Q"),
color=alt.Color(
"type:N",
legend=alt.Legend(title="Attribute"),
scale=color_scale,
),
)
.properties(height=150)
)
return c
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