import math
from copy import deepcopy
from typing import Optional
import plotly.express as px
from dash import Dash, Input, Output, dcc, html
from langcheck.metrics.metric_value import MetricValue
from langcheck.plot._css import GLOBAL_CSS, INPUT_CSS, NUM_RESULTS_CSS
[docs]def scatter(metric_value: MetricValue,
other_metric_value: Optional[MetricValue] = None,
jupyter_mode: str = 'inline') -> None:
'''Shows an interactive scatter plot of all data points in an
:class:`~langcheck.metrics.metric_value.MetricValue`. When run in a
notebook, this usually displays the chart inline in the cell output.
Args:
metric_value: The :class:`~langcheck.metrics.metric_value.MetricValue`
to plot.
other_metric_value: If provided, another
:class:`~langcheck.metrics.metric_value.MetricValue` to plot on the
same chart.
jupyter_mode: Defaults to 'inline', which displays the chart in the
cell output. For Colab, set this to 'external' instead. See the
Dash documentation for more info:
https://dash.plotly.com/workspaces/using-dash-in-jupyter-and-workspaces#display-modes
'''
if other_metric_value is None:
_scatter_one_metric_value(metric_value, jupyter_mode)
else:
_scatter_two_metric_values(metric_value, other_metric_value,
jupyter_mode)
def _scatter_one_metric_value(metric_value: MetricValue,
jupyter_mode: str) -> None:
'''Shows an interactive scatter plot of all data points in one
:class:`~langcheck.metrics.metric_value.MetricValue`.
'''
# Rename some MetricValue fields for display
df = metric_value.to_df()
df.rename(columns={'metric_value': metric_value.metric_name}, inplace=True)
df['prompt'] = df['prompt'].fillna('None')
df['reference_output'] = df['reference_output'].fillna('None')
df['source'] = df['source'].fillna('None')
# Define layout of the Dash app (chart + search boxes)
app = Dash(__name__)
app.layout = html.Div([
html.Div([
html.Label('Filter generated_outputs: '),
dcc.Input(id='filter_generated_outputs',
type='text',
placeholder='Type to search...',
style=INPUT_CSS),
]),
html.Div([
html.Label('Filter reference_outputs: '),
dcc.Input(id='filter_reference_outputs',
type='text',
placeholder='Type to search...',
style=INPUT_CSS),
]),
html.Div([
html.Label('Filter prompts: '),
dcc.Input(id='filter_prompts',
type='text',
placeholder='Type to search...',
style=INPUT_CSS),
]),
html.Div([
html.Label('Filter sources: '),
dcc.Input(id='filter_sources',
type='text',
placeholder='Type to search...',
style=INPUT_CSS),
]),
html.Div([html.Span(id='num_results_message', style=NUM_RESULTS_CSS)]),
dcc.Graph(
id='scatter_plot',
config={
'displaylogo': False,
'modeBarButtonsToRemove': ['select', 'lasso2d', 'resetScale']
})
],
style=GLOBAL_CSS)
# This function gets called whenever the user types in the search boxes
@app.callback(Output('scatter_plot', 'figure'),
Output('num_results_message', 'children'),
Input('filter_generated_outputs', 'value'),
Input('filter_reference_outputs', 'value'),
Input('filter_prompts', 'value'),
Input('filter_sources', 'value'))
def update_figure(filter_generated_outputs, filter_reference_outputs,
filter_prompts, filter_sources):
# Filter data points based on search boxes, case-insensitive
filtered_df = df.copy()
if filter_generated_outputs:
filtered_df = filtered_df[
filtered_df['generated_output'].str.lower().str.contains(
filter_generated_outputs.lower())]
if filter_reference_outputs:
filtered_df = filtered_df[
filtered_df['reference_output'].str.lower().str.contains(
filter_reference_outputs.lower())]
if filter_prompts:
filtered_df = filtered_df[
filtered_df['prompt'].str.lower().str.contains(
filter_prompts.lower())]
if filter_sources:
filtered_df = filtered_df[
filtered_df['source'].str.lower().str.contains(
filter_sources.lower())]
# Configure the actual scatter plot
fig = px.scatter(filtered_df,
x=filtered_df.index,
y=metric_value.metric_name,
hover_data=filtered_df.columns)
# Explicitly set the default axis ranges (with a little padding) so that
# the plot doesn't change when the user types in the search boxes
fig.update_xaxes(range=[-0.1, len(df)])
fig.update_yaxes(range=[
min(-0.1, math.floor(df[metric_value.metric_name].min())),
max(1.1, math.ceil(df[metric_value.metric_name].max()))
])
# However, if the user manually zoomed in, keep that zoom level even
# when update_figure() re-runs
fig.update_layout(uirevision='constant')
# Disable drag-to-zoom by default (the user can still enable it
# in the modebar)
fig.update_layout(dragmode=False)
# Display a message about how many data points are hidden
num_results_message = (
f'Showing {len(filtered_df)} of {len(df)} data points.')
return fig, num_results_message
# Display the Dash app inline in the notebook
# TODO: This doesn't seem to display inline if you click "Run All" in VSCode
# instead of running the cell directly
app.run(jupyter_mode=jupyter_mode) # type: ignore
def _scatter_two_metric_values(metric_value: MetricValue,
other_metric_value: MetricValue,
jupyter_mode: str) -> None:
'''Shows an interactive scatter plot of all data points in two
:class:`~langcheck.metrics.metric_value.MetricValue`.
'''
# Validate that the two MetricValues have the same data points
if metric_value.generated_outputs != other_metric_value.generated_outputs:
raise ValueError(
'Both MetricValues must have the same generated_outputs')
if metric_value.prompts != other_metric_value.prompts:
raise ValueError('Both MetricValues must have the same prompts')
if metric_value.reference_outputs != other_metric_value.reference_outputs:
raise ValueError(
'Both MetricValues must have the same reference_outputs')
if metric_value.language != other_metric_value.language:
raise ValueError('Both MetricValues must have the same language')
# Append "(other)" to the metric name of the second MetricValue if
# necessary. (It's possible to plot two MetricValues from the same metric,
# e.g. if you compute semantic_similarity() with a local model and an OpenAI
# model)
if metric_value.metric_name == other_metric_value.metric_name:
other_metric_value = deepcopy(other_metric_value)
other_metric_value.metric_name += ' (other)'
# Rename some MetricValue fields for display
df = metric_value.to_df()
df.rename(columns={'metric_value': metric_value.metric_name}, inplace=True)
df[other_metric_value.metric_name] = other_metric_value.to_df(
)['metric_value']
df['prompt'] = df['prompt'].fillna('None')
df['reference_output'] = df['reference_output'].fillna('None')
df['source'] = df['source'].fillna('None')
# Define layout of the Dash app (chart + search boxes)
app = Dash(__name__)
app.layout = html.Div([
html.Div([
html.Label('Filter generated_outputs: '),
dcc.Input(id='filter_generated_outputs',
type='text',
placeholder='Type to search...',
style=INPUT_CSS),
]),
html.Div([
html.Label('Filter reference_outputs: '),
dcc.Input(id='filter_reference_outputs',
type='text',
placeholder='Type to search...',
style=INPUT_CSS),
]),
html.Div([
html.Label('Filter prompts: '),
dcc.Input(id='filter_prompts',
type='text',
placeholder='Type to search...',
style=INPUT_CSS),
]),
html.Div([
html.Label('Filter sources: '),
dcc.Input(id='filter_sources',
type='text',
placeholder='Type to search...',
style=INPUT_CSS),
]),
html.Div([html.Span(id='num_results_message', style=NUM_RESULTS_CSS)]),
dcc.Graph(
id='scatter_plot',
config={
'displaylogo': False,
'modeBarButtonsToRemove': ['select', 'lasso2d', 'resetScale']
})
],
style=GLOBAL_CSS)
# This function gets called whenever the user types in the search boxes
@app.callback(Output('scatter_plot', 'figure'),
Output('num_results_message', 'children'),
Input('filter_generated_outputs', 'value'),
Input('filter_reference_outputs', 'value'),
Input('filter_prompts', 'value'),
Input('filter_sources', 'value'))
def update_figure(filter_generated_outputs, filter_reference_outputs,
filter_prompts, filter_sources):
# Filter data points based on search boxes, case-insensitive
filtered_df = df.copy()
if filter_generated_outputs:
filtered_df = filtered_df[
filtered_df['generated_output'].str.lower().str.contains(
filter_generated_outputs.lower())]
if filter_reference_outputs:
filtered_df = filtered_df[
filtered_df['reference_output'].str.lower().str.contains(
filter_reference_outputs.lower())]
if filter_prompts:
filtered_df = filtered_df[
filtered_df['prompt'].str.lower().str.contains(
filter_prompts.lower())]
if filter_sources:
filtered_df = filtered_df[
filtered_df['source'].str.lower().str.contains(
filter_sources.lower())]
# Configure the actual scatter plot
# (We need to explicitly add the index column into hover_data here.
# Unfortunately it's not possible to make "index" show up at the top of
# the tooltip like _scatter_one_metric_value() since Plotly always
# displays the x and y values at the top.)
hover_data = {col: True for col in filtered_df.columns}
hover_data['index'] = filtered_df.index
fig = px.scatter(filtered_df,
x=metric_value.metric_name,
y=other_metric_value.metric_name,
hover_data=hover_data)
# Explicitly set the default axis ranges (with a little padding) so that
# the plot doesn't change when the user types in the search boxes
fig.update_xaxes(range=[
min(-0.1, math.floor(df[metric_value.metric_name].min())),
max(1.1, math.ceil(df[metric_value.metric_name].max()))
])
fig.update_yaxes(range=[
min(-0.1, math.floor(df[other_metric_value.metric_name].min())),
max(1.1, math.ceil(df[other_metric_value.metric_name].max()))
])
# However, if the user manually zoomed in, keep that zoom level even
# when update_figure() re-runs
fig.update_layout(uirevision='constant')
# Disable drag-to-zoom by default (the user can still enable it in the
# modebar)
fig.update_layout(dragmode=False)
# Display a message about how many data points are hidden
num_results_message = (
f'Showing {len(filtered_df)} of {len(df)} data points.')
return fig, num_results_message
# Display the Dash app inline in the notebook
# TODO: This doesn't seem to display inline if you click "Run All" in VSCode
# instead of running the cell directly
app.run(jupyter_mode=jupyter_mode) # type: ignore
