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Add new get_cardinality_plot #453

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Sep 20, 2023
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Add new get_cardinality_plot #453

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06bf48a
move get_cardinality_plot and refactor figure plotting a bit
frances-h Sep 18, 2023
b1b7504
tests
frances-h Sep 18, 2023
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130 changes: 0 additions & 130 deletions sdmetrics/reports/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -456,136 +456,6 @@ def get_column_pair_plot(real_data, synthetic_data, column_names, metadata):
return make_mixed_column_pair_plot(real_data, synthetic_data)


def _get_cardinality(parent_table, child_table, parent_primary_key, child_foreign_key):
"""Return the cardinality of the parent-child relationship.

Args:
parent_table (pandas.DataFrame):
The parent table.
child_table (pandas.DataFrame):
The child table.
parent_primary_key (string):
The name of the primary key column in the parent table.
child_foreign_key (string):
The name of the foreign key column in the child table.

Returns:
pandas.DataFrame
"""
child_counts = child_table[child_foreign_key].value_counts()
child_per_parent = child_counts.reindex(parent_table[parent_primary_key], fill_value=0)
child_per_parent = child_per_parent.reset_index()
child_per_parent.columns = [parent_primary_key, '# children']

cardinalities = child_per_parent.groupby('# children').size().reset_index(name='# parents')

return cardinalities.sort_values('# children')


def _generate_cardinality_plot(data, parent_primary_key, child_foreign_key):
"""Generate a plot of the cardinality of the parent-child relationship.

Args:
data (pandas.DataFrame):
The cardinality data.
parent_primary_key (string):
The name of the primary key column in the parent table.
child_foreign_key (string):
The name of the foreign key column in the child table.

Returns:
plotly.graph_objects._figure.Figure
"""
fig = px.histogram(
data,
x='# children',
y='# parents',
color='data',
barmode='group',
color_discrete_sequence=[PlotConfig.DATACEBO_DARK, PlotConfig.DATACEBO_GREEN],
pattern_shape='data',
pattern_shape_sequence=['', '/'],
nbins=max(data['# children']) - min(data['# children']) + 1,
histnorm='probability density'
)

for name in ['Real', 'Synthetic']:
fig.update_traces(
hovertemplate=f'<b>{name}</b><br>Frequency: %{{y}}<extra></extra>',
selector={'name': name}
)

title = (
f"Relationship (child foreign key='{child_foreign_key}' and parent "
f"primary key='{parent_primary_key}')"
)
fig.update_layout(
title=title,
xaxis_title='# of Children (per Parent)',
yaxis_title='Frequency',
plot_bgcolor=PlotConfig.BACKGROUND_COLOR,
font={'size': PlotConfig.FONT_SIZE},
)
return fig


def get_cardinality_plot(real_data, synthetic_data, child_table_name, parent_table_name,
child_foreign_key, metadata):
"""Return a plot of the cardinality of the parent-child relationship.

Args:
real_data (dict):
The real data.
synthetic_data (dict):
The synthetic data.
child_table_name (string):
The name of the child table.
parent_table_name (string):
The name of the parent table.
child_foreign_key (string):
The name of the foreign key column in the child table.
metadata (dict):
The metadata.

Returns:
plotly.graph_objects._figure.Figure
"""
parent_primary_key = None
for relation in metadata.get('relationships', []):
parent_match = relation['parent_table_name'] == parent_table_name
child_match = relation['child_table_name'] == child_table_name
foreign_key_match = relation['child_foreign_key'] == child_foreign_key
if child_match and parent_match and foreign_key_match:
parent_primary_key = relation['parent_primary_key']

if parent_primary_key is None:
raise ValueError(
f"No relationship found between child table '{child_table_name}' and parent table "
f"'{parent_table_name}' for the foreign key '{child_foreign_key}' "
'in the metadata. Please update the metadata.'
)

real_cardinality = _get_cardinality(
real_data[parent_table_name], real_data[child_table_name],
parent_primary_key, child_foreign_key
)
synth_cardinality = _get_cardinality(
synthetic_data[parent_table_name],
synthetic_data[child_table_name],
parent_primary_key, child_foreign_key
)

real_cardinality['data'] = 'Real'
synth_cardinality['data'] = 'Synthetic'

all_cardinality = pd.concat([real_cardinality, synth_cardinality], ignore_index=True)
fig = _generate_cardinality_plot(
all_cardinality, parent_primary_key, child_foreign_key
)

return fig


def discretize_table_data(real_data, synthetic_data, metadata):
"""Create a copy of the real and synthetic data with discretized data.

Expand Down
261 changes: 261 additions & 0 deletions sdmetrics/visualization.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,261 @@
"""Visualization methods for SDMetrics."""

import pandas as pd
import plotly.express as px
import plotly.figure_factory as ff
from pandas.api.types import is_datetime64_dtype

from sdmetrics.reports.utils import PlotConfig
from sdmetrics.utils import get_missing_percentage


def _generate_column_bar_plot(real_data, synthetic_data, plot_kwargs={}):
"""Generate a bar plot of the real and synthetic data.

Args:
real_column (pandas.Series):
The real data for the desired column.
synthetic_column (pandas.Series):
The synthetic data for the desired column.
plot_kwargs (dict, optional):
Dictionary of keyword arguments to pass to px.histogram. Keyword arguments
provided this way will overwrite defaults.

Returns:
plotly.graph_objects._figure.Figure
"""
all_data = pd.concat([real_data, synthetic_data], axis=0, ignore_index=True)
default_histogram_kwargs = {
'x': 'values',
'color': 'Data',
'barmode': 'group',
'color_discrete_sequence': [PlotConfig.DATACEBO_DARK, PlotConfig.DATACEBO_GREEN],
'pattern_shape': 'Data',
'pattern_shape_sequence': ['', '/'],
'histnorm': 'probability density',
}
fig = px.histogram(
all_data,
**{**default_histogram_kwargs, **plot_kwargs}
)

return fig


def _generate_column_distplot(real_data, synthetic_data, plot_kwargs={}):
"""Plot the real and synthetic data as a distplot.

Args:
real_data (pandas.DataFrame):
The real data for the desired column.
synthetic_data (pandas.DataFrame):
The synthetic data for the desired column.
plot_kwargs (dict, optional):
Dictionary of keyword arguments to pass to px.histogram. Keyword arguments
provided this way will overwrite defaults.

Returns:
plotly.graph_objects._figure.Figure
"""
default_distplot_kwargs = {
'show_hist': False,
'show_rug': False,
'colors': [PlotConfig.DATACEBO_DARK, PlotConfig.DATACEBO_GREEN]
}

fig = ff.create_distplot(
[real_data['values'], synthetic_data['values']],
['Real', 'Synthetic'],
**{**default_distplot_kwargs, **plot_kwargs}
)

return fig


def _generate_column_plot(real_column,
synthetic_column,
plot_type,
plot_kwargs={},
plot_title=None,
x_label=None):
"""Generate a plot of the real and synthetic data.

Args:
real_column (pandas.Series):
The real data for the desired column.
synthetic_column (pandas.Series):
The synthetic data for the desired column.
plot_type (str):
The type of plot to use. Must be one of 'bar' or 'distplot'.
hist_kwargs (dict, optional):
Dictionary of keyword arguments to pass to px.histogram. Keyword arguments
provided this way will overwrite defaults.
plot_title (str, optional):
Title to use for the plot. Defaults to 'Real vs. Synthetic Data for column {column}'
x_label (str, optional):
Label to use for x-axis. Defaults to 'Category'.

Returns:
plotly.graph_objects._figure.Figure
"""
if plot_type not in ['bar', 'distplot']:
raise ValueError(
"Unrecognized plot_type '{plot_type}'. Pleas use one of 'bar' or 'distplot'"
)

column_name = real_column.name if hasattr(real_column, 'name') else ''

real_data = pd.DataFrame({'values': real_column.copy()})
real_data['Data'] = 'Real'
synthetic_data = pd.DataFrame({'values': synthetic_column.copy()})
synthetic_data['Data'] = 'Synthetic'

is_datetime_sdtype = False
if is_datetime64_dtype(real_column.dtype):
is_datetime_sdtype = True
real_data = real_data.astype('int64')
synthetic_data = synthetic_data.astype('int64')

missing_data_real = get_missing_percentage(real_column)
missing_data_synthetic = get_missing_percentage(synthetic_column)

trace_args = {}

if plot_type == 'bar':
fig = _generate_column_bar_plot(real_data, synthetic_data, plot_kwargs)
elif plot_type == 'distplot':
fig = _generate_column_distplot(real_data, synthetic_data, plot_kwargs)
trace_args = {'fill': 'tozeroy'}

for i, name in enumerate(['Real', 'Synthetic']):
fig.update_traces(
x=pd.to_datetime(fig.data[i].x) if is_datetime_sdtype else fig.data[i].x,
hovertemplate=f'<b>{name}</b><br>Frequency: %{{y}}<extra></extra>',
selector={'name': name},
**trace_args
)

show_missing_values = missing_data_real > 0 or missing_data_synthetic > 0
annotations = [] if not show_missing_values else [
{
'xref': 'paper',
'yref': 'paper',
'x': 1.0,
'y': 1.05,
'showarrow': False,
'text': (
f'*Missing Values: Real Data ({missing_data_real}%), '
f'Synthetic Data ({missing_data_synthetic}%)'
),
},
]

if not plot_title:
plot_title = f"Real vs. Synthetic Data for column '{column_name}'"

if not x_label:
x_label = 'Category'

fig.update_layout(
title=plot_title,
xaxis_title=x_label,
yaxis_title='Frequency',
plot_bgcolor=PlotConfig.BACKGROUND_COLOR,
annotations=annotations,
font={'size': PlotConfig.FONT_SIZE},
)
return fig


def _generate_cardinality_plot(real_data,
synthetic_data,
parent_primary_key,
child_foreign_key,
plot_type='bar'):
plot_title = (
f"Relationship (child foreign key='{child_foreign_key}' and parent "
f"primary key='{parent_primary_key}')"
)
x_label = '# of Children (per Parent)'

plot_kwargs = {}
if plot_type == 'bar':
max_cardinality = max(max(real_data), max(synthetic_data))
min_cardinality = min(min(real_data), min(synthetic_data))
plot_kwargs = {
'nbins': max_cardinality - min_cardinality + 1
}

return _generate_column_plot(real_data, synthetic_data, plot_type,
plot_kwargs, plot_title, x_label)


def _get_cardinality(parent_table, child_table, parent_primary_key, child_foreign_key):
"""Return the cardinality of the parent-child relationship.

Args:
parent_table (pandas.DataFrame):
The parent table.
child_table (pandas.DataFrame):
The child table.
parent_primary_key (string):
The name of the primary key column in the parent table.
child_foreign_key (string):
The name of the foreign key column in the child table.

Returns:
pandas.DataFrame
"""
child_counts = child_table[child_foreign_key].value_counts().rename('# children')
cardinalities = child_counts.reindex(parent_table[parent_primary_key], fill_value=0).to_frame()

return cardinalities.sort_values('# children')['# children']


def get_cardinality_plot(real_data, synthetic_data, child_table_name, parent_table_name,
child_foreign_key, parent_primary_key, plot_type='bar'):
"""Return a plot of the cardinality of the parent-child relationship.

Args:
real_data (dict):
The real data.
synthetic_data (dict):
The synthetic data.
child_table_name (string):
The name of the child table.
parent_table_name (string):
The name of the parent table.
child_foreign_key (string):
The name of the foreign key column in the child table.
parent_primary_key (string):
The name of the primary key column in the parent table.
plot_type (string, optional):
The plot type to use to plot the cardinality. Must be either 'bar' or 'distplot'.
Defaults to 'bar'.

Returns:
plotly.graph_objects._figure.Figure
"""
if plot_type not in ['bar', 'distplot']:
raise ValueError(
f"Invalid plot_type '{plot_type}'. Please use one of ['bar', 'distplot'].")

real_cardinality = _get_cardinality(
real_data[parent_table_name], real_data[child_table_name],
parent_primary_key, child_foreign_key
)
synth_cardinality = _get_cardinality(
synthetic_data[parent_table_name],
synthetic_data[child_table_name],
parent_primary_key, child_foreign_key
)

fig = _generate_cardinality_plot(
real_cardinality,
synth_cardinality,
parent_primary_key,
child_foreign_key,
plot_type=plot_type
)

return fig
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