def render_variables_section(dataframe_summary: dict) -> list:
"""Render the HTML for each of the variables in the DataFrame.
Args:
dataframe_summary: The statistics for each variable.
Returns:
The rendered HTML, where each row represents a variable.
"""
type_to_func = {
Boolean: render_boolean,
Real: render_real,
Count: render_real,
Complex: render_complex,
Date: render_date,
Categorical: render_categorical,
Url: render_url,
AbsolutePath: render_path,
ExistingPath: render_path,
# ImagePath: render_path_image,
Generic: render_generic,
}
templs = []
for idx, summary in dataframe_summary["variables"].items():
# Common template variables
warnings = [
warning.fmt()
for warning in dataframe_summary["messages"]
if warning.column_name == idx
]
warning_fields = {
field
for warning in dataframe_summary["messages"]
if warning.column_name == idx
for field in warning.fields
}
warning_types = {
warning.message_type
for warning in dataframe_summary["messages"]
if warning.column_name == idx
}
template_variables = {
"varname": idx,
"varid": hash(idx),
"warnings": warnings,
"warn_fields": warning_fields,
}
template_variables.update(summary)
# Per type template variables
template_variables.update(type_to_func[summary["type"]](template_variables))
# Ignore these
if config["reject_variables"].get(bool):
ignore = MessageType.REJECTED in warning_types
else:
ignore = False
bottom = None
if "bottom" in template_variables and template_variables["bottom"] is not None:
btn = ToggleButton("Toggle details", anchor_id=template_variables["varid"])
bottom = Collapse(btn, template_variables["bottom"])
var = Variable(
template_variables["top"],
bottom=bottom,
anchor_id=template_variables["varid"],
name=idx,
ignore=ignore,
)
templs.append(var)
return templs
def get_correlation_items(summary) -> Optional[Renderable]:
"""Create the list of correlation items
Args:
summary: dict of correlations
Returns:
List of correlation items to show in the interface.
"""
items = get_items()
pearson_description = (
"The Pearson's correlation coefficient (<em>r</em>) is a measure of linear correlation "
"between two variables. It's value lies between -1 and +1, -1 indicating total negative "
"linear correlation, 0 indicating no linear correlation and 1 indicating total positive "
"linear correlation. Furthermore, <em>r</em> is invariant under separate changes in location "
"and scale of the two variables, implying that for a linear function the angle to the "
"x-axis does not affect <em>r</em>.<br /><br />To calculate <em>r</em> for two "
"variables <em>X</em> and <em>Y</em>, one divides the covariance of <em>X</em> and "
"<em>Y</em> by the product of their standard deviations. ")
spearman_description = """The Spearman's rank correlation coefficient (<em>ρ</em>) is a measure of monotonic
correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than
Pearson's <em>r</em>. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation,
0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.<br /><br />To
calculate <em>ρ</em> for two variables <em>X</em> and <em>Y</em>, one divides the covariance of the rank
variables of <em>X</em> and <em>Y</em> by the product of their standard deviations. """
kendall_description = """Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation
coefficient (<em>τ</em>) measures ordinal association between two variables. It's value lies between -1 and +1,
-1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.
<br /><br />To calculate <em>τ</em> for two variables <em>X</em> and <em>Y</em>, one determines the number of
concordant and discordant pairs of observations. <em>τ</em> is given by the number of concordant pairs minus the
discordant pairs divided by the total number of pairs."""
key_to_data = {
"pearson": (-1, "Pearson's r", pearson_description),
"spearman": (-1, "Spearman's ρ", spearman_description),
"kendall": (-1, "Kendall's τ", kendall_description),
"phi_k": (0, "Phik (φk)", ""),
"cramers": (0, "Cramér's V (φc)", ""),
"recoded": (0, "Recoded", ""),
}
image_format = config["plot"]["image_format"].get(str)
for key, item in summary["correlations"].items():
vmin, name, description = key_to_data[key]
diagram = Image(
plot.correlation_matrix(item, vmin=vmin),
image_format=image_format,
alt=name,
anchor_id="{key}_diagram".format(key=key),
name=name,
classes="correlation-diagram",
)
if len(description) > 0:
desc = HTML(
'<div style="padding:20px" class="text-muted"><h3>{name}</h3>{description}</div>'
.format(description=description, name=name),
anchor_id="{key}_html".format(key=key),
classes="correlation-description",
)
tbl = Sequence([diagram, desc],
anchor_id=key,
name=name,
sequence_type="grid")
items.append(tbl)
else:
items.append(diagram)
corr = Sequence(
items,
sequence_type="tabs",
name="Correlations Tab",
anchor_id="correlations_tab",
)
if len(items) > 0:
btn = ToggleButton(
"Toggle correlation descriptions",
anchor_id="toggle-correlation-description",
name="Toggle correlation descriptions",
)
return Collapse(name="Correlations",
anchor_id="correlations",
button=btn,
item=corr)
else:
return None
def get_correlation_items(summary) -> Optional[Renderable]:
"""Create the list of correlation items
Args:
summary: dict of correlations
Returns:
List of correlation items to show in the interface.
"""
items: List[Renderable] = []
pearson_description = (
"The Pearson's correlation coefficient (<em>r</em>) is a measure of linear correlation "
"between two variables. It's value lies between -1 and +1, -1 indicating total negative "
"linear correlation, 0 indicating no linear correlation and 1 indicating total positive "
"linear correlation. Furthermore, <em>r</em> is invariant under separate changes in location "
"and scale of the two variables, implying that for a linear function the angle to the "
"x-axis does not affect <em>r</em>.<br /><br />To calculate <em>r</em> for two "
"variables <em>X</em> and <em>Y</em>, one divides the covariance of <em>X</em> and "
"<em>Y</em> by the product of their standard deviations. ")
spearman_description = """The Spearman's rank correlation coefficient (<em>ρ</em>) is a measure of monotonic
correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than
Pearson's <em>r</em>. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation,
0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.<br /><br />To
calculate <em>ρ</em> for two variables <em>X</em> and <em>Y</em>, one divides the covariance of the rank
variables of <em>X</em> and <em>Y</em> by the product of their standard deviations. """
kendall_description = """Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation
coefficient (<em>τ</em>) measures ordinal association between two variables. It's value lies between -1 and +1,
-1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.
<br /><br />To calculate <em>τ</em> for two variables <em>X</em> and <em>Y</em>, one determines the number of
concordant and discordant pairs of observations. <em>τ</em> is given by the number of concordant pairs minus the
discordant pairs divided by the total number of pairs."""
phi_k_description = """Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case
of a bivariate normal input distribution. There is extensive documentation available <a href='https://phik.readthedocs.io/en/latest/index.html'>here</a>."""
cramers_description = """Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association.
The empirical estimators used for Cramér's V have been proved to be biased, even for large samples.
We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found <a href='http://stats.lse.ac.uk/bergsma/pdf/cramerV3.pdf'>here</a>."""
key_to_data = {
"pearson": (-1, "Pearson's r", pearson_description),
"spearman": (-1, "Spearman's ρ", spearman_description),
"kendall": (-1, "Kendall's τ", kendall_description),
"phi_k": (0, "Phik (φk)", phi_k_description),
"cramers": (0, "Cramér's V (φc)", cramers_description),
}
image_format = config["plot"]["image_format"].get(str)
for key, item in summary["correlations"].items():
vmin, name, description = key_to_data[key]
diagram = Image(
plot.correlation_matrix(item, vmin=vmin),
image_format=image_format,
alt=name,
anchor_id=f"{key}_diagram",
name=name,
classes="correlation-diagram",
)
if len(description) > 0:
desc = HTML(
f'<div style="padding:20px" class="text-muted"><h3>{name}</h3>{description}</div>',
anchor_id=f"{key}_html",
classes="correlation-description",
)
tbl = Container([diagram, desc],
anchor_id=key,
name=name,
sequence_type="grid")
items.append(tbl)
else:
items.append(diagram)
corr = Container(
items,
sequence_type="tabs",
name="Correlations Tab",
anchor_id="correlations_tab",
)
if len(items) > 0:
btn = ToggleButton(
"Toggle correlation descriptions",
anchor_id="toggle-correlation-description",
name="Toggle correlation descriptions",
)
return Collapse(name="Correlations",
anchor_id="correlations",
button=btn,
item=corr)
else:
return None
def render_variables_section(dataframe_summary: dict) -> list:
"""Render the HTML for each of the variables in the DataFrame.
Args:
dataframe_summary: The statistics for each variable.
Returns:
The rendered HTML, where each row represents a variable.
"""
templs = []
descriptions = config["variables"]["descriptions"].get(dict)
show_description = config["show_variable_description"].get(bool)
reject_variables = config["reject_variables"].get(bool)
render_map = get_render_map()
for idx, summary in dataframe_summary["variables"].items():
# Common template variables
warnings = [
warning.fmt() for warning in dataframe_summary["messages"]
if warning.column_name == idx
]
warning_fields = {
field
for warning in dataframe_summary["messages"]
if warning.column_name == idx for field in warning.fields
}
warning_types = {
warning.message_type
for warning in dataframe_summary["messages"]
if warning.column_name == idx
}
template_variables = {
"varname": idx,
"varid": hash(idx),
"warnings": warnings,
"description":
descriptions.get(idx, "") if show_description else "",
"warn_fields": warning_fields,
}
template_variables.update(summary)
# Per type template variables
template_variables.update(
render_map[summary["type"]](template_variables))
# Ignore these
if reject_variables:
ignore = MessageType.REJECTED in warning_types
else:
ignore = False
bottom = None
if "bottom" in template_variables and template_variables[
"bottom"] is not None:
btn = ToggleButton("Toggle details",
anchor_id=template_variables["varid"])
bottom = Collapse(btn, template_variables["bottom"])
var = Variable(
template_variables["top"],
bottom=bottom,
anchor_id=template_variables["varid"],
name=idx,
ignore=ignore,
)
templs.append(var)
return templs
def render_variables_section(config: Settings, dataframe_summary: dict) -> list:
"""Render the HTML for each of the variables in the DataFrame.
Args:
config: report Settings object
dataframe_summary: The statistics for each variable.
Returns:
The rendered HTML, where each row represents a variable.
"""
templs = []
descriptions = config.variables.descriptions
show_description = config.show_variable_description
reject_variables = config.reject_variables
render_map = get_render_map()
for idx, summary in dataframe_summary["variables"].items():
# Common template variables
alerts = [
alert.fmt()
for alert in dataframe_summary["alerts"]
if alert.column_name == idx
]
alert_fields = {
field
for alert in dataframe_summary["alerts"]
if alert.column_name == idx
for field in alert.fields
}
alert_types = {
alert.alert_type
for alert in dataframe_summary["alerts"]
if alert.column_name == idx
}
template_variables = {
"varname": idx,
"varid": hash(idx),
"alerts": alerts,
"description": descriptions.get(idx, "") if show_description else "",
"alert_fields": alert_fields,
}
template_variables.update(summary)
# Per type template variables
template_variables.update(
render_map[summary["type"]](config, template_variables)
)
# Ignore these
if reject_variables:
ignore = AlertType.REJECTED in alert_types
else:
ignore = False
bottom = None
if "bottom" in template_variables and template_variables["bottom"] is not None:
btn = ToggleButton("Toggle details", anchor_id=template_variables["varid"])
bottom = Collapse(btn, template_variables["bottom"])
var = Variable(
template_variables["top"],
bottom=bottom,
anchor_id=template_variables["varid"],
name=idx,
ignore=ignore,
)
templs.append(var)
return templs
