repository
stringclasses 11
values | repo_id
stringlengths 1
3
| target_module_path
stringlengths 16
72
| prompt
stringlengths 298
21.7k
| relavent_test_path
stringlengths 50
99
| full_function
stringlengths 336
33.8k
| function_name
stringlengths 2
51
|
|---|---|---|---|---|---|---|
seaborn | 0 | seaborn/_core/scales.py | def label(
self,
formatter: Formatter | None = None, *,
like: str | Callable | None = None,
base: int | None | Default = default,
unit: str | None = None,
) -> Continuous:
"""
Configure the appearance of tick labels for the scale's axis or legend.
Parameters
----------
formatter : :class:`matplotlib.ticker.Formatter` subclass
Pre-configured formatter to use; other parameters will be ignored.
like : str or callable
Either a format pattern (e.g., `".2f"`), a format string with fields named
`x` and/or `pos` (e.g., `"${x:.2f}"`), or a callable with a signature like
`f(x: float, pos: int) -> str`. In the latter variants, `x` is passed as the
tick value and `pos` is passed as the tick index.
base : number
Use log formatter (with scientific notation) having this value as the base.
Set to `None` to override the default formatter with a log transform.
unit : str or (str, str) tuple
Use SI prefixes with these units (e.g., with `unit="g"`, a tick value
of 5000 will appear as `5 kg`). When a tuple, the first element gives the
separator between the number and unit.
Returns
-------
scale
Copy of self with new label configuration.
"""
| /usr/src/app/target_test_cases/failed_tests_Continuous.label.txt | def label(
self,
formatter: Formatter | None = None, *,
like: str | Callable | None = None,
base: int | None | Default = default,
unit: str | None = None,
) -> Continuous:
"""
Configure the appearance of tick labels for the scale's axis or legend.
Parameters
----------
formatter : :class:`matplotlib.ticker.Formatter` subclass
Pre-configured formatter to use; other parameters will be ignored.
like : str or callable
Either a format pattern (e.g., `".2f"`), a format string with fields named
`x` and/or `pos` (e.g., `"${x:.2f}"`), or a callable with a signature like
`f(x: float, pos: int) -> str`. In the latter variants, `x` is passed as the
tick value and `pos` is passed as the tick index.
base : number
Use log formatter (with scientific notation) having this value as the base.
Set to `None` to override the default formatter with a log transform.
unit : str or (str, str) tuple
Use SI prefixes with these units (e.g., with `unit="g"`, a tick value
of 5000 will appear as `5 kg`). When a tuple, the first element gives the
separator between the number and unit.
Returns
-------
scale
Copy of self with new label configuration.
"""
# Input checks
if formatter is not None and not isinstance(formatter, Formatter):
raise TypeError(
f"Label formatter must be an instance of {Formatter!r}, "
f"not {type(formatter)!r}"
)
if like is not None and not (isinstance(like, str) or callable(like)):
msg = f"`like` must be a string or callable, not {type(like).__name__}."
raise TypeError(msg)
new = copy(self)
new._label_params = {
"formatter": formatter,
"like": like,
"base": base,
"unit": unit,
}
return new
| Continuous.label |
seaborn | 1 | seaborn/_core/plot.py | def add(
self,
mark: Mark,
*transforms: Stat | Move,
orient: str | None = None,
legend: bool = True,
label: str | None = None,
data: DataSource = None,
**variables: VariableSpec,
) -> Plot:
"""
Specify a layer of the visualization in terms of mark and data transform(s).
This is the main method for specifying how the data should be visualized.
It can be called multiple times with different arguments to define
a plot with multiple layers.
Parameters
----------
mark : :class:`Mark`
The visual representation of the data to use in this layer.
transforms : :class:`Stat` or :class:`Move`
Objects representing transforms to be applied before plotting the data.
Currently, at most one :class:`Stat` can be used, and it
must be passed first. This constraint will be relaxed in the future.
orient : "x", "y", "v", or "h"
The orientation of the mark, which also affects how transforms are computed.
Typically corresponds to the axis that defines groups for aggregation.
The "v" (vertical) and "h" (horizontal) options are synonyms for "x" / "y",
but may be more intuitive with some marks. When not provided, an
orientation will be inferred from characteristics of the data and scales.
legend : bool
Option to suppress the mark/mappings for this layer from the legend.
label : str
A label to use for the layer in the legend, independent of any mappings.
data : DataFrame or dict
Data source to override the global source provided in the constructor.
variables : data vectors or identifiers
Additional layer-specific variables, including variables that will be
passed directly to the transforms without scaling.
Examples
--------
.. include:: ../docstrings/objects.Plot.add.rst
"""
| /usr/src/app/target_test_cases/failed_tests_Plot.add.txt | def add(
self,
mark: Mark,
*transforms: Stat | Move,
orient: str | None = None,
legend: bool = True,
label: str | None = None,
data: DataSource = None,
**variables: VariableSpec,
) -> Plot:
"""
Specify a layer of the visualization in terms of mark and data transform(s).
This is the main method for specifying how the data should be visualized.
It can be called multiple times with different arguments to define
a plot with multiple layers.
Parameters
----------
mark : :class:`Mark`
The visual representation of the data to use in this layer.
transforms : :class:`Stat` or :class:`Move`
Objects representing transforms to be applied before plotting the data.
Currently, at most one :class:`Stat` can be used, and it
must be passed first. This constraint will be relaxed in the future.
orient : "x", "y", "v", or "h"
The orientation of the mark, which also affects how transforms are computed.
Typically corresponds to the axis that defines groups for aggregation.
The "v" (vertical) and "h" (horizontal) options are synonyms for "x" / "y",
but may be more intuitive with some marks. When not provided, an
orientation will be inferred from characteristics of the data and scales.
legend : bool
Option to suppress the mark/mappings for this layer from the legend.
label : str
A label to use for the layer in the legend, independent of any mappings.
data : DataFrame or dict
Data source to override the global source provided in the constructor.
variables : data vectors or identifiers
Additional layer-specific variables, including variables that will be
passed directly to the transforms without scaling.
Examples
--------
.. include:: ../docstrings/objects.Plot.add.rst
"""
if not isinstance(mark, Mark):
msg = f"mark must be a Mark instance, not {type(mark)!r}."
raise TypeError(msg)
# TODO This API for transforms was a late decision, and previously Plot.add
# accepted 0 or 1 Stat instances and 0, 1, or a list of Move instances.
# It will take some work to refactor the internals so that Stat and Move are
# treated identically, and until then well need to "unpack" the transforms
# here and enforce limitations on the order / types.
stat: Optional[Stat]
move: Optional[List[Move]]
error = False
if not transforms:
stat, move = None, None
elif isinstance(transforms[0], Stat):
stat = transforms[0]
move = [m for m in transforms[1:] if isinstance(m, Move)]
error = len(move) != len(transforms) - 1
else:
stat = None
move = [m for m in transforms if isinstance(m, Move)]
error = len(move) != len(transforms)
if error:
msg = " ".join([
"Transforms must have at most one Stat type (in the first position),",
"and all others must be a Move type. Given transform type(s):",
", ".join(str(type(t).__name__) for t in transforms) + "."
])
raise TypeError(msg)
new = self._clone()
new._layers.append({
"mark": mark,
"stat": stat,
"move": move,
# TODO it doesn't work to supply scalars to variables, but it should
"vars": variables,
"source": data,
"legend": legend,
"label": label,
"orient": {"v": "x", "h": "y"}.get(orient, orient), # type: ignore
})
return new
| Plot.add |
seaborn | 2 | seaborn/_core/plot.py | def facet(
self,
col: VariableSpec = None,
row: VariableSpec = None,
order: OrderSpec | dict[str, OrderSpec] = None,
wrap: int | None = None,
) -> Plot:
"""
Produce subplots with conditional subsets of the data.
Parameters
----------
col, row : data vectors or identifiers
Variables used to define subsets along the columns and/or rows of the grid.
Can be references to the global data source passed in the constructor.
order : list of strings, or dict with dimensional keys
Define the order of the faceting variables.
wrap : int
When using only `col` or `row`, wrap subplots across a two-dimensional
grid with this many subplots on the faceting dimension.
Examples
--------
.. include:: ../docstrings/objects.Plot.facet.rst
"""
| /usr/src/app/target_test_cases/failed_tests_Plot.facet.txt | def facet(
self,
col: VariableSpec = None,
row: VariableSpec = None,
order: OrderSpec | dict[str, OrderSpec] = None,
wrap: int | None = None,
) -> Plot:
"""
Produce subplots with conditional subsets of the data.
Parameters
----------
col, row : data vectors or identifiers
Variables used to define subsets along the columns and/or rows of the grid.
Can be references to the global data source passed in the constructor.
order : list of strings, or dict with dimensional keys
Define the order of the faceting variables.
wrap : int
When using only `col` or `row`, wrap subplots across a two-dimensional
grid with this many subplots on the faceting dimension.
Examples
--------
.. include:: ../docstrings/objects.Plot.facet.rst
"""
variables: dict[str, VariableSpec] = {}
if col is not None:
variables["col"] = col
if row is not None:
variables["row"] = row
structure = {}
if isinstance(order, dict):
for dim in ["col", "row"]:
dim_order = order.get(dim)
if dim_order is not None:
structure[dim] = list(dim_order)
elif order is not None:
if col is not None and row is not None:
err = " ".join([
"When faceting on both col= and row=, passing `order` as a list"
"is ambiguous. Use a dict with 'col' and/or 'row' keys instead."
])
raise RuntimeError(err)
elif col is not None:
structure["col"] = list(order)
elif row is not None:
structure["row"] = list(order)
spec: FacetSpec = {
"variables": variables,
"structure": structure,
"wrap": wrap,
}
new = self._clone()
new._facet_spec.update(spec)
return new
| Plot.facet |
seaborn | 3 | seaborn/_core/plot.py | def on(self, target: Axes | SubFigure | Figure) -> Plot:
"""
Provide existing Matplotlib figure or axes for drawing the plot.
When using this method, you will also need to explicitly call a method that
triggers compilation, such as :meth:`Plot.show` or :meth:`Plot.save`. If you
want to postprocess using matplotlib, you'd need to call :meth:`Plot.plot`
first to compile the plot without rendering it.
Parameters
----------
target : Axes, SubFigure, or Figure
Matplotlib object to use. Passing :class:`matplotlib.axes.Axes` will add
artists without otherwise modifying the figure. Otherwise, subplots will be
created within the space of the given :class:`matplotlib.figure.Figure` or
:class:`matplotlib.figure.SubFigure`.
Examples
--------
.. include:: ../docstrings/objects.Plot.on.rst
"""
| /usr/src/app/target_test_cases/failed_tests_Plot.on.txt | def on(self, target: Axes | SubFigure | Figure) -> Plot:
"""
Provide existing Matplotlib figure or axes for drawing the plot.
When using this method, you will also need to explicitly call a method that
triggers compilation, such as :meth:`Plot.show` or :meth:`Plot.save`. If you
want to postprocess using matplotlib, you'd need to call :meth:`Plot.plot`
first to compile the plot without rendering it.
Parameters
----------
target : Axes, SubFigure, or Figure
Matplotlib object to use. Passing :class:`matplotlib.axes.Axes` will add
artists without otherwise modifying the figure. Otherwise, subplots will be
created within the space of the given :class:`matplotlib.figure.Figure` or
:class:`matplotlib.figure.SubFigure`.
Examples
--------
.. include:: ../docstrings/objects.Plot.on.rst
"""
accepted_types: tuple # Allow tuple of various length
accepted_types = (
mpl.axes.Axes, mpl.figure.SubFigure, mpl.figure.Figure
)
accepted_types_str = (
f"{mpl.axes.Axes}, {mpl.figure.SubFigure}, or {mpl.figure.Figure}"
)
if not isinstance(target, accepted_types):
err = (
f"The `Plot.on` target must be an instance of {accepted_types_str}. "
f"You passed an instance of {target.__class__} instead."
)
raise TypeError(err)
new = self._clone()
new._target = target
return new
| Plot.on |
seaborn | 4 | seaborn/_core/plot.py | def pair(
self,
x: VariableSpecList = None,
y: VariableSpecList = None,
wrap: int | None = None,
cross: bool = True,
) -> Plot:
"""
Produce subplots by pairing multiple `x` and/or `y` variables.
Parameters
----------
x, y : sequence(s) of data vectors or identifiers
Variables that will define the grid of subplots.
wrap : int
When using only `x` or `y`, "wrap" subplots across a two-dimensional grid
with this many columns (when using `x`) or rows (when using `y`).
cross : bool
When False, zip the `x` and `y` lists such that the first subplot gets the
first pair, the second gets the second pair, etc. Otherwise, create a
two-dimensional grid from the cartesian product of the lists.
Examples
--------
.. include:: ../docstrings/objects.Plot.pair.rst
"""
| /usr/src/app/target_test_cases/failed_tests_Plot.pair.txt | def pair(
self,
x: VariableSpecList = None,
y: VariableSpecList = None,
wrap: int | None = None,
cross: bool = True,
) -> Plot:
"""
Produce subplots by pairing multiple `x` and/or `y` variables.
Parameters
----------
x, y : sequence(s) of data vectors or identifiers
Variables that will define the grid of subplots.
wrap : int
When using only `x` or `y`, "wrap" subplots across a two-dimensional grid
with this many columns (when using `x`) or rows (when using `y`).
cross : bool
When False, zip the `x` and `y` lists such that the first subplot gets the
first pair, the second gets the second pair, etc. Otherwise, create a
two-dimensional grid from the cartesian product of the lists.
Examples
--------
.. include:: ../docstrings/objects.Plot.pair.rst
"""
# TODO Add transpose= arg, which would then draw pair(y=[...]) across rows
# This may also be possible by setting `wrap=1`, but is that too unobvious?
# TODO PairGrid features not currently implemented: diagonals, corner
pair_spec: PairSpec = {}
axes = {"x": [] if x is None else x, "y": [] if y is None else y}
for axis, arg in axes.items():
if isinstance(arg, (str, int)):
err = f"You must pass a sequence of variable keys to `{axis}`"
raise TypeError(err)
pair_spec["variables"] = {}
pair_spec["structure"] = {}
for axis in "xy":
keys = []
for i, col in enumerate(axes[axis]):
key = f"{axis}{i}"
keys.append(key)
pair_spec["variables"][key] = col
if keys:
pair_spec["structure"][axis] = keys
if not cross and len(axes["x"]) != len(axes["y"]):
err = "Lengths of the `x` and `y` lists must match with cross=False"
raise ValueError(err)
pair_spec["cross"] = cross
pair_spec["wrap"] = wrap
new = self._clone()
new._pair_spec.update(pair_spec)
return new
| Plot.pair |
seaborn | 5 | seaborn/_base.py | def _attach(
self,
obj,
allowed_types=None,
log_scale=None,
):
"""Associate the plotter with an Axes manager and initialize its units.
Parameters
----------
obj : :class:`matplotlib.axes.Axes` or :class:'FacetGrid`
Structural object that we will eventually plot onto.
allowed_types : str or list of str
If provided, raise when either the x or y variable does not have
one of the declared seaborn types.
log_scale : bool, number, or pair of bools or numbers
If not False, set the axes to use log scaling, with the given
base or defaulting to 10. If a tuple, interpreted as separate
arguments for the x and y axes.
"""
| /usr/src/app/target_test_cases/failed_tests__base.VectorPlotter._attach.txt | def _attach(
self,
obj,
allowed_types=None,
log_scale=None,
):
"""Associate the plotter with an Axes manager and initialize its units.
Parameters
----------
obj : :class:`matplotlib.axes.Axes` or :class:'FacetGrid`
Structural object that we will eventually plot onto.
allowed_types : str or list of str
If provided, raise when either the x or y variable does not have
one of the declared seaborn types.
log_scale : bool, number, or pair of bools or numbers
If not False, set the axes to use log scaling, with the given
base or defaulting to 10. If a tuple, interpreted as separate
arguments for the x and y axes.
"""
from .axisgrid import FacetGrid
if isinstance(obj, FacetGrid):
self.ax = None
self.facets = obj
ax_list = obj.axes.flatten()
if obj.col_names is not None:
self.var_levels["col"] = obj.col_names
if obj.row_names is not None:
self.var_levels["row"] = obj.row_names
else:
self.ax = obj
self.facets = None
ax_list = [obj]
# Identify which "axis" variables we have defined
axis_variables = set("xy").intersection(self.variables)
# -- Verify the types of our x and y variables here.
# This doesn't really make complete sense being here here, but it's a fine
# place for it, given the current system.
# (Note that for some plots, there might be more complicated restrictions)
# e.g. the categorical plots have their own check that as specific to the
# non-categorical axis.
if allowed_types is None:
allowed_types = ["numeric", "datetime", "categorical"]
elif isinstance(allowed_types, str):
allowed_types = [allowed_types]
for var in axis_variables:
var_type = self.var_types[var]
if var_type not in allowed_types:
err = (
f"The {var} variable is {var_type}, but one of "
f"{allowed_types} is required"
)
raise TypeError(err)
# -- Get axis objects for each row in plot_data for type conversions and scaling
facet_dim = {"x": "col", "y": "row"}
self.converters = {}
for var in axis_variables:
other_var = {"x": "y", "y": "x"}[var]
converter = pd.Series(index=self.plot_data.index, name=var, dtype=object)
share_state = getattr(self.facets, f"_share{var}", True)
# Simplest cases are that we have a single axes, all axes are shared,
# or sharing is only on the orthogonal facet dimension. In these cases,
# all datapoints get converted the same way, so use the first axis
if share_state is True or share_state == facet_dim[other_var]:
converter.loc[:] = getattr(ax_list[0], f"{var}axis")
else:
# Next simplest case is when no axes are shared, and we can
# use the axis objects within each facet
if share_state is False:
for axes_vars, axes_data in self.iter_data():
ax = self._get_axes(axes_vars)
converter.loc[axes_data.index] = getattr(ax, f"{var}axis")
# In the more complicated case, the axes are shared within each
# "file" of the facetgrid. In that case, we need to subset the data
# for that file and assign it the first axis in the slice of the grid
else:
names = getattr(self.facets, f"{share_state}_names")
for i, level in enumerate(names):
idx = (i, 0) if share_state == "row" else (0, i)
axis = getattr(self.facets.axes[idx], f"{var}axis")
converter.loc[self.plot_data[share_state] == level] = axis
# Store the converter vector, which we use elsewhere (e.g comp_data)
self.converters[var] = converter
# Now actually update the matplotlib objects to do the conversion we want
grouped = self.plot_data[var].groupby(self.converters[var], sort=False)
for converter, seed_data in grouped:
if self.var_types[var] == "categorical":
if self._var_ordered[var]:
order = self.var_levels[var]
else:
order = None
seed_data = categorical_order(seed_data, order)
converter.update_units(seed_data)
# -- Set numerical axis scales
# First unpack the log_scale argument
if log_scale is None:
scalex = scaley = False
else:
# Allow single value or x, y tuple
try:
scalex, scaley = log_scale
except TypeError:
scalex = log_scale if self.var_types.get("x") == "numeric" else False
scaley = log_scale if self.var_types.get("y") == "numeric" else False
# Now use it
for axis, scale in zip("xy", (scalex, scaley)):
if scale:
for ax in ax_list:
set_scale = getattr(ax, f"set_{axis}scale")
if scale is True:
set_scale("log", nonpositive="mask")
else:
set_scale("log", base=scale, nonpositive="mask")
# For categorical y, we want the "first" level to be at the top of the axis
if self.var_types.get("y", None) == "categorical":
for ax in ax_list:
ax.yaxis.set_inverted(True)
# TODO -- Add axes labels
| VectorPlotter._attach |
seaborn | 6 | seaborn/_base.py | def iter_data(
self, grouping_vars=None, *,
reverse=False, from_comp_data=False,
by_facet=True, allow_empty=False, dropna=True,
):
"""Generator for getting subsets of data defined by semantic variables.
Also injects "col" and "row" into grouping semantics.
Parameters
----------
grouping_vars : string or list of strings
Semantic variables that define the subsets of data.
reverse : bool
If True, reverse the order of iteration.
from_comp_data : bool
If True, use self.comp_data rather than self.plot_data
by_facet : bool
If True, add faceting variables to the set of grouping variables.
allow_empty : bool
If True, yield an empty dataframe when no observations exist for
combinations of grouping variables.
dropna : bool
If True, remove rows with missing data.
Yields
------
sub_vars : dict
Keys are semantic names, values are the level of that semantic.
sub_data : :class:`pandas.DataFrame`
Subset of ``plot_data`` for this combination of semantic values.
"""
| /usr/src/app/target_test_cases/failed_tests_VectorPlotter.iter_data.txt | def iter_data(
self, grouping_vars=None, *,
reverse=False, from_comp_data=False,
by_facet=True, allow_empty=False, dropna=True,
):
"""Generator for getting subsets of data defined by semantic variables.
Also injects "col" and "row" into grouping semantics.
Parameters
----------
grouping_vars : string or list of strings
Semantic variables that define the subsets of data.
reverse : bool
If True, reverse the order of iteration.
from_comp_data : bool
If True, use self.comp_data rather than self.plot_data
by_facet : bool
If True, add faceting variables to the set of grouping variables.
allow_empty : bool
If True, yield an empty dataframe when no observations exist for
combinations of grouping variables.
dropna : bool
If True, remove rows with missing data.
Yields
------
sub_vars : dict
Keys are semantic names, values are the level of that semantic.
sub_data : :class:`pandas.DataFrame`
Subset of ``plot_data`` for this combination of semantic values.
"""
# TODO should this default to using all (non x/y?) semantics?
# or define grouping vars somewhere?
if grouping_vars is None:
grouping_vars = []
elif isinstance(grouping_vars, str):
grouping_vars = [grouping_vars]
elif isinstance(grouping_vars, tuple):
grouping_vars = list(grouping_vars)
# Always insert faceting variables
if by_facet:
facet_vars = {"col", "row"}
grouping_vars.extend(
facet_vars & set(self.variables) - set(grouping_vars)
)
# Reduce to the semantics used in this plot
grouping_vars = [var for var in grouping_vars if var in self.variables]
if from_comp_data:
data = self.comp_data
else:
data = self.plot_data
if dropna:
data = data.dropna()
levels = self.var_levels.copy()
if from_comp_data:
for axis in {"x", "y"} & set(grouping_vars):
converter = self.converters[axis].iloc[0]
if self.var_types[axis] == "categorical":
if self._var_ordered[axis]:
# If the axis is ordered, then the axes in a possible
# facet grid are by definition "shared", or there is a
# single axis with a unique cat -> idx mapping.
# So we can just take the first converter object.
levels[axis] = converter.convert_units(levels[axis])
else:
# Otherwise, the mappings may not be unique, but we can
# use the unique set of index values in comp_data.
levels[axis] = np.sort(data[axis].unique())
else:
transform = converter.get_transform().transform
levels[axis] = transform(converter.convert_units(levels[axis]))
if grouping_vars:
grouped_data = data.groupby(
grouping_vars, sort=False, as_index=False, observed=False,
)
grouping_keys = []
for var in grouping_vars:
key = levels.get(var)
grouping_keys.append([] if key is None else key)
iter_keys = itertools.product(*grouping_keys)
if reverse:
iter_keys = reversed(list(iter_keys))
for key in iter_keys:
pd_key = (
key[0] if len(key) == 1 and _version_predates(pd, "2.2.0") else key
)
try:
data_subset = grouped_data.get_group(pd_key)
except KeyError:
# XXX we are adding this to allow backwards compatibility
# with the empty artists that old categorical plots would
# add (before 0.12), which we may decide to break, in which
# case this option could be removed
data_subset = data.loc[[]]
if data_subset.empty and not allow_empty:
continue
sub_vars = dict(zip(grouping_vars, key))
yield sub_vars, data_subset.copy()
else:
yield {}, data.copy()
| VectorPlotter.iter_data |
seaborn | 7 | seaborn/_base.py | def scale_categorical(self, axis, order=None, formatter=None):
"""
Enforce categorical (fixed-scale) rules for the data on given axis.
Parameters
----------
axis : "x" or "y"
Axis of the plot to operate on.
order : list
Order that unique values should appear in.
formatter : callable
Function mapping values to a string representation.
Returns
-------
self
"""
| /usr/src/app/target_test_cases/failed_tests__base.VectorPlotter.scale_categorical.txt | def scale_categorical(self, axis, order=None, formatter=None):
"""
Enforce categorical (fixed-scale) rules for the data on given axis.
Parameters
----------
axis : "x" or "y"
Axis of the plot to operate on.
order : list
Order that unique values should appear in.
formatter : callable
Function mapping values to a string representation.
Returns
-------
self
"""
# This method both modifies the internal representation of the data
# (converting it to string) and sets some attributes on self. It might be
# a good idea to have a separate object attached to self that contains the
# information in those attributes (i.e. whether to enforce variable order
# across facets, the order to use) similar to the SemanticMapping objects
# we have for semantic variables. That object could also hold the converter
# objects that get used, if we can decouple those from an existing axis
# (cf. https://github.com/matplotlib/matplotlib/issues/19229).
# There are some interactions with faceting information that would need
# to be thought through, since the converts to use depend on facets.
# If we go that route, these methods could become "borrowed" methods similar
# to what happens with the alternate semantic mapper constructors, although
# that approach is kind of fussy and confusing.
# TODO this method could also set the grid state? Since we like to have no
# grid on the categorical axis by default. Again, a case where we'll need to
# store information until we use it, so best to have a way to collect the
# attributes that this method sets.
# TODO if we are going to set visual properties of the axes with these methods,
# then we could do the steps currently in CategoricalPlotter._adjust_cat_axis
# TODO another, and distinct idea, is to expose a cut= param here
_check_argument("axis", ["x", "y"], axis)
# Categorical plots can be "univariate" in which case they get an anonymous
# category label on the opposite axis.
if axis not in self.variables:
self.variables[axis] = None
self.var_types[axis] = "categorical"
self.plot_data[axis] = ""
# If the "categorical" variable has a numeric type, sort the rows so that
# the default result from categorical_order has those values sorted after
# they have been coerced to strings. The reason for this is so that later
# we can get facet-wise orders that are correct.
# XXX Should this also sort datetimes?
# It feels more consistent, but technically will be a default change
# If so, should also change categorical_order to behave that way
if self.var_types[axis] == "numeric":
self.plot_data = self.plot_data.sort_values(axis, kind="mergesort")
# Now get a reference to the categorical data vector and remove na values
cat_data = self.plot_data[axis].dropna()
# Get the initial categorical order, which we do before string
# conversion to respect the original types of the order list.
# Track whether the order is given explicitly so that we can know
# whether or not to use the order constructed here downstream
self._var_ordered[axis] = order is not None or cat_data.dtype.name == "category"
order = pd.Index(categorical_order(cat_data, order), name=axis)
# Then convert data to strings. This is because in matplotlib,
# "categorical" data really mean "string" data, so doing this artists
# will be drawn on the categorical axis with a fixed scale.
# TODO implement formatter here; check that it returns strings?
if formatter is not None:
cat_data = cat_data.map(formatter)
order = order.map(formatter)
else:
cat_data = cat_data.astype(str)
order = order.astype(str)
# Update the levels list with the type-converted order variable
self.var_levels[axis] = order
# Now ensure that seaborn will use categorical rules internally
self.var_types[axis] = "categorical"
# Put the string-typed categorical vector back into the plot_data structure
self.plot_data[axis] = cat_data
return self
| VectorPlotter.scale_categorical |
seaborn | 8 | seaborn/_base.py | def categorical_order(vector, order=None):
"""Return a list of unique data values.
Determine an ordered list of levels in ``values``.
Parameters
----------
vector : list, array, Categorical, or Series
Vector of "categorical" values
order : list-like, optional
Desired order of category levels to override the order determined
from the ``values`` object.
Returns
-------
order : list
Ordered list of category levels not including null values.
"""
| /usr/src/app/target_test_cases/failed_tests__base.categorical_order.txt | def categorical_order(vector, order=None):
"""Return a list of unique data values.
Determine an ordered list of levels in ``values``.
Parameters
----------
vector : list, array, Categorical, or Series
Vector of "categorical" values
order : list-like, optional
Desired order of category levels to override the order determined
from the ``values`` object.
Returns
-------
order : list
Ordered list of category levels not including null values.
"""
if order is None:
if hasattr(vector, "categories"):
order = vector.categories
else:
try:
order = vector.cat.categories
except (TypeError, AttributeError):
order = pd.Series(vector).unique()
if variable_type(vector) == "numeric":
order = np.sort(order)
order = filter(pd.notnull, order)
return list(order)
| _base.categorical_order |
seaborn | 9 | seaborn/_base.py | def infer_orient(x=None, y=None, orient=None, require_numeric=True):
"""Determine how the plot should be oriented based on the data.
For historical reasons, the convention is to call a plot "horizontally"
or "vertically" oriented based on the axis representing its dependent
variable. Practically, this is used when determining the axis for
numerical aggregation.
Parameters
----------
x, y : Vector data or None
Positional data vectors for the plot.
orient : string or None
Specified orientation. If not None, can be "x" or "y", or otherwise
must start with "v" or "h".
require_numeric : bool
If set, raise when the implied dependent variable is not numeric.
Returns
-------
orient : "x" or "y"
Raises
------
ValueError: When `orient` is an unknown string.
TypeError: When dependent variable is not numeric, with `require_numeric`
"""
| /usr/src/app/target_test_cases/failed_tests_infer_orient.txt | def infer_orient(x=None, y=None, orient=None, require_numeric=True):
"""Determine how the plot should be oriented based on the data.
For historical reasons, the convention is to call a plot "horizontally"
or "vertically" oriented based on the axis representing its dependent
variable. Practically, this is used when determining the axis for
numerical aggregation.
Parameters
----------
x, y : Vector data or None
Positional data vectors for the plot.
orient : string or None
Specified orientation. If not None, can be "x" or "y", or otherwise
must start with "v" or "h".
require_numeric : bool
If set, raise when the implied dependent variable is not numeric.
Returns
-------
orient : "x" or "y"
Raises
------
ValueError: When `orient` is an unknown string.
TypeError: When dependent variable is not numeric, with `require_numeric`
"""
x_type = None if x is None else variable_type(x)
y_type = None if y is None else variable_type(y)
nonnumeric_dv_error = "{} orientation requires numeric `{}` variable."
single_var_warning = "{} orientation ignored with only `{}` specified."
if x is None:
if str(orient).startswith("h"):
warnings.warn(single_var_warning.format("Horizontal", "y"))
if require_numeric and y_type != "numeric":
raise TypeError(nonnumeric_dv_error.format("Vertical", "y"))
return "x"
elif y is None:
if str(orient).startswith("v"):
warnings.warn(single_var_warning.format("Vertical", "x"))
if require_numeric and x_type != "numeric":
raise TypeError(nonnumeric_dv_error.format("Horizontal", "x"))
return "y"
elif str(orient).startswith("v") or orient == "x":
if require_numeric and y_type != "numeric":
raise TypeError(nonnumeric_dv_error.format("Vertical", "y"))
return "x"
elif str(orient).startswith("h") or orient == "y":
if require_numeric and x_type != "numeric":
raise TypeError(nonnumeric_dv_error.format("Horizontal", "x"))
return "y"
elif orient is not None:
err = (
"`orient` must start with 'v' or 'h' or be None, "
f"but `{repr(orient)}` was passed."
)
raise ValueError(err)
elif x_type != "categorical" and y_type == "categorical":
return "y"
elif x_type != "numeric" and y_type == "numeric":
return "x"
elif x_type == "numeric" and y_type != "numeric":
return "y"
elif require_numeric and "numeric" not in (x_type, y_type):
err = "Neither the `x` nor `y` variable appears to be numeric."
raise TypeError(err)
else:
return "x"
| _base.infer_orient |
seaborn | 10 | seaborn/_base.py | def unique_dashes(n):
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings or tuples
Valid arguments for the ``dashes`` parameter on
:class:`matplotlib.lines.Line2D`. The first spec is a solid
line (``""``), the remainder are sequences of long and short
dashes.
"""
| /usr/src/app/target_test_cases/failed_tests__base.unique_dashes.txt | def unique_dashes(n):
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings or tuples
Valid arguments for the ``dashes`` parameter on
:class:`matplotlib.lines.Line2D`. The first spec is a solid
line (``""``), the remainder are sequences of long and short
dashes.
"""
# Start with dash specs that are well distinguishable
dashes = [
"",
(4, 1.5),
(1, 1),
(3, 1.25, 1.5, 1.25),
(5, 1, 1, 1),
]
# Now programmatically build as many as we need
p = 3
while len(dashes) < n:
# Take combinations of long and short dashes
a = itertools.combinations_with_replacement([3, 1.25], p)
b = itertools.combinations_with_replacement([4, 1], p)
# Interleave the combinations, reversing one of the streams
segment_list = itertools.chain(*zip(
list(a)[1:-1][::-1],
list(b)[1:-1]
))
# Now insert the gaps
for segments in segment_list:
gap = min(segments)
spec = tuple(itertools.chain(*((seg, gap) for seg in segments)))
dashes.append(spec)
p += 1
return dashes[:n]
| _base.unique_dashes |
seaborn | 11 | seaborn/_base.py | def unique_markers(n):
"""Build an arbitrarily long list of unique marker styles for points.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
Values for defining :class:`matplotlib.markers.MarkerStyle` objects.
All markers will be filled.
"""
| /usr/src/app/target_test_cases/failed_tests_unique_markers.txt | def unique_markers(n):
"""Build an arbitrarily long list of unique marker styles for points.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
Values for defining :class:`matplotlib.markers.MarkerStyle` objects.
All markers will be filled.
"""
# Start with marker specs that are well distinguishable
markers = [
"o",
"X",
(4, 0, 45),
"P",
(4, 0, 0),
(4, 1, 0),
"^",
(4, 1, 45),
"v",
]
# Now generate more from regular polygons of increasing order
s = 5
while len(markers) < n:
a = 360 / (s + 1) / 2
markers.extend([
(s + 1, 1, a),
(s + 1, 0, a),
(s, 1, 0),
(s, 0, 0),
])
s += 1
# Convert to MarkerStyle object, using only exactly what we need
# markers = [mpl.markers.MarkerStyle(m) for m in markers[:n]]
return markers[:n]
| _base.unique_markers |
seaborn | 12 | seaborn/_base.py | def variable_type(vector, boolean_type="numeric"):
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in two ways:
- Python sequences or object-typed PyData objects are considered numeric if
all of their entries are numeric.
- String or mixed-type data are considered categorical even if not
explicitly represented as a :class:`pandas.api.types.CategoricalDtype`.
Parameters
----------
vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
Input data to test.
boolean_type : 'numeric' or 'categorical'
Type to use for vectors containing only 0s and 1s (and NAs).
Returns
-------
var_type : 'numeric', 'categorical', or 'datetime'
Name identifying the type of data in the vector.
"""
| /usr/src/app/target_test_cases/failed_tests_variable_type.txt | def variable_type(vector, boolean_type="numeric"):
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in two ways:
- Python sequences or object-typed PyData objects are considered numeric if
all of their entries are numeric.
- String or mixed-type data are considered categorical even if not
explicitly represented as a :class:`pandas.api.types.CategoricalDtype`.
Parameters
----------
vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
Input data to test.
boolean_type : 'numeric' or 'categorical'
Type to use for vectors containing only 0s and 1s (and NAs).
Returns
-------
var_type : 'numeric', 'categorical', or 'datetime'
Name identifying the type of data in the vector.
"""
vector = pd.Series(vector)
# If a categorical dtype is set, infer categorical
if isinstance(vector.dtype, pd.CategoricalDtype):
return VariableType("categorical")
# Special-case all-na data, which is always "numeric"
if pd.isna(vector).all():
return VariableType("numeric")
# At this point, drop nans to simplify further type inference
vector = vector.dropna()
# Special-case binary/boolean data, allow caller to determine
# This triggers a numpy warning when vector has strings/objects
# https://github.com/numpy/numpy/issues/6784
# Because we reduce with .all(), we are agnostic about whether the
# comparison returns a scalar or vector, so we will ignore the warning.
# It triggers a separate DeprecationWarning when the vector has datetimes:
# https://github.com/numpy/numpy/issues/13548
# This is considered a bug by numpy and will likely go away.
with warnings.catch_warnings():
warnings.simplefilter(
action='ignore', category=(FutureWarning, DeprecationWarning)
)
try:
if np.isin(vector, [0, 1]).all():
return VariableType(boolean_type)
except TypeError:
# .isin comparison is not guaranteed to be possible under NumPy
# casting rules, depending on the (unknown) dtype of 'vector'
pass
# Defer to positive pandas tests
if pd.api.types.is_numeric_dtype(vector):
return VariableType("numeric")
if pd.api.types.is_datetime64_dtype(vector):
return VariableType("datetime")
# --- If we get to here, we need to check the entries
# Check for a collection where everything is a number
def all_numeric(x):
for x_i in x:
if not isinstance(x_i, Number):
return False
return True
if all_numeric(vector):
return VariableType("numeric")
# Check for a collection where everything is a datetime
def all_datetime(x):
for x_i in x:
if not isinstance(x_i, (datetime, np.datetime64)):
return False
return True
if all_datetime(vector):
return VariableType("datetime")
# Otherwise, our final fallback is to consider things categorical
return VariableType("categorical")
| _base.variable_type |
seaborn | 13 | seaborn/_statistics.py | def __init__(self, k_depth, outlier_prop, trust_alpha):
"""
Compute percentiles of a distribution using various tail stopping rules.
Parameters
----------
k_depth: "tukey", "proportion", "trustworthy", or "full"
Stopping rule for choosing tail percentiled to show:
- tukey: Show a similar number of outliers as in a conventional boxplot.
- proportion: Show approximately `outlier_prop` outliers.
- trust_alpha: Use `trust_alpha` level for most extreme tail percentile.
outlier_prop: float
Parameter for `k_depth="proportion"` setting the expected outlier rate.
trust_alpha: float
Parameter for `k_depth="trustworthy"` setting the confidence threshold.
Notes
-----
Based on the proposal in this paper:
https://vita.had.co.nz/papers/letter-value-plot.pdf
"""
| /usr/src/app/target_test_cases/failed_tests__statistics.LetterValues.__init__.txt | def __init__(self, k_depth, outlier_prop, trust_alpha):
"""
Compute percentiles of a distribution using various tail stopping rules.
Parameters
----------
k_depth: "tukey", "proportion", "trustworthy", or "full"
Stopping rule for choosing tail percentiled to show:
- tukey: Show a similar number of outliers as in a conventional boxplot.
- proportion: Show approximately `outlier_prop` outliers.
- trust_alpha: Use `trust_alpha` level for most extreme tail percentile.
outlier_prop: float
Parameter for `k_depth="proportion"` setting the expected outlier rate.
trust_alpha: float
Parameter for `k_depth="trustworthy"` setting the confidence threshold.
Notes
-----
Based on the proposal in this paper:
https://vita.had.co.nz/papers/letter-value-plot.pdf
"""
k_options = ["tukey", "proportion", "trustworthy", "full"]
if isinstance(k_depth, str):
_check_argument("k_depth", k_options, k_depth)
elif not isinstance(k_depth, int):
err = (
"The `k_depth` parameter must be either an integer or string "
f"(one of {k_options}), not {k_depth!r}."
)
raise TypeError(err)
self.k_depth = k_depth
self.outlier_prop = outlier_prop
self.trust_alpha = trust_alpha
| _statistics.LetterValues.__init__ |
seaborn | 14 | seaborn/_statistics.py | def __init__(self, estimator, errorbar=None, **boot_kws):
"""
Data aggregator that produces a weighted estimate and error bar interval.
Parameters
----------
estimator : string
Function (or method name) that maps a vector to a scalar. Currently
supports only "mean".
errorbar : string or (string, number) tuple
Name of errorbar method or a tuple with a method name and a level parameter.
Currently the only supported method is "ci".
boot_kws
Additional keywords are passed to bootstrap when error_method is "ci".
"""
| /usr/src/app/target_test_cases/failed_tests__statistics.WeightedAggregator.__init__.txt | def __init__(self, estimator, errorbar=None, **boot_kws):
"""
Data aggregator that produces a weighted estimate and error bar interval.
Parameters
----------
estimator : string
Function (or method name) that maps a vector to a scalar. Currently
supports only "mean".
errorbar : string or (string, number) tuple
Name of errorbar method or a tuple with a method name and a level parameter.
Currently the only supported method is "ci".
boot_kws
Additional keywords are passed to bootstrap when error_method is "ci".
"""
if estimator != "mean":
# Note that, while other weighted estimators may make sense (e.g. median),
# I'm not aware of an implementation in our dependencies. We can add one
# in seaborn later, if there is sufficient interest. For now, limit to mean.
raise ValueError(f"Weighted estimator must be 'mean', not {estimator!r}.")
self.estimator = estimator
method, level = _validate_errorbar_arg(errorbar)
if method is not None and method != "ci":
# As with the estimator, weighted 'sd' or 'pi' error bars may make sense.
# But we'll keep things simple for now and limit to (bootstrap) CI.
raise ValueError(f"Error bar method must be 'ci', not {method!r}.")
self.error_method = method
self.error_level = level
self.boot_kws = boot_kws
| _statistics.WeightedAggregator.__init__ |
seaborn | 15 | seaborn/algorithms.py | def bootstrap(*args, **kwargs):
"""Resample one or more arrays with replacement and store aggregate values.
Positional arguments are a sequence of arrays to bootstrap along the first
axis and pass to a summary function.
Keyword arguments:
n_boot : int, default=10000
Number of iterations
axis : int, default=None
Will pass axis to ``func`` as a keyword argument.
units : array, default=None
Array of sampling unit IDs. When used the bootstrap resamples units
and then observations within units instead of individual
datapoints.
func : string or callable, default="mean"
Function to call on the args that are passed in. If string, uses as
name of function in the numpy namespace. If nans are present in the
data, will try to use nan-aware version of named function.
seed : Generator | SeedSequence | RandomState | int | None
Seed for the random number generator; useful if you want
reproducible resamples.
Returns
-------
boot_dist: array
array of bootstrapped statistic values
"""
| /usr/src/app/target_test_cases/failed_tests_algorithms.bootstrap.txt | def bootstrap(*args, **kwargs):
"""Resample one or more arrays with replacement and store aggregate values.
Positional arguments are a sequence of arrays to bootstrap along the first
axis and pass to a summary function.
Keyword arguments:
n_boot : int, default=10000
Number of iterations
axis : int, default=None
Will pass axis to ``func`` as a keyword argument.
units : array, default=None
Array of sampling unit IDs. When used the bootstrap resamples units
and then observations within units instead of individual
datapoints.
func : string or callable, default="mean"
Function to call on the args that are passed in. If string, uses as
name of function in the numpy namespace. If nans are present in the
data, will try to use nan-aware version of named function.
seed : Generator | SeedSequence | RandomState | int | None
Seed for the random number generator; useful if you want
reproducible resamples.
Returns
-------
boot_dist: array
array of bootstrapped statistic values
"""
# Ensure list of arrays are same length
if len(np.unique(list(map(len, args)))) > 1:
raise ValueError("All input arrays must have the same length")
n = len(args[0])
# Default keyword arguments
n_boot = kwargs.get("n_boot", 10000)
func = kwargs.get("func", "mean")
axis = kwargs.get("axis", None)
units = kwargs.get("units", None)
random_seed = kwargs.get("random_seed", None)
if random_seed is not None:
msg = "`random_seed` has been renamed to `seed` and will be removed"
warnings.warn(msg)
seed = kwargs.get("seed", random_seed)
if axis is None:
func_kwargs = dict()
else:
func_kwargs = dict(axis=axis)
# Initialize the resampler
if isinstance(seed, np.random.RandomState):
rng = seed
else:
rng = np.random.default_rng(seed)
# Coerce to arrays
args = list(map(np.asarray, args))
if units is not None:
units = np.asarray(units)
if isinstance(func, str):
# Allow named numpy functions
f = getattr(np, func)
# Try to use nan-aware version of function if necessary
missing_data = np.isnan(np.sum(np.column_stack(args)))
if missing_data and not func.startswith("nan"):
nanf = getattr(np, f"nan{func}", None)
if nanf is None:
msg = f"Data contain nans but no nan-aware version of `{func}` found"
warnings.warn(msg, UserWarning)
else:
f = nanf
else:
f = func
# Handle numpy changes
try:
integers = rng.integers
except AttributeError:
integers = rng.randint
# Do the bootstrap
if units is not None:
return _structured_bootstrap(args, n_boot, units, f,
func_kwargs, integers)
boot_dist = []
for i in range(int(n_boot)):
resampler = integers(0, n, n, dtype=np.intp) # intp is indexing dtype
sample = [a.take(resampler, axis=0) for a in args]
boot_dist.append(f(*sample, **func_kwargs))
return np.array(boot_dist)
| algorithms.bootstrap |
seaborn | 16 | seaborn/axisgrid.py | def facet_data(self):
"""Generator for name indices and data subsets for each facet.
Yields
------
(i, j, k), data_ijk : tuple of ints, DataFrame
The ints provide an index into the {row, col, hue}_names attribute,
and the dataframe contains a subset of the full data corresponding
to each facet. The generator yields subsets that correspond with
the self.axes.flat iterator, or self.axes[i, j] when `col_wrap`
is None.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.facet_data.txt | def facet_data(self):
"""Generator for name indices and data subsets for each facet.
Yields
------
(i, j, k), data_ijk : tuple of ints, DataFrame
The ints provide an index into the {row, col, hue}_names attribute,
and the dataframe contains a subset of the full data corresponding
to each facet. The generator yields subsets that correspond with
the self.axes.flat iterator, or self.axes[i, j] when `col_wrap`
is None.
"""
data = self.data
# Construct masks for the row variable
if self.row_names:
row_masks = [data[self._row_var] == n for n in self.row_names]
else:
row_masks = [np.repeat(True, len(self.data))]
# Construct masks for the column variable
if self.col_names:
col_masks = [data[self._col_var] == n for n in self.col_names]
else:
col_masks = [np.repeat(True, len(self.data))]
# Construct masks for the hue variable
if self.hue_names:
hue_masks = [data[self._hue_var] == n for n in self.hue_names]
else:
hue_masks = [np.repeat(True, len(self.data))]
# Here is the main generator loop
for (i, row), (j, col), (k, hue) in product(enumerate(row_masks),
enumerate(col_masks),
enumerate(hue_masks)):
data_ijk = data[row & col & hue & self._not_na]
yield (i, j, k), data_ijk
| axisgrid.FacetGrid.facet_data |
seaborn | 17 | seaborn/axisgrid.py | def map(self, func, *args, **kwargs):
"""Apply a plotting function to each facet's subset of the data.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. It
must plot to the currently active matplotlib Axes and take a
`color` keyword argument. If faceting on the `hue` dimension,
it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.map.txt | def map(self, func, *args, **kwargs):
"""Apply a plotting function to each facet's subset of the data.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. It
must plot to the currently active matplotlib Axes and take a
`color` keyword argument. If faceting on the `hue` dimension,
it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
# If color was a keyword argument, grab it here
kw_color = kwargs.pop("color", None)
# How we use the function depends on where it comes from
func_module = str(getattr(func, "__module__", ""))
# Check for categorical plots without order information
if func_module == "seaborn.categorical":
if "order" not in kwargs:
warning = ("Using the {} function without specifying "
"`order` is likely to produce an incorrect "
"plot.".format(func.__name__))
warnings.warn(warning)
if len(args) == 3 and "hue_order" not in kwargs:
warning = ("Using the {} function without specifying "
"`hue_order` is likely to produce an incorrect "
"plot.".format(func.__name__))
warnings.warn(warning)
# Iterate over the data subsets
for (row_i, col_j, hue_k), data_ijk in self.facet_data():
# If this subset is null, move on
if not data_ijk.values.size:
continue
# Get the current axis
modify_state = not func_module.startswith("seaborn")
ax = self.facet_axis(row_i, col_j, modify_state)
# Decide what color to plot with
kwargs["color"] = self._facet_color(hue_k, kw_color)
# Insert the other hue aesthetics if appropriate
for kw, val_list in self.hue_kws.items():
kwargs[kw] = val_list[hue_k]
# Insert a label in the keyword arguments for the legend
if self._hue_var is not None:
kwargs["label"] = utils.to_utf8(self.hue_names[hue_k])
# Get the actual data we are going to plot with
plot_data = data_ijk[list(args)]
if self._dropna:
plot_data = plot_data.dropna()
plot_args = [v for k, v in plot_data.items()]
# Some matplotlib functions don't handle pandas objects correctly
if func_module.startswith("matplotlib"):
plot_args = [v.values for v in plot_args]
# Draw the plot
self._facet_plot(func, ax, plot_args, kwargs)
# Finalize the annotations and layout
self._finalize_grid(args[:2])
return self
| axisgrid.FacetGrid.map |
seaborn | 18 | seaborn/axisgrid.py | def map_dataframe(self, func, *args, **kwargs):
"""Like ``.map`` but passes args as strings and inserts data in kwargs.
This method is suitable for plotting with functions that accept a
long-form DataFrame as a `data` keyword argument and access the
data in that DataFrame using string variable names.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. Unlike
the `map` method, a function used here must "understand" Pandas
objects. It also must plot to the currently active matplotlib Axes
and take a `color` keyword argument. If faceting on the `hue`
dimension, it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.map_dataframe.txt | def map_dataframe(self, func, *args, **kwargs):
"""Like ``.map`` but passes args as strings and inserts data in kwargs.
This method is suitable for plotting with functions that accept a
long-form DataFrame as a `data` keyword argument and access the
data in that DataFrame using string variable names.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. Unlike
the `map` method, a function used here must "understand" Pandas
objects. It also must plot to the currently active matplotlib Axes
and take a `color` keyword argument. If faceting on the `hue`
dimension, it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
# If color was a keyword argument, grab it here
kw_color = kwargs.pop("color", None)
# Iterate over the data subsets
for (row_i, col_j, hue_k), data_ijk in self.facet_data():
# If this subset is null, move on
if not data_ijk.values.size:
continue
# Get the current axis
modify_state = not str(func.__module__).startswith("seaborn")
ax = self.facet_axis(row_i, col_j, modify_state)
# Decide what color to plot with
kwargs["color"] = self._facet_color(hue_k, kw_color)
# Insert the other hue aesthetics if appropriate
for kw, val_list in self.hue_kws.items():
kwargs[kw] = val_list[hue_k]
# Insert a label in the keyword arguments for the legend
if self._hue_var is not None:
kwargs["label"] = self.hue_names[hue_k]
# Stick the facet dataframe into the kwargs
if self._dropna:
data_ijk = data_ijk.dropna()
kwargs["data"] = data_ijk
# Draw the plot
self._facet_plot(func, ax, args, kwargs)
# For axis labels, prefer to use positional args for backcompat
# but also extract the x/y kwargs and use if no corresponding arg
axis_labels = [kwargs.get("x", None), kwargs.get("y", None)]
for i, val in enumerate(args[:2]):
axis_labels[i] = val
self._finalize_grid(axis_labels)
return self
| axisgrid.FacetGrid.map_dataframe |
seaborn | 19 | seaborn/axisgrid.py | def refline(self, *, x=None, y=None, color='.5', linestyle='--', **line_kws):
"""Add a reference line(s) to each facet.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies the color of the reference line(s). Pass ``color=None`` to
use ``hue`` mapping.
linestyle : str
Specifies the style of the reference line(s).
line_kws : key, value mappings
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.axvline`
when ``x`` is not None and :meth:`matplotlib.axes.Axes.axhline` when ``y``
is not None.
Returns
-------
:class:`FacetGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.refline.txt | def refline(self, *, x=None, y=None, color='.5', linestyle='--', **line_kws):
"""Add a reference line(s) to each facet.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies the color of the reference line(s). Pass ``color=None`` to
use ``hue`` mapping.
linestyle : str
Specifies the style of the reference line(s).
line_kws : key, value mappings
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.axvline`
when ``x`` is not None and :meth:`matplotlib.axes.Axes.axhline` when ``y``
is not None.
Returns
-------
:class:`FacetGrid` instance
Returns ``self`` for easy method chaining.
"""
line_kws['color'] = color
line_kws['linestyle'] = linestyle
if x is not None:
self.map(plt.axvline, x=x, **line_kws)
if y is not None:
self.map(plt.axhline, y=y, **line_kws)
return self
| axisgrid.FacetGrid.refline |
seaborn | 20 | seaborn/axisgrid.py | def set_titles(self, template=None, row_template=None, col_template=None, **kwargs):
"""Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for all titles with the formatting keys {col_var} and
{col_name} (if using a `col` faceting variable) and/or {row_var}
and {row_name} (if using a `row` faceting variable).
row_template:
Template for the row variable when titles are drawn on the grid
margins. Must have {row_var} and {row_name} formatting keys.
col_template:
Template for the column variable when titles are drawn on the grid
margins. Must have {col_var} and {col_name} formatting keys.
Returns
-------
self: object
Returns self.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.FacetGrid.set_titles.txt | def set_titles(self, template=None, row_template=None, col_template=None, **kwargs):
"""Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for all titles with the formatting keys {col_var} and
{col_name} (if using a `col` faceting variable) and/or {row_var}
and {row_name} (if using a `row` faceting variable).
row_template:
Template for the row variable when titles are drawn on the grid
margins. Must have {row_var} and {row_name} formatting keys.
col_template:
Template for the column variable when titles are drawn on the grid
margins. Must have {col_var} and {col_name} formatting keys.
Returns
-------
self: object
Returns self.
"""
args = dict(row_var=self._row_var, col_var=self._col_var)
kwargs["size"] = kwargs.pop("size", mpl.rcParams["axes.labelsize"])
# Establish default templates
if row_template is None:
row_template = "{row_var} = {row_name}"
if col_template is None:
col_template = "{col_var} = {col_name}"
if template is None:
if self._row_var is None:
template = col_template
elif self._col_var is None:
template = row_template
else:
template = " | ".join([row_template, col_template])
row_template = utils.to_utf8(row_template)
col_template = utils.to_utf8(col_template)
template = utils.to_utf8(template)
if self._margin_titles:
# Remove any existing title texts
for text in self._margin_titles_texts:
text.remove()
self._margin_titles_texts = []
if self.row_names is not None:
# Draw the row titles on the right edge of the grid
for i, row_name in enumerate(self.row_names):
ax = self.axes[i, -1]
args.update(dict(row_name=row_name))
title = row_template.format(**args)
text = ax.annotate(
title, xy=(1.02, .5), xycoords="axes fraction",
rotation=270, ha="left", va="center",
**kwargs
)
self._margin_titles_texts.append(text)
if self.col_names is not None:
# Draw the column titles as normal titles
for j, col_name in enumerate(self.col_names):
args.update(dict(col_name=col_name))
title = col_template.format(**args)
self.axes[0, j].set_title(title, **kwargs)
return self
# Otherwise title each facet with all the necessary information
if (self._row_var is not None) and (self._col_var is not None):
for i, row_name in enumerate(self.row_names):
for j, col_name in enumerate(self.col_names):
args.update(dict(row_name=row_name, col_name=col_name))
title = template.format(**args)
self.axes[i, j].set_title(title, **kwargs)
elif self.row_names is not None and len(self.row_names):
for i, row_name in enumerate(self.row_names):
args.update(dict(row_name=row_name))
title = template.format(**args)
self.axes[i, 0].set_title(title, **kwargs)
elif self.col_names is not None and len(self.col_names):
for i, col_name in enumerate(self.col_names):
args.update(dict(col_name=col_name))
title = template.format(**args)
# Index the flat array so col_wrap works
self.axes.flat[i].set_title(title, **kwargs)
return self
| axisgrid.FacetGrid.set_titles |
seaborn | 21 | seaborn/axisgrid.py | def add_legend(self, legend_data=None, title=None, label_order=None,
adjust_subtitles=False, **kwargs):
"""Draw a legend, maybe placing it outside axes and resizing the figure.
Parameters
----------
legend_data : dict
Dictionary mapping label names (or two-element tuples where the
second element is a label name) to matplotlib artist handles. The
default reads from ``self._legend_data``.
title : string
Title for the legend. The default reads from ``self._hue_var``.
label_order : list of labels
The order that the legend entries should appear in. The default
reads from ``self.hue_names``.
adjust_subtitles : bool
If True, modify entries with invisible artists to left-align
the labels and set the font size to that of a title.
kwargs : key, value pairings
Other keyword arguments are passed to the underlying legend methods
on the Figure or Axes object.
Returns
-------
self : Grid instance
Returns self for easy chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.Grid.add_legend.txt | def add_legend(self, legend_data=None, title=None, label_order=None,
adjust_subtitles=False, **kwargs):
"""Draw a legend, maybe placing it outside axes and resizing the figure.
Parameters
----------
legend_data : dict
Dictionary mapping label names (or two-element tuples where the
second element is a label name) to matplotlib artist handles. The
default reads from ``self._legend_data``.
title : string
Title for the legend. The default reads from ``self._hue_var``.
label_order : list of labels
The order that the legend entries should appear in. The default
reads from ``self.hue_names``.
adjust_subtitles : bool
If True, modify entries with invisible artists to left-align
the labels and set the font size to that of a title.
kwargs : key, value pairings
Other keyword arguments are passed to the underlying legend methods
on the Figure or Axes object.
Returns
-------
self : Grid instance
Returns self for easy chaining.
"""
# Find the data for the legend
if legend_data is None:
legend_data = self._legend_data
if label_order is None:
if self.hue_names is None:
label_order = list(legend_data.keys())
else:
label_order = list(map(utils.to_utf8, self.hue_names))
blank_handle = mpl.patches.Patch(alpha=0, linewidth=0)
handles = [legend_data.get(lab, blank_handle) for lab in label_order]
title = self._hue_var if title is None else title
title_size = mpl.rcParams["legend.title_fontsize"]
# Unpack nested labels from a hierarchical legend
labels = []
for entry in label_order:
if isinstance(entry, tuple):
_, label = entry
else:
label = entry
labels.append(label)
# Set default legend kwargs
kwargs.setdefault("scatterpoints", 1)
if self._legend_out:
kwargs.setdefault("frameon", False)
kwargs.setdefault("loc", "center right")
# Draw a full-figure legend outside the grid
figlegend = self._figure.legend(handles, labels, **kwargs)
self._legend = figlegend
figlegend.set_title(title, prop={"size": title_size})
if adjust_subtitles:
adjust_legend_subtitles(figlegend)
# Draw the plot to set the bounding boxes correctly
_draw_figure(self._figure)
# Calculate and set the new width of the figure so the legend fits
legend_width = figlegend.get_window_extent().width / self._figure.dpi
fig_width, fig_height = self._figure.get_size_inches()
self._figure.set_size_inches(fig_width + legend_width, fig_height)
# Draw the plot again to get the new transformations
_draw_figure(self._figure)
# Now calculate how much space we need on the right side
legend_width = figlegend.get_window_extent().width / self._figure.dpi
space_needed = legend_width / (fig_width + legend_width)
margin = .04 if self._margin_titles else .01
self._space_needed = margin + space_needed
right = 1 - self._space_needed
# Place the subplot axes to give space for the legend
self._figure.subplots_adjust(right=right)
self._tight_layout_rect[2] = right
else:
# Draw a legend in the first axis
ax = self.axes.flat[0]
kwargs.setdefault("loc", "best")
leg = ax.legend(handles, labels, **kwargs)
leg.set_title(title, prop={"size": title_size})
self._legend = leg
if adjust_subtitles:
adjust_legend_subtitles(leg)
return self
| axisgrid.Grid.add_legend |
seaborn | 22 | seaborn/axisgrid.py | def tick_params(self, axis='both', **kwargs):
"""Modify the ticks, tick labels, and gridlines.
Parameters
----------
axis : {'x', 'y', 'both'}
The axis on which to apply the formatting.
kwargs : keyword arguments
Additional keyword arguments to pass to
:meth:`matplotlib.axes.Axes.tick_params`.
Returns
-------
self : Grid instance
Returns self for easy chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.Grid.tick_params.txt | def tick_params(self, axis='both', **kwargs):
"""Modify the ticks, tick labels, and gridlines.
Parameters
----------
axis : {'x', 'y', 'both'}
The axis on which to apply the formatting.
kwargs : keyword arguments
Additional keyword arguments to pass to
:meth:`matplotlib.axes.Axes.tick_params`.
Returns
-------
self : Grid instance
Returns self for easy chaining.
"""
for ax in self.figure.axes:
ax.tick_params(axis=axis, **kwargs)
return self
| axisgrid.Grid.tick_params |
seaborn | 23 | seaborn/axisgrid.py | def plot(self, joint_func, marginal_func, **kwargs):
"""Draw the plot by passing functions for joint and marginal axes.
This method passes the ``kwargs`` dictionary to both functions. If you
need more control, call :meth:`JointGrid.plot_joint` and
:meth:`JointGrid.plot_marginals` directly with specific parameters.
Parameters
----------
joint_func, marginal_func : callables
Functions to draw the bivariate and univariate plots. See methods
referenced above for information about the required characteristics
of these functions.
kwargs
Additional keyword arguments are passed to both functions.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot.txt | def plot(self, joint_func, marginal_func, **kwargs):
"""Draw the plot by passing functions for joint and marginal axes.
This method passes the ``kwargs`` dictionary to both functions. If you
need more control, call :meth:`JointGrid.plot_joint` and
:meth:`JointGrid.plot_marginals` directly with specific parameters.
Parameters
----------
joint_func, marginal_func : callables
Functions to draw the bivariate and univariate plots. See methods
referenced above for information about the required characteristics
of these functions.
kwargs
Additional keyword arguments are passed to both functions.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
self.plot_marginals(marginal_func, **kwargs)
self.plot_joint(joint_func, **kwargs)
return self
| axisgrid.JointGrid.plot |
seaborn | 24 | seaborn/axisgrid.py | def plot_joint(self, func, **kwargs):
"""Draw a bivariate plot on the joint axes of the grid.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y``. Otherwise,
it must accept ``x`` and ``y`` vectors of data as the first two
positional arguments, and it must plot on the "current" axes.
If ``hue`` was defined in the class constructor, the function must
accept ``hue`` as a parameter.
kwargs
Keyword argument are passed to the plotting function.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot_joint.txt | def plot_joint(self, func, **kwargs):
"""Draw a bivariate plot on the joint axes of the grid.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y``. Otherwise,
it must accept ``x`` and ``y`` vectors of data as the first two
positional arguments, and it must plot on the "current" axes.
If ``hue`` was defined in the class constructor, the function must
accept ``hue`` as a parameter.
kwargs
Keyword argument are passed to the plotting function.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
kwargs = kwargs.copy()
if str(func.__module__).startswith("seaborn"):
kwargs["ax"] = self.ax_joint
else:
plt.sca(self.ax_joint)
if self.hue is not None:
kwargs["hue"] = self.hue
self._inject_kwargs(func, kwargs, self._hue_params)
if str(func.__module__).startswith("seaborn"):
func(x=self.x, y=self.y, **kwargs)
else:
func(self.x, self.y, **kwargs)
return self
| axisgrid.JointGrid.plot_joint |
seaborn | 25 | seaborn/axisgrid.py | def plot_marginals(self, func, **kwargs):
"""Draw univariate plots on each marginal axes.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y`` and plot
when only one of them is defined. Otherwise, it must accept a vector
of data as the first positional argument and determine its orientation
using the ``vertical`` parameter, and it must plot on the "current" axes.
If ``hue`` was defined in the class constructor, it must accept ``hue``
as a parameter.
kwargs
Keyword argument are passed to the plotting function.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.plot_marginals.txt | def plot_marginals(self, func, **kwargs):
"""Draw univariate plots on each marginal axes.
Parameters
----------
func : plotting callable
If a seaborn function, it should accept ``x`` and ``y`` and plot
when only one of them is defined. Otherwise, it must accept a vector
of data as the first positional argument and determine its orientation
using the ``vertical`` parameter, and it must plot on the "current" axes.
If ``hue`` was defined in the class constructor, it must accept ``hue``
as a parameter.
kwargs
Keyword argument are passed to the plotting function.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
seaborn_func = (
str(func.__module__).startswith("seaborn")
# deprecated distplot has a legacy API, special case it
and not func.__name__ == "distplot"
)
func_params = signature(func).parameters
kwargs = kwargs.copy()
if self.hue is not None:
kwargs["hue"] = self.hue
self._inject_kwargs(func, kwargs, self._hue_params)
if "legend" in func_params:
kwargs.setdefault("legend", False)
if "orientation" in func_params:
# e.g. plt.hist
orient_kw_x = {"orientation": "vertical"}
orient_kw_y = {"orientation": "horizontal"}
elif "vertical" in func_params:
# e.g. sns.distplot (also how did this get backwards?)
orient_kw_x = {"vertical": False}
orient_kw_y = {"vertical": True}
if seaborn_func:
func(x=self.x, ax=self.ax_marg_x, **kwargs)
else:
plt.sca(self.ax_marg_x)
func(self.x, **orient_kw_x, **kwargs)
if seaborn_func:
func(y=self.y, ax=self.ax_marg_y, **kwargs)
else:
plt.sca(self.ax_marg_y)
func(self.y, **orient_kw_y, **kwargs)
self.ax_marg_x.yaxis.get_label().set_visible(False)
self.ax_marg_y.xaxis.get_label().set_visible(False)
return self
| axisgrid.JointGrid.plot_marginals |
seaborn | 26 | seaborn/axisgrid.py | def refline(
self, *, x=None, y=None, joint=True, marginal=True,
color='.5', linestyle='--', **line_kws
):
"""Add a reference line(s) to joint and/or marginal axes.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
joint, marginal : bools
Whether to add the reference line(s) to the joint/marginal axes.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies the color of the reference line(s).
linestyle : str
Specifies the style of the reference line(s).
line_kws : key, value mappings
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.axvline`
when ``x`` is not None and :meth:`matplotlib.axes.Axes.axhline` when ``y``
is not None.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.refline.txt | def refline(
self, *, x=None, y=None, joint=True, marginal=True,
color='.5', linestyle='--', **line_kws
):
"""Add a reference line(s) to joint and/or marginal axes.
Parameters
----------
x, y : numeric
Value(s) to draw the line(s) at.
joint, marginal : bools
Whether to add the reference line(s) to the joint/marginal axes.
color : :mod:`matplotlib color <matplotlib.colors>`
Specifies the color of the reference line(s).
linestyle : str
Specifies the style of the reference line(s).
line_kws : key, value mappings
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.axvline`
when ``x`` is not None and :meth:`matplotlib.axes.Axes.axhline` when ``y``
is not None.
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
line_kws['color'] = color
line_kws['linestyle'] = linestyle
if x is not None:
if joint:
self.ax_joint.axvline(x, **line_kws)
if marginal:
self.ax_marg_x.axvline(x, **line_kws)
if y is not None:
if joint:
self.ax_joint.axhline(y, **line_kws)
if marginal:
self.ax_marg_y.axhline(y, **line_kws)
return self
| axisgrid.JointGrid.refline |
seaborn | 27 | seaborn/axisgrid.py | def set_axis_labels(self, xlabel="", ylabel="", **kwargs):
"""Set axis labels on the bivariate axes.
Parameters
----------
xlabel, ylabel : strings
Label names for the x and y variables.
kwargs : key, value mappings
Other keyword arguments are passed to the following functions:
- :meth:`matplotlib.axes.Axes.set_xlabel`
- :meth:`matplotlib.axes.Axes.set_ylabel`
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.JointGrid.set_axis_labels.txt | def set_axis_labels(self, xlabel="", ylabel="", **kwargs):
"""Set axis labels on the bivariate axes.
Parameters
----------
xlabel, ylabel : strings
Label names for the x and y variables.
kwargs : key, value mappings
Other keyword arguments are passed to the following functions:
- :meth:`matplotlib.axes.Axes.set_xlabel`
- :meth:`matplotlib.axes.Axes.set_ylabel`
Returns
-------
:class:`JointGrid` instance
Returns ``self`` for easy method chaining.
"""
self.ax_joint.set_xlabel(xlabel, **kwargs)
self.ax_joint.set_ylabel(ylabel, **kwargs)
return self
| axisgrid.JointGrid.set_axis_labels |
seaborn | 28 | seaborn/axisgrid.py | def __init__(
self, data, *, hue=None, vars=None, x_vars=None, y_vars=None,
hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True,
height=2.5, aspect=1, layout_pad=.5, despine=True, dropna=False,
):
"""Initialize the plot figure and PairGrid object.
Parameters
----------
data : DataFrame
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : string (variable name)
Variable in ``data`` to map plot aspects to different colors. This
variable will be excluded from the default x and y variables.
vars : list of variable names
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
hue_kws : dictionary of param -> list of values mapping
Other keyword arguments to insert into the plotting call to let
other plot attributes vary across levels of the hue variable (e.g.
the markers in a scatterplot).
corner : bool
If True, don't add axes to the upper (off-diagonal) triangle of the
grid, making this a "corner" plot.
height : scalar
Height (in inches) of each facet.
aspect : scalar
Aspect * height gives the width (in inches) of each facet.
layout_pad : scalar
Padding between axes; passed to ``fig.tight_layout``.
despine : boolean
Remove the top and right spines from the plots.
dropna : boolean
Drop missing values from the data before plotting.
See Also
--------
pairplot : Easily drawing common uses of :class:`PairGrid`.
FacetGrid : Subplot grid for plotting conditional relationships.
Examples
--------
.. include:: ../docstrings/PairGrid.rst
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.PairGrid.__init__.txt | def __init__(
self, data, *, hue=None, vars=None, x_vars=None, y_vars=None,
hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True,
height=2.5, aspect=1, layout_pad=.5, despine=True, dropna=False,
):
"""Initialize the plot figure and PairGrid object.
Parameters
----------
data : DataFrame
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : string (variable name)
Variable in ``data`` to map plot aspects to different colors. This
variable will be excluded from the default x and y variables.
vars : list of variable names
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
hue_kws : dictionary of param -> list of values mapping
Other keyword arguments to insert into the plotting call to let
other plot attributes vary across levels of the hue variable (e.g.
the markers in a scatterplot).
corner : bool
If True, don't add axes to the upper (off-diagonal) triangle of the
grid, making this a "corner" plot.
height : scalar
Height (in inches) of each facet.
aspect : scalar
Aspect * height gives the width (in inches) of each facet.
layout_pad : scalar
Padding between axes; passed to ``fig.tight_layout``.
despine : boolean
Remove the top and right spines from the plots.
dropna : boolean
Drop missing values from the data before plotting.
See Also
--------
pairplot : Easily drawing common uses of :class:`PairGrid`.
FacetGrid : Subplot grid for plotting conditional relationships.
Examples
--------
.. include:: ../docstrings/PairGrid.rst
"""
super().__init__()
data = handle_data_source(data)
# Sort out the variables that define the grid
numeric_cols = self._find_numeric_cols(data)
if hue in numeric_cols:
numeric_cols.remove(hue)
if vars is not None:
x_vars = list(vars)
y_vars = list(vars)
if x_vars is None:
x_vars = numeric_cols
if y_vars is None:
y_vars = numeric_cols
if np.isscalar(x_vars):
x_vars = [x_vars]
if np.isscalar(y_vars):
y_vars = [y_vars]
self.x_vars = x_vars = list(x_vars)
self.y_vars = y_vars = list(y_vars)
self.square_grid = self.x_vars == self.y_vars
if not x_vars:
raise ValueError("No variables found for grid columns.")
if not y_vars:
raise ValueError("No variables found for grid rows.")
# Create the figure and the array of subplots
figsize = len(x_vars) * height * aspect, len(y_vars) * height
with _disable_autolayout():
fig = plt.figure(figsize=figsize)
axes = fig.subplots(len(y_vars), len(x_vars),
sharex="col", sharey="row",
squeeze=False)
# Possibly remove upper axes to make a corner grid
# Note: setting up the axes is usually the most time-intensive part
# of using the PairGrid. We are foregoing the speed improvement that
# we would get by just not setting up the hidden axes so that we can
# avoid implementing fig.subplots ourselves. But worth thinking about.
self._corner = corner
if corner:
hide_indices = np.triu_indices_from(axes, 1)
for i, j in zip(*hide_indices):
axes[i, j].remove()
axes[i, j] = None
self._figure = fig
self.axes = axes
self.data = data
# Save what we are going to do with the diagonal
self.diag_sharey = diag_sharey
self.diag_vars = None
self.diag_axes = None
self._dropna = dropna
# Label the axes
self._add_axis_labels()
# Sort out the hue variable
self._hue_var = hue
if hue is None:
self.hue_names = hue_order = ["_nolegend_"]
self.hue_vals = pd.Series(["_nolegend_"] * len(data),
index=data.index)
else:
# We need hue_order and hue_names because the former is used to control
# the order of drawing and the latter is used to control the order of
# the legend. hue_names can become string-typed while hue_order must
# retain the type of the input data. This is messy but results from
# the fact that PairGrid can implement the hue-mapping logic itself
# (and was originally written exclusively that way) but now can delegate
# to the axes-level functions, while always handling legend creation.
# See GH2307
hue_names = hue_order = categorical_order(data[hue], hue_order)
if dropna:
# Filter NA from the list of unique hue names
hue_names = list(filter(pd.notnull, hue_names))
self.hue_names = hue_names
self.hue_vals = data[hue]
# Additional dict of kwarg -> list of values for mapping the hue var
self.hue_kws = hue_kws if hue_kws is not None else {}
self._orig_palette = palette
self._hue_order = hue_order
self.palette = self._get_palette(data, hue, hue_order, palette)
self._legend_data = {}
# Make the plot look nice
for ax in axes[:-1, :].flat:
if ax is None:
continue
for label in ax.get_xticklabels():
label.set_visible(False)
ax.xaxis.offsetText.set_visible(False)
ax.xaxis.label.set_visible(False)
for ax in axes[:, 1:].flat:
if ax is None:
continue
for label in ax.get_yticklabels():
label.set_visible(False)
ax.yaxis.offsetText.set_visible(False)
ax.yaxis.label.set_visible(False)
self._tight_layout_rect = [.01, .01, .99, .99]
self._tight_layout_pad = layout_pad
self._despine = despine
if despine:
utils.despine(fig=fig)
self.tight_layout(pad=layout_pad)
| axisgrid.PairGrid.__init__ |
seaborn | 29 | seaborn/axisgrid.py | def pairplot(
data, *,
hue=None, hue_order=None, palette=None,
vars=None, x_vars=None, y_vars=None,
kind="scatter", diag_kind="auto", markers=None,
height=2.5, aspect=1, corner=False, dropna=False,
plot_kws=None, diag_kws=None, grid_kws=None, size=None,
):
"""Plot pairwise relationships in a dataset.
By default, this function will create a grid of Axes such that each numeric
variable in ``data`` will by shared across the y-axes across a single row and
the x-axes across a single column. The diagonal plots are treated
differently: a univariate distribution plot is drawn to show the marginal
distribution of the data in each column.
It is also possible to show a subset of variables or plot different
variables on the rows and columns.
This is a high-level interface for :class:`PairGrid` that is intended to
make it easy to draw a few common styles. You should use :class:`PairGrid`
directly if you need more flexibility.
Parameters
----------
data : `pandas.DataFrame`
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : name of variable in ``data``
Variable in ``data`` to map plot aspects to different colors.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
vars : list of variable names
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
kind : {'scatter', 'kde', 'hist', 'reg'}
Kind of plot to make.
diag_kind : {'auto', 'hist', 'kde', None}
Kind of plot for the diagonal subplots. If 'auto', choose based on
whether or not ``hue`` is used.
markers : single matplotlib marker code or list
Either the marker to use for all scatterplot points or a list of markers
with a length the same as the number of levels in the hue variable so that
differently colored points will also have different scatterplot
markers.
height : scalar
Height (in inches) of each facet.
aspect : scalar
Aspect * height gives the width (in inches) of each facet.
corner : bool
If True, don't add axes to the upper (off-diagonal) triangle of the
grid, making this a "corner" plot.
dropna : boolean
Drop missing values from the data before plotting.
{plot, diag, grid}_kws : dicts
Dictionaries of keyword arguments. ``plot_kws`` are passed to the
bivariate plotting function, ``diag_kws`` are passed to the univariate
plotting function, and ``grid_kws`` are passed to the :class:`PairGrid`
constructor.
Returns
-------
grid : :class:`PairGrid`
Returns the underlying :class:`PairGrid` instance for further tweaking.
See Also
--------
PairGrid : Subplot grid for more flexible plotting of pairwise relationships.
JointGrid : Grid for plotting joint and marginal distributions of two variables.
Examples
--------
.. include:: ../docstrings/pairplot.rst
"""
| /usr/src/app/target_test_cases/failed_tests_axisgrid.pairplot.txt | def pairplot(
data, *,
hue=None, hue_order=None, palette=None,
vars=None, x_vars=None, y_vars=None,
kind="scatter", diag_kind="auto", markers=None,
height=2.5, aspect=1, corner=False, dropna=False,
plot_kws=None, diag_kws=None, grid_kws=None, size=None,
):
"""Plot pairwise relationships in a dataset.
By default, this function will create a grid of Axes such that each numeric
variable in ``data`` will by shared across the y-axes across a single row and
the x-axes across a single column. The diagonal plots are treated
differently: a univariate distribution plot is drawn to show the marginal
distribution of the data in each column.
It is also possible to show a subset of variables or plot different
variables on the rows and columns.
This is a high-level interface for :class:`PairGrid` that is intended to
make it easy to draw a few common styles. You should use :class:`PairGrid`
directly if you need more flexibility.
Parameters
----------
data : `pandas.DataFrame`
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : name of variable in ``data``
Variable in ``data`` to map plot aspects to different colors.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
vars : list of variable names
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
kind : {'scatter', 'kde', 'hist', 'reg'}
Kind of plot to make.
diag_kind : {'auto', 'hist', 'kde', None}
Kind of plot for the diagonal subplots. If 'auto', choose based on
whether or not ``hue`` is used.
markers : single matplotlib marker code or list
Either the marker to use for all scatterplot points or a list of markers
with a length the same as the number of levels in the hue variable so that
differently colored points will also have different scatterplot
markers.
height : scalar
Height (in inches) of each facet.
aspect : scalar
Aspect * height gives the width (in inches) of each facet.
corner : bool
If True, don't add axes to the upper (off-diagonal) triangle of the
grid, making this a "corner" plot.
dropna : boolean
Drop missing values from the data before plotting.
{plot, diag, grid}_kws : dicts
Dictionaries of keyword arguments. ``plot_kws`` are passed to the
bivariate plotting function, ``diag_kws`` are passed to the univariate
plotting function, and ``grid_kws`` are passed to the :class:`PairGrid`
constructor.
Returns
-------
grid : :class:`PairGrid`
Returns the underlying :class:`PairGrid` instance for further tweaking.
See Also
--------
PairGrid : Subplot grid for more flexible plotting of pairwise relationships.
JointGrid : Grid for plotting joint and marginal distributions of two variables.
Examples
--------
.. include:: ../docstrings/pairplot.rst
"""
# Avoid circular import
from .distributions import histplot, kdeplot
# Handle deprecations
if size is not None:
height = size
msg = ("The `size` parameter has been renamed to `height`; "
"please update your code.")
warnings.warn(msg, UserWarning)
if not isinstance(data, pd.DataFrame):
raise TypeError(
f"'data' must be pandas DataFrame object, not: {type(data)}")
plot_kws = {} if plot_kws is None else plot_kws.copy()
diag_kws = {} if diag_kws is None else diag_kws.copy()
grid_kws = {} if grid_kws is None else grid_kws.copy()
# Resolve "auto" diag kind
if diag_kind == "auto":
if hue is None:
diag_kind = "kde" if kind == "kde" else "hist"
else:
diag_kind = "hist" if kind == "hist" else "kde"
# Set up the PairGrid
grid_kws.setdefault("diag_sharey", diag_kind == "hist")
grid = PairGrid(data, vars=vars, x_vars=x_vars, y_vars=y_vars, hue=hue,
hue_order=hue_order, palette=palette, corner=corner,
height=height, aspect=aspect, dropna=dropna, **grid_kws)
# Add the markers here as PairGrid has figured out how many levels of the
# hue variable are needed and we don't want to duplicate that process
if markers is not None:
if kind == "reg":
# Needed until regplot supports style
if grid.hue_names is None:
n_markers = 1
else:
n_markers = len(grid.hue_names)
if not isinstance(markers, list):
markers = [markers] * n_markers
if len(markers) != n_markers:
raise ValueError("markers must be a singleton or a list of "
"markers for each level of the hue variable")
grid.hue_kws = {"marker": markers}
elif kind == "scatter":
if isinstance(markers, str):
plot_kws["marker"] = markers
elif hue is not None:
plot_kws["style"] = data[hue]
plot_kws["markers"] = markers
# Draw the marginal plots on the diagonal
diag_kws = diag_kws.copy()
diag_kws.setdefault("legend", False)
if diag_kind == "hist":
grid.map_diag(histplot, **diag_kws)
elif diag_kind == "kde":
diag_kws.setdefault("fill", True)
diag_kws.setdefault("warn_singular", False)
grid.map_diag(kdeplot, **diag_kws)
# Maybe plot on the off-diagonals
if diag_kind is not None:
plotter = grid.map_offdiag
else:
plotter = grid.map
if kind == "scatter":
from .relational import scatterplot # Avoid circular import
plotter(scatterplot, **plot_kws)
elif kind == "reg":
from .regression import regplot # Avoid circular import
plotter(regplot, **plot_kws)
elif kind == "kde":
from .distributions import kdeplot # Avoid circular import
plot_kws.setdefault("warn_singular", False)
plotter(kdeplot, **plot_kws)
elif kind == "hist":
from .distributions import histplot # Avoid circular import
plotter(histplot, **plot_kws)
# Add a legend
if hue is not None:
grid.add_legend()
grid.tight_layout()
return grid
| axisgrid.pairplot |
seaborn | 30 | seaborn/_marks/base.py | def _resolve(
self,
data: DataFrame | dict[str, Any],
name: str,
scales: dict[str, Scale] | None = None,
) -> Any:
"""Obtain default, specified, or mapped value for a named feature.
Parameters
----------
data : DataFrame or dict with scalar values
Container with data values for features that will be semantically mapped.
name : string
Identity of the feature / semantic.
scales: dict
Mapping from variable to corresponding scale object.
Returns
-------
value or array of values
Outer return type depends on whether `data` is a dict (implying that
we want a single value) or DataFrame (implying that we want an array
of values with matching length).
"""
| /usr/src/app/target_test_cases/failed_tests_base.Mark._resolve.txt | def _resolve(
self,
data: DataFrame | dict[str, Any],
name: str,
scales: dict[str, Scale] | None = None,
) -> Any:
"""Obtain default, specified, or mapped value for a named feature.
Parameters
----------
data : DataFrame or dict with scalar values
Container with data values for features that will be semantically mapped.
name : string
Identity of the feature / semantic.
scales: dict
Mapping from variable to corresponding scale object.
Returns
-------
value or array of values
Outer return type depends on whether `data` is a dict (implying that
we want a single value) or DataFrame (implying that we want an array
of values with matching length).
"""
feature = self._mappable_props[name]
prop = PROPERTIES.get(name, Property(name))
directly_specified = not isinstance(feature, Mappable)
return_multiple = isinstance(data, pd.DataFrame)
return_array = return_multiple and not name.endswith("style")
# Special case width because it needs to be resolved and added to the dataframe
# during layer prep (so the Move operations use it properly).
# TODO how does width *scaling* work, e.g. for violin width by count?
if name == "width":
directly_specified = directly_specified and name not in data
if directly_specified:
feature = prop.standardize(feature)
if return_multiple:
feature = [feature] * len(data)
if return_array:
feature = np.array(feature)
return feature
if name in data:
if scales is None or name not in scales:
# TODO Might this obviate the identity scale? Just don't add a scale?
feature = data[name]
else:
scale = scales[name]
value = data[name]
try:
feature = scale(value)
except Exception as err:
raise PlotSpecError._during("Scaling operation", name) from err
if return_array:
feature = np.asarray(feature)
return feature
if feature.depend is not None:
# TODO add source_func or similar to transform the source value?
# e.g. set linewidth as a proportion of pointsize?
return self._resolve(data, feature.depend, scales)
default = prop.standardize(feature.default)
if return_multiple:
default = [default] * len(data)
if return_array:
default = np.array(default)
return default
| base.Mark._resolve |
seaborn | 31 | seaborn/_marks/base.py | def resolve_color(
mark: Mark,
data: DataFrame | dict,
prefix: str = "",
scales: dict[str, Scale] | None = None,
) -> RGBATuple | ndarray:
"""
Obtain a default, specified, or mapped value for a color feature.
This method exists separately to support the relationship between a
color and its corresponding alpha. We want to respect alpha values that
are passed in specified (or mapped) color values but also make use of a
separate `alpha` variable, which can be mapped. This approach may also
be extended to support mapping of specific color channels (i.e.
luminance, chroma) in the future.
Parameters
----------
mark :
Mark with the color property.
data :
Container with data values for features that will be semantically mapped.
prefix :
Support "color", "fillcolor", etc.
"""
| /usr/src/app/target_test_cases/failed_tests_resolve_color.txt | def resolve_color(
mark: Mark,
data: DataFrame | dict,
prefix: str = "",
scales: dict[str, Scale] | None = None,
) -> RGBATuple | ndarray:
"""
Obtain a default, specified, or mapped value for a color feature.
This method exists separately to support the relationship between a
color and its corresponding alpha. We want to respect alpha values that
are passed in specified (or mapped) color values but also make use of a
separate `alpha` variable, which can be mapped. This approach may also
be extended to support mapping of specific color channels (i.e.
luminance, chroma) in the future.
Parameters
----------
mark :
Mark with the color property.
data :
Container with data values for features that will be semantically mapped.
prefix :
Support "color", "fillcolor", etc.
"""
color = mark._resolve(data, f"{prefix}color", scales)
if f"{prefix}alpha" in mark._mappable_props:
alpha = mark._resolve(data, f"{prefix}alpha", scales)
else:
alpha = mark._resolve(data, "alpha", scales)
def visible(x, axis=None):
"""Detect "invisible" colors to set alpha appropriately."""
# TODO First clause only needed to handle non-rgba arrays,
# which we are trying to handle upstream
return np.array(x).dtype.kind != "f" or np.isfinite(x).all(axis)
# Second check here catches vectors of strings with identity scale
# It could probably be handled better upstream. This is a tricky problem
if np.ndim(color) < 2 and all(isinstance(x, float) for x in color):
if len(color) == 4:
return mpl.colors.to_rgba(color)
alpha = alpha if visible(color) else np.nan
return mpl.colors.to_rgba(color, alpha)
else:
if np.ndim(color) == 2 and color.shape[1] == 4:
return mpl.colors.to_rgba_array(color)
alpha = np.where(visible(color, axis=1), alpha, np.nan)
return mpl.colors.to_rgba_array(color, alpha)
# TODO should we be implementing fill here too?
# (i.e. set fillalpha to 0 when fill=False)
| base.resolve_color |
seaborn | 32 | seaborn/_core/rules.py | def categorical_order(vector: Series, order: list | None = None) -> list:
"""
Return a list of unique data values using seaborn's ordering rules.
Parameters
----------
vector : Series
Vector of "categorical" values
order : list
Desired order of category levels to override the order determined
from the `data` object.
Returns
-------
order : list
Ordered list of category levels not including null values.
"""
| /usr/src/app/target_test_cases/failed_tests_rules.categorical_order.txt | def categorical_order(vector: Series, order: list | None = None) -> list:
"""
Return a list of unique data values using seaborn's ordering rules.
Parameters
----------
vector : Series
Vector of "categorical" values
order : list
Desired order of category levels to override the order determined
from the `data` object.
Returns
-------
order : list
Ordered list of category levels not including null values.
"""
if order is not None:
return order
if vector.dtype.name == "category":
order = list(vector.cat.categories)
else:
order = list(filter(pd.notnull, vector.unique()))
if variable_type(pd.Series(order)) == "numeric":
order.sort()
return order
| categorical_order |
seaborn | 33 | seaborn/palettes.py | def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
"""Return a list of colors or continuous colormap defining a palette.
Possible ``palette`` values include:
- Name of a seaborn palette (deep, muted, bright, pastel, dark, colorblind)
- Name of matplotlib colormap
- 'husl' or 'hls'
- 'ch:<cubehelix arguments>'
- 'light:<color>', 'dark:<color>', 'blend:<color>,<color>',
- A sequence of colors in any format matplotlib accepts
Calling this function with ``palette=None`` will return the current
matplotlib color cycle.
This function can also be used in a ``with`` statement to temporarily
set the color cycle for a plot or set of plots.
See the :ref:`tutorial <palette_tutorial>` for more information.
Parameters
----------
palette : None, string, or sequence, optional
Name of palette or None to return current palette. If a sequence, input
colors are used but possibly cycled and desaturated.
n_colors : int, optional
Number of colors in the palette. If ``None``, the default will depend
on how ``palette`` is specified. Named palettes default to 6 colors,
but grabbing the current palette or passing in a list of colors will
not change the number of colors unless this is specified. Asking for
more colors than exist in the palette will cause it to cycle. Ignored
when ``as_cmap`` is True.
desat : float, optional
Proportion to desaturate each color by.
as_cmap : bool
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
set_palette : Set the default color cycle for all plots.
set_color_codes : Reassign color codes like ``"b"``, ``"g"``, etc. to
colors from one of the seaborn palettes.
Examples
--------
.. include:: ../docstrings/color_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_color_palette.txt | def color_palette(palette=None, n_colors=None, desat=None, as_cmap=False):
"""Return a list of colors or continuous colormap defining a palette.
Possible ``palette`` values include:
- Name of a seaborn palette (deep, muted, bright, pastel, dark, colorblind)
- Name of matplotlib colormap
- 'husl' or 'hls'
- 'ch:<cubehelix arguments>'
- 'light:<color>', 'dark:<color>', 'blend:<color>,<color>',
- A sequence of colors in any format matplotlib accepts
Calling this function with ``palette=None`` will return the current
matplotlib color cycle.
This function can also be used in a ``with`` statement to temporarily
set the color cycle for a plot or set of plots.
See the :ref:`tutorial <palette_tutorial>` for more information.
Parameters
----------
palette : None, string, or sequence, optional
Name of palette or None to return current palette. If a sequence, input
colors are used but possibly cycled and desaturated.
n_colors : int, optional
Number of colors in the palette. If ``None``, the default will depend
on how ``palette`` is specified. Named palettes default to 6 colors,
but grabbing the current palette or passing in a list of colors will
not change the number of colors unless this is specified. Asking for
more colors than exist in the palette will cause it to cycle. Ignored
when ``as_cmap`` is True.
desat : float, optional
Proportion to desaturate each color by.
as_cmap : bool
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
set_palette : Set the default color cycle for all plots.
set_color_codes : Reassign color codes like ``"b"``, ``"g"``, etc. to
colors from one of the seaborn palettes.
Examples
--------
.. include:: ../docstrings/color_palette.rst
"""
if palette is None:
palette = get_color_cycle()
if n_colors is None:
n_colors = len(palette)
elif not isinstance(palette, str):
palette = palette
if n_colors is None:
n_colors = len(palette)
else:
if n_colors is None:
# Use all colors in a qualitative palette or 6 of another kind
n_colors = QUAL_PALETTE_SIZES.get(palette, 6)
if palette in SEABORN_PALETTES:
# Named "seaborn variant" of matplotlib default color cycle
palette = SEABORN_PALETTES[palette]
elif palette == "hls":
# Evenly spaced colors in cylindrical RGB space
palette = hls_palette(n_colors, as_cmap=as_cmap)
elif palette == "husl":
# Evenly spaced colors in cylindrical Lab space
palette = husl_palette(n_colors, as_cmap=as_cmap)
elif palette.lower() == "jet":
# Paternalism
raise ValueError("No.")
elif palette.startswith("ch:"):
# Cubehelix palette with params specified in string
args, kwargs = _parse_cubehelix_args(palette)
palette = cubehelix_palette(n_colors, *args, **kwargs, as_cmap=as_cmap)
elif palette.startswith("light:"):
# light palette to color specified in string
_, color = palette.split(":")
reverse = color.endswith("_r")
if reverse:
color = color[:-2]
palette = light_palette(color, n_colors, reverse=reverse, as_cmap=as_cmap)
elif palette.startswith("dark:"):
# light palette to color specified in string
_, color = palette.split(":")
reverse = color.endswith("_r")
if reverse:
color = color[:-2]
palette = dark_palette(color, n_colors, reverse=reverse, as_cmap=as_cmap)
elif palette.startswith("blend:"):
# blend palette between colors specified in string
_, colors = palette.split(":")
colors = colors.split(",")
palette = blend_palette(colors, n_colors, as_cmap=as_cmap)
else:
try:
# Perhaps a named matplotlib colormap?
palette = mpl_palette(palette, n_colors, as_cmap=as_cmap)
except (ValueError, KeyError): # Error class changed in mpl36
raise ValueError(f"{palette!r} is not a valid palette name")
if desat is not None:
palette = [desaturate(c, desat) for c in palette]
if not as_cmap:
# Always return as many colors as we asked for
pal_cycle = cycle(palette)
palette = [next(pal_cycle) for _ in range(n_colors)]
# Always return in r, g, b tuple format
try:
palette = map(mpl.colors.colorConverter.to_rgb, palette)
palette = _ColorPalette(palette)
except ValueError:
raise ValueError(f"Could not generate a palette for {palette}")
return palette
| color_palette |
seaborn | 34 | seaborn/utils.py | def desaturate(color, prop):
"""Decrease the saturation channel of a color by some percent.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
prop : float
saturation channel of color will be multiplied by this value
Returns
-------
new_color : rgb tuple
desaturated color code in RGB tuple representation
"""
| /usr/src/app/target_test_cases/failed_tests_desaturate.txt | def desaturate(color, prop):
"""Decrease the saturation channel of a color by some percent.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
prop : float
saturation channel of color will be multiplied by this value
Returns
-------
new_color : rgb tuple
desaturated color code in RGB tuple representation
"""
# Check inputs
if not 0 <= prop <= 1:
raise ValueError("prop must be between 0 and 1")
# Get rgb tuple rep
rgb = to_rgb(color)
# Short circuit to avoid floating point issues
if prop == 1:
return rgb
# Convert to hls
h, l, s = colorsys.rgb_to_hls(*rgb)
# Desaturate the saturation channel
s *= prop
# Convert back to rgb
new_color = colorsys.hls_to_rgb(h, l, s)
return new_color
| desaturate |
seaborn | 35 | seaborn/_core/groupby.py | def __init__(self, order: list[str] | dict[str, list | None]):
"""
Initialize the GroupBy from grouping variables and optional level orders.
Parameters
----------
order
List of variable names or dict mapping names to desired level orders.
Level order values can be None to use default ordering rules. The
variables can include names that are not expected to appear in the
data; these will be dropped before the groups are defined.
"""
| /usr/src/app/target_test_cases/failed_tests_groupby.GroupBy.__init__.txt | def __init__(self, order: list[str] | dict[str, list | None]):
"""
Initialize the GroupBy from grouping variables and optional level orders.
Parameters
----------
order
List of variable names or dict mapping names to desired level orders.
Level order values can be None to use default ordering rules. The
variables can include names that are not expected to appear in the
data; these will be dropped before the groups are defined.
"""
if not order:
raise ValueError("GroupBy requires at least one grouping variable")
if isinstance(order, list):
order = {k: None for k in order}
self.order = order
| groupby.GroupBy.__init__ |
seaborn | 36 | seaborn/external/kde.py | def evaluate(self, points):
"""Evaluate the estimated pdf on a set of points.
Parameters
----------
points : (# of dimensions, # of points)-array
Alternatively, a (# of dimensions,) vector can be passed in and
treated as a single point.
Returns
-------
values : (# of points,)-array
The values at each point.
Raises
------
ValueError : if the dimensionality of the input points is different than
the dimensionality of the KDE.
"""
| /usr/src/app/target_test_cases/failed_tests_kde.gaussian_kde.evaluate.txt | def evaluate(self, points):
"""Evaluate the estimated pdf on a set of points.
Parameters
----------
points : (# of dimensions, # of points)-array
Alternatively, a (# of dimensions,) vector can be passed in and
treated as a single point.
Returns
-------
values : (# of points,)-array
The values at each point.
Raises
------
ValueError : if the dimensionality of the input points is different than
the dimensionality of the KDE.
"""
points = atleast_2d(asarray(points))
d, m = points.shape
if d != self.d:
if d == 1 and m == self.d:
# points was passed in as a row vector
points = reshape(points, (self.d, 1))
m = 1
else:
msg = f"points have dimension {d}, dataset has dimension {self.d}"
raise ValueError(msg)
output_dtype = np.common_type(self.covariance, points)
result = zeros((m,), dtype=output_dtype)
whitening = linalg.cholesky(self.inv_cov)
scaled_dataset = dot(whitening, self.dataset)
scaled_points = dot(whitening, points)
if m >= self.n:
# there are more points than data, so loop over data
for i in range(self.n):
diff = scaled_dataset[:, i, newaxis] - scaled_points
energy = sum(diff * diff, axis=0) / 2.0
result += self.weights[i]*exp(-energy)
else:
# loop over points
for i in range(m):
diff = scaled_dataset - scaled_points[:, i, newaxis]
energy = sum(diff * diff, axis=0) / 2.0
result[i] = sum(exp(-energy)*self.weights, axis=0)
result = result / self._norm_factor
return result
| kde.gaussian_kde.evaluate |
seaborn | 37 | seaborn/external/kde.py | def set_bandwidth(self, bw_method=None):
"""Compute the estimator bandwidth with given method.
The new bandwidth calculated after a call to `set_bandwidth` is used
for subsequent evaluations of the estimated density.
Parameters
----------
bw_method : str, scalar or callable, optional
The method used to calculate the estimator bandwidth. This can be
'scott', 'silverman', a scalar constant or a callable. If a
scalar, this will be used directly as `kde.factor`. If a callable,
it should take a `gaussian_kde` instance as only parameter and
return a scalar. If None (default), nothing happens; the current
`kde.covariance_factor` method is kept.
Notes
-----
.. versionadded:: 0.11
"""
| /usr/src/app/target_test_cases/failed_tests_kde.gaussian_kde.set_bandwidth.txt | def set_bandwidth(self, bw_method=None):
"""Compute the estimator bandwidth with given method.
The new bandwidth calculated after a call to `set_bandwidth` is used
for subsequent evaluations of the estimated density.
Parameters
----------
bw_method : str, scalar or callable, optional
The method used to calculate the estimator bandwidth. This can be
'scott', 'silverman', a scalar constant or a callable. If a
scalar, this will be used directly as `kde.factor`. If a callable,
it should take a `gaussian_kde` instance as only parameter and
return a scalar. If None (default), nothing happens; the current
`kde.covariance_factor` method is kept.
Notes
-----
.. versionadded:: 0.11
"""
if bw_method is None:
pass
elif bw_method == 'scott':
self.covariance_factor = self.scotts_factor
elif bw_method == 'silverman':
self.covariance_factor = self.silverman_factor
elif np.isscalar(bw_method) and not isinstance(bw_method, str):
self._bw_method = 'use constant'
self.covariance_factor = lambda: bw_method
elif callable(bw_method):
self._bw_method = bw_method
self.covariance_factor = lambda: self._bw_method(self)
else:
msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
"or a callable."
raise ValueError(msg)
self._compute_covariance()
| kde.gaussian_kde.set_bandwidth |
seaborn | 38 | seaborn/utils.py | def load_dataset(name, cache=True, data_home=None, **kws):
"""Load an example dataset from the online repository (requires internet).
This function provides quick access to a small number of example datasets
that are useful for documenting seaborn or generating reproducible examples
for bug reports. It is not necessary for normal usage.
Note that some of the datasets have a small amount of preprocessing applied
to define a proper ordering for categorical variables.
Use :func:`get_dataset_names` to see a list of available datasets.
Parameters
----------
name : str
Name of the dataset (``{name}.csv`` on
https://github.com/mwaskom/seaborn-data).
cache : boolean, optional
If True, try to load from the local cache first, and save to the cache
if a download is required.
data_home : string, optional
The directory in which to cache data; see :func:`get_data_home`.
kws : keys and values, optional
Additional keyword arguments are passed to passed through to
:func:`pandas.read_csv`.
Returns
-------
df : :class:`pandas.DataFrame`
Tabular data, possibly with some preprocessing applied.
"""
| /usr/src/app/target_test_cases/failed_tests_load_dataset.txt | def load_dataset(name, cache=True, data_home=None, **kws):
"""Load an example dataset from the online repository (requires internet).
This function provides quick access to a small number of example datasets
that are useful for documenting seaborn or generating reproducible examples
for bug reports. It is not necessary for normal usage.
Note that some of the datasets have a small amount of preprocessing applied
to define a proper ordering for categorical variables.
Use :func:`get_dataset_names` to see a list of available datasets.
Parameters
----------
name : str
Name of the dataset (``{name}.csv`` on
https://github.com/mwaskom/seaborn-data).
cache : boolean, optional
If True, try to load from the local cache first, and save to the cache
if a download is required.
data_home : string, optional
The directory in which to cache data; see :func:`get_data_home`.
kws : keys and values, optional
Additional keyword arguments are passed to passed through to
:func:`pandas.read_csv`.
Returns
-------
df : :class:`pandas.DataFrame`
Tabular data, possibly with some preprocessing applied.
"""
# A common beginner mistake is to assume that one's personal data needs
# to be passed through this function to be usable with seaborn.
# Let's provide a more helpful error than you would otherwise get.
if isinstance(name, pd.DataFrame):
err = (
"This function accepts only strings (the name of an example dataset). "
"You passed a pandas DataFrame. If you have your own dataset, "
"it is not necessary to use this function before plotting."
)
raise TypeError(err)
url = f"{DATASET_SOURCE}/{name}.csv"
if cache:
cache_path = os.path.join(get_data_home(data_home), os.path.basename(url))
if not os.path.exists(cache_path):
if name not in get_dataset_names():
raise ValueError(f"'{name}' is not one of the example datasets.")
urlretrieve(url, cache_path)
full_path = cache_path
else:
full_path = url
df = pd.read_csv(full_path, **kws)
if df.iloc[-1].isnull().all():
df = df.iloc[:-1]
# Set some columns as a categorical type with ordered levels
if name == "tips":
df["day"] = pd.Categorical(df["day"], ["Thur", "Fri", "Sat", "Sun"])
df["sex"] = pd.Categorical(df["sex"], ["Male", "Female"])
df["time"] = pd.Categorical(df["time"], ["Lunch", "Dinner"])
df["smoker"] = pd.Categorical(df["smoker"], ["Yes", "No"])
elif name == "flights":
months = df["month"].str[:3]
df["month"] = pd.Categorical(months, months.unique())
elif name == "exercise":
df["time"] = pd.Categorical(df["time"], ["1 min", "15 min", "30 min"])
df["kind"] = pd.Categorical(df["kind"], ["rest", "walking", "running"])
df["diet"] = pd.Categorical(df["diet"], ["no fat", "low fat"])
elif name == "titanic":
df["class"] = pd.Categorical(df["class"], ["First", "Second", "Third"])
df["deck"] = pd.Categorical(df["deck"], list("ABCDEFG"))
elif name == "penguins":
df["sex"] = df["sex"].str.title()
elif name == "diamonds":
df["color"] = pd.Categorical(
df["color"], ["D", "E", "F", "G", "H", "I", "J"],
)
df["clarity"] = pd.Categorical(
df["clarity"], ["IF", "VVS1", "VVS2", "VS1", "VS2", "SI1", "SI2", "I1"],
)
df["cut"] = pd.Categorical(
df["cut"], ["Ideal", "Premium", "Very Good", "Good", "Fair"],
)
elif name == "taxis":
df["pickup"] = pd.to_datetime(df["pickup"])
df["dropoff"] = pd.to_datetime(df["dropoff"])
elif name == "seaice":
df["Date"] = pd.to_datetime(df["Date"])
elif name == "dowjones":
df["Date"] = pd.to_datetime(df["Date"])
return df
| load_dataset |
seaborn | 39 | seaborn/matrix.py | def clustermap(
data, *,
pivot_kws=None, method='average', metric='euclidean',
z_score=None, standard_scale=None, figsize=(10, 10),
cbar_kws=None, row_cluster=True, col_cluster=True,
row_linkage=None, col_linkage=None,
row_colors=None, col_colors=None, mask=None,
dendrogram_ratio=.2, colors_ratio=0.03,
cbar_pos=(.02, .8, .05, .18), tree_kws=None,
**kwargs
):
"""
Plot a matrix dataset as a hierarchically-clustered heatmap.
This function requires scipy to be available.
Parameters
----------
data : 2D array-like
Rectangular data for clustering. Cannot contain NAs.
pivot_kws : dict, optional
If `data` is a tidy dataframe, can provide keyword arguments for
pivot to create a rectangular dataframe.
method : str, optional
Linkage method to use for calculating clusters. See
:func:`scipy.cluster.hierarchy.linkage` documentation for more
information.
metric : str, optional
Distance metric to use for the data. See
:func:`scipy.spatial.distance.pdist` documentation for more options.
To use different metrics (or methods) for rows and columns, you may
construct each linkage matrix yourself and provide them as
`{row,col}_linkage`.
z_score : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to calculate z-scores
for the rows or the columns. Z scores are: z = (x - mean)/std, so
values in each row (column) will get the mean of the row (column)
subtracted, then divided by the standard deviation of the row (column).
This ensures that each row (column) has mean of 0 and variance of 1.
standard_scale : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to standardize that
dimension, meaning for each row or column, subtract the minimum and
divide each by its maximum.
figsize : tuple of (width, height), optional
Overall size of the figure.
cbar_kws : dict, optional
Keyword arguments to pass to `cbar_kws` in :func:`heatmap`, e.g. to
add a label to the colorbar.
{row,col}_cluster : bool, optional
If ``True``, cluster the {rows, columns}.
{row,col}_linkage : :class:`numpy.ndarray`, optional
Precomputed linkage matrix for the rows or columns. See
:func:`scipy.cluster.hierarchy.linkage` for specific formats.
{row,col}_colors : list-like or pandas DataFrame/Series, optional
List of colors to label for either the rows or columns. Useful to evaluate
whether samples within a group are clustered together. Can use nested lists or
DataFrame for multiple color levels of labeling. If given as a
:class:`pandas.DataFrame` or :class:`pandas.Series`, labels for the colors are
extracted from the DataFrames column names or from the name of the Series.
DataFrame/Series colors are also matched to the data by their index, ensuring
colors are drawn in the correct order.
mask : bool array or DataFrame, optional
If passed, data will not be shown in cells where `mask` is True.
Cells with missing values are automatically masked. Only used for
visualizing, not for calculating.
{dendrogram,colors}_ratio : float, or pair of floats, optional
Proportion of the figure size devoted to the two marginal elements. If
a pair is given, they correspond to (row, col) ratios.
cbar_pos : tuple of (left, bottom, width, height), optional
Position of the colorbar axes in the figure. Setting to ``None`` will
disable the colorbar.
tree_kws : dict, optional
Parameters for the :class:`matplotlib.collections.LineCollection`
that is used to plot the lines of the dendrogram tree.
kwargs : other keyword arguments
All other keyword arguments are passed to :func:`heatmap`.
Returns
-------
:class:`ClusterGrid`
A :class:`ClusterGrid` instance.
See Also
--------
heatmap : Plot rectangular data as a color-encoded matrix.
Notes
-----
The returned object has a ``savefig`` method that should be used if you
want to save the figure object without clipping the dendrograms.
To access the reordered row indices, use:
``clustergrid.dendrogram_row.reordered_ind``
Column indices, use:
``clustergrid.dendrogram_col.reordered_ind``
Examples
--------
.. include:: ../docstrings/clustermap.rst
"""
| /usr/src/app/target_test_cases/failed_tests_matrix.clustermap.txt | def clustermap(
data, *,
pivot_kws=None, method='average', metric='euclidean',
z_score=None, standard_scale=None, figsize=(10, 10),
cbar_kws=None, row_cluster=True, col_cluster=True,
row_linkage=None, col_linkage=None,
row_colors=None, col_colors=None, mask=None,
dendrogram_ratio=.2, colors_ratio=0.03,
cbar_pos=(.02, .8, .05, .18), tree_kws=None,
**kwargs
):
"""
Plot a matrix dataset as a hierarchically-clustered heatmap.
This function requires scipy to be available.
Parameters
----------
data : 2D array-like
Rectangular data for clustering. Cannot contain NAs.
pivot_kws : dict, optional
If `data` is a tidy dataframe, can provide keyword arguments for
pivot to create a rectangular dataframe.
method : str, optional
Linkage method to use for calculating clusters. See
:func:`scipy.cluster.hierarchy.linkage` documentation for more
information.
metric : str, optional
Distance metric to use for the data. See
:func:`scipy.spatial.distance.pdist` documentation for more options.
To use different metrics (or methods) for rows and columns, you may
construct each linkage matrix yourself and provide them as
`{row,col}_linkage`.
z_score : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to calculate z-scores
for the rows or the columns. Z scores are: z = (x - mean)/std, so
values in each row (column) will get the mean of the row (column)
subtracted, then divided by the standard deviation of the row (column).
This ensures that each row (column) has mean of 0 and variance of 1.
standard_scale : int or None, optional
Either 0 (rows) or 1 (columns). Whether or not to standardize that
dimension, meaning for each row or column, subtract the minimum and
divide each by its maximum.
figsize : tuple of (width, height), optional
Overall size of the figure.
cbar_kws : dict, optional
Keyword arguments to pass to `cbar_kws` in :func:`heatmap`, e.g. to
add a label to the colorbar.
{row,col}_cluster : bool, optional
If ``True``, cluster the {rows, columns}.
{row,col}_linkage : :class:`numpy.ndarray`, optional
Precomputed linkage matrix for the rows or columns. See
:func:`scipy.cluster.hierarchy.linkage` for specific formats.
{row,col}_colors : list-like or pandas DataFrame/Series, optional
List of colors to label for either the rows or columns. Useful to evaluate
whether samples within a group are clustered together. Can use nested lists or
DataFrame for multiple color levels of labeling. If given as a
:class:`pandas.DataFrame` or :class:`pandas.Series`, labels for the colors are
extracted from the DataFrames column names or from the name of the Series.
DataFrame/Series colors are also matched to the data by their index, ensuring
colors are drawn in the correct order.
mask : bool array or DataFrame, optional
If passed, data will not be shown in cells where `mask` is True.
Cells with missing values are automatically masked. Only used for
visualizing, not for calculating.
{dendrogram,colors}_ratio : float, or pair of floats, optional
Proportion of the figure size devoted to the two marginal elements. If
a pair is given, they correspond to (row, col) ratios.
cbar_pos : tuple of (left, bottom, width, height), optional
Position of the colorbar axes in the figure. Setting to ``None`` will
disable the colorbar.
tree_kws : dict, optional
Parameters for the :class:`matplotlib.collections.LineCollection`
that is used to plot the lines of the dendrogram tree.
kwargs : other keyword arguments
All other keyword arguments are passed to :func:`heatmap`.
Returns
-------
:class:`ClusterGrid`
A :class:`ClusterGrid` instance.
See Also
--------
heatmap : Plot rectangular data as a color-encoded matrix.
Notes
-----
The returned object has a ``savefig`` method that should be used if you
want to save the figure object without clipping the dendrograms.
To access the reordered row indices, use:
``clustergrid.dendrogram_row.reordered_ind``
Column indices, use:
``clustergrid.dendrogram_col.reordered_ind``
Examples
--------
.. include:: ../docstrings/clustermap.rst
"""
if _no_scipy:
raise RuntimeError("clustermap requires scipy to be available")
plotter = ClusterGrid(data, pivot_kws=pivot_kws, figsize=figsize,
row_colors=row_colors, col_colors=col_colors,
z_score=z_score, standard_scale=standard_scale,
mask=mask, dendrogram_ratio=dendrogram_ratio,
colors_ratio=colors_ratio, cbar_pos=cbar_pos)
return plotter.plot(metric=metric, method=method,
colorbar_kws=cbar_kws,
row_cluster=row_cluster, col_cluster=col_cluster,
row_linkage=row_linkage, col_linkage=col_linkage,
tree_kws=tree_kws, **kwargs)
| matrix.clustermap |
seaborn | 40 | seaborn/matrix.py | def dendrogram(
data, *,
linkage=None, axis=1, label=True, metric='euclidean',
method='average', rotate=False, tree_kws=None, ax=None
):
"""Draw a tree diagram of relationships within a matrix
Parameters
----------
data : pandas.DataFrame
Rectangular data
linkage : numpy.array, optional
Linkage matrix
axis : int, optional
Which axis to use to calculate linkage. 0 is rows, 1 is columns.
label : bool, optional
If True, label the dendrogram at leaves with column or row names
metric : str, optional
Distance metric. Anything valid for scipy.spatial.distance.pdist
method : str, optional
Linkage method to use. Anything valid for
scipy.cluster.hierarchy.linkage
rotate : bool, optional
When plotting the matrix, whether to rotate it 90 degrees
counter-clockwise, so the leaves face right
tree_kws : dict, optional
Keyword arguments for the ``matplotlib.collections.LineCollection``
that is used for plotting the lines of the dendrogram tree.
ax : matplotlib axis, optional
Axis to plot on, otherwise uses current axis
Returns
-------
dendrogramplotter : _DendrogramPlotter
A Dendrogram plotter object.
Notes
-----
Access the reordered dendrogram indices with
dendrogramplotter.reordered_ind
"""
| /usr/src/app/target_test_cases/failed_tests_matrix.dendrogram.txt | def dendrogram(
data, *,
linkage=None, axis=1, label=True, metric='euclidean',
method='average', rotate=False, tree_kws=None, ax=None
):
"""Draw a tree diagram of relationships within a matrix
Parameters
----------
data : pandas.DataFrame
Rectangular data
linkage : numpy.array, optional
Linkage matrix
axis : int, optional
Which axis to use to calculate linkage. 0 is rows, 1 is columns.
label : bool, optional
If True, label the dendrogram at leaves with column or row names
metric : str, optional
Distance metric. Anything valid for scipy.spatial.distance.pdist
method : str, optional
Linkage method to use. Anything valid for
scipy.cluster.hierarchy.linkage
rotate : bool, optional
When plotting the matrix, whether to rotate it 90 degrees
counter-clockwise, so the leaves face right
tree_kws : dict, optional
Keyword arguments for the ``matplotlib.collections.LineCollection``
that is used for plotting the lines of the dendrogram tree.
ax : matplotlib axis, optional
Axis to plot on, otherwise uses current axis
Returns
-------
dendrogramplotter : _DendrogramPlotter
A Dendrogram plotter object.
Notes
-----
Access the reordered dendrogram indices with
dendrogramplotter.reordered_ind
"""
if _no_scipy:
raise RuntimeError("dendrogram requires scipy to be installed")
plotter = _DendrogramPlotter(data, linkage=linkage, axis=axis,
metric=metric, method=method,
label=label, rotate=rotate)
if ax is None:
ax = plt.gca()
return plotter.plot(ax=ax, tree_kws=tree_kws)
| matrix.dendrogram |
seaborn | 41 | seaborn/matrix.py | def heatmap(
data, *,
vmin=None, vmax=None, cmap=None, center=None, robust=False,
annot=None, fmt=".2g", annot_kws=None,
linewidths=0, linecolor="white",
cbar=True, cbar_kws=None, cbar_ax=None,
square=False, xticklabels="auto", yticklabels="auto",
mask=None, ax=None,
**kwargs
):
"""Plot rectangular data as a color-encoded matrix.
This is an Axes-level function and will draw the heatmap into the
currently-active Axes if none is provided to the ``ax`` argument. Part of
this Axes space will be taken and used to plot a colormap, unless ``cbar``
is False or a separate Axes is provided to ``cbar_ax``.
Parameters
----------
data : rectangular dataset
2D dataset that can be coerced into an ndarray. If a Pandas DataFrame
is provided, the index/column information will be used to label the
columns and rows.
vmin, vmax : floats, optional
Values to anchor the colormap, otherwise they are inferred from the
data and other keyword arguments.
cmap : matplotlib colormap name or object, or list of colors, optional
The mapping from data values to color space. If not provided, the
default will depend on whether ``center`` is set.
center : float, optional
The value at which to center the colormap when plotting divergent data.
Using this parameter will change the default ``cmap`` if none is
specified.
robust : bool, optional
If True and ``vmin`` or ``vmax`` are absent, the colormap range is
computed with robust quantiles instead of the extreme values.
annot : bool or rectangular dataset, optional
If True, write the data value in each cell. If an array-like with the
same shape as ``data``, then use this to annotate the heatmap instead
of the data. Note that DataFrames will match on position, not index.
fmt : str, optional
String formatting code to use when adding annotations.
annot_kws : dict of key, value mappings, optional
Keyword arguments for :meth:`matplotlib.axes.Axes.text` when ``annot``
is True.
linewidths : float, optional
Width of the lines that will divide each cell.
linecolor : color, optional
Color of the lines that will divide each cell.
cbar : bool, optional
Whether to draw a colorbar.
cbar_kws : dict of key, value mappings, optional
Keyword arguments for :meth:`matplotlib.figure.Figure.colorbar`.
cbar_ax : matplotlib Axes, optional
Axes in which to draw the colorbar, otherwise take space from the
main Axes.
square : bool, optional
If True, set the Axes aspect to "equal" so each cell will be
square-shaped.
xticklabels, yticklabels : "auto", bool, list-like, or int, optional
If True, plot the column names of the dataframe. If False, don't plot
the column names. If list-like, plot these alternate labels as the
xticklabels. If an integer, use the column names but plot only every
n label. If "auto", try to densely plot non-overlapping labels.
mask : bool array or DataFrame, optional
If passed, data will not be shown in cells where ``mask`` is True.
Cells with missing values are automatically masked.
ax : matplotlib Axes, optional
Axes in which to draw the plot, otherwise use the currently-active
Axes.
kwargs : other keyword arguments
All other keyword arguments are passed to
:meth:`matplotlib.axes.Axes.pcolormesh`.
Returns
-------
ax : matplotlib Axes
Axes object with the heatmap.
See Also
--------
clustermap : Plot a matrix using hierarchical clustering to arrange the
rows and columns.
Examples
--------
.. include:: ../docstrings/heatmap.rst
"""
| /usr/src/app/target_test_cases/failed_tests_matrix.heatmap.txt | def heatmap(
data, *,
vmin=None, vmax=None, cmap=None, center=None, robust=False,
annot=None, fmt=".2g", annot_kws=None,
linewidths=0, linecolor="white",
cbar=True, cbar_kws=None, cbar_ax=None,
square=False, xticklabels="auto", yticklabels="auto",
mask=None, ax=None,
**kwargs
):
"""Plot rectangular data as a color-encoded matrix.
This is an Axes-level function and will draw the heatmap into the
currently-active Axes if none is provided to the ``ax`` argument. Part of
this Axes space will be taken and used to plot a colormap, unless ``cbar``
is False or a separate Axes is provided to ``cbar_ax``.
Parameters
----------
data : rectangular dataset
2D dataset that can be coerced into an ndarray. If a Pandas DataFrame
is provided, the index/column information will be used to label the
columns and rows.
vmin, vmax : floats, optional
Values to anchor the colormap, otherwise they are inferred from the
data and other keyword arguments.
cmap : matplotlib colormap name or object, or list of colors, optional
The mapping from data values to color space. If not provided, the
default will depend on whether ``center`` is set.
center : float, optional
The value at which to center the colormap when plotting divergent data.
Using this parameter will change the default ``cmap`` if none is
specified.
robust : bool, optional
If True and ``vmin`` or ``vmax`` are absent, the colormap range is
computed with robust quantiles instead of the extreme values.
annot : bool or rectangular dataset, optional
If True, write the data value in each cell. If an array-like with the
same shape as ``data``, then use this to annotate the heatmap instead
of the data. Note that DataFrames will match on position, not index.
fmt : str, optional
String formatting code to use when adding annotations.
annot_kws : dict of key, value mappings, optional
Keyword arguments for :meth:`matplotlib.axes.Axes.text` when ``annot``
is True.
linewidths : float, optional
Width of the lines that will divide each cell.
linecolor : color, optional
Color of the lines that will divide each cell.
cbar : bool, optional
Whether to draw a colorbar.
cbar_kws : dict of key, value mappings, optional
Keyword arguments for :meth:`matplotlib.figure.Figure.colorbar`.
cbar_ax : matplotlib Axes, optional
Axes in which to draw the colorbar, otherwise take space from the
main Axes.
square : bool, optional
If True, set the Axes aspect to "equal" so each cell will be
square-shaped.
xticklabels, yticklabels : "auto", bool, list-like, or int, optional
If True, plot the column names of the dataframe. If False, don't plot
the column names. If list-like, plot these alternate labels as the
xticklabels. If an integer, use the column names but plot only every
n label. If "auto", try to densely plot non-overlapping labels.
mask : bool array or DataFrame, optional
If passed, data will not be shown in cells where ``mask`` is True.
Cells with missing values are automatically masked.
ax : matplotlib Axes, optional
Axes in which to draw the plot, otherwise use the currently-active
Axes.
kwargs : other keyword arguments
All other keyword arguments are passed to
:meth:`matplotlib.axes.Axes.pcolormesh`.
Returns
-------
ax : matplotlib Axes
Axes object with the heatmap.
See Also
--------
clustermap : Plot a matrix using hierarchical clustering to arrange the
rows and columns.
Examples
--------
.. include:: ../docstrings/heatmap.rst
"""
# Initialize the plotter object
plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,
annot_kws, cbar, cbar_kws, xticklabels,
yticklabels, mask)
# Add the pcolormesh kwargs here
kwargs["linewidths"] = linewidths
kwargs["edgecolor"] = linecolor
# Draw the plot and return the Axes
if ax is None:
ax = plt.gca()
if square:
ax.set_aspect("equal")
plotter.plot(ax, cbar_ax, kwargs)
return ax
| matrix.heatmap |
seaborn | 42 | seaborn/palettes.py | def blend_palette(colors, n_colors=6, as_cmap=False, input="rgb"):
"""Make a palette that blends between a list of colors.
Parameters
----------
colors : sequence of colors in various formats interpreted by `input`
hex code, html color name, or tuple in `input` space.
n_colors : int, optional
Number of colors in the palette.
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
Examples
--------
.. include: ../docstrings/blend_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.blend_palette.txt | def blend_palette(colors, n_colors=6, as_cmap=False, input="rgb"):
"""Make a palette that blends between a list of colors.
Parameters
----------
colors : sequence of colors in various formats interpreted by `input`
hex code, html color name, or tuple in `input` space.
n_colors : int, optional
Number of colors in the palette.
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
Examples
--------
.. include: ../docstrings/blend_palette.rst
"""
colors = [_color_to_rgb(color, input) for color in colors]
name = "blend"
pal = mpl.colors.LinearSegmentedColormap.from_list(name, colors)
if not as_cmap:
rgb_array = pal(np.linspace(0, 1, int(n_colors)))[:, :3] # no alpha
pal = _ColorPalette(map(tuple, rgb_array))
return pal
| palettes.blend_palette |
seaborn | 43 | seaborn/palettes.py | def crayon_palette(colors):
"""Make a palette with color names from Crayola crayons.
Colors are taken from here:
https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors
This is just a simple wrapper around the `seaborn.crayons` dictionary.
Parameters
----------
colors : list of strings
List of keys in the `seaborn.crayons` dictionary.
Returns
-------
palette
A list of colors as RGB tuples.
See Also
--------
xkcd_palette : Make a palette with named colors from the XKCD color survey.
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.crayon_palette.txt | def crayon_palette(colors):
"""Make a palette with color names from Crayola crayons.
Colors are taken from here:
https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors
This is just a simple wrapper around the `seaborn.crayons` dictionary.
Parameters
----------
colors : list of strings
List of keys in the `seaborn.crayons` dictionary.
Returns
-------
palette
A list of colors as RGB tuples.
See Also
--------
xkcd_palette : Make a palette with named colors from the XKCD color survey.
"""
palette = [crayons[name] for name in colors]
return color_palette(palette, len(palette))
| palettes.crayon_palette |
seaborn | 44 | seaborn/palettes.py | def cubehelix_palette(n_colors=6, start=0, rot=.4, gamma=1.0, hue=0.8,
light=.85, dark=.15, reverse=False, as_cmap=False):
"""Make a sequential palette from the cubehelix system.
This produces a colormap with linearly-decreasing (or increasing)
brightness. That means that information will be preserved if printed to
black and white or viewed by someone who is colorblind. "cubehelix" is
also available as a matplotlib-based palette, but this function gives the
user more control over the look of the palette and has a different set of
defaults.
In addition to using this function, it is also possible to generate a
cubehelix palette generally in seaborn using a string starting with
`ch:` and containing other parameters (e.g. `"ch:s=.25,r=-.5"`).
Parameters
----------
n_colors : int
Number of colors in the palette.
start : float, 0 <= start <= 3
The hue value at the start of the helix.
rot : float
Rotations around the hue wheel over the range of the palette.
gamma : float 0 <= gamma
Nonlinearity to emphasize dark (gamma < 1) or light (gamma > 1) colors.
hue : float, 0 <= hue <= 1
Saturation of the colors.
dark : float 0 <= dark <= 1
Intensity of the darkest color in the palette.
light : float 0 <= light <= 1
Intensity of the lightest color in the palette.
reverse : bool
If True, the palette will go from dark to light.
as_cmap : bool
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
choose_cubehelix_palette : Launch an interactive widget to select cubehelix
palette parameters.
dark_palette : Create a sequential palette with dark low values.
light_palette : Create a sequential palette with bright low values.
References
----------
Green, D. A. (2011). "A colour scheme for the display of astronomical
intensity images". Bulletin of the Astromical Society of India, Vol. 39,
p. 289-295.
Examples
--------
.. include:: ../docstrings/cubehelix_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.cubehelix_palette.txt | def cubehelix_palette(n_colors=6, start=0, rot=.4, gamma=1.0, hue=0.8,
light=.85, dark=.15, reverse=False, as_cmap=False):
"""Make a sequential palette from the cubehelix system.
This produces a colormap with linearly-decreasing (or increasing)
brightness. That means that information will be preserved if printed to
black and white or viewed by someone who is colorblind. "cubehelix" is
also available as a matplotlib-based palette, but this function gives the
user more control over the look of the palette and has a different set of
defaults.
In addition to using this function, it is also possible to generate a
cubehelix palette generally in seaborn using a string starting with
`ch:` and containing other parameters (e.g. `"ch:s=.25,r=-.5"`).
Parameters
----------
n_colors : int
Number of colors in the palette.
start : float, 0 <= start <= 3
The hue value at the start of the helix.
rot : float
Rotations around the hue wheel over the range of the palette.
gamma : float 0 <= gamma
Nonlinearity to emphasize dark (gamma < 1) or light (gamma > 1) colors.
hue : float, 0 <= hue <= 1
Saturation of the colors.
dark : float 0 <= dark <= 1
Intensity of the darkest color in the palette.
light : float 0 <= light <= 1
Intensity of the lightest color in the palette.
reverse : bool
If True, the palette will go from dark to light.
as_cmap : bool
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
choose_cubehelix_palette : Launch an interactive widget to select cubehelix
palette parameters.
dark_palette : Create a sequential palette with dark low values.
light_palette : Create a sequential palette with bright low values.
References
----------
Green, D. A. (2011). "A colour scheme for the display of astronomical
intensity images". Bulletin of the Astromical Society of India, Vol. 39,
p. 289-295.
Examples
--------
.. include:: ../docstrings/cubehelix_palette.rst
"""
def get_color_function(p0, p1):
# Copied from matplotlib because it lives in private module
def color(x):
# Apply gamma factor to emphasise low or high intensity values
xg = x ** gamma
# Calculate amplitude and angle of deviation from the black
# to white diagonal in the plane of constant
# perceived intensity.
a = hue * xg * (1 - xg) / 2
phi = 2 * np.pi * (start / 3 + rot * x)
return xg + a * (p0 * np.cos(phi) + p1 * np.sin(phi))
return color
cdict = {
"red": get_color_function(-0.14861, 1.78277),
"green": get_color_function(-0.29227, -0.90649),
"blue": get_color_function(1.97294, 0.0),
}
cmap = mpl.colors.LinearSegmentedColormap("cubehelix", cdict)
x = np.linspace(light, dark, int(n_colors))
pal = cmap(x)[:, :3].tolist()
if reverse:
pal = pal[::-1]
if as_cmap:
x_256 = np.linspace(light, dark, 256)
if reverse:
x_256 = x_256[::-1]
pal_256 = cmap(x_256)
cmap = mpl.colors.ListedColormap(pal_256, "seaborn_cubehelix")
return cmap
else:
return _ColorPalette(pal)
| palettes.cubehelix_palette |
seaborn | 45 | seaborn/palettes.py | def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from dark to ``color``.
This kind of palette is good for data that range between relatively
uninteresting low values and interesting high values.
The ``color`` parameter can be specified in a number of ways, including
all options for defining a color in matplotlib and several additional
color spaces that are handled by seaborn. You can also use the database
of named colors from the XKCD color survey.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_dark_palette` function.
Parameters
----------
color : base color for high values
hex, rgb-tuple, or html color name
n_colors : int, optional
number of colors in the palette
reverse : bool, optional
if True, reverse the direction of the blend
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
input : {'rgb', 'hls', 'husl', xkcd'}
Color space to interpret the input color. The first three options
apply to tuple inputs and the latter applies to string inputs.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
light_palette : Create a sequential palette with bright low values.
diverging_palette : Create a diverging palette with two colors.
Examples
--------
.. include:: ../docstrings/dark_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.dark_palette.txt | def dark_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from dark to ``color``.
This kind of palette is good for data that range between relatively
uninteresting low values and interesting high values.
The ``color`` parameter can be specified in a number of ways, including
all options for defining a color in matplotlib and several additional
color spaces that are handled by seaborn. You can also use the database
of named colors from the XKCD color survey.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_dark_palette` function.
Parameters
----------
color : base color for high values
hex, rgb-tuple, or html color name
n_colors : int, optional
number of colors in the palette
reverse : bool, optional
if True, reverse the direction of the blend
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
input : {'rgb', 'hls', 'husl', xkcd'}
Color space to interpret the input color. The first three options
apply to tuple inputs and the latter applies to string inputs.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
light_palette : Create a sequential palette with bright low values.
diverging_palette : Create a diverging palette with two colors.
Examples
--------
.. include:: ../docstrings/dark_palette.rst
"""
rgb = _color_to_rgb(color, input)
hue, sat, _ = husl.rgb_to_husl(*rgb)
gray_s, gray_l = .15 * sat, 15
gray = _color_to_rgb((hue, gray_s, gray_l), input="husl")
colors = [rgb, gray] if reverse else [gray, rgb]
return blend_palette(colors, n_colors, as_cmap)
| palettes.dark_palette |
seaborn | 46 | seaborn/palettes.py | def diverging_palette(h_neg, h_pos, s=75, l=50, sep=1, n=6, # noqa
center="light", as_cmap=False):
"""Make a diverging palette between two HUSL colors.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_diverging_palette` function.
Parameters
----------
h_neg, h_pos : float in [0, 359]
Anchor hues for negative and positive extents of the map.
s : float in [0, 100], optional
Anchor saturation for both extents of the map.
l : float in [0, 100], optional
Anchor lightness for both extents of the map.
sep : int, optional
Size of the intermediate region.
n : int, optional
Number of colors in the palette (if not returning a cmap)
center : {"light", "dark"}, optional
Whether the center of the palette is light or dark
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
dark_palette : Create a sequential palette with dark values.
light_palette : Create a sequential palette with light values.
Examples
--------
.. include: ../docstrings/diverging_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.diverging_palette.txt | def diverging_palette(h_neg, h_pos, s=75, l=50, sep=1, n=6, # noqa
center="light", as_cmap=False):
"""Make a diverging palette between two HUSL colors.
If you are using the IPython notebook, you can also choose this palette
interactively with the :func:`choose_diverging_palette` function.
Parameters
----------
h_neg, h_pos : float in [0, 359]
Anchor hues for negative and positive extents of the map.
s : float in [0, 100], optional
Anchor saturation for both extents of the map.
l : float in [0, 100], optional
Anchor lightness for both extents of the map.
sep : int, optional
Size of the intermediate region.
n : int, optional
Number of colors in the palette (if not returning a cmap)
center : {"light", "dark"}, optional
Whether the center of the palette is light or dark
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
dark_palette : Create a sequential palette with dark values.
light_palette : Create a sequential palette with light values.
Examples
--------
.. include: ../docstrings/diverging_palette.rst
"""
palfunc = dict(dark=dark_palette, light=light_palette)[center]
n_half = int(128 - (sep // 2))
neg = palfunc((h_neg, s, l), n_half, reverse=True, input="husl")
pos = palfunc((h_pos, s, l), n_half, input="husl")
midpoint = dict(light=[(.95, .95, .95)], dark=[(.133, .133, .133)])[center]
mid = midpoint * sep
pal = blend_palette(np.concatenate([neg, mid, pos]), n, as_cmap=as_cmap)
return pal
| palettes.diverging_palette |
seaborn | 47 | seaborn/palettes.py | def hls_palette(n_colors=6, h=.01, l=.6, s=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HLS system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` values should be between 0 and 1.
.. note::
While the separation of the resulting colors will be mathematically
constant, the HLS system does not construct a perceptually-uniform space,
so their apparent intensity will vary.
Parameters
----------
n_colors : int
Number of colors in the palette.
h : float
The value of the first hue.
l : float
The lightness value.
s : float
The saturation intensity.
as_cmap : bool
If True, return a matplotlib colormap object.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
husl_palette : Make a palette using evenly spaced hues in the HUSL system.
Examples
--------
.. include:: ../docstrings/hls_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.hls_palette.txt | def hls_palette(n_colors=6, h=.01, l=.6, s=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HLS system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` values should be between 0 and 1.
.. note::
While the separation of the resulting colors will be mathematically
constant, the HLS system does not construct a perceptually-uniform space,
so their apparent intensity will vary.
Parameters
----------
n_colors : int
Number of colors in the palette.
h : float
The value of the first hue.
l : float
The lightness value.
s : float
The saturation intensity.
as_cmap : bool
If True, return a matplotlib colormap object.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
husl_palette : Make a palette using evenly spaced hues in the HUSL system.
Examples
--------
.. include:: ../docstrings/hls_palette.rst
"""
if as_cmap:
n_colors = 256
hues = np.linspace(0, 1, int(n_colors) + 1)[:-1]
hues += h
hues %= 1
hues -= hues.astype(int)
palette = [colorsys.hls_to_rgb(h_i, l, s) for h_i in hues]
if as_cmap:
return mpl.colors.ListedColormap(palette, "hls")
else:
return _ColorPalette(palette)
| palettes.hls_palette |
seaborn | 48 | seaborn/palettes.py | def husl_palette(n_colors=6, h=.01, s=.9, l=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HUSL system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` values should be between 0 and 1.
This function is similar to :func:`hls_palette`, but it uses a nonlinear color
space that is more perceptually uniform.
Parameters
----------
n_colors : int
Number of colors in the palette.
h : float
The value of the first hue.
l : float
The lightness value.
s : float
The saturation intensity.
as_cmap : bool
If True, return a matplotlib colormap object.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
hls_palette : Make a palette using evenly spaced hues in the HSL system.
Examples
--------
.. include:: ../docstrings/husl_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.husl_palette.txt | def husl_palette(n_colors=6, h=.01, s=.9, l=.65, as_cmap=False): # noqa
"""
Return hues with constant lightness and saturation in the HUSL system.
The hues are evenly sampled along a circular path. The resulting palette will be
appropriate for categorical or cyclical data.
The `h`, `l`, and `s` values should be between 0 and 1.
This function is similar to :func:`hls_palette`, but it uses a nonlinear color
space that is more perceptually uniform.
Parameters
----------
n_colors : int
Number of colors in the palette.
h : float
The value of the first hue.
l : float
The lightness value.
s : float
The saturation intensity.
as_cmap : bool
If True, return a matplotlib colormap object.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
hls_palette : Make a palette using evenly spaced hues in the HSL system.
Examples
--------
.. include:: ../docstrings/husl_palette.rst
"""
if as_cmap:
n_colors = 256
hues = np.linspace(0, 1, int(n_colors) + 1)[:-1]
hues += h
hues %= 1
hues *= 359
s *= 99
l *= 99 # noqa
palette = [_color_to_rgb((h_i, s, l), input="husl") for h_i in hues]
if as_cmap:
return mpl.colors.ListedColormap(palette, "hsl")
else:
return _ColorPalette(palette)
| palettes.husl_palette |
seaborn | 49 | seaborn/palettes.py | def light_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from light to ``color``.
The ``color`` parameter can be specified in a number of ways, including
all options for defining a color in matplotlib and several additional
color spaces that are handled by seaborn. You can also use the database
of named colors from the XKCD color survey.
If you are using a Jupyter notebook, you can also choose this palette
interactively with the :func:`choose_light_palette` function.
Parameters
----------
color : base color for high values
hex code, html color name, or tuple in `input` space.
n_colors : int, optional
number of colors in the palette
reverse : bool, optional
if True, reverse the direction of the blend
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
input : {'rgb', 'hls', 'husl', xkcd'}
Color space to interpret the input color. The first three options
apply to tuple inputs and the latter applies to string inputs.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
dark_palette : Create a sequential palette with dark low values.
diverging_palette : Create a diverging palette with two colors.
Examples
--------
.. include:: ../docstrings/light_palette.rst
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.light_palette.txt | def light_palette(color, n_colors=6, reverse=False, as_cmap=False, input="rgb"):
"""Make a sequential palette that blends from light to ``color``.
The ``color`` parameter can be specified in a number of ways, including
all options for defining a color in matplotlib and several additional
color spaces that are handled by seaborn. You can also use the database
of named colors from the XKCD color survey.
If you are using a Jupyter notebook, you can also choose this palette
interactively with the :func:`choose_light_palette` function.
Parameters
----------
color : base color for high values
hex code, html color name, or tuple in `input` space.
n_colors : int, optional
number of colors in the palette
reverse : bool, optional
if True, reverse the direction of the blend
as_cmap : bool, optional
If True, return a :class:`matplotlib.colors.ListedColormap`.
input : {'rgb', 'hls', 'husl', xkcd'}
Color space to interpret the input color. The first three options
apply to tuple inputs and the latter applies to string inputs.
Returns
-------
palette
list of RGB tuples or :class:`matplotlib.colors.ListedColormap`
See Also
--------
dark_palette : Create a sequential palette with dark low values.
diverging_palette : Create a diverging palette with two colors.
Examples
--------
.. include:: ../docstrings/light_palette.rst
"""
rgb = _color_to_rgb(color, input)
hue, sat, _ = husl.rgb_to_husl(*rgb)
gray_s, gray_l = .15 * sat, 95
gray = _color_to_rgb((hue, gray_s, gray_l), input="husl")
colors = [rgb, gray] if reverse else [gray, rgb]
return blend_palette(colors, n_colors, as_cmap)
| palettes.light_palette |
seaborn | 50 | seaborn/palettes.py | def set_color_codes(palette="deep"):
"""Change how matplotlib color shorthands are interpreted.
Calling this will change how shorthand codes like "b" or "g"
are interpreted by matplotlib in subsequent plots.
Parameters
----------
palette : {deep, muted, pastel, dark, bright, colorblind}
Named seaborn palette to use as the source of colors.
See Also
--------
set : Color codes can be set through the high-level seaborn style
manager.
set_palette : Color codes can also be set through the function that
sets the matplotlib color cycle.
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.set_color_codes.txt | def set_color_codes(palette="deep"):
"""Change how matplotlib color shorthands are interpreted.
Calling this will change how shorthand codes like "b" or "g"
are interpreted by matplotlib in subsequent plots.
Parameters
----------
palette : {deep, muted, pastel, dark, bright, colorblind}
Named seaborn palette to use as the source of colors.
See Also
--------
set : Color codes can be set through the high-level seaborn style
manager.
set_palette : Color codes can also be set through the function that
sets the matplotlib color cycle.
"""
if palette == "reset":
colors = [
(0., 0., 1.),
(0., .5, 0.),
(1., 0., 0.),
(.75, 0., .75),
(.75, .75, 0.),
(0., .75, .75),
(0., 0., 0.)
]
elif not isinstance(palette, str):
err = "set_color_codes requires a named seaborn palette"
raise TypeError(err)
elif palette in SEABORN_PALETTES:
if not palette.endswith("6"):
palette = palette + "6"
colors = SEABORN_PALETTES[palette] + [(.1, .1, .1)]
else:
err = f"Cannot set colors with palette '{palette}'"
raise ValueError(err)
for code, color in zip("bgrmyck", colors):
rgb = mpl.colors.colorConverter.to_rgb(color)
mpl.colors.colorConverter.colors[code] = rgb
| palettes.set_color_codes |
seaborn | 51 | seaborn/palettes.py | def xkcd_palette(colors):
"""Make a palette with color names from the xkcd color survey.
See xkcd for the full list of colors: https://xkcd.com/color/rgb/
This is just a simple wrapper around the `seaborn.xkcd_rgb` dictionary.
Parameters
----------
colors : list of strings
List of keys in the `seaborn.xkcd_rgb` dictionary.
Returns
-------
palette
A list of colors as RGB tuples.
See Also
--------
crayon_palette : Make a palette with Crayola crayon colors.
"""
| /usr/src/app/target_test_cases/failed_tests_palettes.xkcd_palette.txt | def xkcd_palette(colors):
"""Make a palette with color names from the xkcd color survey.
See xkcd for the full list of colors: https://xkcd.com/color/rgb/
This is just a simple wrapper around the `seaborn.xkcd_rgb` dictionary.
Parameters
----------
colors : list of strings
List of keys in the `seaborn.xkcd_rgb` dictionary.
Returns
-------
palette
A list of colors as RGB tuples.
See Also
--------
crayon_palette : Make a palette with Crayola crayon colors.
"""
palette = [xkcd_rgb[name] for name in colors]
return color_palette(palette, len(palette))
| palettes.xkcd_palette |
seaborn | 52 | seaborn/_core/plot.py | def label(
self, *,
title: str | None = None,
legend: str | None = None,
**variables: str | Callable[[str], str]
) -> Plot:
"""
Control the labels and titles for axes, legends, and subplots.
Additional keywords correspond to variables defined in the plot.
Values can be one of the following types:
- string (used literally; pass "" to clear the default label)
- function (called on the default label)
For coordinate variables, the value sets the axis label.
For semantic variables, the value sets the legend title.
For faceting variables, `title=` modifies the subplot-specific label,
while `col=` and/or `row=` add a label for the faceting variable.
When using a single subplot, `title=` sets its title.
The `legend=` parameter sets the title for the "layer" legend
(i.e., when using `label` in :meth:`Plot.add`).
Examples
--------
.. include:: ../docstrings/objects.Plot.label.rst
"""
| /usr/src/app/target_test_cases/failed_tests_plot.Plot.label.txt | def label(
self, *,
title: str | None = None,
legend: str | None = None,
**variables: str | Callable[[str], str]
) -> Plot:
"""
Control the labels and titles for axes, legends, and subplots.
Additional keywords correspond to variables defined in the plot.
Values can be one of the following types:
- string (used literally; pass "" to clear the default label)
- function (called on the default label)
For coordinate variables, the value sets the axis label.
For semantic variables, the value sets the legend title.
For faceting variables, `title=` modifies the subplot-specific label,
while `col=` and/or `row=` add a label for the faceting variable.
When using a single subplot, `title=` sets its title.
The `legend=` parameter sets the title for the "layer" legend
(i.e., when using `label` in :meth:`Plot.add`).
Examples
--------
.. include:: ../docstrings/objects.Plot.label.rst
"""
new = self._clone()
if title is not None:
new._labels["title"] = title
if legend is not None:
new._labels["legend"] = legend
new._labels.update(variables)
return new
| plot.Plot.label |
seaborn | 53 | seaborn/_core/plot.py | def layout(
self,
*,
size: tuple[float, float] | Default = default,
engine: str | None | Default = default,
extent: tuple[float, float, float, float] | Default = default,
) -> Plot:
"""
Control the figure size and layout.
.. note::
Default figure sizes and the API for specifying the figure size are subject
to change in future "experimental" releases of the objects API. The default
layout engine may also change.
Parameters
----------
size : (width, height)
Size of the resulting figure, in inches. Size is inclusive of legend when
using pyplot, but not otherwise.
engine : {{"tight", "constrained", "none"}}
Name of method for automatically adjusting the layout to remove overlap.
The default depends on whether :meth:`Plot.on` is used.
extent : (left, bottom, right, top)
Boundaries of the plot layout, in fractions of the figure size. Takes
effect through the layout engine; exact results will vary across engines.
Note: the extent includes axis decorations when using a layout engine,
but it is exclusive of them when `engine="none"`.
Examples
--------
.. include:: ../docstrings/objects.Plot.layout.rst
"""
| /usr/src/app/target_test_cases/failed_tests_plot.Plot.layout.txt | def layout(
self,
*,
size: tuple[float, float] | Default = default,
engine: str | None | Default = default,
extent: tuple[float, float, float, float] | Default = default,
) -> Plot:
"""
Control the figure size and layout.
.. note::
Default figure sizes and the API for specifying the figure size are subject
to change in future "experimental" releases of the objects API. The default
layout engine may also change.
Parameters
----------
size : (width, height)
Size of the resulting figure, in inches. Size is inclusive of legend when
using pyplot, but not otherwise.
engine : {{"tight", "constrained", "none"}}
Name of method for automatically adjusting the layout to remove overlap.
The default depends on whether :meth:`Plot.on` is used.
extent : (left, bottom, right, top)
Boundaries of the plot layout, in fractions of the figure size. Takes
effect through the layout engine; exact results will vary across engines.
Note: the extent includes axis decorations when using a layout engine,
but it is exclusive of them when `engine="none"`.
Examples
--------
.. include:: ../docstrings/objects.Plot.layout.rst
"""
# TODO add an "auto" mode for figsize that roughly scales with the rcParams
# figsize (so that works), but expands to prevent subplots from being squished
# Also should we have height=, aspect=, exclusive with figsize? Or working
# with figsize when only one is defined?
new = self._clone()
if size is not default:
new._figure_spec["figsize"] = size
if engine is not default:
new._layout_spec["engine"] = engine
if extent is not default:
new._layout_spec["extent"] = extent
return new
| plot.Plot.layout |
seaborn | 54 | seaborn/_core/plot.py | def limit(self, **limits: tuple[Any, Any]) -> Plot:
"""
Control the range of visible data.
Keywords correspond to variables defined in the plot, and values are a
`(min, max)` tuple (where either can be `None` to leave unset).
Limits apply only to the axis; data outside the visible range are
still used for any stat transforms and added to the plot.
Behavior for non-coordinate variables is currently undefined.
Examples
--------
.. include:: ../docstrings/objects.Plot.limit.rst
"""
| /usr/src/app/target_test_cases/failed_tests_plot.Plot.limit.txt | def limit(self, **limits: tuple[Any, Any]) -> Plot:
"""
Control the range of visible data.
Keywords correspond to variables defined in the plot, and values are a
`(min, max)` tuple (where either can be `None` to leave unset).
Limits apply only to the axis; data outside the visible range are
still used for any stat transforms and added to the plot.
Behavior for non-coordinate variables is currently undefined.
Examples
--------
.. include:: ../docstrings/objects.Plot.limit.rst
"""
new = self._clone()
new._limits.update(limits)
return new
| plot.Plot.limit |
seaborn | 55 | seaborn/_core/plot.py | def save(self, loc, **kwargs) -> Plot:
"""
Compile the plot and write it to a buffer or file on disk.
Parameters
----------
loc : str, path, or buffer
Location on disk to save the figure, or a buffer to write into.
kwargs
Other keyword arguments are passed through to
:meth:`matplotlib.figure.Figure.savefig`.
"""
| /usr/src/app/target_test_cases/failed_tests_plot.Plot.save.txt | def save(self, loc, **kwargs) -> Plot:
"""
Compile the plot and write it to a buffer or file on disk.
Parameters
----------
loc : str, path, or buffer
Location on disk to save the figure, or a buffer to write into.
kwargs
Other keyword arguments are passed through to
:meth:`matplotlib.figure.Figure.savefig`.
"""
# TODO expose important keyword arguments in our signature?
with theme_context(self._theme_with_defaults()):
self._plot().save(loc, **kwargs)
return self
| plot.Plot.save |
seaborn | 56 | seaborn/_core/plot.py | def scale(self, **scales: Scale) -> Plot:
"""
Specify mappings from data units to visual properties.
Keywords correspond to variables defined in the plot, including coordinate
variables (`x`, `y`) and semantic variables (`color`, `pointsize`, etc.).
A number of "magic" arguments are accepted, including:
- The name of a transform (e.g., `"log"`, `"sqrt"`)
- The name of a palette (e.g., `"viridis"`, `"muted"`)
- A tuple of values, defining the output range (e.g. `(1, 5)`)
- A dict, implying a :class:`Nominal` scale (e.g. `{"a": .2, "b": .5}`)
- A list of values, implying a :class:`Nominal` scale (e.g. `["b", "r"]`)
For more explicit control, pass a scale spec object such as :class:`Continuous`
or :class:`Nominal`. Or pass `None` to use an "identity" scale, which treats
data values as literally encoding visual properties.
Examples
--------
.. include:: ../docstrings/objects.Plot.scale.rst
"""
| /usr/src/app/target_test_cases/failed_tests_plot.Plot.scale.txt | def scale(self, **scales: Scale) -> Plot:
"""
Specify mappings from data units to visual properties.
Keywords correspond to variables defined in the plot, including coordinate
variables (`x`, `y`) and semantic variables (`color`, `pointsize`, etc.).
A number of "magic" arguments are accepted, including:
- The name of a transform (e.g., `"log"`, `"sqrt"`)
- The name of a palette (e.g., `"viridis"`, `"muted"`)
- A tuple of values, defining the output range (e.g. `(1, 5)`)
- A dict, implying a :class:`Nominal` scale (e.g. `{"a": .2, "b": .5}`)
- A list of values, implying a :class:`Nominal` scale (e.g. `["b", "r"]`)
For more explicit control, pass a scale spec object such as :class:`Continuous`
or :class:`Nominal`. Or pass `None` to use an "identity" scale, which treats
data values as literally encoding visual properties.
Examples
--------
.. include:: ../docstrings/objects.Plot.scale.rst
"""
new = self._clone()
new._scales.update(scales)
return new
| plot.Plot.scale |
seaborn | 57 | seaborn/_core/plot.py | def share(self, **shares: bool | str) -> Plot:
"""
Control sharing of axis limits and ticks across subplots.
Keywords correspond to variables defined in the plot, and values can be
boolean (to share across all subplots), or one of "row" or "col" (to share
more selectively across one dimension of a grid).
Behavior for non-coordinate variables is currently undefined.
Examples
--------
.. include:: ../docstrings/objects.Plot.share.rst
"""
| /usr/src/app/target_test_cases/failed_tests_plot.Plot.share.txt | def share(self, **shares: bool | str) -> Plot:
"""
Control sharing of axis limits and ticks across subplots.
Keywords correspond to variables defined in the plot, and values can be
boolean (to share across all subplots), or one of "row" or "col" (to share
more selectively across one dimension of a grid).
Behavior for non-coordinate variables is currently undefined.
Examples
--------
.. include:: ../docstrings/objects.Plot.share.rst
"""
new = self._clone()
new._shares.update(shares)
return new
| plot.Plot.share |
seaborn | 58 | seaborn/_core/plot.py | def theme(self, config: Mapping[str, Any], /) -> Plot:
"""
Control the appearance of elements in the plot.
.. note::
The API for customizing plot appearance is not yet finalized.
Currently, the only valid argument is a dict of matplotlib rc parameters.
(This dict must be passed as a positional argument.)
It is likely that this method will be enhanced in future releases.
Matplotlib rc parameters are documented on the following page:
https://matplotlib.org/stable/tutorials/introductory/customizing.html
Examples
--------
.. include:: ../docstrings/objects.Plot.theme.rst
"""
| /usr/src/app/target_test_cases/failed_tests_plot.Plot.theme.txt | def theme(self, config: Mapping[str, Any], /) -> Plot:
"""
Control the appearance of elements in the plot.
.. note::
The API for customizing plot appearance is not yet finalized.
Currently, the only valid argument is a dict of matplotlib rc parameters.
(This dict must be passed as a positional argument.)
It is likely that this method will be enhanced in future releases.
Matplotlib rc parameters are documented on the following page:
https://matplotlib.org/stable/tutorials/introductory/customizing.html
Examples
--------
.. include:: ../docstrings/objects.Plot.theme.rst
"""
new = self._clone()
rc = mpl.RcParams(config)
new._theme.update(rc)
return new
| plot.Plot.theme |
seaborn | 59 | seaborn/_core/properties.py | def _default_values(self, n: int) -> list[DashPatternWithOffset]:
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings or tuples
Valid arguments for the ``dashes`` parameter on
:class:`matplotlib.lines.Line2D`. The first spec is a solid
line (``""``), the remainder are sequences of long and short
dashes.
"""
| /usr/src/app/target_test_cases/failed_tests_properties.LineStyle._default_values.txt | def _default_values(self, n: int) -> list[DashPatternWithOffset]:
"""Build an arbitrarily long list of unique dash styles for lines.
Parameters
----------
n : int
Number of unique dash specs to generate.
Returns
-------
dashes : list of strings or tuples
Valid arguments for the ``dashes`` parameter on
:class:`matplotlib.lines.Line2D`. The first spec is a solid
line (``""``), the remainder are sequences of long and short
dashes.
"""
# Start with dash specs that are well distinguishable
dashes: list[str | DashPattern] = [
"-", (4, 1.5), (1, 1), (3, 1.25, 1.5, 1.25), (5, 1, 1, 1),
]
# Now programmatically build as many as we need
p = 3
while len(dashes) < n:
# Take combinations of long and short dashes
a = itertools.combinations_with_replacement([3, 1.25], p)
b = itertools.combinations_with_replacement([4, 1], p)
# Interleave the combinations, reversing one of the streams
segment_list = itertools.chain(*zip(list(a)[1:-1][::-1], list(b)[1:-1]))
# Now insert the gaps
for segments in segment_list:
gap = min(segments)
spec = tuple(itertools.chain(*((seg, gap) for seg in segments)))
dashes.append(spec)
p += 1
return [self._get_dash_pattern(x) for x in dashes]
| properties.LineStyle._default_values |
seaborn | 60 | seaborn/_core/properties.py | def _default_values(self, n: int) -> list[MarkerStyle]:
"""Build an arbitrarily long list of unique marker styles.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
Values for defining :class:`matplotlib.markers.MarkerStyle` objects.
All markers will be filled.
"""
| /usr/src/app/target_test_cases/failed_tests_properties.Marker._default_values.txt | def _default_values(self, n: int) -> list[MarkerStyle]:
"""Build an arbitrarily long list of unique marker styles.
Parameters
----------
n : int
Number of unique marker specs to generate.
Returns
-------
markers : list of string or tuples
Values for defining :class:`matplotlib.markers.MarkerStyle` objects.
All markers will be filled.
"""
# Start with marker specs that are well distinguishable
markers = [
"o", "X", (4, 0, 45), "P", (4, 0, 0), (4, 1, 0), "^", (4, 1, 45), "v",
]
# Now generate more from regular polygons of increasing order
s = 5
while len(markers) < n:
a = 360 / (s + 1) / 2
markers.extend([(s + 1, 1, a), (s + 1, 0, a), (s, 1, 0), (s, 0, 0)])
s += 1
markers = [MarkerStyle(m) for m in markers[:n]]
return markers
| properties.Marker._default_values |
seaborn | 61 | seaborn/rcmod.py | def set_context(context=None, font_scale=1, rc=None):
"""
Set the parameters that control the scaling of plot elements.
These parameters correspond to label size, line thickness, etc.
Calling this function modifies the global matplotlib `rcParams`. For more
information, see the :doc:`aesthetics tutorial <../tutorial/aesthetics>`.
The base context is "notebook", and the other contexts are "paper", "talk",
and "poster", which are version of the notebook parameters scaled by different
values. Font elements can also be scaled independently of (but relative to)
the other values.
See :func:`plotting_context` to get the parameter values.
Parameters
----------
context : dict, or one of {paper, notebook, talk, poster}
A dictionary of parameters or the name of a preconfigured set.
font_scale : float, optional
Separate scaling factor to independently scale the size of the
font elements.
rc : dict, optional
Parameter mappings to override the values in the preset seaborn
context dictionaries. This only updates parameters that are
considered part of the context definition.
Examples
--------
.. include:: ../docstrings/set_context.rst
"""
| /usr/src/app/target_test_cases/failed_tests_rcmod.set_context.txt | def set_context(context=None, font_scale=1, rc=None):
"""
Set the parameters that control the scaling of plot elements.
These parameters correspond to label size, line thickness, etc.
Calling this function modifies the global matplotlib `rcParams`. For more
information, see the :doc:`aesthetics tutorial <../tutorial/aesthetics>`.
The base context is "notebook", and the other contexts are "paper", "talk",
and "poster", which are version of the notebook parameters scaled by different
values. Font elements can also be scaled independently of (but relative to)
the other values.
See :func:`plotting_context` to get the parameter values.
Parameters
----------
context : dict, or one of {paper, notebook, talk, poster}
A dictionary of parameters or the name of a preconfigured set.
font_scale : float, optional
Separate scaling factor to independently scale the size of the
font elements.
rc : dict, optional
Parameter mappings to override the values in the preset seaborn
context dictionaries. This only updates parameters that are
considered part of the context definition.
Examples
--------
.. include:: ../docstrings/set_context.rst
"""
context_object = plotting_context(context, font_scale, rc)
mpl.rcParams.update(context_object)
| rcmod.set_context |
seaborn | 62 | seaborn/rcmod.py | def set_palette(palette, n_colors=None, desat=None, color_codes=False):
"""Set the matplotlib color cycle using a seaborn palette.
Parameters
----------
palette : seaborn color palette | matplotlib colormap | hls | husl
Palette definition. Should be something :func:`color_palette` can process.
n_colors : int
Number of colors in the cycle. The default number of colors will depend
on the format of ``palette``, see the :func:`color_palette`
documentation for more information.
desat : float
Proportion to desaturate each color by.
color_codes : bool
If ``True`` and ``palette`` is a seaborn palette, remap the shorthand
color codes (e.g. "b", "g", "r", etc.) to the colors from this palette.
See Also
--------
color_palette : build a color palette or set the color cycle temporarily
in a ``with`` statement.
set_context : set parameters to scale plot elements
set_style : set the default parameters for figure style
"""
| /usr/src/app/target_test_cases/failed_tests_rcmod.set_palette.txt | def set_palette(palette, n_colors=None, desat=None, color_codes=False):
"""Set the matplotlib color cycle using a seaborn palette.
Parameters
----------
palette : seaborn color palette | matplotlib colormap | hls | husl
Palette definition. Should be something :func:`color_palette` can process.
n_colors : int
Number of colors in the cycle. The default number of colors will depend
on the format of ``palette``, see the :func:`color_palette`
documentation for more information.
desat : float
Proportion to desaturate each color by.
color_codes : bool
If ``True`` and ``palette`` is a seaborn palette, remap the shorthand
color codes (e.g. "b", "g", "r", etc.) to the colors from this palette.
See Also
--------
color_palette : build a color palette or set the color cycle temporarily
in a ``with`` statement.
set_context : set parameters to scale plot elements
set_style : set the default parameters for figure style
"""
colors = palettes.color_palette(palette, n_colors, desat)
cyl = cycler('color', colors)
mpl.rcParams['axes.prop_cycle'] = cyl
if color_codes:
try:
palettes.set_color_codes(palette)
except (ValueError, TypeError):
pass
| rcmod.set_palette |
seaborn | 63 | seaborn/rcmod.py | def set_style(style=None, rc=None):
"""
Set the parameters that control the general style of the plots.
The style parameters control properties like the color of the background and
whether a grid is enabled by default. This is accomplished using the
matplotlib rcParams system.
The options are illustrated in the
:doc:`aesthetics tutorial <../tutorial/aesthetics>`.
See :func:`axes_style` to get the parameter values.
Parameters
----------
style : dict, or one of {darkgrid, whitegrid, dark, white, ticks}
A dictionary of parameters or the name of a preconfigured style.
rc : dict, optional
Parameter mappings to override the values in the preset seaborn
style dictionaries. This only updates parameters that are
considered part of the style definition.
Examples
--------
.. include:: ../docstrings/set_style.rst
"""
| /usr/src/app/target_test_cases/failed_tests_rcmod.set_style.txt | def set_style(style=None, rc=None):
"""
Set the parameters that control the general style of the plots.
The style parameters control properties like the color of the background and
whether a grid is enabled by default. This is accomplished using the
matplotlib rcParams system.
The options are illustrated in the
:doc:`aesthetics tutorial <../tutorial/aesthetics>`.
See :func:`axes_style` to get the parameter values.
Parameters
----------
style : dict, or one of {darkgrid, whitegrid, dark, white, ticks}
A dictionary of parameters or the name of a preconfigured style.
rc : dict, optional
Parameter mappings to override the values in the preset seaborn
style dictionaries. This only updates parameters that are
considered part of the style definition.
Examples
--------
.. include:: ../docstrings/set_style.rst
"""
style_object = axes_style(style, rc)
mpl.rcParams.update(style_object)
| rcmod.set_style |
seaborn | 64 | seaborn/rcmod.py | def set_theme(context="notebook", style="darkgrid", palette="deep",
font="sans-serif", font_scale=1, color_codes=True, rc=None):
"""
Set aspects of the visual theme for all matplotlib and seaborn plots.
This function changes the global defaults for all plots using the
matplotlib rcParams system. The themeing is decomposed into several distinct
sets of parameter values.
The options are illustrated in the :doc:`aesthetics <../tutorial/aesthetics>`
and :doc:`color palette <../tutorial/color_palettes>` tutorials.
Parameters
----------
context : string or dict
Scaling parameters, see :func:`plotting_context`.
style : string or dict
Axes style parameters, see :func:`axes_style`.
palette : string or sequence
Color palette, see :func:`color_palette`.
font : string
Font family, see matplotlib font manager.
font_scale : float, optional
Separate scaling factor to independently scale the size of the
font elements.
color_codes : bool
If ``True`` and ``palette`` is a seaborn palette, remap the shorthand
color codes (e.g. "b", "g", "r", etc.) to the colors from this palette.
rc : dict or None
Dictionary of rc parameter mappings to override the above.
Examples
--------
.. include:: ../docstrings/set_theme.rst
"""
| /usr/src/app/target_test_cases/failed_tests_rcmod.set_theme.txt | def set_theme(context="notebook", style="darkgrid", palette="deep",
font="sans-serif", font_scale=1, color_codes=True, rc=None):
"""
Set aspects of the visual theme for all matplotlib and seaborn plots.
This function changes the global defaults for all plots using the
matplotlib rcParams system. The themeing is decomposed into several distinct
sets of parameter values.
The options are illustrated in the :doc:`aesthetics <../tutorial/aesthetics>`
and :doc:`color palette <../tutorial/color_palettes>` tutorials.
Parameters
----------
context : string or dict
Scaling parameters, see :func:`plotting_context`.
style : string or dict
Axes style parameters, see :func:`axes_style`.
palette : string or sequence
Color palette, see :func:`color_palette`.
font : string
Font family, see matplotlib font manager.
font_scale : float, optional
Separate scaling factor to independently scale the size of the
font elements.
color_codes : bool
If ``True`` and ``palette`` is a seaborn palette, remap the shorthand
color codes (e.g. "b", "g", "r", etc.) to the colors from this palette.
rc : dict or None
Dictionary of rc parameter mappings to override the above.
Examples
--------
.. include:: ../docstrings/set_theme.rst
"""
set_context(context, font_scale)
set_style(style, rc={"font.family": font})
set_palette(palette, color_codes=color_codes)
if rc is not None:
mpl.rcParams.update(rc)
| rcmod.set_theme |
seaborn | 65 | seaborn/regression.py | def residplot(
data=None, *, x=None, y=None,
x_partial=None, y_partial=None, lowess=False,
order=1, robust=False, dropna=True, label=None, color=None,
scatter_kws=None, line_kws=None, ax=None
):
"""Plot the residuals of a linear regression.
This function will regress y on x (possibly as a robust or polynomial
regression) and then draw a scatterplot of the residuals. You can
optionally fit a lowess smoother to the residual plot, which can
help in determining if there is structure to the residuals.
Parameters
----------
data : DataFrame, optional
DataFrame to use if `x` and `y` are column names.
x : vector or string
Data or column name in `data` for the predictor variable.
y : vector or string
Data or column name in `data` for the response variable.
{x, y}_partial : vectors or string(s) , optional
These variables are treated as confounding and are removed from
the `x` or `y` variables before plotting.
lowess : boolean, optional
Fit a lowess smoother to the residual scatterplot.
order : int, optional
Order of the polynomial to fit when calculating the residuals.
robust : boolean, optional
Fit a robust linear regression when calculating the residuals.
dropna : boolean, optional
If True, ignore observations with missing data when fitting and
plotting.
label : string, optional
Label that will be used in any plot legends.
color : matplotlib color, optional
Color to use for all elements of the plot.
{scatter, line}_kws : dictionaries, optional
Additional keyword arguments passed to scatter() and plot() for drawing
the components of the plot.
ax : matplotlib axis, optional
Plot into this axis, otherwise grab the current axis or make a new
one if not existing.
Returns
-------
ax: matplotlib axes
Axes with the regression plot.
See Also
--------
regplot : Plot a simple linear regression model.
jointplot : Draw a :func:`residplot` with univariate marginal distributions
(when used with ``kind="resid"``).
Examples
--------
.. include:: ../docstrings/residplot.rst
"""
| /usr/src/app/target_test_cases/failed_tests_regression.residplot.txt | def residplot(
data=None, *, x=None, y=None,
x_partial=None, y_partial=None, lowess=False,
order=1, robust=False, dropna=True, label=None, color=None,
scatter_kws=None, line_kws=None, ax=None
):
"""Plot the residuals of a linear regression.
This function will regress y on x (possibly as a robust or polynomial
regression) and then draw a scatterplot of the residuals. You can
optionally fit a lowess smoother to the residual plot, which can
help in determining if there is structure to the residuals.
Parameters
----------
data : DataFrame, optional
DataFrame to use if `x` and `y` are column names.
x : vector or string
Data or column name in `data` for the predictor variable.
y : vector or string
Data or column name in `data` for the response variable.
{x, y}_partial : vectors or string(s) , optional
These variables are treated as confounding and are removed from
the `x` or `y` variables before plotting.
lowess : boolean, optional
Fit a lowess smoother to the residual scatterplot.
order : int, optional
Order of the polynomial to fit when calculating the residuals.
robust : boolean, optional
Fit a robust linear regression when calculating the residuals.
dropna : boolean, optional
If True, ignore observations with missing data when fitting and
plotting.
label : string, optional
Label that will be used in any plot legends.
color : matplotlib color, optional
Color to use for all elements of the plot.
{scatter, line}_kws : dictionaries, optional
Additional keyword arguments passed to scatter() and plot() for drawing
the components of the plot.
ax : matplotlib axis, optional
Plot into this axis, otherwise grab the current axis or make a new
one if not existing.
Returns
-------
ax: matplotlib axes
Axes with the regression plot.
See Also
--------
regplot : Plot a simple linear regression model.
jointplot : Draw a :func:`residplot` with univariate marginal distributions
(when used with ``kind="resid"``).
Examples
--------
.. include:: ../docstrings/residplot.rst
"""
plotter = _RegressionPlotter(x, y, data, ci=None,
order=order, robust=robust,
x_partial=x_partial, y_partial=y_partial,
dropna=dropna, color=color, label=label)
if ax is None:
ax = plt.gca()
# Calculate the residual from a linear regression
_, yhat, _ = plotter.fit_regression(grid=plotter.x)
plotter.y = plotter.y - yhat
# Set the regression option on the plotter
if lowess:
plotter.lowess = True
else:
plotter.fit_reg = False
# Plot a horizontal line at 0
ax.axhline(0, ls=":", c=".2")
# Draw the scatterplot
scatter_kws = {} if scatter_kws is None else scatter_kws.copy()
line_kws = {} if line_kws is None else line_kws.copy()
plotter.plot(ax, scatter_kws, line_kws)
return ax
| regression.residplot |
seaborn | 66 | seaborn/_core/rules.py | def variable_type(
vector: Series,
boolean_type: Literal["numeric", "categorical", "boolean"] = "numeric",
strict_boolean: bool = False,
) -> VarType:
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in a few ways:
- Python sequences or object-typed PyData objects are considered numeric if
all of their entries are numeric.
- String or mixed-type data are considered categorical even if not
explicitly represented as a :class:`pandas.api.types.CategoricalDtype`.
- There is some flexibility about how to treat binary / boolean data.
Parameters
----------
vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
Input data to test.
boolean_type : 'numeric', 'categorical', or 'boolean'
Type to use for vectors containing only 0s and 1s (and NAs).
strict_boolean : bool
If True, only consider data to be boolean when the dtype is bool or Boolean.
Returns
-------
var_type : 'numeric', 'categorical', or 'datetime'
Name identifying the type of data in the vector.
"""
| /usr/src/app/target_test_cases/failed_tests_rules.variable_type.txt | def variable_type(
vector: Series,
boolean_type: Literal["numeric", "categorical", "boolean"] = "numeric",
strict_boolean: bool = False,
) -> VarType:
"""
Determine whether a vector contains numeric, categorical, or datetime data.
This function differs from the pandas typing API in a few ways:
- Python sequences or object-typed PyData objects are considered numeric if
all of their entries are numeric.
- String or mixed-type data are considered categorical even if not
explicitly represented as a :class:`pandas.api.types.CategoricalDtype`.
- There is some flexibility about how to treat binary / boolean data.
Parameters
----------
vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence
Input data to test.
boolean_type : 'numeric', 'categorical', or 'boolean'
Type to use for vectors containing only 0s and 1s (and NAs).
strict_boolean : bool
If True, only consider data to be boolean when the dtype is bool or Boolean.
Returns
-------
var_type : 'numeric', 'categorical', or 'datetime'
Name identifying the type of data in the vector.
"""
# If a categorical dtype is set, infer categorical
if isinstance(getattr(vector, 'dtype', None), pd.CategoricalDtype):
return VarType("categorical")
# Special-case all-na data, which is always "numeric"
if pd.isna(vector).all():
return VarType("numeric")
# Now drop nulls to simplify further type inference
vector = vector.dropna()
# Special-case binary/boolean data, allow caller to determine
# This triggers a numpy warning when vector has strings/objects
# https://github.com/numpy/numpy/issues/6784
# Because we reduce with .all(), we are agnostic about whether the
# comparison returns a scalar or vector, so we will ignore the warning.
# It triggers a separate DeprecationWarning when the vector has datetimes:
# https://github.com/numpy/numpy/issues/13548
# This is considered a bug by numpy and will likely go away.
with warnings.catch_warnings():
warnings.simplefilter(
action='ignore',
category=(FutureWarning, DeprecationWarning) # type: ignore # mypy bug?
)
if strict_boolean:
if isinstance(vector.dtype, pd.core.dtypes.base.ExtensionDtype):
boolean_dtypes = ["bool", "boolean"]
else:
boolean_dtypes = ["bool"]
boolean_vector = vector.dtype in boolean_dtypes
else:
try:
boolean_vector = bool(np.isin(vector, [0, 1]).all())
except TypeError:
# .isin comparison is not guaranteed to be possible under NumPy
# casting rules, depending on the (unknown) dtype of 'vector'
boolean_vector = False
if boolean_vector:
return VarType(boolean_type)
# Defer to positive pandas tests
if pd.api.types.is_numeric_dtype(vector):
return VarType("numeric")
if pd.api.types.is_datetime64_dtype(vector):
return VarType("datetime")
# --- If we get to here, we need to check the entries
# Check for a collection where everything is a number
def all_numeric(x):
for x_i in x:
if not isinstance(x_i, Number):
return False
return True
if all_numeric(vector):
return VarType("numeric")
# Check for a collection where everything is a datetime
def all_datetime(x):
for x_i in x:
if not isinstance(x_i, (datetime, np.datetime64)):
return False
return True
if all_datetime(vector):
return VarType("datetime")
# Otherwise, our final fallback is to consider things categorical
return VarType("categorical")
| rules.variable_type |
seaborn | 67 | seaborn/_core/scales.py | def tick(
self,
locator: Locator | None = None, *,
at: Sequence[float] | None = None,
upto: int | None = None,
count: int | None = None,
every: float | None = None,
between: tuple[float, float] | None = None,
minor: int | None = None,
) -> Continuous:
"""
Configure the selection of ticks for the scale's axis or legend.
Parameters
----------
locator : :class:`matplotlib.ticker.Locator` subclass
Pre-configured matplotlib locator; other parameters will not be used.
at : sequence of floats
Place ticks at these specific locations (in data units).
upto : int
Choose "nice" locations for ticks, but do not exceed this number.
count : int
Choose exactly this number of ticks, bounded by `between` or axis limits.
every : float
Choose locations at this interval of separation (in data units).
between : pair of floats
Bound upper / lower ticks when using `every` or `count`.
minor : int
Number of unlabeled ticks to draw between labeled "major" ticks.
Returns
-------
scale
Copy of self with new tick configuration.
"""
| /usr/src/app/target_test_cases/failed_tests_scales.Continuous.tick.txt | def tick(
self,
locator: Locator | None = None, *,
at: Sequence[float] | None = None,
upto: int | None = None,
count: int | None = None,
every: float | None = None,
between: tuple[float, float] | None = None,
minor: int | None = None,
) -> Continuous:
"""
Configure the selection of ticks for the scale's axis or legend.
Parameters
----------
locator : :class:`matplotlib.ticker.Locator` subclass
Pre-configured matplotlib locator; other parameters will not be used.
at : sequence of floats
Place ticks at these specific locations (in data units).
upto : int
Choose "nice" locations for ticks, but do not exceed this number.
count : int
Choose exactly this number of ticks, bounded by `between` or axis limits.
every : float
Choose locations at this interval of separation (in data units).
between : pair of floats
Bound upper / lower ticks when using `every` or `count`.
minor : int
Number of unlabeled ticks to draw between labeled "major" ticks.
Returns
-------
scale
Copy of self with new tick configuration.
"""
# Input checks
if locator is not None and not isinstance(locator, Locator):
raise TypeError(
f"Tick locator must be an instance of {Locator!r}, "
f"not {type(locator)!r}."
)
log_base, symlog_thresh = self._parse_for_log_params(self.trans)
if log_base or symlog_thresh:
if count is not None and between is None:
raise RuntimeError("`count` requires `between` with log transform.")
if every is not None:
raise RuntimeError("`every` not supported with log transform.")
new = copy(self)
new._tick_params = {
"locator": locator,
"at": at,
"upto": upto,
"count": count,
"every": every,
"between": between,
"minor": minor,
}
return new
| scales.Continuous.tick |
seaborn | 68 | seaborn/_core/scales.py | def label(
self,
formatter: Formatter | None = None, *,
concise: bool = False,
) -> Temporal:
"""
Configure the appearance of tick labels for the scale's axis or legend.
.. note::
This API is under construction and will be enhanced over time.
Parameters
----------
formatter : :class:`matplotlib.ticker.Formatter` subclass
Pre-configured formatter to use; other parameters will be ignored.
concise : bool
If True, use :class:`matplotlib.dates.ConciseDateFormatter` to make
the tick labels as compact as possible.
Returns
-------
scale
Copy of self with new label configuration.
"""
| /usr/src/app/target_test_cases/failed_tests_scales.Temporal.label.txt | def label(
self,
formatter: Formatter | None = None, *,
concise: bool = False,
) -> Temporal:
"""
Configure the appearance of tick labels for the scale's axis or legend.
.. note::
This API is under construction and will be enhanced over time.
Parameters
----------
formatter : :class:`matplotlib.ticker.Formatter` subclass
Pre-configured formatter to use; other parameters will be ignored.
concise : bool
If True, use :class:`matplotlib.dates.ConciseDateFormatter` to make
the tick labels as compact as possible.
Returns
-------
scale
Copy of self with new label configuration.
"""
new = copy(self)
new._label_params = {"formatter": formatter, "concise": concise}
return new
| scales.Temporal.label |
seaborn | 69 | seaborn/_core/scales.py | def tick(
self, locator: Locator | None = None, *,
upto: int | None = None,
) -> Temporal:
"""
Configure the selection of ticks for the scale's axis or legend.
.. note::
This API is under construction and will be enhanced over time.
Parameters
----------
locator : :class:`matplotlib.ticker.Locator` subclass
Pre-configured matplotlib locator; other parameters will not be used.
upto : int
Choose "nice" locations for ticks, but do not exceed this number.
Returns
-------
scale
Copy of self with new tick configuration.
"""
| /usr/src/app/target_test_cases/failed_tests_scales.Temporal.tick.txt | def tick(
self, locator: Locator | None = None, *,
upto: int | None = None,
) -> Temporal:
"""
Configure the selection of ticks for the scale's axis or legend.
.. note::
This API is under construction and will be enhanced over time.
Parameters
----------
locator : :class:`matplotlib.ticker.Locator` subclass
Pre-configured matplotlib locator; other parameters will not be used.
upto : int
Choose "nice" locations for ticks, but do not exceed this number.
Returns
-------
scale
Copy of self with new tick configuration.
"""
if locator is not None and not isinstance(locator, Locator):
err = (
f"Tick locator must be an instance of {Locator!r}, "
f"not {type(locator)!r}."
)
raise TypeError(err)
new = copy(self)
new._tick_params = {"locator": locator, "upto": upto}
return new
| scales.Temporal.tick |
seaborn | 70 | seaborn/utils.py | def ci_to_errsize(cis, heights):
"""Convert intervals to error arguments relative to plot heights.
Parameters
----------
cis : 2 x n sequence
sequence of confidence interval limits
heights : n sequence
sequence of plot heights
Returns
-------
errsize : 2 x n array
sequence of error size relative to height values in correct
format as argument for plt.bar
"""
| /usr/src/app/target_test_cases/failed_tests_utils.ci_to_errsize.txt | def ci_to_errsize(cis, heights):
"""Convert intervals to error arguments relative to plot heights.
Parameters
----------
cis : 2 x n sequence
sequence of confidence interval limits
heights : n sequence
sequence of plot heights
Returns
-------
errsize : 2 x n array
sequence of error size relative to height values in correct
format as argument for plt.bar
"""
cis = np.atleast_2d(cis).reshape(2, -1)
heights = np.atleast_1d(heights)
errsize = []
for i, (low, high) in enumerate(np.transpose(cis)):
h = heights[i]
elow = h - low
ehigh = high - h
errsize.append([elow, ehigh])
errsize = np.asarray(errsize).T
return errsize
| utils.ci_to_errsize |
seaborn | 71 | seaborn/utils.py | def despine(fig=None, ax=None, top=True, right=True, left=False,
bottom=False, offset=None, trim=False):
"""Remove the top and right spines from plot(s).
fig : matplotlib figure, optional
Figure to despine all axes of, defaults to the current figure.
ax : matplotlib axes, optional
Specific axes object to despine. Ignored if fig is provided.
top, right, left, bottom : boolean, optional
If True, remove that spine.
offset : int or dict, optional
Absolute distance, in points, spines should be moved away
from the axes (negative values move spines inward). A single value
applies to all spines; a dict can be used to set offset values per
side.
trim : bool, optional
If True, limit spines to the smallest and largest major tick
on each non-despined axis.
Returns
-------
None
"""
| /usr/src/app/target_test_cases/failed_tests_utils.despine.txt | def despine(fig=None, ax=None, top=True, right=True, left=False,
bottom=False, offset=None, trim=False):
"""Remove the top and right spines from plot(s).
fig : matplotlib figure, optional
Figure to despine all axes of, defaults to the current figure.
ax : matplotlib axes, optional
Specific axes object to despine. Ignored if fig is provided.
top, right, left, bottom : boolean, optional
If True, remove that spine.
offset : int or dict, optional
Absolute distance, in points, spines should be moved away
from the axes (negative values move spines inward). A single value
applies to all spines; a dict can be used to set offset values per
side.
trim : bool, optional
If True, limit spines to the smallest and largest major tick
on each non-despined axis.
Returns
-------
None
"""
# Get references to the axes we want
if fig is None and ax is None:
axes = plt.gcf().axes
elif fig is not None:
axes = fig.axes
elif ax is not None:
axes = [ax]
for ax_i in axes:
for side in ["top", "right", "left", "bottom"]:
# Toggle the spine objects
is_visible = not locals()[side]
ax_i.spines[side].set_visible(is_visible)
if offset is not None and is_visible:
try:
val = offset.get(side, 0)
except AttributeError:
val = offset
ax_i.spines[side].set_position(('outward', val))
# Potentially move the ticks
if left and not right:
maj_on = any(
t.tick1line.get_visible()
for t in ax_i.yaxis.majorTicks
)
min_on = any(
t.tick1line.get_visible()
for t in ax_i.yaxis.minorTicks
)
ax_i.yaxis.set_ticks_position("right")
for t in ax_i.yaxis.majorTicks:
t.tick2line.set_visible(maj_on)
for t in ax_i.yaxis.minorTicks:
t.tick2line.set_visible(min_on)
if bottom and not top:
maj_on = any(
t.tick1line.get_visible()
for t in ax_i.xaxis.majorTicks
)
min_on = any(
t.tick1line.get_visible()
for t in ax_i.xaxis.minorTicks
)
ax_i.xaxis.set_ticks_position("top")
for t in ax_i.xaxis.majorTicks:
t.tick2line.set_visible(maj_on)
for t in ax_i.xaxis.minorTicks:
t.tick2line.set_visible(min_on)
if trim:
# clip off the parts of the spines that extend past major ticks
xticks = np.asarray(ax_i.get_xticks())
if xticks.size:
firsttick = np.compress(xticks >= min(ax_i.get_xlim()),
xticks)[0]
lasttick = np.compress(xticks <= max(ax_i.get_xlim()),
xticks)[-1]
ax_i.spines['bottom'].set_bounds(firsttick, lasttick)
ax_i.spines['top'].set_bounds(firsttick, lasttick)
newticks = xticks.compress(xticks <= lasttick)
newticks = newticks.compress(newticks >= firsttick)
ax_i.set_xticks(newticks)
yticks = np.asarray(ax_i.get_yticks())
if yticks.size:
firsttick = np.compress(yticks >= min(ax_i.get_ylim()),
yticks)[0]
lasttick = np.compress(yticks <= max(ax_i.get_ylim()),
yticks)[-1]
ax_i.spines['left'].set_bounds(firsttick, lasttick)
ax_i.spines['right'].set_bounds(firsttick, lasttick)
newticks = yticks.compress(yticks <= lasttick)
newticks = newticks.compress(newticks >= firsttick)
ax_i.set_yticks(newticks)
| utils.despine |
seaborn | 72 | seaborn/utils.py | def get_color_cycle():
"""Return the list of colors in the current matplotlib color cycle
Parameters
----------
None
Returns
-------
colors : list
List of matplotlib colors in the current cycle, or dark gray if
the current color cycle is empty.
"""
| /usr/src/app/target_test_cases/failed_tests_utils.get_color_cycle.txt | def get_color_cycle():
"""Return the list of colors in the current matplotlib color cycle
Parameters
----------
None
Returns
-------
colors : list
List of matplotlib colors in the current cycle, or dark gray if
the current color cycle is empty.
"""
cycler = mpl.rcParams['axes.prop_cycle']
return cycler.by_key()['color'] if 'color' in cycler.keys else [".15"]
| utils.get_color_cycle |
seaborn | 73 | seaborn/utils.py | def move_legend(obj, loc, **kwargs):
"""
Recreate a plot's legend at a new location.
The name is a slight misnomer. Matplotlib legends do not expose public
control over their position parameters. So this function creates a new legend,
copying over the data from the original object, which is then removed.
Parameters
----------
obj : the object with the plot
This argument can be either a seaborn or matplotlib object:
- :class:`seaborn.FacetGrid` or :class:`seaborn.PairGrid`
- :class:`matplotlib.axes.Axes` or :class:`matplotlib.figure.Figure`
loc : str or int
Location argument, as in :meth:`matplotlib.axes.Axes.legend`.
kwargs
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.legend`.
Examples
--------
.. include:: ../docstrings/move_legend.rst
"""
| /usr/src/app/target_test_cases/failed_tests_utils.move_legend.txt | def move_legend(obj, loc, **kwargs):
"""
Recreate a plot's legend at a new location.
The name is a slight misnomer. Matplotlib legends do not expose public
control over their position parameters. So this function creates a new legend,
copying over the data from the original object, which is then removed.
Parameters
----------
obj : the object with the plot
This argument can be either a seaborn or matplotlib object:
- :class:`seaborn.FacetGrid` or :class:`seaborn.PairGrid`
- :class:`matplotlib.axes.Axes` or :class:`matplotlib.figure.Figure`
loc : str or int
Location argument, as in :meth:`matplotlib.axes.Axes.legend`.
kwargs
Other keyword arguments are passed to :meth:`matplotlib.axes.Axes.legend`.
Examples
--------
.. include:: ../docstrings/move_legend.rst
"""
# This is a somewhat hackish solution that will hopefully be obviated by
# upstream improvements to matplotlib legends that make them easier to
# modify after creation.
from seaborn.axisgrid import Grid # Avoid circular import
# Locate the legend object and a method to recreate the legend
if isinstance(obj, Grid):
old_legend = obj.legend
legend_func = obj.figure.legend
elif isinstance(obj, mpl.axes.Axes):
old_legend = obj.legend_
legend_func = obj.legend
elif isinstance(obj, mpl.figure.Figure):
if obj.legends:
old_legend = obj.legends[-1]
else:
old_legend = None
legend_func = obj.legend
else:
err = "`obj` must be a seaborn Grid or matplotlib Axes or Figure instance."
raise TypeError(err)
if old_legend is None:
err = f"{obj} has no legend attached."
raise ValueError(err)
# Extract the components of the legend we need to reuse
# Import here to avoid a circular import
from seaborn._compat import get_legend_handles
handles = get_legend_handles(old_legend)
labels = [t.get_text() for t in old_legend.get_texts()]
# Handle the case where the user is trying to override the labels
if (new_labels := kwargs.pop("labels", None)) is not None:
if len(new_labels) != len(labels):
err = "Length of new labels does not match existing legend."
raise ValueError(err)
labels = new_labels
# Extract legend properties that can be passed to the recreation method
# (Vexingly, these don't all round-trip)
legend_kws = inspect.signature(mpl.legend.Legend).parameters
props = {k: v for k, v in old_legend.properties().items() if k in legend_kws}
# Delegate default bbox_to_anchor rules to matplotlib
props.pop("bbox_to_anchor")
# Try to propagate the existing title and font properties; respect new ones too
title = props.pop("title")
if "title" in kwargs:
title.set_text(kwargs.pop("title"))
title_kwargs = {k: v for k, v in kwargs.items() if k.startswith("title_")}
for key, val in title_kwargs.items():
title.set(**{key[6:]: val})
kwargs.pop(key)
# Try to respect the frame visibility
kwargs.setdefault("frameon", old_legend.legendPatch.get_visible())
# Remove the old legend and create the new one
props.update(kwargs)
old_legend.remove()
new_legend = legend_func(handles, labels, loc=loc, **props)
new_legend.set_title(title.get_text(), title.get_fontproperties())
# Let the Grid object continue to track the correct legend object
if isinstance(obj, Grid):
obj._legend = new_legend
| utils.move_legend |
seaborn | 74 | seaborn/utils.py | def remove_na(vector):
"""Helper method for removing null values from data vectors.
Parameters
----------
vector : vector object
Must implement boolean masking with [] subscript syntax.
Returns
-------
clean_clean : same type as ``vector``
Vector of data with null values removed. May be a copy or a view.
"""
| /usr/src/app/target_test_cases/failed_tests_utils.remove_na.txt | def remove_na(vector):
"""Helper method for removing null values from data vectors.
Parameters
----------
vector : vector object
Must implement boolean masking with [] subscript syntax.
Returns
-------
clean_clean : same type as ``vector``
Vector of data with null values removed. May be a copy or a view.
"""
return vector[pd.notnull(vector)]
| utils.remove_na |
seaborn | 75 | seaborn/utils.py | def saturate(color):
"""Return a fully saturated color with the same hue.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
Returns
-------
new_color : rgb tuple
saturated color code in RGB tuple representation
"""
| /usr/src/app/target_test_cases/failed_tests_utils.saturate.txt | def saturate(color):
"""Return a fully saturated color with the same hue.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
Returns
-------
new_color : rgb tuple
saturated color code in RGB tuple representation
"""
return set_hls_values(color, s=1)
| utils.saturate |
seaborn | 76 | seaborn/utils.py | def set_hls_values(color, h=None, l=None, s=None): # noqa
"""Independently manipulate the h, l, or s channels of a color.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
h, l, s : floats between 0 and 1, or None
new values for each channel in hls space
Returns
-------
new_color : rgb tuple
new color code in RGB tuple representation
"""
| /usr/src/app/target_test_cases/failed_tests_utils.set_hls_values.txt | def set_hls_values(color, h=None, l=None, s=None): # noqa
"""Independently manipulate the h, l, or s channels of a color.
Parameters
----------
color : matplotlib color
hex, rgb-tuple, or html color name
h, l, s : floats between 0 and 1, or None
new values for each channel in hls space
Returns
-------
new_color : rgb tuple
new color code in RGB tuple representation
"""
# Get an RGB tuple representation
rgb = to_rgb(color)
vals = list(colorsys.rgb_to_hls(*rgb))
for i, val in enumerate([h, l, s]):
if val is not None:
vals[i] = val
rgb = colorsys.hls_to_rgb(*vals)
return rgb
| utils.set_hls_values |
seaborn | 77 | seaborn/utils.py | def to_utf8(obj):
"""Return a string representing a Python object.
Strings (i.e. type ``str``) are returned unchanged.
Byte strings (i.e. type ``bytes``) are returned as UTF-8-decoded strings.
For other objects, the method ``__str__()`` is called, and the result is
returned as a string.
Parameters
----------
obj : object
Any Python object
Returns
-------
s : str
UTF-8-decoded string representation of ``obj``
"""
| /usr/src/app/target_test_cases/failed_tests_utils.to_utf8.txt | def to_utf8(obj):
"""Return a string representing a Python object.
Strings (i.e. type ``str``) are returned unchanged.
Byte strings (i.e. type ``bytes``) are returned as UTF-8-decoded strings.
For other objects, the method ``__str__()`` is called, and the result is
returned as a string.
Parameters
----------
obj : object
Any Python object
Returns
-------
s : str
UTF-8-decoded string representation of ``obj``
"""
if isinstance(obj, str):
return obj
try:
return obj.decode(encoding="utf-8")
except AttributeError: # obj is not bytes-like
return str(obj)
| utils.to_utf8 |
scikit-learn | 0 | sklearn/linear_model/_bayes.py | def fit(self, X, y):
"""Fit the model according to the given training data and parameters.
Iterative procedure to maximize the evidence
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : array-like of shape (n_samples,)
Target values (integers). Will be cast to X's dtype if necessary.
Returns
-------
self : object
Fitted estimator.
"""
| /usr/src/app/target_test_cases/failed_tests_ARDRegression.fit.txt | def fit(self, X, y):
"""Fit the model according to the given training data and parameters.
Iterative procedure to maximize the evidence
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : array-like of shape (n_samples,)
Target values (integers). Will be cast to X's dtype if necessary.
Returns
-------
self : object
Fitted estimator.
"""
X, y = validate_data(
self,
X,
y,
dtype=[np.float64, np.float32],
force_writeable=True,
y_numeric=True,
ensure_min_samples=2,
)
dtype = X.dtype
n_samples, n_features = X.shape
coef_ = np.zeros(n_features, dtype=dtype)
X, y, X_offset_, y_offset_, X_scale_ = _preprocess_data(
X, y, fit_intercept=self.fit_intercept, copy=self.copy_X
)
self.X_offset_ = X_offset_
self.X_scale_ = X_scale_
# Launch the convergence loop
keep_lambda = np.ones(n_features, dtype=bool)
lambda_1 = self.lambda_1
lambda_2 = self.lambda_2
alpha_1 = self.alpha_1
alpha_2 = self.alpha_2
verbose = self.verbose
# Initialization of the values of the parameters
eps = np.finfo(np.float64).eps
# Add `eps` in the denominator to omit division by zero if `np.var(y)`
# is zero.
# Explicitly set dtype to avoid unintended type promotion with numpy 2.
alpha_ = np.asarray(1.0 / (np.var(y) + eps), dtype=dtype)
lambda_ = np.ones(n_features, dtype=dtype)
self.scores_ = list()
coef_old_ = None
def update_coeff(X, y, coef_, alpha_, keep_lambda, sigma_):
coef_[keep_lambda] = alpha_ * np.linalg.multi_dot(
[sigma_, X[:, keep_lambda].T, y]
)
return coef_
update_sigma = (
self._update_sigma
if n_samples >= n_features
else self._update_sigma_woodbury
)
# Iterative procedure of ARDRegression
for iter_ in range(self.max_iter):
sigma_ = update_sigma(X, alpha_, lambda_, keep_lambda)
coef_ = update_coeff(X, y, coef_, alpha_, keep_lambda, sigma_)
# Update alpha and lambda
rmse_ = np.sum((y - np.dot(X, coef_)) ** 2)
gamma_ = 1.0 - lambda_[keep_lambda] * np.diag(sigma_)
lambda_[keep_lambda] = (gamma_ + 2.0 * lambda_1) / (
(coef_[keep_lambda]) ** 2 + 2.0 * lambda_2
)
alpha_ = (n_samples - gamma_.sum() + 2.0 * alpha_1) / (
rmse_ + 2.0 * alpha_2
)
# Prune the weights with a precision over a threshold
keep_lambda = lambda_ < self.threshold_lambda
coef_[~keep_lambda] = 0
# Compute the objective function
if self.compute_score:
s = (lambda_1 * np.log(lambda_) - lambda_2 * lambda_).sum()
s += alpha_1 * log(alpha_) - alpha_2 * alpha_
s += 0.5 * (
fast_logdet(sigma_)
+ n_samples * log(alpha_)
+ np.sum(np.log(lambda_))
)
s -= 0.5 * (alpha_ * rmse_ + (lambda_ * coef_**2).sum())
self.scores_.append(s)
# Check for convergence
if iter_ > 0 and np.sum(np.abs(coef_old_ - coef_)) < self.tol:
if verbose:
print("Converged after %s iterations" % iter_)
break
coef_old_ = np.copy(coef_)
if not keep_lambda.any():
break
self.n_iter_ = iter_ + 1
if keep_lambda.any():
# update sigma and mu using updated params from the last iteration
sigma_ = update_sigma(X, alpha_, lambda_, keep_lambda)
coef_ = update_coeff(X, y, coef_, alpha_, keep_lambda, sigma_)
else:
sigma_ = np.array([]).reshape(0, 0)
self.coef_ = coef_
self.alpha_ = alpha_
self.sigma_ = sigma_
self.lambda_ = lambda_
self._set_intercept(X_offset_, y_offset_, X_scale_)
return self
| ARDRegression.fit |
scikit-learn | 1 | sklearn/linear_model/_bayes.py | def predict(self, X, return_std=False):
"""Predict using the linear model.
In addition to the mean of the predictive distribution, also its
standard deviation can be returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Samples.
return_std : bool, default=False
Whether to return the standard deviation of posterior prediction.
Returns
-------
y_mean : array-like of shape (n_samples,)
Mean of predictive distribution of query points.
y_std : array-like of shape (n_samples,)
Standard deviation of predictive distribution of query points.
"""
| /usr/src/app/target_test_cases/failed_tests_ARDRegression.predict.txt | def predict(self, X, return_std=False):
"""Predict using the linear model.
In addition to the mean of the predictive distribution, also its
standard deviation can be returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Samples.
return_std : bool, default=False
Whether to return the standard deviation of posterior prediction.
Returns
-------
y_mean : array-like of shape (n_samples,)
Mean of predictive distribution of query points.
y_std : array-like of shape (n_samples,)
Standard deviation of predictive distribution of query points.
"""
y_mean = self._decision_function(X)
if return_std is False:
return y_mean
else:
col_index = self.lambda_ < self.threshold_lambda
X = _safe_indexing(X, indices=col_index, axis=1)
sigmas_squared_data = (np.dot(X, self.sigma_) * X).sum(axis=1)
y_std = np.sqrt(sigmas_squared_data + (1.0 / self.alpha_))
return y_mean, y_std
| ARDRegression.predict |
scikit-learn | 2 | sklearn/ensemble/_weight_boosting.py | def decision_function(self, X):
"""Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
score : ndarray of shape of (n_samples, k)
The decision function of the input samples. The order of
outputs is the same as that of the :term:`classes_` attribute.
Binary classification is a special cases with ``k == 1``,
otherwise ``k==n_classes``. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in ``classes_``, respectively.
"""
| /usr/src/app/target_test_cases/failed_tests_AdaBoostClassifier.decision_function.txt | def decision_function(self, X):
"""Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
Returns
-------
score : ndarray of shape of (n_samples, k)
The decision function of the input samples. The order of
outputs is the same as that of the :term:`classes_` attribute.
Binary classification is a special cases with ``k == 1``,
otherwise ``k==n_classes``. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in ``classes_``, respectively.
"""
check_is_fitted(self)
X = self._check_X(X)
n_classes = self.n_classes_
classes = self.classes_[:, np.newaxis]
# TODO(1.6): Remove, because "algorithm" param will be deprecated in 1.6
if self.algorithm == "SAMME.R":
# The weights are all 1. for SAMME.R
pred = sum(
_samme_proba(estimator, n_classes, X) for estimator in self.estimators_
)
else: # self.algorithm == "SAMME"
pred = sum(
np.where(
(estimator.predict(X) == classes).T,
w,
-1 / (n_classes - 1) * w,
)
for estimator, w in zip(self.estimators_, self.estimator_weights_)
)
pred /= self.estimator_weights_.sum()
if n_classes == 2:
pred[:, 0] *= -1
return pred.sum(axis=1)
return pred
| AdaBoostClassifier.decision_function |
scikit-learn | 3 | sklearn/kernel_approximation.py | def fit(self, X, y=None):
"""Only validates estimator's parameters.
This method allows to: (i) validate the estimator's parameters and
(ii) be consistent with the scikit-learn transformer API.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : array-like, shape (n_samples,) or (n_samples, n_outputs), \
default=None
Target values (None for unsupervised transformations).
Returns
-------
self : object
Returns the transformer.
"""
| /usr/src/app/target_test_cases/failed_tests_AdditiveChi2Sampler.fit.txt | def fit(self, X, y=None):
"""Only validates estimator's parameters.
This method allows to: (i) validate the estimator's parameters and
(ii) be consistent with the scikit-learn transformer API.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
y : array-like, shape (n_samples,) or (n_samples, n_outputs), \
default=None
Target values (None for unsupervised transformations).
Returns
-------
self : object
Returns the transformer.
"""
X = validate_data(self, X, accept_sparse="csr", ensure_non_negative=True)
if self.sample_interval is None and self.sample_steps not in (1, 2, 3):
raise ValueError(
"If sample_steps is not in [1, 2, 3],"
" you need to provide sample_interval"
)
return self
| AdditiveChi2Sampler.fit |
scikit-learn | 4 | sklearn/kernel_approximation.py | def transform(self, X):
"""Apply approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Returns
-------
X_new : {ndarray, sparse matrix}, \
shape = (n_samples, n_features * (2*sample_steps - 1))
Whether the return value is an array or sparse matrix depends on
the type of the input X.
"""
| /usr/src/app/target_test_cases/failed_tests_AdditiveChi2Sampler.transform.txt | def transform(self, X):
"""Apply approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where `n_samples` is the number of samples
and `n_features` is the number of features.
Returns
-------
X_new : {ndarray, sparse matrix}, \
shape = (n_samples, n_features * (2*sample_steps - 1))
Whether the return value is an array or sparse matrix depends on
the type of the input X.
"""
X = validate_data(
self, X, accept_sparse="csr", reset=False, ensure_non_negative=True
)
sparse = sp.issparse(X)
if self.sample_interval is None:
# See figure 2 c) of "Efficient additive kernels via explicit feature maps" # noqa
# <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>
# A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, # noqa
# 2011
if self.sample_steps == 1:
sample_interval = 0.8
elif self.sample_steps == 2:
sample_interval = 0.5
elif self.sample_steps == 3:
sample_interval = 0.4
else:
raise ValueError(
"If sample_steps is not in [1, 2, 3],"
" you need to provide sample_interval"
)
else:
sample_interval = self.sample_interval
# zeroth component
# 1/cosh = sech
# cosh(0) = 1.0
transf = self._transform_sparse if sparse else self._transform_dense
return transf(X, self.sample_steps, sample_interval)
| AdditiveChi2Sampler.transform |
scikit-learn | 5 | sklearn/cluster/_affinity_propagation.py | def fit(self, X, y=None):
"""Fit the clustering from features, or affinity matrix.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between
instances if ``affinity='precomputed'``. If a sparse feature matrix
is provided, it will be converted into a sparse ``csr_matrix``.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self
Returns the instance itself.
"""
| /usr/src/app/target_test_cases/failed_tests_AffinityPropagation.fit.txt | def fit(self, X, y=None):
"""Fit the clustering from features, or affinity matrix.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features), or \
array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between
instances if ``affinity='precomputed'``. If a sparse feature matrix
is provided, it will be converted into a sparse ``csr_matrix``.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self
Returns the instance itself.
"""
if self.affinity == "precomputed":
X = validate_data(self, X, copy=self.copy, force_writeable=True)
self.affinity_matrix_ = X
else: # self.affinity == "euclidean"
X = validate_data(self, X, accept_sparse="csr")
self.affinity_matrix_ = -euclidean_distances(X, squared=True)
if self.affinity_matrix_.shape[0] != self.affinity_matrix_.shape[1]:
raise ValueError(
"The matrix of similarities must be a square array. "
f"Got {self.affinity_matrix_.shape} instead."
)
if self.preference is None:
preference = np.median(self.affinity_matrix_)
else:
preference = self.preference
preference = np.asarray(preference)
random_state = check_random_state(self.random_state)
(
self.cluster_centers_indices_,
self.labels_,
self.n_iter_,
) = _affinity_propagation(
self.affinity_matrix_,
max_iter=self.max_iter,
convergence_iter=self.convergence_iter,
preference=preference,
damping=self.damping,
verbose=self.verbose,
return_n_iter=True,
random_state=random_state,
)
if self.affinity != "precomputed":
self.cluster_centers_ = X[self.cluster_centers_indices_].copy()
return self
| AffinityPropagation.fit |
scikit-learn | 6 | sklearn/cluster/_affinity_propagation.py | def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
labels : ndarray of shape (n_samples,)
Cluster labels.
"""
| /usr/src/app/target_test_cases/failed_tests_AffinityPropagation.predict.txt | def predict(self, X):
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
labels : ndarray of shape (n_samples,)
Cluster labels.
"""
check_is_fitted(self)
X = validate_data(self, X, reset=False, accept_sparse="csr")
if not hasattr(self, "cluster_centers_"):
raise ValueError(
"Predict method is not supported when affinity='precomputed'."
)
if self.cluster_centers_.shape[0] > 0:
with config_context(assume_finite=True):
return pairwise_distances_argmin(X, self.cluster_centers_)
else:
warnings.warn(
(
"This model does not have any cluster centers "
"because affinity propagation did not converge. "
"Labeling every sample as '-1'."
),
ConvergenceWarning,
)
return np.array([-1] * X.shape[0])
| AffinityPropagation.predict |
scikit-learn | 7 | sklearn/ensemble/_bagging.py | def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the base
estimators in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
The class log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
| /usr/src/app/target_test_cases/failed_tests_BaggingClassifier.predict_log_proba.txt | def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the base
estimators in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
The class log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
check_is_fitted(self)
if hasattr(self.estimator_, "predict_log_proba"):
# Check data
X = validate_data(
self,
X,
accept_sparse=["csr", "csc"],
dtype=None,
ensure_all_finite=False,
reset=False,
)
# Parallel loop
n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs)
all_log_proba = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
delayed(_parallel_predict_log_proba)(
self.estimators_[starts[i] : starts[i + 1]],
self.estimators_features_[starts[i] : starts[i + 1]],
X,
self.n_classes_,
)
for i in range(n_jobs)
)
# Reduce
log_proba = all_log_proba[0]
for j in range(1, len(all_log_proba)):
log_proba = np.logaddexp(log_proba, all_log_proba[j])
log_proba -= np.log(self.n_estimators)
else:
log_proba = np.log(self.predict_proba(X))
return log_proba
| BaggingClassifier.predict_log_proba |
scikit-learn | 8 | sklearn/ensemble/_bagging.py | def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method, then it resorts to voting and the predicted class probabilities
of an input sample represents the proportion of estimators predicting
each class.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
| /usr/src/app/target_test_cases/failed_tests_BaggingClassifier.predict_proba.txt | def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method, then it resorts to voting and the predicted class probabilities
of an input sample represents the proportion of estimators predicting
each class.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
Returns
-------
p : ndarray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
check_is_fitted(self)
# Check data
X = validate_data(
self,
X,
accept_sparse=["csr", "csc"],
dtype=None,
ensure_all_finite=False,
reset=False,
)
# Parallel loop
n_jobs, _, starts = _partition_estimators(self.n_estimators, self.n_jobs)
all_proba = Parallel(
n_jobs=n_jobs, verbose=self.verbose, **self._parallel_args()
)(
delayed(_parallel_predict_proba)(
self.estimators_[starts[i] : starts[i + 1]],
self.estimators_features_[starts[i] : starts[i + 1]],
X,
self.n_classes_,
)
for i in range(n_jobs)
)
# Reduce
proba = sum(all_proba) / self.n_estimators
return proba
| BaggingClassifier.predict_proba |
scikit-learn | 9 | sklearn/linear_model/_bayes.py | def fit(self, X, y, sample_weight=None):
"""Fit the model.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.
y : ndarray of shape (n_samples,)
Target values. Will be cast to X's dtype if necessary.
sample_weight : ndarray of shape (n_samples,), default=None
Individual weights for each sample.
.. versionadded:: 0.20
parameter *sample_weight* support to BayesianRidge.
Returns
-------
self : object
Returns the instance itself.
"""
| /usr/src/app/target_test_cases/failed_tests_BayesianRidge.fit.txt | def fit(self, X, y, sample_weight=None):
"""Fit the model.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.
y : ndarray of shape (n_samples,)
Target values. Will be cast to X's dtype if necessary.
sample_weight : ndarray of shape (n_samples,), default=None
Individual weights for each sample.
.. versionadded:: 0.20
parameter *sample_weight* support to BayesianRidge.
Returns
-------
self : object
Returns the instance itself.
"""
X, y = validate_data(
self,
X,
y,
dtype=[np.float64, np.float32],
force_writeable=True,
y_numeric=True,
)
dtype = X.dtype
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X, dtype=dtype)
X, y, X_offset_, y_offset_, X_scale_ = _preprocess_data(
X,
y,
fit_intercept=self.fit_intercept,
copy=self.copy_X,
sample_weight=sample_weight,
)
if sample_weight is not None:
# Sample weight can be implemented via a simple rescaling.
X, y, _ = _rescale_data(X, y, sample_weight)
self.X_offset_ = X_offset_
self.X_scale_ = X_scale_
n_samples, n_features = X.shape
# Initialization of the values of the parameters
eps = np.finfo(np.float64).eps
# Add `eps` in the denominator to omit division by zero if `np.var(y)`
# is zero
alpha_ = self.alpha_init
lambda_ = self.lambda_init
if alpha_ is None:
alpha_ = 1.0 / (np.var(y) + eps)
if lambda_ is None:
lambda_ = 1.0
# Avoid unintended type promotion to float64 with numpy 2
alpha_ = np.asarray(alpha_, dtype=dtype)
lambda_ = np.asarray(lambda_, dtype=dtype)
verbose = self.verbose
lambda_1 = self.lambda_1
lambda_2 = self.lambda_2
alpha_1 = self.alpha_1
alpha_2 = self.alpha_2
self.scores_ = list()
coef_old_ = None
XT_y = np.dot(X.T, y)
U, S, Vh = linalg.svd(X, full_matrices=False)
eigen_vals_ = S**2
# Convergence loop of the bayesian ridge regression
for iter_ in range(self.max_iter):
# update posterior mean coef_ based on alpha_ and lambda_ and
# compute corresponding rmse
coef_, rmse_ = self._update_coef_(
X, y, n_samples, n_features, XT_y, U, Vh, eigen_vals_, alpha_, lambda_
)
if self.compute_score:
# compute the log marginal likelihood
s = self._log_marginal_likelihood(
n_samples, n_features, eigen_vals_, alpha_, lambda_, coef_, rmse_
)
self.scores_.append(s)
# Update alpha and lambda according to (MacKay, 1992)
gamma_ = np.sum((alpha_ * eigen_vals_) / (lambda_ + alpha_ * eigen_vals_))
lambda_ = (gamma_ + 2 * lambda_1) / (np.sum(coef_**2) + 2 * lambda_2)
alpha_ = (n_samples - gamma_ + 2 * alpha_1) / (rmse_ + 2 * alpha_2)
# Check for convergence
if iter_ != 0 and np.sum(np.abs(coef_old_ - coef_)) < self.tol:
if verbose:
print("Convergence after ", str(iter_), " iterations")
break
coef_old_ = np.copy(coef_)
self.n_iter_ = iter_ + 1
# return regularization parameters and corresponding posterior mean,
# log marginal likelihood and posterior covariance
self.alpha_ = alpha_
self.lambda_ = lambda_
self.coef_, rmse_ = self._update_coef_(
X, y, n_samples, n_features, XT_y, U, Vh, eigen_vals_, alpha_, lambda_
)
if self.compute_score:
# compute the log marginal likelihood
s = self._log_marginal_likelihood(
n_samples, n_features, eigen_vals_, alpha_, lambda_, coef_, rmse_
)
self.scores_.append(s)
self.scores_ = np.array(self.scores_)
# posterior covariance is given by 1/alpha_ * scaled_sigma_
scaled_sigma_ = np.dot(
Vh.T, Vh / (eigen_vals_ + lambda_ / alpha_)[:, np.newaxis]
)
self.sigma_ = (1.0 / alpha_) * scaled_sigma_
self._set_intercept(X_offset_, y_offset_, X_scale_)
return self
| BayesianRidge.fit |
scikit-learn | 10 | sklearn/linear_model/_bayes.py | def predict(self, X, return_std=False):
"""Predict using the linear model.
In addition to the mean of the predictive distribution, also its
standard deviation can be returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Samples.
return_std : bool, default=False
Whether to return the standard deviation of posterior prediction.
Returns
-------
y_mean : array-like of shape (n_samples,)
Mean of predictive distribution of query points.
y_std : array-like of shape (n_samples,)
Standard deviation of predictive distribution of query points.
"""
| /usr/src/app/target_test_cases/failed_tests_BayesianRidge.predict.txt | def predict(self, X, return_std=False):
"""Predict using the linear model.
In addition to the mean of the predictive distribution, also its
standard deviation can be returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Samples.
return_std : bool, default=False
Whether to return the standard deviation of posterior prediction.
Returns
-------
y_mean : array-like of shape (n_samples,)
Mean of predictive distribution of query points.
y_std : array-like of shape (n_samples,)
Standard deviation of predictive distribution of query points.
"""
y_mean = self._decision_function(X)
if not return_std:
return y_mean
else:
sigmas_squared_data = (np.dot(X, self.sigma_) * X).sum(axis=1)
y_std = np.sqrt(sigmas_squared_data + (1.0 / self.alpha_))
return y_mean, y_std
| BayesianRidge.predict |
scikit-learn | 11 | sklearn/neural_network/_rbm.py | def fit(self, X, y=None):
"""Fit the model to the data X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
Returns
-------
self : BernoulliRBM
The fitted model.
"""
| /usr/src/app/target_test_cases/failed_tests_BernoulliRBM.fit.txt | def fit(self, X, y=None):
"""Fit the model to the data X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
Returns
-------
self : BernoulliRBM
The fitted model.
"""
X = validate_data(self, X, accept_sparse="csr", dtype=(np.float64, np.float32))
n_samples = X.shape[0]
rng = check_random_state(self.random_state)
self.components_ = np.asarray(
rng.normal(0, 0.01, (self.n_components, X.shape[1])),
order="F",
dtype=X.dtype,
)
self._n_features_out = self.components_.shape[0]
self.intercept_hidden_ = np.zeros(self.n_components, dtype=X.dtype)
self.intercept_visible_ = np.zeros(X.shape[1], dtype=X.dtype)
self.h_samples_ = np.zeros((self.batch_size, self.n_components), dtype=X.dtype)
n_batches = int(np.ceil(float(n_samples) / self.batch_size))
batch_slices = list(
gen_even_slices(n_batches * self.batch_size, n_batches, n_samples=n_samples)
)
verbose = self.verbose
begin = time.time()
for iteration in range(1, self.n_iter + 1):
for batch_slice in batch_slices:
self._fit(X[batch_slice], rng)
if verbose:
end = time.time()
print(
"[%s] Iteration %d, pseudo-likelihood = %.2f, time = %.2fs"
% (
type(self).__name__,
iteration,
self.score_samples(X).mean(),
end - begin,
)
)
begin = end
return self
| BernoulliRBM.fit |
scikit-learn | 12 | sklearn/neural_network/_rbm.py | def partial_fit(self, X, y=None):
"""Fit the model to the partial segment of the data X.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
Returns
-------
self : BernoulliRBM
The fitted model.
"""
| /usr/src/app/target_test_cases/failed_tests_BernoulliRBM.partial_fit.txt | def partial_fit(self, X, y=None):
"""Fit the model to the partial segment of the data X.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
Returns
-------
self : BernoulliRBM
The fitted model.
"""
first_pass = not hasattr(self, "components_")
X = validate_data(
self, X, accept_sparse="csr", dtype=np.float64, reset=first_pass
)
if not hasattr(self, "random_state_"):
self.random_state_ = check_random_state(self.random_state)
if not hasattr(self, "components_"):
self.components_ = np.asarray(
self.random_state_.normal(0, 0.01, (self.n_components, X.shape[1])),
order="F",
)
self._n_features_out = self.components_.shape[0]
if not hasattr(self, "intercept_hidden_"):
self.intercept_hidden_ = np.zeros(
self.n_components,
)
if not hasattr(self, "intercept_visible_"):
self.intercept_visible_ = np.zeros(
X.shape[1],
)
if not hasattr(self, "h_samples_"):
self.h_samples_ = np.zeros((self.batch_size, self.n_components))
self._fit(X, self.random_state_)
| BernoulliRBM.partial_fit |
scikit-learn | 13 | sklearn/neural_network/_rbm.py | def score_samples(self, X):
"""Compute the pseudo-likelihood of X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Values of the visible layer. Must be all-boolean (not checked).
Returns
-------
pseudo_likelihood : ndarray of shape (n_samples,)
Value of the pseudo-likelihood (proxy for likelihood).
Notes
-----
This method is not deterministic: it computes a quantity called the
free energy on X, then on a randomly corrupted version of X, and
returns the log of the logistic function of the difference.
"""
| /usr/src/app/target_test_cases/failed_tests_BernoulliRBM.score_samples.txt | def score_samples(self, X):
"""Compute the pseudo-likelihood of X.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Values of the visible layer. Must be all-boolean (not checked).
Returns
-------
pseudo_likelihood : ndarray of shape (n_samples,)
Value of the pseudo-likelihood (proxy for likelihood).
Notes
-----
This method is not deterministic: it computes a quantity called the
free energy on X, then on a randomly corrupted version of X, and
returns the log of the logistic function of the difference.
"""
check_is_fitted(self)
v = validate_data(self, X, accept_sparse="csr", reset=False)
rng = check_random_state(self.random_state)
# Randomly corrupt one feature in each sample in v.
ind = (np.arange(v.shape[0]), rng.randint(0, v.shape[1], v.shape[0]))
if sp.issparse(v):
data = -2 * v[ind] + 1
if isinstance(data, np.matrix): # v is a sparse matrix
v_ = v + sp.csr_matrix((data.A.ravel(), ind), shape=v.shape)
else: # v is a sparse array
v_ = v + sp.csr_array((data.ravel(), ind), shape=v.shape)
else:
v_ = v.copy()
v_[ind] = 1 - v_[ind]
fe = self._free_energy(v)
fe_ = self._free_energy(v_)
# log(expit(x)) = log(1 / (1 + exp(-x)) = -np.logaddexp(0, -x)
return -v.shape[1] * np.logaddexp(0, -(fe_ - fe))
| BernoulliRBM.score_samples |
scikit-learn | 14 | sklearn/cluster/_bisect_k_means.py | def fit(self, X, y=None, sample_weight=None):
"""Compute bisecting k-means clustering.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster.
.. note:: The data will be converted to C ordering,
which will cause a memory copy
if the given data is not C-contiguous.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight. `sample_weight` is not used during
initialization if `init` is a callable.
Returns
-------
self
Fitted estimator.
"""
| /usr/src/app/target_test_cases/failed_tests_BisectingKMeans.fit.txt | def fit(self, X, y=None, sample_weight=None):
"""Compute bisecting k-means clustering.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster.
.. note:: The data will be converted to C ordering,
which will cause a memory copy
if the given data is not C-contiguous.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
The weights for each observation in X. If None, all observations
are assigned equal weight. `sample_weight` is not used during
initialization if `init` is a callable.
Returns
-------
self
Fitted estimator.
"""
X = validate_data(
self,
X,
accept_sparse="csr",
dtype=[np.float64, np.float32],
order="C",
copy=self.copy_x,
accept_large_sparse=False,
)
self._check_params_vs_input(X)
self._random_state = check_random_state(self.random_state)
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
self._n_threads = _openmp_effective_n_threads()
if self.algorithm == "lloyd" or self.n_clusters == 1:
self._kmeans_single = _kmeans_single_lloyd
self._check_mkl_vcomp(X, X.shape[0])
else:
self._kmeans_single = _kmeans_single_elkan
# Subtract of mean of X for more accurate distance computations
if not sp.issparse(X):
self._X_mean = X.mean(axis=0)
X -= self._X_mean
# Initialize the hierarchical clusters tree
self._bisecting_tree = _BisectingTree(
indices=np.arange(X.shape[0]),
center=X.mean(axis=0),
score=0,
)
x_squared_norms = row_norms(X, squared=True)
for _ in range(self.n_clusters - 1):
# Chose cluster to bisect
cluster_to_bisect = self._bisecting_tree.get_cluster_to_bisect()
# Split this cluster into 2 subclusters
self._bisect(X, x_squared_norms, sample_weight, cluster_to_bisect)
# Aggregate final labels and centers from the bisecting tree
self.labels_ = np.full(X.shape[0], -1, dtype=np.int32)
self.cluster_centers_ = np.empty((self.n_clusters, X.shape[1]), dtype=X.dtype)
for i, cluster_node in enumerate(self._bisecting_tree.iter_leaves()):
self.labels_[cluster_node.indices] = i
self.cluster_centers_[i] = cluster_node.center
cluster_node.label = i # label final clusters for future prediction
cluster_node.indices = None # release memory
# Restore original data
if not sp.issparse(X):
X += self._X_mean
self.cluster_centers_ += self._X_mean
_inertia = _inertia_sparse if sp.issparse(X) else _inertia_dense
self.inertia_ = _inertia(
X, sample_weight, self.cluster_centers_, self.labels_, self._n_threads
)
self._n_features_out = self.cluster_centers_.shape[0]
return self
| BisectingKMeans.fit |
scikit-learn | 15 | sklearn/calibration.py | def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit the calibrated model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,)
Target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
**fit_params : dict
Parameters to pass to the `fit` method of the underlying
classifier.
Returns
-------
self : object
Returns an instance of self.
"""
| /usr/src/app/target_test_cases/failed_tests_CalibratedClassifierCV.fit.txt | def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit the calibrated model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,)
Target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
**fit_params : dict
Parameters to pass to the `fit` method of the underlying
classifier.
Returns
-------
self : object
Returns an instance of self.
"""
check_classification_targets(y)
X, y = indexable(X, y)
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X)
estimator = self._get_estimator()
self.calibrated_classifiers_ = []
if self.cv == "prefit":
# `classes_` should be consistent with that of estimator
check_is_fitted(self.estimator, attributes=["classes_"])
self.classes_ = self.estimator.classes_
predictions, _ = _get_response_values(
estimator,
X,
response_method=["decision_function", "predict_proba"],
)
if predictions.ndim == 1:
# Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
predictions = predictions.reshape(-1, 1)
calibrated_classifier = _fit_calibrator(
estimator,
predictions,
y,
self.classes_,
self.method,
sample_weight,
)
self.calibrated_classifiers_.append(calibrated_classifier)
else:
# Set `classes_` using all `y`
label_encoder_ = LabelEncoder().fit(y)
self.classes_ = label_encoder_.classes_
if _routing_enabled():
routed_params = process_routing(
self,
"fit",
sample_weight=sample_weight,
**fit_params,
)
else:
# sample_weight checks
fit_parameters = signature(estimator.fit).parameters
supports_sw = "sample_weight" in fit_parameters
if sample_weight is not None and not supports_sw:
estimator_name = type(estimator).__name__
warnings.warn(
f"Since {estimator_name} does not appear to accept"
" sample_weight, sample weights will only be used for the"
" calibration itself. This can be caused by a limitation of"
" the current scikit-learn API. See the following issue for"
" more details:"
" https://github.com/scikit-learn/scikit-learn/issues/21134."
" Be warned that the result of the calibration is likely to be"
" incorrect."
)
routed_params = Bunch()
routed_params.splitter = Bunch(split={}) # no routing for splitter
routed_params.estimator = Bunch(fit=fit_params)
if sample_weight is not None and supports_sw:
routed_params.estimator.fit["sample_weight"] = sample_weight
# Check that each cross-validation fold can have at least one
# example per class
if isinstance(self.cv, int):
n_folds = self.cv
elif hasattr(self.cv, "n_splits"):
n_folds = self.cv.n_splits
else:
n_folds = None
if n_folds and np.any(np.unique(y, return_counts=True)[1] < n_folds):
raise ValueError(
f"Requesting {n_folds}-fold "
"cross-validation but provided less than "
f"{n_folds} examples for at least one class."
)
if isinstance(self.cv, LeaveOneOut):
raise ValueError(
"LeaveOneOut cross-validation does not allow"
"all classes to be present in test splits. "
"Please use a cross-validation generator that allows "
"all classes to appear in every test and train split."
)
cv = check_cv(self.cv, y, classifier=True)
if self.ensemble:
parallel = Parallel(n_jobs=self.n_jobs)
self.calibrated_classifiers_ = parallel(
delayed(_fit_classifier_calibrator_pair)(
clone(estimator),
X,
y,
train=train,
test=test,
method=self.method,
classes=self.classes_,
sample_weight=sample_weight,
fit_params=routed_params.estimator.fit,
)
for train, test in cv.split(X, y, **routed_params.splitter.split)
)
else:
this_estimator = clone(estimator)
method_name = _check_response_method(
this_estimator,
["decision_function", "predict_proba"],
).__name__
predictions = cross_val_predict(
estimator=this_estimator,
X=X,
y=y,
cv=cv,
method=method_name,
n_jobs=self.n_jobs,
params=routed_params.estimator.fit,
)
if len(self.classes_) == 2:
# Ensure shape (n_samples, 1) in the binary case
if method_name == "predict_proba":
# Select the probability column of the postive class
predictions = _process_predict_proba(
y_pred=predictions,
target_type="binary",
classes=self.classes_,
pos_label=self.classes_[1],
)
predictions = predictions.reshape(-1, 1)
this_estimator.fit(X, y, **routed_params.estimator.fit)
# Note: Here we don't pass on fit_params because the supported
# calibrators don't support fit_params anyway
calibrated_classifier = _fit_calibrator(
this_estimator,
predictions,
y,
self.classes_,
self.method,
sample_weight,
)
self.calibrated_classifiers_.append(calibrated_classifier)
first_clf = self.calibrated_classifiers_[0].estimator
if hasattr(first_clf, "n_features_in_"):
self.n_features_in_ = first_clf.n_features_in_
if hasattr(first_clf, "feature_names_in_"):
self.feature_names_in_ = first_clf.feature_names_in_
return self
| CalibratedClassifierCV.fit |
scikit-learn | 16 | sklearn/calibration.py | def predict_proba(self, X):
"""Calibrated probabilities of classification.
This function returns calibrated probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The samples, as accepted by `estimator.predict_proba`.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
The predicted probas.
"""
| /usr/src/app/target_test_cases/failed_tests_CalibratedClassifierCV.predict_proba.txt | def predict_proba(self, X):
"""Calibrated probabilities of classification.
This function returns calibrated probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The samples, as accepted by `estimator.predict_proba`.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
The predicted probas.
"""
check_is_fitted(self)
# Compute the arithmetic mean of the predictions of the calibrated
# classifiers
mean_proba = np.zeros((_num_samples(X), len(self.classes_)))
for calibrated_classifier in self.calibrated_classifiers_:
proba = calibrated_classifier.predict_proba(X)
mean_proba += proba
mean_proba /= len(self.calibrated_classifiers_)
return mean_proba
| CalibratedClassifierCV.predict_proba |
scikit-learn | 17 | sklearn/calibration.py | def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
"""Plot visualization.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Parameters
----------
ax : Matplotlib Axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
name : str, default=None
Name for labeling curve. If `None`, use `estimator_name` if
not `None`, otherwise no labeling is shown.
ref_line : bool, default=True
If `True`, plots a reference line representing a perfectly
calibrated classifier.
**kwargs : dict
Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
Returns
-------
display : :class:`~sklearn.calibration.CalibrationDisplay`
Object that stores computed values.
"""
| /usr/src/app/target_test_cases/failed_tests_CalibrationDisplay.plot.txt | def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
"""Plot visualization.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Parameters
----------
ax : Matplotlib Axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
name : str, default=None
Name for labeling curve. If `None`, use `estimator_name` if
not `None`, otherwise no labeling is shown.
ref_line : bool, default=True
If `True`, plots a reference line representing a perfectly
calibrated classifier.
**kwargs : dict
Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
Returns
-------
display : :class:`~sklearn.calibration.CalibrationDisplay`
Object that stores computed values.
"""
self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name)
info_pos_label = (
f"(Positive class: {self.pos_label})" if self.pos_label is not None else ""
)
line_kwargs = {"marker": "s", "linestyle": "-"}
if name is not None:
line_kwargs["label"] = name
line_kwargs.update(**kwargs)
ref_line_label = "Perfectly calibrated"
existing_ref_line = ref_line_label in self.ax_.get_legend_handles_labels()[1]
if ref_line and not existing_ref_line:
self.ax_.plot([0, 1], [0, 1], "k:", label=ref_line_label)
self.line_ = self.ax_.plot(self.prob_pred, self.prob_true, **line_kwargs)[0]
# We always have to show the legend for at least the reference line
self.ax_.legend(loc="lower right")
xlabel = f"Mean predicted probability {info_pos_label}"
ylabel = f"Fraction of positives {info_pos_label}"
self.ax_.set(xlabel=xlabel, ylabel=ylabel)
return self
| CalibrationDisplay.plot |
scikit-learn | 18 | sklearn/utils/_mocking.py | def decision_function(self, X):
"""Confidence score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
decision : ndarray of shape (n_samples,) if n_classes == 2\
else (n_samples, n_classes)
Confidence score.
"""
| /usr/src/app/target_test_cases/failed_tests_CheckingClassifier.decision_function.txt | def decision_function(self, X):
"""Confidence score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
decision : ndarray of shape (n_samples,) if n_classes == 2\
else (n_samples, n_classes)
Confidence score.
"""
if (
self.methods_to_check == "all"
or "decision_function" in self.methods_to_check
):
X, y = self._check_X_y(X)
rng = check_random_state(self.random_state)
if len(self.classes_) == 2:
# for binary classifier, the confidence score is related to
# classes_[1] and therefore should be null.
return rng.randn(_num_samples(X))
else:
return rng.randn(_num_samples(X), len(self.classes_))
| CheckingClassifier.decision_function |
scikit-learn | 19 | sklearn/utils/_mocking.py | def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : array-like of shape (n_samples, n_outputs) or (n_samples,), \
default=None
Target relative to X for classification or regression;
None for unsupervised learning.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
**fit_params : dict of string -> object
Parameters passed to the ``fit`` method of the estimator
Returns
-------
self
"""
| /usr/src/app/target_test_cases/failed_tests_CheckingClassifier.fit.txt | def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training vector, where `n_samples` is the number of samples and
`n_features` is the number of features.
y : array-like of shape (n_samples, n_outputs) or (n_samples,), \
default=None
Target relative to X for classification or regression;
None for unsupervised learning.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
**fit_params : dict of string -> object
Parameters passed to the ``fit`` method of the estimator
Returns
-------
self
"""
assert _num_samples(X) == _num_samples(y)
if self.methods_to_check == "all" or "fit" in self.methods_to_check:
X, y = self._check_X_y(X, y, should_be_fitted=False)
self.n_features_in_ = np.shape(X)[1]
self.classes_ = np.unique(check_array(y, ensure_2d=False, allow_nd=True))
if self.expected_fit_params:
missing = set(self.expected_fit_params) - set(fit_params)
if missing:
raise AssertionError(
f"Expected fit parameter(s) {list(missing)} not seen."
)
for key, value in fit_params.items():
if _num_samples(value) != _num_samples(X):
raise AssertionError(
f"Fit parameter {key} has length {_num_samples(value)}"
f"; expected {_num_samples(X)}."
)
if self.expected_sample_weight:
if sample_weight is None:
raise AssertionError("Expected sample_weight to be passed")
_check_sample_weight(sample_weight, X)
return self
| CheckingClassifier.fit |
scikit-learn | 20 | sklearn/utils/_mocking.py | def predict_proba(self, X):
"""Predict probabilities for each class.
Here, the dummy classifier will provide a probability of 1 for the
first class of `classes_` and 0 otherwise.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
proba : ndarray of shape (n_samples, n_classes)
The probabilities for each sample and class.
"""
| /usr/src/app/target_test_cases/failed_tests_CheckingClassifier.predict_proba.txt | def predict_proba(self, X):
"""Predict probabilities for each class.
Here, the dummy classifier will provide a probability of 1 for the
first class of `classes_` and 0 otherwise.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data.
Returns
-------
proba : ndarray of shape (n_samples, n_classes)
The probabilities for each sample and class.
"""
if self.methods_to_check == "all" or "predict_proba" in self.methods_to_check:
X, y = self._check_X_y(X)
rng = check_random_state(self.random_state)
proba = rng.randn(_num_samples(X), len(self.classes_))
proba = np.abs(proba, out=proba)
proba /= np.sum(proba, axis=1)[:, np.newaxis]
return proba
| CheckingClassifier.predict_proba |
scikit-learn | 21 | sklearn/utils/_mocking.py | def score(self, X=None, Y=None):
"""Fake score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.
Y : array-like of shape (n_samples, n_output) or (n_samples,)
Target relative to X for classification or regression;
None for unsupervised learning.
Returns
-------
score : float
Either 0 or 1 depending of `foo_param` (i.e. `foo_param > 1 =>
score=1` otherwise `score=0`).
"""
| /usr/src/app/target_test_cases/failed_tests_CheckingClassifier.score.txt | def score(self, X=None, Y=None):
"""Fake score.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data, where `n_samples` is the number of samples and
`n_features` is the number of features.
Y : array-like of shape (n_samples, n_output) or (n_samples,)
Target relative to X for classification or regression;
None for unsupervised learning.
Returns
-------
score : float
Either 0 or 1 depending of `foo_param` (i.e. `foo_param > 1 =>
score=1` otherwise `score=0`).
"""
if self.methods_to_check == "all" or "score" in self.methods_to_check:
self._check_X_y(X, Y)
if self.foo_param > 1:
score = 1.0
else:
score = 0.0
return score
| CheckingClassifier.score |