from typing import Any, Dict, Optional, Tuple, Union
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from matplotlib.figure import Figure
from slickml.utils import check_var
sns.set_style("ticks")
mpl.rcParams["axes.linewidth"] = 2
mpl.rcParams["lines.linewidth"] = 2
# TODO(amir): take out the bar chart function into `_base.py` plotting
# technically, this is the same as `plot_xgb_feature_importance()`
# and call it for feature frequency and feature importance
[docs]def plot_xfs_feature_frequency(
freq: pd.DataFrame,
*,
figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = (8, 4),
show_freq_pct: Optional[bool] = True,
color: Optional[str] = "#87CEEB",
marker: Optional[str] = "o",
markersize: Optional[Union[int, float]] = 10,
markeredgecolor: Optional[str] = "#1F77B4",
markerfacecolor: Optional[str] = "#1F77B4",
markeredgewidth: Optional[Union[int, float]] = 1,
fontsize: Optional[Union[int, float]] = 12,
save_path: Optional[str] = None,
display_plot: Optional[bool] = True,
return_fig: Optional[bool] = False,
) -> Optional[Figure]:
"""Visualizes the selected features frequency as a bar chart.
Notes
-----
This plotting function can be used along with ``feature_frequency_`` attribute of any
frequency-based feature selection algorithm such as ``XGBoostFeatureSelector``.
Parameters
----------
feature importance : pd.DataFrame
Feature importance (``feature_frequency_`` attribute)
figsize : tuple, optional
Figure size, by default (8, 4)
show_freq_pct : bool, optional
Whether to show the features frequency in percent, by default True
color : str, optional
Color of the horizontal lines of lollipops, by default "#87CEEB"
marker : str, optional
Marker style of the lollipops. More valid marker styles can be found at [markers-api]_, by default "o"
markersize : Union[int, float], optional
Markersize, by default 10
markeredgecolor : str, optional
Marker edge color, by default "#1F77B4"
markerfacecolor : str, optional
Marker face color, by defualt "#1F77B4"
markeredgewidth : Union[int, float], optional
Marker edge width, by default 1
fontsize : Union[int, float], optional
Fontsize for xlabel and ylabel, and ticks parameters, by default 12
save_path : str, optional
The full or relative path to save the plot including the image format such as
"myplot.png" or "../../myplot.pdf", by default None
display_plot : bool, optional
Whether to show the plot, by default True
return_fig : bool, optional
Whether to return figure object, by default False
See Also
--------
:class:`slickml.selection.XGBoostFeatureSelector`
References
----------
.. [markers-api] https://matplotlib.org/stable/api/markers_api.html
Returns
-------
Figure, optional
"""
check_var(
figsize,
var_name="figsize",
dtypes=tuple,
)
check_var(
show_freq_pct,
var_name="show_freq_pct",
dtypes=bool,
)
check_var(
color,
var_name="color",
dtypes=str,
)
check_var(
marker,
var_name="marker",
dtypes=str,
)
check_var(
markersize,
var_name="markersize",
dtypes=(float, int),
)
check_var(
markeredgecolor,
var_name="markeredgecolor",
dtypes=str,
)
check_var(
markerfacecolor,
var_name="markerfacecolor",
dtypes=str,
)
check_var(
markeredgewidth,
var_name="markeredgewidth",
dtypes=(int, float),
)
check_var(
fontsize,
var_name="fontsize",
dtypes=(int, float),
)
check_var(
display_plot,
var_name="display_plot",
dtypes=bool,
)
check_var(
return_fig,
var_name="return_fig",
dtypes=bool,
)
# choose whether the feature frequency is being plotted by count or percentage
if show_freq_pct:
col = "Frequency (%)"
else:
col = "Frequency"
# reindex freq
freq = freq.reindex(
index=range(len(freq) - 1, -1, -1),
)
fig, ax = plt.subplots(figsize=figsize)
# TODO(amir): add vline option too ?
ax.hlines(
y=freq["Feature"],
xmin=0,
xmax=freq[col],
color=color,
)
ax.plot(
freq[col],
freq["Feature"].values,
marker,
markersize=markersize,
markeredgecolor=markeredgecolor,
markerfacecolor=markerfacecolor,
markeredgewidth=markeredgewidth,
)
ax.set_xlabel(
f"{col}",
fontsize=fontsize,
)
ax.set_ylabel(
"Feature",
fontsize=fontsize,
)
ax.set_title(
"Selected Features Frequency",
fontsize=fontsize,
)
ax.tick_params(
axis="both",
which="major",
labelsize=fontsize,
)
if save_path:
plt.savefig(
save_path,
bbox_inches="tight",
dpi=200,
)
if display_plot:
plt.show()
if return_fig:
return fig
return None
[docs]def plot_xfs_cv_results(
*,
figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = (10, 8),
internalcvcolor: Optional[str] = "#4169E1",
externalcvcolor: Optional[str] = "#8A2BE2",
sharex: Optional[bool] = False,
sharey: Optional[bool] = False,
save_path: Optional[str] = None,
display_plot: Optional[bool] = True,
return_fig: Optional[bool] = False,
**kwargs: Dict[str, Any],
) -> Optional[Figure]:
"""Visualizies the cross-validation results of ``XGBoostFeatureSelector``.
Notes
-----
It visualizes the internal and external cross-validiation performance during the selection
process. The `internal` refers to the performance of the train/test folds during the ``xgboost.cv()``
using ``metrics`` rounds to help the best number of boosting round while the `external` refers to
the performance of ``xgboost.train()`` based on watchlist using ``eval_metric``. Additionally,
`sns.distplot` previously was used which is now deprecated. More details in [seaborn-distplot-deprecation]_.
Parameters
----------
figsize : tuple, optional
Figure size, by default (10, 8)
internalcvcolor : str, optional
Color of the histograms for internal cv results, by default "#4169E1"
externalcvcolor : str, optional
Color of the histograms for external cv results, by default "#8A2BE2"
sharex : bool, optional
Whether to share "X" axis for each column of subplots, by default False
sharey : bool, optional
Whether to share "Y" axis for each row of subplots, by default False
save_path : str, optional
The full or relative path to save the plot including the image format such as
"myplot.png" or "../../myplot.pdf", by default None
display_plot : bool, optional
Whether to show the plot, by default True
return_fig : bool, optional
Whether to return figure object, by default False
kwargs : Dict[str, Any]
Required plooting elements (``plotting_cv_`` attribute of ``XGBoostFeatureSelector``)
See Also
--------
:class:`slickml.selection.XGBoostFeatureSelector`
Refereces
---------
.. [seaborn-distplot-deprecation] https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
Returns
-------
Figure, optional
"""
check_var(
figsize,
var_name="figsize",
dtypes=tuple,
)
check_var(
internalcvcolor,
var_name="internalcvcolor",
dtypes=str,
)
check_var(
externalcvcolor,
var_name="externalcvcolor",
dtypes=str,
)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(
nrows=2,
ncols=2,
figsize=figsize,
sharex=sharex,
sharey=sharey,
)
sns.histplot(
data=kwargs["int_cv_train"],
color=internalcvcolor,
ax=ax1,
stat="density",
kde=True,
kde_kws={
"cut": 3,
},
alpha=0.4,
edgecolor=(1, 1, 1, 0.4),
)
sns.histplot(
data=kwargs["int_cv_test"],
color=internalcvcolor,
ax=ax2,
stat="density",
kde=True,
kde_kws={
"cut": 3,
},
alpha=0.4,
edgecolor=(1, 1, 1, 0.4),
)
sns.histplot(
data=kwargs["ext_cv_train"],
color=externalcvcolor,
ax=ax3,
stat="density",
kde=True,
kde_kws={
"cut": 3,
},
alpha=0.4,
edgecolor=(1, 1, 1, 0.4),
)
sns.histplot(
data=kwargs["ext_cv_test"],
color=externalcvcolor,
ax=ax4,
stat="density",
kde=True,
kde_kws={
"cut": 3,
},
alpha=0.4,
edgecolor=(1, 1, 1, 0.4),
)
ax1.set(
title=f"Internal {kwargs['n_splits']}-Folds CV {kwargs['metric']} - Train",
)
ax2.set(
title=f"Internal {kwargs['n_splits']}-Folds CV {kwargs['metric']} - Test",
)
ax3.set(
title=f"External {kwargs['n_splits']}-Folds CV {kwargs['eval_metric']} - Train",
)
ax4.set(
title=f"External {kwargs['n_splits']}-Folds CV {kwargs['eval_metric']} - Test",
)
if save_path:
plt.savefig(
save_path,
bbox_inches="tight",
dpi=200,
)
if display_plot:
plt.show()
if return_fig:
return fig
return None