from typing import 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
# TODO(amir): this options should be set globally too
sns.set_style("ticks")
mpl.rcParams["axes.linewidth"] = 2
mpl.rcParams["lines.linewidth"] = 2
# TODO(amir): we can prolly take out the `bar_plot()` into a general pattern
# as part of base-viz functions and just call it; the same pattern gets repeated in glmnet
# or any time we wanna have a horizontal/vertical bar chart
# TODO(amir): for now we ship this; but we gotta come back to this when the main refactor is done
# TODO(amir): add the functionality for vertical plot as well
[docs]def plot_xgb_feature_importance(
feature_importance: pd.DataFrame,
*,
figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = (8, 5),
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 XGBoost feature importance as a bar chart.
Notes
-----
This plotting function can be used along with ``feature_importance_`` attribute of
any of ``XGBoostClassifier``, ``XGBoostCVClassifier``, ``XGBoostRegressor``, or
``XGBoostCVRegressor`` classes.
Parameters
----------
feature importance : pd.DataFrame
Feature importance (``feature_importance_`` attribute)
figsize : tuple, optional
Figure size, by default (8, 5)
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.classification.XGBoostClassifier`
:class:`slickml.classification.XGBoostCVClassifier`
:class:`slickml.regression.XGBoostRegressor`
:class:`slickml.regression.XGBoostCVRegressor`
References
----------
.. [markers-api] https://matplotlib.org/stable/api/markers_api.html
Returns
-------
Figure, optional
"""
check_var(
figsize,
var_name="figsize",
dtypes=tuple,
)
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,
)
if save_path:
check_var(
save_path,
var_name="save_path",
dtypes=str,
)
# TODO(amir): take this out into a utility functions
# prep feature importance
cols = feature_importance.columns.tolist()
coly, colx = cols[0], cols[1]
feature_importance = feature_importance.reindex(
index=range(len(feature_importance) - 1, -1, -1),
)
fig, ax = plt.subplots(
figsize=figsize,
)
ax.hlines(
y=feature_importance[coly],
xmin=0,
xmax=feature_importance[colx],
color=color,
)
ax.plot(
feature_importance[colx],
feature_importance[coly].values,
marker,
markersize=markersize,
markeredgecolor=markeredgecolor,
markerfacecolor=markerfacecolor,
markeredgewidth=markeredgewidth,
)
# find max value, and put importance values on the plot
max_val = feature_importance[colx].max()
for index, value in enumerate(feature_importance[colx]):
ax.text(
value + 0.05 * max_val,
index * 1.01,
f"{value:.2f}",
)
ax.set_xlabel(
f"{' '.join(colx.split('_')).title()}",
fontsize=fontsize,
)
ax.set_ylabel(
f"{coly.title()}",
fontsize=fontsize,
)
ax.set_title(
"Feature Importance",
fontsize=fontsize,
)
ax.set(
xlim=[
None,
feature_importance[colx].max() * 1.13,
],
)
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_xgb_cv_results(
cv_results: pd.DataFrame,
*,
figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = (8, 5),
linestyle: Optional[str] = "--",
train_label: Optional[str] = "Train",
test_label: Optional[str] = "Test",
train_color: Optional[str] = "navy",
train_std_color: Optional[str] = "#B3C3F3",
test_color: Optional[str] = "purple",
test_std_color: Optional[str] = "#D0AAF3",
save_path: Optional[str] = None,
display_plot: Optional[bool] = False,
return_fig: Optional[bool] = False,
) -> Optional[Figure]:
"""Visualizes the cv_results of ``XGBoostCVClassifier``.
Parameters
----------
cv_results : pd.DataFrame
Cross-validation results
figsize : Tuple[Union[int, float], Union[int, float]], optional
Figure size, by default (8, 5)
linestyle : str, optional
Style of lines [linestyles-api]_, by default "--"
train_label : str, optional
Label in the figure legend for the train line, by default "Train"
test_label : str, optional
Label in the figure legend for the test line, by default "Test"
train_color : str, optional
Color of the training line, by default "navy"
train_std_color : str, optional
Color of the edge color of the training std bars, by default "#B3C3F3"
test_color : str, optional
Color of the testing line, by default "purple"
test_std_color : str, optional
Color of the edge color of the testing std bars, by default "#D0AAF3"
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 False
return_fig : bool, optional
Whether to return figure object, by default False
See Also
--------
:class:`slickml.classification.XGBoostCVClassifier`
References
----------
.. [linestyles-api] https://matplotlib.org/stable/gallery/lines_bars_and_markers/linestyles.html
Returns
-------
Figure, optional
"""
check_var(
figsize,
var_name="figsize",
dtypes=tuple,
)
check_var(
linestyle,
var_name="linestyle",
dtypes=str,
)
check_var(
train_label,
var_name="train_label",
dtypes=str,
)
check_var(
test_label,
var_name="test_label",
dtypes=str,
)
check_var(
train_color,
var_name="train_color",
dtypes=str,
)
check_var(
train_std_color,
var_name="train_std_color",
dtypes=str,
)
check_var(
test_color,
var_name="test_color",
dtypes=str,
)
check_var(
test_std_color,
var_name="test_std_color",
dtypes=str,
)
if save_path:
check_var(
save_path,
var_name="save_path",
dtypes=str,
)
# TODO(amir): optimize this part
# update metrics capitalizations for title/labels
metric = cv_results.columns.tolist()[0].split("-")[1]
metrics = [
"AUC",
"AUCPR",
"Error",
"LogLoss",
"MAE",
"RMSE",
"RMSLE",
]
for m in metrics:
if m.lower() == metric:
metric = m
fig, ax = plt.subplots(
figsize=figsize,
)
ax.errorbar(
x=range(cv_results.shape[0]),
y=cv_results.iloc[:, 0],
yerr=cv_results.iloc[:, 1],
fmt=linestyle,
ecolor=train_std_color,
c=train_color,
label=train_label,
)
ax.errorbar(
x=range(cv_results.shape[0]),
y=cv_results.iloc[:, 2],
yerr=cv_results.iloc[:, 3],
fmt=linestyle,
ecolor=test_std_color,
c=test_color,
label=test_label,
)
ax.set_xlabel(
"# of Boosting Rounds",
fontsize=12,
)
ax.set_ylabel(
f"""{metric}""",
fontsize=12,
)
ax.set_title(
f"""{metric} Evolution vs Boosting Rounds""",
fontsize=12,
)
ax.tick_params(
axis="both",
which="major",
labelsize=12,
)
ax.legend(
loc=0,
prop={
"size": 12,
},
framealpha=0.0,
)
if save_path:
plt.savefig(
save_path,
bbox_inches="tight",
dpi=200,
)
if display_plot:
plt.show()
if return_fig:
return fig
return None