from typing import Any, Dict, Optional, Tuple
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import scipy as scp
import seaborn as sns
from matplotlib.figure import Figure
from sklearn.metrics import accuracy_score
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): globally adjust options, fonts, ...
[docs]def plot_binary_classification_metrics(
figsize: Optional[Tuple[float, float]] = (12, 12),
save_path: Optional[str] = None,
display_plot: Optional[bool] = False,
return_fig: Optional[bool] = False,
**kwargs: Dict[str, Any],
) -> Optional[Figure]:
"""Visualizes binary classification metrics using ``plotting_dict_`` attribute of ``BinaryClassificationMetrics``.
Parameters
----------
figsize : Tuple[float, float], optional
Figure size, by default (12, 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 False
return_fig : bool, optional
Whether to return figure object, by default False
**kwargs : Dict[str, Any]
Key-value pairs of regression metrics plot
Returns
-------
Figure, optional
"""
check_var(
figsize,
var_name="figsize",
dtypes=tuple,
)
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): move this to a function ?
# prepare thresholds for plotting
thr_set1 = np.arange(
min(kwargs["roc_thresholds"]),
max(kwargs["roc_thresholds"]),
0.01,
)
thr_set2 = np.arange(
min(kwargs["pr_thresholds"]),
max(kwargs["pr_thresholds"]),
0.01,
)
f1_list = [
2
* (kwargs["precision_list"][i] * kwargs["recall_list"][i]) # type: ignore
/ (kwargs["precision_list"][i] + kwargs["recall_list"][i]) # type: ignore
for i in range(len(kwargs["precision_list"]))
]
queue_rate_list = [(kwargs["y_pred_proba"] >= thr).mean() for thr in thr_set2]
accuracy_list = [
accuracy_score(kwargs["y_true"], (kwargs["y_pred_proba"] >= thr).astype(int))
for thr in thr_set1
]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(
nrows=2,
ncols=2,
figsize=figsize,
)
# -----------------------------------
# subplot 1: roc curve
ax1.plot(
kwargs["fpr_list"],
kwargs["tpr_list"],
color="red",
label=f"AUC = {kwargs['auc_roc']:.3f}",
)
ax1.plot(
kwargs["fpr_list"][kwargs["youden_index"]], # type: ignore
kwargs["tpr_list"][kwargs["youden_index"]], # type: ignore
marker="o",
color="navy",
ms=10,
)
ax1.set(
xlim=[-0.01, 1.01],
ylim=[-0.01, 1.01],
xlabel="1 - Specificity",
ylabel="Sensitivity",
title="ROC Curve",
)
ax1.tick_params(
axis="both",
which="major",
labelsize=12,
)
ax1.legend(
prop={"size": 12},
loc=0,
framealpha=0.0,
)
ax1.annotate(
f"Threshold = {kwargs['youden_threshold']:.3f}",
xy=(
kwargs["fpr_list"][kwargs["youden_index"]], # type: ignore
kwargs["tpr_list"][kwargs["youden_index"]], # type: ignore
),
xycoords="data",
xytext=(
kwargs["fpr_list"][kwargs["youden_index"]] + 0.4, # type: ignore
kwargs["tpr_list"][kwargs["youden_index"]] - 0.4, # type: ignore
),
arrowprops=dict(
facecolor="black",
shrink=0.05,
),
horizontalalignment="right",
verticalalignment="bottom",
)
# -----------------------------------
# subplot 2: preferred scores vs thresholds
ax2.plot(
kwargs["roc_thresholds"],
1 - kwargs["fpr_list"], # type: ignore
label="Specificity",
)
ax2.plot(
kwargs["roc_thresholds"],
kwargs["tpr_list"],
label="Sensitivity",
)
ax2.plot(
thr_set1,
accuracy_list,
label="Accuracy",
)
ax2.set(
xlim=[-0.01, 1.01],
ylim=[-0.01, 1.01],
xlabel="Threshold",
ylabel="Score",
title="Preferred Scores vs Thresholds",
)
ax2.tick_params(
axis="both",
which="major",
labelsize=12,
)
ax2.legend(
bbox_to_anchor=(1.2, 0.5),
loc="center",
ncol=1,
framealpha=0.0,
)
ax2.axvline(
kwargs["sens_spec_threshold"],
color="k",
ls="--",
)
if isinstance(kwargs["sens_spec_threshold"], float) and kwargs["sens_spec_threshold"] <= 0.5:
ax2.annotate(
f"Threshold = {kwargs['sens_spec_threshold']:.3f}",
xy=(kwargs["sens_spec_threshold"], 0.05),
xycoords="data",
xytext=(kwargs["sens_spec_threshold"] + 0.1, 0.05), # type: ignore
arrowprops=dict(facecolor="black", shrink=0.05),
horizontalalignment="left",
verticalalignment="bottom",
)
else:
ax2.annotate(
f"Threshold = {kwargs['sens_spec_threshold']:.3f}",
xy=(kwargs["sens_spec_threshold"], 0.05),
xycoords="data",
xytext=(kwargs["sens_spec_threshold"] - 0.4, 0.05), # type: ignore
arrowprops=dict(facecolor="black", shrink=0.05),
horizontalalignment="left",
verticalalignment="bottom",
)
# -----------------------------------
# subplot 3: precision-recall curve
ax3.plot(
kwargs["recall_list"],
kwargs["precision_list"],
color="red",
label=f"PR AUC ={kwargs['auc_pr']:.3f}",
)
ax3.plot(
kwargs["recall_list"][kwargs["prec_rec_index"]], # type: ignore
kwargs["precision_list"][kwargs["prec_rec_index"]], # type: ignore
marker="o",
color="navy",
ms=10,
)
ax3.axvline(
x=kwargs["recall_list"][kwargs["prec_rec_index"]], # type: ignore
ymin=kwargs["recall_list"][kwargs["prec_rec_index"]], # type: ignore
ymax=kwargs["precision_list"][kwargs["prec_rec_index"]], # type: ignore
color="navy",
ls="--",
)
ax3.set(
xlim=[-0.01, 1.01],
ylim=[-0.01, 1.01],
xlabel="Recall",
ylabel="Precision",
title="Precision-Recall Curve",
)
ax3.legend(
prop={"size": 12},
loc=0,
framealpha=0.0,
)
ax3.tick_params(
axis="both",
which="major",
labelsize=12,
)
ax3.annotate(
f"Threshold = {kwargs['prec_rec_threshold']:.3f}",
xy=(
kwargs["recall_list"][kwargs["prec_rec_index"]], # type: ignore
kwargs["precision_list"][kwargs["prec_rec_index"]], # type: ignore
),
xycoords="data",
xytext=(
kwargs["recall_list"][kwargs["prec_rec_index"]] - 0.4, # type: ignore
kwargs["precision_list"][kwargs["prec_rec_index"]] - 0.4, # type: ignore
),
arrowprops=dict(facecolor="black", shrink=0.05),
horizontalalignment="left",
verticalalignment="bottom",
)
# -----------------------------------
# subplot 4: preferred Scores vs Thresholds
ax4.plot(
kwargs["pr_thresholds"],
kwargs["precision_list"][1:], # type: ignore
label="Precision",
)
ax4.plot(
kwargs["pr_thresholds"],
kwargs["recall_list"][1:], # type: ignore
label="Recall",
)
ax4.plot(
kwargs["pr_thresholds"],
f1_list[1:],
label="F1-Score",
)
ax4.plot(
thr_set2,
queue_rate_list,
label="Queue Rate",
)
ax4.set(
xlim=[-0.01, 1.01],
ylim=[-0.01, 1.01],
xlabel="Threshold",
ylabel="Score",
title="Preferred Scores vs Thresholds",
)
ax4.tick_params(
axis="both",
which="major",
labelsize=12,
)
ax4.legend(
bbox_to_anchor=(1.2, 0.5),
loc="center",
ncol=1,
framealpha=0.0,
)
ax4.axvline(
kwargs["prec_rec_threshold"],
color="k",
ls="--",
)
if isinstance(kwargs["prec_rec_threshold"], float) and kwargs["prec_rec_threshold"] <= 0.5:
ax4.annotate(
f"Threshold = {kwargs['prec_rec_threshold']:.3f}",
xy=(kwargs["prec_rec_threshold"], 0.03),
xycoords="data",
xytext=(kwargs["prec_rec_threshold"] + 0.1, 0.03), # type: ignore
arrowprops=dict(facecolor="black", shrink=0.05),
horizontalalignment="left",
verticalalignment="bottom",
)
else:
ax4.annotate(
f"Threshold = {kwargs['prec_rec_threshold']:.3f}",
xy=(kwargs["prec_rec_threshold"], 0.03),
xycoords="data",
xytext=(kwargs["prec_rec_threshold"] - 0.4, 0.03), # type: ignore
arrowprops=dict(facecolor="black", shrink=0.05),
horizontalalignment="left",
verticalalignment="bottom",
)
if save_path:
plt.savefig(
save_path,
bbox_inches="tight",
dpi=200,
)
if display_plot:
plt.show()
if return_fig:
return fig
return None
# TODO(amir): standardize all `plotting` params and options
# currently, lots of options are duplicated; this can be done globally
# TODO(amir): we are exposing this function in public API, therefore we need to probably expose
# `kwargs` values; unless this function wont be useful; we can also make this private
# TODO(amir): I think it would make sense to return `fig` so it can be tested
# TODO(amir): there are warnings on seaborn regarding passing positional args instead of kwargs
[docs]def plot_regression_metrics(
figsize: Optional[Tuple[float, float]] = (12, 16),
save_path: Optional[str] = None,
display_plot: Optional[bool] = False,
return_fig: Optional[bool] = False,
**kwargs: Dict[str, Any],
) -> Figure:
"""Visualizes regression metrics using ``plotting_dict_`` attribute of ``RegressionMetrics``.
Parameters
----------
figsize : Tuple[float, float], optional
Figure size, by default (12, 16)
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
**kwargs : Dict[str, Any]
Key-value pairs of regression metrics plot
Returns
-------
Figure, optional
"""
check_var(
figsize,
var_name="figsize",
dtypes=tuple,
)
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,
)
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(
nrows=3,
ncols=2,
figsize=figsize,
)
# subplot 1: actual vs predicted
sns.regplot(
x=kwargs["y_true"],
y=kwargs["y_pred"],
marker="o",
scatter_kws={
"edgecolors": "navy",
},
color="#B3C3F3",
fit_reg=False,
ax=ax1,
)
ax1.plot(
[
kwargs["y_true"].min(), # type: ignore
kwargs["y_true"].max(), # type: ignore
],
[
kwargs["y_true"].min(), # type: ignore
kwargs["y_true"].max(), # type: ignore
],
"r--",
lw=3,
)
ax1.set(
xlabel="Actual Values",
ylabel="Predicted Values",
title="Actual-Predicted",
)
ax1.tick_params(
axis="both",
which="major",
labelsize=12,
)
ax1.text(
0.05,
0.93,
f"MAPE = {kwargs['mape']:.3f}",
fontsize=12,
transform=ax1.transAxes,
)
ax1.text(
0.05,
0.86,
f"$R^2$ = {kwargs['r2']:.3f}",
fontsize=12,
transform=ax1.transAxes,
)
# -----------------------------------
# subplot 2: Q-Q Normal Plot
scp.stats.probplot(
kwargs["y_residual"],
fit=True,
dist="norm",
plot=ax2,
)
ax2.get_lines()[0].set_marker("o")
ax2.get_lines()[0].set_markerfacecolor("#B3C3F3")
ax2.get_lines()[0].set_markeredgecolor("navy")
ax2.get_lines()[0].set_markersize(6.0)
ax2.get_lines()[1].set_linewidth(3.0)
ax2.get_lines()[1].set_linestyle("--")
ax2.set(
xlabel="Quantiles",
ylabel="Residuals",
title="Q-Q",
)
ax2.tick_params(
axis="both",
which="major",
labelsize=12,
)
# -----------------------------------
# subplot 3: Residuals vs Fitted
sns.residplot(
x=kwargs["y_pred"],
y=kwargs["y_true"],
lowess=True,
order=1,
line_kws={
"color": "red",
"lw": 3,
"ls": "--",
"alpha": 1,
},
scatter_kws={
"edgecolors": "navy",
},
color="#B3C3F3",
robust=True,
ax=ax3,
)
ax3.set(
xlabel="Predicted Values",
ylabel="Residuals",
title="Residuals-Predicted",
)
ax3.tick_params(
axis="both",
which="major",
labelsize=12,
)
# -----------------------------------
# subplot 4: Sqrt Standard Residuals vs Fitted
sns.regplot(
x=kwargs["y_pred"],
y=kwargs["y_residual_normsq"],
lowess=True,
line_kws={
"color": "red",
"lw": 3,
"ls": "--",
"alpha": 1,
},
scatter_kws={"edgecolors": "navy"},
color="#B3C3F3",
ax=ax4,
)
ax4.set(
xlabel="Predicted Values",
ylabel="Standardized Residuals Norm",
title="Scale-Location",
)
ax4.tick_params(
axis="both",
which="major",
labelsize=12,
)
# -----------------------------------
# subplot 5: Histogram of Coeff. of Variations
freqs, _, _ = ax5.hist(
kwargs["y_ratio"],
histtype="bar",
bins=np.arange(0.75, 1.25, 0.01),
alpha=1.0,
color="#B3C3F3",
edgecolor="navy",
)
ax5.set_xticks(
[
0.6,
0.7,
0.8,
0.9,
1.0,
1.1,
1.2,
1.3,
1.4,
],
)
ax5.set_xticklabels(
[
"Less",
0.7,
0.8,
0.9,
1.0,
1.1,
1.2,
1.3,
"More",
],
rotation=30,
)
ax5.set(
ylabel="Frequency",
title="Prediction Variation",
)
ax5.tick_params(
axis="both",
which="major",
labelsize=12,
)
ax5_ylim = max(freqs)
ax5.text(
0.65,
ax5_ylim,
rf"""$\mu$ = {kwargs['mean_y_ratio']:.3f}""",
fontsize=12,
)
ax5.text(
0.65,
0.93 * ax5_ylim,
f"CV = {kwargs['cv_y_ratio']:.3f}",
fontsize=12,
)
# -----------------------------------
# subplot 6: REC
ax6.plot(
kwargs["deviation"],
kwargs["accuracy"],
color="red",
label=f"AUC = {kwargs['auc_rec']:.3f}",
)
ax6.set(
xlim=[-0.01, 1.01],
ylim=[-0.01, 1.01],
xlabel="Deviation",
ylabel="Accuracy",
title="REC Curve",
)
ax6.tick_params(
axis="both",
which="major",
labelsize=12,
)
ax6.legend(
prop={"size": 12},
loc=4,
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
# TODO(amir): investigate the options to return axes as well if needed
# TODO(amir): investigate a better option for `plt.show()`; currently, no matter what the
# figure is being shown when returning `fig`; what would be a global pattern here that can be
# tested as well? Should we sacrifice the testing part here?
return None