import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import geopandas as gpd
import seaborn as sns
sns.set_palette('colorblind')
import numpy as np
import os, sys
from sklearn import metrics
from copro import evaluation
[docs]def selected_polygons(polygon_gdf, **kwargs):
"""Creates a plotting instance of the boundaries of all selected polygons.
Args:
polygon_gdf (geo-dataframe): geo-dataframe containing the selected polygons.
Kwargs:
Geopandas-supported keyword arguments.
Returns:
ax: Matplotlib axis object.
"""
ax = polygon_gdf.boundary.plot(**kwargs)
return ax
[docs]def selected_conflicts(conflict_gdf, **kwargs):
"""Creates a plotting instance of the best casualties estimates of the selected conflicts.
Args:
conflict_gdf (geo-dataframe): geo-dataframe containing the selected conflicts.
Kwargs:
Geopandas-supported keyword arguments.
Returns:
ax: Matplotlib axis object.
"""
ax = conflict_gdf.plot(column='best', **kwargs)
return ax
[docs]def metrics_distribution(out_dict, metrics, **kwargs):
"""Plots the value distribution of a range of evaluation metrics based on all model simulations.
Args:
out_dict (dict): dictionary containing metrics score for various metrics and all simulation.
Kwargs:
Matplotlib-supported keyword arguments.
Returns:
ax: Matplotlib axis object.
"""
fig, ax = plt.subplots(1, 1, **kwargs)
for metric, color in zip(metrics, sns.color_palette('colorblind')):
sns.histplot(out_dict[str(metric)], ax=ax, kde=True, stat='density', color=color, label=str(metric))
plt.legend()
return ax
[docs]def correlation_matrix(df, **kwargs):
"""Plots the correlation matrix of a dataframe.
Args:
df (dataframe): dataframe containing columns to be correlated.
Kwargs:
Seaborn-supported keyword arguments.
Returns:
ax: Matplotlib axis object.
"""
df_corr = evaluation.calc_correlation_matrix(df)
ax = sns.heatmap(df_corr, **kwargs)
return ax
[docs]def plot_ROC_curve_n_times(ax, clf, X_test, y_test, tprs, aucs, mean_fpr, **kwargs):
"""Plots the ROC-curve per model simulation to a pre-initiated matplotlib-instance.
Args:
ax (axis): axis of pre-initaited matplotlib-instance
clf (classifier): sklearn-classifier used in the simulation.
X_test (array): array containing test-sample variable values.
y_test (list): list containing test-sample conflict data.
tprs (list): list with false positive rates.
aucs (list): list with area-under-curve values.
mean_fpr (array): array with mean false positive rate.
Returns:
list: lists with true positive rates and area-under-curve values per plot.
"""
viz = metrics.plot_roc_curve(clf, X_test, y_test, ax=ax,
alpha=0.15, color='b', lw=1, label=None, **kwargs)
interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
interp_tpr[0] = 0.0
tprs.append(interp_tpr)
aucs.append(viz.roc_auc)
return tprs, aucs
[docs]def plot_ROC_curve_n_mean(ax, tprs, aucs, mean_fpr, **kwargs):
"""Plots the mean ROC-curve to a pre-initiated matplotlib-instance.
Args:
ax (axis): axis of pre-initaited matplotlib-instance
tprs (list): list with false positive rates.
aucs (list): list with area-under-curve values.
mean_fpr (array): array with mean false positive rate.
"""
mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0
mean_auc = metrics.auc(mean_fpr, mean_tpr)
std_auc = np.std(aucs)
ax.plot(mean_fpr, mean_tpr, color='r',
label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
lw=2, alpha=.8, **kwargs)
std_tpr = np.std(tprs, axis=0)
tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2, label=None, **kwargs)
ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], **kwargs)
ax.legend(loc="lower right")