Github Blog_Code/binary_plots.py at master · apwheele/Blog_Code · GitHub

''' Binary plot helper functions for ROC curve and calibration charts Andy Wheeler [email protected] andrewpwheeler.com '''

import pandas as pd import numpy as np from sklearn.metrics import roc_auc_score, roc_curve import matplotlib import matplotlib.pyplot as plt import seaborn as sns

############################################################### #CALIBRATION

#See below for explanation #https://andrewpwheeler.com/2020/07/04/adjusting-predicted-probabilities-for-sampling/

#Rebalancing function, rewrite from #https://github.com/matloff/regtools/blob/master/inst/UnbalancedClasses.md #and https://www.listendata.com/2015/04/oversampling-for-rare-event.htm def classadjust(condprobs,wrongprob,trueprob): a = condprobs/(wrongprob/trueprob) comp_cond = 1 - condprobs comp_wrong = 1 - wrongprob comp_true = 1 - trueprob b = comp_cond/(comp_wrong/comp_true) return a/(a+b)

#Calibration Chart def cal_data(prob, true, data, bins, plot=False, figsize=(6,4), save_plot=False): cal_dat = data[[prob,true]].copy() cal_dat['Count'] = 1 cal_dat['Bin'] = pd.qcut(cal_dat[prob], bins, range(bins) ).astype(int) + 1 agg_bins = cal_dat.groupby('Bin', as_index=False)['Count',prob,true].sum() agg_bins['Predicted'] = agg_bins[prob]/agg_bins['Count'] agg_bins['Actual'] = agg_bins[true]/agg_bins['Count'] if plot: fig, ax = plt.subplots(figsize=figsize) ax.plot(agg_bins['Bin'], agg_bins['Predicted'], marker='+', label='Predicted') ax.plot(agg_bins['Bin'], agg_bins['Actual'], marker='o', markeredgecolor='w', label='Actual') ax.set_ylabel('Probability') ax.legend(loc='upper left') ax.set_xlim([0,bins+1]) plt.xticks(None) plt.tick_params(length=0) if save_plot: plt.savefig(save_plot, dpi=500, bbox_inches='tight') plt.show() return agg_bins

# Calibration chart by groups (long format, seaborn small multiple) def cal_data_group(prob, true, group, data, bins, plot=False, wrap_col=3, sns_height=4, save_plot=False): cal_dat = data[[prob,true,group]].copy() cal_dat['Count'] = 1 cal_dat['Bin'] = (cal_dat.groupby(group,as_index=False)[prob] ).transform( lambda x: pd.qcut(x, bins, labels=range(bins)) ).astype(int) + 1 agg_bins = cal_dat.groupby([group,'Bin'], as_index=False)['Count',prob,true].sum() agg_bins['Predicted'] = agg_bins[prob]/agg_bins['Count'] agg_bins['Actual'] = agg_bins[true]/agg_bins['Count'] agg_long = pd.melt(agg_bins, id_vars=['Bin',group], value_vars=['Predicted','Actual'], var_name='Type', value_name='Probability') if plot: d = {'marker': ['o','X']} ax = sns.FacetGrid(data=agg_long, col=group, hue='Type', hue_kws=d, col_wrap=wrap_col, despine=False, height=sns_height) ax.map(plt.plot, 'Bin', 'Probability', markeredgecolor="w") ax.set_titles("{col_name}") ax.set_xlabels("") ax.set_xticklabels("") ax.axes[0].legend(loc='upper left') # Setting xlim in FacetGrid not behaving how I want for a in ax.axes: a.set_xlim([0,bins+1]) a.tick_params(length=0) if save_plot: plt.savefig(save_plot, dpi=500, bbox_inches='tight') plt.show() return agg_bins # Calibration chart by multiple variables (wide format) def cal_data_wide(probs, true, data, bins, plot=False, wrap_col=3, sns_height=4, save_plot=False): cal_dat = data[ probs + [true]].copy() cal_dat = pd.melt(cal_dat, id_vars=true, value_vars=probs, var_name='Group', value_name='prob') cal_dat['Count'] = 1 cal_dat['Bin'] = (cal_dat.groupby('Group',as_index=False)['prob'] ).transform( lambda x: pd.qcut(x, bins, labels=range(bins)) ).astype(int) + 1 agg_bins = cal_dat.groupby(['Group','Bin'], as_index=False)['Count','prob',true].sum() agg_bins['Predicted'] = agg_bins['prob']/agg_bins['Count'] agg_bins['Actual'] = agg_bins[true]/agg_bins['Count'] agg_long = pd.melt(agg_bins, id_vars=['Bin','Group'], value_vars=['Predicted','Actual'], var_name='Type', value_name='Probability') if plot: d = {'marker': ['o','X']} ax = sns.FacetGrid(data=agg_long, col='Group', hue='Type', hue_kws=d, col_wrap=wrap_col, despine=False, height=sns_height) ax.map(plt.plot, 'Bin', 'Probability', markeredgecolor="w") ax.set_titles("{col_name}") ax.set_xlabels("") ax.set_xticklabels("") ax.axes[0].legend(loc='upper left') # Setting xlim in FacetGrid not behaving how I want for a in ax.axes: a.set_xlim([0,bins+1]) a.tick_params(length=0) if save_plot: plt.savefig(save_plot, dpi=500, bbox_inches='tight') plt.show() return agg_bins

# Calibration chart, multiple variables and by sub-group def cal_data_wide_group(probs, true, group, data, bins, plot=False, wrap_col=3, sns_height=3, font_title=12, save_plot=False): cal_dat = data[ probs + [true, group]].copy() cal_dat = pd.melt(cal_dat, id_vars=[true,group], value_vars=probs, var_name='Group', value_name='prob') cal_dat['Count'] = 1 cal_dat['Bin'] = (cal_dat.groupby([group, 'Group'],as_index=False)['prob'] ).transform( lambda x: pd.qcut(x, bins, labels=range(bins)) ).astype(int) + 1 agg_bins = cal_dat.groupby([group,'Group','Bin'], as_index=False)['Count','prob',true].sum() agg_bins['Predicted'] = agg_bins['prob']/agg_bins['Count'] agg_bins['Actual'] = agg_bins[true]/agg_bins['Count'] agg_long = pd.melt(agg_bins, id_vars=['Bin',group,'Group'], value_vars=['Predicted','Actual'], var_name='Type', value_name='Probability') agg_long['CombFacet'] = agg_long[group].astype(str) + " | " + agg_long['Group'].str.strip() agg_long.sort_values(by=['Group',group], inplace=True) agg_long.reset_index(drop=True, inplace=True) if plot: d = {'marker': ['o','X']} ax = sns.FacetGrid(data=agg_long, col='CombFacet', hue='Type', hue_kws=d, col_wrap=wrap_col, despine=False, height=sns_height) ax.map(plt.plot, 'Bin', 'Probability', markeredgecolor="w") ax.set_titles(col_template="{col_name}",size=font_title) ax.set_xlabels("") ax.set_xticklabels("") ax.axes[0].legend(loc='upper left') #Setting xlim in FacetGrid not behaving how I want for a in ax.axes: a.set_xlim([0,bins+1]) a.tick_params(length=0) if save_plot: plt.savefig(save_plot, dpi=500, bbox_inches='tight') plt.show() return agg_bins

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############################################################### #MATPLOTLIB THEME

#https://andrewpwheeler.com/2020/05/05/notes-on-matplotlib-and-seaborn-charts-python/

andy_theme = {'axes.grid': True, 'grid.linestyle': '--', 'legend.framealpha': 1, 'legend.facecolor': 'white', 'legend.shadow': True, 'legend.fontsize': 14, 'legend.title_fontsize': 16, 'xtick.labelsize': 14, 'ytick.labelsize': 14, 'axes.labelsize': 16, 'axes.titlesize': 20, 'figure.dpi': 100} #print( matplotlib.rcParams ) matplotlib.rcParams.update(andy_theme)

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############################################################### #AUC CHARTS

#AUC data plot in wide format, y_scores needs to be a list #Of the variables in the dataframe def auc_plot(data, y_true, y_scores, figsize=(6,6), save_plot=False, leg_size=None): fin_dat = [] fig, ax = plt.subplots(figsize=figsize) #Equality line plt.plot([0, 1], [0, 1], color='grey', lw=1, linestyle='-') plt.xlim([-0.03, 1.03]) plt.ylim([-0.03, 1.03]) tick_num = [i/10 for i in range(11)] plt.xticks(tick_num) plt.yticks(tick_num) plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") ax.set_aspect(aspect='equal') for s in y_scores: fpr, tpr, thr = roc_curve(data[y_true], data[s]) sub_dat = pd.DataFrame( zip(fpr, tpr, thr), columns=['FPR','TPR','Thresh'] ) sub_dat['Var'] = s auc_val = roc_auc_score(data[y_true], data[s]) sub_dat['AUC'] = auc_val fin_dat.append(sub_dat) ax.plot(fpr, tpr, lw=2, label=f'{s} (AUC={auc_val:0.2f})') if len(y_scores) == 1: plt.title(f'{y_scores[0]} (AUC={auc_val:0.2f})') else: plt.title('ROC Curves & AUC Scores') ax.legend(loc='lower right', fontsize=leg_size) if save_plot: plt.savefig(save_plot, dpi=500, bbox_inches='tight') plt.show() fin_df = pd.concat(fin_dat, axis=0) return fin_df

#AUC data plot in long format def auc_plot_long(data, y_true, y_score, group, figsize=(6,6), save_plot=False, leg_size=None): fin_dat = [] fig, ax = plt.subplots(figsize=figsize) #Equality line plt.plot([0, 1], [0, 1], color='grey', lw=1, linestyle='-') plt.xlim([-0.03, 1.03]) plt.ylim([-0.03, 1.03]) tick_num = [i/10 for i in range(11)] plt.xticks(tick_num) plt.yticks(tick_num) plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") ax.set_aspect(aspect='equal') grp_list = pd.unique(data[group]) for s in grp_list: subset = data[ data[group] == s ] fpr, tpr, thr = roc_curve(subset[y_true], subset[y_score]) sub_dat = pd.DataFrame( zip(fpr, tpr, thr), columns=['FPR','TPR','Thresh'] ) sub_dat['Var'] = s auc_val = roc_auc_score(subset[y_true], subset[y_score]) sub_dat['AUC'] = auc_val fin_dat.append(sub_dat) ax.plot(fpr, tpr, lw=2, label=f'{s} (AUC={auc_val:0.2f})') if len(grp_list) == 1: plt.title(f'{grp_list[0]} (AUC={auc_val:0.2f})') else: plt.title('ROC Curves & AUC Scores') ax.legend(loc='lower right', fontsize=leg_size) if save_plot: plt.savefig(save_plot, dpi=500, bbox_inches='tight') plt.show() fin_df = pd.concat(fin_dat, axis=0) return fin_df

# AUC Plot long (by group) and multiple variables # AUC data plot in wide format, y_scores needs to be a list # Of the variables in the dataframe def auc_plot_wide_group(data, y_true, y_scores, group, ncols=3, size=3, save_plot=False, leg_size=None): fin_dat = [] # Figuring out number of groups grp_list = pd.unique(data[group]) ncols = min([3,len(grp_list)]) nrows = int(np.ceil( len(grp_list) / ncols ) ) res_order = [(r,c) for r in range(nrows) for c in range(ncols)] fig, ax_grid = plt.subplots(figsize=(size*ncols, size*nrows), nrows=nrows, ncols=ncols, sharex=False, sharey=True, squeeze=False) fig.subplots_adjust(wspace=0.05) #?constrained layout? tick_num = [i/10 for i in range(11)] tick_lab = [0.0,'',0.2,'',0.4,'',0.6,'',0.8,'',1.0] for g,o in zip(grp_list,res_order): ax = ax_grid[o] #Equality line ax.plot([0, 1], [0, 1], color='grey', lw=1, linestyle='-') ax.set_xlim(left=-0.03, right=1.03) ax.set_ylim(bottom=-0.03, top=1.03) ax.set_yticks(tick_num) ax.set_xticks(tick_num) # If last row, give x lab if o[0] == (nrows - 1): ax.set_xlabel("FPR") ax.set_xticklabels(tick_lab) else: ax.tick_params(axis='x', length=0) ax.set_xticklabels([]) # If first column give y lab if o[1] == 0: ax.set_ylabel("TPR") ax.set_yticklabels(tick_lab) else: ax.tick_params(axis='y', length=0) ax.set_aspect(aspect='equal') for v in y_scores: subset = data[ data[group] == g ] fpr, tpr, thr = roc_curve(subset[y_true], subset[v]) sub_dat = pd.DataFrame( zip(fpr, tpr, thr), columns=['FPR','TPR','Thresh'] ) sub_dat['Var'] = v sub_dat[group] = g auc_val = roc_auc_score(subset[y_true], subset[v]) sub_dat['AUC'] = auc_val fin_dat.append(sub_dat) ax.plot(fpr, tpr, lw=2, label=f'{v} (AUC={auc_val:0.2f})') if len(y_scores) == 1: ax.set_title(f'{g}, {y_scores[0]} (AUC={auc_val:0.2f})') else: ax.set_title(f'{g}') ax.legend(loc='lower right', fontsize=leg_size) # The padded extra set as empty ext = len(res_order) - len(grp_list) if ext > 0: for e in res_order[-ext:]: # turn off extra ax_grid[e].axis('off') up_row = (e[0]-1,e[1]) ax_up = ax_grid[up_row] # redo x axis ax_up.tick_params(axis='x', length=3.5) ax_up.set_xticks(tick_num) ax_up.set_xlabel('FPR') ax_up.set_xticklabels(tick_lab) if save_plot: plt.savefig(save_plot, dpi=500, bbox_inches='tight') plt.show() fin_df = pd.concat(fin_dat, axis=0) return fin_df

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