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pdp_tool.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def pdp(df, features, yname, n=4,
writefolder=None, digits=2, figsize=(8,6),
showbincount=True, ylim_origin=True,
even_spaced_ticks=False):
assert isinstance(yname, str), 'yname must be a string. Unique column.'
assert yname in df.columns, 'yname column is not in the dataframe.'
assert isinstance(features, list), 'features must be a list. If single feature use [feature].'
for feature in features:
if feature != yname:
if feature not in df.columns:
print (f'feature {feature} not in df.')
continue
replace_list = ['/', '\\', ' ', '(', ')']
feature_string = str(feature)
for char in replace_list:
feature_string = feature_string.replace(char, '_')
yname_string = str(yname)
for char in replace_list:
yname_string = yname_string.replace(char, '_')
# tirando os nans
try:
#df_temp = df[~np.isnan(df[feature])][[feature, yname]]
df_temp = df[[feature, yname]].dropna()
bins_pos = np.percentile(df_temp[feature].values, np.linspace(0,100,n+1))
except:
print ('feature {} with problems.'.format(feature))
continue
v_mean = list()
v_std = list()
if bins_pos.size == np.unique(bins_pos).size: # variavel continua
hist, _ = np.histogram(df_temp[feature], bins_pos)
xtickslabel = list()
bin_pos_label = list()
for i in range(bins_pos.size-1): # vou pegar cada intervalo agora e calcular a media de y
v = df_temp[(df_temp[feature].values >= bins_pos[i]) & (df_temp[feature].values < bins_pos[i+1])][yname].values
if np.isnan(v.mean()) or np.isnan(v.std()) or abs(v.mean())==float('inf') or abs(v.std())==float('inf'):
continue
else:
xtickslabel.append('['+'{number:.{digits}f}'.format(number=bins_pos[i], digits=digits)+'-'+'{number:.{digits}f}'.format(number=bins_pos[i+1], digits=digits)+'[')
v_mean.append(v.mean())
v_std.append(v.std())
if even_spaced_ticks:
bin_pos_label.append((bins_pos[i]+bins_pos[i+1])/2)
else:
bin_pos_label.append(i)
v_mean = np.array(v_mean)
v_std = np.array(v_std)/np.sqrt(hist)
fig, ax1 = plt.subplots(figsize=figsize)
ax1.set_xlabel(feature)
ax1.set_ylabel('mean ' + yname)
ax1.set_ylim([(v_mean-v_std).min()*0.9, (v_mean+v_std).max()*1.05])
ax1.set_xticks(bin_pos_label)
#ax1.plot(bins_pos[:-1], v_mean, label='mean '+yname)
ax1.plot(bin_pos_label, v_mean, 'o-', label='mean '+yname)
ax1.set_xticklabels(xtickslabel, rotation=35)
#ax1.fill_between(bins_pos[:-1], v_mean + v_std, v_mean - v_std, alpha=0.1, color='b')
ax1.fill_between(bin_pos_label, v_mean + v_std, v_mean - v_std, alpha=0.1, color='b')
if showbincount:
color = 'tab:red'
ax2 = ax1.twinx()
ax2.plot(bin_pos_label, hist, 'o--', label='bin count', color=color)
#ax2.bar(bin_pos_label, hist, label='bin count', color=color)
ax2.set_ylim([0, hist.max()*1.2])
ax2.set_ylabel('bin_count', color=color)
if writefolder:
feature_ = feature.replace(' ', '_')
feature_ = feature_.replace('/', '_')
plt.savefig(writefolder+'/scatter_feature_'+feature_string+'_y_'+yname_string+'.png')
else:
plt.tight_layout()
plt.show()
else: # variavel categorica
bins_pos = np.unique(bins_pos)
hist = list()
for value in bins_pos:
hist.append((df_temp[feature].values==value).sum())
#hist, _ = np.histogram(df[feature], bins_pos)
for i in range(bins_pos.size): # vou pegar cada intervalo agora e calcular a media de y
v = df_temp[df_temp[feature].values == bins_pos[i]][yname].values
v_mean.append(v.mean())
v_std.append(v.std())
v_mean = np.array(v_mean)
v_std = np.array(v_std)/np.sqrt(hist)
fig, ax1 = plt.subplots(figsize=figsize)
ax1.set_xlabel(feature)
ax1.set_ylabel('mean '+yname)
if ylim_origin:
ax1.set_ylim([0,(v_mean+v_std).max()*1.05])
else:
ax1.set_ylim([(v_mean-v_std).min()*0.95,(v_mean+v_std).max()*1.05])
ax1.set_xticks(bins_pos)
ax1.plot(bins_pos, v_mean, 'o-', label='mean '+yname)
ax1.fill_between(bins_pos, v_mean + v_std, v_mean - v_std, alpha=0.1, color='b')
if showbincount:
color = 'tab:red'
ax2 = ax1.twinx()
ax2.plot(bins_pos, hist, 'o--', label='bin count', color=color)
ax2.set_ylim([0, np.array(hist).max()*1.2])
ax2.set_ylabel('bin_count', color=color)
if writefolder:
plt.savefig(writefolder+'/pdp_feature_'+feature_string+'_y_'+yname_string+'.png')
else:
plt.show()