How to get the threshold from a specific precision and recall - scikit-learn

Question

I'm trying to get the threshold for a specific precision and recall. Let's say I want to get the threshold at the precision of 60% and recall of 40%. Are there any straightforward way to do it using the sklearn package?
precision, recall, threshold = precision_recall_curve(y_val, y_e)
df_pr = pd.DataFrame()
df_pr['precision'] = precision
df_pr['recall'] = recall
df_pr['threshold'] = list(threshold) + [1]
precision recall threshold
0 0.247543 1.000000 0.059483
1 0.247486 0.999692 0.059489
2 0.247504 0.999692 0.059512
3 0.247523 0.999692 0.059542

Answer 1

Provided that I've properly understood your question, imo, the point to highlight is that precision and recall are not necessarily coupled as you seem to imply. Here's a toy example:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_curve
X, y = make_classification(n_samples=1000, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=7)
lr = LogisticRegression(random_state=42)
lr.fit(X_train, y_train)
y_scores = lr.predict_proba(X_test)
precision, recall, threshold = precision_recall_curve(y_test, y_scores[:, 1])
plt.plot(threshold, precision[:-1], 'b--', label='Precision')
plt.plot(threshold, recall[:-1], 'r--', label='Recall')
plt.xlabel('Threshold')
plt.legend(loc='lower left')
plt.ylim([0,1])
This said, the problem becomes something you can easily solve either with numpy or pandas, depending on your "setting". For instance, here's a toy function returning precision, recall and threshold at the index where the condition is attained.
def prt(arr, value):
array = np.asarray(arr)
idx = np.where(array[:-1] == value)[0][0]
return precision[idx], recall[idx], threshold[idx]
prt(precision, 0.6) # I checked ex-ante that precision=0.6 is attained. Differently you'll have to go with something custom.
(0.6, 0.9622641509433962, 0.052229434776723364)
Otherwise, to resemble your setting with a pandas DataFrame:
df = pd.DataFrame()
df['precision'] = precision[:-1]
df['recall'] = recall[:-1]
df['threshold'] = threshold
df[df.loc[:, 'precision'] == 0.6]
I would suggest you sklearn precision_recall_curve and threshold that tries to explain how .precision_recall_curve() works under the hood and Why does precision_recall_curve() return different values than confusion matrix? which might be somehow related.