How does sklearn select threshold steps in precision recall curve?

Question

I trained a basic FFNN on a example breast cancer dataset. For the results the precision_recall_curve function gives datapoints for 416 different thresholds. My Data contains 569 unique prediction values, as far as I understand the Precision Recall Curve I could apply 568 different threshold values and check the resulting Precision and Recall.

But how do I do so? is there a way to set the number of thresholds to test with sklearn? Or at least an explanation of how sklearn selects those thresholds?

I mean 417 should be enough, even for bigger data sets, I am just curious how they got selected.

# necessary packages
from sklearn.datasets import load_breast_cancer
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

# load data
sk_data = load_breast_cancer(return_X_y=False)

# safe data in pandas
data = sk_data['data']
target = sk_data['target']
target_names = sk_data['target_names']
feature_names = sk_data['feature_names']
data = pd.DataFrame(data=data, columns=feature_names)

# build ANN
model = Sequential()
model.add(Dense(64, kernel_initializer='random_uniform', input_dim=30, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, kernel_initializer='random_uniform', activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='sigmoid'))

# train ANN
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()

model.fit(data, target, epochs=50, batch_size=10, validation_split=0.2)

# eval
pred = model.predict(data)

# calculate precision-recall curve
from sklearn.metrics import precision_recall_curve
precision, recall, thresholds = precision_recall_curve(target, pred)

# precision-recall curve and f1
import matplotlib.pyplot as plt

#pyplot.plot([0, 1], [0.5, 0.5], linestyle='--')
plt.plot(recall, precision, marker='.')
# show the plot
plt.show()

len(np.unique(pred)) #569
len(thresholds) # 417

Answer 1

Reading the source, precision_recall_curve does compute precision and recall for each unique predicted probability (here pred) but then omits the output for all thresholds that result in full recall (apart from the very first threshold to achieve full recall).