Calculate Confusion Matrix of a FastText Classifier model - machine-learning

Question

I'm calculating for a Facebook FastText classifier model the confusion matrix in this way:
#!/usr/local/bin/python3
import argparse
import numpy as np
from sklearn.metrics import confusion_matrix
def parse_labels(path):
with open(path, 'r') as f:
return np.array(list(map(lambda x: int(x[9:]), f.read().split())))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Display confusion matrix.')
parser.add_argument('test', help='Path to test labels')
parser.add_argument('predict', help='Path to predictions')
args = parser.parse_args()
test_labels = parse_labels(args.test)
pred_labels = parse_labels(args.predict)
print(test_labels)
print(pred_labels)
eq = test_labels == pred_labels
print("Accuracy: " + str(eq.sum() / len(test_labels)))
print(confusion_matrix(test_labels, pred_labels))
My predictions and test set are like
$ head -n10 /root/pexp
__label__spam
__label__verified
__label__verified
__label__spam
__label__verified
__label__verified
__label__verified
__label__verified
__label__verified
__label__verified
$ head -n10 /root/dataset_test.csv
__label__spam
__label__verified
__label__verified
__label__spam
__label__verified
__label__verified
__label__verified
__label__verified
__label__verified
__label__verified
Predictions of the model has been calculated over the test set in this way:
./fasttext predict /root/my_model.bin /root/dataset_test.csv > /root/pexp
I'm then going the calculate the FastText Confusion Matrix:
$ ./confusion.py /root/dataset_test.csv /root/pexp
but I'm stuck with this error:
Traceback (most recent call last):
File "./confusion.py", line 18, in <module>
test_labels = parse_labels(args.test)
File "./confusion.py", line 10, in parse_labels
return np.array(list(map(lambda x: int(x[9:]), f.read().split())))
File "./confusion.py", line 10, in <lambda>
return np.array(list(map(lambda x: int(x[9:]), f.read().split())))
ValueError: invalid literal for int() with base 10: 'spam'
I have fixed the script as suggested to handle non numeric labels:
def parse_labels(path):
with open(path, 'r') as f:
return np.array(list(map(lambda x: x[9:], f.read().split())))
Also, in the case of FastText it's possibile that the test set will have normalized labels (without the prefix __label__) at some point, so to convert back to the prefix you can do like:
awk 'BEGIN{FS=OFS="\t"}{ $1 = "__label__" tolower($1) }1' /root/dataset_test.csv > /root/dataset_test_norm.csv
See here about this.
Also, the input test file must be cut of the other columns than the label column:
cut -f 1 -d$'\t' /root/dataset_test_norm.csv > /root/dataset_test_norm_label.csv
So finally we get the Confusion Matrix:
$ ./confusion.py /root/dataset_test_norm_label.csv /root/pexp
Accuracy: 0.998852852227
[[9432 21]
[ 3 14543]]
My final solution is here.
[UPDATE]
The script is now working fine. I have added the Confusion Matrix calculation script directly in my FastText Node.js implementation, FastText.js here.

Answer 1

from sklearn.metrics import confusion_matrix
# predict the data
df["predicted"] = df["text"].apply(lambda x: model.predict(x)[0][0])
# Create the confusion matrix
confusion_matrix(df["labeled"], df["predicted"])
## OutPut:
# array([[5823, 8, 155, 1],
# [ 199, 51, 22, 0],
# [ 561, 2, 764, 0],
# [ 48, 0, 4, 4]], dtype=int64)