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https://github.com/waikato-datamining/simple-confusion-matrix
Python library for generating a confusion matrix from actual and predicted label.
https://github.com/waikato-datamining/simple-confusion-matrix
Last synced: 5 days ago
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Python library for generating a confusion matrix from actual and predicted label.
- Host: GitHub
- URL: https://github.com/waikato-datamining/simple-confusion-matrix
- Owner: waikato-datamining
- License: mit
- Created: 2021-04-14T23:33:56.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-22T04:53:42.000Z (7 months ago)
- Last Synced: 2024-09-24T02:00:01.897Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 12.7 KB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.rst
- License: LICENSE
Awesome Lists containing this project
README
# simple-confusion-matrix
Python library for generating a confusion matrix from actual and predicted label.
## Functionality:
* load actual, predicted and weight columns from a CSV
* if no weight list (values: 0-1) is provided, a value of 1 is assumed;
this value is used as weight for the counting then
* can generate three types of matrices:
* counts
* percentages (all cells sum up to 1)
* percentages per row (all cells of a row sum up to 1)
* can generate plain text or CSV output
* can write generated matrix back to a CSV file
## Installation
* create virtual environment
```commandline
python3 -m venv venv
./venv/bin/pip install simple-confusion-matrix
```
## Command-line usage
You don't have to write code for using the library, you can just use the
`scm-generate` command-line utility:
```
usage: scm-generate [-h] -i FILE [-d DELIMITER] [-q CHAR] [-H] [-o FILE]
[-O FORMAT] [-a COL] [-A PREFIX] [-p COL] [-P PREFIX]
[-w COL] [-l LABELS] [-C CORNER] [-D NUM] [-t TYPE]
Generates a confusion matrix from a CSV file with actual/predicted label
columns.
optional arguments:
-h, --help show this help message and exit
-i FILE, --input FILE
the CSV file to load the actual/predicted labels from
(default: None)
-d DELIMITER, --delimiter DELIMITER
the column delimited in the CSV file (default: ,)
-q CHAR, --quotechar CHAR
the quote character to use in the CSV file (default:
")
-H, --no_header whether the CSV file has no header row (default: True)
-o FILE, --output FILE
the optional CSV file to write the generated matrix to
(default: None)
-O FORMAT, --output_format FORMAT
the output format to use when writing to the output
file (default: csv)
-a COL, --actual COL the 1-based column index for the actual/ground truth
labels (default: 1)
-A PREFIX, --actual_prefix PREFIX
the prefix to use for the labels depicted in the
'actual' labels column (default: a: )
-p COL, --predicted COL
the 1-based column index for the predicted labels
(default: 2)
-P PREFIX, --predicted_prefix PREFIX
the prefix to use for the labels depicted in the
'predicted' labels row (default: p: )
-w COL, --weight COL the 1-based column index for the weight (0-1) of the
predicted label (default: None)
-l LABELS, --labels LABELS
comma-separated list of predefined labels to use (eg
if not all labels present in CSV file) (default: None)
-C CORNER, --corner CORNER
the text to print in the top-left corner (default: x)
-D NUM, --max_decimals NUM
the maximum number of decimals after the decimal point
to use in case of float values like percentages
(default: 3)
-t TYPE, --matrix_type TYPE
the type of matrix to generate (default: counts)
```
## Code examples
* Generating counts:
```python
from scm.matrix import ConfusionMatrix, MatrixType
actual = ["a", "a", "b", "b", "a", "b", "c", "c", "c", "c"]
predicted = ["a", "c", "b", "a", "a", "b", "a", "b", "c", "c"]
matrix = ConfusionMatrix(actual=actual, predicted=predicted)
result = matrix.generate(matrix_type=MatrixType.COUNTS)
print(result)
```
Generates the following output:
```
x p: a p: b p: c
a: a 2 0 1
a: b 1 2 0
a: c 1 1 2
```
* Using predefined label order:
```python
from scm.matrix import ConfusionMatrix, MatrixType
actual = ["a", "a", "b", "b", "a", "b", "c", "c", "c", "c"]
predicted = ["a", "c", "b", "a", "a", "b", "a", "b", "c", "c"]
labels = ["c", "b", "a"]
matrix = ConfusionMatrix(actual=actual, predicted=predicted, labels=labels)
result = matrix.generate(matrix_type=MatrixType.COUNTS)
print(result)
```
Generates the following output:
```
x p: c p: b p: a
a: c 2 1 1
a: b 0 2 1
a: a 1 0 2
```
* Using global percentages:
```python
from scm.matrix import ConfusionMatrix, MatrixType
actual = ["a", "a", "b", "b", "a", "b", "c", "c", "c", "c"]
predicted = ["a", "c", "b", "a", "a", "b", "a", "b", "c", "c"]
matrix = ConfusionMatrix(actual=actual, predicted=predicted, labels=labels)
result = matrix.generate(matrix_type=MatrixType.PERCENTAGES)
print(result)
```
Generates the following output:
```
x p: a p: b p: c
a: a 0.200 0.000 0.100
a: b 0.100 0.200 0.000
a: c 0.100 0.100 0.200
```
* Using row percentages (and only two decimals after decimal point):
```python
from scm.matrix import ConfusionMatrix, MatrixType
actual = ["a", "a", "b", "b", "a", "b", "c", "c", "c", "c"]
predicted = ["a", "c", "b", "a", "a", "b", "a", "b", "c", "c"]
matrix = ConfusionMatrix(actual=actual, predicted=predicted, labels=labels)
result = matrix.generate(matrix_type=MatrixType.PERCENTAGES_PER_ROW, max_decimals=2)
print(result)
```
Generates the following output:
```
x p: a p: b p: c
a: a 0.67 0.00 0.33
a: b 0.33 0.67 0.00
a: c 0.25 0.25 0.50
```
* Saving the matrix as CSV file:
```python
from scm.matrix import ConfusionMatrix, MatrixType
actual = ["a", "a", "b", "b", "a", "b", "c", "c", "c", "c"]
predicted = ["a", "c", "b", "a", "a", "b", "a", "b", "c", "c"]
matrix = ConfusionMatrix(actual=actual, predicted=predicted, labels=labels)
result = matrix.generate(matrix_type=MatrixType.PERCENTAGES_PER_ROW, max_decimals=2)
result.to_csv(output_file="/some/where/out.csv")
```
* Loading the actual and predicted values from a CSV file and writing the result back to a CSV:
```python
from scm.matrix import ConfusionMatrix, MatrixType, load_csv
actual, predicted, _ = load_csv("/some/where/in.csv", 1, 2)
matrix = ConfusionMatrix(actual=actual, predicted=predicted)
result = matrix.generate(matrix_type=MatrixType.PERCENTAGES_PER_ROW)
result.to_csv(output_file="/some/where/out.csv")
```