Repository: wcipriano/pretty-print-confusion-matrix
Branch: master
Commit: 4220d581799e
Files: 12
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Directory structure:
gitextract_mfctpddf/
├── .flake8
├── .github/
│ └── FUNDING.yml
├── .gitignore
├── .pre-commit-config.yaml
├── LICENSE
├── README.md
├── examples/
│ ├── arrays_example.py
│ ├── df_example.py
│ └── docs.py
├── pretty_confusion_matrix/
│ ├── __init__.py
│ └── pretty_confusion_matrix.py
└── pyproject.toml
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FILE CONTENTS
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FILE: .flake8
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[flake8]
ignore = E203, E266, E501, W503, F403, F401
max-line-length = 79
max-complexity = 18
select = B,C,E,F,W,T4,B9
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FILE: .github/FUNDING.yml
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# These are supported funding model platforms
github: wcipriano
buy_me_a_coffee: wcipriano
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FILE: .gitignore
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__pycache__
dist
.venv/
.idea
bkp/*
*-local-*
matrix-output-*
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FILE: .pre-commit-config.yaml
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repos:
- repo: https://github.com/ambv/black
rev: 20.8b1
hooks:
- id: black
- repo: https://gitlab.com/pycqa/flake8
rev: 3.8.4
hooks:
- id: flake8
- repo: https://github.com/kynan/nbstripout
rev: 0.5.0
hooks:
- id: nbstripout
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FILE: LICENSE
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================================================
FILE: README.md
================================================
# Confusion Matrix in Python
Plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib.
This module get a pretty print confusion matrix from a NumPy matrix or from 2 NumPy arrays (`y_test` and `predictions`).
## Become a sponsor
Please, consider contributing it to the project.
Support the developers who power open source!
Invest in open source. It powers your world.
Become a sponsor now!
If my open source projects could bring you closer to your goals and you want to say thank you, and you can contribute, feel free to do [Buy me a ☕](https://buymeacoffee.com/wcipriano) !
Or Become a sponsor directly on [GitHub sponsors page](https://github.com/sponsors/wcipriano?frequency=recurring&) !
You will receive a **sponsor badge** on your profile.
I will be really thankfull for anything even if it is a coffee or just a kind comment towards my work, because that helps me a lot.
See my projects and my contacts in my [Linktree](https://linktr.ee/wagner.cipriano) if you want to stay connected!
## Installation
```bash
pip install pretty-confusion-matrix
```
## Get Started
### Plotting from DataFrame:
```python
import numpy as np
import pandas as pd
from pretty_confusion_matrix import pp_matrix
array = np.array([[13, 0, 1, 0, 2, 0],
[0, 50, 2, 0, 10, 0],
[0, 13, 16, 0, 0, 3],
[0, 0, 0, 13, 1, 0],
[0, 40, 0, 1, 15, 0],
[0, 0, 0, 0, 0, 20]])
# get pandas dataframe
df_cm = pd.DataFrame(array, index=range(1, 7), columns=range(1, 7))
# colormap: see this and choose your more dear
cmap = 'PuRd'
pp_matrix(df_cm, cmap=cmap)
```
### Plotting from vectors
```python
import numpy as np
from pretty_confusion_matrix import pp_matrix_from_data
y_test = np.array([1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2,
3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5])
predic = np.array([1, 2, 4, 3, 5, 1, 2, 4, 3, 5, 1, 2, 3, 4, 4, 1, 4, 3, 4, 5, 1, 2, 4, 4, 5, 1, 2, 4, 4, 5, 1, 2, 4, 4, 5, 1, 2, 4, 4, 5, 1, 2, 3, 3, 5, 1, 2, 3, 3, 5, 1, 2,
3, 4, 4, 1, 2, 3, 4, 1, 1, 2, 3, 4, 1, 1, 2, 3, 4, 1, 1, 2, 4, 4, 5, 1, 2, 4, 4, 5, 1, 2, 4, 4, 5, 1, 2, 4, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5])
pp_matrix_from_data(y_test, predic)
```
## Using custom labels in axis
You can customize the labels in axis, whether by DataFrame or vectors.
### From DataFrame
To plot the matrix with text labels in axis rather than integer, change the params `index` and `columns` of your dataframe.
Getting the example one above, just change the line `df_cm = pd.DataFrame(array, index=range(1, 7), columns=range(1, 7))` by
```python
col = ['Dog', 'Cat', 'Mouse', 'Fox', 'Bird', 'Chicken']
df_cm = pd.DataFrame(array, index=col, columns=col)
```
It'll replace the integer labels (**1...6**) in the axis, by **Dog, Cat, Mouse**, and so on..
### From vectors
It's very similar, in this case you just need to use the `columns` param like the example below.
This param is a positional array, i.e., the order must be the same of the data representation.
In this example _Dog_ will be assigned to the class 0, _Cat_ will be assigned to the class 1, and so on and so forth.
Getting the example two above, just change the line `pp_matrix_from_data(y_test, predic)`, by
```python
columns = ['Dog', 'Cat', 'Mouse', 'Fox', 'Bird']
pp_matrix_from_data(y_test, predic, columns)
```
It'll replace "class A, ..., class E" in the axis, by **Dog, Cat, ..., Bird**.
More information about "_How to plot confusion matrix with string axis rather than integer in python_" in [this Stackoverflow answer](https://stackoverflow.com/a/51176855/1809554).
## Choosing Colormaps
You can choose the layout of the your matrix by a lot of colors options like PuRd, Oranges and more...
To customizer your color scheme, use the param cmap of funcion pp_matrix.
To see all the colormap available, please do this:
```python
from matplotlib import colormaps
list(colormaps)
```
More information about Choosing Colormaps in Matplotlib is available [here](https://matplotlib.org/stable/users/explain/colors/colormaps.html).
## References:
### 1. MATLAB confusion matrix:
a) [Plot Confusion](https://www.mathworks.com/help/nnet/ref/plotconfusion.html)
b) [Plot Confusion Matrix Using Categorical Labels](https://www.mathworks.com/help/examples/nnet/win64/PlotConfusionMatrixUsingCategoricalLabelsExample_02.png)
### 2. Examples and more on Python:
a) [How to plot confusion matrix with string axis rather than integer in python](https://stackoverflow.com/questions/5821125/how-to-plot-confusion-matrix-with-string-axis-rather-than-integer-in-python/51176855#51176855)
b) [Plot-scikit-learn-classification-report](https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report)
c) [Plot-confusion-matrix-with-string-axis-rather-than-integer-in-Python](https://stackoverflow.com/questions/5821125/how-to-plot-confusion-matrix-with-string-axis-rather-than-integer-in-python)
d) [Seaborn heatmap](https://www.programcreek.com/python/example/96197/seaborn.heatmap)
e) [Sklearn-plot-confusion-matrix-with-labels](https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels/31720054)
f) [Model-selection-plot-confusion-matrix](http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py)
================================================
FILE: examples/arrays_example.py
================================================
import numpy as np
from pretty_confusion_matrix import pp_matrix_from_data
y_test = np.array([1, 2, 3, 4, 5])
predic = np.array([3, 2, 4, 3, 5])
cmap = "PuRd"
path_to_save_img = './matrix-output-from-array.png'
title = 'Confusion matrix from array'
pp_matrix_from_data(y_test, predic, cmap=cmap, path_to_save_img=path_to_save_img, title=title)
================================================
FILE: examples/df_example.py
================================================
import numpy as np
import pandas as pd
from pretty_confusion_matrix import pp_matrix
array = np.array(
[
[13, 0, 1, 0, 2, 0],
[0, 50, 2, 0, 10, 0],
[0, 13, 16, 0, 0, 3],
[0, 0, 0, 13, 1, 0],
[0, 40, 0, 1, 15, 0],
[0, 0, 0, 0, 0, 20],
]
)
# get pandas dataframe
df_cm = pd.DataFrame(array, index=range(1, 7), columns=range(1, 7))
# colormap: see this and choose your more dear
cmap = "PuRd"
title = 'Confusion matrix from dataframe'
pp_matrix(df_cm, cmap=cmap, title=title)
================================================
FILE: examples/docs.py
================================================
import pretty_confusion_matrix
print('VERSION: ', pretty_confusion_matrix.__version__)
================================================
FILE: pretty_confusion_matrix/__init__.py
================================================
import importlib.metadata
__version__ = importlib.metadata.version("pretty_confusion_matrix")
from .pretty_confusion_matrix import pp_matrix, pp_matrix_from_data
================================================
FILE: pretty_confusion_matrix/pretty_confusion_matrix.py
================================================
# -*- coding: utf-8 -*-
"""
plot a pretty confusion matrix with seaborn
Created on Mon Jun 25 14:17:37 2018
@author: Wagner Cipriano - wagnerbhbr - gmail - CEFETMG / MMC
REFerences:
https://www.mathworks.com/help/nnet/ref/plotconfusion.html
https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report
https://stackoverflow.com/questions/5821125/how-to-plot-confusion-matrix-with-string-axis-rather-than-integer-in-python
https://www.programcreek.com/python/example/96197/seaborn.heatmap
https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels/31720054
http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py
"""
import matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sn
from matplotlib.collections import QuadMesh
def get_new_fig(fn, figsize=[9, 9]):
"""Init graphics"""
fig1 = plt.figure(fn, figsize)
ax1 = fig1.gca() # Get Current Axis
ax1.cla() # clear existing plot
return fig1, ax1
def configcell_text_and_colors(
array_df, lin, col, oText, facecolors, posi, fz, fmt, show_null_values=0
):
"""
config cell text and colors
and return text elements to add and to dell
@TODO: use fmt
"""
text_add = []
text_del = []
cell_val = array_df[lin][col]
tot_all = array_df[-1][-1]
per = (float(cell_val) / tot_all) * 100
curr_column = array_df[:, col]
ccl = len(curr_column)
# last line and/or last column
if (col == (ccl - 1)) or (lin == (ccl - 1)):
# tots and percents
if cell_val != 0:
if (col == ccl - 1) and (lin == ccl - 1):
tot_rig = 0
for i in range(array_df.shape[0] - 1):
tot_rig += array_df[i][i]
per_ok = (float(tot_rig) / cell_val) * 100
elif col == ccl - 1:
tot_rig = array_df[lin][lin]
per_ok = (float(tot_rig) / cell_val) * 100
elif lin == ccl - 1:
tot_rig = array_df[col][col]
per_ok = (float(tot_rig) / cell_val) * 100
per_err = 100 - per_ok
else:
per_ok = per_err = 0
per_ok_s = ["%.2f%%" % (per_ok), "100%"][per_ok == 100]
# text to DEL
text_del.append(oText)
# text to ADD
font_prop = fm.FontProperties(weight="bold", size=fz)
text_kwargs = dict(
color="w",
ha="center",
va="center",
gid="sum",
fontproperties=font_prop,
)
lis_txt = ["%d" % (cell_val), per_ok_s, "%.2f%%" % (per_err)]
lis_kwa = [text_kwargs]
dic = text_kwargs.copy()
dic["color"] = "g"
lis_kwa.append(dic)
dic = text_kwargs.copy()
dic["color"] = "r"
lis_kwa.append(dic)
lis_pos = [
(oText._x, oText._y - 0.3),
(oText._x, oText._y),
(oText._x, oText._y + 0.3),
]
for i in range(len(lis_txt)):
newText = dict(
x=lis_pos[i][0],
y=lis_pos[i][1],
text=lis_txt[i],
kw=lis_kwa[i],
)
text_add.append(newText)
# set background color for sum cells (last line and last column)
carr = [0.27, 0.30, 0.27, 1.0]
if (col == ccl - 1) and (lin == ccl - 1):
carr = [0.17, 0.20, 0.17, 1.0]
facecolors[posi] = carr
else:
if per > 0:
txt = "%s\n%.2f%%" % (cell_val, per)
else:
if show_null_values == 0:
txt = ""
elif show_null_values == 1:
txt = "0"
else:
txt = "0\n0.0%"
oText.set_text(txt)
# main diagonal
if col == lin:
# set color of the textin the diagonal to white
oText.set_color("w")
# set background color in the diagonal to blue
facecolors[posi] = [0.35, 0.8, 0.55, 1.0]
else:
oText.set_color("r")
return text_add, text_del
def insert_totals(df_cm):
"""insert total column and line (the last ones)"""
sum_col = []
for c in df_cm.columns:
sum_col.append(df_cm[c].sum())
sum_lin = []
for item_line in df_cm.iterrows():
sum_lin.append(item_line[1].sum())
df_cm["sum_lin"] = sum_lin
sum_col.append(np.sum(sum_lin))
df_cm.loc["sum_col"] = sum_col
def pp_matrix(
df_cm,
annot=True,
cmap="Oranges",
fmt=".2f",
fz=11,
lw=0.5,
cbar=False,
figsize=[8, 8],
show_null_values=0,
pred_val_axis="y",
path_to_save_img="",
title="Confusion matrix",
):
"""
print conf matrix with default layout (like matlab)
params:
df_cm dataframe (pandas) without totals
annot print text in each cell
cmap Oranges,Oranges_r,YlGnBu,Blues,RdBu, ... see:
fz fontsize
lw linewidth
pred_val_axis where to show the prediction values (x or y axis)
'col' or 'x': show predicted values in columns (x axis) instead lines
'lin' or 'y': show predicted values in lines (y axis)
"""
if pred_val_axis in ("col", "x"):
xlbl = "Predicted"
ylbl = "Actual"
else:
xlbl = "Actual"
ylbl = "Predicted"
df_cm = df_cm.T
# create "Total" column
insert_totals(df_cm)
# this is for print allways in the same window
fig, ax1 = get_new_fig("Conf matrix default", figsize)
ax = sn.heatmap(
df_cm,
annot=annot,
annot_kws={"size": fz},
linewidths=lw,
ax=ax1,
cbar=cbar,
cmap=cmap,
linecolor="w",
fmt=fmt,
)
# set ticklabels rotation
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, fontsize=10)
ax.set_yticklabels(ax.get_yticklabels(), rotation=25, fontsize=10)
# Turn off all the ticks
for (tx, ty) in zip(ax.xaxis.get_major_ticks(), ax.yaxis.get_major_ticks()):
tx.tick1line.set_visible(False)
tx.tick2line.set_visible(False)
ty.tick1line.set_visible(False)
ty.tick2line.set_visible(False)
# face colors list
quadmesh = ax.findobj(QuadMesh)[0]
facecolors = quadmesh.get_facecolors()
# iter in text elements
array_df = np.array(df_cm.to_records(index=False).tolist())
text_add = []
text_del = []
posi = -1 # from left to right, bottom to top.
for t in ax.collections[0].axes.texts: # ax.texts:
pos = np.array(t.get_position()) - [0.5, 0.5]
lin = int(pos[1])
col = int(pos[0])
posi += 1
# set text
txt_res = configcell_text_and_colors(
array_df, lin, col, t, facecolors, posi, fz, fmt, show_null_values
)
text_add.extend(txt_res[0])
text_del.extend(txt_res[1])
# remove the old ones
for item in text_del:
item.remove()
# append the new ones
for item in text_add:
ax.text(item["x"], item["y"], item["text"], **item["kw"])
# titles and legends
ax.set_title(title)
ax.set_xlabel(xlbl)
ax.set_ylabel(ylbl)
plt.tight_layout() # set layout slim
# save or show the img result
if not path_to_save_img:
plt.show()
else:
plt.savefig(path_to_save_img)
def pp_matrix_from_data(
y_test,
predictions,
columns=None,
annot=True,
cmap="Oranges",
fmt=".2f",
fz=11,
lw=0.5,
cbar=False,
figsize=[8, 8],
show_null_values=0,
pred_val_axis="lin",
path_to_save_img="",
title="Confusion matrix",
):
"""
plot confusion matrix function with y_test (actual values) and predictions (predic),
whitout a confusion matrix yet
"""
from pandas import DataFrame
from sklearn.metrics import confusion_matrix
# data
if not columns:
from string import ascii_uppercase
columns = [
"class %s" % (i)
for i in list(ascii_uppercase)[0 : len(np.unique(y_test))]
]
confm = confusion_matrix(y_test, predictions)
df_cm = DataFrame(confm, index=columns, columns=columns)
pp_matrix(
df_cm,
fz=fz,
cmap=cmap,
figsize=figsize,
show_null_values=show_null_values,
pred_val_axis=pred_val_axis,
path_to_save_img=path_to_save_img,
title=title,
annot=annot,
fmt=fmt,
lw=lw,
cbar=cbar
)
================================================
FILE: pyproject.toml
================================================
[tool.poetry]
name = "pretty-confusion-matrix"
version = "0.6.0"
description = "plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib"
repository = "https://github.com/wcipriano/pretty-print-confusion-matrix"
authors = ["Wagner Cipriano ", "Khuyen Tran "]
keywords = ["confusion matrix"]
readme = "README.md"
[project.urls]
homepage = "https://pypi.org/project/pretty-confusion-matrix"
source = "https://github.com/wcipriano/pretty-print-confusion-matrix"
download = "https://pypi.org/project/pretty-confusion-matrix/#files"
tracker = "https://github.com/wcipriano/pretty-print-confusion-matrix/issues"
[tool.poetry.dependencies]
python = ">=3.9,<3.12"
numpy = [
{version = "^2.0.0", python = ">=3.9"},
]
matplotlib = [
{version = "^3.9.0", python = ">=3.9"},
]
seaborn = "^0.13.2"
pandas = [
{version = "^2.2.2", python = ">=3.9"},
]
scikit-learn = [
{version = "^1.5", python = ">=3.9"},
]
[tool.poetry.dev-dependencies]
pre-commit = "^3.5.0"
black = "^24.4.2"
flake8 = "^5.0.4"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.black]
line-length = 79
include = '\.pyi?$'
exclude = '''
/(
\.git
| \.hg
| \.mypy_cache
| \.tox
| \.venv
| _build
| buck-out
| build
)/
'''