Full Code of wcipriano/pretty-print-confusion-matrix for AI

Repository: wcipriano/pretty-print-confusion-matrix
Branch: master
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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

================================================
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

================================================
FILE: .github/FUNDING.yml
================================================
# These are supported funding model platforms

github: wcipriano
buy_me_a_coffee: wcipriano


================================================
FILE: .gitignore
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__pycache__
dist
.venv/
.idea
bkp/*
*-local-*
matrix-output-*

================================================
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


================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pretty-confusion-matrix?logo=python&logoColor=%23FFFFFF)
<a href="https://pypi.org/project/pretty-confusion-matrix/"><img alt="PyPI" src="https://img.shields.io/pypi/v/pretty-confusion-matrix?logo=pypi&logoColor=%23FFFFFF"></a>
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![PyPI - Wheel](https://img.shields.io/pypi/wheel/pretty-confusion-matrix)
<a href="https://libraries.io/pypi/pretty-confusion-matrix"><img alt="GitHub Repo stars" src="https://img.shields.io/librariesio/github/wcipriano/pretty-print-confusion-matrix"></a>
<a href="https://github.com/wcipriano"><img alt="GitHub Repo stars" src="https://img.shields.io/github/license/wcipriano/pretty-print-confusion-matrix?logo=apache"></a>
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![PyPI - Downloads](https://img.shields.io/pypi/dm/pretty-confusion-matrix?logo=download)

# 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)
```
![alt text](https://raw.githubusercontent.com/khuyentran1401/pretty-print-confusion-matrix/master/Screenshots/Conf_matrix_default.png)


### 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)
```

![alt text](https://raw.githubusercontent.com/khuyentran1401/pretty-print-confusion-matrix/master/Screenshots/Conf_matrix_default_2.png)


## 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 <wagnao@gmail.com>", "Khuyen Tran <khuyentran1476@gmail.com>"]
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   
)/ 
'''