[ { "path": ".flake8", "content": "[flake8]\nignore = E203, E266, E501, W503, F403, F401\nmax-line-length = 79\nmax-complexity = 18\nselect = B,C,E,F,W,T4,B9" }, { "path": ".github/FUNDING.yml", "content": "# These are supported funding model platforms\n\ngithub: wcipriano\nbuy_me_a_coffee: wcipriano\n" }, { "path": ".gitignore", "content": "__pycache__\ndist\n.venv/\n.idea\nbkp/*\n*-local-*\nmatrix-output-*" }, { "path": ".pre-commit-config.yaml", "content": "repos:\n- repo: https://github.com/ambv/black\n rev: 20.8b1\n hooks:\n - id: black\n- repo: https://gitlab.com/pycqa/flake8\n rev: 3.8.4\n hooks:\n - id: flake8\n- repo: https://github.com/kynan/nbstripout\n rev: 0.5.0\n hooks:\n - id: nbstripout\n" }, { "path": "LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "README.md", "content": "![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pretty-confusion-matrix?logo=python&logoColor=%23FFFFFF)\n\"PyPI\"\n\"Code\n![PyPI - Wheel](https://img.shields.io/pypi/wheel/pretty-confusion-matrix)\n\"GitHub\n\"GitHub\n\"GitHub\n![PyPI - Downloads](https://img.shields.io/pypi/dm/pretty-confusion-matrix?logo=download)\n\n# Confusion Matrix in Python\nPlot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib.\n\nThis module get a pretty print confusion matrix from a NumPy matrix or from 2 NumPy arrays (`y_test` and `predictions`).\n\n\n## Become a sponsor\nPlease, consider contributing it to the project.\nSupport the developers who power open source!\nInvest in open source. It powers your world.\nBecome a sponsor now!\n\nIf 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) !\n\nOr Become a sponsor directly on [GitHub sponsors page](https://github.com/sponsors/wcipriano?frequency=recurring&) ! \nYou will receive a **sponsor badge** on your profile.\n\n\nI 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. \n\nSee my projects and my contacts in my [Linktree](https://linktr.ee/wagner.cipriano) if you want to stay connected!\n\n\n\n\n\n\n## Installation\n```bash\npip install pretty-confusion-matrix\n```\n\n## Get Started\n\n### Plotting from DataFrame:\n```python\nimport numpy as np\nimport pandas as pd\nfrom pretty_confusion_matrix import pp_matrix\n\narray = np.array([[13, 0, 1, 0, 2, 0],\n [0, 50, 2, 0, 10, 0],\n [0, 13, 16, 0, 0, 3],\n [0, 0, 0, 13, 1, 0],\n [0, 40, 0, 1, 15, 0],\n [0, 0, 0, 0, 0, 20]])\n\n# get pandas dataframe\ndf_cm = pd.DataFrame(array, index=range(1, 7), columns=range(1, 7))\n# colormap: see this and choose your more dear\ncmap = 'PuRd'\npp_matrix(df_cm, cmap=cmap)\n```\n![alt text](https://raw.githubusercontent.com/khuyentran1401/pretty-print-confusion-matrix/master/Screenshots/Conf_matrix_default.png)\n\n\n### Plotting from vectors\n\n\n```python\nimport numpy as np\nfrom pretty_confusion_matrix import pp_matrix_from_data\n\ny_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,\n 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])\npredic = 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,\n 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])\n\npp_matrix_from_data(y_test, predic)\n```\n\n![alt text](https://raw.githubusercontent.com/khuyentran1401/pretty-print-confusion-matrix/master/Screenshots/Conf_matrix_default_2.png)\n\n\n## Using custom labels in axis\nYou can customize the labels in axis, whether by DataFrame or vectors.\n\n### From DataFrame\nTo plot the matrix with text labels in axis rather than integer, change the params `index` and `columns` of your dataframe.\nGetting the example one above, just change the line `df_cm = pd.DataFrame(array, index=range(1, 7), columns=range(1, 7))` by\n```python\ncol = ['Dog', 'Cat', 'Mouse', 'Fox', 'Bird', 'Chicken']\ndf_cm = pd.DataFrame(array, index=col, columns=col)\n```\nIt'll replace the integer labels (**1...6**) in the axis, by **Dog, Cat, Mouse**, and so on..\n\n\n### From vectors\nIt's very similar, in this case you just need to use the `columns` param like the example below.\nThis param is a positional array, i.e., the order must be the same of the data representation. \nIn this example _Dog_ will be assigned to the class 0, _Cat_ will be assigned to the class 1, and so on and so forth.\nGetting the example two above, just change the line `pp_matrix_from_data(y_test, predic)`, by\n```python\ncolumns = ['Dog', 'Cat', 'Mouse', 'Fox', 'Bird'] \npp_matrix_from_data(y_test, predic, columns)\n```\nIt'll replace \"class A, ..., class E\" in the axis, by **Dog, Cat, ..., Bird**.\n\nMore 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).\n\n\n\n## Choosing Colormaps\n\nYou can choose the layout of the your matrix by a lot of colors options like PuRd, Oranges and more... \nTo customizer your color scheme, use the param cmap of funcion pp_matrix. \nTo see all the colormap available, please do this:\n```python\nfrom matplotlib import colormaps\nlist(colormaps)\n```\n\nMore information about Choosing Colormaps in Matplotlib is available [here](https://matplotlib.org/stable/users/explain/colors/colormaps.html).\n\n\n\n\n## References:\n### 1. MATLAB confusion matrix:\n\na) [Plot Confusion](https://www.mathworks.com/help/nnet/ref/plotconfusion.html)\n \nb) [Plot Confusion Matrix Using Categorical Labels](https://www.mathworks.com/help/examples/nnet/win64/PlotConfusionMatrixUsingCategoricalLabelsExample_02.png)\n\n\n\n### 2. Examples and more on Python:\n\n 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)\n \n b) [Plot-scikit-learn-classification-report](https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report)\n \n 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)\n \n d) [Seaborn heatmap](https://www.programcreek.com/python/example/96197/seaborn.heatmap)\n \n e) [Sklearn-plot-confusion-matrix-with-labels](https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels/31720054)\n\n 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)\n\n" }, { "path": "examples/arrays_example.py", "content": "import numpy as np\n\nfrom pretty_confusion_matrix import pp_matrix_from_data\n\ny_test = np.array([1, 2, 3, 4, 5])\npredic = np.array([3, 2, 4, 3, 5])\n\ncmap = \"PuRd\"\npath_to_save_img = './matrix-output-from-array.png'\ntitle = 'Confusion matrix from array'\npp_matrix_from_data(y_test, predic, cmap=cmap, path_to_save_img=path_to_save_img, title=title)\n" }, { "path": "examples/df_example.py", "content": "import numpy as np\nimport pandas as pd\n\nfrom pretty_confusion_matrix import pp_matrix\n\narray = np.array(\n [\n [13, 0, 1, 0, 2, 0],\n [0, 50, 2, 0, 10, 0],\n [0, 13, 16, 0, 0, 3],\n [0, 0, 0, 13, 1, 0],\n [0, 40, 0, 1, 15, 0],\n [0, 0, 0, 0, 0, 20],\n ]\n)\n\n# get pandas dataframe\ndf_cm = pd.DataFrame(array, index=range(1, 7), columns=range(1, 7))\n# colormap: see this and choose your more dear\ncmap = \"PuRd\"\ntitle = 'Confusion matrix from dataframe'\npp_matrix(df_cm, cmap=cmap, title=title)\n" }, { "path": "examples/docs.py", "content": "import pretty_confusion_matrix\n\nprint('VERSION: ', pretty_confusion_matrix.__version__)\n" }, { "path": "pretty_confusion_matrix/__init__.py", "content": "import importlib.metadata\n__version__ = importlib.metadata.version(\"pretty_confusion_matrix\")\n\nfrom .pretty_confusion_matrix import pp_matrix, pp_matrix_from_data\n" }, { "path": "pretty_confusion_matrix/pretty_confusion_matrix.py", "content": "# -*- coding: utf-8 -*-\n\"\"\"\nplot a pretty confusion matrix with seaborn\nCreated on Mon Jun 25 14:17:37 2018\n@author: Wagner Cipriano - wagnerbhbr - gmail - CEFETMG / MMC\nREFerences:\n https://www.mathworks.com/help/nnet/ref/plotconfusion.html\n https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-classification-report\n https://stackoverflow.com/questions/5821125/how-to-plot-confusion-matrix-with-string-axis-rather-than-integer-in-python\n https://www.programcreek.com/python/example/96197/seaborn.heatmap\n https://stackoverflow.com/questions/19233771/sklearn-plot-confusion-matrix-with-labels/31720054\n http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py\n\"\"\"\n\nimport matplotlib.font_manager as fm\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sn\nfrom matplotlib.collections import QuadMesh\n\n\ndef get_new_fig(fn, figsize=[9, 9]):\n \"\"\"Init graphics\"\"\"\n fig1 = plt.figure(fn, figsize)\n ax1 = fig1.gca() # Get Current Axis\n ax1.cla() # clear existing plot\n return fig1, ax1\n\n\ndef configcell_text_and_colors(\n array_df, lin, col, oText, facecolors, posi, fz, fmt, show_null_values=0\n):\n \"\"\"\n config cell text and colors\n and return text elements to add and to dell\n @TODO: use fmt\n \"\"\"\n text_add = []\n text_del = []\n cell_val = array_df[lin][col]\n tot_all = array_df[-1][-1]\n per = (float(cell_val) / tot_all) * 100\n curr_column = array_df[:, col]\n ccl = len(curr_column)\n\n # last line and/or last column\n if (col == (ccl - 1)) or (lin == (ccl - 1)):\n # tots and percents\n if cell_val != 0:\n if (col == ccl - 1) and (lin == ccl - 1):\n tot_rig = 0\n for i in range(array_df.shape[0] - 1):\n tot_rig += array_df[i][i]\n per_ok = (float(tot_rig) / cell_val) * 100\n elif col == ccl - 1:\n tot_rig = array_df[lin][lin]\n per_ok = (float(tot_rig) / cell_val) * 100\n elif lin == ccl - 1:\n tot_rig = array_df[col][col]\n per_ok = (float(tot_rig) / cell_val) * 100\n per_err = 100 - per_ok\n else:\n per_ok = per_err = 0\n\n per_ok_s = [\"%.2f%%\" % (per_ok), \"100%\"][per_ok == 100]\n\n # text to DEL\n text_del.append(oText)\n\n # text to ADD\n font_prop = fm.FontProperties(weight=\"bold\", size=fz)\n text_kwargs = dict(\n color=\"w\",\n ha=\"center\",\n va=\"center\",\n gid=\"sum\",\n fontproperties=font_prop,\n )\n lis_txt = [\"%d\" % (cell_val), per_ok_s, \"%.2f%%\" % (per_err)]\n lis_kwa = [text_kwargs]\n dic = text_kwargs.copy()\n dic[\"color\"] = \"g\"\n lis_kwa.append(dic)\n dic = text_kwargs.copy()\n dic[\"color\"] = \"r\"\n lis_kwa.append(dic)\n lis_pos = [\n (oText._x, oText._y - 0.3),\n (oText._x, oText._y),\n (oText._x, oText._y + 0.3),\n ]\n for i in range(len(lis_txt)):\n newText = dict(\n x=lis_pos[i][0],\n y=lis_pos[i][1],\n text=lis_txt[i],\n kw=lis_kwa[i],\n )\n text_add.append(newText)\n\n # set background color for sum cells (last line and last column)\n carr = [0.27, 0.30, 0.27, 1.0]\n if (col == ccl - 1) and (lin == ccl - 1):\n carr = [0.17, 0.20, 0.17, 1.0]\n facecolors[posi] = carr\n\n else:\n if per > 0:\n txt = \"%s\\n%.2f%%\" % (cell_val, per)\n else:\n if show_null_values == 0:\n txt = \"\"\n elif show_null_values == 1:\n txt = \"0\"\n else:\n txt = \"0\\n0.0%\"\n oText.set_text(txt)\n\n # main diagonal\n if col == lin:\n # set color of the textin the diagonal to white\n oText.set_color(\"w\")\n # set background color in the diagonal to blue\n facecolors[posi] = [0.35, 0.8, 0.55, 1.0]\n else:\n oText.set_color(\"r\")\n\n return text_add, text_del\n\n\ndef insert_totals(df_cm):\n \"\"\"insert total column and line (the last ones)\"\"\"\n sum_col = []\n for c in df_cm.columns:\n sum_col.append(df_cm[c].sum())\n sum_lin = []\n for item_line in df_cm.iterrows():\n sum_lin.append(item_line[1].sum())\n df_cm[\"sum_lin\"] = sum_lin\n sum_col.append(np.sum(sum_lin))\n df_cm.loc[\"sum_col\"] = sum_col\n\n\ndef pp_matrix(\n df_cm,\n annot=True,\n cmap=\"Oranges\",\n fmt=\".2f\",\n fz=11,\n lw=0.5,\n cbar=False,\n figsize=[8, 8],\n show_null_values=0,\n pred_val_axis=\"y\",\n path_to_save_img=\"\",\n title=\"Confusion matrix\",\n):\n \"\"\"\n print conf matrix with default layout (like matlab)\n params:\n df_cm dataframe (pandas) without totals\n annot print text in each cell\n cmap Oranges,Oranges_r,YlGnBu,Blues,RdBu, ... see:\n fz fontsize\n lw linewidth\n pred_val_axis where to show the prediction values (x or y axis)\n 'col' or 'x': show predicted values in columns (x axis) instead lines\n 'lin' or 'y': show predicted values in lines (y axis)\n \"\"\"\n if pred_val_axis in (\"col\", \"x\"):\n xlbl = \"Predicted\"\n ylbl = \"Actual\"\n else:\n xlbl = \"Actual\"\n ylbl = \"Predicted\"\n df_cm = df_cm.T\n\n # create \"Total\" column\n insert_totals(df_cm)\n\n # this is for print allways in the same window\n fig, ax1 = get_new_fig(\"Conf matrix default\", figsize)\n\n ax = sn.heatmap(\n df_cm,\n annot=annot,\n annot_kws={\"size\": fz},\n linewidths=lw,\n ax=ax1,\n cbar=cbar,\n cmap=cmap,\n linecolor=\"w\",\n fmt=fmt,\n )\n\n # set ticklabels rotation\n ax.set_xticklabels(ax.get_xticklabels(), rotation=45, fontsize=10)\n ax.set_yticklabels(ax.get_yticklabels(), rotation=25, fontsize=10)\n\n # Turn off all the ticks\n for (tx, ty) in zip(ax.xaxis.get_major_ticks(), ax.yaxis.get_major_ticks()):\n tx.tick1line.set_visible(False)\n tx.tick2line.set_visible(False)\n ty.tick1line.set_visible(False)\n ty.tick2line.set_visible(False)\n\n # face colors list\n quadmesh = ax.findobj(QuadMesh)[0]\n facecolors = quadmesh.get_facecolors()\n\n # iter in text elements\n array_df = np.array(df_cm.to_records(index=False).tolist())\n text_add = []\n text_del = []\n posi = -1 # from left to right, bottom to top.\n for t in ax.collections[0].axes.texts: # ax.texts:\n pos = np.array(t.get_position()) - [0.5, 0.5]\n lin = int(pos[1])\n col = int(pos[0])\n posi += 1\n\n # set text\n txt_res = configcell_text_and_colors(\n array_df, lin, col, t, facecolors, posi, fz, fmt, show_null_values\n )\n\n text_add.extend(txt_res[0])\n text_del.extend(txt_res[1])\n\n # remove the old ones\n for item in text_del:\n item.remove()\n # append the new ones\n for item in text_add:\n ax.text(item[\"x\"], item[\"y\"], item[\"text\"], **item[\"kw\"])\n\n # titles and legends\n ax.set_title(title)\n ax.set_xlabel(xlbl)\n ax.set_ylabel(ylbl)\n plt.tight_layout() # set layout slim\n\n # save or show the img result\n if not path_to_save_img:\n plt.show()\n else:\n plt.savefig(path_to_save_img)\n\n\ndef pp_matrix_from_data(\n y_test,\n predictions,\n columns=None,\n annot=True,\n cmap=\"Oranges\",\n fmt=\".2f\",\n fz=11,\n lw=0.5,\n cbar=False,\n figsize=[8, 8],\n show_null_values=0,\n pred_val_axis=\"lin\",\n path_to_save_img=\"\",\n title=\"Confusion matrix\",\n):\n \"\"\"\n plot confusion matrix function with y_test (actual values) and predictions (predic),\n whitout a confusion matrix yet\n \"\"\"\n from pandas import DataFrame\n from sklearn.metrics import confusion_matrix\n\n # data\n if not columns:\n from string import ascii_uppercase\n\n columns = [\n \"class %s\" % (i)\n for i in list(ascii_uppercase)[0 : len(np.unique(y_test))]\n ]\n\n confm = confusion_matrix(y_test, predictions)\n df_cm = DataFrame(confm, index=columns, columns=columns)\n pp_matrix(\n df_cm,\n fz=fz,\n cmap=cmap,\n figsize=figsize,\n show_null_values=show_null_values,\n pred_val_axis=pred_val_axis,\n path_to_save_img=path_to_save_img,\n title=title,\n annot=annot,\n fmt=fmt,\n lw=lw,\n cbar=cbar\n )\n" }, { "path": "pyproject.toml", "content": "[tool.poetry]\nname = \"pretty-confusion-matrix\"\nversion = \"0.6.0\"\ndescription = \"plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib\"\nrepository = \"https://github.com/wcipriano/pretty-print-confusion-matrix\"\nauthors = [\"Wagner Cipriano \", \"Khuyen Tran \"]\nkeywords = [\"confusion matrix\"]\nreadme = \"README.md\"\n\n[project.urls]\nhomepage = \"https://pypi.org/project/pretty-confusion-matrix\"\nsource = \"https://github.com/wcipriano/pretty-print-confusion-matrix\"\ndownload = \"https://pypi.org/project/pretty-confusion-matrix/#files\"\ntracker = \"https://github.com/wcipriano/pretty-print-confusion-matrix/issues\"\n\n[tool.poetry.dependencies]\npython = \">=3.9,<3.12\"\nnumpy = [\n {version = \"^2.0.0\", python = \">=3.9\"},\n]\nmatplotlib = [\n {version = \"^3.9.0\", python = \">=3.9\"},\n]\nseaborn = \"^0.13.2\"\npandas = [\n {version = \"^2.2.2\", python = \">=3.9\"},\n]\nscikit-learn = [\n {version = \"^1.5\", python = \">=3.9\"},\n]\n\n[tool.poetry.dev-dependencies]\npre-commit = \"^3.5.0\"\nblack = \"^24.4.2\"\nflake8 = \"^5.0.4\"\n\n[build-system]\nrequires = [\"poetry-core\"]\nbuild-backend = \"poetry.core.masonry.api\"\n\n[tool.black]\nline-length = 79\ninclude = '\\.pyi?$'\nexclude = '''\n/(\n\t\\.git\n| \\.hg\n| \\.mypy_cache\n| \\.tox\n| \\.venv\n| _build\n| buck-out\n| build \n)/ \n'''" } ]