From 8cb32d8916e880e6838cb07f7e02c944ecf890ec Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Wed, 21 Aug 2024 08:21:07 +0200 Subject: [PATCH 01/14] removes jupyter artifacts --- .gitignore | 2 + .ipynb_checkpoints/plotFruit-checkpoint.ipynb | 189 ------------------ 2 files changed, 2 insertions(+), 189 deletions(-) delete mode 100644 .ipynb_checkpoints/plotFruit-checkpoint.ipynb diff --git a/.gitignore b/.gitignore index b2a2e85..60891e2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +# Jupyter +.ipynb_checkpoints/ #Eclipse/pydev .project diff --git a/.ipynb_checkpoints/plotFruit-checkpoint.ipynb b/.ipynb_checkpoints/plotFruit-checkpoint.ipynb deleted file mode 100644 index eb9cc7b..0000000 --- a/.ipynb_checkpoints/plotFruit-checkpoint.ipynb +++ /dev/null @@ -1,189 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import sankey\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import pandas as pd\n", - "pd.options.display.max_rows=8" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df = pd.read_csv('./fruits.txt',sep = ' ',names=['true','predicted'])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " true predicted\n", - "0 blueberry blueberry\n", - "1 apple blueberry\n", - "2 blueberry blueberry\n", - "3 lime lime\n", - ".. ... ...\n", - "996 blueberry blueberry\n", - "997 apple apple\n", - "998 apple banana\n", - "999 orange orange\n", - "\n", - "[1000 rows x 2 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "colorDict = {'apple':'#f71b1b','blueberry':'#1b7ef7','banana':'#f3f71b','lime':'#12e23f','orange':'#f78c1b'}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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3v7MXuKBCSCPIR5GU67yErF9L5ivkVsRQGWqooCkINMup8DDyvF8ZeFJIsbjr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HTOI7N7gdcDC1CdxIftASQ8yefubJ+x6qneqcdVMC+Nq+HdekH5lkEqHGcyc34erWl6AcCqd84H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\nMbn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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sankey.sankey(df['true'],df['predicted'],aspect=20,colorDict=colorDict,fontsize=10)\n", - "plt.gcf().set_size_inches(6,6)\n", - "plt.savefig('fruits.png',bbox_inches='tight',dpi=150)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.4.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 20bdd536c385c9f07f8ddb3aaa1a700f0d3d0f06 Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Wed, 21 Aug 2024 08:21:53 +0200 Subject: [PATCH 02/14] 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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) -## Example +## Examples -With fruits.txt : +### Simple expected/predicted example with fruits.txt: + +`pysankey` contains a simple expected/predicted dataset called `fruits.txt` which looks the following:

@@ -80,9 +82,13 @@ import pandas as pd from pysankey import sankey import matplotlib.pyplot as plt + df = pd.read_csv( - 'pysankey/fruits.txt', sep=' ', names=['true', 'predicted'] + 'fruits.txt', + sep=' ', + names=['true', 'predicted'] ) + colorDict = { 'apple':'#f71b1b', 'blueberry':'#1b7ef7', @@ -92,83 +98,77 @@ colorDict = { 'kiwi':'#9BD937' } +labels = list(colorDict.keys()) +leftLabels = [label for label in labels if label in df['true'].values] +rightLabels = [label for label in labels if label in df['predicted'].values] + +# Create the sankey diagram ax = sankey( - df['true'], df['predicted'], aspect=20, colorDict=colorDict, - leftLabels=['banana','orange','blueberry','apple','lime'], - rightLabels=['orange','banana','blueberry','apple','lime','kiwi'], + left=df['true'], + right=df['predicted'], + leftLabels=leftLabels, + rightLabels=rightLabels, + colorDict=colorDict, + aspect=20, fontsize=12 ) + plt.show() # to display -plt.savefig('fruit.png', bbox_inches='tight') # to save ``` -![Fruity Alchemy](pysankey/fruit.png) - -You could also use weight: +![Fruity Alchemy](.github/img/fruits.png) -``` -,customer,good,revenue -0,John,fruit,5.5 -1,Mike,meat,11.0 -2,Betty,drinks,7.0 -3,Ben,fruit,4.0 -4,Betty,bread,2.0 -5,John,bread,2.5 -6,John,drinks,8.0 -7,Ben,bread,2.0 -8,Mike,bread,3.5 -9,John,meat,13.0 -``` +### Plotting preprocessed data using weights -```python -import pandas as pd -from pysankey import sankey -import matplotlib.pyplot as plt +However, not always you have or can have the data available in the format mentioned in the previous example (e.g. if the dataset is too large). In this case, the weights between the true and predicted labels can also be calculated beforehand and used to create the sankey diagram. In this example, we continue to work with the data loaded already in the previous example: -df = pd.read_csv( - 'pysankey/customers-goods.csv', sep=',', - names=['id', 'customer', 'good', 'revenue'] -) -weight = df['revenue'].values[1:].astype(float) +``` python +# Calculate the weights from the fruits dataframe +df = df.groupby(["true", "predicted"]).size() +df = df.reset_index().rename(columns={0: "weight"}) +weights = df['weight'].astype(float) ax = sankey( - left=df['customer'].values[1:], right=df['good'].values[1:], - rightWeight=weight, leftWeight=weight, aspect=20, fontsize=20 + left=df['true'], + right=df['predicted'], + rightWeight=weights, + leftWeight=weights, + leftLabels=leftLabels, + rightLabels=rightLabels, + colorDict=colorDict, + aspect=20, + fontsize=12 ) + plt.show() # to display -plt.savefig('customers-goods.png', bbox_inches='tight') # to save ``` -![Customer goods](pysankey/customers-goods.png) +![Fruity Alchemy](.github/img/fruits_weighted.png) + +### pysankey function overview + +> `sankey(left, right, leftWeight=None, rightWeight=None, colorDict=None, leftLabels=None, rightLabels=None, aspect=4, rightColor=False, fontsize=14, ax=None, color_gradient=False, alphaDict=None)` +> +> **left**, **right** : NumPy array of object labels on the left and right of the diagram +> +> **leftWeight**, **rightWeight** : Numpy arrays of the weights each strip +> +> **colorDict** : Dictionary of colors to use for each label +> +> **leftLabels**, **rightLabels** : order of the left and right labels in the diagram +> +> **aspect** : vertical extent of the diagram in units of horizontal extent +> +> **rightColor** : If true, each strip in the diagram will be be colored + according to its left label +> +> **figSize** : tuple setting the width and height of the sankey diagram. + Defaults to current figure size +> +> **fontsize** : Fontsize to be used for the labels +> +> **ax** : matplotlib axes to plot on, otherwise uses current axes. -Similar to seaborn, you can pass a matplotlib `Axes` to `sankey` function: - -```python -import pandas as pd -from pysankey import sankey -import matplotlib.pyplot as plt - -df = pd.read_csv( - 'pysankey/fruits.txt', - sep=' ', names=['true', 'predicted'] -) -colorDict = { - 'apple': '#f71b1b', - 'blueberry': '#1b7ef7', - 'banana': '#f3f71b', - 'lime': '#12e23f', - 'orange': '#f78c1b' -} - -ax1 = plt.axes() - -sankey( - df['true'], df['predicted'], aspect=20, colorDict=colorDict, - fontsize=12, ax=ax1 -) - -plt.show() -``` ## Important informations diff --git a/pysankey/create_documentation_plots.py b/pysankey/create_documentation_plots.py new file mode 100644 index 0000000..5a97347 --- /dev/null +++ b/pysankey/create_documentation_plots.py @@ -0,0 +1,58 @@ +import pandas as pd +from pysankey import sankey +import matplotlib.pyplot as plt + + +df = pd.read_csv( + 'fruits.txt', + sep=' ', + names=['true', 'predicted'] +) + +colorDict = { + 'apple':'#f71b1b', + 'blueberry':'#1b7ef7', + 'banana':'#f3f71b', + 'lime':'#12e23f', + 'orange':'#f78c1b', + 'kiwi':'#9BD937' +} + +labels = list(colorDict.keys()) +leftLabels = [label for label in labels if label in df['true'].values] +rightLabels = [label for label in labels if label in df['predicted'].values] + +ax = sankey( + left=df['true'], + right=df['predicted'], + leftLabels=leftLabels, + rightLabels=rightLabels, + colorDict=colorDict, + aspect=20, + fontsize=12 +) + +plt.savefig("../.github/img/fruits.png") +plt.close() + + +# This calculates how often the different combinations of "true" and "predicted" co-occure +df = df.groupby(["true", "predicted"]).size().reset_index() +weights = df[0].astype(float) + + + +ax = sankey( + left=df['true'], + right=df['predicted'], + rightWeight=weights, + leftWeight=weights, + leftLabels=leftLabels, + rightLabels=rightLabels, + colorDict=colorDict, + aspect=20, + fontsize=12 +) + + +plt.savefig("../.github/img/fruits_weighted.png") From 46c2a973d2eab2d185be0bc0d623a44ff185c7ad Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Wed, 21 Aug 2024 08:23:58 +0200 Subject: [PATCH 03/14] updates status badge and gives ci workflow the name build to match with travis --- .github/workflows/ci.yaml | 2 ++ README.md | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index f201845..4e66a7f 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -1,3 +1,5 @@ +name: build + on: push: branches: diff --git a/README.md b/README.md index d50d3b5..c34a1a0 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Uses matplotlib to create simple Date: Wed, 21 Aug 2024 06:31:02 +0000 Subject: [PATCH 04/14] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- README.md | 43 +++++++++++++---------- pysankey/create_documentation_plots.py | 47 ++++++++++++-------------- 2 files changed, 46 insertions(+), 44 deletions(-) diff --git a/README.md b/README.md index c34a1a0..bf196e5 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,8 @@ Sankey diagrams flowing only from left to right. ### Simple expected/predicted example with fruits.txt: -`pysankey` contains a simple expected/predicted dataset called `fruits.txt` which looks the following: +`pysankey` contains a simple expected/predicted dataset called `fruits.txt` which looks +the following:
@@ -84,8 +85,8 @@ import matplotlib.pyplot as plt df = pd.read_csv( - 'fruits.txt', - sep=' ', + 'fruits.txt', + sep=' ', names=['true', 'predicted'] ) @@ -104,12 +105,12 @@ rightLabels = [label for label in labels if label in df['predicted'].values] # Create the sankey diagram ax = sankey( - left=df['true'], + left=df['true'], right=df['predicted'], leftLabels=leftLabels, rightLabels=rightLabels, colorDict=colorDict, - aspect=20, + aspect=20, fontsize=12 ) @@ -120,23 +121,27 @@ plt.show() # to display ### Plotting preprocessed data using weights -However, not always you have or can have the data available in the format mentioned in the previous example (e.g. if the dataset is too large). In this case, the weights between the true and predicted labels can also be calculated beforehand and used to create the sankey diagram. In this example, we continue to work with the data loaded already in the previous example: +However, not always you have or can have the data available in the format mentioned in +the previous example (e.g. if the dataset is too large). In this case, the weights +between the true and predicted labels can also be calculated beforehand and used to +create the sankey diagram. In this example, we continue to work with the data loaded +already in the previous example: -``` python +```python # Calculate the weights from the fruits dataframe df = df.groupby(["true", "predicted"]).size() df = df.reset_index().rename(columns={0: "weight"}) weights = df['weight'].astype(float) ax = sankey( - left=df['true'], + left=df['true'], right=df['predicted'], - rightWeight=weights, - leftWeight=weights, + rightWeight=weights, + leftWeight=weights, leftLabels=leftLabels, rightLabels=rightLabels, colorDict=colorDict, - aspect=20, + aspect=20, fontsize=12 ) @@ -149,27 +154,29 @@ plt.show() # to display > `sankey(left, right, leftWeight=None, rightWeight=None, colorDict=None, leftLabels=None, rightLabels=None, aspect=4, rightColor=False, fontsize=14, ax=None, color_gradient=False, alphaDict=None)` > -> **left**, **right** : NumPy array of object labels on the left and right of the diagram +> **left**, **right** : NumPy array of object labels on the left and right of the +> diagram > > **leftWeight**, **rightWeight** : Numpy arrays of the weights each strip -> +> > **colorDict** : Dictionary of colors to use for each label -> -> **leftLabels**, **rightLabels** : order of the left and right labels in the diagram +> +> **leftLabels**, **rightLabels** : order of the left and right labels in the diagram > > **aspect** : vertical extent of the diagram in units of horizontal extent > > **rightColor** : If true, each strip in the diagram will be be colored + according to its left label -> + > **figSize** : tuple setting the width and height of the sankey diagram. + Defaults to current figure size -> + > **fontsize** : Fontsize to be used for the labels > > **ax** : matplotlib axes to plot on, otherwise uses current axes. - ## Important informations Use of `figureName`, `closePlot`, `figSize` in `sankey()` is deprecated and will be diff --git a/pysankey/create_documentation_plots.py b/pysankey/create_documentation_plots.py index 5a97347..da9ff93 100644 --- a/pysankey/create_documentation_plots.py +++ b/pysankey/create_documentation_plots.py @@ -1,35 +1,31 @@ -import pandas as pd -from pysankey import sankey import matplotlib.pyplot as plt +import pandas as pd +from pysankey import sankey -df = pd.read_csv( - 'fruits.txt', - sep=' ', - names=['true', 'predicted'] -) +df = pd.read_csv("fruits.txt", sep=" ", names=["true", "predicted"]) colorDict = { - 'apple':'#f71b1b', - 'blueberry':'#1b7ef7', - 'banana':'#f3f71b', - 'lime':'#12e23f', - 'orange':'#f78c1b', - 'kiwi':'#9BD937' + "apple": "#f71b1b", + "blueberry": "#1b7ef7", + "banana": "#f3f71b", + "lime": "#12e23f", + "orange": "#f78c1b", + "kiwi": "#9BD937", } labels = list(colorDict.keys()) -leftLabels = [label for label in labels if label in df['true'].values] -rightLabels = [label for label in labels if label in df['predicted'].values] +leftLabels = [label for label in labels if label in df["true"].values] +rightLabels = [label for label in labels if label in df["predicted"].values] ax = sankey( - left=df['true'], - right=df['predicted'], + left=df["true"], + right=df["predicted"], leftLabels=leftLabels, rightLabels=rightLabels, colorDict=colorDict, - aspect=20, - fontsize=12 + aspect=20, + fontsize=12, ) plt.savefig("../.github/img/fruits.png") @@ -41,17 +37,16 @@ weights = df[0].astype(float) - ax = sankey( - left=df['true'], - right=df['predicted'], - rightWeight=weights, - leftWeight=weights, + left=df["true"], + right=df["predicted"], + rightWeight=weights, + leftWeight=weights, leftLabels=leftLabels, rightLabels=rightLabels, colorDict=colorDict, - aspect=20, - fontsize=12 + aspect=20, + fontsize=12, ) From 4059d360558adc2f7a799754195849ab6f889922 Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Wed, 21 Aug 2024 08:33:05 +0200 Subject: [PATCH 05/14] fixes too long line --- pysankey/create_documentation_plots.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pysankey/create_documentation_plots.py b/pysankey/create_documentation_plots.py index 5a97347..e5f83cb 100644 --- a/pysankey/create_documentation_plots.py +++ b/pysankey/create_documentation_plots.py @@ -36,7 +36,8 @@ plt.close() -# This calculates how often the different combinations of "true" and "predicted" co-occure +# This calculates how often the different combinations of "true" and +# "predicted" co-occure df = df.groupby(["true", "predicted"]).size().reset_index() weights = df[0].astype(float) From 52f096a0e20366465e3df8457ec1c6615cd19b94 Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Wed, 21 Aug 2024 08:35:14 +0200 Subject: [PATCH 06/14] fixes formatting --- README.md | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index bf196e5..88c1fb0 100644 --- a/README.md +++ b/README.md @@ -166,13 +166,8 @@ plt.show() # to display > **aspect** : vertical extent of the diagram in units of horizontal extent > > **rightColor** : If true, each strip in the diagram will be be colored - - according to its left label - -> **figSize** : tuple setting the width and height of the sankey diagram. - - Defaults to current figure size - +> according to its left label +> > **fontsize** : Fontsize to be used for the labels > > **ax** : matplotlib axes to plot on, otherwise uses current axes. From 4797b965df390467512b4fbbe62497e1bae4bee6 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 21 Aug 2024 06:35:25 +0000 Subject: [PATCH 07/14] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- README.md | 4 ++-- pysankey/create_documentation_plots.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 88c1fb0..5883256 100644 --- a/README.md +++ b/README.md @@ -165,8 +165,8 @@ plt.show() # to display > > **aspect** : vertical extent of the diagram in units of horizontal extent > -> **rightColor** : If true, each strip in the diagram will be be colored -> according to its left label +> **rightColor** : If true, each strip in the diagram will be be colored according to +> its left label > > **fontsize** : Fontsize to be used for the labels > diff --git a/pysankey/create_documentation_plots.py b/pysankey/create_documentation_plots.py index 5fd6117..aa31bb7 100644 --- a/pysankey/create_documentation_plots.py +++ b/pysankey/create_documentation_plots.py @@ -32,7 +32,7 @@ plt.close() -# This calculates how often the different combinations of "true" and +# This calculates how often the different combinations of "true" and # "predicted" co-occure df = df.groupby(["true", "predicted"]).size().reset_index() weights = df[0].astype(float) From df097de57c5adb24188001e1f8aff8991c48267e Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Wed, 21 Aug 2024 09:51:47 +0200 Subject: [PATCH 08/14] moved plot creation script to .github as it has nothing to do with the package and is only used for the repo --- {pysankey => .github}/create_documentation_plots.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) rename {pysankey => .github}/create_documentation_plots.py (86%) diff --git a/pysankey/create_documentation_plots.py b/.github/create_documentation_plots.py similarity index 86% rename from pysankey/create_documentation_plots.py rename to .github/create_documentation_plots.py index aa31bb7..472aca6 100644 --- a/pysankey/create_documentation_plots.py +++ b/.github/create_documentation_plots.py @@ -3,7 +3,7 @@ from pysankey import sankey -df = pd.read_csv("fruits.txt", sep=" ", names=["true", "predicted"]) +df = pd.read_csv("../pysankey/fruits.txt", sep=" ", names=["true", "predicted"]) colorDict = { "apple": "#f71b1b", @@ -28,7 +28,7 @@ fontsize=12, ) -plt.savefig("../.github/img/fruits.png") +plt.savefig("img/fruits.png") plt.close() @@ -51,4 +51,4 @@ ) -plt.savefig("../.github/img/fruits_weighted.png") +plt.savefig("img/fruits_weighted.png") From d29527aaf3a112efa0440e1d5acc822787548c5e Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Wed, 21 Aug 2024 09:52:17 +0200 Subject: [PATCH 09/14] simplifies data aggregation in the preprocessing example --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 5883256..1a9c5b4 100644 --- a/README.md +++ b/README.md @@ -129,9 +129,8 @@ already in the previous example: ```python # Calculate the weights from the fruits dataframe -df = df.groupby(["true", "predicted"]).size() -df = df.reset_index().rename(columns={0: "weight"}) -weights = df['weight'].astype(float) +df = df.groupby(["true", "predicted"]).size().reset_index() +weights = df[0].astype(float) ax = sankey( left=df['true'], From 09354cccf119dc8b3393fc84a0b83bcd8b331fd8 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 21 Aug 2024 07:53:53 +0000 Subject: [PATCH 10/14] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .github/create_documentation_plots.py | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/create_documentation_plots.py b/.github/create_documentation_plots.py index 472aca6..ae9f8b8 100644 --- a/.github/create_documentation_plots.py +++ b/.github/create_documentation_plots.py @@ -1,6 +1,5 @@ import matplotlib.pyplot as plt import pandas as pd - from pysankey import sankey df = pd.read_csv("../pysankey/fruits.txt", sep=" ", names=["true", "predicted"]) From 1bd43b8bdcb99ff309b338a9eb95f44c956fd4a6 Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Mon, 21 Oct 2024 12:51:33 +0200 Subject: [PATCH 11/14] removes old png file from package folder (the ones still used can be found in the .github/ folder --- pysankey/customers-goods.png | Bin 99849 -> 0 bytes pysankey/fruit.png | Bin 202453 -> 0 bytes pysankey/fruits.png | Bin 144441 -> 0 bytes 3 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 pysankey/customers-goods.png delete mode 100644 pysankey/fruit.png delete mode 100644 pysankey/fruits.png diff --git a/pysankey/customers-goods.png b/pysankey/customers-goods.png deleted file mode 100644 index 51ae90f85f6750c3c8b5319fa98ceeaba0564ed3..0000000000000000000000000000000000000000 GIT binary patch literal 0 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+++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 1a9c5b4..2d3ca70 100644 --- a/README.md +++ b/README.md @@ -121,11 +121,11 @@ plt.show() # to display ### Plotting preprocessed data using weights -However, not always you have or can have the data available in the format mentioned in -the previous example (e.g. if the dataset is too large). In this case, the weights -between the true and predicted labels can also be calculated beforehand and used to -create the sankey diagram. In this example, we continue to work with the data loaded -already in the previous example: +However, the data may not always be available in the format mentioned in +the previous example (for instance, if the dataset is too large). In such cases, the weights +between the true and predicted labels can be calculated in advance and used to +create the Sankey diagram. In this example, we will continue working with the data that was loaded +in the previous example: ```python # Calculate the weights from the fruits dataframe From 0298d8cbd79b8598e2e9e8fdd3783213cbc589be Mon Sep 17 00:00:00 2001 From: Marc Weitz Date: Wed, 30 Oct 2024 12:08:52 +0100 Subject: [PATCH 13/14] Update README.md Co-authored-by: Pierre Sassoulas --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2d3ca70..da8ad26 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ Sankey diagrams flowing only from left to right. ### Simple expected/predicted example with fruits.txt: `pysankey` contains a simple expected/predicted dataset called `fruits.txt` which looks -the following: +like the following:

From fa6f37568473f5176c6d1264813cb14c465ed618 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 30 Oct 2024 11:08:53 +0000 Subject: [PATCH 14/14] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index da8ad26..81d1a5d 100644 --- a/README.md +++ b/README.md @@ -121,11 +121,11 @@ plt.show() # to display ### Plotting preprocessed data using weights -However, the data may not always be available in the format mentioned in -the previous example (for instance, if the dataset is too large). In such cases, the weights -between the true and predicted labels can be calculated in advance and used to -create the Sankey diagram. In this example, we will continue working with the data that was loaded -in the previous example: +However, the data may not always be available in the format mentioned in the previous +example (for instance, if the dataset is too large). In such cases, the weights between +the true and predicted labels can be calculated in advance and used to create the Sankey +diagram. In this example, we will continue working with the data that was loaded in the +previous example: ```python # Calculate the weights from the fruits dataframe