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notebook | Jupyter Interactive Notebook | Widget library

 by   jupyter Jupyter Notebook Version: v7.0.0a2 License: Non-SPDX

 by   jupyter Jupyter Notebook Version: v7.0.0a2 License: Non-SPDX

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kandi X-RAY | notebook Summary

notebook is a Jupyter Notebook library typically used in Telecommunications, Media, Advertising, Marketing, User Interface, Widget, Jupyter, Pandas applications. notebook has no bugs, it has no vulnerabilities and it has medium support. However notebook has a Non-SPDX License. You can download it from GitHub.
The Jupyter notebook is a web-based notebook environment for interactive computing.
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Quality
Quality
Security
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kandi-support Support

  • notebook has a medium active ecosystem.
  • It has 8966 star(s) with 3748 fork(s). There are 314 watchers for this library.
  • There were 4 major release(s) in the last 12 months.
  • There are 2014 open issues and 2357 have been closed. On average issues are closed in 375 days. There are 48 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of notebook is v7.0.0a2
notebook Support
Best in #Widget
Average in #Widget
notebook Support
Best in #Widget
Average in #Widget

quality kandi Quality

  • notebook has 0 bugs and 0 code smells.
notebook Quality
Best in #Widget
Average in #Widget
notebook Quality
Best in #Widget
Average in #Widget

securitySecurity

  • notebook has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • notebook code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
notebook Security
Best in #Widget
Average in #Widget
notebook Security
Best in #Widget
Average in #Widget

license License

  • notebook has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
notebook License
Best in #Widget
Average in #Widget
notebook License
Best in #Widget
Average in #Widget

buildReuse

  • notebook releases are available to install and integrate.
  • Installation instructions, examples and code snippets are available.
  • notebook saves you 9168 person hours of effort in developing the same functionality from scratch.
  • It has 18747 lines of code, 1333 functions and 328 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
notebook Reuse
Best in #Widget
Average in #Widget
notebook Reuse
Best in #Widget
Average in #Widget
Top functions reviewed by kandi - BETA

kandi has reviewed notebook and discovered the below as its top functions. This is intended to give you an instant insight into notebook implemented functionality, and help decide if they suit your requirements.

  • Main entry point .
    • Gets shared projects package
      • Get option .
        • Load a component .
          • Create a module with a given scope
            • Load a script

              Get all kandi verified functions for this library.

              Get all kandi verified functions for this library.

              notebook Key Features

              Jupyter Interactive Notebook

              Installation

              copy iconCopydownload iconDownload
              $ pip install notebook
              

              Running in a local installation

              copy iconCopydownload iconDownload
              $ jupyter notebook
              

              Compute class weight function issue in 'sklearn' library when used in 'Keras' classification (Python 3.8, only in VS code)

              copy iconCopydownload iconDownload
              class_weights = compute_class_weight(
                                                      class_weight = "balanced",
                                                      classes = np.unique(train_classes),
                                                      y = train_classes                                                    
                                                  )
              class_weights = dict(zip(np.unique(train_classes), class_weights))
              class_weights
              

              Conflicting Python versions in SageMaker Studio notebook with Python 3.8 kernel

              copy iconCopydownload iconDownload
              !python3 -V
              
              import sys
              sys.version 
              
              import tensorflow as tf
              print(tf.__version__)
              
              !python3 -V
              
              import sys
              sys.version 
              
              import tensorflow as tf
              print(tf.__version__)
              
              !python3 -V
              
              import sys
              sys.version 
              
              import tensorflow as tf
              print(tf.__version__)
              

              How to undo/redo changes inside the selected cell in Jupyter notebook?

              copy iconCopydownload iconDownload
              {
                  "experimentalDisableDocumentWideUndoRedo": true
              }
              
              pip install "jupyterlab<3.1"
              
              conda install -c conda-forge "jupyterlab<3.1"
              
              {
                  "experimentalDisableDocumentWideUndoRedo": true
              }
              
              pip install "jupyterlab<3.1"
              
              conda install -c conda-forge "jupyterlab<3.1"
              
              {
                  "experimentalDisableDocumentWideUndoRedo": true
              }
              
              pip install "jupyterlab<3.1"
              
              conda install -c conda-forge "jupyterlab<3.1"
              

              How to get console output and plot side by side in a R Notebook?

              copy iconCopydownload iconDownload
              ---
              title: "R Notebook"
              output:
                html_document:
              ---
              
              ```{r, echo = F}
              il <- split(iris, iris$Species)
              ```
              
              :::: {.columns}
              
              ::: {.column width="60%"}
              ```{r, echo = F, results = F}
              lapply(il, plot)
              ```
              :::
              
              ::: {.column width="40%"}
              ```{r, echo = F}
              lapply(il, summary)
              ```
              :::
              
              ::::
              
              ---
              title: "R Notebook"
              output:
                html_document:
              ---
              
              ```{r, results = F, fig.show = "hide"}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              
              :::: {.columns}
              
              ::: {.column width="60%"}
              ```{r, echo = F, results = F}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              :::
              
              ::: {.column width="40%"}
              ```{r, echo = F, fig.show = "hide"}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              :::
              
              ::::
              
              ---
              title: "R Notebook"
              output:
                html_document:
              ---
              
              ```{r, echo = F}
              il <- split(iris, iris$Species)
              ```
              
              :::: {.columns}
              
              ::: {.column width="60%"}
              ```{r, echo = F, results = F}
              lapply(il, plot)
              ```
              :::
              
              ::: {.column width="40%"}
              ```{r, echo = F}
              lapply(il, summary)
              ```
              :::
              
              ::::
              
              ---
              title: "R Notebook"
              output:
                html_document:
              ---
              
              ```{r, results = F, fig.show = "hide"}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              
              :::: {.columns}
              
              ::: {.column width="60%"}
              ```{r, echo = F, results = F}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              :::
              
              ::: {.column width="40%"}
              ```{r, echo = F, fig.show = "hide"}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              :::
              
              ::::
              
              ```{r setup, include=FALSE}
              knitr::opts_chunk$set(echo = FALSE)
              
              # confirm engine for 'js' (it was #37 for me)
              names(knitr::knit_engines$get())
              
              # set the output width for chunks' render
              # this is to keep the summaries even (versicolor was doing its own thing)
              options(width = 75)
              library(tidyverse)
              ```
              
              <style>
              .setupCols {
                display:flex;
                flex-direction:row;
                width: 100%;
              }
              .setupCols p{
                display:flex;
                flex-direction: column;
                width: 45%;
              }
              
              .setupCols pre {
                display:flex;
                flex-direction: column;
                width: 55%
              }
              .setupCols pre code {
                font-size: 85%;
              }
              </style>
              
              <div class="setupCols">
              
              ```{r graphOne}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              
              </div>
              
              ```{r styler,results='asis',engine='js'}
              
              // search for class and tags
              elem = document.querySelector('div.setupCols > pre > code');
              // remove hashtags
              elem.innerHTML = elem.innerHTML.replace(/#{2}/g, '');
              // add newlines between summaries
              elem.innerHTML = elem.innerHTML.replace(/\s{9}\n/g, '<br /><br />')
              
              ```
              
              ```{r setup, include=FALSE}
              knitr::opts_chunk$set(echo = FALSE)
              
              # confirm engine for 'js' (it was #37 for me)
              names(knitr::knit_engines$get())
              
              # set the output width for chunks' render
              # this is to keep the summaries even (versicolor was doing its own thing)
              options(width = 75)
              library(tidyverse)
              ```
              
              <style>
              .setupCols {
                display:flex;
                flex-direction:row;
                width: 100%;
              }
              .setupCols p{
                display:flex;
                flex-direction: column;
                width: 45%;
              }
              
              .setupCols pre {
                display:flex;
                flex-direction: column;
                width: 55%
              }
              .setupCols pre code {
                font-size: 85%;
              }
              </style>
              
              <div class="setupCols">
              
              ```{r graphOne}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              
              </div>
              
              ```{r styler,results='asis',engine='js'}
              
              // search for class and tags
              elem = document.querySelector('div.setupCols > pre > code');
              // remove hashtags
              elem.innerHTML = elem.innerHTML.replace(/#{2}/g, '');
              // add newlines between summaries
              elem.innerHTML = elem.innerHTML.replace(/\s{9}\n/g, '<br /><br />')
              
              ```
              
              ```{r setup, include=FALSE}
              knitr::opts_chunk$set(echo = FALSE)
              
              # confirm engine for 'js' (it was #37 for me)
              names(knitr::knit_engines$get())
              
              # set the output width for chunks' render
              # this is to keep the summaries even (versicolor was doing its own thing)
              options(width = 75)
              library(tidyverse)
              ```
              
              <style>
              .setupCols {
                display:flex;
                flex-direction:row;
                width: 100%;
              }
              .setupCols p{
                display:flex;
                flex-direction: column;
                width: 45%;
              }
              
              .setupCols pre {
                display:flex;
                flex-direction: column;
                width: 55%
              }
              .setupCols pre code {
                font-size: 85%;
              }
              </style>
              
              <div class="setupCols">
              
              ```{r graphOne}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              
              </div>
              
              ```{r styler,results='asis',engine='js'}
              
              // search for class and tags
              elem = document.querySelector('div.setupCols > pre > code');
              // remove hashtags
              elem.innerHTML = elem.innerHTML.replace(/#{2}/g, '');
              // add newlines between summaries
              elem.innerHTML = elem.innerHTML.replace(/\s{9}\n/g, '<br /><br />')
              
              ```
              
              ```{r setup, include=FALSE}
              knitr::opts_chunk$set(echo = FALSE)
              
              # confirm engine for 'js' (it was #37 for me)
              names(knitr::knit_engines$get())
              
              # set the output width for chunks' render
              # this is to keep the summaries even (versicolor was doing its own thing)
              options(width = 75)
              library(tidyverse)
              ```
              
              <style>
              .setupCols {
                display:flex;
                flex-direction:row;
                width: 100%;
              }
              .setupCols p{
                display:flex;
                flex-direction: column;
                width: 45%;
              }
              
              .setupCols pre {
                display:flex;
                flex-direction: column;
                width: 55%
              }
              .setupCols pre code {
                font-size: 85%;
              }
              </style>
              
              <div class="setupCols">
              
              ```{r graphOne}
              mapply(FUN = function(.x) {
                plot(.x)
                summary(.x)
              }, split(iris, iris$Species), SIMPLIFY = FALSE)
              ```
              
              </div>
              
              ```{r styler,results='asis',engine='js'}
              
              // search for class and tags
              elem = document.querySelector('div.setupCols > pre > code');
              // remove hashtags
              elem.innerHTML = elem.innerHTML.replace(/#{2}/g, '');
              // add newlines between summaries
              elem.innerHTML = elem.innerHTML.replace(/\s{9}\n/g, '<br /><br />')
              
              ```
              
              ---
              title: "Example"
              output:
                html_document
              ---
              
              ```{r, echo=FALSE, results='asis'}
              out <- mapply(function(x) {
                knitr::knit_child(text = c(
                  '',
                  ':::: {.columns}',
                  '',
                  '::: {.column width="40%"}',
                  '```{r}',
                  'plot(x)',
                  '```',
                  ':::',
                  '',
                  '::: {.column width="60%"}',
                  '```{r}',
                  'summary(x)',
                  '```',
                  ':::',
                  '',
                  '::::'
                ), envir = environment(), quiet = TRUE)
              }, split(iris, iris$Species), SIMPLIFY = TRUE)
              
              cat(unlist(out), sep = '\n')
              ```
              

              Pandas - How to Save A Styled Dataframe to Image

              copy iconCopydownload iconDownload
              import pandas as pd
              import numpy as np
              import dataframe_image as dfi
              
              col_name = 'TestColumn'
              
              temp_df = pd.DataFrame({'TestColumn':['A','B','A',np.nan]})
              
              t1 = (temp_df[col_name].fillna("Unknown").value_counts()/len(temp_df)*100).to_frame().reset_index()
              t1.rename(columns={'index':' '}, inplace=True)
              t1[' '] = t1[' '].astype(str) 
              
              
              style_test = t1.style.bar(subset=[col_name], color='#5e81f2', vmax=100, vmin=0).set_table_attributes('style="font-size: 17px"').set_properties(
                  **{'color': 'black !important',
                     'border': '1px black solid !important'}
              ).set_table_styles([{
                  'selector': 'th',
                  'props': [('border', '1px black solid !important')]
              }]).set_properties( **{'width': '500px'}).hide_index().set_properties(subset=[" "], **{'text-align': 'left'})
              
              dfi.export(style_test, 'successful_test.png')
              

              embedding jupyter notebook/ google colab in Django app

              copy iconCopydownload iconDownload
              from django.shortcuts import redirect,HttpResponse
              import subprocess
              import time
              
              def open_jupiter_notbook(request):
                  b= subprocess.check_output("jupyter-lab list".split()).decode('utf-8')
                  if "9999" not in b:
                      a=subprocess.Popen("jupyter-lab  --no-browser --port 9999".split())
                  start_time = time.time()
                  unreachable_time = 10
                  while "9999" not in b:
                      timer = time.time()
                      elapsed_time = timer-start_time
                      b= subprocess.check_output("jupyter-lab list".split()).decode('utf-8')
                      if "9999" in b:
                          break
                      if elapsed_time > unreachable_time:
                          return HttpResponse("Unreachable")
                  path = b.split('\n')[1].split('::',1)[0]
                  #You can here add data to your path if you want to open file or anything
                  return redirect(path)
              
              <iframe src="{% url 'open_jupiter_notbook' %}" width= 600px height=200px></iframe>
              
              from django.shortcuts import redirect,HttpResponse
              import subprocess
              import time
              
              def open_jupiter_notbook(request):
                  b= subprocess.check_output("jupyter-lab list".split()).decode('utf-8')
                  if "9999" not in b:
                      a=subprocess.Popen("jupyter-lab  --no-browser --port 9999".split())
                  start_time = time.time()
                  unreachable_time = 10
                  while "9999" not in b:
                      timer = time.time()
                      elapsed_time = timer-start_time
                      b= subprocess.check_output("jupyter-lab list".split()).decode('utf-8')
                      if "9999" in b:
                          break
                      if elapsed_time > unreachable_time:
                          return HttpResponse("Unreachable")
                  path = b.split('\n')[1].split('::',1)[0]
                  #You can here add data to your path if you want to open file or anything
                  return redirect(path)
              
              <iframe src="{% url 'open_jupiter_notbook' %}" width= 600px height=200px></iframe>
              
              from google.colab import files
              src = list(files.upload().values())[0]
              open('mylib.py','wb').write(src)
              import mylib
              

              JupyterLab Notebook cells going missing

              copy iconCopydownload iconDownload
              # (or conda-forge equivalent if you use conda/mamba)
              pip install -U "jupyterlab>=3.1.10"
              
              {
                  "renderCellOnIdle": false,
                  "numberCellsToRenderDirectly": 10000000000000
              }
              
              # (or conda-forge equivalent if you use conda/mamba)
              pip install -U "jupyterlab>=3.1.10"
              
              {
                  "renderCellOnIdle": false,
                  "numberCellsToRenderDirectly": 10000000000000
              }
              
              {
                  "markdownCellConfig": {
                      "lineNumbers": true
                  },
                  "codeCellConfig": {
                       "lineNumbers": true
                  },
                  "renderCellOnIdle": false,
                  "numberCellsToRenderDirectly": 10000000000000
              }
              

              html inline style not applying in Jupyter notebook cells anymore

              copy iconCopydownload iconDownload
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              
              <div class='verdana'>This should be Verdana font.</div>
              
              <div class="bigger">Font size 20px</div>
              
              from IPython.core.display import HTML
              def css_styling():
                  styles = open("./styles/custom.css", "r").read()
                  return HTML(styles)
              css_styling()
              
              %%html
              <style>
              // add your CSS styling here
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              </style>
              
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              
              <div class='verdana'>This should be Verdana font.</div>
              
              <div class="bigger">Font size 20px</div>
              
              from IPython.core.display import HTML
              def css_styling():
                  styles = open("./styles/custom.css", "r").read()
                  return HTML(styles)
              css_styling()
              
              %%html
              <style>
              // add your CSS styling here
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              </style>
              
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              
              <div class='verdana'>This should be Verdana font.</div>
              
              <div class="bigger">Font size 20px</div>
              
              from IPython.core.display import HTML
              def css_styling():
                  styles = open("./styles/custom.css", "r").read()
                  return HTML(styles)
              css_styling()
              
              %%html
              <style>
              // add your CSS styling here
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              </style>
              
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              
              <div class='verdana'>This should be Verdana font.</div>
              
              <div class="bigger">Font size 20px</div>
              
              from IPython.core.display import HTML
              def css_styling():
                  styles = open("./styles/custom.css", "r").read()
                  return HTML(styles)
              css_styling()
              
              %%html
              <style>
              // add your CSS styling here
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              </style>
              
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              
              <div class='verdana'>This should be Verdana font.</div>
              
              <div class="bigger">Font size 20px</div>
              
              from IPython.core.display import HTML
              def css_styling():
                  styles = open("./styles/custom.css", "r").read()
                  return HTML(styles)
              css_styling()
              
              %%html
              <style>
              // add your CSS styling here
              div.verdana {    
                  font-family:verdana;
              }
              
              div.bigger {
                  font-size: 20pt;
              }
              </style>
              

              How can I update Google Colab's Python version?

              copy iconCopydownload iconDownload
              # install Anaconda3
              !wget -qO ac.sh https://repo.anaconda.com/archive/Anaconda3-2020.07-Linux-x86_64.sh 
              !bash ./ac.sh -b
              
              # a fake google.colab library
              !ln -s /usr/local/lib/python3.6/dist-packages/google \
                     /root/anaconda3/lib/python3.8/site-packages/google
              
              # start jupyterlab, which now has Python3 = 3.8
              !nohup /root/anaconda3/bin/jupyter-lab --ip=0.0.0.0&
              
              # access through ngrok, click the link
              !pip install pyngrok -q
              from pyngrok import ngrok
              print(ngrok.connect(8888))
              
              # Install the python version
              !apt-get install python3.9
              
              # Select the version
              !python3.9 setup.py
              
              virtualenv env --python=python3.9
              
              # install Anaconda3
              !wget -qO ac.sh https://repo.anaconda.com/archive/Anaconda3-2020.07-Linux-x86_64.sh 
              !bash ./ac.sh -b
              
              # a fake google.colab library
              !ln -s /usr/local/lib/python3.6/dist-packages/google \
                     /root/anaconda3/lib/python3.8/site-packages/google
              
              # start jupyterlab, which now has Python3 = 3.8
              !nohup /root/anaconda3/bin/jupyter-lab --ip=0.0.0.0&
              
              # access through ngrok, click the link
              !pip install pyngrok -q
              from pyngrok import ngrok
              print(ngrok.connect(8888))
              
              # Install the python version
              !apt-get install python3.9
              
              # Select the version
              !python3.9 setup.py
              
              virtualenv env --python=python3.9
              
              # install Anaconda3
              !wget -qO ac.sh https://repo.anaconda.com/archive/Anaconda3-2020.07-Linux-x86_64.sh 
              !bash ./ac.sh -b
              
              # a fake google.colab library
              !ln -s /usr/local/lib/python3.6/dist-packages/google \
                     /root/anaconda3/lib/python3.8/site-packages/google
              
              # start jupyterlab, which now has Python3 = 3.8
              !nohup /root/anaconda3/bin/jupyter-lab --ip=0.0.0.0&
              
              # access through ngrok, click the link
              !pip install pyngrok -q
              from pyngrok import ngrok
              print(ngrok.connect(8888))
              
              # Install the python version
              !apt-get install python3.9
              
              # Select the version
              !python3.9 setup.py
              
              virtualenv env --python=python3.9
              
              !python --version
              #3.7.11
              
              #install python 3.9
              !sudo apt-get update -y
              !sudo apt-get install python3.9
              
              #change alternatives
              !sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1
              !sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 2
              
              #check python version
              !python --version
              #3.9.6
              
              !sudo update-alternatives --config python3
              #after running, enter the row number of the python version you want. 
              
              !python --version
              #3.7.11
              
              #install python 3.9
              !sudo apt-get update -y
              !sudo apt-get install python3.9
              
              #change alternatives
              !sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1
              !sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 2
              
              #check python version
              !python --version
              #3.9.6
              
              !sudo update-alternatives --config python3
              #after running, enter the row number of the python version you want. 
              
              !python --version
              #3.7.11
              
              #install python 3.9
              !sudo apt-get update -y
              !sudo apt-get install python3.9
              
              #change alternatives
              !sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 1
              !sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 2
              
              #check python version
              !python --version
              #3.9.6
              
              !sudo update-alternatives --config python3
              #after running, enter the row number of the python version you want. 
              

              How to calculate correlation coefficients using sklearn CCA module?

              copy iconCopydownload iconDownload
              import numpy as np
              from matplotlib import pyplot as plt
              from sklearn.cross_decomposition import CCA
              
              # rows contain the number of samples for CCA and the number of rvs goes in columns
              X = np.random.randn(2000, 100)
              Y = np.random.randn(2000, 50)
              
              # num of components
              n_comps = min(X.shape[1], Y.shape[1])
              cca = CCA(n_components=n_comps)
              cca.fit(X, Y)
              X_c, Y_c = cca.transform(X, Y)
              
              # calculate and plot the correlations of all components
              corrs = [np.corrcoef(X_c[:, i], Y_c[:, i])[0, 1] for i in range(n_comps)]    
              plt.plot(corrs)
              plt.xlabel('cca_idx')
              plt.ylabel('cca_corr')
              plt.show()
              
              Y = np.dot(X, np.random.randn(100, 100)) 
              
              import numpy as np
              from matplotlib import pyplot as plt
              from sklearn.cross_decomposition import CCA
              
              # rows contain the number of samples for CCA and the number of rvs goes in columns
              X = np.random.randn(2000, 100)
              Y = np.random.randn(2000, 50)
              
              # num of components
              n_comps = min(X.shape[1], Y.shape[1])
              cca = CCA(n_components=n_comps)
              cca.fit(X, Y)
              X_c, Y_c = cca.transform(X, Y)
              
              # calculate and plot the correlations of all components
              corrs = [np.corrcoef(X_c[:, i], Y_c[:, i])[0, 1] for i in range(n_comps)]    
              plt.plot(corrs)
              plt.xlabel('cca_idx')
              plt.ylabel('cca_corr')
              plt.show()
              
              Y = np.dot(X, np.random.randn(100, 100)) 
              

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              QUESTION

              Compute class weight function issue in 'sklearn' library when used in 'Keras' classification (Python 3.8, only in VS code)

              Asked 2022-Mar-27 at 23:14

              The classifier script I wrote is working fine and recently added weight balancing to the fitting. Since I added the weight estimate function using 'sklearn' library I get the following error :

              compute_class_weight() takes 1 positional argument but 3 were given
              

              This error does not make sense per documentation. The script should have three inputs but not sure why it says expecting only one variable. Full error and code information is shown below. Apparently, this is failing only in VS code. I tested in the Jupyter notebook and working fine. So it seems an issue with VS code compiler. Any one notice? ( I am using Python 3.8 with other latest other libraries)

              from sklearn.utils import compute_class_weight
              
              train_classes = train_generator.classes
              
              class_weights = compute_class_weight(
                                                      "balanced",
                                                      np.unique(train_classes),
                                                      train_classes                                                    
                                                  )
              class_weights = dict(zip(np.unique(train_classes), class_weights)),
              class_weights
              

              In Jupyter Notebook,

              enter image description here

              enter image description here

              ANSWER

              Answered 2022-Mar-27 at 23:14

              After spending a lot of time, this is how I fixed it. I still don't know why but when the code is modified as follows, it works fine. I got the idea after seeing this solution for a similar but slightly different issue.

              class_weights = compute_class_weight(
                                                      class_weight = "balanced",
                                                      classes = np.unique(train_classes),
                                                      y = train_classes                                                    
                                                  )
              class_weights = dict(zip(np.unique(train_classes), class_weights))
              class_weights
              

              Source https://stackoverflow.com/questions/69783897

              Community Discussions, Code Snippets contain sources that include Stack Exchange Network

              Vulnerabilities

              No vulnerabilities reported

              Install notebook

              You can find the installation documentation for the Jupyter platform, on ReadTheDocs. The documentation for advanced usage of Jupyter notebook can be found here.
              See CONTRIBUTING.rst for how to set up a local development installation.

              Support

              If you are interested in contributing to the project, see CONTRIBUTING.rst.

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