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[#]: collector: (lujun9972)
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[#]: translator: (chen-ni)
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Jupyter and data science in Fedora)
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[#]: via: (https://fedoramagazine.org/jupyter-and-data-science-in-fedora/)
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[#]: author: (Avi Alkalay https://fedoramagazine.org/author/aviram/)
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Jupyter and data science in Fedora
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======
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![][1]
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In the past, kings and leaders used oracles and magicians to help them predict the future — or at least get some good advice due to their supposed power to perceive hidden information. Nowadays, we live in a society obsessed with quantifying everything. So we have data scientists to do this job.
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Data scientists use statistical models, numerical techniques and advanced algorithms that didn’t come from statistical disciplines, along with the data that exist on databases, to find, to infer, to predict data that doesn’t exist yet. Sometimes this data is about the future. That is why we do a lot of predictive analytics and prescriptive analytics.
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Here are some questions to which data scientists help find answers:
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1. Who are the students with high propensity to abandon the class? For each one, what are the reasons for leaving?
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2. Which house has a price above or below the fair price? What is the fair price for a certain house?
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3. What are the hidden groups that my clients classify themselves?
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4. Which future problems this premature child will develop?
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5. How many calls will I get in my call center tomorrow 11:43 AM?
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6. My bank should or should not lend money to this customer?
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Note how the answer to all these question is not sitting in any database waiting to be queried. These are all data that still doesn’t exist and has to be calculated. That is part of the job we data scientists do.
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Throughout this article you’ll learn how to prepare a Fedora system as a Data Scientist’s development environment and also a production system. Most of the basic software is RPM-packaged, but the most advanced parts can only be installed, nowadays, with Python’s _pip_ tool.
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### Jupyter — the IDE
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Most modern data scientists use Python. And an important part of their work is EDA (exploratory data analysis). EDA is a manual and interactive process that retrieves data, explores its features, searches for correlations, and uses plotted graphics to visualize and understand how data is shaped and prototypes predictive models.
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Jupyter is a web application perfect for this task. Jupyter works with Notebooks, documents that mix rich text including beautifully rendered math formulas (thanks to [mathjax][2]), blocks of code and code output, including graphics.
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Notebook files have extension _.ipynb_, which means Interactive Python Notebook.
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#### Setting up and running Jupyter
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First, install essential packages for Jupyter ([using][3] _[sudo][3]_):
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```
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$ sudo dnf install python3-notebook mathjax sscg
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```
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You might want to install additional and optional Python modules commonly used by data scientists:
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```
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$ sudo dnf install python3-seaborn python3-lxml python3-basemap python3-scikit-image python3-scikit-learn python3-sympy python3-dask+dataframe python3-nltk
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```
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Set a password to log into Notebook web interface and avoid those long tokens. Run the following command anywhere on your terminal:
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```
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$ mkdir -p $HOME/.jupyter
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$ jupyter notebook password
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```
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Now, type a password for yourself. This will create the file _$HOME/.jupyter/jupyter_notebook_config.json_ with your encrypted password.
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Next, prepare for SSLby generating a self-signed HTTPS certificate for Jupyter’s web server:
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```
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$ cd $HOME/.jupyter; sscg
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```
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Finish configuring Jupyter by editing your _$HOME/.jupyter/jupyter_notebook_config.json_ file. Make it look like this:
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```
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{
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"NotebookApp": {
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"password": "sha1:abf58...87b",
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"ip": "*",
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"allow_origin": "*",
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"allow_remote_access": true,
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"open_browser": false,
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"websocket_compression_options": {},
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"certfile": "/home/aviram/.jupyter/service.pem",
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"keyfile": "/home/aviram/.jupyter/service-key.pem",
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"notebook_dir": "/home/aviram/Notebooks"
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}
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}
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```
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The parts in red must be changed to match your folders. Parts in blue were already there after you created your password. Parts in green are the crypto-related files generated by _sscg_.
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Create a folder for your notebook files, as configured in the _notebook_dir_ setting above:
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```
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$ mkdir $HOME/Notebooks
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```
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Now you are all set. Just run Jupyter Notebook from anywhere on your system by typing:
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```
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$ jupyter notebook
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```
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Or add this line to your _$HOME/.bashrc_ file to create a shortcut command called _jn_:
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```
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alias jn='jupyter notebook'
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```
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After running the command _jn_, access _<https://your-fedora-host.com:8888>_ from any browser on the network to see the Jupyter user interface. You’ll need to use the password you set up earlier. Start typing some Python code and markup text. This is how it looks:
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![Jupyter with a simple notebook][4]
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In addition to the IPython environment, you’ll also get a web-based Unix terminal provided by _terminado_. Some people might find this useful, while others find this insecure. You can disable this feature in the config file.
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### JupyterLab — the next generation of Jupyter
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JupyterLab is the next generation of Jupyter, with a better interface and more control over your workspace. It’s currently not RPM-packaged for Fedora at the time of writing, but you can use _pip_ to get it installed easily:
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```
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$ pip3 install jupyterlab --user
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$ jupyter serverextension enable --py jupyterlab
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```
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Then run your regular _jupiter notebook_ command or _jn_ alias. JupyterLab will be accessible from _<http://your-linux-host.com:8888/**lab>_**.
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### Tools used by data scientists
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In this section you can get to know some of these tools, and how to install them. Unless noted otherwise, the module is already packaged for Fedora and was installed as prerequisites for previous components.
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#### **Numpy**
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_Numpy_ is an advanced and C-optimized math library designed to work with large in-memory datasets. It provides advanced multidimensional matrix support and operations, including math functions as log(), exp(), trigonometry etc.
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#### Pandas
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In this author’s opinion, Python is THE platform for data science mostly because of Pandas. Built on top of numpy, Pandas makes easy the work of preparing and displaying data. You can think of it as a no-UI spreadsheet, but ready to work with much larger datasets. Pandas helps with data retrieval from a SQL database, CSV or other types of files, columns and rows manipulation, data filtering and, to some extent, data visualization with matplotlib.
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#### Matplotlib
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Matplotlib is a library to plot 2D and 3D data. It has great support for notations in graphics, labels and overlays
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![matplotlib pair of graphics showing a cost function searching its optimal value through a gradient descent algorithm][5]
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#### Seaborn
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Built on top of matplotlib, Seaborn’s graphics are optimized for a more statistical comprehension of data. It automatically displays regression lines or Gauss curve approximations of plotted data.
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![Linear regression visualised with SeaBorn][6]
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#### [StatsModels][7]
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StatsModels provides algorithms for statistical and econometrics data analysis such as linear and logistic regressions. Statsmodel is also home for the classical family of [time series algorithms][8] known as ARIMA.
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![Normalized number of passengers across time \(blue\) and ARIMA-predicted number of passengers \(red\)][9]
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#### Scikit-learn
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The central piece of the machine-learning ecosystem, [scikit][10] provides predictor algorithms for [regression][11] (Elasticnet, Gradient Boosting, Random Forest etc) and [classification][11] and clustering (K-means, DBSCAN etc). It features a very well designed API. Scikit also has classes for advanced data manipulation, dataset split into train and test parts, dimensionality reduction and data pipeline preparation.
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#### XGBoost
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XGBoost is the most advanced regressor and classifier used nowadays. It’s not part of scikit-learn, but it adheres to scikit’s API. [XGBoost][12] is not packaged for Fedora and should be installed with pip. [XGBoost can be accelerated with your nVidia GPU][13], but not through its _pip_ package. You can get this if you compile it yourself against CUDA. Get it with:
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```
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$ pip3 install xgboost --user
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```
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#### Imbalanced Learn
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[imbalanced-learn][14] provides ways for under-sampling and over-sampling data. It is useful in fraud detection scenarios where known fraud data is very small when compared to non-fraud data. In these cases data augmentation is needed for the known fraud data, to make it more relevant to train predictors. Install it with _pip_:
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```
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$ pip3 install imblearn --user
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```
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#### NLTK
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The [Natural Language toolkit][15], or NLTK, helps you work with human language data for the purpose of building chatbots (just to cite an example).
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#### SHAP
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Machine learning algorithms are very good on predicting, but aren’t good at explaining why they made a prediction. [SHAP][16] solves that, by analyzing trained models.
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![Where SHAP fits into the data analysis process][17]
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Install it with _pip_:
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```
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$ pip3 install shap --user
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```
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#### [Keras][18]
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Keras is a library for deep learning and neural networks. Install it with _pip_:
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```
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$ sudo dnf install python3-h5py
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$ pip3 install keras --user
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```
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#### [TensorFlow][19]
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TensorFlow is a popular neural networks builder. Install it with _pip_:
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```
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$ pip3 install tensorflow --user
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```
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* * *
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_Photo courtesy of [FolsomNatural][20] on [Flickr][21] (CC BY-SA 2.0)._
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--------------------------------------------------------------------------------
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via: https://fedoramagazine.org/jupyter-and-data-science-in-fedora/
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作者：[Avi Alkalay][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://fedoramagazine.org/author/aviram/
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[b]: https://github.com/lujun9972
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[1]: https://fedoramagazine.org/wp-content/uploads/2019/06/jupyter-816x345.jpg
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[2]: http://mathjax.org
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[3]: https://fedoramagazine.org/howto-use-sudo/
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[4]: https://avi.alkalay.net/articlefiles/2018/07/jupyter-fedora.png
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[5]: https://fedoramagazine.org/wp-content/uploads/2019/06/gradient-descent-cost-function-optimization.png
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[6]: https://seaborn.pydata.org/_images/regression_marginals.png
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[7]: https://www.statsmodels.org/
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[8]: https://www.statsmodels.org/stable/examples/index.html#stats
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[9]: https://fedoramagazine.org/wp-content/uploads/2019/06/time-series.png
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[10]: https://scikit-learn.org/stable/
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[11]: https://scikit-learn.org/stable/supervised_learning.html#supervised-learning
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[12]: https://xgboost.ai
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[13]: https://xgboost.readthedocs.io/en/latest/gpu/index.html
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[14]: https://imbalanced-learn.readthedocs.io
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[15]: https://www.nltk.org
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[16]: https://github.com/slundberg/shap
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[17]: https://raw.githubusercontent.com/slundberg/shap/master/docs/artwork/shap_diagram.png
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[18]: https://keras.io
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[19]: https://www.tensorflow.org
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[20]: https://www.flickr.com/photos/87249144@N08/
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[21]: https://www.flickr.com/photos/87249144@N08/45871861611/
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244
translated/tech/20190702 Jupyter and data science in Fedora.md
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244
translated/tech/20190702 Jupyter and data science in Fedora.md
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@ -0,0 +1,244 @@
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[#]: collector: (lujun9972)
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[#]: translator: (chen-ni)
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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[#]: subject: (Jupyter and data science in Fedora)
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[#]: via: (https://fedoramagazine.org/jupyter-and-data-science-in-fedora/)
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[#]: author: (Avi Alkalay https://fedoramagazine.org/author/aviram/)
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在 Fedora 上搭建 Jupyter 和数据科学环境
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======
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![][1]
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在过去，神谕和魔法师被认为拥有发现奥秘的力量，国王和统治者们会借助他们预测未来，或者至少是听取一些建议。如今我们生活在一个痴迷于将一切事情量化的社会里，这份工作就交给数据科学家了。
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数据科学家通过使用统计模型、数值分析，以及统计学之外的高级算法，结合数据库里已经存在的数据，去发掘、推断和预测尚不存在的数据（有时是关于未来的数据）。这就是为什么我们要做这么多的预测分析和规划分析。
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下面是一些可以借助数据科学家回答的问题：
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1. 哪些学生有旷课倾向？每个人旷课的原因分别是什么？
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2. 哪栋房子的售价比合理价格要高或者低？一栋房子的合理价格是多少？
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3. 如何将我们的客户按照潜在的特质进行分组？
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4. 这个孩子的早熟可能会在未来引发什么问题？
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5. 我们的呼叫中心在明天早上 11 点 43 分会接收到多少次呼叫？
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6. 我们的银行是否应该向这位客户发放贷款？
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请注意，这些问题的答案是在任何数据库里都查询不到的，因为它们尚不存在，需要被计算出来才行。这就是我们数据科学家从事的工作。
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在这篇文章中你会学习如何将 Fedora 系统打造成数据科学家的开发环境和生产系统。其中大多数基本软件都有 RPM 软件包，但是最先进的组件目前只能通过 Python 的 **pip** 工具安装。
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### Jupyter IDE
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大多数现代数据科学家使用 Python 工作。他们工作中很重要的一部分是 <ruby>探索性数据分析<rt>Exploratory Data Analysis<rt></ruby>（EDA）。EDA 是一种手动进行的、交互性的过程，包括提取数据、探索数据特征、寻找相关性、通过绘制图形进行数据可视化并理解数据的分布特征，以及实现简易的预测模型。
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Jupyter 是能够完美胜任该工作的一个 web 应用。Jupyter 使用的 Notebook 文件支持丰富的文本，包括渲染精美的数学公式（得益于 [mathjax][2]）、代码块和代码输出（包括图形输出）。
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Notebook 文件的后缀是 **.ipynb**，意思是“交互式 Python Notebook”。
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#### 搭建并运行 Jupyter
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首先，[使用 sudo][3] 安装 Jupyter 核心软件包：
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```
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$ sudo dnf install python3-notebook mathjax sscg
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```
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你或许需要安装数据科学家常用的一些附加可选模块：
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```
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$ sudo dnf install python3-seaborn python3-lxml python3-basemap python3-scikit-image python3-scikit-learn python3-sympy python3-dask+dataframe python3-nltk
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```
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设置一个用来登陆 Notebook 的 web 界面的密码，从而避免使用冗长的令牌。你可以在终端里任何一个位置运行下面的命令：
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```
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$ mkdir -p $HOME/.jupyter
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$ jupyter notebook password
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```
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然后输入你的密码，这时会自动创建 ** $HOME/.jupyter/jupyter_notebook_config.json ** 这个文件，包含了你的密码的加密后版本。
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|
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接下来，通过使用 SSLby 为 Jupyter 的 web 服务器生成一个自签名的 HTTPS 证书：
|
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|
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```
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$ cd $HOME/.jupyter; sscg
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```
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配置 Jupyter 的最后一步是编辑 **$HOME/.jupyter/jupyter_notebook_config.json** 这个文件。按照下面的模版编辑该文件：
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|
||||
```
|
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{
|
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"NotebookApp": {
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"password": "sha1:abf58...87b",
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"ip": "*",
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||||
"allow_origin": "*",
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||||
"allow_remote_access": true,
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"open_browser": false,
|
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"websocket_compression_options": {},
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"certfile": "/home/aviram/.jupyter/service.pem",
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"keyfile": "/home/aviram/.jupyter/service-key.pem",
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"notebook_dir": "/home/aviram/Notebooks"
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}
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}
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```
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红色的部分应该替换为你的文件夹。蓝色的部分在你创建完密码之后就已经自动生成了。绿色的部分是 **sscg** 生成的和加密相关的文件。
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接下来创建一个用来存放 notebook 文件的文件夹，应该和上面配置里 **notebook_dir** 一致：
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```
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$ mkdir $HOME/Notebooks
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```
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你已经完成了配置。现在可以在系统里的任何一个地方通过以下命令启动 Jupyter Notebook：
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```
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$ jupyter notebook
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```
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或者是将下面这行代码添加到 **$HOME/.bashrc** 文件，创建一个叫做 **jn** 的快捷命令：
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```
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alias jn='jupyter notebook'
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```
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运行 **jn** 命令之后，你可以通过网络内部的任何一个浏览器访问 **<https://your-fedora-host.com:8888>** （LCTT 译注：将域名替换为服务器的域名），就可以看到 Jupyter 的用户界面了，需要使用前面设置的密码登录。你可以尝试键入一些 Python 代码和标记文本，看起来会像下面这样：
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|
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![Jupyter with a simple notebook][4]
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除了 IPython 环境，安装过程还会生成一个由 **terminado** 提供的基于 web 的 Unix 终端。有人觉得这很实用，也有人觉得这样不是很安全。你可以在配置文件里禁用这个功能。
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### JupyterLab — 下一代 Jupyter
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||||
|
||||
JupyterLab 是下一代的 Jupyter，拥有更好的用户界面和对工作空间更强的操控性。在写这篇文章的时候 JupyterLab 还没有可用的 RPM 软件包，但是你可以使用 **pip** 轻松完成安装：
|
||||
|
||||
```
|
||||
$ pip3 install jupyterlab --user
|
||||
$ jupyter serverextension enable --py jupyterlab
|
||||
```
|
||||
|
||||
然后运行 **jupiter notebook** 命令或者 **jn** 快捷命令。访问 **<http://your-linux-host.com:8888/**lab**>** LCTT 译注：将域名替换为服务器的域名）就可以使用 JupyterLab 了。
|
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|
||||
### 数据科学家使用的工具
|
||||
|
||||
在下面这一节里，你将会了解到数据科学家使用的一些工具及其安装方法。除非另作说明，这些工具应该已经有 Fedora 软件包版本，并且已经作为前面组件所需要的软件包而被安装了。
|
||||
|
||||
#### **Numpy**
|
||||
|
||||
**Numpy** 是一个针对 C 语言优化过的高级库，用来处理内存里的大型数据集。它支持高级多维矩阵及其运算，并且包含了 log()、exp()、三角函数等数学函数。
|
||||
|
||||
#### Pandas
|
||||
|
||||
在我看来，正是 Pandas 成就了 Python 作为数据科学首选平台的地位。Pandas 构建在 numpy 之上，可以让数据准备和数据呈现工作变得简单很多。你可以把它想象成一个没有用户界面的电子表格程序，但是能够处理的数据集要大得多。Pandas 支持从 SQL 数据库或者 CSV 等格式的文件中提取数据、按列或者按行进行操作、数据筛选，以及通过 matplotlib 实现数据可视化的一部分功能。
|
||||
|
||||
#### Matplotlib
|
||||
|
||||
Matplotlib 是一个用来绘制 2D 和 3D 数据图像的库，在图象注解、标签和叠加层方面都提供了相当不错的支持。
|
||||
|
||||
![matplotlib pair of graphics showing a cost function searching its optimal value through a gradient descent algorithm][5]
|
||||
|
||||
#### Seaborn
|
||||
|
||||
Seaborn 构建在 matplotlib 之上，它的绘图功能经过了优化，更加适合数据的统计学研究，比如说可以自动显示所绘制数据的近似回归线或者正态分布曲线。
|
||||
|
||||
![Linear regression visualised with SeaBorn][6]
|
||||
|
||||
#### [StatsModels][7]
|
||||
|
||||
StatsModels 为统计学和经济计量学的数据分析问题（例如线形回归和逻辑回归）提供算法支持，同时提供经典的 [时间序列算法][8] 家族：ARIMA。
|
||||
|
||||
![Normalized number of passengers across time \(blue\) and ARIMA-predicted number of passengers \(red\)][9]
|
||||
|
||||
#### Scikit-learn
|
||||
|
||||
作为机器学习生态系统的核心部件，[scikit][10] 为不同类型的问题提供预测算法，包括 [回归问题][11]（算法包括 Elasticnet、Gradient Boosting、随机森林等等）、[分类问题][11] 和聚类问题（算法包括 K-means 和 DBSCAN 等等），并且拥有设计精良的 API。Scikit 还定义了一些专门的 Python 类，用来支持数据操作的高级技巧，比如将数据集拆分为训练集和测试集、降维算法、数据准备管道流程等等。
|
||||
|
||||
#### XGBoost
|
||||
|
||||
XGBoost 是目前可以使用的最先进的回归器和分类器。它并不是 scikit-learn 的一部分，但是却遵循了 scikit 的 API。[XGBoost][12] 并没有针对 Fedora 的软件包，但可以使用 pip 安装。[使用英伟达显卡可以提升 XGBoost 算法的性能][13]，但是这并不能通过 **pip** 软件包来实现。如果你希望使用这个功能，可以针对 CUDA （LCTT 译注：英伟达开发的并行计算平台）自己进行编译。使用下面这个命令安装 XGBoost：
|
||||
|
||||
```
|
||||
$ pip3 install xgboost --user
|
||||
```
|
||||
|
||||
#### Imbalanced Learn
|
||||
|
||||
[imbalanced-learn][14] 是一个解决数据欠采样和过采样问题的工具。比如在反欺诈问题中，欺诈数据相对于正常数据来说数量非常小，这个时候就需要对欺诈数据进行数据增强，从而让预测器能够更好地适应数据集。使用 **pip** 安装：
|
||||
|
||||
```
|
||||
$ pip3 install imblearn --user
|
||||
```
|
||||
|
||||
#### NLTK
|
||||
|
||||
[Natural Language toolkit][15]（简称 NLTK）是一个处理人类语言数据的工具，举例来说，它可以被用来开发一个聊天机器人。
|
||||
|
||||
|
||||
#### SHAP
|
||||
|
||||
机器学习算法拥有强大的预测能力，但并不能够很好地解释为什么做出这样或那样的预测。[SHAP][16] 可以通过分析训练后的模型来解决这个问题。
|
||||
|
||||
![Where SHAP fits into the data analysis process][17]
|
||||
|
||||
使用 **pip** 安装：
|
||||
|
||||
```
|
||||
$ pip3 install shap --user
|
||||
```
|
||||
|
||||
#### [Keras][18]
|
||||
|
||||
Keras 是一个深度学习和神经网络模型的库，使用 **pip** 安装：
|
||||
|
||||
```
|
||||
$ sudo dnf install python3-h5py
|
||||
$ pip3 install keras --user
|
||||
```
|
||||
|
||||
#### [TensorFlow][19]
|
||||
|
||||
TensorFlow 是一个非常流行的神经网络模型搭建工具，使用 **pip** 安装：
|
||||
|
||||
```
|
||||
$ pip3 install tensorflow --user
|
||||
```
|
||||
|
||||
* * *
|
||||
|
||||
_Photo courtesy of [FolsomNatural][20] on [Flickr][21] (CC BY-SA 2.0)._
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://fedoramagazine.org/jupyter-and-data-science-in-fedora/
|
||||
|
||||
作者：[Avi Alkalay][a]
|
||||
选题：[lujun9972][b]
|
||||
译者：[chen-ni](https://github.com/chen-ni)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://fedoramagazine.org/author/aviram/
|
||||
[b]: https://github.com/lujun9972
|
||||
[1]: https://fedoramagazine.org/wp-content/uploads/2019/06/jupyter-816x345.jpg
|
||||
[2]: http://mathjax.org
|
||||
[3]: https://fedoramagazine.org/howto-use-sudo/
|
||||
[4]: https://avi.alkalay.net/articlefiles/2018/07/jupyter-fedora.png
|
||||
[5]: https://fedoramagazine.org/wp-content/uploads/2019/06/gradient-descent-cost-function-optimization.png
|
||||
[6]: https://seaborn.pydata.org/_images/regression_marginals.png
|
||||
[7]: https://www.statsmodels.org/
|
||||
[8]: https://www.statsmodels.org/stable/examples/index.html#stats
|
||||
[9]: https://fedoramagazine.org/wp-content/uploads/2019/06/time-series.png
|
||||
[10]: https://scikit-learn.org/stable/
|
||||
[11]: https://scikit-learn.org/stable/supervised_learning.html#supervised-learning
|
||||
[12]: https://xgboost.ai
|
||||
[13]: https://xgboost.readthedocs.io/en/latest/gpu/index.html
|
||||
[14]: https://imbalanced-learn.readthedocs.io
|
||||
[15]: https://www.nltk.org
|
||||
[16]: https://github.com/slundberg/shap
|
||||
[17]: https://raw.githubusercontent.com/slundberg/shap/master/docs/artwork/shap_diagram.png
|
||||
[18]: https://keras.io
|
||||
[19]: https://www.tensorflow.org
|
||||
[20]: https://www.flickr.com/photos/87249144@N08/
|
||||
[21]: https://www.flickr.com/photos/87249144@N08/45871861611/
|
||||
Loading…
Reference in New Issue
Block a user