From ee7d54671e5f63721993d58196de2196f2ad47fb Mon Sep 17 00:00:00 2001 From: geekpi Date: Thu, 2 Jun 2022 08:57:00 +0800 Subject: [PATCH] translated --- ...hine Learning Model to Make Predictions.md | 89 ------------------- ...hine Learning Model to Make Predictions.md | 87 ++++++++++++++++++ 2 files changed, 87 insertions(+), 89 deletions(-) delete mode 100644 sources/tech/20220530 Using a Machine Learning Model to Make Predictions.md create mode 100644 translated/tech/20220530 Using a Machine Learning Model to Make Predictions.md diff --git a/sources/tech/20220530 Using a Machine Learning Model to Make Predictions.md b/sources/tech/20220530 Using a Machine Learning Model to Make Predictions.md deleted file mode 100644 index 7c107e5f39..0000000000 --- a/sources/tech/20220530 Using a Machine Learning Model to Make Predictions.md +++ /dev/null @@ -1,89 +0,0 @@ -[#]: subject: "Using a Machine Learning Model to Make Predictions" -[#]: via: "https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/" -[#]: author: "Jishnu Saurav Mittapalli https://www.opensourceforu.com/author/jishnu-saurav-mittapalli/" -[#]: collector: "lkxed" -[#]: translator: "geekpi" -[#]: reviewer: " " -[#]: publisher: " " -[#]: url: " " - -Using a Machine Learning Model to Make Predictions -====== -Machine learning is basically a subset of artificial intelligence that uses previously existing data to make a prediction on new data. Of course, all of us know this by now! This article demonstrates how a machine learning model developed in Python can be used as a part of a Java code to make predictions. - -![Machine-learning][1] - -This article assumes you are familiar with the basic development skills and understanding of machine learning. We will start with training our model, and then make a machine learning model in Python. - -This article assumes you are familiar with the basic development skills and understanding of machine learning. We will start with training our model, and then make a machine learning model in Python. - -I am taking the example of a flood prediction model. First, import the following libraries: - -``` -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -``` - -Once we have successfully imported the libraries, we need to take in the data sets, as shown in the code below. To predict floods, I am using the river level data set. - -``` -from google.colab import files -uploaded = files.upload() -for fn in uploaded.keys(): print(‘User uploaded file “{name}” with length {length} bytes’.format( -name=fn, length=len(uploaded[fn]))) -Choose files No file chosen -``` - -The upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable*Saving Hoppers Crossing-Hourly-River-Level.csv to Hoppers Crossing-Hourly-River-Level.csv User uploaded file “Hoppers Crossing-Hourly-River-Level.csv”* with length 2207036 bytes. - -Once this is done, we can train our model using the *sklearn library*. For this, we first need to import the library and the algorithm model, as shown in Figure 1. - -![Figure 1: Training the model][2] - -``` -from sklearn.linear_model import LinearRegression -regressor = LinearRegression() -regressor.fit(X_train, y_train) -``` - -Once that is done we have trained our model, and it’s now ready to make predictions, as shown in Figure 2. - -![Figure 2: Making predictions][3] - -### Using ML model in Java - -What we need to do now is to convert the ML model into a model that can be used by a Java program. There is a library called sklearn2pmml that helps us do this: - -``` -# Install the library -pip install sklearn2pmml -``` - -Once the library is installed we can convert our already trained model, as shown below: - -``` -sklearn2pmml(pipeline, ‘model.pmml’, with_repr = True) -``` - -This is it! We can now use the generated `model.pmml` file in our Java code to make predictions. Do try it out! - -（LCTT 译注：Java 中有第三方库 [jpmml/jpmml-evaluator][4]，它能帮助你使用生成的 `model.pmml` 进行预测。） - --------------------------------------------------------------------------------- - -via: https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/ - -作者：[Jishnu Saurav Mittapalli][a] -选题：[lkxed][b] -译者：[译者ID](https://github.com/译者ID) -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://www.opensourceforu.com/author/jishnu-saurav-mittapalli/ -[b]: https://github.com/lkxed -[1]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Machine-learning.jpg -[2]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-1Training-the-model.jpg -[3]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-2-Making-predictions.jpg -[4]: https://github.com/jpmml/jpmml-evaluator diff --git a/translated/tech/20220530 Using a Machine Learning Model to Make Predictions.md b/translated/tech/20220530 Using a Machine Learning Model to Make Predictions.md new file mode 100644 index 0000000000..94a7feb229 --- /dev/null +++ b/translated/tech/20220530 Using a Machine Learning Model to Make Predictions.md @@ -0,0 +1,87 @@ +[#]: subject: "Using a Machine Learning Model to Make Predictions" +[#]: via: "https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/" +[#]: author: "Jishnu Saurav Mittapalli https://www.opensourceforu.com/author/jishnu-saurav-mittapalli/" +[#]: collector: "lkxed" +[#]: translator: "geekpi" +[#]: reviewer: " " +[#]: publisher: " " +[#]: url: " " + +使用机器学习模型进行预测 +====== +机器学习基本上是人工智能的一个子集，它使用以前存在的数据对新数据进行预测。当然，现在我们所有人都知道这个道理了！这篇文章展示了如何将 Python 中开发的机器学习模型作为 Java 代码的一部分来进行预测。 + +![Machine-learning][1] + +本文假设你熟悉基本的开发技巧并理解机器学习。我们将从训练我们的模型开始，然后在 Python 中制作一个机器学习模型。 + +我以一个洪水预测模型为例。首先，导入以下库： + +``` +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +``` + +当我们成功地导入了这些库，我们就需要输入数据集，如下面的代码所示。为了预测洪水，我使用的是河流水位数据集。 + +``` +from google.colab import files +uploaded = files.upload() +for fn in uploaded.keys(): print(‘User uploaded file “{name}” with length {length} bytes’.format( +name=fn, length=len(uploaded[fn]))) +Choose files No file chosen +``` + +只有在当前浏览器会话中执行了该单元格时，上传部件才可用。请重新运行此单元，上传文件 *“Hoppers Crossing-Hourly-River-Level.csv”*，大小 2207036 字节。 + +完成后，我们就可以使用 *sklearn 库*来训练我们的模型。为此，我们首先需要导入该库和算法模型，如图 1 所示。 + +![Figure 1: Training the model][2] + +``` +from sklearn.linear_model import LinearRegression +regressor = LinearRegression() +regressor.fit(X_train, y_train) +``` + +完成后，我们就训练好了我们的模型，现在可以进行预测了，如图 2 所示。 + +![Figure 2: Making predictions][3] + +### 在 Java 中使用 ML 模型 + +我们现在需要做的是把 ML 模型转换成一个可以被 Java 程序使用的模型。有一个叫做 sklearn2pmml 的库可以帮助我们做到这一点： + +``` +# Install the library +pip install sklearn2pmml +``` + +库安装完毕后，我们就可以转换我们已经训练好的模型，如下图所示： + +``` +sklearn2pmml(pipeline, ‘model.pmml’, with_repr = True) +``` + +这就完成了！我们现在可以在我们的 Java 代码中使用生成的 `model.pmml` 文件来进行预测。请试一试吧！ + +（LCTT 译注：Java 中有第三方库 [jpmml/jpmml-evaluator][4]，它能帮助你使用生成的 `model.pmml` 进行预测。） + +-------------------------------------------------------------------------------- + +via: https://www.opensourceforu.com/2022/05/using-a-machine-learning-model-to-make-predictions/ + +作者：[Jishnu Saurav Mittapalli][a] +选题：[lkxed][b] +译者：[geekpi](https://github.com/geekpi) +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://www.opensourceforu.com/author/jishnu-saurav-mittapalli/ +[b]: https://github.com/lkxed +[1]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Machine-learning.jpg +[2]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-1Training-the-model.jpg +[3]: https://www.opensourceforu.com/wp-content/uploads/2022/05/Figure-2-Making-predictions.jpg +[4]: https://github.com/jpmml/jpmml-evaluator