weka.gui.knowledgeflow.templates.pythonScripting.kf Maven / Gradle / Ivy

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Integration with CPython for Weka. Python version 2.7.x or higher is required. Also requires the following packages to be installed in python: numpy, pandas, matplotlib and scikit-learn. This package provides a wrapper classifier and clusterer that, between them, cover 60+ scikit-learn algorithms. It also provides a general scripting step for the Knowlege Flow along with scripting plugin environments for the Explorer and Knowledge Flow.

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{
	"flow_name" : "Python Scripting",
	"steps" : [
		{
			"class" : "weka.knowledgeflow.steps.PythonScriptExecutor",
			"properties" : {
				"debug" : false,
				"name" : "PythonScriptExecutor",
				"pythonScript" : "import matplotlib.pyplot as plt\nfrom pandas.plotting import scatter_matrix\nfrom pandas.plotting import andrews_curves\nfrom pandas.plotting import parallel_coordinates\nfrom pandas.plotting import radviz\nimport numpy as np\nfrom sklearn import *\niris=py_data\n# Summarize the input data\nsummary=str(iris.describe(include='all'))\n# Scatter plot\nscatter = scatter_matrix(iris,figsize=(6,6),diagonal='kde')\nfig=scatter[0][0].get_figure()\n# Andrew's curves\nfig2=plt.figure()\nandrews_curves(iris, 'class')\n# Parallel coordinates\nfig3=plt.figure()\nparallel_coordinates(iris, 'class')\n# RadViz\nfig4=plt.figure()\nradviz(iris, 'class')\n# learn a scikit-learn classifier and get predictions\nX=iris.iloc[:,[0,1,2,3]].values\nY=iris.iloc[:,[4]].values\ndt=tree.DecisionTreeClassifier()\ndt.fit(X,Y)\npreds=dt.predict_proba(X)",
				"scriptFile" : "",
				"variablesToGetFromPython" : "summary,iris,fig,fig2,fig3,fig4,preds"
			},
			"connections" : {
				"text" : [
					"TextViewer"
				],
				"image" : [
					"ImageViewer"
				],
				"dataSet" : [
					"TextViewer2"
				]
			},
			"coordinates" : "320,40"
		},
		{
			"class" : "weka.knowledgeflow.steps.Loader",
			"properties" : {
				"loader" : {
					"type" : "loader",
					"class" : "weka.core.converters.ArffLoader",
					"filePath" : "weka/gui/knowledgeflow/templates/iris.arff",
					"useRelativePath" : false
				},
				"name" : "ArffLoader"
			},
			"connections" : {
				"dataSet" : [
					"PythonScriptExecutor"
				]
			},
			"coordinates" : "40,40"
		},
		{
			"class" : "weka.knowledgeflow.steps.TextViewer",
			"properties" : {
				"name" : "TextViewer"
			},
			"connections" : {
			},
			"coordinates" : "320,200"
		},
		{
			"class" : "weka.knowledgeflow.steps.ImageViewer",
			"properties" : {
				"name" : "ImageViewer"
			},
			"connections" : {
			},
			"coordinates" : "560,200"
		},
		{
			"class" : "weka.knowledgeflow.steps.TextViewer",
			"properties" : {
				"name" : "TextViewer2"
			},
			"connections" : {
			},
			"coordinates" : "560,40"
		},
		{
			"class" : "weka.knowledgeflow.steps.Note",
			"properties" : {
				"name" : "Note",
				"noteText" : "Load the iris data from\nthe classpath"
			},
			"connections" : {
			},
			"coordinates" : "28,137"
		},
		{
			"class" : "weka.knowledgeflow.steps.Note",
			"properties" : {
				"name" : "Note2",
				"noteText" : "Collect textual\nvariables from\nPython"
			},
			"connections" : {
			},
			"coordinates" : "217,217"
		},
		{
			"class" : "weka.knowledgeflow.steps.Note",
			"properties" : {
				"name" : "Note3",
				"noteText" : "Collect images\nfrom Python"
			},
			"connections" : {
			},
			"coordinates" : "450,221"
		},
		{
			"class" : "weka.knowledgeflow.steps.Note",
			"properties" : {
				"name" : "Note4",
				"noteText" : "The iris data\nback from Python"
			},
			"connections" : {
			},
			"coordinates" : "552,130"
		}
	]
}