notebook/Spark Tutorial/4. Spark MlLib_2EZFM3GJA.zpln

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      "text": "%md\n\nThis is a tutorial of how to use Spark MLlib in Zeppelin, we have 2 examples in this note:\n\n* Linear regression, we generate some random data and use a linear regression to fit this data. We use bokeh here to visualize the data and the fitted model.  Besides training, we also visualize the loss value over iteration.\n* Logstic regression, we use the official `sample_binary_classification_data` of spark as the training data. Besides training, we also visualize the loss value over iteration.\n",
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            "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003eThis is a tutorial of how to use Spark MLlib in Zeppelin, we have 2 examples in this note:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLinear regression, we generate some random data and use a linear regression to fit this data. We use bokeh here to visualize the data and the fitted model.  Besides training, we also visualize the loss value over iteration.\u003c/li\u003e\n\u003cli\u003eLogstic regression, we use the official \u003ccode\u003esample_binary_classification_data\u003c/code\u003e of spark as the training data. Besides training, we also visualize the loss value over iteration.\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003c/div\u003e"
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      "text": "%spark.pyspark\n\n\nimport numpy as np\nfrom bokeh.io import output_notebook, show\nfrom bokeh.plotting import figure\n\noutput_notebook()\n\n",
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Possible fixes:\\n\"+\n     \"\u003c/p\u003e\\n\"+\n     \"\u003cul\u003e\\n\"+\n     \"\u003cli\u003ere-rerun `output_notebook()` to attempt to load from CDN again, or\u003c/li\u003e\\n\"+\n     \"\u003cli\u003euse INLINE resources instead, as so:\u003c/li\u003e\\n\"+\n     \"\u003c/ul\u003e\\n\"+\n     \"\u003ccode\u003e\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources\u003dINLINE)\\n\"+\n     \"\u003c/code\u003e\\n\"+\n     \"\u003c/div\u003e\"}};\n\n  function display_loaded() {\n    var el \u003d document.getElementById(\"1002\");\n    if (el !\u003d null) {\n      el.textContent \u003d \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !\u003d\u003d undefined) {\n      if (el !\u003d null) {\n        el.textContent \u003d \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() \u003c root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback !\u003d null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls \u003d\u003d null) css_urls \u003d [];\n    if (js_urls \u003d\u003d null) js_urls \u003d [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading \u003e 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls \u003d\u003d null || js_urls.length \u003d\u003d\u003d 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading \u003d css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading \u003d\u003d\u003d 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i \u003d 0; 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      "text": "%spark.pyspark\n\nimport numpy as np\nfrom bokeh.io import output_notebook, show\nfrom bokeh.plotting import figure\n\n\nnum \u003d 1000\nx \u003d np.linspace(0, 10, num)\ny \u003d 2 * x + np.random.normal(0,4, num)\n\np \u003d figure()\np.circle(x, y)\nshow(p)",
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\u003d\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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      "title": "Linear Regression",
      "text": "%spark.pyspark\n\n\nfrom pyspark.ml.regression import LinearRegression\nimport pandas as pd\nfrom pyspark.ml.linalg import DenseVector, Vectors, VectorUDT\nfrom pyspark.sql.functions import udf\nfrom pyspark.sql.types import UserDefinedType, StringType\n\nto_vector \u003d udf(lambda x: Vectors.dense(x), VectorUDT())\n\ndf \u003d pd.DataFrame({\u0027features\u0027: x, \u0027label\u0027: y})\ntraining \u003d spark.createDataFrame(df).withColumn(\u0027features\u0027, to_vector(\u0027features\u0027))\n\nlr \u003d LinearRegression(maxIter\u003d50, regParam\u003d0.3, elasticNetParam\u003d0.8, solver\u003d\u0027l-bfgs\u0027)\n\n# Fit the model\nlrModel \u003d lr.fit(training)\n\n# Print the coefficients and intercept for linear regression\nprint(\"Coefficients: %s\" % str(lrModel.coefficients))\nprint(\"Intercept: %s\" % str(lrModel.intercept))\n\n# Summarize the model over the training set and print out some metrics\ntrainingSummary \u003d lrModel.summary\nprint(\"numIterations: %d\" % trainingSummary.totalIterations)\nprint(\"objectiveHistory: %s\" % str(trainingSummary.objectiveHistory))\ntrainingSummary.residuals.show()\nprint(\"RMSE: %f\" % trainingSummary.rootMeanSquaredError)\nprint(\"r2: %f\" % trainingSummary.r2)\n\n\n",
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\u003d\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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