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python-How to use jupyter nbconvert

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周杰伦

文章分类 其他 python jupyter ide 阅读数 : 137 阅读时长 : 9分钟


【python-How to use jupyter nbconvert】此文章归类为：[ "python", "jupyter", "ide" ]。

How to use jupyter nbconvert

最近在使用jupyter notebook的时候，发现notebook文件在问题探索方面非常方便，但是交付的话，还是期望能将其转换为python源文件。要实现notebook源文件(.ipynb)与python源文件(.py)之间的相互转换，可以使用命令jupyter nbconvert来完成。举例如下，

这里有一个文件名称为,内容如下：

lanzhou) lwk@qwfys:~/Public/project/python/alink_tutorial_python/pyalink$ cat Chap14.ipynb 
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyalink.alink import *\n",
    "useLocalEnv(1)\n",
    "\n",
    "from utils import *\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.max_colwidth', 1000)\n",
    "\n",
    "DATA_DIR = ROOT_DIR + \"ctr_avazu\" + os.sep\n",
    "\n",
    "SCHEMA_STRING\\\n",
    "    = \"id string, click string, dt string, C1 string, banner_pos int, site_id string, site_domain string, \"\\\n",
    "    + \"site_category string, app_id string, app_domain string, app_category string, device_id string, \"\\\n",
    "    + \"device_ip string, device_model string, device_type string, device_conn_type string, C14 int, C15 int, \"\\\n",
    "    + \"C16 int, C17 int, C18 int, C19 int, C20 int, C21 int\"\n",
    "\n",
    "CATEGORY_COL_NAMES = [\n",
    "    \"C1\", \"banner_pos\", \"site_category\", \"app_domain\",\n",
    "    \"app_category\", \"device_type\", \"device_conn_type\",\n",
    "    \"site_id\", \"site_domain\", \"device_id\", \"device_model\"\n",
    "]\n",
    "\n",
    "NUMERICAL_COL_NAMES = [\"C14\", \"C15\", \"C16\", \"C17\", \"C18\", \"C19\", \"C20\", \"C21\"]\n",
    "\n",
    "FEATURE_MODEL_FILE = \"feature_model.ak\"\n",
    "INIT_MODEL_FILE = \"init_model.ak\"\n",
    "\n",
    "LABEL_COL_NAME = \"click\"\n",
    "VEC_COL_NAME = \"vec\"\n",
    "PREDICTION_COL_NAME = \"pred\"\n",
    "PRED_DETAIL_COL_NAME = \"pred_info\"\n",
    "\n",
    "NUM_HASH_FEATURES = 30000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_2\n",
    "TextSourceBatchOp()\\\n",
    "    .setFilePath(\"http://alink-release.oss-cn-beijing.aliyuncs.com/\"\n",
    "                 + \"data-files/avazu-small.csv\")\\\n",
    "    .firstN(10)\\\n",
    "    .print()\n",
    "\n",
    "trainBatchData = CsvSourceBatchOp()\\\n",
    "    .setFilePath(\"http://alink-release.oss-cn-beijing.aliyuncs.com/\"\n",
    "                 + \"data-files/avazu-small.csv\")\\\n",
    "    .setSchemaStr(SCHEMA_STRING);\n",
    "\n",
    "trainBatchData.firstN(10).print();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_3\n",
    "trainBatchData = CsvSourceBatchOp()\\\n",
    "    .setFilePath(\"http://alink-release.oss-cn-beijing.aliyuncs.com/\"\n",
    "                 + \"data-files/avazu-small.csv\")\\\n",
    "    .setSchemaStr(SCHEMA_STRING);\n",
    "\n",
    "feature_pipeline = Pipeline()\\\n",
    "    .add(\n",
    "        StandardScaler()\\\n",
    "            .setSelectedCols(NUMERICAL_COL_NAMES)\n",
    "    )\\\n",
    "    .add(\n",
    "        FeatureHasher()\\\n",
    "            .setSelectedCols(CATEGORY_COL_NAMES + NUMERICAL_COL_NAMES)\\\n",
    "            .setCategoricalCols(CATEGORY_COL_NAMES)\\\n",
    "            .setOutputCol(VEC_COL_NAME)\\\n",
    "            .setNumFeatures(NUM_HASH_FEATURES)\n",
    "    );\n",
    "\n",
    "if not(os.path.exists(DATA_DIR + FEATURE_MODEL_FILE)) :\n",
    "    feature_pipeline\\\n",
    "        .fit(trainBatchData)\\\n",
    "        .save(DATA_DIR + FEATURE_MODEL_FILE)\n",
    "    BatchOperator.execute()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_4\n",
    "feature_pipelineModel = PipelineModel.load(DATA_DIR + FEATURE_MODEL_FILE)\n",
    "\n",
    "data = CsvSourceStreamOp()\\\n",
    "    .setFilePath(\"http://alink-release.oss-cn-beijing.aliyuncs.com/\"\n",
    "                 + \"data-files/avazu-ctr-train-8M.csv\")\\\n",
    "    .setSchemaStr(SCHEMA_STRING);\n",
    "\n",
    "if not(os.path.exists(DATA_DIR + INIT_MODEL_FILE)) :\n",
    "    trainBatchData = CsvSourceBatchOp()\\\n",
    "        .setFilePath(\"http://alink-release.oss-cn-beijing.aliyuncs.com/\"\n",
    "                     + \"data-files/avazu-small.csv\")\\\n",
    "        .setSchemaStr(SCHEMA_STRING);\n",
    "\n",
    "    lr = LogisticRegressionTrainBatchOp()\\\n",
    "        .setVectorCol(VEC_COL_NAME)\\\n",
    "        .setLabelCol(LABEL_COL_NAME)\\\n",
    "        .setWithIntercept(True)\\\n",
    "        .setMaxIter(10);\n",
    "\n",
    "    feature_pipelineModel\\\n",
    "    .transform(trainBatchData)\\\n",
    "    .link(lr)\\\n",
    "    .link(\n",
    "        AkSinkBatchOp().setFilePath(DATA_DIR + INIT_MODEL_FILE)\n",
    "    );\n",
    "    BatchOperator.execute();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_5 \n",
    "feature_pipelineModel = PipelineModel.load(DATA_DIR + FEATURE_MODEL_FILE);\n",
    "\n",
    "initModel = AkSourceBatchOp().setFilePath(DATA_DIR + INIT_MODEL_FILE);\n",
    "\n",
    "data = CsvSourceStreamOp()\\\n",
    "    .setFilePath(\"http://alink-release.oss-cn-beijing.aliyuncs.com/\"\n",
    "                 + \"data-files/avazu-ctr-train-8M.csv\")\\\n",
    "    .setSchemaStr(SCHEMA_STRING)\\\n",
    "    .setIgnoreFirstLine(True)\n",
    "\n",
    "spliter = SplitStreamOp().setFraction(0.5).linkFrom(data);\n",
    "train_stream_data = feature_pipelineModel.transform(spliter);\n",
    "test_stream_data = feature_pipelineModel.transform(spliter.getSideOutput(0));\n",
    "\n",
    "model = FtrlTrainStreamOp(initModel)\\\n",
    "    .setVectorCol(VEC_COL_NAME)\\\n",
    "    .setLabelCol(LABEL_COL_NAME)\\\n",
    "    .setWithIntercept(True)\\\n",
    "    .setAlpha(0.1)\\\n",
    "    .setBeta(0.1)\\\n",
    "    .setL1(0.01)\\\n",
    "    .setL2(0.01)\\\n",
    "    .setTimeInterval(10)\\\n",
    "    .setVectorSize(NUM_HASH_FEATURES)\\\n",
    "    .linkFrom(train_stream_data);\n",
    "\n",
    "predResult = FtrlPredictStreamOp(initModel)\\\n",
    "    .setVectorCol(VEC_COL_NAME)\\\n",
    "    .setPredictionCol(PREDICTION_COL_NAME)\\\n",
    "    .setReservedCols([LABEL_COL_NAME])\\\n",
    "    .setPredictionDetailCol(PRED_DETAIL_COL_NAME)\\\n",
    "    .linkFrom(model, test_stream_data);\n",
    "\n",
    "# predResult\\\n",
    "#     .sample(0.0001)\\\n",
    "#     .select(\"'Pred Sample' AS out_type, *\")\\\n",
    "#     .print();\n",
    "\n",
    "predResult.print(key=\"predResult\", refreshInterval = 30, maxLimit=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predResult\\\n",
    "    .link(\n",
    "        EvalBinaryClassStreamOp()\\\n",
    "            .setLabelCol(LABEL_COL_NAME)\\\n",
    "            .setPredictionDetailCol(PRED_DETAIL_COL_NAME)\\\n",
    "            .setTimeInterval(10)\n",
    "    )\\\n",
    "    .link(\n",
    "        JsonValueStreamOp()\\\n",
    "            .setSelectedCol(\"Data\")\\\n",
    "            .setReservedCols([\"Statistics\"])\\\n",
    "            .setOutputCols([\"Accuracy\", \"AUC\", \"ConfusionMatrix\"])\\\n",
    "            .setJsonPath([\"$.Accuracy\", \"$.AUC\", \"$.ConfusionMatrix\"])\n",
    "    )\\\n",
    "    .print(key=\"evaluation\", refreshInterval = 30, maxLimit=20)\n",
    "# .select(\"'Eval Metric' AS out_type, *\")\\\n",
    "#     .print();\n",
    "\n",
    "StreamOperator.execute();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#c_6\n",
    "data = CsvSourceStreamOp()\\\n",
    "    .setFilePath(\"http://alink-release.oss-cn-beijing.aliyuncs.com/\"\n",
    "                 + \"data-files/avazu-ctr-train-8M.csv\")\\\n",
    "    .setSchemaStr(SCHEMA_STRING)\\\n",
    "    .setIgnoreFirstLine(True);\n",
    "\n",
    "feature_pipelineModel = PipelineModel.load(DATA_DIR + FEATURE_MODEL_FILE);\n",
    "\n",
    "spliter = SplitStreamOp().setFraction(0.5).linkFrom(data);\n",
    "train_stream_data = feature_pipelineModel.transform(spliter);\n",
    "test_stream_data = feature_pipelineModel.transform(spliter.getSideOutput(0));\n",
    "\n",
    "initModel = AkSourceBatchOp().setFilePath(DATA_DIR + INIT_MODEL_FILE);\n",
    "\n",
    "model = FtrlTrainStreamOp(initModel)\\\n",
    "    .setVectorCol(VEC_COL_NAME)\\\n",
    "    .setLabelCol(LABEL_COL_NAME)\\\n",
    "    .setWithIntercept(True)\\\n",
    "    .setAlpha(0.1)\\\n",
    "    .setBeta(0.1)\\\n",
    "    .setL1(0.01)\\\n",
    "    .setL2(0.01)\\\n",
    "    .setTimeInterval(10)\\\n",
    "    .setVectorSize(NUM_HASH_FEATURES)\\\n",
    "    .linkFrom(train_stream_data);\n",
    "\n",
    "model_filter = FtrlModelFilterStreamOp()\\\n",
    "    .setPositiveLabelValueString(\"1\")\\\n",
    "    .setVectorCol(VEC_COL_NAME)\\\n",
    "    .setLabelCol(LABEL_COL_NAME)\\\n",
    "    .setAccuracyThreshold(0.83)\\\n",
    "    .setAucThreshold(0.71)\\\n",
    "    .linkFrom(model, train_stream_data);\n",
    "\n",
    "model_filter\\\n",
    "    .select(\"'Model' AS out_type, *\")\\\n",
    "    .print();\n",
    "\n",
    "predResult = FtrlPredictStreamOp(initModel)\\\n",
    "    .setVectorCol(VEC_COL_NAME)\\\n",
    "    .setPredictionCol(PREDICTION_COL_NAME)\\\n",
    "    .setReservedCols([LABEL_COL_NAME])\\\n",
    "    .setPredictionDetailCol(PRED_DETAIL_COL_NAME)\\\n",
    "    .linkFrom(model_filter, test_stream_data);\n",
    "\n",
    "predResult\\\n",
    "    .sample(0.0001)\\\n",
    "    .select(\"'Pred Sample' AS out_type, *\")\\\n",
    "    .print();\n",
    "\n",
    "predResult\\\n",
    "    .link(\n",
    "        EvalBinaryClassStreamOp()\\\n",
    "            .setPositiveLabelValueString(\"1\")\\\n",
    "            .setLabelCol(LABEL_COL_NAME)\\\n",
    "            .setPredictionDetailCol(PRED_DETAIL_COL_NAME)\\\n",
    "            .setTimeInterval(10)\n",
    "    )\\\n",
    "    .link(\n",
    "        JsonValueStreamOp()\\\n",
    "            .setSelectedCol(\"Data\")\\\n",
    "            .setReservedCols([\"Statistics\"])\\\n",
    "            .setOutputCols([\"Accuracy\", \"AUC\", \"ConfusionMatrix\"])\\\n",
    "            .setJsonPath([\"$.Accuracy\", \"$.AUC\", \"$.ConfusionMatrix\"])\n",
    "    )\\\n",
    "    .select(\"'Eval Metric' AS out_type, *\")\\\n",
    "    .print();\n",
    "\n",
    "StreamOperator.execute();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
(lanzhou) lwk@qwfys:~/Public/project/python/alink_tutorial_python/pyalink$

接下来，我们借助命令jupyter nbconvert将其转换为.py文件，命令如下：

(lanzhou) lwk@qwfys:~/Public/project/python/alink_tutorial_python/pyalink$ mkdir -p python
(lanzhou) lwk@qwfys:~/Public/project/python/alink_tutorial_python/pyalink$ jupyter nbconvert --to python Chap14.ipynb --output-dir python
[NbConvertApp] Converting notebook Chap14.ipynb to python
[NbConvertApp] Writing 7347 bytes to python/Chap14.py
(lanzhou) lwk@qwfys:~/Public/project/python/alink_tutorial_python/pyalink$

我们看到，已经在python目录下生成了文件Chap14.py。

接下来，我们看一下生成的Chap14.py文件的内容：

(lanzhou) lwk@qwfys:~/Public/project/python/alink_tutorial_python/pyalink$ cat python/Chap14.py 
#!/usr/bin/env python
# coding: utf-8

# In[ ]:


from pyalink.alink import *
useLocalEnv(1)

from utils import *
import os
import pandas as pd

pd.set_option('display.max_colwidth', 1000)

DATA_DIR = ROOT_DIR + "ctr_avazu" + os.sep

SCHEMA_STRING\
    = "id string, click string, dt string, C1 string, banner_pos int, site_id string, site_domain string, "\
    + "site_category string, app_id string, app_domain string, app_category string, device_id string, "\
    + "device_ip string, device_model string, device_type string, device_conn_type string, C14 int, C15 int, "\
    + "C16 int, C17 int, C18 int, C19 int, C20 int, C21 int"

CATEGORY_COL_NAMES = [
    "C1", "banner_pos", "site_category", "app_domain",
    "app_category", "device_type", "device_conn_type",
    "site_id", "site_domain", "device_id", "device_model"
]

NUMERICAL_COL_NAMES = ["C14", "C15", "C16", "C17", "C18", "C19", "C20", "C21"]

FEATURE_MODEL_FILE = "feature_model.ak"
INIT_MODEL_FILE = "init_model.ak"

LABEL_COL_NAME = "click"
VEC_COL_NAME = "vec"
PREDICTION_COL_NAME = "pred"
PRED_DETAIL_COL_NAME = "pred_info"

NUM_HASH_FEATURES = 30000


# In[ ]:


#c_2
TextSourceBatchOp()\
    .setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/"
                 + "data-files/avazu-small.csv")\
    .firstN(10)\
    .print()

trainBatchData = CsvSourceBatchOp()\
    .setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/"
                 + "data-files/avazu-small.csv")\
    .setSchemaStr(SCHEMA_STRING);

trainBatchData.firstN(10).print();


# In[ ]:


#c_3
trainBatchData = CsvSourceBatchOp()\
    .setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/"
                 + "data-files/avazu-small.csv")\
    .setSchemaStr(SCHEMA_STRING);

feature_pipeline = Pipeline()\
    .add(
        StandardScaler()\
            .setSelectedCols(NUMERICAL_COL_NAMES)
    )\
    .add(
        FeatureHasher()\
            .setSelectedCols(CATEGORY_COL_NAMES + NUMERICAL_COL_NAMES)\
            .setCategoricalCols(CATEGORY_COL_NAMES)\
            .setOutputCol(VEC_COL_NAME)\
            .setNumFeatures(NUM_HASH_FEATURES)
    );

if not(os.path.exists(DATA_DIR + FEATURE_MODEL_FILE)) :
    feature_pipeline\
        .fit(trainBatchData)\
        .save(DATA_DIR + FEATURE_MODEL_FILE)
    BatchOperator.execute()


# In[ ]:


#c_4
feature_pipelineModel = PipelineModel.load(DATA_DIR + FEATURE_MODEL_FILE)

data = CsvSourceStreamOp()\
    .setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/"
                 + "data-files/avazu-ctr-train-8M.csv")\
    .setSchemaStr(SCHEMA_STRING);

if not(os.path.exists(DATA_DIR + INIT_MODEL_FILE)) :
    trainBatchData = CsvSourceBatchOp()\
        .setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/"
                     + "data-files/avazu-small.csv")\
        .setSchemaStr(SCHEMA_STRING);

    lr = LogisticRegressionTrainBatchOp()\
        .setVectorCol(VEC_COL_NAME)\
        .setLabelCol(LABEL_COL_NAME)\
        .setWithIntercept(True)\
        .setMaxIter(10);

    feature_pipelineModel\
    .transform(trainBatchData)\
    .link(lr)\
    .link(
        AkSinkBatchOp().setFilePath(DATA_DIR + INIT_MODEL_FILE)
    );
    BatchOperator.execute();


# In[ ]:


#c_5 
feature_pipelineModel = PipelineModel.load(DATA_DIR + FEATURE_MODEL_FILE);

initModel = AkSourceBatchOp().setFilePath(DATA_DIR + INIT_MODEL_FILE);

data = CsvSourceStreamOp()\
    .setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/"
                 + "data-files/avazu-ctr-train-8M.csv")\
    .setSchemaStr(SCHEMA_STRING)\
    .setIgnoreFirstLine(True)

spliter = SplitStreamOp().setFraction(0.5).linkFrom(data);
train_stream_data = feature_pipelineModel.transform(spliter);
test_stream_data = feature_pipelineModel.transform(spliter.getSideOutput(0));

model = FtrlTrainStreamOp(initModel)\
    .setVectorCol(VEC_COL_NAME)\
    .setLabelCol(LABEL_COL_NAME)\
    .setWithIntercept(True)\
    .setAlpha(0.1)\
    .setBeta(0.1)\
    .setL1(0.01)\
    .setL2(0.01)\
    .setTimeInterval(10)\
    .setVectorSize(NUM_HASH_FEATURES)\
    .linkFrom(train_stream_data);

predResult = FtrlPredictStreamOp(initModel)\
    .setVectorCol(VEC_COL_NAME)\
    .setPredictionCol(PREDICTION_COL_NAME)\
    .setReservedCols([LABEL_COL_NAME])\
    .setPredictionDetailCol(PRED_DETAIL_COL_NAME)\
    .linkFrom(model, test_stream_data);

# predResult\
#     .sample(0.0001)\
#     .select("'Pred Sample' AS out_type, *")\
#     .print();

predResult.print(key="predResult", refreshInterval = 30, maxLimit=20)


# In[ ]:


predResult\
    .link(
        EvalBinaryClassStreamOp()\
            .setLabelCol(LABEL_COL_NAME)\
            .setPredictionDetailCol(PRED_DETAIL_COL_NAME)\
            .setTimeInterval(10)
    )\
    .link(
        JsonValueStreamOp()\
            .setSelectedCol("Data")\
            .setReservedCols(["Statistics"])\
            .setOutputCols(["Accuracy", "AUC", "ConfusionMatrix"])\
            .setJsonPath(["$.Accuracy", "$.AUC", "$.ConfusionMatrix"])
    )\
    .print(key="evaluation", refreshInterval = 30, maxLimit=20)
# .select("'Eval Metric' AS out_type, *")\
#     .print();

StreamOperator.execute();


# In[ ]:


#c_6
data = CsvSourceStreamOp()\
    .setFilePath("http://alink-release.oss-cn-beijing.aliyuncs.com/"
                 + "data-files/avazu-ctr-train-8M.csv")\
    .setSchemaStr(SCHEMA_STRING)\
    .setIgnoreFirstLine(True);

feature_pipelineModel = PipelineModel.load(DATA_DIR + FEATURE_MODEL_FILE);

spliter = SplitStreamOp().setFraction(0.5).linkFrom(data);
train_stream_data = feature_pipelineModel.transform(spliter);
test_stream_data = feature_pipelineModel.transform(spliter.getSideOutput(0));

initModel = AkSourceBatchOp().setFilePath(DATA_DIR + INIT_MODEL_FILE);

model = FtrlTrainStreamOp(initModel)\
    .setVectorCol(VEC_COL_NAME)\
    .setLabelCol(LABEL_COL_NAME)\
    .setWithIntercept(True)\
    .setAlpha(0.1)\
    .setBeta(0.1)\
    .setL1(0.01)\
    .setL2(0.01)\
    .setTimeInterval(10)\
    .setVectorSize(NUM_HASH_FEATURES)\
    .linkFrom(train_stream_data);

model_filter = FtrlModelFilterStreamOp()\
    .setPositiveLabelValueString("1")\
    .setVectorCol(VEC_COL_NAME)\
    .setLabelCol(LABEL_COL_NAME)\
    .setAccuracyThreshold(0.83)\
    .setAucThreshold(0.71)\
    .linkFrom(model, train_stream_data);

model_filter\
    .select("'Model' AS out_type, *")\
    .print();

predResult = FtrlPredictStreamOp(initModel)\
    .setVectorCol(VEC_COL_NAME)\
    .setPredictionCol(PREDICTION_COL_NAME)\
    .setReservedCols([LABEL_COL_NAME])\
    .setPredictionDetailCol(PRED_DETAIL_COL_NAME)\
    .linkFrom(model_filter, test_stream_data);

predResult\
    .sample(0.0001)\
    .select("'Pred Sample' AS out_type, *")\
    .print();

predResult\
    .link(
        EvalBinaryClassStreamOp()\
            .setPositiveLabelValueString("1")\
            .setLabelCol(LABEL_COL_NAME)\
            .setPredictionDetailCol(PRED_DETAIL_COL_NAME)\
            .setTimeInterval(10)
    )\
    .link(
        JsonValueStreamOp()\
            .setSelectedCol("Data")\
            .setReservedCols(["Statistics"])\
            .setOutputCols(["Accuracy", "AUC", "ConfusionMatrix"])\
            .setJsonPath(["$.Accuracy", "$.AUC", "$.ConfusionMatrix"])
    )\
    .select("'Eval Metric' AS out_type, *")\
    .print();

StreamOperator.execute();


# In[ ]:



(lanzhou) lwk@qwfys:~/Public/project/python/alink_tutorial_python/pyalink$


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