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Sphynx-HenryAY/Jupyter_apply_KNIME_kNN_model.ipynb

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Jupyter_apply_KNIME_kNN_model.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "6e4a24b4",
"metadata": {},
"source": [
"# 1. Setup env"
]
},
{
"cell_type": "markdown",
"id": "1e4ad3be",
"metadata": {},
"source": [
"## 1.a For Windows"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e21108",
"metadata": {},
"outputs": [],
"source": [
"import knime\n",
"\n",
"knime.executable_path = r\"C://Program Files/KNIME/knime.exe\"\n",
"workspace = r\"C:/Users/yourusername/knime-workspace/\"\n",
"workflow = \"jupyter_kNN\""
]
},
{
"cell_type": "markdown",
"id": "94569299",
"metadata": {},
"source": [
"## 1.b For Linux"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0a14b7f3",
"metadata": {},
"outputs": [],
"source": [
"import knime\n",
"\n",
"knime.executable_path = r\"/home/yourusername/projects/knime/knime\"\n",
"workspace = r\"/home/yourusername/projects/knime-workspace/\"\n",
"workflow = \"jupyter_kNN\""
]
},
{
"cell_type": "markdown",
"id": "68cdae86",
"metadata": {},
"source": [
"## 1.c For WSL"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66fe5c18",
"metadata": {},
"outputs": [],
"source": [
"import knime\n",
"\n",
"knime.executable_path = r\"/home/yourusername/projects/knime/knime\"\n",
"workspace = r\"/mnt/c/Users/yourusername/knime-workspace/\"\n",
"workflow = \"jupyter_kNN\""
]
},
{
"cell_type": "markdown",
"id": "86d284e6",
"metadata": {},
"source": [
"# 2. Import data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "37c623d0",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"data = pd.read_csv( \"data_test.csv\" )"
]
},
{
"cell_type": "markdown",
"id": "ceee79f8",
"metadata": {},
"source": [
"# 3. Run"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c76809a1",
"metadata": {},
"outputs": [
{
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" <polygon clip-path=\"url(#lupWs09seSoclipPath38)\" points=\" 696 154 705 158 696 163\" stroke=\"none\" stroke-width=\"1\"/>\n",
" </g>\n",
" <g fill=\"rgb(150,150,150)\" font-family=\"'Segoe UI'\" stroke=\"rgb(150,150,150)\" stroke-linecap=\"butt\" stroke-width=\"2\" text-rendering=\"optimizeLegibility\">\n",
" <path clip-path=\"url(#lupWs09seSoclipPath39)\" d=\"M565 238 C567 238 571 238 573 238 C603 238 616 167 646 167 C648 167 652 167 654 167\" fill=\"none\" fill-rule=\"evenodd\"/>\n",
" <path clip-path=\"url(#lupWs09seSoclipPath39)\" d=\"M305 147 C307 147 311 147 313 147 C412 147 547 148 646 148 C648 148 652 148 654 148\" fill=\"none\" fill-rule=\"evenodd\"/>\n",
" <path clip-path=\"url(#lupWs09seSoclipPath39)\" d=\"M305 147 C307 147 311 147 313 147 C350 147 309 268 346 268 C348 268 352 268 354 268\" fill=\"none\" fill-rule=\"evenodd\"/>\n",
" <path clip-path=\"url(#lupWs09seSoclipPath39)\" d=\"M405 278 C407 278 411 278 413 278 C442 278 477 247 506 247 C508 247 512 247 514 247\" fill=\"none\" fill-rule=\"evenodd\"/>\n",
" <path clip-path=\"url(#lupWs09seSoclipPath39)\" d=\"M305 128 C307 128 311 128 313 128 C378 128 441 228 506 228 C508 228 512 228 514 228\" fill=\"none\" fill-rule=\"evenodd\"/>\n",
" <path clip-path=\"url(#lupWs09seSoclipPath39)\" d=\"M165 138 C167 138 171 138 173 138 C194 138 225 138 246 138 C248 138 252 138 254 138\" fill=\"none\" fill-rule=\"evenodd\"/>\n",
" <path clip-path=\"url(#lupWs09seSoclipPath39)\" d=\"M165 278 C167 278 171 278 173 278 C224 278 295 287 346 287 C348 287 352 287 354 287\" fill=\"none\" fill-rule=\"evenodd\"/>\n",
" <path clip-path=\"url(#lupWs09seSoclipPath39)\" d=\"M705 158 C707 158 711 158 713 158 C734 158 765 158 786 158 C788 158 792 158 794 158\" fill=\"none\" fill-rule=\"evenodd\"/>\n",
" </g>\n",
" </g>\n",
"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" sepal length sepal width petal length petal width Class [kNN] \\\n",
"0 6.7 3.0 6.0 2.0 Iris-virginica \n",
"1 5.5 4.2 1.0 0.3 Iris-setosa \n",
"2 5.0 2.1 3.3 1.2 Iris-versicolor \n",
"3 4.3 1.7 4.0 1.8 Iris-virginica \n",
"4 5.8 2.7 4.2 1.6 Iris-versicolor \n",
"5 6.1 3.7 3.0 0.8 Iris-setosa \n",
"\n",
" P (class=Iris-setosa) P (class=Iris-versicolor) P (class=Iris-virginica) \n",
"0 0.0 0.0 1.0 \n",
"1 1.0 0.0 0.0 \n",
"2 0.0 1.0 0.0 \n",
"3 0.0 0.0 1.0 \n",
"4 0.0 1.0 0.0 \n",
"5 1.0 0.0 0.0 \n"
]
}
],
"source": [
"with knime.Workflow( workflow_path = workflow, workspace_path = workspace ) as wf:\n",
" wf.display_svg()\n",
" wf.data_table_inputs[0] = data\n",
" wf.execute()\n",
" print( wf.data_table_outputs[0] )"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@Sphynx-HenryAY
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Sphynx-HenryAY commented Jun 17, 2021 •
edited

knime flow modification:

  • knime python package indicates input and output slots by parsing each settings xml in workflow, and look for:
    • input: Container Input (Table)
    • output: Container Output (Table)
  • for each input and output container it fetches, it will create corresponding slot for workflow.data_table_inputs, workflow.data_table_outputs, following the Container node creation order

@Sphynx-HenryAY
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related knime workflow: jupyter_kNN

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