Convert Word Bag Transform to ONNX ML.NET

WordBagToOnnx.ipynb

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       "<div><div></div><div></div><div><strong>Installed Packages</strong><ul><li><span>Microsoft.ML, 1.7.1</span></li><li><span>Microsoft.ML.OnnxConverter, 0.19.1</span></li><li><span>Microsoft.ML.OnnxTransformer, 1.7.1</span></li></ul></div></div>"
      ]
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   "source": [
    "#r \"nuget:Microsoft.ML\"\n",
    "#r \"nuget:Microsoft.ML.OnnxTransformer\"\n",
    "#r \"nuget:Microsoft.ML.OnnxConverter\""
   ]
  },
  {
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   "source": [
    "using System;\n",
    "using System.IO;\n",
    "using System.Linq;\n",
    "using Microsoft.ML;\n",
    "using Microsoft.ML.OnnxRuntime;\n",
    "using Microsoft.ML.Data;\n",
    "using Microsoft.ML.Transforms;"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "var corpus = new [] \n",
    "{\n",
    "\tnew {Text = \"The quick brown fox jumped over the lazy dog. Dog is so lazy. Quick!\"},\n",
    "\tnew {Text = \"The lazy dog was jumped over by the quick brown fox. Fox is not lazy\"}\n",
    "};"
   ]
  },
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   "outputs": [],
   "source": [
    "var ctx = new MLContext();"
   ]
  },
  {
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     "language": "csharp"
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   "outputs": [],
   "source": [
    "var data = ctx.Data.LoadFromEnumerable(corpus);"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   },
   "outputs": [],
   "source": [
    "var pipeline = ctx.Transforms.Text.ProduceWordBags (outputColumnName: \"WB\", inputColumnName:\"Text\", ngramLength:1);"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "var model = pipeline.Fit(data);"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "var mlnetmodelOutputDv = model.Transform(data);"
   ]
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   "outputs": [],
   "source": [
    "var mlnetmodelOutputDv = model.Transform(data);"
   ]
  },
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   "outputs": [],
   "source": [
    "var wordbagOutputmlnet = mlnetmodelOutputDv.GetColumn<float[]>(\"WB\");"
   ]
  },
  {
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    "wordbagOutputmlnet"
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   "source": [
    "using(FileStream stream = File.Create(\"./onnx_model.onnx\"))\n",
    "{\n",
    "    ctx.Model.ConvertToOnnx(model, data, stream);\n",
    "}"
   ]
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   "outputs": [],
   "source": [
    "var onnxModelPipeline = ctx.Transforms.ApplyOnnxModel(\"onnx_model.onnx\");"
   ]
  },
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   "outputs": [],
   "source": [
    "var onnxOutputDv = onnxModelPipeline.Fit(data).Transform(data);"
   ]
  },
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    "onnxOutputDv.Schema"
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   "outputs": [],
   "source": [
    "var wordBagOutputOnnx = onnxOutputDv.GetColumn<float[]>(\"WB\");"
   ]
  },
  {
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       "<table><thead><tr><th><i>index</i></th><th>value</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0 ]</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">[ 1, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1 ]</div></td></tr></tbody></table>"
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    "wordBagOutputOnnx"
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   "outputs": [],
   "source": [
    "var comparison = \n",
    "    wordbagOutputmlnet.Zip(wordBagOutputOnnx, (a,b) => \n",
    "    { \n",
    "        var difference = a.Zip(b,(c,d) => c-d);\n",
    "        return new {MLNET=a, ONNX=b,Difference=difference};\n",
    "    });"
   ]
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       "<table><thead><tr><th><i>index</i></th><th>MLNET</th><th>ONNX</th><th>Difference</th></tr></thead><tbody><tr><td>0</td><td><div class=\"dni-plaintext\">[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0 ]</div></td><td><div class=\"dni-plaintext\">[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0 ]</div></td><td><div class=\"dni-plaintext\">[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ... (more) ]</div></td></tr><tr><td>1</td><td><div class=\"dni-plaintext\">[ 1, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1 ]</div></td><td><div class=\"dni-plaintext\">[ 1, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1 ]</div></td><td><div class=\"dni-plaintext\">[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ... (more) ]</div></td></tr></tbody></table>"
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    "comparison"
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