|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 3, |
| 6 | + "metadata": {}, |
| 7 | + "outputs": [], |
| 8 | + "source": [ |
| 9 | + "from griffe.loader import GriffeLoader\n", |
| 10 | + "from griffe.docstrings.parsers import Parser\n", |
| 11 | + "\n", |
| 12 | + "griffe = GriffeLoader(docstring_parser = Parser(\"numpy\"))\n", |
| 13 | + "mod = griffe.load_module(\"vetiver\") # no editable install\n", |
| 14 | + "\n", |
| 15 | + "#f_obj = mod._modules_collection[\"vetiver.load_pkgs\"]\n" |
| 16 | + ] |
| 17 | + }, |
| 18 | + { |
| 19 | + "cell_type": "code", |
| 20 | + "execution_count": 10, |
| 21 | + "metadata": {}, |
| 22 | + "outputs": [ |
| 23 | + { |
| 24 | + "data": { |
| 25 | + "text/plain": [ |
| 26 | + "<Function('load_pkgs', 6, 33)>" |
| 27 | + ] |
| 28 | + }, |
| 29 | + "execution_count": 10, |
| 30 | + "metadata": {}, |
| 31 | + "output_type": "execute_result" |
| 32 | + } |
| 33 | + ], |
| 34 | + "source": [ |
| 35 | + "mod._modules_collection[\"vetiver.attach_pkgs.load_pkgs\"] # full path" |
| 36 | + ] |
| 37 | + }, |
| 38 | + { |
| 39 | + "cell_type": "code", |
| 40 | + "execution_count": 1, |
| 41 | + "metadata": {}, |
| 42 | + "outputs": [ |
| 43 | + { |
| 44 | + "name": "stdout", |
| 45 | + "output_type": "stream", |
| 46 | + "text": [ |
| 47 | + "# load_pkgs\n", |
| 48 | + "\n", |
| 49 | + "`load_pkgs(model: VetiverModel = None, packages: list = None, path='')`\n", |
| 50 | + "\n", |
| 51 | + "Load packages necessary for predictions\n", |
| 52 | + "\n", |
| 53 | + "Args\n", |
| 54 | + "----\n", |
| 55 | + " model: VetiverModel\n", |
| 56 | + " VetiverModel to extract packages from\n", |
| 57 | + " packages: list\n", |
| 58 | + " List of extra packages to include\n", |
| 59 | + " path: str\n", |
| 60 | + " Where to save output file\n" |
| 61 | + ] |
| 62 | + } |
| 63 | + ], |
| 64 | + "source": [ |
| 65 | + "from quartodoc import get_function, MdRenderer\n", |
| 66 | + "\n", |
| 67 | + "# get function object ---\n", |
| 68 | + "f_obj = get_function(\"vetiver.attach_pkgs\", \"load_pkgs\") # not attach.laod\n", |
| 69 | + "\n", |
| 70 | + "\n", |
| 71 | + "# render ---\n", |
| 72 | + "renderer = MdRenderer(header_level = 1)\n", |
| 73 | + "print(\n", |
| 74 | + " renderer.to_md(f_obj)\n", |
| 75 | + ")" |
| 76 | + ] |
| 77 | + }, |
| 78 | + { |
| 79 | + "cell_type": "code", |
| 80 | + "execution_count": 4, |
| 81 | + "metadata": {}, |
| 82 | + "outputs": [ |
| 83 | + { |
| 84 | + "name": "stdout", |
| 85 | + "output_type": "stream", |
| 86 | + "text": [ |
| 87 | + "# VetiverAPI\n", |
| 88 | + "\n", |
| 89 | + "`VetiverAPI(self, model: VetiverModel, check_ptype: bool = True, app_factory=FastAPI)`\n", |
| 90 | + "\n", |
| 91 | + "Create model aware API\n", |
| 92 | + "\n", |
| 93 | + "## Parameters\n", |
| 94 | + "\n", |
| 95 | + "| Name | Type | Description | Default |\n", |
| 96 | + "|---------------|--------------|------------------------------------------------|-----------|\n", |
| 97 | + "| `model` | VetiverModel | Model to be deployed in API | required |\n", |
| 98 | + "| `check_ptype` | bool | Determine if data prototype should be enforced | `True` |\n", |
| 99 | + "| `app_factory` | | Type of API to be deployed | `FastAPI` |\n", |
| 100 | + "\n", |
| 101 | + "Example\n", |
| 102 | + "-------\n", |
| 103 | + ">>> import vetiver\n", |
| 104 | + ">>> X, y = vetiver.get_mock_data()\n", |
| 105 | + ">>> model = vetiver.get_mock_model().fit(X, y)\n", |
| 106 | + ">>> v = vetiver.VetiverModel(model = model, model_name = \"my_model\", ptype_data = X)\n", |
| 107 | + ">>> v_api = vetiver.VetiverAPI(model = v, check_ptype = True)\n" |
| 108 | + ] |
| 109 | + } |
| 110 | + ], |
| 111 | + "source": [ |
| 112 | + "c_obj = mod._modules_collection[\"vetiver.server.VetiverAPI\"]\n", |
| 113 | + "\n", |
| 114 | + "print(\n", |
| 115 | + " renderer.to_md(c_obj)\n", |
| 116 | + ")" |
| 117 | + ] |
| 118 | + }, |
| 119 | + { |
| 120 | + "cell_type": "code", |
| 121 | + "execution_count": 19, |
| 122 | + "metadata": {}, |
| 123 | + "outputs": [ |
| 124 | + { |
| 125 | + "data": { |
| 126 | + "text/plain": [ |
| 127 | + "'# vetiver_post\\n\\n`vetiver_post(self, endpoint_fx: Callable, endpoint_name: str = \\'custom_endpoint\\')`\\n\\nCreate new POST endpoint that is aware of model input data\\n\\n## Parameters\\n\\n| Name | Type | Description | Default |\\n|-----------------|-----------------|---------------------------------------|---------------------|\\n| `endpoint_fx` | typing.Callable | Custom function to be run at endpoint | required |\\n| `endpoint_name` | str | Name of endpoint | `\\'custom_endpoint\\'` |\\n\\nExample\\n-------\\n>>> import vetiver\\n>>> X, y = vetiver.get_mock_data()\\n>>> model = vetiver.get_mock_model().fit(X, y)\\n>>> v = vetiver.VetiverModel(model = model, model_name = \"model\", ptype_data = X)\\n>>> v_api = vetiver.VetiverAPI(model = v, check_ptype = True)\\n>>> def sum_values(x):\\n... return x.sum()\\n>>> v_api.vetiver_post(sum_values, \"sums\")'" |
| 128 | + ] |
| 129 | + }, |
| 130 | + "execution_count": 19, |
| 131 | + "metadata": {}, |
| 132 | + "output_type": "execute_result" |
| 133 | + } |
| 134 | + ], |
| 135 | + "source": [ |
| 136 | + "renderer.to_md(c_obj.members[\"vetiver_post\"]) # no -> none" |
| 137 | + ] |
| 138 | + }, |
| 139 | + { |
| 140 | + "cell_type": "code", |
| 141 | + "execution_count": null, |
| 142 | + "metadata": {}, |
| 143 | + "outputs": [], |
| 144 | + "source": [] |
| 145 | + } |
| 146 | + ], |
| 147 | + "metadata": { |
| 148 | + "kernelspec": { |
| 149 | + "display_name": "Python 3.9.11 64-bit ('pydemo')", |
| 150 | + "language": "python", |
| 151 | + "name": "python3" |
| 152 | + }, |
| 153 | + "language_info": { |
| 154 | + "codemirror_mode": { |
| 155 | + "name": "ipython", |
| 156 | + "version": 3 |
| 157 | + }, |
| 158 | + "file_extension": ".py", |
| 159 | + "mimetype": "text/x-python", |
| 160 | + "name": "python", |
| 161 | + "nbconvert_exporter": "python", |
| 162 | + "pygments_lexer": "ipython3", |
| 163 | + "version": "3.9.11" |
| 164 | + }, |
| 165 | + "orig_nbformat": 4, |
| 166 | + "vscode": { |
| 167 | + "interpreter": { |
| 168 | + "hash": "974018313955b4988b16ea215671657307c8736770f13695d4ded4c5899ccb5a" |
| 169 | + } |
| 170 | + } |
| 171 | + }, |
| 172 | + "nbformat": 4, |
| 173 | + "nbformat_minor": 2 |
| 174 | +} |
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