gen_class_data/gen_class_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate classification data\n",
    "\n",
    "Use this function to generate sample data sets, wraps scikit-learn's **[make_classification](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html#sklearn-datasets-make-classification)**.  See the link for a description of all parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: ignore\n",
    "import nuclio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "from typing import Optional, List, Any\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "from mlrun.execution import MLClientCtx\n",
    "\n",
    "def gen_class_data(\n",
    "    context: MLClientCtx,\n",
    "    n_samples: int,\n",
    "    m_features: int,\n",
    "    k_classes: int,\n",
    "    header: Optional[List[str]],\n",
    "    label_column: Optional[str] = \"labels\",\n",
    "    weight: float = 0.5,\n",
    "    random_state: int = 1,\n",
    "    key: str = \"classifier-data\", \n",
    "    file_ext: str = \"parquet\",\n",
    "    sk_params = {}\n",
    "):\n",
    "    \"\"\"Create a binary classification sample dataset and save.\n",
    "    If no filename is given it will default to:\n",
    "    \"simdata-{n_samples}X{m_features}.parquet\".\n",
    "    \n",
    "    Additional scikit-learn parameters can be set using **sk_params, please see https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html for more details.\n",
    "    \n",
    "    :param context:       function context\n",
    "    :param n_samples:     number of rows/samples\n",
    "    :param m_features:    number of cols/features\n",
    "    :param k_classes:     number of classes\n",
    "    :param header:        header for features array\n",
    "    :param label_column:  column name of ground-truth series\n",
    "    :param weight:        fraction of sample negative value (ground-truth=0)\n",
    "    :param random_state:  rng seed (see https://scikit-learn.org/stable/glossary.html#term-random-state)\n",
    "    :param key:           key of data in artifact store\n",
    "    :param file_ext:      (pqt) extension for parquet file\n",
    "    :param sk_params:     additional parameters for `sklearn.datasets.make_classification`\n",
    "    \"\"\"\n",
    "    features, labels = make_classification(\n",
    "        n_samples=n_samples,\n",
    "        n_features=m_features,\n",
    "        weights=weight,\n",
    "        n_classes=k_classes,\n",
    "        random_state=random_state, \n",
    "        **sk_params)\n",
    "\n",
    "    # make dataframes, add column names, concatenate (X, y)\n",
    "    X = pd.DataFrame(features)\n",
    "    if not header:\n",
    "        X.columns = [\"feat_\" + str(x) for x in range(m_features)]\n",
    "    else:\n",
    "        X.columns = header\n",
    "\n",
    "    y = pd.DataFrame(labels, columns=[label_column])\n",
    "    data = pd.concat([X, y], axis=1)\n",
    "    \n",
    "    context.log_dataset(key, df=data, format=file_ext, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: end-code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[mlrun] 2020-06-14 10:37:07,647 function spec saved to path: function.yaml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.kubejob.KubejobRuntime at 0x7faf4a975eb8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlrun import code_to_function\n",
    "from mlrun.platforms.other import auto_mount\n",
    "\n",
    "gpus = False\n",
    "\n",
    "fn_params = {\n",
    "    \"name\"        : \"gen_class_data\",\n",
    "    \"handler\"     : \"gen_class_data\",\n",
    "    \"kind\"        : \"job\",\n",
    "    \"image\"       : \"mlrun/ml-models\" if not gpus else \"mlrun/ml-models-gpu\",\n",
    "    \"description\" : \"simulate classification data using scikit-learn\",\n",
    "    \"categories\"  : [\"simulators\", \"ml\"],\n",
    "    \"labels\"      : {\"author\": \"yjb\", 'framework': 'sklearn'},\n",
    "}\n",
    "\n",
    "fn = code_to_function(**fn_params)\n",
    "\n",
    "fn.export(\"function.yaml\")\n",
    "fn.apply(auto_mount())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlrun import NewTask, mlconf\n",
    "\n",
    "task_params = {\n",
    "    \"name\":        \"tasks generate classification data\", \n",
    "    \"params\" : {\n",
    "        \"n_samples\"   : 10_000,\n",
    "        \"m_features\"  : 5,\n",
    "        \"k_classes\"   : 2,\n",
    "        \"weight\"      : [0.5, 0.5],\n",
    "        \"sk_params\"   : {\"n_informative\": 2},\n",
    "        \"file_ext\"    : \"csv\"}}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[mlrun] 2020-06-14 10:33:01,963 starting run tasks generate classification data uid=1d7c5af7e4b04bd98755c87842455105  -> http://mlrun-api:8080\n",
      "[mlrun] 2020-06-14 10:33:02,156 log artifact classifier-data at /User/artifacts/classifier-data.csv, size: 998700, db: Y\n",
      "\n"
     ]
    },
    {
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       "\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"1d7c5af7e4b04bd98755c87842455105\"><a href=\"https://mlrun-ui.default-tenant.app.flobdnzhgtgt.iguazio-cd2.com/projects/default/jobs/1d7c5af7e4b04bd98755c87842455105/info\" target=\"_blank\" >...42455105</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jun 14 10:33:01</td>\n",
       "      <td>completed</td>\n",
       "      <td>tasks generate classification data</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=handler</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=jupyter-7b44c8d958-kklf7</div></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"dictlist\">n_samples=10000</div><div class=\"dictlist\">m_features=5</div><div class=\"dictlist\">k_classes=2</div><div class=\"dictlist\">weight=[0.5, 0.5]</div><div class=\"dictlist\">sk_params={'n_informative': 2}</div><div class=\"dictlist\">file_ext=csv</div></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result755b6617\" title=\"/files/artifacts/classifier-data.csv\">classifier-data</div></td>\n",
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       "    <div class=\"pane-header\">\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 1d7c5af7e4b04bd98755c87842455105 --project default , !mlrun logs 1d7c5af7e4b04bd98755c87842455105 --project default\n",
      "[mlrun] 2020-06-14 10:33:02,198 run executed, status=completed\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.model.RunObject at 0x7fafa49fc160>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlrun import run_local\n",
    "run_local(NewTask(**task_params), handler=gen_class_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### remote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[mlrun] 2020-06-14 10:33:02,619 starting run tasks generate classification data uid=8f2102b308f446f28242c03ac1a835a7  -> http://mlrun-api:8080\n",
      "[mlrun] 2020-06-14 10:33:02,723 Job is running in the background, pod: tasks-generate-classification-data-wjdsf\n",
      "[mlrun] 2020-06-14 10:33:08,285 starting local run: main.py # gen_class_data\n",
      "[mlrun] 2020-06-14 10:33:08,806 log artifact classifier-data at /User/artifacts/classifier-data.csv, size: 998700, db: Y\n",
      "\n",
      "[mlrun] 2020-06-14 10:33:08,823 run executed, status=completed\n",
      "final state: succeeded\n"
     ]
    },
    {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"8f2102b308f446f28242c03ac1a835a7\"><a href=\"https://mlrun-ui.default-tenant.app.flobdnzhgtgt.iguazio-cd2.com/projects/default/jobs/8f2102b308f446f28242c03ac1a835a7/info\" target=\"_blank\" >...c1a835a7</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jun 14 10:33:08</td>\n",
       "      <td>completed</td>\n",
       "      <td>tasks generate classification data</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=tasks-generate-classification-data-wjdsf</div></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"dictlist\">n_samples=10000</div><div class=\"dictlist\">m_features=5</div><div class=\"dictlist\">k_classes=2</div><div class=\"dictlist\">weight=[0.5, 0.5]</div><div class=\"dictlist\">sk_params={'n_informative': 2}</div><div class=\"dictlist\">file_ext=csv</div></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultc9fcf59d\" title=\"/files/artifacts/classifier-data.csv\">classifier-data</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"resultc9fcf59d-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"resultc9fcf59d-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"resultc9fcf59d\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"resultc9fcf59d-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 8f2102b308f446f28242c03ac1a835a7 --project default , !mlrun logs 8f2102b308f446f28242c03ac1a835a7 --project default\n",
      "[mlrun] 2020-06-14 10:33:11,884 run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "run = fn.run(NewTask(**task_params), artifact_path=mlconf.artifact_path)"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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