gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# tutorial https://cloud.google.com/bigquery/docs/bigqueryml-scientist-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from google.cloud import bigquery\n",
    "%load_ext google.cloud.bigquery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/.google.json\"\n",
    "os.environ[\"GOOGLE_CLOUD_PROJECT\"] = \"aqueduct30\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "client = bigquery.Client()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step two create your dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset(DatasetReference('aqueduct30', 'bqml_tutorial'))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = bigquery.Dataset(client.dataset('bqml_tutorial'))\n",
    "dataset.location = 'US'\n",
    "client.create_dataset(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step three: Create your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing query with job ID: 5b59d7e8-cdf4-4733-a263-d6467e66fb4a\n",
      "Query executing: 445.85s"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Table has no schema:  call 'client.get_table()'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-a00c87c72132>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bigquery'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'CREATE OR REPLACE MODEL `bqml_tutorial.sample_model`\\nOPTIONS(model_type=\\'logistic_reg\\') AS\\nSELECT\\n  IF(totals.transactions IS NULL, 0, 1) AS label,\\n  IFNULL(device.operatingSystem, \"\") AS os,\\n  device.isMobile AS is_mobile,\\n  IFNULL(geoNetwork.country, \"\") AS country,\\n  IFNULL(totals.pageviews, 0) AS pageviews\\nFROM\\n  `bigquery-public-data.google_analytics_sample.ga_sessions_*`\\nWHERE\\n  _TABLE_SUFFIX BETWEEN \\'20160801\\' AND \\'20170630\\''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/envs/python35/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2165\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2166\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2167\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2168\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/python35/lib/python3.5/site-packages/google/cloud/bigquery/magics.py\u001b[0m in \u001b[0;36m_cell_magic\u001b[0;34m(line, query)\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0mjob_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbigquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mQueryJobConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m     \u001b[0mjob_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_legacy_sql\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_legacy_sql\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m     \u001b[0mquery_job\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_run_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/python35/lib/python3.5/site-packages/google/cloud/bigquery/magics.py\u001b[0m in \u001b[0;36m_run_query\u001b[0;34m(client, query, job_config)\u001b[0m\n\u001b[1;32m    220\u001b[0m             time.time() - start_time), end='')\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0mquery_job\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/python35/lib/python3.5/site-packages/google/cloud/bigquery/job.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout, retry)\u001b[0m\n\u001b[1;32m   2508\u001b[0m         \u001b[0mdest_table_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestination\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2509\u001b[0m         \u001b[0mdest_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdest_table_ref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2510\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlist_rows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdest_table\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretry\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2512\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/python35/lib/python3.5/site-packages/google/cloud/bigquery/client.py\u001b[0m in \u001b[0;36mlist_rows\u001b[0;34m(self, table, selected_fields, max_results, page_token, start_index, page_size, retry)\u001b[0m\n\u001b[1;32m   1403\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1404\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1405\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_TABLE_HAS_NO_SCHEMA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1406\u001b[0m             \u001b[0mschema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1407\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Table has no schema:  call 'client.get_table()'"
     ]
    }
   ],
   "source": [
    "%%bigquery\n",
    "CREATE OR REPLACE MODEL `bqml_tutorial.sample_model`\n",
    "OPTIONS(model_type='logistic_reg') AS\n",
    "SELECT\n",
    "  IF(totals.transactions IS NULL, 0, 1) AS label,\n",
    "  IFNULL(device.operatingSystem, \"\") AS os,\n",
    "  device.isMobile AS is_mobile,\n",
    "  IFNULL(geoNetwork.country, \"\") AS country,\n",
    "  IFNULL(totals.pageviews, 0) AS pageviews\n",
    "FROM\n",
    "  `bigquery-public-data.google_analytics_sample.ga_sessions_*`\n",
    "WHERE\n",
    "  _TABLE_SUFFIX BETWEEN '20160801' AND '20170630'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "source": []
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