experimental design (notebook)
This commit is contained in:
Steve L. Nyemba -- The Architect
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cbf41d0fc5
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from google.cloud import bigquery as bq\n",
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"\n",
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"client = bq.Client.from_service_account_json('/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"xo = ['person_id','date_of_birth','race']\n",
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"xi = ['person_id','value_as_number','value_source_value']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_tables(client,did,fields=[]):\n",
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" \"\"\"\n",
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" getting table lists from google\n",
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" \"\"\"\n",
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" r = []\n",
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" ref = client.dataset(id)\n",
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" tables = list(client.list_tables(ref))\n",
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" for table in tables :\n",
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" ref = table.reference\n",
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" schema = client.get_table(ref).schema\n",
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" names = [f.field_name for f in schema]\n",
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" x = list(set(names) & set(fields))\n",
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" if x :\n",
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" r.append({\"name\":table.table_id,\"fields\":names})\n",
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" return r\n",
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" \n",
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"def get_fields(**args):\n",
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" \"\"\"\n",
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" This function will generate a random set of fields from two tables. Tables are structured as follows \n",
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" {name,fields:[],\"y\":}, with \n",
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" name table name (needed to generate sql query)\n",
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" fields list of field names, used in the projection\n",
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" y name of the field to be joined.\n",
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" @param xo candidate table in the join\n",
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" @param xi candidate table in the join\n",
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" @param join field by which the tables can be joined.\n",
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" \"\"\"\n",
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" # The set operation will remove redundancies in the field names (not sure it's a good idea)\n",
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" xo = args['xo']['fields']\n",
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" xi = args['xi']['fields']\n",
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" zi = args['xi']['name']\n",
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" return list(set(xo) | set(['.'.join([args['xi']['name'],name]) for name in xi if name != args['join']]) )\n",
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"def generate_sql(**args):\n",
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" \"\"\"\n",
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" This function will generate the SQL query for the resulting join\n",
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" \"\"\"\n",
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" xo = args['xo']\n",
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" xi = args['xi']\n",
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" sql = \"SELECT :fields FROM :xo.name INNER JOIN :xi.name ON :xi.name.:xi.y = :xo.y \"\n",
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" fields = \",\".join(get_fields(xo=xi,xi=xi,join=xi['y']))\n",
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" \n",
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" \n",
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" sql = sql.replace(\":fields\",fields).replace(\":xo.name\",xo['name']).replace(\":xi.name\",xi['name'])\n",
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" sql = sql.replace(\":xi.y\",xi['y']).replace(\":xo.y\",xo['y'])\n",
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" return sql\n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['person_id',\n",
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" 'measurements.value_as_number',\n",
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" 'date_of_birth',\n",
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" 'race',\n",
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" 'measurements.value_source_value']"
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]
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},
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"execution_count": 54,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race']}\n",
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"xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value']}\n",
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"get_fields(xo=xo,xi=xi,join=\"person_id\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'SELECT person_id,value_as_number,measurements.value_source_value,measurements.value_as_number,value_source_value FROM person INNER JOIN measurements ON measurements.person_id = person_id '"
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]
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},
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"execution_count": 55,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race'],\"y\":\"person_id\"}\n",
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"xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value'],\"y\":\"person_id\"}\n",
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"generate_sql(xo=xo,xi=xi)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('a', 'b'), ('a', 'c'), ('b', 'c')]"
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]
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},
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"execution_count": 59,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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" We are designing a process that will take two tables that will generate \n",
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"\"\"\"\n",
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"import itertools\n",
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"list(itertools.combinations(['a','b','c'],2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 87,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"TableReference(DatasetReference(u'aou-res-deid-vumc-test', u'raw'), 'care_site')"
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]
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},
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"execution_count": 87,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ref = client.dataset('raw')\n",
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"tables = list(client.list_tables(ref))\n",
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"names = [table.table_id for table in tables]\n",
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"(tables[0].reference)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 85,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(u'care_site',\n",
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" u'concept',\n",
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" u'concept_ancestor',\n",
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" u'concept_class',\n",
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" u'concept_relationship',\n",
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" u'concept_synonym',\n",
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" u'condition_occurrence',\n",
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" u'criteria',\n",
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" u'death',\n",
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" u'device_exposure',\n",
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" u'domain',\n",
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" u'drug_exposure',\n",
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" u'drug_strength',\n",
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" u'location',\n",
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" u'measurement',\n",
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" u'note',\n",
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" u'observation',\n",
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" u'people_seed',\n",
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" u'person',\n",
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" u'procedure_occurrence',\n",
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" u'relationship',\n",
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" u'visit_occurrence',\n",
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" u'vocabulary')"
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]
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},
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"execution_count": 85,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#\n",
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"# find every table with person id at the very least or a subset of fields\n",
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"#\n",
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"def get_tables\n",
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"q = ['person_id']\n",
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"pairs = list(itertools.combinations(names,len(names)))\n",
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"pairs[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['a']"
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]
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},
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"execution_count": 90,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"list(set(['a','b']) & set(['a']))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.15rc1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,319 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from google.cloud import bigquery as bq\n",
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"\n",
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"client = bq.Client.from_service_account_json('/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"xo = ['person_id','date_of_birth','race']\n",
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"xi = ['person_id','value_as_number','value_source_value']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 181,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_tables(client,id,fields=[]):\n",
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" \"\"\"\n",
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" getting table lists from google\n",
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" \"\"\"\n",
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" r = []\n",
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" ref = client.dataset(id)\n",
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" tables = list(client.list_tables(ref))\n",
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" for table in tables :\n",
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" ref = table.reference\n",
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" schema = client.get_table(ref).schema\n",
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" names = [f.name for f in schema]\n",
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" x = list(set(names) & set(fields))\n",
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" if x :\n",
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" r.append({\"name\":table.table_id,\"fields\":names})\n",
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" return r\n",
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" \n",
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"def get_fields(**args):\n",
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" \"\"\"\n",
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" This function will generate a random set of fields from two tables. Tables are structured as follows \n",
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" {name,fields:[],\"y\":}, with \n",
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" name table name (needed to generate sql query)\n",
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" fields list of field names, used in the projection\n",
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" y name of the field to be joined.\n",
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" @param xo candidate table in the join\n",
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" @param xi candidate table in the join\n",
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" @param join field by which the tables can be joined.\n",
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" \"\"\"\n",
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" # The set operation will remove redundancies in the field names (not sure it's a good idea)\n",
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"# xo = args['xo']['fields']\n",
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"# xi = args['xi']['fields']\n",
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"# zi = args['xi']['name']\n",
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"# return list(set([ \".\".join([args['xo']['name'],name]) for name in xo]) | set(['.'.join([args['xi']['name'],name]) for name in xi if name != args['join']]) )\n",
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" xo = args['xo']\n",
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" fields = [\".\".join([args['xo']['name'],name]) for name in args['xo']['fields']]\n",
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" if not isinstance(args['xi'],list) :\n",
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" x_ = [args['xi']]\n",
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" else:\n",
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" x_ = args['xi']\n",
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" for xi in x_ :\n",
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" fields += (['.'.join([xi['name'],name]) for name in xi['fields'] if name != args['join']])\n",
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" return fields\n",
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"def generate_sql(**args):\n",
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" \"\"\"\n",
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" This function will generate the SQL query for the resulting join\n",
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" \"\"\"\n",
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" \n",
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" xo = args['xo']\n",
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" x_ = args['xi']\n",
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" xo_name = \".\".join([args['prefix'],xo['name'] ]) if 'prefix' in args else xo['name']\n",
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" SQL = \"SELECT :fields FROM :xo.name \".replace(\":xo.name\",xo_name)\n",
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" if not isinstance(x_,list):\n",
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" x_ = [x_]\n",
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" f = []#[\".\".join([args['xo']['name'],args['join']] )] \n",
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" INNER_JOINS = []\n",
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" for xi in x_ :\n",
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" xi_name = \".\".join([args['prefix'],xi['name'] ]) if 'prefix' in args else xi['name']\n",
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" JOIN_SQL = \"INNER JOIN :xi.name ON \".replace(':xi.name',xi_name)\n",
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" value = \".\".join([xi['name'],args['join']])\n",
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" f.append(value) \n",
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" \n",
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" ON_SQL = \"\"\n",
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" tmp = []\n",
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" for term in f :\n",
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" ON_SQL = \":xi.name.:ofield = :xo.name.:ofield\".replace(\":xo.name\",xo['name'])\n",
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" ON_SQL = ON_SQL.replace(\":xi.name.:ofield\",term).replace(\":ofield\",args['join'])\n",
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" tmp.append(ON_SQL)\n",
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" INNER_JOINS += [JOIN_SQL + \" AND \".join(tmp)]\n",
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" return SQL + \" \".join(INNER_JOINS)\n",
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" \n",
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"# sql = \"SELECT :fields FROM :xo.name INNER JOIN :xi.name ON :xi.name.:xi.y = :xo.y \"\n",
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"# fields = \",\".join(get_fields(xo=xi,xi=xi,join=xi['y']))\n",
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" \n",
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" \n",
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"# sql = sql.replace(\":fields\",fields).replace(\":xo.name\",xo['name']).replace(\":xi.name\",xi['name'])\n",
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"# sql = sql.replace(\":xi.y\",xi['y']).replace(\":xo.y\",xo['y'])\n",
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"# return sql\n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 183,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'SELECT :fields FROM raw.person INNER JOIN raw.measurement ON measurement.person_id = person.person_id'"
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]
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},
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"execution_count": 183,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race']}\n",
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"xi = [{\"name\":\"measurement\",\"fields\":['person_id','value_as_number','value_source_value']}] #,{\"name\":\"observation\",\"fields\":[\"person_id\",\"value_as_string\",\"observation_source_value\"]}]\n",
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"generate_sql(xo=xo,xi=xi,join=\"person_id\",prefix='raw')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'SELECT person_id,value_as_number,measurements.value_source_value,measurements.value_as_number,value_source_value FROM person INNER JOIN measurements ON measurements.person_id = person_id '"
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]
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},
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"execution_count": 55,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race'],\"y\":\"person_id\"}\n",
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"xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value'],\"y\":\"person_id\"}\n",
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"generate_sql(xo=xo,xi=xi)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('a', 'b'), ('a', 'c'), ('b', 'c')]"
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]
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},
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"execution_count": 59,
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"metadata": {},
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"output_type": "execute_result"
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}
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||||
],
|
||||
"source": [
|
||||
"\"\"\"\n",
|
||||
" We are designing a process that will take two tables that will generate \n",
|
||||
"\"\"\"\n",
|
||||
"import itertools\n",
|
||||
"list(itertools.combinations(['a','b','c'],2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 111,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[u'condition_occurrence.condition_occurrence_id',\n",
|
||||
" u'condition_occurrence.person_id',\n",
|
||||
" u'condition_occurrence.condition_concept_id',\n",
|
||||
" u'condition_occurrence.condition_start_date',\n",
|
||||
" u'condition_occurrence.condition_start_datetime',\n",
|
||||
" u'condition_occurrence.condition_end_date',\n",
|
||||
" u'condition_occurrence.condition_end_datetime',\n",
|
||||
" u'condition_occurrence.condition_type_concept_id',\n",
|
||||
" u'condition_occurrence.stop_reason',\n",
|
||||
" u'condition_occurrence.provider_id',\n",
|
||||
" u'condition_occurrence.visit_occurrence_id',\n",
|
||||
" u'condition_occurrence.condition_source_value',\n",
|
||||
" u'condition_occurrence.condition_source_concept_id',\n",
|
||||
" u'death.death_date',\n",
|
||||
" u'death.death_datetime',\n",
|
||||
" u'death.death_type_concept_id',\n",
|
||||
" u'death.cause_concept_id',\n",
|
||||
" u'death.cause_source_value',\n",
|
||||
" u'death.cause_source_concept_id',\n",
|
||||
" u'device_exposure.device_exposure_id',\n",
|
||||
" u'device_exposure.device_concept_id',\n",
|
||||
" u'device_exposure.device_exposure_start_date',\n",
|
||||
" u'device_exposure.device_exposure_start_datetime',\n",
|
||||
" u'device_exposure.device_exposure_end_date',\n",
|
||||
" u'device_exposure.device_exposure_end_datetime',\n",
|
||||
" u'device_exposure.device_type_concept_id',\n",
|
||||
" u'device_exposure.unique_device_id',\n",
|
||||
" u'device_exposure.quantity',\n",
|
||||
" u'device_exposure.provider_id',\n",
|
||||
" u'device_exposure.visit_occurrence_id',\n",
|
||||
" u'device_exposure.device_source_value',\n",
|
||||
" u'device_exposure.device_source_concept_id',\n",
|
||||
" u'drug_exposure.drug_exposure_id',\n",
|
||||
" u'drug_exposure.drug_concept_id',\n",
|
||||
" u'drug_exposure.drug_exposure_start_date',\n",
|
||||
" u'drug_exposure.drug_exposure_start_datetime',\n",
|
||||
" u'drug_exposure.drug_exposure_end_date',\n",
|
||||
" u'drug_exposure.drug_exposure_end_datetime',\n",
|
||||
" u'drug_exposure.drug_type_concept_id',\n",
|
||||
" u'drug_exposure.stop_reason',\n",
|
||||
" u'drug_exposure.refills',\n",
|
||||
" u'drug_exposure.quantity',\n",
|
||||
" u'drug_exposure.days_supply',\n",
|
||||
" u'drug_exposure.sig',\n",
|
||||
" u'drug_exposure.route_concept_id',\n",
|
||||
" u'drug_exposure.effective_drug_dose',\n",
|
||||
" u'drug_exposure.dose_unit_concept_id',\n",
|
||||
" u'drug_exposure.lot_number',\n",
|
||||
" u'drug_exposure.provider_id',\n",
|
||||
" u'drug_exposure.visit_occurrence_id',\n",
|
||||
" u'drug_exposure.drug_source_value',\n",
|
||||
" u'drug_exposure.drug_source_concept_id',\n",
|
||||
" u'drug_exposure.route_source_value',\n",
|
||||
" u'drug_exposure.dose_unit_source_value']"
|
||||
]
|
||||
},
|
||||
"execution_count": 111,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# find every table with person id at the very least or a subset of fields\n",
|
||||
"#\n",
|
||||
"info = get_tables(client,'raw',['person_id'])\n",
|
||||
"# get_fields(xo=names[0],xi=names[1:4],join='person_id')\n",
|
||||
"\n",
|
||||
"# q = ['person_id']\n",
|
||||
"# pairs = list(itertools.combinations(names,len(names)))\n",
|
||||
"# pairs[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 90,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['a']"
|
||||
]
|
||||
},
|
||||
"execution_count": 90,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"list(set(['a','b']) & set(['a']))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 120,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x_ = 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 2",
|
||||
"language": "python",
|
||||
"name": "python2"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.15rc1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue