{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data analysis with ibmdbpy and Db2 Warehouse - Part 2: Recommendation\n", "\n", "This notebook shows you how to use ibmdbpy to handle geospatial data, with a point of interest recommandation use case.\n", "___\n", "\n", "### Accelerate Python analytics with in-database processing by using ibmdbpy and IBM Db2 Warehouse\n", "\n", "The ibmdbpy project provides access to in-database algorithms in IBM Db2 Warehouse through a Python interface for data manipulation. It accelerates Python analytics by seamlessly pushing operations written in Python into the underlying database for execution, thereby benefitting from in-database performance-enhancing features, such as columnar storage and parallel processing. For more details about ibmdbpy, please refer to the [documentation](https://pythonhosted.org/ibmdbpy/index.html) and to the dedicated [Git repository](https://github.com/ibmdbanalytics/ibmdbpy/tree/master/ibmdbpy). This notebook provides you with an overview of ibmdbpy functionalities through a tourism use case. \n", "\n", "\n", "__About this notebook__\n", "\n", "In the \"Museums\" notebook series, you get deeper insights about how to perform in-database analytics with ibmdbpy.\n", "Our use case: tourism in the United States. Imagine you have designed a tourism app and want to recommand activities or places to users according to their location, taste and some geographic constraints. Ibmdbpy spatial extension enables you to efficiently query your database for real-time recommandation. There are two notebooks: \n", "\n", "1. Open and preprocess data: [Preprocessing notebook](./ibmdbpy_Museums_DataAnalysis_1-preprocessing.ipynb)\n", "2. Recommend a Point of Interest in the US: here you are!\n", "\n", "\n", "__Prerequisites__\n", "* Db2 account: see [IBM Cloud](https://cloud.ibm.com/login) or [Db2 Warehouse](https://www.ibm.com/support/knowledgecenter/en/SSCJDQ/com.ibm.swg.im.dashdb.kc.doc/welcome.html)\n", "* Db2 driver: learn more on [IBM Knowledge Center](https://www.ibm.com/support/knowledgecenter/en/SSFMBX/com.ibm.swg.im.dashdb.doc/connecting/connect_applications_by_type.html) and see [IBM Support](https://www.ibm.com/support/pages/db2-jdbc-driver-versions-and-downloads)\n", "* Having installed the [ibmdbpy](https://pypi.org/project/ibmdbpy/) python library with pip: \n", "> pip install ibmdbpy \n", "* Optional dependency for JDBC is the [jaydebeapi](https://pypi.org/project/JayDeBeApi/) library. Run the following command to install ibmdbpy, as well as the dependencies for the JDBC feature:\n", "> pip install ibmdbpy[jdbc]\n", "* The [museums.csv](https://www.kaggle.com/imls/museum-directory/data) dataset.\n", "\n", "Note: the museums dataset we use in this notebook has been published under the licence CC0 1.0 Universal (CC0 1.0) /\n", "Public Domain Dedication (see [source](https://www.kaggle.com/imls/museum-directory/data)).\n", "\n", "\n", "__Contents__\n", "\n", "__1. Get started__\n", "* Imports\n", "* Establish JDBC connection\n", "* Load the data: Museums and Tourists datasets\n", "\n", "__2. Point of interest recommendation__\n", "\n", "* step 1: filter on category\n", "* step 2: buffer\n", "* visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Get started\n", "\n", "As usual, we make the necessary imports and establish a connection to Db2 Warehouse. Then we load the data using ibmdbpy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Imports__\n", "\n", "Jaydebeapi is used in order to establish a JDBC connection. We load the necessary classes of ibmdbpy. Pandas, numpy, seaborn and matplotlib are used for data visualization." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# connection to Db2, ibmdbpy classes\n", "from ibmdbpy import IdaDataBase, IdaDataFrame, IdaGeoDataFrame\n", "\n", "# for visualization and data transformation\n", "import pandas\n", "import geopandas\n", "import numpy as np\n", "from shapely.geometry import Point\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Establish JDBC connection__\n", "\n", "The JDBC connection is based on a Java Virtual Machine, so it is available on every machine that can run Java. In ibmdbpy, users can choose to use JDBC to connect to a remote Db2 instance. \n", "\n", "You have to enter your own credentials in the following cell." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Enter the values for you database connection\n", "dsn_database = \"___\" # e.g. \"BLUDB\"\n", "dsn_hostname = \"___\" # e.g.: \"abc.url.example\"\n", "dsn_port = \"___\" # e.g. \"50000\"\n", "dsn_uid = \"___\" # e.g. \"db2_1234\"\n", "dsn_pwd = \"___\" # e.g. \"zorglub\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Establish connection\n", "connection_string='jdbc:db2://'+dsn_hostname+':'+dsn_port+'/'+dsn_database+':user='+dsn_uid+';password='+dsn_pwd+\";\" \n", "idadb=IdaDataBase(dsn=connection_string)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Define your verbosity settings\n", "from ibmdbpy.utils import set_verbose\n", "set_verbose(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The connection is now established! If you leave this notebook open for a while, it might get closed even if your Kernel does not die. No problem! Your variables are not lost, it is only the connection that has been interrupted. Just run the following to reconnect:\n", "\n", "> idadb.reconnect()\n", "\n", "In the end if this notebook, you will close the connection by running:\n", "\n", "> idadb.close()\n", "\n", "By default the environment variable `AUTOCOMMIT` is then set to True, which means that every SQL statement which is submitted through the connection is executed within its own transaction and then committed implicitly. When you close the connection to Db2, if the environment variable `AUTOCOMMIT` is set to False, then all changes after the last explicit commit are discarded. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Load the data: Museums and Tourists datasets__\n", "\n", "Here we directly load the data we have saved to Db2 at the end of the previous notebook. We also create another table containing user information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* GEO_MUSEUMS table\n", "\n", "Note that we specify the geometry column and the index column. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/eva.feillet/.local/lib/python3.7/site-packages/jaydebeapi/__init__.py:250: UserWarning: No type mapping for JDBC type 'STRUCT' (constant value 2002). Using None as a default type_code.\n", " \"Using None as a default type_code.\" % (type_name, jdbc_type_const))\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
IDGEOMMUSEUMTYPEMUSEUMNAME
08400100001POINT (-87.505100 33.295310)GENERAL MUSEUMWESTERVELT-WARNER MUSEUM OF AMERICAN ART
18400100002POINT (-85.719780 31.349360)GENERAL MUSEUMU. S. ARMY AVIATION MUSEUM
28400100004POINT (-86.628410 34.713800)GENERAL MUSEUMU.S. SPACE & ROCKET CENTER
38400100005POINT (-85.955410 32.943940)GENERAL MUSEUMWELLBORN MUSCLECAR MUSEUM
48400100007POINT (-88.044830 30.690950)GENERAL MUSEUMUSS ALABAMA BATTLESHIP
\n", "
" ], "text/plain": [ " ID GEOM MUSEUMTYPE \\\n", "0 8400100001 POINT (-87.505100 33.295310) GENERAL MUSEUM \n", "1 8400100002 POINT (-85.719780 31.349360) GENERAL MUSEUM \n", "2 8400100004 POINT (-86.628410 34.713800) GENERAL MUSEUM \n", "3 8400100005 POINT (-85.955410 32.943940) GENERAL MUSEUM \n", "4 8400100007 POINT (-88.044830 30.690950) GENERAL MUSEUM \n", "\n", " MUSEUMNAME \n", "0 WESTERVELT-WARNER MUSEUM OF AMERICAN ART \n", "1 U. S. ARMY AVIATION MUSEUM \n", "2 U.S. SPACE & ROCKET CENTER \n", "3 WELLBORN MUSCLECAR MUSEUM \n", "4 USS ALABAMA BATTLESHIP " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Download the dataset from a Db2 instance\n", "geo_museums = IdaGeoDataFrame(idadb, 'GEO_MUSEUMS', geometry = 'GEOM', indexer = 'ID')\n", "geo_museums.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* GEO_TOURISTS table\n", "\n", "We show you how to create a sample of user data. We just insert a few user profiles in a new table for this scenario." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Table GEO_TOURISTS already exists. Do you want to erase it and create a new one?\n", "y/n y\n", "Table GEO_TOURISTS created\n", "Values successfully inserted\n" ] } ], "source": [ "def create_tourists(): \n", " #idadb.ida_query('SET CURRENT FUNCTION PATH = CURRENT FUNCTION PATH, db2gse') \n", " idadb.ida_query('CREATE TABLE GEO_TOURISTS(user_id INT GENERATED BY DEFAULT AS IDENTITY NOT NULL, user_loc db2gse.ST_POINT NOT NULL, museum_type VARCHAR(50), max_dist INT, PRIMARY KEY (user_id)) ORGANIZE BY ROW')\n", " print('Table GEO_TOURISTS created')\n", "\n", " values = [\"(db2gse.ST_Point (-74.1824830955078, 40.7705705469009, 1), 'GENERAL MUSEUM', 40)\",\n", " \"(db2gse.ST_Point(-95.1824830955078, 38.9547390131, 1), 'HISTORIC PRESERVATION', 20)\",\n", " \"(db2gse.ST_Point(-120.40035536736, 34.92375843752231, 1), 'SCIENCE & TECHNOLOGY MUSEUM OR PLANETARIUM', 50)\",\n", " \"(db2gse.ST_Point(-111.27845602187497, 40.59386155517787, 1), 'GENERAL MUSEUM', 10)\",\n", " \"(db2gse.ST_Point(-112.00355367812489, 33.572290986838915, 1), 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION', 40)\"]\n", " for v in values:\n", " idadb.ida_query('INSERT INTO GEO_TOURISTS(user_loc, museum_type, max_dist) VALUES'+v)\n", " print('Values successfully inserted')\n", " \n", "if idadb.exists_table(\"GEO_TOURISTS\"):\n", " print(\"Table GEO_TOURISTS already exists. Do you want to erase it and create a new one?\")\n", " ans = input(\"y/n \")\n", " if ans == 'n':\n", " print('Table GEO_TOURISTS will remain as it currently is.') \n", " if ans == 'y':\n", " idadb.ida_query('DROP TABLE GEO_TOURISTS')\n", " create_tourists()\n", "else:\n", " create_tourists()\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
USER_IDUSER_LOCMUSEUM_TYPEMAX_DIST
01POINT (-74.182483 40.770571)GENERAL MUSEUM40
12POINT (-95.182483 38.954739)HISTORIC PRESERVATION20
23POINT (-120.400355 34.923758)SCIENCE & TECHNOLOGY MUSEUM OR PLANETARIUM50
34POINT (-111.278456 40.593862)GENERAL MUSEUM10
45POINT (-112.003554 33.572291)ZOO, AQUARIUM, OR WILDLIFE CONSERVATION40
\n", "
" ], "text/plain": [ " USER_ID USER_LOC \\\n", "0 1 POINT (-74.182483 40.770571) \n", "1 2 POINT (-95.182483 38.954739) \n", "2 3 POINT (-120.400355 34.923758) \n", "3 4 POINT (-111.278456 40.593862) \n", "4 5 POINT (-112.003554 33.572291) \n", "\n", " MUSEUM_TYPE MAX_DIST \n", "0 GENERAL MUSEUM 40 \n", "1 HISTORIC PRESERVATION 20 \n", "2 SCIENCE & TECHNOLOGY MUSEUM OR PLANETARIUM 50 \n", "3 GENERAL MUSEUM 10 \n", "4 ZOO, AQUARIUM, OR WILDLIFE CONSERVATION 40 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# define the corresponding IdaGeoDataFrame\n", "geo_tourists = IdaGeoDataFrame(idadb, 'GEO_TOURISTS', indexer = 'USER_ID', geometry = 'USER_LOC')\n", "geo_tourists.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, we are all set! Let's make some recommendations with ibmdbpy!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Point of Interest Recommendation\n", "\n", "For this demo we simply use the museum type and the maximum distance from user location as criteria." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 1: select user and filter rows on museum type__\n", "\n", "As a reminder we display the different museum types from our dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following cell we define an IdaGeoDataFrame obtained by filtering geo_museums on MuseumType. We take user number 1 as example." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "User with ID 1 would like to go to a point of interest of type GENERAL MUSEUM.\n" ] } ], "source": [ "# Here with an example: userID = 1\n", "\n", "userID = 1\n", "my_user = geo_tourists[geo_tourists['USER_ID']==userID]\n", "my_type = my_user['MUSEUM_TYPE'].head()[0]\n", "\n", "print('User with ID %s would like to go to a point of interest of type %s.' %(userID, my_type))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Filter museums according to this prefered type\n", "my_museums = geo_museums[geo_museums['MUSEUMTYPE']==my_type]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 2: create a buffer around user locations__\n", " \n", "Here we apply the `buffer` method, relying on the function `db2gse.ST_Buffer` from Db2 to add a new column to our dataset view. This new column of type `ST_Geometry` contains polygons corresponding to an area centered on the current location of a user with a MAX_DIST radius (maximum distance allowed by user). " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "User with ID 1 has specifed a maximum distance of 40 km.\n" ] } ], "source": [ "# Here we focus on just one particular user:\n", "my_max_dist = my_user['MAX_DIST'].head()[0]\n", "\n", "print('User with ID %s has specifed a maximum distance of %s km.' %(userID,my_max_dist))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Select only the column with userID\n", "\n", "my_user['BUFFER'] = my_user.buffer(my_max_dist,'KILOMETER')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TYPENAME
USER_IDINTEGER
USER_LOCST_POINT
MUSEUM_TYPEVARCHAR
MAX_DISTINTEGER
BUFFERST_GEOMETRY
\n", "
" ], "text/plain": [ " TYPENAME\n", "USER_ID INTEGER\n", "USER_LOC ST_POINT\n", "MUSEUM_TYPE VARCHAR\n", "MAX_DIST INTEGER\n", "BUFFER ST_GEOMETRY" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Remember that we have several columns eligible to be set as geometry now:\n", "# USER_LOC and BUFFER\n", "my_user.dtypes" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Current geometry attribute for user table:\n", "['USER_LOC']\n", "New geometry attribute for user table:\n", "['BUFFER']\n", "Current geometry attribute for museums table:\n", "['GEOM']\n" ] } ], "source": [ "# Set 'BUFFER' as the new geometry column for the next step\n", "print(\"Current geometry attribute for user table:\")\n", "print(my_user.geometry.columns)\n", "print(\"New geometry attribute for user table:\")\n", "my_user.set_geometry('BUFFER')\n", "print(my_user.geometry.columns)\n", "\n", "# Sanity check\n", "print(\"Current geometry attribute for museums table:\")\n", "print(my_museums.geometry.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 3: Find museums in the target area__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Method 1: use `contains` to detect which museums are in the buffer " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "column1_for_db2gse: DB2GSE.ST_BUFFER(USER_LOC,40,'KILOMETER')\n", "column2_for_db2gse: GEOM\n", "A function pattern was detected in column1_for_db2gse. Please provide the alias of the first geometry column.\n", "ALIAS_1: BUFFER\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
INDEXERIDA1INDEXERIDA2RESULT
0184006001781
1184006005201
2184094000141
3184034005921
4184034005121
\n", "
" ], "text/plain": [ " INDEXERIDA1 INDEXERIDA2 RESULT\n", "0 1 8400600178 1\n", "1 1 8400600520 1\n", "2 1 8409400014 1\n", "3 1 8403400592 1\n", "4 1 8403400512 1" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The following line of code computes for each museum a boolean value:\n", "# Whether geometry from my_museums contains geometry from my_user.\n", "result = my_user.contains(my_museums)\n", "\n", "# Filter out positive results:\n", "# Obtain the IDs of eligible point of interest only\n", "positive_results = result[result['RESULT']==1]\n", "positive_results.head(5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/eva.feillet/anaconda3/lib/python3.7/site-packages/ibmdbpy/frame.py:1899: UserWarning: Table RESULTS_1 already exists.\n", " warnings.warn(message, UserWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Are you sure that you want to overwrite RESULTS_1 [y/n] y\n" ] } ], "source": [ "# Save the positive results only\n", "positive_results.save_as(\"RESULTS_1\", clear_existing = True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
IDMUSEUMTYPEMUSEUMNAME
08409400014GENERAL MUSEUM78TH DIVISION HISTORICAL HOLDING
18403601706GENERAL MUSEUMACCINOSCO
28403600439GENERAL MUSEUMADEPT NEW AMERICAN FOLK CENTER
38403600435GENERAL MUSEUMAFRICAN-AMERICAN WAX MUSEUM OF HARLEM
48403600392GENERAL MUSEUMALTERNATIVE MUSEUM
58403601961GENERAL MUSEUMALUMNI SOCIETY OF THE SCHOOL OF VISUAL ARTS
\n", "
" ], "text/plain": [ " ID MUSEUMTYPE MUSEUMNAME\n", "0 8409400014 GENERAL MUSEUM 78TH DIVISION HISTORICAL HOLDING\n", "1 8403601706 GENERAL MUSEUM ACCINOSCO\n", "2 8403600439 GENERAL MUSEUM ADEPT NEW AMERICAN FOLK CENTER\n", "3 8403600435 GENERAL MUSEUM AFRICAN-AMERICAN WAX MUSEUM OF HARLEM\n", "4 8403600392 GENERAL MUSEUM ALTERNATIVE MUSEUM\n", "5 8403601961 GENERAL MUSEUM ALUMNI SOCIETY OF THE SCHOOL OF VISUAL ARTS" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display the results: museum names\n", "idadb.ida_query('SELECT IDA1.\"ID\", IDA1.\"MUSEUMTYPE\", IDA1.\"MUSEUMNAME\" FROM GEO_MUSEUMS AS IDA1, RESULTS_1 AS IDA2 WHERE IDA1.\"ID\" = IDA2.INDEXERIDA2 ORDER BY IDA1.MUSEUMNAME FETCH FIRST 6 ROWS ONLY')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can now refine your choice using other criteria!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Method 2: compute distance to user and filter on distance\n", "\n", "You can order results by increasing distance to the user for example." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "column1_for_db2gse: GEOM\n", "column2_for_db2gse: USER_LOC\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
INDEXERIDA1INDEXERIDA2RESULT
08403600571139.960883
18403600907139.847335
28403600554139.346915
38403600069139.045445
48403400049138.540799
\n", "
" ], "text/plain": [ " INDEXERIDA1 INDEXERIDA2 RESULT\n", "0 8403600571 1 39.960883\n", "1 8403600907 1 39.847335\n", "2 8403600554 1 39.346915\n", "3 8403600069 1 39.045445\n", "4 8403400049 1 38.540799" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute distance to user location\n", "my_user.set_geometry('USER_LOC')\n", "my_distances = my_museums.distance(my_user, 'KILOMETRE')\n", "\n", "# Filter\n", "my_results = my_distances[my_distances['RESULT'] <= my_max_dist].sort('RESULT', ascending = False)\n", "\n", "my_results.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/eva.feillet/anaconda3/lib/python3.7/site-packages/ibmdbpy/frame.py:1899: UserWarning: Table RESULTS_2 already exists.\n", " warnings.warn(message, UserWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Are you sure that you want to overwrite RESULTS_2 [y/n] y\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
IDMUSEUMTYPEMUSEUMNAME
08409400014GENERAL MUSEUM78TH DIVISION HISTORICAL HOLDING
18403601706GENERAL MUSEUMACCINOSCO
28403600439GENERAL MUSEUMADEPT NEW AMERICAN FOLK CENTER
38403600435GENERAL MUSEUMAFRICAN-AMERICAN WAX MUSEUM OF HARLEM
48403600392GENERAL MUSEUMALTERNATIVE MUSEUM
58403601961GENERAL MUSEUMALUMNI SOCIETY OF THE SCHOOL OF VISUAL ARTS
\n", "
" ], "text/plain": [ " ID MUSEUMTYPE MUSEUMNAME\n", "0 8409400014 GENERAL MUSEUM 78TH DIVISION HISTORICAL HOLDING\n", "1 8403601706 GENERAL MUSEUM ACCINOSCO\n", "2 8403600439 GENERAL MUSEUM ADEPT NEW AMERICAN FOLK CENTER\n", "3 8403600435 GENERAL MUSEUM AFRICAN-AMERICAN WAX MUSEUM OF HARLEM\n", "4 8403600392 GENERAL MUSEUM ALTERNATIVE MUSEUM\n", "5 8403601961 GENERAL MUSEUM ALUMNI SOCIETY OF THE SCHOOL OF VISUAL ARTS" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_results.save_as('RESULTS_2', clear_existing = True)\n", "\n", "idadb.ida_query('SELECT IDA1.\"ID\", IDA1.\"MUSEUMTYPE\", IDA1.\"MUSEUMNAME\" FROM GEO_MUSEUMS AS IDA1, RESULTS_2 AS IDA2 WHERE IDA1.ID = IDA2.INDEXERIDA1 ORDER BY IDA1.MUSEUMNAME FETCH FIRST 6 ROWS ONLY')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "263\n", "263\n" ] } ], "source": [ "print(len(positive_results))\n", "print(len(my_results))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Comment__\n", "\n", "By taking a closer look at the results, you would be able to see that we obtain exactly the same recommandations. In our case, we have found 263 point of interests of type \"general museum\" within 40km of our user. Let's visualize these results on a map!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualization \n", "\n", "After you have processed the data with ibmdbpy, you can convert your data back to a DataFrame or GeoDataFrame format for visualization. Here we visualize the results for user 1." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# save full result data to Db2 - optional, here we do it for visualization purposes\n", "idadb.ida_query('drop table if exists geo_results')\n", "idadb.ida_query('CREATE TABLE GEO_RESULTS AS (SELECT * FROM GEO_MUSEUMS AS IDA1, RESULTS_1 AS IDA2 WHERE IDA1.\"ID\" = IDA2.INDEXERIDA2) WITH DATA ORGANIZE BY ROW')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Overview__" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "results = IdaGeoDataFrame(idadb, 'GEO_RESULTS', indexer = 'ID', geometry = 'GEOM')\n", "\n", "# Data transformation for visualization\n", "\n", "# GeoDataFrame for results\n", "results['X'] = results.x() # Using the spatial function ST_X and ST_Y to extract the coordinates\n", "results['Y'] = results.y()\n", "results_df = results[['X', 'Y', 'MUSEUMTYPE']].as_dataframe()\n", "\n", "geometry = [Point(xy) for xy in zip(results_df['X'],results_df['Y'])]\n", "results_df = geopandas.GeoDataFrame(results_df, geometry = geometry)\n", "\n", "# GeoDataFrame for users\n", "geo_tourists.set_geometry('USER_LOC')\n", "geo_tourists['X'] = geo_tourists.x()\n", "geo_tourists['Y'] = geo_tourists.y()\n", "tourists_df = geo_tourists.as_dataframe()\n", "\n", "geometry = [Point(xy) for xy in zip(tourists_df['X'],tourists_df['Y'])]\n", "tourists_df = geopandas.GeoDataFrame(tourists_df, geometry = geometry)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# load map of the USA\n", "import os\n", "os.system(\"wget -nc https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_5m.zip\")\n", "os.system(\"unzip cb_2018_us_state_5m.zip\")\n", "\n", "usa = geopandas.read_file(\"cb_2018_us_state_5m.shp\")\n", "\n", "# cut out some states in order to be able to zoom in\n", "for stusps in ['HI', 'VI', 'GU', 'MP', 'AS', 'PR', 'AK']: \n", " usa = usa[usa['STUSPS'] != stusps]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1, 'User locations')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = (18,16))\n", "usa.plot(ax=ax)\n", "\n", "x = tourists_df.X\n", "y = tourists_df.Y\n", "c = tourists_df.USER_ID\n", "\n", "scatter = ax.scatter(x, y, c=c, s = 200, marker = '*', cmap = 'spring')\n", "\n", "# produce a legend with the unique colors from the scatter\n", "legend1 = ax.legend(*scatter.legend_elements(), loc=\"lower left\", title=\"User IDs\")\n", "ax.add_artist(legend1)\n", "\n", "legend = ['users']\n", "plt.legend(legend, fontsize = 12)\n", "plt.title('User locations', fontsize = 18)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# Zoom for user 1\n", "\n", "#zoom = ['NY', 'PA', 'VT', 'DE', 'RI', 'MA', 'CT', 'NJ', 'ME', 'NH', 'DC']\n", "zoom = ['NY', 'NJ', 'CT']\n", "usa_zoom = usa.set_index('STUSPS').loc[zoom]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1, 'Selected museums for user 1')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize = (18,16))\n", "usa_zoom.plot(ax=ax)\n", "results_df.plot(ax = ax, marker = '+', markersize = 100, color = 'cyan')\n", "\n", "tourists_df.set_index('USER_ID').loc[[userID]].plot(ax = ax, marker = '*', markersize = 100, color = 'magenta')\n", "legend = ['Points of Interest', 'user 1']\n", "plt.legend(legend, fontsize = 12)\n", "plt.title('Selected museums for user 1', fontsize = 18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "__Congratulations!__ You reached the end of this notebook. Before shutting Jupyter down, close the current connection to Db2 Warehouse." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Connection closed.\n" ] } ], "source": [ "idadb.close()\n", "# idadb.reconnect() if connection closed and you want to restore it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Where to go from here?\n", "\n", "This notebook was a continuation of [Preprocessing](./ibmdbpy_Museums_DataAnalysis_1-preprocessing.ipynb).\n", "\n", "If you haven't already done so, we encourage you to have a look at the **Getting started** notebooks also available on this repository. See also the **Machine learning** notebooks.\n", "\n", "* Getting started with ibmdbpy:\n", " \n", " [Basics](../GettingStarted/ibmdbpy_GettingStarted_1-basics.ipynb)\n", "\n", " [GeoBasics](../GettingStarted/ibmdbpy_GettingStarted_2-geo_basics.ipynb)\n", "\n", " [Extensive Guide](../GettingStarted/ibmdbpy_GettingStarted_3-geo_guide.ipynb)\n", "\n", "\n", "* Machine learning with ibmdbpy: \n", " \n", " [Naïve Bayes](../MachineLearning/ibmdbpy_NaiveBayes.ipynb)\n", " \n", " [Association Rules Mining](../MachineLearning/ibmdbpy_AssociationRulesMining.ipynb)\n", "\n", " [KMeans](../GettingStarted/ibmdbpy_GettingStarted_1-basics.ipynb) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "____\n", "\n", "__Authors__\n", "\n", "Eva Feillet - ML intern, IBM Cloud and Cognitive Software @ IBM Lab in Böblingen, Germany" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }