Page Rank in Python

A Page Rank example using the NYC Taxi dataset

The following is a complete example, using the Python API, of solving a graph created with NYC Taxi data for a page rank problem via the /solve/graph endpoint. For more information on Network Graphs & Solvers, see Network Graphs & Solvers Concepts.

Prerequisites

The prerequisites for running the page rank solve graph example are listed below:

Python API Installation

The native Kinetica Python API is accessible through the following means:


PyPI

The Python package manager, pip, is required to install the API from PyPI.

  1. Install the API:

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    pip install gpudb --upgrade
    
  2. Test the installation:

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    python -c "import gpudb;print('Import Successful')"
    

    If Import Successful is displayed, the API has been installed as is ready for use.

Git

  1. In the desired directory, run the following, but be sure to replace <kinetica-version> with the name of the installed Kinetica version, e.g., v7.1:

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    git clone -b release/<kinetica-version> --single-branch https://github.com/kineticadb/kinetica-api-python.git
    
  2. Change directory into the newly downloaded repository:

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    cd kinetica-api-python
    
  3. In the root directory of the unzipped repository, install the Kinetica API:

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    sudo python setup.py install
    
  4. Test the installation (Python 2.7 (or greater) is necessary for running the API example):

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    python examples/example.py
    

Data File

The example script references the nyc_neighborhood.csv data file, mentioned in the Prerequisites, in the current local directory, by default. This directory can specified as a parameter when running the example script.

Script Detail

This example is going to demonstrate solving for the most popular pickup or dropoff point in New York City by ranking pickup and dropoff locations in terms of how frequently passengers are getting picked up/dropped off there. The more connected a point is in relation to other points, the greater its importance.

Constants

Several constants are defined at the beginning of the script:

  • SCHEMA -- the name of the schema in which the tables supporting the graph creation and match operations will be created

    Important

    The schema is created during the table setup portion of the script because the schema must exist prior to creating the tables that will later support the graph creation and match operations.

  • TABLE_NYC_N -- the name of the table into which the NYC Neighborhood dataset is loaded. This dataset is joined to the TABLE_TAXI table to create the JOIN_TAXI dataset.

  • TABLE_TAXI -- the name of the table into which the NYC taxi dataset is loaded. This dataset is joined to the TABLE_NYC_N table to create the JOIN_TAXI dataset.

  • TABLE_TAXI_E -- the name of the projection derived from the JOIN_TAXI dataset that serves as the edges for the GRAPH_T graph.

  • TABLE_TAXI_N -- the name of the union derived from the JOIN_TAXI dataset that serves as the nodes for the GRAPH_T graph.

  • TABLE_TAXI_N_S -- the same as TABLE_TAXI_N but later sharded so it can be joined to the nyctaxi_graph_id graph.

  • JOIN_TAXI -- the name of the join view that represents the dataset of all the trips found in the TABLE_TAXI dataset that overlap with the neighborhood boundaries found in the TABLE_NYC_N dataset

  • JOIN_PR_RESULTS -- the name of the join view that represents the dataset of all the nodes found in the TABLE_TAXI_N where the ID matches the SOLVERS_NODE_ID found in TABLE_GRAPH_T_PRSOLVED_S

  • GRAPH_T -- the NYC taxi graph

  • TABLE_GRAPH_T_PRSOLVED -- the solved NYC taxi graph using the PAGE_RANK solver type

  • TABLE_GRAPH_T_PRSOLVED_S -- the same as TABLE_GRAPH_T_PRSOLVED but later sharded so it can be joined to the nyctaxi_nodes_sharded table.

Constant Definitions
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SCHEMA = "tutorial_graph"
TABLE_NYC_N = SCHEMA + ".nyc_neighborhood"
TABLE_TAXI = "demo.nyctaxi"
TABLE_TAXI_E = SCHEMA + ".nyctaxi_edges_id"
TABLE_TAXI_N = TABLE_TAXI + "_nodes"
TABLE_TAXI_N_S = TABLE_TAXI_N + "_sharded"

JOIN_TAXI = SCHEMA + ".taxi_tables_joined"
JOIN_PR_RESULTS = SCHEMA + ".page_rank_results_joined"

GRAPH_T = SCHEMA + ".nyctaxi_graph_id"
TABLE_GRAPH_T_PRSOLVED = GRAPH_T + "_page_rank_solved"
TABLE_GRAPH_T_PRSOLVED_S = TABLE_GRAPH_T_PRSOLVED + "_sharded"

Graph Creation

One graph is used for this example: nyctaxi_graph_id, a graph utilizing IDs based on a modified version of the standard NYC Taxi dataset (mentioned in Prerequisites).

To filter out data that could skew graph nyctaxi_graph_id, the NYC Neighborhood dataset must be inserted into Kinetica and joined to the NYC Taxi dataset using STXY_CONTAINS to remove any trip points in the NYC Taxi dataset that are not contained within the geospatial boundaries of the NYC Neighborhood dataset:

Perform Geospatial Filter of Taxi Data by NYC Neighborhoods
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join_taxi_tables_response = kinetica.create_join_table(
    join_table_name = JOIN_TAXI,
    table_names = [TABLE_TAXI + " as t", TABLE_NYC_N + " as n"],
    column_names = [
        "CONCAT(CHAR32(pickup_longitude), CHAR32(pickup_latitude)) as pickup_name",
        "t.pickup_longitude", 
        "t.pickup_latitude",
        "HASH(t.pickup_longitude + t.pickup_latitude) as pickup_id",
        "CONCAT(CHAR32(dropoff_longitude), CHAR32(dropoff_latitude)) as dropoff_name",
        "t.dropoff_longitude", 
        "t.dropoff_latitude",
        "HASH(t.dropoff_longitude + t.dropoff_latitude) as dropoff_id",
        "t.total_amount"
    ],
    expressions = [
        "(STXY_CONTAINS(n.geom, t.pickup_longitude, t.pickup_latitude)) AND"
        "(STXY_CONTAINS(n.geom, t.dropoff_longitude, t.dropoff_latitude)) "
    ]
)["status_info"]["status"]

Before nyctaxi_graph_id can be created, the edges must be derived from the taxi_tables_joined dataset's XY pickup and dropoff pairs to create the nyctaxi_edges_id dataset:

Create Node & Edge Tables from Taxi Trips
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# Union the JOIN_TAXI view to itself to collapse pickup & dropoff
# locations into a unified set of endpoint locations to serve as node IDs
nodes_response = kinetica.execute_sql(
    statement = (
        "CREATE TABLE " + TABLE_TAXI_N + " AS "
        "SELECT "
              "pickup_id as id, "
              "pickup_longitude as lon, "
              "pickup_latitude as lat "
        "FROM " + JOIN_TAXI + " "
        "UNION "
        "SELECT "
              "dropoff_id, "
              "dropoff_longitude, "
              "dropoff_latitude "
        "FROM " + JOIN_TAXI
    ),
    offset = 0,
    limit = gpudb.GPUdb.END_OF_SET,
    encoding = "json",
    options = {}
)["status_info"]["status"]

# Create a projection to contain the graph edges (based on NODE_ID)
edges_id_response = kinetica.create_projection(
    table_name = JOIN_TAXI,
    projection_name = TABLE_TAXI_E,
    column_names = ["pickup_id", "dropoff_id"]
)["status_info"]["status"]

Now, nyctaxi_graph_id is created with the following characteristics:

  • It is not directed because direction is irrelevant to this example
  • The nodes in this graph are represented using the IDs created from the union of pickup and dropoff points IDs of the nyctaxi_nodes table (ID).
  • The edges in this graph are using the individual pickup and dropoff point IDs of the nyctaxi_edges_id table as the edge endpoints (NODE1_ID / NODE2_ID).
  • It has no weights because one does not need to influence the ranking in any way
  • It has no inherent restrictions for any of the nodes or edges in the graph
  • It will be replaced with this instance of the graph if a graph of the same name exists (recreate).
Create NYC Taxi Routes Graph
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create_t_graph_response = kinetica.create_graph(
    graph_name = GRAPH_T,
    directed_graph = False,
    nodes = [
        TABLE_TAXI_N + ".id AS ID"
    ],
    edges = [
        TABLE_TAXI_E + ".pickup_id AS NODE1_ID",
        TABLE_TAXI_E + ".dropoff_id AS NODE2_ID"
    ],
    weights = [],
    restrictions = [],
    options = {
        "recreate": "true"
    }
)

Page Rank

The graph is solved:

Solve Graph
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solve_pr_graph_response = kinetica.solve_graph(
    graph_name = GRAPH_T,
    solver_type = "PAGE_RANK",
    source_nodes = ["129341667930495514"],
    destination_nodes = [],
    solution_table = TABLE_GRAPH_T_PRSOLVED,
    options = {}
)["status_info"]["status"]

Important

A source node ID was selected at random from the nyctaxi_nodes. Since page rank is ranking each node's connectedness in relation to other nodes, any node can be the source

A sharded version of the nyctaxi_nodes union (created earlier) is created so it can be joined:

Shard Node Table for Geospatial Join with Graph Solution
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nodes_sharded_response = kinetica.create_projection(
    table_name = TABLE_TAXI_N,
    projection_name = TABLE_TAXI_N_S,
    column_names = ["id", "lon", "lat"],
    options = {"shard_key": "id"}
)["status_info"]["status"]

A sharded version of the nyctaxi_graph_id_page_rank_solved table is also created so it can be joined to the nyctaxi_nodes union:

Shard Graph Solution for Geospatial Join with Node Table
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graph_sharded_response = kinetica.create_projection(
    table_name = TABLE_GRAPH_T_PRSOLVED,
    projection_name = TABLE_GRAPH_T_PRSOLVED_S,
    column_names = ["SOLVERS_NODE_ID", "SOLVERS_NODE_COSTS"],
    options = {"shard_key": "SOLVERS_NODE_ID"}
)["status_info"]["status"]

The union and graph results table are joined on ID:

Join Node Table with Graph Solution
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# Join the TABLE_TAXI_N_S and TABLE_GRAPH_T_PRSOLVED_S tables to pair the page
# rank results IDs and costs with the longitude/latitude pair of each node
join_pr_response = kinetica.create_join_table(
    join_table_name = JOIN_PR_RESULTS,
    table_names = [
        TABLE_TAXI_N_S + " as n",
        TABLE_GRAPH_T_PRSOLVED_S + " as s"
    ],
    column_names = [
        "n.lon", 
        "n.lat", 
        "s.SOLVERS_NODE_ID", 
        "s.SOLVERS_NODE_COSTS"
    ],
    expressions = ["n.id = s.SOLVERS_NODE_ID"],
    options = {}
)["status_info"]["status"]

The top 10 nodes (sorted by descending cost) are retrieved. The higher the cost, the more frequently the node was visited:

Page Rank Results
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Top 10 nodes sorted by cost (highest to lowest):
Longitude   Latitude   ID                   Cost         
=========== ========== ==================== =============
-73.9823227 40.7401466 -5733631352137098805 0.00019725596
-74.0027618 40.7605858 -5733631352137098805 0.00019725596
-73.9935074 40.7513313 -5733631352137098805 0.00019725596
-73.9873047 40.7451286 -5733631352137098805 0.00019725596
-73.9572525 40.7679901  7374672091319971430 0.00019725596
-73.9771347 40.7878723  7374672091319971430 0.00019725596
-73.9651031 40.7758408  7374672091319971430 0.00019725596
-73.9648056 40.7755432  7374672091319971430 0.00019725596
-73.9375687 40.7583008  5145699326395297532 0.00018814926
-73.9377365 40.7584686  5145699326395297532 0.00018814926

Download & Run

Included below is a complete example containing all the above requests, the data files, and output.

To run the complete sample, ensure that:

  • the NYC Taxi dataset has been loaded into the database
  • the solve_graph_nyctaxi_page_rank.py script is in the current directory
  • the nyc_neighborhood.csv file is in the current directory or use the data_dir parameter to specify the local directory containing it

Then, run the following:

Run Example
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python solve_graph_nyctaxi_page_rank.py [--url <kinetica_url>] --username <username> --password <password> [--data_dir <data_file_directory>]