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Modified the KG mouse gene mapper for Airflow. #484

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Modified the KG mouse gene mapper for Airflow. #484

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241 changes: 115 additions & 126 deletions impc_etl/jobs/load/impc_kg/mouse_gene_mapper.py
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Original file line number Diff line number Diff line change
@@ -1,131 +1,120 @@
import luigi
from impc_etl.jobs.extract.allele_ref_extractor import ExtractAlleleRef
from impc_etl.jobs.extract.gene_ref_extractor import ExtractGeneRef
from impc_etl.jobs.load.impc_bulk_api.impc_api_mapper import to_camel_case
from luigi.contrib.spark import PySparkTask
from pyspark import SparkContext
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, collect_set

from impc_etl.jobs.load.impc_kg.impc_kg_helper import add_unique_id, map_unique_ids
from impc_etl.jobs.load.solr.gene_mapper import GeneLoader
from impc_etl.workflow.config import ImpcConfig


class ImpcKgMouseGeneMapper(PySparkTask):
"""
PySpark Task class to parse GenTar Product report data.
"""

#: Name of the Spark task
name: str = "ImpcKgMouseGeneMapper"

#: Path of the output directory where the new parquet file will be generated.
output_path: luigi.Parameter = luigi.Parameter()

def requires(self):
return [GeneLoader(), ExtractGeneRef(), ExtractAlleleRef()]

def output(self):
"""
Returns the full parquet path as an output for the Luigi Task
(e.g. impc/dr15.2/parquet/product_report_parquet)
"""
return ImpcConfig().get_target(f"{self.output_path}/impc_kg/mouse_gene_json")

def app_options(self):
"""
Generates the options pass to the PySpark job
"""
return [
self.input()[0].path,
self.input()[1].path,
self.input()[2].path,
self.output().path,
]

def main(self, sc: SparkContext, *args):
"""
Module to generate the mouse gene data as JSON for the KG.
"""
import logging
import textwrap

from airflow.sdk import Variable, asset

from impc_etl.utils.airflow import create_input_asset, create_output_asset
from impc_etl.utils.spark import with_spark_session

task_logger = logging.getLogger("airflow.task")
dr_tag = Variable.get("data_release_tag")

gene_ref_parquet_path_asset = create_input_asset("output/gene_ref_parquet")
allele_ref_parquet_path_asset = create_input_asset("output/allele_ref_parquet")
gene_data_include_parquet_path_asset = create_input_asset("output/gene_data_include_parquet")

mouse_gene_output_asset = create_output_asset("/impc_kg/mouse_gene_json")

@asset.multi(
schedule=[gene_ref_parquet_path_asset,
allele_ref_parquet_path_asset,
gene_data_include_parquet_path_asset,
],
outlets=[mouse_gene_output_asset],
dag_id=f"{dr_tag}_impc_kg_mouse_gene_mapper",
description=textwrap.dedent(
"""
Takes in a SparkContext and the list of arguments generated by `app_options` and executes the PySpark job.
PySpark task to create the Knowledge Graph JSON files for
mouse gene data from the gene_ref_parquet, allele_ref_parquet and the output of the gene_mapper.
"""
spark = SparkSession(sc)

# Parsing app options
gene_parquet_path = args[0]
gene_ref_parquet_path = args[1]
allele_ref_parquet_path = args[2]
output_path = args[3]

gene_df = spark.read.parquet(gene_parquet_path)
gene_ref_df = spark.read.parquet(gene_ref_parquet_path)
allele_ref_df = spark.read.parquet(allele_ref_parquet_path)

gene_df = add_unique_id(
gene_df,
"mouse_gene_id",
["mgi_accession_id"],
),
tags=["impc_kg"],
)
@with_spark_session
def impc_kg_mouse_gene_mapper():

from impc_etl.jobs.load.impc_web_api.impc_web_api_helper import to_camel_case
from impc_etl.jobs.load.impc_kg.impc_kg_helper import add_unique_id, map_unique_ids

from pyspark.sql import SparkSession
from pyspark.sql.functions import collect_set, col

spark = SparkSession.builder.getOrCreate()

gene_df = spark.read.parquet(gene_data_include_parquet_path_asset.uri)
gene_ref_df = spark.read.parquet(gene_ref_parquet_path_asset.uri)
allele_ref_df = spark.read.parquet(allele_ref_parquet_path_asset.uri)

gene_df = add_unique_id(
gene_df,
"mouse_gene_id",
["mgi_accession_id"],
)

allele_ref_df = allele_ref_df.select(
"mgi_marker_acc_id", "mgi_allele_acc_id"
).distinct()
allele_ref_df = allele_ref_df.withColumnRenamed(
"mgi_marker_acc_id", "mgi_accession_id"
)

allele_ref_df = allele_ref_df.groupBy("mgi_accession_id").agg(
collect_set("mgi_allele_acc_id").alias("mouse_allele_acc_ids")
)

gene_df = gene_df.join(allele_ref_df, "mgi_accession_id", "left_outer")

gene_df = map_unique_ids(gene_df, "mouse_alleles", "mouse_allele_acc_ids")

gene_ref_df = gene_ref_df.select("mgi_gene_acc_id", "human_gene_acc_id")

gene_df = gene_df.join(
gene_ref_df, col("mgi_accession_id") == col("mgi_gene_acc_id")
).drop("mgi_gene_acc_id")

gene_df = map_unique_ids(
gene_df, "human_gene_orthologues", "human_gene_acc_id"
)

mouse_gene_col_map = {
"marker_name": "name",
"marker_symbol": "symbol",
"mgi_accession_id": "mgiGeneAccessionId",
"marker_synonym": "synonyms",
"ensembl_gene_id": "ensembl_acc_id",
}

output_cols = [
"marker_name",
"marker_symbol",
"mgi_accession_id",
"marker_synonym",
"seq_region_start",
"seq_region_end",
"chr_strand",
"seq_region_id",
"entrezgene_id",
"ensembl_gene_id",
"ccds_id",
"ncbi_id",
"human_gene_orthologues",
"mouse_alleles",
]
output_df = gene_df.select(*output_cols).distinct()
for col_name in output_df.columns:
output_df = output_df.withColumnRenamed(
col_name,
(
to_camel_case(col_name)
if col_name not in mouse_gene_col_map
else to_camel_case(mouse_gene_col_map[col_name])
),
)
output_df.distinct().coalesce(1).write.json(
mouse_gene_output_asset.uri, mode="overwrite", compression="gzip"
)

allele_ref_df = allele_ref_df.select(
"mgi_marker_acc_id", "mgi_allele_acc_id"
).distinct()
allele_ref_df = allele_ref_df.withColumnRenamed(
"mgi_marker_acc_id", "mgi_accession_id"
)

allele_ref_df = allele_ref_df.groupBy("mgi_accession_id").agg(
collect_set("mgi_allele_acc_id").alias("mouse_allele_acc_ids")
)

gene_df = gene_df.join(allele_ref_df, "mgi_accession_id", "left_outer")

gene_df = map_unique_ids(gene_df, "mouse_alleles", "mouse_allele_acc_ids")

gene_ref_df = gene_ref_df.select("mgi_gene_acc_id", "human_gene_acc_id")

gene_df = gene_df.join(
gene_ref_df, col("mgi_accession_id") == col("mgi_gene_acc_id")
).drop("mgi_gene_acc_id")

gene_df = map_unique_ids(
gene_df, "human_gene_orthologues", "human_gene_acc_id"
)

mouse_gene_col_map = {
"marker_name": "name",
"marker_symbol": "symbol",
"mgi_accession_id": "mgiGeneAccessionId",
"marker_synonym": "synonyms",
"ensembl_gene_id": "ensembl_acc_id",
}

output_cols = [
"marker_name",
"marker_symbol",
"mgi_accession_id",
"marker_synonym",
"seq_region_start",
"seq_region_end",
"chr_strand",
"seq_region_id",
"entrezgene_id",
"ensembl_gene_id",
"ccds_id",
"ncbi_id",
"human_gene_orthologues",
"mouse_alleles",
]
output_df = gene_df.select(*output_cols).distinct()
for col_name in output_df.columns:
output_df = output_df.withColumnRenamed(
col_name,
(
to_camel_case(col_name)
if col_name not in mouse_gene_col_map
else to_camel_case(mouse_gene_col_map[col_name])
),
)
output_df.distinct().coalesce(1).write.json(
output_path, mode="overwrite", compression="gzip"
)