i13 python api

.gitignore

@@ -59,3 +59,4 @@ variant-spark_2.11.iml
 build
 dist
 _build
+spark-warehouse

dev/dev-requirements.txt

@@ -1,3 +1,4 @@
 Sphinx>=1.3.5
 pylint==1.8.1
 decorator>=4.1.2
+typedecorator>=0.0.5

python/MANIFEST.in

@@ -0,0 +1,3 @@
+global-exclude *.py[cod] __pycache__ .DS_Store
+recursive-include target/jars *-all.jar
+include README.md

python/README.md

@@ -0,0 +1 @@
+TBP:

python/examples/hipster_index.py

@@ -0,0 +1,25 @@
+'''
+Created on 24 Jan 2018
+
+@author: szu004
+'''
+import os
+from variants import VariantsContext
+from pyspark.sql import SparkSession
+
+PROJECT_DIR=os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir))
+
+def main():
+    spark = SparkSession.builder.appName("HipsterIndex") \
+        .getOrCreate()
+    vs = VariantsContext(spark)
+    features = vs.import_vcf(os.path.join(PROJECT_DIR, 'data/chr22_1000.vcf'))
+    labels = vs.load_label(os.path.join(PROJECT_DIR,'data/chr22-labels.csv'), '22_16050408')
+    model  = features.importance_analysis(labels, mtry_fraction = 0.1, seed = 13, n_trees = 200)
+    print("Oob = %s" % model.oob_error())
+    for entry in model.important_variables(10):
+        print entry
+
+if __name__ == '__main__':
+    main()
+

python/pylintrc

@@ -29,7 +29,7 @@ profile=no
 
 # Add files or directories to the blacklist. They should be base names, not
 # paths.
-ignore=pyspark.heapq3
+ignore=pyspark
 
 # Pickle collected data for later comparisons.
 persistent=yes

python/setup.py

@@ -1,8 +1,77 @@
+from __future__ import print_function
 from setuptools import setup, find_packages
+import sys
+import os
+
+if sys.version_info < (2, 7):
+    print("Python versions prior to 2.7 are not supported.", file=sys.stderr)
+    exit(-1)
 
-setup(
-    name = 'variants',
-    version = '0.1.0',
-    description ='VariantSpark Python API', 
-    packages = find_packages()
-)
+
+#VERSION = __version__
+# A temporary path so we can access above the Python project root and fetch scripts and jars we need
+TEMP_PATH = "target"
+ROOT_DIR = os.path.abspath("../")
+
+# Provide guidance about how to use setup.py
+incorrect_invocation_message = """
+If you are installing variants from variant-spark source, you must first build variant-spark  and
+run sdist.
+    To build  with maven you can run:
+      ./build/mvn -DskipTests clean package
+    Building the source dist is done in the Python directory:
+      cd python
+      python setup.py sdist
+      pip install dist/*.tar.gz"""
+
+JARS_PATH = os.path.join(ROOT_DIR, "target")
+JARS_TARGET = os.path.join(TEMP_PATH, "jars")
+
+in_src = os.path.isfile("../pom.xml") 
+
+if (in_src):
+    # Construct links for setup
+    try:
+        os.mkdir(TEMP_PATH)
+    except:
+        print("Temp path for symlink to parent already exists {0}".format(TEMP_PATH),
+              file=sys.stderr)
+        exit(-1)
+
+
+try:
+
+    if (in_src):
+        # Construct the symlink farm - this is necessary since we can't refer to the path above the
+        # package root and we need to copy the jars and scripts which are up above the python root.
+        os.symlink(JARS_PATH, JARS_TARGET)
+
+    setup(
+        name='variants',
+        version='0.1.0',
+        description='VariantSpark Python API',
+        packages=find_packages(exclude=["*.test"]) + ['variants.jars'], 
+        install_requires=['typedecorator'],
+#        test_suite = 'variants.test',
+#        test_requires = [
+#            'pyspark>=2.1.0'
+#        ],
+        include_package_data=True,
+        package_dir={
+            'variants.jars': 'target/jars',
+        },
+        package_data={
+            'variants.jars': ['*-all.jar'],
+        },
+        entry_points='''
+        [console_scripts]
+        variants-jar=variants.cli:cli
+        ''',
+    )
+finally:
+    # We only cleanup the symlink farm if we were in Spark, otherwise we are installing rather than
+    # packaging.
+    if (in_src):
+        # Depending on cleaning up the symlink farm or copied version
+        os.remove(os.path.join(TEMP_PATH, "jars"))
+        os.rmdir(TEMP_PATH)

python/variants/__init__.py

@@ -0,0 +1,5 @@
+try:
+    from variants.core import VariantsContext
+except:
+    pass
+from variants.setup import find_jar

python/variants/cli.py

@@ -0,0 +1,6 @@
+from __future__ import print_function
+
+from variants import find_jar
+
+def cli():
+    print(find_jar())

python/variants/core.py

@@ -0,0 +1,136 @@
+import sys
+from typedecorator import params, Nullable, Union, setup_typecheck
+from pyspark import SparkConf
+from pyspark.sql import SQLContext
+from variants.setup import find_jar
+
+class VariantsContext(object):
+    """The main entry point for VariantSpark functionality.
+    """
+
+    @classmethod
+    def spark_conf(cls, conf  = SparkConf()):
+        """ Adds the necessary option to the spark configuration.
+        Note: In client mode these need to be setup up using --jars or --driver-class-path
+        """
+        return conf.set("spark.jars", find_jar())
+
+    def __init__(self, ss=None):
+        """The main entry point for VariantSpark functionality.
+        :param ss: SparkSession
+        :type ss: :class:`.pyspark.SparkSession`
+        """
+        self.sc = ss.sparkContext
+        self.sql = SQLContext.getOrCreate(self.sc)
+        self._jsql = self.sql._jsqlContext
+        self._jvm = self.sc._jvm
+        self._vs_api = getattr(self._jvm, 'au.csiro.variantspark.api')
+        jss = ss._jsparkSession
+        self._jvsc = self._vs_api.VSContext.apply(jss)
+
+        setup_typecheck()
+
+        sys.stderr.write('Running on Apache Spark version {}\n'.format(self.sc.version))
+        if self.sc._jsc.sc().uiWebUrl().isDefined():
+            sys.stderr.write('SparkUI available at {}\n'.format(self.sc._jsc.sc().uiWebUrl().get()))
+        sys.stderr.write(
+            'Welcome to\n'
+            ' _    __           _             __  _____                  __    \n'
+            '| |  / /___ ______(_)___ _____  / /_/ ___/____  ____ ______/ /__  \n'
+            '| | / / __ `/ ___/ / __ `/ __ \/ __/\__ \/ __ \/ __ `/ ___/ //_/  \n'
+            '| |/ / /_/ / /  / / /_/ / / / / /_ ___/ / /_/ / /_/ / /  / ,<     \n'
+            '|___/\__,_/_/  /_/\__,_/_/ /_/\__//____/ .___/\__,_/_/  /_/|_|    \n'
+            '                                      /_/                         \n')
+
+    @params(self=object, vcf_file_path=str)
+    def import_vcf(self, vcf_file_path):
+        """ Import features from a VCF file.
+        """
+        return FeatureSource(self._jvm, self._vs_api,
+                             self._jsql, self.sql, self._jvsc.importVCF(vcf_file_path))
+
+    @params(self=object, label_file_path=str, col_name=str)
+    def load_label(self, label_file_path, col_name):
+        """ Loads the label source file
+
+        :param label_file_path: The file path for the label source file
+        :param col_name: the name of the column containing labels
+        """
+        return self._jvsc.loadLabel(label_file_path, col_name)
+
+    def stop(self):
+        """ Shut down the VariantsContext.
+        """
+
+        self.sc.stop()
+        self.sc = None
+
+
+class FeatureSource(object):
+
+    def __init__(self, _jvm, _vs_api, _jsql, sql, _jfs):
+        self._jfs = _jfs
+        self._jvm = _jvm
+        self._vs_api = _vs_api
+        self._jsql = _jsql
+        self.sql = sql
+
+    @params(self=object, label_source=object, n_trees=Nullable(int), mtry_fraction=Nullable(float),
+            oob=Nullable(bool), seed=Nullable(Union(int, long)), batch_size=Nullable(int),
+            var_ordinal_levels=Nullable(int))
+    def importance_analysis(self, label_source, n_trees=1000, mtry_fraction=None,
+                            oob=True, seed=None, batch_size=100, var_ordinal_levels=3):
+        """Builds random forest classifier.
+
+        :param label_source: The ingested label source
+        :param int n_trees: The number of trees to build in the forest.
+        :param float mtry_fraction: The fraction of variables to try at each split.
+        :param bool oob: Should OOB error be calculated.
+        :param long seed: Random seed to use.
+        :param int batch_size: The number of trees to build in one batch.
+        :param int var_ordinal_levels:
+
+        :return: Importance analysis model.
+        :rtype: :py:class:`ImportanceAnalysis`
+        """
+        jrf_params = self._jvm.au.csiro.variantspark.algo.RandomForestParams(bool(oob),
+                                float(mtry_fraction), True, float('nan'), True, long(seed))
+        jia = self._vs_api.ImportanceAnalysis(self._jsql, self._jfs, label_source,
+                                              jrf_params, n_trees, batch_size, var_ordinal_levels)
+        return ImportanceAnalysis(jia, self.sql)
+
+
+class ImportanceAnalysis(object):
+    """ Model for random forest based importance analysis
+    """
+
+    def __init__(self, _jia, sql):
+        self._jia = _jia
+        self.sql = sql
+
+    @params(self=object, limit=Nullable(int))
+    def important_variables(self, limit=10):
+        """ Gets the top limit important variables as a list of tuples (name, importance) where:
+            - name: string - variable name
+            - importance: double - gini importance
+        """
+        jimpvarmap = self._jia.importantVariablesJavaMap(limit)
+        return sorted(jimpvarmap.items(), key=lambda x: x[1], reverse=True)
+
+    def oob_error(self):
+        """ OOB (Out of Bag) error estimate for the model
+
+        :rtype: float
+        """
+        return self._jia.oobError()
+
+    def variable_importance(self):
+        """ Returns a DataFrame with the gini importance of variables.
+        The DataFrame has two columns:
+            - variable: string - variable name
+            - importance: double - gini importance
+        """
+        jdf = self._jia.variableImportance()
+        jdf.count()
+        jdf.createTempView("df")
+        return self.sql.table("df")

python/variants/setup.py

@@ -0,0 +1,10 @@
+import glob
+import os
+import pkg_resources
+
+def find_jar():
+    """Gets the path to the variant spark jar bundled with the
+    python distribution
+    """
+    jars_dir = pkg_resources.resource_filename(__name__, "jars")
+    return glob.glob(os.path.join(jars_dir, "*-all.jar"))[0]

python/variants/test/__init__.py

@@ -0,0 +1,10 @@
+import os
+import glob
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_DIR = os.path.abspath(os.path.join(THIS_DIR, os.pardir, os.pardir, os.pardir))
+
+def find_variants_jar():
+    jar_candidates = glob.glob(os.path.join(PROJECT_DIR, 'target','variant-spark_*-all.jar'))
+    assert len(jar_candidates) == 1, "Expecting one jar, but found: %s" % str(jar_candidates)
+    return jar_candidates[0]

python/variants/test/test_core.py

@@ -0,0 +1,59 @@
+import os
+import unittest
+
+from pyspark import SparkConf
+from pyspark.sql import SparkSession
+
+from variants import VariantsContext
+from variants.test import find_variants_jar, PROJECT_DIR
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+class VariantSparkPySparkTestCase(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(self):
+        sconf = SparkConf(loadDefaults=False)\
+            .set("spark.sql.files.openCostInBytes", 53687091200L)\
+            .set("spark.sql.files.maxPartitionBytes", 53687091200L)\
+            .set("spark.driver.extraClassPath", find_variants_jar())
+        spark = SparkSession.builder.config(conf=sconf)\
+            .appName("test").master("local").getOrCreate()
+        self.sc = spark.sparkContext
+
+    @classmethod
+    def tearDownClass(self):
+        self.sc.stop()
+
+
+class VariantSparkAPITestCase(VariantSparkPySparkTestCase):
+
+    def setUp(self):
+        self.spark = SparkSession(self.sc)
+        self.vc = VariantsContext(self.spark)
+
+    def test_variants_context_parameter_type(self):
+        with self.assertRaises(TypeError) as cm:
+            self.vc.load_label(label_file_path=123, col_name=456)
+        self.assertEqual('keyword argument label_file_path = 123 doesn\'t match signature str',
+                         str(cm.exception))
+
+    def test_importance_analysis_from_vcf(self):
+        label_data_path = os.path.join(PROJECT_DIR, 'data/chr22-labels.csv')
+        label = self.vc.load_label(label_file_path=label_data_path, col_name='22_16050408')
+        feature_data_path = os.path.join(PROJECT_DIR, 'data/chr22_1000.vcf')
+        features = self.vc.import_vcf(vcf_file_path=feature_data_path)
+
+        imp_analysis = features.importance_analysis(label, 100, float('nan'), True, 13, 100, 3)
+        imp_vars = imp_analysis.important_variables(20)
+        most_imp_var = imp_vars[0][0]
+        self.assertEqual('22_16050408', most_imp_var)
+        df = imp_analysis.variable_importance()
+        self.assertEqual('22_16050408',
+                         str(df.orderBy('importance', ascending=False).collect()[0][0]))
+        oob_error = imp_analysis.oob_error()
+        self.assertAlmostEqual(0.016483516483516484, oob_error, 4)
+
+
+if __name__ == '__main__':
+    unittest.main(verbosity=2)