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- Complex Embeddings for Simple Link Prediction, https://arxiv.org/abs/1606.06357 | |
- Vertex AI Matching Engine: https://cloud.google.com/vertex-ai/docs/matching-engine |
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$ ./build/mvn clean test -DmemoryFiles=rerun.txt | |
$ cat | |
TestFailed Some(org.apache.spark.api.python.RepairSuite) org.apache.spark.api.python.RepairSuite None | |
TestFailed Some(org.apache.spark.api.python.DepGraphSuite) org.apache.spark.api.python.DepGraphSuite Some(computeFunctionalDepMap) | |
$ ./build/mvn clean test -DtestsFiles=rerun.txt | |
Run starting. Expected test count is: 5 | |
DepGraphSuite: | |
13:29:58.598 WARN org.apache.spark.util.Utils: Your hostname, maropus-MacBook-Pro.local resolves to a loopback address: 127.0.0.1; using 192.168.3.2 instead (on interface en0) | |
13:29:58.599 WARN org.apache.spark.util.Utils: Set SPARK_LOCAL_IP if you need to bind to another address |
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Spark session available as 'spark'. | |
Welcome to | |
____ __ | |
/ __/__ ___ _____/ /__ | |
_\ \/ _ \/ _ `/ __/ '_/ | |
/___/ .__/\_,_/_/ /_/\_\ version 3.2.0-SNAPSHOT | |
/_/ | |
Using Scala version 2.12.10 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_181) | |
Type in expressions to have them evaluated. |
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import time | |
from collections import Counter | |
from pyspark.accumulators import AccumulatorParam | |
from pyspark.sql.functions import col, pandas_udf, PandasUDFType | |
class UdfMetricAccumulatorParam(AccumulatorParam): | |
def zero(self, value): | |
init_value = {} | |
return init_value.update(value) |
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@pytest.hookimpl(hookwrapper=True) | |
def pytest_report_teststatus(report, config): | |
outcome = yield | |
res = outcome.get_result() | |
attr_name = "___TIME___" | |
if report.when == "setup": | |
# HACK: store the start time in `config` | |
setattr(config, attr_name, time.time()) | |
elif report.when == "call": |
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// export SPARK_HOME=<YOUR_SPARK_V3_0> | |
$ git clone https://github.com/maropu/spark-tpcds-datagen.git | |
$ cd spark-tpcds-datagen | |
$ ./bin/datagen --master=local[*] --conf spark.driver.memory=8g --scale-factor 10 --output-location /tmp/tpcds-sf-10 | |
scala> :paste | |
import org.apache.spark.sql.catalyst.catalog.CatalogColumnStat | |
import org.apache.spark.sql.execution.datasources.LogicalRelation | |
import org.apache.spark.sql.types.DataType | |
sql("SET spark.sql.cbo.enabled=true") |
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# https://qiita.com/9_ties/items/3bdb177384937ddc88df | |
# https://homes.cs.washington.edu/~pedrod/papers/mlj05.pdf | |
import pandas as pd | |
import numpy as np | |
from scipy.special import logsumexp | |
from itertools import product | |
const = ['A', 'B'] | |
preds = [('Smokes', 1), ('Cancer', 1), ('Friends', 2)] # Predicate and arity |
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///////// Invocation of Scala collection object methods ///////// | |
--- | |
scala> import scala.reflect.runtime.universe._ | |
scala> val mapClazz = scala.collection.immutable.Map.getClass | |
mapClazz: Class[_ <: scala.collection.immutable.Map.type] = class scala.collection.immutable.Map$ | |
scala> val mirror = runtimeMirror(mapClazz.getClassLoader) | |
mirror: reflect.runtime.universe.Mirror = JavaMirror with ... |