<commit_before>package org.dizhang.seqspark.ds

import breeze.linalg.{DenseMatrix => DM, DenseVector => DV}
import breeze.stats._
import org.apache.spark.SparkContext
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.dizhang.seqspark.util.Constant.Pheno
import scala.util.{Failure, Success, Try}
import Phenotype._

 /**
  * Created by zhangdi on 9/13/16.
  */
object Phenotype {

  def apply(path: String, sc: SparkContext): Phenotype = {
    //val sqlContext = new SQLContext(sc)

    val spark = SparkSession
      .builder()
      .appName("SeqSpark Phenotype")
      .getOrCreate()

    val options = Map(
      "nullValue" -> Pheno.mis,
      "sep" -> Pheno.delim,<commit_msg>testing<commit_after>package org.dizhang.seqspark.ds

import breeze.linalg.{DenseMatrix => DM, DenseVector => DV, _}
import breeze.stats._
import org.apache.spark.SparkContext
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.dizhang.seqspark.util.Constant.Pheno
import scala.util.{Failure, Success, Try}
import Phenotype._

 /**
  * Created by zhangdi on 9/13/16.
  */
object Phenotype {

  def apply(path: String, sc: SparkContext): Phenotype = {
    //val sqlContext = new SQLContext(sc)

    val spark = SparkSession
      .builder()
      .appName("SeqSpark Phenotype")
      .getOrCreate()

    val options = Map(
      "nullValue" -> Pheno.mis,
      "sep" -> Pheno.delim,
{'fraction_non_alphanumeric': 0.12724658519051044, 'fraction_numerical': 0.007189072609633357, 'mean_word_length': 3.4615384615384617, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 11, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5522990', 'n_tokens_mistral': 507, 'n_tokens_neox': 460, 'n_words': 105}