DF 3.3.5 - R Formula.ipynb

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    "sc"
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   "source": [
    "!rm -rf metastore_db/*.lck\n",
    "\n",
    "from pyspark.sql import SQLContext\n",
    "sqlc = SQLContext(sc)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## R Formula"
   ]
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    "crimes = sqlc.read.format(\"com.databricks.spark.csv\") \\\n",
    "            .option(\"delimiter\", \",\") \\\n",
    "            .option(\"header\", \"true\") \\\n",
    "            .option(\"inferSchema\", \"true\") \\\n",
    "            .load(\"UScrime2-colsLotsOfNAremoved.csv\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "source": [
    "from pyspark.ml.feature import RFormula\n",
    "\n",
    "formula = RFormula().setFormula(\" ViolentCrimesPerPop ~ householdsize + racepctblack + racePctWhite \") \\\n",
    "                    .setFeaturesCol(\"features\") \\\n",
    "                    .setLabelCol(\"label\")"
   ]
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   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "output = formula.fit(crimes).transform(crimes)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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      "+----------------+-----+\n",
      "|        features|label|\n",
      "+----------------+-----+\n",
      "| [0.33,0.02,0.9]|  0.2|\n",
      "|[0.16,0.12,0.74]| 0.67|\n",
      "|[0.42,0.49,0.56]| 0.43|\n",
      "+----------------+-----+\n",
      "only showing top 3 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "output.select(\"features\", \"label\").show(3)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": []
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