使用 PySpark 的機器學習
Andrew Collier
Data Scientist, Fathom Data
+------+ |height| +------+ | 1.42| | 1.45| | 1.47| | 1.50| | 1.52| | 1.57| | 1.60| | 1.75| | 1.85| | 1.88| +------+
+------+----------+ |height|height_bin| +------+----------+ | 1.42| short| | 1.45| short| | 1.47| short| | 1.50| short| | 1.52| average| | 1.57| average| | 1.60| average| | 1.75| average| | 1.85| tall| | 1.88| tall| +------+----------+
汽車 RPM 存在「自然」分界：
from pyspark.ml.feature import Bucketizer bucketizer = Bucketizer(splits=[3500, 4500, 6000, 6500], inputCol="rpm", outputCol="rpm_bin")
將區間套用到 rpm 欄。
rpm
bucketed = bucketizer.transform(cars)
bucketed.select('rpm', 'rpm_bin').show(5)
+----+-------+ | rpm|rpm_bin| +----+-------+ |3800| 0.0| |4500| 1.0| |5750| 1.0| |5300| 1.0| |6200| 2.0| +----+-------+
bucketed.groupBy('rpm_bin').count().show()
+-------+-----+ |rpm_bin|count| +-------+-----+ | 0.0| 8| <- low | 1.0| 67| <- medium | 2.0| 17| <- high +-------+-----+
將 RPM 分箱做 one-hot 編碼為虛擬變數。
+-------+-------------+ |rpm_bin| rpm_dummy| +-------+-------------+ | 0.0|(2,[0],[1.0])| <- low | 1.0|(2,[1],[1.0])| <- medium | 2.0| (2,[],[])| <- high +-------+-------------+
「high」RPM 區間作為參考層級，不會有虛擬變數。
regression.coefficients
DenseVector([1.3814, 0.1433])
regression.intercept
8.1835
「low」RPM 的油耗：
8.1835 + 1.3814 = 9.5649
「medium」RPM 的油耗：
8.1835 + 0.1433 = 8.3268
單一欄位運算：
log()
sqrt()
pow()
兩欄位運算：
+------+-----+----+ |height| mass| bmi| bmi = mass / height^2 +------+-----+----+ | 1.52| 77.1|33.2| | 1.60| 58.1|22.7| | 1.57|122.0|49.4| | 1.75| 95.3|31.0| | 1.80| 99.8|30.7| | 1.65| 90.7|33.3| | 1.60| 70.3|27.5| | 1.78| 81.6|25.8| | 1.65| 77.1|28.3| | 1.78|128.0|40.5| +------+-----+----+
cars = cars.withColumn('density_line', cars.mass / cars.length) # Linear density cars = cars.withColumn('density_quad', cars.mass / cars.length**2) # Area density cars = cars.withColumn('density_cube', cars.mass / cars.length**3) # Volume density
+------+------+------------+------------+------------+ | mass|length|density_line|density_quad|density_cube| +------+------+------------+------------+------------+ |1451.0| 4.775|303.87434554|63.638606397|13.327456837| |1129.0| 4.623|244.21371403|52.825808790|11.426737787| |1399.0| 4.547|307.67539036|67.665579583|14.881367843| +------+------+------------+------------+------------+
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