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Testsfsasfss

Prediksi Kanker Payudara dengan Python dan Apache Spark

In [1]:

from pyspark.sql import SparkSession
!pip install ipython-autotime
%load_ext autotime

from pyspark import SparkConf, SparkContext
spark = SparkSession.builder.appName('SVM-PSO').getOrCreate()

df = spark.read.csv('Dataset/breast_cancer.csv', header = True, inferSchema = True)
df.printSchema()

Requirement already satisfied: ipython-autotime in c:\users\fajar\appdata\local\programs\python\python38\lib\site-packages (0.1)
root
 |-- id: integer (nullable = true)
 |-- diagnosis: string (nullable = true)
 |-- radius_mean: double (nullable = true)
 |-- texture_mean: double (nullable = true)
 |-- perimeter_mean: double (nullable = true)
 |-- area_mean: double (nullable = true)
 |-- smoothness_mean: double (nullable = true)
 |-- compactness_mean: double (nullable = true)
 |-- concavity_mean: double (nullable = true)
 |-- concave points_mean: double (nullable = true)
 |-- symmetry_mean: double (nullable = true)
 |-- fractal_dimension_mean: double (nullable = true)
 |-- radius_se: double (nullable = true)
 |-- texture_se: double (nullable = true)
 |-- perimeter_se: double (nullable = true)
 |-- area_se: double (nullable = true)
 |-- smoothness_se: double (nullable = true)
 |-- compactness_se: double (nullable = true)
 |-- concavity_se: double (nullable = true)
 |-- concave points_se: double (nullable = true)
 |-- symmetry_se: double (nullable = true)
 |-- fractal_dimension_se: double (nullable = true)
 |-- radius_worst: double (nullable = true)
 |-- texture_worst: double (nullable = true)
 |-- perimeter_worst: double (nullable = true)
 |-- area_worst: double (nullable = true)
 |-- smoothness_worst: double (nullable = true)
 |-- compactness_worst: double (nullable = true)
 |-- concavity_worst: double (nullable = true)
 |-- concave points_worst: double (nullable = true)
 |-- symmetry_worst: double (nullable = true)
 |-- fractal_dimension_worst: double (nullable = true)
 |-- _c32: string (nullable = true)

In [2]:

import pandas as pd
df = df.drop('id')
X = df.drop('_c32')
f = X.drop('diagnosis')

y = df['diagnosis']
# df.drop('age').collect()
pd.DataFrame(X.take(5), columns=X.columns).transpose()

Out[2]:

	0	1	2	3	4
diagnosis	M	M	M	M	M
radius_mean	17.99	20.57	19.69	11.42	20.29
texture_mean	10.38	17.77	21.25	20.38	14.34
perimeter_mean	122.8	132.9	130	77.58	135.1
area_mean	1001	1326	1203	386.1	1297
smoothness_mean	0.1184	0.08474	0.1096	0.1425	0.1003
compactness_mean	0.2776	0.07864	0.1599	0.2839	0.1328
concavity_mean	0.3001	0.0869	0.1974	0.2414	0.198
concave points_mean	0.1471	0.07017	0.1279	0.1052	0.1043
symmetry_mean	0.2419	0.1812	0.2069	0.2597	0.1809
fractal_dimension_mean	0.07871	0.05667	0.05999	0.09744	0.05883
radius_se	1.095	0.5435	0.7456	0.4956	0.7572
texture_se	0.9053	0.7339	0.7869	1.156	0.7813
perimeter_se	8.589	3.398	4.585	3.445	5.438
area_se	153.4	74.08	94.03	27.23	94.44
smoothness_se	0.006399	0.005225	0.00615	0.00911	0.01149
compactness_se	0.04904	0.01308	0.04006	0.07458	0.02461
concavity_se	0.05373	0.0186	0.03832	0.05661	0.05688
concave points_se	0.01587	0.0134	0.02058	0.01867	0.01885
symmetry_se	0.03003	0.01389	0.0225	0.05963	0.01756
fractal_dimension_se	0.006193	0.003532	0.004571	0.009208	0.005115
radius_worst	25.38	24.99	23.57	14.91	22.54
texture_worst	17.33	23.41	25.53	26.5	16.67
perimeter_worst	184.6	158.8	152.5	98.87	152.2
area_worst	2019	1956	1709	567.7	1575
smoothness_worst	0.1622	0.1238	0.1444	0.2098	0.1374
compactness_worst	0.6656	0.1866	0.4245	0.8663	0.205
concavity_worst	0.7119	0.2416	0.4504	0.6869	0.4
concave points_worst	0.2654	0.186	0.243	0.2575	0.1625
symmetry_worst	0.4601	0.275	0.3613	0.6638	0.2364
fractal_dimension_worst	0.1189	0.08902	0.08758	0.173	0.07678

time: 4.94 s

In [3]:

from pyspark.sql.functions import when, lit
X = X.withColumn('diagnosis', when(X.diagnosis == 'B', lit(0)).otherwise(1))

time: 248 ms

In [4]:

X.groupby('diagnosis').count().toPandas()

Out[4]:

	diagnosis	count
0	1	212
1	0	357

time: 1.84 s

In [5]:

from pyspark.ml.feature import VectorAssembler
vectorAssembler = VectorAssembler(inputCols = f.schema.names, outputCol = 'features')
X = vectorAssembler.transform(X)

time: 2.8 s

In [6]:

pd.DataFrame(X.take(5), columns=X.columns).transpose()

Out[6]:

	0	1	2	3	4
diagnosis	1	1	1	1	1
radius_mean	17.99	20.57	19.69	11.42	20.29
texture_mean	10.38	17.77	21.25	20.38	14.34
perimeter_mean	122.8	132.9	130	77.58	135.1
area_mean	1001	1326	1203	386.1	1297
smoothness_mean	0.1184	0.08474	0.1096	0.1425	0.1003
compactness_mean	0.2776	0.07864	0.1599	0.2839	0.1328
concavity_mean	0.3001	0.0869	0.1974	0.2414	0.198
concave points_mean	0.1471	0.07017	0.1279	0.1052	0.1043
symmetry_mean	0.2419	0.1812	0.2069	0.2597	0.1809
fractal_dimension_mean	0.07871	0.05667	0.05999	0.09744	0.05883
radius_se	1.095	0.5435	0.7456	0.4956	0.7572
texture_se	0.9053	0.7339	0.7869	1.156	0.7813
perimeter_se	8.589	3.398	4.585	3.445	5.438
area_se	153.4	74.08	94.03	27.23	94.44
smoothness_se	0.006399	0.005225	0.00615	0.00911	0.01149
compactness_se	0.04904	0.01308	0.04006	0.07458	0.02461
concavity_se	0.05373	0.0186	0.03832	0.05661	0.05688
concave points_se	0.01587	0.0134	0.02058	0.01867	0.01885
symmetry_se	0.03003	0.01389	0.0225	0.05963	0.01756
fractal_dimension_se	0.006193	0.003532	0.004571	0.009208	0.005115
radius_worst	25.38	24.99	23.57	14.91	22.54
texture_worst	17.33	23.41	25.53	26.5	16.67
perimeter_worst	184.6	158.8	152.5	98.87	152.2
area_worst	2019	1956	1709	567.7	1575
smoothness_worst	0.1622	0.1238	0.1444	0.2098	0.1374
compactness_worst	0.6656	0.1866	0.4245	0.8663	0.205
concavity_worst	0.7119	0.2416	0.4504	0.6869	0.4
concave points_worst	0.2654	0.186	0.243	0.2575	0.1625
symmetry_worst	0.4601	0.275	0.3613	0.6638	0.2364
fractal_dimension_worst	0.1189	0.08902	0.08758	0.173	0.07678
features	[17.99, 10.38, 122.8, 1001.0, 0.1184, 0.2776, ...	[20.57, 17.77, 132.9, 1326.0, 0.08474, 0.07864...	[19.69, 21.25, 130.0, 1203.0, 0.1096, 0.1599, ...	[11.42, 20.38, 77.58, 386.1, 0.1425, 0.2839, 0...	[20.29, 14.34, 135.1, 1297.0, 0.1003, 0.1328, ...

time: 444 ms

In [7]:

train, test = X.randomSplit([0.7, 0.3], seed = 42)
print("Training Dataset Count: " + str(train.count()))
print("Test Dataset Count: " + str(test.count()))

Training Dataset Count: 426
Test Dataset Count: 143
time: 857 ms

Logistic Regression¶

In [8]:

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
# assembler = VectorAssembler(inputCols=[list_of_header_names],outputCol="features")
lr = LogisticRegression(featuresCol = 'features', labelCol = 'diagnosis')
lrModel = lr.fit(train)
predictions = lrModel.transform(test)

evaluator = MulticlassClassificationEvaluator(predictionCol="prediction", labelCol="diagnosis",metricName="accuracy")
print('Accuracy', evaluator.evaluate(predictions))

Accuracy 0.9300699300699301
time: 11.2 s

Decission Tree¶

In [9]:

from pyspark.ml.classification import DecisionTreeClassifier

dt = DecisionTreeClassifier(labelCol="diagnosis", featuresCol="features")
dtModel = dt.fit(train)
predictions = dtModel.transform(test)

evaluator = MulticlassClassificationEvaluator(predictionCol="prediction", labelCol="diagnosis",metricName="accuracy")
print('Accuracy', evaluator.evaluate(predictions))

Accuracy 0.965034965034965
time: 2.02 s

Naive Bayes¶

In [10]:

from pyspark.ml.classification import NaiveBayes

nb = NaiveBayes(labelCol="diagnosis", featuresCol="features")
nbModel = nb.fit(train)
predictions = nbModel.transform(test)

evaluator = MulticlassClassificationEvaluator(predictionCol="prediction", labelCol="diagnosis",metricName="accuracy")
print('Accuracy', evaluator.evaluate(predictions))

time: 888 ms

SVM¶

In [11]:

from pyspark.ml.classification import LinearSVC

svm = LinearSVC(labelCol="diagnosis", featuresCol="features")
svmModel = svm.fit(train)
predictions = svmModel.transform(test)

evaluator = MulticlassClassificationEvaluator(predictionCol="prediction", labelCol="diagnosis",metricName="accuracy")
print('Accuracy', evaluator.evaluate(predictions))

Accuracy 0.958041958041958
time: 6.36 s