forked from makukha/bigdata19.case04
-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
183 lines (129 loc) · 5.84 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.functions import col
from pyspark.sql.types import FloatType
from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier, LogisticRegression, RandomForestClassifier
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.regression import GeneralizedLinearRegression
from pyspark.ml.stat import Correlation, Summarizer
import sys
import config
DATA_CSV = config.BUILDDIR / 'bd_lab_small_sample.csv'
DATA_PARQUET = DATA_CSV.with_suffix('.parquet')
spark = SparkContext.getOrCreate()
sql = SQLContext(spark)
def convert():
"""Convert CSV to Parquet."""
df = sql.read.csv(str(DATA_CSV), header=True, inferSchema='true')
for field in ['cost', 'call_duration_minutes', 'data_volume_mb', 'LAT', 'LON']:
df = df.withColumn(field, df[field].cast(FloatType()))
df.write.parquet(str(DATA_PARQUET))
def explore():
df = sql.read.parquet(str(DATA_PARQUET))
df.printSchema()
# breakpoint()
df.agg({'target': 'max'}).collect()
from pyspark.sql.types import FloatType
df = df.withColumn('cost', df['cost'].cast(FloatType()))
df.select(df.columns[:10]).show()
from math import ceil
for i in range(ceil(len(df.columns) / 5)):
df.select(df.columns[i*5:(i+1)*5]).show()
df.groupby(df['phone_price_category']).count().toPandas().to_csv('build/test.csv')
df.groupby(df['phone_price_category']).count().coalesce(1).write.csv('build/test3.csv', header=True)
df = df.withColumn('phone_price_category', df['phone_price_category'].cast(FloatType()))
df.corr('cost', 'phone_price_category')
breakpoint()
df.groupBy('hash_number_A')\
.agg({'cost': 'sum', 'phone_price_category': 'max'})\
.dropna().corr('sum(cost)', 'max(phone_price_category)')
df.groupBy('hash_number_A')\
.agg({'cost': 'sum', 'phone_price_category': 'max'})\
.dropna()\
.explain()
df.crosstab('device_type', 'phone_price_category')
df.fillna(0, ['phone_price_category']).crosstab('device_type', 'phone_price_category').show()
df.cube('device_type', 'phone_price_category').sum().show()
df.cube('device_type', 'phone_price_category').sum('cost', 'target').show()
def basic_statistics():
"""Basic statistics."""
df = sql.read.parquet(str(DATA_PARQUET))
numeric = ['cost', 'call_duration_minutes', 'data_volume_mb']
assemble = VectorAssembler(inputCols=numeric, outputCol='features')
features = assemble.transform(df.dropna(subset=numeric+['target']))
breakpoint()
# summarize
summarize = Summarizer().metrics('mean', 'variance', 'count', 'numNonZeros', 'max', 'min', 'normL2', 'normL1')
features.select(summarize.summary(features['features'])).show(truncate=False)
# correlations
r1 = Correlation.corr(features, 'features', 'pearson').head()[0]
small = features.sample(fraction=0.1, seed=100500)
r2 = Correlation.corr(small, 'features', 'spearman').head()[0]
def classify_target():
"""Forecast binary target."""
df = sql.read.parquet(str(DATA_PARQUET))
features = ['cost', 'call_duration_minutes', 'data_volume_mb']
variables = features + ['test_flag', 'target']
pipeline_prepare = Pipeline(stages=[
VectorAssembler(inputCols=features, outputCol='features'),
])
prepared = pipeline_prepare.fit(df).transform(df.dropna(subset=variables))
training = prepared.filter(col('test_flag') == 0)
testing = prepared.filter(col('test_flag') == 1)
training_small = training.sample(fraction=0.3, seed=100500)
evaluator = BinaryClassificationEvaluator(rawPredictionCol='prediction', labelCol='target')
breakpoint()
# Logistic regression
classifier = LogisticRegression(regParam=0.3, elasticNetParam=0,
featuresCol='features', labelCol='target', predictionCol='prediction', probabilityCol='probability')
model = classifier.fit(training_small)
predicted = model.transform(testing)
print('Test Area Under ROC: ', evaluator.evaluate(predicted))
breakpoint()
# Decision Tree Classifier
classifier = DecisionTreeClassifier(featuresCol='features', labelCol='target', maxDepth=3)
model = classifier.fit(training_small)
predicted = model.transform(testing)
print('Test Area Under ROC: ', evaluator.evaluate(predicted))
breakpoint()
# Random Forest Classifier
rf = RandomForestClassifier(featuresCol='features', labelCol='label')
model = classifier.fit(training_small)
predicted = model.transform(testing)
print('Test Area Under ROC: ', evaluator.evaluate(predicted))
breakpoint()
def model():
data = sql.read.parquet(str(DATA_PARQUET))
data.createOrReplaceTempView('data')
sample = sql.sql('''
select
hash_number_A
,interest_1
,interest_2
,interest_3
,interest_4
,interest_5
,device_type
,phone_price_category
,sum(cost) as label
from data
group by {", ".join(str(n) for n in range(1, 8+1))}''')
breakpoint()
pipeline = Pipeline(stages=[
StringIndexer(inputCol='interest_1', outputCol='interest'),
StringIndexer(inputCol='phone_price_category', outputCol='phone_price'),
VectorAssembler(inputCols=['interest', 'phone_price'], outputCol='features'),
])
model_data = pipeline.fit(sample)
sample = model_data.transform(sample)
# 'gaussian', 'binomial', 'poisson', 'gamma', 'tweedie'
regression = GeneralizedLinearRegression(family='gaussian', labelCol='label', featuresCol='features', maxIter=10, regParam=0.3)
model = regression.fit(sample)
breakpoint()
def main():
print('Executing main()')
exec(sys.argv[1])
if __name__ == '__main__':
main()