convert_int = ["NbrMeals_EXCEP", "GrossFormulaPrice", "NetFormulaPrice", "NbrMealsPrice", "ProductDiscount", "FormulaDiscount", "TotalDiscount", "TotalPrice", "TotalCredit"]
for i in convert_int:
table1 = table1.withColumn(i, table1[i].cast("integer"))
#Changing column types from 'Subscriptions' from string to timestamp
convert_date = ["StartDate","EndDate","RenewalDate","PaymentDate"]
for i in convert_date:
table1 = table1.withColumn(i, table1[i].cast("timestamp"))
#Encoding string columns in merged table
table1 = StringIndexer(inputCol = "PaymentStatus", outputCol = "PaymentStatus_index").fit(table1).transform(table1)
#Creating meaningful Time variables
table1 = table1.withColumn("DaysSubscription", datediff(col("EndDate"), col("StartDate")))
table1 = table1.withColumn("MonthsSubscription", months_between(col("EndDate"), col("StartDate")))
table1 = table1.withColumn("Year", year("StartDate"))
# COMMAND ----------
#Feature engineering
#Aggregating variables by CustomerID
subs_totals = table1.groupBy("CustomerID").agg(count("SubscriptionID"), avg("DaysSubscription"),
avg("MonthsSubscription"), sum("NbrMeals_REG"), sum("NbrMeals_EXCEP"),
min("NbrMealsPrice"), max("NbrMealsPrice"), avg("NbrMealsPrice"),
min("ProductDiscount"), max("ProductDiscount"), sum("ProductDiscount"),
min("TotalDiscount"), max("TotalDiscount"), sum("TotalDiscount"),
min("TotalPrice"), max("TotalPrice"), sum("TotalPrice"),
min("TotalCredit"), max("TotalCredit"),
sum("TotalCredit"))
# Average minutes on ground at LGA
avg_ground_lga = inter + regression.regression.coefficients[4]
print(avg_ground_lga)
# RPM buckcket
from pyspark.ml.feature import Bucketizer
bucketizer = Bucketizer(split=[3500, 4500, 6000, 6500],
inputCol='rpm', outpuCol='rpm_bin')
# Apply bucket to rpm column
cars = bucketizer.transform(cars)
# ROM buckets
bucketed.select('rpm', 'rpm_bin').show(5)
cars.groupBy('rpm_bin').count().show()
# Engineering density
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
from pyspark.ml.feature import Bucketizer, OneHotEncoderEstimator
# Create buckets at 3 hour intervals through the day
buckets = Bucketizer(splits=[3 * x for x in range(9)], inputCol='depart', outputCol='depart_bucket')
# Bucket the departure times
bucketed = buckets.transform(flights)
bucketed.select('depart', 'depart_bucket').show(5)
# Create a one-hot encoder
onehot = OneHotEncoderEstimator(inputCols=['depart_bucket'], outputCols=['depart_dummy'])
##StringEncoding of categorical variables
cat_x_vars = ["term", "grade", "home_ownership", "pred_KM", "emp_length"]
#df2 = df #backup in case of trouble
for cat_var in cat_x_vars:
df = StringIndexer(inputCol=cat_var, outputCol=cat_var +
'Idx').fit(df).transform(df).drop(cat_var)
df = df.withColumnRenamed(cat_var + 'Idx', cat_var)
#df.select(cat_x_vars).show(5) #check
##Create y or target variables for neural networks
#probability/indicator for default
df = df.withColumn('probDef',
F.when(df['loan_status'] == 1,
1.0).otherwise(0.0)) #default is 1, repaid is 0
#indicator for early replayment
df = df.withColumn(
'probER',
F.when((df['loan_status'] == 0) & (df['fracNumPmts'] < 1),
1.0).otherwise(0.0))
#indicator for on-schedule repayment can be inferred as probDef=probER=0,0, with
#visually:
#plot of timing of either default or eventual (not early repayment)
#df.filter((df['loan_status']==1)|(df.fracNumPmts >=1)).select(df.fracNumPmts).toPandas().plot.hist()
#plt.show() #This is bi-modal, mostly low over 0,1 and then a spike at 1.
#plot of timing of either repayment (whenever)
#df.filter(df['loan_status']==0).select(df.fracNumPmts).toPandas().plot.hist()
