def genData(mongoid, conn, cursor, impute=True, freq='daily'):
'''
Generate a timeseries dataframe for timeseries modelling.
mongoid: str. string of mongodb id.
conn: sqlite3 connection.
cursor: sqlite3 cursor.
impute:
freq:
actualrevcount:
'''
initial = routines.sqlToDf(conn, cursor)
allproduct = initial.selectReview3(mongoid, impute=impute)
product = routines.tsSalesRateSentiment(allproduct, freq=freq)
return product
# rpy2
# courtesy:
# https://stackoverflow.com/questions/36105701/how-to-run-auto-arima-using-rpy2
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
from rpy2.robjects.packages import importr
from rpy2.robjects import FloatVector
pandas2ri.activate()
# make connection to sqlite db
conn = sqlite3.connect('product.db')
c = conn.cursor()
# enable foreign keys
c.execute("PRAGMA foreign_keys = ON")
conn.commit()
test = routines.sqlToDf(conn, c, 20)
test.prod.head()
product = test.selectReview2(test.prod.loc[3, 'id'])
product2 = routines.tsSalesRateSentiment(product,
freq='daily',
standardize=True)
product2 = product2[['Sales']]
# naive method
ro.r('library(forecast)')
rdf = pandas2ri.py2ri(product2)
ro.globalenv['r_timeseries'] = rdf
ored = ro.r('as.data.frame(forecast(naive(r_timeseries),h=10))')
