def readGammaGammaFitterModel():
gammaGammaFitterModel = GammaGammaFitter()
gammaGammaFitterModel.load_model("GammaGammaFitterModel.pkl")
return gammaGammaFitterModel
def predictSpending(customerId):
# initialize the data dictionary that will be returned
data = {"success": False, "result": {"customerId": "", "y": 0.0}}
# ensure the customer ID was properly uploaded to our endpoint
if customerId:
print("* get data")
data = pandas.read_csv("sample_transactions.csv")
#data = pandas.read_json(baseURL + "/api/transactions")
#data = data.drop(columns="_id")
print("* prepare data")
# prepare and shaping the data
# columns -
# customerId
# frequency : number of repeat purchase transactions
# recency: time (in days) between first purchase and latest purchase
# T: time (in days) between first purchase and end of the period under study
# monetary_value: average transactions amount
today = pandas.to_datetime(datetime.date.today())
summaryData = summary_data_from_transaction_data(
data,
"customerId",
"transactionDate",
monetary_value_col="transactionAmount",
observation_period_end=today)
# filter the customer data that has no transaction
analysisData = summaryData[summaryData["frequency"] > 0]
# get the stat of the particular customer
customer = analysisData.loc[customerId]
# load model
ggf_loaded = GammaGammaFitter()
ggf_loaded.load_model('ggf.pkl')
# estimate the average transaction amount
predict = ggf_loaded.conditional_expected_average_profit(
customer["frequency"], customer['monetary_value'])
# add the input and predicted output to the return data
data = {
"success": True,
"result": {
"customerId": customerId,
"y": predict
}
}
# return the data dictionary as a JSON response
return flask.jsonify(data)
