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(feat) predict spend

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ishaan-jaff 2024-03-01 09:28:22 -08:00
parent 47c5b94c50
commit 03a0b274f7
4 changed files with 90 additions and 27 deletions
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55
enterprise/utils.py
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@ -251,27 +251,30 @@ def _forecast_daily_cost(data: list):
import requests
from datetime import datetime, timedelta
# Get the last entry in the data
first_entry = data[0]
last_entry = data[-1]
# Parse the date from the last entry
last_entry_date = datetime.strptime(last_entry["date"], "%Y-%m-%d").date()
# print("Last Entry Date:", last_entry_date)
# get the date today
today_date = datetime.today().date()
# Get the month of the last entry
last_entry_month = last_entry_date.month
# print("Last Entry Month:", last_entry_month)
today_day_month = today_date.month
# Parse the date from the first entry
first_entry_date = datetime.strptime(first_entry["date"], "%Y-%m-%d").date()
last_entry_date = datetime.strptime(last_entry["date"], "%Y-%m-%d")
print("last entry date", last_entry_date)
# Assuming today_date is a datetime object
today_date = datetime.now()
# Calculate the last day of the month
last_day_of_month = (
datetime(last_entry_date.year, last_entry_date.month % 12 + 1, 1)
- timedelta(days=1)
).day
# print("Last Day of Month:", last_day_of_month)
last_day_of_todays_month = datetime(
today_date.year, today_date.month % 12 + 1, 1
) - timedelta(days=1)
# Calculate the remaining days in the month
remaining_days = last_day_of_month - last_entry_date.day
# print("Remaining Days:", remaining_days)
remaining_days = (last_day_of_todays_month - last_entry_date).days + 1
series = {}
for entry in data:
@ -279,6 +282,17 @@ def _forecast_daily_cost(data: list):
spend = entry["spend"]
series[date] = spend
if len(series) < 10:
num_items_to_fill = 11 - len(series)
# avg spend for all days in series
avg_spend = sum(series.values()) / len(series)
for i in range(num_items_to_fill):
# go backwards from the first entry
date = first_entry_date - timedelta(days=i)
series[date.strftime("%Y-%m-%d")] = avg_spend
series[date.strftime("%Y-%m-%d")] = avg_spend
payload = {"series": series, "count": remaining_days}
print("Prediction Data:", payload)
@ -291,6 +305,8 @@ def _forecast_daily_cost(data: list):
json=payload,
headers=headers,
)
# check the status code
response.raise_for_status()
json_response = response.json()
forecast_data = json_response["forecast"]
@ -314,15 +330,6 @@ def _forecast_daily_cost(data: list):
# _forecast_daily_cost(
# [
# {"date": "2022-01-01", "spend": 100},
# {"date": "2022-01-02", "spend": 200},
# {"date": "2022-01-03", "spend": 300},
# {"date": "2022-01-04", "spend": 400},
# {"date": "2022-01-05", "spend": 500},
# {"date": "2022-01-06", "spend": 600},
# {"date": "2022-01-07", "spend": 700},
# {"date": "2022-01-08", "spend": 800},
# {"date": "2022-01-09", "spend": 900},
# {"date": "2022-01-10", "spend": 1000},
# {"date": "2022-01-11", "spend": 50},
# ]
# )