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remove randomData
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@thelostone-mc
thelostone-mc committed Mar 21, 2020
1 parent 59961d7 commit 662b7f9
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Showing 2 changed files with 32 additions and 65 deletions.
94 changes: 31 additions & 63 deletions app/grants/clr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -224,32 +224,6 @@ def grants_clr_calculate_pos_neg(pos_totals, neg_totals, total_pot=0.0):
return final_bigtot, final_totals


def generate_random_contribution_data():
import random
contrib_data = []
# whatever the range is here, you should have that many grants locally to test with
# this function generates fake contribution data, but assumes the grants are in the db

grants_to_use = 33
low_donation = 3000
high_donation = 6890
number_of_profiles = 17

for grant_id in range(grants_to_use):
contrib_data.append({
'id': grant_id,
'contributions': [
{
str(profile_id): random.randint(low_donation, high_donation)
} for profile_id in range(random.randint(1, number_of_profiles))
]
})
return contrib_data


'''
TODO: ANYTHING LEFT ?
'''
def calculate_clr_for_donation(grant, amount, grant_contributions, total_pot, threshold):

### ADD NEW GRANT CONTRIBUTIONS MODEL IS UPDATED AND PULL SEPARATE POSITIVE AND NEGATIVE CONTRIBUTIONS, INPUT VARIABLES IN THE METHOD grants_clr_calculate LINES 292 AND ON WILL NEED TO BE CHANGED
Expand Down Expand Up @@ -279,7 +253,6 @@ def calculate_clr_for_donation(grant, amount, grant_contributions, total_pot, th
Populate Data needed to calculate CLR
Args:
random_data : boolean - generate random data / use data from db
clr_type : media | tech | None
network : mainnet | rinkeby
Expand All @@ -289,7 +262,7 @@ def calculate_clr_for_donation(grant, amount, grant_contributions, total_pot, th
total_pot: float
threshold : int
'''
def populate_data_for_clr (random_data=False, clr_type=None, network='mainnet'):
def populate_data_for_clr(clr_type=None, network='mainnet'):

from_date = timezone.now()
# get all the eligible contributions and calculate total
Expand All @@ -312,49 +285,44 @@ def populate_data_for_clr (random_data=False, clr_type=None, network='mainnet'):
return None, None, None, None

# set up data to load contributions for each grant
if not random_data:
contrib_data = []

for grant in grants:
# go through all the individual contributions for each grant
g_contributions = copy.deepcopy(contributions).filter(subscription__grant_id=grant.id)

# put in correct format
phantom_funding_profiles = PhantomFunding.objects.filter(grant_id=grant.id, created_on__gte=CLR_START_DATE, created_on__lte=from_date)
all_contributing_profile_ids = list(set([c.subscription.contributor_profile.id for c in g_contributions] + [p.profile_id for p in phantom_funding_profiles]))
all_summed_contributions = []

for profile_id in all_contributing_profile_ids:
# get sum of contributions per grant for each profile
profile_g_contributions = g_contributions.filter(subscription__contributor_profile_id=profile_id)
sum_of_each_profiles_contributions = float(sum([c.subscription.get_converted_monthly_amount() for c in profile_g_contributions]))

phantom_funding = PhantomFunding.objects.filter(created_on__gte=CLR_START_DATE, grant_id=grant.id, profile_id=profile_id, created_on__lte=from_date)
if phantom_funding.exists():
sum_of_each_profiles_contributions = sum_of_each_profiles_contributions + phantom_funding.first().value

all_summed_contributions.append({str(profile_id): sum_of_each_profiles_contributions})

# for each grant, list the contributions in key value pairs like {'profile id': sum of contributions}
grant_id = grant.defer_clr_to.pk if grant.defer_clr_to else grant.id
contrib_data.append({
'id': grant_id,
'contributions': all_summed_contributions
})
contrib_data = []

else:
# use random contribution data for testing
contrib_data = generate_random_contribution_data()
for grant in grants:
# go through all the individual contributions for each grant
g_contributions = copy.deepcopy(contributions).filter(subscription__grant_id=grant.id)

# put in correct format
phantom_funding_profiles = PhantomFunding.objects.filter(grant_id=grant.id, created_on__gte=CLR_START_DATE, created_on__lte=from_date)
all_contributing_profile_ids = list(set([c.subscription.contributor_profile.id for c in g_contributions] + [p.profile_id for p in phantom_funding_profiles]))
all_summed_contributions = []

for profile_id in all_contributing_profile_ids:
# get sum of contributions per grant for each profile
profile_g_contributions = g_contributions.filter(subscription__contributor_profile_id=profile_id)
sum_of_each_profiles_contributions = float(sum([c.subscription.get_converted_monthly_amount() for c in profile_g_contributions]))

phantom_funding = PhantomFunding.objects.filter(created_on__gte=CLR_START_DATE, grant_id=grant.id, profile_id=profile_id, created_on__lte=from_date)
if phantom_funding.exists():
sum_of_each_profiles_contributions = sum_of_each_profiles_contributions + phantom_funding.first().value

all_summed_contributions.append({str(profile_id): sum_of_each_profiles_contributions})

# for each grant, list the contributions in key value pairs like {'profile id': sum of contributions}
grant_id = grant.defer_clr_to.pk if grant.defer_clr_to else grant.id
contrib_data.append({
'id': grant_id,
'contributions': all_summed_contributions
})

#print(f'\n contributions data: {contrib_data} \n')
return (grants, contrib_data, total_pot, threshold)


def predict_clr(random_data=False, save_to_db=False, from_date=None, clr_type=None, network='mainnet'):
def predict_clr(save_to_db=False, from_date=None, clr_type=None, network='mainnet'):
# setup
clr_calc_start_time = timezone.now()
debug_output = []
grants, contrib_data, total_pot, threshold = populate_data_for_clr(random_data, clr_type, network)
grants, contrib_data, total_pot, threshold = populate_data_for_clr(clr_type, network)

# calculate clr given additional donations
for grant in grants:
Expand Down Expand Up @@ -461,7 +429,7 @@ def predict_clr_live(grant, contributor, amount):

clr_type = grant.grant_type
network = grant.network
_, contrib_data, total_pot, threshold = populate_data_for_clr(False, clr_type, network)
_, contrib_data, total_pot, threshold = populate_data_for_clr(clr_type, network)

predicted_clr_match = calculate_clr_for_donation_live(
grant, contributor, amount, contrib_data, total_pot, threshold
Expand Down
3 changes: 1 addition & 2 deletions app/grants/management/commands/estimate_clr.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -34,15 +34,14 @@ class Command(BaseCommand):

def add_arguments(self, parser):

parser.add_argument('clr_type', type=str, default='all', choices=['tech', 'media', 'all'])
parser.add_argument('clr_type', type=str, default='tech', choices=['tech', 'media', 'health'])
parser.add_argument('network', type=str, default='mainnet', choices=['rinkeby', 'mainnet'])

def handle(self, *args, **options):
clr_type = options['clr_type']
network = options['network']

clr_prediction_curves = predict_clr(
random_data=False,
save_to_db=True,
from_date=timezone.now(),
clr_type=clr_type,
Expand Down

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