Stoned Ape Crew

.gitignore

@@ -10,3 +10,4 @@
 data/*
 *.twb
 viz/.DS_Store
+data/mints/*.json

load_data.py

@@ -8,6 +8,7 @@ import requests
 import pandas as pd
 import urllib.request
 import snowflake.connector
+from utils import clean_name, clean_token_id
 
 os.chdir('/Users/kellenblumberg/git/nft-deal-score')
 
@@ -41,6 +42,20 @@ def clean_colnames(df):
 	df.columns = names
 	return(df)
 
+def add_collection_steps():
+	# 1. mint_address_token_id_map
+	# 2. scrape metadata
+	metadata = pd.read_csv('./data/metadata.csv')
+	metadata['collection'] = metadata.collection.apply(lambda x: clean_name(x) )
+	sorted(metadata.collection.unique())
+	metadata.to_csv('./data/metadata.csv', index=False)
+	metadata[metadata.collection == 'Stoned Ape Crew']
+	metadata[metadata.collection == 'Stoned Ape Crew'].feature_name.unique()
+	# 3. scrape howrareis
+	# 4. add sales
+	# 5. run model
+	pass
+
 def manual_clean():
 	for c in [ 'pred_price', 'attributes', 'feature_values', 'model_sales', 'listings', 'coefsdf', 'tokens' ]:
 		df = pd.read_csv('./data/{}.csv'.format(c))
@@ -49,7 +64,32 @@ def manual_clean():
 			df['clean_token_id'] = df.token_id
 		df.to_csv('./data/{}.csv'.format(c), index=False)
 
-def add_solana_sales():
+def mint_address_token_id_map_2():
+	old = pd.read_csv('./data/mint_address_token_id_map.csv')
+	old = pd.DataFrame()
+	mints = pd.read_csv('./data/solana_mints.csv')
+	data = []
+	for collection in [ 'Stoned Ape Crew','DeGods' ]:
+		for m in mints[mints.collection == collection].mint_address.unique():
+			pass
+			f = open('./data/mints/{}/{}.json'.format(collection, m))
+			j = json.load(f)
+			try:
+				token_id = int(re.split('#', j['name'])[1])
+				data += [[ collection, m, token_id, j['uri'] ]]
+			except:
+				print(m)
+		df = pd.DataFrame(data, columns=['collection','mint','token_id','uri'])
+		old = old.append(df).drop_duplicates()
+		print(old[old.token_id.notnull()].groupby('collection').token_id.count())
+	old.to_csv('./data/mint_address_token_id_map.csv', index=False)
+
+def mint_address_token_id_map():
+	mints = pd.read_csv('./data/solana_mints.csv')
+	mints[mints.collection == 'Stoned Ape Crew'][['mint_address']].drop_duplicates().to_csv('~/Downloads/tmp.csv', index=False)
+	mints[mints.collection == 'Degods'][['mint_address']].drop_duplicates().to_csv('~/Downloads/tmp.csv', index=False)
+	mints[mints.collection == 'DeGods'][['mint_address']].drop_duplicates().to_csv('~/Downloads/tmp.csv', index=False)
+	old = pd.read_csv('./data/mint_address_token_id_map.csv')
 	my_file = open('./scripts/solana-rpc-app/output.txt', 'r')
 	content = my_file.read()
 	my_file.close()
@@ -60,9 +100,20 @@ def add_solana_sales():
 		if len(s) > 1 and '#' in s[1]:
 			data += [[ re.split('"', s[0])[1], int(re.split('#', re.split('"', s[1])[1])[1]) ]]
 	df = pd.DataFrame(data, columns=['mint','token_id']).drop_duplicates()
+	df['collection'] = 'DeGods'
+	df.to_csv('./data/mint_address_token_id_map.csv', index=False)
+
+def add_solana_sales():
+	print('Adding Solana sales...')
+	# read id map
+	id_map = pd.read_csv('./data/mint_address_token_id_map.csv')
+	id_map['collection'] = id_map.collection.apply(lambda x: clean_name(x) )
+	id_map.collection.unique()
+
 	query = '''
 		SELECT tx_id
 		, n.mint
+		, l.project_name
 		, n.block_timestamp AS sale_date
 		, (inner_instruction:instructions[0]:parsed:info:lamports 
 		+ inner_instruction:instructions[1]:parsed:info:lamports 
@@ -72,16 +123,23 @@ def add_solana_sales():
 		LEFT JOIN crosschain.address_labels l ON LOWER(n.mint) = LOWER(l.address)
 		WHERE block_timestamp >= CURRENT_DATE - 200
 		AND instruction:data like '3UjLyJvuY4%'
-		AND l.project_name ilike 'degods'
+		AND l.project_name IN ('degods','stoned ape crew')
 	'''
 	sales = ctx.cursor().execute(query)
 	sales = pd.DataFrame.from_records(iter(sales), columns=[x[0] for x in sales.description])
 	sales = clean_colnames(sales)
 	print('Queried {} sales'.format(len(sales)))
 	sales['chain'] = 'Solana'
-	sales['collection'] = 'DeGods'
-	m = sales.merge(df, how='left', on=['mint'])
+	sales['collection'] = sales.project_name.apply(lambda x: clean_name(x) )
+	# m = sales.merge(id_map, how='left', on=['mint','collection'])
+	m = sales.merge(id_map, how='inner', on=['mint','collection'])
+	m.sort_values('collection')
+	m = m[[ 'collection','token_id','sale_date','price','chain' ]]
 	s_df = pd.read_csv('./data/sales.csv')
+	if 'collection_x' in s_df.columns and 'collection_y' in s_df.columns:
+		s_df['collection'] = s_df.collection.fillna(s_df.collection_x).fillna(s_df.collection_y)
+		del s_df['collection_x']
+		del s_df['collection_y']
 	l0 = len(s_df)
 	s_df = s_df[-s_df.collection.isin(sales.collection.unique())]
 	s_df = s_df.append(m)
@@ -91,6 +149,8 @@ def add_solana_sales():
 	for c in [ 'mint','tmp' ]:
 		if c in s_df:
 			del s_df[c]
+	if 'project_name' in s_df.columns:
+		del s_df['project_name']
 	s_df.to_csv('./data/sales.csv', index=False)
 	pass
 
@@ -428,7 +488,7 @@ def add_terra_sales():
 			WHEN 'terra1trn7mhgc9e2wfkm5mhr65p3eu7a2lc526uwny2' THEN 'LunaBulls'
 			WHEN 'terra103z9cnqm8psy0nyxqtugg6m7xnwvlkqdzm4s4k' THEN 'Galactic Punks'
 			WHEN 'terra1vhuyuwwr4rkdpez5f5lmuqavut28h5dt29rpn6' THEN 'Levana Dragons'
-			WHEN 'terra1p70x7jkqhf37qa7qm4v23g4u4g8ka4ktxudxa7' THEN 'Levana Meteor Dust'
+			WHEN 'terra1p70x7jkqhf37qa7qm4v23g4u4g8ka4ktxudxa7' THEN 'Levana Dust'
 			WHEN 'terra1k0y373yxqne22pc9g7jvnr4qclpsxtafevtrpg' THEN 'Levana Eggs'
 			WHEN 'terra14gfnxnwl0yz6njzet4n33erq5n70wt79nm24el' THEN 'Levana Loot'
 			WHEN 'terra1chrdxaef0y2feynkpq63mve0sqeg09acjnp55v' THEN 'Levana Meteors'
@@ -555,6 +615,7 @@ def add_terra_sales():
 	# tokens = pd.read_csv('./data/tokens.csv')
 	# tokens['tmp'] = tokens.token_id.apply(lambda x: (str(x)[:5]))
 	# tokens[tokens.collection == 'Galactic Punks'].to_csv('~/Downloads/tmp.csv', index=False)
+	sales.tokenid.values[:4]
 	sales['tokenid'] = sales.tokenid.apply(lambda x: str(int(float(x))) )
 	# tokens['token_id'] = tokens.token_id.astype(str)
 
@@ -569,6 +630,10 @@ def add_terra_sales():
 		, 'amount': 'price'
 		, 'tokenid': 'token_id'
 	})
+	sales = clean_token_id(sales)
+	sales.token_id.values[:4]
+	# sales['token_id'] = sales.token_id.astype(int)
+
 	# tmp = sales.merge(tokens[['collection','token_id','clean_token_id']])
 	# sales[sales.tx_id.isin(['6CA1966B42D02F07D1FB6A839B8276D501FDF3EF048DECA5601C74D82EBB9D12',
     #    'F5643C0C805F3236F67CFF1A6AC1FC50CF9DB61B846B3CE6F9D4CD3806284D4E',
@@ -577,9 +642,9 @@ def add_terra_sales():
 	# sales.columns
 	sales['chain'] = 'Terra'
 	sales = sales[[ 'chain','collection','token_id','sale_date','price','tx_id' ]]
-	print(sales.groupby(['chain','collection']).token_id.count())
-	sales['token_id'] = sales.token_id.apply(lambda x: re.sub('"', '', x) )
-	sales['collection'] = sales.collection.apply(lambda x: 'Levana Dragon Eggs' if x=='Levana Eggs' else x )
+	# print(sales.groupby(['chain','collection']).token_id.count())
+	# sales['token_id'] = sales.token_id.apply(lambda x: re.sub('"', '', x) )
+	# sales['collection'] = sales.collection.apply(lambda x: 'Levana Dragon Eggs' if x=='Levana Eggs' else x )
 	old = pd.read_csv('./data/sales.csv')
 	# print(old.groupby(['chain','collection']).token_id.count())
 	l0 = len(old)
@@ -588,6 +653,7 @@ def add_terra_sales():
 	old = old[ -(old.collection.isin(sales.collection.unique())) ]
 	old = old.append(sales)
 	old = old[[ 'chain','collection','token_id','sale_date','price','tx_id' ]]
+	# old['collection'] = old.collection.apply(lambda x: 'Levana Dust' if x == 'Levana Meteor Dust' else x )
 	old = old.drop_duplicates(subset=['collection','token_id','price'])
 	# old = old[-(old.collection == 'Levana Dragons')]
 	# old = old[-(old.collection == 'Levana Dragon Eggs')]

prepare_data.py

@@ -28,12 +28,15 @@ def add_att_count():
 
 
 m_df = pd.read_csv('./data/metadata.csv')
+l0 = len(m_df)
 
 solana_rarities = pd.read_csv('./data/solana_rarities.csv')
 tokens = pd.read_csv('./data/tokens.csv')[['collection','token_id','nft_rank']]
 solana_rarities = pd.read_csv('./data/solana_rarities.csv')
 lp_ranks = pd.read_csv('./data/lp_ranks.csv')
 gp_ranks = pd.read_csv('./data/gp_ranks.csv')
+lev_egg_ranks = m_df[m_df.feature_name == 'collection_rank'][['collection','token_id','feature_value']].rename(columns={'feature_value':'nft_rank'})
+lev_egg_ranks['nft_rank'] = lev_egg_ranks.nft_rank.astype(int)
 if False:
 	metadata = pd.read_csv('./data/metadata.csv')
 	levana_ranks = metadata[(metadata.collection == 'Levana Dragon Eggs') & (metadata.feature_name == 'collection_rank')]
@@ -49,9 +52,13 @@ if False:
 	metadata['chain'] = metadata.collection.apply(lambda x: 'Terra' if x in ['LunaBulls','Galactic Punks','Levana Dragon Eggs'] else 'Solana' )
 	metadata.to_csv('./data/metadata.csv', index=False)
 
-rarities = solana_rarities.append(lp_ranks).append(gp_ranks).append(tokens).drop_duplicates(keep='first')
-rarities = rarities[[ 'collection','token_id','nft_rank' ]]
+rarities = solana_rarities.append(lp_ranks).append(gp_ranks).append(lev_egg_ranks).append(tokens)[[ 'collection','token_id','nft_rank' ]].dropna().drop_duplicates(subset=['collection','token_id'], keep='first')
+rarities[rarities.collection == 'Levana Dragon Eggs']
+# rarities = rarities[[ 'collection','token_id','nft_rank' ]].dropna()
 rarities['collection'] = rarities.collection.apply(lambda x: clean_name(x) )
+# rarities[rarities.nft_rank.isnull()]
+# solana_rarities[solana_rarities.nft_rank.isnull()]
+rarities['nft_rank'] = rarities.nft_rank.astype(int)
 # rarities[ (rarities.collection == 'Solana Monkey Business') & (rarities.token_id == 903) ]
 rarities.loc[ (rarities.collection == 'Solana Monkey Business') & (rarities.token_id == 903) , 'nft_rank' ] = 18
 rarities['adj_nft_rank_0'] = rarities.nft_rank.apply(lambda x: (x+1) ** -0.2 )
@@ -104,10 +111,15 @@ print(m_df[(m_df.token_id=='10') & (m_df.collection == 'Aurory')])
 
 m_df['feature_value'] = m_df.feature_value.apply(lambda x: x.strip() if type(x) == str else x )
 m_df['chain'] = m_df.collection.apply(lambda x: 'Terra' if x in ['LunaBulls','Galactic Punks','Levana Dragon Eggs'] else 'Solana' )
-m_df.to_csv('./data/metadata.csv', index=False)
 
 g = m_df[['collection','token_id']].drop_duplicates().groupby('collection').token_id.count().reset_index()
 a = m_df.groupby('collection').token_id.count().reset_index().rename(columns={'token_id':'atts'})
 g = g.merge(a)
 g['rat'] = g.atts / g.token_id
 print(g)
+
+l1 = len(m_df)
+m_df[m_df.collection == 'Levana Dragon Eggs'].feature_name.unique()
+print('Adding {} rows'.format(l1 - l0))
+m_df[m_df.collection == 'Levana Dragon Eggs']
+m_df.to_csv('./data/metadata.csv', index=False)

scrape_sol_nfts.py

@@ -19,7 +19,7 @@ from selenium.webdriver.common.keys import Keys
 os.chdir('/Users/kellenblumberg/git/nft-deal-score')
 os.environ['PATH'] += os.pathsep + '/Users/kellenblumberg/shared/'
 
-from utils import merge, clean_name
+from utils import clean_token_id, merge, clean_name
 
 # browser = webdriver.Chrome()
 
@@ -43,6 +43,69 @@ def scrape_magic_eden_sales():
 	df = pd.DataFrame([ x['parsedTransaction'] for x in results if 'parsedTransaction' in x.keys()])
 	df[[ 'blockTime','collection_symbol','total_amount' ]]
 
+def metadata_from_uri():
+	df = pd.read_csv('./data/mint_address_token_id_map.csv')
+	tokens = pd.read_csv('./data/tokens.csv')
+	tokens['collection'] = tokens.collection.apply(lambda x: clean_name(x))
+	tokens[tokens.collection == 'Stoned Ape Crew']
+	sorted(tokens.collection.unique())
+	collections = [ 'Stoned Ape Crew' ]
+	data = []
+	t_data = []
+	# seen = [ x[1] for x in t_data]
+	for collection in collections:
+		print(collection)
+		it = 0
+		cur = df[df.collection == collection]
+		seen = []
+		for row in cur.iterrows():
+			it += 1
+			if it % 250 == 2:
+				print('{} {} {}'.format(it, len(data), len(t_data)))
+			row = row[1]
+			uri = row['uri']
+			token_id = row['token_id']
+			if token_id in seen:
+				continue
+			try:
+				j = requests.get(uri, timeout=3).json()
+				for a in j['attributes']:
+					data += [[ collection, token_id, a['trait_type'], a['value'] ]]
+				t_data += [[ collection, token_id, j['image'] ]]
+				seen.append(token_id)
+			except:
+				print(row['uri'])
+	old = pd.read_csv('./data/metadata.csv')
+	l0 = len(old)
+	metadata = pd.DataFrame(data, columns=['collection','token_id','feature_name','feature_value'])
+	metadata['chain'] = 'Solana'
+	old['token_id'] = old.token_id.astype(str)
+	metadata['token_id'] = metadata.token_id.astype(str)
+	old = old.append(metadata).drop_duplicates(subset=['collection','token_id','feature_name'])
+	l1 = len(old)
+	print('Adding {} rows to metadata'.format(l1 - l0))
+	# old['chain'] = old.collection.apply(lambda x: 'Terra' if x in ['Galactic Punks','Levana Dragon Eggs','LunaBulls'] else 'Solana')
+	print(old.groupby(['chain','collection']).token_id.count())
+	old.to_csv('./data/metadata.csv', index=False)
+
+	old = pd.read_csv('./data/tokens.csv')
+	l0 = len(old)
+	tokens = pd.DataFrame(t_data, columns=['collection','token_id','image_url'])
+	old['collection'] = old.collection.apply(lambda x: clean_name(x))
+	old['token_id'] = old.token_id.astype(str)
+	tokens['token_id'] = tokens.token_id.astype(str)
+	old = old.merge(tokens, how='left', on=['collection','token_id'])
+	old['image_url'] = old.image_url_y.fillna(old.image_url_x)
+	del old['image_url_x']
+	del old['image_url_y']
+	tmp = old[old.collection == 'Stoned Ape Crew']
+	tmp['tmp'] = tmp.image_url.apply(lambda x: x[:20] )
+	tmp.groupby('tmp').token_id.count()
+	old = old.drop_duplicates(subset=['collection','token_id'], keep='last')
+	l1 = len(old)
+	print('Adding {} rows to tokens'.format(l1 - l0))
+	old.to_csv('./data/tokens.csv', index=False)
+
 def scrape_not_found(browser):
 	url = 'https://notfoundterra.com/lunabulls'
 	browser.get(url)
@@ -198,7 +261,8 @@ def scrape_randomearth(browser):
 				for i in j['items']:
 					data += [[ 'Terra', collection, i['token_id'], i['price'] / (10 ** 6) ]]
 		df = pd.DataFrame(data, columns=['chain','collection','token_id','price'])
-		df.to_csv('~/Downloads/tmp.csv', index=False)
+		df = clean_token_id(df)
+		# df.to_csv('~/Downloads/tmp.csv', index=False)
 		old = pd.read_csv('./data/listings.csv')
 		old = old[-old.collection.isin(df.collection.unique())]
 		old = old.append(df)
@@ -382,7 +446,7 @@ def scrape_solanafloor():
 	df.to_csv('./data/sf_projects.csv', index=False)
 
 
-def scrape_listings(browser, collections = [ 'degods','aurory','thugbirdz','smb','degenapes','peskypenguinclub' ], alerted = [], is_listings = True):
+def scrape_listings(browser, collections = [ 'stoned-ape-crew','degods','aurory','thugbirdz','smb','degenapes','peskypenguinclub' ], alerted = [], is_listings = True):
 	print('Scraping solanafloor listings...')
 	data = []
 	m_data = []
@@ -468,7 +532,16 @@ def scrape_listings(browser, collections = [ 'degods','aurory','thugbirdz','smb'
 							# 	print(row.text)
 					scroll = browser.find_elements_by_class_name('ag-row-even')
 					j = min(j, len(scroll) - 1)
+					try:
 						browser.execute_script("arguments[0].scrollIntoView();", scroll[j] )
+					except:
+						sleep(1)
+						try:
+							browser.execute_script("arguments[0].scrollIntoView();", scroll[j] )
+						except:
+							sleep(10)
+							browser.execute_script("arguments[0].scrollIntoView();", scroll[j] )
+
 					sleep(.1)
 			next = browser.find_elements_by_class_name('ag-icon-next')
 			a = browser.find_element_by_id('ag-17-start-page-number').text
@@ -1045,6 +1118,7 @@ def metadata_from_solscan():
 		[ 'shadowy-super-coder', 'https://sld-gengo.s3.amazonaws.com/{}.json', 0, 10000 ]
 		, [ 'degods', 'https://sld-gengo.s3.amazonaws.com/{}.json', 1, 10000 ]
 		, [ 'balloonsville', 'https://bafybeih5i7lktx6o7rjceuqvlxmpqzwfh4nhr322wq5hjncxbicf4fbq2e.ipfs.dweb.link/{}.json', 0, 5000 ]
+		, [ 'Stoned Ape Crew', 'https://bafybeih5i7lktx6o7rjceuqvlxmpqzwfh4nhr322wq5hjncxbicf4fbq2e.ipfs.dweb.link/{}.json', 0, 5000 ]
 	]
 	data = []
 	token_data = []
@@ -1134,6 +1208,11 @@ def scrape_mints():
 
 	mints = pd.DataFrame()
 	auth_to_mint = {}
+	# metaboss -r https://red-cool-wildflower.solana-mainnet.quiknode.pro/a1674d4ab875dd3f89b34863a86c0f1931f57090/ decode mint --list-file ./data/mints/etc/degods.json -o ~/Downloads/degods
+	# metaboss -r https://red-cool-wildflower.solana-mainnet.quiknode.pro/a1674d4ab875dd3f89b34863a86c0f1931f57090/ derive metadata ChANfqf7AP9x1rFRjZkE6n19u3tQckv65Z6r6xPqkRKR  --output ~/Downloads
+	# metaboss decode mint --list-file <LIST_FILE> -o <OUPUT_DIRECTORY>
+
+
 	for collection, update_authority in d.items():
 		auth_to_mint[update_authority] = collection
 	for fname in [ './data/mints/'+f for f in os.listdir('./data/mints') ]:

scratch.py

@@ -0,0 +1,27 @@
+import os
+import pandas as pd
+
+os.chdir('/Users/kellenblumberg/git/nft-deal-score')
+
+COLLECTION = 'Stoned Ape Crew'
+
+sales = pd.read_csv('./data/sales.csv')
+sales = pd.read_csv('./data/model_sales.csv')
+sorted(sales.collection.unique())
+# sales = sales[sales.exclude == 0]
+metadata = pd.read_csv('./data/metadata.csv')
+metadata = metadata[metadata.collection == COLLECTION]
+sales = sales[sales.collection == COLLECTION]
+features = sorted(metadata.feature_name.unique())
+metadata = metadata.pivot( ['collection','token_id'], ['feature_name'], ['feature_value'] ).reset_index()
+metadata.columns = [ 'collection','token_id' ] + features
+sales['token_id'] = sales.token_id.astype(int)
+metadata['token_id'] = metadata.token_id.astype(int)
+df = sales.merge(metadata)
+# df.sort_values('sale_date', ascending=0).head()
+df = df.fillna('None')
+df['id'] = len(df)
+df['rel_price_0'] = (df.price - df.mn_20).apply(lambda x: max(0, x))
+df['rel_price_1'] = (df.price / df.mn_20).apply(lambda x: max(0, x-1))
+df[ (df.rel_price_0.notnull()) ].to_csv('./data/tableau_data.csv', index=False)
+

scripts/solana-rpc-app/ChANfqf7AP9x1rFRjZkE6n19u3tQckv65Z6r6xPqkRKR.json

@@ -0,0 +1 @@
+{"creators":[{"address":"7RCBr3ZQ8yhY4jHpFFo3Kmh7MnaCPi1bFuUgXUB9WURf","share":0,"verified":true},{"address":"PUFFgnKKhQ23vp8uSPwdzrUhEr7WpLmjM85NB1FQgpb","share":100,"verified":false}],"name":"Stoned Ape #2732","seller_fee_basis_points":742,"symbol":"SAC","uri":"https://arweave.net/EhC3DmvjOUP2346FiEjKhu_xTDvuLfOYMipZTQgg1F8"}

solana_model.py

@@ -2,7 +2,7 @@ import collections
 import os
 import re
 import json
-from textwrap import indent
+import pickle
 import warnings
 import requests
 import numpy as np
@@ -25,7 +25,6 @@ os.chdir('/Users/kellenblumberg/git/nft-deal-score')
 
 warnings.filterwarnings('ignore')
 
-
 ###################################
 #     Define Helper Functions     #
 ###################################
@@ -68,7 +67,8 @@ def get_sales(check_exclude = True, exclude=[]):
 
 	s_df = pd.read_csv('./data/sales.csv').rename(columns={'sale_date':'block_timestamp'})
 	s_df['token_id'] = s_df.token_id.astype(str)
-	s_df['collection'] = s_df.collection.apply(lambda x: clean_name(x))
+	# s_df['collection'] = s_df.collection_x.fillna(s_df.collection_y).fillna(s_df.collection).apply(lambda x: clean_name(x))
+	s_df = s_df.drop_duplicates(subset=['token_id','collection','price'])
 	s_df = s_df[-s_df.collection.isin(['Levana Meteors','Levana Dust'])]
 	s_df = s_df[ -s_df.collection.isin(['boryokudragonz', 'Boryoku Dragonz']) ]
 	s_df = s_df[[ 'chain','collection','block_timestamp','token_id','price','tx_id' ]]
@@ -125,7 +125,7 @@ def get_coefs(cols, coef):
 	coefs = coefs.sort_values('val', ascending=0)
 	return(coefs)
 
-def train_model(check_exclude, supplement_with_listings):
+def train_model(check_exclude, supplement_with_listings, use_saved_params=True):
 	exclude = [
 		( 'aurory', 2239, 3500 )
 		, ( 'aurory', 1876, 789 )
@@ -134,6 +134,7 @@ def train_model(check_exclude, supplement_with_listings):
 		, ( 'aurory', 9239, 1700 )
 	]
 	s_df = get_sales(check_exclude, exclude)
+	s_df[s_df.collection.isnull()]
 	# s_df = pd.read_csv('./data/sales.csv').rename(columns={'sale_date':'block_timestamp'})
 	# s_df['collection'] = s_df.collection.apply(lambda x: clean_name(x))
 	# s_df = s_df[-s_df.collection.isin(['Levana Meteors','Levana Dust'])]
@@ -215,8 +216,10 @@ def train_model(check_exclude, supplement_with_listings):
 	if supplement_with_listings:
 		pred_price = pd.read_csv('./data/pred_price.csv')
 		pred_price['collection'] = pred_price.collection.apply(lambda x: clean_name(x))
+		pred_price['token_id'] = pred_price.token_id.astype(str)
 		listings['collection'] = listings.collection.apply(lambda x: clean_name(x))
 		listings['block_timestamp'] = s_df.block_timestamp.max()
+		listings['token_id'] = listings.token_id.astype(str)
 		# listings = listings[listings.collection.isin(pred_price.collection.unique())]
 		floor = s_df.sort_values('timestamp').groupby('collection').tail(1)[['collection','mn_20']]
 		tmp = merge(listings, pred_price, ensure=False)
@@ -259,7 +262,7 @@ def train_model(check_exclude, supplement_with_listings):
 	ALL_NUMERIC_COLS = ['nft_rank','adj_nft_rank_0','adj_nft_rank_1','adj_nft_rank_2']
 	MODEL_EXCLUDE_COLS = {
 		# 'Levana Dragon Eggs': ['collection_rank','meteor_id','shower','lucky_number','cracking_date','attribute_count','weight','temperature']
-		'Levana Dragon Eggs': ['meteor_id','shower','lucky_number','cracking_date','attribute_count','rarity_score_rank','rarity_score','weight']
+		'Levana Dragon Eggs': ['meteor_id','shower','lucky_number','cracking_date','attribute_count','rarity_score_rank','rarity_score','weight','collection_rank_group']
 		, 'Solana Monkey Business': ['Clothes_Diamond']
 	}
 	MODEL_INCLUDE_COLS = {
@@ -283,12 +286,25 @@ def train_model(check_exclude, supplement_with_listings):
 	# for collection in [ 'Solana Monkey Business' ]:
 	# for collection in [ 'Aurory' ]:
 	# for collection in [ 'Aurory','Solana Monkey Business' ]:
-	collections = list(s_df[['collection']].drop_duplicates().merge(m_df[['collection']].drop_duplicates()).collection.unique())
 	sorted(pred_price.collection.unique())
 	sorted(s_df.collection.unique())
 	print(sorted(m_df.collection.unique()))
-	# for collection in m_df.collection.unique():
-	for collection in [ 'DeGods' ]:
+	# for collection in s_df.collection.unique():
+	saved_params = {}
+	file_to_store = open('./objects/saved_params.pickle', 'rb')
+	saved_params = pickle.load(file_to_store)
+	collection = 'Aurory'
+	collection = 'Levana Dragon Eggs'
+	collection = 'Galactic Punks'
+	collection = 'Stoned Ape Crew'
+	collections = ['Levana Dragon Eggs']
+	collections = list(s_df[['collection']].drop_duplicates().merge(m_df[['collection']].drop_duplicates()).collection.unique())
+	collections = ['Stoned Ape Crew']
+	for collection in collections:
+		# if collection == 'Stoned Ape Crew':
+		# 	continue
+		if not collection in saved_params.keys():
+			saved_params[collection] = {}
 		coefsdf = coefsdf[coefsdf.collection != collection]
 		salesdf = salesdf[salesdf.collection != collection]
 		attributes = attributes[attributes.collection != collection]
@@ -297,6 +313,9 @@ def train_model(check_exclude, supplement_with_listings):
 		print('Working on collection {}'.format(collection))
 		sales = s_df[ s_df.collection == collection ]
 		metadata = m_df[ m_df.collection == collection ]
+		metadata = metadata[metadata.feature_name != 'Genesis Role?']
+		metadata[metadata.token_id=='1']
+		metadata[metadata.feature_name=='Genesis Role?'].feature_value.unique()
 		sorted(metadata.feature_name.unique())
 		# metadata.groupby(['feature_name','feature_value']).token_id.count().reset_index().to_csv('~/Downloads/tmp.csv', index=False)
 		# metadata[metadata.token_id == '1']
@@ -306,7 +325,7 @@ def train_model(check_exclude, supplement_with_listings):
 		metadata = metadata[-metadata.feature_name.isin(['rank','pct','ipfs_image'])]
 		metadata.feature_name.unique()
 		metadata[(metadata.token_id=='1') & (metadata.collection == 'Solana Monkey Business')]
-		print(sorted(metadata.feature_name.unique()))
+		# print(sorted(metadata.feature_name.unique()))
 
 		# categorize columns
 		all_names = sorted(metadata.feature_name.unique())
@@ -314,7 +333,7 @@ def train_model(check_exclude, supplement_with_listings):
 		num_features = sorted((NUMERIC_COLS[collection] if collection in NUMERIC_COLS.keys() else []) + ALL_NUMERIC_COLS)
 		num_features = [ x for x in num_features if x in metadata.feature_name.unique() ]
 		num_metadata = metadata[metadata.feature_name.isin(num_features)]
-		print(sorted(num_metadata.feature_name.unique()))
+		# print(sorted(num_metadata.feature_name.unique()))
 		num_metadata[num_metadata.feature_name == 'nft_rank']
 		cat_features = sorted([ x for x in all_names if not x in (model_exclude + num_features) ])
 		cat_metadata = metadata[metadata.feature_name.isin(cat_features)]
@@ -326,7 +345,7 @@ def train_model(check_exclude, supplement_with_listings):
 		# create dummies for binary variables
 		cat_metadata = cat_metadata.pivot( ['collection','token_id'], ['feature_name'], ['feature_value'] ).reset_index()
 		cat_metadata.columns = [ 'collection','token_id' ] + cat_features
-		cat_metadata = calculate_percentages( cat_metadata, cat_features )
+		# cat_metadata = calculate_percentages( cat_metadata, cat_features )
 		dummies = pd.get_dummies(cat_metadata[cat_features])
 		# dummies.head(1).to_csv('~/Downloads/tmp2.csv', index=False)
 		if collection == 'Solana Monkey Business':
@@ -342,7 +361,7 @@ def train_model(check_exclude, supplement_with_listings):
 			del dummies['matching_black']
 
 		cat_metadata = pd.concat([ cat_metadata.reset_index(drop=True), dummies.reset_index(drop=True) ], axis=1)
-		del cat_metadata['pct']
+		# del cat_metadata['pct']
 
 		for c in model_exclude:
 			if c in dummies.columns:
@@ -358,7 +377,7 @@ def train_model(check_exclude, supplement_with_listings):
 		assert(len(df.columns) < 1000)
 
 		# test dataFrame
-		ensure = not collection in ['Aurory']
+		ensure = not collection in ['Aurory','Stoned Ape Crew']
 		test = merge(num_metadata, cat_metadata, ['collection','token_id'], ensure=False)
 
 		if collection == 'Solana Monkey Business':
@@ -394,6 +413,8 @@ def train_model(check_exclude, supplement_with_listings):
 				else:
 					tmp['is_top_{}'.format(i)] = (tmp.nft_rank <= i).astype(int)
 		pred_cols += [ 'is_top_100','is_top_250','is_top_1000' ]
+		if 'collection_rank' in pred_cols:
+			pred_cols = [ x for x in pred_cols if not x in ['nft_rank'] ]
 		df.sort_values('price', ascending=0)[['price']].head(20)
 		# df.groupby(['rarity','weight']).price.mean()
 
@@ -422,8 +443,8 @@ def train_model(check_exclude, supplement_with_listings):
 		tmp.sort_values('b').head(20)
 		rem = list(tmp[tmp.b==0].a.values)
 		std_pred_cols = [ c for c in std_pred_cols if not c in rem ]
-		if collection == 'Levana Dragon Eggs':
-			std_pred_cols = [ 'std_essence_Dark','std_collection_rank_group_0','std_rarity_Legendary','std_rarity_Rare','std_rarity_Ancient','std_collection_rank','std_transformed_collection_rank' ]
+		# if collection == 'Levana Dragon Eggs':
+		# 	std_pred_cols = [ 'std_essence_Dark','std_collection_rank_group_0','std_rarity_Legendary','std_rarity_Rare','std_rarity_Ancient','std_collection_rank','std_transformed_collection_rank' ]
 		mn = df.timestamp.min()
 		mx = df.timestamp.max()
 		df['wt'] = df.timestamp.apply(lambda x: 3.0 ** ((x - mn) / (mx - mn)) )
@@ -433,18 +454,18 @@ def train_model(check_exclude, supplement_with_listings):
 		#     df['wt'] = df.price.apply(lambda x: 1.0 / (x ** 0.9) )
 		#     df.sort_values('price', ascending=0)[['price','wt']].head(20)
 		# std_pred_cols = [ 'std_Hat_Crown','std_adj_nft_rank_0','std_Hat_None','std_Eyes_None','std_Clothes_None','std_Attribute Count_4','std_Mouth_None','std_adj_nft_rank_1','std_Type_Dark','std_Ears_None','std_Background_Light purple','std_Hat_Black Fedora 2','std_Hat_White Fedora 2','std_Attribute Count_0','std_Type_Skeleton','std_Attribute Count_2','std_Attribute Count_1','std_Hat_Protagonist Black Hat','std_Clothes_Sailor Vest','std_Mouth_Pipe','std_Hat_Protagonist White Hat','std_Clothes_Pirate Vest','std_Hat_Roman Helmet','std_Type_Solana','std_Clothes_Beige Smoking','std_Hat_Military Helmet','std_Hat_White Fedora 1','std_naked_1_att','std_Type_Zombie','std_Clothes_Roman Armor','std_Eyes_3D Glasses','std_Clothes_Orange Kimono','std_Hat_Green Punk Hair','std_Hat_Sombrero','std_Clothes_Military Vest','std_Hat_Space Warrior Hair','std_Hat_Blue Punk Hair','std_Clothes_Orange Jacket','std_Ears_Earing Silver','std_Eyes_Laser Eyes','std_Eyes_Vipers','std_Type_Alien','std_Type_Red','std_Hat_Admiral Hat' ]
-		cur_std_pred_cols = [ 'std_adj_nft_rank_0','std_Hat_Crown','std_adj_nft_rank_1','std_Type_Skeleton','std_Type_Alien','std_Clothes_None','std_Eyes_Vipers','std_Hat_Space Warrior Hair','std_Type_Zombie','std_Clothes_Pirate Vest','std_Clothes_Orange Kimono','std_Eyes_Laser Eyes','std_Type_Solana','std_Hat_Ninja Bandana','std_Hat_Solana Backwards Cap','std_Eyes_Solana Vipers','std_Attribute Count_0','std_Attribute Count_1','std_Attribute Count_2','std_Attribute Count_3','std_Attribute Count_5','std_Hat_Strawhat','std_Hat_Admiral Hat','std_matching_top','std_Hat_Sombrero','std_matching_cop','std_Hat_Cowboy Hat','std_Hat_None' ]
-		cur_std_pred_cols = deepcopy(std_pred_cols)
-		g = df[std_pred_cols].sum().reset_index()
-		g.columns = [ 'col','cnt' ]
-		g = g.sort_values('cnt')
-		g.head(20)
+		# cur_std_pred_cols = [ 'std_adj_nft_rank_0','std_Hat_Crown','std_adj_nft_rank_1','std_Type_Skeleton','std_Type_Alien','std_Clothes_None','std_Eyes_Vipers','std_Hat_Space Warrior Hair','std_Type_Zombie','std_Clothes_Pirate Vest','std_Clothes_Orange Kimono','std_Eyes_Laser Eyes','std_Type_Solana','std_Hat_Ninja Bandana','std_Hat_Solana Backwards Cap','std_Eyes_Solana Vipers','std_Attribute Count_0','std_Attribute Count_1','std_Attribute Count_2','std_Attribute Count_3','std_Attribute Count_5','std_Hat_Strawhat','std_Hat_Admiral Hat','std_matching_top','std_Hat_Sombrero','std_matching_cop','std_Hat_Cowboy Hat','std_Hat_None' ]
+		# cur_std_pred_cols = deepcopy(std_pred_cols)
+		# g = df[std_pred_cols].sum().reset_index()
+		# g.columns = [ 'col','cnt' ]
+		# g = g.sort_values('cnt')
+		# g.head(20)
 		if collection == 'Solana Monkey Busines':
 			df.loc[ df.token_id == '903', 'nft_rank' ] = 18
 			df[df.token_id=='903']
 			df[df.token_id==903]
 		df = df.reset_index(drop=True)
-		X = df[cur_std_pred_cols].values
+		X = df[std_pred_cols].values
 		y_0 = df.rel_price_0.values
 		y_1 = df.rel_price_1.values
 
@@ -480,11 +501,13 @@ def train_model(check_exclude, supplement_with_listings):
 			y_val_rar_adj = df[rar_adj_target_col].values
 			models = ['las','ridge'] if target_col == 'rel_price_1' else ['las','ridge','rfr']
 			for model in models:
-				cur_std_pred_cols = std_pred_cols
+				cur_std_pred_cols = deepcopy(std_pred_cols)
 				print(model)
 				y = y_val_rar_adj if model in ['rfr'] else y_val
 				col = 'y_pred_{}_{}'.format(model, it)
-				df, bst_p, bst_r = ku.get_bst_params( model, df, X, y, target_col, col, verbose = True, wt_col='wt' )
+				params = [saved_params[collection][col]] if col in saved_params[collection].keys() and use_saved_params else []
+				df, bst_p, bst_r = ku.get_bst_params( model, df, X, y, target_col, col, verbose = True, wt_col='wt', params = params )
+				saved_params[collection][col] = bst_p
 
 				# if model == 'ridge':
 				# 	while len(cur_std_pred_cols) > 50:
@@ -506,9 +529,10 @@ def train_model(check_exclude, supplement_with_listings):
 						cur_std_pred_cols = [ c for c in coefs[coefs.val >= 0 ].col.unique() ]
 						X_new = df[cur_std_pred_cols].values
 						clf.fit(X_new, y)
-						# df, bst_p, bst_r = ku.get_bst_params( model, df, df[cur_std_pred_cols].values, y, target_col, col, verbose = True, wt_col='wt' )
 						coefs = get_coefs(cur_std_pred_cols, clf.coef_)
 						mn = coefs.val.min()
+						if mn >= 0:
+							df, bst_p, bst_r = ku.get_bst_params( model, df, X_new, y, target_col, col, verbose = True, wt_col='wt', params = [bst_p] )
 					coefs.to_csv('./data/coefs/{}_{}_{}.csv'.format(collection, model, it), index=False)
 				test = ku.apply_model( model, bst_p, df, test, cur_std_pred_cols, target_col, col)
 				if model in ['rfr']:
@@ -543,11 +567,12 @@ def train_model(check_exclude, supplement_with_listings):
 		clf = LinearRegression(fit_intercept=False)
 		target_col = 'adj_price'
 		clf.fit( df[['pred_lin','pred_log']].values, df[target_col].values, df.wt.values )
-		clf.score( df[['pred_lin','pred_log']].values, df[target_col].values, df.wt.values )
-		df[['pred_lin','pred_log',target_col]].mean()
-		df[['pred_lin','pred_log',target_col]].median()
-		test[['pred_lin','pred_log']].mean()
-		test[['pred_lin','pred_log']].median()
+		score = clf.score( df[['pred_lin','pred_log']].values, df[target_col].values, df.wt.values )
+		print('R-Sq: {}'.format(round(score * 100, 1)))
+		# df[['pred_lin','pred_log',target_col]].mean()
+		# df[['pred_lin','pred_log',target_col]].median()
+		# test[['pred_lin','pred_log']].mean()
+		# test[['pred_lin','pred_log']].median()
 		
 		print('Price = {} * lin + {} * log'.format( round(clf.coef_[0], 2), round(clf.coef_[1], 2) ))
 		tmp = pd.DataFrame([[collection, clf.coef_[0], clf.coef_[1], CUR_FLOOR]], columns=['collection','lin_coef','log_coef','floor_price'])
@@ -591,7 +616,8 @@ def train_model(check_exclude, supplement_with_listings):
 		df[df.pred < 200].err.mean()
 		df['collection'] = collection
 		print('Avg err last 100: {}'.format(round(df.sort_values('block_timestamp').head(100).err.mean(), 2)))
-		salesdf = salesdf.append( df.rename(columns={'collection_rank':'nft_rank'}).merge(s_df[s_df.sim == 0][['collection','token_id','block_timestamp','price']] )[[ 'collection','token_id','block_timestamp','price','pred','mn_20','nft_rank' ]].sort_values('block_timestamp', ascending=0) )
+		# salesdf = salesdf.append( df.rename(columns={'collection_rank':'nft_rank'}).merge(s_df[s_df.sim == 0][['collection','token_id','block_timestamp','price']] )[[ 'collection','token_id','block_timestamp','price','pred','mn_20','nft_rank' ]].sort_values('block_timestamp', ascending=0) )
+		salesdf = salesdf.append( df.merge(s_df[s_df.sim == 0][['collection','token_id','block_timestamp','price']] )[[ 'collection','token_id','block_timestamp','price','pred','mn_20','nft_rank' ]].sort_values('block_timestamp', ascending=0) )
 
 		############################################################
 		#     Create Predictions for Each NFT in The Collection    #
@@ -703,6 +729,9 @@ def train_model(check_exclude, supplement_with_listings):
 	# nft_rank['token_id'] = nft_rank.token_id.astype(str)
 	# pred_price['token_id'] = pred_price.token_id.astype(str)
 	# pred_price = pred_price.merge(nft_rank, how='left', on=['collection','token_id'])
+	# pred_price = pred_price[pred_price.collection != 'LunaBulls']
+	pred_price['collection'] = pred_price.collection.apply(lambda x: clean_name(x))
+	pred_price = pred_price.drop_duplicates(subset=['collection','token_id'], keep='last')
 	pred_price.to_csv('./data/pred_price.csv', index=False)
 	# pred_price = pd.read_csv('./data/pred_price.csv')
 	pred_price.groupby('collection')[['pred_price']].min()
@@ -725,14 +754,17 @@ def train_model(check_exclude, supplement_with_listings):
 
 	feature_values.to_csv('./data/feature_values.csv', index=False)
 
+	file_to_store = open('./objects/saved_params.pickle', 'wb')
+	pickle.dump(saved_params, file_to_store)
+
 	if True or check_exclude:
 		exclude = pd.read_csv('./data/exclude.csv')
 		salesdf['rat'] = salesdf.price / salesdf.pred
 		salesdf['dff'] = salesdf.price - salesdf.pred
-		salesdf['exclude_1'] = (((salesdf.dff >= 20) & (salesdf.rat > 4)) | ((salesdf.dff >= 40) & (salesdf.rat > 3)) | ((salesdf.dff >= 60) & (salesdf.rat > 2.5)) | ((salesdf.dff >= 80) & (salesdf.rat > 2))).astype(int)
+		salesdf['exclude_1'] = (((salesdf.dff >= 20) & (salesdf.rat > 4)) | ((salesdf.dff >= 40) & (salesdf.rat > 3)) | ((salesdf.dff >= 60) & (salesdf.rat > 2.5)) | ((salesdf.dff >= 80) & (salesdf.rat > 2.5))).astype(int)
 		salesdf['rat'] = salesdf.pred / salesdf.price
 		salesdf['dff'] = salesdf.pred - salesdf.price
-		salesdf['exclude_2'] = (((salesdf.dff >= 20) & (salesdf.rat > 4)) | ((salesdf.dff >= 40) & (salesdf.rat > 3)) | ((salesdf.dff >= 60) & (salesdf.rat > 2.5)) | ((salesdf.dff >= 80) & (salesdf.rat > 2))).astype(int)
+		salesdf['exclude_2'] = (((salesdf.dff >= 20) & (salesdf.rat > 4)) | ((salesdf.dff >= 40) & (salesdf.rat > 3)) | ((salesdf.dff >= 60) & (salesdf.rat > 2.5)) | ((salesdf.dff >= 80) & (salesdf.rat > 2.5))).astype(int)
 		salesdf['exclude'] = (salesdf.exclude_1 + salesdf.exclude_2).apply(lambda x: int(x>0))
 		print(salesdf.exclude_1.mean())
 		print(salesdf.exclude_2.mean())
@@ -744,5 +776,5 @@ def train_model(check_exclude, supplement_with_listings):
 
 # train_model(True, False)
 # train_model(False, False)
-# train_model(False, True)
+train_model(False, True)
 

update.py

@@ -5,6 +5,8 @@ from time import sleep
 from copy import deepcopy
 import random
 
+from utils import clean_name
+
 os.chdir('/Users/kellenblumberg/git/nft-deal-score')
 os.environ['PATH'] += os.pathsep + '/Users/kellenblumberg/shared/'
 
@@ -14,8 +16,6 @@ import solana_model as sm
 
 browser = webdriver.Chrome()
 
-
-
 if False:
 	alerted = []
 	for i in range(10):
@@ -50,6 +50,7 @@ if False:
 ssn.scrape_recent_smb_sales(browser)
 ssn.scrape_recent_sales()
 ld.add_terra_sales()
+ld.add_solana_sales()
 
 # update listings
 ssn.scrape_randomearth(browser)
@@ -93,24 +94,29 @@ def add_model_sales():
 
 def update_token_ids():
 	tokens = pd.read_csv('./data/tokens.csv')
+	tokens['collection'] = tokens.collection.apply(lambda x: clean_name(x))
+	tokens = tokens.drop_duplicates(subset=['collection','token_id'], keep='last')
+	tokens.to_csv('./data/tokens.csv', index=False)
 	tokens.groupby('collection').token_id.count()
 	tokens['tmp'] = tokens.token_id.apply(lambda x: (int(float(x))) )
 	tokens[tokens.token_id == 223838831896070003935953339589523931136]
 	tokens[tokens.collection=='Galactic Punks']
 	tokens['token_id'] = tokens.token_id.apply(lambda x: str(int(float(x))) )
-	tokens['tmp'] = tokens.token_id.apply(lambda x: len(x) )
+	# tokens['tmp'] = tokens.token_id.apply(lambda x: len(x) )
 	tokens.tmp.max()
 	# df[ (df.collection == 'Pesky Penguins') & (df.token_id == '3362') ]
 	tokens[ (tokens.collection == 'Pesky Penguins') & (tokens.token_id == '3362') ]
 	tokens[ (tokens.collection == 'Pesky Penguins') & (tokens.token_id == 3362) ]
 	# df.token_id.unique()
-	c = 'listings'
+	c = 'sales'
+	# for c in [ 'listings' ]:
 	for c in [ 'attributes','sales','listings' ]:
 		print(c)
 		df = pd.read_csv('./data/{}.csv'.format(c))
+		df['collection'] = df.collection.apply(lambda x: clean_name(x))
 		# df.token_id.unique()
 		df = df[df.token_id.notnull()]
-		df['token_id'] = df.token_id.apply(lambda x: None if x == 'nan' else str(int(float(x))) )
+		# df['token_id'] = df.token_id.apply(lambda x: None if x == 'nan' else str(int(float(x))) )
 		df['token_id'] = df.token_id.apply(lambda x: None if x == 'nan' else str(int(float(x))) )
 		# df['tmp'] = df.token_id.apply(lambda x: (str(x)[:5]))
 		df['tmp'] = df.token_id.apply(lambda x: x[:10] )

utils.py

@@ -1,3 +1,4 @@
+import re
 import pandas as pd
 
 
@@ -10,11 +11,27 @@ clean_names = {
 	,'meerkatmillionaires': 'Meerkat Millionaires'
 	,'boryokudragonz': 'Boryoku Dragonz'
 	,'degods': 'DeGods'
+	,'lunabulls': 'LunaBulls'
+	# ,'stonedapecrew': 'Stoned Ape Crew'
 }
 
+def clean_token_id(df):
+	tokens = pd.read_csv('./data/tokens.csv')
+	df['collection'] = df.collection.apply(lambda x: clean_name(x))
+	df['token_id'] = df.token_id.apply(lambda x: re.sub('"', '', x) if type(x)==str else x )
+	df['tmp'] = df.token_id.apply(lambda x: x[:10] )
+	tokens['tmp'] = tokens.token_id.apply(lambda x: x[:10] )
+	df = df.merge(tokens[['collection','tmp','clean_token_id']], how='left', on=['collection','tmp'])
+	df['token_id'] = df.clean_token_id.fillna(df.token_id)
+	df['token_id'] = df.token_id.astype(int)
+	del df['tmp']
+	del df['clean_token_id']
+	return(df)
+
 def clean_name(name):
-	if name.lower() in clean_names.keys():
-		return(clean_names[name.lower()])
+	x = re.sub('-', '', name).lower()
+	if x in clean_names.keys():
+		return(clean_names[x])
 	name = re.sub('-', ' ', name.title())
 	return(name)
 
@@ -27,7 +44,7 @@ def merge(left, right, on=None, how='inner', ensure=True, verbose=True, message
 		print('{} -> {}'.format(len(left), len(df)))
 		cur = left.merge(right, on=on, how='left')
 		cols = set(right.columns).difference(set(left.columns))
-		print(cols)
+		# print(cols)
 		if ensure:
 			col = list(cols)[0]
 			missing = cur[cur[col].isnull()]

viz/server.R

@@ -408,7 +408,7 @@ server <- function(input, output, session) {
 		selectInput(
 			inputId = 'collectionname'
 			, label = NULL
-			, selected = 'DeGods'
+			, selected = 'Stoned Ape Crew'
 			, choices = choices
 			, width = "100%"
 		)
@@ -454,10 +454,14 @@ server <- function(input, output, session) {
 				, ifelse(
 					selected == 'Solana Monkey Business'
 					, selected
+					, ifelse(
+						selected == 'Stoned Ape Crew'
+						, 'Stoned Ape'
 						, substr(selected, 1, nchar(selected) - 1)
 					)
 				)
 			)
+		)
 		if (!is.na(id)) {
 			t <- paste0(title," #", id)
 		}