print("Hidden Lyers ", HIDDEN_LAYERS)
print("PAST_DAYS ", PAST_DAYS)
print("TESTING_PERCENTAGE ", TESTING_PERCENTAGE)
print("Epochs Num ", n_epochs)
print("Normalization ", normalization)
Experiment_KEY = 'Hidden_' + str(HIDDEN_LAYERS) + '_DAYS_' + str(
PAST_DAYS) + '_nepoch_' + str(n_epochs) + '_normalization_' + str(
normalization) + '_features_' + str(n_in)
print(Experiment_KEY)
cluster_subset = [1, 5, 11, 19]
airports_clusters = get_clusters()
tst_indexes = get_test_indexes()
airports, airports_data = parse_data(normalization=normalization)
real_test_data = get_real_test_target(tst_indexes)
airports_clusters_data = {}
airports_all_data = {
'features_trn': [],
'targets_trn': [],
'features_tst': [],
'targets_tst': [],
'targets_tst_real': []
}
for air_id in airports:
cluster_id = airports_clusters[air_id]
if cluster_id in cluster_subset:
data = []
for rec in airports_data[air_id]:
data.append(rec)
n_in = 18
n_out = 2
# normalization='zcore'
print("PAST_DAYS ", PAST_DAYS)
print("TESTING_PERCENTAGE ", TESTING_PERCENTAGE)
print("Normalization ", normalization)
cluster_subset=[1,5,11,19]
airports_clusters = get_clusters()
err_counter = 0
err_mintmp = 0
err_maxtmp = 0
tst_indexes = get_test_indexes()
airports, airports_data = parse_data(normalization=normalization)
real_test_data=get_real_test_target(tst_indexes)
airports_clusters_data = {}
airports_all_data = {'features_trn': [], 'targets_trn': [], 'features_tst': [], 'targets_tst': [],'targets_tst_real':[]}
air_count=0
for air_id in airports:
cluster_id = airports_clusters[air_id]
if cluster_id in cluster_subset:
air_count+=1
data = []
for rec in airports_data[air_id]:
data.append(rec)
features_trn, targets_trn, features_tst, targets_tst ,targets_tst_real = divide_to_sequence(data, tst_indexes, feature_num=n_in,
PAST_DAYS=PAST_DAYS,real_test_data=real_test_data)
import numpy as np
from parse_data import *
from download_images import *
from update_prices import *
from add_features import *
labels = ["idd", "city", "exhibition", "artist", "title", "price", "sold", "avg_estimate", "signed", "area", \
"volume", "year_created", "auction_lot", "auction_house", "auction_date", \
"avg_log_price_sold_before", "median_price_sold_before", "num_artworks", \
"num_artists", "sale_rate_before", "img_url", "volatility_returns_before", "mean_returns_before", "skew_prices_before",
"medium", "lots_per_artist", "num_artworks_ratio"]
cities = ["New York", "London", "Paris"]
for city in cities:
print(city)
parse_data(city, labels)
update_prices(city)