def main():
inventory = []
huur, verhuurd, koop, verkocht = get_url_list.get_aparts(60, 230, 390, 760)
url_list = []
#Compile the four lists into one
for item in huur:
url_list.append([item, "huur"])
for item in verhuurd:
url_list.append([item, "verhurrd"])
for item in koop:
url_list.append([item, "koop"])
for item in verkocht:
url_list.append([item, "verkocht"])
print "Done getting all the urls!"
new_url_list = get_chunks(url_list, 1000)
threads = []
for i in range(len(new_url_list)):
t = threading.Thread(target = get_inventory, args=(inventory, new_url_list[i], ))
threads.append(t)
t.start()
for t in threads:
t.join()
print "Done getting all the features!"
threading_output = unicodecsv.writer(open("FundaInventoryLatestExpanded.csv", "wb"), encoding='utf-8', delimiter='|')
sample = get_features.get_features(sample_url)
sample["type"] = "sample"
expand_features.expand_features(sample)
threading_output.writerow(sample.keys())
for row in inventory:
threading_output.writerow(row.values())
print "Done writing output file!"
def get_inventory(inventory, url_list):
# failed_urls = unicodecsv.writer(open("failed_urls_all_threading_20150516.csv", "wb"), encoding='utf-8', delimiter='|')
count = 0
#Loop through all huur and verhuurd urls
for row in url_list:
try:
apart = get_features.get_features(row[0])
apart["type"] = row[1]
expand_features.expand_features(apart)
inventory.append(apart)
except:
print "error url:" + row[0]
print sys.exc_info()
# failed_urls.writerow([row[0], sys.exc_info()])
count += 1
if count % 100 == 0:
print "We have gathered properties: " + str(count)
time.sleep(2)
from load_data import load_data
from write_submission import write_submission
import numpy as np
#from matplotlib import pyplot as plt
from expand_features import expand_features
def rmse(predictions, targets):
return np.sqrt(((predictions - targets)**2).mean())
# load data
[Xtr, Ytr, Xte, testID] = load_data()
# expand features
Xtr_expanded = expand_features(Xtr)
Xte_expanded = expand_features(Xte)
print('Xtr shape', Xtr_expanded.shape, 'Ytr shape', Ytr.shape, 'Xte shape',
Xte.shape)
clf = linear_model.RidgeCV(alphas=[1e-3, 1e-2, 1e-1],
normalize=True,
store_cv_values=True).fit(Xtr_expanded, Ytr)
ridge_preds = clf.predict(Xtr_expanded)
print('RMSE Ridge:', rmse(ridge_preds, Ytr))
print(clf.alpha_)
# clf = kernel_ridge.KernelRidge(alpha=0.01)
# clf.fit(Xtr_expanded, Ytr)
# kridge_preds = clf.predict(Xtr_expanded)
import csv
import get_features
import expand_features
import unicodecsv
sample_url = "http://www.funda.nl/koop/amsterdam/appartement-49453167-van-boetzelaerstraat-34-2/"
header_output = unicodecsv.writer(open("expanded_headers.csv", "wb"), encoding='utf-8', delimiter='|')
sample = get_features.get_features(sample_url)
sample["type"] = "sample"
expand_features.expand_features(sample)
header_output.writerow(sample.keys())
