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main.py
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main.py
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import pandas as pd
from data_clean import clean_data
from doc2vec_multithread import make_vectors
from pca_tsne import pca,tsne
from kmeans_cluster import cluster
import json
from image_compare import create_similar
import warnings
warnings.simplefilter("ignore")
def final(similar):
a=pd.read_csv('tops.csv',usecols=['productId','mrp','size','color','productBrand','sellerName'])
f=open('result.txt','r')
data=json.loads(f.read())
prodid=a['productId'].tolist()
mrp=a['mrp'].tolist()
size=a['size'].tolist()
color=a['color'].tolist()
brand=a['productBrand'].tolist()
seller=a['sellerName'].tolist()
del a
for i in range(0,len(similar)):
conf=5
a=similar[i]
index1=a[0]
index2=a[1]
if (((mrp[index1]*0.8)<mrp[index2]<(mrp[index1]*1.2))):
conf=conf+1
if (size[index1]==size[index2]):
conf=conf+1
if (color[index1]==color[index2]):
conf=conf+1
if (brand[index1]==brand[index2]):
conf=conf+1
if (seller[index1]==seller[index2]):
conf=conf+1
if conf>=9:
prod1=str(prodid[index1])
prod2=str(prodid[index2])
if (prod1 in data.keys()):
data[prod1].append([prod2])
elif (prod2 in data.keys()):
data[prod2].append([prod1])
else:
data[prod1]=[prod2]
f=open('result.txt','w')
f.write(json.dumps(data))
f.close()
if __name__ == "__main__":
clean_data()
a,b,c,d=make_vectors()
pca=pca(a,b,c,d)
tsne=tsne(pca)
clusters=cluster(tsne)
similar=create_similar(cluster)
final(similar)