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code to loop over 25 top dfs #36

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code to loop over 25 top dfs #36

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24 changes: 14 additions & 10 deletions eCommerce_package/functions_reco_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,9 @@
import numpy as np

def get_data_2():
path_df1 = '../data/Oct19_20/latent_df_1_with_100pct_data_50_svd_components_oct19.csv'
path_df2 = '../data/Oct19_20/latent_df_2_with_100pct_data_100_svd_components_oct19.csv'
path_df3 = '../data/Oct19_20/X_meta_with_100pct_data_oct19.csv'
path_df1 = './data/latent_dfs/latent_df_1_with_100pct_data_50_svd_components_oct19.csv'
path_df2 = './data/latent_dfs/latent_df_2_with_100pct_data_100_svd_components_oct19.csv'
path_df3 = './data/latent_dfs/X_meta_with_100pct_data_oct19.csv'
df_1 = pd.read_csv(path_df1,index_col=[0])
df_2 = pd.read_csv(path_df2,index_col=[0])
meta_df = pd.read_csv(path_df3, index_col=[0])
Expand Down Expand Up @@ -66,14 +66,18 @@ def top_n_products(rec_df, meta_df, n=10, ranking='hybrid'):

if __name__ == '__main__':
try:
# 1003363 1002544
product_id= 1000978
top25 = pd.read_csv("./data/top25.csv")
df_1, df_2, meta_df = get_data_2()
# print("Starting recommendation model")
rec_df = recommendation_model(product_id, df_1, df_2, weight_features = 0.8)
# print("Starting product sorting")
new_df = top_n_products(rec_df, meta_df, n=10, ranking='features')
# print(new_df.head())

for product_id in top25['product_id'].to_list():
print(f"Starting recommendation model for product {product_id}")
rec_df = recommendation_model(product_id, df_1, df_2, weight_features = 0.8)

print("Starting product sorting")
new_df = top_n_products(rec_df, meta_df, n=10, ranking='hybrid')

print(f"Saving {product_id} to csv")
new_df.to_csv(f"./data/top25_dfs/{product_id}.csv")

except:
import ipdb, traceback, sys
Expand Down