File size: 1,836 Bytes
33e1108 ce1fcc3 33e1108 ce1fcc3 33e1108 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | from surprise import SVDpp
from surprise import Dataset
from surprise import Reader
import streamlit as st
import pandas as pd
import pickle
def predict():
with open('./model.pkl', 'rb') as f:
model = pickle.load(f)
titles = pd.read_csv('./amazon-books-titles.csv')
predictions = []
for row in titles.iterrows():
predictions.append({'title': row[1]['title'], 'rating': model.predict(x, row[1]['asin']).est})
sorted_predictions = sorted(predictions, key=lambda p: -p['rating'])
st.write(pd.DataFrame(sorted_predictions))
def train_model():
books = pd.read_csv('./amazon-books.zip')
titles = pd.read_csv('./amazon-books-titles.csv')
current_user_book_ids = titles[titles['title'].isin([t.strip() for t in y])]['asin'].values
current_user_ratings = pd.DataFrame({
'reviewerID': [x] * len(current_user_book_ids),
'asin': current_user_book_ids,
'overall': [5] * len(current_user_book_ids)
})
st.write(current_user_ratings)
books = books.append(current_user_ratings)
data = Dataset.load_from_df(books[['reviewerID', 'asin', 'overall']], Reader(line_format='user item rating', rating_scale=(1, 5)))
trainset = data.build_full_trainset()
best_params = {
'n_epochs': 15,
'lr_all': 0.004760245463611792,
'reg_all': 0.40040712444861504,
'random_state': 42
}
algo = SVDpp(**best_params)
algo.fit(trainset)
with open('./model.pkl', 'wb') as f:
pickle.dump(algo, f)
with open('./titles.txt', 'r', encoding='utf8') as f:
options = f.readlines()
x = st.text_input('uid')
y = st.multiselect('Select book', options)
btn = st.button('Submit', on_click=train_model)
btn2 = st.button('Predict', on_click=predict)
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