def predict(rating_dic):
df_clean = pd.read_csv("dataset_clean.csv")
#######################
# Fit surprise model
#######################
final_model = KNNBaseline(k=60, min_k=2, sim_options={'name': 'pearson_baseline', 'user_based': True})
new_user_id = max(df_clean["userID"]) + 1
ratings = np.array(list(rating_dic.values()))
rated_mask = ratings != None
ratings = ratings[rated_mask]
items = np.array(list(rating_dic.keys()))[rated_mask]
user = np.ones(len(items), dtype="int") * new_user_id
new_user_df = pd.DataFrame({"userID": user, "itemID": items, "rating": ratings})
total_df = df_clean.append(new_user_df)
# A reader is still needed but only the rating_scale param is requiered.
reader = Reader(rating_scale=(0, 10))
# The columns must correspond to user id, item id and ratings (in that order).
new_trainset = Dataset.load_from_df(total_df, reader).build_full_trainset()
## Fit the best model
final_model.fit(new_trainset)
predicted_ratings = []
for nootropic in nootropics_list:
predicted_ratings.append(final_model.predict(new_user_id, nootropic).est)
item_baselines = final_model.default_prediction() + final_model.compute_baselines()[
1] # mean rating + item baseline ?
result_df = pd.DataFrame(
{"nootropic": nootropics_list, "predicted_rating": predicted_ratings, "baseline_rating": item_baselines})
nootropics_without_ratings = [nootropic for nootropic in nootropics_list if (nootropic not in rating_dic.keys())]
new_result_df = result_df[result_df["nootropic"].isin(nootropics_without_ratings)]
return new_result_df.sort_values("predicted_rating", ascending=False, ignore_index=True)
def get_item_baseline():
df_clean = pd.read_csv("dataset_clean.csv")
#######################
# Fit surprise model
#######################
final_model = KNNBaseline(k=60, min_k=2, sim_options={'name': 'pearson_baseline', 'user_based': True})
total_df = df_clean
# A reader is still needed but only the rating_scale param is requiered.
reader = Reader(rating_scale=(0, 10))
# The columns must correspond to user id, item id and ratings (in that order).
new_trainset = Dataset.load_from_df(total_df, reader).build_full_trainset()
## Fit the best model
final_model.fit(new_trainset)
item_baselines = final_model.default_prediction() + final_model.compute_baselines()[
1] # mean rating + item baseline ?
return pd.DataFrame({"nootropic": nootropics_list, "item_baselines":item_baselines})
