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config.py
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config.py
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import os
os.environ["KERAS_BACKEND"] = "torch"
from utils import *
config = {
"ml20m": (
Dataset("MovieLens20M"),
{
"filename": "_datasets/ml20m/ratings.csv",
"item_id_name": "movieId",
"user_id_name": "userId",
"value_name": "rating",
"timestamp_name": "timestamp",
"min_value_to_keep": 4.0,
"user_min_support": 5,
"set_all_values_to": 1.0,
"num_test_users": 10000,
"random_state": 42,
"load_previous_splits": False,
"items_raw_data": """pd.read_feather("_datasets/ml20m/item_text_descriptions.feather")""",
"items_item_id_name": "movieId",
"items_preprocess": """f'{row.llama31_description}'""",
"coldstart_fraction": 0.1,
"num_coldstart_items": 2000,
},
),
"goodbooks": (
Dataset("Goodbooks-10k"),
{
"raw_data": """pd.read_csv("_datasets/goodbooks/ratings.csv")""",
"user_id_name": "user_id",
"item_id_name": "book_id",
"value_name": "rating",
"min_value_to_keep": 4.0,
"user_min_support": 5,
"item_min_support": 1,
"set_all_values_to": 1.0,
"num_test_users": 2500,
"random_state": 42,
"load_previous_splits": False,
"items_raw_data": """pd.read_feather("_datasets/goodbooks/item_text_descriptions.feather")""",
"items_item_id_name": "book_id",
"items_preprocess": """f'{row.llama31_description}'""",
"coldstart_fraction": 0.1,
"num_coldstart_items": 2000,
},
),
"amazon-books": (
Dataset("Amazon books"),
{
"raw_data": """pd.read_feather("_datasets/amazbooks/ratings.feather")""",
"value_name": "rating",
"item_id_name": "asin",
"user_id_name": "uid",
"timestamp_name": "timestamp",
"min_value_to_keep": 4.0,
"user_min_support": 5,
"item_min_support": 1,
"set_all_values_to": 1.0,
"num_test_users": 10000,
"random_state": 42,
"max_steps": 1000,
"load_previous_splits": False,
"items_raw_data": """pd.read_feather("_datasets/amazbooks/item_text_descriptions.feather")""",
"items_item_id_name": "asin",
"items_preprocess": """f'{row.llama31_description}'""",
"coldstart_fraction": 0.1,
"num_coldstart_items": 2000,
},
),
}