97 lines
2.5 KiB
YAML
97 lines
2.5 KiB
YAML
base:
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data:
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groups:
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filepath_or_buffer:
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- 'datasets/stratigis/groupsWithHighRatings5.txt'
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- 'datasets/stratigis/groupsWithModerateRatings5untested.txt'
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testdata:
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filepath_or_buffer: 'datasets/fake_data.csv'
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sep: '\t'
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skiprows: 1
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names: [ 'userId', 'itemId', 'rating', 'timestamp']
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test:
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filepath_or_buffer: 'datasets/stratigis/ratings.csv'
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sep: ','
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skiprows: 1
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names: [ 'userId', 'itemId', 'rating', 'timestamp']
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ml32m:
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filepath_or_buffer: 'datasets/ml-32m/ratings.csv'
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sep: ','
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skiprows: 1
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names: [ 'userId', 'itemId', 'rating', 'timestamp']
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ml100k:
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filepath_or_buffer: 'datasets/ml-100k/u.data'
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sep: '\t'
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skiprows: 0
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names: [ 'userId', 'itemId', 'rating', 'timestamp']
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ml1m:
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filepath_or_buffer: 'datasets/ml-1m/ratings.dat'
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sep: '::'
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names: [ 'userId', 'itemId', 'rating', 'timestamp' ]
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tags:
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tags_file: 'datasets/stratigis/tags.csv'
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model:
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gmf:
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learning_rate: 0.005
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weight_decay: 0.0000001
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latent_dim: 8
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epochs: 30
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num_negative: 10
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batch_size: 1024
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cuda: False
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optimizer_name: 'adam'
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mlp:
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learning_rate: 0.005
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weight_decay: 0.0000001
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latent_dim: 8
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epochs: 30
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num_negative: 10
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batch_size: 1024
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cuda: False
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optimizer_name: 'adam'
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als:
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learning_rate: 0.1
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latent_dim: 100
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epochs: 10
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reg_term: 0.001
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bpr:
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learning_rate: 0.01
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latent_dim: 100
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epochs: 10
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reg_term: 0.001
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emf:
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learning_rate: 0.01
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reg_term: 0.001
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expl_reg_term: 0.0
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latent_dim: 80
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epochs: 10
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positive_threshold: 3
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knn: 10
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mf:
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learning_rate: 0.01
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reg_term: 0.001
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expl_reg_term: 0.0
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latent_dim: 80
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epochs: 10
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positive_threshold: 3
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knn: 10
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autoencoder:
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learning_rate: 0.005
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weight_decay: 0.0000001
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hidden_layer_features: 8
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epochs: 30
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cuda: False
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optimizer_name: 'adam'
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positive_threshold: 3
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knn: 10
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expl: true
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explainer:
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lore4groups:
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n_similar_for_tree: 500
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rating_threshold_for_like: 2.8 # Increased significantly
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max_tree_depth: 5
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top_n_labels: 5000
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min_rating_for_history: 1.0
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similarity_threshold: 0.0000 # Added stricter similarity
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tree_stop_threshold: 0.1 # Make sure this is present, as per the paper |