ml-100k results

Author: paulorvdc

config_files/test_entity2rec.yml

@@ -1,20 +1,30 @@
 experiment:
   dataset:
-    name: ml-100k
+    # name: ml-100k
+    # item: # infos related to item dataset (mandatory, at least item_id)
+    #   path: datasets/ml-100k/processed/item.csv
+    #   extra_features: [movie_year, movie_title] # features(columns) beside item_id to be used
+    # user: # mandatory (at least user_id)
+    #   path: datasets/ml-100k/processed/user.csv
+    #   extra_features: [gender, occupation] # features beside user_id
+    # ratings: # mandatory (at least [user_id, item_id, rating])
+    #   path: datasets/ml-100k/processed/rating.csv
+    #   timestamp: True
+    # enrich:
+    #   map_path: datasets/ml-100k/processed/map.csv
+    #   enrich_path: datasets/ml-100k/processed/enriched.csv
+    #   remove_unmatched: False
+    #   properties: [subject, director, abstract]
+    name: ml-1m
     item: # infos related to item dataset (mandatory, at least item_id)
-      path: datasets/ml-100k/processed/item.csv
+      path: datasets/ml-1m/processed/item.csv
       extra_features: [movie_year, movie_title] # features(columns) beside item_id to be used
     user: # mandatory (at least user_id)
-      path: datasets/ml-100k/processed/user.csv
+      path: datasets/ml-1m/processed/user.csv
       extra_features: [gender, occupation] # features beside user_id
     ratings: # mandatory (at least [user_id, item_id, rating])
-      path: datasets/ml-100k/processed/rating.csv
+      path: datasets/ml-1m/processed/rating.csv
       timestamp: True
-    enrich:
-      map_path: datasets/ml-100k/processed/map.csv
-      enrich_path: datasets/ml-100k/processed/enriched.csv
-      remove_unmatched: False
-      properties: [subject, director, abstract]
 
   preprocess:
     # - method: filter_by_rating
@@ -88,6 +98,6 @@ experiment:
     metrics: [MAP, nDCG]
 
   report:
-    file: "experiment_results/e2rec_runs/ml-100k_e2rec_enriched.csv"
+    file: "experiment_results/e2rec_runs/ml-1m_e2rec.csv"
     execution_times:
-      file: "experiment_results/e2rec_runs/ml-100k_e2rec_enriched_times.csv"
+      file: "experiment_results/e2rec_runs/ml-1m_e2rec_times.csv"

experiment_results/e2rec_runs/ml-100k_e2rec.csv

@@ -0,0 +1,2 @@
+model,fold-1_MAP@5,fold-1_nDCG@5,fold-2_MAP@5,fold-2_nDCG@5,fold-3_MAP@5,fold-3_nDCG@5,fold-4_MAP@5,fold-4_nDCG@5,fold-5_MAP@5,fold-5_nDCG@5,MAP@5_mean,MAP@5_std,nDCG@5_mean,nDCG@5_std
+"Entity2Rec;embedding_model=deepwalk_based;embedding_model_kwargs={'config': {'save_weights': True}, 'parameters': {'walk_len': 10, 'p': 1.0, 'q': 1.0, 'n_walks': 50, 'embedding_size': 64, 'epochs': 1}};run_all=False;workers=32;iterations=1;collab_only=False;content_only=False",0.009271827500883704,0.013716478827020675,0.007483209614704843,0.011744302348328722,0.010876634853305056,0.017536916632190015,0.007274655355249204,0.011287936190241333,0.005927889713679745,0.010301140674257251,0.00816684340756451,0.001722801663145888,0.012917354934407599,0.0025633799618442906

experiment_results/e2rec_runs/ml-100k_e2rec_enriched.csv

@@ -0,0 +1,2 @@
+model,fold-1_MAP@5,fold-1_nDCG@5,fold-2_MAP@5,fold-2_nDCG@5,fold-3_MAP@5,fold-3_nDCG@5,fold-4_MAP@5,fold-4_nDCG@5,fold-5_MAP@5,fold-5_nDCG@5,MAP@5_mean,MAP@5_std,nDCG@5_mean,nDCG@5_std
+"Entity2Rec;embedding_model=deepwalk_based;embedding_model_kwargs={'config': {'save_weights': True}, 'parameters': {'walk_len': 10, 'p': 1.0, 'q': 1.0, 'n_walks': 50, 'embedding_size': 64, 'epochs': 1}};run_all=False;workers=32;iterations=1;collab_only=False;content_only=False",0.008308589607635206,0.01336343908887122,0.008579003181336161,0.014144473487556833,0.007553022269353129,0.011679326451523303,0.00562919759632379,0.00874604699575104,0.007963944856839872,0.012184593261743412,0.007606751502297632,0.0010468088103881643,0.012023575857089162,0.0018536488304715785

experiment_results/e2rec_runs/ml-100k_e2rec_enriched_times.csv

@@ -0,0 +1,2 @@
+model,fold-1_execution_time,fold-2_execution_time,fold-3_execution_time,fold-4_execution_time,fold-5_execution_time,execution_time_mean,execution_time_std
+"Entity2Rec;embedding_model=deepwalk_based;embedding_model_kwargs={'config': {'save_weights': True}, 'parameters': {'walk_len': 10, 'p': 1.0, 'q': 1.0, 'n_walks': 50, 'embedding_size': 64, 'epochs': 1}};run_all=False;workers=32;iterations=1;collab_only=False;content_only=False (CPU: AMD EPYC 7502P 32-Core Processor; RAM: 94GB; GPUs: ['NVIDIA A2'])",53289.055238485336,58117.099523067474,75657.94412708282,80494.51433563232,75865.19587182999,68684.7618192196,10847.648839329928

experiment_results/e2rec_runs/ml-100k_e2rec_times.csv

@@ -0,0 +1,2 @@
+model,fold-1_execution_time,fold-2_execution_time,fold-3_execution_time,fold-4_execution_time,fold-5_execution_time,execution_time_mean,execution_time_std
+"Entity2Rec;embedding_model=deepwalk_based;embedding_model_kwargs={'config': {'save_weights': True}, 'parameters': {'walk_len': 10, 'p': 1.0, 'q': 1.0, 'n_walks': 50, 'embedding_size': 64, 'epochs': 1}};run_all=False;workers=32;iterations=1;collab_only=False;content_only=False (CPU: AMD EPYC 7502P 32-Core Processor; RAM: 94GB; GPUs: ['NVIDIA A2'])",61841.76803421974,74184.3076198101,76555.75235724449,78322.35031342506,76628.20624065399,73506.47691307068,5979.3109809194375