Distil bart 20news classification central learning

combine_metrics.py

@@ -0,0 +1,52 @@
+import os
+import pandas as pd
+from pathlib import Path
+
+def combine_metrics():
+    # Directory containing the CSV files
+    results_dir = Path("results")
+
+    # List all CSV files in the results directory
+    csv_files = list(results_dir.glob("training_metrics_*.csv"))
+
+    if not csv_files:
+        print("No CSV files found in the results directory.")
+        return
+
+    print(f"Found {len(csv_files)} CSV files to combine.")
+
+    # Read and combine all CSV files
+    all_dfs = []
+    for file in csv_files:
+        try:
+            df = pd.read_csv(file)
+            # Add a column to track the source file
+            df['source_file'] = file.name
+            all_dfs.append(df)
+            print(f"Processed {file.name} with {len(df)} rows")
+        except Exception as e:
+            print(f"Error processing {file}: {e}")
+
+    if not all_dfs:
+        print("No valid CSV files to combine.")
+        return
+
+    # Combine all DataFrames
+    combined_df = pd.concat(all_dfs, ignore_index=True)
+
+    # Sort by round, client_id, and epoch
+    if 'round' in combined_df.columns and 'client_id' in combined_df.columns and 'epoch' in combined_df.columns:
+        combined_df = combined_df.sort_values(by=['round', 'client_id', 'epoch'])
+
+    # Save the combined DataFrame to a new CSV file
+    output_file = results_dir / "combined_training_metrics.csv"
+    combined_df.to_csv(output_file, index=False)
+
+    print(f"\nCombined metrics saved to: {output_file}")
+    print(f"Total rows: {len(combined_df)}")
+    print("\nColumn names:", combined_df.columns.tolist())
+    print("\nSample data:")
+    print(combined_df.head())
+
+if __name__ == "__main__":
+    combine_metrics()

requirements.txt

@@ -0,0 +1,11 @@
+torch>=1.12.0
+transformers>=4.25.0
+numpy>=1.22.0
+scikit-learn>=1.0.2
+tqdm>=4.64.0
+pandas>=1.4.0
+matplotlib>=3.5.0
+tensorboard>=2.10.0
+wandb>=0.13.0
+sentencepiece>=0.1.96
+protobuf>=3.20.0

results_bart_20news_centralized/training_metrics_centralized_20250806_111734.csv

@@ -0,0 +1,46 @@
+round,client_id,epoch,phase,loss,accuracy,precision,recall,f1,source_file
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results_distilbart_20news_centralized/best_model/config.json

@@ -0,0 +1,110 @@
+{
+  "_name_or_path": "facebook/bart-base",
+  "activation_dropout": 0.1,
+  "activation_function": "gelu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": false,
+  "architectures": [
+    "BartForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "classif_dropout": 0.1,
+  "classifier_dropout": 0.0,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 6,
+  "decoder_start_token_id": 2,
+  "dropout": 0.1,
+  "early_stopping": true,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 6,
+  "eos_token_id": 2,
+  "forced_bos_token_id": 0,
+  "forced_eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
+    "17": "LABEL_17",
+    "18": "LABEL_18",
+    "19": "LABEL_19"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_12": 12,
+    "LABEL_13": 13,
+    "LABEL_14": 14,
+    "LABEL_15": 15,
+    "LABEL_16": 16,
+    "LABEL_17": 17,
+    "LABEL_18": 18,
+    "LABEL_19": 19,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "max_position_embeddings": 1024,
+  "model_type": "bart",
+  "no_repeat_ngram_size": 3,
+  "normalize_before": false,
+  "normalize_embedding": true,
+  "num_beams": 4,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "problem_type": "single_label_classification",
+  "scale_embedding": false,
+  "task_specific_params": {
+    "summarization": {
+      "length_penalty": 1.0,
+      "max_length": 128,
+      "min_length": 12,
+      "num_beams": 4
+    },
+    "summarization_cnn": {
+      "length_penalty": 2.0,
+      "max_length": 142,
+      "min_length": 56,
+      "num_beams": 4
+    },
+    "summarization_xsum": {
+      "length_penalty": 1.0,
+      "max_length": 62,
+      "min_length": 11,
+      "num_beams": 6
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.27.4",
+  "use_cache": true,
+  "vocab_size": 50265
+}