From 23851deba881146aa70fd9913c2db62e798022ed Mon Sep 17 00:00:00 2001 From: Ouael SAHBI Date: Mon, 24 Nov 2025 10:46:20 -0500 Subject: [PATCH 1/2] Adding the documentation --- .gitignore | 2 - .readthedocs.yml | 35 + MEDfl.egg-info/PKG-INFO | 35 +- MEDfl/rw/client.py | 684 ++--- MEDfl/rw/model.py | 52 +- MEDfl/rw/server.py | 19 +- MEDfl/rw/strategy.py | 565 ++-- build/lib/MEDfl/LearningManager/__init__.py | 13 + build/lib/MEDfl/LearningManager/client.py | 150 + .../lib/MEDfl/LearningManager/dynamicModal.py | 287 ++ .../LearningManager/federated_dataset.py | 60 + build/lib/MEDfl/LearningManager/flpipeline.py | 192 ++ build/lib/MEDfl/LearningManager/model.py | 224 ++ build/lib/MEDfl/LearningManager/params.yaml | 14 + .../MEDfl/LearningManager/params_optimiser.py | 442 +++ build/lib/MEDfl/LearningManager/plot.py | 229 ++ build/lib/MEDfl/LearningManager/server.py | 181 ++ build/lib/MEDfl/LearningManager/strategy.py | 82 + build/lib/MEDfl/LearningManager/utils.py | 331 ++ 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create mode 100644 docs/source/index.rst create mode 100644 docs/source/modules.rst create mode 100644 docs/source/tutorials.rst diff --git a/.gitignore b/.gitignore index 58eb08c..497c1c7 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,4 @@ __pycache__/ MEDfl.egg-info/ fl-env/ dist/ -build/ notebooks/ -docs/ diff --git a/.readthedocs.yml b/.readthedocs.yml new file mode 100644 index 0000000..f5c674f --- /dev/null +++ b/.readthedocs.yml @@ -0,0 +1,35 @@ +version: 2 + +sphinx: + configuration: docs/source/conf.py + +python: + version: "3.9" + install: + - method: pip + path: . + + + + + + + + + + + + + + + + + + + + + + + + + - requirements: requirements.txt diff --git a/MEDfl.egg-info/PKG-INFO b/MEDfl.egg-info/PKG-INFO index cf5deb1..f9055c3 100644 --- a/MEDfl.egg-info/PKG-INFO +++ b/MEDfl.egg-info/PKG-INFO @@ -1,6 +1,6 @@ -Metadata-Version: 2.1 +Metadata-Version: 2.4 Name: MEDfl -Version: 2.0.5 +Version: 2.0.5.dev4 Summary: Python Open-source package for simulating federated learning and differential privacy Home-page: https://github.com/MEDomics-UdeS/MEDfl Author: MEDomics consortium @@ -17,6 +17,37 @@ Classifier: Programming Language :: Python :: 3.9 Requires-Python: >=3.8,<3.13 Description-Content-Type: text/markdown License-File: LICENSE +Requires-Dist: flwr~=1.18.0 +Requires-Dist: matplotlib~=3.6.3 +Requires-Dist: numpy~=1.26.4 +Requires-Dist: opacus~=1.5.3 +Requires-Dist: pandas~=1.5.2 +Requires-Dist: PyYAML~=6.0 +Requires-Dist: setuptools~=68.0.0 +Requires-Dist: Sphinx~=5.3.0 +Requires-Dist: SQLAlchemy~=1.4.47 +Requires-Dist: torch>=2.7.0 +Requires-Dist: datetime~=5.1 +Requires-Dist: scikit-learn~=1.2.2 +Requires-Dist: sphinx-jsonschema==1.19.1 +Requires-Dist: sphinx-rtd-dark-mode==1.2.4 +Requires-Dist: plotly==5.19.0 +Requires-Dist: optuna==3.5.0 +Requires-Dist: mysql-connector-python~=9.3.0 +Requires-Dist: seaborn~=0.13.2 +Requires-Dist: flwr[simulation] +Dynamic: author +Dynamic: author-email +Dynamic: classifier +Dynamic: description +Dynamic: description-content-type +Dynamic: home-page +Dynamic: keywords +Dynamic: license-file +Dynamic: project-url +Dynamic: requires-dist +Dynamic: requires-python +Dynamic: summary # MEDfl: A Collaborative Framework for Federated Learning in Medicine diff --git a/MEDfl/rw/client.py b/MEDfl/rw/client.py index 7eda953..4fed968 100644 --- a/MEDfl/rw/client.py +++ b/MEDfl/rw/client.py @@ -1,4 +1,4 @@ -# client.py +# File: client.py import argparse import pandas as pd @@ -21,11 +21,6 @@ except ImportError: GPUtil = None -try: - import xgboost as xgb -except Exception: - xgb = None - class DPConfig: """ @@ -40,10 +35,10 @@ class DPConfig: def __init__( self, - noise_multiplier: float = 1.0, - max_grad_norm: float = 1.0, - batch_size: int = 32, - secure_rng: bool = False, + noise_multiplier=1.0, + max_grad_norm=1.0, + batch_size=32, + secure_rng=False, ): self.noise_multiplier = noise_multiplier self.max_grad_norm = max_grad_norm @@ -51,356 +46,346 @@ def __init__( self.secure_rng = secure_rng -def booster_to_parameters(bst): - # Send JSON so the server can merge trees safely - raw = bst.save_raw(raw_format="json") - # NumPyClient expects a list of numpy arrays - return [np.frombuffer(raw, dtype=np.uint8)] - - -def parameters_to_booster(parameters): - # Handle numpy uint8 array or raw bytes/bytearray - buf = parameters[0] - if isinstance(buf, (bytes, bytearray, memoryview)): - raw = bytes(buf) - else: - arr = np.asarray(buf, dtype=np.uint8) - raw = arr.tobytes() - - booster = xgb.Booster() - # xgboost can load both JSON and binary buffers - booster.load_model(bytearray(raw)) - return booster - - class FlowerClient(fl.client.NumPyClient): def __init__( self, - server_address: str, - data_path: str = "data/data.csv", - dp_config: DPConfig = None, - model_type: str = "nn", - xgb_params: dict = None, - xgb_rounds: int = 50, + server_address, + data_path="data/data.csv", + dp_config=None, + # NEW (optional client overrides; do NOT remove old args) + val_frac=None, + test_frac=None, + id_col=None, + test_ids=None, + seed=42, ): self.server_address = server_address - self.model_type = model_type.lower() - self.xgb_params = xgb_params or {} - self.xgb_rounds = xgb_rounds - - # Store hostname for datasetConfig host-specific overrides - self.hostname = socket.gethostname() - - # Load once; keep the DataFrame so we can rebuild splits from config later - self.df = pd.read_csv(data_path) - - # Defaults at startup (can be overridden dynamically from config)' - default_target = self.df.columns[-1] - default_test_size = 0.20 + self.dp_config = dp_config + self.client_val_frac = val_frac + self.client_test_frac = test_frac + self.id_col = id_col + self.test_ids = test_ids + self.seed = seed + + # Load the CSV once; actual column selection happens on first fit using server config + self._df = pd.read_csv(data_path) + + # Defaults used only for get_properties BEFORE first fit (last column target) + self.feature_names = self._df.columns[:-1].tolist() + self.target_name = self._df.columns[-1] + self.label_counts = self._df[self.target_name].value_counts().to_dict() + self.classes = sorted(self.label_counts.keys()) + + # Tensors for metrics before first fit (fallback to all-but-last as features) + X_default = self._df.iloc[:, :-1].values + y_default = self._df.iloc[:, -1].values + self.X_tensor = torch.tensor(X_default, dtype=torch.float32) + self.y_tensor = torch.tensor(y_default, dtype=torch.float32) + + # Placeholders; we lazily build loaders/model on the first fit when we see server config + self.train_loader = None + self.val_loader = None + self.test_loader = None + + self.model = None + self.criterion = nn.BCEWithLogitsLoss() + self.optimizer = None + + # Effective settings (filled at first fit) + self.effective_features = self.feature_names[:] + self.effective_target = self.target_name + self.effective_val_frac = float(self.client_val_frac) if self.client_val_frac is not None else 0.0 + self.effective_test_frac = float(self.client_test_frac) if self.client_test_frac is not None else 0.0 + + self._initialized = False + self._dp_attached = False # to avoid wrapping twice + + # ---------- helpers + + def _mk_loader(self, X, y, batch_size, shuffle): + x_t = torch.tensor(X, dtype=torch.float32) + y_t = torch.tensor(y, dtype=torch.float32) + return DataLoader(TensorDataset(x_t, y_t), batch_size=batch_size, shuffle=shuffle) + + def _lazy_init_from_server_config(self, config): + """ + Build model and (train, val, test) loaders once, using: + - Server-enforced schema: config['features'] (comma-separated), config['target'] + - Split fractions: client overrides win; else use server's val_fraction/test_fraction + - NEW: if config['test_ids'] is set (per-client from strategy), use ID-based split + """ + # ---------- schema from server (enforced if provided) + srv_features = (config.get("features") or "").strip() + srv_target = (config.get("target") or "").strip() + print(f"[Client] Initializing with server schema: features='{srv_features}', target='{srv_target}'") + + if srv_target: + if srv_target not in self._df.columns: + raise ValueError(f"Server-specified target '{srv_target}' not in CSV columns {list(self._df.columns)}") + target_col = srv_target + else: + target_col = self._df.columns[-1] # fallback (keeps backward compatibility) - # Build initial splits/buffers/metadata - self._prepare_splits( - target_name=default_target, - test_size=default_test_size, - dp_config=dp_config, - ) + if srv_features: + feat_cols = [c.strip() for c in srv_features.split(",") if c.strip()] + missing = [c for c in feat_cols if c not in self._df.columns] + if missing: + raise ValueError(f"Server-specified feature(s) not found in CSV: {missing}") + else: + feat_cols = [c for c in self._df.columns if c != target_col] + + # ---------- fractions: client overrides > server defaults > fallback (0.10/0.10) + srv_val = config.get("val_fraction", None) + srv_test = config.get("test_fraction", None) + val_frac = self.client_val_frac if self.client_val_frac is not None else (float(srv_val) if srv_val is not None else 0.10) + test_frac = self.client_test_frac if self.client_test_frac is not None else (float(srv_test) if srv_test is not None else 0.10) + + if not (0.0 <= val_frac < 1.0): + raise ValueError(f"Invalid val_frac: {val_frac} (must be 0 <= val_frac < 1)") + + # ---------- NEW: adopt test_ids / id_col from server config if provided + if (not self.test_ids or not self.test_ids.strip()) and config.get("test_ids"): + + # strategy (per_client) can inject test_ids as list or CSV; normalize to CSV string + ti = config.get("test_ids") + if isinstance(ti, (list, tuple, set)): + self.test_ids = ",".join(str(x) for x in ti) + else: + self.test_ids = str(ti) + + print(f"[Client] Using server-provided test_ids: {self.test_ids}") + + if (not self.id_col) and config.get("id_col"): + self.id_col = str(config.get("id_col")) + + # ---------- extract arrays with the enforced schema + X_all = self._df[feat_cols].values + y_all = self._df[target_col].values + + # Keep tensors for global metrics logging (same behavior as before) + self.X_tensor = torch.tensor(X_all, dtype=torch.float32) + self.y_tensor = torch.tensor(y_all, dtype=torch.float32) + + # ---------- split + if self.test_ids and self.test_ids.strip(): # ID-based mode (unchanged behavior, now also supports server-provided IDs) + print("[Client] Using ID-based test selection") + test_ids_list = [i.strip() for i in self.test_ids.split(',') if i.strip()] + + if self.id_col and self.id_col in self._df.columns: + id_series = self._df[self.id_col] + # Align types between id_series and test_ids_list + if np.issubdtype(id_series.dtype, np.number): + test_ids_list = [int(i) for i in test_ids_list] + else: + test_ids_list = [str(i) for i in test_ids_list] + + else: + print(f"[Client] Falling back to line numbers (index) as IDs since id_col='{self.id_col}' is invalid or not provided") + id_series = self._df.index + try: + test_ids_list = [int(i) for i in test_ids_list] + except ValueError: + raise ValueError("Test IDs must be integers when using line numbers as IDs") + + test_mask = id_series.isin(test_ids_list) + if not test_mask.any(): + print("[Client] Warning: No matching IDs found for test set; it will be empty") + + X_test = X_all[test_mask] + y_test = y_all[test_mask] + X_trval = X_all[~test_mask] + y_trval = y_all[~test_mask] + + actual_test_frac = len(y_test) / len(y_all) if len(y_all) > 0 else 0.0 + if val_frac + actual_test_frac >= 1.0: + raise ValueError(f"Validation fraction {val_frac} + actual test fraction {actual_test_frac} >= 1.0") + + self.effective_test_frac = actual_test_frac # For logging + + else: # Fraction-based mode (existing) + if not (0.0 <= test_frac < 1.0 and (val_frac + test_frac) < 1.0): + raise ValueError(f"Invalid fractions: val={val_frac}, test={test_frac} (require 0 <= val,test < 1 and val+test < 1)") + + strat_all = y_all if len(np.unique(y_all)) > 1 else None + X_trval, X_test, y_trval, y_test = train_test_split( + X_all, y_all, test_size=test_frac, random_state=self.seed, stratify=strat_all + ) - # Apply DP once at startup (NN only) - self.privacy_engine = None - if dp_config and self.model_type == "nn": + # Split val from trval (common to both modes) + if val_frac > 0 and len(y_trval) > 0: + actual_test_frac = len(y_test) / len(y_all) if len(y_all) > 0 else 0.0 + rel_val = val_frac / (1.0 - actual_test_frac) if (1.0 - actual_test_frac) > 0 else 0.0 + strat_tr = y_trval if len(np.unique(y_trval)) > 1 else None + X_train, X_val, y_train, y_val = train_test_split( + X_trval, y_trval, test_size=rel_val, random_state=self.seed, stratify=strat_tr + ) + else: + X_train, y_train = X_trval, y_trval + X_val, y_val = np.empty((0, X_all.shape[1])), np.empty((0,)) + + # ---------- build loaders + batch_size = self.dp_config.batch_size if self.dp_config else 32 + self.train_loader = self._mk_loader(X_train, y_train, batch_size, shuffle=True) + self.val_loader = self._mk_loader(X_val, y_val, batch_size=batch_size, shuffle=False) if len(y_val) else None + self.test_loader = self._mk_loader(X_test, y_test, batch_size, shuffle=False) + + # ---------- model/optimizer + input_dim = X_all.shape[1] + self.model = Net(input_dim) + self.optimizer = optim.SGD(self.model.parameters(), lr=0.01) + + # ---------- attach DP (same behavior as before; only wraps the train loader) + if self.dp_config and not self._dp_attached: try: from opacus import PrivacyEngine - - self.privacy_engine = PrivacyEngine() - ( - self.model, - self.optimizer, - self.train_loader, - ) = self.privacy_engine.make_private( + privacy_engine = PrivacyEngine() + (self.model, self.optimizer, self.train_loader) = privacy_engine.make_private( module=self.model, optimizer=self.optimizer, data_loader=self.train_loader, - noise_multiplier=dp_config.noise_multiplier, - max_grad_norm=dp_config.max_grad_norm, - secure_rng=dp_config.secure_rng, + noise_multiplier=self.dp_config.noise_multiplier, + max_grad_norm=self.dp_config.max_grad_norm, + secure_rng=self.dp_config.secure_rng, ) + self._dp_attached = True except ImportError: print("Opacus non installé : exécution sans DP.") - # -------------------------------------------------------------------------- - # Helpers to (re)prepare data from a target column and test_size - # -------------------------------------------------------------------------- - def _prepare_splits(self, target_name, test_size, dp_config): - """Create train/test split, tensors/DMatrices, and metadata for the chosen target/test_size.""" - df = self.df - - if target_name not in df.columns: - raise ValueError(f"Target '{target_name}' not found in CSV columns: {list(df.columns)}") - - # Clamp and sanitize test_size - try: - ts = float(test_size) - except Exception: - ts = 0.20 - ts = max(1e-6, min(ts, 0.9)) - - # Build X/y from chosen target - X_df = df.drop(columns=[target_name]) - y_series = df[target_name] - X_full = X_df.values - - # If y isn't numeric, factorize to integers (keep class labels for metadata) - if not np.issubdtype(np.asarray(y_series).dtype, np.number): - y_vals, uniques = pd.factorize(y_series) - y_full = y_vals.astype(np.float32, copy=False) - classes = list(map(str, uniques)) - label_counts = y_series.value_counts().to_dict() + # ---------- record effective settings + self.effective_features = feat_cols + self.effective_target = target_col + self.effective_val_frac = float(val_frac) + # effective_test_frac already set above if ID mode; otherwise use the input + if self.test_ids and self.test_ids.strip(): + pass # Already set else: - y_full = y_series.values.astype(np.float32, copy=False) - classes = sorted(pd.Series(y_full).unique().tolist()) - label_counts = pd.Series(y_full).value_counts().to_dict() - - # Heuristic for stratification (classification-like) - is_classif = np.unique(y_full).shape[0] <= 50 - strat = y_full if is_classif else None - - X_train, X_test, y_train, y_test = train_test_split( - X_full, - y_full, - test_size=ts, - random_state=42, - stratify=strat if strat is not None else None, - ) + self.effective_test_frac = float(test_frac) - # --- Update metadata used by get_properties --- - self.feature_names = X_df.columns.tolist() - self.target_name = target_name - self.label_counts = label_counts - self.classes = classes - - # --- Build per-model buffers --- - if self.model_type == "nn": - # Train tensors - self.X_tensor = torch.tensor(X_train, dtype=torch.float32) - self.y_tensor = torch.tensor(y_train, dtype=torch.float32) - - # Test tensors - self.X_test_tensor = torch.tensor(X_test, dtype=torch.float32) - self.y_test_tensor = torch.tensor(y_test, dtype=torch.float32) - - # DataLoaders - batch_size = getattr(dp_config, "batch_size", 32) if dp_config else 32 - train_ds = TensorDataset(self.X_tensor, self.y_tensor) - self.train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True) - self.test_loader = DataLoader( - TensorDataset(self.X_test_tensor, self.y_test_tensor), - batch_size=batch_size, - shuffle=False, - ) + self._initialized = True + print(f"[Client] Initialized with features={feat_cols}, target={target_col}, val={val_frac}, test={self.effective_test_frac}") - # Create model/criterion/optimizer if not present - input_dim = X_train.shape[1] - if not hasattr(self, "model"): - self.model = Net(input_dim) - self.criterion = nn.BCEWithLogitsLoss() - self.optimizer = optim.SGD(self.model.parameters(), lr=0.01) + # ---------- FL API (unchanged behavior) ---------- - else: - # XGBoost DMatrices - if xgb is None: - raise ImportError("xgboost is not installed. `pip install xgboost`") - - self.X_np = X_train.astype(np.float32, copy=False) # train - self.y_np = y_train.astype(np.float32, copy=False) - self.dtrain = xgb.DMatrix(self.X_np, label=self.y_np) - - self.X_np_test = X_test.astype(np.float32, copy=False) # test - self.y_np_test = y_test.astype(np.float32, copy=False) - self.dtest = xgb.DMatrix(self.X_np_test, label=self.y_np_test) - - # Cold-start booster only if not present yet - if not hasattr(self, "bst"): - self.bst = xgb.train(self.xgb_params, self.dtrain, num_boost_round=1) - - # Remember current prep so we can skip unnecessary rebuilds - self._prepared_key = (self.target_name, float(ts)) - - def _pick_target_and_frac(self, cfg): - """Pick ('target', 'testFrac') from a small dict; support alternative keys.""" - if not isinstance(cfg, dict): - return None, None - tgt = cfg.get("target") or cfg.get("Target") or cfg.get("label") - frac = cfg.get("testFrac", cfg.get("test_size", None)) - try: - frac = float(frac) if frac is not None else None - except Exception: - frac = None - return tgt, frac - - def _resolve_dataset_from_cfg(self, ds_cfg): - """ - Resolve (target, test_size) from datasetConfig: - - If isGlobal=True, take globalConfig.{target,testFrac} - - else take datasetConfig[hostname].{target,testFrac} - - fallbacks: keep current settings or defaults - """ - default_target = getattr(self, "target_name", self.df.columns[-1]) - default_frac = getattr(self, "_prepared_key", (None, 0.2))[1] if hasattr(self, "_prepared_key") else 0.2 - - if not isinstance(ds_cfg, dict): - return default_target, default_frac - - is_global = bool(ds_cfg.get("isGlobal")) - if is_global: - tgt, frac = self._pick_target_and_frac(ds_cfg.get("globalConfig", {})) - else: - host_cfg = ds_cfg.get(self.hostname) - if not isinstance(host_cfg, dict): - lower_map = {str(k).lower(): v for k, v in ds_cfg.items() if isinstance(v, dict)} - host_cfg = lower_map.get(self.hostname.lower()) - tgt, frac = self._pick_target_and_frac(host_cfg or {}) - - target = tgt if tgt else default_target - test_size = frac if frac is not None else default_frac - test_size = max(1e-6, min(float(test_size), 0.9)) - return target, test_size - - def _ensure_prepared_from_config(self, config, dp_config=None): - """Check config['datasetConfig']; rebuild splits if (target,test_size) changed.""" - ds_cfg = config.get("dataset_config") if isinstance(config, dict) else None - if not isinstance(ds_cfg, dict): - return - - target, ts = self._resolve_dataset_from_cfg(ds_cfg) - current = getattr(self, "_prepared_key", None) - desired = (target, float(ts)) - if current != desired: - # Do not re-apply DP dynamically here - self._prepare_splits(target_name=target, test_size=ts, dp_config=None) - - # -------------------------------------------------------------------------- - # Federated API - # -------------------------------------------------------------------------- def get_parameters(self, config): - if self.model_type == "nn": - return [val.cpu().numpy() for val in self.model.state_dict().values()] - else: - return booster_to_parameters(self.bst) + if not self._initialized: + try: + self._lazy_init_from_server_config(config if isinstance(config, dict) else {}) + except Exception as e: + if not self._initialized: + self._lazy_init_from_server_config({}) + return [val.cpu().numpy() for val in self.model.state_dict().values()] def set_parameters(self, parameters): - if self.model_type == "nn": - params_dict = zip(self.model.state_dict().keys(), parameters) - state_dict = {k: torch.tensor(v) for k, v in params_dict} - self.model.load_state_dict(state_dict, strict=True) - else: - if parameters and len(parameters) > 0: - self.bst = parameters_to_booster(parameters) + params_dict = zip(self.model.state_dict().keys(), parameters) + state_dict = {k: torch.tensor(v) for k, v in params_dict} + self.model.load_state_dict(state_dict, strict=True) def fit(self, parameters, config): - self.set_parameters(parameters) + if not self._initialized: + self._lazy_init_from_server_config(config) - # Allow server to override target/test_size dynamically - self._ensure_prepared_from_config(config, dp_config=None) - - if self.model_type == "nn": - self.model.train() - local_epochs = config.get("local_epochs", 5) - total_loss = 0.0 - for _ in range(local_epochs): - for X_batch, y_batch in self.train_loader: - self.optimizer.zero_grad() - outputs = self.model(X_batch) - loss = self.criterion(outputs.squeeze(), y_batch) - loss.backward() - self.optimizer.step() - total_loss += loss.item() * X_batch.size(0) - - avg_loss = total_loss / (len(self.train_loader.dataset) * max(local_epochs, 1)) - with torch.no_grad(): - logits = self.model(self.X_tensor).squeeze() # train set - probs = torch.sigmoid(logits).cpu().numpy() - y_true = self.y_tensor.cpu().numpy() - th = config.get("threshold", 0.5) - binary_preds = (probs >= th).astype(int) - acc = accuracy_score(y_true, binary_preds) + self.set_parameters(parameters) + self.model.train() + + local_epochs = config.get("local_epochs", 5) + total_loss = 0.0 + print(f"Training for {local_epochs} epochs...") + + for epoch in range(local_epochs): + print(f"Epoch {epoch + 1}/{local_epochs}") + for X_batch, y_batch in self.train_loader: + self.optimizer.zero_grad() + outputs = self.model(X_batch) + loss = self.criterion(outputs.squeeze(), y_batch) + loss.backward() + self.optimizer.step() + total_loss += loss.item() * X_batch.size(0) + + avg_loss = total_loss / (len(self.train_loader.dataset) * local_epochs) + + with torch.no_grad(): + logits = self.model(self.X_tensor).squeeze() + probs = torch.sigmoid(logits).cpu().numpy() + y_true = self.y_tensor.cpu().numpy() + binary_preds = (probs >= 0.5).astype(int) + try: auc = roc_auc_score(y_true, probs) + except Exception: + auc = float("nan") + acc = accuracy_score(y_true, binary_preds) - metrics = { - "train_loss": avg_loss, - "train_accuracy": acc, - "train_auc": auc, - } - return self.get_parameters(config), len(self.X_tensor), metrics - - else: - local_rounds = int(config.get("num_local_round", config.get("xgb_rounds", self.xgb_rounds))) - self.bst = xgb.train( - self.xgb_params, - self.dtrain, - num_boost_round=local_rounds, - xgb_model=self.bst, # continue from global - ) - preds = self.bst.predict(self.dtrain) - th = config.get("threshold", 0.5) - binary_preds = (preds >= th).astype(int) - acc = float((binary_preds == self.y_np).mean()) - auc = float(roc_auc_score(self.y_np, preds)) if len(np.unique(self.y_np)) > 1 else 0.0 + hostname = socket.gethostname() + os_type = platform.system() + metrics = { + "hostname": hostname, + "os_type": os_type, + "train_loss": avg_loss, + "train_accuracy": acc, + "train_auc": auc, + "features": ",".join(self.effective_features), + "target": self.effective_target, + "val_fraction": self.effective_val_frac, + "test_fraction": self.effective_test_frac, + } - metrics = {"train_accuracy": acc, "train_auc": auc} - return self.get_parameters(config), len(self.y_np), metrics + return self.get_parameters(config), len(self.train_loader.dataset), metrics def evaluate(self, parameters, config): - self.set_parameters(parameters) + if not self._initialized: + self._lazy_init_from_server_config(config) - # Keep eval consistent with any overridden target/test_size - self._ensure_prepared_from_config(config, dp_config=None) - - if self.model_type == "nn": - self.model.eval() - total_loss = 0.0 - all_probs, all_true = [], [] - with torch.no_grad(): - for X_batch, y_batch in self.test_loader: - outputs = self.model(X_batch) - loss = self.criterion(outputs.squeeze(), y_batch) - total_loss += loss.item() * X_batch.size(0) - probs = torch.sigmoid(outputs.squeeze()).cpu().numpy() - all_probs.extend(probs.tolist()) - all_true.extend(y_batch.cpu().numpy().tolist()) - - avg_loss = total_loss / len(self.test_loader.dataset) - th = config.get("threshold", 0.5) - binary_preds = [1 if p >= th else 0 for p in all_probs] - acc = accuracy_score(all_true, binary_preds) + self.set_parameters(parameters) + self.model.eval() + + total_loss = 0.0 + all_probs, all_true = [], [] + with torch.no_grad(): + for X_batch, y_batch in self.test_loader: + outputs = self.model(X_batch) + loss = self.criterion(outputs.squeeze(), y_batch) + total_loss += loss.item() * X_batch.size(0) + probs = torch.sigmoid(outputs.squeeze()).cpu().numpy() + all_probs.extend(probs.tolist()) + all_true.extend(y_batch.cpu().numpy().tolist()) + + avg_loss = total_loss / len(self.test_loader.dataset) if len(self.test_loader.dataset) > 0 else 0.0 + binary_preds = [1 if p >= 0.5 else 0 for p in all_probs] + try: auc = roc_auc_score(all_true, all_probs) - metrics = {"eval_loss": avg_loss, "eval_accuracy": acc, "eval_auc": auc} - return float(avg_loss), len(self.test_loader.dataset), metrics + except Exception: + auc = float("nan") + acc = accuracy_score(all_true, binary_preds) - else: - th = config.get("threshold", 0.5) - preds = self.bst.predict(self.dtest) - binary = (preds >= th).astype(int) - y_true = self.y_np_test - acc = float((binary == y_true).mean()) - auc = float(roc_auc_score(y_true, preds)) if len(np.unique(y_true)) > 1 else 0.0 - metrics = {"eval_accuracy": acc, "eval_auc": auc} - # loss optional for XGB; return 0.0 to satisfy Flower - return 0.0, len(y_true), metrics + metrics = { + "eval_loss": avg_loss, + "eval_accuracy": acc, + "eval_auc": auc, + } + print(f"Evaluation metrics: {metrics}") + + return float(avg_loss), len(self.test_loader.dataset), metrics def get_properties(self, config): hostname = socket.gethostname() os_type = platform.system() - if self.model_type == "nn": - num_samples = len(self.X_tensor) # train samples - num_features = self.X_tensor.shape[1] + if self._initialized: + num_samples = int(self.X_tensor.shape[0]) + num_features = int(self.X_tensor.shape[1]) + features_str = ",".join(self.effective_features) + target_name = self.effective_target + label_counts = pd.Series(self.y_tensor.numpy()).value_counts().to_dict() + classes = sorted(label_counts.keys()) else: - num_samples = len(self.y_np) # train samples - num_features = self.X_np.shape[1] + num_samples = len(self.X_tensor) + num_features = self.X_tensor.shape[1] + features_str = ",".join(self.feature_names) + target_name = self.target_name + label_counts = self.label_counts + classes = self.classes - features_str = ",".join(self.feature_names) - classes_str = ",".join(map(str, self.classes)) - dist_str = ",".join(f"{cls}:{cnt}" for cls, cnt in self.label_counts.items()) + classes_str = ",".join(map(str, classes)) + dist_str = ",".join(f"{cls}:{cnt}" for cls, cnt in label_counts.items()) cpu_physical = psutil.cpu_count(logical=False) cpu_logical = psutil.cpu_count(logical=True) @@ -419,7 +404,7 @@ def get_properties(self, config): "num_samples": num_samples, "num_features": num_features, "features": features_str, - "target": self.target_name, + "target": target_name, "classes": classes_str, "label_distribution": dist_str, "cpu_physical_cores": cpu_physical, @@ -427,61 +412,10 @@ def get_properties(self, config): "total_memory_gb": total_mem_gb, "gpu_driver_present": str(driver_present), "gpu_count": gpu_count, - "model_type": self.model_type, } - def start(self) -> None: - fl.client.start_numpy_client(server_address=self.server_address, client=self) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Flower client with NN/XGBoost + optional DP for NN") - parser.add_argument("--server_address", type=str, required=True, help="ex: 127.0.0.1:8080") - parser.add_argument("--data_path", type=str, default="data/data.csv", help="CSV path") - - # Mode - parser.add_argument("--model", type=str, default="nn", choices=["nn", "xgb"], help="Client model type") - - # DP (NN only) - parser.add_argument("--dp", action="store_true", help="Activer la confidentialité différentielle (NN uniquement)") - parser.add_argument("--noise_multiplier", type=float, default=1.0) - parser.add_argument("--max_grad_norm", type=float, default=1.0) - parser.add_argument("--batch_size", type=int, default=32) - - # XGBoost params - parser.add_argument("--xgb_eta", type=float, default=0.1) - parser.add_argument("--xgb_max_depth", type=int, default=6) - parser.add_argument("--xgb_subsample", type=float, default=0.8) - parser.add_argument("--xgb_colsample_bytree", type=float, default=0.8) - parser.add_argument("--xgb_rounds", type=int, default=50) - args = parser.parse_args() - - dp_config = None - if args.dp and args.model == "nn": - dp_config = DPConfig( - noise_multiplier=args.noise_multiplier, - max_grad_norm=args.max_grad_norm, - batch_size=args.batch_size, + def start(self): + fl.client.start_numpy_client( + server_address=self.server_address, + client=self, ) - - xgb_params = None - if args.model == "xgb": - xgb_params = { - "objective": "binary:logistic", - "eval_metric": "logloss", - "eta": args.xgb_eta, - "max_depth": args.xgb_max_depth, - "subsample": args.xgb_subsample, - "colsample_bytree": args.xgb_colsample_bytree, - "tree_method": "hist", - } - - client = FlowerClient( - server_address=args.server_address, - data_path=args.data_path, - dp_config=dp_config, - model_type=args.model, - xgb_params=xgb_params, - xgb_rounds=args.xgb_rounds if args.model == "xgb" else 0, - ) - client.start() diff --git a/MEDfl/rw/model.py b/MEDfl/rw/model.py index 2e37ae1..3c50a1f 100644 --- a/MEDfl/rw/model.py +++ b/MEDfl/rw/model.py @@ -1,11 +1,8 @@ import math -from typing import List, Optional, Literal, Callable, Tuple import torch import torch.nn as nn import torch.nn.functional as F -Task = Literal["binary", "multiclass", "multilabel", "regression"] - _ACTS = { "relu": nn.ReLU, "gelu": nn.GELU, @@ -16,7 +13,7 @@ "identity": nn.Identity, } -def _make_activation(name: str) -> nn.Module: +def _make_activation(name): if name not in _ACTS: raise ValueError(f"Unsupported activation '{name}'. Choose from {list(_ACTS)}") return _ACTS[name]() @@ -35,16 +32,16 @@ class Net(nn.Module): def __init__( self, - input_dim: int, - hidden_dims: List[int] = [64, 32], - activation: str = "relu", - batchnorm: bool = True, - dropout: Optional[float | List[float]] = 0.0, - task: Task = "binary", - num_classes: Optional[int] = None, - output_bias: bool = True, - return_logits: bool = True, - weight_init: Literal["kaiming", "xavier", "none"] = "kaiming", + input_dim, + hidden_dims=[64, 32], + activation="relu", + batchnorm=True, + dropout=0.0, + task="binary", + num_classes=None, + output_bias=True, + return_logits=True, + weight_init="kaiming", ): super().__init__() self.task = task @@ -78,10 +75,9 @@ def __init__( else: raise ValueError("dropout must be float|list[float]|None") - layers = [] - in_dim = input_dim self.blocks = nn.ModuleList() - for i, (h, p) in enumerate(zip(hidden_dims, dropout)): + in_dim = input_dim + for h, p in zip(hidden_dims, dropout): block = nn.ModuleDict({ "lin": nn.Linear(in_dim, h, bias=True), "bn": nn.BatchNorm1d(h) if batchnorm else nn.Identity(), @@ -98,11 +94,15 @@ def __init__( self.apply(lambda m: self._init_weights(m, scheme=weight_init, activation=activation)) @staticmethod - def _init_weights(m: nn.Module, scheme: str, activation: str): + def _init_weights(m, scheme, activation): if isinstance(m, nn.Linear): if scheme == "kaiming": nonlinearity = "leaky_relu" if activation == "leaky_relu" else "relu" - nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5) if activation == "leaky_relu" else 0, nonlinearity=nonlinearity) + nn.init.kaiming_uniform_( + m.weight, + a=math.sqrt(5) if activation == "leaky_relu" else 0, + nonlinearity=nonlinearity, + ) elif scheme == "xavier": nn.init.xavier_uniform_(m.weight) if m.bias is not None: @@ -110,7 +110,7 @@ def _init_weights(m: nn.Module, scheme: str, activation: str): bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 nn.init.uniform_(m.bias, -bound, bound) - def forward(self, x: torch.Tensor) -> torch.Tensor: + def forward(self, x): for blk in self.blocks: x = blk["lin"](x) x = blk["bn"](x) @@ -120,7 +120,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return logits if self.return_logits else self._apply_output_activation(logits) # Inference helpers - def _apply_output_activation(self, logits: torch.Tensor) -> torch.Tensor: + def _apply_output_activation(self, logits): if self.task == "binary": return torch.sigmoid(logits) # (B, 1) elif self.task == "multiclass": @@ -128,15 +128,15 @@ def _apply_output_activation(self, logits: torch.Tensor) -> torch.Tensor: elif self.task == "multilabel": return torch.sigmoid(logits) # (B, L) elif self.task == "regression": - return logits # raw regression output + return logits # raw regression output else: raise RuntimeError("Invalid task") @torch.no_grad() - def predict(self, x: torch.Tensor) -> torch.Tensor: - """Apply appropriate post-processing for predictions.""" - logits = self.forward(x) if self.return_logits else x - probs = self._apply_output_activation(logits) if self.return_logits else logits + def predict(self, x): + out = self.forward(x) + probs = self._apply_output_activation(out) if self.return_logits else out + if self.task == "binary": return (probs >= 0.5).long() elif self.task == "multiclass": diff --git a/MEDfl/rw/server.py b/MEDfl/rw/server.py index dc1cd4d..178658a 100644 --- a/MEDfl/rw/server.py +++ b/MEDfl/rw/server.py @@ -1,7 +1,6 @@ import flwr as fl from flwr.server.strategy import FedAvg from flwr.server.server import ServerConfig -from typing import Optional, Any from MEDfl.rw.strategy import Strategy import asyncio from flwr.server.client_manager import ClientManager @@ -29,11 +28,11 @@ class FederatedServer: def __init__( self, - host: str = "0.0.0.0", - port: int = 8080, - num_rounds: int = 3, - strategy: Optional[Strategy] = None, - certificates: Optional[Any] = None, + host="0.0.0.0", + port=8080, + num_rounds=3, + strategy=None, + certificates=None, ): """ Initialize the FederatedServer. @@ -60,7 +59,7 @@ def __init__( self.connected_clients = [] # Track connected client IDs - def start(self) -> None: + def start(self): """ Start the Flower server with the configured strategy and track client connections. """ @@ -89,7 +88,7 @@ class TrackingClientManager(fl.server.client_manager.SimpleClientManager): client_properties (dict): Placeholder for storing client-specific properties. """ - def __init__(self, server: FederatedServer): + def __init__(self, server): """ Initialize the TrackingClientManager. @@ -100,7 +99,7 @@ def __init__(self, server: FederatedServer): self.server = server self.client_properties = {} - def register(self, client: ClientProxy) -> bool: + def register(self, client): """ Register a client and log its connection. @@ -116,7 +115,7 @@ def register(self, client: ClientProxy) -> bool: asyncio.run(self._fetch_and_log_hostname(client)) return success - async def _fetch_and_log_hostname(self, client: ClientProxy): + async def _fetch_and_log_hostname(self, client): """ Asynchronously fetch and log the client's hostname or CID. diff --git a/MEDfl/rw/strategy.py b/MEDfl/rw/strategy.py index fc3d02b..ba1ce42 100644 --- a/MEDfl/rw/strategy.py +++ b/MEDfl/rw/strategy.py @@ -1,117 +1,98 @@ -# MEDfl/rw/strategy.py +# File: MEDfl/rw/strategy.py import os -import json import numpy as np import flwr as fl -from typing import Callable, Optional, Dict, Any, List, Tuple from flwr.common import GetPropertiesIns from flwr.server.client_manager import ClientManager from flwr.server.client_proxy import ClientProxy import time -from flwr.common import parameters_to_ndarrays, ndarrays_to_parameters - - -# ===== unchanged aggregate_* for metrics (works for both) ===== -def aggregate_fit_metrics(results: List[Tuple[int, Dict[str, float]]]) -> Dict[str, float]: - total = max(sum(n for n, _ in results), 1) +from MEDfl.rw.model import Net +import torch + +# =================================================== +# Custom metric aggregation functions +# =================================================== +def aggregate_fit_metrics(results): + total = sum(n for n, _ in results) loss = sum(m.get("train_loss", 0.0) * n for n, m in results) / total - acc = sum(m.get("train_accuracy", 0.0) * n for n, m in results) / total - auc = sum(m.get("train_auc", 0.0) * n for n, m in results) / total + acc = sum(m.get("train_accuracy", 0.0) * n for n, m in results) / total + auc = sum(m.get("train_auc", 0.0) * n for n, m in results) / total return {"train_loss": loss, "train_accuracy": acc, "train_auc": auc} -def aggregate_eval_metrics(results: List[Tuple[int, Dict[str, float]]]) -> Dict[str, float]: - total = max(sum(n for n, _ in results), 1) +def aggregate_eval_metrics(results): + total = sum(n for n, _ in results) loss = sum(m.get("eval_loss", 0.0) * n for n, m in results) / total - acc = sum(m.get("eval_accuracy", 0.0) * n for n, m in results) / total - auc = sum(m.get("eval_auc", 0.0) * n for n, m in results) / total + acc = sum(m.get("eval_accuracy", 0.0) * n for n, m in results) / total + auc = sum(m.get("eval_auc", 0.0) * n for n, m in results) / total return {"eval_loss": loss, "eval_accuracy": acc, "eval_auc": auc} -# ========== NEW: helper to decode XGB booster parameters ========== -# --- helpers for XGB bytes <-> Booster (updated to JSON format) --- -def _booster_to_json_bytes(bst) -> bytes: - """Return XGBoost Booster as JSON bytes (so we can edit trees).""" - # xgboost>=1.7 supports raw_format='json' - raw = bst.save_raw(raw_format='json') - return bytes(raw) - -def _ensure_json_bytes(raw_bytes: bytes) -> bytes: - """If bytes are not JSON (binary model), try converting via Booster load+save.""" - try: - # Quick sniff: JSON starts with '{' or '[' - b0 = raw_bytes.lstrip()[:1] - if b0 in (b'{', b'['): - return raw_bytes - except Exception: - pass - # Convert binary -> JSON - import xgboost as xgb - bst = xgb.Booster() - bst.load_model(bytearray(raw_bytes)) - return _booster_to_json_bytes(bst) - -def _get_tree_nums(xgb_model_org: bytes) -> Tuple[int, int]: - xgb_model = json.loads(bytearray(xgb_model_org)) - tree_num = int(xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_trees"]) - paral_tree_num = int(xgb_model["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_parallel_tree"]) - return tree_num, paral_tree_num - -def _aggregate_trees(bst_prev_org: Optional[bytes], bst_curr_org: bytes) -> bytes: - """Conduct bagging aggregation by appending trees from current to previous.""" - bst_curr_org = _ensure_json_bytes(bst_curr_org) - - if not bst_prev_org: - # First model in the round becomes the base - return bst_curr_org - - bst_prev_org = _ensure_json_bytes(bst_prev_org) - - tree_num_prev, _ = _get_tree_nums(bst_prev_org) - _, paral_tree_num_curr = _get_tree_nums(bst_curr_org) - - bst_prev = json.loads(bytearray(bst_prev_org)) - bst_curr = json.loads(bytearray(bst_curr_org)) - - # Update counts - bst_prev["learner"]["gradient_booster"]["model"]["gbtree_model_param"]["num_trees"] = str( - tree_num_prev + paral_tree_num_curr - ) - - # Update iteration_indptr - iteration_indptr = bst_prev["learner"]["gradient_booster"]["model"]["iteration_indptr"] - iteration_indptr.append(iteration_indptr[-1] + paral_tree_num_curr) - - # Append current trees, re-id them - trees_curr = bst_curr["learner"]["gradient_booster"]["model"]["trees"] - for t in range(paral_tree_num_curr): - trees_curr[t]["id"] = tree_num_prev + t - bst_prev["learner"]["gradient_booster"]["model"]["trees"].append(trees_curr[t]) - bst_prev["learner"]["gradient_booster"]["model"]["tree_info"].append(0) - - return bytes(json.dumps(bst_prev), "utf-8") - +# =================================================== +# Strategy Wrapper +# =================================================== class Strategy: + """ + Flower Strategy wrapper: + - Dynamic hyperparameters via on_fit_config_fn + - Custom metric aggregation + - Per-client & aggregated metric logging + - Synchronous get_properties() inspection in configure_fit() + - Saving global parameters every saveOnRounds to savingPath + + Extended: + - split_mode: + * "global": use global val_fraction/test_fraction for all clients + * "per_client": use client_fractions[hostname] if present + - client_fractions: + { + "HOSTNAME_1": { + "val_fraction": float (optional), + "test_fraction": float (optional), + "test_ids": [..] or "id1,id2" (optional) + }, + ... + } + - In per_client mode: + * if test_ids is present for a client: + -> send test_ids + -> do NOT use that client's test_fraction + * otherwise: + -> use that client's val_fraction/test_fraction if provided, + else fall back to global val_fraction/test_fraction + - client id in this mapping = hostname from client.get_properties() + - id_col: + * column name used on clients to match test_ids (default "id") + """ + def __init__( self, - name: str = "FedAvg", - fraction_fit: float = 1.0, - fraction_evaluate: float = 1.0, - min_fit_clients: int = 2, - min_evaluate_clients: int = 2, - min_available_clients: int = 2, - initial_parameters: Optional[List[Any]] = None, - evaluate_fn: Optional[Callable] = None, - fit_metrics_aggregation_fn: Optional[Callable] = None, - evaluate_metrics_aggregation_fn: Optional[Callable] = None, - local_epochs: int = 1, - threshold: float = 0.5, - learning_rate: float = 0.01, - optimizer_name: str = "SGD", - savingPath: str = "", - saveOnRounds: int = 3, - total_rounds: int = 3, - datasetConfig: Dict[str, Any] = {}, - ) -> None: + name="FedAvg", + fraction_fit=1.0, + fraction_evaluate=1.0, + min_fit_clients=2, + min_evaluate_clients=2, + min_available_clients=2, + initial_parameters=None, + evaluate_fn=None, + fit_metrics_aggregation_fn=None, + evaluate_metrics_aggregation_fn=None, + local_epochs=1, + threshold=0.5, + learning_rate=0.01, + optimizer_name="SGD", + savingPath="", + saveOnRounds=3, + total_rounds=3, + features="", + target="", + val_fraction=0.10, + test_fraction=0.10, + # NEW: splitting control (added at the end to not break existing calls) + split_mode="global", # "global" or "per_client" + client_fractions=None, + # NEW: id column for test_ids mapping + id_col="id", + ): self.name = name self.fraction_fit = fraction_fit self.fraction_evaluate = fraction_evaluate @@ -124,6 +105,7 @@ def __init__( self.fit_metrics_aggregation_fn = fit_metrics_aggregation_fn or aggregate_fit_metrics self.evaluate_metrics_aggregation_fn = evaluate_metrics_aggregation_fn or aggregate_eval_metrics + # Dynamic hyperparams self.local_epochs = local_epochs self.threshold = threshold self.learning_rate = learning_rate @@ -131,299 +113,190 @@ def __init__( self.savingPath = savingPath self.saveOnRounds = saveOnRounds self.total_rounds = total_rounds - self.datasetConfig = datasetConfig + self._features = features # comma-separated or "" + self._target = target # or "" + self._val_fraction = val_fraction + self._test_fraction = test_fraction - self.strategy_object: Optional[fl.server.strategy.Strategy] = None + # NEW + self.split_mode = split_mode + self.client_fractions = client_fractions or {} + self.id_col = id_col - def create_strategy(self) -> None: - # ======== Branch: custom XGB strategy ======== - if self.name == "XGBoostBagging": - self.strategy_object = self._create_xgb_bagging_strategy() - return + self.strategy_object = None - # ======== Default: use Flower built-in by name (e.g., FedAvg) ======== + def create_strategy(self): + # 1) Pick the Flower Strategy class StrategyClass = getattr(fl.server.strategy, self.name) - # ======== Common params for all built-in strategies ======== - def fit_config_fn(server_round: int) -> Dict[str, Any]: - return {"local_epochs": self.local_epochs, "threshold": self.threshold, "learning_rate": self.learning_rate, "optimizer": self.optimizer_name , "dataset_config": self.datasetConfig} - - params: Dict[str, Any] = { - "fraction_fit": self.fraction_fit, - "fraction_evaluate": self.fraction_evaluate, - "min_fit_clients": self.min_fit_clients, - "min_evaluate_clients": self.min_evaluate_clients, - "min_available_clients": self.min_available_clients, - "evaluate_fn": self.evaluate_fn, - "on_fit_config_fn": fit_config_fn, - "fit_metrics_aggregation_fn": self.fit_metrics_aggregation_fn, - "evaluate_metrics_aggregation_fn": self.evaluate_metrics_aggregation_fn, + # 2) Define on_fit_config_fn _before_ instantiation (global defaults) + def fit_config_fn(server_round): + return { + "local_epochs": self.local_epochs, + "threshold": self.threshold, + "learning_rate": self.learning_rate, + "optimizer": self.optimizer_name, + "features": self._features, + "target": self._target, + "val_fraction": float(self._val_fraction), + "test_fraction": float(self._test_fraction), + # NEW: always send id_col so clients know which column to use for test_ids + "id_col": self.id_col, + } + + # 3) Build params including on_fit_config_fn + params = { + "fraction_fit": self.fraction_fit, + "fraction_evaluate": self.fraction_evaluate, + "min_fit_clients": self.min_fit_clients, + "min_evaluate_clients": self.min_evaluate_clients, + "min_available_clients": self.min_available_clients, + "evaluate_fn": self.evaluate_fn, + "on_fit_config_fn": fit_config_fn, + "fit_metrics_aggregation_fn": self.fit_metrics_aggregation_fn, + "evaluate_metrics_aggregation_fn": self.evaluate_metrics_aggregation_fn, } if self.initial_parameters: params["initial_parameters"] = fl.common.ndarrays_to_parameters(self.initial_parameters) + else: + # derive initial params from server-specified features + feat_cols = [c.strip() for c in (self._features or "").split(",") if c.strip()] + if not feat_cols: + raise ValueError( + "No initial_parameters provided and 'features' is empty. " + "Provide Strategy(..., features='col1,col2,...') or pass initial_parameters." + ) + input_dim = len(feat_cols) + _model = Net(input_dim) + _arrays = [t.detach().cpu().numpy() for t in _model.state_dict().values()] + params["initial_parameters"] = fl.common.ndarrays_to_parameters(_arrays) + # 4) Instantiate the real Flower strategy strat = StrategyClass(**params) - # Wrap aggregate_fit for logging + optional saving + # 5) Wrap aggregate_fit for logging (prints unchanged) original_agg_fit = strat.aggregate_fit def logged_agg_fit(server_round, results, failures): print(f"\n[Server] 🔄 Round {server_round} - Client Training Metrics:") - for (client_id, fit_res) in results: + for i, (client_id, fit_res) in enumerate(results): print(f" CTM Round {server_round} Client:{client_id.cid}: {fit_res.metrics}") - agg_params, metrics = original_agg_fit(server_round, results, failures) print(f"[Server] ✅ Round {server_round} - Aggregated Training Metrics: {metrics}\n") - - # ⬇️ Only try to save when we actually have parameters - should_checkpoint = ( - self.savingPath - and ((server_round % self.saveOnRounds == 0) or (self.total_rounds and server_round == self.total_rounds)) - ) - - if should_checkpoint and agg_params is not None: - try: - arrays = fl.common.parameters_to_ndarrays(agg_params) - filename = ( - f"round_{server_round}_final_model.npz" - if (self.total_rounds and server_round == self.total_rounds) - else f"round_{server_round}_model.npz" - ) - os.makedirs(self.savingPath, exist_ok=True) - np.savez(os.path.join(self.savingPath, filename), *arrays) - print(f"[Server] 💾 Saved checkpoint to {os.path.join(self.savingPath, filename)}") - except Exception as e: - print(f"[Server] ⚠️ Skipped saving checkpoint (no parameters or conversion failed): {e}") - elif should_checkpoint and agg_params is None: - print("[Server] ⚠️ Skipped checkpoint: aggregate_fit returned None (no aggregated parameters this round).") - + # save the model parameters if savingPath is set on each saveOnRounds + if self.savingPath and ( + (server_round % self.saveOnRounds == 0) + or (self.total_rounds and server_round == self.total_rounds) + ): + arrays = fl.common.parameters_to_ndarrays(agg_params) + # Determine filename: final_model on last round else round_{n} + filename = ( + f"round_{server_round}_final_model.npz" + if server_round == self.total_rounds + else f"round_{server_round}_model.npz" + ) + filepath = os.path.join(self.savingPath, filename) + np.savez(filepath, *arrays) return agg_params, metrics strat.aggregate_fit = logged_agg_fit - + # 6) Wrap aggregate_evaluate for logging (prints unchanged) original_agg_eval = strat.aggregate_evaluate + def logged_agg_eval(server_round, results, failures): print(f"\n[Server] 📊 Round {server_round} - Client Evaluation Metrics:") - for (client_id, eval_res) in results: + for i, (client_id, eval_res) in enumerate(results): print(f" CEM Round {server_round} Client:{client_id.cid}: {eval_res.metrics}") loss, metrics = original_agg_eval(server_round, results, failures) - print(f"[Server] ✅ Round {server_round} - Aggregated Evaluation Metrics:\n Loss: {loss}, Metrics: {metrics}\n") + print(f"[Server] ✅ Round {server_round} - Aggregated Evaluation Metrics:") + print(f" Loss: {loss}, Metrics: {metrics}\n") return loss, metrics + strat.aggregate_evaluate = logged_agg_eval + # 7) Wrap configure_fit to: + # - log client properties (unchanged) + # - apply split_mode/client_fractions to fit_ins.config (NEW) original_conf_fit = strat.configure_fit - def wrapped_conf_fit(server_round: int, parameters, client_manager: ClientManager): - selected = original_conf_fit(server_round=server_round, parameters=parameters, client_manager=client_manager) - ins = GetPropertiesIns(config={}) - for client, _ in selected: - try: - props = client.get_properties(ins=ins, timeout=10.0, group_id=0) - print(f"\n📋 [Round {server_round}] Client {client.cid} Properties: {props.properties}") - except Exception as e: - print(f"⚠️ Failed to get properties from {client.cid}: {e}") - return selected - strat.configure_fit = wrapped_conf_fit - self.strategy_object = strat + def wrapped_conf_fit( + server_round, + parameters, + client_manager + ): + selected = original_conf_fit( + server_round=server_round, + parameters=parameters, + client_manager=client_manager + ) - # ---------- NEW: Custom XGBoost strategy ---------- - def _create_xgb_bagging_strategy_old(self) -> fl.server.strategy.Strategy: - from flwr.common import FitIns, Parameters, ndarrays_to_parameters - from flwr.server.client_manager import ClientManager - - class XGBoostBagging(fl.server.strategy.Strategy): - def __init__(self, outer: "Strategy"): - self.outer = outer - self.global_parameters: Parameters | None = None - - def initialize_parameters(self, client_manager: ClientManager): - return self.global_parameters - - def configure_fit(self, server_round, parameters, client_manager: ClientManager): - # (unchanged) - sample_n = max(self.outer.min_fit_clients, 1) - clients = client_manager.sample(num_clients=sample_n, min_num_clients=self.outer.min_fit_clients) - fit_ins = FitIns(parameters if parameters is not None else self.global_parameters, - {"xgb_rounds": self.outer.local_epochs if self.outer.local_epochs > 0 else 50}) - return [(client, fit_ins) for client in clients] - - def aggregate_fit(self, server_round, results, failures): - # (unchanged: select-best) - best = None - for (client, fit_res) in results: - m = fit_res.metrics or {} - score = (m.get("train_auc", 0.0), m.get("train_accuracy", 0.0)) - if (best is None) or (score > best["score"]): - best = {"score": score, "parameters": fit_res.parameters} - if best is not None: - self.global_parameters = best["parameters"] - - metrics = self.outer.fit_metrics_aggregation_fn([(r.num_examples, r.metrics) for (_, r) in results]) - print(f"[Server-XGB] ✅ Round {server_round} - Selected best booster (AUC,ACC)={best['score'] if best else None}") - - # Optional saving (unchanged) - if self.outer.savingPath and ((server_round % self.outer.saveOnRounds == 0) or - (self.outer.total_rounds and server_round == self.outer.total_rounds)): - os.makedirs(self.outer.savingPath, exist_ok=True) - if self.global_parameters is not None: - raw = bytes(self.global_parameters.tensors[0]) if hasattr(self.global_parameters, "tensors") \ - else bytes(self.global_parameters[0].tolist()) - with open(os.path.join(self.outer.savingPath, f"round_{server_round}_xgb.model"), "wb") as f: - f.write(raw) - - return self.global_parameters, metrics - - def configure_evaluate(self, server_round, parameters, client_manager: ClientManager): - # No centralized eval: return empty list - return [] - - def aggregate_evaluate(self, server_round, results, failures): - if not results: - return 0.0, {} - loss = 0.0 - metrics = self.outer.evaluate_metrics_aggregation_fn([(r.num_examples, r.metrics) for (_, r) in results]) - return loss, metrics - - # 🔧 ADD THIS METHOD to satisfy the abstract interface - def evaluate(self, server_round, parameters): - # No server-side evaluation; Flower expects Optional[Tuple[float, dict]] - return None - - strat = XGBoostBagging(self) - - # Log client props, like the NN path - original_conf_fit = strat.configure_fit - def wrapped_conf_fit(server_round, parameters, client_manager): - selected = original_conf_fit(server_round, parameters, client_manager) ins = GetPropertiesIns(config={}) - for client, _ in selected: + + for client, fit_ins in selected: + hostname = None try: props = client.get_properties(ins=ins, timeout=10.0, group_id=0) print(f"\n📋 [Round {server_round}] Client {client.cid} Properties: {props.properties}") + hostname = props.properties.get("hostname", None) except Exception as e: print(f"⚠️ Failed to get properties from {client.cid}: {e}") - return selected - strat.configure_fit = wrapped_conf_fit - - return strat - - def _create_xgb_bagging_strategy(self) -> fl.server.strategy.Strategy: - from flwr.common import FitIns, Parameters, ndarrays_to_parameters - from flwr.common import Scalar - from typing import Union, cast - - class XGBoostBagging(fl.server.strategy.Strategy): - def __init__(self, outer: "Strategy"): - self.outer = outer - self.global_model_bytes: Optional[bytes] = None # JSON bytes - - def initialize_parameters(self, client_manager: ClientManager): - if self.global_model_bytes is None: - return None - # wrap the JSON bytes into a uint8 numpy array for NumPyClient - arr = np.frombuffer(self.global_model_bytes, dtype=np.uint8) - return ndarrays_to_parameters([arr]) - - - def configure_fit(self, server_round: int, parameters, client_manager: ClientManager): - # Sample exactly min_fit_clients (or at least 1) - sample_n = max(self.outer.min_fit_clients, 1) - clients = client_manager.sample( - num_clients=sample_n, min_num_clients=self.outer.min_fit_clients - ) - - # Tell clients how many trees to add and return - cfg = { - "num_local_round": int(self.outer.local_epochs) if self.outer.local_epochs > 0 else 50, - "global_round": int(server_round), - } - fit_ins = FitIns( - parameters if parameters is not None else self.initialize_parameters(client_manager), - cfg, - ) - return [(client, fit_ins) for client in clients] - - def aggregate_fit( - self, - server_round: int, - results: List[Tuple[ClientProxy, "flwr.common.FitRes"]], - failures: List[Union[Tuple[ClientProxy, "flwr.common.FitRes"], BaseException]], - ): - # Accept failures policy like FedAvg (optional) - if not results: - return None, {} - if not getattr(self, "accept_failures", True) and failures: - return None, {} - - # Start from current global; append every client's newly trained trees - global_bytes = self.global_model_bytes - for _, fit_res in results: - arrays = parameters_to_ndarrays(fit_res.parameters) # list of np.ndarrays - for arr in arrays: - raw = arr.tobytes() # recover the exact buffer we sent - raw_json = _ensure_json_bytes(raw) # accept binary/JSON; normalize to JSON - # (Optional but recommended) slice only the last K trees trained locally: - # k = int(self.outer.local_epochs) if self.outer.local_epochs > 0 else 50 - # raw_json = _slice_last_k_trees(raw_json, k) - global_bytes = _aggregate_trees(global_bytes, raw_json) - - - self.global_model_bytes = global_bytes - - # Optional metric aggregation for logging - metrics = self.outer.fit_metrics_aggregation_fn( - [(r.num_examples, r.metrics) for (_, r) in results] - ) - - # Optional checkpointing - if self.outer.savingPath and ( - (server_round % self.outer.saveOnRounds == 0) - or (self.outer.total_rounds and server_round == self.outer.total_rounds) - ): - os.makedirs(self.outer.savingPath, exist_ok=True) - if self.global_model_bytes is not None: - with open(os.path.join(self.outer.savingPath, f"round_{server_round}_xgb.model"), "wb") as f: - f.write(self.global_model_bytes) + # Fallback: if no hostname returned, use Flower cid + if not hostname: + hostname = client.cid - arr = np.frombuffer(self.global_model_bytes, dtype=np.uint8) - return ndarrays_to_parameters([arr]), metrics + # Keep same object + cfg = fit_ins.config + if self.split_mode == "per_client": + # Lookup by hostname (preferred) or cid + per_cfg = ( + self.client_fractions.get(hostname) + or self.client_fractions.get(client.cid) + or {} + ) - def configure_evaluate(self, server_round, parameters, client_manager: ClientManager): - # You can keep distributed evaluation if your clients implement it; - # otherwise return [] to disable. - return [] - - def aggregate_evaluate(self, server_round, results, failures): - if not results: - return 0.0, {} - loss = 0.0 - metrics = self.outer.evaluate_metrics_aggregation_fn( - [(r.num_examples, r.metrics) for (_, r) in results] - ) - return loss, metrics - - def evaluate(self, server_round, parameters): - # No server-side eval by default - return None - - strat = XGBoostBagging(self) + # val_fraction: per-client override if present + if "val_fraction" in per_cfg: + try: + cfg["val_fraction"] = float(per_cfg["val_fraction"]) + except Exception: + pass # keep existing if invalid + + # test: prefer test_ids if provided + if "test_ids" in per_cfg and per_cfg["test_ids"]: + test_ids_val = per_cfg["test_ids"] + if isinstance(test_ids_val, (list, tuple, set)): + test_ids_str = ",".join(str(x) for x in test_ids_val) + else: + test_ids_str = str(test_ids_val) + cfg["test_ids"] = test_ids_str + # when using explicit IDs, do not force a test_fraction for this client + if "test_fraction" in cfg: + del cfg["test_fraction"] + # ensure id_col is sent so client can map IDs + cfg["id_col"] = self.id_col + else: + # no test_ids -> use per-client test_fraction if present + if "test_fraction" in per_cfg: + try: + cfg["test_fraction"] = float(per_cfg["test_fraction"]) + except Exception: + pass # keep existing if invalid + # if no test_ids: id_col not strictly required, leave as-is + else: + # split_mode == "global": enforce global fractions, clear any test_ids + if "test_ids" in cfg: + del cfg["test_ids"] + cfg["val_fraction"] = float(self._val_fraction) + cfg["test_fraction"] = float(self._test_fraction) + # also send id_col so clients know column name if needed + cfg["id_col"] = self.id_col - # (Optional) property logging identical to your previous wrapper - original_conf_fit = strat.configure_fit - def wrapped_conf_fit(server_round, parameters, client_manager): - selected = original_conf_fit(server_round, parameters, client_manager) - ins = GetPropertiesIns(config={}) - for client, _ in selected: - try: - props = client.get_properties(ins=ins, timeout=10.0, group_id=0) - print(f"\n📋 [Round {server_round}] Client {client.cid} Properties: {props.properties}") - except Exception as e: - print(f"⚠️ Failed to get properties from {client.cid}: {e}") return selected - strat.configure_fit = wrapped_conf_fit - return strat + strat.configure_fit = wrapped_conf_fit + # 8) Save the ready-to-use strategy + self.strategy_object = strat diff --git a/build/lib/MEDfl/LearningManager/__init__.py b/build/lib/MEDfl/LearningManager/__init__.py new file mode 100644 index 0000000..88a6314 --- /dev/null +++ b/build/lib/MEDfl/LearningManager/__init__.py @@ -0,0 +1,13 @@ +# MEDfl/LearningManager/__init__.py + +# Import modules from this package +# from .client import * +# from .dynamicModal import * +# from .flpipeline import * +# from .federated_dataset import * +# from .model import * +# from .params_optimiser import * +# from .plot import * +# from .server import * +# from .strategy import * +# from .utils import * diff --git a/build/lib/MEDfl/LearningManager/client.py b/build/lib/MEDfl/LearningManager/client.py new file mode 100644 index 0000000..4a18ea8 --- /dev/null +++ b/build/lib/MEDfl/LearningManager/client.py @@ -0,0 +1,150 @@ +#!/usr/bin/env python3 +import flwr as fl +from opacus import PrivacyEngine +from torch.utils.data import DataLoader + +from .model import Model +from .utils import params +import torch + +class FlowerClient(fl.client.NumPyClient): + """ + FlowerClient class for creating MEDfl clients. + + Attributes: + cid (str): Client ID. + local_model (Model): Local model of the federated learning network. + trainloader (DataLoader): DataLoader for training data. + valloader (DataLoader): DataLoader for validation data. + diff_priv (bool): Flag indicating whether to use differential privacy. + """ + def __init__(self, cid: str, local_model: Model, trainloader: DataLoader, valloader: DataLoader, diff_priv: bool = params["diff_privacy"]): + """ + Initializes the FlowerClient instance. + + Args: + cid (str): Client ID. + local_model (Model): Local model of the federated learning network. + trainloader (DataLoader): DataLoader for training data. + valloader (DataLoader): DataLoader for validation data. + diff_priv (bool): Flag indicating whether to use differential privacy. + """ + self.cid = cid + self.local_model = local_model + self.trainloader = trainloader + self.valloader = valloader + if torch.cuda.is_available(): + num_cuda_devices = torch.cuda.device_count() + if num_cuda_devices > 0: + device_idx = int(self.cid) % num_cuda_devices + self.device = torch.device(f"cuda:{device_idx}") + self.local_model.model.to(self.device) + else: + # Handle case where CUDA is available but no CUDA devices are found + raise RuntimeError("CUDA is available, but no CUDA devices are found.") + else: + # Handle case where CUDA is not available + self.device = torch.device("cpu") + self.local_model.model.to(self.device) + + self.privacy_engine = PrivacyEngine(secure_mode=False) + self.diff_priv = diff_priv + self.epsilons = [] + self.accuracies = [] + self.losses = [] + if self.diff_priv: + model, optimizer, self.trainloader = self.privacy_engine.make_private_with_epsilon( + module=self.local_model.model.train(), + optimizer=self.local_model.optimizer, + data_loader=self.trainloader, + epochs=params["train_epochs"], + target_epsilon=float(params["EPSILON"]), + target_delta= float(params["DELTA"]), + max_grad_norm=params["MAX_GRAD_NORM"], + ) + setattr(self.local_model, "model", model) + setattr(self.local_model, "optimizer", optimizer) + self.validate() + + def validate(self): + """Validates cid, local_model, trainloader, valloader.""" + if not isinstance(self.cid, str): + raise TypeError("cid argument must be a string") + + if not isinstance(self.local_model, Model): + raise TypeError("local_model argument must be a MEDfl.LearningManager.model.Model") + + if not isinstance(self.trainloader, DataLoader): + raise TypeError("trainloader argument must be a torch.utils.data.dataloader") + + if not isinstance(self.valloader, DataLoader): + raise TypeError("valloader argument must be a torch.utils.data.dataloader") + + if not isinstance(self.diff_priv, bool): + raise TypeError("diff_priv argument must be a bool") + + def get_parameters(self, config): + """ + Returns the current parameters of the local model. + + Args: + config: Configuration information. + + Returns: + Numpy array: Parameters of the local model. + """ + print(f"[Client {self.cid}] get_parameters") + return self.local_model.get_parameters() + + def fit(self, parameters, config): + """ + Fits the local model to the received parameters using federated learning. + + Args: + parameters: Parameters received from the server. + config: Configuration information. + + Returns: + Tuple: Parameters of the local model, number of training examples, and privacy information. + """ + print('\n -------------------------------- \n this is the config of the client') + print(f"[Client {self.cid}] fit, config: {config}") + # print(config['epochs']) + print('\n -------------------------------- \n ') + self.local_model.set_parameters(parameters) + for _ in range(params["train_epochs"]): + epsilon = self.local_model.train( + self.trainloader, + epoch=_, + device=self.device, + privacy_engine=self.privacy_engine, + diff_priv=self.diff_priv, + ) + self.epsilons.append(epsilon) + print(f"epsilon of client {self.cid} : eps = {epsilon}") + return ( + self.local_model.get_parameters(), + len(self.trainloader), + {"epsilon": epsilon}, + ) + + def evaluate(self, parameters, config): + """ + Evaluates the local model on the validation data and returns the loss and accuracy. + + Args: + parameters: Parameters received from the server. + config: Configuration information. + + Returns: + Tuple: Loss, number of validation examples, and accuracy information. + """ + print(f"[Client {self.cid}] evaluate, config: {config}") + self.local_model.set_parameters(parameters) + loss, accuracy , auc = self.local_model.evaluate( + self.valloader, device=self.device + ) + self.losses.append(loss) + self.accuracies.append(accuracy) + + return float(loss), len(self.valloader), {"accuracy": float(accuracy)} diff --git a/build/lib/MEDfl/LearningManager/dynamicModal.py b/build/lib/MEDfl/LearningManager/dynamicModal.py new file mode 100644 index 0000000..12db5c4 --- /dev/null +++ b/build/lib/MEDfl/LearningManager/dynamicModal.py @@ -0,0 +1,287 @@ +import torch +import torch.nn as nn +from sklearn.svm import SVC + +class DynamicModel: + """DynamicModel class for creating various types of neural network models.""" + + # Create a binary classifier model + @staticmethod + def create_binary_classifier(input_dim, hidden_dims, output_dim, activation='relu', dropout_rate=0.0, + batch_norm=False, use_gpu=False): + """ + Creates a binary classifier neural network model with customizable architecture. + + Args: + input_dim (int): Dimension of the input data. + hidden_dims (List[int]): List of dimensions for hidden layers. + output_dim (int): Dimension of the output (number of classes). + activation (str, optional): Activation function for hidden layers. Default is 'relu'. + dropout_rate (float, optional): Dropout rate for regularization. Default is 0.0 (no dropout). + batch_norm (bool, optional): Whether to apply batch normalization. Default is False. + use_gpu (bool, optional): Whether to use GPU acceleration. Default is False. + + Returns: + torch.nn.Module: Created PyTorch model. + """ + + layers = [] + + for i in range(len(hidden_dims)): + if i == 0: + layers.append(nn.Linear(input_dim, hidden_dims[0])) + else: + layers.append(nn.Linear(hidden_dims[i - 1], hidden_dims[i])) + + if batch_norm: + layers.append(nn.BatchNorm1d(hidden_dims[i])) + + activation_layer = nn.ReLU() if activation == 'relu' else nn.Sigmoid() + layers.append(activation_layer) + + if dropout_rate > 0.0: + layers.append(nn.Dropout(dropout_rate)) + + layers.append(nn.Linear(hidden_dims[-1], output_dim)) + layers.append(nn.Sigmoid()) + + model = nn.Sequential(*layers) + + if use_gpu: + model = model.cuda() + + return model + + # Create a multi-class classifier model + @staticmethod + def create_multiclass_classifier(input_dim, hidden_dims, output_dim, activation='relu', dropout_rate=0.0, + batch_norm=False, use_gpu=False): + """ + Creates a multiclass classifier neural network model with customizable architecture. + + Args: + input_dim (int): Dimension of the input data. + hidden_dims (List[int]): List of dimensions for hidden layers. + output_dim (int): Dimension of the output (number of classes). + activation (str, optional): Activation function for hidden layers. Default is 'relu'. + dropout_rate (float, optional): Dropout rate for regularization. Default is 0.0 (no dropout). + batch_norm (bool, optional): Whether to apply batch normalization. Default is False. + use_gpu (bool, optional): Whether to use GPU acceleration. Default is False. + + Returns: + torch.nn.Module: Created PyTorch model. + """ + layers = [] + + for i in range(len(hidden_dims)): + if i == 0: + layers.append(nn.Linear(input_dim, hidden_dims[0])) + else: + layers.append(nn.Linear(hidden_dims[i - 1], hidden_dims[i])) + + if batch_norm: + layers.append(nn.BatchNorm1d(hidden_dims[i])) + + activation_layer = nn.ReLU() if activation == 'relu' else nn.Sigmoid() + layers.append(activation_layer) + + if dropout_rate > 0.0: + layers.append(nn.Dropout(dropout_rate)) + + layers.append(nn.Linear(hidden_dims[-1], output_dim)) + layers.append(nn.LogSoftmax(dim=1)) + + model = nn.Sequential(*layers) + + if use_gpu: + model = model.cuda() + + return model + + # Create a linear regressor model + @staticmethod + def create_linear_regressor(input_dim, output_dim, use_gpu=False): + """ + Creates a linear regressor neural network model. + + Args: + input_dim (int): Dimension of the input data. + output_dim (int): Dimension of the output. + + Returns: + torch.nn.Module: Created PyTorch model. + """ + class LinearRegressionModel(nn.Module): + def __init__(self): + super(LinearRegressionModel, self).__init__() + self.linear = nn.Linear(input_dim, output_dim) + + def forward(self, x): + return self.linear(x) + + model = LinearRegressionModel() + + if use_gpu: + model = model.cuda() + + return model + + # Create a logistic regressor model + @staticmethod + def create_logistic_regressor(input_dim, use_gpu=False): + """ + Creates a logistic regressor neural network model. + + Args: + input_dim (int): Dimension of the input data. + + Returns: + torch.nn.Module: Created PyTorch model. + """ + class LogisticRegressionModel(nn.Module): + def __init__(self): + super(LogisticRegressionModel, self).__init__() + self.linear = nn.Linear(input_dim, 1) + + def forward(self, x): + return torch.sigmoid(self.linear(x)) + + model = LogisticRegressionModel() + + if use_gpu: + model = model.cuda() + + return model + + @staticmethod + def create_convolutional_neural_network(input_channels, output_channels, kernel_size, use_gpu=False): + """ + Creates a convolutional neural network (CNN) model. + + Args: + input_channels (int): Number of input channels. + output_channels (int): Number of output channels. + kernel_size (int): Size of the convolutional kernel. + + Returns: + torch.nn.Module: Created PyTorch model. + """ + + model = nn.Sequential( + nn.Conv2d(input_channels, output_channels, kernel_size), + nn.ReLU(), + nn.MaxPool2d(2) + ) + + if use_gpu: + model = model.cuda() + + return model + + @staticmethod + def create_recurrent_neural_network(input_size, hidden_size, use_gpu=False): + """ + Creates a recurrent neural network (RNN) model. + + Args: + input_size (int): Size of the input. + hidden_size (int): Size of the hidden layer. + + Returns: + torch.nn.Module: Created PyTorch model. + """ + + model = nn.RNN(input_size, hidden_size, batch_first=True) + + if use_gpu: + model = model.cuda() + + return model + + @staticmethod + def create_lstm_network(input_size, hidden_size, use_gpu=False): + """ + Creates a Long Short-Term Memory (LSTM) network model. + + Args: + input_size (int): Size of the input layer. + hidden_size (int): Size of the hidden layer. + + Returns: + torch.nn.Module: Created PyTorch model. + """ + + model = nn.LSTM(input_size, hidden_size, batch_first=True) + + if use_gpu: + model = model.cuda() + + return model + + # Create the dynamic model + def create_model(self, model_type: str, params_dict={}) -> torch.nn.Module: + """ + Create a specific type of model dynamically based on the given parameters. + + Args: + model_type (str): Type of the model to create ('Binary Classifier', 'Multiclass Classifier', 'Linear Regressor', 'Logistic Regressor', 'SVM', 'Neural Network Classifier', 'Convolutional Neural Network', 'Recurrent Neural Network', 'LSTM Network', 'Autoencoder'). + params_dict (dict): Dictionary containing parameters for model creation. + + Returns: + torch.nn.Module: Created PyTorch model. + """ + if model_type == 'Binary Classifier': + return self.create_binary_classifier( + params_dict['input_dim'], params_dict['hidden_dims'], + params_dict['output_dim'], params_dict.get('activation', 'relu'), + params_dict.get('dropout_rate', 0.0), params_dict.get('batch_norm', False), + params_dict.get('use_gpu', False) + ) + elif model_type == 'Multiclass Classifier': + return self.create_multiclass_classifier( + params_dict['input_dim'], params_dict['hidden_dims'], + params_dict['output_dim'], params_dict.get('activation', 'relu'), + params_dict.get('dropout_rate', 0.0), params_dict.get('batch_norm', False), + params_dict.get('use_gpu', False) + ) + elif model_type == 'Linear Regressor': + return self.create_linear_regressor( + params_dict['input_dim'], params_dict['output_dim'], + params_dict.get('use_gpu', False) + ) + elif model_type == 'Logistic Regressor': + return self.create_logistic_regressor( + params_dict['input_dim'], params_dict.get('use_gpu', False) + ) + elif model_type == 'Neural Network Classifier': + return self.create_neural_network_classifier( + params_dict['input_dim'], params_dict['output_dim'], + params_dict['hidden_dims'], params_dict.get('activation', 'relu'), + params_dict.get('dropout_rate', 0.0), params_dict.get('batch_norm', False), + params_dict.get('num_layers', 2), params_dict.get('use_gpu', False) + ) + elif model_type == 'Convolutional Neural Network': + return self.create_convolutional_neural_network( + params_dict['input_channels'], params_dict['output_channels'], + params_dict['kernel_size'], params_dict.get('use_gpu', False) + ) + elif model_type == 'Recurrent Neural Network': + return self.create_recurrent_neural_network( + params_dict['input_size'], params_dict['hidden_size'], + params_dict.get('use_gpu', False) + ) + elif model_type == 'LSTM Network': + return self.create_lstm_network( + params_dict['input_size'], params_dict['hidden_size'], + params_dict.get('use_gpu', False) + ) + elif model_type == 'Autoencoder': + return self.create_autoencoder( + params_dict['input_size'], params_dict['encoder_hidden_size'], + params_dict.get('use_gpu', False) + ) + else: + raise ValueError("Invalid model type provided") + + + diff --git a/build/lib/MEDfl/LearningManager/federated_dataset.py b/build/lib/MEDfl/LearningManager/federated_dataset.py new file mode 100644 index 0000000..4a451cc --- /dev/null +++ b/build/lib/MEDfl/LearningManager/federated_dataset.py @@ -0,0 +1,60 @@ +from MEDfl.NetManager.net_helper import * +from MEDfl.NetManager.net_manager_queries import * +from MEDfl.NetManager.database_connector import DatabaseManager + +class FederatedDataset: + def __init__( + self, + name: str, + train_nodes: list, + test_nodes: list, + trainloaders: list, + valloaders: list, + testloaders: list, + ): + """ + Represents a Federated Dataset. + + :param name: Name of the Federated Dataset. + :param train_nodes: List of train nodes. + :param test_nodes: List of test nodes. + :param trainloaders: List of train data loaders. + :param valloaders: List of validation data loaders. + :param testloaders: List of test data loaders. + """ + self.name = name + self.train_nodes = train_nodes + self.test_nodes = test_nodes + self.trainloaders = trainloaders + self.valloaders = valloaders + self.testloaders = testloaders + self.size = len(self.trainloaders[0].dataset[0][0]) + + db_manager = DatabaseManager() + db_manager.connect() + self.eng = db_manager.get_connection() + + def create(self, FLsetupId: int): + """ + Create a new Federated Dataset in the database. + + :param FLsetupId: The FLsetup ID associated with the Federated Dataset. + """ + query_params = {"name": self.name, "FLsetupId": FLsetupId} + fedDataId = get_feddataset_id_from_name(self.name) + if fedDataId : + self.id = fedDataId + else: + self.eng.execute(text(INSERT_FLDATASET_QUERY), query_params) + self.id = get_feddataset_id_from_name(self.name) + + + def update(self, FLpipeId: int, FedId: int): + """ + Update the FLpipe ID associated with the Federated Dataset in the database. + + :param FLpipeId: The new FLpipe ID to be updated. + :param FedId: The Federated Dataset ID. + """ + query_params = {"FLpipeId": FLpipeId, "FedId": FedId} + self.eng.execute(text(UPDATE_FLDATASET_QUERY), **query_params) diff --git a/build/lib/MEDfl/LearningManager/flpipeline.py b/build/lib/MEDfl/LearningManager/flpipeline.py new file mode 100644 index 0000000..67c5bd5 --- /dev/null +++ b/build/lib/MEDfl/LearningManager/flpipeline.py @@ -0,0 +1,192 @@ +import datetime +from typing import List +import json +import pandas as pd + + +# File: create_query.py +from sqlalchemy import text +from torch.utils.data import DataLoader, TensorDataset +import torch + +from MEDfl.LearningManager.server import FlowerServer +from MEDfl.LearningManager.utils import params, test +from MEDfl.NetManager.net_helper import get_flpipeline_from_name +from MEDfl.NetManager.net_manager_queries import (CREATE_FLPIPELINE_QUERY, + DELETE_FLPIPELINE_QUERY , CREATE_TEST_RESULTS_QUERY) +from MEDfl.NetManager.database_connector import DatabaseManager + +def create_query(name, description, creation_date, result): + query = text( + f"INSERT INTO FLpipeline(name, description, creation_date, results) " + f"VALUES ('{name}', '{description}', '{creation_date}', '{result}')" + ) + return query + + + +class FLpipeline: + """ + FLpipeline class for managing Federated Learning pipelines. + + Attributes: + name (str): The name of the FLpipeline. + description (str): A description of the FLpipeline. + server (FlowerServer): The FlowerServer object associated with the FLpipeline. + + Methods: + __init__(self, name: str, description: str, server: FlowerServer) -> None: + Initialize FLpipeline with the specified name, description, and server. + + + """ + + def __init__( + self, name: str, description: str, server: FlowerServer + ) -> None: + self.name = name + self.description = description + self.server = server + self.validate() + + db_manager = DatabaseManager() + db_manager.connect() + self.eng = db_manager.get_connection() + + def validate(self) -> None: + """ + Validate the name, description, and server attributes. + Raises: + TypeError: If the name is not a string, the description is not a string, + or the server is not a FlowerServer object. + """ + if not isinstance(self.name, str): + raise TypeError("name argument must be a string") + + if not isinstance(self.description, str): + raise TypeError("description argument must be a string") + + # if not isinstance(self.server, FlowerServer): + # raise TypeError("server argument must be a FlowerServer") + + def create(self, result: str) -> None: + """ + Create a new FLpipeline entry in the database with the given result. + + Args: + result (str): The result string to store in the database. + + """ + creation_date = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + query = CREATE_FLPIPELINE_QUERY.format( + name=self.name, + description=self.description, + creation_date=creation_date, + result=result, + ) + self.eng.execute(text(query)) + self.id = get_flpipeline_from_name(self.name) + try: + self.server.fed_dataset.update( + FLpipeId=self.id, FedId=self.server.fed_dataset.id + ) + except: + pass + + def delete(self) -> None: + """ + Delete the FLpipeline entry from the database based on its name. + + Note: This is a placeholder method and needs to be implemented based on your specific database setup. + + """ + # Placeholder code for deleting the FLpipeline entry from the database based on the name. + # You need to implement the actual deletion based on your database setup. + self.eng.execute(DELETE_FLPIPELINE_QUERY.format(self.name)) + + + def test_by_node(self, node_name: str, test_frac=1) -> dict: + """ + Test the FLpipeline by node with the specified test_frac. + + Args: + node_name (str): The name of the node to test. + test_frac (float, optional): The fraction of the test data to use. Default is 1. + + Returns: + dict: A dictionary containing the node name and the classification report. + + """ + idx = self.server.fed_dataset.test_nodes.index(node_name) + global_model, test_loader = ( + self.server.global_model, + self.server.fed_dataset.testloaders[idx], + ) + + # Move model to GPU if available + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + global_model.model.to(device) + + # Prepare test data + test_data = test_loader.dataset + num_samples = int(test_frac * len(test_data)) + test_data = TensorDataset(test_data[:num_samples][0].to(device), test_data[:num_samples][1].to(device)) + + # Create DataLoader for test data + test_loader = DataLoader(test_data, batch_size=params["test_batch_size"]) + + # Perform testing + classification_report = test(model=global_model.model, test_loader=test_loader, device=device) + + return { + "node_name": node_name, + "classification_report": str(classification_report), + } + + + def auto_test(self, test_frac=1) -> List[dict]: + """ + Automatically test the FLpipeline on all nodes with the specified test_frac. + + Args: + test_frac (float, optional): The fraction of the test data to use. Default is 1. + + Returns: + List[dict]: A list of dictionaries containing the node names and the classification reports. + + """ + result = [ + self.test_by_node(node, test_frac) + for node in self.server.fed_dataset.test_nodes + ] + self.create("\n".join(str(res).replace("'", '"') for res in result)) + + # stockage des resultats des tests + for entry in result: + node_name = entry['node_name'] + classification_report_str = entry['classification_report'] + + # Convert the 'classification_report' string to a dictionary + classification_report_dict = json.loads(classification_report_str.replace("'", "\"")) + try: + # Insert record into the 'testResults' table + query = CREATE_TEST_RESULTS_QUERY.format( + pipelineId = self.id, + nodeName = node_name , + confusion_matrix = json.dumps(classification_report_dict['confusion matrix']), + accuracy =classification_report_dict['Accuracy'] , + sensivity = classification_report_dict['Sensitivity/Recall'] , + ppv = classification_report_dict['PPV/Precision'] , + npv= classification_report_dict['NPV'] , + f1score= classification_report_dict['F1-score'] , + fpr= classification_report_dict['False positive rate'] , + tpr= classification_report_dict['True positive rate'] + ) + self.eng.execute(text(query)) + except Exception as e: + # This block will catch any other exceptions + print(f"An unexpected error occurred: {e}") + + + + return result diff --git a/build/lib/MEDfl/LearningManager/model.py b/build/lib/MEDfl/LearningManager/model.py new file mode 100644 index 0000000..f68dc73 --- /dev/null +++ b/build/lib/MEDfl/LearningManager/model.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python3 +# froked from https://github.com/pythonlessons/mltu/blob/main/mltu/torch/model.py + +import typing +from collections import OrderedDict +from typing import Dict, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +from sklearn.metrics import accuracy_score,roc_auc_score + +from .utils import params + + +class Model: + """ + Model class for training and testing PyTorch neural networks. + + Attributes: + model (torch.nn.Module): PyTorch neural network. + optimizer (torch.optim.Optimizer): PyTorch optimizer. + criterion (typing.Callable): Loss function. + """ + + def __init__( + self, + model: torch.nn.Module, + optimizer: torch.optim.Optimizer, + criterion: typing.Callable, + ) -> None: + """ + Initialize Model class with the specified model, optimizer, and criterion. + + Args: + model (torch.nn.Module): PyTorch neural network. + optimizer (torch.optim.Optimizer): PyTorch optimizer. + criterion (typing.Callable): Loss function. + """ + self.model = model + self.optimizer = optimizer + self.criterion = criterion + # Get device on which model is running + self.validate() + + def validate(self) -> None: + """ + Validate model and optimizer. + """ + if not isinstance(self.model, torch.nn.Module): + raise TypeError("model argument must be a torch.nn.Module") + + if not isinstance(self.optimizer, torch.optim.Optimizer): + raise TypeError( + "optimizer argument must be a torch.optim.Optimizer" + ) + + def get_parameters(self) -> List[np.ndarray]: + """ + Get the parameters of the model as a list of NumPy arrays. + + Returns: + List[np.ndarray]: The parameters of the model as a list of NumPy arrays. + """ + return [ + val.cpu().numpy() for _, val in self.model.state_dict().items() + ] + + def set_parameters(self, parameters: List[np.ndarray]) -> None: + """ + Set the parameters of the model from a list of NumPy arrays. + + Args: + parameters (List[np.ndarray]): The parameters to be set. + """ + params_dict = zip(self.model.state_dict().keys(), parameters) + state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict}) + self.model.load_state_dict(state_dict, strict=True) + + def train( + self, train_loader, epoch, device, privacy_engine, diff_priv=False + ) -> float: + """ + Train the model on the given train_loader for one epoch. + + Args: + train_loader: The data loader for training data. + epoch (int): The current epoch number. + device: The device on which to perform the training. + privacy_engine: The privacy engine used for differential privacy (if enabled). + diff_priv (bool, optional): Whether differential privacy is used. Default is False. + + Returns: + float: The value of epsilon used in differential privacy. + """ + self.model.train() + epsilon = 0 + losses = [] + top1_acc = [] + + for i, (X_train, y_train) in enumerate(train_loader): + X_train, y_train = X_train.to(device), y_train.to(device) + + self.optimizer.zero_grad() + + # compute output + y_hat = torch.squeeze(self.model(X_train), 1) + loss = self.criterion(y_hat, y_train) + + preds = np.argmax(y_hat.detach().cpu().numpy(), axis=0) + labels = y_train.detach().cpu().numpy() + + # measure accuracy and record loss + acc = (preds == labels).mean() + + losses.append(loss.item()) + top1_acc.append(acc) + + loss.backward() + self.optimizer.step() + + if diff_priv: + epsilon = privacy_engine.get_epsilon(float(params["DELTA"])) + + if (i + 1) % 10 == 0: + if diff_priv: + epsilon = privacy_engine.get_epsilon(float(params["DELTA"])) + print( + f"\tTrain Epoch: {epoch} \t" + f"Loss: {np.mean(losses):.6f} " + f"Acc@1: {np.mean(top1_acc) * 100:.6f} " + f"(ε = {epsilon:.2f}, δ = {params['DELTA']})" + ) + else: + print( + f"\tTrain Epoch: {epoch} \t" + f"Loss: {np.mean(losses):.6f} " + f"Acc@1: {np.mean(top1_acc) * 100:.6f}" + ) + + return epsilon + + def evaluate(self, val_loader, device=torch.device("cpu")) -> Tuple[float, float]: + """ + Evaluate the model on the given validation data. + + Args: + val_loader: The data loader for validation data. + device: The device on which to perform the evaluation. Default is 'cpu'. + + Returns: + Tuple[float, float]: The evaluation loss and accuracy. + """ + correct, total, loss, accuracy, auc = 0, 0, 0.0, [], [] + self.model.eval() + + with torch.no_grad(): + for X_test, y_test in val_loader: + X_test, y_test = X_test.to(device), y_test.to(device) # Move data to device + + y_hat = torch.squeeze(self.model(X_test), 1) + + + criterion = self.criterion.to(y_hat.device) + loss += criterion(y_hat, y_test).item() + + + # Move y_hat to CPU for accuracy computation + y_hat_cpu = y_hat.cpu().detach().numpy() + accuracy.append(accuracy_score(y_test.cpu().numpy(), y_hat_cpu.round())) + + # Move y_test to CPU for AUC computation + y_test_cpu = y_test.cpu().numpy() + y_prob_cpu = y_hat.cpu().detach().numpy() + if (len(np.unique(y_test_cpu)) != 1): + auc.append(roc_auc_score(y_test_cpu, y_prob_cpu)) + + total += y_test.size(0) + correct += np.sum(y_hat_cpu.round() == y_test_cpu) + + loss /= len(val_loader.dataset) + return loss, np.mean(accuracy), np.mean(auc) + + + @staticmethod + def save_model(model , model_name:str): + """ + Saves a PyTorch model to a file. + + Args: + model (torch.nn.Module): PyTorch model to be saved. + model_name (str): Name of the model file. + + Raises: + Exception: If there is an issue during the saving process. + + Returns: + None + """ + try: + torch.save(model, '../../notebooks/.ipynb_checkpoints/trainedModels/' + model_name + ".pth") + except Exception as e: + raise Exception(f"Error saving the model: {str(e)}") + + @staticmethod + def load_model(model_path: str): + """ + Loads a PyTorch model from a file. + + Args: + model_path (str): Path to the model file to be loaded. + + Returns: + torch.nn.Module: Loaded PyTorch model. + """ + # Ensure models are loaded onto the CPU when CUDA is not available + torch_kwargs = {"weights_only": False} + if torch.cuda.is_available(): + loaded_model = torch.load(model_path , **torch_kwargs) + else: + loaded_model = torch.load(model_path, map_location=torch.device('cpu') , **torch_kwargs) + return loaded_model + + diff --git a/build/lib/MEDfl/LearningManager/params.yaml b/build/lib/MEDfl/LearningManager/params.yaml new file mode 100644 index 0000000..83fc6fa --- /dev/null +++ b/build/lib/MEDfl/LearningManager/params.yaml @@ -0,0 +1,14 @@ +DELTA: 1.0e-05 +EPSILON: 5.0 +MAX_GRAD_NORM: 1.0 +diff_privacy: true +lr: 0.001 +min_evalclient: 2 +num_rounds: 20 +optimizer: SGD +path_to_master_csv: /home/local/USHERBROOKE/saho6810/MEDfl/code/MEDfl/notebooks/data/masterDataSet/miniDiabete.csv +path_to_test_csv: /home/local/USHERBROOKE/saho6810/MEDfl/code/MEDfl/notebooks/data/masterDataSet/Mimic_train.csv +task: BinaryClassification +test_batch_size: 1 +train_batch_size: 32 +train_epochs: 20 diff --git a/build/lib/MEDfl/LearningManager/params_optimiser.py b/build/lib/MEDfl/LearningManager/params_optimiser.py new file mode 100644 index 0000000..4d4a1ea --- /dev/null +++ b/build/lib/MEDfl/LearningManager/params_optimiser.py @@ -0,0 +1,442 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import torch +import torch.nn as nn +import torch.optim as optim +import torch.nn.functional as F +from torch.utils.data import TensorDataset, DataLoader +from sklearn.model_selection import GridSearchCV, train_test_split +from sklearn.base import BaseEstimator +from sklearn.metrics import make_scorer, precision_score, recall_score, accuracy_score, f1_score,roc_auc_score, balanced_accuracy_score +import optuna + +from MEDfl.LearningManager.model import Model +from MEDfl.LearningManager.strategy import Strategy +from MEDfl.LearningManager.server import FlowerServer +from MEDfl.LearningManager.flpipeline import FLpipeline + +class BinaryClassifier(nn.Module): + def __init__(self, input_size, num_layers, layer_size): + super(BinaryClassifier, self).__init__() + + # Input layer + self.layers = [nn.Linear(input_size, layer_size)] + + # Hidden layers + for _ in range(num_layers - 1): + self.layers.append(nn.Linear(layer_size, layer_size)) + + # Output layer + self.layers.append(nn.Linear(layer_size, 1)) + + # ModuleList to handle dynamic number of layers + self.layers = nn.ModuleList(self.layers) + + def forward(self, x): + for layer in self.layers[:-1]: + x = F.relu(layer(x)) + x = self.layers[-1](x) + return x + +class CustomPyTorchClassifier(BaseEstimator): + def __init__(self, hidden_dim=10, lr=0.001, pos_weight=1, th=0.5, max_epochs=10, batch_size=32): + self.hidden_dim = hidden_dim + self.lr = lr + self.pos_weight = pos_weight + self.max_epochs = max_epochs + self.batch_size = batch_size + self.th = th + self.model = None + + def fit(self, X, y): + if isinstance(X, torch.Tensor): + X = X.numpy() + if isinstance(y, torch.Tensor): + y = y.numpy() + + input_dim = X.shape[1] + self.model = nn.Sequential( + nn.Linear(input_dim, self.hidden_dim), + nn.ReLU(), + nn.Linear(self.hidden_dim, 1), + nn.Sigmoid() + ) + + criterion = nn.BCEWithLogitsLoss(pos_weight=torch.tensor(self.pos_weight)) + optimizer = optim.Adam(self.model.parameters(), lr=self.lr) + + train_data = TensorDataset(torch.from_numpy(X).float(), torch.from_numpy(y).float()) + train_loader = DataLoader(train_data, batch_size=self.batch_size, shuffle=True) + + self.model.train() + for epoch in range(self.max_epochs): + for inputs, labels in train_loader: + optimizer.zero_grad() + outputs = self.model(inputs) + loss = criterion(outputs.squeeze(), labels) + loss.backward() + optimizer.step() + return self + + def predict(self, X): + if isinstance(X, torch.Tensor): + X = X.numpy() + + self.model.eval() + with torch.no_grad(): + outputs = self.model(torch.from_numpy(X).float()) + predictions = (outputs.squeeze() > self.th).float().numpy() + return predictions + + def score(self, X, y): + predictions = self.predict(X) + return accuracy_score(y, predictions) + + +class ParamsOptimiser: + def __init__(self, X_train = None, y_train=None, X_test=None, y_test=None): + if isinstance(X_train, pd.DataFrame): + X_train = X_train.to_numpy() + if isinstance(y_train, pd.Series): + y_train = y_train.to_numpy() + if isinstance(X_test, pd.DataFrame): + X_test = X_test.to_numpy() + if isinstance(y_test, pd.Series): + y_test = y_test.to_numpy() + + self.X_train = X_train + self.y_train = y_train + self.X_test = X_test + self.y_test = y_test + + def perform_grid_search(self, param_grid, scoring_metric='recall', cv=3, verbose=1): + pytorch_model = CustomPyTorchClassifier() + scorer = make_scorer(recall_score, greater_is_better=True) + + if scoring_metric == 'precision': + scorer = make_scorer(precision_score) + elif scoring_metric == 'accuracy': + scorer = make_scorer(accuracy_score) + elif scoring_metric == 'recall': + scorer = make_scorer(recall_score) + elif scoring_metric == 'f1': + scorer = make_scorer(f1_score) + + grid_search = GridSearchCV(pytorch_model, param_grid, scoring=scorer, cv=cv, refit=scoring_metric, verbose=verbose) + grid_search.fit(self.X_train, self.y_train) + + self.grid_search_results = grid_search # Save the grid search results + + return grid_search + + # Inside the CustomModelTrainer class + def plot_results(self, params_to_plot=None): + results = pd.DataFrame(self.grid_search_results.cv_results_) + + if params_to_plot is None: + # Create a column for configuration details + results['config'] = results['params'].apply(lambda x: str(x)) + + # Visualize mean test scores along with configurations + plt.figure(figsize=(15, 8)) + bar_plot = plt.bar(results.index, results['mean_test_score'], color='blue', alpha=0.7) + plt.xticks(results.index, results['config'], rotation='vertical', fontsize=8) + plt.ylabel('Mean Test Score') + plt.title('Mean Test Scores for Each Configuration') + plt.tight_layout() + + # Add values on top of bars + for bar, score in zip(bar_plot, results['mean_test_score']): + plt.text(bar.get_x() + bar.get_width() / 2 - 0.15, bar.get_height() + 0.01, f'{score:.3f}', fontsize=8) + + plt.show() + return + + try: + # Dynamically get the column names for the specified scoring metric + mean_test_col = f'mean_test_{params_to_plot[0]}' + param_cols = [f'param_{param}' for param in params_to_plot] + + if len(params_to_plot) == 1: + # Plotting the heatmap for a single parameter + plt.figure(figsize=(8, 6)) + sns.heatmap(results.pivot_table(index=param_cols[0]), + annot=True, cmap='YlGnBu', fmt=".3f", cbar_kws={'label': mean_test_col}) + plt.title(mean_test_col.capitalize()) + plt.show() + elif len(params_to_plot) == 2: + # Create a pair plot for two parameters + plt.figure(figsize=(8, 6)) + scores = results.pivot_table(index=param_cols[0], columns=param_cols[1], values=f'mean_test_score', aggfunc="mean") + sns.heatmap(scores, annot=True, cmap='YlGnBu', fmt=".3f", cbar_kws={'label': mean_test_col}) + plt.title(mean_test_col.capitalize()) + plt.show() + else: + print("Invalid number of parameters to plot. You can provide either one or two parameters.") + except KeyError as e: + print(f"Error: {e}. Make sure the specified scoring metric exists in the results DataFrame.") + + + + def optuna_optimisation(self, direction, params): + # Create the data loaders here + train_data = TensorDataset(torch.from_numpy(self.X_train).float(), torch.from_numpy(self.y_train).float()) + test_data = TensorDataset(torch.from_numpy(self.X_test).float(), torch.from_numpy(self.y_test).float()) + + + + def objective(trial): + + batch_size=trial.suggest_int('batch_size', **params['batch_size']) + + train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True) + test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False) + + # Create the model with the suggested hyperparameters + model = BinaryClassifier(input_size=self.X_train.shape[1], + num_layers=trial.suggest_int('num_layers', **params['num_layers']) , + layer_size=trial.suggest_int('hidden_size', **params['hidden_size'])) + + # Define the loss function and optimizer + criterion = nn.BCEWithLogitsLoss() + optimizer_name = trial.suggest_categorical('optimizer', params['optimizer']) + learning_rate = trial.suggest_float('learning_rate', **params['learning_rate']) + + + if optimizer_name == 'Adam': + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + elif optimizer_name == 'SGD': + optimizer = optim.SGD(model.parameters(), lr=learning_rate) + elif optimizer_name == 'RMSprop': + optimizer = optim.RMSprop(model.parameters(), lr=learning_rate) + + # Training loop + num_epochs = trial.suggest_int('num_epochs', **params['num_epochs']) + for epoch in range(num_epochs): + model.train() + for batch_X, batch_y in train_loader: + optimizer.zero_grad() + outputs = model(batch_X) + loss = criterion(outputs.squeeze(), batch_y) + loss.backward() + optimizer.step() + + # Evaluation + model.eval() + predictions = [] + true_labels = [] + with torch.no_grad(): + for batch_X, batch_y in test_loader: + outputs = model(batch_X) + predictions.extend(torch.sigmoid(outputs).numpy()) + true_labels.extend(batch_y.numpy()) + + # Calculate F1 score + # f1 = f1_score(true_labels, (np.array(predictions) > 0.5).astype(int)) + auc = roc_auc_score(true_labels, predictions) + + trial.report(auc, epoch) + + # Handle pruning based on the intermediate value + if trial.should_prune(): + raise optuna.TrialPruned() + + return auc + + # Create an Optuna study + study = optuna.create_study(direction=direction) + study.optimize(objective, n_trials=params['n_trials']) + + self.study = study + + # Get the best hyperparameters + best_params = study.best_params + print(f"Best Hyperparameters: {best_params}") + + return study + + def train_optimized_model(self ,trial ,th_min , th_max): + + best_params = self.study.best_params + + threshold = trial.suggest_float('threashhold', th_min, th_max, log=True) + + train_data = TensorDataset(torch.from_numpy(self.X_train).float(), torch.from_numpy(self.y_train).float()) + test_data = TensorDataset(torch.from_numpy(self.X_test).float(), torch.from_numpy(self.y_test).float()) + + train_loader = DataLoader(train_data, batch_size=best_params['batch_size'], shuffle=True) + test_loader = DataLoader(test_data, batch_size=best_params['batch_size'], shuffle=False) + + + # Use the best hyperparameters to train the final model + final_model = BinaryClassifier(input_size=self.X_train.shape[1], layer_size=best_params['hidden_size'] , num_layers=best_params['num_layers']) + final_optimizer = self.get_optimizer(best_params['optimizer'], final_model.parameters(), best_params['learning_rate']) + final_criterion = nn.BCEWithLogitsLoss() + + num_epochs = best_params['num_epochs'] + for epoch in range(num_epochs): + final_model.train() + for batch_X, batch_y in train_loader: + final_optimizer.zero_grad() + outputs = final_model(batch_X) + loss = final_criterion(outputs.squeeze(), batch_y) + loss.backward() + final_optimizer.step() + + # Evaluate the final model on the test set + final_model.eval() + with torch.no_grad(): + predictions = [] + true_labels = [] + for batch_X, batch_y in test_loader: + outputs = final_model(batch_X) + predictions.extend(torch.sigmoid(outputs).numpy()) + true_labels.extend(batch_y.numpy()) + + final_balanced_acc = balanced_accuracy_score(true_labels, (np.array(predictions) > threshold).astype(int)) + print(f"Model balanced accuracy: {final_balanced_acc}") + + return final_balanced_acc + + def get_optimizer(self, optimizer_name, parameters, learning_rate): + if optimizer_name == 'Adam': + return optim.Adam(parameters, lr=learning_rate) + elif optimizer_name == 'SGD': + return optim.SGD(parameters, lr=learning_rate) + elif optimizer_name == 'RMSprop': + return optim.RMSprop(parameters, lr=learning_rate) + else: + raise ValueError(f"Unknown optimizer: {optimizer_name}") + + def perform_grid_search(self, param_grid, scoring_metric='recall', cv=3, verbose=1): + pytorch_model = CustomPyTorchClassifier() + scorer = make_scorer(recall_score, greater_is_better=True) + + if scoring_metric == 'precision': + scorer = make_scorer(precision_score) + elif scoring_metric == 'accuracy': + scorer = make_scorer(accuracy_score) + elif scoring_metric == 'recall': + scorer = make_scorer(recall_score) + elif scoring_metric == 'f1': + scorer = make_scorer(f1_score) + + grid_search = GridSearchCV(pytorch_model, param_grid, scoring=scorer, cv=cv, refit=scoring_metric, verbose=verbose) + grid_search.fit(self.X_train, self.y_train) + + self.grid_search_results = grid_search # Save the grid search results + + return grid_search + + + def plot_param_importances(self): + return optuna.visualization.plot_param_importances(self.study) + + def plot_slice(self , params): + return optuna.visualization.plot_slice(self.study , params=params) + + def plot_parallel_coordinate(self): + return optuna.visualization.plot_parallel_coordinate(self.study) + + def plot_rank(self , params=None): + return optuna.visualization.plot_rank(self.study , params=params) + + def plot_optimization_history(self): + return optuna.visualization.plot_optimization_history(self.study) + + def optimize_model_threashhold(self , n_trials , th_min , th_max): + additional_params = {'th_min': th_min, 'th_max': th_max} + + th_study = optuna.create_study(direction='maximize') + th_study.optimize(lambda trial: self.train_optimized_model(trial , **additional_params) , n_trials) + + # Get the best hyperparameters + best_params = th_study.best_params + print(f"Best Hyperparameters: {best_params}") + + return optuna.visualization.plot_rank(th_study , params=['threashhold']) + + def federated_params_iptim(self , params , direction, model, fl_dataset): + + def objective(trial): + + criterion = nn.BCEWithLogitsLoss() + + optimizer_name = trial.suggest_categorical('optimizer', params['optimizer']) + learning_rate = trial.suggest_float('learning_rate', **params['learning_rate']) + num_rounds = trial.suggest_int('num_rounds', **params['num_rounds']) + diff_privacy = trial.suggest_int('diff_privacy', **params['diff_privacy']) + diff_privacy = True if diff_privacy == 1 else False + + if optimizer_name == 'Adam': + optimizer = optim.Adam(model.parameters(), lr=learning_rate) + elif optimizer_name == 'SGD': + optimizer = optim.SGD(model.parameters(), lr=learning_rate) + elif optimizer_name == 'RMSprop': + optimizer = optim.RMSprop(model.parameters(), lr=learning_rate) + + # Creating a new Model instance using the specific model created by DynamicModel + global_model = Model(model, optimizer, criterion) + + # Get the initial params of the model + init_params = global_model.get_parameters() + + fl_strategy = trial.suggest_categorical('fl_strategy', params['fl_strategy']) + + learning_strategy = Strategy(fl_strategy, + fraction_fit = 1.0 , + fraction_evaluate = 1.0, + min_fit_clients = 2, + min_evaluate_clients = 2, + min_available_clients = 2 , + initial_parameters=init_params) + + learning_strategy.create_strategy() + + # Create The server + server = FlowerServer(global_model, strategy = learning_strategy, num_rounds = num_rounds, + num_clients = len(fl_dataset.trainloaders), + fed_dataset = fl_dataset,diff_privacy = diff_privacy, + # You can change the resources alocated for each client based on your machine + client_resources={'num_cpus': 1.0, 'num_gpus': 0.0} + ) + + ppl_1 = FLpipeline( name ="the first fl_pipeline",description = "this is our first FL pipeline", + server = server) + + # Run the Traning of the model + history = ppl_1.server.run() + + return server.auc[len(server.auc)-1] + + + + study = optuna.create_study(direction=direction) + study.optimize(objective, n_trials=params['n_trials']) + + self.study = study + + # Get the best hyperparameters + best_params = study.best_params + print(f"Best Hyperparameters: {best_params}") + + return study + + + + + + + + + + + + + + + + + diff --git a/build/lib/MEDfl/LearningManager/plot.py b/build/lib/MEDfl/LearningManager/plot.py new file mode 100644 index 0000000..c5f6c6e --- /dev/null +++ b/build/lib/MEDfl/LearningManager/plot.py @@ -0,0 +1,229 @@ +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns + +from .utils import * + +# Replace this with your actual code for data collection +results_dict = { + ("LR: 0.001, Optimizer: Adam", "accuracy"): [0.85, 0.89, 0.92, 0.94, ...], + ("LR: 0.001, Optimizer: Adam", "loss"): [0.2, 0.15, 0.1, 0.08, ...], + ("LR: 0.01, Optimizer: SGD", "accuracy"): [0.88, 0.91, 0.93, 0.95, ...], + ("LR: 0.01, Optimizer: SGD", "loss"): [0.18, 0.13, 0.09, 0.07, ...], + ("LR: 0.1, Optimizer: Adam", "accuracy"): [0.82, 0.87, 0.91, 0.93, ...], + ("LR: 0.1, Optimizer: Adam", "loss"): [0.25, 0.2, 0.15, 0.12, ...], +} +""" +server should have: + #len = num of rounds + self.accuracies + self.losses + +Client should have + # len = num of epochs + self.accuracies + self.losses + self.epsilons + self.deltas + +#common things : LR,SGD, Aggregation + +""" + + +class AccuracyLossPlotter: + """ + A utility class for plotting accuracy and loss metrics based on experiment results. + + Args: + results_dict (dict): Dictionary containing experiment results organized by parameters and metrics. + + Attributes: + results_dict (dict): Dictionary containing experiment results organized by parameters and metrics. + parameters (list): List of unique parameters in the experiment results. + metrics (list): List of unique metrics in the experiment results. + iterations (range): Range of iterations (rounds or epochs) in the experiment. + """ + + def __init__(self, results_dict): + """ + Initialize the AccuracyLossPlotter with experiment results. + + Args: + results_dict (dict): Dictionary containing experiment results organized by parameters and metrics. + """ + self.results_dict = results_dict + self.parameters = list( + set([param[0] for param in results_dict.keys()]) + ) + self.metrics = list(set([param[1] for param in results_dict.keys()])) + self.iterations = range(1, len(list(results_dict.values())[0]) + 1) + + def plot_accuracy_loss(self): + """ + Plot accuracy and loss metrics for different parameters. + """ + + plt.figure(figsize=(8, 6)) + + for param in self.parameters: + for metric in self.metrics: + key = (param, metric) + values = self.results_dict[key] + plt.plot( + self.iterations, + values, + label=f"{param} ({metric})", + marker="o", + linestyle="-", + ) + + plt.xlabel("Rounds") + plt.ylabel("Accuracy / Loss") + plt.title("Accuracy and Loss by Parameters") + plt.legend() + plt.grid(True) + plt.show() + + @staticmethod + def plot_global_confusion_matrix(pipeline_name: str): + """ + Plot a global confusion matrix based on pipeline results. + + Args: + pipeline_name (str): Name of the pipeline. + + Returns: + None + """ + # Get the id of the pipeline by name + pipeline_id = get_pipeline_from_name(pipeline_name) + # get the confusion matrix pf the pipeline + confusion_matrix = get_pipeline_confusion_matrix(pipeline_id) + + # Extracting confusion matrix values + TP = confusion_matrix['TP'] + FP = confusion_matrix['FP'] + FN = confusion_matrix['FN'] + TN = confusion_matrix['TN'] + + # Creating a matrix for visualization + matrix = [[TN, FP], + [FN, TP]] + + # Plotting the confusion matrix as a heatmap + plt.figure(figsize=(6, 4)) + sns.heatmap(matrix, annot=True, fmt='d', cmap='Blues', + xticklabels=['Predicted Negative', 'Predicted Positive'], + yticklabels=['Actual Negative', 'Actual Positive']) + plt.title('Global Confusion Matrix') + plt.xlabel('Predicted label') + plt.ylabel('True label') + plt.tight_layout() + + # Display the confusion matrix heatmap + plt.show() + + @staticmethod + def plot_confusion_Matrix_by_node(node_name: str, pipeline_name: str): + """ + Plot a confusion matrix for a specific node in the pipeline. + + Args: + node_name (str): Name of the node. + pipeline_name (str): Name of the pipeline. + + Returns: + None + """ + + # Get the id of the pipeline by name + pipeline_id = get_pipeline_from_name(pipeline_name) + # get the confusion matrix pf the pipeline + confusion_matrix = get_node_confusion_matrix( + pipeline_id, node_name=node_name) + + # Extracting confusion matrix values + TP = confusion_matrix['TP'] + FP = confusion_matrix['FP'] + FN = confusion_matrix['FN'] + TN = confusion_matrix['TN'] + + # Creating a matrix for visualization + matrix = [[TN, FP], + [FN, TP]] + + # Plotting the confusion matrix as a heatmap + plt.figure(figsize=(6, 4)) + sns.heatmap(matrix, annot=True, fmt='d', cmap='Blues', + xticklabels=['Predicted Negative', 'Predicted Positive'], + yticklabels=['Actual Negative', 'Actual Positive']) + plt.title('Confusion Matrix of node: '+node_name) + plt.xlabel('Predicted label') + plt.ylabel('True label') + plt.tight_layout() + + # Display the confusion matrix heatmap + plt.show() + return + + @staticmethod + def plot_classification_report(pipeline_name: str): + """ + Plot a comparison of classification report metrics between nodes. + + Args: + pipeline_name (str): Name of the pipeline. + + Returns: + None + """ + + colors = ['#FF5733', '#6A5ACD', '#3CB371', '#FFD700', '#FFA500', '#8A2BE2', '#00FFFF', '#FF00FF', '#A52A2A', '#00FF00'] + + # Get the id of the pipeline by name + pipeline_id = get_pipeline_from_name(pipeline_name) + + pipeline_results = get_pipeline_result(pipeline_id) + + nodesList = pipeline_results['nodename'] + classificationReports = [] + + for index, node in enumerate(nodesList): + classificationReports.append({ + 'Accuracy': pipeline_results['accuracy'][index], + 'Sensitivity/Recall': pipeline_results['sensivity'][index], + 'PPV/Precision': pipeline_results['ppv'][index], + 'NPV': pipeline_results['npv'][index], + 'F1-score': pipeline_results['f1score'][index], + 'False positive rate': pipeline_results['fpr'][index], + 'True positive rate': pipeline_results['tpr'][index] + }) + + metric_labels = list(classificationReports[0].keys()) # Assuming both reports have the same keys + + # Set the positions of the bars on the x-axis + x = np.arange(len(metric_labels)) + + # Set the width of the bars + width = 0.35 + + plt.figure(figsize=(12, 6)) + + for index, report in enumerate(classificationReports): + metric = list(report.values()) + plt.bar(x + (index - len(nodesList) / 2) * width / len(nodesList), metric, width / len(nodesList), + label=nodesList[index], color=colors[index % len(colors)]) + + # Adding labels, title, and legend + plt.xlabel('Metrics') + plt.ylabel('Values') + plt.title('Comparison of Classification Report Metrics between Nodes') + plt.xticks(ticks=x, labels=metric_labels, rotation=45) + plt.legend() + + # Show plot + plt.tight_layout() + plt.show() + + return diff --git a/build/lib/MEDfl/LearningManager/server.py b/build/lib/MEDfl/LearningManager/server.py new file mode 100644 index 0000000..fa88c10 --- /dev/null +++ b/build/lib/MEDfl/LearningManager/server.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python3 + +import copy +from typing import Dict, Optional, Tuple + +import flwr as fl +import torch + +from .client import FlowerClient +from .federated_dataset import FederatedDataset +from .model import Model +from .strategy import Strategy + + +class FlowerServer: + """ + A class representing the central server for Federated Learning using Flower. + + Attributes: + global_model (Model): The global model that will be federated among clients. + strategy (Strategy): The strategy used for federated learning, specifying communication and aggregation methods. + num_rounds (int): The number of federated learning rounds to perform. + num_clients (int): The number of clients participating in the federated learning process. + fed_dataset (FederatedDataset): The federated dataset used for training and evaluation. + diff_priv (bool): Whether differential privacy is used during the federated learning process. + accuracies (List[float]): A list to store the accuracy of the global model during each round. + losses (List[float]): A list to store the loss of the global model during each round. + flower_clients (List[FlowerClient]): A list to store the FlowerClient objects representing individual clients. + + """ + + def __init__( + self, + global_model: Model, + strategy: Strategy, + num_rounds: int, + num_clients: int, + fed_dataset: FederatedDataset, + diff_privacy: bool = False, + client_resources: Optional[Dict[str, float]] = {'num_cpus': 1, 'num_gpus': 0.0} + ) -> None: + """ + Initialize a FlowerServer object with the specified parameters. + + Args: + global_model (Model): The global model that will be federated among clients. + strategy (Strategy): The strategy used for federated learning, specifying communication and aggregation methods. + num_rounds (int): The number of federated learning rounds to perform. + num_clients (int): The number of clients participating in the federated learning process. + fed_dataset (FederatedDataset): The federated dataset used for training and evaluation. + diff_privacy (bool, optional): Whether differential privacy is used during the federated learning process. + Default is False. + """ + self.device = torch.device( + f"cuda" if torch.cuda.is_available() else "cpu" + ) + self.global_model = global_model + self.params = global_model.get_parameters() + self.global_model.model = global_model.model.to(self.device) + self.num_rounds = num_rounds + self.num_clients = num_clients + self.fed_dataset = fed_dataset + self.strategy = strategy + self.client_resources = client_resources + setattr( + self.strategy.strategy_object, + "min_available_clients", + self.num_clients, + ) + setattr( + self.strategy.strategy_object, + "initial_parameters", + fl.common.ndarrays_to_parameters(self.params), + ) + setattr(self.strategy.strategy_object, "evaluate_fn", self.evaluate) + self.fed_dataset = fed_dataset + self.diff_priv = diff_privacy + self.accuracies = [] + self.losses = [] + self.auc = [] + self.flower_clients = [] + self.validate() + + def validate(self) -> None: + """Validate global_model, strategy, num_clients, num_rounds, fed_dataset, diff_privacy""" + if not isinstance(self.global_model, Model): + raise TypeError("global_model argument must be a Model instance") + + # if not isinstance(self.strategy, Strategy): + # print(self.strategy) + # print(isinstance(self.strategy, Strategy)) + # raise TypeError("strategy argument must be a Strategy instance") + + if not isinstance(self.num_clients, int): + raise TypeError("num_clients argument must be an int") + + if not isinstance(self.num_rounds, int): + raise TypeError("num_rounds argument must be an int") + + if not isinstance(self.diff_priv, bool): + raise TypeError("diff_priv argument must be a bool") + + def client_fn(self, cid) -> FlowerClient: + """ + Return a FlowerClient object for a specific client ID. + + Args: + cid: The client ID. + + Returns: + FlowerClient: A FlowerClient object representing the individual client. + """ + + device = torch.device( + f"cuda:{int(cid) % 4}" if torch.cuda.is_available() else "cpu" + ) + client_model = copy.deepcopy(self.global_model) + + trainloader = self.fed_dataset.trainloaders[int(cid)] + valloader = self.fed_dataset.valloaders[int(cid)] + # this helps in making plots + + client = FlowerClient( + cid, client_model, trainloader, valloader, self.diff_priv + ) + self.flower_clients.append(client) + return client + + def evaluate( + self, + server_round: int, + parameters: fl.common.NDArrays, + config: Dict[str, fl.common.Scalar], + ) -> Optional[Tuple[float, Dict[str, fl.common.Scalar]]]: + """ + Evaluate the global model on the validation dataset and update the accuracies and losses. + + Args: + server_round (int): The current round of the federated learning process. + parameters (fl.common.NDArrays): The global model parameters. + config (Dict[str, fl.common.Scalar]): Configuration dictionary. + + Returns: + Optional[Tuple[float, Dict[str, fl.common.Scalar]]]: The evaluation loss and accuracy. + """ + testloader = self.fed_dataset.valloaders[0] + + self.global_model.set_parameters( + parameters + ) # Update model with the latest parameters + loss, accuracy ,auc = self.global_model.evaluate(testloader, self.device) + self.auc.append(auc) + self.losses.append(loss) + self.accuracies.append(accuracy) + + return loss, {"accuracy": accuracy} + + def run(self) -> None: + """ + Run the federated learning process using Flower simulation. + + Returns: + History: The history of the accuracies and losses during the training of each node + """ + # Increase the object store memory to the minimum allowed value or higher + ray_init_args = {"include_dashboard": False + , "object_store_memory": 78643200 + } + self.fed_dataset.eng = None + + history = fl.simulation.start_simulation( + client_fn=self.client_fn, + num_clients=self.num_clients, + config=fl.server.ServerConfig(self.num_rounds), + strategy=self.strategy.strategy_object, + ray_init_args=ray_init_args, + client_resources = self.client_resources + ) + + return history + diff --git a/build/lib/MEDfl/LearningManager/strategy.py b/build/lib/MEDfl/LearningManager/strategy.py new file mode 100644 index 0000000..038d8e6 --- /dev/null +++ b/build/lib/MEDfl/LearningManager/strategy.py @@ -0,0 +1,82 @@ + +from collections import OrderedDict +from typing import Dict, List, Optional, Tuple + +import flwr as fl +import numpy as np + +import optuna + + + + +class Strategy: + """ + A class representing a strategy for Federated Learning. + + Attributes: + name (str): The name of the strategy. Default is "FedAvg". + fraction_fit (float): Fraction of clients to use for training during each round. Default is 1.0. + fraction_evaluate (float): Fraction of clients to use for evaluation during each round. Default is 1.0. + min_fit_clients (int): Minimum number of clients to use for training during each round. Default is 2. + min_evaluate_clients (int): Minimum number of clients to use for evaluation during each round. Default is 2. + min_available_clients (int): Minimum number of available clients required to start a round. Default is 2. + initial_parameters (Optional[]): The initial parameters of the server model + Methods: + + """ + + def __init__( + self, + name: str = "FedAvg", + fraction_fit: float = 1.0, + fraction_evaluate: float = 1.0, + min_fit_clients: int = 2, + min_evaluate_clients: int = 2, + min_available_clients: int = 2, + initial_parameters = [], + evaluation_methode = "centralized" + ) -> None: + """ + Initialize a Strategy object with the specified parameters. + + Args: + name (str): The name of the strategy. Default is "FedAvg". + fraction_fit (float): Fraction of clients to use for training during each round. Default is 1.0. + fraction_evaluate (float): Fraction of clients to use for evaluation during each round. Default is 1.0. + min_fit_clients (int): Minimum number of clients to use for training during each round. Default is 2. + min_evaluate_clients (int): Minimum number of clients to use for evaluation during each round. Default is 2. + min_available_clients (int): Minimum number of available clients required to start a round. Default is 2. + initial_parameters (Optional[]): The initial parametres of the server model + evaluation_methode ( "centralized" | "distributed") + """ + self.fraction_fit = fraction_fit + self.fraction_evaluate = fraction_evaluate + self.min_fit_clients = min_fit_clients + self.min_evaluate_clients = min_evaluate_clients + self.min_available_clients = min_available_clients + self.initial_parameters = initial_parameters + self.evaluate_fn = None + self.name = name + + def optuna_fed_optimization(self, direction:str , hpo_rate:int , params_config): + self.study = optuna.create_study(direction=direction) + self.hpo_rate = hpo_rate + self.params_config = params_config + + + def create_strategy(self): + self.strategy_object = self.get_strategy_by_name()( + fraction_fit=self.fraction_fit, + fraction_evaluate=self.fraction_evaluate, + min_fit_clients=self.min_fit_clients, + min_evaluate_clients=self.min_evaluate_clients, + min_available_clients=self.min_available_clients, + initial_parameters=fl.common.ndarrays_to_parameters(self.initial_parameters), + evaluate_fn=self.evaluate_fn + ) + def get_strategy_by_name(self): + return eval(f"fl.server.strategy.{self.name}") + + + diff --git a/build/lib/MEDfl/LearningManager/utils.py b/build/lib/MEDfl/LearningManager/utils.py new file mode 100644 index 0000000..a5bd4dd --- /dev/null +++ b/build/lib/MEDfl/LearningManager/utils.py @@ -0,0 +1,331 @@ +#!/usr/bin/env python3 + +import pkg_resources +import torch +import yaml +from sklearn.metrics import * +from yaml.loader import SafeLoader + + +from MEDfl.NetManager.database_connector import DatabaseManager + +# from scripts.base import * +import json + + +import pandas as pd +import numpy as np + +import os +import configparser + +import subprocess +import ast + +from sqlalchemy import text + + +# Get the directory of the current script +current_directory = os.path.dirname(os.path.abspath(__file__)) + +# Load configuration from the config file +yaml_path = os.path.join(current_directory, 'params.yaml') + +with open(yaml_path) as g: + params = yaml.load(g, Loader=SafeLoader) + +# global_yaml_path = pkg_resources.resource_filename(__name__, "../../global_params.yaml") +# with open(global_yaml_path) as g: +# global_params = yaml.load(g, Loader=SafeLoader) + + +# Default path for the config file +DEFAULT_CONFIG_PATH = 'db_config.ini' + + +def load_db_config_dep(): + config = os.environ.get('MEDfl_DB_CONFIG') + + if config: + return ast.literal_eval(config) + else: + raise ValueError(f"MEDfl db config not found") + +# Function to allow users to set config path programmatically + + +def set_db_config_dep(config_path): + config = configparser.ConfigParser() + config.read(config_path) + if (config['sqllite']): + os.environ['MEDfl_DB_CONFIG'] = str(dict(config['sqllite'])) + else: + raise ValueError(f"mysql key not found in file '{config_path}'") + + + +def load_db_config(): + """Read a dictionary from an environment variable.""" + obj_str = os.getenv("MEDfl_DB_CONFIG") + if obj_str is not None: + return ast.literal_eval(obj_str) + else: + raise ValueError(f"Environment variable MEDfl_DB_CONFIG not found") + +# Function to allow users to set config path programmatically + + +def set_db_config(config_path): + obj = {"database" : config_path} + + """Store a dictionary as a string in an environment variable.""" + obj_str = str(obj) + os.environ['MEDfl_DB_CONFIG'] = obj_str + + + + + + +# Create databas + + +def create_MEDfl_db(): + script_path = os.path.join(os.path.dirname( + __file__), 'scripts', 'create_db.sh') + subprocess.run(['sh', script_path], check=True) + + +def custom_classification_report(y_true, y_pred_prob): + """ + Compute custom classification report metrics including accuracy, sensitivity, specificity, precision, NPV, + F1-score, false positive rate, and true positive rate. + + Args: + y_true (array-like): True labels. + y_pred (array-like): Predicted labels. + + Returns: + dict: A dictionary containing custom classification report metrics. + """ + y_pred = (y_pred_prob).round( + ) # Round absolute values of predicted probabilities to the nearest integer + + auc = roc_auc_score(y_true, y_pred_prob) # Calculate AUC + + tn, fp, fn, tp = confusion_matrix(y_true, y_pred , labels=[0, 1]).ravel() + + # Accuracy + denominator_acc = tp + tn + fp + fn + acc = (tp + tn) / denominator_acc if denominator_acc != 0 else 0.0 + + # Sensitivity/Recall + denominator_sen = tp + fn + sen = tp / denominator_sen if denominator_sen != 0 else 0.0 + + # Specificity + denominator_sp = tn + fp + sp = tn / denominator_sp if denominator_sp != 0 else 0.0 + + # PPV/Precision + denominator_ppv = tp + fp + ppv = tp / denominator_ppv if denominator_ppv != 0 else 0.0 + +# NPV + denominator_npv = tn + fn + npv = tn / denominator_npv if denominator_npv != 0 else 0.0 + + # F1 Score + denominator_f1 = sen + ppv + f1 = 2 * (sen * ppv) / denominator_f1 if denominator_f1 != 0 else 0.0 + + # False Positive Rate + denominator_fpr = fp + tn + fpr = fp / denominator_fpr if denominator_fpr != 0 else 0.0 + + # True Positive Rate + denominator_tpr = tp + fn + tpr = tp / denominator_tpr if denominator_tpr != 0 else 0.0 + + return { + "confusion matrix": {"TP": tp, "FP": fp, "FN": fn, "TN": tn}, + "Accuracy": round(acc, 3), + "Sensitivity/Recall": round(sen, 3), + "Specificity": round(sp, 3), + "PPV/Precision": round(ppv, 3), + "NPV": round(npv, 3), + "F1-score": round(f1, 3), + "False positive rate": round(fpr, 3), + "True positive rate": round(tpr, 3), + "auc": auc + } + + +def test(model, test_loader, device=torch.device("cpu")): + """ + Evaluate a model using a test loader and return a custom classification report. + + Args: + model (torch.nn.Module): PyTorch model to evaluate. + test_loader (torch.utils.data.DataLoader): DataLoader for the test dataset. + device (torch.device, optional): Device for model evaluation. Default is "cpu". + + Returns: + dict: A dictionary containing custom classification report metrics. + """ + + model.eval() + with torch.no_grad(): + X_test, y_test = test_loader.dataset[:][0].to( + device), test_loader.dataset[:][1].to(device) + y_hat_prob = torch.squeeze(model(X_test), 1).cpu() + + return custom_classification_report(y_test.cpu().numpy(), y_hat_prob.cpu().numpy()) + + +column_map = {"object": "VARCHAR(255)", "int64": "INT", "float64": "FLOAT"} + + +def empty_db(): + """ + Empty the database by deleting records from multiple tables and resetting auto-increment counters. + + Returns: + None + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + # my_eng.execute(text(f"DELETE FROM {'DataSets'}")) + my_eng.execute(text(f"DELETE FROM {'Nodes'}")) + my_eng.execute(text(f"DELETE FROM {'FedDatasets'}")) + my_eng.execute(text(f"DELETE FROM {'Networks'}")) + my_eng.execute(text(f"DELETE FROM {'FLsetup'}")) + + my_eng.execute(text(f"DELETE FROM {'FLpipeline'}")) + my_eng.execute(text(f"ALTER TABLE {'Nodes'} AUTO_INCREMENT = 1")) + my_eng.execute(text(f"ALTER TABLE {'Networks'} AUTO_INCREMENT = 1")) + my_eng.execute(text(f"ALTER TABLE {'FedDatasets'} AUTO_INCREMENT = 1")) + my_eng.execute(text(f"ALTER TABLE {'FLsetup'} AUTO_INCREMENT = 1")) + my_eng.execute(text(f"ALTER TABLE {'FLpipeline'} AUTO_INCREMENT = 1")) + my_eng.execute(text(f"DELETE FROM {'testResults'}")) + my_eng.execute(text(f"DROP TABLE IF EXISTS {'MasterDataset'}")) + my_eng.execute(text(f"DROP TABLE IF EXISTS {'DataSets'}")) + + +def get_pipeline_from_name(name): + """ + Get the pipeline ID from its name in the database. + + Args: + name (str): Name of the pipeline. + + Returns: + int: ID of the pipeline. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + NodeId = int( + pd.read_sql( + text(f"SELECT id FROM FLpipeline WHERE name = '{name}'"), my_eng + ).iloc[0, 0] + ) + return NodeId + + +def get_pipeline_confusion_matrix(pipeline_id): + """ + Get the global confusion matrix for a pipeline based on test results. + + Args: + pipeline_id (int): ID of the pipeline. + + Returns: + dict: A dictionary representing the global confusion matrix. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + data = pd.read_sql( + text( + f"SELECT confusionmatrix FROM testResults WHERE pipelineid = '{pipeline_id}'"), my_eng + ) + + # Convert the column of strings into a list of dictionaries representing confusion matrices + confusion_matrices = [ + json.loads(matrix.replace("'", "\"")) for matrix in data['confusionmatrix'] + ] + + # Initialize variables for global confusion matrix + global_TP = global_FP = global_FN = global_TN = 0 + + # Iterate through each dictionary and sum the corresponding values for each category + for matrix in confusion_matrices: + global_TP += matrix['TP'] + global_FP += matrix['FP'] + global_FN += matrix['FN'] + global_TN += matrix['TN'] + + # Create a global confusion matrix as a dictionary + global_confusion_matrix = { + 'TP': global_TP, + 'FP': global_FP, + 'FN': global_FN, + 'TN': global_TN + } + # Return the list of dictionaries representing confusion matrices + return global_confusion_matrix + + +def get_node_confusion_matrix(pipeline_id, node_name): + """ + Get the confusion matrix for a specific node in a pipeline based on test results. + + Args: + pipeline_id (int): ID of the pipeline. + node_name (str): Name of the node. + + Returns: + dict: A dictionary representing the confusion matrix for the specified node. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + data = pd.read_sql( + text( + f"SELECT confusionmatrix FROM testResults WHERE pipelineid = '{pipeline_id}' AND nodename = '{node_name}'"), my_eng + ) + + # Convert the column of strings into a list of dictionaries representing confusion matrices + confusion_matrices = [ + json.loads(matrix.replace("'", "\"")) for matrix in data['confusionmatrix'] + ] + + # Return the list of dictionaries representing confusion matrices + return confusion_matrices[0] + + +def get_pipeline_result(pipeline_id): + """ + Get the test results for a pipeline. + + Args: + pipeline_id (int): ID of the pipeline. + + Returns: + pandas.DataFrame: DataFrame containing test results for the specified pipeline. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + data = pd.read_sql( + text( + f"SELECT * FROM testResults WHERE pipelineid = '{pipeline_id}'"), my_eng + ) + return data diff --git a/build/lib/MEDfl/NetManager/__init__.py b/build/lib/MEDfl/NetManager/__init__.py new file mode 100644 index 0000000..c583635 --- /dev/null +++ b/build/lib/MEDfl/NetManager/__init__.py @@ -0,0 +1,10 @@ +# # MEDfl/NetworkManager/__init__.py + +# # Import modules from this package +# from .dataset import * +# from .flsetup import * +# from .net_helper import * +# from .net_manager_queries import * +# from .network import * +# from .node import * +# from .database_connector import * diff --git a/build/lib/MEDfl/NetManager/database_connector.py b/build/lib/MEDfl/NetManager/database_connector.py new file mode 100644 index 0000000..ad85ab1 --- /dev/null +++ b/build/lib/MEDfl/NetManager/database_connector.py @@ -0,0 +1,45 @@ +import os +import subprocess , sys +from sqlalchemy import create_engine +from configparser import ConfigParser + +class DatabaseManager: + def __init__(self): + from MEDfl.LearningManager.utils import load_db_config + db_config = load_db_config() + if db_config: + self.config = db_config + else: + self.config = None + self.engine = None + + def connect(self): + if not self.config: + raise ValueError("Database configuration not loaded. Use load_db_config() or set_config_path() first.") + # Assuming the SQLite database file path is provided in the config with the key 'database' + database_path = self.config['database'] + connection_string = f"sqlite:///{database_path}" + self.engine = create_engine(connection_string, pool_pre_ping=True) + + def get_connection(self): + if not self.engine: + self.connect() + return self.engine.connect() + + def create_MEDfl_db(self, path_to_csv): + # Get the directory of the current script + current_directory = os.path.dirname(__file__) + + # Define the path to the create_db.py script + create_db_script_path = os.path.join(current_directory, '..', 'scripts', 'create_db.py') + + # Execute the create_db.py script + print(sys.executable) + result = subprocess.run([sys.executable, create_db_script_path, path_to_csv], + capture_output=True, text=True) + + return + + def close(self): + if self.engine: + self.engine.dispose() diff --git a/build/lib/MEDfl/NetManager/dataset.py b/build/lib/MEDfl/NetManager/dataset.py new file mode 100644 index 0000000..2023495 --- /dev/null +++ b/build/lib/MEDfl/NetManager/dataset.py @@ -0,0 +1,92 @@ +import pandas as pd +from sqlalchemy import text + +from .net_helper import * +from .net_manager_queries import (DELETE_DATASET, INSERT_DATASET, + SELECT_ALL_DATASET_NAMES) +from MEDfl.NetManager.database_connector import DatabaseManager + +class DataSet: + def __init__(self, name: str, path: str, engine=None): + """ + Initialize a DataSet object. + + :param name: The name of the dataset. + :type name: str + :param path: The file path of the dataset CSV file. + :type path: str + """ + self.name = name + self.path = path + db_manager = DatabaseManager() + db_manager.connect() + self.engine = db_manager.get_connection() + + def validate(self): + """ + Validate name and path attributes. + + :raises TypeError: If name or path is not a string. + """ + if not isinstance(self.name, str): + raise TypeError("name argument must be a string") + + if not isinstance(self.path, str): + raise TypeError("path argument must be a string") + + def upload_dataset(self, NodeId=-1): + """ + Upload the dataset to the database. + + :param NodeId: The NodeId associated with the dataset. + :type NodeId: int + + Notes: + - Assumes the file at self.path is a valid CSV file. + - The dataset is uploaded to the 'DataSets' table in the database. + """ + + data_df = pd.read_csv(self.path) + nodeId = NodeId + columns = data_df.columns.tolist() + + + data_df = process_eicu(data_df) + for index, row in data_df.iterrows(): + query_1 = "INSERT INTO DataSets(DataSetName,nodeId," + "".join( + f"{x}," for x in columns + ) + query_2 = f" VALUES ('{self.name}',{nodeId}, " + "".join( + f"{is_str(data_df, row, x)}," for x in columns + ) + query = query_1[:-1] + ")" + query_2[:-1] + ")" + + self.engine.execute(text(query)) + + def delete_dataset(self): + """ + Delete the dataset from the database. + + Notes: + - Assumes the dataset name is unique in the 'DataSets' table. + """ + self.engine.execute(text(DELETE_DATASET), {"name": self.name}) + + def update_data(self): + """ + Update the data in the dataset. + + Not implemented yet. + """ + pass + + @staticmethod + def list_alldatasets(engine): + """ + List all dataset names from the 'DataSets' table. + + :returns: A DataFrame containing the names of all datasets in the 'DataSets' table. + :rtype: pd.DataFrame + """ + res = pd.read_sql(text(SELECT_ALL_DATASET_NAMES), engine) + return res diff --git a/build/lib/MEDfl/NetManager/flsetup.py b/build/lib/MEDfl/NetManager/flsetup.py new file mode 100644 index 0000000..10567ad --- /dev/null +++ b/build/lib/MEDfl/NetManager/flsetup.py @@ -0,0 +1,320 @@ +from datetime import datetime + + +from torch.utils.data import random_split, DataLoader, Dataset + +from MEDfl.LearningManager.federated_dataset import FederatedDataset +from .net_helper import * +from .net_manager_queries import * # Import the sql_queries module +from .network import Network + +from .node import Node + +from MEDfl.NetManager.database_connector import DatabaseManager + + +class FLsetup: + def __init__(self, name: str, description: str, network: Network): + """Initialize a Federated Learning (FL) setup. + + Args: + name (str): The name of the FL setup. + description (str): A description of the FL setup. + network (Network): An instance of the Network class representing the network architecture. + """ + self.name = name + self.description = description + self.network = network + self.column_name = None + self.auto = 1 if self.column_name is not None else 0 + self.validate() + self.fed_dataset = None + + db_manager = DatabaseManager() + db_manager.connect() + self.eng = db_manager.get_connection() + + + + def validate(self): + """Validate name, description, and network.""" + if not isinstance(self.name, str): + raise TypeError("name argument must be a string") + + if not isinstance(self.description, str): + raise TypeError("description argument must be a string") + + if not isinstance(self.network, Network): + raise TypeError( + "network argument must be a MEDfl.NetManager.Network " + ) + + def create(self): + """Create an FL setup.""" + creation_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + netid = get_netid_from_name(self.network.name) + self.eng.execute( + text(CREATE_FLSETUP_QUERY), + { + "name": self.name, + "description": self.description, + "creation_date": creation_date, + "net_id": netid, + "column_name": self.column_name, + }, + ) + self.id = get_flsetupid_from_name(self.name) + + def delete(self): + """Delete the FL setup.""" + if self.fed_dataset is not None: + self.fed_dataset.delete_Flsetup(FLsetupId=self.id) + self.eng.execute(text(DELETE_FLSETUP_QUERY), {"name": self.name}) + + @classmethod + def read_setup(cls, FLsetupId: int): + """Read the FL setup by FLsetupId. + + Args: + FLsetupId (int): The id of the FL setup to read. + + Returns: + FLsetup: An instance of the FLsetup class with the specified FLsetupId. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + res = pd.read_sql( + text(READ_SETUP_QUERY), my_eng, params={"flsetup_id": FLsetupId} + ).iloc[0] + + network_res = pd.read_sql( + text(READ_NETWORK_BY_ID_QUERY), + my_eng, + params={"net_id": int(res["NetId"])}, + ).iloc[0] + network = Network(network_res["NetName"]) + setattr(network, "id", res["NetId"]) + fl_setup = cls(res["name"], res["description"], network) + if res["column_name"] == str(None): + res["column_name"] = None + setattr(fl_setup, "column_name", res["column_name"]) + setattr(fl_setup, "id", res["FLsetupId"]) + + return fl_setup + + @staticmethod + def list_allsetups(): + """List all the FL setups. + + Returns: + DataFrame: A DataFrame containing information about all the FL setups. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + Flsetups = pd.read_sql(text(READ_ALL_SETUPS_QUERY), my_eng) + return Flsetups + + def create_nodes_from_master_dataset(self, params_dict: dict): + """Create nodes from the master dataset. + + Args: + params_dict (dict): A dictionary containing parameters for node creation. + - column_name (str): The name of the column in the MasterDataset used to create nodes. + - train_nodes (list): A list of node names that will be used for training. + - test_nodes (list): A list of node names that will be used for testing. + + Returns: + list: A list of Node instances created from the master dataset. + """ + assert "column_name" in params_dict.keys() + column_name, train_nodes, test_nodes = ( + params_dict["column_name"], + params_dict["train_nodes"], + params_dict["test_nodes"], + ) + self.column_name = column_name + self.auto = 1 + + # Update the Column name of the auto flSetup + query = f"UPDATE FLsetup SET column_name = '{column_name}' WHERE name = '{self.name}'" + self.eng.execute(text(query)) + + + # Add Network to DB + # self.network.create_network() + + netid = get_netid_from_name(self.network.name) + + assert self.network.mtable_exists == 1 + node_names = pd.read_sql( + text(READ_DISTINCT_NODES_QUERY.format(column_name)), self.eng + ) + + nodes = [Node(val[0], 1) for val in node_names.values.tolist()] + + used_nodes = [] + + for node in nodes: + if node.name in train_nodes: + node.train = 1 + node.create_node(netid) + used_nodes.append(node) + if node.name in test_nodes: + node.train =0 + node.create_node(netid) + used_nodes.append(node) + return used_nodes + + def create_dataloader_from_node( + self, + node: Node, + output, + fill_strategy="mean", fit_encode=[], to_drop=[], + train_batch_size: int = 32, + test_batch_size: int = 1, + split_frac: float = 0.2, + dataset: Dataset = None, + + ): + """Create DataLoader from a Node. + + Args: + node (Node): The node from which to create DataLoader. + train_batch_size (int): The batch size for training data. + test_batch_size (int): The batch size for test data. + split_frac (float): The fraction of data to be used for training. + dataset (Dataset): The dataset to use. If None, the method will read the dataset from the node. + + Returns: + DataLoader: The DataLoader instances for training and testing. + """ + if dataset is None: + if self.column_name is not None: + dataset = process_data_after_reading( + node.get_dataset(self.column_name), output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop + ) + else: + dataset = process_data_after_reading( + node.get_dataset(), output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop) + + dataset_size = len(dataset) + traindata_size = int(dataset_size * (1 - split_frac)) + traindata, testdata = random_split( + dataset, [traindata_size, dataset_size - traindata_size] + ) + trainloader, testloader = DataLoader( + traindata, batch_size=train_batch_size + ), DataLoader(testdata, batch_size=test_batch_size) + return trainloader, testloader + + def create_federated_dataset( + self, output, fill_strategy="mean", fit_encode=[], to_drop=[], val_frac=0.1, test_frac=0.2 + ) -> FederatedDataset: + """Create a federated dataset. + + Args: + output (string): the output feature of the dataset + val_frac (float): The fraction of data to be used for validation. + test_frac (float): The fraction of data to be used for testing. + + Returns: + FederatedDataset: The FederatedDataset instance containing train, validation, and test data. + """ + + if not self.column_name: + to_drop.extend(["DataSetName" , "NodeId" , "DataSetId"]) + else : + to_drop.extend(["PatientId"]) + + netid = self.network.id + train_nodes = pd.read_sql( + text( + f"SELECT Nodes.NodeName FROM Nodes WHERE Nodes.NetId = {netid} AND Nodes.train = 1 " + ), + self.eng, + ) + test_nodes = pd.read_sql( + text( + f"SELECT Nodes.NodeName FROM Nodes WHERE Nodes.NetId = {netid} AND Nodes.train = 0 " + ), + self.eng, + ) + + train_nodes = [ + Node(val[0], 1, test_frac) for val in train_nodes.values.tolist() + ] + test_nodes = [Node(val[0], 0) for val in test_nodes.values.tolist()] + + trainloaders, valloaders, testloaders = [], [], [] + # if len(test_nodes) == 0: + # raise "test node empty" + if test_nodes is None: + _, testloader = self.create_dataloader_from_node( + train_nodes[0], output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop) + testloaders.append(testloader) + else: + for train_node in train_nodes: + train_valloader, testloader = self.create_dataloader_from_node( + train_node, output, fill_strategy=fill_strategy, + fit_encode=fit_encode, to_drop=to_drop,) + trainloader, valloader = self.create_dataloader_from_node( + train_node, + output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop, + test_batch_size=32, + split_frac=val_frac, + dataset=train_valloader.dataset, + ) + trainloaders.append(trainloader) + valloaders.append(valloader) + testloaders.append(testloader) + + for test_node in test_nodes: + _, testloader = self.create_dataloader_from_node( + test_node, output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop, split_frac=1.0 + ) + testloaders.append(testloader) + train_nodes_names = [node.name for node in train_nodes] + test_nodes_names = train_nodes_names + [ + node.name for node in test_nodes + ] + + # test_nodes_names = [ + # node.name for node in test_nodes + # ] + + # Add FlSetup on to the DataBase + # self.create() + + # self.network.update_network(FLsetupId=self.id) + fed_dataset = FederatedDataset( + self.name + "_Feddataset", + train_nodes_names, + test_nodes_names, + trainloaders, + valloaders, + testloaders, + ) + self.fed_dataset = fed_dataset + self.fed_dataset.create(self.id) + return self.fed_dataset + + + + + def get_flDataSet(self): + """ + Retrieve the federated dataset associated with the FL setup using the FL setup's name. + + Returns: + pandas.DataFrame: DataFrame containing the federated dataset information. + """ + return pd.read_sql( + text( + f"SELECT * FROM FedDatasets WHERE FLsetupId = {get_flsetupid_from_name(self.name)}" + ), + self.eng, + ) diff --git a/build/lib/MEDfl/NetManager/net_helper.py b/build/lib/MEDfl/NetManager/net_helper.py new file mode 100644 index 0000000..2d9aff0 --- /dev/null +++ b/build/lib/MEDfl/NetManager/net_helper.py @@ -0,0 +1,254 @@ +from sklearn.preprocessing import LabelEncoder +from sklearn.impute import SimpleImputer + +from sqlalchemy import text + +import torch +import pandas as pd +from torch.utils.data import TensorDataset +import numpy as np + +from MEDfl.NetManager.database_connector import DatabaseManager + + +def is_str(data_df, row, x): + """ + Check if a column in a DataFrame is of type 'object' and convert the value accordingly. + + Args: + data_df (pandas.DataFrame): DataFrame containing the data. + row (pandas.Series): Data row. + x (str): Column name. + + Returns: + str or float: Processed value based on the column type. + """ + if data_df[x].dtype == "object": + x = f"'{row[x]}'" + else: + x = row[x] + return x + + +def process_eicu(data_df): + """ + Process eICU data by filling missing values with mean and replacing NaNs with 'Unknown'. + + Args: + data_df (pandas.DataFrame): Input data. + + Returns: + pandas.DataFrame: Processed data. + """ + # Identify numeric and non-numeric columns + numeric_columns = data_df.select_dtypes(include=[np.number]).columns + non_numeric_columns = data_df.select_dtypes(exclude=[np.number]).columns + + # Fill NaN in numeric columns with mean + data_df[numeric_columns] = data_df[numeric_columns].fillna( + data_df[numeric_columns].mean()) + + # Fill NaN in non-numeric columns with 'Unknown' + data_df[non_numeric_columns] = data_df[non_numeric_columns].fillna( + 'Unknown') + + try: + data_df = data_df.reset_index(drop=True) + except: + pass + + return data_df + + +# remove indiserd columns after reading from the DB +def process_data_after_reading(data, output, fill_strategy="mean", fit_encode=[], to_drop=[]): + """ + Process data after reading from the database, including encoding, dropping columns, and creating a PyTorch TensorDataset. + + Args: + data (pandas.DataFrame): Input data. + output (str): Output column name. + fill_strategy (str, optional): Imputation strategy for missing values. Default is "mean". + fit_encode (list, optional): List of columns to be label-encoded. Default is an empty list. + to_drop (list, optional): List of columns to be dropped from the DataFrame. Default is an empty list. + + Returns: + torch.utils.data.TensorDataset: Processed data as a PyTorch TensorDataset. + """ + + # Check if there is a DataSet assigned to the node + if (len(data) == 0): + raise "Node doesn't Have dataSet" + + encoder = LabelEncoder() + # En Code some columns + for s in fit_encode: + try: + data[s] = encoder.fit_transform(data[s]) + except: + raise print(s) + + # The output of the DATA + y = data[output] + + X = data + + # remove indisered columns when reading the dataframe from the DB + for column in to_drop: + try: + X = X.drop( + [column], axis=1 + ) + except Exception as e: + raise e + + # Get the DATAset Features + features = [col for col in X.columns if col != output] + + # Impute missing values using the mean strategy + try: + imputer = SimpleImputer(strategy=fill_strategy) + X[features] = imputer.fit_transform(X[features]) + except: + print() + + X = torch.tensor(X.values, dtype=torch.float32) + y = torch.tensor(y.values, dtype=torch.float32) + data = TensorDataset(X, y) + + return data + + +def get_nodeid_from_name(name): + """ + Get the NodeId from the Nodes table based on the NodeName. + + Args: + name (str): Node name. + + Returns: + int or None: NodeId or None if not found. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + result_proxy = my_eng.execute(f"SELECT NodeId FROM Nodes WHERE NodeName = '{name}'") + NodeId = int(result_proxy.fetchone()[0]) + return NodeId + + +def get_netid_from_name(name): + """ + Get the Network Id from the Networks table based on the NetName. + + Args: + name (str): Network name. + + Returns: + int or None: NetId or None if not found. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + try: + result_proxy = my_eng.execute(f"SELECT NetId FROM Networks WHERE NetName = '{name}'") + NetId = int(result_proxy.fetchone()[0]) + except: + NetId = None + return NetId + + +def get_flsetupid_from_name(name): + """ + Get the FLsetupId from the FLsetup table based on the FL setup name. + + Args: + name (str): FL setup name. + + Returns: + int or None: FLsetupId or None if not found. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + try: + + result_proxy = my_eng.execute(f"SELECT FLsetupId FROM FLsetup WHERE name = '{name}'") + id = int(result_proxy.fetchone()[0]) + + except: + id = None + return id + + +def get_flpipeline_from_name(name): + """ + Get the FLpipeline Id from the FLpipeline table based on the FL pipeline name. + + Args: + name (str): FL pipeline name. + + Returns: + int or None: FLpipelineId or None if not found. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + try: + + result_proxy = my_eng.execute(f"SELECT id FROM FLpipeline WHERE name = '{name}'") + id = int(result_proxy.fetchone()[0]) + except: + id = None + return id + + +def get_feddataset_id_from_name(name): + """ + Get the Federated dataset Id from the FedDatasets table based on the federated dataset name. + + Args: + name (str): Federated dataset name. + + Returns: + int or None: FedId or None if not found. + """ + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + try: + + result_proxy = my_eng.execute(f"SELECT FedId FROM FedDatasets WHERE name = '{name}'") + id = int(result_proxy.fetchone()[0]) + except: + id = None + return id + + +def master_table_exists(): + """ + Check if the MasterDataset table exists in the database. + + Returns: + bool: True if the table exists, False otherwise. + """ + try: + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + # SQLite-specific query to check if table exists + sql_query = text("SELECT name FROM sqlite_master WHERE type='table' AND name='MasterDataset'") + result = my_eng.execute(sql_query) + exists = result.fetchone() is not None + return exists + + except Exception as e: + print(f"Error checking MasterDataset table existence: {e}") + return False + diff --git a/build/lib/MEDfl/NetManager/net_manager_queries.py b/build/lib/MEDfl/NetManager/net_manager_queries.py new file mode 100644 index 0000000..dd83969 --- /dev/null +++ b/build/lib/MEDfl/NetManager/net_manager_queries.py @@ -0,0 +1,142 @@ +from .net_helper import is_str + +INSERT_DATASET = """ + INSERT INTO DataSets(DataSetName, NodeId, {columns}) + VALUES (:name, :NodeId, {values}) +""" +DELETE_DATASET = """ + DELETE FROM DataSets WHERE DataSetName = :name +""" + +SELECT_ALL_DATASET_NAMES = """ + SELECT DISTINCT DataSetName,NodeId FROM DataSets +""" + +SELECT_DATASET_BY_NAME = """ + SELECT * FROM DataSets WHERE DataSetName = :name +""" + +# node queries +# sql_queries.py + +INSERT_NODE_QUERY = ( + "INSERT INTO Nodes(NodeName,NetId,train) VALUES ('{}',{}, {})" +) +DELETE_NODE_QUERY = "DELETE FROM Nodes WHERE NodeName = '{}'" +SELECT_MASTER_COLUMNS_QUERY = "SELECT * FROM MasterDataset LIMIT 1" +SELECT_DATASET_BY_COLUMN_QUERY = "SELECT * FROM MasterDataset WHERE {} = '{}'" +SELECT_DATASET_BY_NODE_ID_QUERY = "SELECT * FROM DataSets WHERE NodeId = {}" + +SELECT_ALL_DATASETS_QUERY = "SELECT DISTINCT DataSetName,NodeName FROM DataSets,Nodes WHERE Nodes.NodeName = '{}' and Nodes.NodeId = DataSets.NodeId" +SELECT_ALL_NODES_QUERY = "SELECT * FROM Nodes" + + +# SQL query to insert a new network +INSERT_NETWORK_QUERY = "INSERT INTO Networks(NetName) VALUES (:name)" + +# SQL query to delete a network +DELETE_NETWORK_QUERY = "DELETE FROM Networks WHERE NetName = '{name}'" + +# SQL query to delete a network +GET_NETWORK_QUERY = "SELECT * FROM Networks WHERE NetName = '{name}'" + + +# SQL query to update a network +UPDATE_NETWORK_QUERY = ( + "UPDATE Networks SET FLsetupId = {FLsetupId} WHERE NetId = {id}" +) + +# SQL query to retrieve all nodes for a network +LIST_ALL_NODES_QUERY = """ +SELECT Nodes.NodeName, Networks.NetName +FROM Nodes +JOIN Networks ON Networks.NetId = Nodes.NetId +WHERE Networks.NetName = :name +""" + +# SQL query to create the MasterDataset table (SQLite-compatible) +CREATE_MASTER_DATASET_TABLE_QUERY = """ +CREATE TABLE IF NOT EXISTS MasterDataset ( + PatientId INTEGER PRIMARY KEY AUTOINCREMENT, + {} +); +""" + + +# SQL query to create the datasets table (SQLite-compatible) +CREATE_DATASETS_TABLE_QUERY = """ +CREATE TABLE IF NOT EXISTS Datasets ( + DataSetId INTEGER PRIMARY KEY AUTOINCREMENT, + DataSetName VARCHAR(255), + NodeId INT, + {} +); +""" + + + +# SQL query to insert dataset values +INSERT_DATASET_VALUES_QUERY = "INSERT INTO MasterDataset({columns}, NodeId) VALUES ('{name}', {nodeId}, {values})" + + +# FL setup_queries +# sql_queries.py + +CREATE_FLSETUP_QUERY = """ + INSERT INTO FLsetup (name, description, creation_date, NetId, column_name) + VALUES (:name, :description, :creation_date, :net_id, :column_name) +""" + +DELETE_FLSETUP_QUERY = """ + DELETE FROM FLsetup + WHERE name = :name +""" + +UPDATE_FLSETUP_QUERY = UPDATE_NETWORK_QUERY = ( + "UPDATE FLsetup SET column_name ='{column_name}' WHERE name ='{FLsetupName}'" +) + + +READ_SETUP_QUERY = """ + SELECT * FROM FLsetup + WHERE FLsetupId = :flsetup_id +""" + +READ_ALL_SETUPS_QUERY = """ + SELECT * FROM FLsetup +""" + +READ_NETWORK_BY_ID_QUERY = """ + SELECT * FROM Networks + WHERE NetId = :net_id +""" + +READ_DISTINCT_NODES_QUERY = """ +SELECT DISTINCT {} FROM MasterDataset +""" + + +# FederatedDataset Queries +INSERT_FLDATASET_QUERY = ( + "INSERT INTO FedDatasets(name, FLsetupId) VALUES (:name, :FLsetupId)" +) +DELETE_FLDATASET_BY_SETUP_AND_PIPELINE_QUERY = "DELETE FROM FedDatasets WHERE FLsetupId = :FLsetupId AND FLpipeId = :FLpipeId" + + +UPDATE_FLDATASET_QUERY = ( + "UPDATE FedDatasets SET FLpipeId = :FLpipeId WHERE FedId = :FedId" +) +SELECT_FLDATASET_BY_NAME_QUERY = "SELECT * FROM FedDatasets WHERE name = :name" + +CREATE_FLPIPELINE_QUERY = """ +INSERT INTO FLpipeline (name, description, creation_date, results) +VALUES ('{name}', '{description}', '{creation_date}', '{result}') +""" +DELETE_FLPIPELINE_QUERY = "DELETE FROM FLpipeline WHERE name = '{name}'" + +SELECT_FLPIPELINE_QUERY = "SELECT FROM FLpipeline WHERE name = '{name}'" + +CREATE_TEST_RESULTS_QUERY = """ +INSERT INTO testResults (pipelineid, nodename, confusionmatrix, accuracy , sensivity, ppv , npv , f1score , fpr , tpr ) +VALUES ('{pipelineId}', '{nodeName}', '{confusion_matrix}', '{accuracy}' , '{sensivity}' , '{ppv}' , '{npv}' , '{f1score}' , '{fpr}' , '{tpr}') +""" diff --git a/build/lib/MEDfl/NetManager/network.py b/build/lib/MEDfl/NetManager/network.py new file mode 100644 index 0000000..65cac7c --- /dev/null +++ b/build/lib/MEDfl/NetManager/network.py @@ -0,0 +1,194 @@ +# src/MEDfl/NetManager/network.py + +from MEDfl.LearningManager.utils import * +from .net_helper import * +from .net_manager_queries import (CREATE_MASTER_DATASET_TABLE_QUERY, + CREATE_DATASETS_TABLE_QUERY, + DELETE_NETWORK_QUERY, + INSERT_NETWORK_QUERY, LIST_ALL_NODES_QUERY, + UPDATE_NETWORK_QUERY, GET_NETWORK_QUERY) +from .node import Node +import pandas as pd +from MEDfl.LearningManager.utils import params + +from sqlalchemy import text +from sqlalchemy.exc import SQLAlchemyError + +class Network: + """ + A class representing a network. + + Attributes: + name (str): The name of the network. + mtable_exists (int): An integer flag indicating whether the MasterDataset table exists (1) or not (0). + """ + + def __init__(self, name: str = ""): + """ + Initialize a Network instance. + + Parameters: + name (str): The name of the network. + """ + self.name = name + self.mtable_exists = int(master_table_exists()) + self.validate() + + db_manager = DatabaseManager() + db_manager.connect() + self.eng = db_manager.get_connection() + + def validate(self): + """Validate name""" + + if not isinstance(self.name, str): + raise TypeError("name argument must be a string") + + def create_network(self): + """Create a new network in the database.""" + try: + print(self.name) + self.eng.execute(text(INSERT_NETWORK_QUERY), {"name": self.name}) + self.id = self.get_netid_from_name(self.name) + except SQLAlchemyError as e: + print(f"Error creating network: {e}") + + def use_network(self, network_name: str): + """Use a network in the database. + + Parameters: + network_name (str): The name of the network to use. + + Returns: + Network or None: An instance of the Network class if the network exists, else None. + """ + try: + network = pd.read_sql( + text(GET_NETWORK_QUERY), + self.eng, + params={"name": network_name} + ) + if not network.empty: + self.name = network.iloc[0]['NetName'] + self.id = network.iloc[0]['NetId'] + self.mtable_exists = int(master_table_exists()) + self.validate() + return self + else: + return None + except SQLAlchemyError as e: + print(f"Error using network: {e}") + return None + + def delete_network(self): + """Delete the network from the database.""" + try: + self.eng.execute(text(DELETE_NETWORK_QUERY), {"name": self.name}) + except SQLAlchemyError as e: + print(f"Error deleting network: {e}") + + def update_network(self, FLsetupId: int): + """Update the network's FLsetupId in the database. + + Parameters: + FLsetupId (int): The FLsetupId to update. + """ + try: + self.eng.execute( + text(UPDATE_NETWORK_QUERY), + {"FLsetupId": FLsetupId, "id": self.id} + ) + except SQLAlchemyError as e: + print(f"Error updating network: {e}") + + def add_node(self, node: Node): + """Add a node to the network. + + Parameters: + node (Node): The node to add. + """ + node.create_node(self.id) + + def list_allnodes(self): + """List all nodes in the network. + + Returns: + DataFrame: A DataFrame containing information about all nodes in the network. + """ + try: + query = text(LIST_ALL_NODES_QUERY) + result_proxy = self.eng.execute(query, name=self.name) + result_df = pd.DataFrame(result_proxy.fetchall(), columns=result_proxy.keys()) + return result_df + except SQLAlchemyError as e: + print(f"Error listing all nodes: {e}") + return pd.DataFrame() + + def create_master_dataset(self, path_to_csv: str = params['path_to_master_csv']): + """ + Create the MasterDataset table and insert dataset values. + + :param path_to_csv: Path to the CSV file containing the dataset. + """ + try: + print(path_to_csv) + data_df = pd.read_csv(path_to_csv) + + if self.mtable_exists != 1: + columns = data_df.columns.tolist() + columns_str = ",\n".join( + [ + f"{col} {column_map[str(data_df[col].dtype)]}" + for col in columns + ] + ) + self.eng.execute( + text(CREATE_MASTER_DATASET_TABLE_QUERY.format(columns_str)) + ) + self.eng.execute(text(CREATE_DATASETS_TABLE_QUERY.format(columns_str))) + + # Process data + data_df = process_eicu(data_df) + + # Insert data in batches + batch_size = 1000 # Adjust as needed + for start_idx in range(0, len(data_df), batch_size): + batch_data = data_df.iloc[start_idx:start_idx + batch_size] + insert_query = f"INSERT INTO MasterDataset ({', '.join(columns)}) VALUES ({', '.join([':' + col for col in columns])})" + data_to_insert = batch_data.to_dict(orient='records') + self.eng.execute(text(insert_query), data_to_insert) + + self.mtable_exists = 1 + except SQLAlchemyError as e: + print(f"Error creating master dataset: {e}") + + @staticmethod + def list_allnetworks(): + """List all networks in the database. + + Returns: + DataFrame: A DataFrame containing information about all networks in the database. + """ + try: + db_manager = DatabaseManager() + db_manager.connect() + my_eng = db_manager.get_connection() + + result_proxy = my_eng.execute("SELECT * FROM Networks") + result = result_proxy.fetchall() + return pd.DataFrame(result, columns=result_proxy.keys()) + except SQLAlchemyError as e: + print(f"Error listing all networks: {e}") + return pd.DataFrame() + + def get_netid_from_name(self, name): + """Get network ID from network name.""" + try: + result = self.eng.execute(text("SELECT NetId FROM Networks WHERE NetName = :name"), {"name": name}).fetchone() + if result: + return result[0] + else: + return None + except SQLAlchemyError as e: + print(f"Error fetching network ID: {e}") + return None diff --git a/build/lib/MEDfl/NetManager/node.py b/build/lib/MEDfl/NetManager/node.py new file mode 100644 index 0000000..9029d69 --- /dev/null +++ b/build/lib/MEDfl/NetManager/node.py @@ -0,0 +1,184 @@ +import pandas as pd + +from .net_helper import * +from .net_manager_queries import * +from MEDfl.LearningManager.utils import params +from MEDfl.NetManager.database_connector import DatabaseManager + +from sqlalchemy import text, exc + + +class Node: + """ + A class representing a node in the network. + + Attributes: + name (str): The name of the node. + train (int): An integer flag representing whether the node is used for training (1) or testing (0). + test_fraction (float, optional): The fraction of data used for testing when train=1. Default is 0.2. + """ + + def __init__( + self, name: str, train: int, test_fraction: float = 0.2, engine=None + ): + """ + Initialize a Node instance. + + Parameters: + name (str): The name of the node. + train (int): An integer flag representing whether the node is used for training (1) or testing (0). + test_fraction (float, optional): The fraction of data used for testing when train=1. Default is 0.2. + """ + self.name = name + self.train = train + self.test_fraction = 1.0 if self.train == 0 else test_fraction + + + db_manager = DatabaseManager() ; + db_manager.connect() ; + self.engine = db_manager.get_connection() + + def validate(self): + """Validate name, train, test_fraction""" + if not isinstance(self.name, str): + raise TypeError("name argument must be a string") + + if not isinstance(self.train, int): + raise TypeError("train argument must be an int") + + if not isinstance(self.test_fraction, float): + raise TypeError("test_fraction argument must be a float") + + def create_node(self, NetId: int): + """Create a node in the database. + Parameters: + NetId (int): The ID of the network to which the node belongs. + + Returns: + None + """ + self.engine.execute( + text(INSERT_NODE_QUERY.format(self.name, NetId, self.train)) + ) + + def delete_node(self): + """Delete the node from the database.""" + self.engine.execute(text(DELETE_NODE_QUERY.format(self.name))) + + def check_dataset_compatibility(self, data_df): + """Check if the dataset is compatible with the master dataset. + Parameters: + data_df (DataFrame): The dataset to check. + + Returns: + None + """ + if master_table_exists() != 1: + print("MasterDataset doesn't exist") + else: + columns = data_df.columns.tolist() + + # get master_dataset columns + result_proxy = self.engine.execute(SELECT_MASTER_COLUMNS_QUERY) + master_table_columns = result_proxy.keys() + + + assert [x == y for x, y in zip(master_table_columns, columns)] + + def update_node(self): + """Update the node information (not implemented).""" + pass + + def get_dataset(self, column_name: str = None): + """Get the dataset for the node based on the given column name. + Parameters: + column_name (str, optional): The column name to filter the dataset. Default is None. + + Returns: + DataFrame: The dataset associated with the node. + """ + NodeId = get_nodeid_from_name(self.name) + if column_name is not None: + query = text(SELECT_DATASET_BY_COLUMN_QUERY.format(column_name, self.name)) + else: + query = text(SELECT_DATASET_BY_NODE_ID_QUERY.format(NodeId)) + + result_proxy = self.engine.execute(query) + node_dataset = pd.DataFrame(result_proxy.fetchall(), columns=result_proxy.keys()) + + return node_dataset + + def upload_dataset(self, dataset_name: str, path_to_csv: str = params['path_to_test_csv']): + """Upload the dataset to the database for the node. + + Parameters: + dataset_name (str): The name of the dataset. + path_to_csv (str, optional): Path to the CSV file containing the dataset. Default is the path in params. + + Returns: + None + """ + try: + data_df = pd.read_csv(path_to_csv) + nodeId = get_nodeid_from_name(self.name) + columns = data_df.columns.tolist() + self.check_dataset_compatibility(data_df) + + data_df = process_eicu(data_df) + + # Insert data in batches + batch_size = 1000 # Adjust as needed + for start_idx in range(0, len(data_df), batch_size): + batch_data = data_df.iloc[start_idx:start_idx + batch_size] + insert_query = f"INSERT INTO Datasets (DataSetName, NodeId, {', '.join(columns)}) VALUES (:dataset_name, :nodeId, {', '.join([':' + col for col in columns])})" + data_to_insert = batch_data.to_dict(orient='records') + params = [{"dataset_name": dataset_name, "nodeId": nodeId, **row} for row in data_to_insert] + self.engine.execute(text(insert_query), params) + except exc.SQLAlchemyError as e: + print(f"Error uploading dataset: {e}") + + def assign_dataset(self, dataset_name:str): + """Assigning existing dataSet to node + Parameters: + dataset_name (str): The name of the dataset to assign. + + Returns: + None + """ + + nodeId = get_nodeid_from_name(self.name) + query = f"UPDATE DataSets SET nodeId = {nodeId} WHERE DataSetName = '{dataset_name}'" + self.engine.execute(text(query)) + + def unassign_dataset(self, dataset_name:str): + """unssigning existing dataSet to node + Parameters: + dataset_name (str): The name of the dataset to assign. + + Returns: + None + """ + + query = f"UPDATE DataSets SET nodeId = {-1} WHERE DataSetName = '{dataset_name}'" + self.engine.execute(text(query)) + + def list_alldatasets(self): + """List all datasets associated with the node. + Returns: + DataFrame: A DataFrame containing information about all datasets associated with the node. + + """ + return pd.read_sql( + text(SELECT_ALL_DATASETS_QUERY.format(self.name)), my_eng + ) + + @staticmethod + def list_allnodes(): + """List all nodes in the database. + Returns: + DataFrame: A DataFrame containing information about all nodes in the database. + + """ + query = text(SELECT_ALL_NODES_QUERY) + res = pd.read_sql(query, my_eng) + return res diff --git a/build/lib/MEDfl/__init__.py b/build/lib/MEDfl/__init__.py new file mode 100644 index 0000000..9763e3e --- /dev/null +++ b/build/lib/MEDfl/__init__.py @@ -0,0 +1,4 @@ +from .LearningManager import * +from .NetManager import * +from .scripts import * +from .rw import * \ No newline at end of file diff --git a/build/lib/MEDfl/rw/__init__.py b/build/lib/MEDfl/rw/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build/lib/MEDfl/rw/client.py b/build/lib/MEDfl/rw/client.py new file mode 100644 index 0000000..4fed968 --- /dev/null +++ b/build/lib/MEDfl/rw/client.py @@ -0,0 +1,421 @@ +# File: client.py + +import argparse +import pandas as pd +import flwr as fl +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import TensorDataset, DataLoader +from sklearn.metrics import accuracy_score, roc_auc_score +from sklearn.model_selection import train_test_split +from MEDfl.rw.model import Net # votre définition de modèle +import socket +import platform +import psutil +import shutil +import numpy as np + +try: + import GPUtil +except ImportError: + GPUtil = None + + +class DPConfig: + """ + Configuration for differential privacy. + + Attributes: + noise_multiplier (float): Noise multiplier for DP. + max_grad_norm (float): Maximum gradient norm for clipping. + batch_size (int): Batch size for training. + secure_rng (bool): Use a secure random generator. + """ + + def __init__( + self, + noise_multiplier=1.0, + max_grad_norm=1.0, + batch_size=32, + secure_rng=False, + ): + self.noise_multiplier = noise_multiplier + self.max_grad_norm = max_grad_norm + self.batch_size = batch_size + self.secure_rng = secure_rng + + +class FlowerClient(fl.client.NumPyClient): + def __init__( + self, + server_address, + data_path="data/data.csv", + dp_config=None, + # NEW (optional client overrides; do NOT remove old args) + val_frac=None, + test_frac=None, + id_col=None, + test_ids=None, + seed=42, + ): + self.server_address = server_address + self.dp_config = dp_config + self.client_val_frac = val_frac + self.client_test_frac = test_frac + self.id_col = id_col + self.test_ids = test_ids + self.seed = seed + + # Load the CSV once; actual column selection happens on first fit using server config + self._df = pd.read_csv(data_path) + + # Defaults used only for get_properties BEFORE first fit (last column target) + self.feature_names = self._df.columns[:-1].tolist() + self.target_name = self._df.columns[-1] + self.label_counts = self._df[self.target_name].value_counts().to_dict() + self.classes = sorted(self.label_counts.keys()) + + # Tensors for metrics before first fit (fallback to all-but-last as features) + X_default = self._df.iloc[:, :-1].values + y_default = self._df.iloc[:, -1].values + self.X_tensor = torch.tensor(X_default, dtype=torch.float32) + self.y_tensor = torch.tensor(y_default, dtype=torch.float32) + + # Placeholders; we lazily build loaders/model on the first fit when we see server config + self.train_loader = None + self.val_loader = None + self.test_loader = None + + self.model = None + self.criterion = nn.BCEWithLogitsLoss() + self.optimizer = None + + # Effective settings (filled at first fit) + self.effective_features = self.feature_names[:] + self.effective_target = self.target_name + self.effective_val_frac = float(self.client_val_frac) if self.client_val_frac is not None else 0.0 + self.effective_test_frac = float(self.client_test_frac) if self.client_test_frac is not None else 0.0 + + self._initialized = False + self._dp_attached = False # to avoid wrapping twice + + # ---------- helpers + + def _mk_loader(self, X, y, batch_size, shuffle): + x_t = torch.tensor(X, dtype=torch.float32) + y_t = torch.tensor(y, dtype=torch.float32) + return DataLoader(TensorDataset(x_t, y_t), batch_size=batch_size, shuffle=shuffle) + + def _lazy_init_from_server_config(self, config): + """ + Build model and (train, val, test) loaders once, using: + - Server-enforced schema: config['features'] (comma-separated), config['target'] + - Split fractions: client overrides win; else use server's val_fraction/test_fraction + - NEW: if config['test_ids'] is set (per-client from strategy), use ID-based split + """ + # ---------- schema from server (enforced if provided) + srv_features = (config.get("features") or "").strip() + srv_target = (config.get("target") or "").strip() + print(f"[Client] Initializing with server schema: features='{srv_features}', target='{srv_target}'") + + if srv_target: + if srv_target not in self._df.columns: + raise ValueError(f"Server-specified target '{srv_target}' not in CSV columns {list(self._df.columns)}") + target_col = srv_target + else: + target_col = self._df.columns[-1] # fallback (keeps backward compatibility) + + if srv_features: + feat_cols = [c.strip() for c in srv_features.split(",") if c.strip()] + missing = [c for c in feat_cols if c not in self._df.columns] + if missing: + raise ValueError(f"Server-specified feature(s) not found in CSV: {missing}") + else: + feat_cols = [c for c in self._df.columns if c != target_col] + + # ---------- fractions: client overrides > server defaults > fallback (0.10/0.10) + srv_val = config.get("val_fraction", None) + srv_test = config.get("test_fraction", None) + val_frac = self.client_val_frac if self.client_val_frac is not None else (float(srv_val) if srv_val is not None else 0.10) + test_frac = self.client_test_frac if self.client_test_frac is not None else (float(srv_test) if srv_test is not None else 0.10) + + if not (0.0 <= val_frac < 1.0): + raise ValueError(f"Invalid val_frac: {val_frac} (must be 0 <= val_frac < 1)") + + # ---------- NEW: adopt test_ids / id_col from server config if provided + if (not self.test_ids or not self.test_ids.strip()) and config.get("test_ids"): + + # strategy (per_client) can inject test_ids as list or CSV; normalize to CSV string + ti = config.get("test_ids") + if isinstance(ti, (list, tuple, set)): + self.test_ids = ",".join(str(x) for x in ti) + else: + self.test_ids = str(ti) + + print(f"[Client] Using server-provided test_ids: {self.test_ids}") + + if (not self.id_col) and config.get("id_col"): + self.id_col = str(config.get("id_col")) + + # ---------- extract arrays with the enforced schema + X_all = self._df[feat_cols].values + y_all = self._df[target_col].values + + # Keep tensors for global metrics logging (same behavior as before) + self.X_tensor = torch.tensor(X_all, dtype=torch.float32) + self.y_tensor = torch.tensor(y_all, dtype=torch.float32) + + # ---------- split + if self.test_ids and self.test_ids.strip(): # ID-based mode (unchanged behavior, now also supports server-provided IDs) + print("[Client] Using ID-based test selection") + test_ids_list = [i.strip() for i in self.test_ids.split(',') if i.strip()] + + if self.id_col and self.id_col in self._df.columns: + id_series = self._df[self.id_col] + # Align types between id_series and test_ids_list + if np.issubdtype(id_series.dtype, np.number): + test_ids_list = [int(i) for i in test_ids_list] + else: + test_ids_list = [str(i) for i in test_ids_list] + + else: + print(f"[Client] Falling back to line numbers (index) as IDs since id_col='{self.id_col}' is invalid or not provided") + id_series = self._df.index + try: + test_ids_list = [int(i) for i in test_ids_list] + except ValueError: + raise ValueError("Test IDs must be integers when using line numbers as IDs") + + test_mask = id_series.isin(test_ids_list) + if not test_mask.any(): + print("[Client] Warning: No matching IDs found for test set; it will be empty") + + X_test = X_all[test_mask] + y_test = y_all[test_mask] + X_trval = X_all[~test_mask] + y_trval = y_all[~test_mask] + + actual_test_frac = len(y_test) / len(y_all) if len(y_all) > 0 else 0.0 + if val_frac + actual_test_frac >= 1.0: + raise ValueError(f"Validation fraction {val_frac} + actual test fraction {actual_test_frac} >= 1.0") + + self.effective_test_frac = actual_test_frac # For logging + + else: # Fraction-based mode (existing) + if not (0.0 <= test_frac < 1.0 and (val_frac + test_frac) < 1.0): + raise ValueError(f"Invalid fractions: val={val_frac}, test={test_frac} (require 0 <= val,test < 1 and val+test < 1)") + + strat_all = y_all if len(np.unique(y_all)) > 1 else None + X_trval, X_test, y_trval, y_test = train_test_split( + X_all, y_all, test_size=test_frac, random_state=self.seed, stratify=strat_all + ) + + # Split val from trval (common to both modes) + if val_frac > 0 and len(y_trval) > 0: + actual_test_frac = len(y_test) / len(y_all) if len(y_all) > 0 else 0.0 + rel_val = val_frac / (1.0 - actual_test_frac) if (1.0 - actual_test_frac) > 0 else 0.0 + strat_tr = y_trval if len(np.unique(y_trval)) > 1 else None + X_train, X_val, y_train, y_val = train_test_split( + X_trval, y_trval, test_size=rel_val, random_state=self.seed, stratify=strat_tr + ) + else: + X_train, y_train = X_trval, y_trval + X_val, y_val = np.empty((0, X_all.shape[1])), np.empty((0,)) + + # ---------- build loaders + batch_size = self.dp_config.batch_size if self.dp_config else 32 + self.train_loader = self._mk_loader(X_train, y_train, batch_size, shuffle=True) + self.val_loader = self._mk_loader(X_val, y_val, batch_size=batch_size, shuffle=False) if len(y_val) else None + self.test_loader = self._mk_loader(X_test, y_test, batch_size, shuffle=False) + + # ---------- model/optimizer + input_dim = X_all.shape[1] + self.model = Net(input_dim) + self.optimizer = optim.SGD(self.model.parameters(), lr=0.01) + + # ---------- attach DP (same behavior as before; only wraps the train loader) + if self.dp_config and not self._dp_attached: + try: + from opacus import PrivacyEngine + privacy_engine = PrivacyEngine() + (self.model, self.optimizer, self.train_loader) = privacy_engine.make_private( + module=self.model, + optimizer=self.optimizer, + data_loader=self.train_loader, + noise_multiplier=self.dp_config.noise_multiplier, + max_grad_norm=self.dp_config.max_grad_norm, + secure_rng=self.dp_config.secure_rng, + ) + self._dp_attached = True + except ImportError: + print("Opacus non installé : exécution sans DP.") + + # ---------- record effective settings + self.effective_features = feat_cols + self.effective_target = target_col + self.effective_val_frac = float(val_frac) + # effective_test_frac already set above if ID mode; otherwise use the input + if self.test_ids and self.test_ids.strip(): + pass # Already set + else: + self.effective_test_frac = float(test_frac) + + self._initialized = True + print(f"[Client] Initialized with features={feat_cols}, target={target_col}, val={val_frac}, test={self.effective_test_frac}") + + # ---------- FL API (unchanged behavior) ---------- + + def get_parameters(self, config): + if not self._initialized: + try: + self._lazy_init_from_server_config(config if isinstance(config, dict) else {}) + except Exception as e: + if not self._initialized: + self._lazy_init_from_server_config({}) + return [val.cpu().numpy() for val in self.model.state_dict().values()] + + def set_parameters(self, parameters): + params_dict = zip(self.model.state_dict().keys(), parameters) + state_dict = {k: torch.tensor(v) for k, v in params_dict} + self.model.load_state_dict(state_dict, strict=True) + + def fit(self, parameters, config): + if not self._initialized: + self._lazy_init_from_server_config(config) + + self.set_parameters(parameters) + self.model.train() + + local_epochs = config.get("local_epochs", 5) + total_loss = 0.0 + print(f"Training for {local_epochs} epochs...") + + for epoch in range(local_epochs): + print(f"Epoch {epoch + 1}/{local_epochs}") + for X_batch, y_batch in self.train_loader: + self.optimizer.zero_grad() + outputs = self.model(X_batch) + loss = self.criterion(outputs.squeeze(), y_batch) + loss.backward() + self.optimizer.step() + total_loss += loss.item() * X_batch.size(0) + + avg_loss = total_loss / (len(self.train_loader.dataset) * local_epochs) + + with torch.no_grad(): + logits = self.model(self.X_tensor).squeeze() + probs = torch.sigmoid(logits).cpu().numpy() + y_true = self.y_tensor.cpu().numpy() + binary_preds = (probs >= 0.5).astype(int) + try: + auc = roc_auc_score(y_true, probs) + except Exception: + auc = float("nan") + acc = accuracy_score(y_true, binary_preds) + + hostname = socket.gethostname() + os_type = platform.system() + metrics = { + "hostname": hostname, + "os_type": os_type, + "train_loss": avg_loss, + "train_accuracy": acc, + "train_auc": auc, + "features": ",".join(self.effective_features), + "target": self.effective_target, + "val_fraction": self.effective_val_frac, + "test_fraction": self.effective_test_frac, + } + + return self.get_parameters(config), len(self.train_loader.dataset), metrics + + def evaluate(self, parameters, config): + if not self._initialized: + self._lazy_init_from_server_config(config) + + self.set_parameters(parameters) + self.model.eval() + + total_loss = 0.0 + all_probs, all_true = [], [] + with torch.no_grad(): + for X_batch, y_batch in self.test_loader: + outputs = self.model(X_batch) + loss = self.criterion(outputs.squeeze(), y_batch) + total_loss += loss.item() * X_batch.size(0) + probs = torch.sigmoid(outputs.squeeze()).cpu().numpy() + all_probs.extend(probs.tolist()) + all_true.extend(y_batch.cpu().numpy().tolist()) + + avg_loss = total_loss / len(self.test_loader.dataset) if len(self.test_loader.dataset) > 0 else 0.0 + binary_preds = [1 if p >= 0.5 else 0 for p in all_probs] + try: + auc = roc_auc_score(all_true, all_probs) + except Exception: + auc = float("nan") + acc = accuracy_score(all_true, binary_preds) + + metrics = { + "eval_loss": avg_loss, + "eval_accuracy": acc, + "eval_auc": auc, + } + print(f"Evaluation metrics: {metrics}") + + return float(avg_loss), len(self.test_loader.dataset), metrics + + def get_properties(self, config): + hostname = socket.gethostname() + os_type = platform.system() + + if self._initialized: + num_samples = int(self.X_tensor.shape[0]) + num_features = int(self.X_tensor.shape[1]) + features_str = ",".join(self.effective_features) + target_name = self.effective_target + label_counts = pd.Series(self.y_tensor.numpy()).value_counts().to_dict() + classes = sorted(label_counts.keys()) + else: + num_samples = len(self.X_tensor) + num_features = self.X_tensor.shape[1] + features_str = ",".join(self.feature_names) + target_name = self.target_name + label_counts = self.label_counts + classes = self.classes + + classes_str = ",".join(map(str, classes)) + dist_str = ",".join(f"{cls}:{cnt}" for cls, cnt in label_counts.items()) + + cpu_physical = psutil.cpu_count(logical=False) + cpu_logical = psutil.cpu_count(logical=True) + total_mem_gb = round(psutil.virtual_memory().total / (1024**3), 2) + driver_present = shutil.which('nvidia-smi') is not None + gpu_count = 0 + if GPUtil and driver_present: + try: + gpu_count = len(GPUtil.getGPUs()) + except Exception: + gpu_count = 0 + + return { + "hostname": hostname, + "os_type": os_type, + "num_samples": num_samples, + "num_features": num_features, + "features": features_str, + "target": target_name, + "classes": classes_str, + "label_distribution": dist_str, + "cpu_physical_cores": cpu_physical, + "cpu_logical_cores": cpu_logical, + "total_memory_gb": total_mem_gb, + "gpu_driver_present": str(driver_present), + "gpu_count": gpu_count, + } + + def start(self): + fl.client.start_numpy_client( + server_address=self.server_address, + client=self, + ) diff --git a/build/lib/MEDfl/rw/model.py b/build/lib/MEDfl/rw/model.py new file mode 100644 index 0000000..3c50a1f --- /dev/null +++ b/build/lib/MEDfl/rw/model.py @@ -0,0 +1,147 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +_ACTS = { + "relu": nn.ReLU, + "gelu": nn.GELU, + "leaky_relu": lambda: nn.LeakyReLU(0.01), + "tanh": nn.Tanh, + "elu": nn.ELU, + "silu": nn.SiLU, + "identity": nn.Identity, +} + +def _make_activation(name): + if name not in _ACTS: + raise ValueError(f"Unsupported activation '{name}'. Choose from {list(_ACTS)}") + return _ACTS[name]() + +class Net(nn.Module): + """ + Flexible MLP with task-aware output. + - hidden_dims: e.g., [128, 64, 32] + - activation: 'relu' | 'gelu' | 'leaky_relu' | ... + - batchnorm: apply BatchNorm1d after each linear (except output) + - dropout: float in [0,1] or list per hidden layer + - task: 'binary' | 'multiclass' | 'multilabel' | 'regression' + - num_classes: required for 'multiclass'; for 'binary' ignore; for 'multilabel' set to label count + - return_logits: always True for training (recommended). Use .predict() for post-activation outputs. + """ + + def __init__( + self, + input_dim, + hidden_dims=[64, 32], + activation="relu", + batchnorm=True, + dropout=0.0, + task="binary", + num_classes=None, + output_bias=True, + return_logits=True, + weight_init="kaiming", + ): + super().__init__() + self.task = task + self.num_classes = num_classes + self.return_logits = return_logits + self.act = activation + + if task == "multiclass": + if not num_classes or num_classes < 2: + raise ValueError("For 'multiclass', num_classes >= 2 is required.") + output_dim = num_classes + elif task == "binary": + output_dim = 1 + elif task == "multilabel": + if not num_classes or num_classes < 1: + raise ValueError("For 'multilabel', set num_classes = number of labels.") + output_dim = num_classes + elif task == "regression": + output_dim = 1 + else: + raise ValueError(f"Unknown task: {task}") + + # Normalize dropout to list per hidden layer + if isinstance(dropout, (int, float)): + dropout = [float(dropout)] * len(hidden_dims) + elif dropout is None: + dropout = [0.0] * len(hidden_dims) + elif isinstance(dropout, list): + if len(dropout) != len(hidden_dims): + raise ValueError("Length of dropout list must match hidden_dims.") + else: + raise ValueError("dropout must be float|list[float]|None") + + self.blocks = nn.ModuleList() + in_dim = input_dim + for h, p in zip(hidden_dims, dropout): + block = nn.ModuleDict({ + "lin": nn.Linear(in_dim, h, bias=True), + "bn": nn.BatchNorm1d(h) if batchnorm else nn.Identity(), + "act": _make_activation(activation), + "drop": nn.Dropout(p) if p and p > 0 else nn.Identity(), + }) + self.blocks.append(block) + in_dim = h + + self.out = nn.Linear(in_dim, output_dim, bias=output_bias) + + # Weight init + if weight_init != "none": + self.apply(lambda m: self._init_weights(m, scheme=weight_init, activation=activation)) + + @staticmethod + def _init_weights(m, scheme, activation): + if isinstance(m, nn.Linear): + if scheme == "kaiming": + nonlinearity = "leaky_relu" if activation == "leaky_relu" else "relu" + nn.init.kaiming_uniform_( + m.weight, + a=math.sqrt(5) if activation == "leaky_relu" else 0, + nonlinearity=nonlinearity, + ) + elif scheme == "xavier": + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + fan_in, _ = nn.init._calculate_fan_in_and_fan_out(m.weight) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + nn.init.uniform_(m.bias, -bound, bound) + + def forward(self, x): + for blk in self.blocks: + x = blk["lin"](x) + x = blk["bn"](x) + x = blk["act"](x) + x = blk["drop"](x) + logits = self.out(x) + return logits if self.return_logits else self._apply_output_activation(logits) + + # Inference helpers + def _apply_output_activation(self, logits): + if self.task == "binary": + return torch.sigmoid(logits) # (B, 1) + elif self.task == "multiclass": + return F.softmax(logits, dim=-1) # (B, C) + elif self.task == "multilabel": + return torch.sigmoid(logits) # (B, L) + elif self.task == "regression": + return logits # raw regression output + else: + raise RuntimeError("Invalid task") + + @torch.no_grad() + def predict(self, x): + out = self.forward(x) + probs = self._apply_output_activation(out) if self.return_logits else out + + if self.task == "binary": + return (probs >= 0.5).long() + elif self.task == "multiclass": + return probs.argmax(dim=-1) + elif self.task == "multilabel": + return (probs >= 0.5).long() + elif self.task == "regression": + return probs diff --git a/build/lib/MEDfl/rw/server.py b/build/lib/MEDfl/rw/server.py new file mode 100644 index 0000000..178658a --- /dev/null +++ b/build/lib/MEDfl/rw/server.py @@ -0,0 +1,134 @@ +import flwr as fl +from flwr.server.strategy import FedAvg +from flwr.server.server import ServerConfig +from MEDfl.rw.strategy import Strategy +import asyncio +from flwr.server.client_manager import ClientManager +from flwr.server.client_proxy import ClientProxy +from flwr.common import GetPropertiesIns +from flwr.common import GetPropertiesIns + + +class FederatedServer: + """ + FederatedServer wraps the launch and configuration of a Flower federated learning server. + + Attributes: + server_address (str): Server host and port in the format "host:port". + server_config (ServerConfig): Configuration for the Flower server. + strategy_wrapper (Strategy): Wrapper around the actual Flower strategy. + strategy (flwr.server.Strategy): Actual Flower strategy instance. + certificates (Any): Optional TLS certificates. + connected_clients (list): List of connected client IDs. + + Methods: + start(): + Launch the Flower server with the specified strategy and log client connections. + """ + + def __init__( + self, + host="0.0.0.0", + port=8080, + num_rounds=3, + strategy=None, + certificates=None, + ): + """ + Initialize the FederatedServer. + + Args: + host (str): Hostname or IP to bind the server to. + port (int): Port to listen on. + num_rounds (int): Number of federated learning rounds to execute. + strategy (Optional[Strategy]): Optional custom strategy wrapper. + certificates (Optional[Any]): Optional TLS certificates. + """ + # Server address and configuration + self.server_address = f"{host}:{port}" + self.server_config = ServerConfig(num_rounds=num_rounds) + + # Use custom or default strategy + self.strategy_wrapper = strategy or Strategy() + self.strategy_wrapper.create_strategy() + if self.strategy_wrapper.strategy_object is None: + raise ValueError("Strategy object not initialized. Call create_strategy() first.") + self.strategy = self.strategy_wrapper.strategy_object + + self.certificates = certificates + self.connected_clients = [] # Track connected client IDs + + + def start(self): + """ + Start the Flower server with the configured strategy and track client connections. + """ + print(f"Using strategy: {self.strategy_wrapper.name}") + print(f"Starting Flower server on {self.server_address} with strategy {self.strategy_wrapper.name}") + + # Use a custom client manager that logs client connections + client_manager = TrackingClientManager(self) + + # Launch the Flower server + fl.server.start_server( + server_address=self.server_address, + config=self.server_config, + strategy=self.strategy, + certificates=self.certificates, + client_manager=client_manager, + ) + + +class TrackingClientManager(fl.server.client_manager.SimpleClientManager): + """ + TrackingClientManager extends the default SimpleClientManager to log client connections. + + Attributes: + server (FederatedServer): The FederatedServer instance this manager belongs to. + client_properties (dict): Placeholder for storing client-specific properties. + """ + + def __init__(self, server): + """ + Initialize the TrackingClientManager. + + Args: + server (FederatedServer): Reference to the FederatedServer. + """ + super().__init__() + self.server = server + self.client_properties = {} + + def register(self, client): + """ + Register a client and log its connection. + + Args: + client (ClientProxy): The client proxy being registered. + + Returns: + bool: True if the client was registered successfully. + """ + success = super().register(client) + if success and client.cid not in self.server.connected_clients: + # Run the asynchronous hostname fetch synchronously + asyncio.run(self._fetch_and_log_hostname(client)) + return success + + async def _fetch_and_log_hostname(self, client): + """ + Asynchronously fetch and log the client's hostname or CID. + + Args: + client (ClientProxy): The client proxy. + """ + # Optional: uncomment to fetch hostname from client properties + # try: + # ins = GetPropertiesIns(config={}) + # props = await client.get_properties(ins=ins, timeout=10.0, group_id=0) + # hostname = props.properties.get("hostname", "unknown") + # except Exception as e: + # hostname = f"Error: {e}" + + print(f"✅ Client connected - CID: {client.cid}") + self.server.connected_clients.append(client.cid) diff --git a/build/lib/MEDfl/rw/strategy.py b/build/lib/MEDfl/rw/strategy.py new file mode 100644 index 0000000..ba1ce42 --- /dev/null +++ b/build/lib/MEDfl/rw/strategy.py @@ -0,0 +1,302 @@ +# File: MEDfl/rw/strategy.py + +import os +import numpy as np +import flwr as fl +from flwr.common import GetPropertiesIns +from flwr.server.client_manager import ClientManager +from flwr.server.client_proxy import ClientProxy +import time +from MEDfl.rw.model import Net +import torch + +# =================================================== +# Custom metric aggregation functions +# =================================================== +def aggregate_fit_metrics(results): + total = sum(n for n, _ in results) + loss = sum(m.get("train_loss", 0.0) * n for n, m in results) / total + acc = sum(m.get("train_accuracy", 0.0) * n for n, m in results) / total + auc = sum(m.get("train_auc", 0.0) * n for n, m in results) / total + return {"train_loss": loss, "train_accuracy": acc, "train_auc": auc} + +def aggregate_eval_metrics(results): + total = sum(n for n, _ in results) + loss = sum(m.get("eval_loss", 0.0) * n for n, m in results) / total + acc = sum(m.get("eval_accuracy", 0.0) * n for n, m in results) / total + auc = sum(m.get("eval_auc", 0.0) * n for n, m in results) / total + return {"eval_loss": loss, "eval_accuracy": acc, "eval_auc": auc} + +# =================================================== +# Strategy Wrapper +# =================================================== +class Strategy: + """ + Flower Strategy wrapper: + - Dynamic hyperparameters via on_fit_config_fn + - Custom metric aggregation + - Per-client & aggregated metric logging + - Synchronous get_properties() inspection in configure_fit() + - Saving global parameters every saveOnRounds to savingPath + + Extended: + - split_mode: + * "global": use global val_fraction/test_fraction for all clients + * "per_client": use client_fractions[hostname] if present + - client_fractions: + { + "HOSTNAME_1": { + "val_fraction": float (optional), + "test_fraction": float (optional), + "test_ids": [..] or "id1,id2" (optional) + }, + ... + } + - In per_client mode: + * if test_ids is present for a client: + -> send test_ids + -> do NOT use that client's test_fraction + * otherwise: + -> use that client's val_fraction/test_fraction if provided, + else fall back to global val_fraction/test_fraction + - client id in this mapping = hostname from client.get_properties() + - id_col: + * column name used on clients to match test_ids (default "id") + """ + + def __init__( + self, + name="FedAvg", + fraction_fit=1.0, + fraction_evaluate=1.0, + min_fit_clients=2, + min_evaluate_clients=2, + min_available_clients=2, + initial_parameters=None, + evaluate_fn=None, + fit_metrics_aggregation_fn=None, + evaluate_metrics_aggregation_fn=None, + local_epochs=1, + threshold=0.5, + learning_rate=0.01, + optimizer_name="SGD", + savingPath="", + saveOnRounds=3, + total_rounds=3, + features="", + target="", + val_fraction=0.10, + test_fraction=0.10, + # NEW: splitting control (added at the end to not break existing calls) + split_mode="global", # "global" or "per_client" + client_fractions=None, + # NEW: id column for test_ids mapping + id_col="id", + ): + self.name = name + self.fraction_fit = fraction_fit + self.fraction_evaluate = fraction_evaluate + self.min_fit_clients = min_fit_clients + self.min_evaluate_clients = min_evaluate_clients + self.min_available_clients = min_available_clients + self.initial_parameters = initial_parameters or [] + self.evaluate_fn = evaluate_fn + + self.fit_metrics_aggregation_fn = fit_metrics_aggregation_fn or aggregate_fit_metrics + self.evaluate_metrics_aggregation_fn = evaluate_metrics_aggregation_fn or aggregate_eval_metrics + + # Dynamic hyperparams + self.local_epochs = local_epochs + self.threshold = threshold + self.learning_rate = learning_rate + self.optimizer_name = optimizer_name + self.savingPath = savingPath + self.saveOnRounds = saveOnRounds + self.total_rounds = total_rounds + self._features = features # comma-separated or "" + self._target = target # or "" + self._val_fraction = val_fraction + self._test_fraction = test_fraction + + # NEW + self.split_mode = split_mode + self.client_fractions = client_fractions or {} + self.id_col = id_col + + self.strategy_object = None + + def create_strategy(self): + # 1) Pick the Flower Strategy class + StrategyClass = getattr(fl.server.strategy, self.name) + + # 2) Define on_fit_config_fn _before_ instantiation (global defaults) + def fit_config_fn(server_round): + return { + "local_epochs": self.local_epochs, + "threshold": self.threshold, + "learning_rate": self.learning_rate, + "optimizer": self.optimizer_name, + "features": self._features, + "target": self._target, + "val_fraction": float(self._val_fraction), + "test_fraction": float(self._test_fraction), + # NEW: always send id_col so clients know which column to use for test_ids + "id_col": self.id_col, + } + + # 3) Build params including on_fit_config_fn + params = { + "fraction_fit": self.fraction_fit, + "fraction_evaluate": self.fraction_evaluate, + "min_fit_clients": self.min_fit_clients, + "min_evaluate_clients": self.min_evaluate_clients, + "min_available_clients": self.min_available_clients, + "evaluate_fn": self.evaluate_fn, + "on_fit_config_fn": fit_config_fn, + "fit_metrics_aggregation_fn": self.fit_metrics_aggregation_fn, + "evaluate_metrics_aggregation_fn": self.evaluate_metrics_aggregation_fn, + } + if self.initial_parameters: + params["initial_parameters"] = fl.common.ndarrays_to_parameters(self.initial_parameters) + else: + # derive initial params from server-specified features + feat_cols = [c.strip() for c in (self._features or "").split(",") if c.strip()] + if not feat_cols: + raise ValueError( + "No initial_parameters provided and 'features' is empty. " + "Provide Strategy(..., features='col1,col2,...') or pass initial_parameters." + ) + input_dim = len(feat_cols) + _model = Net(input_dim) + _arrays = [t.detach().cpu().numpy() for t in _model.state_dict().values()] + params["initial_parameters"] = fl.common.ndarrays_to_parameters(_arrays) + + # 4) Instantiate the real Flower strategy + strat = StrategyClass(**params) + + # 5) Wrap aggregate_fit for logging (prints unchanged) + original_agg_fit = strat.aggregate_fit + + def logged_agg_fit(server_round, results, failures): + print(f"\n[Server] 🔄 Round {server_round} - Client Training Metrics:") + for i, (client_id, fit_res) in enumerate(results): + print(f" CTM Round {server_round} Client:{client_id.cid}: {fit_res.metrics}") + agg_params, metrics = original_agg_fit(server_round, results, failures) + print(f"[Server] ✅ Round {server_round} - Aggregated Training Metrics: {metrics}\n") + # save the model parameters if savingPath is set on each saveOnRounds + if self.savingPath and ( + (server_round % self.saveOnRounds == 0) + or (self.total_rounds and server_round == self.total_rounds) + ): + arrays = fl.common.parameters_to_ndarrays(agg_params) + # Determine filename: final_model on last round else round_{n} + filename = ( + f"round_{server_round}_final_model.npz" + if server_round == self.total_rounds + else f"round_{server_round}_model.npz" + ) + filepath = os.path.join(self.savingPath, filename) + np.savez(filepath, *arrays) + return agg_params, metrics + + strat.aggregate_fit = logged_agg_fit + + # 6) Wrap aggregate_evaluate for logging (prints unchanged) + original_agg_eval = strat.aggregate_evaluate + + def logged_agg_eval(server_round, results, failures): + print(f"\n[Server] 📊 Round {server_round} - Client Evaluation Metrics:") + for i, (client_id, eval_res) in enumerate(results): + print(f" CEM Round {server_round} Client:{client_id.cid}: {eval_res.metrics}") + loss, metrics = original_agg_eval(server_round, results, failures) + print(f"[Server] ✅ Round {server_round} - Aggregated Evaluation Metrics:") + print(f" Loss: {loss}, Metrics: {metrics}\n") + return loss, metrics + + strat.aggregate_evaluate = logged_agg_eval + + # 7) Wrap configure_fit to: + # - log client properties (unchanged) + # - apply split_mode/client_fractions to fit_ins.config (NEW) + original_conf_fit = strat.configure_fit + + def wrapped_conf_fit( + server_round, + parameters, + client_manager + ): + selected = original_conf_fit( + server_round=server_round, + parameters=parameters, + client_manager=client_manager + ) + + ins = GetPropertiesIns(config={}) + + for client, fit_ins in selected: + hostname = None + try: + props = client.get_properties(ins=ins, timeout=10.0, group_id=0) + print(f"\n📋 [Round {server_round}] Client {client.cid} Properties: {props.properties}") + hostname = props.properties.get("hostname", None) + except Exception as e: + print(f"⚠️ Failed to get properties from {client.cid}: {e}") + + # Fallback: if no hostname returned, use Flower cid + if not hostname: + hostname = client.cid + + # Keep same object + cfg = fit_ins.config + + if self.split_mode == "per_client": + # Lookup by hostname (preferred) or cid + per_cfg = ( + self.client_fractions.get(hostname) + or self.client_fractions.get(client.cid) + or {} + ) + + # val_fraction: per-client override if present + if "val_fraction" in per_cfg: + try: + cfg["val_fraction"] = float(per_cfg["val_fraction"]) + except Exception: + pass # keep existing if invalid + + # test: prefer test_ids if provided + if "test_ids" in per_cfg and per_cfg["test_ids"]: + test_ids_val = per_cfg["test_ids"] + if isinstance(test_ids_val, (list, tuple, set)): + test_ids_str = ",".join(str(x) for x in test_ids_val) + else: + test_ids_str = str(test_ids_val) + cfg["test_ids"] = test_ids_str + # when using explicit IDs, do not force a test_fraction for this client + if "test_fraction" in cfg: + del cfg["test_fraction"] + # ensure id_col is sent so client can map IDs + cfg["id_col"] = self.id_col + else: + # no test_ids -> use per-client test_fraction if present + if "test_fraction" in per_cfg: + try: + cfg["test_fraction"] = float(per_cfg["test_fraction"]) + except Exception: + pass # keep existing if invalid + # if no test_ids: id_col not strictly required, leave as-is + else: + # split_mode == "global": enforce global fractions, clear any test_ids + if "test_ids" in cfg: + del cfg["test_ids"] + cfg["val_fraction"] = float(self._val_fraction) + cfg["test_fraction"] = float(self._test_fraction) + # also send id_col so clients know column name if needed + cfg["id_col"] = self.id_col + + return selected + + strat.configure_fit = wrapped_conf_fit + + # 8) Save the ready-to-use strategy + self.strategy_object = strat diff --git a/build/lib/MEDfl/scripts/__init__.py b/build/lib/MEDfl/scripts/__init__.py new file mode 100644 index 0000000..0e0ec23 --- /dev/null +++ b/build/lib/MEDfl/scripts/__init__.py @@ -0,0 +1,2 @@ +# from .base import * +# from .create_db import * \ No newline at end of file diff --git a/build/lib/MEDfl/scripts/base.py b/build/lib/MEDfl/scripts/base.py new file mode 100644 index 0000000..1f5030a --- /dev/null +++ b/build/lib/MEDfl/scripts/base.py @@ -0,0 +1,30 @@ +import mysql.connector +from sqlalchemy import create_engine, text +from configparser import ConfigParser +import yaml +import pkg_resources +import os + +# Get the directory of the current script +current_directory = os.path.dirname(os.path.abspath(__file__)) + +# Load configuration from the config file +config_file_path = os.path.join(current_directory, 'db_config.ini') + +config = ConfigParser() +config.read(config_file_path) +mysql_config = config['mysql'] + + + +connection_string = ( + f"mysql+mysqlconnector://{mysql_config['user']}:{mysql_config['password']}@" + f"{mysql_config['host']}:{mysql_config['port']}/{mysql_config['database']}" +) + +eng = create_engine( + connection_string, + execution_options={"autocommit": True}, +) + +my_eng = eng.connect() \ No newline at end of file diff --git a/build/lib/MEDfl/scripts/create_db.py b/build/lib/MEDfl/scripts/create_db.py new file mode 100644 index 0000000..a98cafb --- /dev/null +++ b/build/lib/MEDfl/scripts/create_db.py @@ -0,0 +1,126 @@ +import sys +import sqlite3 +import pandas as pd +from configparser import ConfigParser +import os +import ast + +from MEDfl.LearningManager.utils import * + + +def main(csv_file_path): + try: + # Get the directory of the current script + current_directory = os.path.dirname(os.path.abspath(__file__)) + + # Load configuration from the config file + # config_file_path = os.path.join(current_directory, 'sqllite_config.ini')* + + config_file_path = load_db_config() + + # config = ConfigParser() + # config.read(config_file_path) + # sqlite_config = config['sqllite'] + + sqlite_config = config_file_path + + + print('Im here !') + + # Connect to SQLite database (it will be created if it doesn't exist) + database_path = sqlite_config['database'] + conn = sqlite3.connect(database_path) + cursor = conn.cursor() + + # Drop each table if it exists + tables = ['Networks', 'FLsetup', 'Nodes', 'DataSets', 'FLpipeline', 'testResults', 'FedDatasets'] + for table in tables: + cursor.execute(f"DROP TABLE IF EXISTS {table}") + + # Create Networks table + cursor.execute( + "CREATE TABLE Networks( \ + NetId INTEGER PRIMARY KEY AUTOINCREMENT, \ + NetName TEXT \ + );" + ) + + # Create FLsetup table + cursor.execute("CREATE TABLE FLsetup (\ + FLsetupId INTEGER PRIMARY KEY AUTOINCREMENT,\ + name TEXT NOT NULL, \ + description TEXT NOT NULL,\ + creation_date TEXT NOT NULL,\ + NetId INTEGER NOT NULL,\ + column_name TEXT\ + )") + + # Create Nodes table + cursor.execute("CREATE TABLE Nodes ( \ + NodeId INTEGER PRIMARY KEY AUTOINCREMENT,\ + NodeName TEXT,\ + train BOOLEAN DEFAULT 1,\ + NetId INTEGER\ + )") + + data_df = pd.read_csv(csv_file_path) + columns = data_df.columns.tolist() + column_map = {"object": "TEXT", "int64": "INTEGER", "float64": "REAL"} + sub_query = ", ".join(f"{col} {column_map[str(data_df[col].dtype)]}" for col in columns) + + # Create Datasets table by getting columns from the master csv file + cursor.execute( + f"CREATE TABLE DataSets( \ + DataSetId INTEGER PRIMARY KEY AUTOINCREMENT, \ + DataSetName TEXT, \ + NodeId INTEGER,\ + {sub_query}\ + )" + ) + + # Create FLpipeline table + cursor.execute("CREATE TABLE FLpipeline(\ + id INTEGER PRIMARY KEY AUTOINCREMENT,\ + name TEXT NOT NULL, \ + description TEXT NOT NULL,\ + creation_date TEXT NOT NULL,\ + results TEXT NOT NULL\ + ) ") + + # Create test results table + cursor.execute("CREATE TABLE testResults(\ + pipelineId INTEGER,\ + nodename TEXT NOT NULL, \ + confusionmatrix TEXT,\ + accuracy REAL,\ + sensivity REAL,\ + ppv REAL,\ + npv REAL,\ + f1score REAL,\ + fpr REAL,\ + tpr REAL, \ + PRIMARY KEY (pipelineId, nodename)\ + ) ") + + # Create FederatedDataset table + cursor.execute("CREATE TABLE FedDatasets (\ + FedId INTEGER PRIMARY KEY AUTOINCREMENT,\ + FLsetupId INTEGER,\ + FLpipeId INTEGER,\ + name TEXT NOT NULL\ + )") + + # Commit and close the cursor + conn.commit() + cursor.close() + conn.close() + + except sqlite3.Error as e: + print(f"Error: {e}") + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python script.py ") + sys.exit(1) + csv_file_path = sys.argv[1] + main(csv_file_path) diff --git a/build/lib/alembic/__init__.py b/build/lib/alembic/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build/lib/alembic/env.py b/build/lib/alembic/env.py new file mode 100644 index 0000000..14593a4 --- /dev/null +++ b/build/lib/alembic/env.py @@ -0,0 +1,61 @@ +from logging.config import fileConfig +import logging +from sqlalchemy import engine_from_config, create_engine +from sqlalchemy import pool +import sys +import os +from alembic import context + +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +from scripts.base import my_eng + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +# add your model's MetaData object here +# for 'autogenerate' support +# from myapp import mymodel +# target_metadata = mymodel.Base.metadata +target_metadata = None + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. +def configure_logger(name): + # This is the standard logging configuration + logging.config.fileConfig( + 'alembic_logging.ini', # Path to your logging configuration file + defaults={ + 'logfilename': 'alembic.log', # Log file name + }, + disable_existing_loggers=False, + ) + + return logging.getLogger(name) + + + +def run_migrations_offline(): + """Run migrations in 'offline' mode.""" + pass + +def run_migrations_online(): + """Run migrations in 'online' mode.""" + pass + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_build/.buildinfo b/docs/_build/.buildinfo new file mode 100644 index 0000000..2188cfb --- /dev/null +++ b/docs/_build/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. 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    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    MEDfl.NetManager package

    +
    +

    Submodules

    +
    +
    +

    MEDfl.NetManager.dataset module

    +
    +
    +class MEDfl.NetManager.dataset.DataSet(name: str, path: str, engine=None)[source]
    +

    Bases: object

    +
    +
    +__init__(name: str, path: str, engine=None)[source]
    +

    Initialize a DataSet object.

    +
    +
    Parameters:
    +
      +

    • name (str) – The name of the dataset.

      • +

    • path (str) – The file path of the dataset CSV file.

      • +
    +
    +
    +
    + +
    +
    +delete_dataset()[source]
    +

    Delete the dataset from the database.

    +

    Notes: +- Assumes the dataset name is unique in the ‘DataSets’ table.

    +
    + +
    +
    +static list_alldatasets(engine)[source]
    +

    List all dataset names from the ‘DataSets’ table.

    +
    +
    Returns:
    +

    A DataFrame containing the names of all datasets in the ‘DataSets’ table.

    +
    +
    Return type:
    +

    pd.DataFrame

    +
    +
    +
    + +
    +
    +update_data()[source]
    +

    Update the data in the dataset.

    +

    Not implemented yet.

    +
    + +
    +
    +upload_dataset(NodeId=-1)[source]
    +

    Upload the dataset to the database.

    +
    +
    Parameters:
    +

    NodeId (int) – The NodeId associated with the dataset.

    +
    +
    +

    Notes: +- Assumes the file at self.path is a valid CSV file. +- The dataset is uploaded to the ‘DataSets’ table in the database.

    +
    + +
    +
    +validate()[source]
    +

    Validate name and path attributes.

    +
    +
    Raises:
    +

    TypeError – If name or path is not a string.

    +
    +
    +
    + +
    + +
    +
    +

    MEDfl.NetManager.flsetup module

    +
    +
    +class MEDfl.NetManager.flsetup.FLsetup(name: str, description: str, network: Network)[source]
    +

    Bases: object

    +
    +
    +__init__(name: str, description: str, network: Network)[source]
    +

    Initialize a Federated Learning (FL) setup.

    +
    +
    Parameters:
    +
      +

    • name (str) – The name of the FL setup.

      • +

    • description (str) – A description of the FL setup.

      • +

    • network (Network) – An instance of the Network class representing the network architecture.

      • +
    +
    +
    +
    + +
    +
    +create()[source]
    +

    Create an FL setup.

    +
    + +
    +
    +create_dataloader_from_node(node: Node, output, fill_strategy='mean', fit_encode=[], to_drop=[], train_batch_size: int = 32, test_batch_size: int = 1, split_frac: float = 0.2, dataset: Dataset | None = None)[source]
    +

    Create DataLoader from a Node.

    +
    +
    Parameters:
    +
      +

    • node (Node) – The node from which to create DataLoader.

      • +

    • train_batch_size (int) – The batch size for training data.

      • +

    • test_batch_size (int) – The batch size for test data.

      • +

    • split_frac (float) – The fraction of data to be used for training.

      • +

    • dataset (Dataset) – The dataset to use. If None, the method will read the dataset from the node.

      • +
    +
    +
    Returns:
    +

    The DataLoader instances for training and testing.

    +
    +
    Return type:
    +

    DataLoader

    +
    +
    +
    + +
    +
    +create_federated_dataset(output, fill_strategy='mean', fit_encode=[], to_drop=[], val_frac=0.1, test_frac=0.2) → FederatedDataset[source]
    +

    Create a federated dataset.

    +
    +
    Parameters:
    +
      +

    • output (string) – the output feature of the dataset

      • +

    • val_frac (float) – The fraction of data to be used for validation.

      • +

    • test_frac (float) – The fraction of data to be used for testing.

      • +
    +
    +
    Returns:
    +

    The FederatedDataset instance containing train, validation, and test data.

    +
    +
    Return type:
    +

    FederatedDataset

    +
    +
    +
    + +
    +
    +create_nodes_from_master_dataset(params_dict: dict)[source]
    +

    Create nodes from the master dataset.

    +
    +
    Parameters:
    +

    params_dict (dict) – A dictionary containing parameters for node creation. +- column_name (str): The name of the column in the MasterDataset used to create nodes. +- train_nodes (list): A list of node names that will be used for training. +- test_nodes (list): A list of node names that will be used for testing.

    +
    +
    Returns:
    +

    A list of Node instances created from the master dataset.

    +
    +
    Return type:
    +

    list

    +
    +
    +
    + +
    +
    +delete()[source]
    +

    Delete the FL setup.

    +
    + +
    +
    +get_flDataSet()[source]
    +

    Retrieve the federated dataset associated with the FL setup using the FL setup’s name.

    +
    +
    Returns:
    +

    DataFrame containing the federated dataset information.

    +
    +
    Return type:
    +

    pandas.DataFrame

    +
    +
    +
    + +
    +
    +static list_allsetups()[source]
    +

    List all the FL setups.

    +
    +
    Returns:
    +

    A DataFrame containing information about all the FL setups.

    +
    +
    Return type:
    +

    DataFrame

    +
    +
    +
    + +
    +
    +classmethod read_setup(FLsetupId: int)[source]
    +

    Read the FL setup by FLsetupId.

    +
    +
    Parameters:
    +

    FLsetupId (int) – The id of the FL setup to read.

    +
    +
    Returns:
    +

    An instance of the FLsetup class with the specified FLsetupId.

    +
    +
    Return type:
    +

    FLsetup

    +
    +
    +
    + +
    +
    +validate()[source]
    +

    Validate name, description, and network.

    +
    + +
    + +
    +
    +

    MEDfl.NetManager.net_helper module

    +
    +
    +MEDfl.NetManager.net_helper.get_feddataset_id_from_name(name)[source]
    +

    Get the Federated dataset Id from the FedDatasets table based on the federated dataset name.

    +
    +
    Parameters:
    +

    name (str) – Federated dataset name.

    +
    +
    Returns:
    +

    FedId or None if not found.

    +
    +
    Return type:
    +

    int or None

    +
    +
    +
    + +
    +
    +MEDfl.NetManager.net_helper.get_flpipeline_from_name(name)[source]
    +

    Get the FLpipeline Id from the FLpipeline table based on the FL pipeline name.

    +
    +
    Parameters:
    +

    name (str) – FL pipeline name.

    +
    +
    Returns:
    +

    FLpipelineId or None if not found.

    +
    +
    Return type:
    +

    int or None

    +
    +
    +
    + +
    +
    +MEDfl.NetManager.net_helper.get_flsetupid_from_name(name)[source]
    +

    Get the FLsetupId from the FLsetup table based on the FL setup name.

    +
    +
    Parameters:
    +

    name (str) – FL setup name.

    +
    +
    Returns:
    +

    FLsetupId or None if not found.

    +
    +
    Return type:
    +

    int or None

    +
    +
    +
    + +
    +
    +MEDfl.NetManager.net_helper.get_netid_from_name(name)[source]
    +

    Get the Network Id from the Networks table based on the NetName.

    +
    +
    Parameters:
    +

    name (str) – Network name.

    +
    +
    Returns:
    +

    NetId or None if not found.

    +
    +
    Return type:
    +

    int or None

    +
    +
    +
    + +
    +
    +MEDfl.NetManager.net_helper.get_nodeid_from_name(name)[source]
    +

    Get the NodeId from the Nodes table based on the NodeName.

    +
    +
    Parameters:
    +

    name (str) – Node name.

    +
    +
    Returns:
    +

    NodeId or None if not found.

    +
    +
    Return type:
    +

    int or None

    +
    +
    +
    + +
    +
    +MEDfl.NetManager.net_helper.is_str(data_df, row, x)[source]
    +

    Check if a column in a DataFrame is of type ‘object’ and convert the value accordingly.

    +
    +
    Parameters:
    +
      +

    • data_df (pandas.DataFrame) – DataFrame containing the data.

      • +

    • row (pandas.Series) – Data row.

      • +

    • x (str) – Column name.

      • +
    +
    +
    Returns:
    +

    Processed value based on the column type.

    +
    +
    Return type:
    +

    str or float

    +
    +
    +
    + +
    +
    +MEDfl.NetManager.net_helper.master_table_exists()[source]
    +

    Check if the MasterDataset table exists in the database.

    +
    +
    Returns:
    +

    True if the table exists, False otherwise.

    +
    +
    Return type:
    +

    bool

    +
    +
    +
    + +
    +
    +MEDfl.NetManager.net_helper.process_data_after_reading(data, output, fill_strategy='mean', fit_encode=[], to_drop=[])[source]
    +

    Process data after reading from the database, including encoding, dropping columns, and creating a PyTorch TensorDataset.

    +
    +
    Parameters:
    +
      +

    • data (pandas.DataFrame) – Input data.

      • +

    • output (str) – Output column name.

      • +

    • fill_strategy (str, optional) – Imputation strategy for missing values. Default is “mean”.

      • +

    • fit_encode (list, optional) – List of columns to be label-encoded. Default is an empty list.

      • +

    • to_drop (list, optional) – List of columns to be dropped from the DataFrame. Default is an empty list.

      • +
    +
    +
    Returns:
    +

    Processed data as a PyTorch TensorDataset.

    +
    +
    Return type:
    +

    torch.utils.data.TensorDataset

    +
    +
    +
    + +
    +
    +MEDfl.NetManager.net_helper.process_eicu(data_df)[source]
    +

    Process eICU data by filling missing values with mean and replacing NaNs with ‘Unknown’.

    +
    +
    Parameters:
    +

    data_df (pandas.DataFrame) – Input data.

    +
    +
    Returns:
    +

    Processed data.

    +
    +
    Return type:
    +

    pandas.DataFrame

    +
    +
    +
    + +
    +
    +

    MEDfl.NetManager.net_manager_queries module

    +
    +
    +

    MEDfl.NetManager.network module

    +
    +
    +class MEDfl.NetManager.network.Network(name: str = '')[source]
    +

    Bases: object

    +

    A class representing a network.

    +
    +
    +name
    +

    The name of the network.

    +
    +
    Type:
    +

    str

    +
    +
    +
    + +
    +
    +mtable_exists
    +

    An integer flag indicating whether the MasterDataset table exists (1) or not (0).

    +
    +
    Type:
    +

    int

    +
    +
    +
    + +
    +
    +__init__(name: str = '')[source]
    +

    Initialize a Network instance.

    +
    +
    Parameters:
    +

    name (str) – The name of the network.

    +
    +
    +
    + +
    +
    +add_node(node: Node)[source]
    +

    Add a node to the network.

    +
    +
    Parameters:
    +

    node (Node) – The node to add.

    +
    +
    +
    + +
    +
    +create_master_dataset(path_to_csv: str = 'D:\\ESI\\3CS\\PFE\\last_year\\Code\\MEDfl\\notebooks\\eicu_test.csv')[source]
    +

    Create the MasterDataset table and insert dataset values.

    +
    +
    Parameters:
    +

    path_to_csv – Path to the CSV file containing the dataset.

    +
    +
    +
    + +
    +
    +create_network()[source]
    +

    Create a new network in the database.

    +
    + +
    +
    +delete_network()[source]
    +

    Delete the network from the database.

    +
    + +
    +
    +static list_allnetworks()[source]
    +

    List all networks in the database. +:returns: A DataFrame containing information about all networks in the database. +:rtype: DataFrame

    +
    + +
    +
    +list_allnodes()[source]
    +

    List all nodes in the network.

    +
    +
    Parameters:
    +

    None –

    +
    +
    Returns:
    +

    A DataFrame containing information about all nodes in the network.

    +
    +
    Return type:
    +

    DataFrame

    +
    +
    +
    + +
    +
    +update_network(FLsetupId: int)[source]
    +

    Update the network’s FLsetupId in the database.

    +
    +
    Parameters:
    +

    FLsetupId (int) – The FLsetupId to update.

    +
    +
    +
    + +
    +
    +use_network(network_name: str)[source]
    +

    Use a network in the database.

    +
    +
    Parameters:
    +

    network_name (str) – The name of the network to use.

    +
    +
    Returns:
    +

    An instance of the Network class if the network exists, else None.

    +
    +
    Return type:
    +

    Network or None

    +
    +
    +
    + +
    +
    +validate()[source]
    +

    Validate name

    +
    + +
    + +
    +
    +

    MEDfl.NetManager.node module

    +
    +
    +class MEDfl.NetManager.node.Node(name: str, train: int, test_fraction: float = 0.2, engine=<sqlalchemy.engine.base.Connection object>)[source]
    +

    Bases: object

    +

    A class representing a node in the network.

    +
    +
    +name
    +

    The name of the node.

    +
    +
    Type:
    +

    str

    +
    +
    +
    + +
    +
    +train
    +

    An integer flag representing whether the node is used for training (1) or testing (0).

    +
    +
    Type:
    +

    int

    +
    +
    +
    + +
    +
    +test_fraction
    +

    The fraction of data used for testing when train=1. Default is 0.2.

    +
    +
    Type:
    +

    float, optional

    +
    +
    +
    + +
    +
    +__init__(name: str, train: int, test_fraction: float = 0.2, engine=<sqlalchemy.engine.base.Connection object>)[source]
    +

    Initialize a Node instance.

    +
    +
    Parameters:
    +
      +

    • name (str) – The name of the node.

      • +

    • train (int) – An integer flag representing whether the node is used for training (1) or testing (0).

      • +

    • test_fraction (float, optional) – The fraction of data used for testing when train=1. Default is 0.2.

      • +
    +
    +
    +
    + +
    +
    +assign_dataset(dataset_name: str)[source]
    +

    Assigning existing dataSet to node +:param dataset_name: The name of the dataset to assign. +:type dataset_name: str

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +check_dataset_compatibility(data_df)[source]
    +

    Check if the dataset is compatible with the master dataset. +:param data_df: The dataset to check. +:type data_df: DataFrame

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +create_node(NetId: int)[source]
    +

    Create a node in the database. +:param NetId: The ID of the network to which the node belongs. +:type NetId: int

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +delete_node()[source]
    +

    Delete the node from the database.

    +
    + +
    +
    +get_dataset(column_name: str | None = None)[source]
    +

    Get the dataset for the node based on the given column name. +:param column_name: The column name to filter the dataset. Default is None. +:type column_name: str, optional

    +
    +
    Returns:
    +

    The dataset associated with the node.

    +
    +
    Return type:
    +

    DataFrame

    +
    +
    +
    + +
    +
    +list_alldatasets()[source]
    +

    List all datasets associated with the node. +:returns: A DataFrame containing information about all datasets associated with the node. +:rtype: DataFrame

    +
    + +
    +
    +static list_allnodes()[source]
    +

    List all nodes in the database. +:returns: A DataFrame containing information about all nodes in the database. +:rtype: DataFrame

    +
    + +
    +
    +unassign_dataset(dataset_name: str)[source]
    +

    unssigning existing dataSet to node +:param dataset_name: The name of the dataset to assign. +:type dataset_name: str

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +update_node()[source]
    +

    Update the node information (not implemented).

    +
    + +
    +
    +upload_dataset(dataset_name: str, path_to_csv: str = 'D:\\ESI\\3CS\\PFE\\last_year\\Code\\MEDfl\\notebooks\\eicu_test.csv')[source]
    +

    Upload the dataset to the database for the node. +:param dataset_name: The name of the dataset. +:type dataset_name: str +:param path_to_csv: Path to the CSV file containing the dataset. Default is the path in params. +:type path_to_csv: str, optional

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +validate()[source]
    +

    Validate name, train, test_fraction

    +
    + +
    + +
    +
    +

    Module contents

    +
    +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/Medfl.html b/docs/_build/Medfl.html new file mode 100644 index 0000000..52c3f77 --- /dev/null +++ b/docs/_build/Medfl.html @@ -0,0 +1,362 @@ + + + + + + + MEDfl package — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    MEDfl package

    +
    +

    Subpackages

    +
    + +
    +
    +
    +

    Module contents

    +
    +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/client.html b/docs/_build/_modules/Medfl/LearningManager/client.html new file mode 100644 index 0000000..4286687 --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/client.html @@ -0,0 +1,262 @@ + + + + + + MEDfl.LearningManager.client — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.client

    +#!/usr/bin/env python3
+import flwr as fl
+from opacus import PrivacyEngine
+from torch.utils.data import DataLoader
+
+from .model import Model
+from .utils import params
+import torch
+
+
    
+[docs]
+class FlowerClient(fl.client.NumPyClient):
+    """
+    FlowerClient class for creating MEDfl clients.
+
+    Attributes:
+        cid (str): Client ID.
+        local_model (Model): Local model of the federated learning network.
+        trainloader (DataLoader): DataLoader for training data.
+        valloader (DataLoader): DataLoader for validation data.
+        diff_priv (bool): Flag indicating whether to use differential privacy.
+    """
+
    
+[docs]
+    def __init__(self, cid: str, local_model: Model, trainloader: DataLoader, valloader: DataLoader, diff_priv: bool = params["diff_privacy"]):
+        """
+        Initializes the FlowerClient instance.
+
+        Args:
+            cid (str): Client ID.
+            local_model (Model): Local model of the federated learning network.
+            trainloader (DataLoader): DataLoader for training data.
+            valloader (DataLoader): DataLoader for validation data.
+            diff_priv (bool): Flag indicating whether to use differential privacy.
+        """
+        self.cid = cid
+        self.local_model = local_model
+        self.trainloader = trainloader
+        self.valloader = valloader
+        self.device = torch.device(f"cuda:{int(self.cid) % 4}" if torch.cuda.is_available() else "cpu")
+        self.local_model.model.to(self.device)
+        self.privacy_engine = PrivacyEngine(secure_mode=False)
+        self.diff_priv = diff_priv
+        self.epsilons = []
+        self.accuracies = []
+        self.losses = []
+        if self.diff_priv:
+            model, optimizer, self.trainloader = self.privacy_engine.make_private_with_epsilon(
+                module=self.local_model.model.train(),
+                optimizer=self.local_model.optimizer,
+                data_loader=self.trainloader,
+                epochs=params["train_epochs"],
+                target_epsilon=params["EPSILON"],
+                target_delta=params["DELTA"],
+                max_grad_norm=params["MAX_GRAD_NORM"],
+            )
+            setattr(self.local_model, "model", model)
+            setattr(self.local_model, "optimizer", optimizer)
+        self.validate()
    
+
+
+
    
+[docs]
+    def validate(self):
+        """Validates cid, local_model, trainloader, valloader."""
+        if not isinstance(self.cid, str):
+            raise TypeError("cid argument must be a string")
+
+        if not isinstance(self.local_model, Model):
+            raise TypeError("local_model argument must be a MEDfl.LearningManager.model.Model")
+
+        if not isinstance(self.trainloader, DataLoader):
+            raise TypeError("trainloader argument must be a torch.utils.data.dataloader")
+
+        if not isinstance(self.valloader, DataLoader):
+            raise TypeError("valloader argument must be a torch.utils.data.dataloader")
+
+        if not isinstance(self.diff_priv, bool):
+            raise TypeError("diff_priv argument must be a bool")
    
+
+
+
    
+[docs]
+    def get_parameters(self, config):
+        """
+        Returns the current parameters of the local model.
+
+        Args:
+            config: Configuration information.
+
+        Returns:
+            Numpy array: Parameters of the local model.
+        """
+        print(f"[Client {self.cid}] get_parameters")
+        return self.local_model.get_parameters()
    
+
+
+
    
+[docs]
+    def fit(self, parameters, config):
+        """
+        Fits the local model to the received parameters using federated learning.
+
+        Args:
+            parameters: Parameters received from the server.
+            config: Configuration information.
+
+        Returns:
+            Tuple: Parameters of the local model, number of training examples, and privacy information.
+        """
+        print(f"[Client {self.cid}] fit, config: {config}")
+        self.local_model.set_parameters(parameters)
+        for _ in range(params["train_epochs"]):
+            epsilon = self.local_model.train(
+                self.trainloader,
+                epoch=_,
+                device=self.device,
+                privacy_engine=self.privacy_engine,
+                diff_priv=self.diff_priv,
+            )
+            self.epsilons.append(epsilon)
+        print(f"epsilon of client {self.cid} : eps = {epsilon}")
+        return (
+            self.local_model.get_parameters(),
+            len(self.trainloader),
+            {"epsilon": epsilon},
+        )
    
+
+
+
    
+[docs]
+    def evaluate(self, parameters, config):
+        """
+        Evaluates the local model on the validation data and returns the loss and accuracy.
+
+        Args:
+            parameters: Parameters received from the server.
+            config: Configuration information.
+
+        Returns:
+            Tuple: Loss, number of validation examples, and accuracy information.
+        """
+        print(f"[Client {self.cid}] evaluate, config: {config}")
+        self.local_model.set_parameters(parameters)
+        loss, accuracy = self.local_model.evaluate(
+            self.valloader, device=self.device
+        )
+        self.losses.append(loss)
+        self.accuracies.append(accuracy)
+        return float(loss), len(self.valloader), {"accuracy": float(accuracy)}
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/dynamicModal.html b/docs/_build/_modules/Medfl/LearningManager/dynamicModal.html new file mode 100644 index 0000000..89b94e6 --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/dynamicModal.html @@ -0,0 +1,424 @@ + + + + + + MEDfl.LearningManager.dynamicModal — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    +
      +
      • +
    • Module code
      • +
    • MEDfl.LearningManager.dynamicModal
      • +
    • +
      • +
    +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.dynamicModal

    +import torch
+import torch.nn as nn
+from sklearn.svm import SVC
+
+
    
+[docs]
+class DynamicModel:
+    """DynamicModel class for creating various types of neural network models."""
+
+    # Create a binary classifier model
+
    
+[docs]
+    @staticmethod
+    def create_binary_classifier(input_dim, hidden_dims, output_dim, activation='relu', dropout_rate=0.0,
+                                 batch_norm=False, use_gpu=False):
+        """
+        Creates a binary classifier neural network model with customizable architecture.
+
+        Args:
+            input_dim (int): Dimension of the input data.
+            hidden_dims (List[int]): List of dimensions for hidden layers.
+            output_dim (int): Dimension of the output (number of classes).
+            activation (str, optional): Activation function for hidden layers. Default is 'relu'.
+            dropout_rate (float, optional): Dropout rate for regularization. Default is 0.0 (no dropout).
+            batch_norm (bool, optional): Whether to apply batch normalization. Default is False.
+            use_gpu (bool, optional): Whether to use GPU acceleration. Default is False.
+
+        Returns:
+            torch.nn.Module: Created PyTorch model.
+        """
+
+        layers = []
+
+        for i in range(len(hidden_dims)):
+            if i == 0:
+                layers.append(nn.Linear(input_dim, hidden_dims[0]))
+            else:
+                layers.append(nn.Linear(hidden_dims[i - 1], hidden_dims[i]))
+
+            if batch_norm:
+                layers.append(nn.BatchNorm1d(hidden_dims[i]))
+
+            activation_layer = nn.ReLU() if activation == 'relu' else nn.Sigmoid()
+            layers.append(activation_layer)
+
+            if dropout_rate > 0.0:
+                layers.append(nn.Dropout(dropout_rate))
+
+        layers.append(nn.Linear(hidden_dims[-1], output_dim))
+        layers.append(nn.Sigmoid())
+
+        model = nn.Sequential(*layers)
+
+        if use_gpu:
+            model = model.cuda()
+
+        return model
    
+
+
+    # Create a multi-class classifier model
+
    
+[docs]
+    @staticmethod
+    def create_multiclass_classifier(input_dim, hidden_dims, output_dim, activation='relu', dropout_rate=0.0,
+                                     batch_norm=False, use_gpu=False):
+        """
+        Creates a multiclass classifier neural network model with customizable architecture.
+
+        Args:
+            input_dim (int): Dimension of the input data.
+            hidden_dims (List[int]): List of dimensions for hidden layers.
+            output_dim (int): Dimension of the output (number of classes).
+            activation (str, optional): Activation function for hidden layers. Default is 'relu'.
+            dropout_rate (float, optional): Dropout rate for regularization. Default is 0.0 (no dropout).
+            batch_norm (bool, optional): Whether to apply batch normalization. Default is False.
+            use_gpu (bool, optional): Whether to use GPU acceleration. Default is False.
+
+        Returns:
+            torch.nn.Module: Created PyTorch model.
+        """
+        layers = []
+
+        for i in range(len(hidden_dims)):
+            if i == 0:
+                layers.append(nn.Linear(input_dim, hidden_dims[0]))
+            else:
+                layers.append(nn.Linear(hidden_dims[i - 1], hidden_dims[i]))
+
+            if batch_norm:
+                layers.append(nn.BatchNorm1d(hidden_dims[i]))
+
+            activation_layer = nn.ReLU() if activation == 'relu' else nn.Sigmoid()
+            layers.append(activation_layer)
+
+            if dropout_rate > 0.0:
+                layers.append(nn.Dropout(dropout_rate))
+
+        layers.append(nn.Linear(hidden_dims[-1], output_dim))
+        layers.append(nn.LogSoftmax(dim=1))
+
+        model = nn.Sequential(*layers)
+
+        if use_gpu:
+            model = model.cuda()
+
+        return model
    
+
+
+    # Create a linear regressor model
+
    
+[docs]
+    @staticmethod
+    def create_linear_regressor(input_dim, output_dim, use_gpu=False):
+        """
+        Creates a linear regressor neural network model.
+
+        Args:
+            input_dim (int): Dimension of the input data.
+            output_dim (int): Dimension of the output.
+
+        Returns:
+            torch.nn.Module: Created PyTorch model.
+        """
+        class LinearRegressionModel(nn.Module):
+            def __init__(self):
+                super(LinearRegressionModel, self).__init__()
+                self.linear = nn.Linear(input_dim, output_dim)
+
+            def forward(self, x):
+                return self.linear(x)
+
+        model = LinearRegressionModel()
+
+        if use_gpu:
+            model = model.cuda()
+
+        return model
    
+
+
+    # Create a logistic regressor model
+
    
+[docs]
+    @staticmethod
+    def create_logistic_regressor(input_dim, use_gpu=False):
+        """
+        Creates a logistic regressor neural network model.
+
+        Args:
+            input_dim (int): Dimension of the input data.
+
+        Returns:
+            torch.nn.Module: Created PyTorch model.
+        """
+        class LogisticRegressionModel(nn.Module):
+            def __init__(self):
+                super(LogisticRegressionModel, self).__init__()
+                self.linear = nn.Linear(input_dim, 1)
+
+            def forward(self, x):
+                return torch.sigmoid(self.linear(x))
+
+        model = LogisticRegressionModel()
+
+        if use_gpu:
+            model = model.cuda()
+
+        return model
    
+
+    
+
    
+[docs]
+    @staticmethod
+    def create_convolutional_neural_network(input_channels, output_channels, kernel_size, use_gpu=False):
+        """
+        Creates a convolutional neural network (CNN) model.
+
+        Args:
+            input_channels (int): Number of input channels.
+            output_channels (int): Number of output channels.
+            kernel_size (int): Size of the convolutional kernel.
+
+        Returns:
+            torch.nn.Module: Created PyTorch model.
+        """
+
+        model = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, kernel_size),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+
+        if use_gpu:
+            model = model.cuda()
+
+        return model
    
+
+    
+
    
+[docs]
+    @staticmethod
+    def create_recurrent_neural_network(input_size, hidden_size, use_gpu=False):
+        """
+        Creates a recurrent neural network (RNN) model.
+
+        Args:
+            input_size (int): Size of the input.
+            hidden_size (int): Size of the hidden layer.
+
+        Returns:
+            torch.nn.Module: Created PyTorch model.
+        """
+
+        model = nn.RNN(input_size, hidden_size, batch_first=True)
+
+        if use_gpu:
+            model = model.cuda()
+
+        return model
    
+
+    
+
    
+[docs]
+    @staticmethod
+    def create_lstm_network(input_size, hidden_size, use_gpu=False):
+        """
+        Creates a Long Short-Term Memory (LSTM) network model.
+
+        Args:
+            input_size (int): Size of the input layer.
+            hidden_size (int): Size of the hidden layer.
+
+        Returns:
+            torch.nn.Module: Created PyTorch model.
+        """
+
+        model = nn.LSTM(input_size, hidden_size, batch_first=True)
+
+        if use_gpu:
+            model = model.cuda()
+
+        return model
    
+
+
+    # Create the dynamic model
+
    
+[docs]
+    def create_model(self, model_type: str, params_dict={}) -> torch.nn.Module:
+        """
+        Create a specific type of model dynamically based on the given parameters.
+
+        Args:
+            model_type (str): Type of the model to create ('Binary Classifier', 'Multiclass Classifier', 'Linear Regressor', 'Logistic Regressor', 'SVM', 'Neural Network Classifier', 'Convolutional Neural Network', 'Recurrent Neural Network', 'LSTM Network', 'Autoencoder').
+            params_dict (dict): Dictionary containing parameters for model creation.
+
+        Returns:
+            torch.nn.Module: Created PyTorch model.
+        """
+        if model_type == 'Binary Classifier':
+            return self.create_binary_classifier(
+                params_dict['input_dim'], params_dict['hidden_dims'],
+                params_dict['output_dim'], params_dict.get('activation', 'relu'),
+                params_dict.get('dropout_rate', 0.0), params_dict.get('batch_norm', False),
+                params_dict.get('use_gpu', False)
+            )
+        elif model_type == 'Multiclass Classifier':
+            return self.create_multiclass_classifier(
+                params_dict['input_dim'], params_dict['hidden_dims'],
+                params_dict['output_dim'], params_dict.get('activation', 'relu'),
+                params_dict.get('dropout_rate', 0.0), params_dict.get('batch_norm', False),
+                params_dict.get('use_gpu', False)
+            )
+        elif model_type == 'Linear Regressor':
+            return self.create_linear_regressor(
+                params_dict['input_dim'], params_dict['output_dim'],
+                params_dict.get('use_gpu', False)
+            )
+        elif model_type == 'Logistic Regressor':
+            return self.create_logistic_regressor(
+                params_dict['input_dim'], params_dict.get('use_gpu', False)
+            )
+        elif model_type == 'Neural Network Classifier':
+            return self.create_neural_network_classifier(
+                params_dict['input_dim'], params_dict['output_dim'],
+                params_dict['hidden_dims'], params_dict.get('activation', 'relu'),
+                params_dict.get('dropout_rate', 0.0), params_dict.get('batch_norm', False),
+                params_dict.get('num_layers', 2), params_dict.get('use_gpu', False)
+            )
+        elif model_type == 'Convolutional Neural Network':
+            return self.create_convolutional_neural_network(
+                params_dict['input_channels'], params_dict['output_channels'],
+                params_dict['kernel_size'], params_dict.get('use_gpu', False)
+            )
+        elif model_type == 'Recurrent Neural Network':
+            return self.create_recurrent_neural_network(
+                params_dict['input_size'], params_dict['hidden_size'],
+                params_dict.get('use_gpu', False)
+            )
+        elif model_type == 'LSTM Network':
+            return self.create_lstm_network(
+                params_dict['input_size'], params_dict['hidden_size'],
+                params_dict.get('use_gpu', False)
+            )
+        elif model_type == 'Autoencoder':
+            return self.create_autoencoder(
+                params_dict['input_size'], params_dict['encoder_hidden_size'],
+                params_dict.get('use_gpu', False)
+            )
+        else:
+            raise ValueError("Invalid model type provided")
    
+
    
+
+
+
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/federated_dataset.html b/docs/_build/_modules/Medfl/LearningManager/federated_dataset.html new file mode 100644 index 0000000..37059cf --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/federated_dataset.html @@ -0,0 +1,179 @@ + + + + + + MEDfl.LearningManager.federated_dataset — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    +
      +
      • +
    • Module code
      • +
    • MEDfl.LearningManager.federated_dataset
      • +
    • +
      • +
    +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.federated_dataset

    +from scripts.base import *
+from MEDfl.NetManager.net_helper import *
+from MEDfl.NetManager.net_manager_queries import *
+
+
+
    
+[docs]
+class FederatedDataset:
+
    
+[docs]
+    def __init__(
+        self,
+        name: str,
+        train_nodes: list,
+        test_nodes: list,
+        trainloaders: list,
+        valloaders: list,
+        testloaders: list,
+    ):
+        """
+        Represents a Federated Dataset.
+
+        :param name: Name of the Federated Dataset.
+        :param train_nodes: List of train nodes.
+        :param test_nodes: List of test nodes.
+        :param trainloaders: List of train data loaders.
+        :param valloaders: List of validation data loaders.
+        :param testloaders: List of test data loaders.
+        """
+        self.name = name
+        self.train_nodes = train_nodes
+        self.test_nodes = test_nodes
+        self.trainloaders = trainloaders
+        self.valloaders = valloaders
+        self.testloaders = testloaders
+        self.size = len(self.trainloaders[0].dataset[0][0])
    
+
+
+
    
+[docs]
+    def create(self, FLsetupId: int):
+        """
+        Create a new Federated Dataset in the database.
+
+        :param FLsetupId: The FLsetup ID associated with the Federated Dataset.
+        """
+        query_params = {"name": self.name, "FLsetupId": FLsetupId}
+        fedDataId = get_feddataset_id_from_name(self.name)
+        if fedDataId :
+            self.id = fedDataId
+        else:
+            my_eng.execute(text(INSERT_FLDATASET_QUERY), query_params)
+            self.id = get_feddataset_id_from_name(self.name)
    
+
+
+
+
    
+[docs]
+    def update(self, FLpipeId: int, FedId: int):
+        """
+        Update the FLpipe ID associated with the Federated Dataset in the database.
+
+        :param FLpipeId: The new FLpipe ID to be updated.
+        :param FedId: The Federated Dataset ID.
+        """
+        query_params = {"FLpipeId": FLpipeId, "FedId": FedId}
+        my_eng.execute(text(UPDATE_FLDATASET_QUERY), **query_params)
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/flpipeline.html b/docs/_build/_modules/Medfl/LearningManager/flpipeline.html new file mode 100644 index 0000000..8dfd867 --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/flpipeline.html @@ -0,0 +1,312 @@ + + + + + + MEDfl.LearningManager.flpipeline — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    +
      +
      • +
    • Module code
      • +
    • MEDfl.LearningManager.flpipeline
      • +
    • +
      • +
    +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.flpipeline

    +import datetime
+from typing import List
+import json
+import pandas as pd
+
+
+# File: create_query.py
+from sqlalchemy import text
+from torch.utils.data import DataLoader, TensorDataset
+
+from MEDfl.LearningManager.server import FlowerServer
+from MEDfl.LearningManager.utils import params, test
+from scripts.base import my_eng
+from MEDfl.NetManager.net_helper import get_flpipeline_from_name
+from MEDfl.NetManager.net_manager_queries import (CREATE_FLPIPELINE_QUERY,
+                                                  DELETE_FLPIPELINE_QUERY , CREATE_TEST_RESULTS_QUERY)
+
+
+
    
+[docs]
+def create_query(name, description, creation_date, result):
+    query = text(
+        f"INSERT INTO FLpipeline(name, description, creation_date, results) "
+        f"VALUES ('{name}', '{description}', '{creation_date}', '{result}')"
+    )
+    return query
    
+
+
+
+
+
    
+[docs]
+class FLpipeline:
+    """
+    FLpipeline class for managing Federated Learning pipelines.
+
+    Attributes:
+        name (str): The name of the FLpipeline.
+        description (str): A description of the FLpipeline.
+        server (FlowerServer): The FlowerServer object associated with the FLpipeline.
+
+    Methods:
+        __init__(self, name: str, description: str, server: FlowerServer) -> None:
+            Initialize FLpipeline with the specified name, description, and server.
+  
+
+    """
+
+    def __init__(
+        self, name: str, description: str, server: FlowerServer
+    ) -> None:
+        self.name = name
+        self.description = description
+        self.server = server
+        self.validate()
+
+
    
+[docs]
+    def validate(self) -> None:
+        """
+        Validate the name, description, and server attributes.
+        Raises:
+            TypeError: If the name is not a string, the description is not a string,
+                      or the server is not a FlowerServer object.
+        """
+        if not isinstance(self.name, str):
+            raise TypeError("name argument must be a string")
+
+        if not isinstance(self.description, str):
+            raise TypeError("description argument must be a string")
+
+        if not isinstance(self.server, FlowerServer):
+            raise TypeError("server argument must be a FlowerServer")
    
+
+
+
    
+[docs]
+    def create(self, result: str) -> None:
+        """
+        Create a new FLpipeline entry in the database with the given result.
+
+        Args:
+            result (str): The result string to store in the database.
+
+        """
+        creation_date = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        query = CREATE_FLPIPELINE_QUERY.format(
+            name=self.name,
+            description=self.description,
+            creation_date=creation_date,
+            result=result,
+        )
+        my_eng.execute(text(query))
+        self.id = get_flpipeline_from_name(self.name)
+        try:
+            self.server.fed_dataset.update(
+                FLpipeId=self.id, FedId=self.server.fed_dataset.id
+            )
+        except:
+            pass
    
+
+
+
    
+[docs]
+    def delete(self) -> None:
+        """
+        Delete the FLpipeline entry from the database based on its name.
+
+        Note: This is a placeholder method and needs to be implemented based on your specific database setup.
+
+        """
+        # Placeholder code for deleting the FLpipeline entry from the database based on the name.
+        # You need to implement the actual deletion based on your database setup.
+        my_eng.execute(DELETE_FLPIPELINE_QUERY.format(self.name))
    
+
+    
+
    
+[docs]
+    def test_by_node(self, node_name: str, test_frac=1) -> dict:
+        """
+        Test the FLpipeline by node with the specified test_frac.
+
+        Args:
+            node_name (str): The name of the node to test.
+            test_frac (float, optional): The fraction of the test data to use. Default is 1.
+
+        Returns:
+            dict: A dictionary containing the node name and the classification report.
+
+        """
+        idx = self.server.fed_dataset.test_nodes.index(node_name)
+        global_model, test_loader = (
+            self.server.global_model,
+            self.server.fed_dataset.testloaders[idx],
+        )
+        test_data = test_loader.dataset
+        test_data = TensorDataset(
+            test_data[: int(test_frac * len(test_data))][0],
+            test_data[: int(test_frac * len(test_data))][1],
+        )
+        test_loader = DataLoader(
+            test_data, batch_size=params["test_batch_size"]
+        )
+        classification_report = test(
+            model=global_model.model, test_loader=test_loader
+        )
+        return {
+            "node_name": node_name,
+            "classification_report": str(classification_report),
+        }
    
+
+
+
    
+[docs]
+    def auto_test(self, test_frac=1) -> List[dict]:
+        """
+        Automatically test the FLpipeline on all nodes with the specified test_frac.
+
+        Args:
+            test_frac (float, optional): The fraction of the test data to use. Default is 1.
+
+        Returns:
+            List[dict]: A list of dictionaries containing the node names and the classification reports.
+
+        """
+        result = [
+            self.test_by_node(node, test_frac)
+            for node in self.server.fed_dataset.test_nodes
+        ]
+        self.create("\n".join(str(res).replace("'", '"') for res in result))
+
+        # stockage des resultats des tests
+        for entry in result:
+           node_name = entry['node_name']
+           classification_report_str = entry['classification_report']
+
+           # Convert the 'classification_report' string to a dictionary
+           classification_report_dict = json.loads(classification_report_str.replace("'", "\""))
+           try:
+               # Insert record into the 'testResults' table
+               query = CREATE_TEST_RESULTS_QUERY.format(
+                      pipelineId = self.id, 
+                      nodeName = node_name , 
+                      confusion_matrix = json.dumps(classification_report_dict['confusion matrix']), 
+                      accuracy =classification_report_dict['Accuracy'] , 
+                      sensivity = classification_report_dict['Sensitivity/Recall'] ,
+                      ppv = classification_report_dict['PPV/Precision'] ,
+                      npv= classification_report_dict['NPV'] ,
+                      f1score= classification_report_dict['F1-score'] ,
+                      fpr= classification_report_dict['False positive rate'] ,
+                      tpr= classification_report_dict['True positive rate'] 
+                )
+               my_eng.execute(text(query))
+           except Exception as e:
+                # This block will catch any other exceptions
+                print(f"An unexpected error occurred: {e}")
+               
+           
+
+        return result
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/model.html b/docs/_build/_modules/Medfl/LearningManager/model.html new file mode 100644 index 0000000..e6c903c --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/model.html @@ -0,0 +1,340 @@ + + + + + + MEDfl.LearningManager.model — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.model

    +#!/usr/bin/env python3
+# froked from https://github.com/pythonlessons/mltu/blob/main/mltu/torch/model.py
+
+import typing
+from collections import OrderedDict
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+from sklearn.metrics import accuracy_score
+
+from .utils import params
+
+
+
    
+[docs]
+class Model:
+    """
+    Model class for training and testing PyTorch neural networks.
+
+    Attributes:
+        model (torch.nn.Module): PyTorch neural network.
+        optimizer (torch.optim.Optimizer): PyTorch optimizer.
+        criterion (typing.Callable): Loss function.
+    """
+
+
    
+[docs]
+    def __init__(
+        self,
+        model: torch.nn.Module,
+        optimizer: torch.optim.Optimizer,
+        criterion: typing.Callable,
+    ) -> None:
+        """
+        Initialize Model class with the specified model, optimizer, and criterion.
+
+        Args:
+            model (torch.nn.Module): PyTorch neural network.
+            optimizer (torch.optim.Optimizer): PyTorch optimizer.
+            criterion (typing.Callable): Loss function.
+        """
+        self.model = model
+        self.optimizer = optimizer
+        self.criterion = criterion
+        # Get device on which model is running
+        self.validate()
    
+
+
+
    
+[docs]
+    def validate(self) -> None:
+        """
+        Validate model and optimizer.
+        """
+        if not isinstance(self.model, torch.nn.Module):
+            raise TypeError("model argument must be a torch.nn.Module")
+
+        if not isinstance(self.optimizer, torch.optim.Optimizer):
+            raise TypeError(
+                "optimizer argument must be a torch.optim.Optimizer"
+            )
    
+
+
+
    
+[docs]
+    def get_parameters(self) -> List[np.ndarray]:
+        """
+        Get the parameters of the model as a list of NumPy arrays.
+
+        Returns:
+            List[np.ndarray]: The parameters of the model as a list of NumPy arrays.
+        """
+        return [
+            val.cpu().numpy() for _, val in self.model.state_dict().items()
+        ]
    
+
+
+
    
+[docs]
+    def set_parameters(self, parameters: List[np.ndarray]) -> None:
+        """
+        Set the parameters of the model from a list of NumPy arrays.
+
+        Args:
+            parameters (List[np.ndarray]): The parameters to be set.
+        """
+        params_dict = zip(self.model.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        self.model.load_state_dict(state_dict, strict=True)
    
+
+
+
    
+[docs]
+    def train(
+        self, train_loader, epoch, device, privacy_engine, diff_priv=False
+    ) -> float:
+        """
+        Train the model on the given train_loader for one epoch.
+
+        Args:
+            train_loader: The data loader for training data.
+            epoch (int): The current epoch number.
+            device: The device on which to perform the training.
+            privacy_engine: The privacy engine used for differential privacy (if enabled).
+            diff_priv (bool, optional): Whether differential privacy is used. Default is False.
+
+        Returns:
+            float: The value of epsilon used in differential privacy.
+        """
+        self.model.train()
+        epsilon = 0
+        losses = []
+        top1_acc = []
+
+        for i, (X_train, y_train) in enumerate(train_loader):
+            self.optimizer.zero_grad()
+
+            # compute output
+            y_hat = torch.squeeze(self.model(X_train), 1)
+            loss = self.criterion(y_hat, y_train)
+
+            preds = np.argmax(y_hat.detach().cpu().numpy(), axis=0)
+            labels = y_train.detach().cpu().numpy()
+
+            # measure accuracy and record loss
+            acc = (preds == labels).mean()
+
+            losses.append(loss.item())
+            top1_acc.append(acc)
+
+            loss.backward()
+            self.optimizer.step()
+
+            if diff_priv:
+                epsilon = privacy_engine.get_epsilon(params["DELTA"])
+
+            if (i + 1) % 10 == 0:
+                if diff_priv:
+                    epsilon = privacy_engine.get_epsilon(params["DELTA"])
+                    print(
+                        f"\tTrain Epoch: {epoch} \t"
+                        f"Loss: {np.mean(losses):.6f} "
+                        f"Acc@1: {np.mean(top1_acc) * 100:.6f} "
+                        f"(ε = {epsilon:.2f}, δ = {params['DELTA']})"
+                    )
+                else:
+                    print(
+                        f"\tTrain Epoch: {epoch} \t"
+                        f"Loss: {np.mean(losses):.6f} "
+                        f"Acc@1: {np.mean(top1_acc) * 100:.6f}"
+                    )
+
+        return epsilon
    
+
+
+
    
+[docs]
+    def evaluate(
+        self, val_loader, device=torch.device("cpu")
+    ) -> Tuple[float, float]:
+        """
+        Evaluate the model on the given validation data.
+
+        Args:
+            val_loader: The data loader for validation data.
+            device: The device on which to perform the evaluation. Default is 'cpu'.
+
+        Returns:
+            Tuple[float, float]: The evaluation loss and accuracy.
+        """
+        correct, total, loss, accuracy = 0, 0, 0.0, []
+        self.model.eval()
+
+        with torch.no_grad():
+            for X_test, y_test in val_loader:
+                y_hat = torch.squeeze(self.model(X_test), 1)
+                accuracy.append(accuracy_score(y_test, y_hat.round()))
+                loss += self.criterion(y_hat, y_test).item()
+                total += y_test.size(0)
+                correct += np.sum(
+                    y_hat.round().detach().numpy() == y_test.detach().numpy()
+                )
+
+        loss /= len(val_loader.dataset)
+        return loss, np.mean(accuracy)
    
+
+
+
    
+[docs]
+    @staticmethod
+    def save_model(model , model_name:str):
+        """
+        Saves a PyTorch model to a file.
+
+        Args:
+            model (torch.nn.Module): PyTorch model to be saved.
+            model_name (str): Name of the model file.
+
+        Raises:
+            Exception: If there is an issue during the saving process.
+
+        Returns:
+            None
+        """
+        try:
+            torch.save(model, '../../notebooks/.ipynb_checkpoints/trainedModels/' + model_name + ".pth")
+        except Exception as e:
+            raise Exception(f"Error saving the model: {str(e)}")
    
+
+    
+
    
+[docs]
+    @staticmethod
+    def load_model(model_name:str):
+        """
+        Loads a PyTorch model from a file.
+
+        Args:
+            model_name (str): Name of the model file to be loaded.
+
+        Returns:
+            torch.nn.Module: Loaded PyTorch model.
+        """
+        loadedModel = torch.load('../../notebooks/.ipynb_checkpoints/trainedModels/'+model_name+".pth")
+        return loadedModel
    
+
    
+
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/plot.html b/docs/_build/_modules/Medfl/LearningManager/plot.html new file mode 100644 index 0000000..46cf40b --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/plot.html @@ -0,0 +1,357 @@ + + + + + + MEDfl.LearningManager.plot — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.plot

    +import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+from .utils import *
+
+# Replace this with your actual code for data collection
+results_dict = {
+    ("LR: 0.001, Optimizer: Adam", "accuracy"): [0.85, 0.89, 0.92, 0.94, ...],
+    ("LR: 0.001, Optimizer: Adam", "loss"): [0.2, 0.15, 0.1, 0.08, ...],
+    ("LR: 0.01, Optimizer: SGD", "accuracy"): [0.88, 0.91, 0.93, 0.95, ...],
+    ("LR: 0.01, Optimizer: SGD", "loss"): [0.18, 0.13, 0.09, 0.07, ...],
+    ("LR: 0.1, Optimizer: Adam", "accuracy"): [0.82, 0.87, 0.91, 0.93, ...],
+    ("LR: 0.1, Optimizer: Adam", "loss"): [0.25, 0.2, 0.15, 0.12, ...],
+}
+"""
+server should have:
+ #len = num of rounds
+  self.accuracies
+  self.losses
+  
+Client should have
+  # len = num of epochs
+  self.accuracies
+  self.losses
+  self.epsilons
+  self.deltas
+  
+#common things : LR,SGD, Aggregation
+  
+"""
+
+
+
    
+[docs]
+class AccuracyLossPlotter:
+    """
+    A utility class for plotting accuracy and loss metrics based on experiment results.
+
+    Args:
+        results_dict (dict): Dictionary containing experiment results organized by parameters and metrics.
+
+    Attributes:
+        results_dict (dict): Dictionary containing experiment results organized by parameters and metrics.
+        parameters (list): List of unique parameters in the experiment results.
+        metrics (list): List of unique metrics in the experiment results.
+        iterations (range): Range of iterations (rounds or epochs) in the experiment.
+    """
+
+
    
+[docs]
+    def __init__(self, results_dict):
+        """
+        Initialize the AccuracyLossPlotter with experiment results.
+
+        Args:
+            results_dict (dict): Dictionary containing experiment results organized by parameters and metrics.
+        """
+        self.results_dict = results_dict
+        self.parameters = list(
+            set([param[0] for param in results_dict.keys()])
+        )
+        self.metrics = list(set([param[1] for param in results_dict.keys()]))
+        self.iterations = range(1, len(list(results_dict.values())[0]) + 1)
    
+
+
+
    
+[docs]
+    def plot_accuracy_loss(self):
+        """
+        Plot accuracy and loss metrics for different parameters.
+        """
+
+        plt.figure(figsize=(8, 6))
+
+        for param in self.parameters:
+            for metric in self.metrics:
+                key = (param, metric)
+                values = self.results_dict[key]
+                plt.plot(
+                    self.iterations,
+                    values,
+                    label=f"{param} ({metric})",
+                    marker="o",
+                    linestyle="-",
+                )
+
+            plt.xlabel("Rounds")
+            plt.ylabel("Accuracy / Loss")
+            plt.title("Accuracy and Loss by Parameters")
+            plt.legend()
+            plt.grid(True)
+            plt.show()
    
+
+
+
    
+[docs]
+    @staticmethod
+    def plot_global_confusion_matrix(pipeline_name: str):
+        """
+        Plot a global confusion matrix based on pipeline results.
+
+        Args:
+            pipeline_name (str): Name of the pipeline.
+
+        Returns:
+            None
+        """
+        # Get the id of the pipeline by name
+        pipeline_id = get_pipeline_from_name(pipeline_name)
+        # get the confusion matrix pf the pipeline
+        confusion_matrix = get_pipeline_confusion_matrix(pipeline_id)
+
+        # Extracting confusion matrix values
+        TP = confusion_matrix['TP']
+        FP = confusion_matrix['FP']
+        FN = confusion_matrix['FN']
+        TN = confusion_matrix['TN']
+
+        # Creating a matrix for visualization
+        matrix = [[TN, FP],
+                  [FN, TP]]
+
+        # Plotting the confusion matrix as a heatmap
+        plt.figure(figsize=(6, 4))
+        sns.heatmap(matrix, annot=True, fmt='d', cmap='Blues',
+                    xticklabels=['Predicted Negative', 'Predicted Positive'],
+                    yticklabels=['Actual Negative', 'Actual Positive'])
+        plt.title('Global Confusion Matrix')
+        plt.xlabel('Predicted label')
+        plt.ylabel('True label')
+        plt.tight_layout()
+
+        # Display the confusion matrix heatmap
+        plt.show()
    
+
+
+
    
+[docs]
+    @staticmethod
+    def plot_confusion_Matrix_by_node(node_name: str, pipeline_name: str):
+        """
+        Plot a confusion matrix for a specific node in the pipeline.
+
+        Args:
+            node_name (str): Name of the node.
+            pipeline_name (str): Name of the pipeline.
+
+        Returns:
+            None
+        """
+
+        # Get the id of the pipeline by name
+        pipeline_id = get_pipeline_from_name(pipeline_name)
+        # get the confusion matrix pf the pipeline
+        confusion_matrix = get_node_confusion_matrix(
+            pipeline_id, node_name=node_name)
+
+        # Extracting confusion matrix values
+        TP = confusion_matrix['TP']
+        FP = confusion_matrix['FP']
+        FN = confusion_matrix['FN']
+        TN = confusion_matrix['TN']
+
+        # Creating a matrix for visualization
+        matrix = [[TN, FP],
+                  [FN, TP]]
+
+        # Plotting the confusion matrix as a heatmap
+        plt.figure(figsize=(6, 4))
+        sns.heatmap(matrix, annot=True, fmt='d', cmap='Blues',
+                    xticklabels=['Predicted Negative', 'Predicted Positive'],
+                    yticklabels=['Actual Negative', 'Actual Positive'])
+        plt.title('Confusion Matrix of node: '+node_name)
+        plt.xlabel('Predicted label')
+        plt.ylabel('True label')
+        plt.tight_layout()
+
+        # Display the confusion matrix heatmap
+        plt.show()
+        return
    
+
+    
+
    
+[docs]
+    @staticmethod
+    def plot_classification_report(pipeline_name: str):
+        """
+        Plot a comparison of classification report metrics between nodes.
+
+        Args:
+            pipeline_name (str): Name of the pipeline.
+
+        Returns:
+            None
+        """
+
+        colors = ['#FF5733', '#6A5ACD', '#3CB371', '#FFD700', '#FFA500', '#8A2BE2', '#00FFFF', '#FF00FF', '#A52A2A', '#00FF00']
+
+        # Get the id of the pipeline by name
+        pipeline_id = get_pipeline_from_name(pipeline_name)
+
+        pipeline_results = get_pipeline_result(pipeline_id)
+
+        nodesList = pipeline_results['nodename']
+        classificationReports = []
+
+        for index, node in enumerate(nodesList):
+            classificationReports.append({
+                'Accuracy': pipeline_results['accuracy'][index],
+                'Sensitivity/Recall': pipeline_results['sensivity'][index],
+                'PPV/Precision': pipeline_results['ppv'][index],
+                'NPV': pipeline_results['npv'][index],
+                'F1-score': pipeline_results['f1score'][index],
+                'False positive rate': pipeline_results['fpr'][index],
+                'True positive rate': pipeline_results['tpr'][index]
+            })
+
+        metric_labels = list(classificationReports[0].keys())  # Assuming both reports have the same keys
+
+        # Set the positions of the bars on the x-axis
+        x = np.arange(len(metric_labels))
+
+        # Set the width of the bars
+        width = 0.35
+
+        plt.figure(figsize=(12, 6))
+
+        for index, report in enumerate(classificationReports):
+            metric = list(report.values())
+            plt.bar(x + (index - len(nodesList) / 2) * width / len(nodesList), metric, width / len(nodesList),
+                    label=nodesList[index], color=colors[index % len(colors)])
+
+        # Adding labels, title, and legend
+        plt.xlabel('Metrics')
+        plt.ylabel('Values')
+        plt.title('Comparison of Classification Report Metrics between Nodes')
+        plt.xticks(ticks=x, labels=metric_labels, rotation=45)
+        plt.legend()
+
+        # Show plot
+        plt.tight_layout()
+        plt.show()
+
+        return 
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/server.html b/docs/_build/_modules/Medfl/LearningManager/server.html new file mode 100644 index 0000000..ecd038b --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/server.html @@ -0,0 +1,300 @@ + + + + + + MEDfl.LearningManager.server — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.server

    +#!/usr/bin/env python3
+
+import copy
+from typing import Dict, Optional, Tuple
+
+import flwr as fl
+import torch
+
+from .client import FlowerClient
+from .federated_dataset import FederatedDataset
+from .model import Model
+from .strategy import Strategy
+
+
+
    
+[docs]
+class FlowerServer:
+    """
+    A class representing the central server for Federated Learning using Flower.
+
+    Attributes:
+        global_model (Model): The global model that will be federated among clients.
+        strategy (Strategy): The strategy used for federated learning, specifying communication and aggregation methods.
+        num_rounds (int): The number of federated learning rounds to perform.
+        num_clients (int): The number of clients participating in the federated learning process.
+        fed_dataset (FederatedDataset): The federated dataset used for training and evaluation.
+        diff_priv (bool): Whether differential privacy is used during the federated learning process.
+        accuracies (List[float]): A list to store the accuracy of the global model during each round.
+        losses (List[float]): A list to store the loss of the global model during each round.
+        flower_clients (List[FlowerClient]): A list to store the FlowerClient objects representing individual clients.
+     
+    """
+
+
    
+[docs]
+    def __init__(
+        self,
+        global_model: Model,
+        strategy: Strategy,
+        num_rounds: int,
+        num_clients: int,
+        fed_dataset: FederatedDataset,
+        diff_privacy: bool = False,
+        client_resources: Optional[Dict[str, float]] = {'num_cpus': 1, 'num_gpus': 0.0}
+    ) -> None:
+        """
+        Initialize a FlowerServer object with the specified parameters.
+
+        Args:
+            global_model (Model): The global model that will be federated among clients.
+            strategy (Strategy): The strategy used for federated learning, specifying communication and aggregation methods.
+            num_rounds (int): The number of federated learning rounds to perform.
+            num_clients (int): The number of clients participating in the federated learning process.
+            fed_dataset (FederatedDataset): The federated dataset used for training and evaluation.
+            diff_privacy (bool, optional): Whether differential privacy is used during the federated learning process.
+                                           Default is False.
+        """
+        self.device = torch.device(
+            f"cuda" if torch.cuda.is_available() else "cpu"
+        )
+        self.global_model = global_model
+        self.params = global_model.get_parameters()
+        self.global_model.model = global_model.model.to(self.device)
+        self.num_rounds = num_rounds
+        self.num_clients = num_clients
+        self.fed_dataset = fed_dataset
+        self.strategy = strategy
+        self.client_resources = client_resources
+        setattr(
+            self.strategy.strategy_object,
+            "min_available_clients",
+            self.num_clients,
+        )
+        setattr(
+            self.strategy.strategy_object,
+            "initial_parameters",
+            fl.common.ndarrays_to_parameters(self.params),
+        )
+        setattr(self.strategy.strategy_object, "evaluate_fn", self.evaluate)
+        self.fed_dataset = fed_dataset
+        self.diff_priv = diff_privacy
+        self.accuracies = []
+        self.losses = []
+        self.flower_clients = []
+        self.validate()
    
+
+
+
    
+[docs]
+    def validate(self) -> None:
+        """Validate global_model, strategy, num_clients, num_rounds, fed_dataset, diff_privacy"""
+        if not isinstance(self.global_model, Model):
+            raise TypeError("global_model argument must be a Model instance")
+
+        if not isinstance(self.strategy, Strategy):
+            raise TypeError("strategy argument must be a Strategy instance")
+
+        if not isinstance(self.num_clients, int):
+            raise TypeError("num_clients argument must be an int")
+
+        if not isinstance(self.num_rounds, int):
+            raise TypeError("num_rounds argument must be an int")
+
+        if not isinstance(self.diff_priv, bool):
+            raise TypeError("diff_priv argument must be a bool")
    
+
+
+
    
+[docs]
+    def client_fn(self, cid) -> FlowerClient:
+        """
+        Return a FlowerClient object for a specific client ID.
+
+        Args:
+            cid: The client ID.
+
+        Returns:
+            FlowerClient: A FlowerClient object representing the individual client.
+        """
+        device = torch.device(
+            f"cuda:{int(cid) % 4}" if torch.cuda.is_available() else "cpu"
+        )
+        client_model = copy.deepcopy(self.global_model)
+        trainloader = self.fed_dataset.trainloaders[int(cid)]
+        valloader = self.fed_dataset.valloaders[int(cid)]
+        # this helps in making plots
+        client = FlowerClient(
+            cid, client_model, trainloader, valloader, self.diff_priv
+        )
+        self.flower_clients.append(client)
+        return client
    
+
+
+
    
+[docs]
+    def evaluate(
+        self,
+        server_round: int,
+        parameters: fl.common.NDArrays,
+        config: Dict[str, fl.common.Scalar],
+    ) -> Optional[Tuple[float, Dict[str, fl.common.Scalar]]]:
+        """
+        Evaluate the global model on the validation dataset and update the accuracies and losses.
+
+        Args:
+            server_round (int): The current round of the federated learning process.
+            parameters (fl.common.NDArrays): The global model parameters.
+            config (Dict[str, fl.common.Scalar]): Configuration dictionary.
+
+        Returns:
+            Optional[Tuple[float, Dict[str, fl.common.Scalar]]]: The evaluation loss and accuracy.
+        """
+        testloader = self.fed_dataset.valloaders[0]
+        self.global_model.set_parameters(
+            parameters
+        )  # Update model with the latest parameters
+        loss, accuracy = self.global_model.evaluate(testloader, self.device)
+        self.losses.append(loss)
+        self.accuracies.append(accuracy)
+        print(f"Server-side evaluation loss {loss} / accuracy {accuracy}")
+        return loss, {"accuracy": accuracy}
    
+
+
+
    
+[docs]
+    def run(self) -> None:
+        """
+        Run the federated learning process using Flower simulation.
+
+        Returns:
+            History: The history of the accuracies and losses during the training of each node 
+        """
+         # Increase the object store memory to the minimum allowed value or higher
+        ray_init_args = {"include_dashboard": False
+                         , "object_store_memory": 78643200
+                        }
+
+        history = fl.simulation.start_simulation(
+            client_fn=self.client_fn,
+            num_clients=self.num_clients,
+            config=fl.server.ServerConfig(self.num_rounds),
+            strategy=self.strategy.strategy_object,
+            ray_init_args=ray_init_args,
+            client_resources = self.client_resources
+        )
+
+        return history
    
+
    
+
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/strategy.html b/docs/_build/_modules/Medfl/LearningManager/strategy.html new file mode 100644 index 0000000..c7b8977 --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/strategy.html @@ -0,0 +1,185 @@ + + + + + + MEDfl.LearningManager.strategy — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    +
      +
      • +
    • Module code
      • +
    • MEDfl.LearningManager.strategy
      • +
    • +
      • +
    +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.strategy

    +
+from collections import OrderedDict
+from typing import Dict, List, Optional, Tuple
+
+import flwr as fl
+import numpy as np
+
+
+
+
+
+
    
+[docs]
+class Strategy:
+    """
+    A class representing a strategy for Federated Learning.
+
+    Attributes:
+        name (str): The name of the strategy. Default is "FedAvg".
+        fraction_fit (float): Fraction of clients to use for training during each round. Default is 1.0.
+        fraction_evaluate (float): Fraction of clients to use for evaluation during each round. Default is 1.0.
+        min_fit_clients (int): Minimum number of clients to use for training during each round. Default is 2.
+        min_evaluate_clients (int): Minimum number of clients to use for evaluation during each round. Default is 2.
+        min_available_clients (int): Minimum number of available clients required to start a round. Default is 2.
+        initial_parameters (Optional[]): The initial parameters of the server model 
+    Methods:
+     
+    """
+
+
    
+[docs]
+    def __init__(
+        self,
+        name: str = "FedAvg",
+        fraction_fit: float = 1.0,
+        fraction_evaluate: float = 1.0,
+        min_fit_clients: int = 2,
+        min_evaluate_clients: int = 2,
+        min_available_clients: int = 2,
+        initial_parameters = [],
+        evaluation_methode = "centralized"
+    ) -> None:
+        """
+        Initialize a Strategy object with the specified parameters.
+
+        Args:
+            name (str): The name of the strategy. Default is "FedAvg".
+            fraction_fit (float): Fraction of clients to use for training during each round. Default is 1.0.
+            fraction_evaluate (float): Fraction of clients to use for evaluation during each round. Default is 1.0.
+            min_fit_clients (int): Minimum number of clients to use for training during each round. Default is 2.
+            min_evaluate_clients (int): Minimum number of clients to use for evaluation during each round. Default is 2.
+            min_available_clients (int): Minimum number of available clients required to start a round. Default is 2.
+            initial_parameters (Optional[]): The initial parametres of the server model 
+            evaluation_methode ( "centralized" | "distributed")
+        """
+        self.fraction_fit = fraction_fit
+        self.fraction_evaluate = fraction_evaluate
+        self.min_fit_clients = min_fit_clients
+        self.min_evaluate_clients = min_evaluate_clients
+        self.min_available_clients = min_available_clients
+        self.initial_parameters = initial_parameters
+        self.evaluate_fn = None
+        self.name = name
+        self.strategy_object = eval(
+            f"fl.server.strategy.{self.name}(\
+                   fraction_fit={self.fraction_fit},\
+                  fraction_evaluate= {self.fraction_evaluate},\
+                  min_fit_clients= {self.min_fit_clients},\
+                  min_evaluate_clients= {self.min_evaluate_clients},\
+                  min_available_clients={self.min_available_clients},\
+                  initial_parameters=fl.common.ndarrays_to_parameters(self.initial_parameters),\
+                  evaluate_fn={self.evaluate_fn})"
+        )
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/LearningManager/utils.html b/docs/_build/_modules/Medfl/LearningManager/utils.html new file mode 100644 index 0000000..c3efb2a --- /dev/null +++ b/docs/_build/_modules/Medfl/LearningManager/utils.html @@ -0,0 +1,355 @@ + + + + + + MEDfl.LearningManager.utils — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.LearningManager.utils

    +#!/usr/bin/env python3
+
+import pkg_resources
+import torch
+import yaml
+from sklearn.metrics import *
+from yaml.loader import SafeLoader
+
+from scripts.base import *
+import json
+
+
+import pandas as pd
+
+yaml_path = pkg_resources.resource_filename(__name__, "params.yaml")
+with open(yaml_path) as g:
+    params = yaml.load(g, Loader=SafeLoader)
+
+
+
    
+[docs]
+def custom_classification_report(y_true, y_pred):
+    """
+    Compute custom classification report metrics including accuracy, sensitivity, specificity, precision, NPV,
+    F1-score, false positive rate, and true positive rate.
+
+    Args:
+        y_true (array-like): True labels.
+        y_pred (array-like): Predicted labels.
+
+    Returns:
+        dict: A dictionary containing custom classification report metrics.
+    """
+
+    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
+
+    # Accuracy
+    denominator_acc = tp + tn + fp + fn
+    acc = (tp + tn) / denominator_acc if denominator_acc != 0 else 0.0
+
+    # Sensitivity/Recall
+    denominator_sen = tp + fn
+    sen = tp / denominator_sen if denominator_sen != 0 else 0.0
+
+    # Specificity
+    denominator_sp = tn + fp
+    sp = tn / denominator_sp if denominator_sp != 0 else 0.0
+
+    # PPV/Precision
+    denominator_ppv = tp + fp
+    ppv = tp / denominator_ppv if denominator_ppv != 0 else 0.0
+
+#     NPV
+    denominator_npv = tn + fn
+    npv = tn / denominator_npv if denominator_npv != 0 else 0.0
+
+    # F1 Score
+    denominator_f1 = sen + ppv
+    f1 = 2 * (sen * ppv) / denominator_f1 if denominator_f1 != 0 else 0.0
+
+    # False Positive Rate
+    denominator_fpr = fp + tn
+    fpr = fp / denominator_fpr if denominator_fpr != 0 else 0.0
+
+    # True Positive Rate
+    denominator_tpr = tp + fn
+    tpr = tp / denominator_tpr if denominator_tpr != 0 else 0.0
+
+    return {
+        "confusion matrix": {"TP": tp, "FP": fp, "FN": fn, "TN": tn},
+        "Accuracy": round(acc, 3),
+        "Sensitivity/Recall": round(sen, 3),
+        "Specificity": round(sp, 3),
+        "PPV/Precision": round(ppv, 3),
+        "NPV": round(npv, 3),
+        "F1-score": round(f1, 3),
+        "False positive rate": round(fpr, 3),
+        "True positive rate": round(tpr, 3),
+    }
    
+
+
+
+
    
+[docs]
+def test(model, test_loader, device=torch.device("cpu")):
+    """
+    Evaluate a model using a test loader and return a custom classification report.
+
+    Args:
+        model (torch.nn.Module): PyTorch model to evaluate.
+        test_loader (torch.utils.data.DataLoader): DataLoader for the test dataset.
+        device (torch.device, optional): Device for model evaluation. Default is "cpu".
+
+    Returns:
+        dict: A dictionary containing custom classification report metrics.
+    """
+
+    model.eval()
+    with torch.no_grad():
+        X_test, y_test = test_loader.dataset[:][0], test_loader.dataset[:][1]
+        y_hat = torch.squeeze(model(X_test), 1).round()
+
+    return custom_classification_report(y_test, y_hat)
    
+
+
+
+column_map = {"object": "VARCHAR(255)", "int64": "INT", "float64": "FLOAT"}
+
+
+
    
+[docs]
+def empty_db():
+    """
+    Empty the database by deleting records from multiple tables and resetting auto-increment counters.
+
+    Returns:
+        None
+    """
+
+    # my_eng.execute(text(f"DELETE FROM  {'DataSets'}"))
+    my_eng.execute(text(f"DELETE FROM {'Nodes'}"))
+    my_eng.execute(text(f"DELETE FROM {'FedDatasets'}"))
+    my_eng.execute(text(f"DELETE FROM {'Networks'}"))
+    my_eng.execute(text(f"DELETE FROM {'FLsetup'}"))
+
+    my_eng.execute(text(f"DELETE FROM {'FLpipeline'}"))
+    my_eng.execute(text(f"ALTER TABLE {'Nodes'} AUTO_INCREMENT = 1"))
+    my_eng.execute(text(f"ALTER TABLE {'Networks'} AUTO_INCREMENT = 1"))
+    my_eng.execute(text(f"ALTER TABLE {'FedDatasets'} AUTO_INCREMENT = 1"))
+    my_eng.execute(text(f"ALTER TABLE {'FLsetup'} AUTO_INCREMENT = 1"))
+    my_eng.execute(text(f"ALTER TABLE {'FLpipeline'} AUTO_INCREMENT = 1"))
+    my_eng.execute(text(f"DELETE FROM {'testresults'}"))
+    my_eng.execute(text(f"DROP TABLE IF EXISTS {'masterdataset'}"))
+    my_eng.execute(text(f"DROP TABLE IF EXISTS {'datasets'}"))
    
+
+
+
    
+[docs]
+def get_pipeline_from_name(name):
+    """
+    Get the pipeline ID from its name in the database.
+
+    Args:
+        name (str): Name of the pipeline.
+
+    Returns:
+        int: ID of the pipeline.
+    """
+
+    NodeId = int(
+        pd.read_sql(
+            text(f"SELECT id FROM flpipeline WHERE name = '{name}'"), my_eng
+        ).iloc[0, 0]
+    )
+    return NodeId
    
+
+
+
    
+[docs]
+def get_pipeline_confusion_matrix(pipeline_id):
+    """
+    Get the global confusion matrix for a pipeline based on test results.
+
+    Args:
+        pipeline_id (int): ID of the pipeline.
+
+    Returns:
+        dict: A dictionary representing the global confusion matrix.
+    """
+
+    data = pd.read_sql(
+            text(f"SELECT confusionmatrix FROM testresults WHERE pipelineid = '{pipeline_id}'"), my_eng
+        )
+    
+    # Convert the column of strings into a list of dictionaries representing confusion matrices
+    confusion_matrices = [
+    json.loads(matrix.replace("'", "\"")) for matrix in data['confusionmatrix']
+    ]
+
+    # Initialize variables for global confusion matrix
+    global_TP = global_FP = global_FN = global_TN = 0
+
+    # Iterate through each dictionary and sum the corresponding values for each category
+    for matrix in confusion_matrices:
+        global_TP += matrix['TP']
+        global_FP += matrix['FP']
+        global_FN += matrix['FN']
+        global_TN += matrix['TN']
+
+    # Create a global confusion matrix as a dictionary
+    global_confusion_matrix = {
+        'TP': global_TP,
+        'FP': global_FP,
+        'FN': global_FN,
+        'TN': global_TN
+    }
+    # Return the list of dictionaries representing confusion matrices
+    return global_confusion_matrix
    
+
+
+
    
+[docs]
+def get_node_confusion_matrix(pipeline_id , node_name):
+    """
+    Get the confusion matrix for a specific node in a pipeline based on test results.
+
+    Args:
+        pipeline_id (int): ID of the pipeline.
+        node_name (str): Name of the node.
+
+    Returns:
+        dict: A dictionary representing the confusion matrix for the specified node.
+    """
+
+    data = pd.read_sql(
+            text(f"SELECT confusionmatrix FROM testresults WHERE pipelineid = '{pipeline_id}' AND nodename = '{node_name}'"), my_eng
+        )
+    
+    # Convert the column of strings into a list of dictionaries representing confusion matrices
+    confusion_matrices = [
+    json.loads(matrix.replace("'", "\"")) for matrix in data['confusionmatrix']
+    ]
+
+  
+    # Return the list of dictionaries representing confusion matrices
+    return confusion_matrices[0]
    
+
+
+
    
+[docs]
+def get_pipeline_result(pipeline_id):
+    """
+    Get the test results for a pipeline.
+
+    Args:
+        pipeline_id (int): ID of the pipeline.
+
+    Returns:
+        pandas.DataFrame: DataFrame containing test results for the specified pipeline.
+    """
+    data = pd.read_sql(
+            text(f"SELECT * FROM testresults WHERE pipelineid = '{pipeline_id}'"), my_eng
+        )
+    return data
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/NetManager/dataset.html b/docs/_build/_modules/Medfl/NetManager/dataset.html new file mode 100644 index 0000000..24a4943 --- /dev/null +++ b/docs/_build/_modules/Medfl/NetManager/dataset.html @@ -0,0 +1,222 @@ + + + + + + MEDfl.NetManager.dataset — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.NetManager.dataset

    +import pandas as pd
+from sqlalchemy import text
+
+from scripts.base import my_eng
+from .net_helper import *
+from .net_manager_queries import (DELETE_DATASET, INSERT_DATASET,
+                                  SELECT_ALL_DATASET_NAMES)
+
+
+
    
+[docs]
+class DataSet:
+
    
+[docs]
+    def __init__(self, name: str, path: str, engine=None):
+        """
+        Initialize a DataSet object.
+
+        :param name: The name of the dataset.
+        :type name: str
+        :param path: The file path of the dataset CSV file.
+        :type path: str
+        """
+        self.name = name
+        self.path = path
+        self.engine = engine if engine is not None else my_eng
    
+
+
+
    
+[docs]
+    def validate(self):
+        """
+        Validate name and path attributes.
+
+        :raises TypeError: If name or path is not a string.
+        """
+        if not isinstance(self.name, str):
+            raise TypeError("name argument must be a string")
+
+        if not isinstance(self.path, str):
+            raise TypeError("path argument must be a string")
    
+
+
+
    
+[docs]
+    def upload_dataset(self, NodeId=-1):
+        """
+        Upload the dataset to the database.
+
+        :param NodeId: The NodeId associated with the dataset.
+        :type NodeId: int
+
+        Notes:
+        - Assumes the file at self.path is a valid CSV file.
+        - The dataset is uploaded to the 'DataSets' table in the database.
+        """
+
+        data_df = pd.read_csv(self.path)
+        nodeId = NodeId
+        columns = data_df.columns.tolist()
+        
+
+        data_df = process_eicu(data_df)
+        for index, row in data_df.iterrows():
+            query_1 = "INSERT INTO DataSets(DataSetName,nodeId," + "".join(
+                f"{x}," for x in columns
+            )
+            query_2 = f" VALUES ('{self.name}',{nodeId}, " + "".join(
+                f"{is_str(data_df, row, x)}," for x in columns
+            )
+            query = query_1[:-1] + ")" + query_2[:-1] + ")"
+             
+            self.engine.execute(text(query))
    
+
+
+
    
+[docs]
+    def delete_dataset(self):
+        """
+        Delete the dataset from the database.
+
+        Notes:
+        - Assumes the dataset name is unique in the 'DataSets' table.
+        """
+        self.engine.execute(text(DELETE_DATASET), {"name": self.name})
    
+
+
+
    
+[docs]
+    def update_data(self):
+        """
+        Update the data in the dataset.
+
+        Not implemented yet.
+        """
+        pass
    
+
+
+
    
+[docs]
+    @staticmethod
+    def list_alldatasets(engine):
+        """
+        List all dataset names from the 'DataSets' table.
+
+        :returns: A DataFrame containing the names of all datasets in the 'DataSets' table.
+        :rtype: pd.DataFrame
+        """
+        res = pd.read_sql(text(SELECT_ALL_DATASET_NAMES), engine)
+        return res
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/NetManager/flsetup.html b/docs/_build/_modules/Medfl/NetManager/flsetup.html new file mode 100644 index 0000000..af2adda --- /dev/null +++ b/docs/_build/_modules/Medfl/NetManager/flsetup.html @@ -0,0 +1,447 @@ + + + + + + MEDfl.NetManager.flsetup — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.NetManager.flsetup

    +from datetime import datetime
+
+
+from torch.utils.data import random_split, DataLoader, Dataset
+
+from MEDfl.LearningManager.federated_dataset import FederatedDataset
+from .net_helper import *
+from .net_manager_queries import *  # Import the sql_queries module
+from .network import Network
+
+from .node import Node
+
+
+
    
+[docs]
+class FLsetup:
+
    
+[docs]
+    def __init__(self, name: str, description: str, network: Network):
+        """Initialize a Federated Learning (FL) setup.
+
+        Args:
+            name (str): The name of the FL setup.
+            description (str): A description of the FL setup.
+            network (Network): An instance of the Network class representing the network architecture.
+        """
+        self.name = name
+        self.description = description
+        self.network = network
+        self.column_name = None
+        self.auto = 1 if self.column_name is not None else 0
+        self.validate()
+        self.fed_dataset = None
    
+
+
+
    
+[docs]
+    def validate(self):
+        """Validate name, description, and network."""
+        if not isinstance(self.name, str):
+            raise TypeError("name argument must be a string")
+
+        if not isinstance(self.description, str):
+            raise TypeError("description argument must be a string")
+
+        if not isinstance(self.network, Network):
+            raise TypeError(
+                "network argument must be a MEDfl.NetManager.Network "
+            )
    
+
+
+
    
+[docs]
+    def create(self):
+        """Create an FL setup."""
+        creation_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        netid = get_netid_from_name(self.network.name)
+        my_eng.execute(
+            text(CREATE_FLSETUP_QUERY),
+            {
+                "name": self.name,
+                "description": self.description,
+                "creation_date": creation_date,
+                "net_id": netid,
+                "column_name": self.column_name,
+            },
+        )
+        self.id = get_flsetupid_from_name(self.name)
    
+
+
+
    
+[docs]
+    def delete(self):
+        """Delete the FL setup."""
+        if self.fed_dataset is not None:
+            self.fed_dataset.delete_Flsetup(FLsetupId=self.id)
+        my_eng.execute(text(DELETE_FLSETUP_QUERY), {"name": self.name})
    
+
+
+
    
+[docs]
+    @classmethod
+    def read_setup(cls, FLsetupId: int):
+        """Read the FL setup by FLsetupId.
+
+        Args:
+            FLsetupId (int): The id of the FL setup to read.
+
+        Returns:
+            FLsetup: An instance of the FLsetup class with the specified FLsetupId.
+        """
+        res = pd.read_sql(
+            text(READ_SETUP_QUERY), my_eng, params={"flsetup_id": FLsetupId}
+        ).iloc[0]
+        
+        network_res = pd.read_sql(
+            text(READ_NETWORK_BY_ID_QUERY),
+            my_eng,
+            params={"net_id": int(res["NetId"])},
+        ).iloc[0]
+        network = Network(network_res["NetName"])
+        setattr(network, "id", res["NetId"])
+        fl_setup = cls(res["name"], res["description"], network)
+        if res["column_name"] == str(None):
+            res["column_name"] = None
+        setattr(fl_setup, "column_name", res["column_name"])
+        setattr(fl_setup, "id", res["FLsetupId"])
+
+        return fl_setup
    
+
+
+
    
+[docs]
+    @staticmethod
+    def list_allsetups():
+        """List all the FL setups.
+
+        Returns:
+            DataFrame: A DataFrame containing information about all the FL setups.
+        """
+        Flsetups = pd.read_sql(text(READ_ALL_SETUPS_QUERY), my_eng)
+        return Flsetups
    
+
+
+
    
+[docs]
+    def create_nodes_from_master_dataset(self, params_dict: dict):
+        """Create nodes from the master dataset.
+
+        Args:
+            params_dict (dict): A dictionary containing parameters for node creation.
+                - column_name (str): The name of the column in the MasterDataset used to create nodes.
+                - train_nodes (list): A list of node names that will be used for training.
+                - test_nodes (list): A list of node names that will be used for testing.
+
+        Returns:
+            list: A list of Node instances created from the master dataset.
+        """
+        assert "column_name" in params_dict.keys()
+        column_name, train_nodes, test_nodes = (
+            params_dict["column_name"],
+            params_dict["train_nodes"],
+            params_dict["test_nodes"],
+        )
+        self.column_name = column_name
+        self.auto = 1
+
+        # Update the Column name of the auto flSetup
+        query = f"UPDATE FLsetup SET column_name = '{column_name}' WHERE name = '{self.name}'"
+        my_eng.execute(text(query))
+        
+
+        # Add Network to DB
+        # self.network.create_network()
+
+        netid = get_netid_from_name(self.network.name)
+
+        assert self.network.mtable_exists == 1
+        node_names = pd.read_sql(
+            text(READ_DISTINCT_NODES_QUERY.format(column_name)), my_eng
+        )
+
+        nodes = [Node(val[0], 1) for val in node_names.values.tolist()]
+
+        used_nodes = []
+
+        for node in nodes:
+            if node.name in train_nodes:
+                node.train = 1
+                node.create_node(netid)
+                used_nodes.append(node)
+            if node.name in test_nodes:
+                node.train =0
+                node.create_node(netid) 
+                used_nodes.append(node)
+        return used_nodes
    
+
+
+
    
+[docs]
+    def create_dataloader_from_node(
+        self,
+        node: Node,
+        output,
+        fill_strategy="mean",  fit_encode=[], to_drop=[],
+        train_batch_size: int = 32,
+        test_batch_size: int = 1,
+        split_frac: float = 0.2,
+        dataset: Dataset = None,
+
+    ):
+        """Create DataLoader from a Node.
+
+        Args:
+            node (Node): The node from which to create DataLoader.
+            train_batch_size (int): The batch size for training data.
+            test_batch_size (int): The batch size for test data.
+            split_frac (float): The fraction of data to be used for training.
+            dataset (Dataset): The dataset to use. If None, the method will read the dataset from the node.
+
+        Returns:
+            DataLoader: The DataLoader instances for training and testing.
+        """
+        if dataset is None:
+            if self.column_name is not None:
+                dataset = process_data_after_reading(
+                    node.get_dataset(self.column_name), output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop
+                )
+            else:
+                dataset = process_data_after_reading(
+                    node.get_dataset(), output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop)
+
+        dataset_size = len(dataset)
+        traindata_size = int(dataset_size * (1 - split_frac))
+        traindata, testdata = random_split(
+            dataset, [traindata_size, dataset_size - traindata_size]
+        )
+        trainloader, testloader = DataLoader(
+            traindata, batch_size=train_batch_size
+        ), DataLoader(testdata, batch_size=test_batch_size)
+        return trainloader, testloader
    
+
+
+
    
+[docs]
+    def create_federated_dataset(
+        self, output, fill_strategy="mean",  fit_encode=[], to_drop=[], val_frac=0.1, test_frac=0.2
+    ) -> FederatedDataset:
+        """Create a federated dataset.
+
+        Args:
+            output (string): the output feature of the dataset
+            val_frac (float): The fraction of data to be used for validation.
+            test_frac (float): The fraction of data to be used for testing.
+
+        Returns:
+            FederatedDataset: The FederatedDataset instance containing train, validation, and test data.
+        """
+        
+        if not self.column_name:
+            to_drop.extend(["DataSetName" , "NodeId" , "DataSetId"])
+        else :
+            to_drop.extend(["PatientId"]) 
+            
+        netid = self.network.id
+        train_nodes = pd.read_sql(
+            text(
+                f"SELECT Nodes.NodeName  FROM Nodes WHERE Nodes.NetId = {netid} AND Nodes.train = 1 "
+            ),
+            my_eng,
+        )
+        test_nodes = pd.read_sql(
+            text(
+                f"SELECT Nodes.NodeName  FROM Nodes WHERE Nodes.NetId = {netid} AND Nodes.train = 0 "
+            ),
+            my_eng,
+        )
+
+        train_nodes = [
+            Node(val[0], 1, test_frac) for val in train_nodes.values.tolist()
+        ]
+        test_nodes = [Node(val[0], 0) for val in test_nodes.values.tolist()]
+
+        trainloaders, valloaders, testloaders = [], [], []
+        # if len(test_nodes) == 0:
+        #     raise "test node empty"
+        if test_nodes is None:
+            _, testloader = self.create_dataloader_from_node(
+                train_nodes[0], output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop)
+            testloaders.append(testloader)
+        else:
+            for train_node in train_nodes:
+                train_valloader, testloader = self.create_dataloader_from_node(
+                    train_node, output, fill_strategy=fill_strategy,
+                    fit_encode=fit_encode, to_drop=to_drop,)
+                trainloader, valloader = self.create_dataloader_from_node(
+                    train_node,
+                    output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop,
+                    test_batch_size=32,
+                    split_frac=val_frac,
+                    dataset=train_valloader.dataset,
+                )
+                trainloaders.append(trainloader)
+                valloaders.append(valloader)
+                testloaders.append(testloader)
+
+            for test_node in test_nodes:
+                _, testloader = self.create_dataloader_from_node(
+                    test_node, output, fill_strategy=fill_strategy, fit_encode=fit_encode, to_drop=to_drop, split_frac=1.0
+                )
+                testloaders.append(testloader)
+        train_nodes_names = [node.name for node in train_nodes]
+        test_nodes_names = train_nodes_names + [
+            node.name for node in test_nodes
+        ]
+        
+        # test_nodes_names = [
+        #     node.name for node in test_nodes
+        # ]
+
+        # Add FlSetup on to the DataBase
+        # self.create()
+
+        # self.network.update_network(FLsetupId=self.id)
+        fed_dataset = FederatedDataset(
+            self.name + "_Feddataset",
+            train_nodes_names,
+            test_nodes_names,
+            trainloaders,
+            valloaders,
+            testloaders,
+        )
+        self.fed_dataset = fed_dataset
+        self.fed_dataset.create(self.id)
+        return self.fed_dataset
    
+
+    
+    
+
+
+
    
+[docs]
+    def get_flDataSet(self):
+        """
+        Retrieve the federated dataset associated with the FL setup using the FL setup's name.
+ 
+        Returns:
+            pandas.DataFrame: DataFrame containing the federated dataset information.
+        """
+        return pd.read_sql(
+            text(
+                f"SELECT * FROM feddatasets WHERE FLsetupId = {get_flsetupid_from_name(self.name)}"
+            ),
+            my_eng,
+        )
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/NetManager/net_helper.html b/docs/_build/_modules/Medfl/NetManager/net_helper.html new file mode 100644 index 0000000..0ed8b9d --- /dev/null +++ b/docs/_build/_modules/Medfl/NetManager/net_helper.html @@ -0,0 +1,380 @@ + + + + + + MEDfl.NetManager.net_helper — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.NetManager.net_helper

    +from sklearn.preprocessing import LabelEncoder
+from sklearn.impute import SimpleImputer
+
+from sqlalchemy import text
+
+import torch
+import pandas as pd
+from torch.utils.data import TensorDataset
+import numpy as np
+
+
+from scripts.base import my_eng
+
+
+
    
+[docs]
+def is_str(data_df, row, x):
+    """
+    Check if a column in a DataFrame is of type 'object' and convert the value accordingly.
+
+    Args:
+        data_df (pandas.DataFrame): DataFrame containing the data.
+        row (pandas.Series): Data row.
+        x (str): Column name.
+
+    Returns:
+        str or float: Processed value based on the column type.
+    """
+    if data_df[x].dtype == "object":
+        x = f"'{row[x]}'"
+    else:
+        x = row[x]
+    return x
    
+
+
+
+
    
+[docs]
+def process_eicu(data_df):
+    """
+    Process eICU data by filling missing values with mean and replacing NaNs with 'Unknown'.
+
+    Args:
+        data_df (pandas.DataFrame): Input data.
+
+    Returns:
+        pandas.DataFrame: Processed data.
+    """
+    # Identify numeric and non-numeric columns
+    numeric_columns = data_df.select_dtypes(include=[np.number]).columns
+    non_numeric_columns = data_df.select_dtypes(exclude=[np.number]).columns
+
+    # Fill NaN in numeric columns with mean
+    data_df[numeric_columns] = data_df[numeric_columns].fillna(data_df[numeric_columns].mean())
+
+    # Fill NaN in non-numeric columns with 'Unknown'
+    data_df[non_numeric_columns] = data_df[non_numeric_columns].fillna('Unknown')
+
+    try:
+        data_df = data_df.reset_index(drop=True)
+    except:
+        pass
+
+    return data_df
    
+
+
+
+# remove indiserd columns after reading from the DB
+
    
+[docs]
+def process_data_after_reading(data, output, fill_strategy="mean",  fit_encode=[], to_drop=[]):
+    """
+    Process data after reading from the database, including encoding, dropping columns, and creating a PyTorch TensorDataset.
+
+    Args:
+        data (pandas.DataFrame): Input data.
+        output (str): Output column name.
+        fill_strategy (str, optional): Imputation strategy for missing values. Default is "mean".
+        fit_encode (list, optional): List of columns to be label-encoded. Default is an empty list.
+        to_drop (list, optional): List of columns to be dropped from the DataFrame. Default is an empty list.
+
+    Returns:
+        torch.utils.data.TensorDataset: Processed data as a PyTorch TensorDataset.
+    """
+
+    # Check if there is a DataSet assigned to the node
+    if (len(data) == 0):
+        raise "Node doesn't Have dataSet"
+
+    encoder = LabelEncoder()
+    # En Code some columns
+    for s in fit_encode:
+        try:
+            data[s] = encoder.fit_transform(data[s])
+        except:
+            raise print(s)
+
+    # The output of the DATA
+    y = data[output]
+
+    X = data
+
+    
+    # remove indisered columns when reading the dataframe from the DB
+    for column in to_drop:
+        try:
+            X = X.drop(
+                [column], axis=1
+            )
+        except Exception as e:
+            raise e
+
+    
+    # Get the DATAset Features
+    features = [col for col in X.columns if col != output]
+
+    # Impute missing values using the mean strategy
+    try:
+        imputer = SimpleImputer(strategy=fill_strategy)
+        X[features] = imputer.fit_transform(X[features])
+    except:
+        print()
+
+    X = torch.tensor(X.values, dtype=torch.float32)
+    y = torch.tensor(y.values, dtype=torch.float32)
+    data = TensorDataset(X, y)
+
+    return data
    
+
+
+
+
    
+[docs]
+def get_nodeid_from_name(name):
+    """
+    Get the NodeId from the Nodes table based on the NodeName.
+
+    Args:
+        name (str): Node name.
+
+    Returns:
+        int or None: NodeId or None if not found.
+    """
+
+    NodeId = int(
+        pd.read_sql(
+            text(f"SELECT NodeId FROM Nodes WHERE NodeName = '{name}'"), my_eng
+        ).iloc[0, 0]
+    )
+    return NodeId
    
+
+
+
+
    
+[docs]
+def get_netid_from_name(name):
+    """
+    Get the Network Id from the Networks table based on the NetName.
+
+    Args:
+        name (str): Network name.
+
+    Returns:
+        int or None: NetId or None if not found.
+    """
+    try:
+        NetId = int(
+            pd.read_sql(
+                text(f"SELECT NetId FROM Networks WHERE NetName = '{name}'"),
+                my_eng,
+            ).iloc[0, 0]
+        )
+    except:
+        NetId = None
+    return NetId
    
+
+
+
+
    
+[docs]
+def get_flsetupid_from_name(name):
+    """
+    Get the FLsetupId from the FLsetup table based on the FL setup name.
+
+    Args:
+        name (str): FL setup name.
+
+    Returns:
+        int or None: FLsetupId or None if not found.
+    """
+    try:
+        id = int(
+            pd.read_sql(
+                text(f"SELECT FLsetupId FROM FLsetup WHERE name = '{name}'"),
+                my_eng,
+            ).iloc[0, 0]
+        )
+    except:
+        id = None
+    return id
    
+
+
+
+
    
+[docs]
+def get_flpipeline_from_name(name):
+    """
+    Get the FLpipeline Id from the FLpipeline table based on the FL pipeline name.
+
+    Args:
+        name (str): FL pipeline name.
+
+    Returns:
+        int or None: FLpipelineId or None if not found.
+    """
+    try:
+        id = int(
+            pd.read_sql(
+                text(f"SELECT id FROM FLpipeline WHERE name = '{name}'"),
+                my_eng,
+            ).iloc[0, 0]
+        )
+    except:
+        id = None
+    return id
    
+
+
+
+
    
+[docs]
+def get_feddataset_id_from_name(name):
+    """
+    Get the Federated dataset Id from the FedDatasets table based on the federated dataset name.
+
+    Args:
+        name (str): Federated dataset name.
+
+    Returns:
+        int or None: FedId or None if not found.
+    """
+    try:
+        id = int(
+            pd.read_sql(
+                text(f"SELECT FedId FROM FedDatasets WHERE name = '{name}'"),
+                my_eng,
+            ).iloc[0, 0]
+        )
+    except:
+        id = None
+    return id
    
+
+
+
+
    
+[docs]
+def master_table_exists():
+    """
+    Check if the MasterDataset table exists in the database.
+
+    Returns:
+        bool: True if the table exists, False otherwise.
+    """
+    
+    return pd.read_sql(
+        text(
+            " SELECT EXISTS ( SELECT TABLE_NAME FROM information_schema.TABLES WHERE TABLE_NAME = 'MasterDataset' )"
+        ),
+        my_eng,
+    ).values[0][0]
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/NetManager/network.html b/docs/_build/_modules/Medfl/NetManager/network.html new file mode 100644 index 0000000..e6c2f1b --- /dev/null +++ b/docs/_build/_modules/Medfl/NetManager/network.html @@ -0,0 +1,303 @@ + + + + + + MEDfl.NetManager.network — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.NetManager.network

    +# src/MEDfl/NetManager/network.py
+
+from MEDfl.LearningManager.utils import *
+
+from .net_helper import *
+from .net_manager_queries import (CREATE_MASTER_DATASET_TABLE_QUERY,
+                                  CREATE_DATASETS_TABLE_QUERY,
+                                  DELETE_NETWORK_QUERY,
+                                  INSERT_NETWORK_QUERY, LIST_ALL_NODES_QUERY,
+                                  UPDATE_NETWORK_QUERY, GET_NETWORK_QUERY)
+from .node import Node
+import pandas as pd
+from MEDfl.LearningManager.utils import params
+
+
+
    
+[docs]
+class Network:
+    """
+    A class representing a network.
+
+    Attributes:
+        name (str): The name of the network.
+        mtable_exists (int): An integer flag indicating whether the MasterDataset table exists (1) or not (0).
+    """
+
+
    
+[docs]
+    def __init__(self, name: str = ""):
+        """
+        Initialize a Network instance.
+
+        Parameters:
+            name (str): The name of the network.
+        """
+        self.name = name
+        self.mtable_exists = int(master_table_exists())
+        self.validate()
    
+
+
+
    
+[docs]
+    def validate(self):
+        """Validate name"""
+
+        if not isinstance(self.name, str):
+            raise TypeError("name argument must be a string")
    
+
+
+
    
+[docs]
+    def create_network(self):
+        """Create a new network in the database."""
+        my_eng.execute(text(INSERT_NETWORK_QUERY.format(name=self.name)))
+        self.id = get_netid_from_name(self.name)
    
+
+
+
    
+[docs]
+    def use_network(self, network_name: str):
+        """Use a network in the database.
+
+        Parameters:
+            network_name (str): The name of the network to use.
+
+        Returns:
+            Network or None: An instance of the Network class if the network exists, else None.
+        
+        """
+        network = pd.read_sql(
+            text(GET_NETWORK_QUERY.format(name=network_name)),
+            my_eng,
+        )
+
+        if (network.NetId[0]):
+            self.name = network.NetName[0]
+            self.id = network.NetId[0]
+            self.mtable_exists = int(master_table_exists())
+            self.validate()
+            return self
+        else:
+            return None
    
+
+
+
    
+[docs]
+    def delete_network(self):
+        """Delete the network from the database."""
+        my_eng.execute(text(DELETE_NETWORK_QUERY.format(name=self.name)))
    
+
+
+
    
+[docs]
+    def update_network(self, FLsetupId: int):
+        """Update the network's FLsetupId in the database.
+        
+        Parameters:
+            FLsetupId (int): The FLsetupId to update.
+        """
+        my_eng.execute(
+            text(UPDATE_NETWORK_QUERY.format(FLsetupId=FLsetupId, id=self.id))
+        )
    
+
+
+
    
+[docs]
+    def add_node(self, node: Node):
+        """Add a node to the network.
+
+        Parameters:
+            node (Node): The node to add.
+        """
+        node.create_node(self.id)
    
+
+
+
    
+[docs]
+    def list_allnodes(self):
+        """List all nodes in the network.
+
+        Parameters:
+            None
+
+        Returns:
+            DataFrame: A DataFrame containing information about all nodes in the network.
+        
+        """
+        return pd.read_sql(
+            text(LIST_ALL_NODES_QUERY.format(name=self.name)), my_eng
+        )
    
+
+
+
    
+[docs]
+    def create_master_dataset(self, path_to_csv: str = params['path_to_master_csv']):
+        """
+        Create the MasterDataset table and insert dataset values.
+
+        :param path_to_csv: Path to the CSV file containing the dataset.
+        """
+        print(path_to_csv)
+        # Read the CSV file into a Pandas DataFrame
+        data_df = pd.read_csv(path_to_csv)
+
+        # Process the data if needed (e.g., handle missing values, encode categorical variables)
+        # ...
+
+        # Check if the MasterDataset table exists
+
+        if self.mtable_exists != 1:
+            columns = data_df.columns.tolist()
+            columns_str = ",\n".join(
+                [
+                    f"{col} {column_map[str(data_df[col].dtype)]}"
+                    for col in columns
+                ]
+            )
+            my_eng.execute(
+                text(CREATE_MASTER_DATASET_TABLE_QUERY.format(columns_str))
+            )
+            my_eng.execute(text(CREATE_DATASETS_TABLE_QUERY.format(columns_str)))
+
+            # Get the list of columns in the DataFrame
+
+            data_df = process_eicu(data_df)
+            # Insert the dataset values into the MasterDataset table
+
+            for index, row in data_df.iterrows():
+                query_1 = "INSERT INTO MasterDataset(" + "".join(
+                    f"{x}," for x in columns
+                )
+                query_2 = f"VALUES (" + "".join(
+                    f"{is_str(data_df, row, x)}," for x in columns
+                )
+                query = query_1[:-1] + ")" + query_2[:-1] + ")"
+                my_eng.execute(text(query))
+
+        # Set mtable_exists flag to True
+        self.mtable_exists = 1
    
+
+
+
    
+[docs]
+    @staticmethod
+    def list_allnetworks():
+        """List all networks in the database.
+        Returns:
+            DataFrame: A DataFrame containing information about all networks in the database.
+        
+        """
+        return pd.read_sql(text("SELECT * FROM Networks"), my_eng)
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/Medfl/NetManager/node.html b/docs/_build/_modules/Medfl/NetManager/node.html new file mode 100644 index 0000000..d92d8f8 --- /dev/null +++ b/docs/_build/_modules/Medfl/NetManager/node.html @@ -0,0 +1,330 @@ + + + + + + MEDfl.NetManager.node — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +

    Source code for MEDfl.NetManager.node

    +import pandas as pd
+
+from scripts.base import *
+from .net_helper import *
+from .net_manager_queries import *
+from MEDfl.LearningManager.utils import params
+
+
+
    
+[docs]
+class Node:
+    """
+    A class representing a node in the network.
+
+    Attributes:
+        name (str): The name of the node.
+        train (int): An integer flag representing whether the node is used for training (1) or testing (0).
+        test_fraction (float, optional): The fraction of data used for testing when train=1. Default is 0.2.
+    """
+
+
    
+[docs]
+    def __init__(
+        self, name: str, train: int, test_fraction: float = 0.2, engine=my_eng
+    ):
+        """
+        Initialize a Node instance.
+
+        Parameters:
+            name (str): The name of the node.
+            train (int): An integer flag representing whether the node is used for training (1) or testing (0).
+            test_fraction (float, optional): The fraction of data used for testing when train=1. Default is 0.2.
+        """
+        self.name = name
+        self.train = train
+        self.test_fraction = 1.0 if self.train == 0 else test_fraction
+        self.engine = engine
    
+
+
+
    
+[docs]
+    def validate(self):
+        """Validate name, train, test_fraction"""
+        if not isinstance(self.name, str):
+            raise TypeError("name argument must be a string")
+
+        if not isinstance(self.train, int):
+            raise TypeError("train argument must be an int")
+
+        if not isinstance(self.test_fraction, float):
+            raise TypeError("test_fraction argument must be a float")
    
+
+
+
    
+[docs]
+    def create_node(self, NetId: int):
+        """Create a node in the database.
+        Parameters:
+            NetId (int): The ID of the network to which the node belongs.
+
+        Returns:
+            None
+        """
+        self.engine.execute(
+            text(INSERT_NODE_QUERY.format(self.name, NetId, self.train))
+        )
    
+
+
+
    
+[docs]
+    def delete_node(self):
+        """Delete the node from the database."""
+        self.engine.execute(text(DELETE_NODE_QUERY.format(self.name)))
    
+
+
+
    
+[docs]
+    def check_dataset_compatibility(self, data_df):
+        """Check if the dataset is compatible with the master dataset.
+        Parameters:
+            data_df (DataFrame): The dataset to check.
+
+        Returns:
+            None
+        """
+        if master_table_exists() != 1:
+            print("MasterDataset doesn't exist")
+        else:
+            columns = data_df.columns.tolist()
+            # get master_dataset columns
+            master_table_columns = pd.read_sql(
+                text(SELECT_MASTER_COLUMNS_QUERY), self.engine
+            ).columns.tolist()
+            assert [x == y for x, y in zip(master_table_columns, columns)]
    
+
+
+
    
+[docs]
+    def update_node(self):
+        """Update the node information (not implemented)."""
+        pass
    
+
+
+
    
+[docs]
+    def get_dataset(self, column_name: str = None):
+        """Get the dataset for the node based on the given column name.
+        Parameters:
+            column_name (str, optional): The column name to filter the dataset. Default is None.
+
+        Returns:
+            DataFrame: The dataset associated with the node.
+        """
+        NodeId = get_nodeid_from_name(self.name)
+        if column_name is not None:
+            
+            node_dataset = pd.read_sql(
+                text(
+                    SELECT_DATASET_BY_COLUMN_QUERY.format(
+                        column_name, self.name
+                    )
+                ),
+                self.engine,
+            )
+             
+        else:
+            node_dataset = pd.read_sql(
+                text(SELECT_DATASET_BY_NODE_ID_QUERY.format(NodeId)),
+                self.engine,
+            )
+        return node_dataset
    
+
+
+
    
+[docs]
+    def upload_dataset(self, dataset_name: str, path_to_csv: str = params['path_to_test_csv']):
+        """Upload the dataset to the database for the node.
+        Parameters:
+            dataset_name (str): The name of the dataset.
+            path_to_csv (str, optional): Path to the CSV file containing the dataset. Default is the path in params.
+
+        Returns:
+            None
+        """
+        data_df = pd.read_csv(path_to_csv)
+
+        nodeId = get_nodeid_from_name(self.name)
+        columns = data_df.columns.tolist()
+        self.check_dataset_compatibility(data_df)
+
+        data_df = process_eicu(data_df)
+        for index, row in data_df.iterrows():
+            query_1 = "INSERT INTO DataSets(DataSetName,nodeId," + "".join(
+                f"{x}," for x in columns
+            )
+            query_2 = f" VALUES ('{dataset_name}',{nodeId}, " + "".join(
+                f"{is_str(data_df, row, x)}," for x in columns
+            )
+            query = query_1[:-1] + ")" + query_2[:-1] + ")"
+            self.engine.execute(text(query))
    
+
+
+
    
+[docs]
+    def assign_dataset(self, dataset_name:str):
+        """Assigning existing dataSet to node
+        Parameters:
+            dataset_name (str): The name of the dataset to assign.
+
+        Returns:
+            None
+        """
+
+        nodeId = get_nodeid_from_name(self.name)
+        query = f"UPDATE DataSets SET nodeId = {nodeId} WHERE DataSetName = '{dataset_name}'"
+        self.engine.execute(text(query))
    
+
+
+
    
+[docs]
+    def unassign_dataset(self, dataset_name:str):
+        """unssigning existing dataSet to node
+        Parameters:
+            dataset_name (str): The name of the dataset to assign.
+
+        Returns:
+            None
+        """
+
+        query = f"UPDATE DataSets SET nodeId = {-1} WHERE DataSetName = '{dataset_name}'"
+        self.engine.execute(text(query))
    
+
+
+
    
+[docs]
+    def list_alldatasets(self):
+        """List all datasets associated with the node.
+        Returns:
+            DataFrame: A DataFrame containing information about all datasets associated with the node.
+        
+        """
+        return pd.read_sql(
+            text(SELECT_ALL_DATASETS_QUERY.format(self.name)), my_eng
+        )
    
+
+
+
    
+[docs]
+    @staticmethod
+    def list_allnodes():
+        """List all nodes in the database.
+        Returns:
+            DataFrame: A DataFrame containing information about all nodes in the database.
+        
+        """
+        query = text(SELECT_ALL_NODES_QUERY)
+        res = pd.read_sql(query, my_eng)
+        return res
    
+
    
+
+
    + +
    +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_modules/index.html b/docs/_build/_modules/index.html new file mode 100644 index 0000000..a3c3f28 --- /dev/null +++ b/docs/_build/_modules/index.html @@ -0,0 +1,123 @@ + + + + + + Overview: module code — MEDfl 0.1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    +
      +
      • +
    • Overview: module code
      • +
    • +
      • +
    +
    +
    +
    + +
    +
    + +
    + +
    +

    © Copyright 2023, MEDomics consortium.

    +
    + + Built with Sphinx using a + theme + provided by Read the Docs. + + +
    +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/docs/_build/_sources/Medfl.LearningManager.rst.txt b/docs/_build/_sources/Medfl.LearningManager.rst.txt new file mode 100644 index 0000000..4f9513e --- /dev/null +++ b/docs/_build/_sources/Medfl.LearningManager.rst.txt @@ -0,0 +1,85 @@ +MEDfl.LearningManager package +============================= + +Submodules +---------- + +MEDfl.LearningManager.client module +----------------------------------- + +.. automodule:: MEDfl.LearningManager.client + :members: + :undoc-members: + :show-inheritance: + +MEDfl.LearningManager.dynamicModal module +----------------------------------------- + +.. automodule:: MEDfl.LearningManager.dynamicModal + :members: + :undoc-members: + :show-inheritance: + +MEDfl.LearningManager.federated\_dataset module +----------------------------------------------- + +.. automodule:: MEDfl.LearningManager.federated_dataset + :members: + :undoc-members: + :show-inheritance: + +MEDfl.LearningManager.flpipeline module +--------------------------------------- + +.. automodule:: MEDfl.LearningManager.flpipeline + :members: + :undoc-members: + :show-inheritance: + +MEDfl.LearningManager.model module +---------------------------------- + +.. automodule:: MEDfl.LearningManager.model + :members: + :undoc-members: + :show-inheritance: + +MEDfl.LearningManager.plot module +--------------------------------- + +.. automodule:: MEDfl.LearningManager.plot + :members: + :undoc-members: + :show-inheritance: + +MEDfl.LearningManager.server module +----------------------------------- + +.. automodule:: MEDfl.LearningManager.server + :members: + :undoc-members: + :show-inheritance: + +MEDfl.LearningManager.strategy module +------------------------------------- + +.. automodule:: MEDfl.LearningManager.strategy + :members: + :undoc-members: + :show-inheritance: + +MEDfl.LearningManager.utils module +---------------------------------- + +.. automodule:: MEDfl.LearningManager.utils + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: MEDfl.LearningManager + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/_sources/Medfl.NetManager.rst.txt b/docs/_build/_sources/Medfl.NetManager.rst.txt new file mode 100644 index 0000000..d36ef24 --- /dev/null +++ b/docs/_build/_sources/Medfl.NetManager.rst.txt @@ -0,0 +1,61 @@ +MEDfl.NetManager package +======================== + +Submodules +---------- + +MEDfl.NetManager.dataset module +------------------------------- + +.. automodule:: MEDfl.NetManager.dataset + :members: + :undoc-members: + :show-inheritance: + +MEDfl.NetManager.flsetup module +------------------------------- + +.. automodule:: MEDfl.NetManager.flsetup + :members: + :undoc-members: + :show-inheritance: + +MEDfl.NetManager.net\_helper module +----------------------------------- + +.. automodule:: MEDfl.NetManager.net_helper + :members: + :undoc-members: + :show-inheritance: + +MEDfl.NetManager.net\_manager\_queries module +--------------------------------------------- + +.. automodule:: MEDfl.NetManager.net_manager_queries + :members: + :undoc-members: + :show-inheritance: + +MEDfl.NetManager.network module +------------------------------- + +.. automodule:: MEDfl.NetManager.network + :members: + :undoc-members: + :show-inheritance: + +MEDfl.NetManager.node module +---------------------------- + +.. automodule:: MEDfl.NetManager.node + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: MEDfl.NetManager + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/_sources/Medfl.rst.txt b/docs/_build/_sources/Medfl.rst.txt new file mode 100644 index 0000000..95b5d99 --- /dev/null +++ b/docs/_build/_sources/Medfl.rst.txt @@ -0,0 +1,19 @@ +MEDfl package +============= + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + MEDfl.LearningManager + MEDfl.NetManager + +Module contents +--------------- + +.. automodule:: MEDfl + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/_sources/index.rst.txt b/docs/_build/_sources/index.rst.txt new file mode 100644 index 0000000..53a2125 --- /dev/null +++ b/docs/_build/_sources/index.rst.txt @@ -0,0 +1,20 @@ +.. MEDfl-package-docs documentation master file, created by + sphinx-quickstart on Wed Jan 10 21:46:56 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to MEDfl-package-docs's documentation! +============================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + modules + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/_build/_sources/modules.rst.txt b/docs/_build/_sources/modules.rst.txt new file mode 100644 index 0000000..e332f8f --- /dev/null +++ b/docs/_build/_sources/modules.rst.txt @@ -0,0 +1,7 @@ +MEDfl +===== + +.. toctree:: + :maxdepth: 4 + + MEDfl diff --git a/docs/_build/_static/MEDomics.png b/docs/_build/_static/MEDomics.png new file mode 100644 index 0000000000000000000000000000000000000000..4456fda85a3883a667da90ee5ccf5f63ce769a1e GIT binary patch literal 128504 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See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/docs/_build/_static/basic.css b/docs/_build/_static/basic.css new file mode 100644 index 0000000..30fee9d --- /dev/null +++ b/docs/_build/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + 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    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    MEDfl.LearningManager package

    +
    +

    Submodules

    +
    +
    +

    MEDfl.LearningManager.client module

    +
    +
    +class MEDfl.LearningManager.client.FlowerClient(cid: str, local_model: Model, trainloader: DataLoader, valloader: DataLoader, diff_priv: bool = True)[source]
    +

    Bases: NumPyClient

    +

    FlowerClient class for creating MEDfl clients.

    +
    +
    +cid
    +

    Client ID.

    +
    +
    Type:
    +

    str

    +
    +
    +
    + +
    +
    +local_model
    +

    Local model of the federated learning network.

    +
    +
    Type:
    +

    Model

    +
    +
    +
    + +
    +
    +trainloader
    +

    DataLoader for training data.

    +
    +
    Type:
    +

    DataLoader

    +
    +
    +
    + +
    +
    +valloader
    +

    DataLoader for validation data.

    +
    +
    Type:
    +

    DataLoader

    +
    +
    +
    + +
    +
    +diff_priv
    +

    Flag indicating whether to use differential privacy.

    +
    +
    Type:
    +

    bool

    +
    +
    +
    + +
    +
    +__init__(cid: str, local_model: Model, trainloader: DataLoader, valloader: DataLoader, diff_priv: bool = True)[source]
    +

    Initializes the FlowerClient instance.

    +
    +
    Parameters:
    +
      +

    • cid (str) – Client ID.

      • +

    • local_model (Model) – Local model of the federated learning network.

      • +

    • trainloader (DataLoader) – DataLoader for training data.

      • +

    • valloader (DataLoader) – DataLoader for validation data.

      • +

    • diff_priv (bool) – Flag indicating whether to use differential privacy.

      • +
    +
    +
    +
    + +
    +
    +context: Context
    +
    + +
    +
    +evaluate(parameters, config)[source]
    +

    Evaluates the local model on the validation data and returns the loss and accuracy.

    +
    +
    Parameters:
    +
      +

    • parameters – Parameters received from the server.

      • +

    • config – Configuration information.

      • +
    +
    +
    Returns:
    +

    Loss, number of validation examples, and accuracy information.

    +
    +
    Return type:
    +

    Tuple

    +
    +
    +
    + +
    +
    +fit(parameters, config)[source]
    +

    Fits the local model to the received parameters using federated learning.

    +
    +
    Parameters:
    +
      +

    • parameters – Parameters received from the server.

      • +

    • config – Configuration information.

      • +
    +
    +
    Returns:
    +

    Parameters of the local model, number of training examples, and privacy information.

    +
    +
    Return type:
    +

    Tuple

    +
    +
    +
    + +
    +
    +get_parameters(config)[source]
    +

    Returns the current parameters of the local model.

    +
    +
    Parameters:
    +

    config – Configuration information.

    +
    +
    Returns:
    +

    Parameters of the local model.

    +
    +
    Return type:
    +

    Numpy array

    +
    +
    +
    + +
    +
    +validate()[source]
    +

    Validates cid, local_model, trainloader, valloader.

    +
    + +
    + +
    +
    +

    MEDfl.LearningManager.dynamicModal module

    +
    +
    +class MEDfl.LearningManager.dynamicModal.DynamicModel[source]
    +

    Bases: object

    +

    DynamicModel class for creating various types of neural network models.

    +
    +
    +static create_binary_classifier(input_dim, hidden_dims, output_dim, activation='relu', dropout_rate=0.0, batch_norm=False, use_gpu=False)[source]
    +

    Creates a binary classifier neural network model with customizable architecture.

    +
    +
    Parameters:
    +
      +

    • input_dim (int) – Dimension of the input data.

      • +

    • hidden_dims (List[int]) – List of dimensions for hidden layers.

      • +

    • output_dim (int) – Dimension of the output (number of classes).

      • +

    • activation (str, optional) – Activation function for hidden layers. Default is ‘relu’.

      • +

    • dropout_rate (float, optional) – Dropout rate for regularization. Default is 0.0 (no dropout).

      • +

    • batch_norm (bool, optional) – Whether to apply batch normalization. Default is False.

      • +

    • use_gpu (bool, optional) – Whether to use GPU acceleration. Default is False.

      • +
    +
    +
    Returns:
    +

    Created PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +static create_convolutional_neural_network(input_channels, output_channels, kernel_size, use_gpu=False)[source]
    +

    Creates a convolutional neural network (CNN) model.

    +
    +
    Parameters:
    +
      +

    • input_channels (int) – Number of input channels.

      • +

    • output_channels (int) – Number of output channels.

      • +

    • kernel_size (int) – Size of the convolutional kernel.

      • +
    +
    +
    Returns:
    +

    Created PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +static create_linear_regressor(input_dim, output_dim, use_gpu=False)[source]
    +

    Creates a linear regressor neural network model.

    +
    +
    Parameters:
    +
      +

    • input_dim (int) – Dimension of the input data.

      • +

    • output_dim (int) – Dimension of the output.

      • +
    +
    +
    Returns:
    +

    Created PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +static create_logistic_regressor(input_dim, use_gpu=False)[source]
    +

    Creates a logistic regressor neural network model.

    +
    +
    Parameters:
    +

    input_dim (int) – Dimension of the input data.

    +
    +
    Returns:
    +

    Created PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +static create_lstm_network(input_size, hidden_size, use_gpu=False)[source]
    +

    Creates a Long Short-Term Memory (LSTM) network model.

    +
    +
    Parameters:
    +
      +

    • input_size (int) – Size of the input layer.

      • +

    • hidden_size (int) – Size of the hidden layer.

      • +
    +
    +
    Returns:
    +

    Created PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +create_model(model_type: str, params_dict={}) → Module[source]
    +

    Create a specific type of model dynamically based on the given parameters.

    +
    +
    Parameters:
    +
      +

    • model_type (str) – Type of the model to create (‘Binary Classifier’, ‘Multiclass Classifier’, ‘Linear Regressor’, ‘Logistic Regressor’, ‘SVM’, ‘Neural Network Classifier’, ‘Convolutional Neural Network’, ‘Recurrent Neural Network’, ‘LSTM Network’, ‘Autoencoder’).

      • +

    • params_dict (dict) – Dictionary containing parameters for model creation.

      • +
    +
    +
    Returns:
    +

    Created PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +static create_multiclass_classifier(input_dim, hidden_dims, output_dim, activation='relu', dropout_rate=0.0, batch_norm=False, use_gpu=False)[source]
    +

    Creates a multiclass classifier neural network model with customizable architecture.

    +
    +
    Parameters:
    +
      +

    • input_dim (int) – Dimension of the input data.

      • +

    • hidden_dims (List[int]) – List of dimensions for hidden layers.

      • +

    • output_dim (int) – Dimension of the output (number of classes).

      • +

    • activation (str, optional) – Activation function for hidden layers. Default is ‘relu’.

      • +

    • dropout_rate (float, optional) – Dropout rate for regularization. Default is 0.0 (no dropout).

      • +

    • batch_norm (bool, optional) – Whether to apply batch normalization. Default is False.

      • +

    • use_gpu (bool, optional) – Whether to use GPU acceleration. Default is False.

      • +
    +
    +
    Returns:
    +

    Created PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +static create_recurrent_neural_network(input_size, hidden_size, use_gpu=False)[source]
    +

    Creates a recurrent neural network (RNN) model.

    +
    +
    Parameters:
    +
      +

    • input_size (int) – Size of the input.

      • +

    • hidden_size (int) – Size of the hidden layer.

      • +
    +
    +
    Returns:
    +

    Created PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    + +
    +
    +

    MEDfl.LearningManager.federated_dataset module

    +
    +
    +class MEDfl.LearningManager.federated_dataset.FederatedDataset(name: str, train_nodes: list, test_nodes: list, trainloaders: list, valloaders: list, testloaders: list)[source]
    +

    Bases: object

    +
    +
    +__init__(name: str, train_nodes: list, test_nodes: list, trainloaders: list, valloaders: list, testloaders: list)[source]
    +

    Represents a Federated Dataset.

    +
    +
    Parameters:
    +
      +

    • name – Name of the Federated Dataset.

      • +

    • train_nodes – List of train nodes.

      • +

    • test_nodes – List of test nodes.

      • +

    • trainloaders – List of train data loaders.

      • +

    • valloaders – List of validation data loaders.

      • +

    • testloaders – List of test data loaders.

      • +
    +
    +
    +
    + +
    +
    +create(FLsetupId: int)[source]
    +

    Create a new Federated Dataset in the database.

    +
    +
    Parameters:
    +

    FLsetupId – The FLsetup ID associated with the Federated Dataset.

    +
    +
    +
    + +
    +
    +update(FLpipeId: int, FedId: int)[source]
    +

    Update the FLpipe ID associated with the Federated Dataset in the database.

    +
    +
    Parameters:
    +
      +

    • FLpipeId – The new FLpipe ID to be updated.

      • +

    • FedId – The Federated Dataset ID.

      • +
    +
    +
    +
    + +
    + +
    +
    +

    MEDfl.LearningManager.flpipeline module

    +
    +
    +class MEDfl.LearningManager.flpipeline.FLpipeline(name: str, description: str, server: FlowerServer)[source]
    +

    Bases: object

    +

    FLpipeline class for managing Federated Learning pipelines.

    +
    +
    +name
    +

    The name of the FLpipeline.

    +
    +
    Type:
    +

    str

    +
    +
    +
    + +
    +
    +description
    +

    A description of the FLpipeline.

    +
    +
    Type:
    +

    str

    +
    +
    +
    + +
    +
    +server
    +

    The FlowerServer object associated with the FLpipeline.

    +
    +
    Type:
    +

    FlowerServer

    +
    +
    +
    + +
    +
    +__init__(self, name
    +

    str, description: str, server: FlowerServer) -> None: +Initialize FLpipeline with the specified name, description, and server.

    +
    + +
    +
    +auto_test(test_frac=1) → List[dict][source]
    +

    Automatically test the FLpipeline on all nodes with the specified test_frac.

    +
    +
    Parameters:
    +

    test_frac (float, optional) – The fraction of the test data to use. Default is 1.

    +
    +
    Returns:
    +

    A list of dictionaries containing the node names and the classification reports.

    +
    +
    Return type:
    +

    List[dict]

    +
    +
    +
    + +
    +
    +create(result: str) → None[source]
    +

    Create a new FLpipeline entry in the database with the given result.

    +
    +
    Parameters:
    +

    result (str) – The result string to store in the database.

    +
    +
    +
    + +
    +
    +delete() → None[source]
    +

    Delete the FLpipeline entry from the database based on its name.

    +

    Note: This is a placeholder method and needs to be implemented based on your specific database setup.

    +
    + +
    +
    +test_by_node(node_name: str, test_frac=1) → dict[source]
    +

    Test the FLpipeline by node with the specified test_frac.

    +
    +
    Parameters:
    +
      +

    • node_name (str) – The name of the node to test.

      • +

    • test_frac (float, optional) – The fraction of the test data to use. Default is 1.

      • +
    +
    +
    Returns:
    +

    A dictionary containing the node name and the classification report.

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    + +
    +
    +validate() → None[source]
    +

    Validate the name, description, and server attributes. +:raises TypeError: If the name is not a string, the description is not a string, + or the server is not a FlowerServer object.

    +
    + +
    + +
    +
    +MEDfl.LearningManager.flpipeline.create_query(name, description, creation_date, result)[source]
    +
    + +
    +
    +

    MEDfl.LearningManager.model module

    +
    +
    +class MEDfl.LearningManager.model.Model(model: Module, optimizer: Optimizer, criterion: Callable)[source]
    +

    Bases: object

    +

    Model class for training and testing PyTorch neural networks.

    +
    +
    +model
    +

    PyTorch neural network.

    +
    +
    Type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +optimizer
    +

    PyTorch optimizer.

    +
    +
    Type:
    +

    torch.optim.Optimizer

    +
    +
    +
    + +
    +
    +criterion
    +

    Loss function.

    +
    +
    Type:
    +

    Callable

    +
    +
    +
    + +
    +
    +__init__(model: Module, optimizer: Optimizer, criterion: Callable) → None[source]
    +

    Initialize Model class with the specified model, optimizer, and criterion.

    +
    +
    Parameters:
    +
      +

    • model (torch.nn.Module) – PyTorch neural network.

      • +

    • optimizer (torch.optim.Optimizer) – PyTorch optimizer.

      • +

    • criterion (Callable) – Loss function.

      • +
    +
    +
    +
    + +
    +
    +evaluate(val_loader, device=device(type='cpu')) → Tuple[float, float][source]
    +

    Evaluate the model on the given validation data.

    +
    +
    Parameters:
    +
      +

    • val_loader – The data loader for validation data.

      • +

    • device – The device on which to perform the evaluation. Default is ‘cpu’.

      • +
    +
    +
    Returns:
    +

    The evaluation loss and accuracy.

    +
    +
    Return type:
    +

    Tuple[float, float]

    +
    +
    +
    + +
    +
    +get_parameters() → List[ndarray][source]
    +

    Get the parameters of the model as a list of NumPy arrays.

    +
    +
    Returns:
    +

    The parameters of the model as a list of NumPy arrays.

    +
    +
    Return type:
    +

    List[np.ndarray]

    +
    +
    +
    + +
    +
    +static load_model(model_name: str)[source]
    +

    Loads a PyTorch model from a file.

    +
    +
    Parameters:
    +

    model_name (str) – Name of the model file to be loaded.

    +
    +
    Returns:
    +

    Loaded PyTorch model.

    +
    +
    Return type:
    +

    torch.nn.Module

    +
    +
    +
    + +
    +
    +static save_model(model, model_name: str)[source]
    +

    Saves a PyTorch model to a file.

    +
    +
    Parameters:
    +
      +

    • model (torch.nn.Module) – PyTorch model to be saved.

      • +

    • model_name (str) – Name of the model file.

      • +
    +
    +
    Raises:
    +

    Exception – If there is an issue during the saving process.

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +set_parameters(parameters: List[ndarray]) → None[source]
    +

    Set the parameters of the model from a list of NumPy arrays.

    +
    +
    Parameters:
    +

    parameters (List[np.ndarray]) – The parameters to be set.

    +
    +
    +
    + +
    +
    +train(train_loader, epoch, device, privacy_engine, diff_priv=False) → float[source]
    +

    Train the model on the given train_loader for one epoch.

    +
    +
    Parameters:
    +
      +

    • train_loader – The data loader for training data.

      • +

    • epoch (int) – The current epoch number.

      • +

    • device – The device on which to perform the training.

      • +

    • privacy_engine – The privacy engine used for differential privacy (if enabled).

      • +

    • diff_priv (bool, optional) – Whether differential privacy is used. Default is False.

      • +
    +
    +
    Returns:
    +

    The value of epsilon used in differential privacy.

    +
    +
    Return type:
    +

    float

    +
    +
    +
    + +
    +
    +validate() → None[source]
    +

    Validate model and optimizer.

    +
    + +
    + +
    +
    +

    MEDfl.LearningManager.plot module

    +
    +
    +class MEDfl.LearningManager.plot.AccuracyLossPlotter(results_dict)[source]
    +

    Bases: object

    +

    A utility class for plotting accuracy and loss metrics based on experiment results.

    +
    +
    Parameters:
    +

    results_dict (dict) – Dictionary containing experiment results organized by parameters and metrics.

    +
    +
    +
    +
    +results_dict
    +

    Dictionary containing experiment results organized by parameters and metrics.

    +
    +
    Type:
    +

    dict

    +
    +
    +
    + +
    +
    +parameters
    +

    List of unique parameters in the experiment results.

    +
    +
    Type:
    +

    list

    +
    +
    +
    + +
    +
    +metrics
    +

    List of unique metrics in the experiment results.

    +
    +
    Type:
    +

    list

    +
    +
    +
    + +
    +
    +iterations
    +

    Range of iterations (rounds or epochs) in the experiment.

    +
    +
    Type:
    +

    range

    +
    +
    +
    + +
    +
    +__init__(results_dict)[source]
    +

    Initialize the AccuracyLossPlotter with experiment results.

    +
    +
    Parameters:
    +

    results_dict (dict) – Dictionary containing experiment results organized by parameters and metrics.

    +
    +
    +
    + +
    +
    +plot_accuracy_loss()[source]
    +

    Plot accuracy and loss metrics for different parameters.

    +
    + +
    +
    +static plot_classification_report(pipeline_name: str)[source]
    +

    Plot a comparison of classification report metrics between nodes.

    +
    +
    Parameters:
    +

    pipeline_name (str) – Name of the pipeline.

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +static plot_confusion_Matrix_by_node(node_name: str, pipeline_name: str)[source]
    +

    Plot a confusion matrix for a specific node in the pipeline.

    +
    +
    Parameters:
    +
      +

    • node_name (str) – Name of the node.

      • +

    • pipeline_name (str) – Name of the pipeline.

      • +
    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +static plot_global_confusion_matrix(pipeline_name: str)[source]
    +

    Plot a global confusion matrix based on pipeline results.

    +
    +
    Parameters:
    +

    pipeline_name (str) – Name of the pipeline.

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    + +
    +
    +MEDfl.LearningManager.plot.results_dict = {('LR: 0.001, Optimizer: Adam', 'accuracy'): [0.85, 0.89, 0.92, 0.94, Ellipsis], ('LR: 0.001, Optimizer: Adam', 'loss'): [0.2, 0.15, 0.1, 0.08, Ellipsis], ('LR: 0.01, Optimizer: SGD', 'accuracy'): [0.88, 0.91, 0.93, 0.95, Ellipsis], ('LR: 0.01, Optimizer: SGD', 'loss'): [0.18, 0.13, 0.09, 0.07, Ellipsis], ('LR: 0.1, Optimizer: Adam', 'accuracy'): [0.82, 0.87, 0.91, 0.93, Ellipsis], ('LR: 0.1, Optimizer: Adam', 'loss'): [0.25, 0.2, 0.15, 0.12, Ellipsis]}
    +
    +
    server should have:
    +
    #len = num of rounds

    self.accuracies +self.losses

    +
    +
    +
    +
    Client should have

    # len = num of epochs +self.accuracies +self.losses +self.epsilons +self.deltas

    +
    +
    +

    #common things : LR,SGD, Aggregation

    +
    + +
    +
    +

    MEDfl.LearningManager.server module

    +
    +
    +class MEDfl.LearningManager.server.FlowerServer(global_model: Model, strategy: Strategy, num_rounds: int, num_clients: int, fed_dataset: FederatedDataset, diff_privacy: bool = False, client_resources: Dict[str, float] | None = {'num_cpus': 1, 'num_gpus': 0.0})[source]
    +

    Bases: object

    +

    A class representing the central server for Federated Learning using Flower.

    +
    +
    +global_model
    +

    The global model that will be federated among clients.

    +
    +
    Type:
    +

    Model

    +
    +
    +
    + +
    +
    +strategy
    +

    The strategy used for federated learning, specifying communication and aggregation methods.

    +
    +
    Type:
    +

    Strategy

    +
    +
    +
    + +
    +
    +num_rounds
    +

    The number of federated learning rounds to perform.

    +
    +
    Type:
    +

    int

    +
    +
    +
    + +
    +
    +num_clients
    +

    The number of clients participating in the federated learning process.

    +
    +
    Type:
    +

    int

    +
    +
    +
    + +
    +
    +fed_dataset
    +

    The federated dataset used for training and evaluation.

    +
    +
    Type:
    +

    FederatedDataset

    +
    +
    +
    + +
    +
    +diff_priv
    +

    Whether differential privacy is used during the federated learning process.

    +
    +
    Type:
    +

    bool

    +
    +
    +
    + +
    +
    +accuracies
    +

    A list to store the accuracy of the global model during each round.

    +
    +
    Type:
    +

    List[float]

    +
    +
    +
    + +
    +
    +losses
    +

    A list to store the loss of the global model during each round.

    +
    +
    Type:
    +

    List[float]

    +
    +
    +
    + +
    +
    +flower_clients
    +

    A list to store the FlowerClient objects representing individual clients.

    +
    +
    Type:
    +

    List[FlowerClient]

    +
    +
    +
    + +
    +
    +__init__(global_model: Model, strategy: Strategy, num_rounds: int, num_clients: int, fed_dataset: FederatedDataset, diff_privacy: bool = False, client_resources: Dict[str, float] | None = {'num_cpus': 1, 'num_gpus': 0.0}) → None[source]
    +

    Initialize a FlowerServer object with the specified parameters.

    +
    +
    Parameters:
    +
      +

    • global_model (Model) – The global model that will be federated among clients.

      • +

    • strategy (Strategy) – The strategy used for federated learning, specifying communication and aggregation methods.

      • +

    • num_rounds (int) – The number of federated learning rounds to perform.

      • +

    • num_clients (int) – The number of clients participating in the federated learning process.

      • +

    • fed_dataset (FederatedDataset) – The federated dataset used for training and evaluation.

      • +

    • diff_privacy (bool, optional) – Whether differential privacy is used during the federated learning process. +Default is False.

      • +
    +
    +
    +
    + +
    +
    +client_fn(cid) → FlowerClient[source]
    +

    Return a FlowerClient object for a specific client ID.

    +
    +
    Parameters:
    +

    cid – The client ID.

    +
    +
    Returns:
    +

    A FlowerClient object representing the individual client.

    +
    +
    Return type:
    +

    FlowerClient

    +
    +
    +
    + +
    +
    +evaluate(server_round: int, parameters: List[ndarray[Any, dtype[Any]]], config: Dict[str, bool | bytes | float | int | str]) → Tuple[float, Dict[str, bool | bytes | float | int | str]] | None[source]
    +

    Evaluate the global model on the validation dataset and update the accuracies and losses.

    +
    +
    Parameters:
    +
      +

    • server_round (int) – The current round of the federated learning process.

      • +

    • parameters (fl.common.NDArrays) – The global model parameters.

      • +

    • config (Dict[str, fl.common.Scalar]) – Configuration dictionary.

      • +
    +
    +
    Returns:
    +

    The evaluation loss and accuracy.

    +
    +
    Return type:
    +

    Optional[Tuple[float, Dict[str, fl.common.Scalar]]]

    +
    +
    +
    + +
    +
    +run() → None[source]
    +

    Run the federated learning process using Flower simulation.

    +
    +
    Returns:
    +

    The history of the accuracies and losses during the training of each node

    +
    +
    Return type:
    +

    History

    +
    +
    +
    + +
    +
    +validate() → None[source]
    +

    Validate global_model, strategy, num_clients, num_rounds, fed_dataset, diff_privacy

    +
    + +
    + +
    +
    +

    MEDfl.LearningManager.strategy module

    +
    +
    +class MEDfl.LearningManager.strategy.Strategy(name: str = 'FedAvg', fraction_fit: float = 1.0, fraction_evaluate: float = 1.0, min_fit_clients: int = 2, min_evaluate_clients: int = 2, min_available_clients: int = 2, initial_parameters=[], evaluation_methode='centralized')[source]
    +

    Bases: object

    +

    A class representing a strategy for Federated Learning.

    +
    +
    +name
    +

    The name of the strategy. Default is “FedAvg”.

    +
    +
    Type:
    +

    str

    +
    +
    +
    + +
    +
    +fraction_fit
    +

    Fraction of clients to use for training during each round. Default is 1.0.

    +
    +
    Type:
    +

    float

    +
    +
    +
    + +
    +
    +fraction_evaluate
    +

    Fraction of clients to use for evaluation during each round. Default is 1.0.

    +
    +
    Type:
    +

    float

    +
    +
    +
    + +
    +
    +min_fit_clients
    +

    Minimum number of clients to use for training during each round. Default is 2.

    +
    +
    Type:
    +

    int

    +
    +
    +
    + +
    +
    +min_evaluate_clients
    +

    Minimum number of clients to use for evaluation during each round. Default is 2.

    +
    +
    Type:
    +

    int

    +
    +
    +
    + +
    +
    +min_available_clients
    +

    Minimum number of available clients required to start a round. Default is 2.

    +
    +
    Type:
    +

    int

    +
    +
    +
    + +
    +
    +initial_parameters
    +

    The initial parameters of the server model

    +
    +
    Type:
    +

    Optional[]

    +
    +
    +
    + +

    Methods:

    +
    +
    +__init__(name: str = 'FedAvg', fraction_fit: float = 1.0, fraction_evaluate: float = 1.0, min_fit_clients: int = 2, min_evaluate_clients: int = 2, min_available_clients: int = 2, initial_parameters=[], evaluation_methode='centralized') → None[source]
    +

    Initialize a Strategy object with the specified parameters.

    +
    +
    Parameters:
    +
      +

    • name (str) – The name of the strategy. Default is “FedAvg”.

      • +

    • fraction_fit (float) – Fraction of clients to use for training during each round. Default is 1.0.

      • +

    • fraction_evaluate (float) – Fraction of clients to use for evaluation during each round. Default is 1.0.

      • +

    • min_fit_clients (int) – Minimum number of clients to use for training during each round. Default is 2.

      • +

    • min_evaluate_clients (int) – Minimum number of clients to use for evaluation during each round. Default is 2.

      • +

    • min_available_clients (int) – Minimum number of available clients required to start a round. Default is 2.

      • +

    • initial_parameters (Optional[]) – The initial parametres of the server model

      • +

    • evaluation_methode ("centralized" | "distributed") –

      • +
    +
    +
    +
    + +
    + +
    +
    +

    MEDfl.LearningManager.utils module

    +
    +
    +MEDfl.LearningManager.utils.custom_classification_report(y_true, y_pred)[source]
    +

    Compute custom classification report metrics including accuracy, sensitivity, specificity, precision, NPV, +F1-score, false positive rate, and true positive rate.

    +
    +
    Parameters:
    +
      +

    • y_true (array-like) – True labels.

      • +

    • y_pred (array-like) – Predicted labels.

      • +
    +
    +
    Returns:
    +

    A dictionary containing custom classification report metrics.

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    + +
    +
    +MEDfl.LearningManager.utils.empty_db()[source]
    +

    Empty the database by deleting records from multiple tables and resetting auto-increment counters.

    +
    +
    Returns:
    +

    None

    +
    +
    +
    + +
    +
    +MEDfl.LearningManager.utils.get_node_confusion_matrix(pipeline_id, node_name)[source]
    +

    Get the confusion matrix for a specific node in a pipeline based on test results.

    +
    +
    Parameters:
    +
      +

    • pipeline_id (int) – ID of the pipeline.

      • +

    • node_name (str) – Name of the node.

      • +
    +
    +
    Returns:
    +

    A dictionary representing the confusion matrix for the specified node.

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    + +
    +
    +MEDfl.LearningManager.utils.get_pipeline_confusion_matrix(pipeline_id)[source]
    +

    Get the global confusion matrix for a pipeline based on test results.

    +
    +
    Parameters:
    +

    pipeline_id (int) – ID of the pipeline.

    +
    +
    Returns:
    +

    A dictionary representing the global confusion matrix.

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    + +
    +
    +MEDfl.LearningManager.utils.get_pipeline_from_name(name)[source]
    +

    Get the pipeline ID from its name in the database.

    +
    +
    Parameters:
    +

    name (str) – Name of the pipeline.

    +
    +
    Returns:
    +

    ID of the pipeline.

    +
    +
    Return type:
    +

    int

    +
    +
    +
    + +
    +
    +MEDfl.LearningManager.utils.get_pipeline_result(pipeline_id)[source]
    +

    Get the test results for a pipeline.

    +
    +
    Parameters:
    +

    pipeline_id (int) – ID of the pipeline.

    +
    +
    Returns:
    +

    DataFrame containing test results for the specified pipeline.

    +
    +
    Return type:
    +

    pandas.DataFrame

    +
    +
    +
    + +
    +
    +MEDfl.LearningManager.utils.test(model, test_loader, device=device(type='cpu'))[source]
    +

    Evaluate a model using a test loader and return a custom classification report.

    +
    +
    Parameters:
    +
      +

    • model (torch.nn.Module) – PyTorch model to evaluate.

      • +

    • test_loader (torch.utils.data.DataLoader) – DataLoader for the test dataset.

      • +

    • device (torch.device, optional) – Device for model evaluation. Default is “cpu”.

      • +
    +
    +
    Returns:
    +

    A dictionary containing custom classification report metrics.

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    + +
    +
    +

    Module contents

    +
    +
    + + +
    +
    + +
    +
    +
    +