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updated clients
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@TheColdIce
TheColdIce committed Jan 7, 2025
1 parent c940049 commit 92a9ced
Showing 1 changed file with 67 additions and 79 deletions.
146 changes: 67 additions & 79 deletions flib/train/Clients/clients.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,7 +144,7 @@ def get_state_dict(self):


class DecisionTreeClient():
def __init__(self, name:str, seed:int, nodes_train:str, nodes_test:str, valset_size:float, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0, class_weight='balanced', random_state =42, **kwargs):
def __init__(self, name:str, seed:int, nodes_train:str, nodes_test:str, valset_size:float, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0, class_weight='balanced', random_state=42, **kwargs):
self.name = name

train_df = pd.read_csv(nodes_train).drop(columns=['account', 'bank'])
Expand All @@ -156,20 +156,12 @@ def __init__(self, name:str, seed:int, nodes_train:str, nodes_test:str, valset_s
self.y_train = train_df['is_sar'].to_numpy()
scaler = StandardScaler()
self.X_train = scaler.fit_transform(self.X_train)
if val_df is not None:
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
else:
self.X_val = None
self.y_val = None
if test_df is not None:
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()
else:
self.X_test = None
self.y_test = None
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()

self.model = DecisionTreeClassifier(
criterion=criterion,
Expand Down Expand Up @@ -229,27 +221,24 @@ def load_state_dict(self, state_dict):


class RandomForestClient():
def __init__(self, name:str, train_df:pd.DataFrame, val_df:pd.DataFrame=None, test_df:pd.DataFrame=None, n_estimators=100, criterion='gini', max_depth=None, class_weight='balanced', random_state=42, **kwargs):
def __init__(self, name:str, seed:str, nodes_train:str, nodes_test:str, valset_size:float, n_estimators=100, criterion='gini', max_depth=None, class_weight='balanced', random_state=42, **kwargs):
self.name = name

train_df = pd.read_csv(nodes_train).drop(columns=['account', 'bank'])
val_df = train_df.sample(frac=valset_size, random_state=seed)
train_df = train_df.drop(val_df.index)
test_df = pd.read_csv(nodes_test).drop(columns=['account', 'bank'])

self.X_train = train_df.drop(columns=['is_sar']).to_numpy()
self.y_train = train_df['is_sar'].to_numpy()
scaler = StandardScaler()
self.X_train = scaler.fit_transform(self.X_train)
if val_df is not None:
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
else:
self.X_val = None
self.y_val = None
if test_df is not None:
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()
else:
self.X_test = None
self.y_test = None
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()

self.model = RandomForestClassifier(
n_estimators=n_estimators,
Expand Down Expand Up @@ -302,27 +291,24 @@ def load_state_dict(self, state_dict):


class GradientBoostingClient():
def __init__(self, name:str, train_df:pd.DataFrame, val_df:pd.DataFrame=None, test_df:pd.DataFrame=None, loss='log_loss', learning_rate=0.1, n_estimators=100, criterion='friedman_mse', max_depth=3, random_state=42, **kwargs):
def __init__(self, name:str, seed:int, nodes_train:str, nodes_test:str, valset_size:float, loss='log_loss', learning_rate=0.1, n_estimators=100, criterion='friedman_mse', max_depth=3, random_state=42, **kwargs):
self.name = name

train_df = pd.read_csv(nodes_train).drop(columns=['account', 'bank'])
val_df = train_df.sample(frac=valset_size, random_state=seed)
train_df = train_df.drop(val_df.index)
test_df = pd.read_csv(nodes_test).drop(columns=['account', 'bank'])

self.X_train = train_df.drop(columns=['is_sar']).to_numpy()
self.y_train = train_df['is_sar'].to_numpy()
scaler = StandardScaler()
self.X_train = scaler.fit_transform(self.X_train)
if val_df is not None:
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
else:
self.X_val = None
self.y_val = None
if test_df is not None:
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()
else:
self.X_test = None
self.y_test = None
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()

self.model = GradientBoostingClassifier(
loss=loss,
Expand Down Expand Up @@ -376,27 +362,24 @@ def load_state_dict(self, state_dict):


class SVMClient():
def __init__(self, name:str, train_df:pd.DataFrame, val_df:pd.DataFrame=None, test_df:pd.DataFrame=None, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, class_weight='balanced', random_state=42, cache_size=200, max_iter=-1, **kwargs):
def __init__(self, name:str, seed:int, nodes_train:str, nodes_test:str, valset_size:float, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, class_weight='balanced', random_state=42, cache_size=200, max_iter=-1, **kwargs):
self.name = name

train_df = pd.read_csv(nodes_train).drop(columns=['account', 'bank'])
val_df = train_df.sample(frac=valset_size, random_state=seed)
train_df = train_df.drop(val_df.index)
test_df = pd.read_csv(nodes_test).drop(columns=['account', 'bank'])

self.X_train = train_df.drop(columns=['is_sar']).to_numpy()
self.y_train = train_df['is_sar'].to_numpy()
scaler = StandardScaler()
self.X_train = scaler.fit_transform(self.X_train)
if val_df is not None:
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
else:
self.X_val = None
self.y_val = None
if test_df is not None:
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()
else:
self.X_test = None
self.y_test = None
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()

self.model = SVC(
C=C,
Expand Down Expand Up @@ -455,27 +438,24 @@ def load_state_dict(self, state_dict):


class KNNClient():
def __init__(self, name:str, train_df:pd.DataFrame, val_df:pd.DataFrame=None, test_df:pd.DataFrame=None, n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', n_jobs=-1, **kwargs):
def __init__(self, name:str, seed:int, nodes_train:str, nodes_test:str, valset_size:float, n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', n_jobs=-1, **kwargs):
self.name = name

train_df = pd.read_csv(nodes_train).drop(columns=['account', 'bank'])
val_df = train_df.sample(frac=valset_size, random_state=seed)
train_df = train_df.drop(val_df.index)
test_df = pd.read_csv(nodes_test).drop(columns=['account', 'bank'])

self.X_train = train_df.drop(columns=['is_sar']).to_numpy()
self.y_train = train_df['is_sar'].to_numpy()
scaler = StandardScaler()
self.X_train = scaler.fit_transform(self.X_train)
if val_df is not None:
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
else:
self.X_val = None
self.y_val = None
if test_df is not None:
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()
else:
self.X_test = None
self.y_test = None
self.X_val = val_df.drop(columns=['is_sar']).to_numpy()
self.X_val = scaler.transform(self.X_val)
self.y_val = val_df['is_sar'].to_numpy()
self.X_test = test_df.drop(columns=['is_sar']).to_numpy()
self.X_test = scaler.transform(self.X_test)
self.y_test = test_df['is_sar'].to_numpy()

self.model = KNeighborsClassifier(
n_neighbors=n_neighbors,
Expand Down Expand Up @@ -531,10 +511,15 @@ def load_state_dict(self, state_dict):


class MLPClient():
def __init__(self, name:str, train_df:pd.DataFrame, val_df:pd.DataFrame=None, test_df:pd.DataFrame=None, device:str='cpu', batch_size=64, optimizer='SGD', optimizer_params={}, criterion='ClassBalancedLoss', criterion_params={}, n_hidden_layers=2, hidden_dim=64, **kwargs):
def __init__(self, name:str, seed:int, device:str, nodes_train:str, nodes_test:str, valset_size:float, batch_size=64, optimizer='SGD', optimizer_params={}, criterion='ClassBalancedLoss', criterion_params={}, n_hidden_layers=2, hidden_dim=64, **kwargs):
self.name = name
self.device = device

train_df = pd.read_csv(nodes_train).drop(columns=['account', 'bank'])
val_df = train_df.sample(frac=valset_size, random_state=seed)
train_df = train_df.drop(val_df.index)
test_df = pd.read_csv(nodes_test).drop(columns=['account', 'bank'])

self.trainset, self.valset, self.testset = tensordatasets(train_df, val_df, test_df, normalize=True, device=self.device)
self.trainloader, self.valloader, self.testloader = dataloaders(self.trainset, self.valset, self.testset, batch_size)

Expand Down Expand Up @@ -654,12 +639,15 @@ def get_state_dict(self):


class GraphSAGEClient():
def __init__(self, name:str, train_df:pd.DataFrame, test_df:pd.DataFrame=None, device='cpu', hidden_dim=64, optimizer='SGD', optimizer_params={}, criterion='ClassBalancedLoss', criterion_params={}, **kwargs):
def __init__(self, name:str, seed:int, nodes_train:str, edges_train:str, nodes_test:str, edges_test:str, device='cpu', hidden_dim=64, optimizer='SGD', optimizer_params={}, criterion='ClassBalancedLoss', criterion_params={}, **kwargs):
self.name = name
self.device = device

train_nodes_df, train_edges_df = train_df
test_nodes_df, test_edges_df = test_df if test_df is not None else (None, None)
train_nodes_df = pd.read_csv(nodes_train).drop(columns=['account', 'bank'])
train_edges_df = pd.read_csv(edges_train)
test_nodes_df = pd.read_csv(nodes_test).drop(columns=['account', 'bank'])
test_edges_df = pd.read_csv(edges_test)

self.trainset, self.testset = utils.graphdataset(train_nodes_df, train_edges_df, test_nodes_df, test_edges_df, device=device)
self.trainset = torch_geometric.transforms.RandomNodeSplit(split='train_rest', num_val=0.2, num_test=0)(self.trainset)

Expand Down

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