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Commit 410202b8 authored by Omar El Rifai's avatar Omar El Rifai
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Allow model selection between linear and convolutional

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driver.py
+ 3
− 2
View file @ 410202b8
...@@ -5,6 +5,7 @@ import click ...@@ -5,6 +5,7 @@ import click
@click.option('--exp_type', help="The experiment type to run") @click.option('--exp_type', help="The experiment type to run")
@click.option('--heterogeneity_type', help="The data heterogeneity to test (or dataset)") @click.option('--heterogeneity_type', help="The data heterogeneity to test (or dataset)")
@click.option('--dataset') @click.option('--dataset')
@click.option('--nn_model', help= "The training model to use ('linear (default) or 'convolutional')")
@click.option('--num_clients', type=int) @click.option('--num_clients', type=int)
@click.option('--num_samples_by_label', type=int) @click.option('--num_samples_by_label', type=int)
@click.option('--num_clusters', type=int) @click.option('--num_clusters', type=int)
...@@ -14,14 +15,14 @@ import click ...@@ -14,14 +15,14 @@ import click
def main_driver(exp_type, dataset, heterogeneity_type, num_clients, num_samples_by_label, num_clusters, centralized_epochs, federated_rounds, seed): def main_driver(exp_type, dataset, nn_model, heterogeneity_type, num_clients, num_samples_by_label, num_clusters, centralized_epochs, federated_rounds, seed):
from pathlib import Path from pathlib import Path
import pandas as pd import pandas as pd
from src.utils_data import setup_experiment, get_uid from src.utils_data import setup_experiment, get_uid
row_exp = pd.Series({"exp_type": exp_type, "dataset": dataset, "heterogeneity_type": heterogeneity_type, "num_clients": num_clients, row_exp = pd.Series({"exp_type": exp_type, "dataset": dataset, "nn_model" : nn_model, "heterogeneity_type": heterogeneity_type, "num_clients": num_clients,
"num_samples_by_label": num_samples_by_label, "num_clusters": num_clusters, "centralized_epochs": centralized_epochs, "num_samples_by_label": num_samples_by_label, "num_clusters": num_clusters, "centralized_epochs": centralized_epochs,
"federated_rounds": federated_rounds, "seed": seed}) "federated_rounds": federated_rounds, "seed": seed})
......
src/models.py
+ 9
− 8
View file @ 410202b8
...@@ -3,10 +3,10 @@ import torch.nn as nn ...@@ -3,10 +3,10 @@ import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
class SimpleLinear2(nn.Module): class SimpleLinear(nn.Module):
""" Fully connected neural network with a single hidden layer of default size 200 and ReLU activations""" """ Fully connected neural network with a single hidden layer of default size 200 and ReLU activations"""
def __init__(self, h1=200): def __init__(self, in_size, n_channels):
""" Initialization function """ Initialization function
Arguments: Arguments:
...@@ -14,8 +14,9 @@ class SimpleLinear2(nn.Module): ...@@ -14,8 +14,9 @@ class SimpleLinear2(nn.Module):
Desired size of the hidden layer Desired size of the hidden layer
""" """
super().__init__() super().__init__()
self.fc1 = nn.Linear(28*28, h1) self.fc1 = nn.Linear(in_size*in_size,200)
self.fc2 = nn.Linear(h1, 10) self.fc2 = nn.Linear(200, 10)
self.in_size = in_size
def forward(self, x: torch.Tensor): def forward(self, x: torch.Tensor):
...@@ -23,19 +24,19 @@ class SimpleLinear2(nn.Module): ...@@ -23,19 +24,19 @@ class SimpleLinear2(nn.Module):
Arguments: Arguments:
x : torch.Tensor x : torch.Tensor
input image of size 28 x 28 input image of size in_size x in_size
Returns: Returns:
log_softmax probabilities of the output layer log_softmax probabilities of the output layer
""" """
x = x.view(-1, 28 * 28) x = x.view(-1, self.in_size * self.in_size)
x = F.relu(self.fc1(x)) x = F.relu(self.fc1(x))
x = self.fc2(x) x = self.fc2(x)
return F.log_softmax(x, dim=1) return F.log_softmax(x, dim=1)
class SimpleLinear(nn.Module): class SimpleConv(nn.Module):
""" Convolutional neural network with 3 convolutional layers and one fully connected layer """ Convolutional neural network with 3 convolutional layers and one fully connected layer
""" """
...@@ -43,7 +44,7 @@ class SimpleLinear(nn.Module): ...@@ -43,7 +44,7 @@ class SimpleLinear(nn.Module):
def __init__(self, in_size, n_channels): def __init__(self, in_size, n_channels):
""" Initialization function """ Initialization function
""" """
super(SimpleLinear, self).__init__() super(SimpleConv, self).__init__()
self.conv1 = nn.Conv2d(n_channels, 16, 3, padding=1) self.conv1 = nn.Conv2d(n_channels, 16, 3, padding=1)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1) self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
......
src/utils_data.py
+ 50
− 57
View file @ 410202b8
...@@ -28,38 +28,39 @@ def shuffle_list(list_samples : int, seed : int) -> list: ...@@ -28,38 +28,39 @@ def shuffle_list(list_samples : int, seed : int) -> list:
return shuffled_list return shuffled_list
def create_label_dict(dataset : str, nn_model : str) -> dict:
def create_label_dict(dataset : dict) -> dict:
"""Create a dictionary of dataset samples
""" Create a dictionary of dataset samples
Arguments: Arguments:
dataset: The name of the dataset to use ('fashion-mnist', 'mnist', or 'kmnist') dataset : The name of the dataset to use (e.g 'fashion-mnist', 'mnist', or 'cifar10')
nn_model : the training model type ('linear' or 'convolutional')
Returns: Returns:
A dictionary of data of the form {'x': [], 'y': []} label_dict : A dictionary of data of the form {'x': [], 'y': []}
Raises: Raises:
Error if the dataset name is unrecognized Error : if the dataset name is unrecognized
""" """
import sys import sys
import numpy as np import numpy as np
import torchvision import torchvision
from tensorflow.keras.datasets import mnist, fashion_mnist
from extra_keras_datasets import kmnist from extra_keras_datasets import kmnist
#import torchvision
if dataset == "fashion-mnist": if dataset == "fashion-mnist":
fashion_mnist = torchvision.datasets.MNIST("datasets", download=True) fashion_mnist = torchvision.datasets.MNIST("datasets", download=True)
(x_train, y_train) = fashion_mnist.data, fashion_mnist.targets (x_train, y_train) = fashion_mnist.data, fashion_mnist.targets
if nn_model == "convolutional":
x_train = x_train.unsqueeze(1)
elif dataset == 'mnist': elif dataset == 'mnist':
mnist = torchvision.datasets.MNIST("datasets", download=True) mnist = torchvision.datasets.MNIST("datasets", download=True)
(x_train, y_train) = mnist.data, mnist.targets (x_train, y_train) = mnist.data, mnist.targets
x_train = x_train.unsqueeze(1)
if nn_model == "convolutional":
x_train = x_train.unsqueeze(1)
elif dataset == "cifar10": elif dataset == "cifar10":
cifar10 = torchvision.datasets.CIFAR10("datasets", download=True) cifar10 = torchvision.datasets.CIFAR10("datasets", download=True)
...@@ -68,6 +69,9 @@ def create_label_dict(dataset : dict) -> dict: ...@@ -68,6 +69,9 @@ def create_label_dict(dataset : dict) -> dict:
elif dataset == 'kmnist': elif dataset == 'kmnist':
(x_train, y_train), _ = kmnist.load_data() (x_train, y_train), _ = kmnist.load_data()
if nn_model == "convolutional":
x_train = x_train.unsqueeze(1)
else: else:
sys.exit("Unrecognized dataset. Please make sure you are using one of the following ['mnist', fashion-mnist', 'kmnist']") sys.exit("Unrecognized dataset. Please make sure you are using one of the following ['mnist', fashion-mnist', 'kmnist']")
...@@ -83,14 +87,15 @@ def create_label_dict(dataset : dict) -> dict: ...@@ -83,14 +87,15 @@ def create_label_dict(dataset : dict) -> dict:
return label_dict return label_dict
def get_clients_data(num_clients : int, num_samples_by_label : int, dataset : dict, seed : int) -> dict: def get_clients_data(num_clients : int, num_samples_by_label : int, dataset : str, nn_model : str) -> dict:
"""Distribute a dataset evenly accross num_clients clients. Works with datasets with 10 labels """Distribute a dataset evenly accross num_clients clients. Works with datasets with 10 labels
Arguments: Arguments:
num_clients : Number of clients of interest num_clients : Number of clients of interest
num_samples_by_label : Number of samples of each labels by client num_samples_by_label : Number of samples of each labels by client
dataset: The name of the dataset to use (e.g 'fashion-mnist', 'mnist', or 'cifar10')
nn_model : the training model type ('linear' or 'convolutional')
Returns: Returns:
client_dataset : Dictionnary where each key correspond to a client index. The samples will be contained in the 'x' key and the target in 'y' key client_dataset : Dictionnary where each key correspond to a client index. The samples will be contained in the 'x' key and the target in 'y' key
...@@ -98,7 +103,7 @@ def get_clients_data(num_clients : int, num_samples_by_label : int, dataset : di ...@@ -98,7 +103,7 @@ def get_clients_data(num_clients : int, num_samples_by_label : int, dataset : di
import numpy as np import numpy as np
label_dict = create_label_dict(dataset) label_dict = create_label_dict(dataset, nn_model)
clients_dictionary = {} clients_dictionary = {}
client_dataset = {} client_dataset = {}
...@@ -133,17 +138,22 @@ def rotate_images(client: Client, rotation: int) -> None: ...@@ -133,17 +138,22 @@ def rotate_images(client: Client, rotation: int) -> None:
""" """
import numpy as np import numpy as np
from math import prod
images = client.data['x'] images = client.data['x']
if rotation >0 : if rotation > 0 :
rotated_images = [] rotated_images = []
for img in images: for img in images:
orig_shape = img.shape
img_flatten = img.flatten()
rotated_img = np.rot90(img, k=rotation//90) # Rotate image by specified angle rotated_img = np.rot90(img, k=rotation//90) # Rotate image by specified angle
rotated_img = rotated_img.reshape(*orig_shape)
rotated_images.append(rotated_img) rotated_images.append(rotated_img)
client.data['x'] = np.array(rotated_images) client.data['x'] = np.array(rotated_images)
...@@ -196,7 +206,6 @@ def data_preparation(client : Client, row_exp : dict) -> None: ...@@ -196,7 +206,6 @@ def data_preparation(client : Client, row_exp : dict) -> None:
return return
def get_dataset_heterogeneities(heterogeneity_type: str) -> dict: def get_dataset_heterogeneities(heterogeneity_type: str) -> dict:
""" """
...@@ -205,7 +214,7 @@ def get_dataset_heterogeneities(heterogeneity_type: str) -> dict: ...@@ -205,7 +214,7 @@ def get_dataset_heterogeneities(heterogeneity_type: str) -> dict:
Arguments: Arguments:
heterogeneity_type : The label of the heterogeneity scenario (labels-distribution-skew, concept-shift-on-labels, quantity-skew) heterogeneity_type : The label of the heterogeneity scenario (labels-distribution-skew, concept-shift-on-labels, quantity-skew)
Returns: Returns:
A dictionary of the form {<het>: []} where <het> is the applicable heterogeneity type dict_params: A dictionary of the form {<het>: []} where <het> is the applicable heterogeneity type
""" """
dict_params = {} dict_params = {}
...@@ -221,24 +230,22 @@ def get_dataset_heterogeneities(heterogeneity_type: str) -> dict: ...@@ -221,24 +230,22 @@ def get_dataset_heterogeneities(heterogeneity_type: str) -> dict:
dict_params['skews'] = [0.1,0.2,0.6,1] dict_params['skews'] = [0.1,0.2,0.6,1]
return dict_params return dict_params
def setup_experiment(row_exp: dict) -> Tuple[Server, list]: def setup_experiment(row_exp: dict) -> Tuple[Server, list]:
""" """ Setup function to create and personalize client's data
Setup function to create and personalize client's data
Arguments: Arguments:
row_exp : The current experiment's global parameters row_exp : The current experiment's global parameters
Returns:
model_server : A nn model used the server in the FL protocol
list_clients : A list of Client Objects used as nodes in the FL protocol
Returns:
model_server, list_clients: a nn model used the server in the FL protocol, a list of Client Objects used as nodes in the FL protocol
""" """
from src.models import SimpleLinear from src.models import SimpleLinear, SimpleConv
from src.utils_fed import init_server_cluster from src.utils_fed import init_server_cluster
import torch import torch
...@@ -246,14 +253,20 @@ def setup_experiment(row_exp: dict) -> Tuple[Server, list]: ...@@ -246,14 +253,20 @@ def setup_experiment(row_exp: dict) -> Tuple[Server, list]:
torch.manual_seed(row_exp['seed']) torch.manual_seed(row_exp['seed'])
imgs_params = {'mnist': (24,1) , 'fashion-mnist': (24,1), 'kmnist': (24,1), 'cifar10': (32,3)} imgs_params = {'mnist': (28,1) , 'fashion-mnist': (28,1), 'kmnist': (28,1), 'cifar10': (32,3)}
model_server = Server(SimpleLinear(in_size=imgs_params[row_exp['dataset']][0], n_channels=imgs_params[row_exp['dataset']][1])) if row_exp['nn_model'] == "linear":
model_server = Server(SimpleLinear(in_size=imgs_params[row_exp['dataset']][0], n_channels=imgs_params[row_exp['dataset']][1]))
elif row_exp['nn_model'] == "convolutional":
model_server = Server(SimpleConv(in_size=imgs_params[row_exp['dataset']][0], n_channels=imgs_params[row_exp['dataset']][1]))
dict_clients = get_clients_data(row_exp['num_clients'], dict_clients = get_clients_data(row_exp['num_clients'],
row_exp['num_samples_by_label'], row_exp['num_samples_by_label'],
row_exp['dataset'], row_exp['dataset'],
row_exp['seed']) row_exp['nn_model'])
for i in range(row_exp['num_clients']): for i in range(row_exp['num_clients']):
...@@ -262,7 +275,8 @@ def setup_experiment(row_exp: dict) -> Tuple[Server, list]: ...@@ -262,7 +275,8 @@ def setup_experiment(row_exp: dict) -> Tuple[Server, list]:
list_clients = add_clients_heterogeneity(list_clients, row_exp) list_clients = add_clients_heterogeneity(list_clients, row_exp)
if row_exp['exp_type'] == "client": if row_exp['exp_type'] == "client":
init_server_cluster(model_server, list_clients, row_exp, imgs_params['dataset'])
init_server_cluster(model_server, list_clients, row_exp, imgs_params[row_exp['dataset']])
return model_server, list_clients return model_server, list_clients
...@@ -679,27 +693,6 @@ def erode_images(x_train : ndarray, kernel_size : tuple =(3, 3)) -> ndarray: ...@@ -679,27 +693,6 @@ def erode_images(x_train : ndarray, kernel_size : tuple =(3, 3)) -> ndarray:
return eroded_images return eroded_images
def save_results(model_server : Server, row_exp : dict ) -> None:
"""
Saves model_server in row_exp['output'] as *.pth object
Arguments:
model_server : The nn.Module to save
row_exp : The current experiment's global parameters
"""
import torch
if row_exp['exp_type'] == "client" or "server":
for cluster_id in range(row_exp['num_clusters']):
torch.save(model_server.clusters_models[cluster_id].state_dict(), f"./results/{row_exp['output']}_{row_exp['exp_type']}_model_cluster_{cluster_id}.pth")
return
def get_uid(str_obj: str) -> str: def get_uid(str_obj: str) -> str:
""" """
Generates an (almost) unique Identifier given a string object. Generates an (almost) unique Identifier given a string object.
......
src/utils_results.py
+ 3
− 3
View file @ 410202b8
...@@ -203,9 +203,9 @@ def summarize_results() -> None: ...@@ -203,9 +203,9 @@ def summarize_results() -> None:
list_params = path.stem.split('_') list_params = path.stem.split('_')
dict_exp_results = {"exp_type" : list_params[0], "dataset": list_params[1], "dataset_type": list_params[2], "number_of_clients": list_params[3], dict_exp_results = {"exp_type" : list_params[0], "dataset": list_params[1], "nn_model" : list_params[2], "dataset_type": list_params[3], "number_of_clients": list_params[4],
"samples by_client": list_params[4], "num_clusters": list_params[5], "centralized_epochs": list_params[6], "samples by_client": list_params[5], "num_clusters": list_params[6], "centralized_epochs": list_params[7],
"federated_rounds": list_params[7],"accuracy": accuracy} "federated_rounds": list_params[8],"accuracy": accuracy}
try: try:
......
src/utils_training.py
+ 3
− 4
View file @ 410202b8
...@@ -50,7 +50,7 @@ def run_cfl_server_side(model_server : Server, list_clients : list, row_exp : di ...@@ -50,7 +50,7 @@ def run_cfl_server_side(model_server : Server, list_clients : list, row_exp : di
return df_results return df_results
def run_cfl_client_side(model_server : Server, list_clients : list, row_exp : dict, init_cluster=True) -> pd.DataFrame: def run_cfl_client_side(model_server : Server, list_clients : list, row_exp : dict) -> pd.DataFrame:
""" Driver function for client-side cluster FL algorithm. The algorithm personalize training by clusters obtained """ Driver function for client-side cluster FL algorithm. The algorithm personalize training by clusters obtained
from model weights (k-means). from model weights (k-means).
...@@ -61,7 +61,6 @@ def run_cfl_client_side(model_server : Server, list_clients : list, row_exp : di ...@@ -61,7 +61,6 @@ def run_cfl_client_side(model_server : Server, list_clients : list, row_exp : di
main_model : Type of Server model needed main_model : Type of Server model needed
list_clients : A list of Client Objects used as nodes in the FL protocol list_clients : A list of Client Objects used as nodes in the FL protocol
row_exp : The current experiment's global parameters row_exp : The current experiment's global parameters
init_cluster : A boolean indicating whether to initialize cluster prior to training
""" """
from src.utils_fed import set_client_cluster, fedavg from src.utils_fed import set_client_cluster, fedavg
...@@ -112,7 +111,7 @@ def run_benchmark(main_model : nn.Module, list_clients : list, row_exp : dict) - ...@@ -112,7 +111,7 @@ def run_benchmark(main_model : nn.Module, list_clients : list, row_exp : dict) -
torch.manual_seed(row_exp['seed']) torch.manual_seed(row_exp['seed'])
torch.use_deterministic_algorithms(True) torch.use_deterministic_algorithms(True)
curr_model = main_model if row_exp['exp_type'] == 'global-federated' else SimpleLinear() curr_model = main_model if row_exp['exp_type'] == 'global-federated' else main_model.model
match row_exp['exp_type']: match row_exp['exp_type']:
...@@ -202,7 +201,7 @@ def train_central(main_model, train_loader, row_exp): ...@@ -202,7 +201,7 @@ def train_central(main_model, train_loader, row_exp):
""" """
criterion = nn.CrossEntropyLoss() criterion = nn.CrossEntropyLoss()
optimizer=optim.SGD optimizer=optim.SGD if row_exp['nn_model'] == "linear" else optim.Adam
optimizer = optimizer(main_model.parameters(), lr=0.01) optimizer = optimizer(main_model.parameters(), lr=0.01)
main_model.train() main_model.train()
......
tests/refs/client_fashion-mnist_features-distribution-skew_8_100_3_5_5_42.csv deleted 100644 → 0
+ 0
− 7
View file @ 98f09846
id,cluster_id,heterogeneity_class,accuracy
0,1,none,80.66666666666666
1,1,erosion,57.666666666666664
2,1,dilatation,81.66666666666667
3,1,none,83.33333333333334
4,1,erosion,59.333333333333336
5,1,dilatation,81.0
tests/refs/global-federated_fashion-mnist_features-distribution-skew_8_100_3_5_5_42.csv deleted 100644 → 0
+ 0
− 7
View file @ 98f09846
id,cluster_id,heterogeneity_class,accuracy
0,,none,74.27777777777777
1,,erosion,74.27777777777777
2,,dilatation,74.27777777777777
3,,none,74.27777777777777
4,,erosion,74.27777777777777
5,,dilatation,74.27777777777777
tests/refs/pers-centralized_fashion-mnist_features-distribution-skew_8_100_3_5_5_42.csv deleted 100644 → 0
+ 0
− 7
View file @ 98f09846
id,cluster_id,heterogeneity_class,accuracy
0,,none,64.66666666666666
1,,erosion,39.5
2,,dilatation,79.83333333333333
3,,none,64.66666666666666
4,,erosion,39.5
5,,dilatation,79.83333333333333
tests/refs/server_fashion-mnist_features-distribution-skew_8_100_3_5_5_42.csv deleted 100644 → 0
+ 0
− 7
View file @ 98f09846
id,cluster_id,heterogeneity_class,accuracy
0,2,none,85.66666666666667
1,1,erosion,66.66666666666666
2,0,dilatation,86.66666666666667
3,2,none,88.66666666666667
4,1,erosion,68.66666666666667
5,0,dilatation,89.0
tests/test_utils_training.py
+ 6
− 6
View file @ 410202b8
...@@ -20,7 +20,7 @@ def utils_extract_params(file_path: Path): ...@@ -20,7 +20,7 @@ def utils_extract_params(file_path: Path):
with open (file_path, "r") as fp: with open (file_path, "r") as fp:
keys = ['exp_type', 'dataset' , 'heterogeneity_type' , 'num_clients', keys = ['exp_type', 'dataset', 'nn_model', 'heterogeneity_type' , 'num_clients',
'num_samples_by_label' , 'num_clusters', 'centralized_epochs', 'num_samples_by_label' , 'num_clusters', 'centralized_epochs',
'federated_rounds', 'seed'] 'federated_rounds', 'seed']
...@@ -29,7 +29,7 @@ def utils_extract_params(file_path: Path): ...@@ -29,7 +29,7 @@ def utils_extract_params(file_path: Path):
row_exp = dict( row_exp = dict(
zip(keys, zip(keys,
parameters[:3] + [int(x) for x in parameters[3:]]) parameters[:4] + [int(x) for x in parameters[4:]])
) )
return row_exp return row_exp
...@@ -44,7 +44,7 @@ def test_run_cfl_benchmark_oracle(): ...@@ -44,7 +44,7 @@ def test_run_cfl_benchmark_oracle():
from src.utils_data import setup_experiment from src.utils_data import setup_experiment
from src.utils_training import run_benchmark from src.utils_training import run_benchmark
file_path = Path("tests/refs/pers-centralized_fashion-mnist_features-distribution-skew_8_100_3_5_5_42.csv") file_path = Path("tests/refs/pers-centralized_fashion-mnist_linear_features-distribution-skew_8_100_3_5_5_42.csv")
row_exp = utils_extract_params(file_path) row_exp = utils_extract_params(file_path)
...@@ -64,7 +64,7 @@ def test_run_cfl_benchmark_fl(): ...@@ -64,7 +64,7 @@ def test_run_cfl_benchmark_fl():
from src.utils_data import setup_experiment from src.utils_data import setup_experiment
from src.utils_training import run_benchmark from src.utils_training import run_benchmark
file_path = Path("tests/refs/global-federated_fashion-mnist_features-distribution-skew_8_100_3_5_5_42.csv") file_path = Path("tests/refs/global-federated_fashion-mnist_linear_features-distribution-skew_8_100_3_5_5_42.csv")
row_exp = utils_extract_params(file_path) row_exp = utils_extract_params(file_path)
...@@ -84,7 +84,7 @@ def test_run_cfl_client_side(): ...@@ -84,7 +84,7 @@ def test_run_cfl_client_side():
from src.utils_data import setup_experiment from src.utils_data import setup_experiment
from src.utils_training import run_cfl_client_side from src.utils_training import run_cfl_client_side
file_path = Path("tests/refs/client_fashion-mnist_features-distribution-skew_8_100_3_5_5_42.csv") file_path = Path("tests/refs/client_fashion-mnist_linear_features-distribution-skew_8_100_3_5_5_42.csv")
row_exp = utils_extract_params(file_path) row_exp = utils_extract_params(file_path)
...@@ -104,7 +104,7 @@ def test_run_cfl_server_side(): ...@@ -104,7 +104,7 @@ def test_run_cfl_server_side():
from src.utils_data import setup_experiment from src.utils_data import setup_experiment
from src.utils_training import run_cfl_server_side from src.utils_training import run_cfl_server_side
file_path = Path("tests/refs/server_fashion-mnist_features-distribution-skew_8_100_3_5_5_42.csv") file_path = Path("tests/refs/server_fashion-mnist_linear_features-distribution-skew_8_100_3_5_5_42.csv")
row_exp = utils_extract_params(file_path) row_exp = utils_extract_params(file_path)
......
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