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algorithm.py
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import argparse
from dataclasses import dataclass, asdict
import json
import numpy as np
import pandas as pd
import sys
from encdec_ad.model import EncDecAD
@dataclass
class CustomParameters:
lstm_layers: int = 1
split: float = 0.9
anomaly_window_size: int = 30
latent_size: int = 40
batch_size: int = 32
validation_batch_size: int = 128
test_batch_size: int = 128
epochs: int = 50 # bigger for smaller datasets, smaller for bigger datasets
early_stopping_delta: float = 0.05
early_stopping_patience: int = 10
learning_rate: float = 1e-3
random_state: int = 42
class AlgorithmArgs(argparse.Namespace):
@property
def ts(self) -> np.ndarray:
return self.df.iloc[:, 1:-1].values
@property
def df(self) -> pd.DataFrame:
return pd.read_csv(self.dataInput)
@staticmethod
def from_sys_args() -> 'AlgorithmArgs':
args: dict = json.loads(sys.argv[1])
custom_parameter_keys = dir(CustomParameters())
filtered_parameters = dict(
filter(lambda x: x[0] in custom_parameter_keys, args.get("customParameters", {}).items()))
args["customParameters"] = CustomParameters(**filtered_parameters)
return AlgorithmArgs(**args)
def train(args: AlgorithmArgs):
data = args.ts
model = EncDecAD(input_size=data.shape[1], **asdict(args.customParameters))
model.fit(data, args.modelOutput)
model.save(args.modelOutput)
def execute(args: AlgorithmArgs):
data = args.ts
model = EncDecAD.load(args.modelInput, input_size=data.shape[1], **asdict(args.customParameters))
anomaly_scores = model.anomaly_detection(data)
anomaly_scores.tofile(args.dataOutput, sep="\n")
def set_random_state(config: AlgorithmArgs) -> None:
seed = config.customParameters.random_state
import random, torch
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if __name__ == "__main__":
args = AlgorithmArgs.from_sys_args()
set_random_state(args)
if args.executionType == "train":
train(args)
elif args.executionType == "execute":
execute(args)
else:
raise ValueError(f"No executionType '{args.executionType}' available! Choose either 'train' or 'execute'.")