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metric_dataloader.py
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metric_dataloader.py
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import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GroupShuffleSplit
import numpy as np
import random
import hydra
from omegaconf import OmegaConf
# see https://pytorch.org/docs/stable/notes/randomness.html
def seed_worker(worker_id):
worker_seed = torch.initial_seed() % 2**32
np.random.seed(worker_seed)
random.seed(worker_seed)
class MetricDataPreprocessor:
"""
CMAPSS dataset class, handles data loading, preprocessing and sliding window slicing.
Splits preprocessed data into train and validation datasets.
Generates Train, Test, Validation datasets and dataloaders.
"""
def __init__(self,
dataset_name,
max_rul,
window_size,
sensors,
train_size,
alpha,
dir_path,
fix_seed=True,
downsample_healthy_train=False,
downsample_healthy_validation=False,
downsample_healthy_test=False,
downsample_healthy_train_p=0,
downsample_healthy_validation_p=0,
downsample_healthy_test_p=0,
downsample_rul_threshold_train=None,
downsample_rul_threshold_validation=None,
downsample_rul_threshold_test=None,
train_ds_mode="train",
train_ds_return_pairs=True,
train_ds_eps=3,
train_ds_max_eps=6,
train_ds_triplet_healthy_rul=120,
test_ds_mode="test",
test_ds_return_pairs=False,
test_ds_eps=3,
test_ds_max_eps=6,
test_ds_triplet_healthy_rul=120,
val_ds_mode="train",
val_ds_return_pairs=True,
val_ds_eps=3,
val_ds_max_eps=6,
val_ds_triplet_healthy_rul=120,
train_dl_batch_size=100,
train_dl_shuffle=True,
train_dl_num_workers=2,
test_dl_batch_size=100,
test_dl_shuffle=False,
test_dl_num_workers=2,
val_dl_batch_size=100,
val_dl_shuffle=True,
val_dl_num_workers=2
):
self.dataset_name = dataset_name
self.max_rul = max_rul
self.window_size = window_size
if type(sensors) != list:
self.sensors = list(sensors)
else:
self.sensors = sensors
self.train_size = train_size
self.dir_path = dir_path
self.alpha = alpha
self.scaler = {}
self.fix_seed = fix_seed
self.downsample_healthy_train = downsample_healthy_train
self.downsample_healthy_validation = downsample_healthy_validation
self.downsample_healthy_test = downsample_healthy_test
self.downsample_healthy_train_p = downsample_healthy_train_p
self.downsample_healthy_validation_p = downsample_healthy_validation_p
self.downsample_healthy_test_p = downsample_healthy_test_p
self.downsample_rul_threshold_train=downsample_rul_threshold_train,
self.downsample_rul_threshold_validation=downsample_rul_threshold_validation,
self.downsample_rul_threshold_test=downsample_rul_threshold_test,
self.train_ds_kwargs = {
"mode": train_ds_mode,
"return_pairs": train_ds_return_pairs,
"triplet_eps": train_ds_eps,
"triplet_max_eps": train_ds_max_eps,
"triplet_healthy_rul": train_ds_triplet_healthy_rul,
"downsample_healthy": downsample_healthy_train,
"downsample_healthy_p": downsample_healthy_train_p,
"downsample_rul_threshold": downsample_rul_threshold_train
}
self.test_ds_kwargs = {
"mode": test_ds_mode,
"return_pairs": test_ds_return_pairs,
"triplet_eps": test_ds_eps,
"triplet_max_eps": test_ds_max_eps,
"triplet_healthy_rul": test_ds_triplet_healthy_rul,
"downsample_healthy": downsample_healthy_test,
"downsample_healthy_p": downsample_healthy_test_p,
"downsample_rul_threshold": downsample_rul_threshold_test
}
self.val_ds_kwargs = {
"mode": val_ds_mode,
"return_pairs": val_ds_return_pairs,
"triplet_eps": val_ds_eps,
"triplet_max_eps": val_ds_max_eps,
"triplet_healthy_rul": val_ds_triplet_healthy_rul,
"downsample_healthy": downsample_healthy_validation,
"downsample_healthy_p": downsample_healthy_validation_p,
"downsample_rul_threshold": downsample_rul_threshold_validation
}
self.train_dl_kwargs = {
"batch_size": train_dl_batch_size,
"shuffle": train_dl_shuffle,
"num_workers": train_dl_num_workers
}
self.test_dl_kwargs = {
"batch_size": test_dl_batch_size,
"shuffle": test_dl_shuffle,
"num_workers": test_dl_num_workers
}
self.val_dl_kwargs = {
"batch_size": val_dl_batch_size,
"shuffle": val_dl_shuffle,
"num_workers": val_dl_num_workers
}
def _get_rul(self, df, final_rul):
"""
Groups dataset by unit_nr (unit number) and calculates RUL within each group.
In case of test dataset, where run stops not at RUL=0, final_rul is the RUL value of the last datapoint in group
:param df: DataFrame
:param final_rul: int
:return: DataFrame
"""
# Get the total number of cycles for each unit
grouped_by_unit = df.groupby(by="unit_nr")
max_cycle = grouped_by_unit["time_cycles"].max() + final_rul
# Merge the max cycle back into the original frame
result_frame = df.merge(max_cycle.to_frame(name='max_cycle'), left_on='unit_nr', right_index=True)
# Calculate remaining useful life for each row
remaining_useful_life = result_frame["max_cycle"] - result_frame["time_cycles"]
result_frame["RUL"] = remaining_useful_life
result_frame["RUL"] = result_frame["RUL"].astype(int)
# drop max_cycle as it's no longer needed
result_frame = result_frame.drop("max_cycle", axis=1)
return result_frame
def _exponential_smoothing(self, df, sensors, n_samples, alpha=0.4):
"""
Performs exponential smoothing of the input data
via https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ewm.html
If alpha = 1, no smoothing will be applied.
:param df: DataFrame
:param sensors: number of sensor features, int
:param n_samples:
:param alpha: smoothing factor [0, 1]
:return: DataFrame
"""
df = df.copy()
# first, take the exponential weighted mean
df[sensors] = df.groupby('unit_nr', group_keys=True)[sensors].apply(
lambda x: x.ewm(alpha=alpha).mean()).reset_index(level=0, drop=True)
# second, drop first n_samples of each unit_nr to reduce filter delay
def create_mask(data, samples):
result = np.ones_like(data)
result[0:samples] = 0
return result
mask = df.groupby('unit_nr')['unit_nr'].transform(create_mask, samples=n_samples).astype(bool)
df = df[mask]
return df
def _add_operating_condition(self, df):
"""
Joins 2 setting columns in one string which uniquely identifies the operational settings
:param df: DataFrame
:return: DataFrame
"""
df_op_cond = df.copy()
df_op_cond['setting_1'] = abs(df_op_cond['setting_1'].round())
df_op_cond['setting_2'] = abs(df_op_cond['setting_2'].round(decimals=2))
# converting settings to string and concatenating makes the operating condition into a categorical variable
df_op_cond['op_cond'] = df_op_cond['setting_1'].astype(str) + '_' \
+ df_op_cond['setting_2'].astype(str) + '_' \
+ df_op_cond['setting_3'].astype(str)
return df_op_cond
def _condition_scaler(self, df):
"""
Applies Scaler for each operational condition separately.
:param df: DataFrame
:return: DataFrame
"""
# apply operating condition specific scaling
sensor_names = self.sensors
def is_fit_called(obj):
return hasattr(obj, "n_features_in_")
for condition in df['op_cond'].unique():
scaler = self.scaler.get(condition, StandardScaler())
# Check if the Scaler for this condition is fitted and fit if it is not:
if not is_fit_called(scaler):
scaler.fit(df.loc[df['op_cond'] == condition, sensor_names])
self.scaler[condition] = scaler
df.loc[df['op_cond'] == condition, sensor_names] = scaler.transform(
df.loc[df['op_cond'] == condition, sensor_names])
return df
def _load_data(self):
"""
Loading data from csv files.
:return: Train, Test, Validation DataFrames
"""
dir_path = self.dir_path
dataset_name = self.dataset_name
max_rul = self.max_rul
# columns
index_names = ['unit_nr', 'time_cycles']
setting_names = ['setting_1', 'setting_2', 'setting_3']
sensor_names = ['s_{}'.format(i + 1) for i in range(0, 21)]
col_names = index_names + setting_names + sensor_names
# remove unused sensors
drop_sensors = [element for element in sensor_names if element not in self.sensors]
train_file = 'train_' + dataset_name + '.txt'
# data readout
train = pd.read_csv((dir_path + train_file), sep=r'\s+', header=None,
names=col_names)
# create RUL values according to the piece-wise target function
train_final_rul = np.zeros(train['unit_nr'].nunique())
train = self._get_rul(train, train_final_rul)
# train['RUL'].clip(upper=max_rul, inplace=True)
# FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
train['RUL'] = train['RUL'].clip(upper=max_rul)
train = train.drop(drop_sensors, axis=1)
test_file = 'test_' + dataset_name + '.txt'
# data readout
test = pd.read_csv((dir_path + test_file), sep=r'\s+', header=None,
names=col_names)
y_test = pd.read_csv((dir_path + 'RUL_' + dataset_name + '.txt'), sep=r'\s+', header=None,
names=['RemainingUsefulLife'])
test_final_rul = y_test.values.squeeze()
# create RUL values according to the piece-wise target function
test = self._get_rul(test, test_final_rul)
# test['RUL'].clip(upper=max_rul, inplace=True)
# FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
test['RUL'] = test['RUL'].clip(upper=max_rul)
test = test.drop(drop_sensors, axis=1)
train = self._exponential_smoothing(train, self.sensors, 0, self.alpha)
test = self._exponential_smoothing(test, self.sensors, 0, self.alpha)
train = self._add_operating_condition(train)
test = self._add_operating_condition(test)
train = self._condition_scaler(train)
x_test = self._condition_scaler(test)
# Validation split:
group_split = GroupShuffleSplit(n_splits=1, train_size=self.train_size, random_state=42)
train_unit, val_unit = next(group_split.split(train['unit_nr'].unique(), groups=train['unit_nr'].unique()))
x_train = train[train['unit_nr'].isin(train_unit)]
x_val = train[train['unit_nr'].isin(val_unit)]
return x_train, x_test, x_val
def get_datasets(self):
"""
Calls data loading method and return 3 DataSets.
:return: Train, Test, Validation Datasets
"""
dataset_kwargs = {"max_rul": self.max_rul, "window_size": self.window_size, "sensors": self.sensors}
train_df, test_df, val_df = self._load_data()
train_dataset = MetricDataset(dataset=train_df, **dataset_kwargs, **self.train_ds_kwargs)
test_dataset = MetricDataset(dataset=test_df, **dataset_kwargs, **self.test_ds_kwargs)
val_dataset = MetricDataset(dataset=val_df, **dataset_kwargs, **self.val_ds_kwargs)
return train_dataset, test_dataset, val_dataset
def get_dataloaders(self, seed_worker=seed_worker):
"""
Calls dataset generation method and return 3 DataSets.
:return: Train, Test, Validation DataLoaders
"""
if self.fix_seed:
g = torch.Generator()
g.manual_seed(0)
else:
seed_worker = None
g = None
print(f"fix dataloader: {self.fix_seed}")
train_dataset, test_dataset, val_dataset = self.get_datasets()
train_loader = DataLoader(dataset=train_dataset, worker_init_fn=seed_worker, generator=g, **self.train_dl_kwargs)
test_loader = DataLoader(dataset=test_dataset, worker_init_fn=seed_worker, generator=g, **self.test_dl_kwargs)
val_loader = DataLoader(dataset=val_dataset, worker_init_fn=seed_worker, generator=g, **self.val_dl_kwargs)
return train_loader, test_loader, val_loader
class MetricDataset(Dataset):
def __init__(self,
dataset,
mode,
max_rul,
window_size,
sensors,
return_pairs,
triplet_eps,
triplet_max_eps,
triplet_healthy_rul,
downsample_healthy,
downsample_healthy_p,
downsample_rul_threshold=None
):
self.return_pairs = return_pairs
self.eps = triplet_eps
self.max_eps = triplet_max_eps
self.healthy_rul = triplet_healthy_rul
self.final_rul = None
self.run_id = None
self.sequences = None
self.targets = None
self.ids = None
self.mode = mode
self.max_rul = max_rul
self.window_size = window_size
self.downsample_healthy = downsample_healthy
self.downsample_healthy_p = downsample_healthy_p
self.downsample_rul_threshold = downsample_rul_threshold
if type(sensors) != list:
self.sensors = list(sensors)
else:
self.sensors = sensors
self.df = dataset
self.get_sequences(self.df)
def get_sequences(self, df):
"""
Splits each engine run into moving window slices
If the length of engine run is less than window size, the data will be padded
:param df: DataFrame
"""
window_size = self.window_size
sensors = self.sensors
columns_to_pick = ["unit_nr"] + sensors + ["RUL"]
units = df['unit_nr'].unique()
self.ids = units
temp_sequences = []
for unit in units:
unit_df = df[df['unit_nr'] == unit].sort_values(by='time_cycles', ascending=True)
unit_slice = unit_df[columns_to_pick].values
slice_len = unit_slice.shape[0]
if slice_len >= window_size:
if self.mode == "train":
for i in range(0, slice_len - window_size + 1):
temp_sequences.append(unit_slice[i:i + window_size])
else:
temp_sequences.append(unit_slice[slice_len - window_size:])
else:
# if self.mode == "train":
# row number < sequence length, only one sequence
# pad width first time-cycle value
temp_sequences.append(np.pad(unit_slice, ((window_size - slice_len, 0), (0, 0)), 'edge'))
data = np.stack(temp_sequences)
self.sequences = data[:, :, 1:-1]
self.targets = data[:, -1, -1]
self.run_id = data[:, 0, 0]
def __getitem__(self, index):
"""
Returns sliding window datapoint and target at given index. If option return_pairs is True,
3 datapoints will be returned for Triplet loss (datapoint at given index: anchor, positive and negative
datapoint.
:param index: index of datapoint to be returned, int
:return: 2 or 6 torch.FloatTensors
"""
if self.downsample_healthy:
run_id = self.run_id[index]
rul = self.targets[index]
downsampe_rul_threshold = self.max_rul
if self.downsample_rul_threshold:
downsampe_rul_threshold = self.downsample_rul_threshold
if (rul >= self.max_rul) & (np.random.rand(1) < self.downsample_healthy_p):
candidate_point_mask = (self.targets < downsampe_rul_threshold) & (self.run_id == run_id)
candidate_point_mask_indexes = np.flatnonzero(candidate_point_mask)
idx = random.choice(candidate_point_mask_indexes)
index = idx
else:
index = index
if self.return_pairs:
return self.get_triplet(index)
return torch.FloatTensor(self.sequences[index]), torch.FloatTensor([self.targets[index]])
def get_triplet(self, index):
"""
Returns datapoint at given index (Anchor), positive and negative datapoints
:param index: index of datapoint to be returned, int
:return: 6 torch.FloatTensors
"""
run_id = self.run_id[index]
rul = self.targets[index]
x, y = torch.FloatTensor(self.sequences[index]), torch.FloatTensor([self.targets[index]])
pos_x, pos_y = self.get_positive_sample(run_id, rul)
neg_x, neg_y = self.get_negative_sample(run_id, rul)
return x, pos_x, neg_x, y, pos_y, neg_y
def __len__(self):
"""
Returns length of the dataset.
:return: int
"""
return len(self.sequences)
def get_positive_sample(self, run_id, rul):
"""
Selects positive datapoint within datapoints of the current unit number, which satisfy condition:
RUL of datapoint is within +/- eps from RUL of the RUL of the given datapoint
:param run_id: unit number of provided datapoint
:param rul: RUL of provided datapoint
:return: 2 torch.FloatTensors
"""
if rul >= self.healthy_rul:
candidate_point_mask = (self.targets >= self.healthy_rul) & (self.run_id == run_id)
else:
candidate_point_mask = (abs(self.targets - rul) <= self.eps) & (self.run_id == run_id)
candidate_point_mask_indexes = np.flatnonzero(candidate_point_mask)
idx = random.choice(candidate_point_mask_indexes)
return torch.FloatTensor(self.sequences[idx]), torch.FloatTensor([self.targets[idx]])
def get_negative_sample(self, run_id, rul):
"""
Selects negative datapoint within datapoints of the current unit number, which satisfy condition:
RUL of datapoint is further than +/- eps
AND
RUL of datapoint is within +/- max_eps
from RUL of the given datapoint
:param run_id: unit number of provided datapoint
:param rul: RUL of provided datapoint
:return: 2 torch.FloatTensors
"""
if rul >= self.healthy_rul:
candidate_point_mask = (self.targets >= self.healthy_rul - self.eps) \
& (self.targets < self.healthy_rul) & \
(self.run_id == run_id)
else:
candidate_point_mask = (abs(self.targets - rul) <= self.max_eps) \
& (abs(self.targets - rul) >= self.eps) & \
(self.run_id == run_id)
candidate_point_mask_indexes = np.flatnonzero(candidate_point_mask)
idx = random.choice(candidate_point_mask_indexes)
return torch.FloatTensor(self.sequences[idx]), torch.FloatTensor([self.targets[idx]])
def get_run(self, run_id):
"""
Returns all datapoints within given unit number (run_id)
:param run_id: unit number
:return: 2 torch.FloatTensors
"""
mask = self.run_id == run_id
return torch.FloatTensor(self.sequences[mask]), torch.FloatTensor(self.targets[mask])