def __read_data__(self):
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(self.root_path,
self.data_path))
border1s = [0, 12*30*24 - self.seq_len, 12*30*24+4*30*24 - self.seq_len]
border2s = [12*30*24, 12*30*24+4*30*24, 12*30*24+8*30*24]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
if self.features=='M' or self.features=='MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features=='S':
df_data = df_raw[[self.target]]
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
df_stamp = df_raw[['date']][border1:border2]
df_stamp['date'] = pd.to_datetime(df_stamp.date)
data_stamp = time_features(df_stamp, timeenc=self.timeenc, freq=self.freq)
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
def __read_data__(self):
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path))
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
# cols = list(df_raw.columns);
if self.cols:
cols = self.cols.copy()
cols.remove(self.target)
else:
cols = list(df_raw.columns)
# print(cols)
cols.remove(self.target)
cols.remove('date')
df_raw = df_raw[['date'] + cols + [self.target]]
num_train = int(len(df_raw) * 0.7)
num_test = int(len(df_raw) * 0.2)
num_vali = len(df_raw) - num_train - num_test
border1s = [
0, num_train - self.seq_len,
len(df_raw) - num_test - self.seq_len
]
border2s = [num_train, num_train + num_vali, len(df_raw)]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
df_stamp = df_raw[['date']][border1:border2]
df_stamp['date'] = pd.to_datetime(df_stamp.date)
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq)
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
def __read_data__(self):
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path))
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
if self.cols:
cols = self.cols.copy()
cols.remove(self.target)
else:
cols = list(df_raw.columns)
cols.remove(self.target)
cols.remove('date')
df_raw = df_raw[['date'] + cols + [self.target]]
# 予測では最後の値を使う
border1 = len(df_raw) - self.seq_len
border2 = len(df_raw)
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale:
self.scaler.fit(df_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
tmp_stamp = df_raw[['date']][border1:border2]
tmp_stamp['date'] = pd.to_datetime(tmp_stamp.date)
pred_dates = pd.date_range(tmp_stamp.date.values[-1],
periods=self.pred_len + 1,
freq=self.freq)
# priods 個数指定
df_stamp = pd.DataFrame(columns=['date'])
df_stamp.date = list(tmp_stamp.date.values) + list(pred_dates[1:])
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq[-1:])
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
class Dataset_Pred(Dataset):
def __init__(self,
root_path,
flag='pred',
size=None,
features='S',
data_path='ETTh1.csv',
target='OT',
scale=True,
inverse=False,
timeenc=0,
freq='15min',
cols=None):
# size [seq_len, label_len, pred_len]
# info
if size == None:
self.seq_len = 24 * 4 * 4
self.label_len = 24 * 4
self.pred_len = 24 * 4
else:
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['pred']
self.features = features
self.target = target
self.scale = scale
self.inverse = inverse
self.timeenc = timeenc
self.freq = freq
self.cols = cols
self.root_path = root_path
self.data_path = data_path
self.__read_data__()
def __read_data__(self):
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path))
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
if self.cols:
cols = self.cols.copy()
cols.remove(self.target)
else:
cols = list(df_raw.columns)
cols.remove(self.target)
cols.remove('date')
df_raw = df_raw[['date'] + cols + [self.target]]
border1 = len(df_raw) - self.seq_len
border2 = len(df_raw)
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale:
self.scaler.fit(df_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
tmp_stamp = df_raw[['date']][border1:border2]
tmp_stamp['date'] = pd.to_datetime(tmp_stamp.date)
pred_dates = pd.date_range(tmp_stamp.date.values[-1],
periods=self.pred_len + 1,
freq=self.freq)
df_stamp = pd.DataFrame(columns=['date'])
df_stamp.date = list(tmp_stamp.date.values) + list(pred_dates[1:])
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq[-1:])
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
def __getitem__(self, index):
s_begin = index
s_end = s_begin + self.seq_len
r_begin = s_end - self.label_len
r_end = r_begin + self.label_len + self.pred_len
seq_x = self.data_x[s_begin:s_end]
seq_y = self.data_y[r_begin:r_begin + self.label_len]
seq_x_mark = self.data_stamp[s_begin:s_end]
seq_y_mark = self.data_stamp[r_begin:r_end]
return seq_x, seq_y, seq_x_mark, seq_y_mark
def __len__(self):
return len(self.data_x) - self.seq_len + 1
def inverse_transform(self, data):
return self.scaler.inverse_transform(data)
class Dataset_Custom(Dataset):
def __init__(self,
root_path,
flag='train',
size=None,
features='S',
data_path='ETTh1.csv',
target='OT',
scale=True,
inverse=False,
timeenc=0,
freq='h',
cols=None):
# size [seq_len, label_len, pred_len]
# info
if size == None:
self.seq_len = 24 * 4 * 4
self.label_len = 24 * 4
self.pred_len = 24 * 4
else:
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['train', 'test', 'val']
type_map = {'train': 0, 'val': 1, 'test': 2}
self.set_type = type_map[flag]
self.features = features
self.target = target
self.scale = scale
self.inverse = inverse
self.timeenc = timeenc
self.freq = freq
self.cols = cols
self.root_path = root_path
self.data_path = data_path
self.__read_data__()
def __read_data__(self):
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path))
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
# cols = list(df_raw.columns);
if self.cols:
cols = self.cols.copy()
cols.remove(self.target)
else:
cols = list(df_raw.columns)
cols.remove(self.target)
cols.remove('date')
df_raw = df_raw[['date'] + cols + [self.target]]
num_train = int(len(df_raw) * 0.7)
num_test = int(len(df_raw) * 0.2)
num_vali = len(df_raw) - num_train - num_test
border1s = [
0, num_train - self.seq_len,
len(df_raw) - num_test - self.seq_len
]
border2s = [num_train, num_train + num_vali, len(df_raw)]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
df_stamp = df_raw[['date']][border1:border2]
df_stamp['date'] = pd.to_datetime(df_stamp.date)
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq)
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
def __getitem__(self, index):
s_begin = index
s_end = s_begin + self.seq_len
r_begin = s_end - self.label_len
r_end = r_begin + self.label_len + self.pred_len
seq_x = self.data_x[s_begin:s_end]
seq_y = self.data_y[r_begin:r_end]
seq_x_mark = self.data_stamp[s_begin:s_end]
seq_y_mark = self.data_stamp[r_begin:r_end]
return seq_x, seq_y, seq_x_mark, seq_y_mark
def __len__(self):
return len(self.data_x) - self.seq_len - self.pred_len + 1
def inverse_transform(self, data):
return self.scaler.inverse_transform(data)
class Dataset_ETT_hour(Dataset):
def __init__(self,
root_path,
flag='train',
size=None,
features='S',
data_path='ETTh1.csv',
target='OT',
scale=True,
inverse=False,
timeenc=0,
freq='h',
cols=None):
# size [seq_len, label_len, pred_len]
# info
if size == None:
self.seq_len = 24 * 4 * 4
self.label_len = 24 * 4
self.pred_len = 24 * 4
else:
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['train', 'test', 'val']
type_map = {'train': 0, 'val': 1, 'test': 2}
self.set_type = type_map[flag]
self.features = features
self.target = target
self.scale = scale
self.inverse = inverse
self.timeenc = timeenc
self.freq = freq
self.root_path = root_path
self.data_path = data_path
self.__read_data__()
def __read_data__(self):
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path))
border1s = [
0, 12 * 30 * 24 - self.seq_len,
12 * 30 * 24 + 4 * 30 * 24 - self.seq_len
]
border2s = [
12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24,
12 * 30 * 24 + 8 * 30 * 24
]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
df_stamp = df_raw[['date']][border1:border2]
df_stamp['date'] = pd.to_datetime(df_stamp.date)
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq)
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
def __getitem__(self, index):
s_begin = index
s_end = s_begin + self.seq_len
r_begin = s_end - self.label_len
r_end = r_begin + self.label_len + self.pred_len
seq_x = self.data_x[s_begin:s_end]
seq_y = self.data_y[r_begin:r_end]
seq_x_mark = self.data_stamp[s_begin:s_end]
seq_y_mark = self.data_stamp[r_begin:r_end]
return seq_x, seq_y, seq_x_mark, seq_y_mark
def __len__(self):
return len(self.data_x) - self.seq_len - self.pred_len + 1
def inverse_transform(self, data):
return self.scaler.inverse_transform(data)
def __read_data__(self):
# __get_item__でデータを取得しやすくするための準備
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(
self.root_path, self.data_path)) # ここでは二次元?? というか次元数がない
# 12 month 30 day 24 hour
border1s = [
0, 12 * 30 * 24 - self.seq_len,
12 * 30 * 24 + 4 * 30 * 24 - self.seq_len
]
# train の場合は0から一年
# val : 1年4ヶ月の96時点
# test : 一年4ヶ月から1年八ヶ月
border2s = [
12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24,
12 * 30 * 24 + 8 * 30 * 24
]
border1 = border1s[self.set_type] # 複数形
border2 = border2s[self.set_type]
# 説明変数の数 multi or single 多変量か単変量か?
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:] # 時刻の列いがい
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale: # default True 正規化をするかしないか
train_data = df_data[border1s[0]:border2s[0]] # trainなら0から2年
self.scaler.fit(train_data.values) # データの標準化 標準かをしないほうがいいのか まだしない
# 正規化の実行 訓練データの平均と分散で正規化する 答えをカンニングしないということ?
data = self.scaler.transform(df_data.values)
else:
data = df_data.values # データの取得 nd array
# これは画像であるPIL image または ndarrayのdata「Height×Width×Channel」を
# Tensor型のdata「Channel×Height×Width」に変換するというもので,
# transという変数がその機能を持つことを意味する.
# なぜChannelの順が入れ替わっているかというと,機械学習をしていく上でChannelが最初のほうが
# 都合が良いからだと思ってもらって良い.
# 今の状態でchannelって何??
df_stamp = df_raw[['date']][border1:border2] # 必要な行数の時刻を取得
df_stamp['date'] = pd.to_datetime(df_stamp.date) # 辞書で呼び出せるように??
# datatime型がnp.ndarray方になる
# 二次元 時間方向 × その時刻の表現の埋め込み表現
# ある時刻に対してその位置埋め込み表現の長さはバラバラ
# あるデータに対しては同じ
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq)
# data_xとdata_yで何が違う??
self.data_x = data[border1:border2] # データ 二次元
if self.inverse: # default false
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
# transformしていた :
# していなかった : values[border1:border2]
self.data_stamp = data_stamp
def __read_data__(self):
self.scaler = StandardScaler() # 標準化のため
df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path))
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
# cols = list(df_raw.columns);
# データの列に対して並び替えをする IndexはDatatime型にしない 列の'date'がindexになる
# つまり自分のstepCountを使いたいなら'date'がindexになる
if self.cols:
cols = self.cols.copy()
cols.remove(self.target)
else:
cols = list(df_raw.columns)
cols.remove(self.target)
cols.remove('date')
df_raw = df_raw[['date'] + cols + [self.target]]
num_train = int(len(df_raw) * 0.7)
num_test = int(len(df_raw) * 0.2)
num_vali = len(df_raw) - num_train - num_test # つまり 1 - 0.7 - 0.2
# 複数形のs
# [0 訓練データまでのindex, validation 検証用データまでのindex, テストデータまでのindex]
border1s = [
0, num_train - self.seq_len,
len(df_raw) - num_test - self.seq_len
]
border2s = [num_train, num_train + num_vali, len(df_raw)]
# 取り出したいデータの種類に合わせて
border1 = border1s[self.set_type] # どのindexから
border2 = border2s[self.set_type] # どのindexまでを取り出すのか
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale: # 標準化するかどうか
train_data = df_data[border1s[0]:border2s[0]]
# axis = 0 で平均, 分散を計算
self.scaler.fit(train_data.values)
# 平均, 分散により標準化, dfのtransformではない
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
df_stamp = df_raw[['date']][border1:border2]
# dataframeからはドットで取り出すこともできる
# indexはintで持っておいて, date列名にdatatime型で持っておく
df_stamp['date'] = pd.to_datetime(df_stamp.date)
# 最終的にmarkとして扱われる
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq)
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
class Dataset_StepCount(Dataset):
# freq : つまりデータ取得の最小の時間の幅
# features : 単回帰? 重回帰??
# size : [どんな時であっても常に同じのモデルに入力される長さ,
# 予測をするときの正解ラベルの幅 つまり96のうち,
# 48はすでに答えがわかっている状態でデコーダに入力される,
# 予測する長さ]
def __init__(self,
root_path='./data/StepCount/',
flag='train',
size=[96, 48, 24],
features='S',
data_path='iPhone_StepCount_from_2017_to_2021_6_18.csv',
target='StepCount',
scale=True,
inverse=False,
timeenc=0,
freq='H',
cols=None):
# size [seq_len, label_len, pred_len]
# info
if size == None:
self.seq_len = 24 * 4 * 4 # この4はどこからきてるの??
# ラベルをつけるのは1日づつってこと??
self.label_len = 24 * 4 # この4はどこからきてるの??
self.pred_len = 24 * 4
else:
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['train', 'test', 'val']
type_map = {'train': 0, 'val': 1, 'test': 2}
self.set_type = type_map[flag]
self.features = features
self.target = target
self.scale = scale
self.inverse = inverse
self.timeenc = timeenc
self.freq = freq
self.cols = cols
self.root_path = root_path
self.data_path = data_path
self.__read_data__() # これは継承もとにない関数
def __read_data__(self):
self.scaler = StandardScaler() # 標準化のため
df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path))
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
# cols = list(df_raw.columns);
# データの列に対して並び替えをする IndexはDatatime型にしない 列の'date'がindexになる
# つまり自分のstepCountを使いたいなら'date'がindexになる
if self.cols:
cols = self.cols.copy()
cols.remove(self.target)
else:
cols = list(df_raw.columns)
cols.remove(self.target)
cols.remove('date')
df_raw = df_raw[['date'] + cols + [self.target]]
num_train = int(len(df_raw) * 0.7)
num_test = int(len(df_raw) * 0.2)
num_vali = len(df_raw) - num_train - num_test # つまり 1 - 0.7 - 0.2
# 複数形のs
# [0 訓練データまでのindex, validation 検証用データまでのindex, テストデータまでのindex]
border1s = [
0, num_train - self.seq_len,
len(df_raw) - num_test - self.seq_len
]
border2s = [num_train, num_train + num_vali, len(df_raw)]
# 取り出したいデータの種類に合わせて
border1 = border1s[self.set_type] # どのindexから
border2 = border2s[self.set_type] # どのindexまでを取り出すのか
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale: # 標準化するかどうか
train_data = df_data[border1s[0]:border2s[0]]
# axis = 0 で平均, 分散を計算
self.scaler.fit(train_data.values)
# 平均, 分散により標準化, dfのtransformではない
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
df_stamp = df_raw[['date']][border1:border2]
# dataframeからはドットで取り出すこともできる
# indexはintで持っておいて, date列名にdatatime型で持っておく
df_stamp['date'] = pd.to_datetime(df_stamp.date)
# 最終的にmarkとして扱われる
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq)
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
def __getitem__(self, index):
s_begin = index
s_end = s_begin + self.seq_len
r_begin = s_end - self.label_len
r_end = r_begin + self.label_len + self.pred_len
seq_x = self.data_x[s_begin:s_end]
seq_y = self.data_y[r_begin:r_end]
seq_x_mark = self.data_stamp[s_begin:s_end]
seq_y_mark = self.data_stamp[r_begin:r_end]
# markはtemporalenbedingに使う
return seq_x, seq_y, seq_x_mark, seq_y_mark
def __len__(self):
return len(self.data_x) - self.seq_len - self.pred_len + 1
def inverse_transform(self, data):
return self.scaler.inverse_transform(data)
class Dataset_ETT_hour(Dataset):
def __init__(self,
root_path,
flag='train',
size=None,
features='S',
data_path='ETTh1.csv',
target='OT',
scale=True,
inverse=False,
timeenc=0,
freq='h',
cols=None):
# size [seq_len, label_len, pred_len]
# info
if size == None:
self.seq_len = 24 * 4 * 4
self.label_len = 24 * 4
self.pred_len = 24 * 4
else:
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['train', 'test', 'val']
type_map = {'train': 0, 'val': 1, 'test': 2}
self.set_type = type_map[flag]
self.features = features
self.target = target
self.scale = scale
self.inverse = inverse
self.timeenc = timeenc
self.freq = freq
self.root_path = root_path
self.data_path = data_path
self.__read_data__() # インスタンス時に呼ばれる 実態を作成したらデータの読み込みが始まる
def __read_data__(self):
# __get_item__でデータを取得しやすくするための準備
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(
self.root_path, self.data_path)) # ここでは二次元?? というか次元数がない
# 12 month 30 day 24 hour
border1s = [
0, 12 * 30 * 24 - self.seq_len,
12 * 30 * 24 + 4 * 30 * 24 - self.seq_len
]
# train の場合は0から一年
# val : 1年4ヶ月の96時点
# test : 一年4ヶ月から1年八ヶ月
border2s = [
12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24,
12 * 30 * 24 + 8 * 30 * 24
]
border1 = border1s[self.set_type] # 複数形
border2 = border2s[self.set_type]
# 説明変数の数 multi or single 多変量か単変量か?
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:] # 時刻の列いがい
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale: # default True 正規化をするかしないか
train_data = df_data[border1s[0]:border2s[0]] # trainなら0から2年
self.scaler.fit(train_data.values) # データの標準化 標準かをしないほうがいいのか まだしない
# 正規化の実行 訓練データの平均と分散で正規化する 答えをカンニングしないということ?
data = self.scaler.transform(df_data.values)
else:
data = df_data.values # データの取得 nd array
# これは画像であるPIL image または ndarrayのdata「Height×Width×Channel」を
# Tensor型のdata「Channel×Height×Width」に変換するというもので,
# transという変数がその機能を持つことを意味する.
# なぜChannelの順が入れ替わっているかというと,機械学習をしていく上でChannelが最初のほうが
# 都合が良いからだと思ってもらって良い.
# 今の状態でchannelって何??
df_stamp = df_raw[['date']][border1:border2] # 必要な行数の時刻を取得
df_stamp['date'] = pd.to_datetime(df_stamp.date) # 辞書で呼び出せるように??
# datatime型がnp.ndarray方になる
# 二次元 時間方向 × その時刻の表現の埋め込み表現
# ある時刻に対してその位置埋め込み表現の長さはバラバラ
# あるデータに対しては同じ
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq)
# data_xとdata_yで何が違う??
self.data_x = data[border1:border2] # データ 二次元
if self.inverse: # default false
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
# transformしていた :
# していなかった : values[border1:border2]
self.data_stamp = data_stamp
def __getitem__(self, index): # 自動で呼ばれる 抽象関数の実装?? pythonの機能
# indexは for in で呼び出される時のindex つまり 0, 1, 2, 3, ...
s_begin = index # indexはstart位置
s_end = s_begin + self.seq_len # 16日ぶん?? 24*4*4
r_begin = s_end - self.label_len # 4日ぶん引く?? 24*4
r_end = r_begin + self.label_len + self.pred_len # = s_end + pred_len
# sに関して
# indexを基準に時間軸に進める方向に対してseq_len
# rに関して
# s_endを基準にして 右にlabel_len 左にlabel_len + pred_len
seq_x = self.data_x[s_begin:s_end] # 何時限? 二次元
seq_y = self.data_y[r_begin:r_end]
seq_x_mark = self.data_stamp[s_begin:s_end] # markは時刻のマークという意味??
seq_y_mark = self.data_stamp[r_begin:r_end]
# x とyがfor in でその後どういう使われ方をするのかに注目
return seq_x, seq_y, seq_x_mark, seq_y_mark # for in で呼び出せる
def __len__(self):
return len(self.data_x) - self.seq_len - self.pred_len + 1
def inverse_transform(self, data):
return self.scaler.inverse_transform(data)
class Dataset_Umass(Dataset):
def __init__(self,
target,
flag='train',
size=None,
features='S',
root_path=None,
data_path=None,
scale=True,
inverse=False,
timeenc=0,
freq='h',
useWeather=False):
# size [seq_len, label_len, pred_len]
# info
if size == None:
self.seq_len = 24 * 4 * 4
self.label_len = 24 * 4
self.pred_len = 24 * 4
else:
self.seq_len = size[0]
self.label_len = size[1]
self.pred_len = size[2]
# init
assert flag in ['train', 'test', 'val']
type_map = {'train': 0, 'val': 1, 'test': 2}
self.set_type = type_map[flag]
self.features = features
self.target = target
self.scale = scale
self.inverse = inverse
self.timeenc = timeenc
self.freq = freq
self.root_path = root_path or './data/Umass'
self.data_path = data_path or 'HomeA2014_electrical.csv'
self.useWeather = useWeather
self.__read_data__()
'''
'''
def __read_data__(self):
self.scaler = StandardScaler()
df_raw = pd.read_csv(os.path.join(self.root_path, self.data_path))
'''
df_raw.columns: ['date', ...(other features), target feature]
'''
cols = list(df_raw.columns)
cols.remove(self.target)
cols.remove('date')
df_raw = df_raw[['date'] + cols + [self.target]]
num_train = int(len(df_raw) * 0.7)
num_test = int(len(df_raw) * 0.2)
num_vali = len(df_raw) - num_train - num_test
border1s = [
0, num_train - self.seq_len,
len(df_raw) - num_test - self.seq_len
]
border2s = [num_train, num_train + num_vali, len(df_raw)]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
if self.features == 'M' or self.features == 'MS':
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
elif self.features == 'S':
df_data = df_raw[[self.target]]
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
df_stamp = df_raw[['date']][border1:border2]
df_stamp['date'] = pd.to_datetime(df_stamp.date)
data_stamp = time_features(df_stamp,
timeenc=self.timeenc,
freq=self.freq)
self.data_x = data[border1:border2]
if self.inverse:
self.data_y = df_data.values[border1:border2]
else:
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
def __getitem__(self, index):
s_begin = index
s_end = s_begin + self.seq_len
r_begin = s_end - self.label_len
r_end = r_begin + self.label_len + self.pred_len
seq_x = self.data_x[s_begin:s_end]
seq_y = self.data_y[r_begin:r_end]
seq_x_mark = self.data_stamp[s_begin:s_end]
seq_y_mark = self.data_stamp[r_begin:r_end]
return seq_x, seq_y, seq_x_mark, seq_y_mark
def __len__(self):
return len(self.data_x) - self.seq_len - self.pred_len + 1
def inverse_transform(self, data):
return self.scaler.inverse_transform(data)
def _get_homeElectricalDir(root_path, home, year):
return os.path.join(root_path, 'Home' + home + '-electrical',
str(year))
def _get_homeElectricalDataFileName(home,
meter,
year,
filename_ext='.csv'):
return 'Home' + home + '-meter' + str(meter) + '_' + str(
year) + filename_ext
def _get_homeWeatherDir(root_path, home):
return os.path.join(root_path, 'Home' + home + '-weather')
def _get_homeWeatherDataFileName(home, year, filename_ext='.csv'):
return 'Home' + home + str(year) + filename_ext
