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Merge pull request #89 from google-research/rajat_dev

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standardizing styles across all files.
This commit is contained in:
Yichen Zhou
2024-07-09 10:17:53 -07:00
committed by GitHub
parent 1facd4eaab 954b7ff4ac
commit 28dcfa669b
11 changed files with 734 additions and 782 deletions
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README.md
+16 -1
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@@ -164,4 +164,19 @@ forecast_df = tfm.forecast_on_df(
## Finetuning
We have provided an example of finetuning the model on a new dataset in `notebooks/finetuning.ipynb`.
We have provided an example of finetuning the model on a new dataset in `notebooks/finetuning.ipynb`.
## Contribution Style guide
If you would like to submit a PR please make sure that you use our formatting style. We use [yapf](https://github.com/google/yapf) for formatting with the following options,
```
[style]
based_on_style = google
# Add your custom style rules here
indent_width = 2
spaces_before_comment = 2
```
Please run `yapf --in-place --recursive <filename>` on all affected files.
experiments/baselines/timegpt_pipeline.py
+183 -181
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@@ -31,191 +31,191 @@ from nixtla import NixtlaClient
def get_seasonality(freq: str) -> int:
return _get_seasonality(freq, seasonalities={"D": 7})
return _get_seasonality(freq, seasonalities={"D": 7})
def maybe_convert_col_to_datetime(df: pd.DataFrame, col_name: str) -> pd.DataFrame:
if not pd.api.types.is_datetime64_any_dtype(df[col_name]):
df = df.copy()
df[col_name] = pd.to_datetime(df[col_name])
return df
def maybe_convert_col_to_datetime(df: pd.DataFrame,
col_name: str) -> pd.DataFrame:
if not pd.api.types.is_datetime64_any_dtype(df[col_name]):
df = df.copy()
df[col_name] = pd.to_datetime(df[col_name])
return df
def zero_pad_time_series(df, freq, min_length=36):
"""If time_series length is less than min_length, front pad it with zeros."""
# 1. Calculate required padding for each unique_id
value_counts = df["unique_id"].value_counts()
to_pad = value_counts[value_counts < min_length].index
"""If time_series length is less than min_length, front pad it with zeros."""
# 1. Calculate required padding for each unique_id
value_counts = df["unique_id"].value_counts()
to_pad = value_counts[value_counts < min_length].index
# 2. Create a new DataFrame to hold padded data
padded_data = []
# 2. Create a new DataFrame to hold padded data
padded_data = []
for unique_id in to_pad:
# 2a. Filter data for the specific unique_id
subset = df[df["unique_id"] == unique_id]
if len(subset) > min_length:
padded_data.append(subset)
else:
# 2b. Determine earliest date and calculate padding dates
start_date = subset["ds"].min()
padding_dates = pd.date_range(
end=start_date,
periods=min_length - len(subset) + 1,
freq=freq, # 'MS' for month start
)[
:-1
] # Exclude the start_date itself
for unique_id in to_pad:
# 2a. Filter data for the specific unique_id
subset = df[df["unique_id"] == unique_id]
if len(subset) > min_length:
padded_data.append(subset)
else:
# 2b. Determine earliest date and calculate padding dates
start_date = subset["ds"].min()
padding_dates = pd.date_range(
end=start_date,
periods=min_length - len(subset) + 1,
freq=freq, # 'MS' for month start
)[:-1] # Exclude the start_date itself
# 2c. Create padding data
padding_df = pd.DataFrame(
{"ds": padding_dates, "unique_id": unique_id, "y": 0} # Zero padding
)
# 2c. Create padding data
padding_df = pd.DataFrame({
"ds": padding_dates,
"unique_id": unique_id,
"y": 0
} # Zero padding
)
# 2d. Combine original and padding data, and append to the list
padded_data.append(pd.concat([padding_df, subset]).sort_values("ds"))
# 2d. Combine original and padding data, and append to the list
padded_data.append(pd.concat([padding_df, subset]).sort_values("ds"))
# 3. Combine all padded data and original data (unchanged)
result_df = pd.concat(padded_data + [df[~df["unique_id"].isin(to_pad)]])
return result_df
# 3. Combine all padded data and original data (unchanged)
result_df = pd.concat(padded_data + [df[~df["unique_id"].isin(to_pad)]])
return result_df
class Forecaster:
"""Borrowed from
"""Borrowed from
https://github.com/Nixtla/nixtla/tree/main/experiments/foundation-time-series-arena/xiuhmolpilli/models.
"""
def forecast(
self,
df: pd.DataFrame,
h: int,
freq: str,
) -> pd.DataFrame:
raise NotImplementedError
def forecast(
self,
df: pd.DataFrame,
h: int,
freq: str,
) -> pd.DataFrame:
raise NotImplementedError
def cross_validation(
self,
df: pd.DataFrame,
h: int,
freq: str,
n_windows: int = 1,
step_size: int | None = None,
) -> pd.DataFrame:
df = maybe_convert_col_to_datetime(df, "ds")
# mlforecast cv code
results = []
sort_idxs = maybe_compute_sort_indices(df, "unique_id", "ds")
if sort_idxs is not None:
df = take_rows(df, sort_idxs)
splits = backtest_splits(
df,
n_windows=n_windows,
h=h,
id_col="unique_id",
time_col="ds",
freq=pd.tseries.frequencies.to_offset(freq),
step_size=h if step_size is None else step_size,
)
for _, (cutoffs, train, valid) in tqdm(enumerate(splits)):
if len(valid.columns) > 3:
raise NotImplementedError(
"Cross validation with exogenous variables is not yet supported."
)
y_pred = self.forecast(
df=train,
h=h,
freq=freq,
)
y_pred = join(y_pred, cutoffs, on="unique_id", how="left")
result = join(
valid[["unique_id", "ds", "y"]],
y_pred,
on=["unique_id", "ds"],
)
if result.shape[0] < valid.shape[0]:
raise ValueError(
"Cross validation result produced less results than expected. "
"Please verify that the frequency parameter (freq) matches your series' "
"and that there aren't any missing periods."
)
results.append(result)
out = vertical_concat(results)
out = drop_index_if_pandas(out)
first_out_cols = ["unique_id", "ds", "cutoff", "y"]
remaining_cols = [c for c in out.columns if c not in first_out_cols]
fcst_cv_df = out[first_out_cols + remaining_cols]
return fcst_cv_df
def cross_validation(
self,
df: pd.DataFrame,
h: int,
freq: str,
n_windows: int = 1,
step_size: int | None = None,
) -> pd.DataFrame:
df = maybe_convert_col_to_datetime(df, "ds")
# mlforecast cv code
results = []
sort_idxs = maybe_compute_sort_indices(df, "unique_id", "ds")
if sort_idxs is not None:
df = take_rows(df, sort_idxs)
splits = backtest_splits(
df,
n_windows=n_windows,
h=h,
id_col="unique_id",
time_col="ds",
freq=pd.tseries.frequencies.to_offset(freq),
step_size=h if step_size is None else step_size,
)
for _, (cutoffs, train, valid) in tqdm(enumerate(splits)):
if len(valid.columns) > 3:
raise NotImplementedError(
"Cross validation with exogenous variables is not yet supported.")
y_pred = self.forecast(
df=train,
h=h,
freq=freq,
)
y_pred = join(y_pred, cutoffs, on="unique_id", how="left")
result = join(
valid[["unique_id", "ds", "y"]],
y_pred,
on=["unique_id", "ds"],
)
if result.shape[0] < valid.shape[0]:
raise ValueError(
"Cross validation result produced less results than expected. "
"Please verify that the frequency parameter (freq) matches your series' "
"and that there aren't any missing periods.")
results.append(result)
out = vertical_concat(results)
out = drop_index_if_pandas(out)
first_out_cols = ["unique_id", "ds", "cutoff", "y"]
remaining_cols = [c for c in out.columns if c not in first_out_cols]
fcst_cv_df = out[first_out_cols + remaining_cols]
return fcst_cv_df
class TimeGPT(Forecaster):
"""Borrowed from
"""Borrowed from
https://github.com/Nixtla/nixtla/tree/main/experiments/foundation-time-series-arena/xiuhmolpilli/models.
We modify the class to take care of edge cases.
"""
def __init__(
self,
api_key: str | None = None,
base_url: Optional[str] = None,
max_retries: int = 1,
model: str = "timegpt-1",
alias: str = "TimeGPT",
):
self.api_key = api_key
self.base_url = base_url
self.max_retries = max_retries
self.model = model
self.alias = alias
def __init__(
self,
api_key: str | None = None,
base_url: Optional[str] = None,
max_retries: int = 1,
model: str = "timegpt-1",
alias: str = "TimeGPT",
):
self.api_key = api_key
self.base_url = base_url
self.max_retries = max_retries
self.model = model
self.alias = alias
def _get_client(self) -> NixtlaClient:
if self.api_key is None:
api_key = os.environ["NIXTLA_API_KEY"]
else:
api_key = self.api_key
return NixtlaClient(
api_key=api_key,
base_url=self.base_url,
max_retries=self.max_retries,
def _get_client(self) -> NixtlaClient:
if self.api_key is None:
api_key = os.environ["NIXTLA_API_KEY"]
else:
api_key = self.api_key
return NixtlaClient(
api_key=api_key,
base_url=self.base_url,
max_retries=self.max_retries,
)
def forecast(
self,
df: pd.DataFrame,
h: int,
freq: str,
level: List = [90.0],
chunk_size: Optional[int] = None,
) -> pd.DataFrame:
client = self._get_client()
fcst_df = None
if chunk_size is None:
fcst_df = client.forecast(
df=df,
h=h,
freq=freq,
level=level,
model=self.model,
)
else:
all_unique_ids = df["unique_id"].unique()
all_fcst_df = []
for i in range(0, len(all_unique_ids), chunk_size):
chunk_ids = all_unique_ids[i:i + chunk_size]
chunk_df = df[df["unique_id"].isin(chunk_ids)]
fct_chunk_df = client.forecast(
df=chunk_df,
h=h,
freq=freq,
level=level,
)
def forecast(
self,
df: pd.DataFrame,
h: int,
freq: str,
level: List = [90.0],
chunk_size: Optional[int] = None,
) -> pd.DataFrame:
client = self._get_client()
fcst_df = None
if chunk_size is None:
fcst_df = client.forecast(
df=df,
h=h,
freq=freq,
level=level,
model=self.model,
)
else:
all_unique_ids = df["unique_id"].unique()
all_fcst_df = []
for i in range(0, len(all_unique_ids), chunk_size):
chunk_ids = all_unique_ids[i : i + chunk_size]
chunk_df = df[df["unique_id"].isin(chunk_ids)]
fct_chunk_df = client.forecast(
df=chunk_df,
h=h,
freq=freq,
level=level,
)
all_fcst_df.append(fct_chunk_df)
fcst_df = pd.concat(all_fcst_df)
fcst_df["ds"] = pd.to_datetime(fcst_df["ds"])
replace_dict = {}
for col in fcst_df.columns:
if col.startswith("TimeGPT"):
replace_dict[col] = col.replace("TimeGPT", self.alias)
fcst_df = fcst_df.rename(columns=replace_dict)
return fcst_df
all_fcst_df.append(fct_chunk_df)
fcst_df = pd.concat(all_fcst_df)
fcst_df["ds"] = pd.to_datetime(fcst_df["ds"])
replace_dict = {}
for col in fcst_df.columns:
if col.startswith("TimeGPT"):
replace_dict[col] = col.replace("TimeGPT", self.alias)
fcst_df = fcst_df.rename(columns=replace_dict)
return fcst_df
def run_timegpt(
@@ -227,25 +227,27 @@ def run_timegpt(
dataset: str,
model: str = "timegpt-1",
) -> Tuple[pd.DataFrame, float, str]:
os.environ["NIXTLA_ID_AS_COL"] = "true"
model = TimeGPT(model="timegpt-1", alias=model)
padded_train_df = zero_pad_time_series(train_df, freq)
init_time = time()
# For these datasets the API fails if we do not chunk.
if dataset in ["m5", "m4_quarterly"]:
chunk_size = 5000
else:
chunk_size = None
fcsts_df = model.forecast(
df=padded_train_df, h=horizon, level=level, freq=freq, chunk_size=chunk_size
)
total_time = time() - init_time
# In case levels are not returned we replace the levels with the mean predictions.
# Note that this does not affect the results table as we only compare on point
# forecastign metrics.
for lvl in level:
if f"{model.alias}-lo-{lvl}" not in fcsts_df.columns:
fcsts_df[f"{model.alias}-lo-{lvl}"] = fcsts_df[model.alias]
if f"{model.alias}-hi-{lvl}" not in fcsts_df.columns:
fcsts_df[f"{model.alias}-hi-{lvl}"] = fcsts_df[model.alias]
return fcsts_df, total_time, model.alias
os.environ["NIXTLA_ID_AS_COL"] = "true"
model = TimeGPT(model="timegpt-1", alias=model)
padded_train_df = zero_pad_time_series(train_df, freq)
init_time = time()
# For these datasets the API fails if we do not chunk.
if dataset in ["m5", "m4_quarterly"]:
chunk_size = 5000
else:
chunk_size = None
fcsts_df = model.forecast(df=padded_train_df,
h=horizon,
level=level,
freq=freq,
chunk_size=chunk_size)
total_time = time() - init_time
# In case levels are not returned we replace the levels with the mean predictions.
# Note that this does not affect the results table as we only compare on point
# forecastign metrics.
for lvl in level:
if f"{model.alias}-lo-{lvl}" not in fcsts_df.columns:
fcsts_df[f"{model.alias}-lo-{lvl}"] = fcsts_df[model.alias]
if f"{model.alias}-hi-{lvl}" not in fcsts_df.columns:
fcsts_df[f"{model.alias}-hi-{lvl}"] = fcsts_df[model.alias]
return fcsts_df, total_time, model.alias
experiments/extended_benchmarks/run_timegpt.py
+35 -38
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation script for timegpt."""
import os
@@ -25,7 +24,6 @@ from ..baselines.timegpt_pipeline import run_timegpt
from .utils import ExperimentHandler
dataset_names = [
"m1_monthly",
"m1_quarterly",
@@ -63,46 +61,45 @@ _MODEL_NAME = flags.DEFINE_string(
)
_SAVE_DIR = flags.DEFINE_string("save_dir", "./results", "Save directory")
QUANTILES = list(np.arange(1, 10) / 10.0)
def main():
results_list = []
run_id = np.random.randint(100000)
model_name = _MODEL_NAME.value
for dataset in dataset_names:
print(f"Evaluating model {model_name} on dataset {dataset}", flush=True)
exp = ExperimentHandler(dataset, quantiles=QUANTILES)
train_df = exp.train_df
horizon = exp.horizon
seasonality = exp.seasonality
freq = exp.freq
level = exp.level
fcsts_df, total_time, model_name = run_timegpt(
train_df=train_df,
horizon=exp.horizon,
model=model_name,
seasonality=seasonality,
freq=freq,
dataset=dataset,
level=level,
)
time_df = pd.DataFrame({"time": [total_time], "model": model_name})
fcsts_df = exp.fcst_from_level_to_quantiles(fcsts_df, model_name)
results = exp.evaluate_from_predictions(
models=[model_name], fcsts_df=fcsts_df, times_df=time_df
)
print(results, flush=True)
results_list.append(results)
results_full = pd.concat(results_list)
save_path = os.path.join(_SAVE_DIR.value, str(run_id))
print(f"Saving results to {save_path}", flush=True)
os.makedirs(save_path, exist_ok=True)
results_full.to_csv(f"{save_path}/results.csv")
results_list = []
run_id = np.random.randint(100000)
model_name = _MODEL_NAME.value
for dataset in dataset_names:
print(f"Evaluating model {model_name} on dataset {dataset}", flush=True)
exp = ExperimentHandler(dataset, quantiles=QUANTILES)
train_df = exp.train_df
horizon = exp.horizon
seasonality = exp.seasonality
freq = exp.freq
level = exp.level
fcsts_df, total_time, model_name = run_timegpt(
train_df=train_df,
horizon=exp.horizon,
model=model_name,
seasonality=seasonality,
freq=freq,
dataset=dataset,
level=level,
)
time_df = pd.DataFrame({"time": [total_time], "model": model_name})
fcsts_df = exp.fcst_from_level_to_quantiles(fcsts_df, model_name)
results = exp.evaluate_from_predictions(models=[model_name],
fcsts_df=fcsts_df,
times_df=time_df)
print(results, flush=True)
results_list.append(results)
results_full = pd.concat(results_list)
save_path = os.path.join(_SAVE_DIR.value, str(run_id))
print(f"Saving results to {save_path}", flush=True)
os.makedirs(save_path, exist_ok=True)
results_full.to_csv(f"{save_path}/results.csv")
if __name__ == "__main__":
FLAGS = flags.FLAGS
FLAGS(sys.argv)
main()
FLAGS = flags.FLAGS
FLAGS(sys.argv)
main()
experiments/extended_benchmarks/run_timesfm.py
+5 -9
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation script for timesfm."""
import os
@@ -26,7 +25,6 @@ import timesfm
from .utils import ExperimentHandler
dataset_names = [
"m1_monthly",
"m1_quarterly",
@@ -74,16 +72,14 @@ context_dict = {
"m4_yearly": 64,
}
_MODEL_PATH = flags.DEFINE_string(
"model_path", "/home/timesfm_q10_20240501", "Path to model"
)
_MODEL_PATH = flags.DEFINE_string("model_path", "/home/timesfm_q10_20240501",
"Path to model")
_BATCH_SIZE = flags.DEFINE_integer("batch_size", 64, "Batch size")
_HORIZON = flags.DEFINE_integer("horizon", 128, "Horizon")
_BACKEND = flags.DEFINE_string("backend", "gpu", "Backend")
_NUM_JOBS = flags.DEFINE_integer("num_jobs", 1, "Number of jobs")
_SAVE_DIR = flags.DEFINE_string("save_dir", "./results", "Save directory")
QUANTILES = list(np.arange(1, 10) / 10.0)
@@ -127,9 +123,9 @@ def main():
)
total_time = time.time() - init_time
time_df = pd.DataFrame({"time": [total_time], "model": model_name})
results = exp.evaluate_from_predictions(
models=[model_name], fcsts_df=fcsts_df, times_df=time_df
)
results = exp.evaluate_from_predictions(models=[model_name],
fcsts_df=fcsts_df,
times_df=time_df)
print(results, flush=True)
results_list.append(results)
results_full = pd.concat(results_list)
experiments/extended_benchmarks/utils.py
+24 -36
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Forked from https://github.com/Nixtla/nixtla/blob/main/experiments/amazon-chronos/src/utils.py."""
from functools import partial
@@ -46,11 +45,9 @@ def quantile_loss(
target_col: str = "y",
) -> pd.DataFrame:
delta_y = df[models].sub(df[target_col], axis=0)
res = (
np.maximum(q * delta_y, (q - 1) * delta_y)
.groupby(df[id_col], observed=True)
.mean()
)
res = (np.maximum(q * delta_y,
(q - 1) * delta_y).groupby(df[id_col],
observed=True).mean())
res.index.name = id_col
res = res.reset_index()
return res
@@ -66,10 +63,8 @@ class ExperimentHandler:
models_dir: str = "./models",
):
if dataset not in gluonts_datasets:
raise Exception(
f"dataset {dataset} not found in gluonts "
f"available datasets: {', '.join(gluonts_datasets)}"
)
raise Exception(f"dataset {dataset} not found in gluonts "
f"available datasets: {', '.join(gluonts_datasets)}")
self.dataset = dataset
self.quantiles = quantiles
self.level = self._transform_quantiles_to_levels(quantiles)
@@ -80,10 +75,8 @@ class ExperimentHandler:
gluonts_dataset = get_dataset(self.dataset)
self.horizon = gluonts_dataset.metadata.prediction_length
if self.horizon is None:
raise Exception(
f"horizon not found for dataset {self.dataset} "
"experiment cannot be run"
)
raise Exception(f"horizon not found for dataset {self.dataset} "
"experiment cannot be run")
self.freq = gluonts_dataset.metadata.freq
# get_seasonality() returns 1 for freq='D', override this to 7. This significantly improves the accuracy of
# statistical models on datasets like m5/nn5_daily. The models like AutoARIMA/AutoETS can still set
@@ -122,9 +115,8 @@ class ExperimentHandler:
@staticmethod
def _transform_quantiles_to_levels(quantiles: List[float]) -> List[int]:
level = [
int(100 - 200 * q) for q in quantiles if q < 0.5
] # in this case mean=mediain
level = [int(100 - 200 * q) for q in quantiles if q < 0.5
] # in this case mean=mediain
level = sorted(list(set(level)))
return level
@@ -153,9 +145,8 @@ class ExperimentHandler:
last_n: int | None = None,
) -> pd.DataFrame:
with multiprocessing.Pool(os.cpu_count()) as pool: # Create a process pool
results = pool.map(
parallel_transform, zip(gluonts_dataset, repeat(last_n))
)
results = pool.map(parallel_transform, zip(gluonts_dataset,
repeat(last_n)))
df = pd.concat(results)
df = df.reset_index(drop=True)
return df
@@ -177,9 +168,8 @@ class ExperimentHandler:
def save_dataframe(self, df: pd.DataFrame, file_name: str):
df.to_csv(f"{self.results_dir}/{file_name}", index=False)
def save_results(
self, fcst_df: pd.DataFrame, total_time: float, model_name: str
):
def save_results(self, fcst_df: pd.DataFrame, total_time: float,
model_name: str):
self.save_dataframe(
fcst_df,
f"{model_name}-{self.dataset}-fcst.csv",
@@ -215,23 +205,21 @@ class ExperimentHandler:
times_df = []
for model in models:
fcst_method_df = pd.read_csv(
f"{self.results_dir}/{model}-{self.dataset}-fcst.csv"
).set_index(["unique_id", "ds"])
f"{self.results_dir}/{model}-{self.dataset}-fcst.csv").set_index(
["unique_id", "ds"])
fcsts_df.append(fcst_method_df)
time_method_df = pd.read_csv(
f"{self.results_dir}/{model}-{self.dataset}-time.csv"
)
f"{self.results_dir}/{model}-{self.dataset}-time.csv")
times_df.append(time_method_df)
fcsts_df = pd.concat(fcsts_df, axis=1).reset_index()
fcsts_df["ds"] = pd.to_datetime(fcsts_df["ds"])
times_df = pd.concat(times_df)
return self.evaluate_from_predictions(
models=models, fcsts_df=fcsts_df, times_df=times_df
)
return self.evaluate_from_predictions(models=models,
fcsts_df=fcsts_df,
times_df=times_df)
def evaluate_from_predictions(
self, models: List[str], fcsts_df: pd.DataFrame, times_df: pd.DataFrame
) -> pd.DataFrame:
def evaluate_from_predictions(self, models: List[str], fcsts_df: pd.DataFrame,
times_df: pd.DataFrame) -> pd.DataFrame:
test_df = self.test_df
train_df = self.train_df
test_df = test_df.merge(fcsts_df, how="left")
@@ -262,9 +250,9 @@ class ExperimentHandler:
eval_prob_df["metric"] = "scaled_crps"
eval_df = pd.concat([eval_df, eval_prob_df]).reset_index(drop=True)
eval_df = eval_df.groupby("metric").mean(numeric_only=True).reset_index()
eval_df = eval_df.melt(
id_vars="metric", value_name="value", var_name="model"
)
eval_df = eval_df.melt(id_vars="metric",
value_name="value",
var_name="model")
times_df.insert(0, "metric", "time")
times_df = times_df.rename(columns={"time": "value"})
eval_df = pd.concat([eval_df, times_df])
experiments/long_horizon_benchmarks/run_eval.py
+21 -34
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Eval pipeline."""
import json
@@ -28,38 +27,28 @@ import torch
import tqdm
from timesfm import data_loader
FLAGS = flags.FLAGS
_BATCH_SIZE = flags.DEFINE_integer(
"batch_size", 64, "Batch size for the randomly sampled batch"
)
_BATCH_SIZE = flags.DEFINE_integer("batch_size", 64,
"Batch size for the randomly sampled batch")
_DATASET = flags.DEFINE_string("dataset", "etth1", "The name of the dataset.")
_MODEL_PATH = flags.DEFINE_string(
"model_path", "./timesfm_q10_20240501", "The name of the dataset."
)
_DATETIME_COL = flags.DEFINE_string(
"datetime_col", "date", "Column having datetime."
)
_NUM_COV_COLS = flags.DEFINE_list(
"num_cov_cols", None, "Column having numerical features."
)
_CAT_COV_COLS = flags.DEFINE_list(
"cat_cov_cols", None, "Column having categorical features."
)
_MODEL_PATH = flags.DEFINE_string("model_path", "./timesfm_q10_20240501",
"The name of the dataset.")
_DATETIME_COL = flags.DEFINE_string("datetime_col", "date",
"Column having datetime.")
_NUM_COV_COLS = flags.DEFINE_list("num_cov_cols", None,
"Column having numerical features.")
_CAT_COV_COLS = flags.DEFINE_list("cat_cov_cols", None,
"Column having categorical features.")
_TS_COLS = flags.DEFINE_list("ts_cols", None, "Columns of time-series features")
_NORMALIZE = flags.DEFINE_bool(
"normalize", True, "normalize data for eval or not"
)
_CONTEXT_LEN = flags.DEFINE_integer(
"context_len", 512, "Length of the context window"
)
_NORMALIZE = flags.DEFINE_bool("normalize", True,
"normalize data for eval or not")
_CONTEXT_LEN = flags.DEFINE_integer("context_len", 512,
"Length of the context window")
_PRED_LEN = flags.DEFINE_integer("pred_len", 96, "prediction length.")
_BACKEND = flags.DEFINE_string("backend", "gpu", "backend to use")
_RESULTS_DIR = flags.DEFINE_string(
"results_dir", "./results/long_horizon", "results directory"
)
_RESULTS_DIR = flags.DEFINE_string("results_dir", "./results/long_horizon",
"results directory")
DATA_DICT = {
"ettm2": {
@@ -176,9 +165,8 @@ def eval():
holiday=False,
permute=False,
)
eval_itr = dtl.tf_dataset(
mode="test", shift=_PRED_LEN.value
).as_numpy_iterator()
eval_itr = dtl.tf_dataset(mode="test",
shift=_PRED_LEN.value).as_numpy_iterator()
model_path = _MODEL_PATH.value
if model_path.startswith("amazon"):
model = chronos.ChronosPipeline.from_pretrained(
@@ -213,10 +201,9 @@ def eval():
for batch in tqdm.tqdm(eval_itr):
past = batch[0]
actuals = batch[3]
forecasts = get_forecasts(
model_path, model, past, int_freq, _PRED_LEN.value
)
forecasts = forecasts[:, 0 : actuals.shape[1]]
forecasts = get_forecasts(model_path, model, past, int_freq,
_PRED_LEN.value)
forecasts = forecasts[:, 0:actuals.shape[1]]
mae_run_losses.append(_mae(forecasts, actuals).sum())
mse_run_losses.append(_mse(forecasts, actuals).sum())
smape_run_losses.append(_smape(forecasts, actuals).sum())
src/timesfm/__init__.py
-1
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TimesFM init file."""
from .timesfm import TimesFm, freq_map
src/timesfm/data_loader.py
+8 -14
View File
@@ -11,13 +11,11 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TF dataloaders for general timeseries datasets.
The expected input format is csv file with a datetime index.
"""
from absl import logging
import numpy as np
import pandas as pd
@@ -79,9 +77,8 @@ class TimeSeriesdata(object):
self.data_df['ccol'] = np.zeros(self.data_df.shape[0])
cat_cov_cols = ['ccol']
self.data_df.fillna(0, inplace=True)
self.data_df.set_index(
pd.DatetimeIndex(self.data_df[datetime_col]), inplace=True
)
self.data_df.set_index(pd.DatetimeIndex(self.data_df[datetime_col]),
inplace=True)
self.num_cov_cols = num_cov_cols
self.cat_cov_cols = cat_cov_cols
self.ts_cols = ts_cols
@@ -94,18 +91,16 @@ class TimeSeriesdata(object):
data_df_idx[-1] + pd.Timedelta(1, freq=freq),
periods=pred_len + 1,
freq=freq,
)
)
))
self.time_df = time_features.TimeCovariates(
date_index, holiday=holiday
).get_covariates()
date_index, holiday=holiday).get_covariates()
self.hist_len = hist_len
self.pred_len = pred_len
self.batch_size = batch_size
self.freq = freq
self.normalize = normalize
self.data_mat = self.data_df[self.ts_cols].to_numpy().transpose()
self.data_mat = self.data_mat[:, 0 : self.test_range[1]]
self.data_mat = self.data_mat[:, 0:self.test_range[1]]
self.time_mat = self.time_df.to_numpy().transpose()
self.num_feat_mat = self.data_df[num_cov_cols].to_numpy().transpose()
self.cat_feat_mat, self.cat_sizes = self._get_cat_cols(cat_cov_cols)
@@ -135,7 +130,7 @@ class TimeSeriesdata(object):
def _normalize_data(self):
self.scaler = StandardScaler()
train_mat = self.data_mat[:, self.train_range[0] : self.train_range[1]]
train_mat = self.data_mat[:, self.train_range[0]:self.train_range[1]]
self.scaler = self.scaler.fit(train_mat.transpose())
self.data_mat = self.scaler.transform(self.data_mat.transpose()).transpose()
@@ -253,9 +248,8 @@ class TimeSeriesdata(object):
gen_fn = self.train_gen
else:
gen_fn = lambda: self.test_val_gen(mode, shift)
output_types = tuple(
[tf.float32] * 2 + [tf.int32] + [tf.float32] * 2 + [tf.int32] * 2
)
output_types = tuple([tf.float32] * 2 + [tf.int32] + [tf.float32] * 2 +
[tf.int32] * 2)
dataset = tf.data.Dataset.from_generator(gen_fn, output_types)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
src/timesfm/patched_decoder.py
+318 -323
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pax ML model for patched time-series decoder.
The file implements Residual MLPs, Patched Decoder layers and PAX ML models.
@@ -36,7 +35,6 @@ from praxis.layers import normalizations
from praxis.layers import stochastics
from praxis.layers import transformers
# PAX shortcuts
NestedMap = py_utils.NestedMap
JTensor = pytypes.JTensor
@@ -44,7 +42,6 @@ JTensor = pytypes.JTensor
LayerTpl = pax_fiddle.Config[base_layer.BaseLayer]
template_field = base_layer.template_field
PAD_VAL = 1123581321.0
DEFAULT_QUANTILES = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
@@ -57,36 +54,35 @@ _FREQ = "freq"
_OUTPUT_TOKENS = "output_tokens"
_STATS = "stats"
# Small numerical value.
_TOLERANCE = 1e-7
def _shift_padded_seq(mask: JTensor, seq: JTensor) -> JTensor:
"""Shifts rows of seq based on the first 0 in each row of the mask."""
num = seq.shape[1]
"""Shifts rows of seq based on the first 0 in each row of the mask."""
num = seq.shape[1]
# Find the index of the first 0 in each row of the mask
first_zero_idx = jnp.argmin(mask, axis=1)
# Find the index of the first 0 in each row of the mask
first_zero_idx = jnp.argmin(mask, axis=1)
# Create a range array for indexing
idx_range = jnp.arange(num)
# Create a range array for indexing
idx_range = jnp.arange(num)
def shift_row(carry, x):
seq_row, shift = x
shifted_idx = (idx_range - shift) % num
shifted_row = seq_row[shifted_idx]
return carry, shifted_row
def shift_row(carry, x):
seq_row, shift = x
shifted_idx = (idx_range - shift) % num
shifted_row = seq_row[shifted_idx]
return carry, shifted_row
# Use lax.scan to shift each row of seq based on the corresponding
# first_zero_idx.
_, shifted_seq = lax.scan(shift_row, None, (seq, first_zero_idx))
# Use lax.scan to shift each row of seq based on the corresponding
# first_zero_idx.
_, shifted_seq = lax.scan(shift_row, None, (seq, first_zero_idx))
return shifted_seq
return shifted_seq
class ResidualBlock(base_layer.BaseLayer):
"""Simple feedforward block with residual connection.
"""Simple feedforward block with residual connection.
Attributes:
input_dims: input dimension.
@@ -99,67 +95,68 @@ class ResidualBlock(base_layer.BaseLayer):
act_tpl: config for activation in hidden layer.
"""
input_dims: int = 0
hidden_dims: int = 0
output_dims: int = 0
dropout_prob: float = 0.0
layer_norm: bool = False
dropout_tpl: LayerTpl = template_field(stochastics.Dropout)
ln_tpl: LayerTpl = template_field(normalizations.LayerNorm)
act_tpl: LayerTpl = template_field(activations.Swish)
input_dims: int = 0
hidden_dims: int = 0
output_dims: int = 0
dropout_prob: float = 0.0
layer_norm: bool = False
dropout_tpl: LayerTpl = template_field(stochastics.Dropout)
ln_tpl: LayerTpl = template_field(normalizations.LayerNorm)
act_tpl: LayerTpl = template_field(activations.Swish)
def setup(self):
lnorm_tpl = self.ln_tpl.clone()
lnorm_tpl.dim = self.output_dims
self.create_child("ln_layer", lnorm_tpl)
def setup(self):
lnorm_tpl = self.ln_tpl.clone()
lnorm_tpl.dim = self.output_dims
self.create_child("ln_layer", lnorm_tpl)
dropout_tpl = self.dropout_tpl.clone()
dropout_tpl.keep_prob = 1.0 - self.dropout_prob
self.create_child("dropout", dropout_tpl)
dropout_tpl = self.dropout_tpl.clone()
dropout_tpl.keep_prob = 1.0 - self.dropout_prob
self.create_child("dropout", dropout_tpl)
self.create_child(
"hidden_layer",
pax_fiddle.Config(
linears.FeedForward,
input_dims=self.input_dims,
output_dims=self.hidden_dims,
activation_tpl=self.act_tpl.clone(),
),
)
self.create_child(
"hidden_layer",
pax_fiddle.Config(
linears.FeedForward,
input_dims=self.input_dims,
output_dims=self.hidden_dims,
activation_tpl=self.act_tpl.clone(),
),
)
self.create_child(
"output_layer",
pax_fiddle.Config(
linears.FeedForward,
input_dims=self.hidden_dims,
output_dims=self.output_dims,
activation_tpl=pax_fiddle.Config(activations.Identity),
),
)
self.create_child(
"output_layer",
pax_fiddle.Config(
linears.FeedForward,
input_dims=self.hidden_dims,
output_dims=self.output_dims,
activation_tpl=pax_fiddle.Config(activations.Identity),
),
)
self.create_child(
"residual_layer",
pax_fiddle.Config(
linears.FeedForward,
input_dims=self.input_dims,
output_dims=self.output_dims,
activation_tpl=pax_fiddle.Config(activations.Identity),
),
)
self.create_child(
"residual_layer",
pax_fiddle.Config(
linears.FeedForward,
input_dims=self.input_dims,
output_dims=self.output_dims,
activation_tpl=pax_fiddle.Config(activations.Identity),
),
)
def __call__(self, inputs: JTensor) -> JTensor:
hidden = self.hidden_layer(inputs)
output = self.output_layer(hidden)
output = self.dropout(output)
residual = self.residual_layer(inputs)
if self.layer_norm:
return self.ln_layer(output + residual)
else:
return output + residual
def __call__(self, inputs: JTensor) -> JTensor:
hidden = self.hidden_layer(inputs)
output = self.output_layer(hidden)
output = self.dropout(output)
residual = self.residual_layer(inputs)
if self.layer_norm:
return self.ln_layer(output + residual)
else:
return output + residual
def _masked_mean_std(inputs: JTensor, padding: JTensor) -> Tuple[JTensor, JTensor]:
"""Calculates mean and standard deviation of arr across axis 1.
def _masked_mean_std(inputs: JTensor,
padding: JTensor) -> Tuple[JTensor, JTensor]:
"""Calculates mean and standard deviation of arr across axis 1.
It should exclude values where pad is 1.
@@ -171,48 +168,48 @@ def _masked_mean_std(inputs: JTensor, padding: JTensor) -> Tuple[JTensor, JTenso
A tuple containing the mean and standard deviation of arr. We return the
statistics of the first patch with more than three non-padded values.
"""
# Selecting the first pad with more than 3 unpadded values.
pad_sum = jnp.sum(1 - padding, axis=2)
# Selecting the first pad with more than 3 unpadded values.
pad_sum = jnp.sum(1 - padding, axis=2)
def _get_patch_index(arr: JTensor):
indices = jnp.argmax(arr >= 3, axis=1)
row_sum = (arr >= 3).sum(axis=1)
return jnp.where(row_sum == 0, arr.shape[1] - 1, indices)
def _get_patch_index(arr: JTensor):
indices = jnp.argmax(arr >= 3, axis=1)
row_sum = (arr >= 3).sum(axis=1)
return jnp.where(row_sum == 0, arr.shape[1] - 1, indices)
patch_indices = _get_patch_index(pad_sum)
bidxs = jnp.arange(inputs.shape[0])
patch_indices = _get_patch_index(pad_sum)
bidxs = jnp.arange(inputs.shape[0])
arr = inputs[bidxs, patch_indices, :]
pad = padding[bidxs, patch_indices, :]
arr = inputs[bidxs, patch_indices, :]
pad = padding[bidxs, patch_indices, :]
# Create a mask where P is 0
mask = 1 - pad
# Create a mask where P is 0
mask = 1 - pad
# Calculate the number of valid elements
num_valid_elements = jnp.sum(mask, axis=1)
# Calculate the number of valid elements
num_valid_elements = jnp.sum(mask, axis=1)
num_valid_elements = jnp.where(num_valid_elements == 0, 1, num_valid_elements)
num_valid_elements = jnp.where(num_valid_elements == 0, 1, num_valid_elements)
# Calculate the masked sum and squared sum of M
masked_sum = jnp.sum(arr * mask, axis=1)
masked_squared_sum = jnp.sum((arr * mask) ** 2, axis=1)
# Calculate the masked sum and squared sum of M
masked_sum = jnp.sum(arr * mask, axis=1)
masked_squared_sum = jnp.sum((arr * mask)**2, axis=1)
# Calculate the masked mean and standard deviation
masked_mean = masked_sum / num_valid_elements
masked_var = masked_squared_sum / num_valid_elements - masked_mean**2
masked_var = jnp.where(masked_var < 0.0, 0.0, masked_var)
masked_std = jnp.sqrt(masked_var)
# Calculate the masked mean and standard deviation
masked_mean = masked_sum / num_valid_elements
masked_var = masked_squared_sum / num_valid_elements - masked_mean**2
masked_var = jnp.where(masked_var < 0.0, 0.0, masked_var)
masked_std = jnp.sqrt(masked_var)
return masked_mean, masked_std
return masked_mean, masked_std
def _create_quantiles() -> list[float]:
"""Returns the quantiles for forecasting."""
return DEFAULT_QUANTILES
"""Returns the quantiles for forecasting."""
return DEFAULT_QUANTILES
class PatchedTimeSeriesDecoder(base_layer.BaseLayer):
"""Patch decoder layer for time-series foundation model.
"""Patch decoder layer for time-series foundation model.
Attributes:
patch_len: length of input patches.
@@ -231,137 +228,137 @@ class PatchedTimeSeriesDecoder(base_layer.BaseLayer):
number of output logits. D is model dimension.
"""
patch_len: int = 0
horizon_len: int = 0
model_dims: int = 0
hidden_dims: int = 0
quantiles: list[float] = dataclasses.field(default_factory=_create_quantiles)
residual_block_tpl: LayerTpl = template_field(ResidualBlock)
stacked_transformer_params_tpl: LayerTpl = template_field(
transformers.StackedTransformer
patch_len: int = 0
horizon_len: int = 0
model_dims: int = 0
hidden_dims: int = 0
quantiles: list[float] = dataclasses.field(default_factory=_create_quantiles)
residual_block_tpl: LayerTpl = template_field(ResidualBlock)
stacked_transformer_params_tpl: LayerTpl = template_field(
transformers.StackedTransformer)
use_freq: bool = True
def setup(self) -> None:
"""Construct the model."""
num_outputs = len(self.quantiles) + 1
stl = self.stacked_transformer_params_tpl.clone()
stl.model_dims = self.model_dims
stl.hidden_dims = self.hidden_dims
stl.mask_self_attention = True
self.create_child("stacked_transformer_layer", stl)
input_resl = self.residual_block_tpl.clone()
ff_in_dims = 2 * self.patch_len
input_resl.input_dims = ff_in_dims
input_resl.hidden_dims = self.hidden_dims
input_resl.output_dims = self.model_dims
self.create_child(
"input_ff_layer",
input_resl,
)
use_freq: bool = True
def setup(self) -> None:
"""Construct the model."""
num_outputs = len(self.quantiles) + 1
horizon_resl = self.residual_block_tpl.clone()
horizon_resl.input_dims = self.model_dims
horizon_resl.hidden_dims = self.hidden_dims
horizon_resl.output_dims = self.horizon_len * num_outputs
self.create_child(
"horizon_ff_layer",
horizon_resl,
)
stl = self.stacked_transformer_params_tpl.clone()
stl.model_dims = self.model_dims
stl.hidden_dims = self.hidden_dims
stl.mask_self_attention = True
self.create_child(
"position_emb",
pax_fiddle.Config(layers.PositionalEmbedding,
embedding_dims=self.model_dims),
)
self.create_child("stacked_transformer_layer", stl)
if self.use_freq:
self.create_child(
"freq_emb",
pax_fiddle.Config(
embedding_softmax.Embedding,
num_classes=3,
input_dims=self.model_dims,
),
)
input_resl = self.residual_block_tpl.clone()
ff_in_dims = 2 * self.patch_len
input_resl.input_dims = ff_in_dims
input_resl.hidden_dims = self.hidden_dims
input_resl.output_dims = self.model_dims
self.create_child(
"input_ff_layer",
input_resl,
)
def transform_decode_state(
self, transform_fn: base_layer.DecodeStateTransformFn) -> None:
"""Transforms all decode state variables based on transform_fn."""
self.stacked_transformer_layer.transform_decode_state(transform_fn)
horizon_resl = self.residual_block_tpl.clone()
horizon_resl.input_dims = self.model_dims
horizon_resl.hidden_dims = self.hidden_dims
horizon_resl.output_dims = self.horizon_len * num_outputs
self.create_child(
"horizon_ff_layer",
horizon_resl,
)
def _forward_transform(
self, inputs: JTensor,
patched_pads: JTensor) -> Tuple[JTensor, Tuple[JTensor, JTensor]]:
"""Input is of shape [B, N, P]."""
mu, sigma = _masked_mean_std(inputs, patched_pads)
sigma = jnp.where(sigma < _TOLERANCE, 1.0, sigma)
# Normalize each patch.
outputs = (inputs - mu[:, None, None]) / sigma[:, None, None]
outputs = jnp.where(
jnp.abs(inputs - PAD_VAL) < _TOLERANCE, PAD_VAL, outputs)
return outputs, (mu, sigma)
self.create_child(
"position_emb",
pax_fiddle.Config(
layers.PositionalEmbedding, embedding_dims=self.model_dims
),
)
def _reverse_transform(self, outputs: JTensor,
stats: Tuple[JTensor, JTensor]) -> JTensor:
"""Output is of shape [B, N, P, Q]."""
mu, sigma = stats
return outputs * sigma[:, None, None, None] + mu[:, None, None, None]
if self.use_freq:
self.create_child(
"freq_emb",
pax_fiddle.Config(
embedding_softmax.Embedding,
num_classes=3,
input_dims=self.model_dims,
),
)
def _preprocess_input(
self,
input_ts: JTensor,
input_padding: JTensor,
pos_emb: Optional[JTensor] = None,
) -> Tuple[JTensor, JTensor, Optional[Tuple[JTensor, JTensor]], JTensor]:
"""Preprocess input for stacked transformer."""
# Reshape into patches.
patched_inputs = es.jax_einshape("b(np)->bnp", input_ts, p=self.patch_len)
input_padding = jnp.where(
jnp.abs(input_ts - PAD_VAL) < _TOLERANCE, 1, input_padding)
patched_pads = es.jax_einshape("b(np)->bnp",
input_padding,
p=self.patch_len)
patched_inputs, stats = self._forward_transform(patched_inputs,
patched_pads)
# B x N x D
patched_inputs = patched_inputs * (1.0 - patched_pads)
concat_inputs = jnp.concatenate([patched_inputs, patched_pads], axis=-1)
model_input = self.input_ff_layer(concat_inputs)
# A patch should not be padded even if there is at least one zero.
patched_padding = jnp.min(patched_pads, axis=-1)
def transform_decode_state(
self, transform_fn: base_layer.DecodeStateTransformFn
) -> None:
"""Transforms all decode state variables based on transform_fn."""
self.stacked_transformer_layer.transform_decode_state(transform_fn)
if pos_emb is None:
position_emb = self.position_emb(seq_length=model_input.shape[1])
else:
position_emb = pos_emb
if self.do_eval:
if position_emb.shape[0] != model_input.shape[0]:
position_emb = jnp.repeat(position_emb, model_input.shape[0], axis=0)
position_emb = _shift_padded_seq(patched_padding, position_emb)
model_input += position_emb
def _forward_transform(
self, inputs: JTensor, patched_pads: JTensor
) -> Tuple[JTensor, Tuple[JTensor, JTensor]]:
"""Input is of shape [B, N, P]."""
mu, sigma = _masked_mean_std(inputs, patched_pads)
sigma = jnp.where(sigma < _TOLERANCE, 1.0, sigma)
# Normalize each patch.
outputs = (inputs - mu[:, None, None]) / sigma[:, None, None]
outputs = jnp.where(jnp.abs(inputs - PAD_VAL) < _TOLERANCE, PAD_VAL, outputs)
return outputs, (mu, sigma)
return model_input, patched_padding, stats, patched_inputs
def _reverse_transform(
self, outputs: JTensor, stats: Tuple[JTensor, JTensor]
) -> JTensor:
"""Output is of shape [B, N, P, Q]."""
mu, sigma = stats
return outputs * sigma[:, None, None, None] + mu[:, None, None, None]
def _postprocess_output(
self,
model_output: JTensor,
num_outputs: int,
stats: Tuple[JTensor, JTensor],
) -> JTensor:
"""Postprocess output of stacked transformer."""
# B x N x (H.Q)
output_ts = self.horizon_ff_layer(model_output)
output_ts = es.jax_einshape("bn(hq)->bnhq",
output_ts,
q=num_outputs,
h=self.horizon_len)
return self._reverse_transform(output_ts, stats)
def _preprocess_input(
self,
input_ts: JTensor,
input_padding: JTensor,
pos_emb: Optional[JTensor] = None,
) -> Tuple[JTensor, JTensor, Optional[Tuple[JTensor, JTensor]], JTensor]:
"""Preprocess input for stacked transformer."""
# Reshape into patches.
patched_inputs = es.jax_einshape("b(np)->bnp", input_ts, p=self.patch_len)
input_padding = jnp.where(
jnp.abs(input_ts - PAD_VAL) < _TOLERANCE, 1, input_padding
)
patched_pads = es.jax_einshape("b(np)->bnp", input_padding, p=self.patch_len)
patched_inputs, stats = self._forward_transform(patched_inputs, patched_pads)
# B x N x D
patched_inputs = patched_inputs * (1.0 - patched_pads)
concat_inputs = jnp.concatenate([patched_inputs, patched_pads], axis=-1)
model_input = self.input_ff_layer(concat_inputs)
# A patch should not be padded even if there is at least one zero.
patched_padding = jnp.min(patched_pads, axis=-1)
if pos_emb is None:
position_emb = self.position_emb(seq_length=model_input.shape[1])
else:
position_emb = pos_emb
if self.do_eval:
if position_emb.shape[0] != model_input.shape[0]:
position_emb = jnp.repeat(position_emb, model_input.shape[0], axis=0)
position_emb = _shift_padded_seq(patched_padding, position_emb)
model_input += position_emb
return model_input, patched_padding, stats, patched_inputs
def _postprocess_output(
self,
model_output: JTensor,
num_outputs: int,
stats: Tuple[JTensor, JTensor],
) -> JTensor:
"""Postprocess output of stacked transformer."""
# B x N x (H.Q)
output_ts = self.horizon_ff_layer(model_output)
output_ts = es.jax_einshape(
"bn(hq)->bnhq", output_ts, q=num_outputs, h=self.horizon_len
)
return self._reverse_transform(output_ts, stats)
def __call__(self, inputs: NestedMap) -> NestedMap:
"""PatchTST call.
def __call__(self, inputs: NestedMap) -> NestedMap:
"""PatchTST call.
Args:
inputs: A NestedMap containing (1) input_ts: input sequence of shape [B,
@@ -374,32 +371,34 @@ class PatchedTimeSeriesDecoder(base_layer.BaseLayer):
(2) 'output_ts' of shape [B, N, H, Q]
(3) 'stats' a Tuple of statistics for renormalization.
"""
input_ts, input_padding = inputs[_INPUT_TS], inputs[_INPUT_PADDING]
num_outputs = len(self.quantiles) + 1
model_input, patched_padding, stats, _ = self._preprocess_input(
input_ts=input_ts,
input_padding=input_padding,
)
if self.use_freq:
freq = inputs[_FREQ].astype(jnp.int32)
f_emb = self.freq_emb(freq) # B x 1 x D
f_emb = jnp.repeat(f_emb, model_input.shape[1], axis=1)
model_input += f_emb
model_output = self.stacked_transformer_layer(model_input, patched_padding)
input_ts, input_padding = inputs[_INPUT_TS], inputs[_INPUT_PADDING]
num_outputs = len(self.quantiles) + 1
model_input, patched_padding, stats, _ = self._preprocess_input(
input_ts=input_ts,
input_padding=input_padding,
)
if self.use_freq:
freq = inputs[_FREQ].astype(jnp.int32)
f_emb = self.freq_emb(freq) # B x 1 x D
f_emb = jnp.repeat(f_emb, model_input.shape[1], axis=1)
model_input += f_emb
model_output = self.stacked_transformer_layer(model_input, patched_padding)
output_ts = self._postprocess_output(model_output, num_outputs, stats)
return NestedMap(
{_OUTPUT_TOKENS: model_output, _OUTPUT_TS: output_ts, _STATS: stats}
)
output_ts = self._postprocess_output(model_output, num_outputs, stats)
return NestedMap({
_OUTPUT_TOKENS: model_output,
_OUTPUT_TS: output_ts,
_STATS: stats
})
def decode(
self,
inputs: NestedMap,
horizon_len: int,
output_patch_len: Optional[int] = None,
max_len: int = 512,
) -> tuple[JTensor, JTensor]:
"""Auto-regressive decoding without caching.
def decode(
self,
inputs: NestedMap,
horizon_len: int,
output_patch_len: Optional[int] = None,
max_len: int = 512,
) -> tuple[JTensor, JTensor]:
"""Auto-regressive decoding without caching.
Args:
inputs: input time-series and paddings. Time-series shape B x C, padding
@@ -415,83 +414,80 @@ class PatchedTimeSeriesDecoder(base_layer.BaseLayer):
- Full predictions (mean and quantiles) as a tensor with shape
B x H x (1 + # quantiles).
"""
final_out = inputs[_INPUT_TS]
inp_time_len = final_out.shape[1]
paddings = inputs[_INPUT_PADDING]
if self.use_freq:
freq = inputs[_FREQ].astype(jnp.int32)
else:
freq = jnp.zeros([final_out.shape[0], 1], dtype=jnp.int32)
full_outputs = []
if paddings.shape[1] != final_out.shape[1] + horizon_len:
raise ValueError(
"Length of paddings must match length of input + horizon_len:"
f" {paddings.shape[1]} != {final_out.shape[1]} + {horizon_len}"
)
if output_patch_len is None:
output_patch_len = self.horizon_len
num_decode_patches = (horizon_len + output_patch_len - 1) // output_patch_len
for _ in range(num_decode_patches):
current_padding = paddings[:, 0 : final_out.shape[1]]
input_ts = final_out[:, -max_len:]
input_padding = current_padding[:, -max_len:]
model_input = NestedMap(
input_ts=input_ts,
input_padding=input_padding,
freq=freq,
)
fprop_outputs = self(model_input)[_OUTPUT_TS]
# (full batch, last patch, output_patch_len, index of mean forecast = 0)
new_ts = fprop_outputs[:, -1, :output_patch_len, 0]
# (full batch, last patch, output_patch_len, all output indices)
full_outputs.append(fprop_outputs[:, -1, :output_patch_len, :])
final_out = jnp.concatenate([final_out, new_ts], axis=-1)
final_out = inputs[_INPUT_TS]
inp_time_len = final_out.shape[1]
paddings = inputs[_INPUT_PADDING]
if self.use_freq:
freq = inputs[_FREQ].astype(jnp.int32)
else:
freq = jnp.zeros([final_out.shape[0], 1], dtype=jnp.int32)
full_outputs = []
if paddings.shape[1] != final_out.shape[1] + horizon_len:
raise ValueError(
"Length of paddings must match length of input + horizon_len:"
f" {paddings.shape[1]} != {final_out.shape[1]} + {horizon_len}")
if output_patch_len is None:
output_patch_len = self.horizon_len
num_decode_patches = (horizon_len + output_patch_len -
1) // output_patch_len
for _ in range(num_decode_patches):
current_padding = paddings[:, 0:final_out.shape[1]]
input_ts = final_out[:, -max_len:]
input_padding = current_padding[:, -max_len:]
model_input = NestedMap(
input_ts=input_ts,
input_padding=input_padding,
freq=freq,
)
fprop_outputs = self(model_input)[_OUTPUT_TS]
# (full batch, last patch, output_patch_len, index of mean forecast = 0)
new_ts = fprop_outputs[:, -1, :output_patch_len, 0]
# (full batch, last patch, output_patch_len, all output indices)
full_outputs.append(fprop_outputs[:, -1, :output_patch_len, :])
final_out = jnp.concatenate([final_out, new_ts], axis=-1)
return (
final_out[:, inp_time_len : inp_time_len + horizon_len],
jnp.concatenate(full_outputs, axis=1)[:, 0:horizon_len, :],
)
return (
final_out[:, inp_time_len:inp_time_len + horizon_len],
jnp.concatenate(full_outputs, axis=1)[:, 0:horizon_len, :],
)
class PatchedDecoderFinetuneModel(base_model.BaseModel):
"""Model class for finetuning patched time-series decoder.
"""Model class for finetuning patched time-series decoder.
Attributes:
core_layer_tpl: config for core layer.
freq: freq to finetune on.
"""
core_layer_tpl: LayerTpl = template_field(PatchedTimeSeriesDecoder)
freq: int = 0
core_layer_tpl: LayerTpl = template_field(PatchedTimeSeriesDecoder)
freq: int = 0
def setup(self) -> None:
self.create_child("core_layer", self.core_layer_tpl)
def setup(self) -> None:
self.create_child("core_layer", self.core_layer_tpl)
def compute_predictions(self, input_batch: NestedMap) -> NestedMap:
input_ts = input_batch[_INPUT_TS]
input_padding = jnp.zeros_like(input_ts)
context_len = input_ts.shape[1]
input_patch_len = self.core_layer_tpl.patch_len
context_pad = (
(context_len + input_patch_len - 1) // input_patch_len
) * input_patch_len - context_len
def compute_predictions(self, input_batch: NestedMap) -> NestedMap:
input_ts = input_batch[_INPUT_TS]
input_padding = jnp.zeros_like(input_ts)
context_len = input_ts.shape[1]
input_patch_len = self.core_layer_tpl.patch_len
context_pad = ((context_len + input_patch_len - 1) //
input_patch_len) * input_patch_len - context_len
input_ts = jnp.pad(input_ts, [(0, 0), (context_pad, 0)])
input_padding = jnp.pad(
input_padding, [(0, 0), (context_pad, 0)], constant_values=1
)
freq = jnp.ones([input_ts.shape[0], 1], dtype=jnp.int32) * self.freq
new_input_batch = NestedMap(
input_ts=input_ts,
input_padding=input_padding,
freq=freq,
)
return self.core_layer(new_input_batch)
input_ts = jnp.pad(input_ts, [(0, 0), (context_pad, 0)])
input_padding = jnp.pad(input_padding, [(0, 0), (context_pad, 0)],
constant_values=1)
freq = jnp.ones([input_ts.shape[0], 1], dtype=jnp.int32) * self.freq
new_input_batch = NestedMap(
input_ts=input_ts,
input_padding=input_padding,
freq=freq,
)
return self.core_layer(new_input_batch)
def _quantile_loss(
self, pred: JTensor, actual: JTensor, quantile: float
) -> JTensor:
"""Calculates quantile loss.
def _quantile_loss(self, pred: JTensor, actual: JTensor,
quantile: float) -> JTensor:
"""Calculates quantile loss.
Args:
pred: B x T
@@ -501,21 +497,20 @@ class PatchedDecoderFinetuneModel(base_model.BaseModel):
Returns:
per coordinate loss.
"""
dev = actual - pred
loss_first = dev * quantile
loss_second = -dev * (1.0 - quantile)
return 2 * jnp.where(loss_first >= 0, loss_first, loss_second)
dev = actual - pred
loss_first = dev * quantile
loss_second = -dev * (1.0 - quantile)
return 2 * jnp.where(loss_first >= 0, loss_first, loss_second)
def compute_loss(
self, prediction_output: NestedMap, input_batch: NestedMap
) -> Tuple[NestedMap, NestedMap]:
output_ts = prediction_output[_OUTPUT_TS]
actual_ts = input_batch[_TARGET_FUTURE]
pred_ts = output_ts[:, -1, 0 : actual_ts.shape[1], :]
loss = jnp.square(pred_ts[:, :, 0] - actual_ts)
for i, quantile in enumerate(self.core_layer.quantiles):
loss += self._quantile_loss(pred_ts[:, :, i + 1], actual_ts, quantile)
loss = loss.mean()
loss_weight = jnp.array(1.0, dtype=jnp.float32)
per_example_out = NestedMap()
return {"avg_qloss": (loss, loss_weight)}, per_example_out
def compute_loss(self, prediction_output: NestedMap,
input_batch: NestedMap) -> Tuple[NestedMap, NestedMap]:
output_ts = prediction_output[_OUTPUT_TS]
actual_ts = input_batch[_TARGET_FUTURE]
pred_ts = output_ts[:, -1, 0:actual_ts.shape[1], :]
loss = jnp.square(pred_ts[:, :, 0] - actual_ts)
for i, quantile in enumerate(self.core_layer.quantiles):
loss += self._quantile_loss(pred_ts[:, :, i + 1], actual_ts, quantile)
loss = loss.mean()
loss_weight = jnp.array(1.0, dtype=jnp.float32)
per_example_out = NestedMap()
return {"avg_qloss": (loss, loss_weight)}, per_example_out
src/timesfm/time_features.py
+14 -14
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Directory to extract time covariates.
Extract time covariates from datetime.
@@ -36,7 +35,6 @@ from pandas.tseries.offsets import Easter
from sklearn.preprocessing import StandardScaler
from tqdm import tqdm
# This is 183 to cover half a year (in both directions), also for leap years
# + 17 as Eastern can be between March, 22 - April, 25
MAX_WINDOW = 183 + 17
@@ -50,9 +48,8 @@ def _distance_to_holiday(holiday):
index - pd.Timedelta(days=MAX_WINDOW),
index + pd.Timedelta(days=MAX_WINDOW),
)
assert (
len(holiday_date) != 0 # pylint: disable=g-explicit-length-test
), f"No closest holiday for the date index {index} found."
assert (len(holiday_date) != 0 # pylint: disable=g-explicit-length-test
), f"No closest holiday for the date index {index} found."
# It sometimes returns two dates if it is exactly half a year after the
# holiday. In this case, the smaller distance (182 days) is returned.
return (index - holiday_date[0]).days
@@ -60,16 +57,19 @@ def _distance_to_holiday(holiday):
return _distance_to_day
EasterSunday = Holiday(
"Easter Sunday", month=1, day=1, offset=[Easter(), Day(0)]
)
EasterSunday = Holiday("Easter Sunday",
month=1,
day=1,
offset=[Easter(), Day(0)])
NewYearsDay = Holiday("New Years Day", month=1, day=1)
SuperBowl = Holiday(
"Superbowl", month=2, day=1, offset=DateOffset(weekday=SU(1))
)
MothersDay = Holiday(
"Mothers Day", month=5, day=1, offset=DateOffset(weekday=SU(2))
)
SuperBowl = Holiday("Superbowl",
month=2,
day=1,
offset=DateOffset(weekday=SU(1)))
MothersDay = Holiday("Mothers Day",
month=5,
day=1,
offset=DateOffset(weekday=SU(2)))
IndependenceDay = Holiday("Independence Day", month=7, day=4)
ChristmasEve = Holiday("Christmas", month=12, day=24)
ChristmasDay = Holiday("Christmas", month=12, day=25)
src/timesfm/timesfm.py
+110 -131
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TimesFM forecast API for inference."""
import logging
@@ -52,23 +51,16 @@ def moving_average(arr, window_size):
"""Calculates the moving average using NumPy's convolution function."""
# Pad with zeros to handle initial window positions
arr_padded = np.pad(arr, (window_size - 1, 0), "constant")
smoothed_arr = (
np.convolve(arr_padded, np.ones(window_size), "valid") / window_size
)
smoothed_arr = (np.convolve(arr_padded, np.ones(window_size), "valid") /
window_size)
return [smoothed_arr, arr - smoothed_arr]
def freq_map(freq: str):
"""Returns the frequency map for the given frequency string."""
freq = str.upper(freq)
if (
freq.endswith("H")
or freq.endswith("T")
or freq.endswith("MIN")
or freq.endswith("D")
or freq.endswith("B")
or freq.endswith("U")
):
if (freq.endswith("H") or freq.endswith("T") or freq.endswith("MIN") or
freq.endswith("D") or freq.endswith("B") or freq.endswith("U")):
return 0
elif freq.endswith(("W", "M", "MS")):
return 1
@@ -179,9 +171,7 @@ class TimesFm:
num_layers=num_layers,
transformer_layer_params_tpl=pax_fiddle.Config(
transformers.Transformer,
ln_tpl=pax_fiddle.Config(
normalizations.RmsNorm,
),
ln_tpl=pax_fiddle.Config(normalizations.RmsNorm,),
),
),
)
@@ -199,34 +189,38 @@ class TimesFm:
def _get_sample_inputs(self):
return {
"input_ts": jnp.zeros(
(
self.per_core_batch_size,
self.context_len + self.output_patch_len,
"input_ts":
jnp.zeros(
(
self.per_core_batch_size,
self.context_len + self.output_patch_len,
),
dtype=jnp.float32,
),
dtype=jnp.float32,
),
"input_padding": jnp.zeros(
(
self.per_core_batch_size,
self.context_len + self.output_patch_len,
"input_padding":
jnp.zeros(
(
self.per_core_batch_size,
self.context_len + self.output_patch_len,
),
dtype=jnp.float32,
),
dtype=jnp.float32,
),
"freq": jnp.zeros(
(
self.per_core_batch_size,
1,
"freq":
jnp.zeros(
(
self.per_core_batch_size,
1,
),
dtype=jnp.int32,
),
dtype=jnp.int32,
),
}
def load_from_checkpoint(
self,
checkpoint_path: Optional[str] = None,
repo_id: str = "google/timesfm-1.0-200m",
checkpoint_type: checkpoints.CheckpointType = checkpoints.CheckpointType.FLAX,
checkpoint_type: checkpoints.CheckpointType = checkpoints.CheckpointType.
FLAX,
step: int | None = None,
) -> None:
"""Loads a checkpoint and compiles the decoder.
@@ -246,8 +240,7 @@ class TimesFm:
start_time = time.time()
self._model = instantiate(self.model_p)
var_weight_hparams = self._model.abstract_init_with_metadata(
self._get_sample_inputs(), do_eval=True
)
self._get_sample_inputs(), do_eval=True)
train_state_partition_specs = tasks_lib.create_state_partition_specs(
var_weight_hparams,
mesh_shape=self.mesh_shape,
@@ -261,8 +254,7 @@ class TimesFm:
learners=None,
)
self._logging(
f"Constructed model weights in {time.time() - start_time:.2f} seconds."
)
f"Constructed model weights in {time.time() - start_time:.2f} seconds.")
# Load the model weights.
self._logging(f"Restoring checkpoint from {checkpoint_path}.")
@@ -275,12 +267,12 @@ class TimesFm:
step=step,
)
self._logging(
f"Restored checkpoint in {time.time() - start_time:.2f} seconds."
)
f"Restored checkpoint in {time.time() - start_time:.2f} seconds.")
self.jit_decode()
def jit_decode(self):
"""Jitting decoding function."""
# Initialize and jit the decode fn.
def _decode(inputs):
assert self._model is not None
@@ -310,34 +302,36 @@ class TimesFm:
with base_layer.JaxContext.new_context(hparams=self._eval_context):
_ = self._pmapped_decode(
NestedMap({
"input_ts": jnp.zeros(
(
self.num_devices,
self.per_core_batch_size,
self.context_len,
"input_ts":
jnp.zeros(
(
self.num_devices,
self.per_core_batch_size,
self.context_len,
),
dtype=jnp.float32,
),
dtype=jnp.float32,
),
"input_padding": jnp.zeros(
(
self.num_devices,
self.per_core_batch_size,
self.context_len + self.horizon_len,
"input_padding":
jnp.zeros(
(
self.num_devices,
self.per_core_batch_size,
self.context_len + self.horizon_len,
),
dtype=jnp.float32,
),
dtype=jnp.float32,
),
"date_features": None,
"freq": jnp.zeros(
(self.num_devices, self.per_core_batch_size, 1),
dtype=jnp.int32,
),
})
)
"date_features":
None,
"freq":
jnp.zeros(
(self.num_devices, self.per_core_batch_size, 1),
dtype=jnp.int32,
),
}))
self._logging(f"Jitted decoding in {time.time() - start_time:.2f} seconds.")
def _preprocess(
self, inputs: Sequence[np.array], freq: Sequence[int]
) -> tuple[np.array, np.array, int]:
def _preprocess(self, inputs: Sequence[np.array],
freq: Sequence[int]) -> tuple[np.array, np.array, int]:
"""Formats and pads raw inputs to feed into the model.
This function both pads each time series to match the context length, and
@@ -358,24 +352,21 @@ class TimesFm:
input_ts, input_padding, inp_freq = [], [], []
pmap_pad = (
(len(inputs) - 1) // self.global_batch_size + 1
) * self.global_batch_size - len(inputs)
pmap_pad = ((len(inputs) - 1) // self.global_batch_size +
1) * self.global_batch_size - len(inputs)
for i, ts in enumerate(inputs):
input_len = ts.shape[0]
padding = np.zeros(shape=(input_len + self.horizon_len,), dtype=float)
if input_len < self.context_len:
num_front_pad = self.context_len - input_len
ts = np.concatenate(
[np.zeros(shape=(num_front_pad,), dtype=float), ts], axis=0
)
ts = np.concatenate([np.zeros(shape=(num_front_pad,), dtype=float), ts],
axis=0)
padding = np.concatenate(
[np.ones(shape=(num_front_pad,), dtype=float), padding], axis=0
)
[np.ones(shape=(num_front_pad,), dtype=float), padding], axis=0)
elif input_len > self.context_len:
ts = ts[-self.context_len :]
padding = padding[-(self.context_len + self.horizon_len) :]
ts = ts[-self.context_len:]
padding = padding[-(self.context_len + self.horizon_len):]
input_ts.append(ts)
input_padding.append(padding)
@@ -425,8 +416,7 @@ class TimesFm:
if not self._train_state or not self._model:
raise ValueError(
"Checkpoint not loaded. Call `load_from_checkpoint` before"
" `forecast`."
)
" `forecast`.")
if forecast_context_len is None:
forecast_context_len = self.context_len
inputs = [np.array(ts)[-forecast_context_len:] for ts in inputs]
@@ -448,47 +438,45 @@ class TimesFm:
full_outputs = []
assert input_ts.shape[0] % self.global_batch_size == 0
for i in range(input_ts.shape[0] // self.global_batch_size):
input_ts_in = jnp.array(
input_ts[
i * self.global_batch_size : (i + 1) * self.global_batch_size
]
)
input_ts_in = jnp.array(input_ts[i * self.global_batch_size:(i + 1) *
self.global_batch_size])
input_padding_in = jnp.array(
input_padding[
i * self.global_batch_size : (i + 1) * self.global_batch_size
],
)
input_padding[i * self.global_batch_size:(i + 1) *
self.global_batch_size],)
inp_freq_in = jnp.array(
inp_freq[
i * self.global_batch_size : (i + 1) * self.global_batch_size, :
],
inp_freq[i * self.global_batch_size:(i + 1) *
self.global_batch_size, :],
dtype=jnp.int32,
)
pmapped_inputs = NestedMap({
"input_ts": es.jax_einshape(
"(db)...->db...",
input_ts_in,
d=self.num_devices,
),
"input_padding": es.jax_einshape(
"(db)...->db...",
input_padding_in,
d=self.num_devices,
),
"date_features": None,
"freq": es.jax_einshape(
"(db)...->db...",
inp_freq_in,
d=self.num_devices,
),
"input_ts":
es.jax_einshape(
"(db)...->db...",
input_ts_in,
d=self.num_devices,
),
"input_padding":
es.jax_einshape(
"(db)...->db...",
input_padding_in,
d=self.num_devices,
),
"date_features":
None,
"freq":
es.jax_einshape(
"(db)...->db...",
inp_freq_in,
d=self.num_devices,
),
})
mean_output, full_output = self._pmapped_decode(pmapped_inputs)
mean_output = es.jax_einshape(
"db...->(db)...", mean_output, d=self.num_devices
)
full_output = es.jax_einshape(
"db...->(db)...", full_output, d=self.num_devices
)
mean_output = es.jax_einshape("db...->(db)...",
mean_output,
d=self.num_devices)
full_output = es.jax_einshape("db...->(db)...",
full_output,
d=self.num_devices)
mean_output = np.array(mean_output)
full_output = np.array(full_output)
mean_outputs.append(mean_output)
@@ -539,14 +527,10 @@ class TimesFm:
Returns:
Future forecasts dataframe.
"""
if not (
"unique_id" in inputs.columns
and "ds" in inputs.columns
and value_name in inputs.columns
):
if not ("unique_id" in inputs.columns and "ds" in inputs.columns and
value_name in inputs.columns):
raise ValueError(
f"DataFrame must have unique_id, ds and {value_name} columns."
)
f"DataFrame must have unique_id, ds and {value_name} columns.")
if not forecast_context_len:
forecast_context_len = self.context_len
logging.info("Preprocessing dataframe.")
@@ -571,17 +555,15 @@ class TimesFm:
with multiprocessing.Pool(processes=num_jobs) as pool:
results = pool.starmap(
process_group,
[
(key, group, value_name, forecast_context_len)
for key, group in df_sorted.groupby("unique_id")
],
[(key, group, value_name, forecast_context_len)
for key, group in df_sorted.groupby("unique_id")],
)
new_inputs, uids = zip(*results)
print("Finished preprocessing dataframe.")
freq_inps = [freq_map(freq)] * len(new_inputs)
_, full_forecast = self.forecast(
new_inputs, freq=freq_inps, window_size=window_size
)
_, full_forecast = self.forecast(new_inputs,
freq=freq_inps,
window_size=window_size)
print("Finished forecasting.")
fcst_df = make_future_dataframe(
uids=uids,
@@ -589,16 +571,13 @@ class TimesFm:
h=self.horizon_len,
freq=freq,
)
fcst_df[model_name] = full_forecast[:, 0 : self.horizon_len, 0].reshape(
-1, 1
)
fcst_df[model_name] = full_forecast[:, 0:self.horizon_len, 0].reshape(-1, 1)
if self._model.quantiles is not None:
for i, q in enumerate(self._model.quantiles):
q_col = f"{model_name}-q-{q}"
fcst_df[q_col] = full_forecast[:, 0 : self.horizon_len, 1 + i].reshape(
-1, 1
)
fcst_df[q_col] = full_forecast[:, 0:self.horizon_len,
1 + i].reshape(-1, 1)
if q == 0.5:
fcst_df[model_name] = fcst_df[q_col]
logging.info("Finished creating output dataframe.")