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standardizing styles accross all files.

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This commit is contained in:
Rajat Sen
2024-07-09 17:10:18 +00:00
parent 938bbac874
commit 41929ba643
10 changed files with 718 additions and 781 deletions
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experiments/long_horizon_benchmarks/run_eval.py
+21 -34
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@@ -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())