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[FEAT] finetune loss #218

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Jan 30, 2024
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[FEAT] finetune loss #218

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1a6b616
finetune loss
cchallu Jan 29, 2024
49e4274
restore os vars
cchallu Jan 29, 2024
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4 changes: 4 additions & 0 deletions nbs/distributed.timegpt.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -161,6 +161,7 @@
" X_df: Optional[fugue.AnyDataFrame] = None,\n",
" level: Optional[List[Union[int, float]]] = None,\n",
" finetune_steps: int = 0,\n",
" finetune_loss: str = 'default',\n",
" clean_ex_first: bool = True,\n",
" validate_token: bool = False,\n",
" add_history: bool = False,\n",
Expand All @@ -177,6 +178,7 @@
" target_col=target_col,\n",
" level=level,\n",
" finetune_steps=finetune_steps,\n",
" finetune_loss=finetune_loss,\n",
" clean_ex_first=clean_ex_first,\n",
" validate_token=validate_token,\n",
" add_history=add_history,\n",
Expand Down Expand Up @@ -245,6 +247,7 @@
" target_col: str = 'y',\n",
" level: Optional[List[Union[int, float]]] = None,\n",
" finetune_steps: int = 0,\n",
" finetune_loss: str = 'default',\n",
" clean_ex_first: bool = True,\n",
" validate_token: bool = False,\n",
" date_features: Union[bool, List[str]] = False,\n",
Expand All @@ -262,6 +265,7 @@
" target_col=target_col,\n",
" level=level,\n",
" finetune_steps=finetune_steps,\n",
" finetune_loss=finetune_loss,\n",
" clean_ex_first=clean_ex_first,\n",
" validate_token=validate_token,\n",
" date_features=date_features,\n",
Expand Down
21 changes: 19 additions & 2 deletions nbs/timegpt.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -164,6 +164,7 @@
" freq: str = None,\n",
" level: Optional[List[Union[int, float]]] = None,\n",
" finetune_steps: int = 0,\n",
" finetune_loss: str = 'default',\n",
" clean_ex_first: bool = True,\n",
" date_features: Union[bool, List[str]] = False,\n",
" date_features_to_one_hot: Union[bool, List[str]] = True,\n",
Expand All @@ -180,6 +181,7 @@
" self.base_freq = freq\n",
" self.level = level\n",
" self.finetune_steps = finetune_steps\n",
" self.finetune_loss = finetune_loss\n",
" self.clean_ex_first = clean_ex_first\n",
" self.date_features = date_features\n",
" self.date_features_to_one_hot = date_features_to_one_hot\n",
Expand Down Expand Up @@ -510,6 +512,7 @@
" freq=self.freq,\n",
" level=self.level,\n",
" finetune_steps=self.finetune_steps,\n",
" finetune_loss=self.finetune_loss,\n",
" clean_ex_first=self.clean_ex_first,\n",
" model=self.model,\n",
" )\n",
Expand Down Expand Up @@ -798,6 +801,7 @@
" X_df: Optional[pd.DataFrame] = None,\n",
" level: Optional[List[Union[int, float]]] = None,\n",
" finetune_steps: int = 0,\n",
" finetune_loss: str = 'default',\n",
" clean_ex_first: bool = True,\n",
" validate_token: bool = False,\n",
" add_history: bool = False,\n",
Expand All @@ -819,6 +823,7 @@
" freq=freq,\n",
" level=level,\n",
" finetune_steps=finetune_steps,\n",
" finetune_loss=finetune_loss,\n",
" clean_ex_first=clean_ex_first,\n",
" date_features=date_features,\n",
" date_features_to_one_hot=date_features_to_one_hot,\n",
Expand Down Expand Up @@ -887,6 +892,7 @@
" n_windows: int = 1,\n",
" step_size: Optional[int] = None,\n",
" finetune_steps: int = 0,\n",
" finetune_loss: str = 'default',\n",
" clean_ex_first: bool = True,\n",
" date_features: Union[bool, List[str]] = False,\n",
" date_features_to_one_hot: Union[bool, List[str]] = True,\n",
Expand All @@ -906,6 +912,7 @@
" freq=freq,\n",
" level=level,\n",
" finetune_steps=finetune_steps,\n",
" finetune_loss=finetune_loss,\n",
" clean_ex_first=clean_ex_first,\n",
" date_features=date_features,\n",
" date_features_to_one_hot=date_features_to_one_hot,\n",
Expand Down Expand Up @@ -1059,6 +1066,7 @@
" X_df: Optional[pd.DataFrame] = None,\n",
" level: Optional[List[Union[int, float]]] = None,\n",
" finetune_steps: int = 0,\n",
" finetune_loss: str = 'default',\n",
" clean_ex_first: bool = True,\n",
" validate_token: bool = False,\n",
" add_history: bool = False,\n",
Expand Down Expand Up @@ -1101,6 +1109,8 @@
" finetune_steps : int (default=0)\n",
" Number of steps used to finetune TimeGPT in the\n",
" new data.\n",
" finetune_loss : str (default='default')\n",
" Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`.\n",
" clean_ex_first : bool (default=True)\n",
" Clean exogenous signal before making forecasts\n",
" using TimeGPT.\n",
Expand Down Expand Up @@ -1145,6 +1155,7 @@
" X_df=X_df,\n",
" level=level,\n",
" finetune_steps=finetune_steps,\n",
" finetune_loss=finetune_loss,\n",
" clean_ex_first=clean_ex_first,\n",
" validate_token=validate_token,\n",
" add_history=add_history,\n",
Expand All @@ -1165,6 +1176,7 @@
" X_df=X_df,\n",
" level=level,\n",
" finetune_steps=finetune_steps,\n",
" finetune_loss=finetune_loss,\n",
" clean_ex_first=clean_ex_first,\n",
" validate_token=validate_token,\n",
" add_history=add_history,\n",
Expand Down Expand Up @@ -1291,6 +1303,7 @@
" n_windows: int = 1,\n",
" step_size: Optional[int] = None,\n",
" finetune_steps: int = 0,\n",
" finetune_loss: str = 'default',\n",
" clean_ex_first: bool = True,\n",
" date_features: Union[bool, List[str]] = False,\n",
" date_features_to_one_hot: Union[bool, List[str]] = True,\n",
Expand Down Expand Up @@ -1336,6 +1349,8 @@
" finetune_steps : int (default=0)\n",
" Number of steps used to finetune TimeGPT in the\n",
" new data.\n",
" finetune_loss : str (default='default')\n",
" Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`.\n",
" clean_ex_first : bool (default=True)\n",
" Clean exogenous signal before making forecasts\n",
" using TimeGPT.\n",
Expand Down Expand Up @@ -1373,6 +1388,7 @@
" target_col=target_col,\n",
" level=level,\n",
" finetune_steps=finetune_steps,\n",
" finetune_loss=finetune_loss,\n",
" clean_ex_first=clean_ex_first,\n",
" validate_token=validate_token,\n",
" date_features=date_features,\n",
Expand All @@ -1393,6 +1409,7 @@
" target_col=target_col,\n",
" level=level,\n",
" finetune_steps=finetune_steps,\n",
" finetune_loss=finetune_loss,\n",
" clean_ex_first=clean_ex_first,\n",
" validate_token=validate_token,\n",
" date_features=date_features,\n",
Expand Down Expand Up @@ -1862,7 +1879,7 @@
"#| hide\n",
"#test same results custom url\n",
"timegpt_custom = TimeGPT(\n",
" token=os.environ['TIMEGPT_CUSTOM_URL_TOKEN'],\n",
" token=os.environ['TIMEGPT_CUSTOM_URL_TOKEN'], \n",
" environment=os.environ['TIMEGPT_CUSTOM_URL'],\n",
")\n",
"# forecast method\n",
Expand Down Expand Up @@ -2294,7 +2311,7 @@
"#| hide \n",
"# test short horizon with finetunning\n",
"test_fail(\n",
" lambda: timegpt.forecast(df=df.tail(3), h=12, time_col='timestamp', target_col='value', finetune_steps=10),\n",
" lambda: timegpt.forecast(df=df.tail(3), h=12, time_col='timestamp', target_col='value', finetune_steps=10, finetune_loss='mae'),\n",
" contains='be sure'\n",
")"
]
Expand Down
9 changes: 9 additions & 0 deletions nixtlats/_modidx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,8 +119,17 @@
'nixtlats.timegpt.validate_model_parameter': ( 'timegpt.html#validate_model_parameter',
'nixtlats/timegpt.py')},
'nixtlats.types.http_validation_error': {},
'nixtlats.types.multi_series_anomaly_model': {},
'nixtlats.types.multi_series_cross_validation_finetune_loss': {},
'nixtlats.types.multi_series_cross_validation_model': {},
'nixtlats.types.multi_series_forecast_finetune_loss': {},
'nixtlats.types.multi_series_forecast_model': {},
'nixtlats.types.multi_series_input': {},
'nixtlats.types.multi_series_insample_forecast_model': {},
'nixtlats.types.single_series_forecast': {},
'nixtlats.types.single_series_forecast_finetune_loss': {},
'nixtlats.types.single_series_forecast_model': {},
'nixtlats.types.single_series_insample_forecast_model': {},
'nixtlats.types.validation_error': {},
'nixtlats.types.validation_error_loc_item': {},
'nixtlats.utils': {'nixtlats.utils.colab_badge': ('utils.html#colab_badge', 'nixtlats/utils.py')}}}
4 changes: 4 additions & 0 deletions nixtlats/distributed/timegpt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,6 +125,7 @@ def forecast(
X_df: Optional[fugue.AnyDataFrame] = None,
level: Optional[List[Union[int, float]]] = None,
finetune_steps: int = 0,
finetune_loss: str = "default",
clean_ex_first: bool = True,
validate_token: bool = False,
add_history: bool = False,
Expand All @@ -141,6 +142,7 @@ def forecast(
target_col=target_col,
level=level,
finetune_steps=finetune_steps,
finetune_loss=finetune_loss,
clean_ex_first=clean_ex_first,
validate_token=validate_token,
add_history=add_history,
Expand Down Expand Up @@ -211,6 +213,7 @@ def cross_validation(
target_col: str = "y",
level: Optional[List[Union[int, float]]] = None,
finetune_steps: int = 0,
finetune_loss: str = "default",
clean_ex_first: bool = True,
validate_token: bool = False,
date_features: Union[bool, List[str]] = False,
Expand All @@ -228,6 +231,7 @@ def cross_validation(
target_col=target_col,
level=level,
finetune_steps=finetune_steps,
finetune_loss=finetune_loss,
clean_ex_first=clean_ex_first,
validate_token=validate_token,
date_features=date_features,
Expand Down
17 changes: 17 additions & 0 deletions nixtlats/timegpt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,6 +100,7 @@ def __init__(
freq: str = None,
level: Optional[List[Union[int, float]]] = None,
finetune_steps: int = 0,
finetune_loss: str = "default",
clean_ex_first: bool = True,
date_features: Union[bool, List[str]] = False,
date_features_to_one_hot: Union[bool, List[str]] = True,
Expand All @@ -116,6 +117,7 @@ def __init__(
self.base_freq = freq
self.level = level
self.finetune_steps = finetune_steps
self.finetune_loss = finetune_loss
self.clean_ex_first = clean_ex_first
self.date_features = date_features
self.date_features_to_one_hot = date_features_to_one_hot
Expand Down Expand Up @@ -481,6 +483,7 @@ def forecast(
freq=self.freq,
level=self.level,
finetune_steps=self.finetune_steps,
finetune_loss=self.finetune_loss,
clean_ex_first=self.clean_ex_first,
model=self.model,
)
Expand Down Expand Up @@ -749,6 +752,7 @@ def _forecast(
X_df: Optional[pd.DataFrame] = None,
level: Optional[List[Union[int, float]]] = None,
finetune_steps: int = 0,
finetune_loss: str = "default",
clean_ex_first: bool = True,
validate_token: bool = False,
add_history: bool = False,
Expand All @@ -768,6 +772,7 @@ def _forecast(
freq=freq,
level=level,
finetune_steps=finetune_steps,
finetune_loss=finetune_loss,
clean_ex_first=clean_ex_first,
date_features=date_features,
date_features_to_one_hot=date_features_to_one_hot,
Expand Down Expand Up @@ -834,6 +839,7 @@ def _cross_validation(
n_windows: int = 1,
step_size: Optional[int] = None,
finetune_steps: int = 0,
finetune_loss: str = "default",
clean_ex_first: bool = True,
date_features: Union[bool, List[str]] = False,
date_features_to_one_hot: Union[bool, List[str]] = True,
Expand All @@ -851,6 +857,7 @@ def _cross_validation(
freq=freq,
level=level,
finetune_steps=finetune_steps,
finetune_loss=finetune_loss,
clean_ex_first=clean_ex_first,
date_features=date_features,
date_features_to_one_hot=date_features_to_one_hot,
Expand Down Expand Up @@ -999,6 +1006,7 @@ def forecast(
X_df: Optional[pd.DataFrame] = None,
level: Optional[List[Union[int, float]]] = None,
finetune_steps: int = 0,
finetune_loss: str = "default",
clean_ex_first: bool = True,
validate_token: bool = False,
add_history: bool = False,
Expand Down Expand Up @@ -1041,6 +1049,8 @@ def forecast(
finetune_steps : int (default=0)
Number of steps used to finetune TimeGPT in the
new data.
finetune_loss : str (default='default')
Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`.
clean_ex_first : bool (default=True)
Clean exogenous signal before making forecasts
using TimeGPT.
Expand Down Expand Up @@ -1085,6 +1095,7 @@ def forecast(
X_df=X_df,
level=level,
finetune_steps=finetune_steps,
finetune_loss=finetune_loss,
clean_ex_first=clean_ex_first,
validate_token=validate_token,
add_history=add_history,
Expand All @@ -1105,6 +1116,7 @@ def forecast(
X_df=X_df,
level=level,
finetune_steps=finetune_steps,
finetune_loss=finetune_loss,
clean_ex_first=clean_ex_first,
validate_token=validate_token,
add_history=add_history,
Expand Down Expand Up @@ -1231,6 +1243,7 @@ def cross_validation(
n_windows: int = 1,
step_size: Optional[int] = None,
finetune_steps: int = 0,
finetune_loss: str = "default",
clean_ex_first: bool = True,
date_features: Union[bool, List[str]] = False,
date_features_to_one_hot: Union[bool, List[str]] = True,
Expand Down Expand Up @@ -1276,6 +1289,8 @@ def cross_validation(
finetune_steps : int (default=0)
Number of steps used to finetune TimeGPT in the
new data.
finetune_loss : str (default='default')
Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`.
clean_ex_first : bool (default=True)
Clean exogenous signal before making forecasts
using TimeGPT.
Expand Down Expand Up @@ -1313,6 +1328,7 @@ def cross_validation(
target_col=target_col,
level=level,
finetune_steps=finetune_steps,
finetune_loss=finetune_loss,
clean_ex_first=clean_ex_first,
validate_token=validate_token,
date_features=date_features,
Expand All @@ -1333,6 +1349,7 @@ def cross_validation(
target_col=target_col,
level=level,
finetune_steps=finetune_steps,
finetune_loss=finetune_loss,
clean_ex_first=clean_ex_first,
validate_token=validate_token,
date_features=date_features,
Expand Down
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