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feat: restrict input forecast #314

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Apr 26, 2024
Merged

feat: restrict input forecast #314

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603c666
feat: restrict input forecast
AzulGarza Apr 25, 2024
e1c2789
fix: remove consufing comment
AzulGarza Apr 25, 2024
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26 changes: 18 additions & 8 deletions nbs/nixtla_client.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -613,16 +613,26 @@
" 'Please consider using a smaller horizon.'\n",
" )\n",
" # restrict input if\n",
" # - we dont want to finetune\n",
" # - we dont have exogenous regegressors\n",
" # - and we dont want to produce pred intervals\n",
" # - no add history\n",
" restrict_input = self.finetune_steps == 0 and X_df is None and self.level is not None and not add_history\n",
" # we dont want to fine-tune\n",
" # we dont have exogenous regressors\n",
" # no add history\n",
" restrict_input = self.finetune_steps == 0 and X_df is None and not add_history\n",
" if restrict_input:\n",
" # add sufficient info to compute\n",
" # conformal interval\n",
" main_logger.info('Restricting input...')\n",
" new_input_size = 3 * self.input_size + max(self.model_horizon, self.h)\n",
" if self.level is not None:\n",
" # add sufficient info to compute\n",
" # conformal interval\n",
" # @AzulGarza\n",
" # this is an old opinionated decision\n",
" # about reducing the data sent to the api\n",
" # to reduce latency when\n",
" # a user passes level. since currently the model\n",
" # uses conformal prediction, we can change a minimum\n",
" # amount of data if the series are too large\n",
" new_input_size = 3 * self.input_size + max(self.model_horizon, self.h)\n",
" else:\n",
" # we only want to forecast\n",
" new_input_size = self.input_size\n",
" Y_df = Y_df.groupby(\"unique_id\").tail(new_input_size)\n",
" if X_df is not None:\n",
" X_df = X_df.groupby(\"unique_id\").tail(\n",
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31 changes: 18 additions & 13 deletions nixtla/nixtla_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -555,21 +555,26 @@ def forecast(
"Please consider using a smaller horizon."
)
# restrict input if
# - we dont want to finetune
# - we dont have exogenous regegressors
# - and we dont want to produce pred intervals
# - no add history
restrict_input = (
self.finetune_steps == 0
and X_df is None
and self.level is not None
and not add_history
)
# we dont want to fine-tune
# we dont have exogenous regressors
# no add history
restrict_input = self.finetune_steps == 0 and X_df is None and not add_history
if restrict_input:
# add sufficient info to compute
# conformal interval
main_logger.info("Restricting input...")
new_input_size = 3 * self.input_size + max(self.model_horizon, self.h)
if self.level is not None:
jmoralez marked this conversation as resolved.
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# add sufficient info to compute
# conformal interval
# @AzulGarza
# this is an old opinionated decision
# about reducing the data sent to the api
# to reduce latency when
# a user passes level. since currently the model
# uses conformal prediction, we can change a minimum
# amount of data if the series are too large
new_input_size = 3 * self.input_size + max(self.model_horizon, self.h)
else:
# we only want to forecast
new_input_size = self.input_size
Y_df = Y_df.groupby("unique_id").tail(new_input_size)
if X_df is not None:
X_df = X_df.groupby("unique_id").tail(
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