Feat/token function #277
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Feat/token function #277
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AzulGarza
requested changes
Apr 8, 2024
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hey @MMenchero! awesome progress! 🙌
i left a couple of comments. i think solving them will prevent the ci/cd from crashing. :)
nixtlats/nixtla_client.py
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| res = response["data"] | ||
| res["requestID"] = response["requestID"] | ||
| local_time = time.localtime(time.time()) | ||
| created_at = time.strftime("%Y-%m-%d %H:%M:%S", local_time) |
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i think we can use created_at = pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S")), instead of importing time. probably this creates a compatibility issue with tenacity that explains the ci/cdm but i'm not completely sure.
nixtlats/nixtla_client.py
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| @@ -889,7 +1066,7 @@ def _forecast( | |||
| ): | |||
| if validate_api_key and not self.validate_api_key(log=False): | |||
| raise Exception("API Key not valid, please email [email protected]") | |||
| nixtla_client_model = _NixtlaClientModel( | |||
| self.nixtla_client_model = _NixtlaClientModel( | |||
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probably it might be better not to store _NixtlaClientModel to prevent the object from being heavy. We could use logic similar to self.weights_x. here, _NixtlaClientModel stores weights_x and then NixtlaClient recovers that object. in the cases of the requests_df, _NixtlaClientModel might be storing that dataframe and then NixtlaClient appends new dataframes to the previously created one, something like:
class NixtlaClient(_NixtlaClient):
requests_df: pd.DataFrame = pd.DataFrame(columns=[...]) # add columns of request_df
def _forecast(...):
nixtla_client_model = _NixtlaClientModel(...)
self.request_df = pd.concat([
self.request_df,
nixtla_client_model.request_df], ignore_index=True,
)
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This PR is ready for review @AzulGarza |
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Description:
Implemented new method for token information retrieval.
Changes:
request_dfmethod to TimeGPT class.forecast,detect_anomalies, andcross_validation.Testing:
See tests at the end of nb.
Notes:
cross_validationmethod.