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[FIX] Add tests for custom endpoint and abstract call api method #143

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Nov 1, 2023
Merged

[FIX] Add tests for custom endpoint and abstract call api method #143

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113 changes: 83 additions & 30 deletions nbs/timegpt.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -398,11 +398,19 @@
" y = Y_df.to_dict(**to_dict_args)\n",
" x = X_df.to_dict(**to_dict_args) if X_df is not None else None\n",
" return y, x\n",
"\n",
" @staticmethod\n",
" def _call_api(method, kwargs):\n",
" response = method(**kwargs)\n",
" if 'data' in response:\n",
" response = response['data']\n",
" return response\n",
" \n",
" def set_model_params(self):\n",
" model_params = self.client.timegpt_model_params(request=SingleSeriesForecast(freq=self.freq))\n",
" if 'data' in model_params:\n",
" model_params = model_params['data']\n",
" model_params = self._call_api(\n",
" self.client.timegpt_model_params,\n",
" {'request': SingleSeriesForecast(freq=self.freq)},\n",
" )\n",
" model_params = model_params['detail']\n",
" self.input_size, self.model_horizon = model_params['input_size'], model_params['horizon']\n",
" \n",
Expand Down Expand Up @@ -449,17 +457,18 @@
" self.validate_input_size(Y_df=Y_df)\n",
" y, x = self.dataframes_to_dict(Y_df, X_df)\n",
" main_logger.info('Calling Forecast Endpoint...')\n",
" response_timegpt = self.client.timegpt_multi_series(\n",
" y=y,\n",
" x=x,\n",
" fh=self.h,\n",
" freq=self.freq,\n",
" level=self.level,\n",
" finetune_steps=self.finetune_steps,\n",
" clean_ex_first=self.clean_ex_first,\n",
" response_timegpt = self._call_api(\n",
" self.client.timegpt_multi_series,\n",
" dict(\n",
" y=y,\n",
" x=x,\n",
" fh=self.h,\n",
" freq=self.freq,\n",
" level=self.level,\n",
" finetune_steps=self.finetune_steps,\n",
" clean_ex_first=self.clean_ex_first,\n",
" ),\n",
" )\n",
" if 'data' in response_timegpt:\n",
" response_timegpt = response_timegpt['data']\n",
" if 'weights_x' in response_timegpt:\n",
" self.weights_x = pd.DataFrame({\n",
" 'features': self.x_cols,\n",
Expand All @@ -469,16 +478,17 @@
" if add_history:\n",
" main_logger.info('Calling Historical Forecast Endpoint...')\n",
" self.validate_input_size(Y_df=Y_df)\n",
" if 'data' in response_timegpt:\n",
" response_timegpt = response_timegpt['data']\n",
" response_timegpt = self.client.timegpt_multi_series_historic(\n",
" y=y,\n",
" x=x,\n",
" freq=self.freq,\n",
" level=self.level,\n",
" clean_ex_first=self.clean_ex_first,\n",
" response_timegpt = self._call_api(\n",
" self.client.timegpt_multi_series_historic,\n",
" dict(\n",
" y=y,\n",
" x=x,\n",
" freq=self.freq,\n",
" level=self.level,\n",
" clean_ex_first=self.clean_ex_first,\n",
" ),\n",
" )\n",
" fitted_df = pd.DataFrame(**response_timegpt['data']['forecast'])\n",
" fitted_df = pd.DataFrame(**response_timegpt['forecast'])\n",
" fitted_df = fitted_df.drop(columns='y')\n",
" fcst_df = pd.concat([fitted_df, fcst_df]).sort_values(['unique_id', 'ds'])\n",
" fcst_df = self.transform_outputs(fcst_df)\n",
Expand All @@ -497,15 +507,16 @@
" Y_df, X_df = self.preprocess_dataframes(df=df, X_df=None)\n",
" main_logger.info('Calling Anomaly Detector Endpoint...')\n",
" y, x = self.dataframes_to_dict(Y_df, X_df)\n",
" response_timegpt = self.client.timegpt_multi_series_anomalies(\n",
" y=y,\n",
" x=x,\n",
" freq=self.freq,\n",
" level=[self.level] if (isinstance(self.level, int) or isinstance(self.level, float)) else [self.level[0]],\n",
" clean_ex_first=self.clean_ex_first,\n",
" response_timegpt = self._call_api(\n",
" self.client.timegpt_multi_series_anomalies,\n",
" dict(\n",
" y=y,\n",
" x=x,\n",
" freq=self.freq,\n",
" level=[self.level] if (isinstance(self.level, int) or isinstance(self.level, float)) else [self.level[0]],\n",
" clean_ex_first=self.clean_ex_first,\n",
" ),\n",
" )\n",
" if 'data' in response_timegpt:\n",
" response_timegpt = response_timegpt['data']\n",
" if 'weights_x' in response_timegpt:\n",
" self.weights_x = pd.DataFrame({\n",
" 'features': self.x_cols,\n",
Expand Down Expand Up @@ -1214,6 +1225,48 @@
"show_doc(TimeGPT.forecast, title_level=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"#test same results custom url\n",
"timegpt_custom = TimeGPT(\n",
" token=os.environ['TIMEGPT_CUSTOM_URL_TOKEN'],\n",
" environment=os.environ['TIMEGPT_CUSTOM_URL'],\n",
")\n",
"# forecast method\n",
"fcst_kwargs = dict(\n",
" df=df, \n",
" h=12, \n",
" level=[90, 95], \n",
" add_history=True, \n",
" time_col='timestamp', \n",
" target_col='value',\n",
")\n",
"fcst_df = timegpt.forecast(**fcst_kwargs)\n",
"fcst_df_custom = timegpt_custom.forecast(**fcst_kwargs)\n",
"pd.testing.assert_frame_equal(\n",
" fcst_df,\n",
" fcst_df_custom,\n",
")\n",
"# anomalies method\n",
"anomalies_kwargs = dict(\n",
" df=df, \n",
" level=99,\n",
" time_col='timestamp', \n",
" target_col='value',\n",
")\n",
"anomalies_df = timegpt.detect_anomalies(**anomalies_kwargs)\n",
"anomalies_df_custom = timegpt.detect_anomalies(**anomalies_kwargs)\n",
"pd.testing.assert_frame_equal(\n",
" anomalies_df,\n",
" anomalies_df_custom,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down
2 changes: 2 additions & 0 deletions nixtlats/_modidx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,8 @@
'nixtlats/timegpt.py'),
'nixtlats.timegpt._TimeGPTModel': ('timegpt.html#_timegptmodel', 'nixtlats/timegpt.py'),
'nixtlats.timegpt._TimeGPTModel.__init__': ('timegpt.html#_timegptmodel.__init__', 'nixtlats/timegpt.py'),
'nixtlats.timegpt._TimeGPTModel._call_api': ( 'timegpt.html#_timegptmodel._call_api',
'nixtlats/timegpt.py'),
'nixtlats.timegpt._TimeGPTModel.add_date_features': ( 'timegpt.html#_timegptmodel.add_date_features',
'nixtlats/timegpt.py'),
'nixtlats.timegpt._TimeGPTModel.compute_date_feature': ( 'timegpt.html#_timegptmodel.compute_date_feature',
Expand Down
75 changes: 42 additions & 33 deletions nixtlats/timegpt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -345,12 +345,18 @@ def dataframes_to_dict(self, Y_df: pd.DataFrame, X_df: pd.DataFrame):
x = X_df.to_dict(**to_dict_args) if X_df is not None else None
return y, x

@staticmethod
def _call_api(method, kwargs):
response = method(**kwargs)
if "data" in response:
response = response["data"]
return response

def set_model_params(self):
model_params = self.client.timegpt_model_params(
request=SingleSeriesForecast(freq=self.freq)
model_params = self._call_api(
self.client.timegpt_model_params,
{"request": SingleSeriesForecast(freq=self.freq)},
)
if "data" in model_params:
model_params = model_params["data"]
model_params = model_params["detail"]
self.input_size, self.model_horizon = (
model_params["input_size"],
Expand Down Expand Up @@ -406,17 +412,18 @@ def forecast(
self.validate_input_size(Y_df=Y_df)
y, x = self.dataframes_to_dict(Y_df, X_df)
main_logger.info("Calling Forecast Endpoint...")
response_timegpt = self.client.timegpt_multi_series(
y=y,
x=x,
fh=self.h,
freq=self.freq,
level=self.level,
finetune_steps=self.finetune_steps,
clean_ex_first=self.clean_ex_first,
response_timegpt = self._call_api(
self.client.timegpt_multi_series,
dict(
y=y,
x=x,
fh=self.h,
freq=self.freq,
level=self.level,
finetune_steps=self.finetune_steps,
clean_ex_first=self.clean_ex_first,
),
)
if "data" in response_timegpt:
response_timegpt = response_timegpt["data"]
if "weights_x" in response_timegpt:
self.weights_x = pd.DataFrame(
{
Expand All @@ -428,16 +435,17 @@ def forecast(
if add_history:
main_logger.info("Calling Historical Forecast Endpoint...")
self.validate_input_size(Y_df=Y_df)
if "data" in response_timegpt:
response_timegpt = response_timegpt["data"]
response_timegpt = self.client.timegpt_multi_series_historic(
y=y,
x=x,
freq=self.freq,
level=self.level,
clean_ex_first=self.clean_ex_first,
response_timegpt = self._call_api(
self.client.timegpt_multi_series_historic,
dict(
y=y,
x=x,
freq=self.freq,
level=self.level,
clean_ex_first=self.clean_ex_first,
),
)
fitted_df = pd.DataFrame(**response_timegpt["data"]["forecast"])
fitted_df = pd.DataFrame(**response_timegpt["forecast"])
fitted_df = fitted_df.drop(columns="y")
fcst_df = pd.concat([fitted_df, fcst_df]).sort_values(["unique_id", "ds"])
fcst_df = self.transform_outputs(fcst_df)
Expand All @@ -456,17 +464,18 @@ def detect_anomalies(self, df: pd.DataFrame):
Y_df, X_df = self.preprocess_dataframes(df=df, X_df=None)
main_logger.info("Calling Anomaly Detector Endpoint...")
y, x = self.dataframes_to_dict(Y_df, X_df)
response_timegpt = self.client.timegpt_multi_series_anomalies(
y=y,
x=x,
freq=self.freq,
level=[self.level]
if (isinstance(self.level, int) or isinstance(self.level, float))
else [self.level[0]],
clean_ex_first=self.clean_ex_first,
response_timegpt = self._call_api(
self.client.timegpt_multi_series_anomalies,
dict(
y=y,
x=x,
freq=self.freq,
level=[self.level]
if (isinstance(self.level, int) or isinstance(self.level, float))
else [self.level[0]],
clean_ex_first=self.clean_ex_first,
),
)
if "data" in response_timegpt:
response_timegpt = response_timegpt["data"]
if "weights_x" in response_timegpt:
self.weights_x = pd.DataFrame(
{
Expand Down
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