feat: use new endpoints and rename models (#263)

Nixtla · Mar 22, 2024 · 0e56620 · 0e56620
1 parent fe224d5
commit 0e56620
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Showing 4 changed files with 126 additions and 72 deletions.
diff --git a/nbs/distributed.timegpt.ipynb b/nbs/distributed.timegpt.ipynb
@@ -168,7 +168,7 @@
     "            add_history: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
-    "            model: str = 'timegpt-1',\n",
+    "            model: str = 'short-horizon',\n",
     "            num_partitions: Optional[int] = None,\n",
     "        ) -> fugue.AnyDataFrame:\n",
     "        kwargs = dict(\n",
@@ -217,7 +217,7 @@
     "            validate_token: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
-    "            model: str = 'timegpt-1',\n",
+    "            model: str = 'short-horizon',\n",
     "            num_partitions: Optional[int] = None,\n",
     "        ) -> fugue.AnyDataFrame:\n",
     "        kwargs = dict(\n",
@@ -260,7 +260,7 @@
     "            validate_token: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
-    "            model: str = 'timegpt-1',\n",
+    "            model: str = 'short-horizon',\n",
     "            n_windows: int = 1,\n",
     "            step_size: Optional[int] = None,\n",
     "            num_partitions: Optional[int] = None,\n",
@@ -448,7 +448,7 @@
     "        num_partitions=1,\n",
     "        id_col=id_col,\n",
     "        time_col=time_col,\n",
-    "        model='timegpt-1',\n",
+    "        model='short-horizon',\n",
     "        **fcst_kwargs\n",
     "    )\n",
     "    fcst_df = fa.as_pandas(fcst_df)\n",
@@ -458,7 +458,7 @@
     "        num_partitions=1,\n",
     "        id_col=id_col,\n",
     "        time_col=time_col,\n",
-    "        model='timegpt-1-long-horizon',\n",
+    "        model='long-horizon',\n",
     "        **fcst_kwargs\n",
     "    )\n",
     "    fcst_df_2 = fa.as_pandas(fcst_df_2)\n",
@@ -557,7 +557,7 @@
     "def test_forecast_dataframe(df: fugue.AnyDataFrame):\n",
     "    test_cv_same_results_num_partitions(df, n_windows=2, step_size=1)\n",
     "    test_cv_same_results_num_partitions(df, n_windows=3, step_size=None, horizon=1)\n",
-    "    test_cv_same_results_num_partitions(df, model='timegpt-1-long-horizon', horizon=1)\n",
+    "    test_cv_same_results_num_partitions(df, model='long-horizon', horizon=1)\n",
     "    test_forecast_diff_results_diff_models(df)\n",
     "    test_forecast(df, num_partitions=1)\n",
     "    test_forecast(df, level=[90, 80], num_partitions=1)\n",
@@ -771,7 +771,7 @@
     "        num_partitions=1,\n",
     "        id_col=id_col,\n",
     "        time_col=time_col,\n",
-    "        model='timegpt-1',\n",
+    "        model='short-horizon',\n",
     "        **anomalies_kwargs\n",
     "    )\n",
     "    anomalies_df = fa.as_pandas(anomalies_df)\n",
@@ -780,7 +780,7 @@
     "        num_partitions=1,\n",
     "        id_col=id_col,\n",
     "        time_col=time_col,\n",
-    "        model='timegpt-1-long-horizon',\n",
+    "        model='long-horizon',\n",
     "        **anomalies_kwargs\n",
     "    )\n",
     "    anomalies_df_2 = fa.as_pandas(anomalies_df_2)\n",

diff --git a/nbs/timegpt.ipynb b/nbs/timegpt.ipynb
@@ -206,7 +206,7 @@
     "            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",
-    "            model: str = 'timegpt-1',\n",
+    "            model: str = 'short-horizon',\n",
     "            max_retries: int = 6,\n",
     "            retry_interval: int = 10,\n",
     "            max_wait_time: int = 6 * 60,\n",
@@ -525,7 +525,7 @@
     "    \n",
     "    def set_model_params(self):\n",
     "        model_params = self._call_api(\n",
-    "            self.client.timegpt_model_params,\n",
+    "            self.client.model_params,\n",
     "            SingleSeriesForecast(freq=self.freq, model=self.model),\n",
     "        )\n",
     "        model_params = model_params['detail']\n",
@@ -589,7 +589,7 @@
     "            model=self.model,\n",
     "        )\n",
     "        response_timegpt = self._call_api(\n",
-    "            self.client.timegpt_multi_series,\n",
+    "            self.client.forecast_multi_series,\n",
     "            payload,\n",
     "        )\n",
     "        if 'weights_x' in response_timegpt:\n",
@@ -602,7 +602,7 @@
     "            main_logger.info('Calling Historical Forecast Endpoint...')\n",
     "            self.validate_input_size(Y_df=Y_df)\n",
     "            response_timegpt = self._call_api(\n",
-    "                self.client.timegpt_multi_series_historic,\n",
+    "                self.client.historic_forecast_multi_series,\n",
     "                MultiSeriesInsampleForecast(\n",
     "                    y=y,\n",
     "                    x=x,\n",
@@ -632,7 +632,7 @@
     "        main_logger.info('Calling Anomaly Detector Endpoint...')\n",
     "        y, x = self.dataframes_to_dict(Y_df, X_df)\n",
     "        response_timegpt = self._call_api(\n",
-    "            self.client.timegpt_multi_series_anomalies,\n",
+    "            self.client.anomaly_detection_multi_series,\n",
     "            MultiSeriesAnomaly(\n",
     "                y=y,\n",
     "                x=x,\n",
@@ -722,11 +722,21 @@
     "#| exporti\n",
     "def validate_model_parameter(func):\n",
     "    def wrapper(self, *args, **kwargs):\n",
-    "        if 'model' in kwargs and kwargs['model'] not in self.supported_models:\n",
-    "            raise ValueError(\n",
-    "                f'unsupported model: {kwargs[\"model\"]} '\n",
-    "                f'supported models: {\", \".join(self.supported_models)}'\n",
-    "            )\n",
+    "        if 'model' in kwargs:\n",
+    "            model = kwargs['model']\n",
+    "            rename_models_dict = {\n",
+    "                'timegpt-1': 'short-horizon',\n",
+    "                'timegpt-1-long-horizon': 'long-horizon',\n",
+    "            }\n",
+    "            if model in rename_models_dict.keys():\n",
+    "                new_model = rename_models_dict[model]\n",
+    "                warnings.warn(f\"'{model}' is deprecated; use '{new_model}' instead.\", FutureWarning)\n",
+    "                model = new_model\n",
+    "            if model not in self.supported_models:\n",
+    "                raise ValueError(\n",
+    "                    f'unsupported model: {kwargs[\"model\"]} '\n",
+    "                    f'supported models: {\", \".join(self.supported_models)}'\n",
+    "                )\n",
     "        return func(self, *args, **kwargs)\n",
     "    return wrapper"
    ]
@@ -842,7 +852,7 @@
     "        self.max_retries = max_retries\n",
     "        self.retry_interval = retry_interval\n",
     "        self.max_wait_time = max_wait_time\n",
-    "        self.supported_models = ['timegpt-1', 'timegpt-1-long-horizon']\n",
+    "        self.supported_models = ['short-horizon', 'long-horizon'] \n",
     "        # custom attr\n",
     "        self.weights_x: pd.DataFrame = None\n",
     "\n",
@@ -880,7 +890,7 @@
     "            add_history: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
-    "            model: str = 'timegpt-1',\n",
+    "            model: str = 'short-horizon',\n",
     "            num_partitions: int = 1,\n",
     "        ):\n",
     "        if validate_token and not self.validate_token(log=False):\n",
@@ -924,7 +934,7 @@
     "            validate_token: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
-    "            model: str = 'timegpt-1',\n",
+    "            model: str = 'short-horizon',\n",
     "            num_partitions: int = 1,\n",
     "        ):\n",
     "        if validate_token and not self.validate_token(log=False):\n",
@@ -971,7 +981,7 @@
     "        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",
-    "        model: str = 'timegpt-1',\n",
+    "        model: str = 'short-horizon',\n",
     "        num_partitions: int = 1,\n",
     "    ):\n",
     "        if validate_token and not self.validate_token(log=False):\n",
@@ -1151,7 +1161,7 @@
     "            add_history: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
-    "            model: str = 'timegpt-1',\n",
+    "            model: str = 'short-horizon',\n",
     "            num_partitions: Optional[int] = None,\n",
     "        ):\n",
     "        \"\"\"Forecast your time series using TimeGPT.\n",
@@ -1214,9 +1224,9 @@
     "            Apply one-hot encoding to these date features.\n",
     "            If `date_features=True`, then all date features are\n",
     "            one-hot encoded by default.\n",
-    "        model : str (default='timegpt=1')\n",
-    "            Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon`. \n",
-    "            We recommend using `timegpt-1-long-horizon` for forecasting \n",
+    "        model : str (default='short-horizon')\n",
+    "            Model to use as a string. Options are: `short-horizon`, and `long-horizon`. \n",
+    "            We recommend using `long-horizon` for forecasting \n",
     "            if you want to predict more than one seasonal \n",
     "            period given the frequency of your data.\n",
     "        num_partitions : int (default=None)\n",
@@ -1286,7 +1296,7 @@
     "            validate_token: bool = False,\n",
     "            date_features: Union[bool, List[str]] = False,\n",
     "            date_features_to_one_hot: Union[bool, List[str]] = True,\n",
-    "            model: str = 'timegpt-1',\n",
+    "            model: str = 'short-horizon',\n",
     "            num_partitions: Optional[int] = None,\n",
     "        ):\n",
     "        \"\"\"Detect anomalies in your time series using TimeGPT.\n",
@@ -1331,9 +1341,9 @@
     "            Apply one-hot encoding to these date features.\n",
     "            If `date_features=True`, then all date features are\n",
     "            one-hot encoded by default.\n",
-    "        model : str (default='timegpt=1')\n",
-    "            Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon`. \n",
-    "            We recommend using `timegpt-1-long-horizon` for forecasting \n",
+    "        model : str (default='short-horizon')\n",
+    "            Model to use as a string. Options are: `short-horizon`, and `long-horizon`. \n",
+    "            We recommend using `long-horizon` for forecasting \n",
     "            if you want to predict more than one seasonal \n",
     "            period given the frequency of your data.\n",
     "        num_partitions : int (default=None)\n",
@@ -1398,7 +1408,7 @@
     "        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",
-    "        model: str = 'timegpt-1',\n",
+    "        model: str = 'short-horizon',\n",
     "        num_partitions: Optional[int] = None,\n",
     "    ):\n",
     "        \"\"\"Perform cross validation in your time series using TimeGPT.\n",
@@ -1461,9 +1471,9 @@
     "            Apply one-hot encoding to these date features.\n",
     "            If `date_features=True`, then all date features are\n",
     "            one-hot encoded by default.\n",
-    "        model : str (default='timegpt=1')\n",
-    "            Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon`. \n",
-    "            We recommend using `timegpt-1-long-horizon` for forecasting \n",
+    "        model : str (default='short-horizon')\n",
+    "            Model to use as a string. Options are: `short-horizon`, and `long-horizon`. \n",
+    "            We recommend using `long-horizon` for forecasting \n",
     "            if you want to predict more than one seasonal \n",
     "            period given the frequency of your data.\n",
     "        num_partitions : int (default=None)\n",
@@ -1629,7 +1639,7 @@
     "#| hide\n",
     "# test input_size\n",
     "test_eq(\n",
-    "    timegpt.client.timegpt_model_params(request=SingleSeriesForecast(freq='D'))['data']['detail'],\n",
+    "    timegpt.client.model_params(request=SingleSeriesForecast(freq='D'))['data']['detail'],\n",
     "    {'input_size': 28, 'horizon': 7},\n",
     ")"
    ]
@@ -1659,10 +1669,34 @@
    "outputs": [],
    "source": [
     "#| hide\n",
-    "# test finetune deprecation\n",
+    "# test model change\n",
     "df_test = df.copy()\n",
     "df_test.columns = [\"ds\", \"y\"]\n",
     "test_warns(\n",
+    "    lambda: timegpt.forecast(df_test, finetune_steps=2, h=12, model=\"timegpt-1\"),\n",
+    ")\n",
+    "test_warns(\n",
+    "    lambda: timegpt.forecast(df_test, finetune_steps=2, h=12, model=\"timegpt-1-long-horizon\"),\n",
+    ")\n",
+    "test_eq(\n",
+    "    timegpt.forecast(df_test, h=12, model=\"timegpt-1\"),\n",
+    "    timegpt.forecast(df_test, h=12),\n",
+    ")\n",
+    "test_eq(\n",
+    "    timegpt.forecast(df_test, h=12, model=\"timegpt-1-long-horizon\"),\n",
+    "    timegpt.forecast(df_test, h=12, model=\"long-horizon\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "# test finetune deprecation\n",
+    "test_warns(\n",
     "    lambda: timegpt.forecast(df_test, finetune_steps=2, h=12),\n",
     ")\n",
     "test_warns(\n",
@@ -1848,7 +1882,7 @@
     "        max_wait_time=max_wait_time,\n",
     "    )\n",
     "    init_time = time()\n",
-    "    with patch('nixtlats.client.Nixtla.timegpt_multi_series', side_effect=side_effect):\n",
+    "    with patch('nixtlats.client.Nixtla.forecast_multi_series', side_effect=side_effect):\n",
     "        test_fail(\n",
     "            lambda: mock_timegpt.forecast(df=df, h=12, time_col='timestamp', target_col='value'),\n",
     "        )\n",
@@ -2028,8 +2062,8 @@
     "        df=df_freq,\n",
     "        h=h,\n",
     "    )\n",
-    "    fcst_1_df = test_equal_fcsts_add_history(**{**kwargs, 'model': 'timegpt-1'})\n",
-    "    fcst_2_df = test_equal_fcsts_add_history(**{**kwargs, 'model': 'timegpt-1-long-horizon'})\n",
+    "    fcst_1_df = test_equal_fcsts_add_history(**{**kwargs, 'model': 'short-horizon'})\n",
+    "    fcst_2_df = test_equal_fcsts_add_history(**{**kwargs, 'model': 'long-horizon'})\n",
     "    test_fail(\n",
     "        lambda: pd.testing.assert_frame_equal(fcst_1_df, fcst_2_df),\n",
     "        contains='(column name=\"TimeGPT\") are different',\n",
@@ -2088,9 +2122,9 @@
    "source": [
     "#| hide\n",
     "# test different results for different models\n",
-    "fcst_kwargs['model'] = 'timegpt-1'\n",
+    "fcst_kwargs['model'] = 'short-horizon'\n",
     "fcst_timegpt_1 = timegpt.forecast(**fcst_kwargs)\n",
-    "fcst_kwargs['model'] = 'timegpt-1-long-horizon'\n",
+    "fcst_kwargs['model'] = 'long-horizon'\n",
     "fcst_timegpt_long = timegpt.forecast(**fcst_kwargs)\n",
     "test_fail(\n",
     "    lambda: pd.testing.assert_frame_equal(fcst_timegpt_1[['TimeGPT']], fcst_timegpt_long[['TimeGPT']]),\n",
@@ -2107,9 +2141,9 @@
     "#| hide\n",
     "# test different results for different models\n",
     "# anomalies\n",
-    "anomalies_kwargs['model'] = 'timegpt-1'\n",
+    "anomalies_kwargs['model'] = 'short-horizon'\n",
     "anomalies_timegpt_1 = timegpt.detect_anomalies(**anomalies_kwargs)\n",
-    "anomalies_kwargs['model'] = 'timegpt-1-long-horizon'\n",
+    "anomalies_kwargs['model'] = 'long-horizon'\n",
     "anomalies_timegpt_long = timegpt.detect_anomalies(**anomalies_kwargs)\n",
     "test_fail(\n",
     "    lambda: pd.testing.assert_frame_equal(anomalies_timegpt_1[['TimeGPT']], anomalies_timegpt_long[['TimeGPT']]),\n",
@@ -2548,6 +2582,13 @@
    "source": [
     "show_doc(TimeGPT.detect_anomalies, title_level=2)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

diff --git a/nixtlats/distributed/timegpt.py b/nixtlats/distributed/timegpt.py
@@ -133,7 +133,7 @@ def forecast(
         add_history: bool = False,
         date_features: Union[bool, List[str]] = False,
         date_features_to_one_hot: Union[bool, List[str]] = True,
-        model: str = "timegpt-1",
+        model: str = "short-horizon",
         num_partitions: Optional[int] = None,
     ) -> fugue.AnyDataFrame:
         kwargs = dict(
@@ -182,7 +182,7 @@ def detect_anomalies(
         validate_token: bool = False,
         date_features: Union[bool, List[str]] = False,
         date_features_to_one_hot: Union[bool, List[str]] = True,
-        model: str = "timegpt-1",
+        model: str = "short-horizon",
         num_partitions: Optional[int] = None,
     ) -> fugue.AnyDataFrame:
         kwargs = dict(
@@ -225,7 +225,7 @@ def cross_validation(
         validate_token: bool = False,
         date_features: Union[bool, List[str]] = False,
         date_features_to_one_hot: Union[bool, List[str]] = True,
-        model: str = "timegpt-1",
+        model: str = "short-horizon",
         n_windows: int = 1,
         step_size: Optional[int] = None,
         num_partitions: Optional[int] = None,