[DOCS] Fix examples (#1092)

* fix_examples * comments_marco_and_slack_comment_on_default_config_auto_models * fix_mint
Nixtla · Jul 30, 2024 · 926937a · 926937a
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diff --git a/nbs/docs/capabilities/01_overview.ipynb b/nbs/docs/capabilities/01_overview.ipynb
@@ -40,7 +40,7 @@
     "|`TCN` | `AutoTCN` | CNN | Univariate | Recursive  | F/H/S | \n",
     "|`TFT` | `AutoTFT` | Transformer | Univariate | Direct  | F/H/S | \n",
     "|`TiDE` | `AutoTiDE` | MLP | Univariate | Direct  | F/H/S | \n",
-    "|`TimeMixer` | `AutoTimeMixer` | MLP | Multivariate | Direct  | F | \n",
+    "|`TimeMixer` | `AutoTimeMixer` | MLP | Multivariate | Direct  | - | \n",
     "|`TimeLLM` | - | LLM | Univariate | Direct  | - | \n",
     "|`TimesNet` | `AutoTimesNet` | CNN | Univariate | Direct  | F | \n",
     "|`TSMixer` | `AutoTSMixer` | MLP | Multivariate | Direct  | - | \n",

diff --git a/nbs/mint.json b/nbs/mint.json
@@ -79,7 +79,7 @@
             "docs/tutorials/temporal_classification.html",
             "docs/tutorials/transfer_learning.html",
             "docs/tutorials/adding_models.html",
-            "docs/tutorials/large_datasets_tutorial.html"    
+            "docs/tutorials/large_datasets.html"    
           ]
         }               
       ]

diff --git a/nbs/models.autoformer.ipynb b/nbs/models.autoformer.ipynb
@@ -693,16 +693,12 @@
    "outputs": [],
    "source": [
     "#| eval: false\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
-    "from neuralforecast.models import MLP\n",
-    "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss\n",
-    "from neuralforecast.tsdataset import TimeSeriesDataset\n",
-    "from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n",
+    "from neuralforecast.models import Autoformer\n",
+    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n",
     "\n",
     "AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')\n",
     "\n",

diff --git a/nbs/models.bitcn.ipynb b/nbs/models.bitcn.ipynb
@@ -403,22 +403,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "#| eval: false\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
-    "from neuralforecast.losses.pytorch import GMM, DistributionLoss\n",
-    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "from neuralforecast.models import BiTCN\n",
+    "from neuralforecast.losses.pytorch import GMM\n",
+    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic\n",
+    "\n",
     "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train\n",
     "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
     "\n",

diff --git a/nbs/models.deepar.ipynb b/nbs/models.deepar.ipynb
@@ -601,18 +601,6 @@
     "## Usage Example"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from neuralforecast import NeuralForecast\n",
-    "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss, GMM, PMM\n",
-    "from neuralforecast.tsdataset import TimeSeriesDataset\n",
-    "from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -621,16 +609,13 @@
    "source": [
     "#| eval: false\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
-    "#from neuralforecast.models import DeepAR\n",
-    "from neuralforecast.losses.pytorch import DistributionLoss, HuberMQLoss\n",
-    "from neuralforecast.tsdataset import TimeSeriesDataset\n",
-    "from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic\n",
+    "from neuralforecast.models import DeepAR\n",
+    "from neuralforecast.losses.pytorch import DistributionLoss\n",
+    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic\n",
     "\n",
-    "#AirPassengersPanel['y'] = AirPassengersPanel['y'] + 10\n",
     "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train\n",
     "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
     "\n",
@@ -661,7 +646,6 @@
     "\n",
     "plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)\n",
     "plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n",
-    "#plt.plot(plot_df['ds'], plot_df['DeepAR'], c='purple', label='mean')\n",
     "plt.plot(plot_df['ds'], plot_df['DeepAR-median'], c='blue', label='median')\n",
     "plt.fill_between(x=plot_df['ds'][-12:], \n",
     "                 y1=plot_df['DeepAR-lo-90'][-12:].values, \n",
@@ -671,13 +655,6 @@
     "plt.grid()\n",
     "plt.plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/nbs/models.deepnpts.ipynb b/nbs/models.deepnpts.ipynb
@@ -313,19 +313,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| eval: false\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
-    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "from neuralforecast.models import DeepNPTS\n",
+    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic\n",
+    "\n",
     "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train\n",
     "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
     "\n",

diff --git a/nbs/models.dilated_rnn.ipynb b/nbs/models.dilated_rnn.ipynb
@@ -565,16 +565,13 @@
    "outputs": [],
    "source": [
     "#| eval: false\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
     "from neuralforecast.models import DilatedRNN\n",
-    "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss\n",
+    "from neuralforecast.losses.pytorch import DistributionLoss\n",
     "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic\n",
-    "from neuralforecast.tsdataset import TimeSeriesDataset, TimeSeriesLoader\n",
     "\n",
     "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train\n",
     "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
@@ -611,13 +608,6 @@
     "plt.grid()\n",
     "plt.plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/nbs/models.dlinear.ipynb b/nbs/models.dlinear.ipynb
@@ -315,16 +315,12 @@
    "outputs": [],
    "source": [
     "#| eval: false\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
-    "from neuralforecast.models import MLP\n",
-    "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss\n",
-    "from neuralforecast.tsdataset import TimeSeriesDataset\n",
-    "from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n",
+    "from neuralforecast.models import DLinear\n",
+    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n",
     "\n",
     "AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')\n",
     "\n",
@@ -334,7 +330,6 @@
     "model = DLinear(h=12,\n",
     "                 input_size=24,\n",
     "                 loss=MAE(),\n",
-    "                 #loss=DistributionLoss(distribution='StudentT', level=[80, 90], return_params=True),\n",
     "                 scaler_type='robust',\n",
     "                 learning_rate=1e-3,\n",
     "                 max_steps=500,\n",
@@ -370,13 +365,6 @@
     "    plt.legend()\n",
     "    plt.grid()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/nbs/models.fedformer.ipynb b/nbs/models.fedformer.ipynb
@@ -697,30 +697,18 @@
    "outputs": [],
    "source": [
     "#| eval: false\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
-    "from neuralforecast.models import MLP\n",
-    "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss, MSE\n",
-    "from neuralforecast.tsdataset import TimeSeriesDataset\n",
-    "from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n",
+    "from neuralforecast.models import FEDformer\n",
+    "from neuralforecast.utils import AirPassengersPanel, augment_calendar_df\n",
     "\n",
     "AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')\n",
     "\n",
     "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train\n",
-    "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#| eval: false\n",
+    "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
+    "\n",
     "model = FEDformer(h=12,\n",
     "                 input_size=24,\n",
     "                 modes=64,\n",

diff --git a/nbs/models.gru.ipynb b/nbs/models.gru.ipynb
@@ -315,16 +315,13 @@
    "outputs": [],
    "source": [
     "#| eval: false\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
     "from neuralforecast.models import GRU\n",
-    "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss\n",
+    "from neuralforecast.losses.pytorch import DistributionLoss\n",
     "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic\n",
-    "from neuralforecast.tsdataset import TimeSeriesDataset, TimeSeriesLoader\n",
     "\n",
     "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train\n",
     "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
@@ -364,13 +361,6 @@
     "plt.grid()\n",
     "plt.plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/nbs/models.hint.ipynb b/nbs/models.hint.ipynb
@@ -537,7 +537,6 @@
    "outputs": [],
    "source": [
     "#| eval: false\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast.losses.pytorch import GMM, sCRPS\n",

diff --git a/nbs/models.informer.ipynb b/nbs/models.informer.ipynb
@@ -531,16 +531,12 @@
    "outputs": [],
    "source": [
     "#| eval: false\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from neuralforecast import NeuralForecast\n",
-    "from neuralforecast.models import MLP\n",
-    "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss\n",
-    "from neuralforecast.tsdataset import TimeSeriesDataset\n",
-    "from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n",
+    "from neuralforecast.models import Informer\n",
+    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n",
     "\n",
     "AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')\n",
     "\n",
@@ -552,7 +548,6 @@
     "                 hidden_size = 16,\n",
     "                 conv_hidden_size = 32,\n",
     "                 n_head = 2,\n",
-    "                 #loss=DistributionLoss(distribution='StudentT', level=[80, 90]),\n",
     "                 loss=MAE(),\n",
     "                 futr_exog_list=calendar_cols,\n",
     "                 scaler_type='robust',\n",