model_deploy.ipynb

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      "/home/teo/dev/narxmodel/narx_darts/venv/lib/python3.10/site-packages/statsforecast/core.py:27: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from darts import TimeSeries\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.models import RNNModel\n"
   ]
  },
  {
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   "source": [
    "df = pd.read_csv('datasets/ph_dataset.csv', header=None, names=['x1', 'x2','y'])\n",
    "data = df[:20]\n",
    "data"
   ]
  },
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   "execution_count": 6,
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   "source": [
    "# Create the target time series\n",
    "target_series = TimeSeries.from_dataframe(df, value_cols='y')\n",
    "\n",
    "# Create the covariate time series\n",
    "covariate_series = TimeSeries.from_dataframe(df, value_cols=['x1', 'x2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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   "source": [
    "import joblib\n",
    "scaler_target = joblib.load('scaler_target.save')\n",
    "scaler_covariate = joblib.load('scaler_covariate.save')\n",
    "\n",
    "# Scale real data\n",
    "target_scaled = scaler_target.fit_transform(target_series)\n",
    "covariate_scaled = scaler_covariate.transform(covariate_series)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
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     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "/home/teo/dev/narxmodel/narx_darts/venv/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:424: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
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     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 76.39it/s]\n"
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   "source": [
    "model = RNNModel.load('model_futurecov')\n",
    "predictions = model.predict(\n",
    "   n=5,  series=target_scaled[:10], future_covariates=covariate_scaled\n",
    ")"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 1, sample: 1)&gt; Size: 40B\n",
       "array([[[11.14258475]],\n",
       "\n",
       "       [[11.24126753]],\n",
       "\n",
       "       [[11.25133821]],\n",
       "\n",
       "       [[11.3103636 ]],\n",
       "\n",
       "       [[11.31991208]]])\n",
       "Coordinates:\n",
       "  * time       (time) int64 40B 10 11 12 13 14\n",
       "  * component  (component) object 8B &#x27;y&#x27;\n",
       "Dimensions without coordinates: sample\n",
       "Attributes:\n",
       "    static_covariates:  None\n",
       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2bcb05aa-fe32-4c54-9f20-3cc71d9a42f2' class='xr-array-in' type='checkbox' checked><label for='section-2bcb05aa-fe32-4c54-9f20-3cc71d9a42f2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>11.14 11.24 11.25 11.31 11.32</span></div><div class='xr-array-data'><pre>array([[[11.14258475]],\n",
       "\n",
       "       [[11.24126753]],\n",
       "\n",
       "       [[11.25133821]],\n",
       "\n",
       "       [[11.3103636 ]],\n",
       "\n",
       "       [[11.31991208]]])</pre></div></div></li><li class='xr-section-item'><input id='section-a488e754-4981-4845-9c8c-bc4e8faffddd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a488e754-4981-4845-9c8c-bc4e8faffddd' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>10 11 12 13 14</div><input id='attrs-45bbd161-77ee-4b87-9e07-3a66ad238fd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45bbd161-77ee-4b87-9e07-3a66ad238fd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-147eb845-235e-47db-a213-33b0f5138919' class='xr-var-data-in' type='checkbox'><label for='data-147eb845-235e-47db-a213-33b0f5138919' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([10, 11, 12, 13, 14])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;y&#x27;</div><input id='attrs-a46c86e6-5db0-4449-a804-1f9b6f3999e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a46c86e6-5db0-4449-a804-1f9b6f3999e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92831062-5886-434d-81ae-3a0e3c05f259' class='xr-var-data-in' type='checkbox'><label for='data-92831062-5886-434d-81ae-3a0e3c05f259' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;y&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80cf1ad9-beb4-4a0c-b3fa-cef9cec358c2' class='xr-section-summary-in' type='checkbox'  ><label for='section-80cf1ad9-beb4-4a0c-b3fa-cef9cec358c2' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9dafab4a-9562-4a10-9c63-5b416451bed3' class='xr-index-data-in' type='checkbox'/><label for='index-9dafab4a-9562-4a10-9c63-5b416451bed3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(RangeIndex(start=10, stop=15, step=1, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c43c29bf-9527-4139-8cb0-df89236fdf19' class='xr-index-data-in' type='checkbox'/><label for='index-c43c29bf-9527-4139-8cb0-df89236fdf19' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;y&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-28dc8da7-717f-46c3-b1d4-3dff2a79e941' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28dc8da7-717f-46c3-b1d4-3dff2a79e941' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)> Size: 40B\n",
       "array([[[11.14258475]],\n",
       "\n",
       "       [[11.24126753]],\n",
       "\n",
       "       [[11.25133821]],\n",
       "\n",
       "       [[11.3103636 ]],\n",
       "\n",
       "       [[11.31991208]]])\n",
       "Coordinates:\n",
       "  * time       (time) int64 40B 10 11 12 13 14\n",
       "  * component  (component) object 8B 'y'\n",
       "Dimensions without coordinates: sample\n",
       "Attributes:\n",
       "    static_covariates:  None\n",
       "    hierarchy:          None"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_real = scaler_target.inverse_transform(predictions)\n",
    "predictions_real"
   ]
  }
 ],
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