autogluon_SOC_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded data from: Data/phil_socdata_trainLSTM.csv | Columns = 8 / 8 | Rows = 421709 -> 421709\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>SOC</th>\n",
       "      <th>V</th>\n",
       "      <th>I</th>\n",
       "      <th>T</th>\n",
       "      <th>V_avg</th>\n",
       "      <th>I_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th>timestamp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">SOC</th>\n",
       "      <th>1970-01-01 00:00:00</th>\n",
       "      <td>100.000031</td>\n",
       "      <td>400.125</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>16.5</td>\n",
       "      <td>400.125</td>\n",
       "      <td>-1.50</td>\n",
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       "      <th>1970-01-01 00:00:01</th>\n",
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       "      <td>-1.56</td>\n",
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       "      <td>400.125</td>\n",
       "      <td>-1.53</td>\n",
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       "    <tr>\n",
       "      <th>1970-01-01 00:00:02</th>\n",
       "      <td>100.000031</td>\n",
       "      <td>400.125</td>\n",
       "      <td>-1.62</td>\n",
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       "      <td>400.125</td>\n",
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       "      <th>1970-01-01 00:00:03</th>\n",
       "      <td>100.000031</td>\n",
       "      <td>400.125</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>16.5</td>\n",
       "      <td>400.125</td>\n",
       "      <td>-1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:04</th>\n",
       "      <td>100.000031</td>\n",
       "      <td>400.125</td>\n",
       "      <td>-1.74</td>\n",
       "      <td>16.5</td>\n",
       "      <td>400.125</td>\n",
       "      <td>-1.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                    SOC        V     I     T    V_avg  I_avg\n",
       "item_id timestamp                                                           \n",
       "SOC     1970-01-01 00:00:00  100.000031  400.125 -1.50  16.5  400.125  -1.50\n",
       "        1970-01-01 00:00:01  100.000031  400.125 -1.56  16.5  400.125  -1.53\n",
       "        1970-01-01 00:00:02  100.000031  400.125 -1.62  16.5  400.125  -1.56\n",
       "        1970-01-01 00:00:03  100.000031  400.125 -1.68  16.5  400.125  -1.59\n",
       "        1970-01-01 00:00:04  100.000031  400.125 -1.74  16.5  400.125  -1.62"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = TimeSeriesDataFrame.from_path(f'Data/phil_socdata_trainLSTM.csv')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    421709.000000\n",
       "mean         68.186101\n",
       "std          24.503003\n",
       "min           0.000000\n",
       "25%          49.019623\n",
       "50%          73.333359\n",
       "75%          89.019638\n",
       "max         100.000031\n",
       "Name: SOC, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = 'SOC'\n",
    "train_data[label].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No path specified. Models will be saved in: \"AutogluonModels/ag-20230405_175457/\"\n",
      "================ TimeSeriesPredictor ================\n",
      "TimeSeriesPredictor.fit() called\n",
      "Setting presets to: best_quality\n",
      "Fitting with arguments:\n",
      "{'enable_ensemble': True,\n",
      " 'evaluation_metric': None,\n",
      " 'hyperparameter_tune_kwargs': {'num_trials': 10,\n",
      "                                'scheduler': 'local',\n",
      "                                'searcher': 'auto'},\n",
      " 'hyperparameters': 'best_quality',\n",
      " 'prediction_length': 10,\n",
      " 'random_seed': None,\n",
      " 'target': 'SOC',\n",
      " 'time_limit': 60}\n",
      "Provided training data set with 421709 rows, 1 items (item = single time series). Average time series length is 421709.0.\n",
      "Training artifacts will be saved to: /home/witvoetk/SOCprediction/AutogluonModels/ag-20230405_175457\n",
      "=====================================================\n",
      "AutoGluon will save models to AutogluonModels/ag-20230405_175457/\n",
      "AutoGluon will gauge predictive performance using evaluation metric: 'mean_wQuantileLoss'\n",
      "\tThis metric's sign has been flipped to adhere to being 'higher is better'. The reported score can be multiplied by -1 to get the metric value.\n",
      "\n",
      "Provided dataset contains following columns:\n",
      "\ttarget:           'SOC'\n",
      "\tpast covariates:  ['V', 'I', 'T']\n",
      "tuning_data is None. Will use the last prediction_length = 10 time steps of each time series as a hold-out validation set.\n",
      "\n",
      "Starting training. Start time is 2023-04-05 17:54:59\n",
      "Models that will be trained: ['Naive', 'SeasonalNaive', 'ETS', 'Theta', 'ARIMA', 'AutoETS', 'DynamicOptimizedTheta', 'DeepAR', 'AutoARIMA', 'TemporalFusionTransformer', 'SimpleFeedForward']\n",
      "Hyperparameter tuning model: Naive. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\t-0.0099       = Validation score (-mean_wQuantileLoss)\n",
      "\t0.00    s     = Training runtime\n",
      "\t3.80    s     = Validation (prediction) runtime\n",
      "Hyperparameter tuning model: SeasonalNaive. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\t-0.0099       = Validation score (-mean_wQuantileLoss)\n",
      "\t0.00    s     = Training runtime\n",
      "\t3.11    s     = Validation (prediction) runtime\n",
      "Hyperparameter tuning model: ETS. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "ETS with seasonal = add requires seasonal_period > 1 (received seasonal_period = 1). Disabling seasonality.\n",
      "\t-0.0099       = Validation score (-mean_wQuantileLoss)\n",
      "\t0.00    s     = Training runtime\n",
      "\t4.98    s     = Validation (prediction) runtime\n",
      "Hyperparameter tuning model: Theta. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\tStopping HPO to satisfy time limit...\n",
      "\tTrained 1 models while tuning Theta.\n",
      "\t-0.0099       = Validation score (-mean_wQuantileLoss)\n",
      "\t19.73   s     = Total tuning time\n",
      "Hyperparameter tuning model: ARIMA. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\t-0.0101       = Validation score (-mean_wQuantileLoss)\n",
      "\t0.00    s     = Training runtime\n",
      "\t50.49   s     = Validation (prediction) runtime\n",
      "Hyperparameter tuning model: AutoETS. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\tWarning: Exception caused AutoETS to fail during training... Skipping this model.\n",
      "\t\n",
      "Hyperparameter tuning model: DynamicOptimizedTheta. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\tWarning: Exception caused DynamicOptimizedTheta to fail during training... Skipping this model.\n",
      "\t\n",
      "Hyperparameter tuning model: DeepAR. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\tStopping HPO to satisfy time limit...\n",
      "\tTrained 1 models while tuning DeepAR.\n",
      "\t-0.1102       = Validation score (-mean_wQuantileLoss)\n",
      "\t6.32    s     = Total tuning time\n",
      "Hyperparameter tuning model: AutoARIMA. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\tWarning: Exception caused AutoARIMA to fail during training... Skipping this model.\n",
      "\t\n",
      "Hyperparameter tuning model: TemporalFusionTransformer. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\t-0.0000       = Validation score (-mean_wQuantileLoss)\n",
      "\t7.76    s     = Training runtime\n",
      "\t0.36    s     = Validation (prediction) runtime\n",
      "Hyperparameter tuning model: SimpleFeedForward. Tuning model for up to 5.33s of the 58.65s remaining.\n",
      "\tWarning: Exception caused SimpleFeedForward/T1 to fail during training... Skipping this model.\n",
      "\tExpected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!\n",
      "\tStopping HPO to satisfy time limit...\n",
      "\tTrained 0 models while tuning SimpleFeedForward.\n",
      "Not fitting ensemble due to lack of time remaining. Time left: -43.60 seconds\n",
      "Training complete. Models trained: ['Naive', 'SeasonalNaive', 'ETS', 'Theta/T1', 'ARIMA', 'DeepAR/T1', 'TemporalFusionTransformer']\n",
      "Total runtime: 102.26 s\n",
      "Best model: TemporalFusionTransformer\n",
      "Best model score: -0.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<autogluon.timeseries.predictor.TimeSeriesPredictor at 0x7fc5c47a37c0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor = TimeSeriesPredictor(prediction_length=10,\n",
    "                                target=\"SOC\",\n",
    "                                ignore_time_index=True\n",
    "                                )\n",
    "\n",
    "#drop the V_avg and I_avg columns\n",
    "train_data = train_data.drop(columns=['V_avg','I_avg'])\n",
    "\n",
    "predictor.fit(train_data, presets='best_quality', time_limit=60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loaded data from: Data/phil_socdata_testLSTM.csv | Columns = 6 / 6 | Rows = 18307 -> 18307\n"
     ]
    },
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       "      <th>SOC</th>\n",
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       "      <th>I</th>\n",
       "      <th>T</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">SOC</th>\n",
       "      <th>1970-01-01 00:00:00</th>\n",
       "      <td>97.647087</td>\n",
       "      <td>397.125</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>13.5</td>\n",
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       "      <th>1970-01-01 00:00:01</th>\n",
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       "    <tr>\n",
       "      <th>1970-01-01 00:00:03</th>\n",
       "      <td>97.647087</td>\n",
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       "      <td>-1.35</td>\n",
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       "    <tr>\n",
       "      <th>1970-01-01 00:00:04</th>\n",
       "      <td>97.647087</td>\n",
       "      <td>397.125</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                   SOC        V     I     T\n",
       "item_id timestamp                                          \n",
       "SOC     1970-01-01 00:00:00  97.647087  397.125 -1.35  13.5\n",
       "        1970-01-01 00:00:01  97.647087  397.125 -1.35  13.5\n",
       "        1970-01-01 00:00:02  97.647087  397.125 -1.35  13.5\n",
       "        1970-01-01 00:00:03  97.647087  397.125 -1.35  13.5\n",
       "        1970-01-01 00:00:04  97.647087  397.125 -1.35  13.5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = TimeSeriesDataFrame(f'Data/phil_socdata_testLSTM.csv')\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 123\n",
      "Model not specified in predict, will default to the model with the best validation score: TemporalFusionTransformer\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">SOC</th>\n",
       "      <th>1900-01-01 05:05:07</th>\n",
       "      <td>13.331537</td>\n",
       "      <td>13.328155</td>\n",
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       "      <td>13.333343</td>\n",
       "      <td>13.331282</td>\n",
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       "      <td>13.331570</td>\n",
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       "      <td>13.331225</td>\n",
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       "      <td>13.331582</td>\n",
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       "      <td>13.328981</td>\n",
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       "      <td>13.331178</td>\n",
       "      <td>13.333972</td>\n",
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       "      <th>1900-01-01 05:05:11</th>\n",
       "      <td>13.331589</td>\n",
       "      <td>13.328172</td>\n",
       "      <td>13.328993</td>\n",
       "      <td>13.330102</td>\n",
       "      <td>13.330377</td>\n",
       "      <td>13.331589</td>\n",
       "      <td>13.333760</td>\n",
       "      <td>13.333470</td>\n",
       "      <td>13.331174</td>\n",
       "      <td>13.333991</td>\n",
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      ],
      "text/plain": [
       "                                  mean        0.1        0.2        0.3  \\\n",
       "item_id timestamp                                                         \n",
       "SOC     1900-01-01 05:05:07  13.331537  13.328155  13.328960  13.330254   \n",
       "        1900-01-01 05:05:08  13.331570  13.328162  13.328974  13.330175   \n",
       "        1900-01-01 05:05:09  13.331582  13.328168  13.328981  13.330133   \n",
       "        1900-01-01 05:05:10  13.331587  13.328171  13.328987  13.330112   \n",
       "        1900-01-01 05:05:11  13.331589  13.328172  13.328993  13.330102   \n",
       "\n",
       "                                   0.4        0.5        0.6        0.7  \\\n",
       "item_id timestamp                                                         \n",
       "SOC     1900-01-01 05:05:07  13.330401  13.331537  13.333875  13.333343   \n",
       "        1900-01-01 05:05:08  13.330372  13.331570  13.333809  13.333415   \n",
       "        1900-01-01 05:05:09  13.330368  13.331582  13.333780  13.333447   \n",
       "        1900-01-01 05:05:10  13.330371  13.331587  13.333766  13.333463   \n",
       "        1900-01-01 05:05:11  13.330377  13.331589  13.333760  13.333470   \n",
       "\n",
       "                                   0.8        0.9  \n",
       "item_id timestamp                                  \n",
       "SOC     1900-01-01 05:05:07  13.331282  13.333893  \n",
       "        1900-01-01 05:05:08  13.331225  13.333921  \n",
       "        1900-01-01 05:05:09  13.331193  13.333949  \n",
       "        1900-01-01 05:05:10  13.331178  13.333972  \n",
       "        1900-01-01 05:05:11  13.331174  13.333991  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = predictor.predict(test_data)\n",
    "y_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model not specified in predict, will default to the model with the best validation score: TemporalFusionTransformer\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.00010470281926864162"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Additional data provided, testing on additional data. Resulting leaderboard will be sorted according to test score (`score_test`).\n",
      "/home/witvoetk/anaconda3/envs/tf/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:700: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>score_test</th>\n",
       "      <th>score_val</th>\n",
       "      <th>pred_time_test</th>\n",
       "      <th>pred_time_val</th>\n",
       "      <th>fit_time_marginal</th>\n",
       "      <th>fit_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TemporalFusionTransformer</td>\n",
       "      <td>-0.000105</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>0.129628</td>\n",
       "      <td>0.363127</td>\n",
       "      <td>7.762802</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SeasonalNaive</td>\n",
       "      <td>-0.002135</td>\n",
       "      <td>-0.009907</td>\n",
       "      <td>5.191964</td>\n",
       "      <td>3.111771</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Naive</td>\n",
       "      <td>-0.002135</td>\n",
       "      <td>-0.009907</td>\n",
       "      <td>2.875815</td>\n",
       "      <td>3.804099</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Theta/T1</td>\n",
       "      <td>-0.002210</td>\n",
       "      <td>-0.009902</td>\n",
       "      <td>1.001933</td>\n",
       "      <td>19.692640</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>-0.002565</td>\n",
       "      <td>-0.010148</td>\n",
       "      <td>0.915862</td>\n",
       "      <td>50.485954</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ETS</td>\n",
       "      <td>-0.002803</td>\n",
       "      <td>-0.009934</td>\n",
       "      <td>2.816763</td>\n",
       "      <td>4.984004</td>\n",
       "      <td>0.001280</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DeepAR/T1</td>\n",
       "      <td>-0.117356</td>\n",
       "      <td>-0.110197</td>\n",
       "      <td>0.085516</td>\n",
       "      <td>0.377995</td>\n",
       "      <td>5.891390</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       model  score_test  score_val  pred_time_test  \\\n",
       "0  TemporalFusionTransformer   -0.000105  -0.000029        0.129628   \n",
       "1              SeasonalNaive   -0.002135  -0.009907        5.191964   \n",
       "2                      Naive   -0.002135  -0.009907        2.875815   \n",
       "3                   Theta/T1   -0.002210  -0.009902        1.001933   \n",
       "4                      ARIMA   -0.002565  -0.010148        0.915862   \n",
       "5                        ETS   -0.002803  -0.009934        2.816763   \n",
       "6                  DeepAR/T1   -0.117356  -0.110197        0.085516   \n",
       "\n",
       "   pred_time_val  fit_time_marginal  fit_order  \n",
       "0       0.363127           7.762802          7  \n",
       "1       3.111771           0.000247          2  \n",
       "2       3.804099           0.000292          1  \n",
       "3      19.692640           0.000275          4  \n",
       "4      50.485954           0.000395          5  \n",
       "5       4.984004           0.001280          3  \n",
       "6       0.377995           5.891390          6  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.leaderboard(test_data, silent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'value' must be an instance of str or bytes, not a tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[26], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      3\u001b[0m plt\u001b[39m.\u001b[39mplot(y_pred[\u001b[39m'\u001b[39m\u001b[39mmean\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m0\u001b[39m])\n\u001b[0;32m----> 4\u001b[0m plt\u001b[39m.\u001b[39;49mplot(test_data[\u001b[39m'\u001b[39;49m\u001b[39mSOC\u001b[39;49m\u001b[39m'\u001b[39;49m])\n\u001b[1;32m      5\u001b[0m plt\u001b[39m.\u001b[39mshow()\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/pyplot.py:2812\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2810\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mplot)\n\u001b[1;32m   2811\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mplot\u001b[39m(\u001b[39m*\u001b[39margs, scalex\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, scaley\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m-> 2812\u001b[0m     \u001b[39mreturn\u001b[39;00m gca()\u001b[39m.\u001b[39;49mplot(\n\u001b[1;32m   2813\u001b[0m         \u001b[39m*\u001b[39;49margs, scalex\u001b[39m=\u001b[39;49mscalex, scaley\u001b[39m=\u001b[39;49mscaley,\n\u001b[1;32m   2814\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m({\u001b[39m\"\u001b[39;49m\u001b[39mdata\u001b[39;49m\u001b[39m\"\u001b[39;49m: data} \u001b[39mif\u001b[39;49;00m data \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m {}), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1688\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1445\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1446\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1447\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1685\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1686\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1687\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1688\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m   1689\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m   1690\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_base.py:311\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m     this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m    310\u001b[0m     args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 311\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(\n\u001b[1;32m    312\u001b[0m     this, kwargs, ambiguous_fmt_datakey\u001b[39m=\u001b[39;49mambiguous_fmt_datakey)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    496\u001b[0m     x, y \u001b[39m=\u001b[39m index_of(xy[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m])\n\u001b[1;32m    498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39mxaxis \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49maxes\u001b[39m.\u001b[39;49mxaxis\u001b[39m.\u001b[39;49mupdate_units(x)\n\u001b[1;32m    500\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39myaxis \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    501\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mupdate_units(y)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axis.py:1675\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1673\u001b[0m neednew \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconverter \u001b[39m!=\u001b[39m converter\n\u001b[1;32m   1674\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconverter \u001b[39m=\u001b[39m converter\n\u001b[0;32m-> 1675\u001b[0m default \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconverter\u001b[39m.\u001b[39;49mdefault_units(data, \u001b[39mself\u001b[39;49m)\n\u001b[1;32m   1676\u001b[0m \u001b[39mif\u001b[39;00m default \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39munits \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1677\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_units(default)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/category.py:105\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[0;34m(data, axis)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[39m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[1;32m    104\u001b[0m \u001b[39mif\u001b[39;00m axis\u001b[39m.\u001b[39munits \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 105\u001b[0m     axis\u001b[39m.\u001b[39mset_units(UnitData(data))\n\u001b[1;32m    106\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    107\u001b[0m     axis\u001b[39m.\u001b[39munits\u001b[39m.\u001b[39mupdate(data)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/category.py:181\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    179\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_counter \u001b[39m=\u001b[39m itertools\u001b[39m.\u001b[39mcount()\n\u001b[1;32m    180\u001b[0m \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 181\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mupdate(data)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/category.py:216\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    213\u001b[0m convertible \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    214\u001b[0m \u001b[39mfor\u001b[39;00m val \u001b[39min\u001b[39;00m OrderedDict\u001b[39m.\u001b[39mfromkeys(data):\n\u001b[1;32m    215\u001b[0m     \u001b[39m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m     _api\u001b[39m.\u001b[39;49mcheck_isinstance((\u001b[39mstr\u001b[39;49m, \u001b[39mbytes\u001b[39;49m), value\u001b[39m=\u001b[39;49mval)\n\u001b[1;32m    217\u001b[0m     \u001b[39mif\u001b[39;00m convertible:\n\u001b[1;32m    218\u001b[0m         \u001b[39m# this will only be called so long as convertible is True.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m         convertible \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_str_is_convertible(val)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/_api/__init__.py:93\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[0;34m(_types, **kwargs)\u001b[0m\n\u001b[1;32m     91\u001b[0m     names\u001b[39m.\u001b[39mremove(\u001b[39m\"\u001b[39m\u001b[39mNone\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     92\u001b[0m     names\u001b[39m.\u001b[39mappend(\u001b[39m\"\u001b[39m\u001b[39mNone\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 93\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[1;32m     94\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m{!r}\u001b[39;00m\u001b[39m must be an instance of \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, not a \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m     95\u001b[0m         k,\n\u001b[1;32m     96\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(names[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]) \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m or \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m names[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n\u001b[1;32m     97\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(names) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m \u001b[39melse\u001b[39;00m names[\u001b[39m0\u001b[39m],\n\u001b[1;32m     98\u001b[0m         type_name(\u001b[39mtype\u001b[39m(v))))\n",
      "\u001b[0;31mTypeError\u001b[0m: 'value' must be an instance of str or bytes, not a tuple"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plots the mean column in y_pred as well as the actual SOC values\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(y_pred['mean'])\n",
    "plt.plot(test_data['SOC'][0])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'value' must be an instance of str or bytes, not a tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m#plots the predicted vs actual values of the top performing model using matplotlib\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m plt\u001b[39m.\u001b[39;49mplot(y_pred[\u001b[39m\"\u001b[39;49m\u001b[39mmean\u001b[39;49m\u001b[39m\"\u001b[39;49m], label\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mPredictions\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      4\u001b[0m plt\u001b[39m.\u001b[39mplot(test_data[label], label\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mTrue Values\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m plt\u001b[39m.\u001b[39mlegend()\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/pyplot.py:2812\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2810\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mplot)\n\u001b[1;32m   2811\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mplot\u001b[39m(\u001b[39m*\u001b[39margs, scalex\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, scaley\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m-> 2812\u001b[0m     \u001b[39mreturn\u001b[39;00m gca()\u001b[39m.\u001b[39;49mplot(\n\u001b[1;32m   2813\u001b[0m         \u001b[39m*\u001b[39;49margs, scalex\u001b[39m=\u001b[39;49mscalex, scaley\u001b[39m=\u001b[39;49mscaley,\n\u001b[1;32m   2814\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m({\u001b[39m\"\u001b[39;49m\u001b[39mdata\u001b[39;49m\u001b[39m\"\u001b[39;49m: data} \u001b[39mif\u001b[39;49;00m data \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m {}), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1688\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1445\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1446\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1447\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1685\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1686\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1687\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1688\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m   1689\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m   1690\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_base.py:311\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m     this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m    310\u001b[0m     args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 311\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(\n\u001b[1;32m    312\u001b[0m     this, kwargs, ambiguous_fmt_datakey\u001b[39m=\u001b[39;49mambiguous_fmt_datakey)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_base.py:499\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    496\u001b[0m     x, y \u001b[39m=\u001b[39m index_of(xy[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m])\n\u001b[1;32m    498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39mxaxis \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49maxes\u001b[39m.\u001b[39;49mxaxis\u001b[39m.\u001b[39;49mupdate_units(x)\n\u001b[1;32m    500\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39myaxis \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    501\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mupdate_units(y)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axis.py:1675\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1673\u001b[0m neednew \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconverter \u001b[39m!=\u001b[39m converter\n\u001b[1;32m   1674\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconverter \u001b[39m=\u001b[39m converter\n\u001b[0;32m-> 1675\u001b[0m default \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconverter\u001b[39m.\u001b[39;49mdefault_units(data, \u001b[39mself\u001b[39;49m)\n\u001b[1;32m   1676\u001b[0m \u001b[39mif\u001b[39;00m default \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39munits \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1677\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_units(default)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/category.py:105\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[0;34m(data, axis)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[39m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[1;32m    104\u001b[0m \u001b[39mif\u001b[39;00m axis\u001b[39m.\u001b[39munits \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 105\u001b[0m     axis\u001b[39m.\u001b[39mset_units(UnitData(data))\n\u001b[1;32m    106\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    107\u001b[0m     axis\u001b[39m.\u001b[39munits\u001b[39m.\u001b[39mupdate(data)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/category.py:181\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    179\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_counter \u001b[39m=\u001b[39m itertools\u001b[39m.\u001b[39mcount()\n\u001b[1;32m    180\u001b[0m \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 181\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mupdate(data)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/category.py:216\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    213\u001b[0m convertible \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    214\u001b[0m \u001b[39mfor\u001b[39;00m val \u001b[39min\u001b[39;00m OrderedDict\u001b[39m.\u001b[39mfromkeys(data):\n\u001b[1;32m    215\u001b[0m     \u001b[39m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m     _api\u001b[39m.\u001b[39;49mcheck_isinstance((\u001b[39mstr\u001b[39;49m, \u001b[39mbytes\u001b[39;49m), value\u001b[39m=\u001b[39;49mval)\n\u001b[1;32m    217\u001b[0m     \u001b[39mif\u001b[39;00m convertible:\n\u001b[1;32m    218\u001b[0m         \u001b[39m# this will only be called so long as convertible is True.\u001b[39;00m\n\u001b[1;32m    219\u001b[0m         convertible \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_str_is_convertible(val)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/_api/__init__.py:93\u001b[0m, in \u001b[0;36mcheck_isinstance\u001b[0;34m(_types, **kwargs)\u001b[0m\n\u001b[1;32m     91\u001b[0m     names\u001b[39m.\u001b[39mremove(\u001b[39m\"\u001b[39m\u001b[39mNone\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     92\u001b[0m     names\u001b[39m.\u001b[39mappend(\u001b[39m\"\u001b[39m\u001b[39mNone\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 93\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[1;32m     94\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m{!r}\u001b[39;00m\u001b[39m must be an instance of \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, not a \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m     95\u001b[0m         k,\n\u001b[1;32m     96\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(names[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]) \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m or \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m names[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n\u001b[1;32m     97\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(names) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m \u001b[39melse\u001b[39;00m names[\u001b[39m0\u001b[39m],\n\u001b[1;32m     98\u001b[0m         type_name(\u001b[39mtype\u001b[39m(v))))\n",
      "\u001b[0;31mTypeError\u001b[0m: 'value' must be an instance of str or bytes, not a tuple"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plots the predicted vs actual values of the top performing model using matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(y_pred[\"mean\"], label=\"Predictions\")\n",
    "plt.plot(test_data[label], label=\"True Values\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data rmse 3.6227905364757076\n"
     ]
    }
   ],
   "source": [
    "mse_test = np.mean(((y_pred - test_data[label])**2))\n",
    "rmse_test = math.sqrt(mse_test)\n",
    "print(\"test data rmse\", rmse_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}