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mosex.ipynb
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{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7HTnJwZ85d-K"
},
"outputs": [],
"source": [
"!pip install autogluon\n",
"!pip install dask[dataframe]"
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor\n",
"import plotly.graph_objects as go"
],
"metadata": {
"id": "Ng_gNqjI6Io1"
},
"execution_count": 99,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = pd.read_csv(\"https://gist.githubusercontent.com/d8rt8v/1557598f0135695dbcad8ce463dfc2fd/raw/e41f637f43725263dae92ad0d1b06f148a46af7c/MoscowExchangeIndex.csv\",sep=\";\")"
],
"metadata": {
"id": "CO-URU6A6L0c"
},
"execution_count": 125,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = df.drop(columns=['VALUE', 'DURATION', 'YIELD','OPEN'])\n",
"df['TRADEDATE'] = pd.to_datetime(df['TRADEDATE'], format='%d.%m.%Y')\n",
"df = df.rename(columns={'CLOSE': 'target'})\n",
"df = df.sort_values(by=['TRADEDATE'])"
],
"metadata": {
"id": "bs0UWHm37v7J"
},
"execution_count": 127,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df.head()"
],
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" ID NAME TRADEDATE HIGH LOW target\n",
"0 IMOEX Индекс МосБиржи 2012-01-03 1445.57 1401.21 1444.76\n",
"1 IMOEX Индекс МосБиржи 2012-01-04 1453.27 1437.62 1447.59\n",
"2 IMOEX Индекс МосБиржи 2012-01-05 1458.14 1432.88 1434.91\n",
"3 IMOEX Индекс МосБиржи 2012-01-06 1453.92 1431.97 1440.60\n",
"4 IMOEX Индекс МосБиржи 2012-01-09 1449.55 1432.01 1448.36"
],
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" <td>Индекс МосБиржи</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>IMOEX</td>\n",
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" <th>3</th>\n",
" <td>IMOEX</td>\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 3250,\n \"fields\": [\n {\n \"column\": \"ID\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"IMOEX\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"NAME\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"\\u0418\\u043d\\u0434\\u0435\\u043a\\u0441 \\u041c\\u043e\\u0441\\u0411\\u0438\\u0440\\u0436\\u0438\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"TRADEDATE\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2012-01-03 00:00:00\",\n \"max\": \"2024-12-10 00:00:00\",\n \"num_unique_values\": 3250,\n \"samples\": [\n \"2021-07-09 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"HIGH\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 734.0660786057459,\n \"min\": 1248.29,\n \"max\": 4292.68,\n \"num_unique_values\": 3230,\n \"samples\": [\n 1537.08\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LOW\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 726.4325807662012,\n \"min\": 1182.86,\n \"max\": 4259.13,\n \"num_unique_values\": 3220,\n \"samples\": [\n 1493.55\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"target\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 730.3755801471954,\n \"min\": 1237.43,\n \"max\": 4287.52,\n \"num_unique_values\": 3212,\n \"samples\": [\n 1515.36\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 128
}
]
},
{
"cell_type": "code",
"source": [
"train_df = df[:-100].copy()\n",
"test_df = df[-100:].copy()"
],
"metadata": {
"id": "ZkF3k2WmJn97"
},
"execution_count": 155,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train_data = TimeSeriesDataFrame.from_data_frame(\n",
" train_df,\n",
" id_column=\"ID\",\n",
" timestamp_column=\"TRADEDATE\",\n",
")\n",
"\n",
"test_data = TimeSeriesDataFrame.from_data_frame(\n",
" test_df,\n",
" id_column=\"ID\",\n",
" timestamp_column=\"TRADEDATE\",\n",
")\n",
"\n",
"\n",
"print(f\"Length of train_data: {len(train_data)}\")\n",
"print(f\"Length of test_data: {len(test_data)}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "pmnvxKSX9bJJ",
"outputId": "b907dd68-e83b-4fc2-fef6-57b1865a47fd"
},
"execution_count": 156,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Length of train_data: 3150\n",
"Length of test_data: 100\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"predictor = TimeSeriesPredictor(\n",
" prediction_length=100,\n",
" target=\"target\",\n",
" freq='B'\n",
").fit(train_data=train_data,\n",
" presets=\"high_quality\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "olZUH3SO81FS",
"outputId": "a28c736b-7c0c-4e68-f2c4-fc08e45c1042"
},
"execution_count": 157,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Beginning AutoGluon training...\n",
"AutoGluon will save models to '/content/AutogluonModels/ag-20241210_203038'\n",
"=================== System Info ===================\n",
"AutoGluon Version: 1.2\n",
"Python Version: 3.10.12\n",
"Operating System: Linux\n",
"Platform Machine: x86_64\n",
"Platform Version: #1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024\n",
"CPU Count: 2\n",
"GPU Count: 1\n",
"Memory Avail: 9.20 GB / 12.67 GB (72.6%)\n",
"Disk Space Avail: 77.63 GB / 112.64 GB (68.9%)\n",
"===================================================\n",
"Setting presets to: high_quality\n",
"\n",
"Fitting with arguments:\n",
"{'enable_ensemble': True,\n",
" 'eval_metric': WQL,\n",
" 'freq': 'B',\n",
" 'hyperparameters': 'default',\n",
" 'known_covariates_names': [],\n",
" 'num_val_windows': 1,\n",
" 'prediction_length': 100,\n",
" 'quantile_levels': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n",
" 'random_seed': 123,\n",
" 'refit_every_n_windows': 1,\n",
" 'refit_full': False,\n",
" 'skip_model_selection': False,\n",
" 'target': 'target',\n",
" 'verbosity': 2}\n",
"\n",
"train_data with frequency 'None' has been resampled to frequency 'B'.\n",
"Provided train_data has 3276 rows (NaN fraction=4.2%), 1 time series. Median time series length is 3276 (min=3276, max=3276). \n",
"\n",
"Provided data contains following columns:\n",
"\ttarget: 'target'\n",
"\tpast_covariates:\n",
"\t\tcategorical: ['NAME']\n",
"\t\tcontinuous (float): ['HIGH', 'LOW']\n",
"\n",
"To learn how to fix incorrectly inferred types, please see documentation for TimeSeriesPredictor.fit\n",
"\n",
"AutoGluon will gauge predictive performance using evaluation metric: 'WQL'\n",
"\tThis metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.\n",
"===================================================\n",
"\n",
"Starting training. Start time is 2024-12-10 20:30:39\n",
"Models that will be trained: ['SeasonalNaive', 'RecursiveTabular', 'DirectTabular', 'NPTS', 'DynamicOptimizedTheta', 'AutoETS', 'ChronosZeroShot[bolt_base]', 'ChronosFineTuned[bolt_small]', 'TemporalFusionTransformer', 'DeepAR', 'PatchTST', 'TiDE']\n",
"Training timeseries model SeasonalNaive. \n",
"\t-0.0316 = Validation score (-WQL)\n",
"\t0.02 s = Training runtime\n",
"\t0.02 s = Validation (prediction) runtime\n",
"Training timeseries model RecursiveTabular. \n",
"\t-0.0298 = Validation score (-WQL)\n",
"\t2.31 s = Training runtime\n",
"\t0.90 s = Validation (prediction) runtime\n",
"Training timeseries model DirectTabular. \n",
"\t-0.1053 = Validation score (-WQL)\n",
"\t132.67 s = Training runtime\n",
"\t2.06 s = Validation (prediction) runtime\n",
"Training timeseries model NPTS. \n",
"\t-0.1548 = Validation score (-WQL)\n",
"\t0.02 s = Training runtime\n",
"\t2.53 s = Validation (prediction) runtime\n",
"Training timeseries model DynamicOptimizedTheta. \n",
"\t-0.0310 = Validation score (-WQL)\n",
"\t0.03 s = Training runtime\n",
"\t1.49 s = Validation (prediction) runtime\n",
"Training timeseries model AutoETS. \n",
"\t-0.0354 = Validation score (-WQL)\n",
"\t0.03 s = Training runtime\n",
"\t0.99 s = Validation (prediction) runtime\n",
"Training timeseries model ChronosZeroShot[bolt_base]. \n",
"\t-0.0495 = Validation score (-WQL)\n",
"\t0.02 s = Training runtime\n",
"\t1.59 s = Validation (prediction) runtime\n",
"Training timeseries model ChronosFineTuned[bolt_small]. \n",
"\tSkipping covariate_regressor since the dataset contains no covariates or static features.\n",
"\tSaving fine-tuned model to /content/AutogluonModels/ag-20241210_203038/models/ChronosFineTuned[bolt_small]/W0/fine-tuned-ckpt\n",
"\t-0.0840 = Validation score (-WQL)\n",
"\t238.77 s = Training runtime\n",
"\t0.06 s = Validation (prediction) runtime\n",
"Training timeseries model TemporalFusionTransformer. \n",
"\t-0.0322 = Validation score (-WQL)\n",
"\t54.84 s = Training runtime\n",
"\t0.03 s = Validation (prediction) runtime\n",
"Training timeseries model DeepAR. \n",
"\t-0.0721 = Validation score (-WQL)\n",
"\t48.71 s = Training runtime\n",
"\t0.62 s = Validation (prediction) runtime\n",
"Training timeseries model PatchTST. \n",
"\t-0.0357 = Validation score (-WQL)\n",
"\t42.41 s = Training runtime\n",
"\t0.02 s = Validation (prediction) runtime\n",
"Training timeseries model TiDE. \n",
"\t-0.0382 = Validation score (-WQL)\n",
"\t116.83 s = Training runtime\n",
"\t0.02 s = Validation (prediction) runtime\n",
"Fitting simple weighted ensemble.\n",
"\tEnsemble weights: {'DeepAR': 0.51, 'DirectTabular': 0.28, 'DynamicOptimizedTheta': 0.01, 'RecursiveTabular': 0.2}\n",
"\t-0.0272 = Validation score (-WQL)\n",
"\t1.74 s = Training runtime\n",
"\t5.07 s = Validation (prediction) runtime\n",
"Training complete. Models trained: ['SeasonalNaive', 'RecursiveTabular', 'DirectTabular', 'NPTS', 'DynamicOptimizedTheta', 'AutoETS', 'ChronosZeroShot[bolt_base]', 'ChronosFineTuned[bolt_small]', 'TemporalFusionTransformer', 'DeepAR', 'PatchTST', 'TiDE', 'WeightedEnsemble']\n",
"Total runtime: 648.99 s\n",
"Best model: WeightedEnsemble\n",
"Best model score: -0.0272\n"
]
}
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" model score_val pred_time_val fit_time_marginal \\\n",
"0 WeightedEnsemble -0.027209 5.066502 1.744842 \n",
"1 RecursiveTabular -0.029814 0.896858 2.313147 \n",
"2 DynamicOptimizedTheta -0.031044 1.487603 0.031497 \n",
"3 SeasonalNaive -0.031620 0.020513 0.020091 \n",
"4 TemporalFusionTransformer -0.032169 0.029134 54.835537 \n",
"5 AutoETS -0.035396 0.987028 0.026445 \n",
"6 PatchTST -0.035678 0.020437 42.410388 \n",
"7 TiDE -0.038242 0.023532 116.829893 \n",
"8 ChronosZeroShot[bolt_base] -0.049451 1.593165 0.023069 \n",
"9 DeepAR -0.072094 0.618030 48.714457 \n",
"10 ChronosFineTuned[bolt_small] -0.084037 0.057849 238.773691 \n",
"11 DirectTabular -0.105333 2.064010 132.667889 \n",
"12 NPTS -0.154756 2.529972 0.023121 \n",
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"data with frequency 'None' has been resampled to frequency 'B'.\n",
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" <th></th>\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>0.1</th>\n",
" <th>0.2</th>\n",
" <th>0.3</th>\n",
" <th>0.4</th>\n",
" <th>0.5</th>\n",
" <th>0.6</th>\n",
" <th>0.7</th>\n",
" <th>0.8</th>\n",
" <th>0.9</th>\n",
" </tr>\n",
" <tr>\n",
" <th>item_id</th>\n",
" <th>timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"11\" valign=\"top\">IMOEX</th>\n",
" <th>2024-07-24</th>\n",
" <td>3008.914874</td>\n",
" <td>2922.311135</td>\n",
" <td>2944.682207</td>\n",
" <td>2968.006330</td>\n",
" <td>2990.174574</td>\n",
" <td>3007.069628</td>\n",
" <td>3035.458276</td>\n",
" <td>3085.459008</td>\n",
" <td>3149.336404</td>\n",
" <td>3217.635547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-07-25</th>\n",
" <td>3010.768138</td>\n",
" <td>2914.337804</td>\n",
" <td>2948.492360</td>\n",
" <td>2978.252730</td>\n",
" <td>2991.506532</td>\n",
" <td>3009.843512</td>\n",
" <td>3032.272263</td>\n",
" <td>3077.540903</td>\n",
" <td>3128.516866</td>\n",
" <td>3198.342007</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-07-26</th>\n",
" <td>2999.331151</td>\n",
" <td>2889.921963</td>\n",
" <td>2924.243520</td>\n",
" <td>2956.909769</td>\n",
" <td>2982.269323</td>\n",
" <td>2999.127397</td>\n",
" <td>3034.732508</td>\n",
" <td>3084.380862</td>\n",
" <td>3140.158888</td>\n",
" <td>3215.372497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-07-29</th>\n",
" <td>2992.711704</td>\n",
" <td>2882.007061</td>\n",
" <td>2920.431484</td>\n",
" <td>2955.721430</td>\n",
" <td>2975.747103</td>\n",
" <td>2994.034515</td>\n",
" <td>3029.618459</td>\n",
" <td>3080.394259</td>\n",
" <td>3139.843491</td>\n",
" <td>3203.220033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-07-30</th>\n",
" <td>2992.102271</td>\n",
" <td>2888.924828</td>\n",
" <td>2919.961703</td>\n",
" <td>2944.577150</td>\n",
" <td>2958.976916</td>\n",
" <td>2990.420596</td>\n",
" <td>3034.446933</td>\n",
" <td>3080.464807</td>\n",
" <td>3134.010704</td>\n",
" <td>3207.059828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-04</th>\n",
" <td>2972.474599</td>\n",
" <td>2779.735031</td>\n",
" <td>2839.797352</td>\n",
" <td>2908.074135</td>\n",
" <td>2948.806683</td>\n",
" <td>2972.503593</td>\n",
" <td>3008.831930</td>\n",
" <td>3103.987832</td>\n",
" <td>3145.104049</td>\n",
" <td>3225.996208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-05</th>\n",
" <td>2982.996115</td>\n",
" <td>2791.793610</td>\n",
" <td>2842.069417</td>\n",
" <td>2915.595789</td>\n",
" <td>2959.869701</td>\n",
" <td>2985.270798</td>\n",
" <td>3019.256634</td>\n",
" <td>3109.522671</td>\n",
" <td>3156.594743</td>\n",
" <td>3234.723523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-06</th>\n",
" <td>2989.271622</td>\n",
" <td>2789.402877</td>\n",
" <td>2841.905468</td>\n",
" <td>2915.011780</td>\n",
" <td>2959.693154</td>\n",
" <td>2989.740851</td>\n",
" <td>3024.463422</td>\n",
" <td>3112.861785</td>\n",
" <td>3150.049267</td>\n",
" <td>3252.600919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-09</th>\n",
" <td>2989.288059</td>\n",
" <td>2791.211183</td>\n",
" <td>2841.785203</td>\n",
" <td>2918.116043</td>\n",
" <td>2967.209654</td>\n",
" <td>2992.514217</td>\n",
" <td>3029.533032</td>\n",
" <td>3118.333435</td>\n",
" <td>3158.829189</td>\n",
" <td>3251.203921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-10</th>\n",
" <td>2998.497659</td>\n",
" <td>2797.673709</td>\n",
" <td>2848.533348</td>\n",
" <td>2920.832650</td>\n",
" <td>2970.823996</td>\n",
" <td>2998.789960</td>\n",
" <td>3032.683994</td>\n",
" <td>3119.897387</td>\n",
" <td>3163.658813</td>\n",
" <td>3257.819560</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 10 columns</p>\n",
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}
},
"metadata": {},
"execution_count": 159
}
]
},
{
"cell_type": "code",
"source": [
"item_id_to_plot = train_data.item_ids[0]\n",
"\n",
"# Get past data, predictions, and ground truth (test data) for the chosen item_id\n",
"y_past = train_data.loc[item_id_to_plot, 'target'][-100:]\n",
"y_pred = predictions.loc[item_id_to_plot]\n",
"y_test = test_data.loc[item_id_to_plot, 'target']\n",
"\n",
"# Create the Plotly figure\n",
"fig = go.Figure()\n",
"\n",
"# Past time series values\n",
"fig.add_trace(go.Scatter(\n",
" x=y_past.index,\n",
" y=y_past,\n",
" mode='lines',\n",
" name='Past time series values',\n",
" line=dict(color='blue')\n",
"))\n",
"\n",
"# Mean forecast\n",
"fig.add_trace(go.Scatter(\n",
" x=y_pred.index,\n",
" y=y_pred['mean'],\n",
" mode='lines',\n",
" name='Mean forecast',\n",
" line=dict(color='orange', dash='dash')\n",
"))\n",
"\n",
"# Confidence interval\n",
"fig.add_trace(go.Scatter(\n",
" x=y_pred.index.tolist() + y_pred.index.tolist()[::-1], # x, then x reversed\n",
" y=y_pred['0.9'].tolist() + y_pred['0.1'].tolist()[::-1], # upper, then lower reversed\n",
" fill='toself',\n",
" fillcolor='rgba(255,0,0,0.2)',\n",
" line=dict(color='rgba(255,255,255,0)'),\n",
" hoverinfo=\"skip\",\n",
" name='10%-90% confidence interval'\n",
"))\n",
"\n",
"# Actual time series values (ground truth)\n",
"fig.add_trace(go.Scatter(\n",
" x=y_test.index,\n",
" y=y_test,\n",
" mode='lines',\n",
" name='Actual time series values',\n",
" line=dict(color='green', dash='dot')\n",
"))\n",
"\n",
"# Update layout for better visualization\n",
"fig.update_layout(\n",
" title=f'Forecast vs. Actuals on Test Set for item_id: {item_id_to_plot}',\n",
" yaxis_title='Target Value',\n",
" legend=dict(\n",
" orientation=\"h\",\n",
" yanchor=\"bottom\",\n",
" y=1.02,\n",
" xanchor=\"right\",\n",
" x=1\n",
" )\n",
")\n",
"\n",
"fig.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "8qYRMp3sJE5h",
"outputId": "f25836c2-2625-4979-b6a0-9cdeadb0d93c"
},
"execution_count": 160,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
"<body>\n",
" <div> <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script> <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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