diff --git a/notebooks/flowstate.ipynb b/notebooks/flowstate.ipynb index fbf92a3..e120565 100644 --- a/notebooks/flowstate.ipynb +++ b/notebooks/flowstate.ipynb @@ -30,8 +30,10 @@ "metadata": {}, "source": [ "The FlowState source codes will be installed from the [granite-tsfm repository](https://github.com/ibm-granite/granite-tsfm).\n", + "\n", + "Recommended Python version: `python=3.12`.\n", "Note that `granite-tsfm` installs `pandas==2.2.3` but GIFT-EVAL requires `pandas==2.0.0`.\n", - "Hence, after installing TTM from `granite-tsfm`, we forece reinstall `pandas==2.0.0`.\n", + "Hence, after installing TTM from `granite-tsfm`, we force reinstall `pandas==2.0.0`.\n", "\n", "Run the following code once to install granite-tsfm in your working python environment." ] @@ -54,6 +56,7 @@ " !git switch gift-flowstate\n", " ! pip install \".[notebooks]\"\n", " ! pip install pandas==2.0.0\n", + " ! pip install tabulate\n", " %cd ..\n", "else:\n", " print(\"Folder 'granite-tsfm' already exists. Skipping git clone.\")\n", @@ -70,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f28e43ee", "metadata": {}, "outputs": [], @@ -125,11 +128,13 @@ "# Path configurations\n", "model_dir = './checkpoints'\n", "config_dir = './configs'\n", - "out_dir = './results/FlowState-9.1M'\n", + "out_dir = './results/FlowState-r1.1'\n", + "# out_dir = './results/FlowState-9.1M' # to recreate the results of FlowState-9.1M uncomment this line\n", "# out_dir = './results/granite-flowstate-r1' # to recreate the results of granite-flowstate-r1 uncomment this line and the one below\n", "\n", "# Model configurations\n", "model_name = 'ibm-research/FlowState'\n", + "revision = 'r1.1' # change this to 'main' to recreate any of the FlowState 1.0 results\n", "# model_name = 'ibm-granite/granite-timeseries-flowstate-r1' # to recreate the results of granite-flowstate-r1 uncomment this line and the one above\n", "\n", "# Auxiliary configurations\n", @@ -140,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "18a02f1e", "metadata": {}, "outputs": [], @@ -169,7 +174,7 @@ " torch.backends.cudnn.benchmark = False\n", " \n", "def LoadFlowState(pred_length, n_ch, freq, device='cpu', domain=None, nd=False):\n", - " flowstate = FlowStateForPrediction.from_pretrained(model_name).to(device)\n", + " flowstate = FlowStateForPrediction.from_pretrained(model_name, revision=revision).to(device)\n", "\n", " config = flowstate.config\n", " config.min_context = 0\n", @@ -189,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "4c4b0470", "metadata": {}, "outputs": [], @@ -213,7 +218,7 @@ " \"num_variates\"\n", " ]\n", "\n", - "def run_gift_eval(zs=False, save=False, verbose=True): \n", + "def run_gift_eval(save=False, verbose=True): \n", " set_seed(seed)\n", "\n", " # Get union of short and med_long datasets\n", @@ -260,10 +265,7 @@ " done_datasets = []\n", " df_res = pd.DataFrame(columns=base_row)\n", "\n", - " if zs: # the zero-shot subset whithout data leakage for the chronos pretraining corpus (to fairly compare with tirex and chronos models)\n", - " excluded = [\"solar/H\", \"m4_monthly\", \"m4_weekly\", \"m4_daily\", \"m4_hourly\", \"electricity/15T\", \"electricity/H\", \"electricity/W\", \"kdd_cup_2018_with_missing/D\", \"kdd_cup_2018_with_missing/H\", \"temperature_rain_with_missing\"]\n", - " else:\n", - " excluded = []\n", + " excluded = []\n", "\n", " for ds_name in all_datasets:\n", " if ds_name in excluded:\n", @@ -412,118 +414,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "7f29951d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "| | dataset | freq | eval_metrics/MASE[0.5] | eval_metrics/mean_weighted_sum_quantile_loss | normalized MASE | normalized CRPS |\n", - "|---:|:---------------------------------|:-------|-------------------------:|-----------------------------------------------:|------------------:|------------------:|\n", - "| 17 | bitbrains_fast_storage/5T/long | 5T | 1.03746 | 0.825588 | 0.91274 | 0.701251 |\n", - "| 16 | bitbrains_fast_storage/5T/medium | 5T | 1.26089 | 0.779547 | 1.03329 | 0.650754 |\n", - "| 15 | bitbrains_fast_storage/5T/short | 5T | 1.00325 | 0.511307 | 0.883125 | 0.422433 |\n", - "| 18 | bitbrains_fast_storage/H/short | H | 1.10441 | 0.699613 | 0.850518 | 0.684393 |\n", - "| 21 | bitbrains_rnd/5T/long | 5T | 3.5251 | 0.727061 | 1.00681 | 0.618659 |\n", - "| 20 | bitbrains_rnd/5T/medium | 5T | 4.58166 | 0.723809 | 1.00865 | 0.619015 |\n", - "| 19 | bitbrains_rnd/5T/short | 5T | 1.96205 | 0.463204 | 0.995553 | 0.420487 |\n", - "| 22 | bitbrains_rnd/H/short | H | 5.9266 | 0.664596 | 0.981653 | 0.534578 |\n", - "| 25 | bizitobs_application/10S/long | 10S | 3.34113 | 0.0526771 | 1.04204 | 1.15189 |\n", - "| 24 | bizitobs_application/10S/medium | 10S | 2.58931 | 0.0420479 | 0.962063 | 0.98498 |\n", - "| 23 | bizitobs_application/10S/short | 10S | 1.33771 | 0.0135584 | 0.596573 | 0.389176 |\n", - "| 28 | bizitobs_l2c/5T/long | 5T | 0.517741 | 0.230333 | 0.356011 | 0.355182 |\n", - "| 27 | bizitobs_l2c/5T/medium | 5T | 0.519392 | 0.213891 | 0.417652 | 0.411019 |\n", - "| 26 | bizitobs_l2c/5T/short | 5T | 0.312318 | 0.0845809 | 0.316745 | 0.322743 |\n", - "| 31 | bizitobs_l2c/H/long | H | 0.546947 | 0.24757 | 0.383539 | 0.263074 |\n", - "| 30 | bizitobs_l2c/H/medium | H | 0.470457 | 0.221082 | 0.311502 | 0.244504 |\n", - "| 29 | bizitobs_l2c/H/short | H | 0.449897 | 0.188621 | 0.370571 | 0.361921 |\n", - "| 34 | bizitobs_service/10S/long | 10S | 1.38227 | 0.0535224 | 1.01103 | 1.0013 |\n", - "| 33 | bizitobs_service/10S/medium | 10S | 1.14405 | 0.0267494 | 0.866321 | 0.562454 |\n", - "| 32 | bizitobs_service/10S/short | 10S | 0.805999 | 0.0127154 | 0.657795 | 0.318025 |\n", - "| 35 | car_parts/M/short | M | 0.894406 | 1.00765 | 0.74443 | 0.585251 |\n", - "| 36 | covid_deaths/D/short | D | 34.606 | 0.0429938 | 0.737674 | 0.339273 |\n", - "| 39 | electricity/15T/long | 15T | 0.905367 | 0.074951 | 0.77812 | 0.665915 |\n", - "| 38 | electricity/15T/medium | 15T | 0.855655 | 0.0756433 | 0.743538 | 0.670865 |\n", - "| 37 | electricity/15T/short | 15T | 1.08174 | 0.0926971 | 0.629998 | 0.562166 |\n", - "| 40 | electricity/D/short | D | 1.43444 | 0.0550442 | 0.721898 | 0.528966 |\n", - "| 43 | electricity/H/long | H | 1.21109 | 0.08753 | 0.794635 | 0.570531 |\n", - "| 42 | electricity/H/medium | H | 1.09298 | 0.0767882 | 0.784929 | 0.602794 |\n", - "| 41 | electricity/H/short | H | 0.935138 | 0.0667723 | 0.688729 | 0.631925 |\n", - "| 44 | electricity/W/short | W | 1.36356 | 0.0435538 | 0.652513 | 0.438594 |\n", - "| 47 | ett1/15T/long | 15T | 1.01262 | 0.229851 | 0.85031 | 0.675677 |\n", - "| 46 | ett1/15T/medium | 15T | 0.989923 | 0.229325 | 0.833054 | 0.713184 |\n", - "| 45 | ett1/15T/short | 15T | 0.688856 | 0.161747 | 0.737411 | 0.670136 |\n", - "| 48 | ett1/D/short | D | 1.62889 | 0.27995 | 0.915948 | 0.685423 |\n", - "| 51 | ett1/H/long | H | 1.33916 | 0.269986 | 0.905633 | 0.573142 |\n", - "| 50 | ett1/H/medium | H | 1.22679 | 0.247674 | 0.782493 | 0.569481 |\n", - "| 49 | ett1/H/short | H | 0.815035 | 0.176215 | 0.833951 | 0.733105 |\n", - "| 52 | ett1/W/short | W | 1.4509 | 0.245007 | 0.82022 | 0.785718 |\n", - "| 55 | ett2/15T/long | 15T | 0.8656 | 0.0903115 | 0.854781 | 0.679932 |\n", - "| 54 | ett2/15T/medium | 15T | 0.847032 | 0.087208 | 0.805747 | 0.702561 |\n", - "| 53 | ett2/15T/short | 15T | 0.754792 | 0.0658127 | 0.707187 | 0.682815 |\n", - "| 56 | ett2/D/short | D | 1.46356 | 0.103255 | 1.05283 | 0.673502 |\n", - "| 59 | ett2/H/long | H | 1.09652 | 0.111435 | 0.971743 | 0.535985 |\n", - "| 58 | ett2/H/medium | H | 1.03795 | 0.104315 | 0.83812 | 0.560216 |\n", - "| 57 | ett2/H/short | H | 0.721571 | 0.0631823 | 0.781625 | 0.710289 |\n", - "| 60 | ett2/W/short | W | 0.977832 | 0.10407 | 1.25601 | 0.778514 |\n", - "| 61 | hierarchical_sales/D/short | D | 0.749529 | 0.582806 | 0.660493 | 0.335629 |\n", - "| 62 | hierarchical_sales/W/short | W | 0.712427 | 0.339858 | 0.695041 | 0.408372 |\n", - "| 63 | hospital/M/short | M | 0.758454 | 0.0478925 | 0.823934 | 0.766425 |\n", - "| 66 | jena_weather/10T/long | 10T | 0.66081 | 0.0507003 | 0.867783 | 0.21375 |\n", - "| 65 | jena_weather/10T/medium | 10T | 0.625403 | 0.04947 | 0.873456 | 0.23353 |\n", - "| 64 | jena_weather/10T/short | 10T | 0.276762 | 0.0287052 | 0.372549 | 0.184955 |\n", - "| 67 | jena_weather/D/short | D | 1.04854 | 0.0475475 | 0.666409 | 0.225782 |\n", - "| 70 | jena_weather/H/long | H | 1.08511 | 0.0666639 | 0.855953 | 0.159048 |\n", - "| 69 | jena_weather/H/medium | H | 0.857148 | 0.0528815 | 0.96459 | 0.154261 |\n", - "| 68 | jena_weather/H/short | H | 0.526017 | 0.0419073 | 0.727638 | 0.271758 |\n", - "| 71 | kdd_cup_2018/D/short | D | 1.22327 | 0.381202 | 0.817131 | 0.565157 |\n", - "| 74 | kdd_cup_2018/H/long | H | 1.05343 | 0.45749 | 0.78877 | 0.488623 |\n", - "| 73 | kdd_cup_2018/H/medium | H | 1.08468 | 0.436805 | 0.759051 | 0.57566 |\n", - "| 72 | kdd_cup_2018/H/short | H | 0.954226 | 0.381719 | 0.711883 | 0.697156 |\n", - "| 2 | loop_seattle/5T/long | 5T | 0.825018 | 0.0739626 | 0.659653 | 0.58162 |\n", - "| 1 | loop_seattle/5T/medium | 5T | 0.773453 | 0.0691045 | 0.670652 | 0.589229 |\n", - "| 0 | loop_seattle/5T/short | 5T | 0.576032 | 0.0499616 | 0.75561 | 0.618092 |\n", - "| 3 | loop_seattle/D/short | D | 0.875111 | 0.0419987 | 0.505145 | 0.406748 |\n", - "| 6 | loop_seattle/H/long | H | 0.920199 | 0.0631912 | 0.595202 | 0.33775 |\n", - "| 5 | loop_seattle/H/medium | H | 0.980202 | 0.068154 | 0.662069 | 0.420494 |\n", - "| 4 | loop_seattle/H/short | H | 0.857866 | 0.0607178 | 0.663551 | 0.582508 |\n", - "| 75 | m4_daily/D/short | D | 3.59338 | 0.0243155 | 1.09607 | 0.998329 |\n", - "| 76 | m4_hourly/H/short | H | 0.73087 | 0.0204907 | 0.612524 | 0.545364 |\n", - "| 77 | m4_monthly/M/short | M | 0.921779 | 0.0925676 | 0.731735 | 0.759245 |\n", - "| 78 | m4_quarterly/Q/short | Q | 1.15335 | 0.0754948 | 0.719831 | 0.769931 |\n", - "| 79 | m4_weekly/W/short | W | 2.02264 | 0.0366341 | 0.728276 | 0.601838 |\n", - "| 80 | m4_yearly/A/short | A | 2.95725 | 0.106938 | 0.745658 | 0.779739 |\n", - "| 7 | m_dense/D/short | D | 0.732415 | 0.0698466 | 0.438746 | 0.307876 |\n", - "| 10 | m_dense/H/long | H | 0.744425 | 0.121175 | 0.503699 | 0.289234 |\n", - "| 9 | m_dense/H/medium | H | 0.691854 | 0.114626 | 0.440686 | 0.303739 |\n", - "| 8 | m_dense/H/short | H | 0.794889 | 0.131305 | 0.534363 | 0.477823 |\n", - "| 81 | restaurant/D/short | D | 0.681671 | 0.255921 | 0.677555 | 0.378012 |\n", - "| 82 | saugeen/D/short | D | 2.73633 | 0.328865 | 0.801727 | 0.56215 |\n", - "| 83 | saugeen/M/short | M | 0.748076 | 0.295643 | 0.766178 | 0.664241 |\n", - "| 84 | saugeen/W/short | W | 1.16713 | 0.356549 | 0.586303 | 0.485739 |\n", - "| 87 | solar/10T/long | 10T | 0.851155 | 0.337313 | 0.977293 | 0.500576 |\n", - "| 86 | solar/10T/medium | 10T | 0.89924 | 0.356401 | 0.970054 | 0.544071 |\n", - "| 85 | solar/10T/short | 10T | 1.02586 | 0.51998 | 0.927786 | 0.604952 |\n", - "| 88 | solar/D/short | D | 0.99146 | 0.277712 | 0.857767 | 0.496744 |\n", - "| 91 | solar/H/long | H | 0.942006 | 0.333536 | 0.879396 | 0.309409 |\n", - "| 90 | solar/H/medium | H | 0.876472 | 0.345018 | 0.937435 | 0.364718 |\n", - "| 89 | solar/H/short | H | 0.872445 | 0.333144 | 0.916495 | 0.563071 |\n", - "| 92 | solar/W/short | W | 0.838795 | 0.122358 | 0.570473 | 0.583356 |\n", - "| 13 | sz_taxi/15T/long | 15T | 0.529082 | 0.206915 | 0.765739 | 0.48348 |\n", - "| 12 | sz_taxi/15T/medium | 15T | 0.550901 | 0.207774 | 0.772127 | 0.547966 |\n", - "| 11 | sz_taxi/15T/short | 15T | 0.551477 | 0.202554 | 0.721435 | 0.656136 |\n", - "| 14 | sz_taxi/H/short | H | 0.575661 | 0.138328 | 0.779851 | 0.646921 |\n", - "| 93 | temperature_rain/D/short | D | 1.34978 | 0.549786 | 0.671019 | 0.433604 |\n", - "| 94 | us_births/D/short | D | 0.457365 | 0.0236868 | 0.245257 | 0.198172 |\n", - "| 95 | us_births/M/short | M | 0.69953 | 0.0149233 | 0.919792 | 0.886943 |\n", - "| 96 | us_births/W/short | W | 1.01749 | 0.0118816 | 0.650808 | 0.615731 |\n", - "Final GIFT-Eval Performance of ortner/FlowState:\n", - "MASE = 0.7261873224938316, CRPS = 0.5019395365878786\n" - ] - } - ], + "outputs": [], "source": [ "mase, crps = run_gift_eval(verbose=True, save=True)\n", "print(f'Final GIFT-Eval Performance of {model_name}:\\nMASE = {mase}, CRPS = {crps}')" @@ -532,7 +426,7 @@ ], "metadata": { "kernelspec": { - "display_name": "gift", + "display_name": "flowstate11", "language": "python", "name": "python3" }, @@ -546,7 +440,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/results/FlowState-r1.1/all_results.csv b/results/FlowState-r1.1/all_results.csv new file mode 100644 index 0000000..4c8734a --- /dev/null +++ b/results/FlowState-r1.1/all_results.csv @@ -0,0 +1,98 @@ +dataset,model,eval_metrics/MSE[mean],eval_metrics/MSE[0.5],eval_metrics/MAE[mean],eval_metrics/MAE[0.5],eval_metrics/MASE[0.5],eval_metrics/MAPE[0.5],eval_metrics/sMAPE[0.5],eval_metrics/MSIS,eval_metrics/RMSE[mean],eval_metrics/NRMSE[mean],eval_metrics/ND[0.5],eval_metrics/mean_weighted_sum_quantile_loss,domain,num_variates +loop_seattle/5T/short,FlowState-r1.1,41.18631665492575,41.18631665492575,3.5313914894442564,3.5313914894442564,0.5585164871955189,0.0924224082715002,0.07574458536840936,7.1386863681154065,6.417656632675649,0.1100486425372225,0.06055556754191994,0.04832590599299439,Transport,1 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b/results/FlowState-r1.1/config.json @@ -0,0 +1,10 @@ +{ + "model": "FlowState-r1.1", + "model_type": "zero-shot", + "model_dtype": "float32", + "model_link": "https://huggingface.co/ibm-research/flowstate", + "code_link": "https://github.com/SalesforceAIResearch/gift-eval/blob/main/notebooks/flowstate.ipynb", + "org": "IBM TSFM", + "testdata_leakage": "No", + "replication_code_available": "Yes" +} \ No newline at end of file