diff --git a/notebooks/05.0-mc-leaderboard.ipynb b/notebooks/05.0-mc-leaderboard.ipynb index 766ae31..f3932b0 100644 --- a/notebooks/05.0-mc-leaderboard.ipynb +++ b/notebooks/05.0-mc-leaderboard.ipynb @@ -47,8 +47,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:17.677408Z", - "start_time": "2020-10-31T07:15:17.218831Z" + "end_time": "2020-11-01T00:13:26.590150Z", + "start_time": "2020-11-01T00:13:26.122799Z" } }, "outputs": [], @@ -70,8 +70,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:17.697085Z", - "start_time": "2020-10-31T07:15:17.681533Z" + "end_time": "2020-11-01T00:13:26.610237Z", + "start_time": "2020-11-01T00:13:26.594095Z" } }, "outputs": [], @@ -87,8 +87,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:19.330137Z", - "start_time": "2020-10-31T07:15:17.700894Z" + "end_time": "2020-11-01T00:13:28.273951Z", + "start_time": "2020-11-01T00:13:26.623917Z" }, "lines_to_next_cell": 0 }, @@ -118,8 +118,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:22.850470Z", - "start_time": "2020-10-31T07:15:19.336362Z" + "end_time": "2020-11-01T00:13:31.877650Z", + "start_time": "2020-11-01T00:13:28.279980Z" }, "lines_to_next_cell": 2 }, @@ -1613,8 +1613,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:22.909146Z", - "start_time": "2020-10-31T07:15:22.855127Z" + "end_time": "2020-11-01T00:13:31.934276Z", + "start_time": "2020-11-01T00:13:31.882053Z" } }, "outputs": [], @@ -1641,8 +1641,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:22.997834Z", - "start_time": "2020-10-31T07:15:22.914693Z" + "end_time": "2020-11-01T00:13:32.026351Z", + "start_time": "2020-11-01T00:13:31.940205Z" } }, "outputs": [ @@ -1669,8 +1669,8 @@ "print(f'using {device}')\n", "\n", "window_past = 48*2\n", - "window_future = 48*2\n", - "batch_size = 128\n", + "window_future = 48\n", + "batch_size = 16\n", "num_workers = 4\n", "datasets_root = Path('../data/processed/')\n", "window_past" @@ -1688,8 +1688,8 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:23.053171Z", - "start_time": "2020-10-31T07:15:23.002407Z" + "end_time": "2020-11-01T00:13:32.081304Z", + "start_time": "2020-11-01T00:13:32.030668Z" } }, "outputs": [], @@ -1746,8 +1746,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:23.111172Z", - "start_time": "2020-10-31T07:15:23.056938Z" + "end_time": "2020-11-01T00:13:32.137302Z", + "start_time": "2020-11-01T00:13:32.084851Z" }, "lines_to_end_of_cell_marker": 2, "lines_to_next_cell": 0 @@ -1810,8 +1810,8 @@ "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:23.164448Z", - "start_time": "2020-10-31T07:15:23.114871Z" + "end_time": "2020-11-01T00:13:32.195406Z", + "start_time": "2020-11-01T00:13:32.141042Z" }, "lines_to_next_cell": 0 }, @@ -1877,8 +1877,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:23.515878Z", - "start_time": "2020-10-31T07:15:23.173605Z" + "end_time": "2020-11-01T00:13:32.551022Z", + "start_time": "2020-11-01T00:13:32.204839Z" }, "lines_to_next_cell": 0 }, @@ -1923,8 +1923,8 @@ "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T07:15:30.245326Z", - "start_time": "2020-10-31T07:15:23.521182Z" + "end_time": "2020-11-01T00:13:39.425559Z", + "start_time": "2020-11-01T00:13:32.555851Z" } }, "outputs": [ @@ -1943,12 +1943,12 @@ "\n", "\n", "\n", - "
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" - ], - "text/plain": [ - " BaselineLast RANP Transformer \\\n", - "BejingPM25 rmse 1.394592 1.143054 1.198529 \n", - " smape 0.273048 0.230133 0.236831 \n", - " nll 1.725607 1.646027 2.523306 \n", - "GasSensor rmse 36.481861 18.331436 7.037924 \n", - " smape 1.318018 0.770784 0.332106 \n", - " nll 1.742590 30.651764 0.338983 \n", - "AppliancesEnergyPrediction rmse 0.902880 0.683983 0.633746 \n", - " smape 0.148381 0.108471 0.103799 \n", - " nll 1.748227 1.564963 1.574110 \n", - "MetroInterstateTraffic rmse 2787.273926 1248.838501 826.047119 \n", - " smape 0.813702 0.457659 0.183467 \n", - " nll 1.758536 1.230699 2.206728 \n", - "IMOSCurrentsVel rmse 0.099627 0.133368 0.131893 \n", - " smape 0.373574 0.468599 0.457730 \n", - " nll 0.821903 1.489647 2.632229 \n", - "\n", - " TCNSeq2Seq \n", - "BejingPM25 rmse 1.088298 \n", - " smape 0.218957 \n", - " nll 1.576179 \n", - "GasSensor rmse 11.869325 \n", - " smape 0.873217 \n", - " nll 95.340050 \n", - "AppliancesEnergyPrediction rmse 0.598277 \n", - " smape 0.095323 \n", - " nll 1.398728 \n", - "MetroInterstateTraffic rmse 1241.640869 \n", - " smape 0.162155 \n", - " nll 1497.094360 \n", - "IMOSCurrentsVel rmse 0.117519 \n", - " smape 0.419145 \n", - " nll 1.215010 " - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -15945,8 +3257,8 @@ " amp_level='O1',\n", " precision=16,\n", " \n", - " limit_train_batches=800,\n", - " limit_val_batches=150,\n", + " limit_train_batches=1200,\n", + " limit_val_batches=250,\n", " logger=CSVLogger(\"../outputs\", name=f'{dataset_name}_{model_name}'),\n", " callbacks=[\n", " EarlyStopping(monitor='loss/val', patience=patience * 2),\n", @@ -16001,71 +3313,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T09:04:03.482501Z", - "start_time": "2020-10-31T09:04:03.392691Z" + "start_time": "2020-11-01T00:13:26.242Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Negative Log-Likelihood (NLL).\n", - "over 96 steps\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nll BejingPM25 GasSensor AppliancesEnergyPrediction MetroInterstateTraffic IMOSCurrentsVel
BaselineLast1.731.741.751.760.82
RANP1.6530.651.561.231.49
Transformer2.520.341.572.212.63
TCNSeq2Seq1.5895.341.401497.091.22
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print(f'Negative Log-Likelihood (NLL).\\nover {window_future} steps')\n", "df_results = pd.concat({k:pd.DataFrame(v) for k,v in results.items()})\n", @@ -16081,26 +3335,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T09:04:04.317603Z", - "start_time": "2020-10-31T09:04:03.565498Z" + "start_time": "2020-11-01T00:13:26.246Z" } }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "() missing 2 required positional arguments: 'xs' and 'ys'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdataset_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm_fn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpt_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpt_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: () missing 2 required positional arguments: 'xs' and 'ys'" - ] - } - ], + "outputs": [], "source": [ "\n", "# Load saved preds\n", @@ -16124,8 +3365,7 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T09:04:04.322573Z", - "start_time": "2020-10-31T07:15:17.310Z" + "start_time": "2020-11-01T00:13:26.248Z" } }, "outputs": [], @@ -16138,8 +3378,7 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T09:04:04.324660Z", - "start_time": "2020-10-31T07:15:17.314Z" + "start_time": "2020-11-01T00:13:26.251Z" }, "scrolled": false }, @@ -16163,8 +3402,7 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T09:04:04.326584Z", - "start_time": "2020-10-31T07:15:17.318Z" + "start_time": "2020-11-01T00:13:26.255Z" } }, "outputs": [], @@ -16186,8 +3424,7 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-10-31T09:04:04.328632Z", - "start_time": "2020-10-31T07:15:17.322Z" + "start_time": "2020-11-01T00:13:26.258Z" } }, "outputs": [], @@ -16200,21 +3437,41 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-10-29T13:05:05.698677Z", - "start_time": "2020-10-29T13:05:05.618208Z" + "start_time": "2020-11-01T00:13:26.261Z" } }, "outputs": [], - "source": [] + "source": [ + "def plot_at_i(data_i):\n", + " d = ds_preds.isel(t_source=data_i)\n", + " return hv_plot_prediction(d).relabel(label=f\"{model}\")\n", + "dmap = hv.DynamicMap(plot_at_i, kdims=['t_source'])\n", + "t = ds_preds.t_source.values\n", + "dmap = dmap.redim.values(t_source=range(len(t)))\n", + "dmap.opts(framewise=True)" + ] }, { "cell_type": "code", "execution_count": null, "metadata": { + "ExecuteTime": { + "start_time": "2020-11-01T00:13:26.264Z" + }, "lines_to_next_cell": 2 }, "outputs": [], - "source": [] + "source": [ + "# Plot series of predictions\n", + "t_ahead_i=6\n", + "d = ds_preds.isel(t_ahead=t_ahead_i)\n", + "p = datashade(hv.Scatter({\n", + " 'x': d.t_target,\n", + " 'y': d.y_true\n", + "}, label='true').opts(color='black'), cmap='black')\n", + "p *= datashade(hv.Curve({'x': d.t_target, 'y':d.y_pred}), cmap='blue')\n", + "p.opts(title=f'ahead by {d.freq} * {t_ahead_i}')" + ] }, { "cell_type": "code", diff --git a/notebooks/05.0-mc-leaderboard.py b/notebooks/05.0-mc-leaderboard.py index 80245b4..f7e3504 100644 --- a/notebooks/05.0-mc-leaderboard.py +++ b/notebooks/05.0-mc-leaderboard.py @@ -98,8 +98,8 @@ device = "cuda" if torch.cuda.is_available() else "cpu" print(f'using {device}') window_past = 48*2 -window_future = 48*2 -batch_size = 128 +window_future = 48 +batch_size = 16 num_workers = 4 datasets_root = Path('../data/processed/') window_past @@ -356,10 +356,10 @@ def free_mem(): # + -hidden_size = 32 -dropout=0.25 -layers=6 -nhead=8 +hidden_size = 16 +dropout=0.0 +layers=8 +nhead=2 models = [ lambda xs, ys: BaselineLast(), @@ -478,8 +478,8 @@ for Dataset in datasets: amp_level='O1', precision=16, - limit_train_batches=800, - limit_val_batches=150, + limit_train_batches=1200, + limit_val_batches=250, logger=CSVLogger("../outputs", name=f'{dataset_name}_{model_name}'), callbacks=[ EarlyStopping(monitor='loss/val', patience=patience * 2), @@ -573,8 +573,23 @@ n.cols(1) plot_performance(ds_preds, full=True) +def plot_at_i(data_i): + d = ds_preds.isel(t_source=data_i) + return hv_plot_prediction(d).relabel(label=f"{model}") +dmap = hv.DynamicMap(plot_at_i, kdims=['t_source']) +t = ds_preds.t_source.values +dmap = dmap.redim.values(t_source=range(len(t))) +dmap.opts(framewise=True) - +# Plot series of predictions +t_ahead_i=6 +d = ds_preds.isel(t_ahead=t_ahead_i) +p = datashade(hv.Scatter({ + 'x': d.t_target, + 'y': d.y_true +}, label='true').opts(color='black'), cmap='black') +p *= datashade(hv.Curve({'x': d.t_target, 'y':d.y_pred}), cmap='blue') +p.opts(title=f'ahead by {d.freq} * {t_ahead_i}') diff --git a/seq2seq_time/data/util.py b/seq2seq_time/data/util.py index 6015769..4ae5dd4 100644 --- a/seq2seq_time/data/util.py +++ b/seq2seq_time/data/util.py @@ -24,6 +24,6 @@ def timeseries_split(df, test_fraction=0.2, dropna=None): else: index = df.index - i = int(len(index)*test_fraction) + i = int(len(index)*(1- test_fraction)) dt = index.values[i] return df.loc[:dt], df.loc[dt:]