diff --git a/notebooks/05.4-mc-leaderboard-prevent_overfit.ipynb b/notebooks/05.5-mc-leaderboard.ipynb similarity index 67% rename from notebooks/05.4-mc-leaderboard-prevent_overfit.ipynb rename to notebooks/05.5-mc-leaderboard.ipynb index 07144ce..864149c 100644 --- a/notebooks/05.4-mc-leaderboard-prevent_overfit.ipynb +++ b/notebooks/05.5-mc-leaderboard.ipynb @@ -46,8 +46,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:16:54.539105Z", - "start_time": "2020-11-02T10:16:54.136971Z" + "end_time": "2020-11-02T12:00:57.453551Z", + "start_time": "2020-11-02T12:00:57.027678Z" } }, "outputs": [], @@ -69,8 +69,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:16:56.550689Z", - "start_time": "2020-11-02T10:16:54.543943Z" + "end_time": "2020-11-02T12:00:59.103287Z", + "start_time": "2020-11-02T12:00:57.458194Z" }, "lines_to_next_cell": 0 }, @@ -100,8 +100,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:00.076283Z", - "start_time": "2020-11-02T10:16:56.555605Z" + "end_time": "2020-11-02T12:01:02.628614Z", + "start_time": "2020-11-02T12:00:59.107092Z" }, "lines_to_next_cell": 2 }, @@ -1595,8 +1595,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:00.147121Z", - "start_time": "2020-11-02T10:17:00.083455Z" + "end_time": "2020-11-02T12:01:02.685794Z", + "start_time": "2020-11-02T12:01:02.633908Z" } }, "outputs": [], @@ -1611,8 +1611,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:00.227032Z", - "start_time": "2020-11-02T10:17:00.152632Z" + "end_time": "2020-11-02T12:01:02.740390Z", + "start_time": "2020-11-02T12:01:02.689143Z" } }, "outputs": [], @@ -1647,8 +1647,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:00.357235Z", - "start_time": "2020-11-02T10:17:00.230843Z" + "end_time": "2020-11-02T12:01:02.842266Z", + "start_time": "2020-11-02T12:01:02.744471Z" }, "lines_to_next_cell": 0 }, @@ -1658,7 +1658,7 @@ "output_type": "stream", "text": [ "using cuda\n", - "20201102-181700\n" + "20201102-200102\n" ] }, { @@ -1711,8 +1711,8 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:00.440513Z", - "start_time": "2020-11-02T10:17:00.364247Z" + "end_time": "2020-11-02T12:01:02.899834Z", + "start_time": "2020-11-02T12:01:02.847536Z" } }, "outputs": [], @@ -1766,8 +1766,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:00.511695Z", - "start_time": "2020-11-02T10:17:00.445774Z" + "end_time": "2020-11-02T12:01:02.963123Z", + "start_time": "2020-11-02T12:01:02.904269Z" }, "lines_to_end_of_cell_marker": 2, "lines_to_next_cell": 0 @@ -1838,8 +1838,8 @@ "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:00.576679Z", - "start_time": "2020-11-02T10:17:00.517026Z" + "end_time": "2020-11-02T12:01:03.022627Z", + "start_time": "2020-11-02T12:01:02.968692Z" }, "lines_to_next_cell": 0 }, @@ -1901,8 +1901,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:00.947336Z", - "start_time": "2020-11-02T10:17:00.584589Z" + "end_time": "2020-11-02T12:01:03.398812Z", + "start_time": "2020-11-02T12:01:03.027763Z" }, "lines_to_next_cell": 0 }, @@ -1946,8 +1946,8 @@ "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:01.005162Z", - "start_time": "2020-11-02T10:17:00.952221Z" + "end_time": "2020-11-02T12:01:03.453178Z", + "start_time": "2020-11-02T12:01:03.404391Z" } }, "outputs": [], @@ -1984,8 +1984,8 @@ "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:01.246769Z", - "start_time": "2020-11-02T10:17:01.008756Z" + "end_time": "2020-11-02T12:01:03.514583Z", + "start_time": "2020-11-02T12:01:03.457007Z" } }, "outputs": [], @@ -2035,8 +2035,8 @@ "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:01.305470Z", - "start_time": "2020-11-02T10:17:01.251009Z" + "end_time": "2020-11-02T12:01:03.577478Z", + "start_time": "2020-11-02T12:01:03.518763Z" }, "lines_to_next_cell": 2 }, @@ -2060,8 +2060,8 @@ "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:01.367121Z", - "start_time": "2020-11-02T10:17:01.309146Z" + "end_time": "2020-11-02T12:01:03.639061Z", + "start_time": "2020-11-02T12:01:03.581402Z" }, "lines_to_end_of_cell_marker": 2, "lines_to_next_cell": 0 @@ -2085,8 +2085,8 @@ "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:01.423947Z", - "start_time": "2020-11-02T10:17:01.370271Z" + "end_time": "2020-11-02T12:01:03.692032Z", + "start_time": "2020-11-02T12:01:03.642598Z" }, "lines_to_next_cell": 0 }, @@ -2115,11 +2115,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:36.363587Z", - "start_time": "2020-11-02T10:17:36.292994Z" + "end_time": "2020-11-02T12:01:03.771436Z", + "start_time": "2020-11-02T12:01:03.699336Z" }, "lines_to_next_cell": 0 }, @@ -2127,15 +2127,15 @@ "source": [ "# PARAMS: model\n", "## Some datasets are easier, so we will vary the hidden size to predict overfitting\n", - "hidden_size={'IMOSCurrentsVel': 6, #?\n", - " 'AppliancesEnergyPrediction': 6, # ?\n", + "hidden_size={'IMOSCurrentsVel': 8, #?\n", + " 'AppliancesEnergyPrediction': 8, # ?\n", " 'BejingPM25': 8, # OK\n", " 'GasSensor': 8, # OK\n", " 'MetroInterstateTraffic': 16 # OK\n", " }\n", "dropout=0.0\n", "layers=6\n", - "nhead=2\n", + "nhead=4\n", "\n", "models = [\n", "# lambda xs, ys: BaselineLast(),\n", @@ -2143,7 +2143,7 @@ " lambda xs, ys, hidden_size: Transformer(xs,\n", " ys,\n", " attention_dropout=dropout,\n", - " nhead=nhead*2,\n", + " nhead=nhead,\n", " nlayers=layers,\n", " hidden_size=hidden_size),\n", "\n", @@ -2154,7 +2154,7 @@ " lambda xs, ys, hidden_size:TCNSeq(xs, ys, hidden_size=hidden_size, nlayers=layers, dropout=dropout, kernel_size=2),\n", " lambda xs, ys, hidden_size: RANP(xs,\n", " ys, hidden_dim=hidden_size, dropout=dropout, \n", - " latent_dim=hidden_size//2, n_decoder_layers=layers),\n", + " latent_dim=hidden_size//2, n_decoder_layers=layers, n_latent_encoder_layers=layers, n_det_encoder_layers=layers),\n", " lambda xs, ys, hidden_size: TransformerSeq2Seq(xs,\n", " ys,\n", " hidden_size=hidden_size,\n", @@ -2170,7 +2170,7 @@ " lambda xs, ys, hidden_size: LSTMSeq2Seq(xs,\n", " ys,\n", " hidden_size=hidden_size,\n", - " lstm_layers=layers,\n", + " lstm_layers=layers//2,\n", " lstm_dropout=dropout),\n", " lambda xs, ys, hidden_size: CrossAttention(xs,\n", " ys,\n", @@ -2199,11 +2199,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:17:37.891945Z", - "start_time": "2020-11-02T10:17:36.745134Z" + "end_time": "2020-11-02T12:01:08.449737Z", + "start_time": "2020-11-02T12:01:03.956006Z" }, "lines_to_next_cell": 0 }, @@ -2212,7 +2212,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel_launcher.py:30: ResourceWarning: unclosed file <_io.TextIOWrapper name='../outputs/20201102-181700_models.md' mode='w' encoding='UTF-8'>\n" + "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel_launcher.py:30: ResourceWarning: unclosed file <_io.TextIOWrapper name='../outputs/20201102-200102_models.md' mode='w' encoding='UTF-8'>\n" ] }, { @@ -2273,10 +2273,10 @@ " \n", " \n", " RANP\n", - " 19.578k\n", - " 19.578k\n", + " 21.626k\n", + " 21.626k\n", " 0.0\n", - " 21.184k\n", + " 24.256k\n", " \n", " \n", " TransformerSeq2Seq\n", @@ -2294,10 +2294,10 @@ " \n", " \n", " LSTMSeq2Seq\n", - " 25.058k\n", - " 25.058k\n", + " 12.002k\n", + " 12.002k\n", " 0.0\n", - " 23.52k\n", + " 11.232k\n", " \n", " \n", " CrossAttention\n", @@ -2323,10 +2323,10 @@ "Transformer 32.562k 32.562k 0.0 \n", "TransformerProcess 72.722k 72.722k 0.0 \n", "TCNSeq 6.258k 6.258k 0.0 \n", - "RANP 19.578k 19.578k 0.0 \n", + "RANP 21.626k 21.626k 0.0 \n", "TransformerSeq2Seq 71.794k 71.794k 0.0 \n", "LSTM 6.05k 6.05k 0.0 \n", - "LSTMSeq2Seq 25.058k 25.058k 0.0 \n", + "LSTMSeq2Seq 12.002k 12.002k 0.0 \n", "CrossAttention 44.642k 44.642k 0.0 \n", "InceptionTimeSeq 46.346k 46.346k 0.0 \n", "\n", @@ -2335,15 +2335,15 @@ "Transformer 31.088k \n", "TransformerProcess 101.088k \n", "TCNSeq 1.84272M \n", - "RANP 21.184k \n", + "RANP 24.256k \n", "TransformerSeq2Seq 68.368k \n", "LSTM 5.664k \n", - "LSTMSeq2Seq 23.52k \n", + "LSTMSeq2Seq 11.232k \n", "CrossAttention 42.64k \n", "InceptionTimeSeq 6.543744M " ] }, - "execution_count": 21, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2396,11 +2396,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:18:49.452676Z", - "start_time": "2020-11-02T10:18:49.381399Z" + "end_time": "2020-11-02T12:01:08.506907Z", + "start_time": "2020-11-02T12:01:08.454072Z" }, "lines_to_next_cell": 2 }, @@ -2412,11 +2412,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:18:49.514448Z", - "start_time": "2020-11-02T10:18:49.458343Z" + "end_time": "2020-11-02T12:01:08.561793Z", + "start_time": "2020-11-02T12:01:08.510389Z" }, "lines_to_next_cell": 2 }, @@ -2427,11 +2427,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": { "ExecuteTime": { - "end_time": "2020-11-02T10:18:49.579065Z", - "start_time": "2020-11-02T10:18:49.518824Z" + "end_time": "2020-11-02T12:01:08.619719Z", + "start_time": "2020-11-02T12:01:08.565869Z" } }, "outputs": [ @@ -2440,7 +2440,7 @@ "output_type": "stream", "text": [ "For tensorboard run:\n", - "tensorboard --logdir=\"/media/wassname/Storage5/projects2/3ST/seq2seq-time/outputs/20201102-181700\"\n" + "tensorboard --logdir=\"/media/wassname/Storage5/projects2/3ST/seq2seq-time/outputs/20201102-200102\"\n" ] } ], @@ -2451,10 +2451,122 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "ExecuteTime": { - "start_time": "2020-11-02T10:18:49.393Z" + "end_time": "2020-11-02T12:01:25.441589Z", + "start_time": "2020-11-02T12:01:08.623017Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20201102-200102 GasSensor BaselineMean\n", + "20201102-200102 GasSensor Transformer\n", + "20201102-200102 GasSensor TransformerProcess\n", + "20201102-200102 GasSensor TCNSeq\n", + "20201102-200102 GasSensor RANP\n", + "20201102-200102 GasSensor TransformerSeq2Seq\n", + "20201102-200102 GasSensor LSTM\n", + "20201102-200102 GasSensor LSTMSeq2Seq\n", + "20201102-200102 GasSensor CrossAttention\n", + "20201102-200102 GasSensor InceptionTimeSeq\n", + "20201102-200102 IMOSCurrentsVel BaselineMean\n", + "20201102-200102 IMOSCurrentsVel Transformer\n", + "20201102-200102 IMOSCurrentsVel TransformerProcess\n", + "20201102-200102 IMOSCurrentsVel TCNSeq\n", + "20201102-200102 IMOSCurrentsVel RANP\n", + "20201102-200102 IMOSCurrentsVel TransformerSeq2Seq\n", + "20201102-200102 IMOSCurrentsVel LSTM\n", + "20201102-200102 IMOSCurrentsVel LSTMSeq2Seq\n", + "20201102-200102 IMOSCurrentsVel CrossAttention\n", + "20201102-200102 IMOSCurrentsVel InceptionTimeSeq\n", + "20201102-200102 AppliancesEnergyPrediction BaselineMean\n", + "20201102-200102 AppliancesEnergyPrediction Transformer\n", + "20201102-200102 AppliancesEnergyPrediction TransformerProcess\n", + "20201102-200102 AppliancesEnergyPrediction TCNSeq\n", + "20201102-200102 AppliancesEnergyPrediction RANP\n", + "20201102-200102 AppliancesEnergyPrediction TransformerSeq2Seq\n", + "20201102-200102 AppliancesEnergyPrediction LSTM\n", + "20201102-200102 AppliancesEnergyPrediction LSTMSeq2Seq\n", + "20201102-200102 AppliancesEnergyPrediction CrossAttention\n", + "20201102-200102 AppliancesEnergyPrediction InceptionTimeSeq\n", + "20201102-200102 BejingPM25 BaselineMean\n", + "20201102-200102 BejingPM25 Transformer\n", + "20201102-200102 BejingPM25 TransformerProcess\n", + "20201102-200102 BejingPM25 TCNSeq\n", + "20201102-200102 BejingPM25 RANP\n", + "20201102-200102 BejingPM25 TransformerSeq2Seq\n", + "20201102-200102 BejingPM25 LSTM\n", + "20201102-200102 BejingPM25 LSTMSeq2Seq\n", + "20201102-200102 BejingPM25 CrossAttention\n", + "20201102-200102 BejingPM25 InceptionTimeSeq\n", + "20201102-200102 MetroInterstateTraffic BaselineMean\n", + "20201102-200102 MetroInterstateTraffic Transformer\n", + "20201102-200102 MetroInterstateTraffic TransformerProcess\n", + "20201102-200102 MetroInterstateTraffic TCNSeq\n", + "20201102-200102 MetroInterstateTraffic RANP\n", + "20201102-200102 MetroInterstateTraffic TransformerSeq2Seq\n", + "20201102-200102 MetroInterstateTraffic LSTM\n", + "20201102-200102 MetroInterstateTraffic LSTMSeq2Seq\n", + "20201102-200102 MetroInterstateTraffic CrossAttention\n", + "20201102-200102 MetroInterstateTraffic InceptionTimeSeq\n" + ] + } + ], + "source": [ + "# DEBUG: sanity check\n", + "\n", + "for Dataset in datasets:\n", + " dataset_name = Dataset.__name__\n", + " dataset = Dataset(datasets_root)\n", + " ds_train, ds_val, ds_test = dataset.to_datasets(window_past=window_past,\n", + " window_future=window_future)\n", + "\n", + " # Init data\n", + " x_past, y_past, x_future, y_future = ds_train.get_rows(10)\n", + " xs = x_past.shape[-1]\n", + " ys = y_future.shape[-1]\n", + "\n", + " # Loaders\n", + " dl_train = DataLoader(ds_train,\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", + " pin_memory=num_workers == 0,\n", + " num_workers=num_workers)\n", + " dl_val = DataLoader(ds_val,\n", + " shuffle=True,\n", + " batch_size=batch_size,\n", + " num_workers=num_workers)\n", + "\n", + " for m_fn in models:\n", + " free_mem()\n", + " pt_model = m_fn(xs, ys, hidden_size[dataset_name])\n", + " model_name = type(pt_model).__name__\n", + " print(timestamp, dataset_name, model_name)\n", + "\n", + " # Wrap in lightning\n", + " model = PL_MODEL(pt_model,\n", + " lr=3e-4\n", + " ).to(device)\n", + " trainer = pl.Trainer(\n", + " fast_dev_run=True,\n", + " # GPU\n", + " gpus=1,\n", + " amp_level='O1',\n", + " precision=16,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2020-11-02T18:57:29.955188Z", + "start_time": "2020-11-02T12:01:25.445939Z" }, "lines_to_next_cell": 0, "scrolled": true @@ -2463,7 +2575,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e5fbd786cfc74c1fbfdf65979d34b4ba", + "model_id": "e1e87ea98e7b4d49b34868d28e969f2d", "version_major": 2, "version_minor": 0 }, @@ -2477,7 +2589,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "544cf6d661034d4189589d0fab9c3955", + "model_id": "90aa8f7130d04d809452f5ecee15aedf", "version_major": 2, "version_minor": 0 }, @@ -2492,7 +2604,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "20201102-181700 GasSensor BaselineMean\n" + "20201102-200102 GasSensor BaselineMean\n" ] }, { @@ -2512,7 +2624,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "711cb2e3a3b54f18ba2ae56648458e41", + "model_id": "b1463ae0dfe34b989b7d5f429fe747ac", "version_major": 2, "version_minor": 0 }, @@ -2607,11 +2719,53 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value='Validating'), FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), m…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value='Validating'), FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), m…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(HTML(value='Validating'), FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), m…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 6: reducing learning rate of group 0 to 3.0000e-05.\n" + "Epoch 9: reducing learning rate of group 0 to 3.0000e-05.\n" ] }, { @@ -2653,17 +2807,17 @@ "data": { "text/html": [ "\n", + " }
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nll GasSensor IMOSCurrentsVel AppliancesEnergyPrediction BejingPM25 MetroInterstateTraffic
BaselineMean1.541.101.411.591.43
Transformer-1.180.931.801.31-0.37
TransformerProcess-0.841.021.171.43-0.33
TCNSeq-0.470.881.101.28-0.15
RANP-1.910.931.251.39-0.36
TransformerSeq2Seq0.691.491.541.49-0.31
LSTM-0.200.971.341.29-0.05
LSTMSeq2Seq0.000.951.201.28-0.29
CrossAttention-0.581.271.241.45-0.34
InceptionTimeSeq-2.071.314.651.32-0.03
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BaselineMeanTransformerTransformerProcessTCNSeqRANPTransformerSeq2SeqLSTMLSTMSeq2SeqCrossAttentionInceptionTimeSeq
GasSensorrmse30.1767169.41651412.9785787.5895963.16676624.89487311.5180307.31176513.9614672.193936
smape1.3024540.3071480.4689180.7264400.2420540.8508120.6000070.6699140.5591430.320668
nll1.542041-1.180013-0.836722-0.474132-1.9063680.692709-0.1999220.004298-0.575165-2.067098
IMOSCurrentsVelrmse0.1313770.1101480.1239720.1058020.1128760.1373020.1143110.1116750.1358250.117366
smape0.4088970.3488600.3931120.3348140.3544700.4216090.3599090.3499480.4260590.371904
nll1.1010740.9290531.0188850.8830370.9268971.4919900.9705320.9513661.2704081.313702
AppliancesEnergyPredictionrmse0.6381130.5893630.5428090.5405300.5640920.5987250.5785090.5479810.5800380.603225
smape0.1074270.0949390.0878700.0869850.0912820.0922750.0921420.0882270.0948180.099783
nll1.4130431.8031101.1655981.0993871.2453241.5404741.3436331.2003631.2402504.647202
BejingPM25rmse1.2250980.9754901.0787000.9630571.0300461.0568090.9663190.9679231.0847770.954078
smape0.2548010.1986670.2273480.1969290.2135560.2180120.1976360.1971640.2284360.193136
nll1.5911471.3059411.4284141.2763561.3882931.4897351.2882471.2781781.4542511.319761
MetroInterstateTrafficrmse2016.557251434.905823460.456573500.520752430.651825460.495270506.585785456.087280445.651703512.640198
smape0.6137510.1003430.1055770.1337560.1046020.1072070.1205300.1051680.1050910.130188
nll1.434845-0.367540-0.333287-0.153166-0.357861-0.307665-0.053744-0.294607-0.336011-0.026490
\n", + "
" + ], + "text/plain": [ + " BaselineMean Transformer \\\n", + "GasSensor rmse 30.176716 9.416514 \n", + " smape 1.302454 0.307148 \n", + " nll 1.542041 -1.180013 \n", + "IMOSCurrentsVel rmse 0.131377 0.110148 \n", + " smape 0.408897 0.348860 \n", + " nll 1.101074 0.929053 \n", + "AppliancesEnergyPrediction rmse 0.638113 0.589363 \n", + " smape 0.107427 0.094939 \n", + " nll 1.413043 1.803110 \n", + "BejingPM25 rmse 1.225098 0.975490 \n", + " smape 0.254801 0.198667 \n", + " nll 1.591147 1.305941 \n", + "MetroInterstateTraffic rmse 2016.557251 434.905823 \n", + " smape 0.613751 0.100343 \n", + " nll 1.434845 -0.367540 \n", + "\n", + " TransformerProcess TCNSeq RANP \\\n", + "GasSensor rmse 12.978578 7.589596 3.166766 \n", + " smape 0.468918 0.726440 0.242054 \n", + " nll -0.836722 -0.474132 -1.906368 \n", + "IMOSCurrentsVel rmse 0.123972 0.105802 0.112876 \n", + " smape 0.393112 0.334814 0.354470 \n", + " nll 1.018885 0.883037 0.926897 \n", + "AppliancesEnergyPrediction rmse 0.542809 0.540530 0.564092 \n", + " smape 0.087870 0.086985 0.091282 \n", + " nll 1.165598 1.099387 1.245324 \n", + "BejingPM25 rmse 1.078700 0.963057 1.030046 \n", + " smape 0.227348 0.196929 0.213556 \n", + " nll 1.428414 1.276356 1.388293 \n", + "MetroInterstateTraffic rmse 460.456573 500.520752 430.651825 \n", + " smape 0.105577 0.133756 0.104602 \n", + " nll -0.333287 -0.153166 -0.357861 \n", + "\n", + " TransformerSeq2Seq LSTM LSTMSeq2Seq \\\n", + "GasSensor rmse 24.894873 11.518030 7.311765 \n", + " smape 0.850812 0.600007 0.669914 \n", + " nll 0.692709 -0.199922 0.004298 \n", + "IMOSCurrentsVel rmse 0.137302 0.114311 0.111675 \n", + " smape 0.421609 0.359909 0.349948 \n", + " nll 1.491990 0.970532 0.951366 \n", + "AppliancesEnergyPrediction rmse 0.598725 0.578509 0.547981 \n", + " smape 0.092275 0.092142 0.088227 \n", + " nll 1.540474 1.343633 1.200363 \n", + "BejingPM25 rmse 1.056809 0.966319 0.967923 \n", + " smape 0.218012 0.197636 0.197164 \n", + " nll 1.489735 1.288247 1.278178 \n", + "MetroInterstateTraffic rmse 460.495270 506.585785 456.087280 \n", + " smape 0.107207 0.120530 0.105168 \n", + " nll -0.307665 -0.053744 -0.294607 \n", + "\n", + " CrossAttention InceptionTimeSeq \n", + "GasSensor rmse 13.961467 2.193936 \n", + " smape 0.559143 0.320668 \n", + " nll -0.575165 -2.067098 \n", + "IMOSCurrentsVel rmse 0.135825 0.117366 \n", + " smape 0.426059 0.371904 \n", + " nll 1.270408 1.313702 \n", + "AppliancesEnergyPrediction rmse 0.580038 0.603225 \n", + " smape 0.094818 0.099783 \n", + " nll 1.240250 4.647202 \n", + "BejingPM25 rmse 1.084777 0.954078 \n", + " smape 0.228436 0.193136 \n", + " nll 1.454251 1.319761 \n", + "MetroInterstateTraffic rmse 445.651703 512.640198 \n", + " smape 0.105091 0.130188 \n", + " nll -0.336011 -0.026490 " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -4929,13 +19482,129 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "ExecuteTime": { - "start_time": "2020-11-02T10:18:49.399Z" + "end_time": "2020-11-02T18:57:30.544597Z", + "start_time": "2020-11-02T18:57:29.960613Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Negative Log-Likelihood (NLL).\n", + "over 48 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", + " \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", + " \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 GasSensor IMOSCurrentsVel AppliancesEnergyPrediction BejingPM25 MetroInterstateTraffic mean(e-e_baseline)
RANP-1.910.931.251.39-0.36-1.16
TransformerProcess-0.841.021.171.43-0.33-0.93
Transformer-1.180.931.801.31-0.37-0.92
TCNSeq-0.470.881.101.28-0.15-0.89
CrossAttention-0.581.271.241.45-0.34-0.81
LSTMSeq2Seq0.000.951.201.28-0.29-0.79
LSTM-0.200.971.341.29-0.05-0.75
TransformerSeq2Seq0.691.491.541.49-0.31-0.43
InceptionTimeSeq-2.071.314.651.32-0.03-0.38
BaselineMean1.541.101.411.591.430.00
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "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", @@ -4944,13 +19613,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "ExecuteTime": { - "start_time": "2020-11-02T10:18:49.403Z" + "end_time": "2020-11-02T18:57:30.627925Z", + "start_time": "2020-11-02T18:57:30.551929Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved to ../outputs/20201102-200102_leaderboard.html\n", + "../outputs/20201102-200102_leaderboard.md\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wassname/anaconda/envs/seq2seq-time/lib/python3.7/site-packages/ipykernel_launcher.py:9: ResourceWarning: unclosed file <_io.TextIOWrapper name='../outputs/20201102-200102_leaderboard.md' mode='w' encoding='UTF-8'>\n", + " if __name__ == '__main__':\n" + ] + } + ], "source": [ "def results_html(results, metric='nll', strformat=\"{:.2f}\"):\n", " df_results = format_results(results, metric=metric)\n", @@ -4976,10 +19663,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "ExecuteTime": { - "start_time": "2020-11-02T10:18:49.408Z" + "end_time": "2020-11-02T18:57:35.418763Z", + "start_time": "2020-11-02T18:57:30.631842Z" } }, "outputs": [], @@ -5003,10 +19691,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "ExecuteTime": { - "start_time": "2020-11-02T10:18:49.412Z" + "end_time": "2020-11-02T18:57:35.471313Z", + "start_time": "2020-11-02T18:57:35.422299Z" } }, "outputs": [], @@ -5016,14 +19705,135 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "ExecuteTime": { - "start_time": "2020-11-02T10:18:49.417Z" + "end_time": "2020-11-02T18:57:42.161977Z", + "start_time": "2020-11-02T18:57:35.478168Z" }, "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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"dmap = dmap.redim.default(t_ahead_i=10, window_steps=400)\n", + "dmap = dmap.redim.default(t_ahead_i=10, window_steps=400, dataset='IMOSCurrentsVel')\n", "dmap" ] }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "ExecuteTime": { + "end_time": "2020-11-02T22:10:42.000730Z", + "start_time": "2020-11-02T22:10:41.946367Z" + } + }, + "outputs": [], + "source": [ + "# def plot_at_i(time_i, dataset, model):\n", + "# d = ds_predss[dataset][model].isel(t_source=time_i)\n", + "# return hv_plot_prediction(d).relabel(label=f\"{model}\")\n", + "\n", + "# dmap = hv.DynamicMap(plot_at_i, kdims=['t_source', 'dataset', 'model'])\n", + "# t = ds_preds.t_source.values\n", + "# models = list(next(iter(ds_predss.values())).keys())\n", + "# dmap = dmap.redim.values(\n", + "# t_source=range(len(t)), \n", + "# dataset=list(ds_predss.keys()),\n", + "# model=models,\n", + "# )\n", + "# dmap.opts(framewise=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "ExecuteTime": { + "end_time": "2020-11-02T22:07:42.023943Z", + "start_time": "2020-11-02T22:07:41.959039Z" + } + }, + "outputs": [], + "source": [ + "# plot_performance(ds_preds, full=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "ExecuteTime": { + "end_time": "2020-11-02T22:14:14.301909Z", + "start_time": "2020-11-02T22:14:14.244048Z" + }, + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "# # Explore predictions with dynamic map\n", + "\n", + "# def plot_predictions_ahead(dataset='IMOSCurrentsVel', model='', t_ahead_i=6, start=0, window_steps=1800):\n", + "# d = next(iter(ds_predss[dataset].values())).isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps))\n", + "\n", + "# p = hv.Scatter({\n", + "# 'x': d.t_target,\n", + "# 'y': d.y_true\n", + "# }, label='true').opts(color='black', framewise=True)\n", + " \n", + "# ds_preds = ds_predss[dataset][model]\n", + "# d = ds_preds.isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps))\n", + "# x = d.t_target\n", + "# y = d.y_pred\n", + "# s = d.y_pred_std\n", + "# p *= hv.Curve({'x': x, 'y':y}, label=model).relabel(label=f\"{model}\")\n", + "# p *= hv.Spread((x, y, s * 2),\n", + "# label='2*std').opts(alpha=0.5, line_width=0)\n", + " \n", + "# p = p.opts(title=f\"Dataset: {dataset}, model={model}, {d.freq}*{t_ahead_i} ahead\", height=250, legend_position='top', ylabel=d.targets)\n", + "# return p.opts(framewise=True)\n", + " \n", + "# dmap = hv.DynamicMap(plot_predictions_ahead, kdims=['dataset', 'model', 't_ahead_i', 'start', 'window_steps'])\n", + "# dmap = dmap.redim.values(dataset=list(ds_predss.keys()), model=models)\n", + "# dmap = dmap.redim.range(t_ahead_i=(0, window_future), start=(0, 5000), window_steps=(10, 5000))\n", + "# dmap = dmap.redim.default(t_ahead_i=10, window_steps=1000)\n", + "# dmap" + ] + }, { "cell_type": "code", "execution_count": null, @@ -5300,7 +20390,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [] } diff --git a/notebooks/05.4-mc-leaderboard-prevent_overfit.py b/notebooks/05.5-mc-leaderboard.py similarity index 87% rename from notebooks/05.4-mc-leaderboard-prevent_overfit.py rename to notebooks/05.5-mc-leaderboard.py index e105a3a..adf6086 100644 --- a/notebooks/05.4-mc-leaderboard-prevent_overfit.py +++ b/notebooks/05.5-mc-leaderboard.py @@ -376,15 +376,15 @@ def free_mem(): # + # PARAMS: model ## Some datasets are easier, so we will vary the hidden size to predict overfitting -hidden_size={'IMOSCurrentsVel': 6, #? - 'AppliancesEnergyPrediction': 6, # ? +hidden_size={'IMOSCurrentsVel': 8, #? + 'AppliancesEnergyPrediction': 8, # ? 'BejingPM25': 8, # OK 'GasSensor': 8, # OK 'MetroInterstateTraffic': 16 # OK } dropout=0.0 layers=6 -nhead=2 +nhead=4 models = [ # lambda xs, ys: BaselineLast(), @@ -392,7 +392,7 @@ models = [ lambda xs, ys, hidden_size: Transformer(xs, ys, attention_dropout=dropout, - nhead=nhead*2, + nhead=nhead, nlayers=layers, hidden_size=hidden_size), @@ -403,7 +403,7 @@ models = [ lambda xs, ys, hidden_size:TCNSeq(xs, ys, hidden_size=hidden_size, nlayers=layers, dropout=dropout, kernel_size=2), lambda xs, ys, hidden_size: RANP(xs, ys, hidden_dim=hidden_size, dropout=dropout, - latent_dim=hidden_size//2, n_decoder_layers=layers), + latent_dim=hidden_size//2, n_decoder_layers=layers, n_latent_encoder_layers=layers, n_det_encoder_layers=layers), lambda xs, ys, hidden_size: TransformerSeq2Seq(xs, ys, hidden_size=hidden_size, @@ -419,7 +419,7 @@ models = [ lambda xs, ys, hidden_size: LSTMSeq2Seq(xs, ys, hidden_size=hidden_size, - lstm_layers=layers, + lstm_layers=layers//2, lstm_dropout=dropout), lambda xs, ys, hidden_size: CrossAttention(xs, ys, @@ -480,6 +480,49 @@ max_iters=20000 tensorboard_dir = Path(f"../outputs/{timestamp}").resolve() print(f'For tensorboard run:\ntensorboard --logdir="{tensorboard_dir}"') +# + +# DEBUG: sanity check + +for Dataset in datasets: + dataset_name = Dataset.__name__ + dataset = Dataset(datasets_root) + ds_train, ds_val, ds_test = dataset.to_datasets(window_past=window_past, + window_future=window_future) + + # Init data + x_past, y_past, x_future, y_future = ds_train.get_rows(10) + xs = x_past.shape[-1] + ys = y_future.shape[-1] + + # Loaders + dl_train = DataLoader(ds_train, + batch_size=batch_size, + shuffle=True, + pin_memory=num_workers == 0, + num_workers=num_workers) + dl_val = DataLoader(ds_val, + shuffle=True, + batch_size=batch_size, + num_workers=num_workers) + + for m_fn in models: + free_mem() + pt_model = m_fn(xs, ys, hidden_size[dataset_name]) + model_name = type(pt_model).__name__ + print(timestamp, dataset_name, model_name) + + # Wrap in lightning + model = PL_MODEL(pt_model, + lr=3e-4 + ).to(device) + trainer = pl.Trainer( + fast_dev_run=True, + # GPU + gpus=1, + amp_level='O1', + precision=16, + ) + # + results = defaultdict(dict) @@ -630,7 +673,7 @@ for dataset in ds_predss.keys(): n += p.opts(title=dataset, legend_position='top_left') n.cols(1).opts(shared_axes=False) -1/0 + dataset='IMOSCurrentsVel' data_i=844 @@ -646,26 +689,7 @@ n.cols(1) # + -# plot_performance(ds_preds, full=True) - -# + -def plot_at_i(time_i, dataset, model): - d = ds_predss[dataset][model].isel(t_source=time_i) - return hv_plot_prediction(d).relabel(label=f"{model}") - -dmap = hv.DynamicMap(plot_at_i, kdims=['t_source', 'dataset', 'model']) -t = ds_preds.t_source.values -models = list(next(iter(ds_predss.values())).keys()) -dmap = dmap.redim.values( - t_source=range(len(t)), - dataset=list(ds_predss.keys()), - model=models, -) -dmap.opts(framewise=True) -# - - -1/0 - +# 1/0 # + # Explore predictions with dynamic map @@ -696,36 +720,6 @@ dmap 1/0 -# + -# Explore predictions with dynamic map - -def plot_predictions_ahead(dataset='IMOSCurrentsVel', model='', t_ahead_i=6, start=0, window_steps=1800): - d = next(iter(ds_predss[dataset].values())).isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps)) - - p = hv.Scatter({ - 'x': d.t_target, - 'y': d.y_true - }, label='true').opts(color='black', framewise=True) - - ds_preds = ds_predss[dataset][model] - d = ds_preds.isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps)) - x = d.t_target - y = d.y_pred - s = d.y_pred_std - p *= hv.Curve({'x': x, 'y':y}, label=model).relabel(label=f"{model}") - p *= hv.Spread((x, y, s * 2), - label='2*std').opts(alpha=0.5, line_width=0) - - p = p.opts(title=f"Dataset: {dataset}, model={model}, {d.freq}*{t_ahead_i} ahead", height=250, legend_position='top', ylabel=d.targets) - return p.opts(framewise=True) - -dmap = hv.DynamicMap(plot_predictions_ahead, kdims=['dataset', 'model', 't_ahead_i', 'start', 'window_steps']) -dmap = dmap.redim.values(dataset=list(ds_predss.keys()), model=models) -dmap = dmap.redim.range(t_ahead_i=(0, window_future), start=(0, 5000), window_steps=(10, 5000)) -dmap = dmap.redim.default(t_ahead_i=10, window_steps=1000) -dmap -# - - # + @@ -752,8 +746,54 @@ def plot_predictions_ahead(dataset='IMOSCurrentsVel', t_ahead_i=6, start=0, wind dmap = hv.DynamicMap(plot_predictions_ahead, kdims=['dataset', 't_ahead_i', 'start', 'window_steps']) dmap = dmap.redim.values(dataset=list(ds_predss.keys())) dmap = dmap.redim.range(t_ahead_i=(0, window_future), start=(0, 5000), window_steps=(10, 5000)) -dmap = dmap.redim.default(t_ahead_i=10, window_steps=400) +dmap = dmap.redim.default(t_ahead_i=10, window_steps=400, dataset='IMOSCurrentsVel') dmap +# + +# def plot_at_i(time_i, dataset, model): +# d = ds_predss[dataset][model].isel(t_source=time_i) +# return hv_plot_prediction(d).relabel(label=f"{model}") + +# dmap = hv.DynamicMap(plot_at_i, kdims=['t_source', 'dataset', 'model']) +# t = ds_preds.t_source.values +# models = list(next(iter(ds_predss.values())).keys()) +# dmap = dmap.redim.values( +# t_source=range(len(t)), +# dataset=list(ds_predss.keys()), +# model=models, +# ) +# dmap.opts(framewise=True) + +# + +# plot_performance(ds_preds, full=True) + +# + +# # Explore predictions with dynamic map + +# def plot_predictions_ahead(dataset='IMOSCurrentsVel', model='', t_ahead_i=6, start=0, window_steps=1800): +# d = next(iter(ds_predss[dataset].values())).isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps)) + +# p = hv.Scatter({ +# 'x': d.t_target, +# 'y': d.y_true +# }, label='true').opts(color='black', framewise=True) + +# ds_preds = ds_predss[dataset][model] +# d = ds_preds.isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps)) +# x = d.t_target +# y = d.y_pred +# s = d.y_pred_std +# p *= hv.Curve({'x': x, 'y':y}, label=model).relabel(label=f"{model}") +# p *= hv.Spread((x, y, s * 2), +# label='2*std').opts(alpha=0.5, line_width=0) + +# p = p.opts(title=f"Dataset: {dataset}, model={model}, {d.freq}*{t_ahead_i} ahead", height=250, legend_position='top', ylabel=d.targets) +# return p.opts(framewise=True) + +# dmap = hv.DynamicMap(plot_predictions_ahead, kdims=['dataset', 'model', 't_ahead_i', 'start', 'window_steps']) +# dmap = dmap.redim.values(dataset=list(ds_predss.keys()), model=models) +# dmap = dmap.redim.range(t_ahead_i=(0, window_future), start=(0, 5000), window_steps=(10, 5000)) +# dmap = dmap.redim.default(t_ahead_i=10, window_steps=1000) +# dmap # - diff --git a/seq2seq_time/models/neural_process.py b/seq2seq_time/models/neural_process.py index 6902ba6..0b4472c 100644 --- a/seq2seq_time/models/neural_process.py +++ b/seq2seq_time/models/neural_process.py @@ -162,6 +162,7 @@ class LatentEncoder(nn.Module): min_std=0.01, batchnorm=False, dropout=0, + nhead=8, attention_dropout=0, attention_layers=2, ): @@ -178,6 +179,7 @@ class LatentEncoder(nn.Module): self._self_attention = Attention( hidden_dim, attention_layers, + n_heads=nhead, rep="identity", dropout=attention_dropout, ) @@ -218,6 +220,7 @@ class DeterministicEncoder(nn.Module): attention_layers=2, batchnorm=False, dropout=0, + nhead=8, attention_dropout=0, ): super().__init__() @@ -232,12 +235,14 @@ class DeterministicEncoder(nn.Module): self._self_attention = Attention( hidden_dim, attention_layers, + n_heads=nhead, rep="identity", dropout=attention_dropout, ) self._cross_attention = Attention( hidden_dim, x_dim=x_dim, + n_heads=nhead, attention_layers=attention_layers, ) @@ -325,6 +330,7 @@ class RANP(nn.Module): use_deterministic_path=True, min_std=0.01, # To avoid collapse use a minimum standard deviation, should be much smaller than variation in labels dropout=0, + nhead=8, attention_dropout=0, batchnorm=False, attention_layers=2, @@ -353,6 +359,7 @@ class RANP(nn.Module): n_encoder_layers=n_latent_encoder_layers, attention_layers=attention_layers, dropout=dropout, + nhead=nhead, attention_dropout=attention_dropout, batchnorm=batchnorm, min_std=min_std, @@ -365,6 +372,7 @@ class RANP(nn.Module): n_d_encoder_layers=n_det_encoder_layers, attention_layers=attention_layers, dropout=dropout, + nhead=nhead, batchnorm=batchnorm, attention_dropout=attention_dropout, )