diff --git a/docs/lstm_std.png b/docs/lstm_std.png new file mode 100644 index 0000000..feea638 Binary files /dev/null and b/docs/lstm_std.png differ diff --git a/readme.md b/readme.md index cede842..72c8dce 100644 --- a/readme.md +++ b/readme.md @@ -12,6 +12,7 @@ This repo implements ["Recurrent Attentive Neural Process for Sequential Data"]( - [Example NP](#example-np) - [Example ANP outputs (sequential)](#example-anp-outputs-sequential) - [Example ANP-RNN outputs](#example-anp-rnn-outputs) + - [Example LSTM baseline](#example-lstm-baseline) - [Replicating DeepMind's tensorflow ANP behaviour](#replicating-deepminds-tensorflow-anp-behaviour) - [Usage](#usage) - [Smartmeter Data](#smartmeter-data) @@ -80,6 +81,11 @@ This has a better calibrated uncertainty and a better fit ![](docs/anp-rnn_4.png) +### Example LSTM baseline + +Note that the LSTM has access to the y values for the first half of the plot (the context) to match the NP setup. It just predicts an output as opposed to uncertainty + +![](docs/lstm_std.png) ## Replicating DeepMind's tensorflow ANP behaviour diff --git a/smartmeters-ANP-RNN.ipynb b/smartmeters-ANP-RNN.ipynb index 42a5926..c51da5c 100644 --- a/smartmeters-ANP-RNN.ipynb +++ b/smartmeters-ANP-RNN.ipynb @@ -42,8 +42,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:07.980740Z", - "start_time": "2020-02-15T07:47:05.580318Z" + "end_time": "2020-02-16T04:11:54.655625Z", + "start_time": "2020-02-16T04:11:52.295232Z" } }, "outputs": [], @@ -72,8 +72,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:08.033410Z", - "start_time": "2020-02-15T07:47:07.983641Z" + "end_time": "2020-02-16T04:11:54.706242Z", + "start_time": "2020-02-16T04:11:54.658550Z" } }, "outputs": [], @@ -88,8 +88,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:08.084885Z", - "start_time": "2020-02-15T07:47:08.036402Z" + "end_time": "2020-02-16T04:11:54.757392Z", + "start_time": "2020-02-16T04:11:54.710107Z" } }, "outputs": [], @@ -104,8 +104,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:08.196304Z", - "start_time": "2020-02-15T07:47:08.087704Z" + "end_time": "2020-02-16T04:11:54.879893Z", + "start_time": "2020-02-16T04:11:54.759690Z" } }, "outputs": [], @@ -122,8 +122,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:08.247423Z", - "start_time": "2020-02-15T07:47:08.198756Z" + "end_time": "2020-02-16T04:11:54.930092Z", + "start_time": "2020-02-16T04:11:54.882227Z" } }, "outputs": [], @@ -145,8 +145,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:17.060772Z", - "start_time": "2020-02-15T07:47:08.249894Z" + "end_time": "2020-02-16T04:12:03.804568Z", + "start_time": "2020-02-16T04:11:54.933255Z" } }, "outputs": [], @@ -159,15 +159,15 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:17.703484Z", - "start_time": "2020-02-15T07:47:17.063181Z" + "end_time": "2020-02-16T04:12:04.536468Z", + "start_time": "2020-02-16T04:12:03.808105Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -219,8 +219,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:17.759610Z", - "start_time": "2020-02-15T07:47:17.707705Z" + "end_time": "2020-02-16T04:12:04.608091Z", + "start_time": "2020-02-16T04:12:04.540302Z" } }, "outputs": [ @@ -251,11 +251,11 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T13:38:55.225017Z", - "start_time": "2020-02-15T13:38:55.140283Z" + "end_time": "2020-02-16T04:12:04.687280Z", + "start_time": "2020-02-16T04:12:04.611238Z" } }, "outputs": [], @@ -362,8 +362,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T07:47:17.991425Z", - "start_time": "2020-02-15T07:47:17.937745Z" + "end_time": "2020-02-16T04:12:04.745268Z", + "start_time": "2020-02-16T04:12:04.690125Z" } }, "outputs": [], @@ -1949,11 +1949,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 18, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T22:55:19.598323Z", - "start_time": "2020-02-15T22:55:19.482586Z" + "end_time": "2020-02-16T04:17:40.782349Z", + "start_time": "2020-02-16T04:17:40.702206Z" }, "scrolled": true }, @@ -1962,7 +1962,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[I 2020-02-16 06:55:19,584] Using an existing study with name 'anp-rnn1' instead of creating a new one.\n" + "[I 2020-02-16 12:17:40,768] Using an existing study with name 'anp-rnn1' instead of creating a new one.\n" ] } ], @@ -1973,9 +1973,10 @@ "parser.add_argument('--pruning', '-p', action='store_true',\n", " help='Activate the pruning feature. `MedianPruner` stops unpromising '\n", " 'trials at the early stages of training.')\n", - "args = parser.parse_args([])\n", + "args = parser.parse_args(['-p'])\n", "\n", - "pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()\n", + "pruner = optuna.pruners.MedianPruner(n_warmup_steps=1, n_startup_trials=20) if args.pruning else optuna.pruners.NopPruner()\n", + "pruner = optuna.pruners.PercentilePruner(75.0)\n", "name = 'anp-rnn1'\n", "study = optuna.create_study(direction='minimize', pruner=pruner, storage=f'sqlite:///optuna_result/{name}.db', study_name=name, load_if_exists=True)\n" ] @@ -1985,301 +1986,16 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-02-15T22:55:38.900Z" - } + "start_time": "2020-02-16T04:17:42.400Z" + }, + "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "trial 29 params {'attention_dropout': 0.2, 'attention_layers': 2, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.0058703726639809896, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 8, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': True, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:gpu available: True, used: True\n", - 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] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 17559, {'val_loss': '-1.3719918727874756', 'val/kl': '0.00039435975486412644', 'val/mse': '0.0042431410402059555', 'val/std': '0.059302836656570435'}\n", - "Epoch 7: reducing learning rate of group 0 to 5.8704e-04.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 19754, {'val_loss': '-1.5102424621582031', 'val/kl': '0.00029231738881208', 'val/mse': '0.0033912782091647387', 'val/std': '0.046705130487680435'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 21949, {'val_loss': '-1.423159122467041', 'val/kl': '0.00035497310454957187', 'val/mse': '0.0035869088023900986', 'val/std': '0.047349609434604645'}\n", - "\n", - "logger.metrics [{'val_loss': -1.423159122467041, 'val/kl': 0.00035497310454957187, 'val/mse': 0.0035869088023900986, 'val/std': 0.047349609434604645, 'epoch': 9}]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2020-02-16 07:44:25,994] Finished trial#29 resulted in value: -1.423159122467041. Current best value is -1.5058155059814453 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.008663362578308754, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "trial 30 params {'attention_dropout': 0.2, 'attention_layers': 2, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.0022419054847050398, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': False, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n" + "trial 40 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.009979117958707866, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n" ] }, { @@ -2323,14 +2039,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 0, {'val_loss': '0.7910884022712708', 'val/kl': '0.00010800049494719133', 'val/mse': '0.2537727653980255', 'val/std': '0.654058575630188'}\n", + "step 0, {'val_loss': '1.53555166721344', 'val/kl': '0.5122055411338806', 'val/mse': '0.29894599318504333', 'val/std': '0.9359065890312195'}\n", "\r" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8d7f1df292af4d14ba291db5e80b3bec", + "model_id": "d39aa0b996f341b6866fa828dcac3b33", "version_major": 2, "version_minor": 0 }, @@ -2359,7 +2075,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 2194, {'val_loss': '-1.1204354763031006', 'val/kl': '0.0014955311780795455', 'val/mse': '0.00617215083912015', 'val/std': '0.09348662197589874'}\n" + "step 2194, {'val_loss': '-1.3771708011627197', 'val/kl': '0.0009297789074480534', 'val/mse': '0.004502739757299423', 'val/std': '0.06497536599636078'}\n" ] }, { @@ -2380,7 +2096,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 4389, {'val_loss': '-1.3880571126937866', 'val/kl': '0.0010640228865668178', 'val/mse': '0.004546670243144035', 'val/std': '0.06830815225839615'}\n" + "step 4389, {'val_loss': '-1.4605064392089844', 'val/kl': '0.000608345028012991', 'val/mse': '0.004014032427221537', 'val/std': '0.06412609666585922'}\n" ] }, { @@ -2401,7 +2117,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 6584, {'val_loss': '-1.3446733951568604', 'val/kl': '0.0006483710603788495', 'val/mse': '0.005048147868365049', 'val/std': '0.06271157413721085'}\n" + "step 6584, {'val_loss': '8.564395904541016', 'val/kl': '0.006522171664983034', 'val/mse': '24820252672.0', 'val/std': '1585.9449462890625'}\n" ] }, { @@ -2422,7 +2138,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 8779, {'val_loss': '-1.5619605779647827', 'val/kl': '0.0007974352920427918', 'val/mse': '0.003406693460419774', 'val/std': '0.05695287138223648'}\n" + "step 8779, {'val_loss': '-1.5419411659240723', 'val/kl': '0.0009605502709746361', 'val/mse': '0.0035048630088567734', 'val/std': '0.05785220488905907'}\n" ] }, { @@ -2443,7 +2159,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 10974, {'val_loss': '-1.567822813987732', 'val/kl': '0.0007052362198010087', 'val/mse': '0.0034684455022215843', 'val/std': '0.0511687695980072'}\n" + "step 10974, {'val_loss': '-1.4154374599456787', 'val/kl': '0.0018635217566043139', 'val/mse': '0.004522555973380804', 'val/std': '0.05777614936232567'}\n" ] }, { @@ -2464,7 +2180,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 13169, {'val_loss': '-1.469671368598938', 'val/kl': '0.0006657325429841876', 'val/mse': '0.0036809651646763086', 'val/std': '0.04665883630514145'}\n" + "step 13169, {'val_loss': '-1.4261882305145264', 'val/kl': '0.0008237186702899635', 'val/mse': '0.0040297918021678925', 'val/std': '0.05604660511016846'}\n" ] }, { @@ -2485,7 +2201,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 15364, {'val_loss': '-1.4495060443878174', 'val/kl': '0.0009186150855384767', 'val/mse': '0.003881386946886778', 'val/std': '0.04494263231754303'}\n" + "step 15364, {'val_loss': '-1.4118669033050537', 'val/kl': '0.0005158120184205472', 'val/mse': '0.00455522770062089', 'val/std': '0.05931553989648819'}\n", + "Epoch 6: reducing learning rate of group 0 to 9.9791e-04.\n" ] }, { @@ -2506,8 +2223,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 17559, {'val_loss': '-1.4232021570205688', 'val/kl': '0.0007268059998750687', 'val/mse': '0.0037214024923741817', 'val/std': '0.04474468529224396'}\n", - "Epoch 7: reducing learning rate of group 0 to 2.2419e-04.\n" + "step 17559, {'val_loss': '-1.5386149883270264', 'val/kl': '0.0005057148518972099', 'val/mse': '0.0036372821778059006', 'val/std': '0.05127168074250221'}\n" ] }, { @@ -2528,7 +2244,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 19754, {'val_loss': '-1.4388623237609863', 'val/kl': '0.0007217807578854263', 'val/mse': '0.003411452053114772', 'val/std': '0.03921413794159889'}\n" + "step 19754, {'val_loss': '-1.5309010744094849', 'val/kl': '0.0003634715103544295', 'val/mse': '0.0035632983781397343', 'val/std': '0.0472351610660553'}\n" ] }, { @@ -2549,23 +2265,311 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 21949, {'val_loss': '-1.4176470041275024', 'val/kl': '0.0006476619746536016', 'val/mse': '0.003419178072363138', 'val/std': '0.03866790235042572'}\n", + "step 21949, {'val_loss': '-1.5311908721923828', 'val/kl': '0.00033483945298939943', 'val/mse': '0.003650220111012459', 'val/std': '0.04958202689886093'}\n", + "Epoch 9: reducing learning rate of group 0 to 9.9791e-05.\n", "\n", - "logger.metrics [{'val_loss': -1.4176470041275024, 'val/kl': 0.0006476619746536016, 'val/mse': 0.003419178072363138, 'val/std': 0.03866790235042572, 'epoch': 9}]\n" + "logger.metrics [{'val_loss': -1.5311908721923828, 'val/kl': 0.00033483945298939943, 'val/mse': 0.003650220111012459, 'val/std': 0.04958202689886093, 'epoch': 9}]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[I 2020-02-16 08:18:44,472] Finished trial#30 resulted in value: -1.4176470041275024. Current best value is -1.5058155059814453 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.008663362578308754, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n" + "[I 2020-02-16 12:48:05,046] Finished trial#40 resulted in value: -1.5311908721923828. Current best value is -1.5311908721923828 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.009979117958707866, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "trial 31 params {'attention_dropout': 0, 'attention_layers': 3, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'multihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.0065892056326513826, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n" + "trial 41 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.00825221580671433, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:gpu available: True, used: True\n", + "INFO:root:VISIBLE GPUS: 0\n", + "INFO:root:\n", + " Name Type Params\n", + "0 model LatentModel 94 K\n", + "1 model._latent_encoder LatentEncoder 8 K\n", + "2 model._latent_encoder._input_layer Linear 608 \n", + "3 model._latent_encoder._encoder ModuleList 2 K\n", + "4 model._latent_encoder._encoder.0 NPBlockRelu2d 1 K\n", + ".. ... ... ...\n", + "87 model._decoder._decoder.7.act ReLU 0 \n", + "88 model._decoder._decoder.7.dropout Dropout2d 0 \n", + "89 model._decoder._decoder.7.norm BatchNorm2d 192 \n", + "90 model._decoder._mean Linear 97 \n", + "91 model._decoder._std Linear 97 \n", + "\n", + "[92 rows x 3 columns]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 0, {'val_loss': '1.5108540058135986', 'val/kl': '0.5089762806892395', 'val/mse': '0.2757703363895416', 'val/std': '0.9246620535850525'}\n", + "\r" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6329689d60764058a7a32d654cac0e5e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), max=1.0), HTML(value='')), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, 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] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 6584, {'val_loss': '-1.5108121633529663', 'val/kl': '0.0008597009000368416', 'val/mse': '0.003719923784956336', 'val/std': '0.05571730062365532'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 8779, {'val_loss': '-1.564136266708374', 'val/kl': '0.000897054560482502', 'val/mse': '0.0033463523723185062', 'val/std': '0.05561968311667442'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 10974, {'val_loss': '-1.4187949895858765', 'val/kl': '0.0005841613747179508', 'val/mse': '0.0040751127526164055', 'val/std': '0.05243219807744026'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 13169, {'val_loss': '-1.4812263250350952', 'val/kl': '0.0007523014210164547', 'val/mse': '0.0037672489415854216', 'val/std': '0.049325522035360336'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 15364, {'val_loss': '-1.2123048305511475', 'val/kl': '0.0014134023804217577', 'val/mse': '0.006530694663524628', 'val/std': '0.07238736003637314'}\n", + "Epoch 6: reducing learning rate of group 0 to 8.2522e-04.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 17559, {'val_loss': '-1.4875582456588745', 'val/kl': '0.0005257382290437818', 'val/mse': '0.003740638727322221', 'val/std': '0.044684037566185'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 19754, {'val_loss': '-1.4734346866607666', 'val/kl': '0.00039242839557118714', 'val/mse': '0.003947222139686346', 'val/std': '0.04682338610291481'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 21949, {'val_loss': '-1.4678099155426025', 'val/kl': '0.0003087829682044685', 'val/mse': '0.0037555007729679346', 'val/std': '0.04366892948746681'}\n", + "Epoch 9: reducing learning rate of group 0 to 8.2522e-05.\n", + "\n", + "logger.metrics [{'val_loss': -1.4678099155426025, 'val/kl': 0.0003087829682044685, 'val/mse': 0.0037555007729679346, 'val/std': 0.04366892948746681, 'epoch': 9}]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2020-02-16 13:19:27,299] Finished trial#41 resulted in value: -1.4678099155426025. Current best value is -1.5311908721923828 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.009979117958707866, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "trial 42 params {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.004675772626655719, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 4, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n" ] }, { @@ -2576,19 +2580,19 @@ "INFO:root:VISIBLE GPUS: 0\n", "INFO:root:\n", " Name Type Params\n", - "0 model LatentModel 124 K\n", + "0 model LatentModel 96 K\n", "1 model._latent_encoder LatentEncoder 8 K\n", "2 model._latent_encoder._input_layer Linear 608 \n", "3 model._latent_encoder._encoder ModuleList 2 K\n", "4 model._latent_encoder._encoder.0 NPBlockRelu2d 1 K\n", ".. ... ... ...\n", - "146 model._decoder._decoder.7.act ReLU 0 \n", - "147 model._decoder._decoder.7.dropout Dropout2d 0 \n", - "148 model._decoder._decoder.7.norm BatchNorm2d 192 \n", - "149 model._decoder._mean Linear 97 \n", - "150 model._decoder._std Linear 97 \n", + "97 model._decoder._decoder.7.act ReLU 0 \n", + "98 model._decoder._decoder.7.dropout Dropout2d 0 \n", + "99 model._decoder._decoder.7.norm BatchNorm2d 192 \n", + "100 model._decoder._mean Linear 97 \n", + "101 model._decoder._std Linear 97 \n", "\n", - "[151 rows x 3 columns]\n" + "[102 rows x 3 columns]\n" ] }, { @@ -2609,14 +2613,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 0, {'val_loss': '1.5510637760162354', 'val/kl': '0.504607617855072', 'val/mse': '0.33672571182250977', 'val/std': '0.9382523894309998'}\n", + "step 0, {'val_loss': '1.6367992162704468', 'val/kl': '0.5042330622673035', 'val/mse': '0.32986244559288025', 'val/std': '1.0728768110275269'}\n", "\r" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e68b6e1b39af43209e02eec071204c46", + "model_id": "85ce296b1876436cad83962e2f28c8cb", "version_major": 2, "version_minor": 0 }, @@ -2645,7 +2649,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 2194, {'val_loss': '-0.9993453621864319', 'val/kl': '0.00492581631988287', 'val/mse': '0.0076696309261024', 'val/std': '0.10437598824501038'}\n" + "step 2194, {'val_loss': '-1.3915525674819946', 'val/kl': '0.0012034019455313683', 'val/mse': '0.003971943166106939', 'val/std': '0.07428822666406631'}\n" ] }, { @@ -2666,7 +2670,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 4389, {'val_loss': '-1.3951125144958496', 'val/kl': '0.0013331789523363113', 'val/mse': '0.004176048096269369', 'val/std': '0.05822036787867546'}\n" + "step 4389, {'val_loss': '-1.3431251049041748', 'val/kl': '0.0008934412035159767', 'val/mse': '0.005613301880657673', 'val/std': '0.07552073895931244'}\n" ] }, { @@ -2687,7 +2691,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 6584, {'val_loss': '-1.4472817182540894', 'val/kl': '0.001394702005200088', 'val/mse': '0.0040388815104961395', 'val/std': '0.05575721710920334'}\n" + "step 6584, {'val_loss': '-1.4798474311828613', 'val/kl': '0.0007770339143462479', 'val/mse': '0.004133238922804594', 'val/std': '0.05946439132094383'}\n" ] }, { @@ -2708,7 +2712,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 8779, {'val_loss': '-1.4240511655807495', 'val/kl': '0.0011677203001454473', 'val/mse': '0.003808689536526799', 'val/std': '0.05027220398187637'}\n" + "step 8779, {'val_loss': '-1.4810700416564941', 'val/kl': '0.0005357018671929836', 'val/mse': '0.003644324606284499', 'val/std': '0.05902468413114548'}\n" ] }, { @@ -2729,7 +2733,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 10974, {'val_loss': '-1.51090669631958', 'val/kl': '0.001719470601528883', 'val/mse': '0.0037067916709929705', 'val/std': '0.05691952630877495'}\n" + "step 10974, {'val_loss': '-1.569221019744873', 'val/kl': '0.0005315328016877174', 'val/mse': '0.003372111590579152', 'val/std': '0.0521407350897789'}\n" ] }, { @@ -2750,7 +2754,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 13169, {'val_loss': '-1.556814193725586', 'val/kl': '0.0011921778786927462', 'val/mse': '0.003143388545140624', 'val/std': '0.04716252535581589'}\n" + "step 13169, {'val_loss': '-1.3642808198928833', 'val/kl': '0.00046485415077768266', 'val/mse': '0.004272697493433952', 'val/std': '0.04868315905332565'}\n" ] }, { @@ -2771,336 +2775,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 15364, {'val_loss': '-1.5254632234573364', 'val/kl': '0.0011767667019739747', 'val/mse': '0.003485812107101083', 'val/std': '0.048899002373218536'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 17559, {'val_loss': '-1.4824448823928833', 'val/kl': '0.0012150286929681897', 'val/mse': '0.0035667528863996267', 'val/std': '0.0452319011092186'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 19754, {'val_loss': '-1.4969969987869263', 'val/kl': '0.0013163189869374037', 'val/mse': '0.00328625226393342', 'val/std': '0.04428704082965851'}\n", - "Epoch 8: reducing learning rate of group 0 to 6.5892e-04.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 21949, {'val_loss': '-1.4131932258605957', 'val/kl': '0.0012195135932415724', 'val/mse': '0.0033923806622624397', 'val/std': '0.0400848314166069'}\n", - "\n", - "logger.metrics [{'val_loss': -1.4131932258605957, 'val/kl': 0.0012195135932415724, 'val/mse': 0.0033923806622624397, 'val/std': 0.0400848314166069, 'epoch': 9}]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2020-02-16 08:47:42,528] Finished trial#31 resulted in value: -1.4131932258605957. Current best value is -1.5058155059814453 with parameters: {'attention_dropout': 0.2, 'attention_layers': 1, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'ptmultihead', 'learning_rate': 0.008663362578308754, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 2, 'n_latent_encoder_layers': 2, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "trial 32 params {'attention_dropout': 0.2, 'attention_layers': 2, 'batch_size': 16, 'batchnorm': True, 'context_in_target': True, 'det_enc_cross_attn_type': 'ptmultihead', 'det_enc_self_attn_type': 'multihead', 'dropout': 0, 'grad_clip': 40, 'hidden_dim': 32, 'latent_dim': 64, 'latent_enc_self_attn_type': 'multihead', 'learning_rate': 0.004388551085821375, 'max_nb_epochs': 10, 'min_std': 0.005, 'n_decoder_layers': 8, 'n_det_encoder_layers': 4, 'n_latent_encoder_layers': 8, 'num_context': 96, 'num_extra_target': 96, 'num_heads': 8, 'num_workers': 3, 'use_deterministic_path': False, 'use_lvar': True, 'use_rnn': False, 'use_self_attn': False, 'vis_i': 670, 'x_dim': 17, 'y_dim': 1}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:gpu available: True, used: True\n", - "INFO:root:VISIBLE GPUS: 0\n", - "INFO:root:\n", - " Name Type Params\n", - "0 model LatentModel 102 K\n", - "1 model._latent_encoder LatentEncoder 14 K\n", - "2 model._latent_encoder._input_layer Linear 608 \n", - "3 model._latent_encoder._encoder ModuleList 8 K\n", - "4 model._latent_encoder._encoder.0 NPBlockRelu2d 1 K\n", - ".. ... ... ...\n", - "127 model._decoder._decoder.7.act ReLU 0 \n", - "128 model._decoder._decoder.7.dropout Dropout2d 0 \n", - "129 model._decoder._decoder.7.norm BatchNorm2d 192 \n", - "130 model._decoder._mean Linear 97 \n", - "131 model._decoder._std Linear 97 \n", - "\n", - "[132 rows x 3 columns]\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 0, {'val_loss': '1.6433210372924805', 'val/kl': '0.502083420753479', 'val/mse': '0.3020164668560028', 'val/std': '1.1032137870788574'}\n", - "\r" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5e06748306f340e3809faaac521f4844", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), max=1.0), HTML(value='')), …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 2194, {'val_loss': '-0.5562781095504761', 'val/kl': '0.004524527583271265', 'val/mse': '0.01594124175608158', 'val/std': '0.20407840609550476'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 4389, {'val_loss': '-1.2800618410110474', 'val/kl': '0.001454153680242598', 'val/mse': '0.004913512151688337', 'val/std': '0.07995617389678955'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 6584, {'val_loss': '-1.4909727573394775', 'val/kl': '0.0005825856351293623', 'val/mse': '0.0038258214481174946', 'val/std': '0.05889587849378586'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 8779, {'val_loss': '-1.4236963987350464', 'val/kl': '0.0012530366657301784', 'val/mse': '0.0038965647108852863', 'val/std': '0.051243484020233154'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 10974, {'val_loss': '-1.4802192449569702', 'val/kl': '0.0008309829281643033', 'val/mse': '0.0034029236994683743', 'val/std': '0.05015803128480911'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 13169, {'val_loss': '1679.2408447265625', 'val/kl': '1679.53564453125', 'val/mse': '606.5656127929688', 'val/std': '752.9714965820312'}\n", - "Epoch 5: reducing learning rate of group 0 to 4.3886e-04.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 15364, {'val_loss': '-1.515012502670288', 'val/kl': '0.0006384316366165876', 'val/mse': '0.0032132412306964397', 'val/std': '0.04366536810994148'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 17559, {'val_loss': '23501292.0', 'val/kl': '23501292.0', 'val/mse': '8748267.0', 'val/std': '6837.646484375'}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=117.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 19754, {'val_loss': '-1.4798544645309448', 'val/kl': '0.0004828128730878234', 'val/mse': '0.0033622710034251213', 'val/std': '0.04285237193107605'}\n" + "step 15364, {'val_loss': '-1.293749213218689', 'val/kl': '0.0004639705002773553', 'val/mse': '0.00423781294375658', 'val/std': '0.04431043565273285'}\n" ] } ], @@ -3113,8 +2788,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-02-01T22:48:16.540204Z", - "start_time": "2020-02-01T22:47:55.013103Z" + "end_time": "2020-02-16T04:12:26.187191Z", + "start_time": "2020-02-16T04:12:26.081801Z" } }, "outputs": [], @@ -3122,34 +2797,52 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-02-15T13:33:41.400966Z", - "start_time": "2020-02-15T13:33:41.306952Z" - } + "end_time": "2020-02-16T04:13:11.822277Z", + "start_time": "2020-02-16T04:13:11.638482Z" + }, + "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of finished trials: 2\n", - "Best trial:\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Record does not exist.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\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 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Best trial:'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_trial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' Value: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\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[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36mbest_trial\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 87\u001b[0m \"\"\"\n\u001b[1;32m 88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_best_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_study_id\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 90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/storages/rdb/storage.py\u001b[0m in \u001b[0;36mget_best_trial\u001b[0;34m(self, study_id)\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_max_value_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 713\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 714\u001b[0;31m \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_min_value_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\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 715\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 716\u001b[0m \u001b[0;31m# Terminate transaction explicitly to avoid connection timeout during transaction.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/storages/rdb/models.py\u001b[0m in \u001b[0;36mfind_min_value_trial\u001b[0;34m(cls, study_id, session)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0morder_by\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_or_none\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[1;32m 187\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrial\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNOT_FOUND_MSG\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 189\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Record does not exist." + "Number of finished trials: 39\n", + "Best trial:\n", + " Value: -1.5058155059814453\n", + " Params: \n", + " attention_dropout: 0.2\n", + " attention_layers: 1\n", + " batch_size: 16\n", + " batchnorm: True\n", + " context_in_target: True\n", + " det_enc_cross_attn_type: ptmultihead\n", + " det_enc_self_attn_type: multihead\n", + " dropout: 0\n", + " grad_clip: 40\n", + " hidden_dim: 32\n", + " latent_dim: 64\n", + " latent_enc_self_attn_type: ptmultihead\n", + " learning_rate: 0.008663362578308754\n", + " max_nb_epochs: 10\n", + " min_std: 0.005\n", + " n_decoder_layers: 8\n", + " n_det_encoder_layers: 2\n", + " n_latent_encoder_layers: 2\n", + " num_context: 96\n", + " num_extra_target: 96\n", + " num_heads: 8\n", + " num_workers: 3\n", + " use_deterministic_path: False\n", + " use_lvar: True\n", + " use_rnn: False\n", + " use_self_attn: False\n", + " vis_i: 670\n", + " x_dim: 17\n", + " y_dim: 1\n" ] } ], diff --git a/smartmeters-lstm.ipynb b/smartmeters-lstm.ipynb deleted file mode 100644 index 8b1ba99..0000000 --- a/smartmeters-lstm.ipynb +++ /dev/null @@ -1,711 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# This notebook uses pytorch lightning & optuna & an LSTM\n", - "\n", - "This notebook trains an LSTM on timeseries data from smartmeters.\n", - "\n", - "It uses pytorch lighting for the training loop. And Optuna for the hyperparameter optimisation.\n", - "\n", - "It also pushes results to the tensorboard hyperparameter dashboard for examination.\n", - "\n", - "- https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:51:52.095766Z", - "start_time": "2020-02-01T12:51:47.852973Z" - } - }, - "outputs": [], - "source": [ - "import sys, re, os, itertools, functools, collections\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import collections\n", - "from pathlib import Path\n", - "from tqdm.auto import tqdm\n", - "\n", - "import pytorch_lightning as pl\n", - "import optuna\n", - "from optuna.integration import PyTorchLightningPruningCallback\n", - "\n", - "\n", - "import math\n", - "%matplotlib inline\n", - "%reload_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:51:52.229222Z", - "start_time": "2020-02-01T12:51:52.117820Z" - } - }, - "outputs": [], - "source": [ - "import logging\n", - "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", - "logger = logging.getLogger(\"smartmeters.ipynb\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:51:52.320060Z", - "start_time": "2020-02-01T12:51:52.232182Z" - } - }, - "outputs": [], - "source": [ - "import torch\n", - "from torch import nn\n", - "import torch.nn.functional as F" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:51:52.631797Z", - "start_time": "2020-02-01T12:51:52.323591Z" - } - }, - "outputs": [], - "source": [ - "from src.models.model import LatentModel\n", - "from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df\n", - "# from src.plot import plot_from_loader\n", - "from src.models.lstm import SequenceDfDataSet, LSTM_PL, plot_from_loader\n", - "from src.dict_logger import DictLogger" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:51:52.688293Z", - "start_time": "2020-02-01T12:51:52.634674Z" - } - }, - "outputs": [], - "source": [ - "# Params\n", - "device='cuda'\n", - "use_logy=False" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load kaggle smart meter data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:07.575506Z", - "start_time": "2020-02-01T12:51:52.692536Z" - } - }, - "outputs": [], - "source": [ - "df_train, df_test = get_smartmeter_df()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:08.620008Z", - "start_time": "2020-02-01T12:52:07.579719Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Show split\n", - "df_train['energy(kWh/hh)'].plot(label='train')\n", - "df_test['energy(kWh/hh)'].plot(label='test')\n", - "plt.title('energy(kWh/hh)')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-01-25T05:07:36.234226Z", - "start_time": "2020-01-25T05:07:36.198552Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train helpers" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:08.844311Z", - "start_time": "2020-02-01T12:52:08.720012Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/lstm\n" - ] - } - ], - "source": [ - "print(f\"now run `tensorboard --logdir {MODEL_DIR}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:08.936273Z", - "start_time": "2020-02-01T12:52:08.846579Z" - } - }, - "outputs": [], - "source": [ - "def main(trial, train=True):\n", - " # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint\n", - " # filenames match. Therefore, the filenames for each trial must be made unique.\n", - " \n", - " checkpoint_callback = pl.callbacks.ModelCheckpoint(\n", - " os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode='min')\n", - "\n", - " # The default logger in PyTorch Lightning writes to event files to be consumed by\n", - " # TensorBoard. We create a simple logger instead that holds the log in memory so that the\n", - " # final accuracy can be obtained after optimization. When using the default logger, the\n", - " # final accuracy could be stored in an attribute of the `Trainer` instead.\n", - " logger = DictLogger(MODEL_DIR, name=name, version=trial.number)\n", - "# print(\"log_dir\", logger.experiment.log_dir)\n", - "\n", - " trainer = pl.Trainer(\n", - " logger=logger,\n", - " val_percent_check=PERCENT_TEST_EXAMPLES,\n", - " checkpoint_callback=checkpoint_callback,\n", - " max_epochs=trial.params['max_nb_epochs'],\n", - " gpus=-1 if torch.cuda.is_available() else None,\n", - " early_stop_callback=PyTorchLightningPruningCallback(trial, monitor='val_loss')\n", - " )\n", - " model = LSTM_PL(trial.params)\n", - " if train:\n", - " trainer.fit(model)\n", - " return model, trainer" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T13:06:23.208888Z", - "start_time": "2020-02-01T13:06:23.092666Z" - } - }, - "outputs": [], - "source": [ - "def add_suggest(trial):\n", - " trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-2)\n", - " trial.suggest_uniform(\"lstm_dropout\", 0, 0.75)\n", - " trial.suggest_categorical(\"hidden_size\", [1, 2, 4, 8, 16, 32, 64, 128]) \n", - " trial.suggest_categorical(\"lstm_layers\", [1, 2, 4, 8]) \n", - " trial.suggest_categorical(\"bidirectional\", [False, True]) \n", - " \n", - " # constants\n", - " trial.suggest_int(\"window_length\", 24 * 2, 24 * 2)\n", - " trial.suggest_int(\"target_length\", 24, 24)\n", - " trial.suggest_int(\"max_nb_epochs\", 20, 20)\n", - " trial.suggest_int(\"num_workers\", 4, 4)\n", - " trial.suggest_int(\"grad_clip\", 40, 40)\n", - " trial.suggest_int(\"vis_i\", 670, 670)\n", - " trial.suggest_int(\"input_size\", 17, 17)\n", - " trial.suggest_int(\"batch_size\", 16, 16) \n", - " return trial" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T13:06:23.437364Z", - "start_time": "2020-02-01T13:06:23.311890Z" - } - }, - "outputs": [], - "source": [ - "\n", - "def objective(trial):\n", - " # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n", - " trial = add_suggest(trial)\n", - "\n", - " \n", - " print('trial', trial.number, 'params', trial.params)\n", - " \n", - " model, trainer = main(trial)\n", - " \n", - " # also report to tensorboard & print\n", - " print('logger.metrics', model.logger.metrics[-1:])\n", - " model.logger.experiment.add_hparams(trial.params, logger.metrics[-1])\n", - " model.logger.save()\n", - " \n", - " return model.logger.metrics[-1]['val_loss']\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:08.712221Z", - "start_time": "2020-02-01T12:52:08.628358Z" - } - }, - "outputs": [], - "source": [ - "PERCENT_TEST_EXAMPLES = 0.3\n", - "EPOCHS = 2\n", - "DIR = Path(os.getcwd())\n", - "MODEL_DIR = DIR/ 'optuna_result'/ 'lstm'\n", - "name = \"lstm1\"\n", - "MODEL_DIR.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[I 2020-01-29 08:42:54,398] Finished trial#8 resulted in value: 0.005635036621242762. Current best value is 0.005635036621242762 with parameters: {'batch_size': 16, 'bidirectional': False, 'grad_clip': 40, 'hidden_size': 128, 'input_size': 17, 'learning_rate': 0.00019134834148401144, 'lstm_dropout': 0.4080689425353674, 'lstm_layers': 8, 'max_nb_epochs': 20, 'num_workers': 4, 'target_length': 24, 'vis_i': 670, 'window_length': 48}." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:22.492191Z", - "start_time": "2020-02-01T12:52:22.416653Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T13:16:23.050700Z", - "start_time": "2020-02-01T13:12:23.739975Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:gpu available: True, used: True\n", - "INFO:root:VISIBLE GPUS: 0\n", - "INFO:root:\n", - " Name Type Params\n", - "0 _model LSTMNet 1 M\n", - "1 _model.lstm1 LSTM 999 K\n", - "2 _model.linear Linear 1 K\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "516c0dacaa1047fc9af021bc54e64f95", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "step 0, {'val_loss': '0.23856914043426514'}\n", - "\r" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f5a24ba06eb142cab3bbd7dfb04f1d99", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), max=1.0), HTML(value='')), …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\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 2\u001b[0m \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_suggest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m103\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mmain\u001b[0;34m(trial, train)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLSTM_PL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 26\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;31m# ON CPU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py\u001b[0m in \u001b[0;36msingle_gpu_train\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_pretrain_routine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdp_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\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[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mrun_pretrain_routine\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 839\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 840\u001b[0m \u001b[0;31m# CORE TRAINING LOOP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\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 842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0;31m# RUN TNG EPOCH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 332\u001b[0m \u001b[0;31m# -----------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 333\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_epoch\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 334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;31m# update LR schedulers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;31m# RUN TRAIN STEP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[0;31m# ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\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 389\u001b[0m \u001b[0mbatch_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_norm_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_step_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_batch\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;31m# calculate loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer_closure\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 513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 514\u001b[0m \u001b[0;31m# nan grads\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36moptimizer_closure\u001b[0;34m()\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[0;31m# backward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 493\u001b[0m \u001b[0mmodel_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_model\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[0;32m--> 494\u001b[0;31m \u001b[0mmodel_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_amp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\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 495\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# track metrics for callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/core/hooks.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, use_amp, loss, optimizer, optimizer_idx)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[1;32m 154\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \"\"\"\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\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 151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\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[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m 97\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 98\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m allow_unreachable=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "trial = optuna.trial.FixedTrial(\n", - " params={\n", - " 'number': 45,\n", - " 'batch_size': 16,\n", - " 'bidirectional': False,\n", - " 'grad_clip': 40,\n", - " 'hidden_size': 128,\n", - " 'input_size': 17,\n", - " 'learning_rate': 0.00019134834148401144,\n", - " 'lstm_dropout': 0.4080689425353674,\n", - " 'lstm_layers': 8,\n", - " 'max_nb_epochs': 20,\n", - " 'num_workers': 4,\n", - " 'target_length': 24,\n", - " 'vis_i': 670,\n", - " 'window_length': 48\n", - " })\n", - "trial = add_suggest(trial)\n", - "trial.number = 103\n", - "main(trial)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T13:16:23.065715Z", - "start_time": "2020-02-01T13:12:25.400Z" - } - }, - "outputs": [], - "source": [ - "# plot\n", - "loader = model.val_dataloader()[0]\n", - "vis_i=640\n", - "plot_from_loader(loader, model, vis_i=vis_i, n=5)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T13:16:23.067381Z", - "start_time": "2020-02-01T13:12:25.500Z" - } - }, - "outputs": [], - "source": [ - "# test\n", - "trainer.test(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2020-01-27T09:03:28.792465Z", - "start_time": "2020-01-27T09:03:28.346446Z" - } - }, - "source": [ - "# Hyperparam opt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:09.197594Z", - "start_time": "2020-02-01T12:52:09.072932Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[I 2020-02-01 20:52:09,183] Using an existing study with name 'no-name-b60e37fc-4ab6-4793-8a0a-c87a1b40c5c0' instead of creating a new one.\n" - ] - } - ], - "source": [ - "import argparse \n", - "\n", - "parser = argparse.ArgumentParser(description='PyTorch Lightning example.')\n", - "parser.add_argument('--pruning', '-p', action='store_true',\n", - " help='Activate the pruning feature. `MedianPruner` stops unpromising '\n", - " 'trials at the early stages of training.')\n", - "args = parser.parse_args(['-p'])\n", - "\n", - "pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()\n", - "\n", - "study = optuna.create_study(direction='minimize', pruner=pruner, storage=f'sqlite:///optuna_result/{name}.db', study_name='no-name-b60e37fc-4ab6-4793-8a0a-c87a1b40c5c0', load_if_exists=True)\n", - "\n", - "# shutil.rmtree(MODEL_DIR)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-01-27T23:38:16.813496Z", - "start_time": "2020-01-27T23:38:16.766674Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:20.456952Z", - "start_time": "2020-02-01T12:52:09.200082Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "trial 10 params {'batch_size': 16, 'bidirectional': False, 'grad_clip': 40, 'hidden_size': 64, 'input_size': 17, 'learning_rate': 0.00010639577108177795, 'lstm_dropout': 0.5343276200876307, 'lstm_layers': 4, 'max_nb_epochs': 20, 'num_workers': 4, 'target_length': 24, 'vis_i': 670, 'window_length': 48}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:gpu available: True, used: True\n", - "INFO:root:VISIBLE GPUS: 0\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(self, func, catch, gc_after_trial)\u001b[0m\n\u001b[1;32m 568\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\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 570\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\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 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mmain\u001b[0;34m(trial, train)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 26\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/distrib_parts.py\u001b[0m in \u001b[0;36msingle_gpu_train\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\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[0;32m--> 430\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot_gpu\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 431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mcuda\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m 304\u001b[0m \"\"\"\n\u001b[0;32m--> 305\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\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 306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\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[0;32m--> 202\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\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 203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: ", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6000\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 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of finished trials: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\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[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Best trial:'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m self._optimize_sequential(func, n_trials, timeout, catch, callbacks,\n\u001b[0;32m--> 302\u001b[0;31m gc_after_trial, None)\n\u001b[0m\u001b[1;32m 303\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0mtime_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\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[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(self, func, n_trials, timeout, catch, callbacks, gc_after_trial, time_start)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial_and_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\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 539\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_session\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[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial_and_callbacks\u001b[0;34m(self, func, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;31m# type: (...) -> None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\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 551\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(self, func, catch, gc_after_trial)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[0;31m# https://github.com/optuna/optuna/pull/325.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 592\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect\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 594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "study.optimize(objective, n_trials=200, timeout=6000)\n", - "\n", - "print('Number of finished trials: {}'.format(len(study.trials)))\n", - "\n", - "print('Best trial:')\n", - "trial = study.best_trial\n", - "\n", - "print(' Value: {}'.format(trial.value))\n", - "\n", - "print(' Params: ')\n", - "for key, value in trial.params.items():\n", - " print(' {}: {}'.format(key, value))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:20.462742Z", - "start_time": "2020-02-01T12:51:48.200Z" - } - }, - "outputs": [], - "source": [ - "study.optimize(objective, n_trials=200, timeout=6000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-01T12:52:20.473924Z", - "start_time": "2020-02-01T12:51:48.200Z" - } - }, - "outputs": [], - "source": [ - "df = study.trials_dataframe(attrs=('number', 'value', 'params', 'state'))\n", - "df.sort_values('value')" - ] - } - ], - "metadata": { - "file_extension": ".py", - "kernelspec": { - "display_name": "jup3.7.3", - "language": "python", - "name": "jup3.7.3" - }, - "mimetype": "text/x-python", - "name": "python", - "npconvert_exporter": "python", - "pygments_lexer": "ipython3", - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "384px" - }, - "toc_section_display": true, - "toc_window_display": true - }, - "version": 3 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/smartmeters-lstm_std.ipynb b/smartmeters-lstm_std.ipynb new file mode 100644 index 0000000..4a1928e --- /dev/null +++ b/smartmeters-lstm_std.ipynb @@ -0,0 +1,1492 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# This notebook uses pytorch lightning & optuna & an LSTM\n", + "\n", + "This notebook trains an LSTM on timeseries data from smartmeters.\n", + "\n", + "It uses pytorch lighting for the training loop. And Optuna for the hyperparameter optimisation.\n", + "\n", + "It also pushes results to the tensorboard hyperparameter dashboard for examination.\n", + "\n", + "- https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:35.113203Z", + "start_time": "2020-02-16T04:41:35.005263Z" + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%reload_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:35.216618Z", + "start_time": "2020-02-16T04:41:35.117396Z" + } + }, + "outputs": [], + "source": [ + "import sys, re, os, itertools, functools, collections\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import collections\n", + "from pathlib import Path\n", + "from tqdm.auto import tqdm\n", + "\n", + "import pytorch_lightning as pl\n", + "import optuna\n", + "from optuna.integration import PyTorchLightningPruningCallback\n", + "\n", + "\n", + "import math\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:35.314619Z", + "start_time": "2020-02-16T04:41:35.219904Z" + } + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:35.411551Z", + "start_time": "2020-02-16T04:41:35.317474Z" + } + }, + "outputs": [], + "source": [ + "from src.models.model import LatentModel\n", + "from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df\n", + "# from src.plot import plot_from_loader\n", + "from src.models.lstm_std import SequenceDfDataSet, LSTM_PL, plot_from_loader\n", + "from src.dict_logger import DictLogger" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:35.539221Z", + "start_time": "2020-02-16T04:41:35.414134Z" + } + }, + "outputs": [], + "source": [ + "import logging\n", + "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", + "logger = logging.getLogger(\"smartmeters.ipynb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:35.649940Z", + "start_time": "2020-02-16T04:41:35.542849Z" + } + }, + "outputs": [], + "source": [ + "# Params\n", + "device='cuda'\n", + "use_logy=False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load kaggle smart meter data" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:44.771767Z", + "start_time": "2020-02-16T04:41:35.652485Z" + } + }, + "outputs": [], + "source": [ + "df_train, df_test = get_smartmeter_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:45.429236Z", + "start_time": "2020-02-16T04:41:44.775741Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Show split\n", + "df_train['energy(kWh/hh)'].plot(label='train')\n", + "df_test['energy(kWh/hh)'].plot(label='test')\n", + "plt.title('energy(kWh/hh)')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T00:24:15.905017Z", + "start_time": "2020-02-16T00:24:15.747859Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train helpers" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:45.536704Z", + "start_time": "2020-02-16T04:41:45.433290Z" + } + }, + "outputs": [], + "source": [ + "def main(trial, train=True):\n", + " # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint\n", + " # filenames match. Therefore, the filenames for each trial must be made unique.\n", + " \n", + " checkpoint_callback = pl.callbacks.ModelCheckpoint(\n", + " os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode='min')\n", + "\n", + " # The default logger in PyTorch Lightning writes to event files to be consumed by\n", + " # TensorBoard. We create a simple logger instead that holds the log in memory so that the\n", + " # final accuracy can be obtained after optimization. When using the default logger, the\n", + " # final accuracy could be stored in an attribute of the `Trainer` instead.\n", + " logger = DictLogger(MODEL_DIR, name=name, version=trial.number)\n", + "# print(\"log_dir\", logger.experiment.log_dir)\n", + "\n", + " trainer = pl.Trainer(\n", + " logger=logger,\n", + " val_percent_check=PERCENT_TEST_EXAMPLES,\n", + " checkpoint_callback=checkpoint_callback,\n", + " max_epochs=trial.params['max_nb_epochs'],\n", + " gpus=-1 if torch.cuda.is_available() else None,\n", + " early_stop_callback=PyTorchLightningPruningCallback(trial, monitor='val_loss')\n", + " )\n", + " model = LSTM_PL(trial.params)\n", + " if train:\n", + " trainer.fit(model)\n", + " return model, trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:45.638524Z", + "start_time": "2020-02-16T04:41:45.539048Z" + } + }, + "outputs": [], + "source": [ + "def add_suggest(trial):\n", + " trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-2)\n", + " trial.suggest_uniform(\"lstm_dropout\", 0, 0.75)\n", + " trial.suggest_categorical(\"hidden_size\", [1, 2, 4, 8, 16, 32, 64, 128]) \n", + " trial.suggest_categorical(\"lstm_layers\", [1, 2, 4, 8]) \n", + " trial.suggest_categorical(\"bidirectional\", [False, True]) \n", + " \n", + " # constants\n", + " trial.suggest_int(\"window_length\", 24 * 4, 24 * 4)\n", + " trial.suggest_int(\"target_length\", 24*4, 24*4)\n", + " trial.suggest_int(\"max_nb_epochs\", 20, 20)\n", + " trial.suggest_int(\"num_workers\", 4, 4)\n", + " trial.suggest_int(\"grad_clip\", 40, 40)\n", + " trial.suggest_int(\"vis_i\", 670, 670)\n", + " trial.suggest_int(\"input_size\", 17, 17)\n", + " trial.suggest_int(\"batch_size\", 16, 16) \n", + " return trial" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:45.734851Z", + "start_time": "2020-02-16T04:41:45.640792Z" + } + }, + "outputs": [], + "source": [ + "\n", + "def objective(trial):\n", + " # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n", + " trial = add_suggest(trial)\n", + "\n", + " \n", + " print('trial', trial.number, 'params', trial.params)\n", + " \n", + " model, trainer = main(trial)\n", + " \n", + " # also report to tensorboard & print\n", + " print('logger.metrics', model.logger.metrics[-1:])\n", + " model.logger.experiment.add_hparams(trial.params, logger.metrics[-1])\n", + " model.logger.save()\n", + " \n", + " return model.logger.metrics[-1]['val_loss']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:45.831844Z", + "start_time": "2020-02-16T04:41:45.737167Z" + } + }, + "outputs": [], + "source": [ + "PERCENT_TEST_EXAMPLES = 0.3\n", + "EPOCHS = 2\n", + "DIR = Path(os.getcwd())\n", + "MODEL_DIR = DIR/ 'optuna_result'/ 'lstm_std'\n", + "name = \"lstm_std1\"\n", + "MODEL_DIR.mkdir(parents=True, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T04:41:45.926287Z", + "start_time": "2020-02-16T04:41:45.834229Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/lstm_std\n" + ] + } + ], + "source": [ + "print(f\"now run `tensorboard --logdir {MODEL_DIR}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[I 2020-01-29 08:42:54,398] Finished trial#8 resulted in value: 0.005635036621242762. Current best value is 0.005635036621242762 with parameters: {'batch_size': 16, 'bidirectional': False, 'grad_clip': 40, 'hidden_size': 128, 'input_size': 17, 'learning_rate': 0.00019134834148401144, 'lstm_dropout': 0.4080689425353674, 'lstm_layers': 8, 'max_nb_epochs': 20, 'num_workers': 4, 'target_length': 24, 'vis_i': 670, 'window_length': 48}." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2020-02-16T05:17:11.600Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:gpu available: True, used: True\n", + "INFO:root:VISIBLE GPUS: 0\n", + "INFO:root:\n", + " Name Type Params\n", + "0 _model LSTMNet 54 K\n", + "1 _model.lstm1 LSTM 54 K\n", + "2 _model.mean Linear 65 \n", + "3 _model.std Linear 65 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "32f4e522972e482bb6ae621f1882e650", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 17559, {'val_loss': '-0.4357861876487732', 'val/loss': '-0.4357861876487732', 'val/loss_mse': '0.009126412682235241', 'val/loss_p': '-0.4357861876487732', 'val/sigma': '0.060174521058797836'}\n", + "Epoch 7: reducing learning rate of group 0 to 2.0000e-05.\n" + ] + } + ], + "source": [ + "trial = optuna.trial.FixedTrial(\n", + " params={\n", + " 'batch_size': 16,\n", + " 'bidirectional': False,\n", + " 'grad_clip': 40,\n", + " 'hidden_size': 64,\n", + " 'input_size': 17,\n", + " 'learning_rate': 2e-4,\n", + " 'lstm_layers': 2,\n", + " 'lstm_dropout': 0.0,\n", + " 'max_nb_epochs': 20,\n", + " 'num_workers': 4,\n", + " 'target_length': 24*4,\n", + " 'vis_i': '670',\n", + " 'window_length': 24*4\n", + " })\n", + "trial = add_suggest(trial)\n", + "trial.number = 124\n", + "model, trainer = main(trial, train=False)\n", + "trainer.fit(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T01:47:59.598921Z", + "start_time": "2020-02-16T01:47:02.100Z" + } + }, + "source": [ + "Note that the LSTM has access to the y values for the first half of the plot (the context) to match the NP setup." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:16:21.617811Z", + "start_time": "2020-02-16T05:01:04.949560Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:gpu available: True, used: True\n", + "INFO:root:VISIBLE GPUS: 0\n", + "INFO:root:\n", + " Name Type Params\n", + "0 _model LSTMNet 1 M\n", + "1 _model.lstm1 LSTM 999 K\n", + "2 _model.mean Linear 129 \n", + "3 _model.std Linear 129 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b38b6ecd37d1475297c891dc76c1f24c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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+ "text/plain": [ + "
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388\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\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 389\u001b[0m \u001b[0mbatch_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_norm_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_step_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_batch\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;31m# calculate loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer_closure\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 513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 514\u001b[0m \u001b[0;31m# nan grads\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/pytorch-lightning/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36moptimizer_closure\u001b[0;34m()\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[0;31m# backward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 493\u001b[0m \u001b[0mmodel_ref\u001b[0m \u001b[0;34m=\u001b[0m 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optimizer_idx)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[1;32m 154\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m 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"\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m 97\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 98\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m allow_unreachable=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# trial = optuna.trial.FixedTrial(\n", + "# params={\n", + "# 'number': 45,\n", + "# 'batch_size': 16,\n", + "# 'bidirectional': False,\n", + "# 'grad_clip': 40,\n", + "# 'hidden_size': 128,\n", + "# 'input_size': 17,\n", + "# 'learning_rate': 1e-3,\n", + "# 'lstm_dropout': 0.4,\n", + "# 'lstm_layers': 8,\n", + "# 'max_nb_epochs': 20,\n", + "# 'num_workers': 4,\n", + "# 'target_length': 24*4,\n", + "# 'vis_i': '670',\n", + "# 'window_length': 24*4\n", + "# })\n", + "# trial = add_suggest(trial)\n", + "# trial.number = 123\n", + "# model, trainer = main(trial, train=False)\n", + "# trainer.fit(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.330351Z", + "start_time": "2020-02-16T04:41:35.100Z" + } + }, + "outputs": [], + "source": [ + "loader = model.val_dataloader()[0]\n", + "dset_test = loader.dataset\n", + "label_names = dset_test.label_names\n", + "plot_from_loader(loader, model, vis_i=670)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.332301Z", + "start_time": "2020-02-16T04:41:35.100Z" + } + }, + "outputs": [], + "source": [ + "# test\n", + "trainer.test(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T00:49:01.520212Z", + "start_time": "2020-02-16T00:49:01.461810Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-27T09:03:28.792465Z", + "start_time": "2020-01-27T09:03:28.346446Z" + } + }, + "source": [ + "# Hyperparam opt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.333887Z", + "start_time": "2020-02-16T04:41:35.100Z" + } + }, + "outputs": [], + "source": [ + "import argparse \n", + "\n", + "parser = argparse.ArgumentParser(description='PyTorch Lightning example.')\n", + "parser.add_argument('--pruning', '-p', action='store_true',\n", + " help='Activate the pruning feature. `MedianPruner` stops unpromising '\n", + " 'trials at the early stages of training.')\n", + "args = parser.parse_args(['-p'])\n", + "\n", + "pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()\n", + "\n", + "study = optuna.create_study(direction='minimize', pruner=pruner, storage=f'sqlite:///optuna_result/{name}.db', study_name='no-name-b60e37fc-4ab6-4793-8a0a-c87a1b40c5c0', load_if_exists=True)\n", + "\n", + "# shutil.rmtree(MODEL_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-01-27T23:38:16.813496Z", + "start_time": "2020-01-27T23:38:16.766674Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.335900Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "study.optimize(objective, n_trials=200, timeout=6000)\n", + "\n", + "print('Number of finished trials: {}'.format(len(study.trials)))\n", + "\n", + "print('Best trial:')\n", + "trial = study.best_trial\n", + "\n", + "print(' Value: {}'.format(trial.value))\n", + "\n", + "print(' Params: ')\n", + "for key, value in trial.params.items():\n", + " print(' {}: {}'.format(key, value))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.337445Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "study.optimize(objective, n_trials=200, timeout=6000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.338898Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "df = study.trials_dataframe(attrs=('number', 'value', 'params', 'state'))\n", + "df.sort_values('value')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scratch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.340348Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "model, trainer = main(trial, train=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.341841Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "loader = model.val_dataloader()[0]\n", + "dset_test = loader.dataset\n", + "label_names = dset_test.label_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.343323Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "x_rows, y_rows = dset_test.iloc(10)\n", + "x_rows.loc[x_rows.index[model.hparams.window_length:], dset_test.label_names] = 0.\n", + "x_rows" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.344989Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "x_rows.loc[x_rows.index[model.hparams.window_length:]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.346671Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "self=dset_test\n", + "idx=10\n", + "k = idx + self.hparams.window_length + self.hparams.target_length\n", + "j = k - self.hparams.target_length\n", + "i = j - self.hparams.window_length\n", + "idx, k, j, i, self.hparams.window_length + self.hparams.target_length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.349116Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "j-i" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-02-16T05:01:00.351704Z", + "start_time": "2020-02-16T04:41:35.200Z" + } + }, + "outputs": [], + "source": [ + "self.hparams.window_length + self.hparams.target_length" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "file_extension": ".py", + "kernelspec": { + "display_name": "jup3.7.3", + "language": "python", + "name": "jup3.7.3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "mimetype": "text/x-python", + "name": "python", + "npconvert_exporter": "python", + "pygments_lexer": "ipython3", + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "384px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "version": 3 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/models/lightning_anp.py b/src/models/lightning_anp.py index e75df10..0ddc6dc 100644 --- a/src/models/lightning_anp.py +++ b/src/models/lightning_anp.py @@ -87,7 +87,7 @@ class LatentModelPL(pl.LightningModule): return self.validation_end(*args, **kwargs) def configure_optimizers(self): - optim = torch.optim.WAdam(self.parameters(), lr=self.hparams["learning_rate"]) + optim = torch.optim.AdamW(self.parameters(), lr=self.hparams["learning_rate"]) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optim, patience=2, verbose=True, min_lr=1e-5) # note early stopping has patient 3 return [optim], [scheduler] diff --git a/src/models/lstm.py b/src/models/lstm.py index 05c5211..54d5175 100644 --- a/src/models/lstm.py +++ b/src/models/lstm.py @@ -42,21 +42,20 @@ class SequenceDfDataSet(torch.utils.data.Dataset): assert i >= 0 assert idx <= len(self.data) - x_rows = self.data.iloc[i:j].copy() + x_rows = self.data.iloc[i:k].copy() # x_rows = x_rows.drop(columns=self.label_names) # Note the NP models do have access to the previous labels for the context, we will allow the LSTM to do the same. Although it will likely just return an autoregressive solution for the first half... x_rows.loc[x_rows.index[self.hparams.window_length:], self.label_names] = 0 + assert len(x_rows.loc[x_rows.index[self.hparams.window_length:], self.label_names])>0 assert (x_rows.loc[x_rows.index[self.hparams.window_length:], self.label_names]==0).all().all() - y_rows = self.data[self.label_names].iloc[i+1:j+1].copy() + y_rows = self.data[self.label_names].iloc[i+1:k+1].copy() # print(i,j,k) # add seconds since start of window index x_rows["tstp"] = ( x_rows["tstp"] - x_rows["tstp"].iloc[0] ).dt.total_seconds() / 86400.0 - - # TODO we could augment by removing and backfilling some return x_rows, y_rows def __getitem__(self, idx): @@ -93,7 +92,7 @@ class LSTMNet(nn.Module): outputs, (h_out, _) = self.lstm1(x) # outputs: [B, T, num_direction * H] y = self.linear(outputs).squeeze(2) - return y + return y class LSTM_PL(pl.LightningModule): @@ -115,6 +114,8 @@ class LSTM_PL(pl.LightningModule): # REQUIRED x, y = batch y_hat = self.forward(x) + y = y[:, self.hparams.window_length:] + y_hat = y_hat[:, self.hparams.window_length:] loss = F.mse_loss(y_hat, y) tensorboard_logs = {"train_loss": loss} return {"loss": loss, "log": tensorboard_logs} @@ -122,6 +123,8 @@ class LSTM_PL(pl.LightningModule): def validation_step(self, batch, batch_idx): x, y = batch y_hat = self.forward(x) + y = y[:, self.hparams.window_length:] + y_hat = y_hat[:, self.hparams.window_length:] loss = F.mse_loss(y_hat, y) tensorboard_logs = {"val_loss": loss} return {"val_loss": loss, "log": tensorboard_logs} @@ -132,10 +135,10 @@ class LSTM_PL(pl.LightningModule): loader = self.val_dataloader()[0] vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset)) if isinstance(self.hparams["vis_i"], str): - image = plot_from_loader(loader, self, vis_i=vis_i) + image = plot_from_loader(loader, self, vis_i=vis_i, window_len=self.hparams["window_length"]) plt.show() else: - image = plot_from_loader_to_tensor(loader, self, vis_i=vis_i) + image = plot_from_loader_to_tensor(loader, self, vis_i=vis_i, window_len=self.hparams["window_length"]) self.logger.experiment.add_image( "val/image", image, self.trainer.global_step ) @@ -151,7 +154,6 @@ class LSTM_PL(pl.LightningModule): assert torch.isfinite(avg_loss) return {"avg_val_loss": avg_loss, "log": tensorboard_logs} - def test_step(self, *args, **kwargs): return self.validation_step(*args, **kwargs) @@ -237,7 +239,7 @@ class LSTM_PL(pl.LightningModule): return parser -def plot_from_loader(loader, model, vis_i=670, n=1): +def plot_from_loader(loader, model, vis_i=670, n=1, window_len=0): dset_test = loader.dataset label_names = dset_test.label_names y_trues = [] @@ -263,7 +265,7 @@ def plot_from_loader(loader, model, vis_i=670, n=1): plt.figure() pd.concat(y_trues)[label_names[0]].plot(label="y_true") ylims = plt.ylim() - pd.concat(y_preds)[label_names[0]].plot(label="y_pred") + pd.concat(y_preds)[label_names[0]][window_len:].plot(label="y_pred") plt.legend() t_ahead = pd.Timedelta("30T") * model.hparams.target_length plt.title(f"predicting {t_ahead} ahead") diff --git a/src/models/lstm_std.py b/src/models/lstm_std.py new file mode 100644 index 0000000..8e9e004 --- /dev/null +++ b/src/models/lstm_std.py @@ -0,0 +1,336 @@ +import os +import numpy as np +import pandas as pd +import torch +from tqdm.auto import tqdm +from torch import nn +from torch.nn import functional as F +from torch.utils.data import DataLoader +from torchvision.datasets import MNIST +from test_tube import Experiment, HyperOptArgumentParser +import torchvision.transforms as transforms +from argparse import ArgumentParser +import json +import pytorch_lightning as pl +import math +from matplotlib import pyplot as plt +import torch +import io +import PIL +from torchvision.transforms import ToTensor + +from src.data.smart_meter import get_smartmeter_df + +from src.utils import ObjectDict + +def log_prob_sigma(value, loc, log_scale): + """A slightly more stable (not confirmed yet) log prob taking in log_var instead of scale. + modified from https://github.com/pytorch/pytorch/blob/2431eac7c011afe42d4c22b8b3f46dedae65e7c0/torch/distributions/normal.py#L65 + """ + var = torch.exp(log_scale * 2) + return ( + -((value - loc) ** 2) / (2 * var) - log_scale - math.log(math.sqrt(2 * math.pi)) + ) + +class SequenceDfDataSet(torch.utils.data.Dataset): + def __init__(self, df, hparams, label_names=None, train=True, transforms=None): + super().__init__() + self.data = df + self.hparams = hparams + self.label_names = label_names + self.train = train + self.transforms = transforms + + def __len__(self): + return len(self.data) - self.hparams.window_length - self.hparams.target_length - 1 + + def iloc(self, idx): + k = idx + self.hparams.window_length + self.hparams.target_length + j = k - self.hparams.target_length + i = j - self.hparams.window_length + assert i >= 0 + assert idx <= len(self.data) + + x_rows = self.data.iloc[i:k].copy() + # x_rows = x_rows.drop(columns=self.label_names) + # Note the NP models do have access to the previous labels for the context, we will allow the LSTM to do the same. Although it will likely just return an autoregressive solution for the first half... + x_rows.loc[x_rows.index[self.hparams.window_length:], self.label_names] = 0 + assert len(x_rows.loc[x_rows.index[self.hparams.window_length:], self.label_names])>0 + assert (x_rows.loc[x_rows.index[self.hparams.window_length:], self.label_names]==0).all().all() + + y_rows = self.data[self.label_names].iloc[i+1:k+1].copy() + # print(i,j,k) + + # add seconds since start of window index + x_rows["tstp"] = ( + x_rows["tstp"] - x_rows["tstp"].iloc[0] + ).dt.total_seconds() / 86400.0 + return x_rows, y_rows + + def __getitem__(self, idx): + x_rows, y_rows = self.iloc(idx) + + x = x_rows.astype(np.float32).values + y = y_rows[self.label_names].astype(np.float32).values + return ( + self.transforms(x).squeeze(0).float(), + self.transforms(y).squeeze(0).squeeze(-1).float(), + ) + + +class LSTMNet(nn.Module): + def __init__(self, hparams, _min_std = 0.05): + super().__init__() + self.hparams = hparams + self._min_std = _min_std + + self.lstm1 = nn.LSTM( + input_size=self.hparams.input_size, + hidden_size=self.hparams.hidden_size, + batch_first=True, + num_layers=self.hparams.lstm_layers, + bidirectional=self.hparams.bidirectional, + dropout=self.hparams.lstm_dropout, + ) + self.hidden_out_size = ( + self.hparams.hidden_size + * (self.hparams.bidirectional + 1) + ) + self.mean = nn.Linear(self.hidden_out_size, 1) + self.std = nn.Linear(self.hidden_out_size, 1) + + def forward(self, x): + outputs, (h_out, _) = self.lstm1(x) + # outputs: [B, T, num_direction * H] + mean = self.mean(outputs).squeeze(2) + log_sigma = self.std(outputs).squeeze(2) + log_sigma = torch.clamp(log_sigma, math.log(self._min_std), -math.log(1e-5)) + return mean, log_sigma + + +class LSTM_PL(pl.LightningModule): + def __init__(self, hparams): + # TODO make label name configurable + # TODO make data source configurable + super().__init__() + self.hparams = ObjectDict() + self.hparams.update( + hparams.__dict__ if hasattr(hparams, "__dict__") else hparams + ) + self._model = LSTMNet(self.hparams) + self._dfs = None + + def forward(self, x): + return self._model(x) + + def training_step(self, batch, batch_idx): + # REQUIRED + x, y = batch + mean, log_sigma = self.forward(x) + + # Don't catch loss on context window + mean = mean[:, self.hparams.window_length:] + log_sigma = log_sigma[:, self.hparams.window_length:] + + sigma = torch.exp(log_sigma) + y_dist = torch.distributions.Normal(mean, sigma) + + y = y[:, self.hparams.window_length:] + + loss_mse = F.mse_loss(mean, y) + loss_p = - log_prob_sigma(y, mean, log_sigma).mean() + loss = loss_p # + loss_mse + tensorboard_logs = {"train/loss": loss, 'train/loss_mse': loss_mse, "train/loss_p": loss_p, "train/sigma": sigma.mean()} + return {"loss": loss, "log": tensorboard_logs} + + def validation_step(self, batch, batch_idx): + x, y = batch + mean, log_sigma = self.forward(x) + + # Don't catch loss on context window + mean = mean[:, self.hparams.window_length:] + log_sigma = log_sigma[:, self.hparams.window_length:] + + sigma = torch.exp(log_sigma) + y_dist = torch.distributions.Normal(mean, sigma) + + y = y[:, self.hparams.window_length:] + + loss_mse = F.mse_loss(mean, y) + loss_p = -log_prob_sigma(y, mean, log_sigma).mean() + loss = loss_p # + loss_mse + tensorboard_logs = {"val_loss": loss, 'val/loss':loss, 'val/loss_mse': loss_mse, "val/loss_p": loss_p, "val/sigma": sigma.mean()} + return {"val_loss": loss, "log": tensorboard_logs} + + def validation_end(self, outputs): + # TODO send an image to tensroboard, like in the lighting_anp.py file + if int(self.hparams["vis_i"]) > 0: + loader = self.val_dataloader()[0] + vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset)) + if isinstance(self.hparams["vis_i"], str): + image = plot_from_loader(loader, self, vis_i=vis_i) + plt.show() + else: + image = plot_from_loader_to_tensor(loader, self, vis_i=vis_i) + self.logger.experiment.add_image( + "val/image", image, self.trainer.global_step + ) + + avg_loss = torch.stack([x["val_loss"] for x in outputs]).mean() + keys = outputs[0]["log"].keys() + tensorboard_logs = { + k: torch.stack([x["log"][k] for x in outputs if k in x["log"]]).mean() + for k in keys + } + tensorboard_logs_str = {k: f"{v}" for k, v in tensorboard_logs.items()} + print(f"step {self.trainer.global_step}, {tensorboard_logs_str}") + assert torch.isfinite(avg_loss) + return {"avg_val_loss": avg_loss, "log": tensorboard_logs} + + def test_step(self, *args, **kwargs): + return self.validation_step(*args, **kwargs) + + def test_end(self, *args, **kwargs): + return self.validation_end(*args, **kwargs) + + def configure_optimizers(self): + optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"]) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optim, patience=2, verbose=True, min_lr=1e-5 + ) # note early stopping has patient 3 + return [optim], [scheduler] + + def _get_cache_dfs(self): + if self._dfs is None: + df_train, df_test = get_smartmeter_df() + # self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600]) + self._dfs = dict(df_train=df_train, df_test=df_test) + return self._dfs + + @pl.data_loader + def train_dataloader(self): + df_train = self._get_cache_dfs()["df_train"] + dset_train = SequenceDfDataSet( + df_train, + self.hparams, + label_names=["energy(kWh/hh)"], + transforms=transforms.ToTensor(), + train=True, + ) + return DataLoader( + dset_train, + batch_size=self.hparams.batch_size, + shuffle=True, + num_workers=self.hparams.num_workers, + ) + + @pl.data_loader + def val_dataloader(self): + df_test = self._get_cache_dfs()["df_test"] + dset_test = SequenceDfDataSet( + df_test, + self.hparams, + label_names=["energy(kWh/hh)"], + train=False, + transforms=transforms.ToTensor(), + ) + return DataLoader(dset_test, batch_size=self.hparams.batch_size, shuffle=False) + + @pl.data_loader + def test_dataloader(self): + df_test = self._get_cache_dfs()["df_test"] + dset_test = SequenceDfDataSet( + df_test, + self.hparams, + label_names=["energy(kWh/hh)"], + train=False, + transforms=transforms.ToTensor(), + ) + return DataLoader(dset_test, batch_size=self.hparams.batch_size, shuffle=False) + + @staticmethod + def add_model_specific_args(parent_parser): + """ + Specify the hyperparams for this LightningModule + """ + # MODEL specific + parser = HyperOptArgumentParser(parents=[parent_parser]) + parser.add_argument("--learning_rate", default=0.002, type=float) + parser.add_argument("--batch_size", default=16, type=int) + parser.add_argument("--lstm_dropout", default=0.5, type=float) + parser.add_argument("--hidden_size", default=16, type=int) + parser.add_argument("--input_size", default=8, type=int) + parser.add_argument("--lstm_layers", default=8, type=int) + parser.add_argument("--bidirectional", default=False, type=bool) + + # training specific (for this model) + parser.add_argument("--window_length", type=int, default=12) + parser.add_argument("--target_length", type=int, default=2) + parser.add_argument("--max_nb_epochs", default=10, type=int) + parser.add_argument("--num_workers", default=4, type=int) + + return parser + + +def plot_from_loader(loader, model, vis_i=670, n=1): + dset_test = loader.dataset + label_names = dset_test.label_names + y_trues = [] + y_preds = [] + vis_i = min(vis_i, len(dset_test)) + for i in tqdm(range(vis_i, vis_i + n)): + x_rows, y_rows = dset_test.iloc(i) + x, y = dset_test[i] + device = next(model.parameters()).device + x = x[None, :].to(device) + model.eval() + with torch.no_grad(): + y_hat, log_sigma = model.forward(x) + y_hat = y_hat.cpu().squeeze(0).numpy() + sigma = log_sigma.exp().cpu().squeeze(0).numpy() + + dt = y_rows.iloc[0].name + + y_hat_rows = y_rows.copy() + y_hat_rows[label_names[0]] = y_hat + y_hat_rows['sigma'] = sigma + y_trues.append(y_rows) + y_preds.append(y_hat_rows) + + df_trues = pd.concat(y_trues) + df_preds = pd.concat(y_preds) + + plt.figure() + df_trues[label_names[0]].plot(label="y_true") + ylims = plt.ylim() + df_preds[label_names[0]][window_len:].plot(label="y_pred") + + std = df_preds['sigma'][window_len:] + mean = df_preds[label_names[0]][window_len:] + plt.fill_between( + df_preds.index[window_len:], + mean - std, + mean + std, + alpha=0.25, + facecolor="blue", + interpolate=True, + label="uncertainty", + ) + plt.legend() + t_ahead = pd.Timedelta("30T") * model.hparams.target_length + plt.title(f"predicting {t_ahead} ahead") + plt.ylim(*ylims) + # plt.show() + + +def plot_from_loader_to_tensor(*args, **kwargs): + plot_from_loader(*args, **kwargs) + + # Send fig to tensorboard + buf = io.BytesIO() + plt.savefig(buf, format="jpeg") + plt.close() + buf.seek(0) + image = PIL.Image.open(buf) + image = ToTensor()(image) # .unsqueeze(0) + return image diff --git a/src/utils.py b/src/utils.py index b6f646f..caadf0b 100644 --- a/src/utils.py +++ b/src/utils.py @@ -13,7 +13,7 @@ class ObjectDict(dict): if attr.startswith('_'): # https://stackoverflow.com/questions/10364332/how-to-pickle-python-object-derived-from-dict raise AttributeError - return self[attr] + return dict(self)[attr] @property def __dict__(self):