diff --git "a/insurance/tab_ddpm_concat/mlu-eval.ipynb" "b/insurance/tab_ddpm_concat/mlu-eval.ipynb" --- "a/insurance/tab_ddpm_concat/mlu-eval.ipynb" +++ "b/insurance/tab_ddpm_concat/mlu-eval.ipynb" @@ -6,16 +6,16 @@ "id": "982e76f5", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:50.558870Z", - "iopub.status.busy": "2024-02-29T18:26:50.558154Z", - "iopub.status.idle": "2024-02-29T18:26:50.590106Z", - "shell.execute_reply": "2024-02-29T18:26:50.589458Z" + "iopub.execute_input": "2024-03-03T11:01:44.570944Z", + "iopub.status.busy": "2024-03-03T11:01:44.569998Z", + "iopub.status.idle": "2024-03-03T11:01:44.611904Z", + "shell.execute_reply": "2024-03-03T11:01:44.611054Z" }, "papermill": { - "duration": 0.046189, - "end_time": "2024-02-29T18:26:50.591955", + "duration": 0.057756, + "end_time": "2024-03-03T11:01:44.614024", "exception": false, - "start_time": "2024-02-29T18:26:50.545766", + "start_time": "2024-03-03T11:01:44.556268", "status": "completed" }, "tags": [] @@ -33,16 +33,16 @@ "id": "675f0b41", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:50.617349Z", - "iopub.status.busy": "2024-02-29T18:26:50.616718Z", - "iopub.status.idle": "2024-02-29T18:26:50.624173Z", - "shell.execute_reply": "2024-02-29T18:26:50.623371Z" + "iopub.execute_input": "2024-03-03T11:01:44.642117Z", + "iopub.status.busy": "2024-03-03T11:01:44.641699Z", + "iopub.status.idle": "2024-03-03T11:01:44.648593Z", + "shell.execute_reply": "2024-03-03T11:01:44.647728Z" }, "papermill": { - "duration": 0.022172, - "end_time": "2024-02-29T18:26:50.626060", + "duration": 0.024578, + "end_time": "2024-03-03T11:01:44.650963", "exception": false, - "start_time": "2024-02-29T18:26:50.603888", + "start_time": "2024-03-03T11:01:44.626385", "status": "completed" }, "tags": [] @@ -76,16 +76,16 @@ "id": "5ae30f5c", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:50.649180Z", - "iopub.status.busy": "2024-02-29T18:26:50.648911Z", - "iopub.status.idle": "2024-02-29T18:26:50.653522Z", - "shell.execute_reply": "2024-02-29T18:26:50.652094Z" + "iopub.execute_input": "2024-03-03T11:01:44.683632Z", + "iopub.status.busy": "2024-03-03T11:01:44.683035Z", + "iopub.status.idle": "2024-03-03T11:01:44.687788Z", + "shell.execute_reply": "2024-03-03T11:01:44.686964Z" }, "papermill": { - "duration": 0.019003, - "end_time": "2024-02-29T18:26:50.655938", + "duration": 0.02277, + "end_time": "2024-03-03T11:01:44.689713", "exception": false, - "start_time": "2024-02-29T18:26:50.636935", + "start_time": "2024-03-03T11:01:44.666943", "status": "completed" }, "tags": [] @@ -102,10 +102,10 @@ "id": "9f42c810", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:50.679562Z", - "iopub.status.busy": "2024-02-29T18:26:50.679302Z", - "iopub.status.idle": "2024-02-29T18:26:50.683027Z", - "shell.execute_reply": "2024-02-29T18:26:50.682249Z" + "iopub.execute_input": "2024-03-03T11:01:44.713693Z", + "iopub.status.busy": "2024-03-03T11:01:44.713372Z", + "iopub.status.idle": "2024-03-03T11:01:44.717894Z", + "shell.execute_reply": "2024-03-03T11:01:44.717086Z" }, "executionInfo": { "elapsed": 678, @@ -119,10 +119,10 @@ }, "id": "ns5hFcVL2yvs", "papermill": { - "duration": 0.01756, - "end_time": "2024-02-29T18:26:50.684861", + "duration": 0.019153, + "end_time": "2024-03-03T11:01:44.719881", "exception": false, - "start_time": "2024-02-29T18:26:50.667301", + "start_time": "2024-03-03T11:01:44.700728", "status": "completed" }, "tags": [] @@ -144,16 +144,16 @@ "id": "85d0c8ce", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:50.707664Z", - "iopub.status.busy": "2024-02-29T18:26:50.707390Z", - "iopub.status.idle": "2024-02-29T18:26:50.712716Z", - "shell.execute_reply": "2024-02-29T18:26:50.711943Z" + "iopub.execute_input": "2024-03-03T11:01:44.745548Z", + "iopub.status.busy": "2024-03-03T11:01:44.745224Z", + "iopub.status.idle": "2024-03-03T11:01:44.751772Z", + "shell.execute_reply": "2024-03-03T11:01:44.750911Z" }, "papermill": { - "duration": 0.018823, - "end_time": "2024-02-29T18:26:50.714583", + "duration": 0.021918, + "end_time": "2024-03-03T11:01:44.753823", "exception": false, - "start_time": "2024-02-29T18:26:50.695760", + "start_time": "2024-03-03T11:01:44.731905", "status": "completed" }, "tags": [ @@ -179,25 +179,26 @@ "debug = False\n", "path = None\n", "param_index = 0\n", - "allow_same_prediction = False" + "allow_same_prediction = True\n", + "log_wandb = False" ] }, { "cell_type": "code", "execution_count": 6, - "id": "1800468a", + "id": "4e90a1af", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:50.739163Z", - "iopub.status.busy": "2024-02-29T18:26:50.738835Z", - "iopub.status.idle": "2024-02-29T18:26:50.744203Z", - "shell.execute_reply": "2024-02-29T18:26:50.743458Z" + "iopub.execute_input": "2024-03-03T11:01:44.780315Z", + "iopub.status.busy": "2024-03-03T11:01:44.780032Z", + "iopub.status.idle": "2024-03-03T11:01:44.785324Z", + "shell.execute_reply": "2024-03-03T11:01:44.784384Z" }, "papermill": { - "duration": 0.019989, - "end_time": "2024-02-29T18:26:50.746255", + "duration": 0.022708, + "end_time": "2024-03-03T11:01:44.787932", "exception": false, - "start_time": "2024-02-29T18:26:50.726266", + "start_time": "2024-03-03T11:01:44.765224", "status": "completed" }, "tags": [ @@ -217,8 +218,9 @@ "folder = \"eval\"\n", "path_prefix = \"../../../../\"\n", "path = \"eval/insurance/tab_ddpm_concat/4\"\n", - "param_index = 2\n", - "allow_same_prediction = True\n" + "param_index = 3\n", + "allow_same_prediction = True\n", + "log_wandb = False\n" ] }, { @@ -227,10 +229,10 @@ "id": "bd7c02d6", "metadata": { "papermill": { - "duration": 0.010925, - "end_time": "2024-02-29T18:26:50.768318", + "duration": 0.012007, + "end_time": "2024-03-03T11:01:44.816037", "exception": false, - "start_time": "2024-02-29T18:26:50.757393", + "start_time": "2024-03-03T11:01:44.804030", "status": "completed" }, "tags": [] @@ -244,10 +246,10 @@ "id": "5f45b1d0", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:50.791282Z", - "iopub.status.busy": "2024-02-29T18:26:50.791016Z", - "iopub.status.idle": "2024-02-29T18:26:50.799559Z", - "shell.execute_reply": "2024-02-29T18:26:50.798806Z" + "iopub.execute_input": "2024-03-03T11:01:44.840912Z", + "iopub.status.busy": "2024-03-03T11:01:44.840199Z", + "iopub.status.idle": "2024-03-03T11:01:44.851377Z", + "shell.execute_reply": "2024-03-03T11:01:44.850578Z" }, "executionInfo": { "elapsed": 7, @@ -261,10 +263,10 @@ }, "id": "UdvXYv3c3LXy", "papermill": { - "duration": 0.022153, - "end_time": "2024-02-29T18:26:50.801373", + "duration": 0.02564, + "end_time": "2024-03-03T11:01:44.853397", "exception": false, - "start_time": "2024-02-29T18:26:50.779220", + "start_time": "2024-03-03T11:01:44.827757", "status": "completed" }, "tags": [] @@ -298,16 +300,16 @@ "id": "f85bf540", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:50.825158Z", - "iopub.status.busy": "2024-02-29T18:26:50.824897Z", - "iopub.status.idle": "2024-02-29T18:26:52.939311Z", - "shell.execute_reply": "2024-02-29T18:26:52.938380Z" + "iopub.execute_input": "2024-03-03T11:01:44.879869Z", + "iopub.status.busy": "2024-03-03T11:01:44.879593Z", + "iopub.status.idle": "2024-03-03T11:01:47.059565Z", + "shell.execute_reply": "2024-03-03T11:01:47.058615Z" }, "papermill": { - "duration": 2.128694, - "end_time": "2024-02-29T18:26:52.941418", + "duration": 2.195792, + "end_time": "2024-03-03T11:01:47.061639", "exception": false, - "start_time": "2024-02-29T18:26:50.812724", + "start_time": "2024-03-03T11:01:44.865847", "status": "completed" }, "tags": [] @@ -317,7 +319,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Set seed to \n" + "Set seed to \n" ] } ], @@ -336,16 +338,16 @@ "id": "8489feae", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:52.968131Z", - "iopub.status.busy": "2024-02-29T18:26:52.967214Z", - "iopub.status.idle": "2024-02-29T18:26:52.979057Z", - "shell.execute_reply": "2024-02-29T18:26:52.978209Z" + "iopub.execute_input": "2024-03-03T11:01:47.086553Z", + "iopub.status.busy": "2024-03-03T11:01:47.086168Z", + "iopub.status.idle": "2024-03-03T11:01:47.097582Z", + "shell.execute_reply": "2024-03-03T11:01:47.096887Z" }, "papermill": { - "duration": 0.027034, - "end_time": "2024-02-29T18:26:52.981011", + "duration": 0.02616, + "end_time": "2024-03-03T11:01:47.099577", "exception": false, - "start_time": "2024-02-29T18:26:52.953977", + "start_time": "2024-03-03T11:01:47.073417", "status": "completed" }, "tags": [] @@ -368,10 +370,10 @@ "id": "debcc684", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:53.005698Z", - "iopub.status.busy": "2024-02-29T18:26:53.005020Z", - "iopub.status.idle": "2024-02-29T18:26:53.011733Z", - "shell.execute_reply": "2024-02-29T18:26:53.010953Z" + "iopub.execute_input": "2024-03-03T11:01:47.125152Z", + "iopub.status.busy": "2024-03-03T11:01:47.124166Z", + "iopub.status.idle": "2024-03-03T11:01:47.132075Z", + "shell.execute_reply": "2024-03-03T11:01:47.131245Z" }, "executionInfo": { "elapsed": 6, @@ -385,10 +387,10 @@ }, "id": "Vrl2QkoV3o_8", "papermill": { - "duration": 0.021252, - "end_time": "2024-02-29T18:26:53.013739", + "duration": 0.02292, + "end_time": "2024-03-03T11:01:47.134095", "exception": false, - "start_time": "2024-02-29T18:26:52.992487", + "start_time": "2024-03-03T11:01:47.111175", "status": "completed" }, "tags": [] @@ -412,10 +414,10 @@ "id": "7538184a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:53.037402Z", - "iopub.status.busy": "2024-02-29T18:26:53.037158Z", - "iopub.status.idle": "2024-02-29T18:26:53.136345Z", - "shell.execute_reply": "2024-02-29T18:26:53.135661Z" + "iopub.execute_input": "2024-03-03T11:01:47.161414Z", + "iopub.status.busy": "2024-03-03T11:01:47.161162Z", + "iopub.status.idle": "2024-03-03T11:01:47.266060Z", + "shell.execute_reply": "2024-03-03T11:01:47.265250Z" }, "executionInfo": { "elapsed": 6, @@ -429,10 +431,10 @@ }, "id": "TilUuFk9vqMb", "papermill": { - "duration": 0.113075, - "end_time": "2024-02-29T18:26:53.138309", + "duration": 0.121905, + "end_time": "2024-03-03T11:01:47.268153", "exception": false, - "start_time": "2024-02-29T18:26:53.025234", + "start_time": "2024-03-03T11:01:47.146248", "status": "completed" }, "tags": [] @@ -465,10 +467,10 @@ "id": "cca61838", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:53.163865Z", - "iopub.status.busy": "2024-02-29T18:26:53.163568Z", - "iopub.status.idle": "2024-02-29T18:26:57.713583Z", - "shell.execute_reply": "2024-02-29T18:26:57.712809Z" + "iopub.execute_input": "2024-03-03T11:01:47.292550Z", + "iopub.status.busy": "2024-03-03T11:01:47.292267Z", + "iopub.status.idle": "2024-03-03T11:01:51.912455Z", + "shell.execute_reply": "2024-03-03T11:01:51.911426Z" }, "executionInfo": { "elapsed": 3113, @@ -482,10 +484,10 @@ }, "id": "7Abt8nStvr9Z", "papermill": { - "duration": 4.565534, - "end_time": "2024-02-29T18:26:57.715908", + "duration": 4.635182, + "end_time": "2024-03-03T11:01:51.914891", "exception": false, - "start_time": "2024-02-29T18:26:53.150374", + "start_time": "2024-03-03T11:01:47.279709", "status": "completed" }, "tags": [] @@ -495,9 +497,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-02-29 18:26:55.362648: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-02-29 18:26:55.362701: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-02-29 18:26:55.364282: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" + "2024-03-03 11:01:49.494405: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-03-03 11:01:49.494461: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-03-03 11:01:49.496116: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] } ], @@ -519,16 +521,16 @@ "id": "6f83b7b6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:26:57.741084Z", - "iopub.status.busy": "2024-02-29T18:26:57.740440Z", - "iopub.status.idle": "2024-02-29T18:26:57.747224Z", - 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"2024-03-03T11:02:00.346124Z", + "shell.execute_reply": "2024-03-03T11:02:00.344770Z" }, "executionInfo": { "elapsed": 20137, @@ -569,10 +571,10 @@ }, "id": "tbaguWxAvtPi", "papermill": { - "duration": 8.004862, - "end_time": "2024-02-29T18:27:05.766044", + "duration": 8.386665, + "end_time": "2024-03-03T11:02:00.348756", "exception": false, - "start_time": "2024-02-29T18:26:57.761182", + "start_time": "2024-03-03T11:01:51.962091", "status": "completed" }, "tags": [] @@ -617,7 +619,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 1/1 [00:00<00:00, 2.96it/s]" + "100%|██████████| 1/1 [00:00<00:00, 2.93it/s]" ] }, { @@ -625,7 +627,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 1/1 [00:00<00:00, 2.95it/s]" + "100%|██████████| 1/1 [00:00<00:00, 2.92it/s]" ] }, { @@ -670,10 +672,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-02-29T18:27:05.793951Z", - "iopub.status.busy": "2024-02-29T18:27:05.793616Z", - "iopub.status.idle": "2024-02-29T18:27:05.800096Z", - "shell.execute_reply": "2024-02-29T18:27:05.799321Z" + "iopub.execute_input": "2024-03-03T11:02:00.379529Z", + "iopub.status.busy": "2024-03-03T11:02:00.379155Z", + "iopub.status.idle": "2024-03-03T11:02:00.386832Z", + "shell.execute_reply": "2024-03-03T11:02:00.385808Z" }, "executionInfo": { "elapsed": 13, @@ -688,10 +690,10 @@ "id": "OxUH_GBEv2qK", "outputId": "76464c90-3baf-4bdc-a955-6f4fddc16b9c", "papermill": { - "duration": 0.022395, - "end_time": "2024-02-29T18:27:05.801957", + "duration": 0.02652, + "end_time": "2024-03-03T11:02:00.389175", "exception": false, - "start_time": "2024-02-29T18:27:05.779562", + "start_time": "2024-03-03T11:02:00.362655", "status": "completed" }, "tags": [] @@ -721,16 +723,16 @@ "id": "3cb9ed90", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:27:05.827106Z", - "iopub.status.busy": "2024-02-29T18:27:05.826850Z", - "iopub.status.idle": "2024-02-29T18:27:05.831397Z", - "shell.execute_reply": "2024-02-29T18:27:05.830675Z" + "iopub.execute_input": "2024-03-03T11:02:00.417130Z", + "iopub.status.busy": "2024-03-03T11:02:00.416762Z", + "iopub.status.idle": "2024-03-03T11:02:00.421720Z", + "shell.execute_reply": "2024-03-03T11:02:00.420819Z" }, "papermill": { - "duration": 0.019112, - "end_time": "2024-02-29T18:27:05.833194", + "duration": 0.021361, + "end_time": "2024-03-03T11:02:00.423823", "exception": false, - "start_time": "2024-02-29T18:27:05.814082", + "start_time": "2024-03-03T11:02:00.402462", "status": "completed" }, "tags": [] @@ -753,16 +755,16 @@ "id": "ad1eb833", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:27:05.858118Z", - "iopub.status.busy": "2024-02-29T18:27:05.857860Z", - "iopub.status.idle": "2024-02-29T18:27:05.888185Z", - "shell.execute_reply": "2024-02-29T18:27:05.887355Z" + "iopub.execute_input": "2024-03-03T11:02:00.450628Z", + "iopub.status.busy": "2024-03-03T11:02:00.450361Z", + "iopub.status.idle": "2024-03-03T11:02:00.483556Z", + "shell.execute_reply": "2024-03-03T11:02:00.482632Z" }, "papermill": { - "duration": 0.044834, - "end_time": "2024-02-29T18:27:05.889997", + "duration": 0.0492, + "end_time": "2024-03-03T11:02:00.485754", "exception": false, - "start_time": "2024-02-29T18:27:05.845163", + "start_time": "2024-03-03T11:02:00.436554", "status": "completed" }, "tags": [] @@ -798,10 +800,10 @@ "id": "14ff8b40", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:27:05.917077Z", - "iopub.status.busy": "2024-02-29T18:27:05.916535Z", - "iopub.status.idle": "2024-02-29T18:27:06.229267Z", - "shell.execute_reply": "2024-02-29T18:27:06.228385Z" + "iopub.execute_input": "2024-03-03T11:02:00.514528Z", + "iopub.status.busy": "2024-03-03T11:02:00.514233Z", + "iopub.status.idle": "2024-03-03T11:02:00.852976Z", + "shell.execute_reply": "2024-03-03T11:02:00.852085Z" }, "executionInfo": { "elapsed": 588, @@ -815,10 +817,10 @@ }, "id": "NgahtU1q9uLO", "papermill": { - "duration": 0.328598, - "end_time": "2024-02-29T18:27:06.231292", + "duration": 0.35501, + "end_time": "2024-03-03T11:02:00.855021", "exception": false, - "start_time": "2024-02-29T18:27:05.902694", + "start_time": "2024-03-03T11:02:00.500011", "status": "completed" }, "tags": [] @@ -827,8 +829,46 @@ { "data": { "text/plain": [ - "{'Body': 'twin_encoder',\n", - " 'loss_balancer_meta': True,\n", + "{'loss_balancer_beta': 0.7520229775744602,\n", + " 'loss_balancer_r': 0.9706519501751338,\n", + " 'tf_pma_low': 64,\n", + " 'grad_loss_fn': torch.Tensor>,\n", + " 'pma_ffn_mode': 'shared',\n", + " 'patience': 10,\n", + " 'inds_init_mode': 'fixnorm',\n", + " 'grad_clip': 0.6896836352825375,\n", + " 'head_final_mul': 'identity',\n", + " 'gradient_penalty_mode': {'gradient_penalty': False,\n", + " 'calc_grad_m': False,\n", + " 'avg_non_role_model_m': False,\n", + " 'inverse_avg_non_role_model_m': False},\n", + " 'dataset_size': 2048,\n", + " 'batch_size': 4,\n", + " 'epochs': 100,\n", + " 'lr_mul': 0.08030439779404704,\n", + " 'n_warmup_steps': 85,\n", + " 'Optim': torch_optimizer.diffgrad.DiffGrad,\n", + " 'fixed_role_model': 'tab_ddpm_concat',\n", + " 'd_model': 256,\n", + " 'attn_activation': torch.nn.modules.activation.Sigmoid,\n", + " 'tf_d_inner': 256,\n", + " 'tf_n_layers_enc': 5,\n", + " 'tf_n_head': 128,\n", + " 'tf_activation': ml_utility_loss.activations.LeakyHardsigmoid,\n", + " 'tf_activation_final': ml_utility_loss.activations.LeakyHardtanh,\n", + " 'ada_d_hid': 256,\n", + " 'ada_n_layers': 8,\n", + " 'ada_activation': torch.nn.modules.activation.ReLU6,\n", + " 'ada_activation_final': ml_utility_loss.activations.LeakyHardtanh,\n", + " 'head_d_hid': 256,\n", + " 'head_n_layers': 8,\n", + " 'head_n_head': 32,\n", + " 'head_activation': torch.nn.modules.activation.ReLU6,\n", + " 'head_activation_final': torch.nn.modules.activation.Softsign,\n", + " 'single_model': True,\n", + " 'models': ['tab_ddpm_concat'],\n", + " 'max_seconds': 3600,\n", + " 'Body': 'twin_encoder',\n", " 'loss_balancer_log': False,\n", " 'loss_balancer_lbtw': False,\n", " 'pma_skip_small': False,\n", @@ -841,52 +881,14 @@ " 'tf_layer_norm': False,\n", " 'tf_pma_start': -1,\n", " 'head_n_seeds': 0,\n", - " 'tf_pma_low': 16,\n", " 'dropout': 0,\n", " 'combine_mode': 'diff_left',\n", " 'tf_isab_mode': 'separate',\n", - " 'grad_loss_fn': torch.Tensor>,\n", - " 'single_model': True,\n", " 'bias': True,\n", " 'bias_final': True,\n", - " 'pma_ffn_mode': 'shared',\n", - " 'patience': 10,\n", - " 'inds_init_mode': 'fixnorm',\n", - " 'grad_clip': 0.77,\n", - " 'head_final_mul': 'identity',\n", - " 'gradient_penalty_mode': {'gradient_penalty': False,\n", - " 'calc_grad_m': False,\n", - " 'avg_non_role_model_m': False,\n", - " 'inverse_avg_non_role_model_m': False},\n", " 'synth_data': 2,\n", - " 'dataset_size': 2048,\n", - " 'batch_size': 8,\n", - " 'epochs': 100,\n", - " 'n_warmup_steps': 100,\n", - " 'Optim': torch_optimizer.diffgrad.DiffGrad,\n", - " 'loss_balancer_beta': 0.75,\n", - " 'loss_balancer_r': 0.95,\n", - " 'fixed_role_model': 'tab_ddpm_concat',\n", - " 'd_model': 256,\n", - " 'attn_activation': torch.nn.modules.activation.LeakyReLU,\n", - " 'tf_d_inner': 512,\n", - " 'tf_n_layers_enc': 4,\n", - " 'tf_n_head': 64,\n", - " 'tf_activation': torch.nn.modules.activation.ReLU6,\n", - " 'tf_activation_final': ml_utility_loss.activations.LeakyHardsigmoid,\n", - " 'ada_d_hid': 1024,\n", - " 'ada_n_layers': 7,\n", - " 'ada_activation': torch.nn.modules.activation.ReLU,\n", - " 'ada_activation_final': torch.nn.modules.activation.Softsign,\n", - " 'head_d_hid': 128,\n", - " 'head_n_layers': 9,\n", - " 'head_n_head': 64,\n", - " 'head_activation': torch.nn.modules.activation.RReLU,\n", - " 'head_activation_final': torch.nn.modules.activation.Softsign,\n", - " 'models': ['tab_ddpm_concat'],\n", - " 'max_seconds': 3600,\n", " 'tf_lora': False,\n", - " 'tf_num_inds': 32,\n", + " 'tf_num_inds': 64,\n", " 'ada_n_seeds': 0,\n", " 'gradient_penalty_kwargs': {'mag_loss': True,\n", " 'mse_mag': False,\n", @@ -895,7 +897,7 @@ " 'cos_loss': False,\n", " 'mag_corr_kwargs': {'only_sign': False},\n", " 'cos_loss_kwargs': {'only_sign': True, 'cos_matrix': False},\n", - " 'mse_mag_kwargs': {'target': 0.1, 'multiply': False}}}" + " 'mse_mag_kwargs': {'target': 0.13044551835398707, 'multiply': True}}}" ] }, "execution_count": 18, @@ -954,16 +956,16 @@ "id": "a48bd9e9", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:27:06.257693Z", - "iopub.status.busy": "2024-02-29T18:27:06.257389Z", - "iopub.status.idle": "2024-02-29T18:27:06.330588Z", - "shell.execute_reply": "2024-02-29T18:27:06.329733Z" + "iopub.execute_input": "2024-03-03T11:02:00.882262Z", + "iopub.status.busy": "2024-03-03T11:02:00.881551Z", + "iopub.status.idle": "2024-03-03T11:02:00.955360Z", + "shell.execute_reply": "2024-03-03T11:02:00.954296Z" }, "papermill": { - "duration": 0.088632, - "end_time": "2024-02-29T18:27:06.332688", + "duration": 0.08959, + "end_time": "2024-03-03T11:02:00.957400", "exception": false, - "start_time": "2024-02-29T18:27:06.244056", + "start_time": "2024-03-03T11:02:00.867810", "status": "completed" }, "tags": [] @@ -1005,10 +1007,10 @@ "height": 1000 }, "execution": { - "iopub.execute_input": "2024-02-29T18:27:06.361058Z", - "iopub.status.busy": "2024-02-29T18:27:06.360443Z", - "iopub.status.idle": "2024-02-29T18:27:06.783264Z", - "shell.execute_reply": "2024-02-29T18:27:06.782352Z" + "iopub.execute_input": "2024-03-03T11:02:00.985850Z", + "iopub.status.busy": "2024-03-03T11:02:00.985495Z", + "iopub.status.idle": "2024-03-03T11:02:01.418904Z", + "shell.execute_reply": "2024-03-03T11:02:01.417808Z" }, "executionInfo": { "elapsed": 396850, @@ -1023,10 +1025,10 @@ "id": "_bt1MQc5kpSk", "outputId": "01c1d3e5-ac64-461d-835a-b76f4a66e6d6", "papermill": { - "duration": 0.439025, - "end_time": "2024-02-29T18:27:06.785332", + "duration": 0.4505, + "end_time": "2024-03-03T11:02:01.421338", "exception": false, - "start_time": "2024-02-29T18:27:06.346307", + "start_time": "2024-03-03T11:02:00.970838", "status": "completed" }, "tags": [] @@ -1073,16 +1075,16 @@ "id": "938f94fc", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:27:06.814758Z", - "iopub.status.busy": "2024-02-29T18:27:06.813947Z", - "iopub.status.idle": "2024-02-29T18:27:06.818524Z", - "shell.execute_reply": "2024-02-29T18:27:06.817759Z" + "iopub.execute_input": "2024-03-03T11:02:01.451176Z", + "iopub.status.busy": "2024-03-03T11:02:01.450374Z", + "iopub.status.idle": "2024-03-03T11:02:01.454846Z", + "shell.execute_reply": "2024-03-03T11:02:01.453995Z" }, "papermill": { - "duration": 0.021262, - "end_time": "2024-02-29T18:27:06.820462", + "duration": 0.021568, + "end_time": "2024-03-03T11:02:01.456847", "exception": false, - "start_time": "2024-02-29T18:27:06.799200", + "start_time": "2024-03-03T11:02:01.435279", "status": "completed" }, "tags": [] @@ -1098,16 +1100,16 @@ "id": "12fb613e", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:27:06.848255Z", - "iopub.status.busy": "2024-02-29T18:27:06.847981Z", - "iopub.status.idle": "2024-02-29T18:27:06.854747Z", - "shell.execute_reply": "2024-02-29T18:27:06.853941Z" + "iopub.execute_input": "2024-03-03T11:02:01.484109Z", + "iopub.status.busy": "2024-03-03T11:02:01.483802Z", + "iopub.status.idle": "2024-03-03T11:02:01.491045Z", + "shell.execute_reply": "2024-03-03T11:02:01.490241Z" }, "papermill": { - "duration": 0.02231, - "end_time": "2024-02-29T18:27:06.856576", + "duration": 0.023247, + "end_time": "2024-03-03T11:02:01.493037", "exception": false, - "start_time": "2024-02-29T18:27:06.834266", + "start_time": "2024-03-03T11:02:01.469790", "status": "completed" }, "tags": [] @@ -1116,7 +1118,7 @@ { "data": { "text/plain": [ - "9613953" + "8696065" ] }, "execution_count": 22, @@ -1137,16 +1139,16 @@ "id": "bd386e57", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:27:06.882937Z", - "iopub.status.busy": "2024-02-29T18:27:06.882671Z", - "iopub.status.idle": "2024-02-29T18:27:06.968606Z", - "shell.execute_reply": "2024-02-29T18:27:06.967758Z" + "iopub.execute_input": "2024-03-03T11:02:01.520098Z", + "iopub.status.busy": "2024-03-03T11:02:01.519804Z", + "iopub.status.idle": "2024-03-03T11:02:01.622148Z", + "shell.execute_reply": "2024-03-03T11:02:01.621289Z" }, "papermill": { - "duration": 0.101194, - "end_time": "2024-02-29T18:27:06.970459", + "duration": 0.11822, + "end_time": "2024-03-03T11:02:01.624199", "exception": false, - "start_time": "2024-02-29T18:27:06.869265", + "start_time": "2024-03-03T11:02:01.505979", "status": "completed" }, "tags": [] @@ -1161,306 +1163,355 @@ "MLUtilitySingle [2, 1071, 12] --\n", "├─Adapter: 1-1 [2, 1071, 12] --\n", "│ └─Sequential: 2-1 [2, 1071, 256] --\n", - "│ │ └─FeedForward: 3-1 [2, 1071, 1024] --\n", - "│ │ │ └─Linear: 4-1 [2, 1071, 1024] 13,312\n", - "│ │ │ └─ReLU: 4-2 [2, 1071, 1024] --\n", - "│ │ └─FeedForward: 3-2 [2, 1071, 1024] --\n", - "│ │ │ └─Linear: 4-3 [2, 1071, 1024] 1,049,600\n", - "│ │ │ └─ReLU: 4-4 [2, 1071, 1024] --\n", - "│ │ └─FeedForward: 3-3 [2, 1071, 1024] --\n", - "│ │ │ └─Linear: 4-5 [2, 1071, 1024] 1,049,600\n", - "│ │ │ └─ReLU: 4-6 [2, 1071, 1024] --\n", - "│ │ └─FeedForward: 3-4 [2, 1071, 1024] --\n", - "│ │ │ └─Linear: 4-7 [2, 1071, 1024] 1,049,600\n", - "│ │ │ └─ReLU: 4-8 [2, 1071, 1024] --\n", - "│ │ └─FeedForward: 3-5 [2, 1071, 1024] --\n", - "│ │ │ └─Linear: 4-9 [2, 1071, 1024] 1,049,600\n", - "│ │ │ └─ReLU: 4-10 [2, 1071, 1024] --\n", - "│ │ └─FeedForward: 3-6 [2, 1071, 1024] --\n", - "│ │ │ └─Linear: 4-11 [2, 1071, 1024] 1,049,600\n", - "│ │ │ └─ReLU: 4-12 [2, 1071, 1024] --\n", + "│ │ └─FeedForward: 3-1 [2, 1071, 256] --\n", + "│ │ │ └─Linear: 4-1 [2, 1071, 256] 3,328\n", + "│ │ │ └─ReLU6: 4-2 [2, 1071, 256] --\n", + "│ │ └─FeedForward: 3-2 [2, 1071, 256] --\n", + "│ │ │ └─Linear: 4-3 [2, 1071, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-4 [2, 1071, 256] --\n", + "│ │ └─FeedForward: 3-3 [2, 1071, 256] --\n", + "│ │ │ └─Linear: 4-5 [2, 1071, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-6 [2, 1071, 256] --\n", + "│ │ └─FeedForward: 3-4 [2, 1071, 256] --\n", + "│ │ │ └─Linear: 4-7 [2, 1071, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-8 [2, 1071, 256] --\n", + "│ │ └─FeedForward: 3-5 [2, 1071, 256] --\n", + "│ │ │ └─Linear: 4-9 [2, 1071, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-10 [2, 1071, 256] --\n", + "│ │ └─FeedForward: 3-6 [2, 1071, 256] --\n", + "│ │ │ └─Linear: 4-11 [2, 1071, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-12 [2, 1071, 256] --\n", "│ │ └─FeedForward: 3-7 [2, 1071, 256] --\n", - "│ │ │ └─Linear: 4-13 [2, 1071, 256] 262,400\n", - "│ │ │ └─Softsign: 4-14 [2, 1071, 256] --\n", + "│ │ │ └─Linear: 4-13 [2, 1071, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-14 [2, 1071, 256] --\n", + "│ │ └─FeedForward: 3-8 [2, 1071, 256] --\n", + "│ │ │ └─Linear: 4-15 [2, 1071, 256] 65,792\n", + "│ │ │ └─LeakyHardtanh: 4-16 [2, 1071, 256] --\n", "├─Adapter: 1-2 [2, 267, 12] (recursive)\n", "│ └─Sequential: 2-2 [2, 267, 256] (recursive)\n", - "│ │ └─FeedForward: 3-8 [2, 267, 1024] (recursive)\n", - "│ │ │ └─Linear: 4-15 [2, 267, 1024] (recursive)\n", - "│ │ │ └─ReLU: 4-16 [2, 267, 1024] --\n", - "│ │ └─FeedForward: 3-9 [2, 267, 1024] (recursive)\n", - "│ │ │ └─Linear: 4-17 [2, 267, 1024] (recursive)\n", - "│ │ │ └─ReLU: 4-18 [2, 267, 1024] --\n", - "│ │ └─FeedForward: 3-10 [2, 267, 1024] (recursive)\n", - "│ │ │ └─Linear: 4-19 [2, 267, 1024] (recursive)\n", - "│ │ │ └─ReLU: 4-20 [2, 267, 1024] --\n", - "│ │ └─FeedForward: 3-11 [2, 267, 1024] (recursive)\n", - "│ │ │ └─Linear: 4-21 [2, 267, 1024] (recursive)\n", - "│ │ │ └─ReLU: 4-22 [2, 267, 1024] --\n", - "│ │ └─FeedForward: 3-12 [2, 267, 1024] (recursive)\n", - "│ │ │ └─Linear: 4-23 [2, 267, 1024] (recursive)\n", - "│ │ │ └─ReLU: 4-24 [2, 267, 1024] --\n", - "│ │ └─FeedForward: 3-13 [2, 267, 1024] (recursive)\n", - "│ │ │ └─Linear: 4-25 [2, 267, 1024] (recursive)\n", - "│ │ │ └─ReLU: 4-26 [2, 267, 1024] --\n", + "│ │ └─FeedForward: 3-9 [2, 267, 256] (recursive)\n", + "│ │ │ └─Linear: 4-17 [2, 267, 256] (recursive)\n", + "│ │ │ └─ReLU6: 4-18 [2, 267, 256] --\n", + "│ │ └─FeedForward: 3-10 [2, 267, 256] (recursive)\n", + "│ │ │ └─Linear: 4-19 [2, 267, 256] (recursive)\n", + "│ │ │ └─ReLU6: 4-20 [2, 267, 256] --\n", + "│ │ └─FeedForward: 3-11 [2, 267, 256] (recursive)\n", + "│ │ │ └─Linear: 4-21 [2, 267, 256] (recursive)\n", + "│ │ │ └─ReLU6: 4-22 [2, 267, 256] --\n", + "│ │ └─FeedForward: 3-12 [2, 267, 256] (recursive)\n", + "│ │ │ └─Linear: 4-23 [2, 267, 256] (recursive)\n", + "│ │ │ └─ReLU6: 4-24 [2, 267, 256] --\n", + "│ │ └─FeedForward: 3-13 [2, 267, 256] (recursive)\n", + "│ │ │ └─Linear: 4-25 [2, 267, 256] (recursive)\n", + "│ │ │ └─ReLU6: 4-26 [2, 267, 256] --\n", "│ │ └─FeedForward: 3-14 [2, 267, 256] (recursive)\n", "│ │ │ └─Linear: 4-27 [2, 267, 256] (recursive)\n", - "│ │ │ └─Softsign: 4-28 [2, 267, 256] --\n", - "├─TwinEncoder: 1-3 [2, 4096] --\n", - "│ └─Encoder: 2-3 [2, 16, 256] --\n", - "│ │ └─ModuleList: 3-16 -- (recursive)\n", - "│ │ │ └─EncoderLayer: 4-29 [2, 1071, 256] --\n", + "│ │ │ └─ReLU6: 4-28 [2, 267, 256] --\n", + "│ │ └─FeedForward: 3-15 [2, 267, 256] (recursive)\n", + "│ │ │ └─Linear: 4-29 [2, 267, 256] (recursive)\n", + "│ │ │ └─ReLU6: 4-30 [2, 267, 256] --\n", + "│ │ └─FeedForward: 3-16 [2, 267, 256] (recursive)\n", + "│ │ │ └─Linear: 4-31 [2, 267, 256] (recursive)\n", + "│ │ │ └─LeakyHardtanh: 4-32 [2, 267, 256] --\n", + "├─TwinEncoder: 1-3 [2, 16384] --\n", + "│ └─Encoder: 2-3 [2, 64, 256] --\n", + "│ │ └─ModuleList: 3-18 -- (recursive)\n", + "│ │ │ └─EncoderLayer: 4-33 [2, 1071, 256] --\n", "│ │ │ │ └─SimpleInducedSetAttention: 5-1 [2, 1071, 256] --\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-1 [2, 32, 256] 8,192\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-2 [2, 32, 256] --\n", - "│ │ │ │ │ │ └─Linear: 7-1 [2, 32, 256] 65,536\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-1 [2, 64, 256] 16,384\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-2 [2, 64, 256] --\n", + "│ │ │ │ │ │ └─Linear: 7-1 [2, 64, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-2 [2, 1071, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-3 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-4 [2, 64, 32, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-1 [2, 64, 32, 1071] --\n", - "│ │ │ │ │ │ └─Linear: 7-5 [2, 32, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-6 [2, 32, 256] --\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-4 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-1 [2, 128, 64, 1071] --\n", + "│ │ │ │ │ │ └─Linear: 7-5 [2, 64, 256] 65,792\n", + "│ │ │ │ │ │ └─Sigmoid: 7-6 [2, 64, 256] --\n", "│ │ │ │ │ └─MultiHeadAttention: 6-3 [2, 1071, 256] --\n", "│ │ │ │ │ │ └─Linear: 7-7 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─Linear: 7-8 [2, 32, 256] 65,536\n", - "│ │ │ │ │ │ └─Linear: 7-9 [2, 32, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-10 [2, 64, 1071, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-2 [2, 64, 1071, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-8 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─Linear: 7-9 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-10 [2, 128, 1071, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-2 [2, 128, 1071, 64] --\n", "│ │ │ │ │ │ └─Linear: 7-11 [2, 1071, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-12 [2, 1071, 256] --\n", + "│ │ │ │ │ │ └─Sigmoid: 7-12 [2, 1071, 256] --\n", "│ │ │ │ └─DoubleFeedForward: 5-2 [2, 1071, 256] --\n", - "│ │ │ │ │ └─Linear: 6-4 [2, 1071, 512] 131,584\n", - "│ │ │ │ │ └─ReLU6: 6-5 [2, 1071, 512] --\n", - "│ │ │ │ │ └─Linear: 6-6 [2, 1071, 256] 131,328\n", - "│ │ │ └─EncoderLayer: 4-30 [2, 1071, 256] --\n", + "│ │ │ │ │ └─Linear: 6-4 [2, 1071, 256] 65,792\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-5 [2, 1071, 256] --\n", + "│ │ │ │ │ └─Linear: 6-6 [2, 1071, 256] 65,792\n", + "│ │ │ └─EncoderLayer: 4-34 [2, 1071, 256] --\n", "│ │ │ │ └─SimpleInducedSetAttention: 5-3 [2, 1071, 256] --\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-7 [2, 32, 256] 8,192\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-8 [2, 32, 256] --\n", - "│ │ │ │ │ │ └─Linear: 7-13 [2, 32, 256] 65,536\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-7 [2, 64, 256] 16,384\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-8 [2, 64, 256] --\n", + "│ │ │ │ │ │ └─Linear: 7-13 [2, 64, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-14 [2, 1071, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-15 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-16 [2, 64, 32, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-3 [2, 64, 32, 1071] --\n", - "│ │ │ │ │ │ └─Linear: 7-17 [2, 32, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-18 [2, 32, 256] --\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-16 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-3 [2, 128, 64, 1071] --\n", + "│ │ │ │ │ │ └─Linear: 7-17 [2, 64, 256] 65,792\n", + "│ │ │ │ │ │ └─Sigmoid: 7-18 [2, 64, 256] --\n", "│ │ │ │ │ └─MultiHeadAttention: 6-9 [2, 1071, 256] --\n", "│ │ │ │ │ │ └─Linear: 7-19 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─Linear: 7-20 [2, 32, 256] 65,536\n", - "│ │ │ │ │ │ └─Linear: 7-21 [2, 32, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-22 [2, 64, 1071, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-4 [2, 64, 1071, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-20 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─Linear: 7-21 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-22 [2, 128, 1071, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-4 [2, 128, 1071, 64] --\n", "│ │ │ │ │ │ └─Linear: 7-23 [2, 1071, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-24 [2, 1071, 256] --\n", + "│ │ │ │ │ │ └─Sigmoid: 7-24 [2, 1071, 256] --\n", "│ │ │ │ └─DoubleFeedForward: 5-4 [2, 1071, 256] --\n", - "│ │ │ │ │ └─Linear: 6-10 [2, 1071, 512] 131,584\n", - "│ │ │ │ │ └─ReLU6: 6-11 [2, 1071, 512] --\n", - "│ │ │ │ │ └─Linear: 6-12 [2, 1071, 256] 131,328\n", - "│ │ │ └─EncoderLayer: 4-31 [2, 1071, 256] --\n", + "│ │ │ │ │ └─Linear: 6-10 [2, 1071, 256] 65,792\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-11 [2, 1071, 256] --\n", + "│ │ │ │ │ └─Linear: 6-12 [2, 1071, 256] 65,792\n", + "│ │ │ └─EncoderLayer: 4-35 [2, 1071, 256] --\n", "│ │ │ │ └─SimpleInducedSetAttention: 5-5 [2, 1071, 256] --\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-13 [2, 32, 256] 8,192\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-14 [2, 32, 256] --\n", - "│ │ │ │ │ │ └─Linear: 7-25 [2, 32, 256] 65,536\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-13 [2, 64, 256] 16,384\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-14 [2, 64, 256] --\n", + "│ │ │ │ │ │ └─Linear: 7-25 [2, 64, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-26 [2, 1071, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-27 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-28 [2, 64, 32, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-5 [2, 64, 32, 1071] --\n", - "│ │ │ │ │ │ └─Linear: 7-29 [2, 32, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-30 [2, 32, 256] --\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-28 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-5 [2, 128, 64, 1071] --\n", + "│ │ │ │ │ │ └─Linear: 7-29 [2, 64, 256] 65,792\n", + "│ │ │ │ │ │ └─Sigmoid: 7-30 [2, 64, 256] --\n", "│ │ │ │ │ └─MultiHeadAttention: 6-15 [2, 1071, 256] --\n", "│ │ │ │ │ │ └─Linear: 7-31 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─Linear: 7-32 [2, 32, 256] 65,536\n", - "│ │ │ │ │ │ └─Linear: 7-33 [2, 32, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-34 [2, 64, 1071, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-6 [2, 64, 1071, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-32 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─Linear: 7-33 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-34 [2, 128, 1071, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-6 [2, 128, 1071, 64] --\n", "│ │ │ │ │ │ └─Linear: 7-35 [2, 1071, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-36 [2, 1071, 256] --\n", + "│ │ │ │ │ │ └─Sigmoid: 7-36 [2, 1071, 256] --\n", "│ │ │ │ └─DoubleFeedForward: 5-6 [2, 1071, 256] --\n", - "│ │ │ │ │ └─Linear: 6-16 [2, 1071, 512] 131,584\n", - "│ │ │ │ │ └─ReLU6: 6-17 [2, 1071, 512] --\n", - "│ │ │ │ │ └─Linear: 6-18 [2, 1071, 256] 131,328\n", - "│ │ │ └─EncoderLayer: 4-32 [2, 16, 256] --\n", + "│ │ │ │ │ └─Linear: 6-16 [2, 1071, 256] 65,792\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-17 [2, 1071, 256] --\n", + "│ │ │ │ │ └─Linear: 6-18 [2, 1071, 256] 65,792\n", + "│ │ │ └─EncoderLayer: 4-36 [2, 1071, 256] --\n", "│ │ │ │ └─SimpleInducedSetAttention: 5-7 [2, 1071, 256] --\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-19 [2, 32, 256] 8,192\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-20 [2, 32, 256] --\n", - "│ │ │ │ │ │ └─Linear: 7-37 [2, 32, 256] 65,536\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-19 [2, 64, 256] 16,384\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-20 [2, 64, 256] --\n", + "│ │ │ │ │ │ └─Linear: 7-37 [2, 64, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-38 [2, 1071, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-39 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-40 [2, 64, 32, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-7 [2, 64, 32, 1071] --\n", - "│ │ │ │ │ │ └─Linear: 7-41 [2, 32, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-42 [2, 32, 256] --\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-40 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-7 [2, 128, 64, 1071] --\n", + "│ │ │ │ │ │ └─Linear: 7-41 [2, 64, 256] 65,792\n", + "│ │ │ │ │ │ └─Sigmoid: 7-42 [2, 64, 256] --\n", "│ │ │ │ │ └─MultiHeadAttention: 6-21 [2, 1071, 256] --\n", "│ │ │ │ │ │ └─Linear: 7-43 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─Linear: 7-44 [2, 32, 256] 65,536\n", - "│ │ │ │ │ │ └─Linear: 7-45 [2, 32, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-46 [2, 64, 1071, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-8 [2, 64, 1071, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-44 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─Linear: 7-45 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-46 [2, 128, 1071, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-8 [2, 128, 1071, 64] --\n", "│ │ │ │ │ │ └─Linear: 7-47 [2, 1071, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-48 [2, 1071, 256] --\n", + "│ │ │ │ │ │ └─Sigmoid: 7-48 [2, 1071, 256] --\n", "│ │ │ │ └─DoubleFeedForward: 5-8 [2, 1071, 256] --\n", - "│ │ │ │ │ └─Linear: 6-22 [2, 1071, 512] 131,584\n", - "│ │ │ │ │ └─LeakyHardsigmoid: 6-23 [2, 1071, 512] --\n", - "│ │ │ │ │ └─Linear: 6-24 [2, 1071, 256] 131,328\n", - "│ │ │ │ └─PoolingByMultiheadAttention: 5-9 [2, 16, 256] --\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-25 [2, 16, 256] 4,096\n", - "│ │ │ │ │ └─SimpleMultiHeadAttention: 6-26 [2, 16, 256] --\n", - "│ │ │ │ │ │ └─Linear: 7-49 [2, 16, 256] 65,536\n", + "│ │ │ │ │ └─Linear: 6-22 [2, 1071, 256] 65,792\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-23 [2, 1071, 256] --\n", + "│ │ │ │ │ └─Linear: 6-24 [2, 1071, 256] 65,792\n", + "│ │ │ └─EncoderLayer: 4-37 [2, 64, 256] --\n", + "│ │ │ │ └─SimpleInducedSetAttention: 5-9 [2, 1071, 256] --\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-25 [2, 64, 256] 16,384\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-26 [2, 64, 256] --\n", + "│ │ │ │ │ │ └─Linear: 7-49 [2, 64, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-50 [2, 1071, 256] 65,536\n", "│ │ │ │ │ │ └─Linear: 7-51 [2, 1071, 256] 65,536\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-52 [2, 64, 16, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-9 [2, 64, 16, 1071] --\n", - "│ │ │ │ │ │ └─Linear: 7-53 [2, 16, 256] 65,792\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-54 [2, 16, 256] --\n", - "│ │ │ │ └─DoubleFeedForward: 5-10 [2, 16, 256] (recursive)\n", - "│ │ │ │ │ └─Linear: 6-27 [2, 16, 512] (recursive)\n", - "│ │ │ │ │ └─LeakyHardsigmoid: 6-28 [2, 16, 512] --\n", - "│ │ │ │ │ └─Linear: 6-29 [2, 16, 256] (recursive)\n", - "│ └─Encoder: 2-4 [2, 16, 256] (recursive)\n", - "│ │ └─ModuleList: 3-16 -- (recursive)\n", - "│ │ │ └─EncoderLayer: 4-33 [2, 267, 256] (recursive)\n", - "│ │ │ │ └─SimpleInducedSetAttention: 5-11 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-30 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-31 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-55 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-56 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-57 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-58 [2, 64, 32, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-10 [2, 64, 32, 267] --\n", - "│ │ │ │ │ │ └─Linear: 7-59 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-60 [2, 32, 256] --\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-32 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-61 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-62 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-63 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-64 [2, 64, 267, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-11 [2, 64, 267, 32] --\n", - "│ │ │ │ │ │ └─Linear: 7-65 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-66 [2, 267, 256] --\n", - "│ │ │ │ └─DoubleFeedForward: 5-12 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ └─Linear: 6-33 [2, 267, 512] (recursive)\n", - "│ │ │ │ │ └─ReLU6: 6-34 [2, 267, 512] --\n", - "│ │ │ │ │ └─Linear: 6-35 [2, 267, 256] (recursive)\n", - "│ │ │ └─EncoderLayer: 4-34 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-52 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-9 [2, 128, 64, 1071] --\n", + "│ │ │ │ │ │ └─Linear: 7-53 [2, 64, 256] 65,792\n", + "│ │ │ │ │ │ └─Sigmoid: 7-54 [2, 64, 256] --\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-27 [2, 1071, 256] --\n", + "│ │ │ │ │ │ └─Linear: 7-55 [2, 1071, 256] 65,536\n", + "│ │ │ │ │ │ └─Linear: 7-56 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─Linear: 7-57 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-58 [2, 128, 1071, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-10 [2, 128, 1071, 64] --\n", + "│ │ │ │ │ │ └─Linear: 7-59 [2, 1071, 256] 65,792\n", + "│ │ │ │ │ │ └─Sigmoid: 7-60 [2, 1071, 256] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-10 [2, 1071, 256] --\n", + "│ │ │ │ │ └─Linear: 6-28 [2, 1071, 256] 65,792\n", + "│ │ │ │ │ └─LeakyHardtanh: 6-29 [2, 1071, 256] --\n", + "│ │ │ │ │ └─Linear: 6-30 [2, 1071, 256] 65,792\n", + "│ │ │ │ └─PoolingByMultiheadAttention: 5-11 [2, 64, 256] --\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-31 [2, 64, 256] 16,384\n", + "│ │ │ │ │ └─SimpleMultiHeadAttention: 6-32 [2, 64, 256] --\n", + "│ │ │ │ │ │ └─Linear: 7-61 [2, 64, 256] 65,536\n", + "│ │ │ │ │ │ └─Linear: 7-62 [2, 1071, 256] 65,536\n", + "│ │ │ │ │ │ └─Linear: 7-63 [2, 1071, 256] 65,536\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-64 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-11 [2, 128, 64, 1071] --\n", + "│ │ │ │ │ │ └─Linear: 7-65 [2, 64, 256] 65,792\n", + "│ │ │ │ │ │ └─Sigmoid: 7-66 [2, 64, 256] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-12 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─Linear: 6-33 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─LeakyHardtanh: 6-34 [2, 64, 256] --\n", + "│ │ │ │ │ └─Linear: 6-35 [2, 64, 256] (recursive)\n", + "│ └─Encoder: 2-4 [2, 64, 256] (recursive)\n", + "│ │ └─ModuleList: 3-18 -- (recursive)\n", + "│ │ │ └─EncoderLayer: 4-38 [2, 267, 256] (recursive)\n", "│ │ │ │ └─SimpleInducedSetAttention: 5-13 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-36 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-37 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-67 [2, 32, 256] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-36 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-37 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-67 [2, 64, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-68 [2, 267, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-69 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-70 [2, 64, 32, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-12 [2, 64, 32, 267] --\n", - "│ │ │ │ │ │ └─Linear: 7-71 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-72 [2, 32, 256] --\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-70 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-12 [2, 128, 64, 267] --\n", + "│ │ │ │ │ │ └─Linear: 7-71 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Sigmoid: 7-72 [2, 64, 256] --\n", "│ │ │ │ │ └─MultiHeadAttention: 6-38 [2, 267, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-73 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-74 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-75 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-76 [2, 64, 267, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-13 [2, 64, 267, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-74 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-75 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-76 [2, 128, 267, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-13 [2, 128, 267, 64] --\n", "│ │ │ │ │ │ └─Linear: 7-77 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-78 [2, 267, 256] --\n", + "│ │ │ │ │ │ └─Sigmoid: 7-78 [2, 267, 256] --\n", "│ │ │ │ └─DoubleFeedForward: 5-14 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ └─Linear: 6-39 [2, 267, 512] (recursive)\n", - "│ │ │ │ │ └─ReLU6: 6-40 [2, 267, 512] --\n", + "│ │ │ │ │ └─Linear: 6-39 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-40 [2, 267, 256] --\n", "│ │ │ │ │ └─Linear: 6-41 [2, 267, 256] (recursive)\n", - "│ │ │ └─EncoderLayer: 4-35 [2, 267, 256] (recursive)\n", + "│ │ │ └─EncoderLayer: 4-39 [2, 267, 256] (recursive)\n", "│ │ │ │ └─SimpleInducedSetAttention: 5-15 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-42 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-43 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-79 [2, 32, 256] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-42 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-43 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-79 [2, 64, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-80 [2, 267, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-81 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-82 [2, 64, 32, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-14 [2, 64, 32, 267] --\n", - "│ │ │ │ │ │ └─Linear: 7-83 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-84 [2, 32, 256] --\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-82 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-14 [2, 128, 64, 267] --\n", + "│ │ │ │ │ │ └─Linear: 7-83 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Sigmoid: 7-84 [2, 64, 256] --\n", "│ │ │ │ │ └─MultiHeadAttention: 6-44 [2, 267, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-85 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-86 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-87 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-88 [2, 64, 267, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-15 [2, 64, 267, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-86 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-87 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-88 [2, 128, 267, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-15 [2, 128, 267, 64] --\n", "│ │ │ │ │ │ └─Linear: 7-89 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-90 [2, 267, 256] --\n", + "│ │ │ │ │ │ └─Sigmoid: 7-90 [2, 267, 256] --\n", "│ │ │ │ └─DoubleFeedForward: 5-16 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ └─Linear: 6-45 [2, 267, 512] (recursive)\n", - "│ │ │ │ │ └─ReLU6: 6-46 [2, 267, 512] --\n", + "│ │ │ │ │ └─Linear: 6-45 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-46 [2, 267, 256] --\n", "│ │ │ │ │ └─Linear: 6-47 [2, 267, 256] (recursive)\n", - "│ │ │ └─EncoderLayer: 4-36 [2, 16, 256] (recursive)\n", + "│ │ │ └─EncoderLayer: 4-40 [2, 267, 256] (recursive)\n", "│ │ │ │ └─SimpleInducedSetAttention: 5-17 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-48 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ └─MultiHeadAttention: 6-49 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-91 [2, 32, 256] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-48 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-49 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-91 [2, 64, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-92 [2, 267, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-93 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-94 [2, 64, 32, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-16 [2, 64, 32, 267] --\n", - "│ │ │ │ │ │ └─Linear: 7-95 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-96 [2, 32, 256] --\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-94 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-16 [2, 128, 64, 267] --\n", + "│ │ │ │ │ │ └─Linear: 7-95 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Sigmoid: 7-96 [2, 64, 256] --\n", "│ │ │ │ │ └─MultiHeadAttention: 6-50 [2, 267, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-97 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-98 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-99 [2, 32, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-100 [2, 64, 267, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-17 [2, 64, 267, 32] --\n", + "│ │ │ │ │ │ └─Linear: 7-98 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-99 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-100 [2, 128, 267, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-17 [2, 128, 267, 64] --\n", "│ │ │ │ │ │ └─Linear: 7-101 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-102 [2, 267, 256] --\n", + "│ │ │ │ │ │ └─Sigmoid: 7-102 [2, 267, 256] --\n", "│ │ │ │ └─DoubleFeedForward: 5-18 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ └─Linear: 6-51 [2, 267, 512] (recursive)\n", - "│ │ │ │ │ └─LeakyHardsigmoid: 6-52 [2, 267, 512] --\n", + "│ │ │ │ │ └─Linear: 6-51 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-52 [2, 267, 256] --\n", "│ │ │ │ │ └─Linear: 6-53 [2, 267, 256] (recursive)\n", - "│ │ │ │ └─PoolingByMultiheadAttention: 5-19 [2, 16, 256] (recursive)\n", - "│ │ │ │ │ └─TensorInductionPoint: 6-54 [2, 16, 256] (recursive)\n", - "│ │ │ │ │ └─SimpleMultiHeadAttention: 6-55 [2, 16, 256] (recursive)\n", - "│ │ │ │ │ │ └─Linear: 7-103 [2, 16, 256] (recursive)\n", + "│ │ │ └─EncoderLayer: 4-41 [2, 267, 256] (recursive)\n", + "│ │ │ │ └─SimpleInducedSetAttention: 5-19 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-54 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-55 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-103 [2, 64, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-104 [2, 267, 256] (recursive)\n", "│ │ │ │ │ │ └─Linear: 7-105 [2, 267, 256] (recursive)\n", - "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-106 [2, 64, 16, 4] --\n", - "│ │ │ │ │ │ │ └─Softmax: 8-18 [2, 64, 16, 267] --\n", - "│ │ │ │ │ │ └─Linear: 7-107 [2, 16, 256] (recursive)\n", - "│ │ │ │ │ │ └─LeakyReLU: 7-108 [2, 16, 256] --\n", - "│ │ │ │ └─DoubleFeedForward: 5-20 [2, 16, 256] (recursive)\n", - "│ │ │ │ │ └─Linear: 6-56 [2, 16, 512] (recursive)\n", - "│ │ │ │ │ └─LeakyHardsigmoid: 6-57 [2, 16, 512] --\n", - "│ │ │ │ │ └─Linear: 6-58 [2, 16, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-106 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-18 [2, 128, 64, 267] --\n", + "│ │ │ │ │ │ └─Linear: 7-107 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Sigmoid: 7-108 [2, 64, 256] --\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-56 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-109 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-110 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-111 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-112 [2, 128, 267, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-19 [2, 128, 267, 64] --\n", + "│ │ │ │ │ │ └─Linear: 7-113 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─Sigmoid: 7-114 [2, 267, 256] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-20 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─Linear: 6-57 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─LeakyHardsigmoid: 6-58 [2, 267, 256] --\n", + "│ │ │ │ │ └─Linear: 6-59 [2, 267, 256] (recursive)\n", + "│ │ │ └─EncoderLayer: 4-42 [2, 64, 256] (recursive)\n", + "│ │ │ │ └─SimpleInducedSetAttention: 5-21 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-60 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-61 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-115 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-116 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-117 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-118 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-20 [2, 128, 64, 267] --\n", + "│ │ │ │ │ │ └─Linear: 7-119 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Sigmoid: 7-120 [2, 64, 256] --\n", + "│ │ │ │ │ └─MultiHeadAttention: 6-62 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-121 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-122 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-123 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-124 [2, 128, 267, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-21 [2, 128, 267, 64] --\n", + "│ │ │ │ │ │ └─Linear: 7-125 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─Sigmoid: 7-126 [2, 267, 256] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-22 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─Linear: 6-63 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ └─LeakyHardtanh: 6-64 [2, 267, 256] --\n", + "│ │ │ │ │ └─Linear: 6-65 [2, 267, 256] (recursive)\n", + "│ │ │ │ └─PoolingByMultiheadAttention: 5-23 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─TensorInductionPoint: 6-66 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─SimpleMultiHeadAttention: 6-67 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-127 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-128 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─Linear: 7-129 [2, 267, 256] (recursive)\n", + "│ │ │ │ │ │ └─ScaledDotProductAttention: 7-130 [2, 128, 64, 2] --\n", + "│ │ │ │ │ │ │ └─Softmax: 8-22 [2, 128, 64, 267] --\n", + "│ │ │ │ │ │ └─Linear: 7-131 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ │ └─Sigmoid: 7-132 [2, 64, 256] --\n", + "│ │ │ │ └─DoubleFeedForward: 5-24 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─Linear: 6-68 [2, 64, 256] (recursive)\n", + "│ │ │ │ │ └─LeakyHardtanh: 6-69 [2, 64, 256] --\n", + "│ │ │ │ │ └─Linear: 6-70 [2, 64, 256] (recursive)\n", "├─Head: 1-4 [2] --\n", "│ └─Sequential: 2-5 [2, 1] --\n", - "│ │ └─FeedForward: 3-17 [2, 128] --\n", - "│ │ │ └─Linear: 4-37 [2, 128] 524,416\n", - "│ │ │ └─RReLU: 4-38 [2, 128] --\n", - "│ │ └─FeedForward: 3-18 [2, 128] --\n", - "│ │ │ └─Linear: 4-39 [2, 128] 16,512\n", - "│ │ │ └─RReLU: 4-40 [2, 128] --\n", - "│ │ └─FeedForward: 3-19 [2, 128] --\n", - "│ │ │ └─Linear: 4-41 [2, 128] 16,512\n", - "│ │ │ └─RReLU: 4-42 [2, 128] --\n", - "│ │ └─FeedForward: 3-20 [2, 128] --\n", - "│ │ │ └─Linear: 4-43 [2, 128] 16,512\n", - "│ │ │ └─RReLU: 4-44 [2, 128] --\n", - "│ │ └─FeedForward: 3-21 [2, 128] --\n", - "│ │ │ └─Linear: 4-45 [2, 128] 16,512\n", - "│ │ │ └─RReLU: 4-46 [2, 128] --\n", - "│ │ └─FeedForward: 3-22 [2, 128] --\n", - "│ │ │ └─Linear: 4-47 [2, 128] 16,512\n", - "│ │ │ └─RReLU: 4-48 [2, 128] --\n", - "│ │ └─FeedForward: 3-23 [2, 128] --\n", - "│ │ │ └─Linear: 4-49 [2, 128] 16,512\n", - "│ │ │ └─RReLU: 4-50 [2, 128] --\n", - "│ │ └─FeedForward: 3-24 [2, 128] --\n", - "│ │ │ └─Linear: 4-51 [2, 128] 16,512\n", - "│ │ │ └─RReLU: 4-52 [2, 128] --\n", - "│ │ └─FeedForward: 3-25 [2, 1] --\n", - "│ │ │ └─Linear: 4-53 [2, 1] 129\n", - "│ │ │ └─Softsign: 4-54 [2, 1] --\n", + "│ │ └─FeedForward: 3-19 [2, 256] --\n", + "│ │ │ └─Linear: 4-43 [2, 256] 4,194,560\n", + "│ │ │ └─ReLU6: 4-44 [2, 256] --\n", + "│ │ └─FeedForward: 3-20 [2, 256] --\n", + "│ │ │ └─Linear: 4-45 [2, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-46 [2, 256] --\n", + "│ │ └─FeedForward: 3-21 [2, 256] --\n", + "│ │ �� └─Linear: 4-47 [2, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-48 [2, 256] --\n", + "│ │ └─FeedForward: 3-22 [2, 256] --\n", + "│ │ │ └─Linear: 4-49 [2, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-50 [2, 256] --\n", + "│ │ └─FeedForward: 3-23 [2, 256] --\n", + "│ │ │ └─Linear: 4-51 [2, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-52 [2, 256] --\n", + "│ │ └─FeedForward: 3-24 [2, 256] --\n", + "│ │ │ └─Linear: 4-53 [2, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-54 [2, 256] --\n", + "│ │ └─FeedForward: 3-25 [2, 256] --\n", + "│ │ │ └─Linear: 4-55 [2, 256] 65,792\n", + "│ │ │ └─ReLU6: 4-56 [2, 256] --\n", + "│ │ └─FeedForward: 3-26 [2, 1] --\n", + "│ │ │ └─Linear: 4-57 [2, 1] 257\n", + "│ │ │ └─Softsign: 4-58 [2, 1] --\n", "========================================================================================================================\n", - "Total params: 9,613,953\n", - "Trainable params: 9,613,953\n", + "Total params: 8,696,065\n", + "Trainable params: 8,696,065\n", "Non-trainable params: 0\n", - "Total mult-adds (M): 38.08\n", + "Total mult-adds (M): 25.74\n", "========================================================================================================================\n", "Input size (MB): 0.13\n", - "Forward/backward pass size (MB): 307.47\n", - "Params size (MB): 38.46\n", - "Estimated Total Size (MB): 346.05\n", + "Forward/backward pass size (MB): 234.98\n", + "Params size (MB): 34.78\n", + "Estimated Total Size (MB): 269.89\n", "========================================================================================================================" ] }, @@ -1483,36 +1534,21 @@ "id": "0f42c4d1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:27:07.000684Z", - "iopub.status.busy": "2024-02-29T18:27:07.000395Z", - "iopub.status.idle": "2024-02-29T18:42:55.818307Z", - "shell.execute_reply": "2024-02-29T18:42:55.817254Z" + "iopub.execute_input": "2024-03-03T11:02:01.656538Z", + "iopub.status.busy": "2024-03-03T11:02:01.655477Z", + "iopub.status.idle": "2024-03-03T11:57:24.427329Z", + "shell.execute_reply": "2024-03-03T11:57:24.426341Z" }, "papermill": { - "duration": 948.852675, - "end_time": "2024-02-29T18:42:55.837260", + "duration": 3322.809941, + "end_time": "2024-03-03T11:57:24.448804", "exception": false, - "start_time": "2024-02-29T18:27:06.984585", + "start_time": "2024-03-03T11:02:01.638863", "status": "completed" }, "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.3\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: W&B syncing is set to \u001b[1m`offline`\u001b[0m in this directory. \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb online`\u001b[0m or set \u001b[1mWANDB_MODE=online\u001b[0m to enable cloud syncing.\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -1525,14 +1561,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train loss {'avg_role_model_loss': 0.0265493107464863, 'avg_role_model_std_loss': 9.705848431753656, 'avg_role_model_mean_pred_loss': 0.0019637997826472465, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.0265493107464863, 'n_size': 320, 'n_batch': 40, 'duration': 39.08715486526489, 'duration_batch': 0.9771788716316223, 'duration_size': 0.12214735895395279, 'avg_pred_std': 0.04609664692543447}\n" + "Train loss {'avg_role_model_loss': 0.11390677978924942, 'avg_role_model_std_loss': 1.6075929376992917, 'avg_role_model_mean_pred_loss': 0.05657142685045635, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.11390677978924942, 'n_size': 320, 'n_batch': 80, 'duration': 84.47398400306702, 'duration_batch': 1.0559248000383377, 'duration_size': 0.2639812000095844, 'avg_pred_std': 0.09700191575684584}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Val loss {'avg_role_model_loss': 0.012864274116873275, 'avg_role_model_std_loss': 8.93672634124523, 'avg_role_model_mean_pred_loss': 3.463389237516879e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012864274116873275, 'n_size': 80, 'n_batch': 10, 'duration': 8.234524965286255, 'duration_batch': 0.8234524965286255, 'duration_size': 0.10293156206607819, 'avg_pred_std': 0.023089123656973243}\n", + "Val loss {'avg_role_model_loss': 0.016991531396342907, 'avg_role_model_std_loss': 16.768777330477747, 'avg_role_model_mean_pred_loss': 0.0011311913316453647, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.016991531396342907, 'n_size': 80, 'n_batch': 20, 'duration': 17.97755455970764, 'duration_batch': 0.8988777279853821, 'duration_size': 0.22471943199634553, 'avg_pred_std': 0.016752634930890055}\n", "Epoch 1\n" ] }, @@ -1540,14 +1576,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train loss {'avg_role_model_loss': 0.013430703204357996, 'avg_role_model_std_loss': 10.238072396071818, 'avg_role_model_mean_pred_loss': 0.0001760885078965657, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.013430703204357996, 'n_size': 320, 'n_batch': 40, 'duration': 38.923088788986206, 'duration_batch': 0.9730772197246551, 'duration_size': 0.12163465246558189, 'avg_pred_std': 0.027457697270438074}\n" + "Train loss {'avg_role_model_loss': 0.02035002698939934, 'avg_role_model_std_loss': 3.3028829722441513, 'avg_role_model_mean_pred_loss': 0.0012390867967089977, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.02035002698939934, 'n_size': 320, 'n_batch': 80, 'duration': 84.68451976776123, 'duration_batch': 1.0585564970970154, 'duration_size': 0.26463912427425385, 'avg_pred_std': 0.06200033854111098}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Val loss {'avg_role_model_loss': 0.01386686596670188, 'avg_role_model_std_loss': 9.424022936335371, 'avg_role_model_mean_pred_loss': 5.71949209714262e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.01386686596670188, 'n_size': 80, 'n_batch': 10, 'duration': 8.236119270324707, 'duration_batch': 0.8236119270324707, 'duration_size': 0.10295149087905883, 'avg_pred_std': 0.019944945629686118}\n", + "Val loss {'avg_role_model_loss': 0.011681753185985144, 'avg_role_model_std_loss': 4.98499947126902, 'avg_role_model_mean_pred_loss': 0.00011886494331716512, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011681753185985144, 'n_size': 80, 'n_batch': 20, 'duration': 18.428929567337036, 'duration_batch': 0.9214464783668518, 'duration_size': 0.23036161959171295, 'avg_pred_std': 0.022514818981289864}\n", "Epoch 2\n" ] }, @@ -1555,14 +1591,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train loss {'avg_role_model_loss': 0.013098158335196786, 'avg_role_model_std_loss': 6.953670260656827, 'avg_role_model_mean_pred_loss': 7.627181049958409e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.013098158335196786, 'n_size': 320, 'n_batch': 40, 'duration': 38.896809816360474, 'duration_batch': 0.9724202454090118, 'duration_size': 0.12155253067612648, 'avg_pred_std': 0.03701225146651268}\n" + "Train loss {'avg_role_model_loss': 0.015553263086258085, 'avg_role_model_std_loss': 4.40048983076071, 'avg_role_model_mean_pred_loss': 0.0012610154882150097, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.015553263086258085, 'n_size': 320, 'n_batch': 80, 'duration': 84.47719740867615, 'duration_batch': 1.055964967608452, 'duration_size': 0.263991241902113, 'avg_pred_std': 0.04350193784048315}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Val loss {'avg_role_model_loss': 0.011231413613131735, 'avg_role_model_std_loss': 4.642900250397725, 'avg_role_model_mean_pred_loss': 1.232088975626766e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011231413613131735, 'n_size': 80, 'n_batch': 10, 'duration': 8.272239923477173, 'duration_batch': 0.8272239923477173, 'duration_size': 0.10340299904346466, 'avg_pred_std': 0.031016640178859235}\n", + "Val loss {'avg_role_model_loss': 0.012001678717297182, 'avg_role_model_std_loss': 2.0838609129365295, 'avg_role_model_mean_pred_loss': 0.00018355202230315414, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012001678717297182, 'n_size': 80, 'n_batch': 20, 'duration': 18.23084259033203, 'duration_batch': 0.9115421295166015, 'duration_size': 0.22788553237915038, 'avg_pred_std': 0.026969157496932895}\n", "Epoch 3\n" ] }, @@ -1570,14 +1606,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train loss {'avg_role_model_loss': 0.013012661421089432, 'avg_role_model_std_loss': 6.77741541211999, 'avg_role_model_mean_pred_loss': 0.00014677781123761946, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.013012661421089432, 'n_size': 320, 'n_batch': 40, 'duration': 39.03108096122742, 'duration_batch': 0.9757770240306854, 'duration_size': 0.12197212800383568, 'avg_pred_std': 0.040795679786242545}\n" + "Train loss {'avg_role_model_loss': 0.012385944992274744, 'avg_role_model_std_loss': 3.628137231519884, 'avg_role_model_mean_pred_loss': 0.0006446951736899908, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012385944992274744, 'n_size': 320, 'n_batch': 80, 'duration': 84.30943512916565, 'duration_batch': 1.0538679391145707, 'duration_size': 0.26346698477864267, 'avg_pred_std': 0.03804389561410062}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Val loss {'avg_role_model_loss': 0.010680149483960122, 'avg_role_model_std_loss': 5.439762359634369, 'avg_role_model_mean_pred_loss': 8.51207419643174e-06, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.010680149483960122, 'n_size': 80, 'n_batch': 10, 'duration': 8.236795425415039, 'duration_batch': 0.8236795425415039, 'duration_size': 0.10295994281768799, 'avg_pred_std': 0.02782872337847948}\n", + "Val loss {'avg_role_model_loss': 0.011361928719270508, 'avg_role_model_std_loss': 2.9160453390245267, 'avg_role_model_mean_pred_loss': 0.00016351417628470699, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011361928719270508, 'n_size': 80, 'n_batch': 20, 'duration': 18.110987901687622, 'duration_batch': 0.9055493950843811, 'duration_size': 0.2263873487710953, 'avg_pred_std': 0.02250743337208405}\n", "Epoch 4\n" ] }, @@ -1585,14 +1621,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train loss {'avg_role_model_loss': 0.012592662169481628, 'avg_role_model_std_loss': 6.8064604322151805, 'avg_role_model_mean_pred_loss': 0.00012719917820476213, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012592662169481628, 'n_size': 320, 'n_batch': 40, 'duration': 38.966336727142334, 'duration_batch': 0.9741584181785583, 'duration_size': 0.12176980227231979, 'avg_pred_std': 0.03671876427251845}\n" + "Train loss {'avg_role_model_loss': 0.012263958598123282, 'avg_role_model_std_loss': 3.76344175813676, 'avg_role_model_mean_pred_loss': 0.0004104777633484336, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012263958598123282, 'n_size': 320, 'n_batch': 80, 'duration': 84.6977150440216, 'duration_batch': 1.05872143805027, 'duration_size': 0.2646803595125675, 'avg_pred_std': 0.03591603521199431}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Val loss {'avg_role_model_loss': 0.012881963208201341, 'avg_role_model_std_loss': 16.157494982505522, 'avg_role_model_mean_pred_loss': 0.00010115250418607502, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012881963208201341, 'n_size': 80, 'n_batch': 10, 'duration': 8.331452369689941, 'duration_batch': 0.8331452369689941, 'duration_size': 0.10414315462112426, 'avg_pred_std': 0.012491705431602895}\n", + "Val loss {'avg_role_model_loss': 0.011301952104258817, 'avg_role_model_std_loss': 2.2325485425771605, 'avg_role_model_mean_pred_loss': 0.00016856359858614668, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011301952104258817, 'n_size': 80, 'n_batch': 20, 'duration': 18.113478422164917, 'duration_batch': 0.9056739211082458, 'duration_size': 0.22641848027706146, 'avg_pred_std': 0.028445655293762685}\n", "Epoch 5\n" ] }, @@ -1600,14 +1636,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Train loss {'avg_role_model_loss': 0.013670370759791694, 'avg_role_model_std_loss': 10.748200260194086, 'avg_role_model_mean_pred_loss': 0.0001568969438597634, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.013670370759791694, 'n_size': 320, 'n_batch': 40, 'duration': 38.94208788871765, 'duration_batch': 0.9735521972179413, 'duration_size': 0.12169402465224266, 'avg_pred_std': 0.029897483938839287}\n" + "Train loss {'avg_role_model_loss': 0.011240640739742958, 'avg_role_model_std_loss': 2.6737590567842524, 'avg_role_model_mean_pred_loss': 0.00015413710455663006, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011240640739742958, 'n_size': 320, 'n_batch': 80, 'duration': 84.4088442325592, 'duration_batch': 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{'avg_role_model_loss': 0.012064280622871593, 'avg_role_model_std_loss': 9.317451603279006, 'avg_role_model_mean_pred_loss': 3.690295125249321e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012064280622871593, 'n_size': 320, 'n_batch': 40, 'duration': 39.005112171173096, 'duration_batch': 0.9751278042793274, 'duration_size': 0.12189097553491593, 'avg_pred_std': 0.0359303968725726}\n" + "Train loss {'avg_role_model_loss': 0.011383894624395907, 'avg_role_model_std_loss': 2.930524671732963, 'avg_role_model_mean_pred_loss': 0.00018457749493845378, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011383894624395907, 'n_size': 320, 'n_batch': 80, 'duration': 84.58404278755188, 'duration_batch': 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{'avg_role_model_loss': 0.01384369531297125, 'avg_role_model_std_loss': 8.665561918970889, 'avg_role_model_mean_pred_loss': 0.00016956335028766033, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.01384369531297125, 'n_size': 320, 'n_batch': 40, 'duration': 38.970547676086426, 'duration_batch': 0.9742636919021607, 'duration_size': 0.12178296148777008, 'avg_pred_std': 0.03315324831055477}\n" + "Train loss {'avg_role_model_loss': 0.011128615495272243, 'avg_role_model_std_loss': 1.7709791813538458, 'avg_role_model_mean_pred_loss': 0.00021516576313122763, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011128615495272243, 'n_size': 320, 'n_batch': 80, 'duration': 85.58019542694092, 'duration_batch': 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'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.010162418862455525, 'n_size': 320, 'n_batch': 80, 'duration': 84.28248190879822, 'duration_batch': 1.0535310238599778, 'duration_size': 0.26338275596499444, 'avg_pred_std': 0.0519078379671555}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Val loss {'avg_role_model_loss': 0.012275835702894256, 'avg_role_model_std_loss': 9.627914267603774, 'avg_role_model_mean_pred_loss': 4.899254280417153e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.012275835702894256, 'n_size': 80, 'n_batch': 10, 'duration': 8.323761701583862, 'duration_batch': 0.8323761701583863, 'duration_size': 0.10404702126979828, 'avg_pred_std': 0.018597377510741354}\n", - "Stopped False\n" + "Val loss {'avg_role_model_loss': 0.009476123469903541, 'avg_role_model_std_loss': 1.0078544585018676, 'avg_role_model_mean_pred_loss': 1.8715846066100564e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.009476123469903541, 'n_size': 80, 'n_batch': 20, 'duration': 17.901034355163574, 'duration_batch': 0.8950517177581787, 'duration_size': 0.22376292943954468, 'avg_pred_std': 0.03873717384412885}\n", + "Epoch 19\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: \n", - "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_loss_test ▅▆▂▁▅▇▁▃█▄▄▄▂▂▃▂▃▄\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_loss_train █▂▂▂▁▂▂▁▁▁▁▁▂▂▂▂▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_non_role_model_embed_loss_test ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: 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"\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_loss_test ▅▆▂▁▅▇▁▃█▄▄▄▂▂▃▂▃▄\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_loss_train █▂▂▂▁▂▂▁▁▁▁▁▂▂▂▂▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_mean_pred_loss_test ▂▃▁▁▄█▁▂▆▃▃▃▁▁▂▁▂▂\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_mean_pred_loss_train █▂▁▁▁▁▁▁▁▁▁▁▂▂▁▁▁▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_std_loss_test ▃▃▁▂▆█▁▇▃▇▃▇▁▂▂▂▃▃\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_std_loss_train ▇▇▄▃▃██▆▄▃▆▅▇▄▅▆▄▁\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_batch_test ▁▁▃▁▅▃▄▆▁█▅▁▂▄▃▄▃▄\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_batch_train █▄▄▇▅▅▅▁▃▅▆▅▆▅▃▅█▅\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_size_test ▁▁▃▁▅▃▄▆▁█▅▁▂▄▃▄▃▄\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_size_train █▄▄▇▅▅▅▁▃▅▆▅▆▅▃▅█▅\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_test ▁▁▃▁▅▃▄▆▁█▅▁▂▄▃▄▃▄\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_train 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avg_non_role_model_g_mag_loss_train 0.0\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_pred_std_test 0.0186\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_pred_std_train 0.0434\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_g_cos_loss_test 0.0\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_g_cos_loss_train 0.0\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_g_mag_loss_test 0.0\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_g_mag_loss_train 0.0\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_loss_test 0.01228\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_loss_train 0.01238\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_mean_pred_loss_test 5e-05\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_mean_pred_loss_train 0.00012\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_std_loss_test 9.62791\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: avg_role_model_std_loss_train 4.77404\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_batch_test 0.83238\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_batch_train 0.97355\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_size_test 0.10405\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_size_train 0.12169\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_test 8.32376\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: duration_train 38.94201\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: n_batch_test 10\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: n_batch_train 40\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: n_size_test 80\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: n_size_train 320\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \n" + "Train loss {'avg_role_model_loss': 0.010770342201794847, 'avg_role_model_std_loss': 2.009929773015307, 'avg_role_model_mean_pred_loss': 0.00013072784897420476, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.010770342201794847, 'n_size': 320, 'n_batch': 80, 'duration': 84.82972049713135, 'duration_batch': 1.060371506214142, 'duration_size': 0.2650928765535355, 'avg_pred_std': 0.04557969000888988}\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: You can sync this run to the cloud by running:\n", - "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[1mwandb sync /kaggle/working/eval/insurance/tab_ddpm_concat/4/wandb/offline-run-20240229_182708-etiddam5\u001b[0m\n" + "Val loss {'avg_role_model_loss': 0.009593866099567094, 'avg_role_model_std_loss': 0.7345563380621798, 'avg_role_model_mean_pred_loss': 1.3211068784391156e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.009593866099567094, 'n_size': 80, 'n_batch': 20, 'duration': 18.183232307434082, 'duration_batch': 0.9091616153717041, 'duration_size': 0.22729040384292604, 'avg_pred_std': 0.04364799705799669}\n", + "Epoch 20\n" ] }, { - "name": "stderr", + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.011184974256775605, 'avg_role_model_std_loss': 1.735422194222258, 'avg_role_model_mean_pred_loss': 0.00015653051535653267, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011184974256775605, 'n_size': 320, 'n_batch': 80, 'duration': 84.4685800075531, 'duration_batch': 1.0558572500944137, 'duration_size': 0.2639643125236034, 'avg_pred_std': 0.04943769198143855}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.00922505634080153, 'avg_role_model_std_loss': 1.1232440773048438, 'avg_role_model_mean_pred_loss': 2.5773531761252856e-06, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.00922505634080153, 'n_size': 80, 'n_batch': 20, 'duration': 18.073370695114136, 'duration_batch': 0.9036685347557067, 'duration_size': 0.22591713368892669, 'avg_pred_std': 0.0375345426844433}\n", + "Epoch 21\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.010670667553040403, 'avg_role_model_std_loss': 1.6657181285418345, 'avg_role_model_mean_pred_loss': 0.00026890914427888377, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.010670667553040403, 'n_size': 320, 'n_batch': 80, 'duration': 84.49483013153076, 'duration_batch': 1.0561853766441345, 'duration_size': 0.2640463441610336, 'avg_pred_std': 0.0491774610709399}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.011584205193139496, 'avg_role_model_std_loss': 4.0657152492325626, 'avg_role_model_mean_pred_loss': 3.660291929232784e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011584205193139496, 'n_size': 80, 'n_batch': 20, 'duration': 17.99208927154541, 'duration_batch': 0.8996044635772705, 'duration_size': 0.22490111589431763, 'avg_pred_std': 0.030582628422416748}\n", + "Epoch 22\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.011918405044889368, 'avg_role_model_std_loss': 1.9266218843145224, 'avg_role_model_mean_pred_loss': 0.000546820462109244, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.011918405044889368, 'n_size': 320, 'n_batch': 80, 'duration': 84.82066988945007, 'duration_batch': 1.060258373618126, 'duration_size': 0.2650645934045315, 'avg_pred_std': 0.049488182202912866}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.009260490916494746, 'avg_role_model_std_loss': 2.0702868502448837, 'avg_role_model_mean_pred_loss': 1.115468241086326e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.009260490916494746, 'n_size': 80, 'n_batch': 20, 'duration': 18.213539123535156, 'duration_batch': 0.9106769561767578, 'duration_size': 0.22766923904418945, 'avg_pred_std': 0.03006206527352333}\n", + "Epoch 23\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.010364986025706457, 'avg_role_model_std_loss': 6.042574923066354, 'avg_role_model_mean_pred_loss': 0.00012542121491789134, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.010364986025706457, 'n_size': 320, 'n_batch': 80, 'duration': 84.50646162033081, 'duration_batch': 1.0563307702541351, 'duration_size': 0.2640826925635338, 'avg_pred_std': 0.045613451191994156}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.009321882369113155, 'avg_role_model_std_loss': 1.242719502127511, 'avg_role_model_mean_pred_loss': 2.0408335805721654e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.009321882369113155, 'n_size': 80, 'n_batch': 20, 'duration': 17.993885278701782, 'duration_batch': 0.8996942639350891, 'duration_size': 0.22492356598377228, 'avg_pred_std': 0.03894345450680703}\n", + "Epoch 24\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.010127854481288523, 'avg_role_model_std_loss': 1.3548822252588457, 'avg_role_model_mean_pred_loss': 9.947211313221551e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.010127854481288523, 'n_size': 320, 'n_batch': 80, 'duration': 84.6096601486206, 'duration_batch': 1.0576207518577576, 'duration_size': 0.2644051879644394, 'avg_pred_std': 0.05492281899787486}\n" + ] + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/offline-run-20240229_182708-etiddam5/logs\u001b[0m\n" + "Val loss {'avg_role_model_loss': 0.00973101281633717, 'avg_role_model_std_loss': 2.803459626334097, 'avg_role_model_mean_pred_loss': 3.2155193029839366e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.00973101281633717, 'n_size': 80, 'n_batch': 20, 'duration': 18.34537410736084, 'duration_batch': 0.917268705368042, 'duration_size': 0.2293171763420105, 'avg_pred_std': 0.02301093057030812}\n", + "Epoch 25\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.010673620513580317, 'avg_role_model_std_loss': 1.630422155917519, 'avg_role_model_mean_pred_loss': 0.0002799555220394656, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 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[ + "Train loss {'avg_role_model_loss': 0.009991624747397055, 'avg_role_model_std_loss': 1.627927773291799, 'avg_role_model_mean_pred_loss': 8.125562307790029e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.009991624747397055, 'n_size': 320, 'n_batch': 80, 'duration': 84.53830814361572, 'duration_batch': 1.0567288517951965, 'duration_size': 0.2641822129487991, 'avg_pred_std': 0.050572005746653305}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.01111485290057317, 'avg_role_model_std_loss': 2.272924814505939, 'avg_role_model_mean_pred_loss': 0.000244029233883869, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 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1.450164285198241, 'avg_role_model_mean_pred_loss': 1.8529243547119787e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.009433183281817036, 'n_size': 80, 'n_batch': 20, 'duration': 18.082876205444336, 'duration_batch': 0.9041438102722168, 'duration_size': 0.2260359525680542, 'avg_pred_std': 0.03382655227323994}\n", + "Epoch 28\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.009869320008056093, 'avg_role_model_std_loss': 1.5068146906768447, 'avg_role_model_mean_pred_loss': 9.115362455692777e-05, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.009869320008056093, 'n_size': 320, 'n_batch': 80, 'duration': 84.38812756538391, 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0.035569448093883696}\n", + "Epoch 30\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train loss {'avg_role_model_loss': 0.010348305049683404, 'avg_role_model_std_loss': 1.0208274961811328, 'avg_role_model_mean_pred_loss': 0.00023567322375602772, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.010348305049683404, 'n_size': 320, 'n_batch': 80, 'duration': 84.40145015716553, 'duration_batch': 1.055018126964569, 'duration_size': 0.2637545317411423, 'avg_pred_std': 0.05335634221555665}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Val loss {'avg_role_model_loss': 0.009130275297502521, 'avg_role_model_std_loss': 1.2715821872590367, 'avg_role_model_mean_pred_loss': 7.716895467813068e-06, 'avg_role_model_g_mag_loss': 0.0, 'avg_role_model_g_cos_loss': 0.0, 'avg_non_role_model_g_mag_loss': 0.0, 'avg_non_role_model_g_cos_loss': 0.0, 'avg_non_role_model_embed_loss': 0.0, 'avg_loss': 0.009130275297502521, 'n_size': 80, 'n_batch': 20, 'duration': 18.11615824699402, 'duration_batch': 0.9058079123497009, 'duration_size': 0.22645197808742523, 'avg_pred_std': 0.03190355768892914}\n", + "Stopped False\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Eval loss {'role_model': 'tab_ddpm_concat', 'n_size': 399, 'n_batch': 50, 'role_model_metrics': {'avg_loss': 0.01993635216760531, 'avg_g_mag_loss': 0.6526490935406888, 'avg_g_cos_loss': 4.4486220484027385e-08, 'pred_duration': 0.8737220764160156, 'grad_duration': 0.5640714168548584, 'total_duration': 1.437793493270874, 'pred_std': 0.053181204944849014, 'std_loss': 0.7849577069282532, 'mean_pred_loss': 1.8548063962953165e-05, 'pred_rmse': 0.141196146607399, 'pred_mae': 0.0972040668129921, 'pred_mape': 0.7692168354988098, 'grad_rmse': 0.28230687975883484, 'grad_mae': 0.19419056177139282, 'grad_mape': 0.9970712065696716}, 'non_role_model_metrics': {'avg_loss': 0, 'avg_g_mag_loss': 0, 'avg_g_cos_loss': 0, 'avg_pred_duration': 0, 'avg_grad_duration': 0, 'avg_total_duration': 0, 'avg_pred_std': 0, 'avg_std_loss': 0, 'avg_mean_pred_loss': 0}, 'avg_metrics': {'avg_loss': 0.01993635216760531, 'avg_g_mag_loss': 0.6526490935406888, 'avg_g_cos_loss': 4.4486220484027385e-08, 'avg_pred_duration': 0.8737220764160156, 'avg_grad_duration': 0.5640714168548584, 'avg_total_duration': 1.437793493270874, 'avg_pred_std': 0.053181204944849014, 'avg_std_loss': 0.7849577069282532, 'avg_mean_pred_loss': 1.8548063962953165e-05}, 'min_metrics': {'avg_loss': 0.01993635216760531, 'avg_g_mag_loss': 0.6526490935406888, 'avg_g_cos_loss': 4.4486220484027385e-08, 'pred_duration': 0.8737220764160156, 'grad_duration': 0.5640714168548584, 'total_duration': 1.437793493270874, 'pred_std': 0.053181204944849014, 'std_loss': 0.7849577069282532, 'mean_pred_loss': 1.8548063962953165e-05, 'pred_rmse': 0.141196146607399, 'pred_mae': 0.0972040668129921, 'pred_mape': 0.7692168354988098, 'grad_rmse': 0.28230687975883484, 'grad_mae': 0.19419056177139282, 'grad_mape': 0.9970712065696716}, 'model_metrics': {'tab_ddpm_concat': {'avg_loss': 0.01993635216760531, 'avg_g_mag_loss': 0.6526490935406888, 'avg_g_cos_loss': 4.4486220484027385e-08, 'pred_duration': 0.8737220764160156, 'grad_duration': 0.5640714168548584, 'total_duration': 1.437793493270874, 'pred_std': 0.053181204944849014, 'std_loss': 0.7849577069282532, 'mean_pred_loss': 1.8548063962953165e-05, 'pred_rmse': 0.141196146607399, 'pred_mae': 0.0972040668129921, 'pred_mape': 0.7692168354988098, 'grad_rmse': 0.28230687975883484, 'grad_mae': 0.19419056177139282, 'grad_mape': 0.9970712065696716}}}\n" + "Eval loss {'role_model': 'tab_ddpm_concat', 'n_size': 399, 'n_batch': 100, 'role_model_metrics': {'avg_loss': 0.019684911810006377, 'avg_g_mag_loss': nan, 'avg_g_cos_loss': 0.0021236211053054007, 'pred_duration': 2.178314447402954, 'grad_duration': 1.2552392482757568, 'total_duration': 3.433553695678711, 'pred_std': 0.06186112388968468, 'std_loss': 0.5796476006507874, 'mean_pred_loss': 4.718968921224587e-05, 'pred_rmse': 0.14030292630195618, 'pred_mae': 0.10000143945217133, 'pred_mape': 0.7038320302963257, 'grad_rmse': 0.2803622782230377, 'grad_mae': 0.19953711330890656, 'grad_mape': 0.9926933646202087}, 'non_role_model_metrics': {'avg_loss': 0, 'avg_g_mag_loss': 0, 'avg_g_cos_loss': 0, 'avg_pred_duration': 0, 'avg_grad_duration': 0, 'avg_total_duration': 0, 'avg_pred_std': 0, 'avg_std_loss': 0, 'avg_mean_pred_loss': 0}, 'avg_metrics': {'avg_loss': 0.019684911810006377, 'avg_g_mag_loss': nan, 'avg_g_cos_loss': 0.0021236211053054007, 'avg_pred_duration': 2.178314447402954, 'avg_grad_duration': 1.2552392482757568, 'avg_total_duration': 3.433553695678711, 'avg_pred_std': 0.06186112388968468, 'avg_std_loss': 0.5796476006507874, 'avg_mean_pred_loss': 4.718968921224587e-05}, 'min_metrics': {'avg_loss': 0.019684911810006377, 'avg_g_mag_loss': nan, 'avg_g_cos_loss': 0.0021236211053054007, 'pred_duration': 2.178314447402954, 'grad_duration': 1.2552392482757568, 'total_duration': 3.433553695678711, 'pred_std': 0.06186112388968468, 'std_loss': 0.5796476006507874, 'mean_pred_loss': 4.718968921224587e-05, 'pred_rmse': 0.14030292630195618, 'pred_mae': 0.10000143945217133, 'pred_mape': 0.7038320302963257, 'grad_rmse': 0.2803622782230377, 'grad_mae': 0.19953711330890656, 'grad_mape': 0.9926933646202087}, 'model_metrics': {'tab_ddpm_concat': {'avg_loss': 0.019684911810006377, 'avg_g_mag_loss': nan, 'avg_g_cos_loss': 0.0021236211053054007, 'pred_duration': 2.178314447402954, 'grad_duration': 1.2552392482757568, 'total_duration': 3.433553695678711, 'pred_std': 0.06186112388968468, 'std_loss': 0.5796476006507874, 'mean_pred_loss': 4.718968921224587e-05, 'pred_rmse': 0.14030292630195618, 'pred_mae': 0.10000143945217133, 'pred_mape': 0.7038320302963257, 'grad_rmse': 0.2803622782230377, 'grad_mae': 0.19953711330890656, 'grad_mape': 0.9926933646202087}}}\n" ] } ], @@ -1920,7 +2051,7 @@ " checkpoint_dir=\"checkpoints\",\n", " verbose=True,\n", " allow_same_prediction=allow_same_prediction,\n", - " wandb=wandb,\n", + " wandb=wandb if log_wandb else None,\n", " study_name=study_name,\n", " **params\n", ")" @@ -1932,16 +2063,16 @@ "id": "9b514a07", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:42:55.875215Z", - "iopub.status.busy": "2024-02-29T18:42:55.874907Z", - "iopub.status.idle": "2024-02-29T18:42:55.879007Z", - "shell.execute_reply": "2024-02-29T18:42:55.878119Z" + "iopub.execute_input": "2024-03-03T11:57:24.490374Z", + "iopub.status.busy": "2024-03-03T11:57:24.489536Z", + "iopub.status.idle": "2024-03-03T11:57:24.493889Z", + "shell.execute_reply": "2024-03-03T11:57:24.493023Z" }, "papermill": { - "duration": 0.025079, - "end_time": "2024-02-29T18:42:55.880936", + "duration": 0.027217, + "end_time": "2024-03-03T11:57:24.495842", "exception": false, - "start_time": "2024-02-29T18:42:55.855857", + "start_time": "2024-03-03T11:57:24.468625", "status": "completed" }, "tags": [] @@ -1958,16 +2089,16 @@ "id": "331a49e1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:42:55.915833Z", - "iopub.status.busy": "2024-02-29T18:42:55.915549Z", - "iopub.status.idle": "2024-02-29T18:42:55.991917Z", - "shell.execute_reply": "2024-02-29T18:42:55.990964Z" + "iopub.execute_input": "2024-03-03T11:57:24.536429Z", + "iopub.status.busy": "2024-03-03T11:57:24.535822Z", + "iopub.status.idle": "2024-03-03T11:57:24.615653Z", + "shell.execute_reply": "2024-03-03T11:57:24.614420Z" }, "papermill": { - "duration": 0.096139, - "end_time": "2024-02-29T18:42:55.994052", + "duration": 0.103308, + "end_time": "2024-03-03T11:57:24.618128", "exception": false, - "start_time": "2024-02-29T18:42:55.897913", + "start_time": "2024-03-03T11:57:24.514820", "status": "completed" }, "tags": [] @@ -1987,16 +2118,16 @@ "id": "123b4b17", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:42:56.032185Z", - "iopub.status.busy": "2024-02-29T18:42:56.031896Z", - "iopub.status.idle": "2024-02-29T18:42:56.328976Z", - "shell.execute_reply": "2024-02-29T18:42:56.328057Z" + "iopub.execute_input": "2024-03-03T11:57:24.659332Z", + "iopub.status.busy": "2024-03-03T11:57:24.659016Z", + "iopub.status.idle": "2024-03-03T11:57:24.929981Z", + "shell.execute_reply": "2024-03-03T11:57:24.928997Z" }, "papermill": { - "duration": 0.319257, - "end_time": "2024-02-29T18:42:56.331162", + "duration": 0.294048, + "end_time": "2024-03-03T11:57:24.931923", "exception": false, - "start_time": "2024-02-29T18:42:56.011905", + "start_time": "2024-03-03T11:57:24.637875", "status": "completed" }, "tags": [] @@ -2014,7 +2145,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2035,16 +2166,16 @@ "id": "2586ba0a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:42:56.370376Z", - "iopub.status.busy": "2024-02-29T18:42:56.370066Z", - "iopub.status.idle": "2024-02-29T18:43:43.371513Z", - "shell.execute_reply": "2024-02-29T18:43:43.370520Z" + "iopub.execute_input": "2024-03-03T11:57:24.972376Z", + "iopub.status.busy": "2024-03-03T11:57:24.972050Z", + "iopub.status.idle": "2024-03-03T11:59:09.938182Z", + "shell.execute_reply": "2024-03-03T11:59:09.937120Z" }, "papermill": { - "duration": 47.023831, - "end_time": "2024-02-29T18:43:43.374040", + "duration": 104.989386, + "end_time": "2024-03-03T11:59:09.940839", "exception": false, - "start_time": "2024-02-29T18:42:56.350209", + "start_time": "2024-03-03T11:57:24.951453", "status": "completed" }, "tags": [] @@ -2069,16 +2200,16 @@ "id": "187137f6", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:43:43.414397Z", - "iopub.status.busy": "2024-02-29T18:43:43.413556Z", - "iopub.status.idle": "2024-02-29T18:43:43.433770Z", - "shell.execute_reply": "2024-02-29T18:43:43.432945Z" + "iopub.execute_input": "2024-03-03T11:59:09.984105Z", + "iopub.status.busy": "2024-03-03T11:59:09.983752Z", + "iopub.status.idle": "2024-03-03T11:59:10.005079Z", + "shell.execute_reply": "2024-03-03T11:59:10.004227Z" }, "papermill": { - "duration": 0.042663, - "end_time": "2024-02-29T18:43:43.435796", + "duration": 0.045165, + "end_time": "2024-03-03T11:59:10.007079", "exception": false, - "start_time": "2024-02-29T18:43:43.393133", + "start_time": "2024-03-03T11:59:09.961914", "status": "completed" }, "tags": [] @@ -2125,21 +2256,21 @@ " \n", " \n", " tab_ddpm_concat\n", - " 5.952382e-08\n", - " 0.609263\n", - " 0.019936\n", - " 0.559148\n", - " 0.194191\n", - " 0.997071\n", - " 0.282307\n", - " 0.000019\n", - " 0.876673\n", - " 0.097204\n", - " 0.769236\n", - " 0.141196\n", - " 0.053181\n", - " 0.784962\n", - " 1.435821\n", + " 0.002556\n", + " 0.524113\n", + " 0.019685\n", + " 1.261741\n", + " 0.199537\n", + " 0.992693\n", + " 0.280362\n", + " 0.000047\n", + " 2.181129\n", + " 0.100001\n", + " 0.703832\n", + " 0.140303\n", + " 0.061861\n", + " 0.579648\n", + " 3.442869\n", " \n", " \n", "\n", @@ -2147,16 +2278,16 @@ ], "text/plain": [ " avg_g_cos_loss avg_g_mag_loss avg_loss grad_duration \\\n", - "tab_ddpm_concat 5.952382e-08 0.609263 0.019936 0.559148 \n", + "tab_ddpm_concat 0.002556 0.524113 0.019685 1.261741 \n", "\n", " grad_mae grad_mape grad_rmse mean_pred_loss \\\n", - "tab_ddpm_concat 0.194191 0.997071 0.282307 0.000019 \n", + "tab_ddpm_concat 0.199537 0.992693 0.280362 0.000047 \n", "\n", " pred_duration pred_mae pred_mape pred_rmse pred_std \\\n", - "tab_ddpm_concat 0.876673 0.097204 0.769236 0.141196 0.053181 \n", + "tab_ddpm_concat 2.181129 0.100001 0.703832 0.140303 0.061861 \n", "\n", " std_loss total_duration \n", - "tab_ddpm_concat 0.784962 1.435821 " + "tab_ddpm_concat 0.579648 3.442869 " ] }, "execution_count": 29, @@ -2178,16 +2309,16 @@ "id": "123d305b", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:43:43.475408Z", - "iopub.status.busy": "2024-02-29T18:43:43.474824Z", - "iopub.status.idle": "2024-02-29T18:43:43.909232Z", - "shell.execute_reply": "2024-02-29T18:43:43.908268Z" + "iopub.execute_input": "2024-03-03T11:59:10.059613Z", + "iopub.status.busy": "2024-03-03T11:59:10.059114Z", + "iopub.status.idle": "2024-03-03T11:59:10.519619Z", + "shell.execute_reply": "2024-03-03T11:59:10.518672Z" }, "papermill": { - "duration": 0.456458, - "end_time": "2024-02-29T18:43:43.911370", + "duration": 0.492551, + "end_time": "2024-03-03T11:59:10.521766", "exception": false, - "start_time": "2024-02-29T18:43:43.454912", + "start_time": "2024-03-03T11:59:10.029215", "status": "completed" }, "tags": [] @@ -2204,16 +2335,16 @@ "id": "a3eecc2a", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:43:43.951744Z", - "iopub.status.busy": "2024-02-29T18:43:43.950961Z", - "iopub.status.idle": "2024-02-29T18:44:32.670044Z", - "shell.execute_reply": "2024-02-29T18:44:32.669005Z" + "iopub.execute_input": "2024-03-03T11:59:10.564330Z", + "iopub.status.busy": "2024-03-03T11:59:10.563999Z", + "iopub.status.idle": "2024-03-03T12:01:02.226778Z", + "shell.execute_reply": "2024-03-03T12:01:02.225923Z" }, "papermill": { - "duration": 48.741699, - "end_time": "2024-02-29T18:44:32.672417", + "duration": 111.687045, + "end_time": "2024-03-03T12:01:02.229479", "exception": false, - "start_time": "2024-02-29T18:43:43.930718", + "start_time": "2024-03-03T11:59:10.542434", "status": "completed" }, "tags": [] @@ -2244,16 +2375,16 @@ "id": "6ab51db8", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:44:32.713356Z", - "iopub.status.busy": "2024-02-29T18:44:32.713056Z", - "iopub.status.idle": "2024-02-29T18:44:32.730203Z", - "shell.execute_reply": "2024-02-29T18:44:32.729517Z" + "iopub.execute_input": "2024-03-03T12:01:02.273874Z", + "iopub.status.busy": 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"end_time": "2024-02-29T18:44:32.775758", + "duration": 0.027945, + "end_time": "2024-03-03T12:01:02.341506", "exception": false, - "start_time": "2024-02-29T18:44:32.750001", + "start_time": "2024-03-03T12:01:02.313561", "status": "completed" }, "tags": [] @@ -2296,7 +2427,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'tab_ddpm_concat': 0.05795487974159697}\n" + "{'tab_ddpm_concat': 0.038165719780903475}\n" ] } ], @@ -2310,16 +2441,16 @@ "id": "3b3ff322", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:44:32.813941Z", - "iopub.status.busy": "2024-02-29T18:44:32.813678Z", - "iopub.status.idle": "2024-02-29T18:44:33.119654Z", - "shell.execute_reply": "2024-02-29T18:44:33.118662Z" + "iopub.execute_input": "2024-03-03T12:01:02.383613Z", + "iopub.status.busy": "2024-03-03T12:01:02.383327Z", + "iopub.status.idle": "2024-03-03T12:01:02.706189Z", + "shell.execute_reply": "2024-03-03T12:01:02.705209Z" }, "papermill": { - "duration": 0.327687, - "end_time": "2024-02-29T18:44:33.121766", + "duration": 0.346517, + "end_time": "2024-03-03T12:01:02.708385", "exception": false, - "start_time": "2024-02-29T18:44:32.794079", + "start_time": "2024-03-03T12:01:02.361868", "status": "completed" }, "tags": [] @@ -2327,7 +2458,7 @@ "outputs": [ { "data": { - "image/png": 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RuKW3FuQcdR1hKUrdgYMHDzrc/uGHHzBgwACIxc5jGcOHD8eZM2fQv79zLzAjIwNmsxlHjhwRpm/nzp1DTU2NX+3uiXAc184rJgJHWK6+dQeKioqwZMkSnDt3Djt27MBrr72Gp556yuX45557Dt999x0WLVqEY8eO4cKFC/jwww+xaNEiAMCgQYMwZcoUPPHEEzh48CCOHDmCefPmQalUdtVL6vYcKazC2z8U4qei6mCb0qMhUQpRZs+ejaamJowaNQoLFy7EU089JSz9O2Po0KHYv38/zp8/jzvuuANZWVlYvny5Q4vu/Px8JCcnIycnBzNnzsSCBQsQHx/fFS+n2/LNhUp8eKwEJTVNaDRaUFlngL7JFGyzejQ0fQtRpFIp1q9fj02bNrW778qVK04fk52djS+++MLlORMTE/HJJ584HHvsscc6ZWdPp6S6CWX6ZgxO1kIm5n/DXaWIEP6BPCWCcIPBlgArl4ggldhFiZInAwmJEkG4wWDmUzDkUlErT4lEKZAEVZQsFguef/559OnTB0qlEv369cOqVavCfm/Rvn37aOd+iGAw2T0lsZDyYfBx+xDhGUGNKa1ZswabNm3C1q1bMXjwYBw+fBhz5syBVqt1mm9DEF2J2WKF2cr/QMolIkjJU+oSgipK3333HaZPn45p06YB4Dea7tixA4cOHQqmWQQBoMUj4jhelOQSEVRyMRQS2vcWSII6fRs7diy+/PJLIdv4+PHj+PbbbzF16lSn4w0GA2prax0uBBEojGYrxCIOMokIHMchOUqJBeP64f4RtN0kkATVU1q6dClqa2uRkZEBsVgMi8WCl156CbNmzXI6fvXq1Vi5cmUXW0mEK9EqGf77zv6wWMM7xtnVBNVT+uc//4nt27fjnXfewdGjR7F161a8/PLL2Lp1q9Pxy5Ytg16vFy7FxcVdbDERbnAcB4mYFqm7kqB6Sr/73e+wdOlSPPTQQwCAIUOGoLCwEKtXr0Zubm678XK5HHK5vKvNJAgAfOB7508lMFms+NXIFCHwTfiXoIpSY2Nju75iYrEYViutbhDBp7iqET8V1yBJq0B2egzEIg4l1U0A+HgTiVJgCKoo3XvvvXjppZeQmpqKwYMH46effsLatWsxd+7cYJpFEACAmkYTCirqhdscxwe9nZVCJvxHUEXptddew/PPP48nn3wSFRUVSE5OxhNPPIHly5cH0yyCAAAYbcIjE7fULZGKORjNLfcR/ieooqRWq7F+/fquy15mDLAEYYe3WNq+TKULtm3bhmeeeQalpaUO8bMZM2ZArVbjrbfeCpSVRBvs3pCkVYiB/9sCM23KDRjhVSXAYgK+eaXrn/eO3wISzzqrPvDAA1i8eDE++ugjPPDAAwD4LiS7du1yWwGA8D92UZK2qigqtXlNJEqBgyJ1IYZSqcQjjzwidCABgLfffhupqakYP3588AwLQ+zCI201fbOnB5hpMSZghJenJJbyXkswntcL5s+fj+zsbJSUlKBXr17YsmUL8vLyHBoJEIHHHjdqvcqmkIqovVKACS9R4jiPp1HBJCsrC7fccgu2bduGSZMm4fTp09i1a1ewzQo7WjylFlG6L4u2mASa8BKlbsS8efOwfv16lJSUYOLEiUhJSen4QYRfuXtIIiYPTgi2GWEHxZRClEceeQRXr17Fm2++SXlbQcK+xYS2mXQt9G6HKFqtFvfffz8iIyMxY8aMYJtD2DhaVI33DxfjVIk+2Kb0WEiUQpiSkhLMmjWL9vsFib0/V+DzU+WoaTQKx/RNJlytbqKOJgGEYkohSHV1Nfbt24d9+/Zh48aNwTYnbCmorEddsxnDU6OEY1KRPSWA8pQCBYlSCJKVlYXq6mqsWbMGgwYNCrY5YYuzlACxyJ48SXlKgYJEKQRx1deN6DoYYzCZeW9I0mbvG0C93wIJxZQIwgkWK4OVtc9Tsq/EUTXKwNHjRSnc2zV1lnB9/1rHjGStRck+faNtJgGjx4qSVMpv7WhsbAyyJd0b+/tnfz/DBaNQIYCDSNR6+iaCRMTRlp8A0mNjSmKxGFFRUaioqAAARERE0D+SFzDG0NjYiIqKCkRFRUEsDq/9XiZbe6W2iZODEtUYlKgOhklhQ48VJQBITEwEAEGYCO+JiooS3sdwIkYlw8IJ1MkkGPRoUeI4DklJSYiPj4fJRMlu3iKVSsPOQ7LDl74lzzoY9GhRsiMWi8P2y0X4F32jCfvOV0AqFuHuIUnBNqdHEhaiRBDeUlLThFMleujUcgxPjRaOm6xWXKpsoJpKAaTHrr4RRGeobjDiTGktiqscV2+FbSaU0R0wSJQIwgkmJ1tMgJbsbrOVhW0OV6AhUSIIJ9i3kUhEjsFu+943xiirO1CQKBGEE5x1MgEcPSeqFBAYSJQIwgktjSgdvyIirqWFH4lSYCBRIggnOGsaAPD5S1KxCGIRBwtVCggIlBJAEE4QuuOK2ydQ/iann8N+OMK/kCgRhBP+6+YE5AzUORUlEqTAQqJEEE6QikXtpm5E10CiRBBe8s2FSlQ3mnBrnxjEaxTBNqfHQT8FBOGE7y5ex95zFdA3tt/IfbW6CQUV9agzmINgWc+HRIkgnHCmrBbHimrQZLK0u8+eUEnJk4GBRIkgnGASUgLaB7UlQvMA2v8WCEiUCMIJZhcZ3QAgFlHzgEBCokQQbbBamZCtba8K0BqpqGVTLuF/SJSIsKXZZMGuE2U4UljtcNzYalrmbPrW0pCSRCkQkCgRYcux4hqcv1aHr89XoqHVSpo9ViTiOEGAWtNSvoRiSoEg6KJUUlKCRx99FLGxsVAqlRgyZAgOHz4cbLOIMKCiziD8XV7bLPwt7HuTOG+lNG6ADv99Z3+M6RsbeCPDkKAmT1ZXV+O2227DhAkT8Nlnn0Gn0+HChQuIjo7u+MEE0Un6xqlQUFEPALhRb0Q/HX9cq5Ri/ri+Ljfctm27JHCjAKi6BCRkAhqq3+0rQRWlNWvWICUlBfn5+cKxPn36BNEiIpzI7KVFbbMJBy9Voa65JUlSJOIQKffyq1FTBJx8n6/+VnYcyJ4HKKP8a3CYENTp20cffYSRI0figQceQHx8PLKysvDmm2+6HG8wGFBbW+twIYjOoFHwnX/rmj3Pzi680YDPT5XhaFGrAHnhd7wgAYDFBBQf8qeZYUVQRenSpUvYtGkTBgwYgN27d+M3v/kNFi9ejK1btzodv3r1ami1WuGSkpLSxRYTPQWTxYqaRiMSNApMHZKIsf1a4kPXapux71wFTpXonT62ptGEs2V1KKlu4g8064Gqy/zfAyfx15VnAQqE+0RQRclqtWL48OH44x//iKysLCxYsADz58/HG2+84XT8smXLoNfrhUtxcXEXW0z0FK7VNiP/wBXsOlGKjESNw8ba6/UG/FRUg4u2eFNb2q2+2QVJ2wtIGgZI5ICxEagtCeRL6LEEVZSSkpJw8803Oxy76aabUFRU5HS8XC6HRqNxuBCELzQZ+T1tEbL2sSOhaYCTHCUAkAhtlmzTtZpC/jo6HRCJ+WsA0F/1m73hRFBF6bbbbsO5c+ccjp0/fx5paWlBsogIFxptoqSQiVFQWY+TV/UwmPljrtor2RG33pDLGB/kBoCoVP5a25u/JlHyiaCuvj3zzDMYO3Ys/vjHP+JXv/oVDh06hL///e/4+9//HkyziDDAvvs/QirGnjPX0GS0IClKAXmkGCaz86YBduxZ3iYr4+NJhnqAEwEamxjZRam2hBctJ7lOhGuC6illZ2fjgw8+wI4dO5CZmYlVq1Zh/fr1mDVrVjDNIsIAg014FFIxImwtuO1TOqOnnpLFCtRX8AdVcYDY9huv0vFCZGoCjA2Begk9lqBXnrznnntwzz33BNsMIsww2r0hiQhKKS9K9imd2U3ZEv64LaZkZUD9Nf5gZELLALEUUEYDjVVAQwUgjwzES+ixBF2UCCIYOIiS3VMyOcaUXGVux0XK8eucfnwg/MxR/mBrUQJ4b6mxCmi4DsT0DcAr6LmQKBFhSVpsBBRSEeIiZahq4PfANRr5BMrxg+Ixum+s4EG1RSziBCFDQyV/HalzHKTSAZXnWqZ3hMeQKBFhSWYvLTJ7aQEAxVV8EqQ9pqSUiVtExx0WEx/oBngRao39duMNv9gbTpAoEWFP2+lbR5gsVnx9vhKixkrkWK0QSRWANKLNSW2bypuq25+AcAuJEhGW1DWbIBWLIJeIkBYTgbuHJCE6gt8Hd/DSDVgYw5BeWqhte+PacuKqHjGNV2GNZBBFxLRf9rdvxjU1AWYDn+VNeASJEhGWvPVDIQwmK3LHpiNGJUO0Sibcd6y4Bo1GCwbEq6F20tbN3s1EadLze3AjnNRVksgBWQS/3aSpGlAnBuiV9DyCXuSNILoaxhhMZn7ZX+akMUDrlTlncBwHiYiDwlwDC2OAMsb5Eymi+Oumms6aHFb4JEqXLl3ytx0E0WWYrQxWW5kRmVgEq5XhYkUdTl7Vw2SxCg0B5C5ECeDTBVo8JReiRHEln/BJlPr3748JEybg7bffRnNzc8cPIIgQwu4JcVxLguTHx8vw/85eg76ppdibq20mAGyekh5WqxtPyR5Xaq7xh9lhg0+idPToUQwdOhRLlixBYmIinnjiCRw6REWtiO5B6+kZx3EQiTgobDlJNbY23TKJCCInTQPsSGGCxGrgPS5XFSbJU/IJn0Rp2LBh2LBhA0pLS7F582aUlZXh9ttvR2ZmJtauXYvKykp/20kQfsO+t621J2Tf/6ZvMgJwP3UDgAjG72mziOWuV9YUfB6UkMtEeESnAt0SiQQzZ87E+++/jzVr1uDixYt49tlnkZKSgtmzZ6OsrMxfdhKE37B7Sq033CrbeEodidK0gZEYkRYNTZSbjiZyW70vQ11LqVyiQzolSocPH8aTTz6JpKQkrF27Fs8++ywKCgqwZ88elJaWYvr06f6ykyD8RoRMjCG9tBgQ37JR1p5AGRUhxewxaZg6xH03EhVrgEwsgsjuDTlDruYDV1YLYGr0i+3hgE95SmvXrkV+fj7OnTuHu+++G9u2bcPdd98Nka0iX58+fbBlyxakp6f701aC8AuxkXJMvNlxA619+mY0M8RGepDo2GxrWiFXux4jEgMyFV9vqbmW/5voEJ9EadOmTZg7dy7y8vKQlOT8FyU+Ph7/+Mc/OmUcQXQV9ulbk8mzriZF5ddgud4AlU6GeHcD5WpelAx1AKgXnCf4JEp79uxBamqq4BnZYYyhuLgYqampkMlkyM3N9YuRBOFPTBYrLFYGqVgkFGwbkKBGnFqO6/UGfFdwHX3iVEjSKl2eo+rGdTTVNiPKrOhYlFAGGKgdmKf4FFPq168frl+/3u54VVUVNZMkQp6TJXps2leAL06XC8d0ajkGJqhR3cA3pyzXu8+/k1v4TicmSQcF3OS2mBOJksf4JErMxUpCfX09FAonm4UIIoQwC91K2v/7Nxj46ZvKXYdcxiAz8ykBzWI3MSWgJeZkqPPe0DDFq+nbkiVLAPB7f5YvX46IiJZyDRaLBQcPHsSwYcP8aiBB+BuzUFmyJTmSMYYrNxpRUsPXVtIqnVcHAAAYGyCGBQAHoyjC9TigRZSayVPyFK9E6aeffgLAf4AnT56ETNays1omk+GWW27Bs88+618LCcLPmGx726StYqIcx+Hj46XCbbeiZKiDiONgFCvB0EGnEkWrXCXCI7wSpb179wIA5syZgw0bNlAzSKJb4sxTAoAkrQJXq5uglImFbSdOMdRCxAFGcSQ4SwdJka2nb1YrIKLCHB3h0zuUn59PgkR0W0wuupXcPiAOvaKVmHiT2/U0oLkWIo6DQRIpVBRwicyWQMmsgInaLXmCx57SzJkzsWXLFmg0GsycOdPt2J07d3baMIIIFGarzVNq47UkaZX41ciUjk9g0CNeI8f4gf0gGahzP1Yk4kvlGhv4i7tkSwKAF6Kk1WrB2Up+arVuUusJIsRJjlJCzHGIjpB1PNgZzbWQiESI1MQA7qZ5dmQqXpAM9QBpUod4LEr5+flO/yaI7sbw1OjOncAetFZ4GMKQq/lWS8b6zj1vmOBTTKmpqQmNjS0bDAsLC7F+/Xp88cUXfjOMIEIWQy2aTBYcKjXjaJEHtZLse96ohbdH+CRK06dPx7Zt2wAANTU1GDVqFF555RVMnz4dmzZt8quBBOFvjGYrXzHSF6wWwNiAZpMFB8vMOFvmQf6RzJb1TZ6SR/hcefKOO+4AAPzrX/9CYmIiCgsLsW3bNrz66qt+NZAg/M3bPxRiw5cXUGpLlPQKQy3AGERiCcwiBSyeiBuJklf4JEqNjY1Qq/mI3RdffIGZM2dCJBLh1ltvRWFhoV8NJAh/I6y+iTtIfHSGPTNboQE4TkgvcIvcJkoGEiVP8LlxwH/+8x8UFxdj9+7dmDRpEgCgoqKC8peIkEfIU/IlkdEW5OZsQW6LTeDcQjElr/BJlJYvX45nn30W6enpGD16NMaMGQOA95qysrL8aiBB+JuWDbk+eEq23f72ipMeeUqtp29UFrdDfKqn9Mtf/hK33347ysrKcMsttwjH77rrLtx3331+M44g/I2lVc83qZsWSi6xTd84pd1T8kKUrBbA3AxIXddpIjrRtjsxMRGJiY6tiEeNGtVpgwgikJhbTbckblooucTmKYltbZUsVgarlbltxwSxBJAqAFMzH1ciUXKLT6LU0NCAP/3pT/jyyy9RUVEBa5t5NXXQJUIV+9SN4yBUnfQKW7skWYQWs27tBalIBM6T08gieVEy1gPoYGtKmOOTKM2bNw/79+/HY489hqSkJGH7CUGEOhwHDExQg4H59n9rC3SLlFrEq7woaCiLBBquU1qAB/gkSp999hl27dqF2267zd/2EERAiZBJMG2ojwX8Tc2A2cD/LfdylZlW4DzGJ1GKjo5GTIyL/ukE0VOx73mTKgCJDEcKq2AwWTEsNQoRsg6+SpSr5DE+pQSsWrUKy5cvd9j/1ln+9Kc/geM4PP300347J0G0hTHmssZ8h9iL/9u8pCOF1Th4uQoNBkvHj6Wsbo/xyVN65ZVXUFBQgISEBKSnp0MqdSwdevToUa/O9+OPP+Jvf/sbhg4d6os5BOExV2404j8/lSA5SoEHs1O9e7AtyG0XJbFIBMDisKLnEhIlj/FJlGbMmOE3A+rr6zFr1iy8+eab+MMf/uB2rMFggMFgEG7X1lIxdsI77KVwfQtyt9pigpaUArNHCZQUU/IUn0RpxYoVfjNg4cKFmDZtGiZOnNihKK1evRorV67023MT4YerUrgeYY8p2Twle0Y4bcr1Lz5XMa+pqcH//d//YdmyZaiqqgLAT9tKSko8Pse7776Lo0ePYvXq1R6NX7ZsGfR6vXApLi72yXYifLFPtcS+7HtrduEpeTJ9swe6zUb+QrjEJ0/pxIkTmDhxIrRaLa5cuYL58+cjJiYGO3fuRFFRkVBryR3FxcV46qmnsGfPHo8bWMrlcsjlcl9MJggArTfjdmL65hBTQsfNAwBALOMzuy1mvoGAxMdSvGGAT57SkiVLkJeXhwsXLjgIyt13342vv/7ao3McOXIEFRUVGD58OCQSCSQSCfbv349XX30VEokEFosHKxoE4SUt7ZW8/NdnrNX0jS/bY58CehRT4riWKRylBbjFJ0/JvlrWll69eqG8vNzJI9pz11134eTJkw7H5syZg4yMDDz33HMQiz0oyE4QXmL3aryuEGCs5zfUcpwgSncM0OHWvrHuG1e2RqYCmmoo2N0BPomSXC53uvJ1/vx56HSe7etRq9XIzMx0OKZSqRAbG9vuOEH4C61Sij5xKsSpvAwD2L0kWSQg4n8wY1ReTsFoBc4jfJq+/eIXv8CLL74Ik8kEgF9eLSoqwnPPPYf777/frwYShD/J7KXFjKxeGNLbyzZhbYLcPiGz9VeiFTi3+Jw8+ctf/hI6nQ5NTU3IyclBeXk5xowZg5deeslnY/bt2+fzYwkioLQJcgNAcVUjymubkaBWIDU2ouNzCJ4SiZI7fBIlrVaLPXv24MCBAzh+/Djq6+sxfPhwTJw40d/2EURoYPeUWnW4vXKjAYevVGN4WrSXokTTN3d4LUpWqxVbtmzBzp07ceXKFXAchz59+iAxMRGM+VgOgiC6iA+PleBqdRPuuikeGYleTMUMti0mipZpn73tt0d1ugFKoPQQr2JKjDH84he/wLx581BSUoIhQ4Zg8ODBKCwsRF5eHpXCJUIeo9kKo9kKkbc/nm2yuYGWFTyP6nQDLQmU5Cm5xStPacuWLfj666/x5ZdfYsKECQ73ffXVV5gxYwa2bduG2bNn+9VIgvAX9pQAr6tO2jfjtgp028/h0TYToNX0rRGwWgFfssrDAK/elR07duD3v/99O0ECgDvvvBNLly7F9u3b/WYcQfgbe/KkV+2VLCZeSAAHT0nqTUY3AEhVfJ4TswIm/5X96Wl4JUonTpzAlClTXN4/depUHD9+vNNGEUSg8Cl50j51E0sdiv6LhSoBHsaURKKWx9MUziVeiVJVVRUSEhJc3p+QkIDq6upOG0UQgcKnnm/NNfy1QovWXQLs5/DYUwIoLcADvIopWSwWSCSuHyIWi2E2mzttFEEECpO9Zbc30zchcdIx4bJ3tBK/HNEbSpkXW6JkagCV5Cm5wStRYowhLy/P5U791gXYCCIU6RWlhMFkhVzihSg5SZwE+CYEETFeZtVQrlKHePWO5ubmdjiGVt6IUGb6sF7eP8jJypvP0PStQ7wSpfz8/EDZQRChi4vpW5PRgnPX6sABuCUlyrNzUQJlh/jctpsgwgYX07dmkwV7f66ATCLyXJQogbJDSJSIsKG22YS3vi+EQirG47f38exBVqvLCgEyW1zKZLF6vsWKYkodQimlRNhgsm0xMZo9zCsC+GkWswKcqKX0iA27KDEGGD3NVRKqT9Z5bkOYQaJEhA327SBedTIxtKoO0CaNQCLihD10Hgud3VOymKiBgAtIlIiwwWTP5vZm35ublTeO41pN4TxMoLQ3EAAo2O0CEiUibLBvBxF70zTAxcqbHbsoeewpcVyrCpQUV3IGiRIRNvjUXsnFypsdr0UJoGB3B9DqGxE2WITNuN54Su4TJycM0oExQKf2ohEBJVC6hUSJCBvkEhGSoxSIjfSiC0lTDX+tiHJ6d+9oD8rgtoUSKN1CokSEDelxKqTHqTx/AGMtFQKUUf4zhBIo3UKiRBCuMNTaGlCKALnzQHeZvgkVtQbo1HIkRymdjmkHxZTcQoFugnCFMHXTuixde/5aPb76uQIFlV5MxWj65hbylIiw4YdLN3Dyqh5De2sxum9sxw9oshUsVEa7HCITd2L1zUCi5AzylIiwoclkQb3B7HmlSA/iSZ1KCTDZGggQDpAoEWGDUArX0zwl+/TNjadkLxZn8EaUhAYCjBoIOIFEiQgb7BndHucp2advLtIBAAilcJtMFs8NEYkAqS2VgILd7SBRIsIGs7cbcoXpm2tPSSm1iZLRC1ECKIHSDSRKRNhg9qZpgKkJMDXzf7uJKQmi5I2nBNAKnBto9Y0IG0zetFeyT91kKr7fmwsiFRLce0sSFFIvOpoAlEDpBhIlImzQKqUwmK2Cd+OWxir+OsJ96oBULEL/eLXbMU6hBEqXkCgRYcPkwYmeD268zl93IEo+Q9M3l5AoEYQzGm/w1x6I0pXrDdA3mZAeq4I2wvVUzwFKoHQJBboJwhn26ZuqY1H68UoVvvq5AqX6Js/PL7dN+ahWdzvIUyLChm3fXwFjwH3De0GjcOPRWC0tgW4PPCW1QgqgCXXNXrSst4uSsZ5PovSkE0qYQKJEhAWMMVQ1GMEYhGL/LmnW88IklrisONkajYL/GtU1mzw3SKbmhchq4YVJ7kOwvIcS1Onb6tWrkZ2dDbVajfj4eMyYMQPnzp0LpklED8ViZWC2LW8dJk82tApye+DBaJS819XaU9I3mrD35wp8V3AdBrOTHCaRiNotuSCoorR//34sXLgQP/zwA/bs2QOTyYRJkyahoYGWSQn/0rrbiLSj5EkvgtwAoLZ5SrU2T8lkseJfR6/iWHENDl6qwsfHy2B1tgmY4kpOCer07fPPP3e4vWXLFsTHx+PIkSMYN25ckKwieiL2ZpESEQdRRxtyvRSlaBVfXre6wQSzxYofr1ShtskEqZgDY0BxVSPOlNUis1ebQnEKDVBb2tIxhQAQYjElvZ4v0h4TE+P0foPBAIPBINyuraUPk/AMk02UpBIPJgcNFfy1SufRudVyCZQyMZqMFly63oAjV/gg+eTBiag3mKFvMqGfLrL9AwVPif6PWxMyomS1WvH000/jtttuQ2ZmptMxq1evxsqVK7vYMqInIIhSRxUCrBagweYpeShKHMdh8uBEqGRi/FxeB7OVITUmAv3jI8G5i0nZg+gkSg6EjCgtXLgQp06dwrfffutyzLJly7BkyRLhdm1tLVJSUrrCPKKbI+I4xEbKECnv4F++sQqwmgGJzG11gLb0sTUk0KnlUMnFSI9VuRckoJUoUUypNSEhSosWLcInn3yCr7/+Gr1793Y5Ti6XQy73or8WQdhI0Cgwe0x6xwPrr/HXqnifcoc4jsOINMfwA2MMBZX1uFhRjwkZ8ZBLbHvv7NM3iik5ENTVN8YYFi1ahA8++ABfffUV+vTpE0xzCKJFlCLj/XraAxdv4GxZHS5fb7WybG9waaynsritCKooLVy4EG+//TbeeecdqNVqlJeXo7y8HE1NXqTrE4Q/aajkr/0oShzHoX88H+i+VNlKlKQqvn0TY4CRpnB2gipKmzZtgl6vx/jx45GUlCRc3nvvvWCaRfRATpXose37K/ju4nXXgxhr5Skl+PX502L58rdXqxvB7FmcIlFLXSWKKwkENaYkfDgEEWAaDGbcqDciWeumQqSxHjA28rEkD1fePCVRo4BUzKHBYMGNBiPiIm2xUYWWjyk16wGt63hqOEFVAoiwwJ7R7TZPqa6cv46IdVtt0hckYpHQQbe4qlUHE3tTAnvnFIJEiQgPhDwld9nctSX8taZXQGzoZROlcn1zy0F7/W97kwKCRIkID4yeZHTr7aKUHBAbEjQKAEBj684n5Cm1IyTylAgi0NgbUbrM6LZagboy/u8AeUq9o5X4dU4/oVccAPKUnECiRIQFLdtMXEzfGioBi4nP5FbFBcQGiVgESdueBXZPyVDHb3ERedkVpQdC0zciLFBIRVArJK5bIdnjSerkrq0CKVPxxeQY41fgCPKUiPBgSmaS+wG1gY0n2SmuasShy1WIipDirpsSeAFURPGF5ZprgAjnFTLCCfKUCIIxoKaI/zsqsBu8LVaGoqpGXK1utWuBgt0OkCgRRFM1n8AoEgPawIpSnJpPmqxuNApxLiHYbW9WEOaQKBE9HsYY3vqhEO/9WIQmo5OM7ppC/lqT7PekybaoZGIoZWIwBlQ3GPmD9imbva1TmEOiRPR4TBaG63UGlNY0Q+wsebLaJkpRaQG3heM4xNrK51bW26qo2svu2svwhjkkSkSPx95NRMRx7VMCGGvxlKIDL0pAyxTuer3dU7KJUnMNYPGid1wPhUSJ6PEYzXzsRiYRta8GWX+N34QrlgQsabItOttm3Bt2T0kWCUjkvEA20RSORIno8RhaiVI7blzkr6P7dFniYmykDEqZuKUCJcfRFK4VlKdE9HjsnpLcmShdv8Bfxw3oMnsSNQo8Ma6vo9cWEcu3WyJRIlEiej4uPaXmWr5cCccBMf26zB6nDQXsW1sa3BShCxNo+kb0eDiO72LbrpOJfeqmTmqpANnFCJ1zhekbiRJ5SkSPZ2CCGgMT1O3vqDzHX3fh1M3OufI6fHOhEikxEZg8OLGlJnjDDX4FThy+X03ylIjwxFDXkgoQf1OXP71YxKGu2YzKOtsKnFwDSJUAs7Y0LwhTSJSI8KTiZ34JXtvLq6aT/kJny1WqajDCYmX8HNPerKC+vMvtCSVIlIgez96fK/Dej0WOPdcqTvPX8YODYpNGIYFMIoLFylBl326itolS3bWg2BQqkCgRPZ5K2xYTs30DbH0lUFvG91yLzwiKTRzHCUmUwhQuMtFmH3lKBNGjaTLx20yEAm+lR/nruP58kbUgoRO2m9hESW0XpQq+CmaYQqJE9HjshfqVMjFgNgDlJ/k7kocH0SoIvd8EUVJG8yJptbTUCw9DSJSIHo3VyoQNuUqpGCg/xXshEbFAdHpQbUvQyJGkVSBezXc5Ace1FJmrKQ6eYUEmfJMhiLCg2WyBvRGzUgyg+Af+Rq8RXVuL2wnxGgUeGpXqeFCbwq8M6q8Gx6gQgESJCB6MAeZmwNTE5+cwK3+MWflia2IZn7vTicJr9Qa+FEiETAxRxUl+a4lMBSQN9der8C/2ypf64rBNogy/V0wED1MzcOMCcKOALxnSVMMLkDs4jo+1aHoBukFATF+vdvObLQwapRRqiRW4coA/mDom4BUmvcFksaLRYIE2QspndstUgLEB0BfxrzfMIFEiAo+xkZ82lRx1vqoklgIiCS9AnAgAB1jNfFCaWfkysY1VfIBargZSbwWShnnkRSRHKfH47X3ALn4FFNcBCi2QPMzfr9BnLl9vwEfHSqFTy/HI6FT+PYjtD5Qd58WbRInolljMfG6LsYH/UkfE8t5FkGMmYAyoOANc2MNP0QDetvgMQNsbiIgDpBGuxYUx/jXVXwOqLvPnMtTx5ys5Cgyc5FmwWl8C7uqP/N8D/iukvKQYlQxWxlBZZ4DJYuU7+AqidBHoPzH4n2MXQ6LUnam7Blw9xAdGrW3KqCqjgOQs/iKRd71tFjNw/vOW5XdVHNB3AhDbz/MvGcfxu/flkfzj+o4Hyo8DV77l6w4d2wEkDAb63el6l7+hDjjzH97jis8IyuZbd2hs1QvqDWaU1jQhLVbFC61Yyk9v9VcD3vYp1CBR6o5YzMCVb4DigxCWlmQRvHdkNfM7zZtqgIK9QNEP/K9twuCu+8Vt1gOndrbUKkq/nY/jdLayo1jCr5rFDwYuf80nQV47zcep0scBvYY7PkfDDZzY/Q+Im6qQ0qsXNAOndu75AwDHcUiNjcCZ0loU3mjkRUkiA3QZvKCXnyBRIkKc2jLg509aioHpBgEpo/n2QHbRMRuByp+Bou/5WMzZj4Frp4BBU/mYSiCpKQZO7+TjSFIlcPN0IKaPf59DquCnbolDgAu7+ffk4v/jX69uEP8aG66DVZyBQV+JBk6F+MG/4h8XgqTHqmyi1ABAxx9MGsqLUsVZ3hOUKoNqY1dCotRdsFqAwgNA4ff8VEQWAQycwn8J2yKR8f/UCYN5b+rKAT4m8+P/8V5T4tDAeE2lx4ALX/C2RsYDmfe3NFoMBJokYHguUHaMn9IZ6vlYkw2zxYobsl4oiM3BbTG6wNnRSdJiI8BxfHcTfaOJX4XTpgCROn6f3tUfgT7jgm1ml0Gi1B2or+C9nfoK/nZ8BjBgMi9M7hCJgbSx/FTg508AfQnw86d8cbNBU/mVLH9gtfBTRXswOT4DyLinawLKHMfHzRKH8qtV+iI+qC6LRIUoGWetgDZCxgeQQxSFVIyU6AgUVTXibHktbu0ba5v23sFPg6/+yL9Gf31eIQ6JUihjMQNF3/FxIauFd+EHTva+KFlEDDDsUT4ofvkb/st76E1gwKTOx5oaq4CzH/FTKADocweQdlvXrxiJxIBuIH+xUVRwA+BuIEkbmtO21gxPi0Z6nAoZia2EJ24g7w3WlgHnd/OeZxisxIXEz8df//pXpKenQ6FQYPTo0Th06FCwTQoujPEraof/wU+9rBb+y5Y9z/cqiSIRn98zcg6/G91s4L2vU//mA+PeYjHxth3+B/+lkciBzJl8UDtEvjjF1Y0A+FylUKdPnAoj0qKhal1HnOOAQdN4wb1+Abi8v2VhowcTdE/pvffew5IlS/DGG29g9OjRWL9+PSZPnoxz584hPj4+2OZ1LaZmPhen9KeWqZpMxXs0ukH++bKr4vg4TNH3fIzq+gU+HyZuAJAwhE/Wc5eU2FjFr3iV/sTnEAF8Z9mMaYEPonuBvtGE0ho+N6qPLnjlSXzBYNuvp5CK+bjSgEnAuc/4eKKhng98dzR178ZwjAVXekePHo3s7Gy8/vrrAACr1YqUlBT893//N5YuXer2sbW1tdBqtdDr9dBoNF1hrn+wmABTI79C1XiD72BRU8R7HPZtF2IpkDIK6D0qcKtG9RX80rq99xnAZ1ZHxvPiJY3gkzGtJj7FoKGSv7aj0PC5R/E3hYx3ZKe22YRvzl+HwWzBzOG9g22Ox1yvN2DXiTIopCJMyIiHLlLOt2S6ephfYWTMljJwE/8DEhnPx5pCKCG0Nb58R4MqSkajEREREfjXv/6FGTNmCMdzc3NRU1ODDz/80GG8wWCAwWAQbtfW1iIlJaXjF1x1mf9A2yK8dObdMYe3rO2xDs5lMbdPdGyNKo7fQpEwuOt+Desr+aTEip/5ZEN3cCIgKhVIuoX33rqoq6wdq5Xh7YOFYAywMuZwzcCQGhOBKZlJwnizxQpJCAe523Ktthn/PnoVBlNLrzqlVAyJmIOqqRTjRccRy2oAADWNRhRW8VNUmUyGm3rr+M+DE7VcAsnQB/kfJjf4IkpBnb5dv34dFosFCQkJDscTEhLw888/txu/evVqrFy50vsnMhtCr8kfJ+ID1xEx/NYLdRKfyRvIJXRXROr4VIF+dwFN1XyBsaaalt37YgkgU/OCqU4Kar4PxwE36o0u7282OW7w7U6CBAAJGgUezk7FgYLruFTZAKPZKnT4vYE43Lh5FmJlN4Dr52G4egn1pisQMQsYM7ZMp7uKjjZT+0jQY0resGzZMixZskS4bfeUOiQqBbjloTZTDNvfbo+1uq/tMV/PJRLZ9nvJQm7Kw/e0j+EvIQrHcfjlCH46JhJx4ACIOA4iDgAHyCVd67kFgmiVDPcMTYbZYoW+yQSD2cp3PAEQGykDZBogpg8i08xIGWoArEZIrc2AStSqBIwVYBbvntjbSVOASgkHVZTi4uIgFotx7Zpj94Zr164hMTGx3Xi5XA653Id9XDKV/7OKiaCREtNzg7ytkYhFiI10/f+ukktsq3XdK5DfEUH1bWUyGUaMGIEvv/xSOGa1WvHll19izJgxQbSMIIhgEfTp25IlS5Cbm4uRI0di1KhRWL9+PRoaGjBnzpxgm0YQRBAIuig9+OCDqKysxPLly1FeXo5hw4bh888/bxf8JggiPAh6nlJn6LZ5SgQRJvjyHe1e66UEQfR4SJQIgggpSJQIgggpgh7o7gz2cFhtbW2QLSEIwhn276Y3oetuLUp1dfw+LY+yugmCCBp1dXXQaj2rItGtV9+sVitKS0uhVqv5ndRhiH2rTXFxMa1A+gC9f52jo/ePMYa6ujokJydDJPIsWtStPSWRSITevbtPWYpAotFo6EvVCej96xzu3j9PPSQ7FOgmCCKkIFEiCCKkIFHq5sjlcqxYscK36gkEvX+dJBDvX7cOdBME0fMgT4kgiJCCRIkgiJCCRIkgiJCCRIkgiJCCRKkbUlVVhVmzZkGj0SAqKgqPP/446uvr3T5m/Pjx4DjO4fLrX/+6iywOLt52YH7//feRkZEBhUKBIUOG4NNPP+0iS0MTb96/LVu2tPs/Uyi87H7DiG7HlClT2C233MJ++OEH9s0337D+/fuzhx9+2O1jcnJy2Pz581lZWZlw0ev1XWRx8Hj33XeZTCZjmzdvZqdPn2bz589nUVFR7Nq1a07HHzhwgInFYvbnP/+ZnTlzhv3v//4vk0ql7OTJk11seWjg7fuXn5/PNBqNw/9ZeXm5V89JotTNOHPmDAPAfvzxR+HYZ599xjiOYyUlJS4fl5OTw5566qkusDC0GDVqFFu4cKFw22KxsOTkZLZ69Wqn43/1q1+xadOmORwbPXo0e+KJJwJqZ6ji7fuXn5/PtFptp56Tpm/djO+//x5RUVEYOXKkcGzixIkQiUQ4ePCg28du374dcXFxyMzMxLJly9DY2Bhoc4OK0WjEkSNHMHHiROGYSCTCxIkT8f333zt9zPfff+8wHgAmT57scnxPxpf3DwDq6+uRlpaGlJQUTJ8+HadPn/bqebv1htxwpLy8HPHx8Q7HJBIJYmJiUF5e7vJxjzzyCNLS0pCcnIwTJ07gueeew7lz57Bz585Amxw0vO3ADPDvr7Px7t7bnoov79+gQYOwefNmDB06FHq9Hi+//DLGjh2L06dPe7x5nkQpRFi6dCnWrFnjdszZs2d9Pv+CBQuEv4cMGYKkpCTcddddKCgoQL9+/Xw+L0G0ZsyYMQ49G8eOHYubbroJf/vb37Bq1SqPzkGiFCL89re/RV5entsxffv2RWJiIioqKhyOm81mVFVVOe0q7IrRo0cDAC5evNhjRcnbDswAkJiY6NX4nowv719bpFIpsrKycPHiRY+fl2JKIYJOp0NGRobbi0wmw5gxY1BTU4MjR44Ij/3qq69gtVoFofGEY8eOAQCSkpL8/VJCBl86MI8ZM8ZhPADs2bMnLDs2+6ODtcViwcmTJ737P+tUmJwIClOmTGFZWVns4MGD7Ntvv2UDBgxwSAm4evUqGzRoEDt48CBjjLGLFy+yF198kR0+fJhdvnyZffjhh6xv375s3LhxwXoJXca7777L5HI527JlCztz5gxbsGABi4qKEpapH3vsMbZ06VJh/IEDB5hEImEvv/wyO3v2LFuxYkXYpwR48/6tXLmS7d69mxUUFLAjR46whx56iCkUCnb69GmPn5NEqRty48YN9vDDD7PIyEim0WjYnDlzWF1dnXD/5cuXGQC2d+9exhhjRUVFbNy4cSwmJobJ5XLWv39/9rvf/S4s8pQYY+y1115jqampTCaTsVGjRrEffvhBuC8nJ4fl5uY6jP/nP//JBg4cyGQyGRs8eDDbtWtXF1scWnjz/j399NPC2ISEBHb33Xezo0ePevV8VLqEIIiQgmJKBEGEFCRKBEGEFCRKBEGEFCRKBEGEFCRKBEGEFCRKBEGEFCRKBEGEFCRKBEGEFCRKRLdiy5YtiIqKEm6/8MILGDZsmHA7Ly8PM2bM6HK7CP9BokQ4JS8vz2Ud74ULF4LjOIeqBoEQg/T0dKxfv97h2IMPPojz58+7fMyGDRuwZcsW4fb48ePx9NNP+9UuIrCQKBEuSUlJwbvvvoumpibhWHNzM9555x2kpqYGxSalUtmuyF1rtFqtgydFdD9IlAiXDB8+HCkpKQ7VKXfu3InU1FRkZWV16tzOPJgZM2YI3tf48eNRWFiIZ555RuiKAbSfvrWltceWl5eH/fv3Y8OGDcI5Ll++jP79++Pll192eNyxY8fAcZxXdX+IwECiRLhl7ty5yM/PF25v3rwZc+bMCfjz7ty5E71798aLL76IsrIylJWVeX2ODRs2YMyYMZg/f75wjtTU1HavCQDy8/Mxbtw49O/f318vgfAREiXCLY8++ii+/fZbFBYWorCwEAcOHMCjjz4a8OeNiYmBWCyGWq1GYmKiT5UftVotZDIZIiIihHOIxWLk5eXh3LlzQv8yk8mEd955B3PnzvX3yyB8gMrhEm7R6XSYNm0atmzZAsYYpk2bhri4uGCb1SmSk5Mxbdo0bN68GaNGjcLHH38Mg8GABx54INimESBPifCAuXPnYsuWLdi6davfvAmRSIS2pbxMJpNfzu0J8+bNE4L4+fn5ePDBBxEREdFlz0+4hkSJ6JApU6bAaDTCZDJh8uTJfjmnTqdziBNZLBacOnXKYYxMJoPFYunU87g6x9133w2VSoVNmzbh888/p6lbCEHTN6JDxGKx0N5JLBa7HKfX64WGBHZiY2ORkpLSbuydd96JJUuWYNeuXejXrx/Wrl2LmpoahzHp6en4+uuv8dBDD0Eul/s0bUxPT8fBgwdx5coVREZGIiYmBiKRSIgtLVu2DAMGDAjLxgChCnlKhEdoNBpoNBq3Y/bt24esrCyHy8qVK52OnTt3LnJzczF79mzk5OSgb9++mDBhgsOYF198EVeuXEG/fv2g0+l8svvZZ5+FWCzGzTffDJ1Oh6KiIuG+xx9/HEajsUtWEwnPoRrdRNjyzTff4K677kJxcXG7LrBE8CBRIsIOg8GAyspK5ObmIjExEdu3bw+2SUQraPpGhB07duxAWloaampq8Oc//znY5hBtIE+JIIiQgjwlgiBCChIlgiBCChIlgiBCChIlgiBCChIlgiBCChIlgiBCChIlgiBCChIlgiBCiv8P2IKlB2EsQyoAAAAASUVORK5CYII=", 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", 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" ] @@ -2348,16 +2479,16 @@ "id": "e79e4b0f", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:44:33.160005Z", - "iopub.status.busy": "2024-02-29T18:44:33.159736Z", - "iopub.status.idle": "2024-02-29T18:44:33.460975Z", - "shell.execute_reply": "2024-02-29T18:44:33.460154Z" + "iopub.execute_input": "2024-03-03T12:01:02.750620Z", + "iopub.status.busy": "2024-03-03T12:01:02.750272Z", + "iopub.status.idle": "2024-03-03T12:01:03.054973Z", + "shell.execute_reply": "2024-03-03T12:01:03.054008Z" }, "papermill": { - "duration": 0.322423, - "end_time": "2024-02-29T18:44:33.462943", + "duration": 0.328422, + "end_time": "2024-03-03T12:01:03.057207", "exception": false, - "start_time": "2024-02-29T18:44:33.140520", + "start_time": "2024-03-03T12:01:02.728785", "status": "completed" }, "tags": [] @@ -2365,7 +2496,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2386,16 +2517,16 @@ "id": "745adde1", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:44:33.503100Z", - "iopub.status.busy": "2024-02-29T18:44:33.502818Z", - "iopub.status.idle": "2024-02-29T18:44:33.729512Z", - "shell.execute_reply": "2024-02-29T18:44:33.728656Z" + "iopub.execute_input": "2024-03-03T12:01:03.102557Z", + "iopub.status.busy": "2024-03-03T12:01:03.101747Z", + "iopub.status.idle": "2024-03-03T12:01:03.274006Z", + "shell.execute_reply": "2024-03-03T12:01:03.273036Z" }, "papermill": { - "duration": 0.248914, - "end_time": "2024-02-29T18:44:33.731368", + "duration": 0.197286, + "end_time": "2024-03-03T12:01:03.276114", "exception": false, - "start_time": "2024-02-29T18:44:33.482454", + "start_time": "2024-03-03T12:01:03.078828", "status": "completed" }, "tags": [] @@ -2403,7 +2534,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2424,16 +2555,16 @@ "id": "eabe1bab", "metadata": { "execution": { - "iopub.execute_input": "2024-02-29T18:44:33.772296Z", - "iopub.status.busy": "2024-02-29T18:44:33.772018Z", - "iopub.status.idle": "2024-02-29T18:44:34.042778Z", - "shell.execute_reply": "2024-02-29T18:44:34.041888Z" + "iopub.execute_input": "2024-03-03T12:01:03.322849Z", + "iopub.status.busy": "2024-03-03T12:01:03.322537Z", + "iopub.status.idle": "2024-03-03T12:01:03.618114Z", + "shell.execute_reply": "2024-03-03T12:01:03.617173Z" }, "papermill": { - "duration": 0.293553, - "end_time": "2024-02-29T18:44:34.044775", + "duration": 0.322102, + "end_time": "2024-03-03T12:01:03.620373", "exception": false, - "start_time": "2024-02-29T18:44:33.751222", + "start_time": "2024-03-03T12:01:03.298271", "status": "completed" }, "tags": [] @@ -2441,7 +2572,7 @@ "outputs": [ { "data": { - "image/png": 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", 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DcfDgQbPusWrVKrz33ntITExEv379EBcXx4wZjh8/HosXL8aCBQswZMgQZGVlYdWqVazrX331VYwZMwajR49Gt27ddFJWmsPlcpGeno4HDx5g5MiReOONN7By5UoAaHEZaEJCAoYNGwaRSITnnnsOQqFQZzWRKb9vW0HXDlvI4752uGdXZ7gbmRjpLHA4HHz33XdWX8JHUXdzo6OjUVhYiF69erX5/a21dph2hykUikl89913zHBIYWEhFi5ciKeffrpdBNCa0O4wpc0pKSmBi4uLwUdJSUl7u2gUY77//vvv7e2eRXz55ZcG39OAAQMAADU1NZg/fz5CQ0Mxc+ZMjBw5EkeOHGlnz1sPjQQpbY6vr69Onl7z89bCFqM9xnz38/Oz+v3agvHjx7Py9LTRpOtMnz4d06dPb0u32gQqgpQ2x97eHr17925vNyzmSfbdEK6urqzk7M4E7Q53WOh8F4ViClQEOypUAykUk6AiSKFQOjVUBCkUSqem3UVw165dCAwMhEAgQEREBE6fPm3U/vDhwwgNDYVAIEBYWBh+/PFH1nlCCBITE+Hj4wNHR0fExMQw2fraHD16FBEREXB0dISnp2eHS6alvWEKxTTaVQQPHjyI+Ph4JCUl4dy5cxg8eDBEIpHBkkVZWVmYMmUKZs+ejby8PMTGxiI2NhaXL19mbDZu3Ijt27cjNTUVOTk5cHZ2hkgkQn19PWPzzTffYNq0aZg1axYuXLiAkydP4rXXXrP5++2sNN/8SLsaMYXS7rRYcdCGhIeHk/nz5zOvlUol8fX1JcnJyXrtJ02aRMaNG8c6FhERQebNm0cIIUSlUhGhUEg2bdrEnK+qqiJ8Pp989dVXhBBCGhsbiZ+fH6uIpiU87puv369tsOl9zKFnz56sQqd37twh9fX17ecQpUNgraKq7RYJyuVy5ObmIiYmhjlmZ2eHmJgYZGdn670mOzubZQ8AIpGIsS8uLoZEImHZuLu7IyIigrE5d+4cbt++DTs7OwwdOhQ+Pj4YO3YsK5rUR0NDA2QyGetBsQyhUGi0YklbQAiBQqFoVx8ojwftJoIVFRVQKpVMFV8N3t7ekEgkeq+RSCRG7TXPxmxu3LgBQF3uKCEhAT/88AM8PT3x3HPPobKy0qC/ycnJcHd3Zx4t1cQzCUIAea1VH5zGh+A0PmzZ1syVFDU1NZg6dSqcnZ3h4+ODDz/80OKtOPVtzvPtt99i9OjRcHJywuDBg3X+Ef7xxx/MRkj+/v549913UVtby5z/4osvMGLECLi6ukIoFOK1115jDascP34cHA4HP/30E4YPHw4+n48//vjDbN8pHY9Ot2JEU5135cqVePXVVwGoSzJpNg6aN2+e3uuWL1+O+Ph45rVMJmu9EDY+BP5tvSViABDWsomaFWUAz7lluybi4+Nx8uRJfP/99/D29kZiYqJVN8JZuXIlNm/ejJCQEKxcuRJTpkxBYWEh7O3tUVRUhDFjxmD9+vXYu3cvysvLsWDBAixYsACfffYZAPXWjuvWrUPfvn1x9+5dxMfHY+bMmToTZ8uWLcPmzZsRHBwMT09Pq/hOebJpNxH08vICl8tlVaoFAKlUytrHQBuhUGjUXvMslUrh4+PDstF8WTXHtevJ8fl8BAcHG124z+fz270L117U1NTg888/x4EDB5h9LTS1/KzF+++/j3HjxgFQ1/8bMGAACgsLERoaiuTkZEydOpWJOkNCQrB9+3aMGjUKu3fvhkAgwOuvv860FRwcjO3bt2PkyJF48OABXFxcmHNr167FCy+8YDW/KU8+7SaCPB4Pw4cPh1gsZtJTVCoVxGIxa49WbSIjIyEWi1ldsMzMTGYjnaCgIAiFQojFYkb0ZDIZcnJy8NZbbwEA0xUqKChAdHQ0AHUUcfPmTaOb/9gEByd1RGZFLjVttNTD0xGeTjzj9zaRGzduoLGxkbWxjru7u1U3wtHe6Ejzj+ru3bsIDQ3FhQsXcPHiRda+woQQqFQqFBcXo1+/fsjNzcXq1atx4cIF3L9/n4n4S0pKWP/wTK3eTOk8tGt3OD4+HjNmzMCIESMQHh6OlJQU1NbWYtasWQDUVSv8/PyQnJwMAFi4cCFGjRqFLVu2YNy4cUhLS8PZs2exZ88eAOqxpkWLFmH9+vUICQlBUFAQVq1aBV9fX0ZoNZuAJyUlwd/fHz179sSmTZsAABMnTmzbD4DDMatLagrEQb3BNnhOAM+ICD5m6NvoSCNkDx48wLx58/Duu+/qXBcQEIDa2lqIRCKIRCJ8+eWX6NatG0pKSiASiXQ282m+CRCF0q4iGBcXh/LyciQmJkIikWDIkCHIyMhgJjZKSkpY+wxERUXhwIEDSEhIwIoVKxASEoL09HQMHDiQsVm6dClqa2sxd+5cVFVVITo6GhkZGazKs5s2bYK9vT2mTZuGuro6RERE4Ndff+1YY0RWzJYODg6Gg4MDzpw5g4CAAABAdXU1rl27hmeffdZ6NzLAsGHDcOXKFYPVWy5duoR79+5hw4YNzDjt2bNnbe4XpYNg/eydzsHjkCd4p6qOFEpriFKlYo5p8gQrH1g3T/CNN94gQUFB5NdffyWXL18mr776KnF1dSWLFi1q8drmeYLQs2F3Xl4ec/7+/fsEADl27BghhJALFy4QR0dHMn/+fJKXl0euXbtG0tPTmRzTu3fvEh6PR5YsWUKKiorIkSNHSJ8+fVjtHjt2jAAg9+/ft8KnQXkceOLzBCmt525NPWrlClQ/bLT5vbZu3YrIyEi89NJLiImJwdNPP41+/frZdL8UDYMGDcKJEydw7do1PPPMMxg6dCgSExOZiZlu3bph3759OHz4MPr3748NGzZg8+bNNveL0jGgGy1ZyOOw0ZJmU6Ueno7o4sxnHfP3dIKns+3GBGtra+Hn54ctW7Zg9uzZNrsPhWIIutESxSjW/s+Wl5eHq1evIjw8HNXV1Vi7di0AYMKECVa+E4XStlARpOilTq5AxQM5vN0E4NmrR002b96MgoICJr3p999/R35+PsaOHWuwnQcPHrSVyxSKRVARpOjl+l21eMmVKvTq5oKhQ4ciNzdXx66urs7oxkMUyuMOFUGKURoaVUbPOzo6dsiNhyidBzo73MbQeSgKxTpY67tERbCN0KyIePjwYRvdkYotpWOjWQ3E5XJb1Q7tDrcRXC4XHh4eTHknJycnZnmYpRCF+o+gocEO9VzCOibXOtaatlV2dqyq3BTK44BKpUJ5eTmcnJxgb986GaMi2IZoqtwY2j7AXO7erwMANFY5oIpvr3PsPt/yX6+mHS4H4NY6ttJTCsX62NnZISAgoNXBBBXBNoTD4cDHxwfdu3dHY2PrV3m88e1xAMDiF/rgpVBf9rGYR8da07a7owO+fbu/cWMKpR3g8Xis2gKWQkWwHeByua0exwCA2zVKAEAj7JmMec2xBnBbtTJF006dqnXtUCiPO3RihEKhdGqoCHYA9GUK0EwcCsU0qAh2UKgGUiimQUWwo2KlUJAmd1M6OlQEOyhUuigU06Ai2AEgeiSPBnAUimlQEaQYhWoppaPzWIjgrl27EBgYCIFAgIiICJw+fdqo/eHDhxEaGgqBQICwsDCdDbYJIUhMTISPjw8cHR0RExOD69evs2wCAwPB4XBYjw0bNlj9vVEolMebdhfBgwcPIj4+HklJSTh37hwGDx4MkUhkcGlZVlYWpkyZgtmzZyMvLw+xsbGIjY3F5cuXGZuNGzdi+/btSE1NRU5ODpydnSESiXTWwK5duxZ37txhHu+8845N32tbQic0KBTTaHcR3Lp1K+bMmYNZs2ahf//+SE1NhZOTE/bu3avXftu2bRgzZgyWLFmCfv36Yd26dRg2bBh27twJQP3lT0lJQUJCAiZMmIBBgwZh//79KCsrQ3p6OqstV1dXCIVC5tGR9qSlEkihmEarRPDBgweQyWSshznI5XLk5uYiJibmkUN2doiJiUF2drbea7Kzs1n2ACASiRj74uJiSCQSlo27uzsiIiJ02tywYQO6du2KoUOHYtOmTVAoFAZ9bWhoaNV7tSW2TJamASWlo2P22uHi4mIsWLAAx48fZ3UvCSHgcDhQKpUmt1VRUQGlUslstq7B29sbV69e1XuNRCLRay+RSJjzmmOGbADg3XffxbBhw9ClSxdkZWVh+fLluHPnDrZu3ar3vsnJyVizZo3J7629odpFoZiG2SL4z3/+E4QQ7N27F97e3q0uY9NexMfHMz8PGjQIPB4P8+bNQ3JyMvh8vo798uXLWdfIZDL4+/u3ia8UCsV2mC2CFy5cQG5uLvr27dvqm3t5eYHL5UIqlbKOS6VSpvZec4RCoVF7zbNUKoWPjw/LZsiQIQZ9iYiIgEKhwM2bN/W+Nz6fr1ccH1foxAiFYhpmjwmOHDkSpaWlVrm5ZutGsVjMHFOpVBCLxYiMjNR7TWRkJMseADIzMxn7oKAgCIVClo1MJkNOTo7BNgHg/PnzsLOzQ/fu3VvzltoFKncUiuWYHQl+8sknePPNN3H79m0MHDiQ2TtDw6BBg8xqLz4+HjNmzMCIESMQHh6OlJQU1NbWYtasWQCA6dOnw8/PD8nJyQCAhQsXYtSoUdiyZQvGjRuHtLQ0nD17Fnv27AGgLly6aNEirF+/HiEhIQgKCsKqVavg6+uL2NhYAOrJlZycHIwePRqurq7Izs7G4sWL8c9//hOenp7mfiSPJdabGKESS+nYmC2C5eXlKCoqYkQKUAuPJRMjABAXF4fy8nIkJiZCIpFgyJAhyMjIYCY2SkpKWNVjo6KicODAASQkJGDFihUICQlBeno6Bg4cyNgsXboUtbW1mDt3LqqqqhAdHY2MjAymOCifz0daWhpWr16NhoYGBAUFYfHixawxvycdfUvpKBSKLhxi5r/6/v37o1+/fli6dKneiZGePXta1cHHFZlMBnd3d1RXV8PNza1dfAhcdhQAsC52IKY91ZN1bPnYUMwb1avVbbsJ7HFxtaiVnlIobYs530+zI8G//voL33//Pd1w+3HChl1WGk9SOjpmT4z87W9/w4ULF2zhC8WKUPGiUEzD7Ejw5ZdfxuLFi3Hp0iWEhYXpTIyMHz/eas5RLIfOZ1AopmG2CL755psA1MUHmmPJxAjFNtCJEQrFNMwWQZVKZQs/KFaGRoIUimmYNSbY2NgIe3t7VtkqSvtjU72jYkrp4Jglgg4ODggICKBdXgqF0mEwe3Z45cqVWLFiBSorK23hD6UN2PDTVcT9JxuNSjq0QaGYPSa4c+dOFBYWwtfXFz179tQpRHru3DmrOUexHGM58IfPluJerRxF5Q8QKjSeSEp7w5SOjtkiqFl/S3l8MLeoqkKlPknnuCgUC0QwKSnJFn5QrIyxCE7R1A2maTQUigUiqCE3Nxf5+fkAgAEDBmDo0KFWc4rSekyJBGkaDYVigQjevXsXkydPxvHjx+Hh4QEAqKqqwujRo5GWloZu3bpZ20eKlVGqqPpRKBrMnh1+5513UFNTgz///BOVlZWorKzE5cuXIZPJ8O6779rCxw7J/Vo5VFYSI32TIIa6uoQQsyJBWk+Q0tExWwQzMjLw0UcfoV+/fsyx/v37Y9euXfjpp5+s6lxH5VzJfQxdl4k5+8/a7B6GtEs7ClRRgaNQzBdBlUqlUzQBUCdS0yV1pvHZyZsAAPFV/RvMWwNteVOqCDZmXMWJa+VMFNjchkLprFhUSmvhwoUoKytjjt2+fRuLFy/G888/b1XnKK1AK8o7X3ofHx0vwv/+dJUVCZrS1aVCSenomC2CO3fuhEwmQ2BgIHr16oVevXohKCgIMpkMO3bssIWPHY622KRUW7zq5OoIvV6hpJEghdIMs2eH/f39ce7cOfzyyy/MBun9+vVDTEyM1Z3rqFh7q+aWxEzRNEyhUpFmkaB1/aBQnkQsyhPkcDh44YUX8MILL1jbH4qV0BY4zQSIijxKlG6yalunKJTHELO7wwAgFouxYsUKvPHGG3j99ddZD0vYtWsXAgMDIRAIEBERgdOnTxu1P3z4MEJDQyEQCBAWFoYff/yRdZ4QgsTERPj4+MDR0RExMTG4fv263rYaGhowZMgQcDgcnD9/3iL/H0e0U2Q0uqfSSo9Rv25rryiUxw+zRXDNmjX4+9//DrFYjIqKCty/f5/1MJeDBw8iPj4eSUlJOHfuHAYPHgyRSIS7d/XPnGZlZWHKlCmYPXs28vLyEBsbi9jYWFaNw40bN2L79u1ITU1FTk4OnJ2dIRKJUF9fr9Pe0qVL4evra7bfjzvakaCSWStsfneYdpkpHR2zu8OpqanYt28fpk2bZhUHtm7dijlz5jD7GKempuLo0aPYu3cvli1bpmO/bds2jBkzBkuWLAEArFu3DpmZmdi5cydSU1NBCEFKSgoSEhIwYcIEAMD+/fvh7e2N9PR0TJ48mWnrp59+ws8//4xvvvmmTXMcrT0xoreAgtbPrO6wmbPDFEpHx+xIUC6XIyoqyio3l8vlyM3NZU2q2NnZISYmBtnZ2Xqvyc7O1pmEEYlEjH1xcTEkEgnLxt3dHREREaw2pVIp5syZgy+++AJOTk4t+trQ0ACZTMZ6WErzvZptDRMJEgKlVi4nlUAKxQIRfOONN3DgwAGr3LyiogJKpRLe3t6s497e3pBIJHqvkUgkRu01z8ZsCCGYOXMm3nzzTYwYMcIkX5OTk+Hu7s48/P39TbruceBRJEjQqDSzO0ylktLBMbs7XF9fjz179uCXX37BoEGDdFaPbN261WrO2YodO3agpqYGy5cvN/ma5cuXIz4+nnktk8ksFsI2yRPUNyZIYHayNIXS0TFbBC9evIghQ4YAgM6GS+Z287y8vMDlciGVSlnHpVIphEKh3muEQqFRe82zVCqFj48Py0bj96+//ors7Gzw+XxWOyNGjMDUqVPx+eef69yXz+fr2D/OaEdwCq3uME2WplDYmC2Cx44ds9rNeTwehg8fDrFYzFSsVqlUEIvFWLBggd5rIiMjIRaLsWjRIuZYZmYmIiMjAQBBQUEQCoUQi8WM6MlkMuTk5OCtt94CAGzfvh3r169nri8rK4NIJMLBgwcRERFhtffXVugVM+08QdbssNaYIFVBCsXyoqrWIj4+HjNmzMCIESMQHh6OlJQU1NbWMrPF06dPh5+fH5KTkwEACxcuxKhRo7BlyxaMGzcOaWlpOHv2LPbs2QNAHY0uWrQI69evR0hICIKCgrBq1Sr4+voyQhsQEMDywcXFBQDQq1cv9OjRw/Zv2sL+cE19I1wFusUr9MEqoKA1O8waE6SxIIXS/iIYFxeH8vJyJCYmQiKRYMiQIcjIyGAmNkpKSmBn92j+JioqCgcOHEBCQgJWrFiBkJAQpKenY+DAgYzN0qVLUVtbi7lz56KqqgrR0dHIyMiAQCBo8/enD44FKrjl5wLs+LUQu6cOw9gwnxbttcf7VKzZYZonSKFo0+4iCAALFiww2P09fvy4zrGJEydi4sSJBtvjcDhYu3Yt1q5da9L9AwMDH/tJgh2/FgIAEr//0yQR1EZpYEyQ1hOkUCxcNkdpHdrzR7UNila3p7eytPbscNPPKhVoniCF0gyzRfC3336DQqH7xVUoFPjtt9+s4lRnYujaTJu0y1oxoh0Jao0JmqKCVCgpHR2zRXD06NGorKzUOV5dXY3Ro0dbxanOhFxpXjVuU3uw2naGU2SoxFEoZosgIURvPuC9e/fg7OxsFac6Om1TVFV37E89O0xTZCgUbUyeGPnHP/4BQD3pMHPmTFbisFKpxMWLF622prij05qlw6Zeq2/FCABWd5iW0qJQzBBBd3d3AOpI0NXVFY6Ojsw5Ho+Hp556CnPmzLG+hxQWlkRv2iLIjgSpClIoJovgZ599BkCdTvL+++/Tru8ThHYqjPYYpEkSSHWS0sExO08wKSnJFn5QjFBZKzf7Gu0oTzsSlCvomCCFoo3ZEyNSqRTTpk2Dr68v7O3tweVyWQ9Ky5i7YuSjY4Vm30Pfsjmg+Ww0VUEKxexIcObMmSgpKcGqVavg4+PT5gVCOwLmfmT1CqXR8y1FdCoaCVIoBjFbBP/44w/8/vvvTIUWiu2xRKz05QkC7IkROjtMoVjQHfb396eziq3E3EiwtZ+2iiWC5iVL04RqSkfHbBFMSUnBsmXLcPPmTRu4Q9EH+3+OaaLE2nKT0O4whWIIs7vDcXFxePjwIXr16gUnJyed8vr6ltRRWotxtdIXrbGTpR/93KCgBRQoFG3MFsGUlBQbuNHZMK8/zI7YTLtWXwEFgCZLUyjNMVsEZ8yYYQs/OhVmjwk26w4TQvD/Lt4x+RraHaZQDGNRPcGioiIkJCRgypQpuHv3LgD1RuZ//vmnVZ2jqGne3f35ihTvfpXX4lUaDKbImDIxQoWS0sExWwRPnDiBsLAw5OTk4Ntvv8WDBw8AABcuXKCrSUyktZmV50urzLLXjgRpFRkKhY3ZIrhs2TKsX78emZmZ4PF4zPG//e1vOHXqlFWdo6hpLlYtvW5+TDtPUG6mCCpoMiGlg2O2CF66dAmvvPKKzvHu3bujoqLCIid27dqFwMBACAQCRERE4PTp00btDx8+jNDQUAgEAoSFheHHH39knSeEIDExET4+PnB0dERMTAyuX7/Oshk/fjwCAgIgEAjg4+ODadOmoayszCL/bY0lMqQtcIa7wxQKxWwR9PDwwJ07uoPyeXl58PPzM9uBgwcPIj4+HklJSTh37hwGDx4MkUjEjDU2JysrC1OmTMHs2bORl5eH2NhYxMbGsjaC37hxI7Zv347U1FTk5OTA2dkZIpEI9fX1jM3o0aNx6NAhFBQU4JtvvkFRURH+53/+x2z/LaF1EyMmXqOdJ2hwxQiVQQrFbBGcPHkyPvjgA0gkEnA4HKhUKpw8eRLvv/8+pk+fbrYDW7duxZw5czBr1iz0798fqampcHJywt69e/Xab9u2DWPGjMGSJUvQr18/rFu3DsOGDcPOnTsBqKPAlJQUJCQkYMKECRg0aBD279+PsrIypKenM+0sXrwYTz31FHr27ImoqCgsW7YMp06dQmNjo9nvwVzMLaBgyaoNViRoqIAC1UAKxXwR/Pe//43Q0FD4+/vjwYMH6N+/P5599llERUUhISHBrLbkcjlyc3MRExPzyCE7O8TExCA7O1vvNdnZ2Sx7ABCJRIx9cXExJBIJy8bd3R0REREG26ysrMSXX36JqKgoneTvx4LmY4DNDujTMlYVmVbMDlMoHR2zRZDH4+Hjjz9GUVERfvjhB/z3v//F1atX8cUXX5hdSquiogJKpZLZaF2Dt7c3JBKJ3mskEolRe82zKW1+8MEHcHZ2RteuXVFSUoIjR44Y9LWhoQEymYz1aCtaK1XaG8yx1g6bvGkTFUtKx8XifYcDAgLw4osvYtKkSQgJCbGmT23GkiVLkJeXh59//hlcLhfTp083+IVPTk6Gu7s78/D3928TH0srH+KHi+ZP2FhzYoRqIKUjY9KKkfj4eKxbtw7Ozs6Ij483art161aTb+7l5QUulwupVMo6LpVKIRQK9V4jFAqN2muepVIpfHx8WDbNy395eXnBy8sLffr0Qb9+/eDv749Tp04hMjJS577Lly9nvXeZTGaxEJozMfLMxmOs1yZHbwYmRuQWTIxQDaR0ZEwSwby8PGbCIC/P8EoFcwus8ng8DB8+HGKxGLGxsQAAlUoFsViMBQsW6L0mMjISYrEYixYtYo5lZmYywhUUFAShUAixWMyInkwmQ05ODt566y2DvqhUanFoaGjQe57P57N22GsNrS5Da0KeoLZNa8vrq6NjWjyX0jExSQSPHTum92drEB8fjxkzZmDEiBEIDw9HSkoKamtrMWvWLADA9OnT4efnh+TkZADAwoULMWrUKGzZsgXjxo1DWloazp49iz179gBQC/GiRYuwfv16hISEICgoCKtWrYKvry8jtDk5OThz5gyio6Ph6emJoqIirFq1Cr169dIbBT5OWFLI2+CKEROvp5EgpSNjdgEFaxMXF4fy8nIkJiZCIpFgyJAhyMjIYCY2SkpKYGf3aOgyKioKBw4cQEJCAlasWIGQkBCkp6dj4MCBjM3SpUtRW1uLuXPnoqqqCtHR0cjIyIBAIAAAODk54dtvv0VSUhJqa2vh4+ODMWPGICEhwWrRnjFasyWBvuhNX3OmzA7TwT4KxUQR1Gy8bgrffvut2U4sWLDAYPf3+PHjOscmTpyIiRMnGmyPw+Fg7dq1WLt2rd7zYWFh+PXXX83280lFe+xPexkcnRihUEycHdaeFXVzc4NYLMbZs2eZ87m5uRCLxcwG7RTbYoomGdpyk21j6v2oClI6LiZFgpqN1wF1bt2kSZOQmprK5AUqlUq8/fbbcHNzs42XFKPoLaCg9bMhETR5dphqIKUDY3ae4N69e/H++++zEqO5XC7i4+MNLnWjsGmLXUoNLZszZEOhdFbMFkGFQoGrV6/qHL969SqTZkKxLaas4DAlEqRjghSKBbPDs2bNwuzZs1FUVITw8HAA6pSTDRs2MGktFOOYW0DBEkwbE2QfP3XjHj7MvIZ1sQPZdnRMkNKBMVsEN2/eDKFQiC1btjAltXx8fLBkyRK89957VneQwkZ/XrRxkVKaGModPnsLOcWV+OkSe401jQQpHRmzRdDOzg5Lly7F0qVLmSICdELk8YO925x+m+ZjhVUP5QCAukalwbYolI5Gq5KlqfhZRmsmRjgwMTIzsGyOZdLscFWdemlkfXMRpKEgpQNjkQh+/fXXOHToEEpKSiCXy1nnzp07ZxXHOjKtGRE0famb1pigodnhZq81kWCDgkaClM6D2bPD27dvx6xZs+Dt7Y28vDyEh4eja9euuHHjBsaOHWsLHykt0NJGSypTI8GHmkhQZdSOQulImC2CH330Efbs2YMdO3aAx+Nh6dKlyMzMxLvvvovq6mpb+NhhqG1Q4JWPTuKTP4ptfi9Dm6+zbLRiPEKIwe4wDQUpHRmzRbCkpARRUVEAAEdHR9TU1AAApk2bhq+++sq63nUwvjj1F/JKqnSOmzPmxoF+TcoruY+vc2/pvcaUMcEHDQrGTkcEKZQOjNljgkKhEJWVlejZsycCAgJw6tQpDB48GMXFxXQAvQXq5K0XF0Of8CsfZTWzMy9PUNMVBvTNDtPfK6XjYnYk+Le//Q3ff/89AHXi9OLFi/HCCy8gLi5O737ElJaxxf8OYubssLYI0jFBSmfC7Ehwz549zPK4+fPno2vXrsjKysL48eMxb948qzvYkbCGlsgVKojzpS0bamFw7bDWz1V1j2b5dVJkzLobhfJkYZYIKhQK/Pvf/8brr7+OHj16AFDvQzx58mSbONfhMDFVxRgPGhR40KBo+VZaP5sbCTYomkeCVAYpHRezusP29vbYuHEjFIqWv4SU9kWjW4QQGNBAVoSomRkGaCRI6VyYPSb4/PPP48SJE7bwpdNiy0jLkAACzbrDtUa6w1QFKR0Ys8cEx44di2XLluHSpUsYPnw4nJ2dWefHjx9vNecopqFfRNXHDHWFmy5kfmRHgs26wzQWpHRgzBbBt99+G4D+/YU5HA6USppjZghDUmILidHom7Hq0axIUHt2WEGTpSmdB7O7wyqVyuDDUgHctWsXAgMDIRAIEBERgdOnTxu1P3z4MEJDQyEQCBAWFoYff/yRdZ4QgsTERPj4+MDR0RExMTG4fv06c/7mzZuYPXs2goKC4OjoiF69eiEpKUlnHbS1actupeZWCiORoLY/1Vqzw839tJXbtSZO8lAotsRsEbQ2Bw8eRHx8PJKSknDu3DkMHjwYIpEId+/e1WuflZWFKVOmYPbs2cjLy0NsbCxiY2Nx+fJlxmbjxo3Yvn07UlNTkZOTA2dnZ4hEItTX1wN4VAX7P//5D/788098+OGHSE1NxYoVK9rkPTfHNnmCLXeHtbu52pGgblvW80tDfaMSf9tyHOO2/05XqFDaFZO7w3V1dRCLxXjppZcAAMuXL0dDQwNznsvlYt26dczevqaydetWzJkzh6lKnZqaiqNHj2Lv3r1YtmyZjv22bdswZswYLFmyBACwbt06ZGZmYufOnUhNTQUhBCkpKUhISMCECRMAAPv374e3tzfS09MxefJkjBkzBmPGjGHaDA4ORkFBAXbv3o3Nmzeb5b85tOXYmuZOhoonAOxJk/sPDUfBtvC7tPIhpDL138/hs6WYFhlo9XtQKKZgciT4+eef4z//+Q/zeufOncjKykJeXh7y8vLw3//+F7t37zbr5nK5HLm5uYiJiXnkkJ0dYmJikJ2drfea7Oxslj0AiEQixr64uBgSiYRl4+7ujoiICINtAkB1dTW6dOli8HxDQwNkMhnrYS6GIioCArlChdy/KqFQmr9Pi7FIzVhVaXZ3uG0jwdtVdczPe36/YdH7plCsgcki+OWXX2Lu3LmsYwcOHMCxY8dw7NgxbNq0CYcOHTLr5hUVFVAqlfD29mYd9/b2hkQi0XuNRCIxaq95NqfNwsJC7Nixw+iKl+TkZNb+y/7+/sbfnJms/O4SXt2djfVH863SHjMxYkJ3mBBitDtsC+5U1zM/l1bW4afL+n83FIqtMVkECwsLERYWxrwWCASws3t0eXh4OK5cuWJd79qA27dvY8yYMZg4cSLmzJlj0G758uWorq5mHqWlpVbzgRDgcFMFmH1ZN63TZtOz0f1Fmk7VypXMBIqnk4PBtqzJnaZIkG+v/htKPVFEV6ZQ2gWTRbCqqoo1BlheXo7AwEDmtUqlYp03BS8vL3C5XEil7LWwUqkUQqFQ7zVCodCovebZlDbLysowevRoREVFYc+ePUZ95fP5cHNzYz3MpS2/4qZNjKi535QozbO3g6cTz2Bb1qSsKRKc9lRPODpw8WeZDH8UVlj9PhRKS5gsgj169GDNwDbn4sWLzHpiU+HxeBg+fDjEYjFzTKVSQSwWIzIyUu81kZGRLHsAyMzMZOyDgoIgFApZNjKZDDk5Oaw2b9++jeeeew7Dhw/HZ599xopqbYWtAh1jzRrbClrTVdaMB3o6OYDvwNVt3wZ+lzVFgv183DA5XD20kHqiyPo3olBawORv/osvvojExEQmzUSburo6rFmzBuPGjTPbgfj4eHz88cf4/PPPkZ+fj7feegu1tbXMbPH06dOxfPlyxn7hwoXIyMjAli1bcPXqVaxevRpnz57FggULAKgTthctWoT169fj+++/x6VLlzB9+nT4+voiNjYWwCMBDAgIwObNm1FeXg6JRGJwzPBJRCNcCiMqqNE2zXighyMPAoe2yZrSjAn6ejhidnQQuHYcnCy8h0u3aHVyStticorMihUrcOjQIfTt2xcLFixAnz59AAAFBQXYuXMnFAqFRXl2cXFxKC8vR2JiIiQSCYYMGYKMjAxmYqOkpIQVpUVFReHAgQNISEjAihUrEBISgvT0dAwc+GjD8KVLl6K2thZz585FVVUVoqOjkZGRwaTvZGZmorCwEIWFhTrRa3uMS9nylkZXjDSd0pTRcndyAFfPVnjW9o8QwkSCvh4C9PB0wvjBvvgu7zZSfyvCrteGWfeGFIoRTBZBb29vZGVl4a233sKyZcsYseBwOHjhhRfw0Ucf6czImsqCBQuYSK45x48f1zk2ceJETJw40WB7HA4Ha9euxdq1a/WenzlzJmbOnGmJq62ibfMENWOCLdvcZyJBBzTqucDaft9/2MiU6xK6q/8xzRsVjO/ybuOnS3fw171a9OzqbKwJCsVqmLV2OCgoCBkZGaisrERhYSEAoHfv3kbz6yhaGMkTbFWzRnabMzox0nSquilR2tOJB1m9bqpMayNBQgg4WhGmJgr0cuGBb68egwwVumF03244VlCOPb/dwL9eCdPbFoVibSwaAOrSpQvCw8MRHh5OBfAxx3h3WH2OGRN0coBA38RIK+4/Z/9ZTNh1kiXGj7rCjizbeaN6AVCnC5XXmJdpQKFYSruvHe5MGBKTZzces/69TIkEm541ZbTcDYlgK0LBzCtSXLxVjfw7j1bYaCZFfNzZSywjgrpgiL8H5AoV9mXZfltSCgWgIvhYUPHA+tVrmDFBUyZGmrrDhmaHLZVAQ+JZVq2OBH3c2ZEgh8PBm03R4BfZf9EKM5Q2gYpgB8eUZXOa7rCnwUjQsntrX2enNSZ4p0qTHqNbbOPv/b0R3M0ZsnoF0k6XWHZjCsUMqAi2IbZKv9E3sfIoT9CUFBmt7rC9rghaGgtqR6HaueiGxgTVdhzMezYYAPDJ78WQK2hhBYptoSLYhrRHUVVTSmnZKllae1KGA61IkBkT1BVBAIgd6ofurnxIZPU4cv621fyhUPRBRbCjopkYMaq8pKmCTNOYoJMDHLi6fxLGtikxhqzu0ZieXZMGKlUEEpnh7jAA8O25eD06CADwn99uGBVyCqW1UBHsoDxKljbeHWZXkOHBnqu7YsRYmo0xtosfbWmgyRMsr2mAUkXAteOgu6vhAryvRQTAlW+PwrsP8OtV/VXGKRRrQEWwDbFVPGMsWbqlZXOaKJBnbweBgx1rAkODsSIMxtAunKqJBDUzw96ufHDtdO+lwU3ggKlP9QRACytQbAsVwTakPcrltbRsrkpryRyHw4G9HmGyNBLURhMJGpsUac7rTweCx7XD2b/u4+zNylb7QKHog4pgB4UpqtpCd1h7tQgAvdGZNURQ06wmPcbHBBHs7ibAP4b5AaDRIMV2UBHsoGjScYwJmIo8qiDj0VRMVb8IWs8vTXfY1920DbnmPhsMDgf4Jf8urktrrOcIhdIEFcE2xFZVZErvP9Q5lldahZJ7D43nCTbrDgP6RdBYNGkM7ZY0WsxEgiaKYHA3F4j6qyuC/+e3Gxb5QaEYg4pgG2KrMcFvz+nm0hECPLvpmPH0EvKoqrSx7rA1krxP3bgHQCsSNKE7rGHeKHXy9JHzt3Gnuq4FawrFPKgIPoaU1zQY3QLTHPRFcRqdIwBTOstVoBZB/RMjrfdDU0ChrOpRRWlTGRrgiYigLmhUEnz6Oy2sQLEuVAQfQ0b+6xcMXvOzVdrSlyytSYgmhKChUT197Ni0ZpirZ68Vi7vDWnpa36hCaeVDVDxQl8gytTus4c3n1IUVvjpdguo23h6U0rGhItjB0dcd1oigigD1jUoAYJbL6VkwYlF3mBCCMzfvM68P55bimaaSYXx7O3Rx1t3VzhjP9emGUKErauVK/DfnL7P9oVAMQUWwDTFXTKyxXExfJKhZFUKgLYKGI0FL3DheUM7q0mu34evhyKo0bQraZbY+O1nM+E2htJZ2F8Fdu3YhMDAQAoEAEREROH36tFH7w4cPIzQ0FAKBAGFhYfjxxx9Z5wkhSExMhI+PDxwdHRETE4Pr16+zbP71r38hKioKTk5O8PDwsPZbshrWyM8zFgkSQlDf1B3WbLWpb6Ml4+uPdVGqCGbtO2PwvLldYQ3jBvnAz8MRFQ/k+Lpps3oKpbW0qwgePHgQ8fHxSEpKwrlz5zB48GCIRCLcvat/rWhWVhamTJmC2bNnIy8vD7GxsYiNjWXth7xx40Zs374dqampyMnJgbOzM0QiEWurULlcjokTJ+Ktt96y+XtsDeaKj9429ImgnVYkqGiKBO013eHWJ0v/ki81et5Q9ZiWcODa4Y1n1IUVPv79hsVjlRSKNu0qglu3bsWcOXMwa9Ys9O/fH6mpqXBycsLevXv12m/btg1jxozBkiVL0K9fP6xbtw7Dhg3Dzp07Aagjm5SUFCQkJGDChAkYNGgQ9u/fj7KyMqSnpzPtrFmzBosXL0ZYWNtu5mPuV9bSNbva6MsTdGgSPBDd7rC+AgrmdOMJIdjx63WjNoaqx5hC3Eh/eDo54K97D/HT5TsWt0OhaGg3EZTL5cjNzUVMTMwjZ+zsEBMTg+zsbL3XZGdns+wBQCQSMfbFxcWQSCQsG3d3d0RERBhssy0xN7DTlwRtLvqiOE0ajEqrO6wRQX01DW5X1eMfH53E4bOlLd4v84oUl2/LjNqYkx7THCeePaZHBgIA/nPiRrvsE03pWLSbCFZUVECpVOrsVezt7Q2JRKL3GolEYtRe82xOm6bS0NAAmUzGepiLud3Kv3/4m9n3aI6mgIKDVoT3aExQd3ZYX8GFAzklOFdShSVfX2xRdG5U1Lbok5cL3xTXDTIjKhACBztcul2NrKJ7rWqLQmn3iZEnheTkZLi7uzMPf39/s9uwxkSHpffULpbKiCAIswm6JhJU6FFBN8Gj7amLWxA5U+Z8zU2P0Xf95JEBAGhhBUrraTcR9PLyApfLhVTKHkSXSqUQCoV6rxEKhUbtNc/mtGkqy5cvR3V1NfMoLW25a9gca4zxmYtm8kB7JQiTIqMdCTbtLdKoZwxRew/gJV9fNHo/UzJfhgV4tGzUArOjg8C14+D36xW4fLu61e1ROi/tJoI8Hg/Dhw+HWCxmjqlUKojFYkRGRuq9JjIykmUPAJmZmYx9UFAQhEIhy0YmkyEnJ8dgm6bC5/Ph5ubGepiLNWZ7zb5nk6jx7PVFgrrdYX2RoHYXN/ev+zrntdFXlFWb9bEDzc4R1Id/Fye8NMgHAI0GKa3DvmUT2xEfH48ZM2ZgxIgRCA8PR0pKCmprazFr1iwAwPTp0+Hn54fk5GQAwMKFCzFq1Chs2bIF48aNQ1paGs6ePYs9e/YAUCfULlq0COvXr0dISAiCgoKwatUq+Pr6IjY2lrlvSUkJKisrUVJSAqVSifPnzwMAevfuDRcXF5u93/bIbdN0h+3ttEVQEwnqToyE9XC3qT/aYtxa5j3bC0fOl+HHS3dQcu8hAro6Wa1tymMKIUBjHcCz3u+6XUUwLi4O5eXlSExMhEQiwZAhQ5CRkcFMbJSUlMBO68sbFRWFAwcOICEhAStWrEBISAjS09MxcOBAxmbp0qWora3F3LlzUVVVhejoaGRkZEAgeJSWkZiYiM8//5x5PXToUADAsWPH8Nxzz9nkvd6V1bdsZAM0KTLaqS8aQSTkUZ4gvykS7O4qQH8fN1y5Y/7EjynwrSiC/X3dMKpPN5y4Vo6Pf7+BdbEDW76I8mSgUgJVfwHl14CKAvZz8Cgg7gur3apdRRAAFixYgAULFug9d/z4cZ1jEydOxMSJEw22x+FwsHbtWqxdu9agzb59+7Bv3z5zXW0VmmotbY1mxQiPqxsJypUqJm1He9P1ri6WT1ysP5pv9Ly+3exaw7xRwThxrRyHzpZiYUxIq2eeKW2MogG4VwiUFwAV1x493ysEFAYCh3uFVnWh3UWw89D6cTBL0IwJ6psdrpM/Wn+rven6q8N64PfrFTbxh2dlEYwM7orBPdxx4VY1Ps+6iff+3teq7VOsRL1MS+S0Irv7NwFiYMaQywe69ga69QG8+qqfu4UCXXpZ1TUqgm1G+yT1aiZj7PXkCdY1TYrYcdh5hLFD/fDSIB/0XvmTWff65Hfdys9jBgiR8eejHE1rjgkCjworvPXlOezP/gtvjuoFZz79s24XCAEe3G0SuWaRXY2R1T18N8CrD9CtL/vZMxCw4xq+zkrQv5YOjiba044ENYKoOSdw4OrM2NpbELHp6wpvmjgIeaX3IZWp02ysLYIA8PcBQgR5OaO4ohZpZ0oxu2njdoqNUKnU43U6kd01oL7K8HUu3loi1/dRhOcqNC23ykZQEWwz2ueXnHZGnc/IWjFix44EtccDtfHv4ojSSt1y9telNQjxdjXp/vZ2dogd4sfsD2LtMUFAXfRh7rPBWP7tJXz6+w1Mj+xpk/t0OhRyoLKoWVRXAFRcNzxeBw7g2ZMtcprIztGjLb03GSqCnQTtFBkmEmxkV5BpzldznsKyby6hZ1cnfJlTwhw/V3LfZBEEwIoyrTk7rM0rQ/2w5edrKKuux/fny/Dq8B42uU+HpKGmSeSazcRWFgPEQN1GLk89Xte8G9u1N+Bg+drw9oCKYCfBQU+ytHZ3WB89PJ3w3zciAIAlgsZ2sGsOh8Pu6diiOwyo38Pr0YHYmFGA//xWhFeG+sFOXzWIzgohQG2F/vE6me5GXQw8V/bEhCay8+gJcDuGfHSMd/EE0I5DHgAe1RAEHgmRZt0w34AIGkKh1BVBY/t+aGuRtWeHtZka0RMfHSvCNekDHL92F38L9W75oo6GSgVUl+qfia0zstrHubvuxES3voCrT/v/8doYKoKdBHayNPuPWrNkzlRq5QoQQljd3PkHzhm052iNhzrYKBIEAHdHB0yNCMB/fruB1OM3OrYIKuRA5Y1micQF6hy6RkMl2DiAR0AzsWuK8Bw929T9xwkqgp0E9uwwW4i0cwRNYWNGAY4XlOPQvEfrsf8o1J9XqNMdtvGExevRQdh7shinb1Yi96/7GN7zCf9yNzxQR3Xa3dfyAuB+MaBS6L/GzqFZfl2T6HXtbdXlZh0FKoKdBNaKkVZGggBwuriS+blRXxFCLbTL4NtqTFCDt5sArwz1w6Gzt5B6oggfTx9h0/tZjdoK3e5r+TVAZmS9Oc9FT35dX3V+XQcZr2sL6CfVSWAlSzcTIkMTIy3RoFCCb8/Fjl8NL2PigMOaSLHV7LA2c5/thcO5t5B5RYrCuzXo3d30mWybolKpRU27+6qJ7OoqDV/n3K3ZxETTs5tvhx+vawuoCLYR7f2nqm/ZnAZLRVBWp8B16X1sFxvfU0Q7UmyL/L3e3V3wQj9v/HxFij2/3cDG/xls83uyUDaqx+uaR3YVhUCjkaK0HgHs7qvm2alL2/neCaEi2Elw0FNAQYMl3WFAXRTitU9yjNpwOOzZZH272dmCN5/rhZ+vSPFd3m3Ev9AXQgu3+TSKvFadOMyIXVNkV3nDyHidvXrtq/Z4Xbe+QNcQOl7XTlAR7CSwKks322Cdb+bEiAZZXcuVcThoeczQFgwL8ER4YBecvlmJvSeLseLFfpY39rBS/3hddYnhaxycAa8Q3fG6LkEA18FyXyhWh4qgrTn3BVBxDS78nhjGqUEh8YUMtivcagjtccDm22pa2h2uNkEEAaBRT15hW/Dmc8E4va8SB3JKMH90b7g7GhEfQtRJw80TicsLgIdGKuo4dTUwXucH2NGle08CVARtzZUjQGEmugP4tqnUXTlxRxHxRaHKV/1M/FCk8kUZusJWo4fOvEdC1zxNxdLu8P2H8hZtOBwOFO2xuQqA0X27o6+3KwqkNfjvqb8wf3RvQKlQp5fojNddB+QPDDfm7q9/Jta5a9u9IYpNoCJoawbFAV2C8bDsCqpK/4QvpxLdONXoxqnGU3bsqiu1hI8bxIcRxULihyLii5tEiMZW/qqiensBP18DYL2JkWNXy02y07fCxObIH4Jz7zrW9bqCU/eyEfqbBKo/78Ou8gagMhDB2tkDXYJ1E4m7hgD8to/eKW0DFUFbM2giMGgibklr8PcPf4Mz6hDMuYPenNvoZVeG3pwy9OKUIZAjgTOnAWGcmwjDTUBLlxTEDiWkO4qaRLGQ+KKoKYqUwdkkN7pqbXPZPFfPUAGFlrhdpVthpjk2HxN8WKnbfa0oAKpKARCEAwi3h7qco6ZX6+CkHq9rvh62SzAdr+uEUBFsIzRl7GvhiEskGJdIMKClDfZQIIBzVy2OnDvobXcbvZoE0pVTh2COBMGQ4AXkstq9SzzY3eqmbrYEXaDpWqfEDWHtAhce1AWDerjj4i31VpVOFhYhLak0tDzrERyOFUSQEEBWpjsxUVEA1BqJRh27AN364qrSB4dvOqHGJRjJ8/4HXA9/Ol5HYaAi2EaQFipLK2CPG8QXN4iv+gBTwYigO6oYUVSLZBl625VByLmP7pwqdOdWIQpXWO09IAIUEV90CxoI35p83LsRiF6cSpRxhHB3dMB3bz+NA6dLcLq4En8L7W7Re9Lej9joezO16oxSoS63rlPp5DogrzF8nVsP/ZVOnL0AAP4NCny94VdUVzfiuds8vNiFCiDlEY+FCO7atQubNm2CRCLB4MGDsWPHDoSHhxu0P3z4MFatWoWbN28iJCQE//u//4sXX3yROU8IQVJSEj7++GNUVVXh6aefxu7duxESEsLYVFZW4p133sH/+3//D3Z2dnj11Vexbds2m225afmWwxzchSfuqjyRBfZuai54yESLve3KGJHsyZHChVOPwZwbwF83gL++R1cAYj6gBBfYEQyuVx9M69YH0/r1AapUgH0IIDB/L+UWvedwdMcEG+vUwta80kllEaA0MNnC4aq7q80Tib36tDhe58y3x4zIntj+ayFSTxRh7EChVfY+pnQM2l0EDx48iPj4eKSmpiIiIgIpKSkQiUQoKChA9+66EUpWVhamTJmC5ORkvPTSSzhw4ABiY2Nx7tw5ZuvNjRs3Yvv27fj888+ZvYdFIhGuXLnCbL05depU3LlzB5mZmWhsbMSsWbMwd+5cHDhwwCbv0xb7rj+AEy6Q3rhAerO61g5QIIAjRW9OGdZGOcBbXgJUFIBUXAdX/gC4d139KDjKbtDV59FYmVefpqiqj/q4BbjhAVCSg2ce/ITR9tfRm3MbSFkOVJXA4J4r9o5a+XVakV2XYMDe8l3wZkQFYs/vN3DxVjWyb9xDVC8vi9uidCw4hNji62k6ERERGDlyJHbu3AkAUKlU8Pf3xzvvvINly5bp2MfFxaG2thY//PADc+ypp57CkCFDkJqaCkIIfH198d577+H9998HAFRXV8Pb2xv79u3D5MmTkZ+fj/79++PMmTMYMUK9wD4jIwMvvvgibt26BV9f3xb9lslkcHd3R3V1NdzcWo6g/iyrxrjtf5j0mViT4+8/h0CvpskTZmztGvtRfg14IDHcCM8V5+u7qSdmVE0TM8QXfxFvKMBluuu9ObfRm1OGEM5t9La7jW6casNtOnrqL8HubrvxusQjl7E/+y8826cb9r9uuKdBefIx5/vZrpGgXC5Hbm4uli9fzhyzs7NDTEwMsrOz9V6TnZ2N+Ph41jGRSIT09HQAQHFxMSQSCWJiYpjz7u7uiIiIQHZ2NiZPnozs7Gx4eHgwAggAMTExsLOzQ05ODl555RWd+zY0NKCh4dEYmExm2ubkSUcuI6e4EvWNBsqU2xgXgdavmMMB3P3Uj16j2Yb11c26qNcflViX12CIXQ2G4AZr1rqRcFEPHlw5RmaJ3fxwrq47LtR1RyHxw7/mvNqUX+fV5ov/34gOxn9P/YXfrpVD9OFvtPbAE0qYnzs2TbTeevB2FcGKigoolUp4e7OLX3p7e+Pq1at6r5FIJHrtJRIJc15zzJhN8662vb09unTpwtg0Jzk5GWvWrDHxnT3idlUdrkqMDOrbGA9jqyS0EbgDPUaoH9o0Fe/c8+1PqC69wpq1duY0wAF1UBIO/iLeKCJ+KCS+KFT5MdHi5dUTcfPcLaw5dAEx/boDgSOt/yZNJKCrE2KH+uHbc7dRIG2/3wmldRhd+WMB7T4m+KSwfPlyVgQqk8ng7+/f4nWLX+iDmVHqLSBr6hvh38UJrgJ7dHPl40Z5LSpr5XAV2KO2QQkvVx6qHjaipl6B+kYlePZ2IATo6sLDQ7kSDnYcONjbQaEkuHCrCtekNaislSMyuCuu332AIC9n+HoIENDFCZduVePlwb4WbZ3Jwp4HdA/F63P64Oa9hwAIpLIG1HZ3xpcnzoAjf4DgPmHgOPCQU3gPfx8gRKS9HRRKFXp1U09Y/GNYDwzq4YGeXdu/QMC/XwnDpBH+7ZPATbEKHUoEvby8wOVyIZVKWcelUimEQqHea4RCoVF7zbNUKoWPjw/LZsiQIYzN3bt3WW0oFApUVlYavC+fzwefzzf9zTUxwNfd4LmBfobPtURkL+PLtYb3tG75JXuuHXp3V4uapj7f3JefZdkYK2evuba9EThw8VQwXepGeUS7JkzxeDwMHz4cYrGYOaZSqSAWixEZGan3msjISJY9AGRmZjL2QUFBEAqFLBuZTIacnBzGJjIyElVVVcjNfZR4/Ouvv0KlUiEiIsJq749CoTwBkHYmLS2N8Pl8sm/fPnLlyhUyd+5c4uHhQSQSCSGEkGnTppFly5Yx9idPniT29vZk8+bNJD8/nyQlJREHBwdy6dIlxmbDhg3Ew8ODHDlyhFy8eJFMmDCBBAUFkbq6OsZmzJgxZOjQoSQnJ4f88ccfJCQkhEyZMsVkv6urqwkAUl1dbYVPgUKhWBNzvp/tLoKEELJjxw4SEBBAeDweCQ8PJ6dOnWLOjRo1isyYMYNlf+jQIdKnTx/C4/HIgAEDyNGjR1nnVSoVWbVqFfH29iZ8Pp88//zzpKCggGVz7949MmXKFOLi4kLc3NzIrFmzSE1Njck+UxGkUB5fzPl+tnue4JOKuXmCFAql7TDn+0kXUVIolE4NFUEKhdKpoSJIoVA6NTRZ2kI0Q6mmLp+jUChth+Z7acqUBxVBC6mpUS+7MmXVCIVCaR9qamrg7m58UQKdHbYQlUqFsrIyuLq6dtradJqlg6WlpZ1+hpx+Fmoel8+BEIKamhr4+vrCroWqRDQStBA7Ozv06NGjvd14LHBzc+vUX3xt6Geh5nH4HFqKADXQiREKhdKpoSJIoVA6NVQEKRbD5/ORlJRkUXWdjgb9LNQ8iZ8DnRihUCidGhoJUiiUTg0VQQqF0qmhIkihUDo1VAQpFEqnhoogxSi7du1CYGAgBAIBIiIicPr0aaP2hw8fRmhoKAQCAcLCwvDjjz+2kae2xZzP4eOPP8YzzzwDT09PeHp6IiYmpsXP7UnC3L8JDWlpaeBwOIiNjbWtg+Ziu9qulCedtLQ0wuPxyN69e8mff/5J5syZQzw8PIhUKtVrf/LkScLlcsnGjRvJlStXSEJCgs7WB08i5n4Or732Gtm1axfJy8sj+fn5ZObMmcTd3Z3cunWrjT23PuZ+FhqKi4uJn58feeaZZ8iECRPaxlkToSJIMUh4eDiZP38+81qpVBJfX1+SnJys137SpElk3LhxrGMRERFk3rx5NvXT1pj7OTRHoVAQV1dX8vnnn9vKxTbDks9CoVCQqKgo8sknn5AZM2Y8diJIu8MUvcjlcuTm5iImJoY5Zmdnh5iYGGRnZ+u9Jjs7m2UPACKRyKD9k4Aln0NzHj58iMbGRnTpYt1tUNsaSz+LtWvXonv37pg9e3ZbuGk2tIACRS8VFRVQKpXw9mbvJezt7Y2rV6/qvUYikei1l0gkNvPT1ljyOTTngw8+gK+vr84/iCcNSz6LP/74A59++inOnz/fBh5aBhVBCsWGbNiwAWlpaTh+/DgEAkF7u9Om1NTUYNq0afj444/h5eXV3u4YhIogRS9eXl7gcrmQSqWs41KpFEKhUO81QqHQLPsnAUs+Bw2bN2/Ghg0b8Msvv2DQoEG2dLNNMPezKCoqws2bN/Hyyy8zx1QqFQDA3t4eBQUF6NWrl22dNgE6JkjRC4/Hw/DhwyEWi5ljKpUKYrEYkZGReq+JjIxk2QNAZmamQfsnAUs+BwDYuHEj1q1bh4yMDIwYMaItXLU55n4WoaGhuHTpEs6fP888xo8fj9GjR+P8+fOPT1X29p6ZoTy+pKWlET6fT/bt20euXLlC5s6dSzw8PIhEIiGEEDJt2jSybNkyxv7kyZPE3t6ebN68meTn55OkpKQOkyJjzuewYcMGwuPxyNdff03u3LnDPGpqatrrLVgNcz+L5jyOs8NUBClG2bFjBwkICCA8Ho+Eh4eTU6dOMedGjRpFZsyYwbI/dOgQ6dOnD+HxeGTAgAHk6NGjbeyxbTDnc+jZsycBoPNISkpqe8dtgLl/E9o8jiJIS2lRKJRODR0TpFAonRoqghQKpVNDRZBCoXRqqAhSKJRODRVBCoXSqaEiSKFQOjVUBCkUSqeGiiCFQunUUBGkdBhmzpwJDoej8xgzZkx7u0Z5jKFVZCgdijFjxuCzzz5jHePz+XptGxsb4eDgwDoml8vB4/HMvq+l11HaHxoJUjoUfD4fQqGQ9fD09AQAcDgc7N69G+PHj4ezszP+9a9/YfXq1RgyZAg++eQTBAUFMTX/SkpKMGHCBLi4uMDNzQ2TJk1ilZAydB3lyYOKIKVTsXr1arzyyiu4dOkSXn/9dQBAYWEhvvnmG3z77bc4f/48VCoVJkyYgMrKSpw4cQKZmZm4ceMG4uLiWG01v47yZEK7w5QOxQ8//AAXFxfWsRUrVmDFihUAgNdeew2zZs1inZfL5di/fz+6desGQF0D8dKlSyguLmZq3u3fvx8DBgzAmTNnMHLkSL3XUZ5MqAhSOhSjR4/G7t27Wce0NzjSV+C0Z8+eLCHLz8+Hv78/q+hn//794eHhgfz8fEYEm19HeTKhIkjpUDg7O6N3795Gz5tyzNR7UZ586JgghdKMfv36obS0FKWlpcyxK1euoKqqCv37929Hzyi2gEaClA5FQ0ODzhaf9vb2Zu12FhMTg7CwMEydOhUpKSlQKBR4++23MWrUqA6zXwjlETQSpHQoMjIy4OPjw3pER0eb1QaHw8GRI0fg6emJZ599FjExMQgODsbBgwdt5DWlPaHl9SkUSqeGRoIUCqVTQ0WQQqF0aqgIUiiUTg0VQQqF0qmhIkihUDo1VAQpFEqnhooghULp1FARpFAonRoqghQKpVNDRZBCoXRqqAhSKJRODRVBCoXSqfn/Pccbx7YiBd4AAAAASUVORK5CYII=", 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" ] @@ -2469,10 +2600,10 @@ "id": "54c0e9f3", "metadata": { "papermill": { - "duration": 0.019946, - "end_time": "2024-02-29T18:44:34.084886", + "duration": 0.022768, + "end_time": "2024-03-03T12:01:03.666008", "exception": false, - "start_time": "2024-02-29T18:44:34.064940", + "start_time": "2024-03-03T12:01:03.643240", "status": "completed" }, "tags": [] @@ -2518,8 +2649,8 @@ }, "papermill": { "default_parameters": {}, - "duration": 1067.648958, - "end_time": "2024-02-29T18:44:36.826930", + "duration": 3563.29435, + "end_time": "2024-03-03T12:01:06.412574", "environment_variables": {}, "exception": null, "input_path": "eval/insurance/tab_ddpm_concat/4/mlu-eval.ipynb", @@ -2532,13 +2663,14 @@ "folder": "eval", "gp": false, "gp_multiply": false, - "param_index": 2, + "log_wandb": false, + "param_index": 3, "path": "eval/insurance/tab_ddpm_concat/4", "path_prefix": "../../../../", "random_seed": 4, "single_model": "tab_ddpm_concat" }, - "start_time": "2024-02-29T18:26:49.177972", + "start_time": "2024-03-03T11:01:43.118224", "version": "2.5.0" }, "toc": {