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pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nimport scipy.stats\nfrom tqdm import tqdm\n\nfrom sklearn.model_selection import cross_val_predict\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import r2_score, mean_squared_error\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom fastprogress import master_bar, progress_bar\nfrom torch.optim import Adam\nfrom torch.optim.lr_scheduler import CosineAnnealingLR\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision.transforms import transforms\nimport torchvision.models as models\nfrom torchvision.models.efficientnet import _efficientnet_conf, _efficientnet\nfrom functools import partial\nimport random, os\nfrom scipy.optimize import minimize\n\n\ndef seed_everything(seed: int):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n \nseed_everything(239)","metadata":{"papermill":{"duration":7.393406,"end_time":"2024-09-25T09:37:54.54632","exception":false,"start_time":"2024-09-25T09:37:47.152914","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-10-07T20:35:33.754363Z","iopub.execute_input":"2024-10-07T20:35:33.754915Z","iopub.status.idle":"2024-10-07T20:35:40.531874Z","shell.execute_reply.started":"2024-10-07T20:35:33.754862Z","shell.execute_reply":"2024-10-07T20:35:40.531049Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class ParticipantVisibleError(Exception):\n pass\ndef ariel_score(\n solution,\n submission,\n naive_mean,\n naive_sigma,\n sigma_true\n ):\n '''\n This is a Gaussian Log Likelihood based metric. For a submission, which contains the predicted mean (x_hat) and variance (x_hat_std),\n we calculate the Gaussian Log-likelihood (GLL) value to the provided ground truth (x). We treat each pair of x_hat,\n x_hat_std as a 1D gaussian, meaning there will be 283 1D gaussian distributions, hence 283 values for each test spectrum,\n the GLL value for one spectrum is the sum of all of them.\n\n Inputs:\n - solution: Ground Truth spectra (from test set)\n - shape: (nsamples, n_wavelengths)\n - submission: Predicted spectra and errors (from participants)\n - shape: (nsamples, n_wavelengths*2)\n naive_mean: (float) mean from the train set.\n naive_sigma: (float) standard deviation from the train set.\n sigma_true: (float) essentially sets the scale of the outputs.\n '''\n\n if submission.min() < 0:\n raise ParticipantVisibleError('Negative values in the submission')\n\n n_wavelengths = 283\n\n y_pred = submission[:, :n_wavelengths]\n # Set a non-zero minimum sigma pred to prevent division by zero errors.\n sigma_pred = np.clip(submission[:, n_wavelengths:], a_min=10**-15, a_max=None)\n y_true = solution\n\n GLL_pred = np.sum(scipy.stats.norm.logpdf(y_true, loc=y_pred, scale=sigma_pred))\n GLL_true = np.sum(scipy.stats.norm.logpdf(y_true, loc=y_true, scale=sigma_true * np.ones_like(y_true)))\n GLL_mean = np.sum(scipy.stats.norm.logpdf(y_true, loc=naive_mean * np.ones_like(y_true), scale=naive_sigma * np.ones_like(y_true)))\n\n #print(GLL_pred, GLL_true, GLL_mean)\n submit_score = (GLL_pred - GLL_mean)/(GLL_true - GLL_mean)\n return submit_score #float(np.clip(submit_score, 0.0, 1.0))","metadata":{"papermill":{"duration":0.019163,"end_time":"2024-09-25T09:37:54.57272","exception":false,"start_time":"2024-09-25T09:37:54.553557","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-10-07T20:35:40.533356Z","iopub.execute_input":"2024-10-07T20:35:40.533808Z","iopub.status.idle":"2024-10-07T20:35:40.543249Z","shell.execute_reply.started":"2024-10-07T20:35:40.533773Z","shell.execute_reply":"2024-10-07T20:35:40.542409Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_adc_info = pd.read_csv('/kaggle/input/ariel-data-challenge-2024/train_adc_info.csv',\n index_col='planet_id')\ntrain_labels = pd.read_csv('/kaggle/input/ariel-data-challenge-2024/train_labels.csv',\n index_col='planet_id')\n\nfull_train = np.load('/kaggle/input/ariel-train-clean-full/train_clean_full.npy')","metadata":{"papermill":{"duration":52.621947,"end_time":"2024-09-25T09:38:47.201192","exception":false,"start_time":"2024-09-25T09:37:54.579245","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-10-07T20:35:40.544323Z","iopub.execute_input":"2024-10-07T20:35:40.544743Z","iopub.status.idle":"2024-10-07T20:36:59.382698Z","shell.execute_reply.started":"2024-10-07T20:35:40.544704Z","shell.execute_reply":"2024-10-07T20:36:59.381679Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12,7))\nplt.plot(train_labels.values.mean(axis=0))\nplt.grid()\nplt.xticks(np.arange(283)[::10]);","metadata":{"papermill":{"duration":0.425256,"end_time":"2024-09-25T09:38:47.634583","exception":false,"start_time":"2024-09-25T09:38:47.209327","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-10-07T20:36:59.385058Z","iopub.execute_input":"2024-10-07T20:36:59.385368Z","iopub.status.idle":"2024-10-07T20:36:59.879068Z","shell.execute_reply.started":"2024-10-07T20:36:59.385334Z","shell.execute_reply":"2024-10-07T20:36:59.878082Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def rmse(y, y_signal):\n n = y_signal.shape[0]\n se = (y - np.repeat(y_signal, 283).reshape((n,283))) ** 2.0\n return np.mean(se) ** 0.5\n\ndef prmse(y, y_signal):\n se = (y - y_signal) ** 2.0\n return np.mean(se) ** 0.5","metadata":{"papermill":{"duration":0.015686,"end_time":"2024-09-25T09:38:47.658121","exception":false,"start_time":"2024-09-25T09:38:47.642435","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-10-07T20:36:59.880111Z","iopub.execute_input":"2024-10-07T20:36:59.880396Z","iopub.status.idle":"2024-10-07T20:36:59.886115Z","shell.execute_reply.started":"2024-10-07T20:36:59.880363Z","shell.execute_reply":"2024-10-07T20:36:59.885202Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"x = np.array([0,1,2,3,4,5,6])\ny = np.exp(-(x-3)**2.0/2.7)\ny = y / np.sum(y)\nplt.plot(x,y)\ny","metadata":{"execution":{"iopub.status.busy":"2024-10-07T20:36:59.887076Z","iopub.execute_input":"2024-10-07T20:36:59.887367Z","iopub.status.idle":"2024-10-07T20:37:00.148607Z","shell.execute_reply.started":"2024-10-07T20:36:59.887333Z","shell.execute_reply":"2024-10-07T20:37:00.147443Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from scipy.signal import savgol_filter\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom scipy.optimize import minimize_scalar\n\ndef smooth_data(data, window_size, deg=3):\n return savgol_filter(data, window_size, deg) # window size 51, polynomial order 3\n\ndef phase_detector(signal, w=5, margin=1, scale=30):\n phase1, phase2 = None, None\n midpoint = signal.shape[0] // 2\n best_drop = 0\n for i in range(40*scale,82*scale): \n t1 = signal[i-w:i+w].max() - signal[i-w:i+w].min()\n if t1 > best_drop:\n i_max = i - w + np.argmax(signal[i-w:i+w])\n i_min = i - w + np.argmax(-signal[i-w:i+w])\n if i_max > i_min:\n continue\n\n best_drop = t1\n \n phase1 = (i_max - margin, i_min + margin)\n\n best_drop = 0\n for i in range(105*scale,147*scale): \n t1 = signal[i-w:i+w].max() - signal[i-w:i+w].min()\n if t1 > best_drop:\n i_max = i - w + np.argmax(signal[i-w:i+w])\n i_min = i - w + np.argmax(-signal[i-w:i+w])\n if i_min > i_max:\n continue\n\n best_drop = t1\n \n phase2 = (i_min - margin, i_max + margin)\n\n\n return phase1[0], phase1[1], phase2[0], phase2[1]\n\n\nclass SignalPoly():\n def __init__(self, deg):\n self.deg = deg\n self.poly_features = PolynomialFeatures(degree=deg, include_bias=True)\n self.model = LinearRegression()\n \n def fit(self, x, y):\n X_poly = self.poly_features.fit_transform(np.array(x).reshape(-1, 1))\n self.model.fit(X_poly, y)\n\n def predict(self, x):\n X_poly = self.poly_features.transform(np.array(x).reshape(-1, 1))\n return self.model.predict(X_poly)\n \ndef f_coef(px, y, s):\n q = np.abs(px - y * s).mean()\n return q \n\ndef try_s_alpenglow2(px, y, y2, y3, s):\n q = np.abs(px - y * (y2 +y3 * s)).mean()\n return q\n\ndef calibrate_train_alpenglow2(signal, p0, p1, p2, p3, max_deg=6, min_deg=1):\n best_deg, best_score, best_s, best_poly = 1, 1e12, 0, None\n signal = np.asarray(signal) # ensure signal is a numpy array for faster slicing\n out = np.concatenate((np.arange(p0), np.arange(p3, signal.shape[0]))) # faster range handling\n x, y = out, signal[out]\n x2 = np.arange(p1, p2)\n y2 = signal[p1:p2]\n\n x = np.concatenate((x, x2)) # concatenate arrays directly\n y = np.concatenate((y, y2)) # combine arrays without list conversion\n y2 = np.ones_like(y)\n y3 = np.zeros_like(y)\n y3[out.shape[0]:] = 1.0\n for deg in range(min_deg, max_deg): \n p = SignalPoly(deg)\n p.fit(x[:len(out)], y[:len(out)]) \n px = p.predict(x)\n \n f = partial(try_s_alpenglow2, px, y, y2, y3)\n #r = minimize(f, [0.0025], method='Nelder-Mead', options={'xatol':1e-7})\n r = minimize_scalar(f)\n s = r.x\n q = r.fun\n\n if q < best_score:\n best_score = q\n best_poly = p\n best_s = s\n best_deg = deg\n\n return best_s, best_deg, best_poly, best_score\n\ndef smooth_data_lambda(train_signal, win=3):\n q = train_signal[:,40-win:322+win]\n q = q / train_signal[:,40-win:322+win].mean(axis=1, keepdims=True)\n q_coef = q.mean(axis=0)\n gauss_coefs = np.array([0.01227215, 0.07819333, 0.23753036, 0.34400831, 0.23753036, 0.07819333, 0.01227215])\n \n t_smooth = train_signal[:,40-win:322+win].copy()\n for l in range(win,t_smooth.shape[1]-win):\n coefs = q_coef[l-win:l+win+1] / q_coef[l]\n \n t_smooth[:,l] = np.dot(train_signal[:,40-win+l-win:40-win+l+win+1] * coefs, gauss_coefs)\n if win > 0:\n t_smooth = t_smooth[:,win:-win][:,::-1]\n else:\n t_smooth = t_smooth[:,::-1]\n return np.concatenate([train_signal[:,0:1], t_smooth], axis=1)","metadata":{"papermill":{"duration":0.101902,"end_time":"2024-09-25T09:38:47.767364","exception":false,"start_time":"2024-09-25T09:38:47.665462","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-10-07T20:37:00.149993Z","iopub.execute_input":"2024-10-07T20:37:00.150316Z","iopub.status.idle":"2024-10-07T20:37:00.221612Z","shell.execute_reply.started":"2024-10-07T20:37:00.15028Z","shell.execute_reply":"2024-10-07T20:37:00.220838Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"feats = np.zeros((len(full_train),283,9))\nmax_degs = np.zeros(len(full_train)).astype(int)\n\nfor i in tqdm(range(len(full_train))): \n train = smooth_data_lambda(full_train[i], 3)\n \n ranges = [(0,283)]\n for r1,r2 in ranges:\n signal = train[:,r1:r2].mean(axis=1)\n signal = smooth_data(signal, 330, 3) \n p0,p1,p2,p3 = phase_detector(signal, 90, margin=30)\n s,max_deg,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3)\n max_degs[i] = max_deg+1\n feats[i,r1:r2,0] = s\n\n x_out = np.concatenate((np.arange(p0), np.arange(p3, signal.shape[0])))\n x_in = np.arange(p1, p2)\n \n ranges = [(0,133),(133,283)]\n for r1,r2 in ranges:\n signal = train[:,r1:r2].mean(axis=1)\n signal = smooth_data(signal, 330, 3) \n s,_,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, max_degs[i])\n feats[i,r1:r2,1] = s\n\n ranges = [(0,62),(62,133),(133,200),(200,283)]\n for r1,r2 in ranges:\n signal = train[:,r1:r2].mean(axis=1)\n signal = smooth_data(signal, 330, 3) \n s,_,p,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, max_degs[i])\n feats[i,r1:r2,2] = s\n\n px = p.predict(np.arange(full_train.shape[1]))\n for l in range(r1,r2):\n signal = smooth_data(train[:,l], 330, 3)\n \n f = partial(f_coef, px[x_out], signal[x_out])\n r = minimize_scalar(f)\n a = r.x\n score_a = r.fun\n \n f = partial(f_coef, px[x_in], signal[x_in])\n r = minimize_scalar(f)\n b = r.x\n score_b = r.fun\n \n feats[i,l,7] = (1.0 - a / b)\n\n ranges = [(j*50,50+j*50) for j in range(283//50+1)]\n for r1,r2 in ranges:\n signal = train[:,r1:r2].mean(axis=1)\n signal = smooth_data(signal, 330, 3) \n s,_,p,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, 4)\n feats[i,r1:r2,3] = s\n \n px = p.predict(np.arange(full_train.shape[1]))\n if r2 > 283:\n r2 = 283\n for l in range(r1,r2):\n signal = smooth_data(train[:,l], 330, 3)\n \n f = partial(f_coef, px[x_out], signal[x_out])\n r = minimize_scalar(f)\n a = r.x\n score_a = r.fun\n \n f = partial(f_coef, px[x_in], signal[x_in])\n r = minimize_scalar(f)\n b = r.x\n score_b = r.fun\n \n feats[i,l,8] = (1.0 - a / b)\n\n ranges = [(0,1),(1,16),(16,31),(31,46),(46,62),(62,77),(77,97),(97,112),(112,133),(133,149),(149,168),\n (168,185),(185,200),(200,215),(215,240),(240,255),(255,283)]\n for r1,r2 in ranges:\n signal = train[:,r1:r2].mean(axis=1)\n signal = smooth_data(signal, 330, 3) \n s,_,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, 3)\n feats[i,r1:r2,4] = s\n \n ranges = [(j*8,8+j*8) for j in range(283//8+1)]\n for r1,r2 in ranges:\n signal = train[:,r1:r2].mean(axis=1)\n signal = smooth_data(signal, 330, 3) \n s,_,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, 3)\n feats[i,r1:r2,5] = s\n\n q_train = smooth_data(train.transpose(1,0), 330, 3)\n q_train = q_train / q_train.mean(axis=1, keepdims=True)\n \n signal = q_train.mean(axis=0)\n s,_,p,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, max_degs[i])\n \n px = p.predict(np.arange(full_train.shape[1]))\n\n for l in range(283):\n signal = q_train[l]\n\n f = partial(f_coef, px[x_out], signal[x_out])\n r = minimize_scalar(f)\n a = r.x\n score_a = r.fun\n\n f = partial(f_coef, px[x_in], signal[x_in])\n r = minimize_scalar(f)\n b = r.x\n score_b = r.fun\n\n feats[i,l,6] = (1.0 - a / b)","metadata":{"execution":{"iopub.status.busy":"2024-10-07T20:37:00.222809Z","iopub.execute_input":"2024-10-07T20:37:00.223261Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for i in range(9):\n print(i, prmse(train_labels.values, feats[...,i]))","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"cnn_train = feats.transpose(0,2,1).copy()\nlabels = train_labels.values\n\ncnn_train = (cnn_train - 0.0025)*1e3\nlabels = (labels - 0.0025)*1e3\n\ncnn_train.mean(), labels.mean()","metadata":{"papermill":{"duration":0.057602,"end_time":"2024-09-25T10:04:40.315122","exception":false,"start_time":"2024-09-25T10:04:40.25752","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"cnn_train.shape","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass CustomCNN(nn.Module):\n def __init__(self):\n super(CustomCNN, self).__init__()\n \n self.spectra = nn.Sequential(\n nn.Conv1d(9, 256, 3, padding='same', bias=False),\n nn.ReLU(),\n nn.Conv1d(256, 256, 5, padding='same', bias=False),\n nn.ReLU(),\n nn.Conv1d(256, 256, 7, padding='same', bias=False),\n nn.ReLU(),\n nn.Conv1d(256, 256, 9, padding='same', bias=False),\n nn.ReLU(),\n nn.Conv1d(256, 256, 11, padding='same', bias=False),\n nn.ReLU(),\n nn.Conv1d(256, 256, 13, padding='same', bias=False),\n nn.ReLU(),\n nn.Conv1d(256, 1, 1, padding='same', bias=False),\n )\n \n self.sigma = nn.Sequential(\n nn.Conv1d(1, 32, kernel_size=3, bias=False),\n nn.ReLU(),\n nn.MaxPool1d(kernel_size=2, stride=2),\n nn.Conv1d(32, 64, kernel_size=3, bias=False),\n nn.ReLU(),\n nn.MaxPool1d(kernel_size=2, stride=2),\n nn.Conv1d(64, 128, kernel_size=3, bias=False),\n nn.ReLU(),\n nn.MaxPool1d(kernel_size=2, stride=2),\n nn.Flatten()\n )\n \n # Вычисляем размер выхода после сверток и пулинга\n self._to_linear = None\n self._get_conv_output((1, 283))\n \n # Полносвязные слои\n self.sigma_out = nn.Linear(self._to_linear, 1)\n \n def _get_conv_output(self, shape):\n batch_size = 1\n input = torch.autograd.Variable(torch.rand(batch_size, *shape))\n output = self.sigma(input)\n self._to_linear = int(torch.numel(output) / batch_size)\n \n def forward(self, x_in):\n x = self.spectra(x_in[:,:9])\n spectrum = torch.flatten(x, start_dim=1)\n #y = torch.cat([x_in[:,7:], x-x_in[:,:1,:]], dim=1)\n y = x-x_in[:,:1,:]\n sigma = self.sigma_out(self.sigma(y))\n \n return spectrum, sigma","metadata":{"papermill":{"duration":0.034724,"end_time":"2024-09-25T10:04:40.368377","exception":false,"start_time":"2024-09-25T10:04:40.333653","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.model_selection import KFold\n\nkf = KFold(n_splits=5)\nX = list(range(len(full_train)))\n\nmean_pred = np.zeros_like(train_labels.values)\nmean_sigma = np.zeros_like(train_labels.values)\n\nl2loss = nn.MSELoss()\n\ndef create_mean_model():\n model = CustomCNN()\n return model\n\nfor ifold, (train_index, test_index) in enumerate(kf.split(X)):\n train_x = torch.from_numpy(cnn_train[train_index]).float()\n train_y = torch.from_numpy(labels[train_index]).float() \n train_dataset = torch.utils.data.TensorDataset(train_x, train_y)\n training_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)\n\n val_x = torch.from_numpy(cnn_train[test_index]).float()\n val_y = torch.from_numpy(labels[test_index]).float() \n val_dataset = torch.utils.data.TensorDataset(val_x, val_y)\n validation_loader = torch.utils.data.DataLoader(val_dataset, batch_size=16, shuffle=False)\n \n model = create_mean_model().cuda()\n\n best_metric = 0\n total_train_losses = []\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=0.0001)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 200, eta_min=0, last_epoch=-1)\n for epoch in range(200):\n ep_losses = [] \n model.train()\n for i, data in enumerate(training_loader):\n # Every data instance is an input + label pair\n inputs, tlabels = data\n\n #z = torch.randn_like(inputs) * 1e-5\n #u = (torch.rand_like(inputs) > 0.8).float()\n #inputs += z*u \n\n # Zero your gradients for every batch!\n optimizer.zero_grad()\n\n # Make predictions for this batch\n outputs, sigma = model(inputs.cuda())\n \n # Compute the loss and its gradients\n loss1 = l2loss(outputs, tlabels.cuda())\n \n mean_diff = torch.mean((outputs - tlabels.cuda()) ** 2.0, dim=1, keepdims=True) ** 0.5\n loss2 = F.smooth_l1_loss(sigma, mean_diff)\n #print(sigma.shape, mean_diff.shape)\n loss = loss1 + loss2 * 1e-3\n \n loss.backward()\n\n # Adjust learning weights\n optimizer.step()\n\n # Gather data and report\n #print(epoch, i, loss.item())\n ep_losses.append(loss.item())\n\n avg_loss = np.mean(ep_losses)\n total_train_losses.append(avg_loss)\n scheduler.step()\n\n model.eval()\n running_vloss = 0\n preds = np.zeros((len(val_dataset), 283))\n ss = np.zeros((len(val_dataset), 283))\n v_offset = 0\n with torch.no_grad(): \n for i, vdata in enumerate(validation_loader):\n vinputs, vlabels = vdata\n voutputs, vsigma = model(vinputs.cuda())\n preds[v_offset:v_offset+len(vinputs)] = voutputs.detach().cpu().numpy() * 1e-3 + 0.0025\n ss[v_offset:v_offset+len(vinputs)] = vsigma.detach().cpu().numpy().clip(0) * 1e-3\n vloss = l2loss(voutputs, vlabels.cuda())\n running_vloss += vloss\n v_offset += len(vinputs)\n\n avg_vloss = running_vloss / (i + 1)\n\n metric1 = prmse(train_labels.values[test_index], preds)\n metric = ariel_score(train_labels.values[test_index],\n np.concatenate([preds.clip(0), ss.clip(0)], axis=1), \n train_labels.values[train_index].mean(),\n train_labels.values[train_index].std(),\n sigma_true=1e-5)\n\n print('fold {} epoch {} train {} valid {} rmse {} ariel {}'.format(ifold, epoch, \n round(avg_loss,6), \n round(avg_vloss.item(),6), \n round(metric1,6),\n round(metric,6)\n ))\n\n \n mean_pred[test_index] = preds\n mean_sigma[test_index] = ss\n torch.save(model.state_dict(), 'wvn_model_wide_{}'.format(ifold))","metadata":{"papermill":{"duration":603.271491,"end_time":"2024-09-25T10:14:43.648561","exception":false,"start_time":"2024-09-25T10:04:40.37707","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"prmse(train_labels.values, mean_pred)","metadata":{"papermill":{"duration":0.018932,"end_time":"2024-09-25T10:14:43.676654","exception":false,"start_time":"2024-09-25T10:14:43.657722","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sigma = mean_sigma.copy()\nsigma[mean_pred.max(axis=1)-mean_pred.min(axis=1) > 0.00018] += 2e-5\n\npreds = mean_pred.copy()\npreds[(mean_pred[:,:250].max(axis=1) - mean_pred[:,:250].min(axis=1)) < (mean_pred[:,250:].max(axis=1) - mean_pred[:,250:].min(axis=1)), 250:] = mean_pred[(mean_pred[:,:250].max(axis=1) - mean_pred[:,:250].min(axis=1)) < (mean_pred[:,250:].max(axis=1) - mean_pred[:,250:].min(axis=1)), 250:251]\npreds[(mean_pred[:,:200].max(axis=1) - mean_pred[:,:200].min(axis=1)) < (mean_pred[:,200:].max(axis=1) - mean_pred[:,200:].min(axis=1)), 200:] = mean_pred[(mean_pred[:,:200].max(axis=1) - mean_pred[:,:200].min(axis=1)) < (mean_pred[:,200:].max(axis=1) - mean_pred[:,200:].min(axis=1)), 200:201]\npreds[mean_pred.max(axis=1)-mean_pred.min(axis=1) < 0.0002] = smooth_data(preds[mean_pred.max(axis=1)-mean_pred.min(axis=1) < 0.0002], 100, 3)\n\nariel_score(train_labels.values,\n np.concatenate([preds.clip(0), sigma], axis=1), \n train_labels.values.mean(),\n train_labels.values.std(),\n sigma_true=1e-5)","metadata":{"papermill":{"duration":0.084638,"end_time":"2024-09-25T10:14:43.770533","exception":false,"start_time":"2024-09-25T10:14:43.685895","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"n = 26\nplt.plot(preds[n])\nplt.plot(train_labels.values[n])\nnp.mean((train_labels.values[n] - preds[n])**2.0) ** 0.5, sigma[n,0]","metadata":{"papermill":{"duration":0.297351,"end_time":"2024-09-25T10:14:44.087724","exception":false,"start_time":"2024-09-25T10:14:43.790373","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.009874,"end_time":"2024-09-25T10:14:44.128304","exception":false,"start_time":"2024-09-25T10:14:44.11843","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.009895,"end_time":"2024-09-25T10:14:44.148329","exception":false,"start_time":"2024-09-25T10:14:44.138434","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.010809,"end_time":"2024-09-25T10:14:44.16924","exception":false,"start_time":"2024-09-25T10:14:44.158431","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.009898,"end_time":"2024-09-25T10:14:44.189287","exception":false,"start_time":"2024-09-25T10:14:44.179389","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.009814,"end_time":"2024-09-25T10:14:44.20912","exception":false,"start_time":"2024-09-25T10:14:44.199306","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.009965,"end_time":"2024-09-25T10:14:44.229301","exception":false,"start_time":"2024-09-25T10:14:44.219336","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.009993,"end_time":"2024-09-25T10:14:44.249397","exception":false,"start_time":"2024-09-25T10:14:44.239404","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.009872,"end_time":"2024-09-25T10:14:44.269393","exception":false,"start_time":"2024-09-25T10:14:44.259521","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.009883,"end_time":"2024-09-25T10:14:44.289356","exception":false,"start_time":"2024-09-25T10:14:44.279473","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.010159,"end_time":"2024-09-25T10:14:44.309652","exception":false,"start_time":"2024-09-25T10:14:44.299493","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}