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pandas as pd\n\nimport matplotlib.pyplot as plt\n\nimport numpy as np\n\nimport seaborn as sns\n\nimport scipy.stats\n\nfrom tqdm import tqdm\n\n\n\nfrom sklearn.model_selection import cross_val_predict\n\nfrom sklearn.linear_model import Ridge\n\nfrom sklearn.metrics import r2_score, mean_squared_error\n\nimport torch\n\nimport torch.nn as nn\n\nimport torch.nn.functional as F\n\nfrom fastprogress import master_bar, progress_bar\n\nfrom torch.optim import Adam\n\nfrom torch.optim.lr_scheduler import CosineAnnealingLR\n\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom torchvision.transforms import transforms\n\nimport torchvision.models as models\n\nfrom torchvision.models.efficientnet import _efficientnet_conf, _efficientnet\n\nfrom functools import partial\n\nimport random, os\n\nfrom scipy.optimize import minimize\n\n\n\n\n\ndef seed_everything(seed: int):\n\n random.seed(seed)\n\n os.environ['PYTHONHASHSEED'] = str(seed)\n\n np.random.seed(seed)\n\n torch.manual_seed(seed)\n\n torch.cuda.manual_seed(seed)\n\n torch.backends.cudnn.deterministic = True\n\n torch.backends.cudnn.benchmark = False\n\n \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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T16:23:33.584436Z","iopub.execute_input":"2024-11-07T16:23:33.585203Z","iopub.status.idle":"2024-11-07T16:23:39.389608Z","shell.execute_reply.started":"2024-11-07T16:23:33.585151Z","shell.execute_reply":"2024-11-07T16:23:39.38879Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class ParticipantVisibleError(Exception):\n\n pass\n\ndef ariel_score(\n\n solution,\n\n submission,\n\n naive_mean,\n\n naive_sigma,\n\n sigma_true\n\n ):\n\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\n we calculate the Gaussian Log-likelihood (GLL) value to the provided ground truth (x). We treat each pair of x_hat,\n\n x_hat_std as a 1D gaussian, meaning there will be 283 1D gaussian distributions, hence 283 values for each test spectrum,\n\n the GLL value for one spectrum is the sum of all of them.\n\n\n\n Inputs:\n\n - solution: Ground Truth spectra (from test set)\n\n - shape: (nsamples, n_wavelengths)\n\n - submission: Predicted spectra and errors (from participants)\n\n - shape: (nsamples, n_wavelengths*2)\n\n naive_mean: (float) mean from the train set.\n\n naive_sigma: (float) standard deviation from the train set.\n\n sigma_true: (float) essentially sets the scale of the outputs.\n\n '''\n\n\n\n if submission.min() < 0:\n\n raise ParticipantVisibleError('Negative values in the submission')\n\n\n\n n_wavelengths = 283\n\n\n\n y_pred = submission[:, :n_wavelengths]\n\n # Set a non-zero minimum sigma pred to prevent division by zero errors.\n\n sigma_pred = np.clip(submission[:, n_wavelengths:], a_min=10**-15, a_max=None)\n\n y_true = solution\n\n\n\n GLL_pred = np.sum(scipy.stats.norm.logpdf(y_true, loc=y_pred, scale=sigma_pred))\n\n GLL_true = np.sum(scipy.stats.norm.logpdf(y_true, loc=y_true, scale=sigma_true * np.ones_like(y_true)))\n\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\n\n #print(GLL_pred, GLL_true, GLL_mean)\n\n submit_score = (GLL_pred - GLL_mean)/(GLL_true - GLL_mean)\n\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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T16:23:39.391085Z","iopub.execute_input":"2024-11-07T16:23:39.391551Z","iopub.status.idle":"2024-11-07T16:23:39.401169Z","shell.execute_reply.started":"2024-11-07T16:23:39.391517Z","shell.execute_reply":"2024-11-07T16:23:39.400203Z"}},"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')\n\ntrain_labels = pd.read_csv('/kaggle/input/ariel-data-challenge-2024/train_labels.csv',\n index_col='planet_id')\n\n\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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T16:23:39.40247Z","iopub.execute_input":"2024-11-07T16:23:39.403099Z","iopub.status.idle":"2024-11-07T16:24:25.578112Z","shell.execute_reply.started":"2024-11-07T16:23:39.403056Z","shell.execute_reply":"2024-11-07T16:24:25.577306Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(12,7))\n\nplt.plot(train_labels.values.mean(axis=0))\n\nplt.grid()\n\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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T16:24:25.58009Z","iopub.execute_input":"2024-11-07T16:24:25.580423Z","iopub.status.idle":"2024-11-07T16:24:26.056518Z","shell.execute_reply.started":"2024-11-07T16:24:25.580391Z","shell.execute_reply":"2024-11-07T16:24:26.055619Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def rmse(y, y_signal):\n\n n = y_signal.shape[0]\n\n se = (y - np.repeat(y_signal, 283).reshape((n,283))) ** 2.0\n\n return np.mean(se) ** 0.5\n\n\n\ndef prmse(y, y_signal):\n\n se = (y - y_signal) ** 2.0\n\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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T16:24:26.057723Z","iopub.execute_input":"2024-11-07T16:24:26.05809Z","iopub.status.idle":"2024-11-07T16:24:26.063831Z","shell.execute_reply.started":"2024-11-07T16:24:26.057992Z","shell.execute_reply":"2024-11-07T16:24:26.062902Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"x = np.array([0,1,2,3,4,5,6])\n\ny = np.exp(-(x-3)**2.0/2.7)\n\ny = y / np.sum(y)\n\nplt.plot(x,y)\n\ny","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T16:24:26.065003Z","iopub.execute_input":"2024-11-07T16:24:26.065341Z","iopub.status.idle":"2024-11-07T16:24:26.323019Z","shell.execute_reply.started":"2024-11-07T16:24:26.065283Z","shell.execute_reply":"2024-11-07T16:24:26.322185Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from scipy.signal import savgol_filter\n\nfrom sklearn.preprocessing import PolynomialFeatures\n\nfrom sklearn.linear_model import LinearRegression\n\nfrom scipy.optimize import minimize_scalar\n\nfrom scipy.stats import norm\n\n\n\n\n\ndef smooth_data(data, window_size, deg=3):\n\n return savgol_filter(data, window_size, deg) # window size 51, polynomial order 3\n\n\n\ndef phase_detector(signal, w=5, margin=1, scale=30):\n\n phase1, phase2 = None, None\n\n midpoint = signal.shape[0] // 2\n\n best_drop = 0\n\n for i in range(40*scale,82*scale): \n\n t1 = signal[i-w:i+w].max() - signal[i-w:i+w].min()\n\n if t1 > best_drop:\n\n i_max = i - w + np.argmax(signal[i-w:i+w])\n\n i_min = i - w + np.argmax(-signal[i-w:i+w])\n\n if i_max > i_min:\n\n continue\n\n\n\n best_drop = t1\n\n \n\n phase1 = (i_max - margin, i_min + margin)\n\n\n\n best_drop = 0\n\n for i in range(105*scale,147*scale): \n\n t1 = signal[i-w:i+w].max() - signal[i-w:i+w].min()\n\n if t1 > best_drop:\n\n i_max = i - w + np.argmax(signal[i-w:i+w])\n\n i_min = i - w + np.argmax(-signal[i-w:i+w])\n\n if i_min > i_max:\n\n continue\n\n\n\n best_drop = t1\n\n \n\n phase2 = (i_min - margin, i_max + margin)\n\n\n\n\n\n return phase1[0], phase1[1], phase2[0], phase2[1]\n\n\n\n\n\nclass SignalPoly():\n\n def __init__(self, deg):\n\n self.deg = deg\n\n self.poly_features = PolynomialFeatures(degree=deg, include_bias=True)\n\n self.model = LinearRegression()\n\n \n\n def fit(self, x, y):\n\n X_poly = self.poly_features.fit_transform(np.array(x).reshape(-1, 1))\n\n self.model.fit(X_poly, y)\n\n\n\n def predict(self, x):\n\n X_poly = self.poly_features.transform(np.array(x).reshape(-1, 1))\n\n return self.model.predict(X_poly)\n\n \n\ndef f_coef(px, y, s):\n\n q = -np.sum(norm.logpdf(y * s, px, 1.0)) \n\n return q \n\n\n\ndef try_s_alpenglow2(px, y, s):\n\n q = -np.sum(norm.logpdf(y * (1 + s), px, 1.0)) \n\n return q\n\n\n\ndef calibrate_train_alpenglow2(signal, p0, p1, p2, p3, max_deg=6, min_deg=1):\n\n best_deg, best_score, best_s, best_poly = 1, 1e12, 0, None\n\n signal = np.asarray(signal) # ensure signal is a numpy array for faster slicing\n\n out = np.concatenate((np.arange(p0), np.arange(p3, signal.shape[0]))) # faster range handling\n\n x, y = out, signal[out]\n\n x2 = np.arange(p1, p2)\n\n y2 = signal[p1:p2]\n\n\n\n for deg in range(min_deg, max_deg): \n\n p = SignalPoly(deg)\n\n p.fit(x, y) \n\n px = p.predict(x)\n\n q0 = -np.sum(norm.logpdf(y, px, 1.0)) \n\n\n\n px2 = p.predict(x2)\n\n f = partial(try_s_alpenglow2, px2, y2)\n\n r = minimize_scalar(f, bounds=(1e-5, 1))\n\n s = r.x\n\n q = q0 + r.fun\n\n\n\n if q < best_score:\n\n best_score = q\n\n best_poly = p\n\n best_s = (1.0 - 1.0 / (1.0 + s))\n\n best_deg = deg\n\n\n\n return best_s, best_deg, best_poly, best_score\n\n\n\ndef smooth_data_lambda(train_signal, win=3):\n\n q = train_signal[:,40-win:322+win]\n\n q = q / train_signal[:,40-win:322+win].mean(axis=1, keepdims=True)\n\n q_coef = q.mean(axis=0)\n\n gauss_coefs = np.array([0.01227215, 0.07819333, 0.23753036, 0.34400831, 0.23753036, 0.07819333, 0.01227215])\n\n \n\n t_smooth = train_signal[:,40-win:322+win].copy()\n\n for l in range(win,t_smooth.shape[1]-win):\n\n coefs = q_coef[l-win:l+win+1] / q_coef[l]\n\n \n\n t_smooth[:,l] = np.dot(train_signal[:,40-win+l-win:40-win+l+win+1] * coefs, gauss_coefs)\n\n if win > 0:\n\n t_smooth = t_smooth[:,win:-win][:,::-1]\n\n else:\n\n t_smooth = t_smooth[:,::-1]\n\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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T16:24:26.32447Z","iopub.execute_input":"2024-11-07T16:24:26.32474Z","iopub.status.idle":"2024-11-07T16:24:26.406769Z","shell.execute_reply.started":"2024-11-07T16:24:26.324711Z","shell.execute_reply":"2024-11-07T16:24:26.406019Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"feats = np.zeros((len(full_train),283,11))\n\nmax_degs = np.zeros(len(full_train)).astype(int)\n\n\n\nfor i in tqdm(range(len(full_train))): \n\n train = smooth_data_lambda(full_train[i], 3)\n\n \n\n ranges = [(0,283)]\n\n for r1,r2 in ranges:\n\n signal = train[:,r1:r2].mean(axis=1)\n\n signal = smooth_data(signal, 330, 3) \n\n p0,p1,p2,p3 = phase_detector(signal, 90, margin=30)\n\n s,max_deg,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3)\n\n max_degs[i] = max_deg+1\n\n feats[i,r1:r2,0] = s\n\n\n\n x_out = np.concatenate((np.arange(p0), np.arange(p3, signal.shape[0])))\n\n x_in = np.arange(p1, p2)\n\n \n\n ranges = [(0,133),(133,283)]\n\n for r1,r2 in ranges:\n\n signal = train[:,r1:r2].mean(axis=1)\n\n signal = smooth_data(signal, 330, 3) \n\n s,_,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, max_degs[i])\n\n feats[i,r1:r2,1] = s\n\n\n\n\n\n ranges = [(0,62),(62,133),(133,200),(200,283)]\n\n for r1,r2 in ranges:\n\n signal = train[:,r1:r2].mean(axis=1)\n\n signal = smooth_data(signal, 330, 3) \n\n s,_,p,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, max_degs[i])\n\n feats[i,r1:r2,2] = s\n\n\n\n px = p.predict(np.arange(full_train.shape[1]))\n\n for l in range(r1,r2):\n\n signal = smooth_data(train[:,l], 330, 3)\n\n \n\n f = partial(f_coef, px[x_out], signal[x_out])\n\n r = minimize_scalar(f)\n\n a = r.x\n\n score_a = r.fun\n\n \n\n f = partial(f_coef, px[x_in], signal[x_in])\n\n r = minimize_scalar(f)\n\n b = r.x\n\n score_b = r.fun\n\n \n\n feats[i,l,7] = (1.0 - a / b)\n\n\n\n\n\n ranges = [(j*50,50+j*50) for j in range(283//50+1)]\n\n for r1,r2 in ranges:\n\n signal = train[:,r1:r2].mean(axis=1)\n\n signal = smooth_data(signal, 330, 3) \n\n s,_,p,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, 4)\n\n feats[i,r1:r2,3] = s\n\n \n\n px = p.predict(np.arange(full_train.shape[1]))\n\n if r2 > 283:\n\n r2 = 283\n\n for l in range(r1,r2):\n\n signal = smooth_data(train[:,l], 330, 3)\n\n \n\n f = partial(f_coef, px[x_out], signal[x_out])\n\n r = minimize_scalar(f)\n\n a = r.x\n\n score_a = r.fun\n\n \n\n f = partial(f_coef, px[x_in], signal[x_in])\n\n r = minimize_scalar(f)\n\n b = r.x\n\n score_b = r.fun\n\n \n\n feats[i,l,8] = (1.0 - a / b)\n\n\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\n (168,185),(185,200),(200,215),(215,240),(240,255),(255,283)]\n\n for r1,r2 in ranges:\n\n signal = train[:,r1:r2].mean(axis=1)\n\n signal = smooth_data(signal, 330, 3) \n\n s,_,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, 3)\n\n feats[i,r1:r2,4] = s\n\n \n\n ranges = [(j*8,8+j*8) for j in range(283//8+1)]\n\n for r1,r2 in ranges:\n\n signal = train[:,r1:r2].mean(axis=1)\n\n signal = smooth_data(signal, 330, 3) \n\n s,_,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, 3)\n\n feats[i,r1:r2,5] = s\n\n\n\n ranges = [(j*13,13+j*13) for j in range(283//13+1)]\n\n for r1,r2 in ranges:\n\n signal = train[:,r1:r2].mean(axis=1)\n\n signal = smooth_data(signal, 330, 3) \n\n s,_,_,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, 3)\n\n feats[i,r1:r2,9] = s\n\n\n\n feats[i,:,10] = (train[x_out].mean(axis=0) - train[x_out].mean()) / train[x_out].mean()\n\n\n\n q_train = smooth_data(train.transpose(1,0), 330, 3)\n\n q_train = q_train / q_train.mean(axis=1, keepdims=True)\n\n \n\n signal = q_train.mean(axis=0)\n\n s,_,p,_ = calibrate_train_alpenglow2(signal, p0,p1,p2,p3, max_degs[i])\n\n \n\n px = p.predict(np.arange(full_train.shape[1]))\n\n\n\n for l in range(283):\n\n signal = q_train[l]\n\n\n\n f = partial(f_coef, px[x_out], signal[x_out])\n\n r = minimize_scalar(f)\n\n a = r.x\n\n score_a = r.fun\n\n\n\n f = partial(f_coef, px[x_in], signal[x_in])\n\n r = minimize_scalar(f)\n\n b = r.x\n\n score_b = r.fun\n\n\n\n feats[i,l,6] = (1.0 - a / b)\n\n\n\n #for k in range(10):\n\n # print(k, prmse(train_labels.values[i], feats[i,:,k]), np.mean(np.abs(train_labels.values[i] - feats[i,:,k])))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T16:24:26.407944Z","iopub.execute_input":"2024-11-07T16:24:26.408429Z","iopub.status.idle":"2024-11-07T18:02:06.680739Z","shell.execute_reply.started":"2024-11-07T16:24:26.408395Z","shell.execute_reply":"2024-11-07T18:02:06.67982Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for i in range(10):\n\n print(i, prmse(train_labels.values, feats[...,i]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:02:06.682124Z","iopub.execute_input":"2024-11-07T18:02:06.682808Z","iopub.status.idle":"2024-11-07T18:02:06.703635Z","shell.execute_reply.started":"2024-11-07T18:02:06.682763Z","shell.execute_reply":"2024-11-07T18:02:06.70269Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for i in range(10):\n\n print(i, prmse(train_labels.values, feats[...,i]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:02:06.707188Z","iopub.execute_input":"2024-11-07T18:02:06.707512Z","iopub.status.idle":"2024-11-07T18:02:06.724945Z","shell.execute_reply.started":"2024-11-07T18:02:06.707477Z","shell.execute_reply":"2024-11-07T18:02:06.72396Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"np.save('feats_11_two_ns.npy', feats)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:02:06.726061Z","iopub.execute_input":"2024-11-07T18:02:06.726482Z","iopub.status.idle":"2024-11-07T18:02:06.743516Z","shell.execute_reply.started":"2024-11-07T18:02:06.726433Z","shell.execute_reply":"2024-11-07T18:02:06.742775Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#np.save('feats_two_ns.npy', feats)\n\n#feats = np.load('feats_two_ns.npy')\n\nfor i in range(9):\n\n print(i, prmse(train_labels.values, feats[...,i]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:02:06.744549Z","iopub.execute_input":"2024-11-07T18:02:06.744842Z","iopub.status.idle":"2024-11-07T18:02:06.762458Z","shell.execute_reply.started":"2024-11-07T18:02:06.744811Z","shell.execute_reply":"2024-11-07T18:02:06.761583Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for i in range(9):\n\n print(i, prmse(train_labels.values, feats[...,i]))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:02:06.763523Z","iopub.execute_input":"2024-11-07T18:02:06.763795Z","iopub.status.idle":"2024-11-07T18:02:06.779492Z","shell.execute_reply.started":"2024-11-07T18:02:06.763766Z","shell.execute_reply":"2024-11-07T18:02:06.778575Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"cnn_train = feats.transpose(0,2,1).copy()\n\nlabels = train_labels.values\n\n\n\ncnn_train[:,:10] = (cnn_train[:,:10] - 0.0025)*1e3\n\nlabels = (labels - 0.0025)*1e3\n\n\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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:02:06.780835Z","iopub.execute_input":"2024-11-07T18:02:06.781191Z","iopub.status.idle":"2024-11-07T18:02:06.803034Z","shell.execute_reply.started":"2024-11-07T18:02:06.78112Z","shell.execute_reply":"2024-11-07T18:02:06.802039Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"labels.std(), cnn_train.std()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:02:06.804213Z","iopub.execute_input":"2024-11-07T18:02:06.804542Z","iopub.status.idle":"2024-11-07T18:02:06.820951Z","shell.execute_reply.started":"2024-11-07T18:02:06.804509Z","shell.execute_reply":"2024-11-07T18:02:06.820028Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.plot(cnn_train[0,10,50:80],alpha=0.1)\n\nplt.plot(cnn_train[2,10,50:80],alpha=0.1)\n\nplt.plot(cnn_train[65,10,50:80],alpha=0.1)\n\nplt.plot(cnn_train[210,10,50:80],alpha=0.1)\n\nplt.plot(cnn_train[656,10,50:80],alpha=0.1)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:02:06.822194Z","iopub.execute_input":"2024-11-07T18:02:06.822893Z","iopub.status.idle":"2024-11-07T18:02:07.072987Z","shell.execute_reply.started":"2024-11-07T18:02:06.822848Z","shell.execute_reply":"2024-11-07T18:02:07.07212Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\n\nimport torch.nn as nn\n\nimport torch.nn.functional as F\n\n\n\nclass CustomCNN(nn.Module):\n\n def __init__(self):\n\n super(CustomCNN, self).__init__()\n\n \n\n self.spectra = nn.Sequential(\n\n nn.Conv1d(11, 256, 3, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 5, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 7, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 9, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 11, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 13, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 1, 1, padding='same', bias=False),\n\n )\n\n \n\n self.spectra200 = nn.Sequential(\n\n nn.Conv1d(11, 256, 3, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 5, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 7, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 9, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 11, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 256, 13, padding='same', bias=False),\n\n nn.ReLU(),\n\n nn.Conv1d(256, 1, 1, padding='same', bias=False),\n\n )\n\n \n\n self.sigma = nn.Sequential(\n\n nn.Conv1d(1, 32, kernel_size=3, bias=False),\n\n nn.ReLU(),\n\n nn.MaxPool1d(kernel_size=2, stride=2),\n\n nn.Conv1d(32, 64, kernel_size=3, bias=False),\n\n nn.ReLU(),\n\n nn.MaxPool1d(kernel_size=2, stride=2),\n\n nn.Conv1d(64, 128, kernel_size=3, bias=False),\n\n nn.ReLU(),\n\n nn.MaxPool1d(kernel_size=2, stride=2),\n\n nn.Flatten()\n\n )\n\n \n\n # Вычисляем размер выхода после сверток и пулинга\n\n self._to_linear = None\n\n self._get_conv_output((1, 283))\n\n \n\n # Полносвязные слои\n\n self.sigma_out = nn.Linear(self._to_linear, 1)\n\n \n\n def _get_conv_output(self, shape):\n\n batch_size = 1\n\n input = torch.autograd.Variable(torch.rand(batch_size, *shape))\n\n output = self.sigma(input)\n\n self._to_linear = int(torch.numel(output) / batch_size)\n\n \n\n def forward(self, x_in):\n\n \n\n x1 = self.spectra(x_in[:,:11,:221])\n\n x2 = self.spectra200(x_in[:,:11,200-21:])\n\n x = torch.cat([x1[:,:,:200],x2[:,:,-83:]], dim=-1)\n\n spectrum = torch.flatten(x, start_dim=1)\n\n #y = torch.cat([x_in[:,7:], x-x_in[:,:1,:]], dim=1)\n\n y = x-x_in[:,:1,:]\n\n sigma = self.sigma_out(self.sigma(y))\n\n \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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:06:56.537261Z","iopub.execute_input":"2024-11-07T18:06:56.537958Z","iopub.status.idle":"2024-11-07T18:06:56.556672Z","shell.execute_reply.started":"2024-11-07T18:06:56.537921Z","shell.execute_reply":"2024-11-07T18:06:56.555629Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.model_selection import KFold\n\n\n\nkf = KFold(n_splits=5)\n\nX = list(range(len(full_train)))\n\n\n\nmean_pred = np.zeros_like(train_labels.values)\n\nmean_sigma = np.zeros_like(train_labels.values)\n\n\n\nl2loss = nn.MSELoss()\n\ngsloss = nn.GaussianNLLLoss(eps=1e-6)\n\n\n\n\n\ndef create_mean_model():\n\n model = CustomCNN()\n\n return model\n\n\n\nfor ifold, (train_index, test_index) in enumerate(kf.split(X)):\n\n train_x = torch.from_numpy(cnn_train[train_index]).float()\n\n train_y = torch.from_numpy(labels[train_index]).float() \n\n train_dataset = torch.utils.data.TensorDataset(train_x, train_y)\n\n training_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)\n\n\n\n val_x = torch.from_numpy(cnn_train[test_index]).float()\n\n val_y = torch.from_numpy(labels[test_index]).float() \n\n val_dataset = torch.utils.data.TensorDataset(val_x, val_y)\n\n validation_loader = torch.utils.data.DataLoader(val_dataset, batch_size=16, shuffle=False)\n\n \n\n model = create_mean_model().cuda()\n\n\n\n best_metric = 0\n\n total_train_losses = []\n\n\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=0.0003)\n\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 200, eta_min=0, last_epoch=-1)\n\n for epoch in range(200):\n\n ep_losses = [] \n\n model.train()\n\n for i, data in enumerate(training_loader):\n\n # Every data instance is an input + label pair\n\n inputs, tlabels = data\n\n\n\n #z = torch.randn_like(inputs) * 1e-5\n\n #u = (torch.rand_like(inputs) > 0.8).float()\n\n #inputs += z*u \n\n\n\n # Zero your gradients for every batch!\n\n optimizer.zero_grad()\n\n\n\n # Make predictions for this batch\n\n outputs, sigma = model(inputs.cuda())\n\n \n\n # Compute the loss and its gradients\n\n #loss1 = l2loss(outputs, tlabels.cuda()) \n\n #mean_diff = torch.mean((outputs - tlabels.cuda()) ** 2.0, dim=1, keepdims=True)\n\n #loss2 = F.smooth_l1_loss(sigma * 1e-3, mean_diff)\n\n #print(sigma.shape, mean_diff.shape)\n\n #loss = loss1 + loss2\n\n loss = gsloss(outputs, tlabels.cuda(), sigma.abs() * 1e-3) \n\n loss.backward()\n\n\n\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1e3)\n\n # Adjust learning weights\n\n optimizer.step()\n\n\n\n # Gather data and report\n\n #print(epoch, i, loss.item())\n\n ep_losses.append(loss.item())\n\n\n\n avg_loss = np.mean(ep_losses)\n\n total_train_losses.append(avg_loss)\n\n scheduler.step()\n\n\n\n model.eval()\n\n running_vloss = 0\n\n preds = np.zeros((len(val_dataset), 283))\n\n ss = np.zeros((len(val_dataset), 283))\n\n v_offset = 0\n\n with torch.no_grad(): \n\n for i, vdata in enumerate(validation_loader):\n\n vinputs, vlabels = vdata\n\n voutputs, vsigma = model(vinputs.cuda())\n\n preds[v_offset:v_offset+len(vinputs)] = voutputs.detach().cpu().numpy() * 1e-3 + 0.0025\n\n ss[v_offset:v_offset+len(vinputs)] = (vsigma.abs().detach().cpu().numpy() * 1e-3) ** 0.5 * 1e-3\n\n vloss = l2loss(voutputs, vlabels.cuda())\n\n running_vloss += vloss\n\n v_offset += len(vinputs)\n\n\n\n avg_vloss = running_vloss / (i + 1)\n\n\n\n metric1 = prmse(train_labels.values[test_index], preds)\n\n metric = ariel_score(train_labels.values[test_index],\n\n np.concatenate([preds.clip(0), ss.clip(0)], axis=1), \n\n train_labels.values[train_index].mean(),\n\n train_labels.values[train_index].std(),\n\n sigma_true=1e-5)\n\n\n\n print('fold {} epoch {} train {} valid {} rmse {} ariel {}'.format(ifold, epoch, \n\n round(avg_loss,6), \n\n round(avg_vloss.item(),6), \n\n round(metric1,6),\n\n round(metric,6)\n\n ))\n\n\n\n \n\n mean_pred[test_index] = preds\n\n mean_sigma[test_index] = ss\n\n torch.save(model.state_dict(), 'f11_wide_pdf_{}'.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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:10:04.262303Z","iopub.execute_input":"2024-11-07T18:10:04.262964Z","iopub.status.idle":"2024-11-07T18:24:02.301336Z","shell.execute_reply.started":"2024-11-07T18:10:04.262922Z","shell.execute_reply":"2024-11-07T18:24:02.300404Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for ifold, (train_index, test_index) in enumerate(kf.split(X)):\n if ifold != 1:\n continue\n\n train_x = torch.from_numpy(cnn_train[train_index]).float()\n\n train_y = torch.from_numpy(labels[train_index]).float() \n\n train_dataset = torch.utils.data.TensorDataset(train_x, train_y)\n\n training_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)\n\n\n\n val_x = torch.from_numpy(cnn_train[test_index]).float()\n\n val_y = torch.from_numpy(labels[test_index]).float() \n\n val_dataset = torch.utils.data.TensorDataset(val_x, val_y)\n\n validation_loader = torch.utils.data.DataLoader(val_dataset, batch_size=16, shuffle=False)\n\n \n\n model = create_mean_model().cuda()\n\n\n\n best_metric = 0\n\n total_train_losses = []\n\n\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=0.0003)\n\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 200, eta_min=0, last_epoch=-1)\n\n for epoch in range(200):\n\n ep_losses = [] \n\n model.train()\n\n for i, data in enumerate(training_loader):\n\n # Every data instance is an input + label pair\n\n inputs, tlabels = data\n\n\n\n #z = torch.randn_like(inputs) * 1e-5\n\n #u = (torch.rand_like(inputs) > 0.8).float()\n\n #inputs += z*u \n\n\n\n # Zero your gradients for every batch!\n\n optimizer.zero_grad()\n\n\n\n # Make predictions for this batch\n\n outputs, sigma = model(inputs.cuda())\n\n \n\n # Compute the loss and its gradients\n\n #loss1 = l2loss(outputs, tlabels.cuda()) \n\n #mean_diff = torch.mean((outputs - tlabels.cuda()) ** 2.0, dim=1, keepdims=True)\n\n #loss2 = F.smooth_l1_loss(sigma * 1e-3, mean_diff)\n\n #print(sigma.shape, mean_diff.shape)\n\n #loss = loss1 + loss2\n\n loss = gsloss(outputs, tlabels.cuda(), sigma.abs() * 1e-3) \n\n loss.backward()\n\n\n\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1e3)\n\n # Adjust learning weights\n\n optimizer.step()\n\n\n\n # Gather data and report\n\n #print(epoch, i, loss.item())\n\n ep_losses.append(loss.item())\n\n\n\n avg_loss = np.mean(ep_losses)\n\n total_train_losses.append(avg_loss)\n\n scheduler.step()\n\n\n\n model.eval()\n\n running_vloss = 0\n\n preds = np.zeros((len(val_dataset), 283))\n\n ss = np.zeros((len(val_dataset), 283))\n\n v_offset = 0\n\n with torch.no_grad(): \n\n for i, vdata in enumerate(validation_loader):\n\n vinputs, vlabels = vdata\n\n voutputs, vsigma = model(vinputs.cuda())\n\n preds[v_offset:v_offset+len(vinputs)] = voutputs.detach().cpu().numpy() * 1e-3 + 0.0025\n\n ss[v_offset:v_offset+len(vinputs)] = (vsigma.abs().detach().cpu().numpy() * 1e-3) ** 0.5 * 1e-3\n\n vloss = l2loss(voutputs, vlabels.cuda())\n\n running_vloss += vloss\n\n v_offset += len(vinputs)\n\n\n\n avg_vloss = running_vloss / (i + 1)\n\n\n\n metric1 = prmse(train_labels.values[test_index], preds)\n\n metric = ariel_score(train_labels.values[test_index],\n\n np.concatenate([preds.clip(0), ss.clip(0)], axis=1), \n\n train_labels.values[train_index].mean(),\n\n train_labels.values[train_index].std(),\n\n sigma_true=1e-5)\n\n\n\n print('fold {} epoch {} train {} valid {} rmse {} ariel {}'.format(ifold, epoch, \n\n round(avg_loss,6), \n\n round(avg_vloss.item(),6), \n\n round(metric1,6),\n\n round(metric,6)\n\n ))\n\n\n\n \n\n mean_pred[test_index] = preds\n\n mean_sigma[test_index] = ss\n\n torch.save(model.state_dict(), 'f11_wide_pdf_{}'.format(ifold))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:24:07.458272Z","iopub.execute_input":"2024-11-07T18:24:07.458881Z","iopub.status.idle":"2024-11-07T18:26:54.960257Z","shell.execute_reply.started":"2024-11-07T18:24:07.45884Z","shell.execute_reply":"2024-11-07T18:26:54.959259Z"}},"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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:26:54.961976Z","iopub.execute_input":"2024-11-07T18:26:54.96231Z","iopub.status.idle":"2024-11-07T18:26:54.969408Z","shell.execute_reply.started":"2024-11-07T18:26:54.962277Z","shell.execute_reply":"2024-11-07T18:26:54.968523Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sigma = mean_sigma.copy()\n\nsigma[mean_pred.max(axis=1)-mean_pred.min(axis=1) > 0.00018] += 2e-5\n\n\n\npreds = mean_pred.copy()\n\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]\n\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]\n\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\n\n\nariel_score(train_labels.values,\n\n np.concatenate([preds.clip(0), sigma], axis=1), \n\n train_labels.values.mean(),\n\n train_labels.values.std(),\n\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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:26:54.970417Z","iopub.execute_input":"2024-11-07T18:26:54.970709Z","iopub.status.idle":"2024-11-07T18:26:55.043061Z","shell.execute_reply.started":"2024-11-07T18:26:54.970678Z","shell.execute_reply":"2024-11-07T18:26:55.041737Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null},{"cell_type":"code","source":"n = 301\n\nplt.plot(preds[n])\n\nplt.plot(train_labels.values[n])\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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:26:55.046248Z","iopub.execute_input":"2024-11-07T18:26:55.046734Z","iopub.status.idle":"2024-11-07T18:26:55.278701Z","shell.execute_reply.started":"2024-11-07T18:26:55.046678Z","shell.execute_reply":"2024-11-07T18:26:55.277876Z"}},"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":"np.save('feats_11_pdf_crit_1.npy', feats)\n\nnp.save('preds_11_pdf_crit_1.npy', mean_pred)\n\nnp.save('sigma_11_pdf_crit_1.npy', mean_sigma)","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":[],"trusted":true,"execution":{"iopub.status.busy":"2024-11-07T18:26:55.279991Z","iopub.execute_input":"2024-11-07T18:26:55.280425Z","iopub.status.idle":"2024-11-07T18:26:55.301878Z","shell.execute_reply.started":"2024-11-07T18:26:55.280374Z","shell.execute_reply":"2024-11-07T18:26:55.300991Z"}},"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}]}