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on","metadata":{"execution":{"iopub.status.busy":"2024-11-03T18:54:44.61591Z","iopub.execute_input":"2024-11-03T18:54:44.61628Z","iopub.status.idle":"2024-11-03T18:54:56.115185Z","shell.execute_reply.started":"2024-11-03T18:54:44.616243Z","shell.execute_reply":"2024-11-03T18:54:56.114024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nimport scipy.stats\nimport polars as pl\nfrom tqdm import tqdm\nfrom pqdm.processes import pqdm\nimport pickle\nimport gc\n\nfrom sklearn.model_selection import cross_val_predict\nfrom sklearn.linear_model import Ridge\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import r2_score, mean_squared_error, root_mean_squared_error\nimport itertools\nimport torch\nfrom time import time\nfrom scipy.optimize import minimize\nfrom functools import partial\nimport random, os\nfrom astropy.stats import sigma_clip\nfrom scipy.signal import savgol_filter\n\nfrom catboost import CatBoostRegressor","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":10.379471,"end_time":"2024-10-31T14:48:42.203064","exception":false,"start_time":"2024-10-31T14:48:31.823593","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:54:56.116639Z","iopub.execute_input":"2024-11-03T18:54:56.117Z","iopub.status.idle":"2024-11-03T18:54:56.837615Z","shell.execute_reply.started":"2024-11-03T18:54:56.116949Z","shell.execute_reply":"2024-11-03T18:54:56.83683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class MetaModel:\n def __init__(self, models):\n self._models = models\n\n def fit(self, X, y, eval_set):\n for model in self._models:\n model.fit(X=X, y=y, eval_set=eval_set)\n\n def predict(self, X):\n predictions = np.zeros(len(X))\n for model in self._models:\n predictions += model.predict(X)\n predictions /= len(self._models)\n\n return predictions","metadata":{"papermill":{"duration":0.016996,"end_time":"2024-10-31T14:48:42.228359","exception":false,"start_time":"2024-10-31T14:48:42.211363","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:54:56.839882Z","iopub.execute_input":"2024-11-03T18:54:56.840379Z","iopub.status.idle":"2024-11-03T18:54:56.846276Z","shell.execute_reply.started":"2024-11-03T18:54:56.840344Z","shell.execute_reply":"2024-11-03T18:54:56.84539Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_adc_info = pd.read_csv('/kaggle/input/ariel-data-challenge-2024/test_adc_info.csv', index_col='planet_id')\naxis_info = pd.read_parquet('/kaggle/input/ariel-data-challenge-2024/axis_info.parquet')\nDEVICE = \"cuda\"\nbinn = 10\n\nstars = set(test_adc_info.star)\nif len(stars) == 1:\n first_star_out_of_0_1 = 2\nelse:\n first_star_out_of_0_1 = sorted([star for star in stars if star not in [0, 1]])[0]","metadata":{"papermill":{"duration":0.314091,"end_time":"2024-10-31T14:48:42.550294","exception":false,"start_time":"2024-10-31T14:48:42.236203","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:54:56.84765Z","iopub.execute_input":"2024-11-03T18:54:56.848315Z","iopub.status.idle":"2024-11-03T18:54:57.083641Z","shell.execute_reply.started":"2024-11-03T18:54:56.84826Z","shell.execute_reply":"2024-11-03T18:54:57.082686Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n\ndef apply_linear_corr(linear_corr, clean_signal):\n linear_corr = torch.flip(linear_corr, dims=[0])\n\n x_range = torch.arange(clean_signal.shape[1], device=clean_signal.device)\n y_range = torch.arange(clean_signal.shape[2], device=clean_signal.device)\n xx, yy = torch.meshgrid(x_range, y_range, indexing=\"ij\")\n\n clean_signal = clean_signal.cuda()\n result_signal = torch.zeros_like(clean_signal)\n\n for i in range(linear_corr.shape[0]):\n result_signal += linear_corr[i, xx, yy] * clean_signal ** (linear_corr.shape[0] - 1 - i)\n\n return result_signal\n\ndef clean_dark(signal, dark, dt):\n dark_expanded = dark.unsqueeze(0)\n dt_expanded = dt.unsqueeze(1).unsqueeze(2)\n\n signal = signal - dark_expanded * dt_expanded\n\n return signal\n\ndef preproc(dataset, adc_info, sensor, binning = 15):\n cut_inf, cut_sup = 39, 321\n sensor_sizes_dict = {\"AIRS-CH0\":[[11250, 32, 356], [1, 32, cut_sup-cut_inf]], \"FGS1\":[[135000, 32, 32], [1, 32, 32]]}\n binned_dict = {\"AIRS-CH0\":[11250 // binning // 2, 282], \"FGS1\":[135000 // binning // 2]}\n linear_corr_dict = {\"AIRS-CH0\":(6, 32, 356), \"FGS1\":(6, 32, 32)}\n planet_ids = adc_info.index\n DEVICE = \"cuda:0\"\n\n feats = []\n feats_center = []\n for i, planet_id in tqdm(list(enumerate(planet_ids))):\n signal = torch.Tensor(pl.read_parquet(f'/kaggle/input/ariel-data-challenge-2024/{dataset}/{planet_id}/{sensor}_signal.parquet').to_numpy().astype(np.float32)).to(DEVICE)\n dark_frame = pl.read_parquet(f'/kaggle/input/ariel-data-challenge-2024/{dataset}/' + str(planet_id) + '/' + sensor + '_calibration/dark.parquet').to_numpy()\n dead_frame = pl.read_parquet(f'/kaggle/input/ariel-data-challenge-2024/{dataset}/' + str(planet_id) + '/' + sensor + '_calibration/dead.parquet').to_numpy()\n flat_frame = pl.read_parquet(f'/kaggle/input/ariel-data-challenge-2024/{dataset}/' + str(planet_id) + '/' + sensor + '_calibration/flat.parquet').to_numpy()\n linear_corr = torch.Tensor(pl.read_parquet(f'/kaggle/input/ariel-data-challenge-2024/{dataset}/' + str(planet_id) + '/' + sensor + '_calibration/linear_corr.parquet').to_numpy().astype(np.float32).reshape(linear_corr_dict[sensor])).to(DEVICE)\n\n signal = signal.reshape(sensor_sizes_dict[sensor][0]) \n gain = adc_info[f'{sensor}_adc_gain'].values[i]\n offset = adc_info[f'{sensor}_adc_offset'].values[i]\n signal = signal / gain + offset\n\n hot = sigma_clip(\n dark_frame, sigma=5, maxiters=5\n ).mask\n dark_frame = torch.Tensor(dark_frame).to(DEVICE)\n\n if sensor == \"AIRS-CH0\":\n signal = signal[:, :, cut_inf:cut_sup] #11250 * 32 * 282\n #dt = axis_info['AIRS-CH0-integration_time'].dropna().values\n dt = torch.ones(len(signal)).to(DEVICE)*0.1 \n dt[1::2] += 4.5 #@bilzard idea\n linear_corr = linear_corr[:, :, cut_inf:cut_sup]\n dark_frame = dark_frame[:, cut_inf:cut_sup]\n dead_frame = dead_frame[:, cut_inf:cut_sup]\n flat_frame = flat_frame[:, cut_inf:cut_sup]\n hot = hot[:, cut_inf:cut_sup]\n else:\n dt = torch.ones(len(signal)).to(DEVICE)*0.1\n dt[1::2] += 0.1\n\n signal = signal.clip(0) #@graySnow idea\n linear_corr_signal = apply_linear_corr(linear_corr, signal)\n signal = clean_dark(linear_corr_signal, dark_frame, dt).cpu().numpy()\n\n flat = flat_frame.reshape(sensor_sizes_dict[sensor][1])\n flat[dead_frame.reshape(sensor_sizes_dict[sensor][1])] = np.nan\n flat[hot.reshape(sensor_sizes_dict[sensor][1])] = np.nan\n signal = signal / flat\n \n if sensor == \"AIRS-CH0\":\n signal_center = signal[:, 10:22, :]\n elif sensor == \"FGS1\":\n signal_center = signal[:, 10:22, 10:22]\n signal_center = signal_center.reshape(\n signal_center.shape[0], signal_center.shape[1] * signal_center.shape[2]\n )\n \n if sensor == \"FGS1\":\n signal = signal.reshape((sensor_sizes_dict[sensor][0][0], sensor_sizes_dict[sensor][0][1]*sensor_sizes_dict[sensor][0][2]))\n\n mean_signal = np.nanmean(signal, axis=1) # mean over the 32*32(FGS1) or 32(CH0) pixels\n cds_signal = (mean_signal[1::2] - mean_signal[0::2])\n binned = np.zeros((binned_dict[sensor]))\n for j in range(cds_signal.shape[0] // binning):\n binned[j] = cds_signal[j*binning:j*binning+binning].mean(axis=0)\n if sensor == \"FGS1\":\n binned = binned.reshape((binned.shape[0],1))\n feats.append(binned)\n \n \n mean_signal = np.nanmean(signal_center, axis=1) # mean over the 32*32(FGS1) or 32(CH0) pixels\n cds_signal = (mean_signal[1::2] - mean_signal[0::2])\n\n binned = np.zeros((binned_dict[sensor]))\n for j in range(cds_signal.shape[0] // binning):\n binned[j] = cds_signal[j*binning:j*binning+binning].mean(axis=0)\n \n if sensor == \"FGS1\":\n binned = binned.reshape((binned.shape[0],1))\n\n feats_center.append(binned)\n \n \n\n return np.stack(feats), np.stack(feats_center)\n\nfgs1_feats, fgs1_feats_center = preproc('test', test_adc_info, \"FGS1\", binn*12)\ntorch.cuda.empty_cache()\nairsch0_feats, airsch0_feats_center = preproc('test', test_adc_info, \"AIRS-CH0\", binn)\ntorch.cuda.empty_cache()\n\ntrain_binn = np.concatenate([fgs1_feats_center, airsch0_feats_center], axis=2)\ntrain_binn_not_centered = np.concatenate([fgs1_feats, airsch0_feats], axis=2)\n\npickle.dump(train_binn_not_centered, open('train_binn_not_centered.pkl', 'wb'))\n\ndel fgs1_feats\ndel fgs1_feats_center\ndel airsch0_feats\ndel airsch0_feats_center\ndel train_binn_not_centered\ngc.collect()\ntorch.cuda.empty_cache()","metadata":{"papermill":{"duration":6.8835,"end_time":"2024-10-31T14:48:49.442173","exception":false,"start_time":"2024-10-31T14:48:42.558673","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:54:57.085181Z","iopub.execute_input":"2024-11-03T18:54:57.085488Z","iopub.status.idle":"2024-11-03T18:55:03.153388Z","shell.execute_reply.started":"2024-11-03T18:54:57.085456Z","shell.execute_reply":"2024-11-03T18:55:03.152482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Old features","metadata":{"papermill":{"duration":0.008514,"end_time":"2024-10-31T14:48:49.459544","exception":false,"start_time":"2024-10-31T14:48:49.45103","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def phase_detector(signal):\n shift_start_end = 3 * 10\n shift_transit = 3 * 20\n signal = savgol_filter(signal, 3 * 5, 1)\n\n first_derivative1 = np.gradient(signal[shift_start_end:len(signal) - 3 * 70])\n phase1 = np.argmin(first_derivative1) + shift_start_end\n\n signal2 = signal[phase1 + shift_transit: len(signal) - shift_start_end]\n first_derivative2 = np.gradient(signal2)\n\n phase2 = np.argmax(first_derivative2) + phase1 + shift_transit\n\n return phase1, phase2\n\n\n\ndef func(i):\n def objective(s):\n\n best_q = 1e10\n for i in range(4) :\n delta = 3 * 2\n\n shift = 3 * 40\n y = signal[max(0, p1 - delta - shift):p1-delta].tolist() + (signal[p1+delta:p2 - delta] * (1 + s)).tolist() + signal[p2+delta: min(p2 + delta + shift, len(signal))].tolist()\n x = list(range(len(y)))\n z = np.polyfit(x, y, deg=i)\n p = np.poly1d(z)\n q = np.abs(p(x) - y).mean()\n\n if q < best_q :\n best_q = q\n\n return q\n planet = train_binn[i,:,:]\n\n signal = planet[:,1:].mean(axis=1)\n\n p1,p2 = phase_detector(signal)\n\n r = minimize(\n objective,\n [0.0001],\n method= 'Nelder-Mead'\n )\n s = r.x[0]\n result = {'phase_1': p1, 'phase_2': p2, 's_all': s}\n\n return result\n\nresults = pqdm(range(len(test_adc_info)), func, n_jobs=4)\n\ndf_features_old = pd.DataFrame(results)\ndf_features_old","metadata":{"papermill":{"duration":0.263977,"end_time":"2024-10-31T14:48:49.732259","exception":false,"start_time":"2024-10-31T14:48:49.468282","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:55:03.154559Z","iopub.execute_input":"2024-11-03T18:55:03.154855Z","iopub.status.idle":"2024-11-03T18:55:03.411412Z","shell.execute_reply.started":"2024-11-03T18:55:03.154822Z","shell.execute_reply":"2024-11-03T18:55:03.410339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# New features","metadata":{"papermill":{"duration":0.009548,"end_time":"2024-10-31T14:48:49.751529","exception":false,"start_time":"2024-10-31T14:48:49.741981","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def get_features(normalized_planet, p1_st, p2_st, p1, p2, t_st, t, d_st, all_d, poly, binn):\n features = [d_st, p1_st, p2_st, p1, p2, t_st, t]\n\n def pad_with_zeros(arr, target_length=5):\n padding = target_length - len(arr)\n return np.pad(arr, (padding, 0), 'constant')\n\n features.extend(pad_with_zeros(poly.coeffs.tolist()))\n\n binn_coefs = [283, 100, 50]\n for coef in binn_coefs:\n lhs = 0\n for rhs in range(coef, 283, coef):\n d_binned = np.array(all_d[lhs: rhs])\n features.append(d_binned.mean())\n features.append(np.median(d_binned))\n features.append(d_binned.max())\n features.append(d_binned.min())\n features.append(d_binned.std())\n features.append(np.quantile(d_binned, 0.5))\n features.append(np.quantile(d_binned, 0.2))\n features.append(np.quantile(d_binned, 0.8))\n lhs = rhs\n\n rmse = root_mean_squared_error(np.concatenate([normalized_planet[: int(p1) - 100 // binn], normalized_planet[int(p2) + 100 // binn: ]], axis=0).mean(axis=1),\n poly(np.concatenate([np.arange(normalized_planet.shape[0])[: int(p1) - 100 // binn],\n np.arange(normalized_planet.shape[0])[int(p2) + 100 // binn: ]],\n axis=0)))\n\n features.append(rmse)\n return features\n\n\ndef moving_window(signal, window_size):\n half_window = window_size // 2\n filtered_signal = np.zeros_like(signal, dtype=float)\n n = len(signal)\n\n for i in range(n):\n start = max(0, i - half_window)\n end = min(n, i + half_window + 1)\n\n filtered_signal[i] = np.mean(signal[start:end])\n\n return filtered_signal\n\n\ndef biweight(y_true, y_pred, c=1e-2):\n \"\"\"\n Реализация Tukey's Biweight Loss на numpy.\n\n Параметры:\n - y_true: реальные значения\n - y_pred: предсказанные значения\n - c: порог отсечения ошибок, после которого они не учитываются\n\n Возвращает:\n - Среднее значение Tukey's Biweight Loss по всем наблюдениям\n \"\"\"\n error = y_true - y_pred\n abs_error = np.abs(error)\n is_small_error = abs_error <= c\n loss = np.zeros_like(error)\n\n # Вычисляем Tukey Loss для малых ошибок\n small_error_part = (1 - (error / c) ** 2) ** 3\n loss[is_small_error] = c ** 2 / 6 * (1 - small_error_part[is_small_error])\n\n return np.mean(loss)","metadata":{"papermill":{"duration":0.029367,"end_time":"2024-10-31T14:48:49.790277","exception":false,"start_time":"2024-10-31T14:48:49.76091","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:55:03.412846Z","iopub.execute_input":"2024-11-03T18:55:03.413193Z","iopub.status.idle":"2024-11-03T18:55:03.429383Z","shell.execute_reply.started":"2024-11-03T18:55:03.413156Z","shell.execute_reply":"2024-11-03T18:55:03.428465Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def phase_detector(signal):\n # FIXME ARTEM 228 (220)\n\n phase1, phase2 = None, None\n drawer = []\n for i in range(750//binn,2250//binn):\n t1 = signal[i:i+150//binn].mean() - signal[i+150//binn:i+300//binn].mean()\n drawer.append(t1)\n\n phase1 = np.argmax(drawer)\n\n d1 = drawer\n\n drawer = []\n for i in range(3000//binn,4500//binn):\n t1 = signal[i:i+150//binn].mean() - signal[i+150//binn:i+300//binn].mean()\n drawer.append(t1)\n\n phase2 = np.argmin(drawer)\n\n min_error = float('inf')\n best_split_index = None\n\n for i in range(1, phase1 - 1):\n x1 = np.arange(i).reshape(-1, 1)\n y1 = d1[: i]\n model1 = LinearRegression().fit(x1, y1)\n y1_pred = model1.predict(x1)\n\n x2 = np.arange(i, phase1).reshape(-1, 1)\n y2 = d1[i: phase1]\n model2 = LinearRegression().fit(x2, y2)\n y2_pred = model2.predict(x2)\n\n total_error = np.mean(np.concatenate([(y1 - y1_pred) ** 2, (y2 - y2_pred) ** 2]))\n\n if total_error < min_error:\n min_error = total_error\n best_split_index = i\n\n return phase1 + 900//binn, phase2 + 3150//binn, 2 * (phase1 - best_split_index) + 220//binn\n\n\ndef secret(p1, p2, t, d, x):\n c = (p1 + p2) / 2\n x = x.astype(np.float128)\n ans = 1 - d / (1 + ((x - c)/(p1 - c))**2 * np.power(99 * ((p2 - c) / (p2 - c + t / 2))**2, (x - p1)*(x - p2) * 2 / (t * (2*(p2 - c) + t / 2))))\n return ans.astype(np.float32)\n\ndef optimized_polyfit(x, y, deg):\n X = np.vander(x, N=deg + 1, increasing=False)\n Q, R = np.linalg.qr(X)\n coeffs = np.linalg.solve(R, Q.T @ y)\n return coeffs\n\n\ndef try_s_poly(signal, deg, x, p1_p2_t_d):\n p1, p2, t, d = p1_p2_t_d\n\n my_function = partial(secret, p1, p2, t, d)\n function_offset = my_function(x)\n\n y = signal / function_offset\n\n z = np.polyfit(x, y, deg)\n p = np.poly1d(z)\n q = np.abs(y - p(x)).mean()\n\n return q\n\n\ndef calibrate_train_poly(signal, p1_st, p2_st, t_st, x, d_st = 0.001, best_deg = None, d_true = None):\n\n best_score = 1e12\n if best_deg is None:\n for deg in range(3, 5, 1):\n f = partial(try_s_poly, signal, deg, x)\n r = minimize(f, [p1_st, p2_st, t_st, d_st], method = 'Nelder-Mead', bounds=[(p1_st - 15, p1_st + 15), (p2_st - 15, p2_st + 15), (t_st*0.5, t_st*2), (1e-4, 0.5)])\n p1, p2, t, d = r.x[0], r.x[1], r.x[2], r.x[3]\n r = minimize(f, [p1, p2, t, d ], method = 'Nelder-Mead', bounds=[(p1 - 15, p1 + 15), (p2 - 15, p2 + 15), (t*0.5, t*2), (d*0.1, d*10)])\n p1, p2, t, d = r.x[0], r.x[1], r.x[2], r.x[3]\n\n my_function = partial(secret, p1, p2, t, d)\n function_offset = my_function(x)\n y = signal / function_offset\n z = np.polyfit(x, y, deg)\n poly = np.poly1d(z)\n q = np.abs(y - poly(x)).mean()\n\n if q < best_score:\n best_score = q\n best_deg = deg\n best_poly = poly\n best_t = t\n best_p1 = p1\n best_p2 = p2\n best_d = d\n best_y = y\n else:\n\n f = partial(try_s_poly, signal, best_deg, x)\n r = minimize(f, [p1_st, p2_st, t_st, d_st], method = 'Nelder-Mead', bounds=[(p1_st - 15, p1_st + 15), (p2_st - 15, p2_st + 15), (t_st*0.5, t_st*2), (d_st*0.1, d_st*10)])\n p1_st, p2_st, t_st, d_st = r.x[0], r.x[1], r.x[2], r.x[3]\n r = minimize(f, [p1_st, p2_st, t_st, d_st], method = 'Nelder-Mead', bounds=[(p1_st - 15, p1_st + 15), (p2_st - 15, p2_st + 15), (t_st*0.5, t_st*2), (d_st*0.1, d_st*10)])\n best_p1, best_p2, best_t, best_d = r.x[0], r.x[1], r.x[2], r.x[3]\n\n my_function = partial(secret, best_p1, best_p2, best_t, best_d)\n function_offset = my_function(x)\n best_y = signal / function_offset\n z = np.polyfit(x, best_y, best_deg)\n best_poly = np.poly1d(z)\n\n return best_t, best_d, best_p1, best_p2, best_poly, best_deg\n\n\ndef try_s(signal, poly, p1, p2, t, x, d):\n\n my_function = partial(secret, p1, p2, t, d)\n function_offset = my_function(x)\n\n y = signal / function_offset\n\n q = np.abs(y - poly(x)).mean()\n\n return q\n\n\ndef calibrate_train(signal, p1, p2, t, poly, d_st, x, method):\n\n f = partial(try_s, signal, poly, p1, p2, t, x)\n r = minimize(f, [d_st], method = method, bounds=[(d_st*0.25, d_st*4)])\n d = r.x[0]\n\n return d\n\ndef get_features_one(i):\n star_id = test_adc_info.iloc[i].star\n\n planet_d = {}\n for w_idx in range(283):\n planet_d[w_idx] = []\n\n planet_d_30 = {}\n for w_idx in range(283):\n planet_d_30[w_idx] = []\n\n planet = np.concatenate([train_binn[i][:, 1:], train_binn[i][:, 0, None]], axis=1)\n normalized_planet = planet / planet.mean(axis=0)\n\n p1, p2, t = phase_detector(normalized_planet[:, :-1].mean(axis=1))\n t_st, d_st, p1_st, p2_st, poly, deg = calibrate_train_poly(normalized_planet[:, :-1].mean(axis=1), p1, p2, t, x)\n\n for k_binn_i in [90]:\n for k in range(100, 283, k_binn_i // 2):\n for j in range(8):\n subsample = np.sort(np.random.choice(np.arange(max(k - k_binn_i, 0), min(k + k_binn_i, 283)), k_binn_i, replace=False))\n\n signal = normalized_planet[:, subsample].mean(axis=1)\n\n t_st_j, d_st_j, p1_j, p2_j, poly_j, _ = calibrate_train_poly(signal, p1_st, p2_st, t_st, x, d_st=d_st, best_deg=deg)\n binn_j = 5 * (k // 100 + 1)\n for w_idxs in subsample.reshape((subsample.shape[0] // binn_j, binn_j)):\n signal = normalized_planet[:, w_idxs].mean(axis=1)\n d = calibrate_train(signal, p1_j, p2_j, t_st_j, poly_j, d_st_j, x, method=\"Nelder-Mead\")\n for w_idx_i in w_idxs:\n planet_d[w_idx_i].append(d)\n \n for k_binn_i in [30]:\n for k in range(100, 283, k_binn_i // 2):\n for j in range(8):\n subsample = np.sort(np.random.choice(np.arange(max(k - k_binn_i, 0), min(k + k_binn_i, 283)), k_binn_i, replace=False))\n\n signal = normalized_planet[:, subsample].mean(axis=1)\n\n t_st_j, d_st_j, p1_j, p2_j, poly_j, _ = calibrate_train_poly(signal, p1_st, p2_st, t_st, x, d_st=d_st, best_deg=deg)\n binn_j = 5 * (k // 100 + 1)\n for w_idxs in subsample.reshape((subsample.shape[0] // binn_j, binn_j)):\n signal = normalized_planet[:, w_idxs].mean(axis=1)\n d = calibrate_train(signal, p1_j, p2_j, t_st_j, poly_j, d_st_j, x, method=\"Nelder-Mead\")\n for w_idx_i in w_idxs:\n planet_d_30[w_idx_i].append(d)\n\n\n for j in range(60):\n subsample = np.sort(np.random.choice(np.arange(283), 100, replace=False))\n signal = normalized_planet[:, subsample].mean(axis=1)\n t_st_j, d_st_j, p1_j, p2_j, poly_j, _ = calibrate_train_poly(signal, p1_st, p2_st, t_st, x, d_st=d_st, best_deg=deg)\n\n for w_idx in subsample.reshape((10, subsample.shape[0] // 10)):\n signal = normalized_planet[:, w_idx].mean(axis=1)\n d = calibrate_train(signal, p1_j, p2_j, t_st_j, poly_j, d_st_j, x, method=\"Nelder-Mead\")\n for w_idx_i in w_idx:\n if w_idx_i <= 100:\n planet_d[w_idx_i].append(d)\n planet_d_30[w_idx_i].append(d)\n\n all_d = []\n for w_idx, preds in planet_d.items():\n if preds:\n all_d.append(np.mean(preds))\n else:\n all_d.append(d_st)\n\n all_d_30 = []\n for w_idx, preds in planet_d_30.items():\n if preds:\n all_d_30.append(np.mean(preds))\n else:\n all_d_30.append(d_st)\n\n planet_features = get_features(planet, p1_st, p2_st, p1, p2, t_st, t, d_st, all_d, poly, binn)\n planet_features_30 = get_features(planet, p1_st, p2_st, p1, p2, t_st, t, d_st, all_d_30, poly, binn)\n return all_d, planet_features, all_d_30, planet_features_30","metadata":{"papermill":{"duration":0.068576,"end_time":"2024-10-31T14:48:49.868864","exception":false,"start_time":"2024-10-31T14:48:49.800288","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:55:03.430951Z","iopub.execute_input":"2024-11-03T18:55:03.431472Z","iopub.status.idle":"2024-11-03T18:55:03.47999Z","shell.execute_reply.started":"2024-11-03T18:55:03.431429Z","shell.execute_reply":"2024-11-03T18:55:03.479129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nx = np.arange(train_binn.shape[1], dtype=np.float32)\narange_283 = np.arange(283)\n\nresults = pqdm(range(train_binn.shape[0]), get_features_one, n_jobs=4)\npred_df = pd.DataFrame(np.flip(np.array([i[0] for i in results]), axis=1))\nfeatures = np.array([i[1] for i in results])\nfeatures2 = np.array([i[3] for i in results])\npred_df2 = pd.DataFrame(np.flip(np.array([i[2] for i in results]), axis=1))\n\ngc.collect()","metadata":{"papermill":{"duration":44.153825,"end_time":"2024-10-31T14:49:34.033376","exception":false,"start_time":"2024-10-31T14:48:49.879551","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:55:03.481092Z","iopub.execute_input":"2024-11-03T18:55:03.481375Z","iopub.status.idle":"2024-11-03T18:55:46.436095Z","shell.execute_reply.started":"2024-11-03T18:55:03.481343Z","shell.execute_reply":"2024-11-03T18:55:46.435009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train_binn\ngc.collect()\n\ntrain_binn = pickle.load(open('train_binn_not_centered.pkl', 'rb'))","metadata":{"papermill":{"duration":0.120873,"end_time":"2024-10-31T14:49:34.164486","exception":false,"start_time":"2024-10-31T14:49:34.043613","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:55:46.437706Z","iopub.execute_input":"2024-11-03T18:55:46.438235Z","iopub.status.idle":"2024-11-03T18:55:46.563327Z","shell.execute_reply.started":"2024-11-03T18:55:46.438184Z","shell.execute_reply":"2024-11-03T18:55:46.562344Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_features_one(i):\n star_id = test_adc_info.iloc[i].star\n\n planet_d = {}\n for w_idx in range(283):\n planet_d[w_idx] = []\n\n planet = np.concatenate([train_binn[i][:, 1:], train_binn[i][:, 0, None]], axis=1)\n normalized_planet = planet / planet.mean(axis=0)\n\n p1, p2, t = phase_detector(normalized_planet[:, :-1].mean(axis=1))\n t_st, d_st, p1_st, p2_st, poly, deg = calibrate_train_poly(normalized_planet[:, :-1].mean(axis=1), p1, p2, t, x)\n\n for k_binn_i in k_binn_wl:\n for k in range(100, 283, k_binn_i // 2):\n for j in range(8):\n subsample = np.sort(np.random.choice(np.arange(max(k - k_binn_i, 0), min(k + k_binn_i, 283)), k_binn_i, replace=False))\n \n signal = normalized_planet[:, subsample].mean(axis=1)\n \n t_st_j, d_st_j, p1_j, p2_j, poly_j, _ = calibrate_train_poly(signal, p1_st, p2_st, t_st, x, d_st=d_st, best_deg=deg)\n binn_j = 5 * (k // 100 + 1)\n for w_idxs in subsample.reshape((subsample.shape[0] // binn_j, binn_j)):\n signal = normalized_planet[:, w_idxs].mean(axis=1)\n d = calibrate_train(signal, p1_j, p2_j, t_st_j, poly_j, d_st_j, x, method=\"Nelder-Mead\")\n for w_idx_i in w_idxs:\n planet_d[w_idx_i].append(d)\n\n for j in range(30):\n subsample = np.sort(np.random.choice(np.arange(283), 100, replace=False))\n signal = normalized_planet[:, subsample].mean(axis=1)\n t_st_j, d_st_j, p1_j, p2_j, poly_j, _ = calibrate_train_poly(signal, p1_st, p2_st, t_st, x, d_st=d_st, best_deg=deg)\n\n for w_idx in subsample.reshape((10, subsample.shape[0] // 10)):\n signal = normalized_planet[:, w_idx].mean(axis=1)\n d = calibrate_train(signal, p1_j, p2_j, t_st_j, poly_j, d_st_j, x, method=\"Nelder-Mead\")\n for w_idx_i in w_idx:\n if w_idx_i <= 100:\n planet_d[w_idx_i].append(d)\n\n all_d = []\n for w_idx, preds in planet_d.items():\n if preds:\n all_d.append(np.mean(preds))\n else:\n all_d.append(d_st)\n\n planet_features = get_features(planet, p1_st, p2_st, p1, p2, t_st, t, d_st, all_d, poly, binn)\n return all_d, planet_features","metadata":{"papermill":{"duration":0.028562,"end_time":"2024-10-31T14:49:34.203422","exception":false,"start_time":"2024-10-31T14:49:34.17486","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:55:46.564914Z","iopub.execute_input":"2024-11-03T18:55:46.565256Z","iopub.status.idle":"2024-11-03T18:55:46.58373Z","shell.execute_reply.started":"2024-11-03T18:55:46.565223Z","shell.execute_reply":"2024-11-03T18:55:46.582723Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nx = np.arange(train_binn.shape[1], dtype=np.float32)\narange_283 = np.arange(283)\n\nk_binn_wl = [90]\n\nresults = pqdm(range(train_binn.shape[0]), get_features_one, n_jobs=4)\npred_d3 = [i[0] for i in results]\nfeatures3 = [i[1] for i in results]\n\n\nfeatures3 = np.array(features3)\npred_df3 = pd.DataFrame(np.flip(np.array(pred_d3), axis=1))\ngc.collect()","metadata":{"papermill":{"duration":15.43337,"end_time":"2024-10-31T14:49:49.646787","exception":false,"start_time":"2024-10-31T14:49:34.213417","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:55:46.587402Z","iopub.execute_input":"2024-11-03T18:55:46.587704Z","iopub.status.idle":"2024-11-03T18:56:01.986407Z","shell.execute_reply.started":"2024-11-03T18:55:46.587672Z","shell.execute_reply":"2024-11-03T18:56:01.985468Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"features = np.concatenate([features, features2, features3], axis=1)\n\nw1 = 0.7\nw2 = 0.6\npred_df = (pred_df * w1 + pred_df2 * (1 - w1)) * w2 + (pred_df3) * (1 - w2)","metadata":{"papermill":{"duration":0.020081,"end_time":"2024-10-31T14:49:49.678087","exception":false,"start_time":"2024-10-31T14:49:49.658006","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:56:01.98765Z","iopub.execute_input":"2024-11-03T18:56:01.987961Z","iopub.status.idle":"2024-11-03T18:56:01.994265Z","shell.execute_reply.started":"2024-11-03T18:56:01.987926Z","shell.execute_reply":"2024-11-03T18:56:01.993361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = []\nfor idx, (f, s) in enumerate(zip(features, test_adc_info.star.dropna())):\n f = f.tolist()\n f.append(s)\n df.append(f)\ndf = pd.DataFrame(df, columns=[f\"f_{i}\" for i in range(len(f))])\n\nstar_target_name = f\"f_{len(f) - 1}\"","metadata":{"papermill":{"duration":0.024044,"end_time":"2024-10-31T14:49:49.712883","exception":false,"start_time":"2024-10-31T14:49:49.688839","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:56:01.995288Z","iopub.execute_input":"2024-11-03T18:56:01.995585Z","iopub.status.idle":"2024-11-03T18:56:02.011627Z","shell.execute_reply.started":"2024-11-03T18:56:01.995534Z","shell.execute_reply":"2024-11-03T18:56:02.010747Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n\ncontexsts = pd.read_pickle('/kaggle/input/ariel-2024-data/model_x20_depth4_old_params_step_183_20_w_0.6__contexsts_d_st_rol300_0.15_v1_my_old_d_st_1_w_0.4__double_opt_V1_V2.pkl')\n\nfor context in contexsts:\n print(context['start_idx'], context['end_idx'], context['w'])\n if 'preds' in context['star_0']:\n del context['star_0']['preds']\n if 'preds' in context['star_1']:\n del context['star_1']['preds']\n if 'star_2' in context:\n if 'preds' in context['star_2']:\n del context['star_2']['preds']\n \n \ncontexsts2 = pd.read_pickle('/kaggle/input/ariel-2024-data/F2_model_x20_depth4_old_params_step_183_20_w_0.6__contexsts_d_st_rol300_0.15_v1_my_old_d_st_1_w_0.4__double_opt_V1_V2.pkl')\n\nfor context in contexsts2:\n print(context['start_idx'], context['end_idx'], context['w'])\n if 'preds' in context['star_0']:\n del context['star_0']['preds']\n if 'preds' in context['star_1']:\n del context['star_1']['preds']\n if 'star_2' in context:\n if 'preds' in context['star_2']:\n del context['star_2']['preds']\n","metadata":{"papermill":{"duration":3.087877,"end_time":"2024-10-31T14:49:52.811598","exception":false,"start_time":"2024-10-31T14:49:49.723721","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:56:02.01299Z","iopub.execute_input":"2024-11-03T18:56:02.013293Z","iopub.status.idle":"2024-11-03T18:56:04.83592Z","shell.execute_reply.started":"2024-11-03T18:56:02.013256Z","shell.execute_reply":"2024-11-03T18:56:04.834921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n\nstar_target_name = f\"f_{len(f) - 1}\"\n\nfor context, context2 in tqdm(zip(contexsts, contexsts2)):\n for star in [0, 1]:\n model = context[f'star_{star}']['model']\n model_opposite = context[f'star_{1 - star}']['model']\n mask_val = df[star_target_name] == star\n\n X_val = df[mask_val][[f\"f_{i}\" for i in range(len(f) - 1)]]\n if star == 0:\n pred_sigmas = model.predict(X_val) * 0.99 + model_opposite.predict(X_val) * 0.01\n elif star == 1:\n pred_sigmas = model.predict(X_val) * 0.99 + model_opposite.predict(X_val) * 0.01\n else:\n pred_sigmas = model.predict(X_val)\n\n context[f'star_{star}']['preds'] = pred_sigmas\n \n star = 2\n mask_val = df[star_target_name] > 1\n X_val = df[mask_val][[f\"f_{i}\" for i in range(len(f) - 1)]]\n\n pred_sigmas = (\n context[f'star_0']['model'].predict(X_val) * 0.25 + context[f'star_1']['model'].predict(X_val) * 0.25\n + context2[f'star_0']['model'].predict(X_val) * 0.25 + context2[f'star_1']['model'].predict(X_val) * 0.25\n )\n context[f'star_{star}'] = {\n 'preds': pred_sigmas\n }\n ","metadata":{"papermill":{"duration":11.825516,"end_time":"2024-10-31T14:50:04.648707","exception":false,"start_time":"2024-10-31T14:49:52.823191","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nGLOBAL_W = 0.85\nGLOBAL_W2 = 0.6\n\nnew_pred = []\nsigmas = []\n\nidx_0, idx_1, idx_2 = 0, 0, 0\nfor idx in range(pred_df.shape[0]):\n star_id = test_adc_info.star.values[idx]\n\n row = pred_df.values[idx, :]\n row = (moving_window(row, 20) * GLOBAL_W + moving_window(row, 300) * (1 - GLOBAL_W) * 0.4 + features[idx, 0] * (1 - GLOBAL_W) * 0.6) * GLOBAL_W2 + df_features_old.iloc[idx, 2] * (1 - GLOBAL_W2)\n \n \n shift = 0.6 * 1e-5\n \n if star_id == 0:\n row_sigma = np.zeros(283)\n for context in contexsts:\n row_sigma[context['start_idx']:context['end_idx']] += context[f'star_0']['preds'][idx_0] * context['w']\n elif star_id == 1:\n row_sigma = np.zeros(283)\n for context in contexsts:\n row_sigma[context['start_idx']:context['end_idx']] += context[f'star_1']['preds'][idx_1] * context['w']\n else:\n row_sigma = np.zeros(283)\n for context in contexsts:\n row_sigma[context['start_idx']:context['end_idx']] += context[f'star_2']['preds'][idx_2] * context['w']\n \n row_sigma += shift\n \n if star_id == 0:\n idx_0 += 1\n elif star_id == 1:\n idx_1 += 1\n else:\n idx_2 += 1\n\n new_pred.append(row)\n sigmas.append(row_sigma)\n\nss = pd.read_csv('/kaggle/input/ariel-data-challenge-2024/sample_submission.csv')\nsubmission = pd.DataFrame(np.concatenate([new_pred, sigmas], axis=1), columns=ss.columns[1:])\nsubmission.index = test_adc_info.index\nsubmission.to_csv('submission.csv')","metadata":{"papermill":{"duration":0.071728,"end_time":"2024-10-31T14:50:04.738746","exception":false,"start_time":"2024-10-31T14:50:04.667018","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:56:15.984022Z","iopub.execute_input":"2024-11-03T18:56:15.984318Z","iopub.status.idle":"2024-11-03T18:56:16.032943Z","shell.execute_reply.started":"2024-11-03T18:56:15.984279Z","shell.execute_reply":"2024-11-03T18:56:16.032015Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.plot(row)","metadata":{"papermill":{"duration":0.31247,"end_time":"2024-10-31T14:50:05.069025","exception":false,"start_time":"2024-10-31T14:50:04.756555","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:56:16.034195Z","iopub.execute_input":"2024-11-03T18:56:16.035252Z","iopub.status.idle":"2024-11-03T18:56:16.312623Z","shell.execute_reply.started":"2024-11-03T18:56:16.035213Z","shell.execute_reply":"2024-11-03T18:56:16.311456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission","metadata":{"papermill":{"duration":0.040666,"end_time":"2024-10-31T14:50:05.128529","exception":false,"start_time":"2024-10-31T14:50:05.087863","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:56:16.313771Z","iopub.execute_input":"2024-11-03T18:56:16.314113Z","iopub.status.idle":"2024-11-03T18:56:16.336637Z","shell.execute_reply.started":"2024-11-03T18:56:16.314077Z","shell.execute_reply":"2024-11-03T18:56:16.33573Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"GLOBAL_W, GLOBAL_W2, w1, w2","metadata":{"papermill":{"duration":0.026758,"end_time":"2024-10-31T14:50:05.173635","exception":false,"start_time":"2024-10-31T14:50:05.146877","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-11-03T18:56:16.337879Z","iopub.execute_input":"2024-11-03T18:56:16.338203Z","iopub.status.idle":"2024-11-03T18:56:16.349799Z","shell.execute_reply.started":"2024-11-03T18:56:16.33817Z","shell.execute_reply":"2024-11-03T18:56:16.34887Z"},"trusted":true},"execution_count":null,"outputs":[]}]}