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install --no-index --find-links=/kaggle/input/ariel-2024-pqdm pqdm > /dev/null","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2025-09-19T07:11:42.95838Z","iopub.status.busy":"2025-09-19T07:11:42.958127Z","iopub.status.idle":"2025-09-19T07:11:47.254137Z","shell.execute_reply":"2025-09-19T07:11:47.253106Z"},"papermill":{"duration":4.302424,"end_time":"2025-09-19T07:11:47.255904","exception":false,"start_time":"2025-09-19T07:11:42.95348","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"7972675e","cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport multiprocessing as mp\nimport torch.nn as nn\nimport os\nimport matplotlib.pyplot as plt\nimport itertools\n\nfrom tqdm import tqdm\nfrom pqdm.threads import pqdm\nfrom astropy.stats import sigma_clip\nfrom scipy.optimize import minimize\nfrom torch.utils.data import DataLoader, TensorDataset, random_split\nfrom sklearn.preprocessing import StandardScaler\nfrom scipy.signal import savgol_filter\nfrom sklearn.metrics import mean_squared_error","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:11:47.263425Z","iopub.status.busy":"2025-09-19T07:11:47.262914Z","iopub.status.idle":"2025-09-19T07:11:55.869552Z","shell.execute_reply":"2025-09-19T07:11:55.868704Z"},"papermill":{"duration":8.611729,"end_time":"2025-09-19T07:11:55.870946","exception":false,"start_time":"2025-09-19T07:11:47.259217","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"08eb79e7","cell_type":"code","source":"ROOT_PATH = \"/kaggle/input/ariel-data-challenge-2025\"\nMODE = \"test\"\n\nclass Config:\n    DATA_PATH = '/kaggle/input/ariel-data-challenge-2025'\n    DATASET = \"test\"\n\n    SCALE = 0.946\n    SIGMA = 0.00056\n    \n    CUT_INF = 39\n    CUT_SUP = 321\n    \n    SENSOR_CONFIG = {\n        \"AIRS-CH0\": {\n            \"raw_shape\": [11250, 32, 356],\n            \"calibrated_shape\": [1, 32, CUT_SUP - CUT_INF],\n            \"linear_corr_shape\": (6, 32, 356),\n            \"dt_pattern\": (0.1, 4.5), \n            \"binning\": 30\n        },\n        \"FGS1\": {\n            \"raw_shape\": [135000, 32, 32],\n            \"calibrated_shape\": [1, 32, 32],\n            \"linear_corr_shape\": (6, 32, 32),\n            \"dt_pattern\": (0.1, 0.1),\n            \"binning\": 30 * 12\n        }\n    }\n    \n    MODEL_PHASE_DETECTION_SLICE = slice(30, 140)\n    MODEL_OPTIMIZATION_DELTA = 11\n    MODEL_POLYNOMIAL_DEGREE = 3\n    \n    N_JOBS = 3\n\ndef _phase_detector_signal(signal, cfg):\n    sl = cfg.MODEL_PHASE_DETECTION_SLICE\n    min_idx = int(np.argmin(signal[sl])) + sl.start\n    s1 = signal[:min_idx]; s2 = signal[min_idx:]\n    \n    if s1.size < 3 or s2.size < 3:\n        return 0, len(signal) - 1\n    \n    g1 = np.gradient(s1); g1_max = np.max(g1) if np.size(g1) else 0.0\n    g2 = np.gradient(s2); g2_max = np.max(g2) if np.size(g2) else 0.0\n    \n    if g1_max != 0:\n        g1 /= g1_max\n    if g2_max != 0:\n        g2 /= g2_max\n    \n    phase1 = int(np.argmin(g1))\n    phase2 = int(np.argmax(g2)) + min_idx\n    \n    return phase1, phase2\n\ndef estimate_sigma_fgs(preprocessed_data, cfg):\n    sig_rel = []\n    delta = cfg.MODEL_OPTIMIZATION_DELTA\n    eps = 1e-12\n    \n    for single in preprocessed_data:\n        air_white = savgol_filter(single[:, 1:].mean(axis=1), 20, 2)\n        p1, p2 = _phase_detector_signal(air_white, cfg)\n        p1 = max(delta, p1)\n        p2 = min(len(air_white) - delta - 1, p2)\n\n        fgs = single[:, 0]\n        oot = (fgs[: p1 - delta] if p1 - delta > 0 else np.empty(0, fgs.dtype))\n        if p2 + delta < fgs.size:\n            oot = np.concatenate([oot, fgs[p2 + delta :]])\n        inn = fgs[p1 + delta : max(p1 + delta, p2 - delta)]\n\n        if oot.size == 0 or inn.size == 0:\n            sig_rel.append(np.nan); continue\n\n        n_oot, n_in = len(oot), len(inn)\n        var_oot = np.nanvar(oot, ddof=1)\n        var_in  = np.nanvar(inn, ddof=1)\n        oot_mean = float(np.nanmean(oot)) if np.isfinite(np.nanmean(oot)) else float(np.nanmean(fgs))\n        sigma_rel = np.sqrt(var_oot / max(n_oot,1) + var_in / max(n_in,1)) / max(oot_mean, eps)\n        sig_rel.append(sigma_rel)\n\n    s = np.asarray(sig_rel, dtype=float)\n    mask = np.isfinite(s) & (s > 0)\n    med = float(np.nanmedian(s[mask])) if mask.any() else 1.0\n\n    k = np.ones_like(s)\n    if med > 0 and np.isfinite(med):\n        k[mask] = np.sqrt(s[mask] / med)\n    \n    k = np.clip(k, 0.85, 1.30) \n    \n    return k * cfg.SIGMA * 1.04\n\ndef estimate_sigma_air(preprocessed_data, cfg):\n    \"\"\"Return sigma_air length N_planets.\"\"\"\n    sig_rel = []\n    delta = cfg.MODEL_OPTIMIZATION_DELTA\n    eps = 1e-12\n\n    for single in preprocessed_data:\n        white = np.nanmean(single[:, 1:], axis=1)\n        white_s = savgol_filter(white, 20, 2)\n\n        p1, p2 = _phase_detector_signal(white_s, cfg)\n        p1 = max(delta, p1)\n        p2 = min(len(white) - delta - 1, p2)\n\n        oot_left = white[: p1 - delta] if p1 - delta > 0 else np.empty(0, white.dtype)\n        oot_right = white[p2 + delta :] if (p2 + delta) < white.size else np.empty(0, white.dtype)\n        oot = np.concatenate([oot_left, oot_right]) if (oot_left.size + oot_right.size) else oot_left\n        inn = white[p1 + delta : max(p1 + delta, p2 - delta)]\n\n        if oot.size == 0 or inn.size == 0:\n            sig_rel.append(np.nan); continue\n\n        n_oot, n_in = len(oot), len(inn)\n        var_oot = np.nanvar(oot, ddof=1)\n        var_in  = np.nanvar(inn, ddof=1)\n        oot_mean = float(np.nanmean(oot)) if np.isfinite(np.nanmean(oot)) else float(np.nanmean(white))\n\n        sigma_rel = np.sqrt(var_oot / max(n_oot,1) + var_in / max(n_in,1)) / max(oot_mean, eps)\n        sig_rel.append(sigma_rel)\n\n    s = np.asarray(sig_rel, dtype=float)\n    mask = np.isfinite(s) & (s > 0)\n    med = float(np.nanmedian(s[mask])) if mask.any() else 1.0\n\n    k = np.ones_like(s)\n    if med > 0 and np.isfinite(med):\n        k[mask] = np.sqrt(s[mask] / med)\n    \n    k = np.clip(k, 0.92, 1.22)\n\n    return k * cfg.SIGMA * 1.04\n\n\nclass SignalProcessor:\n    def __init__(self, config):\n        self.cfg = config\n        self.adc_info = pd.read_csv(f\"{self.cfg.DATA_PATH}/adc_info.csv\")\n        self.planet_ids = pd.read_csv(f'{self.cfg.DATA_PATH}/{self.cfg.DATASET}_star_info.csv', index_col='planet_id').index.astype(int)\n\n    def _apply_linear_corr(self, linear_corr, signal):\n        coeffs = np.flip(linear_corr, axis=0)\n        x = signal.astype(np.float64, copy=False)\n        out = np.empty_like(x, dtype=np.float64)\n        out[...] = coeffs[0]\n        for k in range(1, coeffs.shape[0]):\n            np.multiply(out, x, out=out)\n            out += coeffs[k]\n\n        return out.astype(signal.dtype, copy=False)\n\n    def _calibrate_single_signal(self, planet_id, sensor):\n        sensor_cfg = self.cfg.SENSOR_CONFIG[sensor]\n\n        signal = pd.read_parquet(f\"{self.cfg.DATA_PATH}/{self.cfg.DATASET}/{planet_id}/{sensor}_signal_0.parquet\").to_numpy()\n        dark = pd.read_parquet(f\"{self.cfg.DATA_PATH}/{self.cfg.DATASET}/{planet_id}/{sensor}_calibration_0/dark.parquet\").to_numpy()\n        dead = pd.read_parquet(f\"{self.cfg.DATA_PATH}/{self.cfg.DATASET}/{planet_id}/{sensor}_calibration_0/dead.parquet\").to_numpy()\n        flat = pd.read_parquet(f\"{self.cfg.DATA_PATH}/{self.cfg.DATASET}/{planet_id}/{sensor}_calibration_0/flat.parquet\").to_numpy()\n        linear_corr = pd.read_parquet(f\"{self.cfg.DATA_PATH}/{self.cfg.DATASET}/{planet_id}/{sensor}_calibration_0/linear_corr.parquet\").values.astype(np.float64).reshape(sensor_cfg[\"linear_corr_shape\"])\n\n        signal = signal.reshape(sensor_cfg[\"raw_shape\"])\n        gain = self.adc_info[f\"{sensor}_adc_gain\"].iloc[0]\n        offset = self.adc_info[f\"{sensor}_adc_offset\"].iloc[0]\n        signal = signal / gain + offset\n\n        hot = sigma_clip(dark, sigma=5, maxiters=5).mask\n\n        if sensor == \"AIRS-CH0\":\n            signal = signal[:, :, self.cfg.CUT_INF : self.cfg.CUT_SUP]\n            linear_corr = linear_corr[:, :, self.cfg.CUT_INF : self.cfg.CUT_SUP]\n            dark = dark[:, self.cfg.CUT_INF : self.cfg.CUT_SUP]\n            dead = dead[:, self.cfg.CUT_INF : self.cfg.CUT_SUP]\n            flat = flat[:, self.cfg.CUT_INF : self.cfg.CUT_SUP]\n            hot = hot[:, self.cfg.CUT_INF : self.cfg.CUT_SUP]\n\n\n        if sensor == \"FGS1\":\n            y0, y1, x0, x1 = 10, 22, 10, 22\n            signal = signal[:, y0:y1, x0:x1]\n            dark   = dark[y0:y1, x0:x1]\n            dead   = dead[y0:y1, x0:x1]\n            flat   = flat[y0:y1, x0:x1]\n            linear_corr = linear_corr[:, y0:y1, x0:x1]\n            hot    = hot[y0:y1, x0:x1]\n\n        np.maximum(signal, 0, out=signal)\n\n        if sensor == \"FGS1\":\n            signal = self._apply_linear_corr(linear_corr, signal)\n        elif sensor == \"AIRS-CH0\":\n            sl = (slice(None), slice(10, 22), slice(None))\n            signal[sl] = self._apply_linear_corr(linear_corr[:, 10:22, :], signal[sl])\n        else:\n            signal = self._apply_linear_corr(linear_corr, signal)\n\n        base_dt, increment = sensor_cfg[\"dt_pattern\"]\n        even_scale = base_dt\n        odd_scale  = base_dt + increment\n\n        signal[::2] -= dark * even_scale\n        signal[1::2] -= dark * odd_scale\n\n        \n        return signal\n\n    def _preprocess_calibrated_signal(self, calibrated_signal, sensor):\n        sensor_cfg = self.cfg.SENSOR_CONFIG[sensor]\n        binning = sensor_cfg[\"binning\"]\n\n        if sensor == \"AIRS-CH0\":\n            signal_roi = calibrated_signal[:, 10:22, :]\n        elif sensor == \"FGS1\":\n            signal_roi = calibrated_signal[:, 10:22, 10:22]\n            signal_roi = signal_roi.reshape(signal_roi.shape[0], -1)\n        \n        mean_signal = np.nanmean(signal_roi, axis=1)\n\n        cds_signal = mean_signal[1::2] - mean_signal[0::2]\n\n        n_bins = cds_signal.shape[0] // binning\n        binned = np.array([\n            cds_signal[j*binning : (j+1)*binning].mean(axis=0) \n            for j in range(n_bins)\n        ])\n\n        if sensor == \"AIRS-CH0\":\n            q_lo = np.nanpercentile(binned, 5.0, axis=1, keepdims=True)\n            q_hi = np.nanpercentile(binned, 95.0, axis=1, keepdims=True)\n            np.clip(binned, q_lo, q_hi, out=binned)\n\n        if sensor == \"FGS1\":\n            binned = binned.reshape((binned.shape[0], 1))\n\n        if sensor == \"AIRS-CH0\":\n            var = np.nanvar(binned, axis=0, ddof=1)\n            med = np.nanmedian(var)\n\n            safe_var = np.where(~np.isfinite(var) | (var <= 0), med if (np.isfinite(med) and med > 0) else 1.0, var)\n            w = 1.0 / safe_var\n\n            lo, hi = np.nanpercentile(w, 5.0), np.nanpercentile(w, 95.0)\n            if np.isfinite(lo) and np.isfinite(hi) and lo < hi:\n                w = np.clip(w, lo, hi)\n\n            M = binned.shape[1]\n            s = np.nansum(w)\n            if np.isfinite(s) and s > 0:\n                w = w * (M / s)\n            else:\n                w = np.ones_like(w)\n\n            binned *= w[None, :]\n\n\n        return binned\n\n    def _process_planet_sensor(self, args):\n        planet_id, sensor = args['planet_id'], args['sensor']\n        calibrated = self._calibrate_single_signal(planet_id, sensor)\n        preprocessed = self._preprocess_calibrated_signal(calibrated, sensor)\n        return preprocessed\n\n    def process_all_data(self):\n        args_fgs1 = [dict(planet_id=planet_id, sensor=\"FGS1\") for planet_id in self.planet_ids]\n        preprocessed_fgs1 = pqdm(args_fgs1, self._process_planet_sensor, n_jobs=self.cfg.N_JOBS)\n\n        args_airs_ch0 = [dict(planet_id=planet_id, sensor=\"AIRS-CH0\") for planet_id in self.planet_ids]\n        preprocessed_airs_ch0 = pqdm(args_airs_ch0, self._process_planet_sensor, n_jobs=self.cfg.N_JOBS)\n\n        preprocessed_signal = np.concatenate(\n            [np.stack(preprocessed_fgs1), np.stack(preprocessed_airs_ch0)], axis=2\n        )\n        \n        return preprocessed_signal\n    \n\nclass TransitModel:\n    def __init__(self, config):\n        self.cfg = config\n\n    def _phase_detector(self, signal):\n        search_slice = self.cfg.MODEL_PHASE_DETECTION_SLICE\n        min_index = np.argmin(signal[search_slice]) + search_slice.start\n        \n        signal1 = signal[:min_index]\n        signal2 = signal[min_index:]\n\n        grad1 = np.gradient(signal1)\n        grad1 /= grad1.max()\n        \n        grad2 = np.gradient(signal2)\n        grad2 /= grad2.max()\n\n        phase1 = np.argmin(grad1)\n        phase2 = np.argmax(grad2) + min_index\n\n        return phase1, phase2\n    \n    def _objective_function(self, s, signal, phase1, phase2):\n        delta = self.cfg.MODEL_OPTIMIZATION_DELTA\n        power = self.cfg.MODEL_POLYNOMIAL_DEGREE\n\n        if phase1 - delta <= 0 or phase2 + delta >= len(signal) or phase2 - delta - (phase1 + delta) < 5:\n            delta = 2\n\n        y = np.concatenate([\n            signal[: phase1 - delta],\n            signal[phase1 + delta : phase2 - delta] * (1 + s),\n            signal[phase2 + delta :]\n        ])\n        x = np.arange(len(y))\n\n        coeffs = np.polyfit(x, y, deg=power)\n        poly = np.poly1d(coeffs)\n        error = np.abs(poly(x) - y).mean()\n        \n        return error\n\n    def predict(self, single_preprocessed_signal):\n        signal_1d = single_preprocessed_signal[:, 1:].mean(axis=1)\n        signal_1d = savgol_filter(signal_1d, 20, 2)\n        \n        phase1, phase2 = self._phase_detector(signal_1d)\n\n        phase1 = max(self.cfg.MODEL_OPTIMIZATION_DELTA, phase1)\n        phase2 = min(len(signal_1d) - self.cfg.MODEL_OPTIMIZATION_DELTA - 1, phase2)    \n\n        result = minimize(\n            fun=self._objective_function,\n            x0=[0.0001],\n            args=(signal_1d, phase1, phase2),\n            method=\"Nelder-Mead\"\n        )\n        \n        return result.x[0]\n\n    def predict_all(self, preprocessed_signals):\n        predictions = [\n            self.predict(preprocessed_signal)\n            for preprocessed_signal in tqdm(preprocessed_signals)\n        ]\n        return np.array(predictions) * self.cfg.SCALE\n    \nclass SubmissionGenerator:\n    def __init__(self, config):\n        self.cfg = config\n        self.sample_submission = pd.read_csv(\"/kaggle/input/ariel-data-challenge-2025/sample_submission.csv\", index_col=\"planet_id\")\n\n    def create(self, predictions1, predictions2, predictions, sigma_fgs=None, sigma_air=None):\n        planet_ids = self.sample_submission.index\n        n_mu = self.sample_submission.shape[1] // 2\n\n        preds = np.asarray(predictions, dtype=float).reshape(-1)\n        mu = np.tile(preds.reshape(-1, 1), (1, n_mu))\n        mu = np.clip(mu, 0, None)\n\n        sigmas = np.full_like(mu, self.cfg.SIGMA, dtype=float)\n        if sigma_fgs is not None:\n            sigma_fgs = np.asarray(sigma_fgs, dtype=float).reshape(-1)\n            sigmas[:, 0] = np.clip(sigma_fgs, 1e-6, 0.1)\n        if sigma_air is not None:\n            sigma_air = np.asarray(sigma_air, dtype=float).reshape(-1, 1)\n            sigmas[:, 1:] = np.clip(sigma_air, 1e-6, 0.1)\n\n        submission_df = pd.DataFrame(\n            np.concatenate([mu, sigmas], axis=1),\n            columns=self.sample_submission.columns,\n            index=planet_ids\n        )\n        submission_df.iloc[:, 1:283] = predictions2\n        submission_df.iloc[:, 0] = predictions1\n\n        submission_df.to_csv(\"submission.csv\")\n        return submission_df\n\n\n\nconfig = Config()\n    \nsignal_processor = SignalProcessor(config)\npreprocessed_data = signal_processor.process_all_data()\n\nmodel = TransitModel(config)\npredictions = model.predict_all(preprocessed_data)","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:11:55.878125Z","iopub.status.busy":"2025-09-19T07:11:55.877762Z","iopub.status.idle":"2025-09-19T07:12:01.436796Z","shell.execute_reply":"2025-09-19T07:12:01.435927Z"},"papermill":{"duration":5.564138,"end_time":"2025-09-19T07:12:01.438184","exception":false,"start_time":"2025-09-19T07:11:55.874046","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"0e0e4c50","cell_type":"code","source":"processor = SignalProcessor(Config)\nStarInfo = pd.read_csv(ROOT_PATH + f\"/{MODE}_star_info.csv\")\nStarInfo[\"planet_id\"] = StarInfo[\"planet_id\"].astype(int)\nPlanetIds = StarInfo[\"planet_id\"].tolist()\nStarInfo = StarInfo.set_index(\"planet_id\")\nStarInfo","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.448727Z","iopub.status.busy":"2025-09-19T07:12:01.44845Z","iopub.status.idle":"2025-09-19T07:12:01.474981Z","shell.execute_reply":"2025-09-19T07:12:01.474235Z"},"papermill":{"duration":0.033405,"end_time":"2025-09-19T07:12:01.476283","exception":false,"start_time":"2025-09-19T07:12:01.442878","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"620c5ade","cell_type":"code","source":"predictions = pd.DataFrame(predictions)\npredictions = predictions.rename(columns={0: \"transit_depth\"})\npredictions","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.485096Z","iopub.status.busy":"2025-09-19T07:12:01.484832Z","iopub.status.idle":"2025-09-19T07:12:01.491488Z","shell.execute_reply":"2025-09-19T07:12:01.490905Z"},"papermill":{"duration":0.012145,"end_time":"2025-09-19T07:12:01.492559","exception":false,"start_time":"2025-09-19T07:12:01.480414","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"2046d9f7","cell_type":"code","source":"input_df = StarInfo.copy()\npred_series = predictions[\"transit_depth\"].set_axis(input_df.index, copy=False)\n\ninput_df.insert(0, \"transit_depth\", (pred_series * 10000).to_numpy())\n\nfeatures = [\"transit_depth\", \"Rs\", \"i\"]\nX = input_df[features].values.astype(\"float32\")","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.500817Z","iopub.status.busy":"2025-09-19T07:12:01.500272Z","iopub.status.idle":"2025-09-19T07:12:01.507467Z","shell.execute_reply":"2025-09-19T07:12:01.506953Z"},"papermill":{"duration":0.012284,"end_time":"2025-09-19T07:12:01.50855","exception":false,"start_time":"2025-09-19T07:12:01.496266","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"5263d2d0","cell_type":"code","source":"X","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.516441Z","iopub.status.busy":"2025-09-19T07:12:01.516234Z","iopub.status.idle":"2025-09-19T07:12:01.520645Z","shell.execute_reply":"2025-09-19T07:12:01.519981Z"},"papermill":{"duration":0.009487,"end_time":"2025-09-19T07:12:01.521671","exception":false,"start_time":"2025-09-19T07:12:01.512184","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"4f7f8efa","cell_type":"code","source":"class ResidualBlock(nn.Module):\n    def __init__(self, dim, p=0.2):\n        super().__init__()\n        self.fc1 = nn.Linear(dim, dim)\n        self.bn1 = nn.BatchNorm1d(dim)\n        self.fc2 = nn.Linear(dim, dim)\n        self.bn2 = nn.BatchNorm1d(dim)\n        self.relu = nn.ReLU()\n        self.dropout = nn.Dropout(p)\n\n    def forward(self, x):\n        identity = x\n        out = self.relu(self.bn1(self.fc1(x)))\n        out = self.dropout(out)\n        out = self.bn2(self.fc2(out))\n        return self.relu(out + identity)\n\n\nclass ResNetMLP(nn.Module):\n    def __init__(self, input_dim=3, hidden_dim=32, output_dim=1, num_blocks=3, dropout_rate=0.2):\n        super().__init__()\n        self.input_layer = nn.Linear(input_dim, hidden_dim)\n        self.blocks = nn.Sequential(*[ResidualBlock(hidden_dim, p=dropout_rate) for _ in range(num_blocks)])\n        self.output_layer = nn.Linear(hidden_dim, output_dim)\n\n    def forward(self, x):\n        x = self.input_layer(x)\n        x = self.blocks(x)\n        x = self.output_layer(x)\n        return x\nmodel = ResNetMLP(num_blocks=80, dropout_rate=0.2)\nmodel.load_state_dict(torch.load(\"/kaggle/input/fgs1/pytorch/default/1/best_model.pth\"))\nmodel.eval()","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.530526Z","iopub.status.busy":"2025-09-19T07:12:01.53017Z","iopub.status.idle":"2025-09-19T07:12:01.790104Z","shell.execute_reply":"2025-09-19T07:12:01.789338Z"},"papermill":{"duration":0.266015,"end_time":"2025-09-19T07:12:01.791349","exception":false,"start_time":"2025-09-19T07:12:01.525334","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"1ad449e4","cell_type":"code","source":"X_tensor = torch.tensor(X, dtype=torch.float32)\nwith torch.no_grad():\n    predictions1 = model(X_tensor).numpy()\npredictions1 /= 10000","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.80115Z","iopub.status.busy":"2025-09-19T07:12:01.80068Z","iopub.status.idle":"2025-09-19T07:12:01.856151Z","shell.execute_reply":"2025-09-19T07:12:01.85557Z"},"papermill":{"duration":0.061549,"end_time":"2025-09-19T07:12:01.85738","exception":false,"start_time":"2025-09-19T07:12:01.795831","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"10b87c11","cell_type":"code","source":"predictions1","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.867485Z","iopub.status.busy":"2025-09-19T07:12:01.867182Z","iopub.status.idle":"2025-09-19T07:12:01.871339Z","shell.execute_reply":"2025-09-19T07:12:01.870791Z"},"papermill":{"duration":0.010167,"end_time":"2025-09-19T07:12:01.87234","exception":false,"start_time":"2025-09-19T07:12:01.862173","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"770d67b3","cell_type":"code","source":"class ResidualBlock2(nn.Module):\n    def __init__(self, dim, p=0.2):\n        super().__init__()\n        self.fc1 = nn.Linear(dim, dim)\n        self.bn1 = nn.BatchNorm1d(dim)\n        self.fc2 = nn.Linear(dim, dim)\n        self.bn2 = nn.BatchNorm1d(dim)\n        self.relu = nn.ReLU()\n        self.dropout = nn.Dropout(p)\n\n    def forward(self, x):\n        identity = x\n        out = self.relu(self.bn1(self.fc1(x)))\n        out = self.dropout(out)\n        out = self.bn2(self.fc2(out))\n        return self.relu(out + identity)\n\n\nclass ResNetMLP2(nn.Module):\n    def __init__(self, input_dim=3, hidden_dim=128, output_dim = 282, num_blocks=3, dropout_rate=0.2):\n        super().__init__()\n        self.input_layer = nn.Linear(input_dim, hidden_dim)\n        self.blocks = nn.Sequential(*[ResidualBlock(hidden_dim, p=dropout_rate) for _ in range(num_blocks)])\n        self.output_layer = nn.Linear(hidden_dim, output_dim)\n\n    def forward(self, x):\n        x = self.input_layer(x)\n        x = self.blocks(x)\n        x = self.output_layer(x)\n        return x","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.881752Z","iopub.status.busy":"2025-09-19T07:12:01.881298Z","iopub.status.idle":"2025-09-19T07:12:01.887578Z","shell.execute_reply":"2025-09-19T07:12:01.887027Z"},"papermill":{"duration":0.012091,"end_time":"2025-09-19T07:12:01.88868","exception":false,"start_time":"2025-09-19T07:12:01.876589","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"f71fe661","cell_type":"code","source":"model2 = ResNetMLP2(num_blocks=80, dropout_rate=0.3)\nmodel2.load_state_dict(torch.load(\"/kaggle/input/airs/pytorch/default/1/best_model_airs.pth\"))\nmodel2.eval()\n\nwith torch.no_grad():\n    predictions2 = model2(X_tensor).numpy()\npredictions2 /= 10000","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:01.897657Z","iopub.status.busy":"2025-09-19T07:12:01.897483Z","iopub.status.idle":"2025-09-19T07:12:02.209574Z","shell.execute_reply":"2025-09-19T07:12:02.208864Z"},"papermill":{"duration":0.318232,"end_time":"2025-09-19T07:12:02.211175","exception":false,"start_time":"2025-09-19T07:12:01.892943","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"7846445e","cell_type":"code","source":"predictions2","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:02.22369Z","iopub.status.busy":"2025-09-19T07:12:02.223459Z","iopub.status.idle":"2025-09-19T07:12:02.229095Z","shell.execute_reply":"2025-09-19T07:12:02.228478Z"},"papermill":{"duration":0.012663,"end_time":"2025-09-19T07:12:02.230096","exception":false,"start_time":"2025-09-19T07:12:02.217433","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"id":"752e8379","cell_type":"code","source":"sigma_fgs_vec = estimate_sigma_fgs(preprocessed_data, config)\nsigma_air_vec = estimate_sigma_air(preprocessed_data, config)\n\n\nsubmission_generator = SubmissionGenerator(config)\nsubmission = submission_generator.create(predictions1, predictions2, predictions, sigma_fgs=sigma_fgs_vec, sigma_air=sigma_air_vec)\nsubmission","metadata":{"execution":{"iopub.execute_input":"2025-09-19T07:12:02.239497Z","iopub.status.busy":"2025-09-19T07:12:02.239128Z","iopub.status.idle":"2025-09-19T07:12:02.265758Z","shell.execute_reply":"2025-09-19T07:12:02.265123Z"},"papermill":{"duration":0.032549,"end_time":"2025-09-19T07:12:02.266924","exception":false,"start_time":"2025-09-19T07:12:02.234375","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}