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notbook forked from [Vitaly Kudelya](https://www.kaggle.com/vitalykudelya): [aNeurIPS: Non-ML Transit Curve Fitting](https://www.kaggle.com/code/vitalykudelya/neurips-non-ml-transit-curve-fitting)\n\n\nMy idea inspired by [GROMML](https://www.kaggle.com/gromml): [NeurIPS - Scale Sigmas](https://www.kaggle.com/code/gromml/neurips-scale-sigmas)\n\nWe can scale $\\textbf{sigma}$ by Linear Regression, input features is preprocessed signal and target is standard deviation of $y_{train}$. Detail in cell 10 (train) and cell 12 (infer), extra less then 5 line.\n\n\nThis notebook builds upon the excellent work done in the following baselines: <br />\n- [Sergei Fironov](https://www.kaggle.com/sergeifironov): [ariel_only_correlation](https://www.kaggle.com/code/sergeifironov/ariel-only-correlation)\n- [Laurent Pourchot](https://www.kaggle.com/pourchot): [Ariel Data Challenge 2024](https://www.kaggle.com/code/pourchot/ariel-data-challenge-2024?scriptVersionId=195970765)\n- [qianc](https://www.kaggle.com/xiaocao123): [Ariel Data Challenge 2024](https://www.kaggle.com/code/xiaocao123/ariel-data-challenge-2024)\n\nThank you to the authors for sharing their insights and code 🙏\n\n## Granularity\nGranularity of predictions is one planet.\nFor each planet we need to predict `283` `spectras` (float number) for corresponding wave lengths and each with an associated uncertainty `sigma`.\n\n## Data\nFor each planet we have:\n- **FGS1** signal (`FGS1_signal_0.parquet`) `[135000, 32 * 32]` (`135000` time steps, each representing `0.1` seconds and `32*32` is sensor data) \n- **AIRS-CH0** signal (`AIRS-CH0_signal_0.parquet`) `[11250, 32 * 356]` (`11250` time steps, each representing `1.2` seconds and `32*356` is sensor data `32` spatial and `356` different wave lengths)\n\nAlso we have additional calibration data to make raw data from sensors more informative.\n\n## Baseline overview\n    \n### Preprocessing\n* Calibration of raw signal. It consists of scaling the signal, eliminating dead pixels and other complicated techniques [calibration discussion](https://www.kaggle.com/competitions/ariel-data-challenge-2024/discussion/528066). Only `40-321` wave_length positions (they should relate to `2-283` positions in targets) are taken for **AIRS-CH0**\n* Calibrated signal is divided into time chunks and aggregated (mean). It reduces number of time points and we get\n  `[187, 32, 32]` for **FGS1** (`187` time steps) and `[187, 32, 282]` for **AIRS-CH0** (`187` time steps)\n* Only center spatial pixels are taken and get `[187, 12, 12]` for **FGS1** and `[187, 12, 282]` for **AIRS-CH0**\n* Aggregate (mean) sensor data per special dimension. We get `[187, 1]` for **FGS1** and `[187, 282]` for **AIRS-CH0**\n* Concatenate **FGS1** and **AIRS-CH0** to `[187, 283]` array. Let's call it a `preprocessed_signal`\n\n### Spectra prediction\n* Aggregate (mean) of our `preprocessed_signal` for `2-283` wave_length positions and get `[187]` data points (time dimension) as an input to further optimizations\n* Find time points `phase1` and `phase2` - start and end of planet going in front of the star (`phase_detector`)\n* Next we need to find a constant `s` (actually our further prediction of `spectra`) that after multiplication\n`signal[phase1:phase2] * (1 + s)` we can't see a \"step\" in the signal (more formally, the error after fitting 3nd power polynomial is minimal)","metadata":{}},{"cell_type":"code","source":"# install pqdm for parallel processing\n!pip install --no-index --find-links=/kaggle/input/ariel-2024-pqdm pqdm","metadata":{"_uuid":"551c5f29-196c-4b4f-ae13-8e330a26d5cf","_cell_guid":"a1797359-3baf-4c1a-a0e0-7448f15d7d26","trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:57:53.607001Z","iopub.execute_input":"2025-08-11T19:57:53.607731Z","iopub.status.idle":"2025-08-11T19:57:57.798277Z","shell.execute_reply.started":"2025-08-11T19:57:53.607683Z","shell.execute_reply":"2025-08-11T19:57:57.796059Z"},"papermill":{"duration":14.056395,"end_time":"2024-10-17T09:42:27.397019","exception":false,"start_time":"2024-10-17T09:42:13.340624","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Librairies","metadata":{"_uuid":"fa38d47c-49ca-48f8-b877-19cf09a1850e","_cell_guid":"fd700e01-cba4-437c-b5d9-862c227e1bb7","trusted":true,"collapsed":false,"papermill":{"duration":0.005981,"end_time":"2024-10-17T09:42:27.409388","exception":false,"start_time":"2024-10-17T09:42:27.403407","status":"completed"},"tags":[],"jupyter":{"outputs_hidden":false}}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas.api.types\nimport scipy.stats\n\nfrom tqdm import tqdm\nfrom pqdm.threads import pqdm\nimport itertools\nimport pickle\n\nfrom scipy.optimize import minimize\nfrom sklearn.metrics import mean_squared_error\n\nimport plotly.express as px\n\nfrom astropy.stats import sigma_clip\nfrom scipy.signal import savgol_filter","metadata":{"_uuid":"24798c1e-8b26-4548-b007-fe790adb4e88","_cell_guid":"7167a660-8bc2-4b2c-951a-71fc5a4eb8e8","trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:57:57.801936Z","iopub.execute_input":"2025-08-11T19:57:57.802968Z","iopub.status.idle":"2025-08-11T19:57:57.813816Z","shell.execute_reply.started":"2025-08-11T19:57:57.802906Z","shell.execute_reply":"2025-08-11T19:57:57.812607Z"},"papermill":{"duration":3.680946,"end_time":"2024-10-17T09:42:31.096532","exception":false,"start_time":"2024-10-17T09:42:27.415586","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Config","metadata":{}},{"cell_type":"code","source":"class Config:\n    DATA_PATH = '/kaggle/input/ariel-data-challenge-2025'\n    DATASET = \"test\"\n\n    SCALE = 0.93960\n    SIGMA = 0.0009\n    \n    CUT_INF = 39\n    CUT_SUP = 250\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 = 7\n    MODEL_POLYNOMIAL_DEGREE = 3\n    \n    N_JOBS = 4","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:57:57.814961Z","iopub.execute_input":"2025-08-11T19:57:57.815304Z","iopub.status.idle":"2025-08-11T19:57:57.837211Z","shell.execute_reply.started":"2025-08-11T19:57:57.815283Z","shell.execute_reply":"2025-08-11T19:57:57.836215Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# SignalProcessor","metadata":{}},{"cell_type":"code","source":"class TransitModel:\n    def __init__(self, config):\n        self.cfg = config\n\n    def _phase_detector(self, signal):\n        # signal have shape as pot ==> use gradient to detect edge of this pot\n        # and truncate signal (phase1:phase2)\n        \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        # ? chi dung airs thoi a\n        signal_1d = single_preprocessed_signal.mean(axis=1) # (187, 211)\n        signal_1d = savgol_filter(signal_1d, 30, 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] * self.cfg.SCALE\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\nignore_cfg = Config\nnonML = TransitModel(ignore_cfg)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:57:57.838492Z","iopub.execute_input":"2025-08-11T19:57:57.838821Z","iopub.status.idle":"2025-08-11T19:57:57.865687Z","shell.execute_reply.started":"2025-08-11T19:57:57.838793Z","shell.execute_reply":"2025-08-11T19:57:57.864402Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class SignalProcessor:\n    def __init__(self, config):\n        self.cfg = config\n        # scale signal\n        self.adc_info = pd.read_csv(f\"{self.cfg.DATA_PATH}/adc_info.csv\")\n\n        # ids, (chieu cao can nang chua dung)\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        linear_corr_flipped = np.flip(linear_corr, axis=0)\n        corrected_signal = signal.copy()\n        \n        for x, y in itertools.product(range(signal.shape[1]), range(signal.shape[2])):\n            poly = np.poly1d(linear_corr_flipped[:, x, y])\n            corrected_signal[:, x, y] = poly(corrected_signal[:, x, y])\n            \n        return corrected_signal\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\"])   # shape (n_frame, w, h)\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        base_dt, increment = sensor_cfg[\"dt_pattern\"]   # 2 const\n        dt = np.ones(len(signal)) * base_dt\n        dt[1::2] += increment\n        \n        signal = signal.clip(0)\n        signal = self._apply_linear_corr(linear_corr, signal)\n        signal -= dark * dt[:, np.newaxis, np.newaxis]\n        \n        flat = flat.reshape(sensor_cfg[\"calibrated_shape\"])\n        flat[dead.reshape(sensor_cfg[\"calibrated_shape\"])] = np.nan\n        flat[hot.reshape(sensor_cfg[\"calibrated_shape\"])] = np.nan\n        \n        signal = signal / flat\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 == \"FGS1\":\n            binned = binned.reshape((binned.shape[0], 1))\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, just_for_test=False):\n        planet_ids = (self.planet_ids[:2] if just_for_test else self.planet_ids)\n\n        # process fgs1: out shape (batch, 187(sampled), 1)\n        args_fgs1 = [dict(planet_id=planet_id, sensor=\"FGS1\") for planet_id in planet_ids]\n        preprocessed_fgs1 = pqdm(args_fgs1, self._process_planet_sensor, n_jobs=self.cfg.N_JOBS)\n\n        # process airs-ch0: out shape (batch, 187, 211)\n        args_airs_ch0 = [dict(planet_id=planet_id, sensor=\"AIRS-CH0\") for planet_id in 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","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:57:57.868585Z","iopub.execute_input":"2025-08-11T19:57:57.868868Z","iopub.status.idle":"2025-08-11T19:57:57.896178Z","shell.execute_reply.started":"2025-08-11T19:57:57.868848Z","shell.execute_reply":"2025-08-11T19:57:57.895143Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Load","metadata":{}},{"cell_type":"code","source":"# cfg = Config\n# cfg.DATASET = \"train\"\n# preprocessor = SignalProcessor(cfg)\n# X = preprocessor.process_all_data()\n\n# To save time, preprossed data I save to file'preprocessed_data.csv'","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:57:57.897227Z","iopub.execute_input":"2025-08-11T19:57:57.897547Z","iopub.status.idle":"2025-08-11T19:57:57.920902Z","shell.execute_reply.started":"2025-08-11T19:57:57.897518Z","shell.execute_reply":"2025-08-11T19:57:57.92005Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pre_proc_path = '/kaggle/input/arieal-processed-signal/preprocessed_data.csv'\ntrain_path = '/kaggle/input/ariel-data-challenge-2025/train.csv'\ndf = pd.read_csv(pre_proc_path)\ny = pd.read_csv(train_path)\ny = pd.merge(df.iloc[:, :1], y, on='planet_id')\n\nX = df.iloc[:, 1:].to_numpy().reshape(-1, 187, 212)\nX = X[:, :, 1:]\ny = y.iloc[:, 1:].to_numpy()\n\nX.shape, y.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:57:57.921968Z","iopub.execute_input":"2025-08-11T19:57:57.922336Z","iopub.status.idle":"2025-08-11T19:58:36.07129Z","shell.execute_reply.started":"2025-08-11T19:57:57.922309Z","shell.execute_reply":"2025-08-11T19:58:36.070276Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Linear Regression for Sigma","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\nlin_reg = LinearRegression()\nlin_reg.fit(X.mean(axis=2), y.std(axis=1))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:58:36.072369Z","iopub.execute_input":"2025-08-11T19:58:36.072627Z","iopub.status.idle":"2025-08-11T19:58:36.154044Z","shell.execute_reply.started":"2025-08-11T19:58:36.072606Z","shell.execute_reply":"2025-08-11T19:58:36.153242Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Infer","metadata":{}},{"cell_type":"code","source":"test_cfg = Config()\nsignal_processor = SignalProcessor(test_cfg)\nX_test = signal_processor.process_all_data()  # shape(n, 187, 212)\nX_test = X_test[:, :, 1:]\nX_test.shape # (n, 187, 211)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:58:36.154877Z","iopub.execute_input":"2025-08-11T19:58:36.155176Z","iopub.status.idle":"2025-08-11T19:58:59.374847Z","shell.execute_reply.started":"2025-08-11T19:58:36.155154Z","shell.execute_reply":"2025-08-11T19:58:59.373837Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"preds = nonML.predict_all(X_test)\npreds = preds.clip(0)\n\npreds = np.repeat(preds, 283).reshape(len(preds), -1)\nsigma = lin_reg.predict(X_test.mean(axis=2))\nsigma = sigma.clip(0.0008)\nsigmas = np.repeat(sigma, 283).reshape(len(sigma), -1)\n\nsubmission = np.concatenate([preds, sigmas], axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:58:59.375889Z","iopub.execute_input":"2025-08-11T19:58:59.376362Z","iopub.status.idle":"2025-08-11T19:58:59.404844Z","shell.execute_reply.started":"2025-08-11T19:58:59.376327Z","shell.execute_reply":"2025-08-11T19:58:59.403939Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sample_submission = pd.read_csv('/kaggle/input/ariel-data-challenge-2025/sample_submission.csv', index_col=\"planet_id\")\nplanet_ids = sample_submission.index\ndf = pd.DataFrame(\n            submission,\n            columns=sample_submission.columns,\n            index=planet_ids\n        )\n\ndf.to_csv(\"submission.csv\")\n\ndf","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-11T19:58:59.40583Z","iopub.execute_input":"2025-08-11T19:58:59.40627Z","iopub.status.idle":"2025-08-11T19:58:59.445942Z","shell.execute_reply.started":"2025-08-11T19:58:59.406229Z","shell.execute_reply":"2025-08-11T19:58:59.445256Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}