{"cells":[{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5"},"cell_type":"markdown","source":"## General information\n\nJust Andrew's Data Munging plus a quick Genetic Programming Model"},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_kg_hide-input":true,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom tqdm import tqdm_notebook\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm import NuSVR, SVR\nfrom sklearn.metrics import mean_absolute_error\npd.options.display.precision = 15\n\nimport lightgbm as lgb\nimport xgboost as xgb\nimport time\nimport datetime\nfrom catboost import CatBoostRegressor\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import StratifiedKFold, KFold, RepeatedKFold\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.linear_model import LinearRegression\nimport gc\nimport seaborn as sns\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom scipy.signal import hilbert\nfrom scipy.signal import hann\nfrom scipy.signal import convolve\nfrom scipy import stats\nfrom sklearn.kernel_ridge import KernelRidge","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"bc314853a64c3ee3b7b14dd2ad99c06e77e16561","trusted":true},"cell_type":"code","source":"%%time\ntrain = pd.read_csv('../input/train.csv', dtype={'acoustic_data': np.int16, 'time_to_failure': np.float32})","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_uuid":"029a1f0d374a39d7fac9a7c81d417973bce1c884","trusted":true},"cell_type":"code","source":"train_acoustic_data_small = train['acoustic_data'].values[::50]\ntrain_time_to_failure_small = train['time_to_failure'].values[::50]\n\nfig, ax1 = plt.subplots(figsize=(16, 8))\nplt.title(\"Trends of acoustic_data and time_to_failure. 2% of data (sampled)\")\nplt.plot(train_acoustic_data_small, color='b')\nax1.set_ylabel('acoustic_data', color='b')\nplt.legend(['acoustic_data'])\nax2 = ax1.twinx()\nplt.plot(train_time_to_failure_small, color='g')\nax2.set_ylabel('time_to_failure', color='g')\nplt.legend(['time_to_failure'], loc=(0.875, 0.9))\nplt.grid(False)\n\ndel train_acoustic_data_small\ndel train_time_to_failure_small","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_uuid":"e75947cef3bff46114dd09ce164625871d54b30e","trusted":true},"cell_type":"code","source":"# Create a training file with simple derived features\nrows = 150_000\nsegments = int(np.floor(train.shape[0] / rows))\n\ndef add_trend_feature(arr, abs_values=False):\n idx = np.array(range(len(arr)))\n if abs_values:\n arr = np.abs(arr)\n lr = LinearRegression()\n lr.fit(idx.reshape(-1, 1), arr)\n return lr.coef_[0]\n\ndef classic_sta_lta(x, length_sta, length_lta):\n \n sta = np.cumsum(x ** 2)\n\n # Convert to float\n sta = np.require(sta, dtype=np.float)\n\n # Copy for LTA\n lta = sta.copy()\n\n # Compute the STA and the LTA\n sta[length_sta:] = sta[length_sta:] - sta[:-length_sta]\n sta /= length_sta\n lta[length_lta:] = lta[length_lta:] - lta[:-length_lta]\n lta /= length_lta\n\n # Pad zeros\n sta[:length_lta - 1] = 0\n\n # Avoid division by zero by setting zero values to tiny float\n dtiny = np.finfo(0.0).tiny\n idx = lta < dtiny\n lta[idx] = dtiny\n\n return sta / lta\n\nX_tr = pd.DataFrame(index=range(segments), dtype=np.float64)\n\ny_tr = pd.DataFrame(index=range(segments), dtype=np.float64, columns=['time_to_failure'])\n\ntotal_mean = train['acoustic_data'].mean()\ntotal_std = train['acoustic_data'].std()\ntotal_max = train['acoustic_data'].max()\ntotal_min = train['acoustic_data'].min()\ntotal_sum = train['acoustic_data'].sum()\ntotal_abs_sum = np.abs(train['acoustic_data']).sum()\n\nfor segment in tqdm_notebook(range(segments)):\n seg = train.iloc[segment*rows:segment*rows+rows]\n x = pd.Series(seg['acoustic_data'].values)\n y = seg['time_to_failure'].values[-1]\n \n y_tr.loc[segment, 'time_to_failure'] = y\n X_tr.loc[segment, 'mean'] = x.mean()\n X_tr.loc[segment, 'std'] = x.std()\n X_tr.loc[segment, 'max'] = x.max()\n X_tr.loc[segment, 'min'] = x.min()\n \n \n X_tr.loc[segment, 'mean_change_abs'] = np.mean(np.diff(x))\n X_tr.loc[segment, 'mean_change_rate'] = np.mean(np.nonzero((np.diff(x) / x[:-1]))[0])\n X_tr.loc[segment, 'abs_max'] = np.abs(x).max()\n X_tr.loc[segment, 'abs_min'] = np.abs(x).min()\n \n X_tr.loc[segment, 'std_first_50000'] = x[:50000].std()\n X_tr.loc[segment, 'std_last_50000'] = x[-50000:].std()\n X_tr.loc[segment, 'std_first_10000'] = x[:10000].std()\n X_tr.loc[segment, 'std_last_10000'] = x[-10000:].std()\n \n X_tr.loc[segment, 'avg_first_50000'] = x[:50000].mean()\n X_tr.loc[segment, 'avg_last_50000'] = x[-50000:].mean()\n X_tr.loc[segment, 'avg_first_10000'] = x[:10000].mean()\n X_tr.loc[segment, 'avg_last_10000'] = x[-10000:].mean()\n \n X_tr.loc[segment, 'min_first_50000'] = x[:50000].min()\n X_tr.loc[segment, 'min_last_50000'] = x[-50000:].min()\n X_tr.loc[segment, 'min_first_10000'] = x[:10000].min()\n X_tr.loc[segment, 'min_last_10000'] = x[-10000:].min()\n \n X_tr.loc[segment, 'max_first_50000'] = x[:50000].max()\n X_tr.loc[segment, 'max_last_50000'] = x[-50000:].max()\n X_tr.loc[segment, 'max_first_10000'] = x[:10000].max()\n X_tr.loc[segment, 'max_last_10000'] = x[-10000:].max()\n \n X_tr.loc[segment, 'max_to_min'] = x.max() / np.abs(x.min())\n X_tr.loc[segment, 'max_to_min_diff'] = x.max() - np.abs(x.min())\n X_tr.loc[segment, 'count_big'] = len(x[np.abs(x) > 500])\n X_tr.loc[segment, 'sum'] = x.sum()\n \n X_tr.loc[segment, 'mean_change_rate_first_50000'] = np.mean(np.nonzero((np.diff(x[:50000]) / x[:50000][:-1]))[0])\n X_tr.loc[segment, 'mean_change_rate_last_50000'] = np.mean(np.nonzero((np.diff(x[-50000:]) / x[-50000:][:-1]))[0])\n X_tr.loc[segment, 'mean_change_rate_first_10000'] = np.mean(np.nonzero((np.diff(x[:10000]) / x[:10000][:-1]))[0])\n X_tr.loc[segment, 'mean_change_rate_last_10000'] = np.mean(np.nonzero((np.diff(x[-10000:]) / x[-10000:][:-1]))[0])\n \n X_tr.loc[segment, 'q95'] = np.quantile(x, 0.95)\n X_tr.loc[segment, 'q99'] = np.quantile(x, 0.99)\n X_tr.loc[segment, 'q05'] = np.quantile(x, 0.05)\n X_tr.loc[segment, 'q01'] = np.quantile(x, 0.01)\n \n X_tr.loc[segment, 'abs_q95'] = np.quantile(np.abs(x), 0.95)\n X_tr.loc[segment, 'abs_q99'] = np.quantile(np.abs(x), 0.99)\n X_tr.loc[segment, 'abs_q05'] = np.quantile(np.abs(x), 0.05)\n X_tr.loc[segment, 'abs_q01'] = np.quantile(np.abs(x), 0.01)\n \n X_tr.loc[segment, 'trend'] = add_trend_feature(x)\n X_tr.loc[segment, 'abs_trend'] = add_trend_feature(x, abs_values=True)\n X_tr.loc[segment, 'abs_mean'] = np.abs(x).mean()\n X_tr.loc[segment, 'abs_std'] = np.abs(x).std()\n \n X_tr.loc[segment, 'mad'] = x.mad()\n X_tr.loc[segment, 'kurt'] = x.kurtosis()\n X_tr.loc[segment, 'skew'] = x.skew()\n X_tr.loc[segment, 'med'] = x.median()\n \n X_tr.loc[segment, 'Hilbert_mean'] = np.abs(hilbert(x)).mean()\n X_tr.loc[segment, 'Hann_window_mean'] = (convolve(x, hann(150), mode='same') / sum(hann(150))).mean()\n X_tr.loc[segment, 'classic_sta_lta1_mean'] = classic_sta_lta(x, 500, 10000).mean()\n X_tr.loc[segment, 'classic_sta_lta2_mean'] = classic_sta_lta(x, 5000, 100000).mean()\n X_tr.loc[segment, 'classic_sta_lta3_mean'] = classic_sta_lta(x, 3333, 6666).mean()\n X_tr.loc[segment, 'classic_sta_lta4_mean'] = classic_sta_lta(x, 10000, 25000).mean()\n X_tr.loc[segment, 'Moving_average_700_mean'] = x.rolling(window=700).mean().mean(skipna=True)\n X_tr.loc[segment, 'Moving_average_1500_mean'] = x.rolling(window=1500).mean().mean(skipna=True)\n X_tr.loc[segment, 'Moving_average_3000_mean'] = x.rolling(window=3000).mean().mean(skipna=True)\n X_tr.loc[segment, 'Moving_average_6000_mean'] = x.rolling(window=6000).mean().mean(skipna=True)\n ewma = pd.Series.ewm\n X_tr.loc[segment, 'exp_Moving_average_300_mean'] = (ewma(x, span=300).mean()).mean(skipna=True)\n X_tr.loc[segment, 'exp_Moving_average_3000_mean'] = ewma(x, span=3000).mean().mean(skipna=True)\n X_tr.loc[segment, 'exp_Moving_average_30000_mean'] = ewma(x, span=6000).mean().mean(skipna=True)\n no_of_std = 2\n X_tr.loc[segment, 'MA_700MA_std_mean'] = x.rolling(window=700).std().mean()\n X_tr.loc[segment,'MA_700MA_BB_high_mean'] = (X_tr.loc[segment, 'Moving_average_700_mean'] + no_of_std * X_tr.loc[segment, 'MA_700MA_std_mean']).mean()\n X_tr.loc[segment,'MA_700MA_BB_low_mean'] = (X_tr.loc[segment, 'Moving_average_700_mean'] - no_of_std * X_tr.loc[segment, 'MA_700MA_std_mean']).mean()\n X_tr.loc[segment, 'MA_400MA_std_mean'] = x.rolling(window=400).std().mean()\n X_tr.loc[segment,'MA_400MA_BB_high_mean'] = (X_tr.loc[segment, 'Moving_average_700_mean'] + no_of_std * X_tr.loc[segment, 'MA_400MA_std_mean']).mean()\n X_tr.loc[segment,'MA_400MA_BB_low_mean'] = (X_tr.loc[segment, 'Moving_average_700_mean'] - no_of_std * X_tr.loc[segment, 'MA_400MA_std_mean']).mean()\n X_tr.loc[segment, 'MA_1000MA_std_mean'] = x.rolling(window=1000).std().mean()\n \n X_tr.loc[segment, 'iqr'] = np.subtract(*np.percentile(x, [75, 25]))\n X_tr.loc[segment, 'q999'] = np.quantile(x,0.999)\n X_tr.loc[segment, 'q001'] = np.quantile(x,0.001)\n X_tr.loc[segment, 'ave10'] = stats.trim_mean(x, 0.1)\n \n for windows in [10, 100, 1000]:\n x_roll_std = x.rolling(windows).std().dropna().values\n x_roll_mean = x.rolling(windows).mean().dropna().values\n \n X_tr.loc[segment, 'ave_roll_std_' + str(windows)] = x_roll_std.mean()\n X_tr.loc[segment, 'std_roll_std_' + str(windows)] = x_roll_std.std()\n X_tr.loc[segment, 'max_roll_std_' + str(windows)] = x_roll_std.max()\n X_tr.loc[segment, 'min_roll_std_' + str(windows)] = x_roll_std.min()\n X_tr.loc[segment, 'q01_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.01)\n X_tr.loc[segment, 'q05_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.05)\n X_tr.loc[segment, 'q95_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.95)\n X_tr.loc[segment, 'q99_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.99)\n X_tr.loc[segment, 'av_change_abs_roll_std_' + str(windows)] = np.mean(np.diff(x_roll_std))\n X_tr.loc[segment, 'av_change_rate_roll_std_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0])\n X_tr.loc[segment, 'abs_max_roll_std_' + str(windows)] = np.abs(x_roll_std).max()\n \n X_tr.loc[segment, 'ave_roll_mean_' + str(windows)] = x_roll_mean.mean()\n X_tr.loc[segment, 'std_roll_mean_' + str(windows)] = x_roll_mean.std()\n X_tr.loc[segment, 'max_roll_mean_' + str(windows)] = x_roll_mean.max()\n X_tr.loc[segment, 'min_roll_mean_' + str(windows)] = x_roll_mean.min()\n X_tr.loc[segment, 'q01_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.01)\n X_tr.loc[segment, 'q05_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.05)\n X_tr.loc[segment, 'q95_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.95)\n X_tr.loc[segment, 'q99_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.99)\n X_tr.loc[segment, 'av_change_abs_roll_mean_' + str(windows)] = np.mean(np.diff(x_roll_mean))\n X_tr.loc[segment, 'av_change_rate_roll_mean_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0])\n X_tr.loc[segment, 'abs_max_roll_mean_' + str(windows)] = np.abs(x_roll_mean).max()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"6ad94de98384e95f8a769ce9670d8b55f636eec3","trusted":true},"cell_type":"code","source":"print(f'{X_tr.shape[0]} samples in new train data and {X_tr.shape[1]} columns.')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"b3e87358c38c3dc2a675be51a241d8bfa7171bbd","trusted":true},"cell_type":"code","source":"np.abs(X_tr.corrwith(y_tr['time_to_failure'])).sort_values(ascending=False).head(12)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"a2bd1c5cd7178d083b99b19a226e89486aa02d8b","trusted":true},"cell_type":"code","source":"plt.figure(figsize=(44, 24))\ncols = list(np.abs(X_tr.corrwith(y_tr['time_to_failure'])).sort_values(ascending=False).head(24).index)\nfor i, col in enumerate(cols):\n plt.subplot(6, 4, i + 1)\n plt.plot(X_tr[col], color='blue')\n plt.title(col)\n ax1.set_ylabel(col, color='b')\n\n ax2 = ax1.twinx()\n plt.plot(y_tr, color='g')\n ax2.set_ylabel('time_to_failure', color='g')\n plt.legend([col, 'time_to_failure'], loc=(0.875, 0.9))\n plt.grid(False)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_uuid":"a1b46d0a370c3817a1cc98c022f9c62ebd135331","scrolled":true,"trusted":true},"cell_type":"code","source":"submission = pd.read_csv('../input/sample_submission.csv', index_col='seg_id')\nX_test = pd.DataFrame(columns=X_tr.columns, dtype=np.float64, index=submission.index)\nplt.figure(figsize=(22, 16))\n\nfor i, seg_id in enumerate(tqdm_notebook(X_test.index)):\n seg = pd.read_csv('../input/test/' + seg_id + '.csv')\n \n x = pd.Series(seg['acoustic_data'].values)\n X_test.loc[seg_id, 'mean'] = x.mean()\n X_test.loc[seg_id, 'std'] = x.std()\n X_test.loc[seg_id, 'max'] = x.max()\n X_test.loc[seg_id, 'min'] = x.min()\n \n X_test.loc[seg_id, 'mean_change_abs'] = np.mean(np.diff(x))\n X_test.loc[seg_id, 'mean_change_rate'] = np.mean(np.nonzero((np.diff(x) / x[:-1]))[0])\n X_test.loc[seg_id, 'abs_max'] = np.abs(x).max()\n X_test.loc[seg_id, 'abs_min'] = np.abs(x).min()\n \n X_test.loc[seg_id, 'std_first_50000'] = x[:50000].std()\n X_test.loc[seg_id, 'std_last_50000'] = x[-50000:].std()\n X_test.loc[seg_id, 'std_first_10000'] = x[:10000].std()\n X_test.loc[seg_id, 'std_last_10000'] = x[-10000:].std()\n \n X_test.loc[seg_id, 'avg_first_50000'] = x[:50000].mean()\n X_test.loc[seg_id, 'avg_last_50000'] = x[-50000:].mean()\n X_test.loc[seg_id, 'avg_first_10000'] = x[:10000].mean()\n X_test.loc[seg_id, 'avg_last_10000'] = x[-10000:].mean()\n \n X_test.loc[seg_id, 'min_first_50000'] = x[:50000].min()\n X_test.loc[seg_id, 'min_last_50000'] = x[-50000:].min()\n X_test.loc[seg_id, 'min_first_10000'] = x[:10000].min()\n X_test.loc[seg_id, 'min_last_10000'] = x[-10000:].min()\n \n X_test.loc[seg_id, 'max_first_50000'] = x[:50000].max()\n X_test.loc[seg_id, 'max_last_50000'] = x[-50000:].max()\n X_test.loc[seg_id, 'max_first_10000'] = x[:10000].max()\n X_test.loc[seg_id, 'max_last_10000'] = x[-10000:].max()\n \n X_test.loc[seg_id, 'max_to_min'] = x.max() / np.abs(x.min())\n X_test.loc[seg_id, 'max_to_min_diff'] = x.max() - np.abs(x.min())\n X_test.loc[seg_id, 'count_big'] = len(x[np.abs(x) > 500])\n X_test.loc[seg_id, 'sum'] = x.sum()\n \n X_test.loc[seg_id, 'mean_change_rate_first_50000'] = np.mean(np.nonzero((np.diff(x[:50000]) / x[:50000][:-1]))[0])\n X_test.loc[seg_id, 'mean_change_rate_last_50000'] = np.mean(np.nonzero((np.diff(x[-50000:]) / x[-50000:][:-1]))[0])\n X_test.loc[seg_id, 'mean_change_rate_first_10000'] = np.mean(np.nonzero((np.diff(x[:10000]) / x[:10000][:-1]))[0])\n X_test.loc[seg_id, 'mean_change_rate_last_10000'] = np.mean(np.nonzero((np.diff(x[-10000:]) / x[-10000:][:-1]))[0])\n \n X_test.loc[seg_id, 'q95'] = np.quantile(x,0.95)\n X_test.loc[seg_id, 'q99'] = np.quantile(x,0.99)\n X_test.loc[seg_id, 'q05'] = np.quantile(x,0.05)\n X_test.loc[seg_id, 'q01'] = np.quantile(x,0.01)\n \n X_test.loc[seg_id, 'abs_q95'] = np.quantile(np.abs(x), 0.95)\n X_test.loc[seg_id, 'abs_q99'] = np.quantile(np.abs(x), 0.99)\n X_test.loc[seg_id, 'abs_q05'] = np.quantile(np.abs(x), 0.05)\n X_test.loc[seg_id, 'abs_q01'] = np.quantile(np.abs(x), 0.01)\n \n X_test.loc[seg_id, 'trend'] = add_trend_feature(x)\n X_test.loc[seg_id, 'abs_trend'] = add_trend_feature(x, abs_values=True)\n X_test.loc[seg_id, 'abs_mean'] = np.abs(x).mean()\n X_test.loc[seg_id, 'abs_std'] = np.abs(x).std()\n \n X_test.loc[seg_id, 'mad'] = x.mad()\n X_test.loc[seg_id, 'kurt'] = x.kurtosis()\n X_test.loc[seg_id, 'skew'] = x.skew()\n X_test.loc[seg_id, 'med'] = x.median()\n \n X_test.loc[seg_id, 'Hilbert_mean'] = np.abs(hilbert(x)).mean()\n X_test.loc[seg_id, 'Hann_window_mean'] = (convolve(x, hann(150), mode='same') / sum(hann(150))).mean()\n X_test.loc[seg_id, 'classic_sta_lta1_mean'] = classic_sta_lta(x, 500, 10000).mean()\n X_test.loc[seg_id, 'classic_sta_lta2_mean'] = classic_sta_lta(x, 5000, 100000).mean()\n X_test.loc[seg_id, 'classic_sta_lta3_mean'] = classic_sta_lta(x, 3333, 6666).mean()\n X_test.loc[seg_id, 'classic_sta_lta4_mean'] = classic_sta_lta(x, 10000, 25000).mean()\n X_test.loc[seg_id, 'Moving_average_700_mean'] = x.rolling(window=700).mean().mean(skipna=True)\n X_test.loc[seg_id, 'Moving_average_1500_mean'] = x.rolling(window=1500).mean().mean(skipna=True)\n X_test.loc[seg_id, 'Moving_average_3000_mean'] = x.rolling(window=3000).mean().mean(skipna=True)\n X_test.loc[seg_id, 'Moving_average_6000_mean'] = x.rolling(window=6000).mean().mean(skipna=True)\n ewma = pd.Series.ewm\n X_test.loc[seg_id, 'exp_Moving_average_300_mean'] = (ewma(x, span=300).mean()).mean(skipna=True)\n X_test.loc[seg_id, 'exp_Moving_average_3000_mean'] = ewma(x, span=3000).mean().mean(skipna=True)\n X_test.loc[seg_id, 'exp_Moving_average_30000_mean'] = ewma(x, span=6000).mean().mean(skipna=True)\n no_of_std = 2\n X_test.loc[seg_id, 'MA_700MA_std_mean'] = x.rolling(window=700).std().mean()\n X_test.loc[seg_id,'MA_700MA_BB_high_mean'] = (X_test.loc[seg_id, 'Moving_average_700_mean'] + no_of_std * X_test.loc[seg_id, 'MA_700MA_std_mean']).mean()\n X_test.loc[seg_id,'MA_700MA_BB_low_mean'] = (X_test.loc[seg_id, 'Moving_average_700_mean'] - no_of_std * X_test.loc[seg_id, 'MA_700MA_std_mean']).mean()\n X_test.loc[seg_id, 'MA_400MA_std_mean'] = x.rolling(window=400).std().mean()\n X_test.loc[seg_id,'MA_400MA_BB_high_mean'] = (X_test.loc[seg_id, 'Moving_average_700_mean'] + no_of_std * X_test.loc[seg_id, 'MA_400MA_std_mean']).mean()\n X_test.loc[seg_id,'MA_400MA_BB_low_mean'] = (X_test.loc[seg_id, 'Moving_average_700_mean'] - no_of_std * X_test.loc[seg_id, 'MA_400MA_std_mean']).mean()\n X_test.loc[seg_id, 'MA_1000MA_std_mean'] = x.rolling(window=1000).std().mean()\n \n X_test.loc[seg_id, 'iqr'] = np.subtract(*np.percentile(x, [75, 25]))\n X_test.loc[seg_id, 'q999'] = np.quantile(x,0.999)\n X_test.loc[seg_id, 'q001'] = np.quantile(x,0.001)\n X_test.loc[seg_id, 'ave10'] = stats.trim_mean(x, 0.1)\n \n for windows in [10, 100, 1000]:\n x_roll_std = x.rolling(windows).std().dropna().values\n x_roll_mean = x.rolling(windows).mean().dropna().values\n \n X_test.loc[seg_id, 'ave_roll_std_' + str(windows)] = x_roll_std.mean()\n X_test.loc[seg_id, 'std_roll_std_' + str(windows)] = x_roll_std.std()\n X_test.loc[seg_id, 'max_roll_std_' + str(windows)] = x_roll_std.max()\n X_test.loc[seg_id, 'min_roll_std_' + str(windows)] = x_roll_std.min()\n X_test.loc[seg_id, 'q01_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.01)\n X_test.loc[seg_id, 'q05_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.05)\n X_test.loc[seg_id, 'q95_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.95)\n X_test.loc[seg_id, 'q99_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.99)\n X_test.loc[seg_id, 'av_change_abs_roll_std_' + str(windows)] = np.mean(np.diff(x_roll_std))\n X_test.loc[seg_id, 'av_change_rate_roll_std_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0])\n X_test.loc[seg_id, 'abs_max_roll_std_' + str(windows)] = np.abs(x_roll_std).max()\n \n X_test.loc[seg_id, 'ave_roll_mean_' + str(windows)] = x_roll_mean.mean()\n X_test.loc[seg_id, 'std_roll_mean_' + str(windows)] = x_roll_mean.std()\n X_test.loc[seg_id, 'max_roll_mean_' + str(windows)] = x_roll_mean.max()\n X_test.loc[seg_id, 'min_roll_mean_' + str(windows)] = x_roll_mean.min()\n X_test.loc[seg_id, 'q01_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.01)\n X_test.loc[seg_id, 'q05_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.05)\n X_test.loc[seg_id, 'q95_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.95)\n X_test.loc[seg_id, 'q99_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.99)\n X_test.loc[seg_id, 'av_change_abs_roll_mean_' + str(windows)] = np.mean(np.diff(x_roll_mean))\n X_test.loc[seg_id, 'av_change_rate_roll_mean_' + str(windows)] = np.mean(np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0])\n X_test.loc[seg_id, 'abs_max_roll_mean_' + str(windows)] = np.abs(x_roll_mean).max()\n \n if i < 12:\n plt.subplot(6, 4, i + 1)\n plt.plot(seg['acoustic_data'])\n plt.title(seg_id)\n \n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d07969d0669845a0fb362ec6b32b73c0353c30c1"},"cell_type":"code","source":"def GPI(data):\n return (5.591662 +\n 0.0399999991*np.tanh(((data[\"mean_change_rate_first_10000\"]) - (((73.0) * ((((((7.0) * (((((((((data[\"q95\"]) + (((0.3183098733) + (data[\"q05_roll_std_100\"]))))) * 2.0)) * 2.0)) + (((data[\"q95_roll_std_10\"]) + (data[\"iqr\"]))))))) + (data[\"iqr\"]))/2.0)))))) +\n 0.0399999991*np.tanh(((((3.6923100948) + (data[\"Moving_average_6000_mean\"]))) * (((73.0) * (((data[\"MA_400MA_BB_low_mean\"]) - (((((data[\"Moving_average_6000_mean\"]) * (data[\"q05_roll_std_10\"]))) + (((data[\"iqr\"]) + (((data[\"q05_roll_std_10\"]) * (((3.6923100948) * 2.0)))))))))))))) +\n 0.0399999991*np.tanh(((((((((((((((((((((((data[\"q05\"]) * 2.0)) * 2.0)) - (data[\"iqr\"]))) - (data[\"max_roll_std_1000\"]))) * 2.0)) * 2.0)) * 2.0)) - (data[\"iqr\"]))) - (3.6923100948))) - (((data[\"q05_roll_std_10\"]) * (73.0))))) * 2.0)) +\n 0.0399999991*np.tanh(((((((((((((((((((data[\"q05_roll_std_10\"]) * (((((-3.0) * 2.0)) * 2.0)))) - (((data[\"iqr\"]) + (data[\"max_roll_std_1000\"]))))) * 2.0)) * 2.0)) * 2.0)) - (data[\"max_roll_mean_100\"]))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((73.0) * ((((data[\"max_first_50000\"]) + ((((data[\"max_first_50000\"]) + (((73.0) * (((data[\"q05\"]) - (((data[\"q05_roll_std_100\"]) + (((0.5555559993) + (((data[\"mad\"]) + (data[\"q95\"]))))))))))))/2.0)))/2.0)))) +\n 0.0399999991*np.tanh(((((((data[\"q05_roll_std_10\"]) * (73.0))) * 2.0)) - ((((((((data[\"q05_roll_std_10\"]) * (73.0))) + (((((((((data[\"iqr\"]) + (((data[\"MA_1000MA_std_mean\"]) + (data[\"abs_max_roll_mean_10\"]))))) * 2.0)) * 2.0)) * 2.0)))/2.0)) * (73.0))))) +\n 0.0399999991*np.tanh(((((data[\"mean_change_abs\"]) - (((((data[\"q95\"]) + (((data[\"q95\"]) + (((((data[\"q05_roll_std_100\"]) / 2.0)) + (((((((0.3183098733) * 2.0)) - (data[\"q05\"]))) - (data[\"q05\"]))))))))) * (73.0))))) * 2.0)) +\n 0.0399999991*np.tanh((-1.0*(((((((7.48210620880126953)) * 2.0)) + (((((data[\"iqr\"]) + ((((-1.0*((data[\"avg_first_10000\"])))) * 2.0)))) + (((73.0) * (((data[\"iqr\"]) + (((((data[\"q05_roll_std_10\"]) * 2.0)) * 2.0))))))))))))) +\n 0.0399999991*np.tanh((((((data[\"q01_roll_std_10\"]) + (((((data[\"q05_roll_mean_10\"]) - (((((data[\"q05_roll_std_100\"]) - (((data[\"q05\"]) - (0.3026320040))))) + (((((((0.5434780121) + (0.3026320040))/2.0)) + (data[\"iqr\"]))/2.0)))))) * (73.0))))/2.0)) * (73.0))) +\n 0.0399999991*np.tanh(((((data[\"trend\"]) + (((data[\"q95_roll_std_10\"]) + (data[\"trend\"]))))) - (((73.0) * (((((((data[\"ave_roll_std_10\"]) + (((((((data[\"q95_roll_mean_10\"]) + (data[\"q05_roll_std_1000\"]))/2.0)) + (data[\"q05_roll_std_1000\"]))/2.0)))/2.0)) + ((((0.5434780121) + (data[\"q95_roll_mean_10\"]))/2.0)))/2.0)))))) +\n 0.0399999991*np.tanh(((73.0) - (((((73.0) * 2.0)) * (((73.0) * ((((((((((data[\"q05_roll_std_1000\"]) / 2.0)) + (data[\"iqr\"]))/2.0)) + (data[\"q05_roll_std_10\"]))) + (((0.2183910012) + (data[\"q95\"]))))))))))) +\n 0.0399999991*np.tanh(((73.0) * (((73.0) * (((((73.0) * (((data[\"q05_roll_mean_10\"]) - (((((data[\"q05_roll_std_100\"]) + (data[\"q95\"]))) + (0.5434780121))))))) + (data[\"q05_roll_std_100\"]))))))) +\n 0.0399999991*np.tanh((-1.0*((((73.0) * (((73.0) * (((73.0) * (((data[\"q05_roll_std_100\"]) + ((((data[\"q95_roll_mean_10\"]) + ((((data[\"Moving_average_3000_mean\"]) + (((((data[\"iqr\"]) + (0.9756100178))) + (data[\"abs_mean\"]))))/2.0)))/2.0))))))))))))) +\n 0.0399999991*np.tanh(((73.0) * (((((data[\"classic_sta_lta3_mean\"]) + (data[\"mad\"]))) + (((((((73.0) * (((data[\"q05_roll_mean_10\"]) - (((data[\"q05_roll_std_100\"]) + (((data[\"mad\"]) + (0.5555559993))))))))) - (data[\"iqr\"]))) - (data[\"iqr\"]))))))) +\n 0.0399999991*np.tanh(((((np.tanh((data[\"q95_roll_std_10\"]))) + (((((((data[\"q05_roll_std_10\"]) + ((((((0.2439019978) + (data[\"ave10\"]))/2.0)) / 2.0)))) * 2.0)) * 2.0)))) * (((data[\"q05_roll_std_10\"]) - (((73.0) * (((73.0) * (73.0))))))))) +\n 0.0399999991*np.tanh(((3.6923100948) * (((73.0) * (((3.6923100948) * ((((((-1.0*((data[\"q99_roll_mean_1000\"])))) * (data[\"avg_last_50000\"]))) - (((data[\"q05_roll_std_10\"]) * (((3.6923100948) + (((3.6923100948) + (data[\"abs_max_roll_mean_100\"]))))))))))))))) +\n 0.0399999991*np.tanh(((((((data[\"Moving_average_1500_mean\"]) + (((((((((data[\"MA_400MA_BB_low_mean\"]) + ((((-1.0*((data[\"q05_roll_std_1000\"])))) - (data[\"iqr\"]))))) * (73.0))) - (data[\"min_roll_std_1000\"]))) - (data[\"q05_roll_std_1000\"]))))) * (73.0))) * (73.0))) +\n 0.0399999991*np.tanh(((73.0) - (((73.0) * (((73.0) * (((data[\"q05_roll_std_10\"]) + (np.tanh(((((((data[\"exp_Moving_average_3000_mean\"]) + (((data[\"iqr\"]) + (np.tanh((((data[\"q05_roll_std_100\"]) + (data[\"exp_Moving_average_30000_mean\"]))))))))/2.0)) / 2.0)))))))))))) +\n 0.0399999991*np.tanh(((73.0) * (((73.0) * (((data[\"q05_roll_mean_10\"]) - (((np.tanh((np.tanh((np.tanh((np.tanh((np.tanh((data[\"q05_roll_std_100\"]))))))))))) + ((((data[\"q95_roll_std_10\"]) + (np.tanh((2.8437500000))))/2.0)))))))))) +\n 0.0399999991*np.tanh(((((((data[\"iqr\"]) / 2.0)) + (((data[\"mean\"]) + (((data[\"q05_roll_std_100\"]) + (((data[\"q05_roll_std_10\"]) * 2.0)))))))) * ((-1.0*((((73.0) - (((((((((data[\"mean\"]) * 2.0)) * 2.0)) * 2.0)) * 2.0))))))))) +\n 0.0399999991*np.tanh(((((((((0.5217390060) - (((((data[\"q05_roll_std_10\"]) * ((-1.0*((7.0)))))) + (data[\"exp_Moving_average_3000_mean\"]))))) * ((-1.0*((73.0)))))) - (((data[\"classic_sta_lta4_mean\"]) * (7.0))))) - ((((7.0) + (data[\"classic_sta_lta4_mean\"]))/2.0)))) +\n 0.0399999991*np.tanh(((((((((((((((((((np.tanh(((-1.0*((((data[\"q05_roll_mean_100\"]) + (0.2183910012)))))))) - ((((data[\"q05_roll_std_100\"]) + (((data[\"q05_roll_std_10\"]) * 2.0)))/2.0)))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) - (data[\"exp_Moving_average_30000_mean\"]))) * 2.0)) +\n 0.0399999991*np.tanh(((data[\"trend\"]) - (((73.0) * ((((3.6923100948) + ((((9.0)) * (((np.tanh((np.tanh((np.tanh((np.tanh((np.tanh((np.tanh((np.tanh((data[\"q05_roll_std_100\"]))))))))))))))) - (data[\"q05_roll_mean_10\"]))))))/2.0)))))) +\n 0.0399999991*np.tanh(((data[\"q05_roll_std_1000\"]) - (((73.0) * (((data[\"avg_last_50000\"]) + (((((73.0) * 2.0)) * (((data[\"ave_roll_std_100\"]) + (((data[\"Moving_average_6000_mean\"]) + (((data[\"iqr\"]) + (((data[\"q05_roll_std_1000\"]) + (((data[\"q95\"]) * 2.0)))))))))))))))))) +\n 0.0399999991*np.tanh(((((data[\"q05_roll_mean_10\"]) - (2.0769200325))) - ((((((6.08570814132690430)) * (((2.0769200325) + (((((((((data[\"q05_roll_std_10\"]) - (data[\"q05_roll_mean_10\"]))) * 2.0)) * 2.0)) * 2.0)))))) + (((((data[\"av_change_rate_roll_std_100\"]) - (data[\"avg_last_10000\"]))) * 2.0)))))) +\n 0.0399999991*np.tanh((-1.0*((((((((((((((((((((((((((((((((data[\"q05_roll_std_1000\"]) + (data[\"q05_roll_mean_100\"]))/2.0)) * 2.0)) + (data[\"iqr\"]))/2.0)) + (data[\"q95_roll_mean_10\"]))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0))))) +\n 0.0399999991*np.tanh((((-1.0*((((((((((data[\"q05_roll_std_100\"]) - (np.tanh(((-1.0*((((data[\"MA_400MA_std_mean\"]) + (((data[\"exp_Moving_average_300_mean\"]) * (((data[\"q05_roll_std_100\"]) * 2.0))))))))))))) * 2.0)) * 2.0)) + (np.tanh((data[\"q05_roll_std_100\"])))))))) * (73.0))) +\n 0.0399999991*np.tanh(((((data[\"abs_q05\"]) - ((((((2.0769200325) * ((((((73.0) * ((((((0.5555559993) + (((data[\"q05_roll_std_100\"]) + (0.7446810007))))/2.0)) + (data[\"q05_roll_std_100\"]))))) + (0.5555559993))/2.0)))) + (0.5555559993))/2.0)))) * 2.0)) +\n 0.0399999991*np.tanh((((10.75933361053466797)) - (((7.0) * (((7.0) * (((7.0) * ((((((data[\"q95_roll_mean_10\"]) + (data[\"q05_roll_std_100\"]))) + ((((data[\"q95_roll_mean_10\"]) + (((data[\"iqr\"]) + (((data[\"Moving_average_700_mean\"]) * 2.0)))))/2.0)))/2.0)))))))))) +\n 0.0399999991*np.tanh(((73.0) * (((((data[\"q05_roll_std_1000\"]) + ((((data[\"q05_roll_std_10\"]) + ((((((data[\"q05_roll_std_1000\"]) + (np.tanh((data[\"q05_roll_std_1000\"]))))/2.0)) - (((data[\"q95_roll_mean_1000\"]) - (np.tanh((73.0))))))))/2.0)))) * (((2.8437500000) - (73.0))))))) +\n 0.0399999991*np.tanh(((((((((-3.0) + (data[\"Moving_average_3000_mean\"]))) - (((73.0) * (((data[\"ave_roll_mean_10\"]) - ((((((data[\"q05_roll_std_100\"]) + (data[\"q95_roll_mean_10\"]))/2.0)) * (((-3.0) - (((data[\"Moving_average_700_mean\"]) + (data[\"q95_roll_mean_10\"]))))))))))))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((data[\"classic_sta_lta3_mean\"]) * 2.0)) + (data[\"q05_roll_mean_100\"]))) - (((73.0) * (((((((0.6582279801) + (data[\"q05_roll_std_10\"]))) - (((data[\"q05_roll_mean_10\"]) * 2.0)))) + ((((-1.0*((data[\"abs_q05\"])))) + (((data[\"q05_roll_std_100\"]) * 2.0)))))))))) +\n 0.0399999991*np.tanh(((((data[\"skew\"]) + (((data[\"kurt\"]) + (data[\"med\"]))))) - ((((7.02540111541748047)) * (((((data[\"iqr\"]) + (((data[\"q95_roll_std_10\"]) + (((((data[\"q95_roll_mean_10\"]) + (((data[\"iqr\"]) * (data[\"Moving_average_1500_mean\"]))))) * 2.0)))))) * 2.0)))))) +\n 0.0399999991*np.tanh(((((data[\"q05_roll_std_100\"]) - (((-2.0) + (((((data[\"iqr\"]) * (-2.0))) - ((-1.0*((data[\"av_change_rate_roll_std_10\"])))))))))) - ((((((data[\"q05_roll_std_100\"]) + (np.tanh((((data[\"q05_roll_std_100\"]) * (data[\"ave_roll_mean_10\"]))))))/2.0)) * (73.0))))) +\n 0.0399999991*np.tanh(((((((((((data[\"q05_roll_std_10\"]) + (((data[\"av_change_abs_roll_std_1000\"]) - (((data[\"av_change_abs_roll_mean_10\"]) * 2.0)))))) * 2.0)) + (data[\"av_change_abs_roll_std_1000\"]))) * 2.0)) - (((73.0) * (((((data[\"abs_max_roll_mean_1000\"]) + (((0.5555559993) + (data[\"q05_roll_std_10\"]))))) * 2.0)))))) +\n 0.0399999991*np.tanh((((((((((((((((((np.tanh((((((data[\"q95_roll_mean_10\"]) + (data[\"q95_roll_std_100\"]))) - (data[\"q05_roll_std_1000\"]))))) + (data[\"q95_roll_std_100\"]))/2.0)) + (data[\"q95_roll_std_100\"]))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) - (((73.0) * (data[\"q95_roll_mean_10\"]))))) * 2.0)) +\n 0.0399999991*np.tanh(((73.0) - (((((((((((data[\"q05_roll_std_10\"]) * 2.0)) - (data[\"ave10\"]))) + (((data[\"q05_roll_std_100\"]) + (0.9756100178))))) * (73.0))) * (((73.0) * 2.0)))))) +\n 0.0399999991*np.tanh(((((data[\"Moving_average_6000_mean\"]) + (((7.0) + (((data[\"ave10\"]) * 2.0)))))) * (((((((((data[\"MA_700MA_BB_low_mean\"]) * (data[\"avg_last_50000\"]))) + ((-1.0*((((data[\"q95_roll_mean_10\"]) * 2.0))))))) - (((data[\"ave_roll_mean_10\"]) + (data[\"q05_roll_std_100\"]))))) * 2.0)))) +\n 0.0399999991*np.tanh(((73.0) * (((data[\"q05_roll_std_100\"]) - (((((((data[\"iqr\"]) - (np.tanh((((73.0) * (((data[\"ave10\"]) - (((data[\"iqr\"]) + (((((data[\"q05_roll_std_1000\"]) + (0.5434780121))) * 2.0)))))))))))) * 2.0)) * 2.0)))))) +\n 0.0399999991*np.tanh(((((((((((((((data[\"exp_Moving_average_30000_mean\"]) * (((data[\"exp_Moving_average_30000_mean\"]) + ((-1.0*((data[\"q01_roll_std_10\"])))))))) - (((data[\"q05_roll_std_10\"]) + (((data[\"q05_roll_std_10\"]) + (((data[\"q95_roll_mean_100\"]) + (data[\"q05_roll_std_10\"]))))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((73.0) - (((((data[\"q05_roll_std_100\"]) * (73.0))) * (((73.0) * (((data[\"q05_roll_std_100\"]) * (((data[\"q05_roll_std_100\"]) - (((data[\"q05\"]) - ((((0.2525250018) + (2.0))/2.0)))))))))))))) +\n 0.0399999991*np.tanh(((((((((7.0) - ((((data[\"sum\"]) + (((73.0) * (((((data[\"sum\"]) + (data[\"q05_roll_std_10\"]))) + (((((((np.tanh((data[\"abs_q05\"]))) + (data[\"q05_roll_std_10\"]))) * 2.0)) * 2.0)))))))/2.0)))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh((((((1.0)) - (((((((((data[\"q05_roll_std_100\"]) + (np.tanh((((data[\"abs_q05\"]) * (((((data[\"exp_Moving_average_30000_mean\"]) - (data[\"abs_q05\"]))) + (((data[\"q05_roll_std_100\"]) + (((data[\"q95_roll_mean_10\"]) * 2.0)))))))))))) * 2.0)) * 2.0)) * 2.0)))) * 2.0)) +\n 0.0399999991*np.tanh(((((np.tanh((((np.tanh((data[\"Moving_average_1500_mean\"]))) * 2.0)))) - (((data[\"Moving_average_1500_mean\"]) + ((-1.0*((((((data[\"q05_roll_std_100\"]) + (np.tanh((0.6582279801))))) * 2.0))))))))) * ((((-1.0*((((data[\"Moving_average_1500_mean\"]) * 2.0))))) - (73.0))))) +\n 0.0399999991*np.tanh(((73.0) - ((((((((((((data[\"q05_roll_std_100\"]) + (((data[\"q05_roll_std_100\"]) * (data[\"Moving_average_6000_mean\"]))))/2.0)) + (((data[\"Moving_average_6000_mean\"]) + (((data[\"q05_roll_std_100\"]) + (data[\"q95\"]))))))) * 2.0)) * 2.0)) * (((73.0) + (data[\"Moving_average_6000_mean\"]))))))) +\n 0.0399999991*np.tanh(((((((((((((data[\"mean_change_rate_last_10000\"]) - (data[\"mean_change_abs\"]))) - (((((((((((((data[\"q05_roll_std_10\"]) + (((0.5434780121) - (data[\"q05\"]))))) * 2.0)) - (data[\"Hann_window_mean\"]))) * 2.0)) * 2.0)) * 2.0)))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((-1.0*((((data[\"avg_last_50000\"]) * (3.6923100948)))))) + (((73.0) / 2.0)))/2.0)) - (((73.0) * ((((((data[\"Moving_average_6000_mean\"]) + (((data[\"q05_roll_std_10\"]) - (((data[\"exp_Moving_average_30000_mean\"]) * (data[\"exp_Moving_average_30000_mean\"]))))))/2.0)) + (data[\"q05_roll_std_10\"]))))))) +\n 0.0399999991*np.tanh(((73.0) * ((-1.0*((((data[\"q01_roll_std_1000\"]) + (((73.0) * (((data[\"q05_roll_std_100\"]) + ((((1.4411799908) + ((((((data[\"q01_roll_std_1000\"]) - (data[\"q99_roll_mean_1000\"]))) + (((data[\"q99_roll_mean_1000\"]) * (data[\"q99_roll_mean_1000\"]))))/2.0)))/2.0))))))))))))) +\n 0.0399999991*np.tanh((((12.50784111022949219)) + ((((12.50784111022949219)) - ((((((np.tanh((((data[\"q05_roll_std_10\"]) * (data[\"q95_roll_mean_1000\"]))))) + (((((((data[\"q05_roll_std_10\"]) + (np.tanh((((data[\"q05_roll_std_10\"]) * (data[\"exp_Moving_average_30000_mean\"]))))))) * 2.0)) * 2.0)))/2.0)) * (73.0))))))) +\n 0.0399999991*np.tanh(((((((data[\"skew\"]) - (((data[\"ave_roll_mean_100\"]) + (((((((data[\"ave_roll_mean_100\"]) * (data[\"abs_q05\"]))) + (((((2.0) + (((((data[\"q05_roll_std_100\"]) - (data[\"q05_roll_mean_10\"]))) * 2.0)))) * 2.0)))) * 2.0)))))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((((data[\"q95_roll_mean_1000\"]) * (data[\"ave_roll_mean_100\"]))) * 2.0)) - (((((data[\"exp_Moving_average_30000_mean\"]) + (((((((((data[\"exp_Moving_average_30000_mean\"]) * (data[\"q05_roll_std_10\"]))) + (((((data[\"q05_roll_std_10\"]) - (0.2439019978))) * 2.0)))) * 2.0)) * 2.0)))) * 2.0)))) * 2.0)) +\n 0.0399999991*np.tanh(((((((((data[\"q05_roll_std_10\"]) - (((((data[\"q95\"]) - (((((data[\"iqr\"]) - (((data[\"exp_Moving_average_3000_mean\"]) * (np.tanh((((data[\"q05_roll_std_10\"]) * 2.0)))))))) - (((data[\"q05_roll_std_10\"]) * 2.0)))))) * 2.0)))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((data[\"q95_roll_mean_100\"]) + (((data[\"mean_change_rate_first_10000\"]) + (((data[\"abs_q05\"]) + (data[\"abs_q05\"]))))))) * (3.6923100948))) + (((((((((data[\"q05_roll_std_1000\"]) + (0.7446810007))) * (((3.6923100948) - (73.0))))) * 2.0)) * 2.0)))) +\n 0.0399999991*np.tanh((-1.0*((((((73.0) * ((((((data[\"q01_roll_mean_1000\"]) - (((data[\"q05\"]) * 2.0)))) + (((data[\"q05_roll_std_1000\"]) + (((((data[\"q05_roll_std_100\"]) - (data[\"q95_roll_mean_1000\"]))) * (data[\"Moving_average_700_mean\"]))))))/2.0)))) + (((((data[\"Moving_average_1500_mean\"]) * 2.0)) * 2.0))))))) +\n 0.0399999991*np.tanh(((((((((data[\"q05\"]) - (((73.0) * ((((-1.0*((((data[\"q05\"]) * 2.0))))) + (((data[\"q05_roll_std_1000\"]) + (1.9019600153))))))))) + (((((((data[\"q05\"]) + (data[\"max_to_min\"]))) * 2.0)) * 2.0)))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((data[\"av_change_abs_roll_mean_100\"]) - (((data[\"q01_roll_mean_1000\"]) * 2.0)))) - (((73.0) * (((((((data[\"Hann_window_mean\"]) * (((data[\"q95\"]) - (data[\"q95_roll_mean_1000\"]))))) / 2.0)) + (((((data[\"q95\"]) * 2.0)) + (data[\"q01_roll_mean_1000\"]))))))))) * 2.0)) +\n 0.0399999991*np.tanh(((((((((((data[\"skew\"]) - (((((data[\"q05_roll_mean_10\"]) + (((((data[\"iqr\"]) + (((3.1415927410) * (((1.0) - (((data[\"q05_roll_mean_10\"]) * 2.0)))))))) * 2.0)))) * 2.0)))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh((((((((6.0)) - (((((((((data[\"q05_roll_std_100\"]) + (((data[\"q05_roll_std_100\"]) * 2.0)))) * 2.0)) + (((((data[\"ave10\"]) * (((((data[\"q05_roll_std_100\"]) * 2.0)) * 2.0)))) + (np.tanh((data[\"q05_roll_std_100\"]))))))) * 2.0)))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((((data[\"q05_roll_std_10\"]) + (((((((data[\"q95_roll_mean_1000\"]) + (((((((data[\"skew\"]) + (((((((data[\"abs_q05\"]) - (((data[\"q05_roll_std_10\"]) * 2.0)))) * 2.0)) * 2.0)))) * 2.0)) * 2.0)))) * 2.0)) * 2.0)))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((((data[\"max_to_min\"]) + (data[\"q95_roll_std_1000\"]))) - (((((73.0) * ((((data[\"q05_roll_std_1000\"]) + (((0.2183910012) + (data[\"abs_q05\"]))))/2.0)))) * (((data[\"q05_roll_std_1000\"]) * (((data[\"q05_roll_std_10\"]) + (data[\"q01_roll_std_10\"]))))))))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((((((data[\"abs_q05\"]) + (data[\"q05_roll_std_100\"]))) - (((7.0) * (((data[\"iqr\"]) - (((data[\"q99_roll_mean_1000\"]) + ((-1.0*((((((((data[\"q05_roll_std_100\"]) + (1.0))) * 2.0)) * 2.0))))))))))))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh((-1.0*((((7.0) * (((73.0) * (((((7.0) * (((((data[\"Moving_average_6000_mean\"]) * (((((-1.0*((data[\"Moving_average_6000_mean\"])))) + (np.tanh((np.tanh((data[\"q01_roll_std_10\"]))))))/2.0)))) + (data[\"q95_roll_mean_10\"]))))) + (data[\"q95_roll_mean_10\"])))))))))) +\n 0.0399999991*np.tanh(((((((((((data[\"skew\"]) + (data[\"ave10\"]))) + (data[\"q95_roll_std_100\"]))) - (((7.0) * (((((data[\"q05_roll_std_100\"]) * (data[\"q95\"]))) * ((((((data[\"q05_roll_std_100\"]) * (7.0))) + (7.0))/2.0)))))))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh((((((9.64285850524902344)) * (((((data[\"Moving_average_700_mean\"]) - ((((((9.64285850524902344)) * ((((data[\"q05_roll_std_100\"]) + (data[\"exp_Moving_average_30000_mean\"]))/2.0)))) - ((((data[\"skew\"]) + ((3.98402547836303711)))/2.0)))))) * 2.0)))) * (((data[\"iqr\"]) * (data[\"q05_roll_std_100\"]))))) +\n 0.0399999991*np.tanh(((((((((((((((((data[\"Moving_average_1500_mean\"]) - (((np.tanh((data[\"exp_Moving_average_30000_mean\"]))) + (np.tanh((((data[\"abs_q05\"]) + ((((data[\"q95_roll_std_10\"]) + (data[\"q01_roll_std_10\"]))/2.0)))))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) - (data[\"iqr\"]))) * 2.0)) +\n 0.0399999991*np.tanh((((-1.0*((data[\"q95\"])))) * (((73.0) + ((-1.0*((((data[\"abs_q05\"]) * ((((((((-1.0*((data[\"abs_q05\"])))) - (data[\"min_roll_std_10\"]))) - (73.0))) - ((((-1.0*((data[\"q05_roll_mean_10\"])))) * (73.0)))))))))))))) +\n 0.0399999991*np.tanh(((data[\"q05_roll_std_10\"]) * (((((((((data[\"q05_roll_std_100\"]) * ((11.90774154663085938)))) * (((data[\"ave10\"]) - (-2.0))))) - ((11.90774154663085938)))) * ((-1.0*((((data[\"q05_roll_std_1000\"]) + (((data[\"q05_roll_std_100\"]) - (-2.0)))))))))))) +\n 0.0399999991*np.tanh(((((((data[\"iqr\"]) * ((9.63457870483398438)))) * (((data[\"q95_roll_mean_1000\"]) - ((((9.63457870483398438)) * (((data[\"q05_roll_std_10\"]) * ((((data[\"q01_roll_std_10\"]) + (((data[\"iqr\"]) * (data[\"q95_roll_mean_1000\"]))))/2.0)))))))))) - (((-3.0) * (data[\"q01_roll_std_10\"]))))) +\n 0.0399999991*np.tanh(((((((((-1.0) - (((np.tanh((data[\"q05_roll_std_10\"]))) + (((((((data[\"q05_roll_std_100\"]) - (np.tanh((((data[\"classic_sta_lta1_mean\"]) + (((((((data[\"q95_roll_std_100\"]) * 2.0)) * 2.0)) * 2.0)))))))) * 2.0)) * 2.0)))))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((((data[\"q95_roll_mean_10\"]) - (((((((data[\"q95_roll_mean_10\"]) + (((((data[\"q05_roll_std_10\"]) + (((3.6923100948) * ((((data[\"ave10\"]) + (((data[\"q05_roll_std_10\"]) + (-1.0))))/2.0)))))) * 2.0)))) * 2.0)) * 2.0)))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((data[\"q05_roll_std_100\"]) + (((((data[\"q95_roll_mean_1000\"]) + (((((data[\"q05_roll_std_100\"]) + ((((3.1415927410) + (data[\"q05_roll_std_1000\"]))/2.0)))) * (((((((data[\"q05_roll_mean_10\"]) * 2.0)) * 2.0)) * (((3.1415927410) * (data[\"q05_roll_std_100\"]))))))))) * 2.0)))) * 2.0)) +\n 0.0399999991*np.tanh(((((((((((((data[\"mean_change_rate_first_10000\"]) - (data[\"Moving_average_6000_mean\"]))) * 2.0)) - (data[\"q01_roll_mean_1000\"]))) - (((data[\"av_change_abs_roll_mean_10\"]) + (((data[\"mean_change_rate_last_50000\"]) + (((data[\"av_change_abs_roll_std_100\"]) - ((((((13.93686485290527344)) / 2.0)) * (data[\"q05\"]))))))))))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((((data[\"q05_roll_std_10\"]) * (data[\"q05_roll_std_10\"]))) * ((((-1.0*((((2.0) - ((-1.0*((((data[\"q05_roll_std_100\"]) + (((((data[\"q05_roll_std_100\"]) * (data[\"abs_q05\"]))) * 2.0)))))))))))) * 2.0)))) - ((-1.0*((data[\"kurt\"])))))) * 2.0)) +\n 0.0399999991*np.tanh(((((((((((((((data[\"q05\"]) - (np.tanh((((data[\"iqr\"]) + (((((data[\"Moving_average_1500_mean\"]) + (((data[\"q05\"]) + (data[\"Moving_average_3000_mean\"]))))) * 2.0)))))))) * 2.0)) + (data[\"q05\"]))) * 2.0)) * 2.0)) - (data[\"av_change_abs_roll_mean_10\"]))) * 2.0)) +\n 0.0399999991*np.tanh(((((((((np.tanh((((((((((((((data[\"Moving_average_6000_mean\"]) - (data[\"q05_roll_std_100\"]))) * 2.0)) * 2.0)) - (((1.6428600550) + (data[\"q01_roll_std_10\"]))))) * 2.0)) - (data[\"av_change_abs_roll_std_100\"]))))) * 2.0)) - (data[\"abs_q05\"]))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh((((((((((((((data[\"skew\"]) + (data[\"q95_roll_std_10\"]))/2.0)) - (((((data[\"ave_roll_std_100\"]) + (((data[\"max_roll_mean_10\"]) * 2.0)))) * (data[\"abs_q05\"]))))) + (((((np.tanh((data[\"q95_roll_std_100\"]))) - (data[\"q01_roll_std_10\"]))) * 2.0)))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((data[\"skew\"]) + (((((data[\"q01_roll_std_100\"]) * 2.0)) + (((data[\"Moving_average_700_mean\"]) + (((data[\"q05_roll_std_10\"]) * (((data[\"q05\"]) * ((((((73.0) * (((data[\"Moving_average_700_mean\"]) + (((data[\"q05_roll_std_100\"]) / 2.0)))))) + (data[\"q05_roll_std_10\"]))/2.0)))))))))))) +\n 0.0399999991*np.tanh(((((((data[\"q05_roll_mean_10\"]) * (((data[\"q05_roll_std_100\"]) * (data[\"q05_roll_std_100\"]))))) * (((((data[\"q05_roll_std_100\"]) * 2.0)) * (((data[\"q05_roll_std_1000\"]) * 2.0)))))) - (((((((data[\"q05_roll_std_1000\"]) * 2.0)) * (data[\"q05_roll_std_100\"]))) - (((data[\"q01_roll_std_1000\"]) * 2.0)))))) +\n 0.0399999991*np.tanh(((((((-1.0) + (((((((np.tanh((data[\"q01_roll_std_10\"]))) * 2.0)) * 2.0)) + (((data[\"max_to_min_diff\"]) + (((data[\"q05\"]) * ((((14.98138904571533203)) + (((data[\"mean_change_rate_last_10000\"]) * 2.0)))))))))))) * (73.0))) * (73.0))) +\n 0.0399999991*np.tanh(((((((data[\"q05_roll_std_10\"]) - (((((data[\"q05_roll_std_1000\"]) + (0.7446810007))) * (((data[\"q05_roll_std_10\"]) * (((((((data[\"q01_roll_std_10\"]) * (((data[\"ave10\"]) + (((data[\"q95_roll_mean_10\"]) * (data[\"q05_roll_std_10\"]))))))) * 2.0)) * 2.0)))))))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((np.tanh((data[\"q01_roll_std_100\"]))) - (((((((((data[\"q05_roll_std_1000\"]) + (((((data[\"q95\"]) - (np.tanh((data[\"q95_roll_std_100\"]))))) * 2.0)))) - (np.tanh((np.tanh((data[\"q95\"]))))))) - (data[\"std_roll_mean_1000\"]))) * 2.0)))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((data[\"q05_roll_std_10\"]) + (((data[\"ave_roll_mean_1000\"]) * 2.0)))) * (((((((data[\"ave_roll_mean_1000\"]) * (data[\"ave_roll_mean_1000\"]))) - (data[\"q05_roll_std_1000\"]))) + (((data[\"q05_roll_mean_10\"]) * (((data[\"Moving_average_3000_mean\"]) + (((((data[\"q05_roll_std_10\"]) * 2.0)) * 2.0)))))))))) +\n 0.0399999991*np.tanh(((((((((((data[\"q05_roll_std_1000\"]) + (((data[\"q05_roll_std_100\"]) - (((data[\"Moving_average_6000_mean\"]) * (data[\"Moving_average_6000_mean\"]))))))) * (((data[\"q95_roll_std_100\"]) * ((((-1.0*((data[\"iqr\"])))) - (((data[\"Moving_average_6000_mean\"]) + (data[\"Moving_average_1500_mean\"]))))))))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((((data[\"sum\"]) + (0.9756100178))) * 2.0)) * 2.0)) * (((data[\"q01_roll_std_100\"]) - (((np.tanh((((data[\"q05_roll_std_10\"]) * (data[\"q05_roll_std_10\"]))))) + (((data[\"q05_roll_std_1000\"]) * (((((data[\"q01_roll_std_100\"]) * (data[\"q01_roll_std_10\"]))) * 2.0)))))))))) +\n 0.0399999991*np.tanh((((((((((-1.0*((data[\"q05_roll_std_100\"])))) - (((data[\"q01_roll_std_100\"]) * (data[\"mean\"]))))) * (((data[\"q05_roll_std_100\"]) * (data[\"q05_roll_std_100\"]))))) * (((data[\"q05_roll_std_100\"]) * (data[\"q05_roll_std_100\"]))))) + (((((data[\"av_change_rate_roll_std_10\"]) * (data[\"max_to_min\"]))) * 2.0)))) +\n 0.0399999991*np.tanh(((((((data[\"q05_roll_std_10\"]) * 2.0)) * 2.0)) * (((((0.5434780121) * 2.0)) - (((((data[\"q05_roll_std_10\"]) * (((((((((data[\"q95_roll_std_10\"]) * 2.0)) * (data[\"q05_roll_std_10\"]))) + (data[\"q05_roll_std_10\"]))) * (data[\"q05_roll_std_10\"]))))) + (data[\"skew\"]))))))) +\n 0.0399999991*np.tanh(((((data[\"abs_q05\"]) * (data[\"q95_roll_std_100\"]))) - (((((data[\"q05_roll_std_100\"]) * (((data[\"q05_roll_std_100\"]) * (((data[\"q05_roll_std_100\"]) * (((data[\"q05_roll_std_100\"]) * (((data[\"abs_q05\"]) * (data[\"q05_roll_std_100\"]))))))))))) + ((((data[\"min_roll_std_100\"]) + (data[\"av_change_abs_roll_mean_100\"]))/2.0)))))) +\n 0.0399999991*np.tanh(((((data[\"Moving_average_6000_mean\"]) + (0.8873239756))) * (((((data[\"q05_roll_std_100\"]) + (data[\"q95_roll_std_1000\"]))) * (((1.6428600550) - (((data[\"q05_roll_std_1000\"]) * (((((data[\"q05_roll_std_100\"]) + (((data[\"exp_Moving_average_3000_mean\"]) + (0.9756100178))))) + (data[\"q05_roll_std_100\"]))))))))))) +\n 0.0399999991*np.tanh(((((data[\"q95_roll_mean_10\"]) + (((data[\"q95_roll_std_1000\"]) * (((data[\"med\"]) + (((((data[\"q05_roll_mean_100\"]) + (((data[\"q95_roll_mean_10\"]) * (data[\"q95_roll_mean_10\"]))))) * (data[\"q05_roll_std_100\"]))))))))) * (((((data[\"exp_Moving_average_30000_mean\"]) - (data[\"q05_roll_std_100\"]))) - (data[\"q05_roll_std_1000\"]))))) +\n 0.0399999991*np.tanh(((((((data[\"min\"]) + ((((-1.0*((data[\"MA_400MA_BB_high_mean\"])))) + (np.tanh((((((0.3183098733) + (((((data[\"kurt\"]) * 2.0)) * 2.0)))) * ((((3.0)) + ((-1.0*((data[\"kurt\"])))))))))))))) * 2.0)) * 2.0)) +\n 0.0399999991*np.tanh(((((((data[\"min_roll_mean_1000\"]) * 2.0)) * 2.0)) + ((((-1.0*((((((data[\"q05_roll_std_1000\"]) + (((data[\"q01_roll_std_10\"]) * (data[\"q95\"]))))) + (((data[\"avg_last_50000\"]) * (((data[\"q01_roll_std_10\"]) - (data[\"avg_last_50000\"])))))))))) * (data[\"q01_roll_std_100\"]))))) +\n 0.0399921872*np.tanh(((((data[\"q95_roll_std_1000\"]) - ((((((((data[\"av_change_abs_roll_mean_10\"]) + (data[\"std_first_10000\"]))/2.0)) + (((data[\"q95\"]) - (data[\"min_last_10000\"]))))) - (((data[\"min\"]) + (((data[\"q95_roll_std_1000\"]) + (data[\"mean_change_rate_last_10000\"]))))))))) - (((data[\"q95\"]) + (data[\"q95\"]))))) +\n 0.0399999991*np.tanh(((((((data[\"min_roll_mean_1000\"]) - (((data[\"q05_roll_std_1000\"]) * (((np.tanh((((data[\"med\"]) * 2.0)))) + ((((((data[\"skew\"]) * (data[\"kurt\"]))) + (data[\"q05_roll_std_1000\"]))/2.0)))))))) + (((data[\"kurt\"]) * (data[\"skew\"]))))) + (data[\"min_roll_mean_1000\"]))) +\n 0.0399921872*np.tanh(((data[\"exp_Moving_average_30000_mean\"]) * (((((data[\"max_roll_std_100\"]) - (((((data[\"exp_Moving_average_30000_mean\"]) * (data[\"MA_400MA_std_mean\"]))) + (data[\"q05_roll_mean_10\"]))))) * (((((data[\"mean_change_rate_first_10000\"]) * (73.0))) - (((((data[\"abs_mean\"]) * (73.0))) * (73.0))))))))) +\n 0.0399999991*np.tanh(((data[\"q01_roll_std_10\"]) + ((-1.0*((((data[\"q05_roll_std_1000\"]) * (((data[\"av_change_rate_roll_mean_100\"]) + ((((-1.0*((data[\"classic_sta_lta4_mean\"])))) + (((data[\"med\"]) + (((data[\"q05_roll_std_1000\"]) + (((data[\"iqr\"]) * (((data[\"iqr\"]) + (data[\"max_last_10000\"])))))))))))))))))))) +\n 0.0399921872*np.tanh((((((((data[\"q05\"]) + (data[\"mean_change_rate_first_10000\"]))/2.0)) + (((data[\"kurt\"]) - (((((data[\"kurt\"]) * (data[\"abs_max_roll_std_1000\"]))) * (data[\"kurt\"]))))))) + (((((data[\"av_change_abs_roll_std_100\"]) * ((-1.0*((data[\"classic_sta_lta3_mean\"])))))) * 2.0)))) +\n 0.0399999991*np.tanh(((((((((((((((data[\"skew\"]) - ((((data[\"min_roll_std_1000\"]) + (data[\"q95_roll_mean_100\"]))/2.0)))) + (((data[\"q95_roll_mean_100\"]) * (data[\"q05\"]))))) * (data[\"MA_400MA_std_mean\"]))) * (data[\"MA_400MA_BB_high_mean\"]))) * 2.0)) * 2.0)) + ((((data[\"mean_change_abs\"]) + (data[\"min_roll_std_1000\"]))/2.0)))) +\n 0.0399921872*np.tanh((((((4.12892913818359375)) * ((((((data[\"exp_Moving_average_300_mean\"]) + (data[\"med\"]))/2.0)) - (data[\"q01_roll_std_100\"]))))) * (((data[\"skew\"]) + (((0.2439019978) + ((((data[\"MA_1000MA_std_mean\"]) + ((((((data[\"av_change_abs_roll_std_1000\"]) + (data[\"med\"]))) + (data[\"iqr\"]))/2.0)))/2.0)))))))) +\n 0.0399999991*np.tanh(((((((((data[\"av_change_abs_roll_std_10\"]) * (((((data[\"mean_change_rate_last_50000\"]) + (data[\"av_change_abs_roll_mean_10\"]))) + (data[\"min_last_50000\"]))))) * 2.0)) * 2.0)) + ((((((((1.0)) + (((data[\"av_change_rate_roll_std_10\"]) + (data[\"skew\"]))))/2.0)) + (data[\"q01_roll_mean_1000\"]))/2.0)))) +\n 0.0399999991*np.tanh(((((((((data[\"q05_roll_std_10\"]) * (((((((data[\"med\"]) + (data[\"q05_roll_std_10\"]))) * (data[\"q05_roll_mean_10\"]))) * (data[\"q95_roll_std_1000\"]))))) * (data[\"q05_roll_std_10\"]))) + (((((data[\"q05_roll_mean_10\"]) + (data[\"q95_roll_std_100\"]))) + (data[\"q95_roll_std_100\"]))))) * 2.0)) +\n 0.0399921872*np.tanh((((data[\"skew\"]) + (((((((((data[\"min_roll_std_10\"]) * ((-1.0*((data[\"min_roll_std_10\"])))))) + (data[\"q05_roll_mean_1000\"]))) * (data[\"Hilbert_mean\"]))) - ((((data[\"av_change_rate_roll_std_100\"]) + (((data[\"max_roll_std_100\"]) * (((data[\"skew\"]) * (data[\"skew\"]))))))/2.0)))))/2.0)) +\n 0.0399999991*np.tanh(((data[\"q05_roll_std_10\"]) * (((((((((data[\"q01_roll_mean_1000\"]) - (np.tanh((((data[\"Moving_average_6000_mean\"]) - (((data[\"ave_roll_std_10\"]) * (((data[\"kurt\"]) - ((((data[\"av_change_abs_roll_mean_10\"]) + ((((data[\"Moving_average_6000_mean\"]) + (data[\"ave_roll_std_10\"]))/2.0)))/2.0)))))))))))) * 2.0)) * 2.0)) * 2.0)))) +\n 0.0399843715*np.tanh(((data[\"min\"]) + ((-1.0*((((data[\"av_change_rate_roll_std_100\"]) * ((((((((data[\"mean_change_rate\"]) + (data[\"min\"]))/2.0)) + (data[\"min_roll_std_1000\"]))) + (((np.tanh((data[\"min_last_10000\"]))) + ((((((data[\"min_roll_std_100\"]) * (data[\"av_change_rate_roll_mean_10\"]))) + (data[\"av_change_abs_roll_std_10\"]))/2.0))))))))))))) +\n 0.0399999991*np.tanh(((data[\"min_roll_mean_100\"]) * (((((data[\"min_roll_std_1000\"]) * (((data[\"q01_roll_std_1000\"]) * (((((((data[\"q01_roll_std_1000\"]) * (data[\"q99_roll_std_100\"]))) + (data[\"exp_Moving_average_300_mean\"]))) + (data[\"avg_first_10000\"]))))))) + (((((data[\"mean_change_rate\"]) * (3.1415927410))) - (data[\"avg_first_10000\"]))))))) +\n 0.0399921872*np.tanh(((((data[\"q95_roll_mean_1000\"]) * (((((((((data[\"q95_roll_mean_1000\"]) + (((data[\"avg_last_10000\"]) + (((3.0) / 2.0)))))/2.0)) + (data[\"q05_roll_mean_100\"]))/2.0)) - (data[\"q95_roll_std_1000\"]))))) * (((((((data[\"q95_roll_std_1000\"]) * 2.0)) + (data[\"q99_roll_mean_10\"]))) + (data[\"classic_sta_lta4_mean\"]))))) +\n 0.0399296731*np.tanh((((((((((((data[\"q01_roll_std_100\"]) + (np.tanh((((data[\"q01\"]) + ((-1.0*((((data[\"q01_roll_std_100\"]) * 2.0))))))))))/2.0)) * ((-1.0*(((((data[\"q95\"]) + (((data[\"exp_Moving_average_30000_mean\"]) * 2.0)))/2.0))))))) * 2.0)) * 2.0)) * 2.0)) +\n 0.0399921872*np.tanh(((((np.tanh((data[\"q99\"]))) + ((-1.0*(((((data[\"max_first_10000\"]) + (((data[\"av_change_rate_roll_mean_1000\"]) - (data[\"q99\"]))))/2.0))))))) - ((((((((data[\"av_change_rate_roll_mean_1000\"]) * (data[\"av_change_rate_roll_mean_1000\"]))) + (data[\"q05_roll_std_10\"]))/2.0)) * (((data[\"q05_roll_std_10\"]) * (data[\"iqr\"]))))))) +\n 0.0399999991*np.tanh(((data[\"min_roll_mean_1000\"]) * (((((((((((data[\"std_roll_std_100\"]) + (((data[\"q05\"]) + (((((data[\"q95_roll_mean_1000\"]) * (data[\"q95_roll_std_1000\"]))) + (((data[\"q95_roll_mean_1000\"]) * (((data[\"q95_roll_mean_1000\"]) * (data[\"q95_roll_std_1000\"]))))))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) +\n 0.0399921872*np.tanh(((((data[\"mean_change_abs\"]) * (data[\"min_roll_std_10\"]))) + (((((((data[\"std_last_50000\"]) * (3.6923100948))) * (((data[\"q95_roll_mean_1000\"]) + (((data[\"classic_sta_lta4_mean\"]) - (data[\"min_roll_std_10\"]))))))) * ((((((data[\"min_roll_std_10\"]) + (data[\"q05_roll_mean_100\"]))/2.0)) - (data[\"max_roll_std_10\"]))))))) +\n 0.0399999991*np.tanh(((((data[\"q01_roll_std_10\"]) + (data[\"min_roll_std_100\"]))) * (((data[\"abs_q05\"]) - (((data[\"avg_last_50000\"]) - (((((data[\"classic_sta_lta2_mean\"]) / 2.0)) + (((data[\"max_roll_mean_1000\"]) * (((((data[\"avg_last_50000\"]) - (np.tanh((data[\"min_roll_std_100\"]))))) - (data[\"min_roll_std_100\"]))))))))))))) +\n 0.0399999991*np.tanh(((((data[\"q95_roll_mean_100\"]) * (data[\"min_roll_mean_1000\"]))) - (((((data[\"av_change_abs_roll_std_100\"]) * (((((data[\"classic_sta_lta3_mean\"]) + (data[\"mean_change_rate_first_50000\"]))) + (((data[\"q95_roll_mean_100\"]) * (data[\"classic_sta_lta3_mean\"]))))))) + (((data[\"mean_change_abs\"]) * (((data[\"classic_sta_lta3_mean\"]) - (data[\"mean_change_rate_first_50000\"]))))))))) +\n 0.0398359038*np.tanh(((data[\"q95_roll_mean_10\"]) * (((((data[\"q95_roll_mean_10\"]) * (((data[\"min_roll_mean_1000\"]) + (data[\"av_change_rate_roll_std_10\"]))))) - (((data[\"max_to_min\"]) + (((((((data[\"max_to_min\"]) * 2.0)) * 2.0)) * (((data[\"av_change_rate_roll_std_10\"]) + (data[\"q01_roll_mean_1000\"]))))))))))) +\n 0.0399999991*np.tanh(((data[\"q01_roll_std_10\"]) * (((((((data[\"q01_roll_std_10\"]) * (data[\"skew\"]))) + (((data[\"classic_sta_lta4_mean\"]) - (data[\"classic_sta_lta2_mean\"]))))) + ((((data[\"av_change_abs_roll_mean_1000\"]) + (((((-1.0*((data[\"q01_roll_std_100\"])))) + (((data[\"q01_roll_std_10\"]) * ((-1.0*((data[\"q01_roll_std_10\"])))))))/2.0)))/2.0)))))) +\n 0.0385153331*np.tanh((((((data[\"std_roll_mean_1000\"]) + (data[\"av_change_abs_roll_mean_100\"]))) + (((data[\"max_first_10000\"]) * (((data[\"MA_700MA_std_mean\"]) * (((73.0) * (((np.tanh((data[\"max_to_min\"]))) - (((data[\"av_change_rate_roll_mean_1000\"]) + (((((data[\"std_roll_mean_1000\"]) * 2.0)) * 2.0)))))))))))))/2.0)) +\n 0.0399999991*np.tanh(((((data[\"std_roll_mean_1000\"]) * (((data[\"av_change_rate_roll_std_1000\"]) - (((((((data[\"skew\"]) - (data[\"min_roll_std_10\"]))) - (data[\"classic_sta_lta4_mean\"]))) + (((data[\"std_roll_mean_1000\"]) - (2.0))))))))) + (((data[\"classic_sta_lta4_mean\"]) * (((data[\"av_change_abs_roll_mean_1000\"]) - (data[\"q99\"]))))))) +\n 0.0399843715*np.tanh((-1.0*((((data[\"kurt\"]) * (((data[\"kurt\"]) * ((((((data[\"av_change_abs_roll_mean_10\"]) + (((((data[\"med\"]) - (((data[\"med\"]) * (data[\"av_change_abs_roll_mean_10\"]))))) - (data[\"min_roll_std_10\"]))))/2.0)) + (((data[\"q99_roll_std_1000\"]) * (data[\"kurt\"])))))))))))) +\n 0.0399921872*np.tanh(((data[\"mean_change_rate_first_10000\"]) * (((data[\"av_change_abs_roll_mean_100\"]) * ((-1.0*((((((np.tanh((((((data[\"classic_sta_lta3_mean\"]) + (data[\"mean_change_rate_first_10000\"]))) * 2.0)))) + (((1.9019600153) - (data[\"av_change_abs_roll_mean_100\"]))))) - (((((data[\"classic_sta_lta3_mean\"]) * 2.0)) * (data[\"mean_change_rate_first_10000\"])))))))))))) +\n 0.0399921872*np.tanh(((((data[\"av_change_rate_roll_std_100\"]) * (((data[\"classic_sta_lta3_mean\"]) * (((((data[\"min_roll_std_1000\"]) - (data[\"min\"]))) * (data[\"q05\"]))))))) + (((data[\"mean_change_rate_last_10000\"]) * (((((data[\"av_change_abs_roll_std_1000\"]) - (data[\"min\"]))) - (((data[\"classic_sta_lta3_mean\"]) * (data[\"q01_roll_std_1000\"]))))))))) +\n 0.0399999991*np.tanh(((data[\"q95_roll_std_1000\"]) + (((data[\"q05_roll_mean_10\"]) - ((-1.0*((((data[\"q95_roll_std_1000\"]) * (((data[\"q95_roll_std_1000\"]) * (((((data[\"q01_roll_std_10\"]) * ((((((((data[\"q95_roll_std_1000\"]) / 2.0)) + (data[\"q01_roll_mean_1000\"]))/2.0)) - (data[\"med\"]))))) * (data[\"q95_roll_std_1000\"])))))))))))))) +\n 0.0399843715*np.tanh(((data[\"av_change_abs_roll_mean_100\"]) * (((data[\"classic_sta_lta4_mean\"]) * (((((((((3.65123486518859863)) + ((((3.65123486518859863)) * (data[\"abs_max_roll_std_10\"]))))/2.0)) + (((((data[\"min_last_50000\"]) * (((data[\"abs_max_roll_std_1000\"]) - (data[\"av_change_abs_roll_mean_10\"]))))) - (data[\"classic_sta_lta4_mean\"]))))) * (data[\"ave_roll_mean_10\"]))))))) +\n 0.0399999991*np.tanh(((((data[\"max_to_min_diff\"]) * (((data[\"min_roll_std_100\"]) * (((data[\"av_change_rate_roll_std_100\"]) + (((data[\"q01_roll_std_100\"]) + (((((((data[\"av_change_rate_roll_mean_100\"]) + (data[\"Hilbert_mean\"]))) * (data[\"q95_roll_mean_1000\"]))) + (((data[\"q01\"]) * (data[\"min_roll_std_100\"]))))))))))))) * 2.0)) +\n 0.0399999991*np.tanh(((((np.tanh((((((data[\"q05\"]) + (((np.tanh((data[\"avg_first_10000\"]))) + (data[\"mean_change_rate_last_10000\"]))))) + (data[\"q05\"]))))) + (((((data[\"kurt\"]) * (data[\"mean_change_rate_last_10000\"]))) + (data[\"q95_roll_std_1000\"]))))) * (((data[\"q01_roll_mean_1000\"]) + (data[\"av_change_rate_roll_mean_100\"]))))) +\n 0.0399921872*np.tanh(((data[\"min_roll_mean_1000\"]) + ((((((((((((data[\"av_change_rate_roll_std_10\"]) + (((data[\"av_change_abs_roll_std_1000\"]) * (data[\"min_roll_mean_1000\"]))))/2.0)) * (data[\"classic_sta_lta3_mean\"]))) + (data[\"av_change_rate_roll_std_10\"]))/2.0)) + (((data[\"abs_max_roll_mean_100\"]) - (((data[\"av_change_abs_roll_std_1000\"]) * (data[\"classic_sta_lta1_mean\"]))))))/2.0)))) +\n 0.0399999991*np.tanh(((data[\"max_to_min\"]) * (((((data[\"MA_400MA_std_mean\"]) - (data[\"q95_roll_std_100\"]))) + ((((((data[\"min_roll_std_10\"]) * (((((((data[\"min_roll_std_10\"]) * (data[\"classic_sta_lta1_mean\"]))) - (data[\"min_roll_std_10\"]))) - (data[\"kurt\"]))))) + ((((data[\"kurt\"]) + (data[\"min_roll_std_10\"]))/2.0)))/2.0)))))) +\n 0.0399921872*np.tanh(((data[\"min_roll_std_10\"]) * ((((((((((((np.tanh((data[\"min_roll_std_10\"]))) + (data[\"avg_last_10000\"]))/2.0)) - (data[\"q95_roll_std_100\"]))) - (data[\"MA_700MA_BB_high_mean\"]))) + (((data[\"abs_max_roll_mean_100\"]) - (data[\"classic_sta_lta4_mean\"]))))) * (((data[\"min_roll_std_10\"]) - (((3.0) / 2.0)))))))) +\n 0.0399765596*np.tanh(((((data[\"avg_last_10000\"]) * (((data[\"min_roll_mean_1000\"]) + (((((data[\"avg_last_10000\"]) + (np.tanh((data[\"mean_change_rate_last_50000\"]))))) * (((data[\"min_roll_std_10\"]) * (data[\"min_last_10000\"]))))))))) + (np.tanh((np.tanh((((((data[\"mean_change_rate_last_50000\"]) * (data[\"min_roll_std_1000\"]))) * 2.0)))))))) +\n 0.0399921872*np.tanh(np.tanh((np.tanh((np.tanh((((((((data[\"q95_roll_std_10\"]) - (data[\"q05\"]))) * (((data[\"abs_q05\"]) * (((data[\"mean_change_rate_first_10000\"]) + (((((data[\"mean_change_rate_first_10000\"]) + (data[\"q05\"]))) * (((data[\"abs_q05\"]) * (data[\"q95_roll_mean_1000\"]))))))))))) * 2.0)))))))) +\n 0.0399843715*np.tanh(((data[\"q95_roll_std_1000\"]) * (((data[\"max_roll_mean_100\"]) + (((((((((data[\"q01_roll_mean_1000\"]) * 2.0)) * 2.0)) * (data[\"min_roll_mean_1000\"]))) * (((((((data[\"q01_roll_mean_1000\"]) + ((-1.0*((data[\"max_to_min\"])))))) * 2.0)) * 2.0)))))))) +\n 0.0399765596*np.tanh(((data[\"q05_roll_std_100\"]) * ((-1.0*((np.tanh((((((data[\"med\"]) + (((((data[\"med\"]) + (((data[\"q01_roll_std_10\"]) * (((data[\"avg_first_50000\"]) + (data[\"q01_roll_std_10\"]))))))) * ((((1.16074943542480469)) + (data[\"avg_first_50000\"]))))))) * 2.0))))))))) +\n 0.0399062298*np.tanh((-1.0*((((data[\"classic_sta_lta2_mean\"]) * ((((((np.tanh((data[\"kurt\"]))) + (data[\"avg_last_50000\"]))/2.0)) + (((data[\"q05_roll_mean_100\"]) * ((-1.0*(((((((((data[\"kurt\"]) + (data[\"q05_roll_mean_100\"]))/2.0)) - (np.tanh((data[\"kurt\"]))))) + (data[\"std_last_10000\"]))))))))))))))) +\n 0.0399453007*np.tanh(((data[\"std_last_50000\"]) * (((((np.tanh((((((((((((data[\"av_change_abs_roll_mean_100\"]) + (np.tanh((np.tanh((((((((((data[\"skew\"]) + (data[\"Moving_average_6000_mean\"]))) * 2.0)) * 2.0)) * 2.0)))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) * 2.0)) * 2.0)))) +\n 0.0390935726*np.tanh((-1.0*((((data[\"max_roll_mean_100\"]) * (((((((np.tanh((((((data[\"classic_sta_lta3_mean\"]) + ((((data[\"q99_roll_mean_1000\"]) + (data[\"mean_change_rate_first_10000\"]))/2.0)))) + (((data[\"av_change_abs_roll_mean_10\"]) + (((data[\"classic_sta_lta3_mean\"]) * (data[\"q95_roll_mean_1000\"]))))))))) * 2.0)) * 2.0)) * 2.0))))))) +\n 0.0399296731*np.tanh(((data[\"q95_roll_std_100\"]) + ((-1.0*((((data[\"q95_roll_std_10\"]) + ((((((((data[\"kurt\"]) * ((((data[\"mean_change_rate_last_10000\"]) + (data[\"trend\"]))/2.0)))) * (data[\"kurt\"]))) + ((((((data[\"med\"]) + (data[\"max_first_50000\"]))/2.0)) * (data[\"min_roll_std_100\"]))))/2.0))))))))) +\n 0.0398984179*np.tanh(((((np.tanh((((data[\"q01\"]) - (data[\"q05\"]))))) + (((((((((np.tanh((data[\"q05_roll_std_100\"]))) + (data[\"q01\"]))) * 2.0)) + (data[\"q95_roll_mean_1000\"]))) * 2.0)))) * ((-1.0*((((data[\"q01\"]) - (data[\"q05\"])))))))) +\n 0.0399999991*np.tanh(np.tanh((np.tanh(((-1.0*((((data[\"mean_change_rate_first_10000\"]) * (((data[\"min_last_10000\"]) + (((data[\"min_last_10000\"]) + (((((data[\"abs_q95\"]) + (((data[\"avg_last_50000\"]) + ((((data[\"min_last_10000\"]) + (0.8873239756))/2.0)))))) * ((-1.0*((data[\"q05_roll_std_100\"]))))))))))))))))))) +\n 0.0399999991*np.tanh(((data[\"min_first_50000\"]) * ((((-1.0*((data[\"av_change_abs_roll_std_100\"])))) - (((data[\"ave_roll_mean_10\"]) * ((-1.0*((((data[\"max_to_min\"]) - (((((((data[\"MA_400MA_BB_low_mean\"]) - (((data[\"av_change_rate_roll_mean_100\"]) + (((data[\"q01_roll_mean_1000\"]) * (data[\"av_change_rate_roll_mean_100\"]))))))) * 2.0)) * 2.0))))))))))))) +\n 0.0399999991*np.tanh(((((((((data[\"max_to_min_diff\"]) * (((data[\"av_change_rate_roll_std_100\"]) + (((((data[\"q95_roll_std_10\"]) + (data[\"max_first_50000\"]))) + (((data[\"mean_change_abs\"]) + (((((data[\"MA_400MA_BB_low_mean\"]) * (data[\"max_first_50000\"]))) * (data[\"max_first_50000\"]))))))))))) * 2.0)) * 2.0)) * (data[\"min_roll_std_100\"]))) +\n 0.0399999991*np.tanh(((((data[\"max\"]) * (((data[\"std_first_50000\"]) * ((((((-1.0*((data[\"max_roll_std_100\"])))) + (((((((((data[\"q001\"]) + (((data[\"q001\"]) * (data[\"classic_sta_lta4_mean\"]))))) + (data[\"classic_sta_lta4_mean\"]))) * 2.0)) * 2.0)))) * 2.0)))))) * (data[\"q05_roll_std_1000\"]))) +\n 0.0399765596*np.tanh(((((data[\"abs_max_roll_std_1000\"]) * 2.0)) * (((data[\"q95_roll_std_10\"]) - (((data[\"q999\"]) - (np.tanh((((data[\"max_last_10000\"]) * (((((((((((((data[\"abs_max_roll_std_1000\"]) + (data[\"Moving_average_6000_mean\"]))) * 2.0)) + (data[\"mean_change_abs\"]))) * 2.0)) * 2.0)) * 2.0)))))))))))) +\n 0.0399687439*np.tanh(((data[\"min_roll_std_10\"]) * ((((((data[\"avg_last_50000\"]) + (((((((data[\"q01_roll_std_1000\"]) * (data[\"classic_sta_lta4_mean\"]))) * (data[\"mean_change_rate_last_50000\"]))) - (((data[\"min_roll_std_10\"]) * (data[\"av_change_abs_roll_mean_10\"]))))))/2.0)) + ((((((data[\"classic_sta_lta4_mean\"]) * (data[\"Moving_average_6000_mean\"]))) + (data[\"mean_change_rate_last_50000\"]))/2.0)))))) +\n 0.0398906022*np.tanh((((np.tanh((((data[\"av_change_abs_roll_mean_1000\"]) - (((data[\"Hann_window_mean\"]) - (data[\"skew\"]))))))) + (((((((data[\"min_roll_mean_1000\"]) * 2.0)) * 2.0)) * (((data[\"skew\"]) + (((((data[\"std_roll_mean_1000\"]) - (data[\"q99_roll_mean_10\"]))) - (data[\"med\"]))))))))/2.0)) +\n 0.0399999991*np.tanh(((data[\"std_first_10000\"]) * ((-1.0*((((data[\"min_roll_std_10\"]) * (((((((((data[\"kurt\"]) + (((((data[\"av_change_rate_roll_std_1000\"]) - (data[\"trend\"]))) + (data[\"mean_change_rate_first_10000\"]))))) - (data[\"max_last_10000\"]))) + (((data[\"trend\"]) * (data[\"trend\"]))))) * 2.0))))))))) +\n 0.0399765596*np.tanh(((data[\"q99\"]) * ((-1.0*((((data[\"iqr\"]) - (((((((1.9019600153) + (((data[\"abs_max_roll_mean_100\"]) + (data[\"q99\"]))))) * (((((data[\"std_roll_mean_1000\"]) * 2.0)) * (((data[\"max_roll_mean_1000\"]) - (data[\"std_roll_mean_1000\"]))))))) + (data[\"min_roll_std_1000\"])))))))))) +\n 0.0399765596*np.tanh(((((data[\"mean_change_rate_last_10000\"]) + (np.tanh((((((np.tanh((data[\"skew\"]))) + (((data[\"max_roll_std_1000\"]) * 2.0)))) * 2.0)))))) * (((data[\"abs_max_roll_std_10\"]) + ((-1.0*((((data[\"skew\"]) * (((((data[\"abs_max\"]) / 2.0)) - (data[\"ave_roll_std_1000\"])))))))))))) +\n 0.0355928876*np.tanh((((((((data[\"min_roll_std_100\"]) * (data[\"q05_roll_mean_1000\"]))) * (data[\"min_roll_std_1000\"]))) + (((((data[\"min_roll_std_100\"]) * (data[\"min_roll_std_100\"]))) - (((((data[\"max_to_min\"]) + ((((((data[\"q999\"]) + (data[\"min_roll_std_100\"]))/2.0)) + (data[\"q999\"]))))) * (data[\"classic_sta_lta3_mean\"]))))))/2.0)) +\n 0.0399843715*np.tanh((((((-1.0*((((((((data[\"q01_roll_std_10\"]) * (data[\"q05_roll_std_1000\"]))) * (data[\"max_first_50000\"]))) * (data[\"q01_roll_std_10\"])))))) * (((data[\"max_first_50000\"]) + (data[\"max_to_min_diff\"]))))) * (((((data[\"min\"]) - (data[\"q05\"]))) - (data[\"q05\"]))))) +\n 0.0399765596*np.tanh(((data[\"med\"]) * (((np.tanh((((((((((data[\"av_change_abs_roll_std_100\"]) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) - (((((data[\"min_roll_std_10\"]) + ((((data[\"q01_roll_std_1000\"]) + ((((data[\"max_to_min_diff\"]) + (data[\"min_roll_std_1000\"]))/2.0)))/2.0)))) * (((data[\"av_change_abs_roll_std_100\"]) * 2.0)))))))) +\n 0.0387106836*np.tanh((-1.0*((((((data[\"av_change_rate_roll_mean_10\"]) * (((data[\"q95_roll_std_1000\"]) + (((((((((((data[\"min_roll_mean_1000\"]) * 2.0)) * 2.0)) * (((data[\"q95_roll_std_1000\"]) + (data[\"min_roll_mean_1000\"]))))) * 2.0)) * (((data[\"q95_roll_std_100\"]) - (((data[\"min_roll_mean_1000\"]) * 2.0)))))))))) * 2.0))))) +\n 0.0399999991*np.tanh(((data[\"max_last_10000\"]) * ((((((((data[\"MA_700MA_BB_low_mean\"]) * (((((data[\"avg_first_10000\"]) - (data[\"av_change_abs_roll_mean_1000\"]))) - (((data[\"avg_first_10000\"]) * (((data[\"avg_first_10000\"]) - (((data[\"min_roll_std_1000\"]) * 2.0)))))))))) * 2.0)) + (((data[\"avg_first_10000\"]) - (data[\"mean_change_rate_last_10000\"]))))/2.0)))) +\n 0.0399999991*np.tanh(np.tanh((((((((((data[\"av_change_rate_roll_std_100\"]) * (((data[\"min\"]) * (((data[\"q05_roll_std_100\"]) + (((((data[\"av_change_abs_roll_std_100\"]) + (((data[\"mean_change_rate_first_10000\"]) * (data[\"av_change_rate_roll_std_100\"]))))) + (((data[\"med\"]) - (data[\"mean_change_rate_first_10000\"]))))))))))) * 2.0)) * 2.0)) * 2.0)))) +\n 0.0359679610*np.tanh((-1.0*((((np.tanh((((((((((((((((((data[\"min_roll_std_1000\"]) - (data[\"av_change_rate_roll_std_1000\"]))) - (((-2.0) + ((-1.0*((data[\"min_roll_std_1000\"])))))))) - (np.tanh((data[\"av_change_abs_roll_std_100\"]))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) / 2.0))))) +\n 0.0373354182*np.tanh((-1.0*((((data[\"max_to_min\"]) * (((data[\"avg_first_50000\"]) * (((((data[\"ave_roll_std_100\"]) - (((data[\"classic_sta_lta1_mean\"]) + (data[\"mean_change_rate_first_10000\"]))))) * (((data[\"classic_sta_lta2_mean\"]) - ((-1.0*((((data[\"ave_roll_std_100\"]) / 2.0)))))))))))))))) +\n 0.0398671627*np.tanh(((((0.8873239756) + (data[\"avg_last_50000\"]))) * (((data[\"iqr\"]) * (((((0.8873239756) + (data[\"Moving_average_6000_mean\"]))) + (((data[\"q05_roll_mean_10\"]) * (((((((data[\"ave10\"]) + (data[\"q01_roll_std_10\"]))) * (data[\"q01_roll_std_10\"]))) * (data[\"iqr\"]))))))))))) +\n 0.0399453007*np.tanh((((np.tanh((((((((((((data[\"mean_change_rate_last_10000\"]) + (np.tanh(((((((-1.0*((data[\"mean_change_rate_last_50000\"])))) * 2.0)) * (data[\"q95_roll_mean_1000\"]))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) + (np.tanh((np.tanh(((((-1.0*((data[\"mean_change_rate_last_50000\"])))) * 2.0)))))))/2.0)) +\n 0.0399687439*np.tanh(((((((data[\"max_roll_std_1000\"]) + (((data[\"max_roll_std_1000\"]) + (data[\"trend\"]))))) + (data[\"count_big\"]))) * (((data[\"max_to_min\"]) * (((((((-1.0*((data[\"max_to_min\"])))) + (data[\"max_roll_mean_1000\"]))/2.0)) - (data[\"q99_roll_std_10\"]))))))) +\n 0.0399843715*np.tanh(((data[\"classic_sta_lta2_mean\"]) * (((data[\"abs_mean\"]) * (((data[\"classic_sta_lta1_mean\"]) * (((((((data[\"mean_change_rate_first_50000\"]) + ((-1.0*((data[\"mean_change_abs\"])))))) + (data[\"classic_sta_lta4_mean\"]))) + (((((((-2.0) + (data[\"av_change_rate_roll_std_10\"]))/2.0)) + (data[\"av_change_abs_roll_std_1000\"]))/2.0)))))))))) +\n 0.0399687439*np.tanh(((data[\"min_roll_std_100\"]) * (((((((data[\"mean_change_abs\"]) + (((((((data[\"av_change_abs_roll_std_100\"]) * (data[\"min_roll_std_100\"]))) + (data[\"min_roll_std_1000\"]))) * (((data[\"std_roll_mean_1000\"]) * (((data[\"min_last_10000\"]) + (data[\"av_change_abs_roll_std_100\"]))))))))) * 2.0)) * (data[\"av_change_abs_roll_std_100\"]))))) +\n 0.0399374850*np.tanh(((((data[\"min_first_10000\"]) - (((data[\"mean_change_rate_first_10000\"]) * (data[\"kurt\"]))))) * (((((((data[\"abs_trend\"]) * (data[\"kurt\"]))) - (data[\"mean_change_abs\"]))) + (((data[\"std_roll_mean_1000\"]) + (((data[\"std_roll_mean_1000\"]) + (((data[\"min_first_10000\"]) + (data[\"abs_std\"]))))))))))) +\n 0.0399921872*np.tanh(((data[\"q95_roll_std_10\"]) * ((-1.0*((((((-1.0*((data[\"abs_max_roll_mean_100\"])))) + ((((-1.0*((((data[\"classic_sta_lta4_mean\"]) / 2.0))))) - (((data[\"q001\"]) * (((data[\"q95_roll_mean_1000\"]) - ((-1.0*((((((((data[\"classic_sta_lta4_mean\"]) / 2.0)) / 2.0)) / 2.0))))))))))))/2.0))))))) +\n 0.0399843715*np.tanh(((((data[\"mean_change_rate_last_10000\"]) * (((data[\"abs_max\"]) * (((((((((((data[\"min_roll_std_1000\"]) + (data[\"max_to_min\"]))) - (data[\"count_big\"]))) * 2.0)) - (data[\"max_to_min\"]))) + (((data[\"av_change_abs_roll_std_1000\"]) + (data[\"mean_change_abs\"]))))))))) + (data[\"count_big\"]))) +\n 0.0383434258*np.tanh(((((data[\"med\"]) * ((-1.0*((((((((data[\"trend\"]) * (((np.tanh((data[\"av_change_abs_roll_std_100\"]))) - (data[\"mean_change_abs\"]))))) + (data[\"mean_change_abs\"]))) * 2.0))))))) * (((((data[\"av_change_abs_roll_std_100\"]) + (np.tanh((data[\"min_last_10000\"]))))) + (data[\"av_change_abs_roll_std_100\"]))))) +\n 0.0399999991*np.tanh(np.tanh((((((((data[\"min_roll_mean_100\"]) * 2.0)) * 2.0)) * (((((((data[\"max_last_10000\"]) - (0.5434780121))) * (data[\"av_change_rate_roll_mean_100\"]))) + (((data[\"av_change_rate_roll_std_10\"]) - (((data[\"min_roll_std_10\"]) * (data[\"av_change_rate_roll_std_10\"]))))))))))) +\n 0.0399921872*np.tanh((-1.0*((((((((((data[\"min_roll_std_100\"]) + (data[\"av_change_abs_roll_std_10\"]))/2.0)) * (((data[\"av_change_abs_roll_mean_1000\"]) + (data[\"av_change_abs_roll_std_10\"]))))) + (((data[\"av_change_abs_roll_std_100\"]) * (((data[\"classic_sta_lta4_mean\"]) * (((data[\"av_change_abs_roll_std_10\"]) + (((data[\"min_roll_std_1000\"]) * (data[\"av_change_abs_roll_std_100\"]))))))))))/2.0))))) +\n 0.0293416679*np.tanh(np.tanh((((data[\"abs_q95\"]) * (((data[\"q01_roll_std_10\"]) * (((data[\"q01_roll_std_10\"]) * (((((data[\"max_last_50000\"]) - ((((((data[\"av_change_abs_roll_mean_10\"]) - (data[\"classic_sta_lta4_mean\"]))) + (data[\"av_change_abs_roll_mean_100\"]))/2.0)))) - ((-1.0*((((data[\"av_change_abs_roll_mean_10\"]) * (data[\"classic_sta_lta4_mean\"])))))))))))))))) +\n 0.0399765596*np.tanh(((((data[\"trend\"]) * (data[\"max_first_10000\"]))) * (((data[\"med\"]) + (((data[\"av_change_rate_roll_mean_1000\"]) + (((((2.0769200325) - (((np.tanh((((((((((data[\"classic_sta_lta1_mean\"]) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) * 2.0)))) / 2.0)))))))) +\n 0.0399921872*np.tanh(((((data[\"mean_change_rate_last_50000\"]) * ((-1.0*((((data[\"min_first_10000\"]) - (data[\"std_first_10000\"])))))))) * ((((((((((-1.0*((data[\"std_first_10000\"])))) - (data[\"min_last_50000\"]))) - (data[\"abs_max_roll_std_10\"]))) - (((data[\"mean_change_rate_last_50000\"]) * (data[\"std_first_10000\"]))))) - (data[\"classic_sta_lta2_mean\"]))))) +\n 0.0399921872*np.tanh(((data[\"av_change_abs_roll_mean_10\"]) * (((data[\"abs_trend\"]) + ((((((((data[\"mean_change_abs\"]) * (data[\"min_last_10000\"]))) + (((data[\"mean_change_rate_first_10000\"]) * (data[\"max_first_10000\"]))))) + ((((data[\"av_change_abs_roll_mean_10\"]) + (((((data[\"med\"]) - (data[\"q95_roll_mean_1000\"]))) - (data[\"mean_change_rate_first_10000\"]))))/2.0)))/2.0)))))) +\n 0.0399531126*np.tanh(((data[\"ave_roll_std_100\"]) * (((data[\"min_roll_std_100\"]) * (((((data[\"max_roll_mean_100\"]) + ((((data[\"max_to_min_diff\"]) + (data[\"q99\"]))/2.0)))) * (((((((data[\"max_to_min_diff\"]) * ((((-1.0*((data[\"std_first_50000\"])))) * (data[\"q95_roll_std_100\"]))))) - (data[\"skew\"]))) * 2.0)))))))) +\n 0.0399843715*np.tanh(((data[\"std_roll_std_100\"]) * (((data[\"min_roll_std_1000\"]) * (((data[\"max_roll_mean_10\"]) - (((data[\"kurt\"]) * ((((((data[\"avg_last_10000\"]) * (data[\"avg_last_10000\"]))) + ((((data[\"q99_roll_mean_100\"]) + ((((((data[\"avg_last_10000\"]) * (data[\"avg_last_10000\"]))) + (data[\"avg_first_10000\"]))/2.0)))/2.0)))/2.0)))))))))) +\n 0.0353584699*np.tanh(((data[\"med\"]) * (((np.tanh((((np.tanh(((((((-1.0*((data[\"max_to_min\"])))) - (data[\"trend\"]))) * 2.0)))) - (((data[\"min_roll_std_100\"]) * (data[\"med\"]))))))) - (np.tanh((np.tanh((data[\"min_roll_std_100\"]))))))))) +\n 0.0399765596*np.tanh((((((((data[\"ave_roll_mean_10\"]) + (data[\"min_roll_std_100\"]))/2.0)) * (((((data[\"classic_sta_lta4_mean\"]) + (data[\"kurt\"]))) + (((((data[\"q95_roll_mean_10\"]) + (((data[\"q95\"]) * (data[\"q95\"]))))) * (data[\"sum\"]))))))) * ((((data[\"q05_roll_std_100\"]) + (data[\"ave_roll_mean_10\"]))/2.0)))) +\n 0.0399843715*np.tanh(((data[\"min_roll_std_10\"]) * ((((((-1.0*((((data[\"av_change_abs_roll_std_10\"]) * (data[\"av_change_abs_roll_mean_100\"])))))) * (data[\"av_change_abs_roll_std_10\"]))) + (np.tanh(((((-1.0*((data[\"q05\"])))) + (((data[\"min_roll_std_1000\"]) * ((-1.0*((((data[\"av_change_abs_roll_mean_100\"]) * (data[\"av_change_abs_roll_mean_100\"])))))))))))))))) +\n 0.0399999991*np.tanh((-1.0*((((data[\"classic_sta_lta1_mean\"]) * (((data[\"max_last_50000\"]) + (((((data[\"abs_q95\"]) + (((data[\"av_change_abs_roll_std_10\"]) * 2.0)))) * (((((data[\"abs_std\"]) + (data[\"max_last_50000\"]))) * (((((data[\"iqr\"]) * 2.0)) * (data[\"avg_last_10000\"])))))))))))))) +\n 0.0388200805*np.tanh(((((data[\"min_first_10000\"]) * (((data[\"mean_change_rate\"]) * (((((data[\"min_roll_std_100\"]) + (data[\"max_last_50000\"]))) + (((((data[\"mean_change_rate\"]) * (((data[\"mean_change_abs\"]) * (((data[\"min_roll_std_100\"]) + (data[\"min_first_10000\"]))))))) - (data[\"mean_change_abs\"]))))))))) * 2.0)) +\n 0.0389529206*np.tanh(((np.tanh((np.tanh((((data[\"max_to_min\"]) + (data[\"max_to_min\"]))))))) - (np.tanh((((((((((((((((((((data[\"min_first_10000\"]) * 2.0)) + (data[\"max_to_min\"]))) * 2.0)) * 2.0)) + (data[\"max_last_50000\"]))) * 2.0)) * 2.0)) * 2.0)) * 2.0)))))) +\n 0.0399843715*np.tanh((-1.0*((((((data[\"max_first_50000\"]) * 2.0)) * (((np.tanh(((((((-1.0*((((data[\"q95_roll_std_1000\"]) + (((data[\"q01_roll_std_10\"]) + (data[\"av_change_abs_roll_std_1000\"])))))))) * 2.0)) * 2.0)))) + (((data[\"q01_roll_std_10\"]) + (((data[\"max_first_50000\"]) * (data[\"mean_change_abs\"])))))))))))) +\n 0.0399453007*np.tanh((((((data[\"av_change_abs_roll_std_1000\"]) + (data[\"mean_change_abs\"]))/2.0)) * (((data[\"av_change_abs_roll_std_100\"]) + (((data[\"trend\"]) + (((((data[\"av_change_abs_roll_mean_1000\"]) + ((((data[\"av_change_abs_roll_std_100\"]) + (((data[\"av_change_rate_roll_std_100\"]) + (((data[\"av_change_abs_roll_std_1000\"]) + (data[\"min_roll_std_10\"]))))))/2.0)))) * (data[\"av_change_abs_roll_mean_1000\"]))))))))) +\n 0.0399140455*np.tanh(((data[\"std_first_50000\"]) * (((data[\"mean_change_rate_last_50000\"]) * ((-1.0*((((data[\"avg_first_50000\"]) * ((((data[\"max_first_10000\"]) + (((data[\"min_roll_mean_10\"]) - (((data[\"avg_first_50000\"]) - (((((data[\"mean_change_rate_last_50000\"]) - (data[\"min_last_10000\"]))) * (data[\"min_last_10000\"]))))))))/2.0))))))))))) +\n 0.0399999991*np.tanh(((((data[\"min_last_10000\"]) + (np.tanh((((data[\"ave_roll_mean_1000\"]) + (((((((data[\"av_change_abs_roll_mean_100\"]) + (data[\"av_change_abs_roll_std_10\"]))) * (data[\"ave_roll_mean_1000\"]))) * (((data[\"min_roll_std_1000\"]) * 2.0)))))))))) * (((data[\"min_roll_std_1000\"]) * (((((data[\"std_roll_mean_1000\"]) * 2.0)) * 2.0)))))) +\n 0.0399765596*np.tanh((((((data[\"av_change_abs_roll_mean_100\"]) + (data[\"q99_roll_std_10\"]))/2.0)) * (np.tanh((((np.tanh((((((((((((((data[\"avg_first_10000\"]) + (data[\"max_roll_std_1000\"]))) + (data[\"count_big\"]))) * (73.0))) - (data[\"av_change_rate_roll_mean_100\"]))) * 2.0)) + (data[\"avg_first_10000\"]))))) * 2.0)))))) +\n 0.0371556953*np.tanh(((((data[\"max_last_10000\"]) * (((((data[\"std_last_50000\"]) * (data[\"avg_last_50000\"]))) + (((((((data[\"min_roll_std_10\"]) * 2.0)) + (((data[\"min_last_10000\"]) * (((data[\"max_first_50000\"]) * (((((data[\"min_roll_std_10\"]) * 2.0)) * 2.0)))))))) * (data[\"max_first_10000\"]))))))) * 2.0)) +\n 0.0399609283*np.tanh(((data[\"av_change_abs_roll_std_10\"]) * ((((((((((data[\"min_last_50000\"]) + (((data[\"trend\"]) * (data[\"av_change_abs_roll_mean_10\"]))))/2.0)) + (data[\"max_to_min\"]))/2.0)) + (((data[\"min_last_50000\"]) - (((data[\"min_last_50000\"]) * (((data[\"min_last_50000\"]) * (data[\"q05_roll_mean_1000\"]))))))))/2.0)))) +\n 0.0391638987*np.tanh(((((data[\"trend\"]) - ((((data[\"min_roll_std_100\"]) + (data[\"q001\"]))/2.0)))) * (((np.tanh((np.tanh((((((data[\"classic_sta_lta3_mean\"]) + ((((data[\"classic_sta_lta3_mean\"]) + ((((data[\"kurt\"]) + (data[\"q95_roll_mean_100\"]))/2.0)))/2.0)))) * 2.0)))))) * ((-1.0*((data[\"min_roll_std_100\"])))))))) +\n 0.0398749746*np.tanh(((data[\"ave10\"]) * ((-1.0*((((data[\"av_change_abs_roll_mean_10\"]) * (((data[\"av_change_abs_roll_mean_100\"]) + ((-1.0*(((((data[\"av_change_abs_roll_mean_10\"]) + (((((data[\"classic_sta_lta2_mean\"]) * 2.0)) + (((data[\"mean_change_rate_last_10000\"]) * (data[\"av_change_rate_roll_mean_100\"]))))))/2.0)))))))))))))) +\n 0.0385778472*np.tanh((-1.0*(((((np.tanh((((((((data[\"trend\"]) + (((data[\"min_roll_std_10\"]) + (data[\"Moving_average_6000_mean\"]))))) * 2.0)) * 2.0)))) + (((((-1.0*((((data[\"q95_roll_std_100\"]) * (data[\"ave10\"])))))) + (((data[\"mean_change_rate_first_50000\"]) * (data[\"av_change_abs_roll_mean_10\"]))))/2.0)))/2.0))))) +\n 0.0399921872*np.tanh(((((((((data[\"min_roll_std_10\"]) * ((((data[\"ave_roll_std_10\"]) + (data[\"avg_last_50000\"]))/2.0)))) + (((((((((data[\"avg_last_10000\"]) + (data[\"min_roll_std_10\"]))/2.0)) + (data[\"min_roll_std_10\"]))/2.0)) - (data[\"ave_roll_std_10\"]))))/2.0)) + (((((data[\"q95_roll_std_100\"]) - (data[\"ave_roll_std_10\"]))) * 2.0)))/2.0)) +\n 0.0399921872*np.tanh((((((-1.0*((((data[\"mean_change_rate_first_10000\"]) * 2.0))))) * ((((((((((data[\"av_change_rate_roll_std_10\"]) * (data[\"avg_last_10000\"]))) + (((data[\"min_last_10000\"]) * (data[\"av_change_rate_roll_std_10\"]))))) - (((data[\"avg_first_10000\"]) / 2.0)))) + (data[\"av_change_rate_roll_std_1000\"]))/2.0)))) * (data[\"av_change_abs_roll_std_1000\"]))) +\n 0.0399453007*np.tanh(((data[\"av_change_abs_roll_std_1000\"]) * (((data[\"avg_last_10000\"]) * (((((((data[\"avg_last_10000\"]) * (((data[\"avg_last_10000\"]) * (((((data[\"med\"]) * (((data[\"q001\"]) - (data[\"classic_sta_lta1_mean\"]))))) - (data[\"min_roll_std_10\"]))))))) - (data[\"max_roll_mean_10\"]))) - (data[\"min_roll_std_10\"]))))))) +\n 0.0398749746*np.tanh(np.tanh((np.tanh((((((((((((-1.0*((data[\"av_change_abs_roll_std_100\"])))) + ((-1.0*((data[\"mean_change_rate_first_50000\"])))))/2.0)) / 2.0)) - (((data[\"mean_change_abs\"]) * (((((((data[\"avg_last_50000\"]) * (data[\"q99_roll_mean_10\"]))) - (data[\"mean_change_rate_first_50000\"]))) - (data[\"std_last_10000\"]))))))) * 2.0)))))) +\n 0.0363899209*np.tanh(((data[\"max_last_10000\"]) * (((((np.tanh((((((((data[\"av_change_rate_roll_mean_100\"]) + (((data[\"min_roll_std_1000\"]) * 2.0)))) * 2.0)) * 2.0)))) + (((data[\"av_change_abs_roll_std_100\"]) + ((-1.0*((data[\"mean_change_abs\"])))))))) + (((((data[\"av_change_rate_roll_mean_10\"]) * (data[\"max_last_50000\"]))) * 2.0)))))) +\n 0.0399999991*np.tanh(((data[\"min_roll_std_100\"]) * (((np.tanh((data[\"min_last_10000\"]))) + (((((np.tanh((data[\"std_first_10000\"]))) - ((((np.tanh((data[\"min_last_10000\"]))) + (((data[\"exp_Moving_average_30000_mean\"]) * (data[\"min_last_10000\"]))))/2.0)))) * ((((((data[\"avg_last_50000\"]) + (data[\"mean_change_abs\"]))/2.0)) * 2.0)))))))) +\n 0.0351396762*np.tanh(np.tanh((((((data[\"av_change_abs_roll_std_1000\"]) * (data[\"mean_change_rate_last_10000\"]))) + (((data[\"med\"]) * (np.tanh((((data[\"av_change_rate_roll_std_100\"]) - (((((data[\"avg_last_10000\"]) - (((data[\"mean_change_rate_first_10000\"]) - (((data[\"avg_last_10000\"]) * (data[\"mean_change_rate_last_10000\"]))))))) - (data[\"av_change_abs_roll_std_1000\"]))))))))))))) +\n 0.0399921872*np.tanh((((np.tanh((((((((((data[\"min_roll_std_10\"]) + (((data[\"mean_change_rate_first_50000\"]) + (((((data[\"mean_change_rate_first_50000\"]) + (data[\"mean_change_rate_first_50000\"]))) * (data[\"classic_sta_lta4_mean\"]))))))) * (data[\"av_change_rate_roll_mean_1000\"]))) * 2.0)) * 2.0)))) + (((data[\"av_change_abs_roll_mean_10\"]) * (np.tanh((data[\"med\"]))))))/2.0)) +\n 0.0379214697*np.tanh((-1.0*((((((data[\"max_to_min_diff\"]) * (((((((((((((data[\"min_last_10000\"]) - (data[\"med\"]))) - (data[\"mean_change_rate_first_10000\"]))) + (data[\"q05_roll_std_100\"]))) + (data[\"q01_roll_std_1000\"]))) * 2.0)) * 2.0)))) * ((-1.0*((((data[\"mean_change_rate_first_10000\"]) * (data[\"min_last_10000\"]))))))))))) +\n 0.0399218611*np.tanh(((data[\"min_first_10000\"]) * (np.tanh((((((((((np.tanh((((data[\"max_to_min\"]) * 2.0)))) + (((((((data[\"min_first_50000\"]) * 2.0)) + (((np.tanh((np.tanh((data[\"mean_change_rate_last_10000\"]))))) + (data[\"min_roll_mean_10\"]))))) * 2.0)))) * 2.0)) * 2.0)) * 2.0)))))) +\n 0.0328423530*np.tanh((-1.0*((((np.tanh((((data[\"kurt\"]) * (((((((data[\"mean_change_rate_last_50000\"]) * 2.0)) * 2.0)) * (data[\"skew\"]))))))) + (np.tanh((((data[\"skew\"]) * (((((data[\"mean_change_rate_last_50000\"]) * 2.0)) * (data[\"skew\"])))))))))))) +\n 0.0346083194*np.tanh((((((data[\"av_change_abs_roll_std_10\"]) / 2.0)) + ((-1.0*((((np.tanh((data[\"q05_roll_mean_1000\"]))) + (np.tanh((((((data[\"min_roll_std_100\"]) - (data[\"mean_change_rate_last_50000\"]))) - (((((data[\"q95_roll_mean_1000\"]) - (data[\"av_change_abs_roll_std_10\"]))) - (((data[\"av_change_abs_roll_mean_1000\"]) - (data[\"mean_change_rate_last_50000\"])))))))))))))))/2.0)) +\n 0.0395155288*np.tanh(((data[\"avg_last_50000\"]) * ((-1.0*(((((np.tanh((np.tanh((((((((((((data[\"av_change_rate_roll_mean_100\"]) * ((-1.0*((data[\"max_to_min\"])))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)))))) + ((((((data[\"av_change_rate_roll_std_10\"]) * (data[\"avg_last_10000\"]))) + (data[\"av_change_rate_roll_std_10\"]))/2.0)))/2.0))))))) +\n 0.0316311792*np.tanh(((data[\"max_first_50000\"]) - (((data[\"classic_sta_lta3_mean\"]) * (((((data[\"max_to_min_diff\"]) + (data[\"mean_change_abs\"]))) - (((data[\"trend\"]) * (((((data[\"mean_change_abs\"]) - (((np.tanh((data[\"max_to_min_diff\"]))) * 2.0)))) - (data[\"av_change_abs_roll_mean_100\"]))))))))))) +\n 0.0399609283*np.tanh(((data[\"trend\"]) * (((((((data[\"min_roll_std_1000\"]) * (data[\"std_last_10000\"]))) * ((((data[\"av_change_abs_roll_mean_10\"]) + (((data[\"std_last_50000\"]) - ((((((data[\"av_change_rate_roll_mean_10\"]) * (data[\"q05_roll_std_1000\"]))) + (data[\"min_roll_std_1000\"]))/2.0)))))/2.0)))) + (((data[\"min_roll_std_1000\"]) - (data[\"q05_roll_std_1000\"]))))))) +\n 0.0362180099*np.tanh((((np.tanh((((((((((((data[\"q01_roll_std_100\"]) - (data[\"mean_change_rate_last_10000\"]))) - ((((data[\"av_change_abs_roll_mean_10\"]) + (data[\"abs_max_roll_mean_100\"]))/2.0)))) - (0.2525250018))) * (data[\"min_roll_std_10\"]))) * (73.0))))) + (((data[\"min_roll_mean_1000\"]) + (data[\"abs_max_roll_mean_100\"]))))/2.0)) +\n 0.0399999991*np.tanh((((np.tanh((((73.0) * (((((73.0) * (data[\"av_change_abs_roll_std_100\"]))) - (data[\"av_change_abs_roll_mean_100\"]))))))) + (np.tanh((((((((((data[\"av_change_rate_roll_mean_100\"]) - (data[\"av_change_abs_roll_std_100\"]))) - (data[\"av_change_abs_roll_std_100\"]))) - (data[\"std_first_50000\"]))) - (data[\"av_change_abs_roll_std_100\"]))))))/2.0)) +\n 0.0374213718*np.tanh((((-1.0*((((data[\"q05\"]) * (((data[\"max_last_10000\"]) * (((((data[\"min_roll_std_10\"]) - (data[\"std_last_10000\"]))) - (data[\"std_last_10000\"])))))))))) + (((data[\"max_last_50000\"]) * (((data[\"mean_change_rate_last_10000\"]) - (np.tanh((((((data[\"av_change_rate_roll_std_10\"]) * 2.0)) * 2.0)))))))))) +\n 0.0331470966*np.tanh(((((((((data[\"abs_max_roll_std_1000\"]) * 2.0)) * 2.0)) * 2.0)) * (((((data[\"mean_change_rate_last_50000\"]) * ((((((data[\"abs_max_roll_std_1000\"]) * (((data[\"mean_change_abs\"]) * 2.0)))) + ((((data[\"std_first_50000\"]) + (data[\"mean_change_rate_last_50000\"]))/2.0)))/2.0)))) + (((data[\"mean_change_abs\"]) * (data[\"av_change_rate_roll_std_1000\"]))))))) +\n 0.0396796241*np.tanh(((np.tanh((np.tanh((((((data[\"avg_first_10000\"]) * (((((data[\"Moving_average_3000_mean\"]) + (data[\"skew\"]))) * 2.0)))) + (np.tanh(((-1.0*((((((data[\"ave_roll_mean_100\"]) + (((data[\"av_change_abs_roll_std_1000\"]) + (((data[\"avg_first_10000\"]) * 2.0)))))) * 2.0))))))))))))) / 2.0)) +\n 0.0399765596*np.tanh(((np.tanh((((((data[\"av_change_abs_roll_mean_10\"]) + (data[\"exp_Moving_average_300_mean\"]))) * (((((data[\"classic_sta_lta4_mean\"]) + (data[\"av_change_abs_roll_mean_10\"]))) + (((((data[\"av_change_abs_roll_mean_10\"]) + (((np.tanh((((np.tanh((data[\"avg_last_50000\"]))) * 2.0)))) * 2.0)))) * (data[\"av_change_abs_roll_mean_10\"]))))))))) / 2.0)) +\n 0.0396014862*np.tanh((((((data[\"max_last_50000\"]) + (((((np.tanh((np.tanh((((((np.tanh((np.tanh(((((data[\"q95\"]) + (data[\"mean_change_abs\"]))/2.0)))))) + (((data[\"av_change_abs_roll_std_100\"]) * 2.0)))) * 2.0)))))) * 2.0)) - (data[\"av_change_abs_roll_std_100\"]))))/2.0)) * (data[\"classic_sta_lta4_mean\"]))) +\n 0.0317015015*np.tanh(((np.tanh((((((data[\"avg_last_10000\"]) - (((data[\"iqr\"]) - (((((data[\"avg_last_10000\"]) - (((data[\"iqr\"]) * (data[\"av_change_rate_roll_std_100\"]))))) - (((((((((data[\"min_roll_std_100\"]) * (data[\"av_change_rate_roll_std_100\"]))) * 2.0)) * 2.0)) * 2.0)))))))) * 2.0)))) / 2.0)) +\n 0.0399296731*np.tanh((-1.0*((((data[\"q05_roll_std_1000\"]) * (((((((((data[\"avg_first_10000\"]) + (data[\"mean_change_abs\"]))/2.0)) * (data[\"std_last_50000\"]))) + (((((((data[\"avg_last_10000\"]) + (((data[\"mean_change_rate_first_10000\"]) * (data[\"mean_change_abs\"]))))/2.0)) + (data[\"max_to_min_diff\"]))/2.0)))/2.0))))))) +\n 0.0377104878*np.tanh((((np.tanh((np.tanh((((((((data[\"count_big\"]) + (((data[\"min_last_10000\"]) * (data[\"min_first_50000\"]))))) * 2.0)) * 2.0)))))) + ((((data[\"count_big\"]) + ((-1.0*((((data[\"max_roll_mean_100\"]) - (np.tanh((((data[\"min_last_10000\"]) * (data[\"avg_last_10000\"])))))))))))/2.0)))/2.0)) +\n 0.0309357308*np.tanh(((np.tanh((((((((data[\"max_to_min\"]) * (data[\"classic_sta_lta2_mean\"]))) + (((data[\"classic_sta_lta2_mean\"]) * (((data[\"max_to_min\"]) * (data[\"classic_sta_lta2_mean\"]))))))) * 2.0)))) / 2.0)) +\n 0.0355303772*np.tanh(((((np.tanh((np.tanh((((((((((data[\"av_change_abs_roll_std_10\"]) - (np.tanh((((data[\"skew\"]) * 2.0)))))) * (data[\"mean_change_rate_first_10000\"]))) * (data[\"mean_change_rate_first_10000\"]))) * (((data[\"mean_change_rate_first_10000\"]) + (data[\"av_change_abs_roll_std_10\"]))))))))) * (data[\"mean_change_rate_first_10000\"]))) * (data[\"mean_change_rate_first_10000\"]))) +\n 0.0399843715*np.tanh(((data[\"av_change_abs_roll_std_1000\"]) * (((((((data[\"trend\"]) + (np.tanh((((data[\"min_roll_std_1000\"]) * ((((8.0)) + (((data[\"min_roll_std_1000\"]) * ((8.0)))))))))))/2.0)) + ((-1.0*((((data[\"q01_roll_std_1000\"]) + (((data[\"q01_roll_std_1000\"]) * (data[\"mean_change_rate_first_50000\"])))))))))/2.0)))) +\n 0.0398437195*np.tanh((-1.0*((((data[\"std_first_10000\"]) * ((((((data[\"q99\"]) / 2.0)) + (((data[\"skew\"]) + (((((data[\"std_first_10000\"]) * 2.0)) * (((data[\"std_first_10000\"]) * (((data[\"std_first_10000\"]) * (((data[\"std_first_10000\"]) - (((data[\"MA_700MA_std_mean\"]) * 2.0)))))))))))))/2.0))))))) +\n 0.0399843715*np.tanh((-1.0*((((data[\"mean_change_abs\"]) * ((((((data[\"av_change_abs_roll_mean_1000\"]) + ((((((((data[\"abs_max_roll_mean_10\"]) + (((((data[\"min_roll_mean_100\"]) * (data[\"av_change_abs_roll_mean_1000\"]))) * (data[\"min_roll_std_10\"]))))) - ((((data[\"min_roll_std_10\"]) + (data[\"av_change_abs_roll_std_100\"]))/2.0)))) + (data[\"skew\"]))/2.0)))/2.0)) / 2.0))))))) +\n 0.0295057632*np.tanh(((((((((((((((data[\"q99_roll_mean_100\"]) + (((data[\"q99_roll_mean_100\"]) + (data[\"q95_roll_mean_10\"]))))) * (((data[\"q05_roll_std_100\"]) + ((((((data[\"av_change_abs_roll_std_1000\"]) - (data[\"min_roll_std_100\"]))) + (data[\"av_change_abs_roll_mean_100\"]))/2.0)))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * (data[\"av_change_abs_roll_std_1000\"]))) +\n 0.0346864611*np.tanh(((np.tanh((((((((data[\"mean_change_rate\"]) * (((data[\"mean_change_rate_first_10000\"]) + (((data[\"mean_change_rate_first_10000\"]) + (data[\"mean_change_rate_last_50000\"]))))))) + (((data[\"q01_roll_std_100\"]) * (((((data[\"mean_change_rate_first_10000\"]) * (((data[\"q99_roll_std_10\"]) * 2.0)))) + (data[\"mean_change_rate_first_10000\"]))))))) * 2.0)))) / 2.0)) +\n 0.0397265106*np.tanh(np.tanh((((((data[\"min_roll_std_100\"]) * ((-1.0*((((data[\"av_change_abs_roll_mean_100\"]) * ((((data[\"q05_roll_mean_100\"]) + (data[\"mean_change_rate_first_10000\"]))/2.0))))))))) - (((((((-1.0*((data[\"classic_sta_lta4_mean\"])))) / 2.0)) + ((((data[\"av_change_rate_roll_mean_100\"]) + ((((data[\"Moving_average_6000_mean\"]) + (data[\"mean_change_rate_first_10000\"]))/2.0)))/2.0)))/2.0)))))) +\n 0.0399687439*np.tanh(((np.tanh((((data[\"min_first_50000\"]) * (((((((((np.tanh((((((data[\"q01_roll_std_1000\"]) + (np.tanh(((-1.0*((((((data[\"skew\"]) + (data[\"classic_sta_lta2_mean\"]))) + (data[\"q95_roll_std_10\"])))))))))) * 2.0)))) * 2.0)) * 2.0)) * 2.0)) * 2.0)))))) / 2.0)) +\n 0.0365696438*np.tanh(((np.tanh((((((((((((((((data[\"mean_change_rate_last_10000\"]) * (((data[\"ave_roll_std_10\"]) - (((data[\"q95\"]) - ((((((data[\"abs_mean\"]) + (((data[\"iqr\"]) / 2.0)))/2.0)) / 2.0)))))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) / 2.0)) +\n 0.0393358096*np.tanh(((((data[\"max_roll_std_10\"]) * (((((data[\"q95_roll_mean_100\"]) + (((data[\"q95_roll_mean_100\"]) + (((data[\"q05_roll_mean_1000\"]) * (data[\"classic_sta_lta2_mean\"]))))))) * (((data[\"max_roll_std_10\"]) * (((((data[\"skew\"]) * (((data[\"q05_roll_mean_100\"]) + (data[\"classic_sta_lta2_mean\"]))))) * 2.0)))))))) * 2.0)) +\n 0.0381871462*np.tanh((((np.tanh((((((((data[\"q05_roll_mean_10\"]) - (((data[\"q05\"]) + (data[\"ave_roll_std_100\"]))))) * (73.0))) * (73.0))))) + (((((((data[\"abs_q05\"]) * (((data[\"std_roll_mean_1000\"]) * (data[\"av_change_rate_roll_std_10\"]))))) * 2.0)) - (data[\"q05\"]))))/2.0)) +\n 0.0383043550*np.tanh(((data[\"max_last_50000\"]) * (((((data[\"av_change_rate_roll_std_100\"]) * 2.0)) * (np.tanh((((((((((((((np.tanh((((data[\"mean_change_abs\"]) - (data[\"classic_sta_lta3_mean\"]))))) - (data[\"classic_sta_lta3_mean\"]))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)))))))) +\n 0.0397812054*np.tanh(np.tanh((np.tanh((((data[\"min_roll_mean_100\"]) * (((((data[\"av_change_rate_roll_mean_1000\"]) - (data[\"min_roll_mean_100\"]))) - (((((((data[\"min_last_50000\"]) * (((data[\"av_change_rate_roll_mean_1000\"]) - (np.tanh((((data[\"min_roll_std_10\"]) - (((data[\"min_roll_mean_100\"]) * 2.0)))))))))) * 2.0)) * 2.0)))))))))) +\n 0.0399921872*np.tanh((-1.0*((((((np.tanh((((((((((((((((((data[\"mean_change_abs\"]) * 2.0)) - ((((((-1.0*((data[\"mean_change_rate_last_50000\"])))) * 2.0)) - (data[\"trend\"]))))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) / 2.0)) / 2.0))))) +\n 0.0329673775*np.tanh(np.tanh(((-1.0*((((data[\"std_first_10000\"]) * (((((((((data[\"av_change_abs_roll_mean_10\"]) + (data[\"max_last_10000\"]))) * (((((data[\"max_last_10000\"]) * (0.8873239756))) + (data[\"classic_sta_lta2_mean\"]))))) * 2.0)) * (data[\"max_last_10000\"])))))))))) +\n 0.0367962494*np.tanh(((data[\"avg_last_10000\"]) * (((((data[\"std_roll_mean_1000\"]) * ((-1.0*(((((((((data[\"mean_change_rate_first_50000\"]) - (((data[\"mean_change_abs\"]) * (data[\"max_last_10000\"]))))) + (data[\"max_to_min_diff\"]))/2.0)) - (((data[\"max_last_10000\"]) * (((data[\"max_to_min\"]) * (data[\"q05_roll_mean_100\"])))))))))))) * 2.0)))) +\n 0.0399921872*np.tanh(((data[\"av_change_abs_roll_mean_10\"]) * (((((data[\"av_change_abs_roll_std_1000\"]) - ((((data[\"av_change_abs_roll_mean_10\"]) + ((((data[\"min_roll_std_10\"]) + (((((((data[\"av_change_rate_roll_mean_10\"]) * (data[\"av_change_abs_roll_std_10\"]))) + (data[\"av_change_abs_roll_mean_10\"]))) * (data[\"av_change_abs_roll_mean_10\"]))))/2.0)))/2.0)))) * (data[\"min_roll_std_10\"]))))) +\n 0.0395311601*np.tanh(((np.tanh(((-1.0*((((data[\"std_first_10000\"]) + (data[\"min_first_10000\"])))))))) + (np.tanh((((data[\"max_roll_std_10\"]) * (((data[\"q95_roll_mean_100\"]) * ((((((((data[\"mean_change_abs\"]) + (data[\"max_roll_std_10\"]))/2.0)) - ((-1.0*((data[\"min_first_10000\"])))))) + (data[\"mean_change_rate_last_50000\"]))))))))))) +\n 0.0399296731*np.tanh(((((data[\"min_roll_std_10\"]) + (((data[\"min_roll_std_10\"]) * (data[\"med\"]))))) * ((((((data[\"classic_sta_lta1_mean\"]) + (data[\"min_roll_mean_1000\"]))/2.0)) - (((data[\"q05_roll_std_10\"]) * ((((((data[\"med\"]) * ((((data[\"classic_sta_lta1_mean\"]) + (data[\"min_roll_mean_1000\"]))/2.0)))) + (data[\"med\"]))/2.0)))))))) +\n 0.0399453007*np.tanh(np.tanh((((((((np.tanh((data[\"min_roll_std_10\"]))) * (data[\"std_roll_mean_1000\"]))) * 2.0)) + ((((((((((((data[\"max_to_min_diff\"]) * 2.0)) * (data[\"std_last_50000\"]))) + (data[\"min_roll_std_10\"]))/2.0)) / 2.0)) + (((data[\"min_roll_std_10\"]) * (np.tanh((data[\"max_to_min_diff\"]))))))))))) +\n 0.0394999012*np.tanh(((data[\"mean_change_rate_first_10000\"]) * (((data[\"min_first_10000\"]) * ((((((((data[\"min_roll_std_10\"]) * (((data[\"av_change_abs_roll_std_100\"]) + (data[\"av_change_abs_roll_std_100\"]))))) + ((((data[\"mean_change_rate_first_10000\"]) + (((data[\"min_roll_std_10\"]) * (data[\"mean_change_rate_first_10000\"]))))/2.0)))/2.0)) + (((data[\"min_roll_std_10\"]) - (data[\"av_change_abs_roll_std_100\"]))))))))) +\n 0.0399687439*np.tanh(((data[\"max_first_10000\"]) * (((((data[\"std_first_10000\"]) * (((data[\"max_first_10000\"]) * (((data[\"std_first_10000\"]) * (((np.tanh((((data[\"std_first_10000\"]) * (data[\"av_change_abs_roll_mean_10\"]))))) + (data[\"std_roll_mean_100\"]))))))))) - (((((data[\"av_change_abs_roll_mean_10\"]) * (data[\"min_roll_std_100\"]))) / 2.0)))))) +\n 0.0397499502*np.tanh(((data[\"trend\"]) * ((((((data[\"std_first_50000\"]) + (np.tanh((np.tanh((((data[\"av_change_rate_roll_mean_10\"]) + (((((((data[\"av_change_rate_roll_mean_1000\"]) + ((((data[\"min_roll_std_100\"]) + (((data[\"Hilbert_mean\"]) + (data[\"av_change_rate_roll_mean_10\"]))))/2.0)))) * 2.0)) * 2.0)))))))))/2.0)) * (data[\"min_roll_std_100\"]))))) +\n 0.0274897423*np.tanh((((np.tanh((((((((((((((data[\"q95_roll_std_100\"]) - (data[\"ave_roll_std_10\"]))) - (data[\"abs_max_roll_mean_10\"]))) - (data[\"max_last_50000\"]))) - (((data[\"min_first_50000\"]) + (data[\"ave_roll_std_10\"]))))) * 2.0)) * 2.0)))) + (((((data[\"q95_roll_std_100\"]) - (data[\"ave_roll_std_10\"]))) * 2.0)))/2.0)) +\n 0.0399531126*np.tanh(((((data[\"mean_change_rate_first_10000\"]) * (np.tanh((((((((((data[\"av_change_abs_roll_mean_10\"]) + (data[\"mean_change_rate_last_10000\"]))/2.0)) * (((data[\"avg_first_10000\"]) - (np.tanh((data[\"av_change_abs_roll_mean_100\"]))))))) + (np.tanh((np.tanh((data[\"avg_first_10000\"]))))))/2.0)))))) * ((((-1.0*((data[\"mean_change_abs\"])))) * 2.0)))) +\n 0.0340613388*np.tanh(((data[\"skew\"]) * (((data[\"max_first_50000\"]) * (((((((((np.tanh((((((((data[\"iqr\"]) * 2.0)) - (data[\"av_change_abs_roll_mean_10\"]))) - (data[\"std_last_10000\"]))))) + (data[\"av_change_abs_roll_mean_10\"]))) - (((data[\"av_change_rate_roll_mean_10\"]) * (data[\"avg_first_50000\"]))))) * 2.0)) * 2.0)))))) +\n 0.0399921872*np.tanh((((((data[\"min_roll_std_10\"]) + (((data[\"min_roll_std_10\"]) * (data[\"avg_first_10000\"]))))/2.0)) * (np.tanh((((73.0) * (((data[\"avg_first_10000\"]) * (((data[\"min_roll_std_10\"]) + (((73.0) * (((73.0) * ((-1.0*((data[\"av_change_abs_roll_std_1000\"])))))))))))))))))) +\n 0.0356475860*np.tanh(np.tanh((((data[\"av_change_abs_roll_mean_1000\"]) * ((((((((((((((data[\"avg_last_50000\"]) + (data[\"kurt\"]))) + (0.2783510089))/2.0)) + (data[\"av_change_rate_roll_std_1000\"]))) + (0.2783510089))) + (data[\"kurt\"]))) * ((((np.tanh((data[\"avg_first_10000\"]))) + (data[\"mean_change_rate_first_50000\"]))/2.0)))))))) +\n 0.0329986326*np.tanh((((((((((data[\"kurt\"]) + (data[\"q05_roll_mean_10\"]))) * (data[\"min_roll_mean_10\"]))) + (np.tanh((((((data[\"q05_roll_mean_10\"]) - (((((data[\"max_to_min\"]) - (data[\"kurt\"]))) + (((data[\"q05_roll_mean_10\"]) * (data[\"max_to_min\"]))))))) * 2.0)))))/2.0)) / 2.0)) +\n 0.0399999991*np.tanh(((data[\"std_roll_mean_1000\"]) * (((data[\"med\"]) * ((((-3.0) + (((((data[\"avg_first_10000\"]) * 2.0)) * (((data[\"mean_change_rate_first_10000\"]) - (((((((data[\"std_roll_mean_1000\"]) / 2.0)) * (((data[\"std_roll_mean_1000\"]) * (-3.0))))) * (data[\"abs_max_roll_mean_1000\"]))))))))/2.0)))))) +\n 0.0303496774*np.tanh((-1.0*((((np.tanh((np.tanh((((((((data[\"classic_sta_lta2_mean\"]) + (((data[\"av_change_abs_roll_std_10\"]) + (((data[\"av_change_abs_roll_mean_10\"]) + (data[\"classic_sta_lta1_mean\"]))))))) * (data[\"abs_q05\"]))) * (3.6923100948))))))) / 2.0))))) +\n 0.0399687439*np.tanh((((-1.0*((data[\"max_first_50000\"])))) * (((((data[\"std_roll_mean_1000\"]) + (((data[\"av_change_abs_roll_std_10\"]) * (((data[\"ave_roll_mean_10\"]) + (((data[\"med\"]) + (1.0))))))))) * (((data[\"avg_last_50000\"]) + (((data[\"med\"]) + (2.0769200325))))))))) +\n 0.0399843715*np.tanh(((np.tanh((((data[\"av_change_rate_roll_mean_100\"]) * (((np.tanh((((((((((data[\"av_change_abs_roll_mean_100\"]) * 2.0)) + (data[\"mean_change_rate_first_50000\"]))) * 2.0)) * 2.0)))) - (((data[\"av_change_abs_roll_mean_100\"]) * (((((data[\"av_change_rate_roll_std_1000\"]) - (data[\"classic_sta_lta2_mean\"]))) - (data[\"classic_sta_lta2_mean\"]))))))))))) / 2.0)) +\n 0.0399999991*np.tanh(((data[\"mean_change_abs\"]) * ((((data[\"av_change_abs_roll_std_100\"]) + (np.tanh((((((((((((((((((data[\"min_first_10000\"]) - (data[\"av_change_rate_roll_std_10\"]))) - (((data[\"av_change_abs_roll_std_100\"]) * (3.6923100948))))) * 2.0)) * 2.0)) * 2.0)) + (data[\"mean_change_abs\"]))) * 2.0)) * 2.0)))))/2.0)))) +\n 0.0285446383*np.tanh(((np.tanh((((((73.0) * (data[\"mean_change_rate_first_10000\"]))) + (((((((((data[\"trend\"]) + (((data[\"av_change_abs_roll_mean_10\"]) + (data[\"min_last_50000\"]))))) + (data[\"mean_change_rate_first_10000\"]))) * 2.0)) * 2.0)))))) + ((-1.0*((np.tanh((data[\"mean_change_rate_first_10000\"])))))))) +\n 0.0399687439*np.tanh(((((np.tanh((((((((((((((((((((np.tanh(((((((data[\"kurt\"]) + (data[\"classic_sta_lta4_mean\"]))/2.0)) - (data[\"max_first_10000\"]))))) + (data[\"skew\"]))) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)) * 2.0)))) / 2.0)) / 2.0)) +\n 0.0398593470*np.tanh(((np.tanh(((-1.0*((np.tanh((((data[\"avg_first_10000\"]) * (((data[\"min_roll_std_10\"]) - (((((data[\"mean_change_rate_last_10000\"]) * (data[\"min_roll_std_10\"]))) + ((((data[\"mean_change_abs\"]) + ((((((data[\"ave_roll_mean_100\"]) + (data[\"avg_first_10000\"]))/2.0)) + (data[\"skew\"]))))/2.0))))))))))))))) / 2.0)) +\n 0.0399296731*np.tanh(((np.tanh((np.tanh((np.tanh((np.tanh((((((((data[\"mean_change_abs\"]) * ((((data[\"av_change_abs_roll_mean_100\"]) + (0.7446810007))/2.0)))) - (((np.tanh((data[\"skew\"]))) + ((((data[\"trend\"]) + (0.7446810007))/2.0)))))) * 2.0)))))))))) / 2.0)) +\n 0.0387341268*np.tanh((((((((data[\"min_roll_std_100\"]) * (np.tanh((((((((data[\"av_change_rate_roll_std_100\"]) * 2.0)) + (((data[\"abs_q05\"]) + (np.tanh((data[\"iqr\"]))))))) * 2.0)))))) + (np.tanh((((np.tanh((((data[\"iqr\"]) * 2.0)))) - (data[\"min_roll_std_10\"]))))))/2.0)) / 2.0)) +\n 0.0384997055*np.tanh(((data[\"av_change_abs_roll_mean_1000\"]) * (((np.tanh((((((np.tanh((data[\"std_first_50000\"]))) + ((((-1.0*((data[\"max_to_min\"])))) * (data[\"std_first_10000\"]))))) + (((data[\"av_change_abs_roll_mean_1000\"]) * (data[\"min_roll_mean_1000\"]))))))) + ((((-1.0*((data[\"min_roll_std_10\"])))) * (data[\"std_first_10000\"]))))))) +\n 0.0384918936*np.tanh(((np.tanh((np.tanh((((((73.0) * (((73.0) * (((((data[\"q95_roll_std_100\"]) + (data[\"std_roll_mean_1000\"]))) * 2.0)))))) * (((data[\"std_first_50000\"]) + (((np.tanh((np.tanh((data[\"max_roll_std_1000\"]))))) * (data[\"kurt\"]))))))))))) / 2.0)) +\n 0.0343192033*np.tanh(((((-1.0) - (np.tanh((((((((0.8873239756) + (data[\"mean_change_abs\"]))) - (data[\"Hann_window_mean\"]))) * (((((((((data[\"Hann_window_mean\"]) - (data[\"min_last_10000\"]))) * 2.0)) * 2.0)) + (data[\"av_change_rate_roll_std_100\"]))))))))) / 2.0)) +\n 0.0372416489*np.tanh(((np.tanh((((((data[\"classic_sta_lta2_mean\"]) + (((data[\"q95_roll_mean_1000\"]) * (data[\"classic_sta_lta2_mean\"]))))) + ((((-1.0*((((((data[\"std_last_10000\"]) + (data[\"MA_400MA_BB_low_mean\"]))) + (np.tanh((((data[\"min_roll_std_100\"]) + (data[\"classic_sta_lta2_mean\"])))))))))) * (data[\"avg_last_10000\"]))))))) / 2.0)) +\n 0.0377808139*np.tanh(np.tanh((((((data[\"mean_change_rate_last_10000\"]) * (((((((data[\"mean_change_rate_first_10000\"]) + ((((data[\"min_roll_std_10\"]) + ((-1.0*((-3.0)))))/2.0)))/2.0)) + (data[\"Moving_average_1500_mean\"]))/2.0)))) * (np.tanh((((((data[\"mean_change_rate_last_10000\"]) + (((data[\"av_change_abs_roll_std_1000\"]) + (1.9019600153))))) * 2.0)))))))) +\n 0.0369056463*np.tanh(np.tanh((np.tanh((((((data[\"max_first_50000\"]) * (data[\"max_first_50000\"]))) * ((((((((data[\"max_first_50000\"]) + (data[\"kurt\"]))/2.0)) - (data[\"avg_last_50000\"]))) - (((np.tanh((((data[\"mean_change_rate_first_10000\"]) * 2.0)))) - (((data[\"kurt\"]) - (0.5555559993))))))))))))))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2550fa808c2ee6f61aab6cf85b12d12704e715ee"},"cell_type":"code","source":"X_tr.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d2449b3c66e1428a70b6f000fe43063e04a2f01d"},"cell_type":"code","source":"X_test.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"73a6e164e8f2a12b87c9607d57a6355df7f92676"},"cell_type":"code","source":"#Only real difference is scaling the lot in one go rather than scling train and then using this to scale test\nalldata = pd.concat([X_tr,\n X_test])\n\nscaler = StandardScaler()\nalldata = pd.DataFrame(scaler.fit_transform(alldata), columns=alldata.columns)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5decbd45734406e5d2c6e53b52f266da3ea9c388"},"cell_type":"code","source":"mean_absolute_error(y_tr,GPI(alldata[:X_tr.shape[0]])) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"f480d9524ef47fd79832c06af7cb85c65b837fcf"},"cell_type":"code","source":"plt.figure(figsize=(15,10))\nplt.plot(y_tr)\nplt.plot(GPI(alldata[:X_tr.shape[0]]))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6d907465cec694a654d4095e57dd51502391bfa3"},"cell_type":"code","source":"submission.time_to_failure = GPI(alldata[X_tr.shape[0]:]).values\nsubmission.to_csv('gpsubmission.csv',index=True)\nsubmission.head()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}