tracking-parametrisation-tuner/parameterisations/notebooks/bend_y_params.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import awkward as ak\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import mplhep\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "input_tree = uproot.open(\n",
    "    {\n",
    "        \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_BJpsi_def_selected.root\": \"Selected\"\n",
    "    }\n",
    ")\n",
    "array = input_tree.arrays()\n",
    "\n",
    "array[\"yStraightOut\"] = array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n",
    "    array[\"ideal_state_10000_z\"] - array[\"ideal_state_770_z\"]\n",
    ")\n",
    "array[\"yDiffOut\"] = array[\"ideal_state_10000_y\"] - array[\"yStraightOut\"]\n",
    "array[\"yStraightEndT\"] = array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n",
    "    9410.0 - array[\"ideal_state_770_z\"]\n",
    ")\n",
    "array[\"yDiffEndT\"] = array[\"ideal_state_9410_y\"] - array[\"yStraightEndT\"]\n",
    "\n",
    "array[\"dSlope_xEndT\"] = array[\"ideal_state_9410_tx\"] - array[\"ideal_state_770_tx\"]\n",
    "array[\"dSlope_yEndT\"] = array[\"ideal_state_9410_ty\"] - array[\"ideal_state_770_ty\"]\n",
    "array[\"dSlope_xEndT_abs\"] = abs(array[\"dSlope_xEndT\"])\n",
    "array[\"dSlope_yEndT_abs\"] = abs(array[\"dSlope_yEndT\"])\n",
    "\n",
    "sel_array = array[(array[\"yDiffOut\"] < 100) & (array[\"yDiffOut\"] > -100)]\n",
    "# sel_array = array\n",
    "\n",
    "\n",
    "def format_array(name, coef):\n",
    "    coef = [str(c) + \"f\" for c in coef if c != 0.0]\n",
    "    code = f\"constexpr std::array {name}\"\n",
    "    code += \"{\" + \", \".join(list(coef)) + \"};\"\n",
    "    return code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.hist(\n",
    "    sel_array[\"yDiffOut\"],\n",
    "    bins=100,\n",
    "    # range=[5100, 5700],\n",
    "    color=\"#2A9D8F\",\n",
    "    density=True,\n",
    ")\n",
    "plt.xlabel(r\"$y_{corr}$ [mm]\")\n",
    "plt.ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/magnet_kink_dist.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-200, 200, 50)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(sel_array[\"ideal_state_770_y\"]),\n",
    "    y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.xlabel(\"y (VELO)\")\n",
    "plt.ylabel(\"$y_{corr}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-200, 200, 50)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(sel_array[\"ideal_state_9410_y\"]),\n",
    "    y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.xlabel(\"y (T)\")\n",
    "plt.ylabel(\"$y_{corr}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-1, 1, 50)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(sel_array[\"ideal_state_9410_ty\"]),\n",
    "    y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.xlabel(\"ty (T)\")\n",
    "plt.ylabel(\"$y_{corr}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-0.5, 0.5, 50)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(sel_array[\"ideal_state_770_ty\"]),\n",
    "    y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.xlabel(\"$t_y$\")\n",
    "plt.ylabel(\"$y_{corr}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_ty_dist.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-0.5, 0.5, 100)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(sel_array[\"dSlope_yEndT\"]),\n",
    "    y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.xlabel(\"$\\Delta t_y$\")\n",
    "plt.ylabel(\"$y_{corr}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_deltaty_dist.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-1.0, 1.0, 50)\n",
    "sns.regplot(\n",
    "    x=ak.to_numpy(sel_array[\"dSlope_xEndT\"]),\n",
    "    y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    ")\n",
    "plt.xlabel(\"$\\Delta t_x$\")\n",
    "plt.ylabel(\"$y_{corr}$ [mm]\")\n",
    "mplhep.lhcb.text(\"Simulation\")\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_deltatx_dist.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intercept= 0.0\n",
      "coef= {'dSlope_yEndT': 4089.1594362560113, 'dSlope_xEndT dSlope_yEndT': 25.0456971896117, 'ideal_state_770_ty dSlope_xEndT^2': 1049.7443418962382, 'ideal_state_770_ty dSlope_yEndT^2': 77388.96417801932}\n",
      "r2 score= 0.7591964011096296\n",
      "RMSE = 9.026349818744066\n"
     ]
    }
   ],
   "source": [
    "features = [\n",
    "    \"ideal_state_770_ty\",\n",
    "    # \"ideal_state_9410_ty\",\n",
    "    # \"ideal_state_770_y\",\n",
    "    # \"ideal_state_9410_y\",\n",
    "    \"dSlope_xEndT\",\n",
    "    \"dSlope_yEndT\",\n",
    "]\n",
    "target_feat = \"yDiffOut\"\n",
    "order = 3\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(sel_array[feat]) for feat in features])\n",
    "target = ak.to_numpy(sel_array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=order, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "\n",
    "poly_features = poly.get_feature_names_out(input_features=features)\n",
    "\n",
    "# keep = [\n",
    "#     \"ideal_state_770_ty\",  #\n",
    "#     \"dSlope_xEndT\",  #\n",
    "#     \"dSlope_yEndT\",  # keep\n",
    "#     \"ideal_state_770_ty^2\",  #\n",
    "#     \"ideal_state_770_ty dSlope_xEndT\",  #\n",
    "#     \"ideal_state_770_ty dSlope_yEndT\",  #\n",
    "#     \"dSlope_xEndT^2\",  # do not keep\n",
    "#     \"dSlope_xEndT dSlope_yEndT\",  # keep\n",
    "#     \"dSlope_yEndT^2\",  #\n",
    "#     \"ideal_state_770_ty^3\",\n",
    "#     \"ideal_state_770_ty^2 dSlope_xEndT\",  #\n",
    "#     \"ideal_state_770_ty^2 dSlope_yEndT\",\n",
    "#     \"ideal_state_770_ty dSlope_xEndT^2\",  # keep\n",
    "#     \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT\",  #\n",
    "#     \"ideal_state_770_ty dSlope_yEndT^2\",  # keep\n",
    "#     \"dSlope_xEndT^3\",  # do not keep\n",
    "#     \"dSlope_xEndT^2 dSlope_yEndT\",  #\n",
    "#     \"dSlope_xEndT dSlope_yEndT^2\",  #\n",
    "#     \"dSlope_yEndT^3\",\n",
    "#     \"ideal_state_770_ty^4\",\n",
    "#     \"ideal_state_770_ty^3 dSlope_xEndT\",\n",
    "#     \"ideal_state_770_ty^3 dSlope_yEndT\",\n",
    "#     \"ideal_state_770_ty^2 dSlope_xEndT^2\",  #\n",
    "#     \"ideal_state_770_ty^2 dSlope_xEndT dSlope_yEndT\",\n",
    "#     \"ideal_state_770_ty^2 dSlope_yEndT^2\",\n",
    "#     \"ideal_state_770_ty dSlope_xEndT^3\",  # do not keep\n",
    "#     \"ideal_state_770_ty dSlope_xEndT^2 dSlope_yEndT\",  #\n",
    "#     \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT^2\",\n",
    "#     \"ideal_state_770_ty dSlope_yEndT^3\",\n",
    "#     \"dSlope_xEndT^4\",  #\n",
    "#     \"dSlope_xEndT^3 dSlope_yEndT\",  # keep\n",
    "#     \"dSlope_xEndT^2 dSlope_yEndT^2\",  #\n",
    "#     \"dSlope_xEndT dSlope_yEndT^3\",\n",
    "#     \"dSlope_yEndT^4\",\n",
    "#     \"ideal_state_770_ty^5\",\n",
    "#     \"ideal_state_770_ty^4 dSlope_xEndT\",\n",
    "#     \"ideal_state_770_ty^4 dSlope_yEndT\",\n",
    "#     \"ideal_state_770_ty^3 dSlope_xEndT^2\",\n",
    "#     \"ideal_state_770_ty^3 dSlope_xEndT dSlope_yEndT\",\n",
    "#     \"ideal_state_770_ty^3 dSlope_yEndT^2\",\n",
    "#     \"ideal_state_770_ty^2 dSlope_xEndT^3\",  #\n",
    "#     \"ideal_state_770_ty^2 dSlope_xEndT^2 dSlope_yEndT\",\n",
    "#     \"ideal_state_770_ty^2 dSlope_xEndT dSlope_yEndT^2\",\n",
    "#     \"ideal_state_770_ty^2 dSlope_yEndT^3\",\n",
    "#     \"ideal_state_770_ty dSlope_xEndT^4\",  # keep\n",
    "#     \"ideal_state_770_ty dSlope_xEndT^3 dSlope_yEndT\",  #\n",
    "#     \"ideal_state_770_ty dSlope_xEndT^2 dSlope_yEndT^2\",\n",
    "#     \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT^3\",\n",
    "#     \"ideal_state_770_ty dSlope_yEndT^4\",\n",
    "#     \"dSlope_xEndT^5\",  #\n",
    "#     \"dSlope_xEndT^4 dSlope_yEndT\",  # keep\n",
    "#     \"dSlope_xEndT^3 dSlope_yEndT^2\",  #\n",
    "#     \"dSlope_xEndT^2 dSlope_yEndT^3\",\n",
    "#     \"dSlope_xEndT dSlope_yEndT^4\",\n",
    "#     \"dSlope_yEndT^5\",\n",
    "# ]\n",
    "\n",
    "keep = [\n",
    "    \"dSlope_yEndT\",  # keep\n",
    "    \"dSlope_xEndT dSlope_yEndT\",  # keep\n",
    "    \"ideal_state_770_ty dSlope_xEndT^2\",  # keep\n",
    "    \"ideal_state_770_ty dSlope_yEndT^2\",  # keep\n",
    "    # \"dSlope_xEndT^3 dSlope_yEndT\",  # do not keep\n",
    "    # \"ideal_state_770_ty dSlope_xEndT^4\",  # keep\n",
    "    # \"dSlope_xEndT^4 dSlope_yEndT\",  # keep\n",
    "    ###\n",
    "    # \"ideal_state_770_ty^5\",\n",
    "    # \"dSlope_xEndT^5\",\n",
    "    # \"dSlope_yEndT^5\",\n",
    "]\n",
    "\n",
    "# keep = [\n",
    "#     \"ideal_state_770_ty dSlope_yEndT^2\",\n",
    "#     \"ideal_state_770_ty dSlope_xEndT^2\",\n",
    "# ]\n",
    "remove = [i for i, f in enumerate(poly_features) if f not in keep]\n",
    "\n",
    "X_train_model = np.delete(X_train_model, remove, axis=1)\n",
    "X_test_model = np.delete(X_test_model, remove, axis=1)\n",
    "poly_features = np.delete(poly_features, remove)\n",
    "# print(poly_features)\n",
    "lin_reg = LinearRegression(fit_intercept=False)\n",
    "# lin_reg = Lasso(fit_intercept=False, alpha=0.000001)\n",
    "# lin_reg = Lasso(alpha=0.1)\n",
    "# lin_reg = LassoCV(max_iter=2000)\n",
    "# lin_reg = ElasticNet(alpha=0.1)\n",
    "# lin_reg = Ridge(alpha=0.1)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\"coef=\", dict(zip(poly_features, lin_reg.coef_)))\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "koeffs = dict(zip(poly_features, lin_reg.coef_))\n",
    "\n",
    "remove = []\n",
    "for itr in koeffs.items():\n",
    "    if abs(itr[1]) < 1.0:\n",
    "        remove.append(itr[0])\n",
    "remove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dSlope_yEndT',\n",
       " 'dSlope_xEndT dSlope_yEndT',\n",
       " 'ideal_state_770_ty dSlope_xEndT^2',\n",
       " 'ideal_state_770_ty dSlope_yEndT^2',\n",
       " 'ideal_state_770_ty dSlope_xEndT^4',\n",
       " 'dSlope_xEndT^4 dSlope_yEndT']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls_koeffs = []\n",
    "for itr in koeffs.items():\n",
    "    ls_koeffs.append(itr[0])\n",
    "ls_koeffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(-40, 40, 41)\n",
    "ax = sns.regplot(\n",
    "    x=y_test,\n",
    "    y=y_test - y_pred_test,\n",
    "    x_bins=bins,\n",
    "    fit_reg=None,\n",
    "    x_estimator=np.mean,\n",
    "    label=\"bla\",\n",
    ")\n",
    "ax2 = ax.twinx()\n",
    "ax2.hist(\n",
    "    y_test,\n",
    "    bins=30,\n",
    "    range=[-40, 40],\n",
    "    color=\"#2A9D8F\",\n",
    "    alpha=0.5,\n",
    "    align=\"mid\",\n",
    "    density=True,\n",
    ")\n",
    "ax.set_xlabel(r\"$y_{\\mathrm{corr}}$ [mm]\")\n",
    "ax.set_ylabel(\n",
    "    r\"$\\left\\langle y_{\\mathrm{corr}}-y_{\\mathrm{corr}}^{\\mathrm{pred}}\\right\\rangle$ [mm]\"\n",
    ")\n",
    "ax2.set_ylabel(\"Number of Tracks (normalised)\")\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_regression_plot.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\n",
    "    \"ideal_state_770_ty\",\n",
    "    \"ideal_state_770_tx\",\n",
    "    \"dSlope_xEndT\",\n",
    "    \"dSlope_yEndT\",\n",
    "    \"dSlope_xEndT_abs\",\n",
    "    \"dSlope_yEndT_abs\",\n",
    "]\n",
    "target_feat = \"yDiffEndT\"\n",
    "\n",
    "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
    "target = ak.to_numpy(array[target_feat])\n",
    "X_train, X_test, y_train, y_test = train_test_split(data,\n",
    "                                                    target,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=6, include_bias=False)\n",
    "X_train_model = poly.fit_transform(X_train)\n",
    "X_test_model = poly.fit_transform(X_test)\n",
    "poly_features = poly.get_feature_names_out(input_features=features)\n",
    "keep = [\n",
    "    #'dSlope_xEndT',\n",
    "    \"dSlope_yEndT\",  # keep\n",
    "    #'dSlope_yEndT_abs',\n",
    "    #'ty dSlope_xEndT',\n",
    "    #'ty dSlope_yEndT',\n",
    "    \"ideal_state_770_ty dSlope_xEndT_abs\",  # keep\n",
    "    \"ideal_state_770_ty dSlope_yEndT_abs\",  # keep\n",
    "    \"ideal_state_770_ty dSlope_yEndT^2\",  # keep\n",
    "    \"ideal_state_770_ty dSlope_xEndT^2\",  # keep\n",
    "    #'tx dSlope_xEndT',\n",
    "    #'tx dSlope_xEndT_abs',\n",
    "    #'tx dSlope_yEndT',\n",
    "    \"ideal_state_770_ty ideal_state_770_tx dSlope_xEndT\",  # keep\n",
    "    \"ideal_state_770_tx^2 dSlope_yEndT\",  # keep\n",
    "    #'ty^2 dSlope_xEndT',\n",
    "    #'ty^2 dSlope_yEndT',\n",
    "    #'ty^2 dSlope_xEndT_abs',\n",
    "    #'ty^2 tx dSlope_xEndT',\n",
    "    #'ty tx^2 dSlope_yEndT',\n",
    "    \"ideal_state_770_ty ideal_state_770_tx^2 dSlope_xEndT_abs\",  # keep\n",
    "    \"ideal_state_770_ty^3 ideal_state_770_tx dSlope_xEndT\",  # keep\n",
    "    #'ty tx^3 dSlope_xEndT',\n",
    "    #'ty^3 dSlope_yEndT_abs',\n",
    "]\n",
    "do_not_keep = [\n",
    "    \"dSlope_xEndT\",\n",
    "    \"dSlope_yEndT_abs\",\n",
    "    \"ideal_state_770_ty dSlope_xEndT\",\n",
    "    \"ideal_state_770_tx dSlope_xEndT\",\n",
    "    \"ideal_state_770_tx dSlope_xEndT_abs\",\n",
    "    \"ideal_state_770_tx dSlope_yEndT\",\n",
    "    \"ideal_state_770_ty^2 dSlope_xEndT\",\n",
    "    \"ideal_state_770_ty^3 dSlope_yEndT_abs\",\n",
    "    \"ideal_state_770_ty ideal_state_770_tx dSlope_yEndT\",\n",
    "    \"ideal_state_770_ty ideal_state_770_tx^3 dSlope_xEndT\",\n",
    "    \"ideal_state_770_ty ideal_state_770_tx^2 dSlope_yEndT\",\n",
    "]\n",
    "reduce = True\n",
    "if reduce:\n",
    "    remove = [\n",
    "        i for i, f in enumerate(poly_features) if (keep and f not in keep)\n",
    "    ]\n",
    "    X_train_model = np.delete(X_train_model, remove, axis=1)\n",
    "    X_test_model = np.delete(X_test_model, remove, axis=1)\n",
    "    poly_features = np.delete(poly_features, remove)\n",
    "    print(poly_features)\n",
    "if not reduce:\n",
    "    remove = [\n",
    "        i for i, f in enumerate(poly_features)\n",
    "        if (\"dSlope_\" not in f) or (\"EndT^\" in f) or (\"abs^\" in f) or (\n",
    "            \"EndT dSlope\" in f) or (\"abs dSlope\" in f)\n",
    "    ]\n",
    "    X_train_model = np.delete(X_train_model, remove, axis=1)\n",
    "    X_test_model = np.delete(X_test_model, remove, axis=1)\n",
    "    poly_features = np.delete(poly_features, remove)\n",
    "    # print(poly_features)\n",
    "    lin_reg = Lasso(fit_intercept=False, alpha=0.000001)\n",
    "else:\n",
    "    lin_reg = LinearRegression(fit_intercept=False)\n",
    "lin_reg.fit(X_train_model, y_train)\n",
    "y_pred_test = lin_reg.predict(X_test_model)\n",
    "print(\"intercept=\", lin_reg.intercept_)\n",
    "print(\n",
    "    \"coef=\",\n",
    "    {\n",
    "        k: v\n",
    "        for k, v in zip(poly_features, lin_reg.coef_)\n",
    "        if abs(v) > 1.0 and k not in keep and k not in do_not_keep\n",
    "    },\n",
    ")\n",
    "print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
    "print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
    "print(\n",
    "    \"straight RMSE =\",\n",
    "    mean_squared_error(\n",
    "        array[\"ideal_state_9410_y\"],\n",
    "        array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] *\n",
    "        (array[\"ideal_state_9410_z\"] - array[\"ideal_state_770_z\"]),\n",
    "        squared=False,\n",
    "    ),\n",
    ")\n",
    "print(format_array(\"y_xEndT_diff\", lin_reg.coef_))"
   ]
  }
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