2023-12-19 13:00:59 +01:00
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{
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"cells": [
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{
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"cell_type": "code",
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2024-03-11 12:39:36 +01:00
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"execution_count": 106,
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2023-12-19 13:00:59 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\n",
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"import awkward as ak\n",
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2024-02-27 15:59:53 +01:00
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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2023-12-19 13:00:59 +01:00
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"import numpy as np\n",
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2024-02-27 15:59:53 +01:00
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"import mplhep\n",
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"\n",
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"from sklearn.preprocessing import PolynomialFeatures\n",
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"from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.metrics import mean_squared_error\n",
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"\n",
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"mplhep.style.use([\"LHCbTex2\"])\n",
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2024-03-11 12:39:36 +01:00
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"input_tree = uproot.open(\n",
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" {\n",
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" \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_BJpsi_def_selected.root\": \"Selected\"\n",
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" }\n",
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")\n",
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2023-12-19 13:00:59 +01:00
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"array = input_tree.arrays()\n",
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2024-02-27 15:59:53 +01:00
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"\n",
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2024-03-11 12:39:36 +01:00
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"array[\"yStraightOut\"] = array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n",
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" array[\"ideal_state_10000_z\"] - array[\"ideal_state_770_z\"]\n",
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")\n",
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2024-02-27 15:59:53 +01:00
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"array[\"yDiffOut\"] = array[\"ideal_state_10000_y\"] - array[\"yStraightOut\"]\n",
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2024-03-11 12:39:36 +01:00
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"array[\"yStraightEndT\"] = array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n",
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" 9410.0 - array[\"ideal_state_770_z\"]\n",
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")\n",
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2024-02-27 15:59:53 +01:00
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"array[\"yDiffEndT\"] = array[\"ideal_state_9410_y\"] - array[\"yStraightEndT\"]\n",
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"\n",
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2024-03-11 12:39:36 +01:00
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"array[\"dSlope_xEndT\"] = array[\"ideal_state_9410_tx\"] - array[\"ideal_state_770_tx\"]\n",
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"array[\"dSlope_yEndT\"] = array[\"ideal_state_9410_ty\"] - array[\"ideal_state_770_ty\"]\n",
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2023-12-19 13:00:59 +01:00
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"array[\"dSlope_xEndT_abs\"] = abs(array[\"dSlope_xEndT\"])\n",
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"array[\"dSlope_yEndT_abs\"] = abs(array[\"dSlope_yEndT\"])\n",
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2024-02-27 15:59:53 +01:00
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"\n",
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2024-03-11 12:39:36 +01:00
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"sel_array = array[(array[\"yDiffOut\"] < 100) & (array[\"yDiffOut\"] > -100)]\n",
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"# sel_array = array\n",
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"\n",
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2023-12-19 13:00:59 +01:00
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"\n",
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"def format_array(name, coef):\n",
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2024-02-27 15:59:53 +01:00
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" coef = [str(c) + \"f\" for c in coef if c != 0.0]\n",
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2023-12-19 13:00:59 +01:00
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" code = f\"constexpr std::array {name}\"\n",
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2024-02-27 15:59:53 +01:00
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" code += \"{\" + \", \".join(list(coef)) + \"};\"\n",
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2023-12-19 13:00:59 +01:00
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" return code"
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]
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},
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{
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"cell_type": "code",
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2024-03-11 12:39:36 +01:00
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"execution_count": 107,
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2024-02-27 15:59:53 +01:00
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"metadata": {},
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2024-03-11 12:39:36 +01:00
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 1200x900 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig = plt.figure()\n",
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"plt.hist(\n",
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" sel_array[\"yDiffOut\"],\n",
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" bins=100,\n",
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" # range=[5100, 5700],\n",
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" color=\"#2A9D8F\",\n",
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" density=True,\n",
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")\n",
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"plt.xlabel(r\"$y_{corr}$ [mm]\")\n",
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"plt.ylabel(\"Number of Tracks (normalised)\")\n",
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"mplhep.lhcb.text(\"Simulation\")\n",
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"plt.show()\n",
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"# plt.savefig(\n",
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"# \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/magnet_kink_dist.pdf\",\n",
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"# format=\"PDF\",\n",
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"# )"
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]
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2024-02-27 15:59:53 +01:00
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},
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{
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"cell_type": "code",
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2024-03-11 12:39:36 +01:00
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"execution_count": 108,
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2024-02-27 15:59:53 +01:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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2024-03-11 12:39:36 +01:00
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"image/png": "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
|
2024-02-27 15:59:53 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
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|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"bins = np.linspace(-200, 200, 50)\n",
|
|
|
|
|
"sns.regplot(\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
" x=ak.to_numpy(sel_array[\"ideal_state_770_y\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" 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",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 109,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2024-03-11 12:39:36 +01:00
|
|
|
"image/png": "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
|
2024-02-27 15:59:53 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"bins = np.linspace(-200, 200, 50)\n",
|
|
|
|
|
"sns.regplot(\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
" x=ak.to_numpy(sel_array[\"ideal_state_9410_y\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" 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",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 110,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2024-03-11 12:39:36 +01:00
|
|
|
"image/png": "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
|
2024-02-27 15:59:53 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
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|
]
|
|
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},
|
|
|
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"metadata": {},
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"output_type": "display_data"
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|
}
|
|
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|
|
],
|
|
|
|
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"source": [
|
|
|
|
|
"bins = np.linspace(-1, 1, 50)\n",
|
|
|
|
|
"sns.regplot(\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
" x=ak.to_numpy(sel_array[\"ideal_state_9410_ty\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
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|
")\n",
|
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|
|
|
"plt.xlabel(\"ty (T)\")\n",
|
|
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
|
|
|
|
"plt.show()"
|
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|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 111,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2024-03-11 12:39:36 +01:00
|
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|
"image/png": "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
|
2024-02-27 15:59:53 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2024-02-29 15:54:19 +01:00
|
|
|
"bins = np.linspace(-0.5, 0.5, 50)\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"sns.regplot(\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
" x=ak.to_numpy(sel_array[\"ideal_state_770_ty\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
")\n",
|
2024-02-29 15:54:19 +01:00
|
|
|
"plt.xlabel(\"$t_y$\")\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
2024-02-29 15:54:19 +01:00
|
|
|
"plt.show()\n",
|
|
|
|
|
"# plt.savefig(\n",
|
|
|
|
|
"# \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_ty_dist.pdf\",\n",
|
|
|
|
|
"# format=\"PDF\",\n",
|
|
|
|
|
"# )"
|
2024-02-27 15:59:53 +01:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 112,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2024-03-11 12:39:36 +01:00
|
|
|
"image/png": "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
|
2024-02-27 15:59:53 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
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|
|
},
|
|
|
|
|
"metadata": {},
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|
|
|
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"output_type": "display_data"
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}
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|
|
],
|
|
|
|
|
"source": [
|
2024-02-29 15:54:19 +01:00
|
|
|
"bins = np.linspace(-0.5, 0.5, 100)\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"sns.regplot(\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
" x=ak.to_numpy(sel_array[\"dSlope_yEndT\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
")\n",
|
2024-02-29 15:54:19 +01:00
|
|
|
"plt.xlabel(\"$\\Delta t_y$\")\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
2024-02-29 15:54:19 +01:00
|
|
|
"plt.show()\n",
|
|
|
|
|
"# plt.savefig(\n",
|
|
|
|
|
"# \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_deltaty_dist.pdf\",\n",
|
|
|
|
|
"# format=\"PDF\",\n",
|
|
|
|
|
"# )"
|
2024-02-27 15:59:53 +01:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 113,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2024-03-11 12:39:36 +01:00
|
|
|
"image/png": "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
|
2024-02-27 15:59:53 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2024-02-29 15:54:19 +01:00
|
|
|
"bins = np.linspace(-1.0, 1.0, 50)\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"sns.regplot(\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
" x=ak.to_numpy(sel_array[\"dSlope_xEndT\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
")\n",
|
2024-02-29 15:54:19 +01:00
|
|
|
"plt.xlabel(\"$\\Delta t_x$\")\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
2024-02-29 15:54:19 +01:00
|
|
|
"plt.show()\n",
|
|
|
|
|
"# plt.savefig(\n",
|
|
|
|
|
"# \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_deltatx_dist.pdf\",\n",
|
|
|
|
|
"# format=\"PDF\",\n",
|
|
|
|
|
"# )"
|
2024-02-27 15:59:53 +01:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 119,
|
2023-12-19 13:00:59 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
2024-03-11 12:39:36 +01:00
|
|
|
"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"
|
2023-12-19 13:00:59 +01:00
|
|
|
]
|
|
|
|
|
}
|
|
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],
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"source": [
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2024-02-27 15:59:53 +01:00
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"features = [\n",
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" \"ideal_state_770_ty\",\n",
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|
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" # \"ideal_state_9410_ty\",\n",
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" # \"ideal_state_770_y\",\n",
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" # \"ideal_state_9410_y\",\n",
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" \"dSlope_xEndT\",\n",
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" \"dSlope_yEndT\",\n",
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"]\n",
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"target_feat = \"yDiffOut\"\n",
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"order = 3\n",
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"\n",
|
2024-03-11 12:39:36 +01:00
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|
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"data = np.column_stack([ak.to_numpy(sel_array[feat]) for feat in features])\n",
|
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|
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"target = ak.to_numpy(sel_array[target_feat])\n",
|
2024-02-27 15:59:53 +01:00
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|
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"X_train, X_test, y_train, y_test = train_test_split(data,\n",
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" target,\n",
|
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|
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" test_size=0.2,\n",
|
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|
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" random_state=42)\n",
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"\n",
|
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"poly = PolynomialFeatures(degree=order, include_bias=False)\n",
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|
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"X_train_model = poly.fit_transform(X_train)\n",
|
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|
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"X_test_model = poly.fit_transform(X_test)\n",
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"\n",
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|
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"poly_features = poly.get_feature_names_out(input_features=features)\n",
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"\n",
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|
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"# keep = [\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# \"ideal_state_770_ty\", #\n",
|
|
|
|
|
"# \"dSlope_xEndT\", #\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# \"dSlope_yEndT\", # keep\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# \"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",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# \"dSlope_xEndT dSlope_yEndT\", # keep\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# \"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",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# \"ideal_state_770_ty dSlope_xEndT^2\", # keep\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT\", #\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# \"ideal_state_770_ty dSlope_yEndT^2\", # keep\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# \"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",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# \"dSlope_xEndT^3 dSlope_yEndT\", # keep\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# \"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",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# \"ideal_state_770_ty dSlope_xEndT^4\", # keep\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# \"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",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# \"dSlope_xEndT^4 dSlope_yEndT\", # keep\n",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# \"dSlope_xEndT^3 dSlope_yEndT^2\", #\n",
|
|
|
|
|
"# \"dSlope_xEndT^2 dSlope_yEndT^3\",\n",
|
|
|
|
|
"# \"dSlope_xEndT dSlope_yEndT^4\",\n",
|
|
|
|
|
"# \"dSlope_yEndT^5\",\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# ]\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",
|
2024-03-11 12:39:36 +01:00
|
|
|
" ###\n",
|
|
|
|
|
" # \"ideal_state_770_ty^5\",\n",
|
|
|
|
|
" # \"dSlope_xEndT^5\",\n",
|
|
|
|
|
" # \"dSlope_yEndT^5\",\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"]\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",
|
2024-03-11 12:39:36 +01:00
|
|
|
"lin_reg = LinearRegression(fit_intercept=False)\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"# 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",
|
2024-03-11 12:39:36 +01:00
|
|
|
"# lin_reg = Ridge(alpha=0.1)\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"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",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 115,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2024-03-11 12:39:36 +01:00
|
|
|
"[]"
|
2024-02-27 15:59:53 +01:00
|
|
|
]
|
|
|
|
|
},
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 115,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"koeffs = dict(zip(poly_features, lin_reg.coef_))\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
"\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"remove = []\n",
|
|
|
|
|
"for itr in koeffs.items():\n",
|
|
|
|
|
" if abs(itr[1]) < 1.0:\n",
|
|
|
|
|
" remove.append(itr[0])\n",
|
|
|
|
|
"remove"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 116,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2024-03-11 12:39:36 +01:00
|
|
|
"['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']"
|
2024-02-27 15:59:53 +01:00
|
|
|
]
|
|
|
|
|
},
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 116,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
2024-03-11 12:39:36 +01:00
|
|
|
"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": [],
|
2024-02-27 15:59:53 +01:00
|
|
|
"source": []
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-03-11 12:39:36 +01:00
|
|
|
"execution_count": 120,
|
2024-02-27 15:59:53 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2024-03-11 12:39:36 +01:00
|
|
|
"image/png": "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2024-02-27 15:59:53 +01:00
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"text/plain": [
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"<Figure size 1200x900 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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2024-02-29 15:54:19 +01:00
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"bins = np.linspace(-40, 40, 41)\n",
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2024-02-27 15:59:53 +01:00
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"ax = sns.regplot(\n",
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" x=y_test,\n",
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2024-02-29 15:54:19 +01:00
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" y=y_test - y_pred_test,\n",
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2024-02-27 15:59:53 +01:00
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" x_bins=bins,\n",
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" fit_reg=None,\n",
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" x_estimator=np.mean,\n",
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" label=\"bla\",\n",
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")\n",
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"ax2 = ax.twinx()\n",
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2024-02-29 15:54:19 +01:00
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"ax2.hist(\n",
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" y_test,\n",
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" bins=30,\n",
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" range=[-40, 40],\n",
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" color=\"#2A9D8F\",\n",
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" alpha=0.5,\n",
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" align=\"mid\",\n",
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" density=True,\n",
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")\n",
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"ax.set_xlabel(r\"$y_{\\mathrm{corr}}$ [mm]\")\n",
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"ax.set_ylabel(\n",
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" r\"$\\left\\langle y_{\\mathrm{corr}}-y_{\\mathrm{corr}}^{\\mathrm{pred}}\\right\\rangle$ [mm]\"\n",
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")\n",
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"ax2.set_ylabel(\"Number of Tracks (normalised)\")\n",
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2024-02-27 15:59:53 +01:00
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"mplhep.lhcb.text(\"Simulation\", loc=0)\n",
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2024-03-11 12:39:36 +01:00
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"plt.show()\n",
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"# plt.savefig(\n",
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"# \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_regression_plot.pdf\",\n",
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"# format=\"PDF\",\n",
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"# )"
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2024-02-27 15:59:53 +01:00
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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2024-02-29 15:54:19 +01:00
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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2024-02-27 15:59:53 +01:00
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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2023-12-19 13:00:59 +01:00
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"features = [\n",
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2024-02-27 15:59:53 +01:00
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" \"ideal_state_770_ty\",\n",
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" \"ideal_state_770_tx\",\n",
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2023-12-19 13:00:59 +01:00
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" \"dSlope_xEndT\",\n",
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" \"dSlope_yEndT\",\n",
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" \"dSlope_xEndT_abs\",\n",
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" \"dSlope_yEndT_abs\",\n",
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"]\n",
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"target_feat = \"yDiffEndT\"\n",
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"\n",
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"data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
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"target = ak.to_numpy(array[target_feat])\n",
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2024-02-29 15:54:19 +01:00
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"X_train, X_test, y_train, y_test = train_test_split(data,\n",
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" target,\n",
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" test_size=0.2,\n",
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" random_state=42)\n",
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2023-12-19 13:00:59 +01:00
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"\n",
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"poly = PolynomialFeatures(degree=6, include_bias=False)\n",
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2024-02-27 15:59:53 +01:00
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"X_train_model = poly.fit_transform(X_train)\n",
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"X_test_model = poly.fit_transform(X_test)\n",
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2023-12-19 13:00:59 +01:00
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"poly_features = poly.get_feature_names_out(input_features=features)\n",
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"keep = [\n",
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" #'dSlope_xEndT',\n",
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2024-02-27 15:59:53 +01:00
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" \"dSlope_yEndT\", # keep\n",
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2023-12-19 13:00:59 +01:00
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" #'dSlope_yEndT_abs',\n",
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" #'ty dSlope_xEndT',\n",
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" #'ty dSlope_yEndT',\n",
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2024-02-27 15:59:53 +01:00
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" \"ideal_state_770_ty dSlope_xEndT_abs\", # keep\n",
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" \"ideal_state_770_ty dSlope_yEndT_abs\", # keep\n",
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" \"ideal_state_770_ty dSlope_yEndT^2\", # keep\n",
|
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" \"ideal_state_770_ty dSlope_xEndT^2\", # keep\n",
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2023-12-19 13:00:59 +01:00
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" #'tx dSlope_xEndT',\n",
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" #'tx dSlope_xEndT_abs',\n",
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" #'tx dSlope_yEndT',\n",
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2024-02-27 15:59:53 +01:00
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" \"ideal_state_770_ty ideal_state_770_tx dSlope_xEndT\", # keep\n",
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" \"ideal_state_770_tx^2 dSlope_yEndT\", # keep\n",
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2023-12-19 13:00:59 +01:00
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" #'ty^2 dSlope_xEndT',\n",
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2024-02-27 15:59:53 +01:00
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" #'ty^2 dSlope_yEndT',\n",
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2023-12-19 13:00:59 +01:00
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" #'ty^2 dSlope_xEndT_abs',\n",
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" #'ty^2 tx dSlope_xEndT',\n",
|
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" #'ty tx^2 dSlope_yEndT',\n",
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2024-02-27 15:59:53 +01:00
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" \"ideal_state_770_ty ideal_state_770_tx^2 dSlope_xEndT_abs\", # keep\n",
|
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" \"ideal_state_770_ty^3 ideal_state_770_tx dSlope_xEndT\", # keep\n",
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2023-12-19 13:00:59 +01:00
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" #'ty tx^3 dSlope_xEndT',\n",
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" #'ty^3 dSlope_yEndT_abs',\n",
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"]\n",
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"do_not_keep = [\n",
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2024-02-27 15:59:53 +01:00
|
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" \"dSlope_xEndT\",\n",
|
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" \"dSlope_yEndT_abs\",\n",
|
|
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|
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" \"ideal_state_770_ty dSlope_xEndT\",\n",
|
|
|
|
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" \"ideal_state_770_tx dSlope_xEndT\",\n",
|
|
|
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" \"ideal_state_770_tx dSlope_xEndT_abs\",\n",
|
|
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|
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" \"ideal_state_770_tx dSlope_yEndT\",\n",
|
|
|
|
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" \"ideal_state_770_ty^2 dSlope_xEndT\",\n",
|
|
|
|
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" \"ideal_state_770_ty^3 dSlope_yEndT_abs\",\n",
|
|
|
|
|
" \"ideal_state_770_ty ideal_state_770_tx dSlope_yEndT\",\n",
|
|
|
|
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" \"ideal_state_770_ty ideal_state_770_tx^3 dSlope_xEndT\",\n",
|
|
|
|
|
" \"ideal_state_770_ty ideal_state_770_tx^2 dSlope_yEndT\",\n",
|
2023-12-19 13:00:59 +01:00
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|
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"]\n",
|
|
|
|
|
"reduce = True\n",
|
|
|
|
|
"if reduce:\n",
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2024-02-29 15:54:19 +01:00
|
|
|
" remove = [\n",
|
|
|
|
|
" i for i, f in enumerate(poly_features) if (keep and f not in keep)\n",
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" ]\n",
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2024-02-27 15:59:53 +01:00
|
|
|
" 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",
|
2023-12-19 13:00:59 +01:00
|
|
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" print(poly_features)\n",
|
|
|
|
|
"if not reduce:\n",
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2024-02-27 15:59:53 +01:00
|
|
|
" remove = [\n",
|
2024-02-29 15:54:19 +01:00
|
|
|
" 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",
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2024-02-27 15:59:53 +01:00
|
|
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" ]\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",
|
|
|
|
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" poly_features = np.delete(poly_features, remove)\n",
|
|
|
|
|
" # print(poly_features)\n",
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2023-12-19 13:00:59 +01:00
|
|
|
" lin_reg = Lasso(fit_intercept=False, alpha=0.000001)\n",
|
|
|
|
|
"else:\n",
|
|
|
|
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" lin_reg = LinearRegression(fit_intercept=False)\n",
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2024-02-27 15:59:53 +01:00
|
|
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"lin_reg.fit(X_train_model, y_train)\n",
|
|
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|
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"y_pred_test = lin_reg.predict(X_test_model)\n",
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2023-12-19 13:00:59 +01:00
|
|
|
"print(\"intercept=\", lin_reg.intercept_)\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"print(\n",
|
|
|
|
|
" \"coef=\",\n",
|
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" {\n",
|
|
|
|
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" k: v\n",
|
|
|
|
|
" for k, v in zip(poly_features, lin_reg.coef_)\n",
|
|
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|
|
" if abs(v) > 1.0 and k not in keep and k not in do_not_keep\n",
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" },\n",
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")\n",
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2023-12-19 13:00:59 +01:00
|
|
|
"print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
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|
|
|
|
"print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
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2024-02-27 15:59:53 +01:00
|
|
|
"print(\n",
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" \"straight RMSE =\",\n",
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" mean_squared_error(\n",
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" array[\"ideal_state_9410_y\"],\n",
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2024-02-29 15:54:19 +01:00
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" array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] *\n",
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" (array[\"ideal_state_9410_z\"] - array[\"ideal_state_770_z\"]),\n",
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2024-02-27 15:59:53 +01:00
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" squared=False,\n",
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" ),\n",
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")\n",
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2023-12-19 13:00:59 +01:00
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"print(format_array(\"y_xEndT_diff\", lin_reg.coef_))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.10.6 (conda)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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2024-02-27 15:59:53 +01:00
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"version": "3.10.12"
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2023-12-19 13:00:59 +01:00
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "a2eff8b4da8b8eebf5ee2e5f811f31a557e0a202b4d2f04f849b065340a6eda6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
|
