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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import uproot\n", "import awkward as ak\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "input_tree = uproot.open({\"/work/guenther/reco_tuner/data/param_data_selected_all_p.root\": \"Selected\"})\n", "array = input_tree.arrays()\n", "array[\"dSlope_fringe\"] = array[\"tx_ref\"] - array[\"tx\"]\n", "array[\"z_mag_x_fringe\"] = (array[\"x\"] - array[\"x_ref\"] - array[\"tx\"] * array[\"z\"] + array[\"tx_ref\"] * array[\"z_ref\"] ) / array[\"dSlope_fringe\"]\n", "array[\"x_straight_diff_ref\"] = array[\"x\"] + array[\"tx\"] * (array[\"z_ref\"] - array[\"z\"]) - array[\"x_ref\"]\n", "array[\"x_straight_diff_ref_abs\"] = abs(array[\"x_straight_diff_ref\"])\n", "array[\"inv_p_gev\"] = 1000. / array[\"p\"]\n", "array[\"pol_qop_gev\"] = array[\"signed_rel_current\"] * array[\"qop\"] * 1000.\n", "array[\"qop_gev\"] = array[\"qop\"] * 1000.\n", "array[\"deflection_sign\"] = array[\"pol_qop_gev\"] / array[\"inv_p_gev\"]\n", "print(array[\"deflection_sign\"])\n", "print(array[\"x_straight_diff_ref\"] / array[\"x_straight_diff_ref_abs\"])\n", "plt.figure()\n", "plt.hist(array[\"x_straight_diff_ref\"], bins=50,\n", " range=[-1200, 1200], alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['inv_p_gev' 'ty^2 inv_p_gev' 'tx^2 inv_p_gev' 'tx inv_p_gev pol_qop_gev'\n", " 'inv_p_gev^3' 'tx^3 pol_qop_gev' 'tx^2 inv_p_gev^2'\n", " 'tx inv_p_gev^2 pol_qop_gev' 'inv_p_gev^4' 'ty^2 tx^2 inv_p_gev'\n", " 'ty^2 tx inv_p_gev pol_qop_gev' 'ty^2 inv_p_gev^3' 'tx^4 inv_p_gev']\n", "intercept= 0.0\n", "coef= {'inv_p_gev': 4018.896625676043, 'ty^2 inv_p_gev': 6724.789549369031, 'tx^2 inv_p_gev': 3970.9093976497766, 'tx inv_p_gev pol_qop_gev': -4363.5807241252905, 'inv_p_gev^3': 1421.1056758688073, 'tx^3 pol_qop_gev': 4934.07761471779, 'tx^2 inv_p_gev^2': 6985.252911263751, 'tx inv_p_gev^2 pol_qop_gev': -5538.28013195104, 'inv_p_gev^4': 1642.8616070452542, 'ty^2 tx^2 inv_p_gev': 106068.96918885755, 'ty^2 tx inv_p_gev pol_qop_gev': -94446.81037767915, 'ty^2 inv_p_gev^3': 26489.793756692892, 'tx^4 inv_p_gev': -23936.54391006025}\n", "r2 score= 0.999838251106393\n", "RMSE = 6.897104452420935\n", "['inv_p_gev', 'ty^2 inv_p_gev', 'tx^2 inv_p_gev', 'tx inv_p_gev pol_qop_gev', 'inv_p_gev^3', 'tx^3 pol_qop_gev', 'tx^2 inv_p_gev^2', 'tx inv_p_gev^2 pol_qop_gev', 'inv_p_gev^4', 'ty^2 tx^2 inv_p_gev', 'ty^2 tx inv_p_gev pol_qop_gev', 'ty^2 inv_p_gev^3', 'tx^4 inv_p_gev']\n" ] } ], "source": [ "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.metrics import mean_squared_error\n", "import numpy as np\n", "\n", "features = [\n", " \"ty\", \n", " \"tx\",\n", " \"inv_p_gev\",\n", " \"pol_qop_gev\"\n", "]\n", "target_feat = \"x_straight_diff_ref_abs\"\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, target, test_size=0.2, random_state=42)\n", "\n", "poly = PolynomialFeatures(degree=5, 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", "reduce = True\n", "if reduce:\n", " keep = [\n", " 'inv_p_gev', \n", " 'ty^2 inv_p_gev', \n", " 'tx^2 inv_p_gev', \n", " 'tx inv_p_gev pol_qop_gev', \n", " 'inv_p_gev^3', \n", " 'tx^3 pol_qop_gev', \n", " 'tx^2 inv_p_gev^2',\n", " 'tx inv_p_gev^2 pol_qop_gev',\n", " 'inv_p_gev^4', \n", " 'ty^2 tx^2 inv_p_gev', \n", " 'ty^2 tx inv_p_gev pol_qop_gev',\n", " 'ty^2 inv_p_gev^3', \n", " 'tx^4 inv_p_gev', \n", " ]\n", " remove = [i for i, f in enumerate(poly_features) if (keep and f not in keep ) or \"p_gev\" not in f]\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", "\n", "#lin_reg = Lasso( fit_intercept=False, alpha=0.00001)#Lasso(fit_intercept=False, alpha=0.001)\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(\"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))\n", "print([key for key, val in dict(zip(poly_features, lin_reg.coef_)).items() if val != 0.0])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(y_pred_test-y_test, bins=100, range=[-5,5])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " ...,\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan],\n", " [nan, nan, nan, ..., nan, nan, nan]]),\n", " array([-60. , -58.8, -57.6, -56.4, -55.2, -54. , -52.8, -51.6, -50.4,\n", " -49.2, -48. , -46.8, -45.6, -44.4, -43.2, -42. , -40.8, -39.6,\n", " -38.4, -37.2, -36. , -34.8, -33.6, -32.4, -31.2, -30. , -28.8,\n", " -27.6, -26.4, -25.2, -24. , -22.8, -21.6, -20.4, -19.2, -18. ,\n", " -16.8, -15.6, -14.4, -13.2, -12. , -10.8, -9.6, -8.4, -7.2,\n", " -6. , -4.8, -3.6, -2.4, -1.2, 0. , 1.2, 2.4, 3.6,\n", " 4.8, 6. , 7.2, 8.4, 9.6, 10.8, 12. , 13.2, 14.4,\n", " 15.6, 16.8, 18. , 19.2, 20.4, 21.6, 22.8, 24. , 25.2,\n", " 26.4, 27.6, 28.8, 30. , 31.2, 32.4, 33.6, 34.8, 36. ,\n", " 37.2, 38.4, 39.6, 40.8, 42. , 43.2, 44.4, 45.6, 46.8,\n", " 48. , 49.2, 50.4, 51.6, 52.8, 54. , 55.2, 56.4, 57.6,\n", " 58.8, 60. ]),\n", " array([-200., -196., -192., -188., -184., -180., -176., -172., -168.,\n", " -164., -160., -156., -152., -148., -144., -140., -136., -132.,\n", " -128., -124., -120., -116., -112., -108., -104., -100., -96.,\n", " -92., -88., -84., -80., -76., -72., -68., -64., -60.,\n", " -56., -52., -48., -44., -40., -36., -32., -28., -24.,\n", " -20., -16., -12., -8., -4., 0., 4., 8., 12.,\n", " 16., 20., 24., 28., 32., 36., 40., 44., 48.,\n", " 52., 56., 60., 64., 68., 72., 76., 80., 84.,\n", " 88., 92., 96., 100., 104., 108., 112., 116., 120.,\n", " 124., 128., 132., 136., 140., 144., 148., 152., 156.,\n", " 160., 164., 168., 172., 176., 180., 184., 188., 192.,\n", " 196., 200.]),\n", " <matplotlib.collections.QuadMesh at 0x7f92bc504160>)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "noise = np.random.normal(0, 0.15, X_test[:,2].shape)\n", "noisey_X_test = X_test.copy()\n", "noisey_X_test[:, 2] = 1./(1./noisey_X_test[:,2] + noise * (1./noisey_X_test[:,2]))\n", "noisey_X_test[:, 3] = noisey_X_test[:, 2] * np.sign( noisey_X_test[:, 3] )\n", "noisey_X_test_model = np.delete( poly.fit_transform( noisey_X_test ), remove, axis=1)\n", "noisy_y_pred_test = lin_reg.predict( noisey_X_test_model )\n", "plt.figure()\n", "plt.hist2d(np.sign(noisey_X_test[:, 3])/noisey_X_test[:, 2], noisy_y_pred_test - y_test, bins=100, range=[[-60,60], [-200,200]],cmin=1, norm=matplotlib.colors.LogNorm())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.6 (conda)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "a2eff8b4da8b8eebf5ee2e5f811f31a557e0a202b4d2f04f849b065340a6eda6" } } }, "nbformat": 4, "nbformat_minor": 2}
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