From 02d26d974ff6dc44f109dd53a718f23ba1f05525 Mon Sep 17 00:00:00 2001 From: cetin Date: Wed, 24 Jan 2024 16:17:45 +0100 Subject: [PATCH] lasso --- methods/fit_linear_regression_model.py | 4 +- trackinglosses_endVelo_momEff.ipynb | 70 ++++++++++++++++--------- trackinglosses_rad_length_endVelo.ipynb | 46 ++++++++-------- 3 files changed, 69 insertions(+), 51 deletions(-) diff --git a/methods/fit_linear_regression_model.py b/methods/fit_linear_regression_model.py index c4a4655..b35a870 100644 --- a/methods/fit_linear_regression_model.py +++ b/methods/fit_linear_regression_model.py @@ -1,6 +1,6 @@ import awkward as ak from sklearn.preprocessing import PolynomialFeatures -from sklearn.linear_model import LinearRegression +from sklearn.linear_model import LinearRegression, Lasso from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import numpy as np @@ -71,7 +71,7 @@ def fit_linear_regression_model( X_test_model = np.delete(X_test_model, remove, axis=1) poly_features = np.delete(poly_features, remove) - lin_reg = LinearRegression(fit_intercept=fit_intercept) + lin_reg = LinearRegression(fit_intercept=fit_intercept) # Lasso(alpha=0.01) lin_reg.fit(X_train_model, y_train) y_pred_test = lin_reg.predict(X_test_model) print(f"Parameterisation for {target_feat}:") diff --git a/trackinglosses_endVelo_momEff.ipynb b/trackinglosses_endVelo_momEff.ipynb index 669bcdc..efaf4e7 100644 --- a/trackinglosses_endVelo_momEff.ipynb +++ b/trackinglosses_endVelo_momEff.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -19,21 +19,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "298 72\n", - "370\n" + "40402 10099\n", + "91861\n" ] } ], "source": [ "file = uproot.open(\n", - " \"tracking_losses_ntuple_test_endVelo_momEff.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", + " \"tracking_losses_ntuple_B_endVelo_idealstateP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", ")\n", "\n", "# selektiere nur elektronen von B->K*ee\n", @@ -45,23 +45,25 @@ " (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n", "] # B: 1466\n", "\n", - "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n", + "notelectrons = allcolumns[\n", + " (~allcolumns.isElectron) & (allcolumns.fromB) & (~allcolumns.lost)\n", + "]\n", "\n", "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n", - "print(ak.num(electrons, axis=0))\n", + "print(ak.num(notelectrons, axis=0))\n", "# ak.count(found, axis=None)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "stretch factor: 0.24161073825503357\n" + "stretch factor: 0.24996287312509283\n" ] } ], @@ -69,22 +71,19 @@ "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n", "eta_found = ak.to_numpy(found[\"eta\"])\n", "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n", - "eta_lost = ak.to_numpy(lost[\"eta\"])\n", - "\n", - "stretch_factor = ak.num(eta_lost, axis=0) / ak.num(eta_found, axis=0)\n", - "print(\"stretch factor: \", stretch_factor)" + "eta_lost = ak.to_numpy(lost[\"eta\"])" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -94,10 +93,18 @@ "source": [ "endVeloP_found = ak.to_numpy(found[\"ideal_state_770_p\"])\n", "trueP_found = ak.to_numpy(found[\"p\"])\n", - "nbins = 50\n", - "vmax = 5\n", "\n", - "a0 = plt.hist2d(\n", + "endVeloP_notelectrons = ak.to_numpy(notelectrons[\"ideal_state_770_p\"])\n", + "trueP_notelectrons = ak.to_numpy(notelectrons[\"p\"])\n", + "\n", + "stretch_factor = ak.num(trueP_notelectrons, axis=0) / ak.num(trueP_found, axis=0)\n", + "\n", + "nbins = 100\n", + "vmax = 50\n", + "\n", + "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n", + "\n", + "a0 = ax0.hist2d(\n", " trueP_found,\n", " endVeloP_found,\n", " density=False,\n", @@ -107,13 +114,26 @@ " vmax=vmax,\n", " range=[[0, 30000], [0, 30000]],\n", ")\n", - "# plt.plot([-0.1, 1.0], [-0.1, 1.0], marker=\"\", alpha=0.8)\n", - "plt.xlabel(f\"True $P$\")\n", - "plt.ylabel(f\"endVelo $P$\")\n", - "plt.title(f\"found P\")\n", - "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n", + "ax0.set_xlabel(f\"True $P$\")\n", + "ax0.set_ylabel(f\"endVelo $P$\")\n", + "ax0.set_title(f\"found P\")\n", + "\n", + "a1 = ax1.hist2d(\n", + " trueP_notelectrons,\n", + " endVeloP_notelectrons,\n", + " density=False,\n", + " bins=nbins,\n", + " cmap=plt.cm.jet,\n", + " cmin=1,\n", + " vmax=vmax * stretch_factor,\n", + " range=[[0, 30000], [0, 30000]],\n", + ")\n", + "ax1.set_xlabel(f\"True $P$\")\n", + "ax1.set_ylabel(f\"endVelo $P$\")\n", + "ax1.set_title(f\"notelectrons P\")\n", "\n", - "plt.colorbar(a0[3])\n", + "plt.suptitle(\"True and idealstate_endVelo Momentum\")\n", + "plt.colorbar(a0[3], ax=ax1)\n", "plt.show()" ] }, diff --git a/trackinglosses_rad_length_endVelo.ipynb b/trackinglosses_rad_length_endVelo.ipynb index 9a2a2cb..61ebc12 100644 --- a/trackinglosses_rad_length_endVelo.ipynb +++ b/trackinglosses_rad_length_endVelo.ipynb @@ -38,12 +38,10 @@ "\n", "# selektiere nur elektronen von B->K*ee\n", "allcolumns = file.arrays()\n", - "found = allcolumns[\n", - " (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n", - "] # B: 9056\n", - "lost = allcolumns[\n", - " (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n", - "] # B: 1466\n", + "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n", + " (allcolumns.fromB)] # B: 9056\n", + "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n", + " (allcolumns.fromB)] # B: 1466\n", "\n", "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n", "\n", @@ -225,14 +223,16 @@ " y = found[\"ideal_state_9410_y\"]\n", " qop = found[\"ideal_state_9410_qop\"]\n", "\n", - "data = ak.zip({\n", - " \"rad_length_frac\": rad_length_frac,\n", - " \"x\": x,\n", - " \"y\": y,\n", - " \"tx\": slopex,\n", - " \"ty\": slopey,\n", - " \"qop\": qop,\n", - "})\n", + "data = ak.zip(\n", + " {\n", + " \"rad_length_frac\": rad_length_frac,\n", + " \"x\": x,\n", + " \"y\": y,\n", + " \"tx\": slopex,\n", + " \"ty\": slopey,\n", + " \"qop\": qop,\n", + " }\n", + ")\n", "lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n", " data,\n", " \"rad_length_frac\",\n", @@ -357,16 +357,14 @@ " y = lost[\"ideal_state_9410_y\"]\n", " qop = lost[\"ideal_state_9410_qop\"]\n", "\n", - "data = ak.zip(\n", - " {\n", - " \"rad_length_frac\": rad_length_frac,\n", - " \"x\": x,\n", - " \"y\": y,\n", - " \"tx\": slopex,\n", - " \"ty\": slopey,\n", - " \"qop\": qop,\n", - " }\n", - ")\n", + "data = ak.zip({\n", + " \"rad_length_frac\": rad_length_frac,\n", + " \"x\": x,\n", + " \"y\": y,\n", + " \"tx\": slopex,\n", + " \"ty\": slopey,\n", + " \"qop\": qop,\n", + "})\n", "lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n", " data,\n", " \"rad_length_frac\",\n",