lasso

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}:")

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",
+    "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",
+    ")\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",
+    "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",
-    ")\n",
+    "})\n",
     "lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n",
     "    data,\n",
     "    \"rad_length_frac\",\n",