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fix minimize to work with tree models

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franzi
2022-10-17 19:17:05 +02:00
parent 40f2d06348
commit a2305c0a23
1 changed files with 2 additions and 11 deletions
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notebooks/5_quality_prediction.ipynb
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@@ -23,20 +23,11 @@
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import pearsonr\n",
"from scipy.optimize import minimize\n",
"# machine learning stuff\n",
"from sklearn.metrics import mean_absolute_error\n",
"from sklearn.dummy import DummyRegressor\n",
"from sklearn.preprocessing import MaxAbsScaler\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import ElasticNetCV\n",
"from sklearn.svm import SVR\n",
"from sklearn.ensemble import RandomForestRegressor, StackingRegressor\n",
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
"from sklearn import tree\n",
"from sklearn.inspection import plot_partial_dependence, permutation_importance\n",
"from sklearn.manifold import TSNE\n",
"from sklearn.model_selection import train_test_split\n",
"# interactive plotting (parallel coordinate plot)\n",
"import plotly.express as px\n",
"# suppress unnecessary warnings\n",
@@ -350,7 +341,7 @@
" # the start value for the optimization is the original water content.\n",
" # to get realistic values, we additionaly specify bounds\n",
" # based on the actual min/max values for the water content\n",
" res = minimize(_loss_fun, np.array([water_org]), bounds=[(value_min, value_max)])\n",
" res = minimize(_loss_fun, np.array([water_org]), bounds=[(value_min, value_max)], method=\"Powell\")\n",
" # the optimized water content is stored in res.x (again a np.array)\n",
" water_new = res.x[0]\n",
" # check the final strength prediction\n",