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v0.6.29
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‎notebooks/00_spotPython_tests.ipynb‎

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"fun_control"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Attribute Name Attribute Value\n",
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"0 age 30\n",
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"1 name John\n",
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"2 salary 50000\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"def class_attributes_to_dataframe(class_obj):\n",
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" # Get the attributes and their values of the class object\n",
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" attributes = [attr for attr in dir(class_obj) if not callable(getattr(class_obj, attr)) and not attr.startswith(\"__\")]\n",
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" values = [getattr(class_obj, attr) for attr in attributes]\n",
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" \n",
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" # Create a DataFrame from the attributes and values\n",
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" df = pd.DataFrame({'Attribute Name': attributes, 'Attribute Value': values})\n",
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" \n",
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" return df\n",
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"\n",
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"# Example usage:\n",
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"class MyClass:\n",
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" def __init__(self):\n",
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" self.name = \"John\"\n",
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" self.age = 30\n",
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" self.salary = 50000\n",
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"\n",
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"my_instance = MyClass()\n",
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"df = class_attributes_to_dataframe(my_instance)\n",
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"print(df)\n",
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"\n"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"spotPython tuning: 2.3692002778610106e-10 [########--] 80.00% \n",
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"spotPython tuning: 2.3692002778610106e-10 [#########-] 90.00% \n",
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"spotPython tuning: 2.3692002778610106e-10 [##########] 100.00% Done...\n",
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"\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<spotPython.spot.spot.Spot at 0x17717ddd0>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"from math import inf\n",
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"from spotPython.fun.objectivefunctions import analytical\n",
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"from spotPython.spot import spot\n",
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"# number of initial points:\n",
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"ni = 7\n",
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"# number of points\n",
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"n = 10\n",
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"\n",
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"fun = analytical().fun_sphere\n",
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"lower = np.array([-1])\n",
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"upper = np.array([1])\n",
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"design_control={\"init_size\": ni}\n",
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"\n",
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"spot_1 = spot.Spot(fun=fun,\n",
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" lower = lower,\n",
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" upper= upper,\n",
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" fun_evals = n,\n",
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" show_progress=True,\n",
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" design_control=design_control,)\n",
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"spot_1.run()\n"
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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": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Attribute Name Attribute Value\n",
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"0 X [[-0.3378148180708981], [0.698908280342222], [0.07989717920782535], [-0.7669633744853341], [0.3783125996899912], [0.7679729980983505], [-0.6923842851505875], [1.5392206722432657e-05], [3.219839249186828e-05], [2.5933080782055762e-05]]\n",
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"1 all_lower [-1]\n",
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"2 all_upper [1]\n",
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"3 all_var_name [x0]\n",
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"4 all_var_type [num]\n",
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"5 counter 10\n",
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"6 de_bounds [[-1, 1]]\n",
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"7 design <spotPython.design.spacefilling.spacefilling object at 0x1773d88d0>\n",
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"8 design_control {'init_size': 7, 'repeats': 1}\n",
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"9 eps 0.0\n",
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"10 fun_control {'sigma': 0, 'seed': None}\n",
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"11 fun_evals 10\n",
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"12 fun_repeats 1\n",
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"13 ident [False]\n",
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"14 infill_criterion y\n",
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"15 k 1\n",
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"16 log_level 50\n",
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"17 lower [-1]\n",
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"18 max_time inf\n",
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"19 mean_X None\n",
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"20 mean_y None\n",
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"21 min_X [1.5392206722432657e-05]\n",
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"22 min_mean_X None\n",
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"23 min_mean_y None\n",
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"24 min_y 0.0\n",
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"25 n_points 1\n",
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"26 noise False\n",
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"27 ocba_delta 0\n",
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"28 optimizer_control {'max_iter': 1000, 'seed': 125}\n",
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"29 red_dim False\n",
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"30 rng Generator(PCG64)\n",
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"31 seed 123\n",
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"32 show_models False\n",
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"33 show_progress True\n",
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"34 spot_writer None\n",
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"35 surrogate <spotPython.build.kriging.Kriging object at 0x17733b090>\n",
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"36 surrogate_control {'noise': False, 'model_optimizer': <function differential_evolution at 0x169b6f420>, 'model_fun_evals': None, 'min_theta': -3.0, 'max_theta': 3.0, 'n_theta': 1, 'n_p': 1, 'optim_p': False, 'cod_type': 'norm', 'var_type': ['num'], 'seed': 124, 'use_cod_y': False}\n",
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"37 tolerance_x 0\n",
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"38 upper [1]\n",
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"39 var_name [x0]\n",
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"40 var_type [num]\n",
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"41 var_y None\n",
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"42 y [0.11411885130827397, 0.48847278433092195, 0.00638355924536736, 0.5882328178019309, 0.14312042308419953, 0.589782525808169, 0.47939599832349, 2.3692002778610106e-10, 1.0367364790603999e-09, 6.725246788486299e-10]"
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]
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},
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{
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"data": {
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"text/plain": [
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"12627"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sys import stdout\n",
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"df = spot_1.class_attributes_to_dataframe()\n",
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"stdout.write(df.to_string())"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Accuracy: 89.28%"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from river import datasets\n",
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"from river import evaluate\n",
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"from river.linear_model import LogisticRegression\n",
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"from river import metrics\n",
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"from river import optim\n",
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"from river import preprocessing\n",
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"\n",
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"dataset = datasets.Phishing()\n",
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"\n",
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"model = (\n",
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" preprocessing.StandardScaler() |\n",
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" LogisticRegression()\n",
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")\n",
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"\n",
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"metric = metrics.Accuracy()\n",
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"\n",
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"evaluate.progressive_val_score(dataset, model, metric)\n"
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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,

‎pyproject.toml‎

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[project]
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name = "spotPython"
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version = "0.6.28"
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version = "0.6.29"
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authors = [
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{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
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]

‎src/spotPython/build/kriging.py‎

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None
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"""
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self.p = ones(self.n_p) * 2.0
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self.pen_val = self.n * log(var(self.nat_y)) + 1e4
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# if var(self.nat_y) is > 0, then self.pen_val = self.n * log(var(self.nat_y)) + 1e4
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# else self.pen_val = self.n * var(self.nat_y) + 1e4
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if var(self.nat_y) > 0:
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self.pen_val = self.n * log(var(self.nat_y)) + 1e4
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else:
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self.pen_val = self.n * var(self.nat_y) + 1e4
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self.negLnLike = None
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self.gen = spacefilling(k=self.k, seed=self.seed)
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self.LnDetPsi = None

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