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Update 10_spot_hpt_sklearn.ipynb
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‎notebooks/10_spot_hpt_sklearn.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"MAX_TIME = 5\n",
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"INIT_SIZE = 10\n",
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"MAX_TIME = 10\n",
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"INIT_SIZE = 50\n",
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"CLASSIFICATION = True\n",
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"REGRESSION = False\n",
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"MOONS = True\n",
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{
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"data": {
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"text/plain": [
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"'10-sklearn_p040025_5min_10init_2023-05-08_23-24-38'"
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"'10-sklearn_p040025_10min_50init_2023-05-09_00-04-46'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"source": [
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"# Chapter 10: Sequential Parameter Optimization\n",
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"## Hyperparameter Tuning: sklearn decision tree"
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"## Hyperparameter Tuning: sklearn"
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]
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},
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{
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"from sklearn.ensemble import HistGradientBoostingRegressor\n",
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"from sklearn.model_selection import cross_validate\n",
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"from sklearn.datasets import fetch_openml\n",
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"from sklearn.metrics import mean_absolute_error, accuracy_score, roc_curve, roc_auc_score\n",
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"from sklearn.metrics import mean_absolute_error, accuracy_score, roc_curve, roc_auc_score, log_loss, mean_squared_error\n",
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"from sklearn.tree import DecisionTreeRegressor\n",
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"from sklearn.datasets import make_regression\n",
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"from sklearn.preprocessing import OneHotEncoder\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from sklearn.ensemble import GradientBoostingClassifier\n",
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"from sklearn.ensemble import GradientBoostingRegressor\n",
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"from sklearn.linear_model import ElasticNet\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"outputs": [],
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"source": [
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"# core_model = RidgeCV\n",
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"# core_model = RandomForestClassifier\n",
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"# core_model = GradientBoostingRegressor\n",
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"# core_model = ElasticNet\n",
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"core_model = RandomForestClassifier\n",
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"# core_model = SVC\n",
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"# core_model = LogisticRegression\n",
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"# core_model = KNeighborsClassifier\n",
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"core_model = GradientBoostingClassifier\n",
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"# core_model = GradientBoostingClassifier\n",
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"fun_control = add_core_model_to_fun_control(core_model=core_model,\n",
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" fun_control=fun_control,\n",
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" hyper_dict=SklearnHyperDict,\n",
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"outputs": [],
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"source": [
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"fun = HyperSklearn(seed=123, log_level=50).fun_sklearn\n",
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"weights = -1.0\n",
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"\n",
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"# metric_sklearn = roc_auc_score\n",
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"# weights = -1.0\n",
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"metric_sklearn = log_loss\n",
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"weights = 1.0\n",
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"\n",
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"fun_control.update({\n",
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" \"horizon\": None,\n",
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" \"log_level\": 50,\n",
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" \"weight_coeff\": None,\n",
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" \"metric\": None,\n",
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" \"metric_sklearn\": roc_auc_score\n",
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" \"metric_sklearn\": metric_sklearn\n",
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" })"
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]
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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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"| name | type | default | lower | upper | transform |\n",
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"|--------------------------|--------|--------------|---------|---------|------------------------|\n",
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"| loss | factor | log_loss | 0 | 1 | None |\n",
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"| learning_rate | float | 0.1 | 0.001 | 0.2 | None |\n",
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"| n_estimators | int | 7 | 3 | 10 | transform_power_2_int |\n",
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"| subsample | float | 0.0 | -10 | 0 | transform_power_2 |\n",
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"| criterion | factor | friedman_mse | 0 | 1 | None |\n",
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"| min_samples_split | int | 1 | 1 | 10 | transform_power_2_int |\n",
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"| min_samples_leaf | int | 0 | 0 | 10 | transform_power_2_int |\n",
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"| min_weight_fraction_leaf | float | 0.0 | 0 | 0.5 | None |\n",
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"| max_depth | int | 2 | 1 | 20 | transform_power_2_int |\n",
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"| min_impurity_decrease | float | 0.0 | 0 | 1e+06 | None |\n",
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"| max_features | factor | none | 0 | 3 | transform_none_to_None |\n",
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"| max_leaf_nodes | int | 10 | 1 | 12 | transform_power_2_int |\n",
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"| tol | float | 0.0001 | 1e-05 | 0.001 | None |\n"
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"| name | type | default | lower | upper | transform |\n",
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"|--------------------------|--------|-----------|---------|---------|------------------------|\n",
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"| n_estimators | int | 7 | 5 | 9 | transform_power_2_int |\n",
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"| criterion | factor | gini | 0 | 2 | None |\n",
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"| max_depth | int | 10 | 1 | 20 | transform_power_2_int |\n",
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"| min_samples_split | int | 2 | 2 | 100 | None |\n",
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"| min_samples_leaf | int | 1 | 1 | 10 | None |\n",
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"| min_weight_fraction_leaf | float | 0.0 | 0 | 0.01 | None |\n",
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"| max_features | factor | sqrt | 0 | 1 | transform_none_to_None |\n",
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"| max_leaf_nodes | int | 10 | 7 | 12 | transform_power_2_int |\n",
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"| min_impurity_decrease | float | 0.0 | 0 | 0.01 | None |\n",
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"| bootstrap | factor | 1 | 0 | 1 | None |\n"
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]
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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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"array([[0.e+00, 1.e-01, 7.e+00, 0.e+00, 0.e+00, 1.e+00, 0.e+00, 0.e+00,\n",
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" 2.e+00, 0.e+00, 3.e+00, 1.e+01, 1.e-04]])"
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"array([[ 7., 0., 10., 2., 1., 0., 0., 10., 0., 1.]])"
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]
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},
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"execution_count": 18,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"spotPython tuning: [##########] 97.10% \r"
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"spotPython tuning: [##--------] 23.07% \r"
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]
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}
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],

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