diff --git a/notebooks/grids.ipynb b/notebooks/grids.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..73eaabe1371a753811b7dee164f9d7edeb26de72
--- /dev/null
+++ b/notebooks/grids.ipynb
@@ -0,0 +1,586 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9824f12e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fdc05dfa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "parameters = {'max_depth':(5,10,15)}\n",
+ "rf = RandomForestRegressor(n_estimators=15, try_features = 'third')\n",
+ "searcher = GridSearchCV(rf, parameters, cv=3)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4f464219",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import GridSearchCV\n",
+ "from sklearn.ensemble import RandomForestRegressor\n",
+ "\n",
+ "from sklearn.datasets import make_regression\n",
+ "import matplotlib.pyplot as plt "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "25a04ec5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X, y = make_regression(n_features=1, n_informative=1, random_state=0, shuffle=False, noise=10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "24b05b14",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(X, y);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "8532c5eb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rf = RandomForestRegressor()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "528b3452",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "searcher = GridSearchCV(estimator=rf, param_grid={'max_depth':(5,10,15)}, cv= 3)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "ec7bef2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=RandomForestRegressor(),\n",
+ " param_grid={'max_depth': (5, 10, 15)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=RandomForestRegressor(),\n",
+ " param_grid={'max_depth': (5, 10, 15)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "GridSearchCV(cv=3, estimator=RandomForestRegressor(),\n",
+ " param_grid={'max_depth': (5, 10, 15)})"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "searcher.fit(X, y)\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "a20fe377",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'mean_fit_time': array([0.08331521, 0.07211264, 0.0718383 ]),\n",
+ " 'std_fit_time': array([0.01629742, 0.00011223, 0.00013308]),\n",
+ " 'mean_score_time': array([0.00523392, 0.00518481, 0.00502459]),\n",
+ " 'std_score_time': array([1.56304468e-04, 1.14600801e-04, 5.38271269e-05]),\n",
+ " 'param_max_depth': masked_array(data=[5, 10, 15],\n",
+ " mask=[False, False, False],\n",
+ " fill_value='?',\n",
+ " dtype=object),\n",
+ " 'params': [{'max_depth': 5}, {'max_depth': 10}, {'max_depth': 15}],\n",
+ " 'split0_test_score': array([0.92873148, 0.92620142, 0.92348871]),\n",
+ " 'split1_test_score': array([0.90228693, 0.90530648, 0.90144398]),\n",
+ " 'split2_test_score': array([0.89852812, 0.8944555 , 0.89516853]),\n",
+ " 'mean_test_score': array([0.90984884, 0.90865446, 0.9067004 ]),\n",
+ " 'std_test_score': array([0.01343993, 0.01317466, 0.01214443]),\n",
+ " 'rank_test_score': array([1, 2, 3], dtype=int32)}"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "searcher.cv_results_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "f58df0df",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "max_depth 5\n",
+ "0.9098488414768675\n",
+ "0.08331521352132161\n",
+ "max_depth 10\n",
+ "0.908654464483059\n",
+ "0.07211263974507649\n",
+ "max_depth 15\n",
+ "0.9067004042768522\n",
+ "0.0718382994333903\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i, p in enumerate(searcher.cv_results_['params']):\n",
+ " for parname, parvalue in p.items():\n",
+ " print(parname, parvalue)\n",
+ " print(searcher.cv_results_['mean_test_score'][i])\n",
+ " print(searcher.cv_results_['mean_fit_time'][i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f44a0964",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6168dc37",
+ "metadata": {},
+ "source": [
+ "## Log search results to model repo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1c8a9237",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import mlflow"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "19952b7e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mlflow.set_tracking_uri('https://modelrepository.eflows4hpc.eu/')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c5f64c94",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024/01/15 10:53:31 INFO mlflow.tracking.fluent: Experiment with name 'gridsearch-example' does not exist. Creating a new experiment.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<Experiment: artifact_location='mlflow-artifacts:/2', creation_time=1705312411739, experiment_id='2', last_update_time=1705312411739, lifecycle_stage='active', name='gridsearch-example', tags={}>"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mlflow.set_experiment('gridsearch-example')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "544a9c35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metrics=['mean_test_score', 'mean_fit_time']\n",
+ "\n",
+ "for i, p in enumerate(searcher.cv_results_['params']):\n",
+ " with mlflow.start_run():\n",
+ " for parname, parvalue in p.items():\n",
+ " mlflow.log_param(parname, value=parvalue)\n",
+ " \n",
+ " for m in metrics:\n",
+ " mlflow.log_metric(m, searcher.cv_results_[m][i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "899abf9c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8afb1656",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ace8a2d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a9c1680f",
+ "metadata": {},
+ "source": [
+ "## Serialize results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "a874f869",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "74f4b85f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame.from_dict(searcher.cv_results_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "68f37d48",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>mean_fit_time</th>\n",
+ " <th>std_fit_time</th>\n",
+ " <th>mean_score_time</th>\n",
+ " <th>std_score_time</th>\n",
+ " <th>param_max_depth</th>\n",
+ " <th>params</th>\n",
+ " <th>split0_test_score</th>\n",
+ " <th>split1_test_score</th>\n",
+ " <th>split2_test_score</th>\n",
+ " <th>mean_test_score</th>\n",
+ " <th>std_test_score</th>\n",
+ " <th>rank_test_score</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.083315</td>\n",
+ " <td>0.016297</td>\n",
+ " <td>0.005234</td>\n",
+ " <td>0.000156</td>\n",
+ " <td>5</td>\n",
+ " <td>{'max_depth': 5}</td>\n",
+ " <td>0.928731</td>\n",
+ " <td>0.902287</td>\n",
+ " <td>0.898528</td>\n",
+ " <td>0.909849</td>\n",
+ " <td>0.013440</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.072113</td>\n",
+ " <td>0.000112</td>\n",
+ " <td>0.005185</td>\n",
+ " <td>0.000115</td>\n",
+ " <td>10</td>\n",
+ " <td>{'max_depth': 10}</td>\n",
+ " <td>0.926201</td>\n",
+ " <td>0.905306</td>\n",
+ " <td>0.894456</td>\n",
+ " <td>0.908654</td>\n",
+ " <td>0.013175</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.071838</td>\n",
+ " <td>0.000133</td>\n",
+ " <td>0.005025</td>\n",
+ " <td>0.000054</td>\n",
+ " <td>15</td>\n",
+ " <td>{'max_depth': 15}</td>\n",
+ " <td>0.923489</td>\n",
+ " <td>0.901444</td>\n",
+ " <td>0.895169</td>\n",
+ " <td>0.906700</td>\n",
+ " <td>0.012144</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " mean_fit_time std_fit_time mean_score_time std_score_time \\\n",
+ "0 0.083315 0.016297 0.005234 0.000156 \n",
+ "1 0.072113 0.000112 0.005185 0.000115 \n",
+ "2 0.071838 0.000133 0.005025 0.000054 \n",
+ "\n",
+ " param_max_depth params split0_test_score split1_test_score \\\n",
+ "0 5 {'max_depth': 5} 0.928731 0.902287 \n",
+ "1 10 {'max_depth': 10} 0.926201 0.905306 \n",
+ "2 15 {'max_depth': 15} 0.923489 0.901444 \n",
+ "\n",
+ " split2_test_score mean_test_score std_test_score rank_test_score \n",
+ "0 0.898528 0.909849 0.013440 1 \n",
+ "1 0.894456 0.908654 0.013175 2 \n",
+ "2 0.895169 0.906700 0.012144 3 "
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "630a4d63",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.to_csv('search.results')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "a604829e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "search.results\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "! ls search.results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7b090b0b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c4c2addd",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "79b51d03",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "a533b1fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('search.results', index_col=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "09989aa2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dct = df.to_dict()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "7ffcd844",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024/01/15 11:19:33 INFO mlflow.tracking.fluent: Experiment with name 'serializedgridsearch-example' does not exist. Creating a new experiment.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<Experiment: artifact_location='mlflow-artifacts:/3', creation_time=1705313973974, experiment_id='3', last_update_time=1705313973974, lifecycle_stage='active', name='serializedgridsearch-example', tags={}>"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mlflow.set_experiment('serializedgridsearch-example')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "1f5a6864",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metrics=['mean_test_score', 'mean_fit_time']\n",
+ "\n",
+ "for i, p in enumerate(dct['params'].values()):\n",
+ " with mlflow.start_run():\n",
+ " p = json.loads(p.replace('\\'', '\"'))\n",
+ " for parname, parvalue in p.items():\n",
+ " mlflow.log_param(parname, value=parvalue)\n",
+ " \n",
+ " for m in metrics:\n",
+ " mlflow.log_metric(m, searcher.cv_results_[m][i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "976d5e87",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}