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Tpot NN Colab test

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"source": [
"!pip install deap update_checker tqdm stopit xgboost\n",
"!pip install dask[delayed] dask[dataframe] dask-ml fsspec>=0.3.3 distributed>=2.10.0\n",
"!pip install scikit-mdr skrebate\n",
"!pip install tpot"
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"text": [
"Collecting deap\n",
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"Building wheels for collected packages: stopit\n",
" Building wheel for stopit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for stopit: filename=stopit-1.1.2-py3-none-any.whl size=11937 sha256=4915a58d356a870dbd83a619555155796895ae1868a1f566245606afe7205dcb\n",
" Stored in directory: /root/.cache/pip/wheels/af/f9/87/bf5b3d565c2a007b4dae9d8142dccc85a9f164e517062dd519\n",
"Successfully built stopit\n",
"Installing collected packages: stopit, deap, update_checker\n",
"Successfully installed deap-1.4.1 stopit-1.1.2 update_checker-0.18.0\n",
"Collecting scikit-mdr\n",
" Downloading scikit_MDR-0.4.5-py3-none-any.whl (15 kB)\n",
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" Downloading skrebate-0.62.tar.gz (19 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Stored in directory: /root/.cache/pip/wheels/dd/67/40/683074a684607162bd0e34dcf7ccdfcab5861c3b2a83286f3a\n",
"Successfully built skrebate\n",
"Installing collected packages: skrebate, scikit-mdr\n",
"Successfully installed scikit-mdr-0.4.5 skrebate-0.62\n",
"Collecting tpot\n",
" Downloading TPOT-0.12.1-py3-none-any.whl (87 kB)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2.0.7)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2023.11.17)\n",
"Installing collected packages: tpot\n",
"Successfully installed tpot-0.12.1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"print(torch.__version__)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5UH_q7gJL3YU",
"outputId": "13ebf85c-bc0a-4c4c-9466-e7194d1d6bc8"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2.1.0+cu121\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 448,
"referenced_widgets": [
"301a7a8edb37404da369a8607c8e7f39",
"550f9e30494a4cedb8265819b10eda9f",
"305334ab788d4c939ef090481cd37014",
"5e6d831eb4da408db14779d0bfa1c3ce",
"59def110c5334bc7865801cc2c2b1201",
"30407bbfe0534d839965fda6ff2ad7a6",
"7a64c0004a5b4f63a5e3e123a1bedb42",
"09b1baa49b8e4261aa12044843037665",
"b895890e16f04226ae3379d0a2b061b2",
"72f3f1a3683e4b7d93549d9bf998f1c7",
"b6633832327f415c85a7d0fc13a1aca3"
]
},
"id": "luBYuKTlKPlX",
"outputId": "b13f1749-e3b4-4152-d48f-cb857d408727"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Optimization Progress: 0%| | 0/110 [00:00<?, ?pipeline/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "301a7a8edb37404da369a8607c8e7f39"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Generation 1 - Current best internal CV score: 1.0\n",
"\n",
"Generation 2 - Current best internal CV score: 1.0\n",
"\n",
"Generation 3 - Current best internal CV score: 1.0\n",
"\n",
"Generation 4 - Current best internal CV score: 1.0\n",
"\n",
"Generation 5 - Current best internal CV score: 1.0\n",
"\n",
"Generation 6 - Current best internal CV score: 1.0\n",
"\n",
"Generation 7 - Current best internal CV score: 1.0\n",
"\n",
"Generation 8 - Current best internal CV score: 1.0\n",
"\n",
"Generation 9 - Current best internal CV score: 1.0\n",
"\n",
"Generation 10 - Current best internal CV score: 1.0\n",
"\n",
"Best pipeline: PytorchLRClassifier(MaxAbsScaler(SelectPercentile(input_matrix, percentile=57)), batch_size=4, learning_rate=1.0, num_epochs=15, weight_decay=0)\n",
"1.0\n"
]
}
],
"source": [
"from tpot import TPOTClassifier\n",
"from sklearn.datasets import make_blobs\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"X, y = make_blobs(n_samples=100, centers=2, n_features=3, random_state=42)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25)\n",
"\n",
"clf = TPOTClassifier(config_dict='TPOT NN', template='Selector-Transformer-PytorchLRClassifier',\n",
" verbosity=2, population_size=10, generations=10)\n",
"clf.fit(X_train, y_train)\n",
"print(clf.score(X_test, y_test))\n",
"clf.export('tpot_nn_demo_pipeline.py')"
]
},
{
"cell_type": "code",
"source": [
"from deap import creator\n",
"from sklearn.model_selection import cross_val_score\n",
"import numpy as np\n",
"\n",
"print(clf.score(X_test, y_test))\n",
"# print part of the pipeline dictionary\n",
"print(dict(list(clf.evaluated_individuals_.items())[0:2]))\n",
"# print a pipeline and its values\n",
"pipeline_str = list(clf.evaluated_individuals_.keys())[0]\n",
"print(pipeline_str)\n",
"print(clf.evaluated_individuals_[pipeline_str])\n",
"# convert pipeline string to scikit-learn pipeline object\n",
"optimized_pipeline = creator.Individual.from_string(pipeline_str, clf._pset) # deap object\n",
"fitted_pipeline = clf._toolbox.compile(expr=optimized_pipeline) # scikit-learn pipeline object\n",
"# print scikit-learn pipeline object\n",
"print(fitted_pipeline)\n",
"\n",
"# Fix random state when the operator allows (optional) just for getting consistent CV score\n",
"for step in fitted_pipeline.steps:\n",
" if 'random_state' in step[1].get_params():\n",
" step[1].set_params(random_state=42)\n",
"\n",
"# CV scores from scikit-learn\n",
"scores = cross_val_score(fitted_pipeline, X_train, y_train, cv=5, scoring='accuracy', verbose=0)\n",
"print(np.mean(scores))\n",
"print(clf.evaluated_individuals_[pipeline_str])\n",
""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mq0lkWNuO1WU",
"outputId": "f2615618-f61f-4f75-91da-c6fd50745777"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.0\n",
"{'PytorchLRClassifier(MaxAbsScaler(SelectPercentile(input_matrix, SelectPercentile__percentile=57)), PytorchLRClassifier__batch_size=4, PytorchLRClassifier__learning_rate=1.0, PytorchLRClassifier__num_epochs=15, PytorchLRClassifier__weight_decay=0)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 3, 'internal_cv_score': 1.0}, 'PytorchLRClassifier(OneHotEncoder(SelectFwe(input_matrix, SelectFwe__alpha=0.039), OneHotEncoder__minimum_fraction=0.2, OneHotEncoder__sparse=False, OneHotEncoder__threshold=10), PytorchLRClassifier__batch_size=8, PytorchLRClassifier__learning_rate=0.01, PytorchLRClassifier__num_epochs=15, PytorchLRClassifier__weight_decay=0.001)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 3, 'internal_cv_score': 1.0}}\n",
"PytorchLRClassifier(MaxAbsScaler(SelectPercentile(input_matrix, SelectPercentile__percentile=57)), PytorchLRClassifier__batch_size=4, PytorchLRClassifier__learning_rate=1.0, PytorchLRClassifier__num_epochs=15, PytorchLRClassifier__weight_decay=0)\n",
"{'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 3, 'internal_cv_score': 1.0}\n",
"Pipeline(steps=[('selectpercentile', SelectPercentile(percentile=57)),\n",
" ('maxabsscaler', MaxAbsScaler()),\n",
" ('pytorchlrclassifier',\n",
" PytorchLRClassifier(batch_size=4, learning_rate=1.0,\n",
" num_epochs=15, weight_decay=0))])\n",
"1.0\n",
"{'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 3, 'internal_cv_score': 1.0}\n"
]
}
]
}
]
}