diff --git a/Auto-SKLearn_AutoML/Classification.ipynb b/Auto-SKLearn_AutoML/Classification.ipynb
index d4dfef7e13a64b026fb5f08334a570d8dfaba3fc..57391f91c37791f41850721ac11e14e56af26d18 100644
--- a/Auto-SKLearn_AutoML/Classification.ipynb
+++ b/Auto-SKLearn_AutoML/Classification.ipynb
@@ -1,226 +1,4 @@
{
-<<<<<<< HEAD
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "name": "Classification.ipynb",
- "provenance": []
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- }
- },
- "cells": [
- {
- "cell_type": "markdown",
- "source": [
- "# Classification Using Auto-SKLearn",
- "\n",
- "**_NOTE_** autosklearn only will run in linux (feb 26, 2022)\n",
- "\n"
- ],
- "metadata": {
- "id": "-I9i52jCjML_"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "[](https://colab.research.google.com/github/mcint170/DataTools_Tutorial_Demo/blob/main/Auto-SKLearn_AutoML/Classification.ipynb)"
- ],
- "metadata": {
- "id": "-ZrgwiL9kR_L"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "!pip install auto-sklearn"
- ],
- "metadata": {
- "id": "XAjlAHVRenet"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "If running on Google Colab: After running this cell, Click Runtime -> Restart runtime. Then you can run the following cells."
- ],
- "metadata": {
- "id": "yqIcMA8hgZ8W"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "# imports\n",
- "from pprint import pprint\n",
- "\n",
- "import sklearn.datasets\n",
- "import sklearn.metrics\n",
- "import pickle\n",
- "\n",
- "import autosklearn.classification"
- ],
- "metadata": {
- "id": "BXuKNodQe7QZ"
- },
- "execution_count": 4,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# split the dataset\n",
- "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n",
- "X_train, X_test, y_train, y_test = \\\n",
- " sklearn.model_selection.train_test_split(X, y, random_state=1)"
- ],
- "metadata": {
- "id": "ExulDsEAfAoO"
- },
- "execution_count": 5,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Fit the classifier\n",
- "automl = autosklearn.classification.AutoSklearnClassifier(\n",
- " time_left_for_this_task=120,\n",
- " per_run_time_limit=30,\n",
- " tmp_folder='/tmp/autosklearn_classification_example_tmp',\n",
- ")\n",
- "automl.fit(X_train, y_train, dataset_name='breast_cancer')"
- ],
- "metadata": {
- "id": "-0zi5I38fNMM",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "e732438b-610c-4d82-bd38-b1a5497541c6"
- },
- "execution_count": 6,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "AutoSklearnClassifier(per_run_time_limit=30, time_left_for_this_task=120,\n",
- " tmp_folder='/tmp/autosklearn_classification_example_tmp')"
- ]
- },
- "metadata": {},
- "execution_count": 6
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "# Different Models run by autosklearn\n",
- "print(automl.leaderboard())"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "SxtOkluYiVHe",
- "outputId": "29e44357-b2cb-404d-a024-cda5bd61b65a"
- },
- "execution_count": 7,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- " rank ensemble_weight type cost duration\n",
- "model_id \n",
- "7 1 0.10 extra_trees 0.014184 1.502508\n",
- "2 2 0.02 random_forest 0.028369 2.024807\n",
- "36 3 0.06 k_nearest_neighbors 0.028369 0.853534\n",
- "26 4 0.04 extra_trees 0.028369 2.240347\n",
- "19 5 0.02 extra_trees 0.028369 2.791073\n",
- "22 6 0.02 gradient_boosting 0.028369 1.149980\n",
- "3 7 0.14 mlp 0.028369 1.667622\n",
- "12 8 0.04 gradient_boosting 0.035461 1.240657\n",
- "17 9 0.02 gradient_boosting 0.035461 1.510491\n",
- "8 10 0.02 random_forest 0.035461 1.958862\n",
- "37 11 0.06 gradient_boosting 0.035461 1.585859\n",
- "5 12 0.04 random_forest 0.035461 2.075770\n",
- "27 13 0.10 extra_trees 0.042553 1.910083\n",
- "34 14 0.08 random_forest 0.042553 1.884860\n",
- "9 15 0.04 extra_trees 0.042553 1.799630\n",
- "23 16 0.02 mlp 0.049645 2.405247\n",
- "35 17 0.06 extra_trees 0.056738 1.586217\n",
- "32 18 0.02 extra_trees 0.063830 1.650489\n",
- "38 19 0.02 extra_trees 0.063830 2.128083\n",
- "20 20 0.02 passive_aggressive 0.078014 0.774718\n",
- "30 21 0.04 adaboost 0.078014 3.121010\n",
- "29 22 0.02 gaussian_nb 0.141844 1.951357\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "# Show the different models\n",
- "pprint(automl.show_models(), indent=4)"
- ],
- "metadata": {
- "id": "25xOtCJ7icgh"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Predict the test labels\n",
- "predictions = automl.predict(X_test)\n",
- "print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_test, predictions))"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "XvbhWaZpidYt",
- "outputId": "7a153d86-4d3b-474a-f867-8adf7e07318b"
- },
- "execution_count": 9,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Accuracy score: 0.9440559440559441\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "# Export the model with the highest rank\n",
- "clf = automl.show_models()[7]['sklearn_classifier']\n",
- "pickle.dump(clf,open('model.pickle','wb'))"
- ],
- "metadata": {
- "id": "iCFcuh9EikR_"
- },
- "execution_count": 10,
- "outputs": []
- }
- ]
-=======
"cells": [
{
"cell_type": "markdown",
@@ -234,26 +12,56 @@
"Example coming from [here](https://automl.github.io/auto-sklearn/master/examples/20_basic/example_classification.html#sphx-glr-examples-20-basic-example-classification-py)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "c5dad4c0",
+ "metadata": {},
+ "source": [
+ "**Classification doesn't work with current version of scipy/github and requires different packages/updates to run notebook**\n",
+ "- Note from professor Colbry: Notebook can't be fixed in classtime, write note of what needs to be fixed and push to gitlab as is."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"id": "c69433ce",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "ImportError",
+ "evalue": "cannot import name 'apply' from 'dask.compatibility' (/home/weinbren/.local/lib/python3.8/site-packages/dask/compatibility.py)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-23-910f3a285265>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mautosklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m~/.local/lib/python3.8/site-packages/autosklearn/classification.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mautosklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoSklearnClassifier\u001b[0m \u001b[0;31m# noqa (imported but unused)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m~/.local/lib/python3.8/site-packages/autosklearn/estimators.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjoblib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/.local/lib/python3.8/site-packages/dask/distributed.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdistributed\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"No module named 'distributed'\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/distributed/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mactor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mActor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mActorFuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m from .client import (\n\u001b[1;32m 9\u001b[0m \u001b[0mClient\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/distributed/actor.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mqueue\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mQueue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFuture\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault_client\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_serialize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0miscoroutinefunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/distributed/client.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcollections_to_dsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompatibility\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mapply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mflatten\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhighlevelgraph\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHighLevelGraph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mImportError\u001b[0m: cannot import name 'apply' from 'dask.compatibility' (/home/weinbren/.local/lib/python3.8/site-packages/dask/compatibility.py)"
+ ]
+ }
+ ],
"source": [
"# imports\n",
"from pprint import pprint\n",
"\n",
"import sklearn.datasets\n",
"import sklearn.metrics\n",
+ "# Fixed import model_selection\n",
+ "import sklearn.model_selection\n",
"import pickle\n",
"\n",
- "import autosklearn.classification"
+ "# This does not work on current version of github, needs update. \n",
+ "import autosklearn.classification\n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"id": "2b1e1930",
"metadata": {},
"outputs": [],
@@ -266,10 +74,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"id": "15e5f821",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "module 'autosklearn' has no attribute 'classification'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-22-6c1473e893d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Fit the classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m automl = autosklearn.classification.AutoSklearnClassifier(\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mtime_left_for_this_task\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mper_run_time_limit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtmp_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/tmp/autosklearn_classification_example_tmp'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mAttributeError\u001b[0m: module 'autosklearn' has no attribute 'classification'"
+ ]
+ }
+ ],
"source": [
"# Fit the classifier\n",
"automl = autosklearn.classification.AutoSklearnClassifier(\n",
@@ -282,10 +102,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "2d4e4d9f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'automl' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-11-6dfffdcd8374>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Different Models run by autosklearn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mautoml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleaderboard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'automl' is not defined"
+ ]
+ }
+ ],
"source": [
"# Different Models run by autosklearn\n",
"print(automl.leaderboard())"
@@ -293,10 +125,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "72e580e7",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'automl' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-12-ab76765f6a20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Show the different models\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mautoml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'automl' is not defined"
+ ]
+ }
+ ],
"source": [
"# Show the different models\n",
"pprint(automl.show_models(), indent=4)"
@@ -304,10 +148,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "027039cd",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'automl' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-13-596897413c8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Predict the test labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mautoml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy score:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'automl' is not defined"
+ ]
+ }
+ ],
"source": [
"# Predict the test labels\n",
"predictions = automl.predict(X_test)\n",
@@ -316,10 +172,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "acd372ea",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'automl' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-14-14e40d77d77d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Export the model with the highest rank\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mautoml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sklearn_classifier'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model.pickle'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'automl' is not defined"
+ ]
+ }
+ ],
"source": [
"# Export the model with the highest rank\n",
"clf = automl.show_models()[7]['sklearn_classifier']\n",
@@ -328,10 +196,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "a3324782",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'clf' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-15-b9c89d294f77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'clf' is not defined"
+ ]
+ }
+ ],
"source": [
"clf"
]
@@ -347,7 +227,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -361,10 +241,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
->>>>>>> 7e6d5ac (Adding minor comments and updates to AutoML tutorial)
}