diff --git a/tpot_tutorial.ipynb b/tpot_tutorial.ipynb
index 3aabfb921e9368a9efed3d8b7e5758c107be3065..d4730f78a05f6d3efdcc197a7c29a663339ba78f 100644
--- a/tpot_tutorial.ipynb
+++ b/tpot_tutorial.ipynb
@@ -2,6 +2,7 @@
"cells": [
{
"cell_type": "markdown",
+ "id": "4b3db991",
"metadata": {},
"source": [
"### Run this cell to install commands"
@@ -9,9 +10,57 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
+ "execution_count": 6,
+ "id": "9849686b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Defaulting to user installation because normal site-packages is not writeable\n",
+ "Requirement already satisfied: torch in /opt/miniconda3/lib/python3.11/site-packages (2.1.1+cu118)\n",
+ "Requirement already satisfied: filelock in /opt/miniconda3/lib/python3.11/site-packages (from torch) (3.13.1)\n",
+ "Requirement already satisfied: typing-extensions in /opt/miniconda3/lib/python3.11/site-packages (from torch) (4.9.0)\n",
+ "Requirement already satisfied: sympy in /opt/miniconda3/lib/python3.11/site-packages (from torch) (1.12)\n",
+ "Requirement already satisfied: networkx in /opt/miniconda3/lib/python3.11/site-packages (from torch) (3.0)\n",
+ "Requirement already satisfied: jinja2 in /opt/miniconda3/lib/python3.11/site-packages (from torch) (3.1.2)\n",
+ "Requirement already satisfied: fsspec in /opt/miniconda3/lib/python3.11/site-packages (from torch) (2023.4.0)\n",
+ "Requirement already satisfied: triton==2.1.0 in /opt/miniconda3/lib/python3.11/site-packages (from torch) (2.1.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /opt/miniconda3/lib/python3.11/site-packages (from jinja2->torch) (2.1.3)\n",
+ "Requirement already satisfied: mpmath>=0.19 in /opt/miniconda3/lib/python3.11/site-packages (from sympy->torch) (1.3.0)\n",
+ "Note: you may need to restart the kernel to use updated packages.\n",
+ "Defaulting to user installation because normal site-packages is not writeable\n",
+ "Requirement already satisfied: xgboost in /home/kozlow86/.local/lib/python3.11/site-packages (2.0.3)\n",
+ "Requirement already satisfied: numpy in /opt/miniconda3/lib/python3.11/site-packages (from xgboost) (1.26.2)\n",
+ "Requirement already satisfied: scipy in /opt/miniconda3/lib/python3.11/site-packages (from xgboost) (1.10.1)\n",
+ "Note: you may need to restart the kernel to use updated packages.\n",
+ "Defaulting to user installation because normal site-packages is not writeable\n",
+ "Requirement already satisfied: tpot in /home/kozlow86/.local/lib/python3.11/site-packages (0.12.1)\n",
+ "Requirement already satisfied: numpy>=1.16.3 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (1.26.2)\n",
+ "Requirement already satisfied: scipy>=1.3.1 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (1.10.1)\n",
+ "Requirement already satisfied: scikit-learn>=0.22.0 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (1.3.2)\n",
+ "Requirement already satisfied: deap>=1.2 in /home/kozlow86/.local/lib/python3.11/site-packages (from tpot) (1.4.1)\n",
+ "Requirement already satisfied: update-checker>=0.16 in /home/kozlow86/.local/lib/python3.11/site-packages (from tpot) (0.18.0)\n",
+ "Requirement already satisfied: tqdm>=4.36.1 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (4.66.1)\n",
+ "Requirement already satisfied: stopit>=1.1.1 in /home/kozlow86/.local/lib/python3.11/site-packages (from tpot) (1.1.2)\n",
+ "Requirement already satisfied: pandas>=0.24.2 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (2.0.3)\n",
+ "Requirement already satisfied: joblib>=0.13.2 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (1.3.2)\n",
+ "Requirement already satisfied: xgboost>=1.1.0 in /home/kozlow86/.local/lib/python3.11/site-packages (from tpot) (2.0.3)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/miniconda3/lib/python3.11/site-packages (from pandas>=0.24.2->tpot) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /opt/miniconda3/lib/python3.11/site-packages (from pandas>=0.24.2->tpot) (2023.3.post1)\n",
+ "Requirement already satisfied: tzdata>=2022.1 in /opt/miniconda3/lib/python3.11/site-packages (from pandas>=0.24.2->tpot) (2023.3)\n",
+ "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/miniconda3/lib/python3.11/site-packages (from scikit-learn>=0.22.0->tpot) (3.2.0)\n",
+ "Requirement already satisfied: requests>=2.3.0 in /opt/miniconda3/lib/python3.11/site-packages (from update-checker>=0.16->tpot) (2.31.0)\n",
+ "Requirement already satisfied: six>=1.5 in /opt/miniconda3/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas>=0.24.2->tpot) (1.16.0)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/miniconda3/lib/python3.11/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2.0.4)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /opt/miniconda3/lib/python3.11/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.4)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/miniconda3/lib/python3.11/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (1.26.18)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /opt/miniconda3/lib/python3.11/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2023.11.17)\n",
+ "Note: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
"source": [
"%pip install torch\n",
"%pip install xgboost\n",
@@ -20,6 +69,7 @@
},
{
"cell_type": "markdown",
+ "id": "f0f5a4dc",
"metadata": {},
"source": [
"* What is TPOT?\n",
@@ -48,6 +98,7 @@
},
{
"cell_type": "markdown",
+ "id": "b48af34c",
"metadata": {},
"source": [
"### Run this cell to import all of the necessary libraries"
@@ -55,7 +106,8 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 1,
+ "id": "c1c41528",
"metadata": {},
"outputs": [],
"source": [
@@ -68,6 +120,7 @@
},
{
"cell_type": "markdown",
+ "id": "4da91ecb",
"metadata": {},
"source": [
"## Example 1"
@@ -75,9 +128,32 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
+ "execution_count": 2,
+ "id": "cd96a5e7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([[5.1, 3.5, 1.4, 0.2],\n",
+ " [4.9, 3. , 1.4, 0.2],\n",
+ " [4.7, 3.2, 1.3, 0.2],\n",
+ " [4.6, 3.1, 1.5, 0.2],\n",
+ " [5. , 3.6, 1.4, 0.2]]),\n",
+ " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+ " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+ " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]))"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"#load in all of the data\n",
"iris = load_iris()\n",
@@ -86,9 +162,21 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
+ "id": "60d59c8f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((112, 4), (38, 4), (112,), (38,))"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"#split data into a test and train data set\n",
"X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, train_size=0.75, test_size=0.25)\n",
@@ -97,15 +185,53 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
+ "execution_count": 4,
+ "id": "0316f5a5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7575a243384549d8aceebdfd353bcd3b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Optimization Progress: 0%| | 0/100 [00:00<?, ?pipeline/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Generation 1 - Current best internal CV score: 0.9739130434782609\n",
+ "\n",
+ "Generation 2 - Current best internal CV score: 0.9739130434782609\n",
+ "\n",
+ "Generation 3 - Current best internal CV score: 0.9739130434782609\n",
+ "\n",
+ "3.09 minutes have elapsed. TPOT will close down.\n",
+ "TPOT closed during evaluation in one generation.\n",
+ "WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation.\n",
+ "\n",
+ "\n",
+ "TPOT closed prematurely. Will use the current best pipeline.\n",
+ "\n",
+ "Best pipeline: MLPClassifier(input_matrix, alpha=0.01, learning_rate_init=0.001)\n",
+ "0.9736842105263158\n"
+ ]
+ }
+ ],
"source": [
"# Fit the model based on the training data, get a score based on testing data.\n",
"# Will report the score of the best found pipeline\n",
"# Change max_time_mins to a higher time to allow TPOT to run without interruption. #issue number 25\n",
- "# It is currently at 2 mins for sake of not taking to long\n",
- "tpot = TPOTClassifier(verbosity=2, max_time_mins=3)\n",
+ "# It is currently at 2 mins for sake of not taking too long\n",
+ "tpot = TPOTClassifier(verbosity=2, max_time_mins=10) # increased max time to give best results\n",
"tpot.fit(X_train, y_train)\n",
"print(tpot.score(X_test, y_test))"
]
@@ -115,12 +241,13 @@
"id": "22bb780f",
"metadata": {},
"source": [
- "Issued warning of TPOT closed prematurely. I am increasing the max_time to 4 so tpot can completely run and the results are more accurate"
+ "Issued warning of TPOT closed prematurely. I am increasing the max_time to 10 so TPOT can completely run and the results are more accurate"
]
},
{
"cell_type": "code",
"execution_count": 11,
+ "id": "013af850",
"metadata": {},
"outputs": [],
"source": [
@@ -130,6 +257,7 @@
},
{
"cell_type": "markdown",
+ "id": "3f331d68",
"metadata": {},
"source": [
"## Example 2"
@@ -137,9 +265,153 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
+ "execution_count": 5,
+ "id": "2b756a95",
+ "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>PassengerId</th>\n",
+ " <th>Survived</th>\n",
+ " <th>Pclass</th>\n",
+ " <th>Name</th>\n",
+ " <th>Sex</th>\n",
+ " <th>Age</th>\n",
+ " <th>SibSp</th>\n",
+ " <th>Parch</th>\n",
+ " <th>Ticket</th>\n",
+ " <th>Fare</th>\n",
+ " <th>Cabin</th>\n",
+ " <th>Embarked</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Braund, Mr. Owen Harris</td>\n",
+ " <td>male</td>\n",
+ " <td>22.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>A/5 21171</td>\n",
+ " <td>7.2500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+ " <td>female</td>\n",
+ " <td>38.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>PC 17599</td>\n",
+ " <td>71.2833</td>\n",
+ " <td>C85</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " <td>Heikkinen, Miss. Laina</td>\n",
+ " <td>female</td>\n",
+ " <td>26.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>STON/O2. 3101282</td>\n",
+ " <td>7.9250</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+ " <td>female</td>\n",
+ " <td>35.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>113803</td>\n",
+ " <td>53.1000</td>\n",
+ " <td>C123</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Allen, Mr. William Henry</td>\n",
+ " <td>male</td>\n",
+ " <td>35.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>373450</td>\n",
+ " <td>8.0500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"#read in data\n",
"titanic = pd.read_csv('titanic_train.csv')\n",
@@ -148,7 +420,8 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 6,
+ "id": "1b663c97",
"metadata": {},
"outputs": [],
"source": [
@@ -158,9 +431,22 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 7,
+ "id": "50d6673f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of levels in category 'Name': 891.00 \n",
+ "Number of levels in category 'Sex': 2.00 \n",
+ "Number of levels in category 'Ticket': 681.00 \n",
+ "Number of levels in category 'Cabin': 148.00 \n",
+ "Number of levels in category 'Embarked': 4.00 \n"
+ ]
+ }
+ ],
"source": [
"# Find out how many different categories there are for each of these 5 features\n",
"for cat in ['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']:\n",
@@ -169,9 +455,19 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
+ "execution_count": 8,
+ "id": "d160467e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Levels for catgeory 'Sex': ['male' 'female']\n",
+ "Levels for catgeory 'Embarked': ['S' 'C' 'Q' nan]\n"
+ ]
+ }
+ ],
"source": [
"#print out what those categories are for 'Sex' and 'Embarked'\n",
"for cat in ['Sex', 'Embarked']:\n",
@@ -180,7 +476,8 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 9,
+ "id": "ab1f5255",
"metadata": {},
"outputs": [],
"source": [
@@ -191,9 +488,33 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
+ "execution_count": 10,
+ "id": "6f88cded",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "PassengerId False\n",
+ "class False\n",
+ "Pclass False\n",
+ "Name False\n",
+ "Sex False\n",
+ "Age False\n",
+ "SibSp False\n",
+ "Parch False\n",
+ "Ticket False\n",
+ "Fare False\n",
+ "Cabin False\n",
+ "Embarked False\n",
+ "dtype: bool"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# fill na values and then double check there are non left\n",
"titanic = titanic.fillna(-999)\n",
@@ -202,7 +523,8 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 11,
+ "id": "7b82a7fc",
"metadata": {},
"outputs": [],
"source": [
@@ -214,16 +536,35 @@
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
+ "execution_count": 12,
+ "id": "6c7468e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 0, 0, ..., 0, 0, 0],\n",
+ " [0, 0, 0, ..., 0, 0, 0],\n",
+ " [1, 0, 0, ..., 0, 0, 0],\n",
+ " ...,\n",
+ " [1, 0, 0, ..., 0, 0, 0],\n",
+ " [0, 0, 0, ..., 0, 0, 0],\n",
+ " [1, 0, 0, ..., 0, 0, 0]])"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"CabinTrans"
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 13,
+ "id": "b9e9c1d2",
"metadata": {},
"outputs": [],
"source": [
@@ -233,7 +574,8 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 14,
+ "id": "8c78ab53",
"metadata": {},
"outputs": [],
"source": [
@@ -243,7 +585,8 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 15,
+ "id": "2f2c8be2",
"metadata": {},
"outputs": [],
"source": [
@@ -254,9 +597,21 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [],
+ "execution_count": 16,
+ "id": "76fc3385",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# make sure there are no nas in the data\n",
"np.isnan(titanic_new).any()"
@@ -264,16 +619,29 @@
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
+ "execution_count": 17,
+ "id": "d26a0d0f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "156"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"titanic_new[0].size"
]
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 18,
+ "id": "38fe863d",
"metadata": {},
"outputs": [],
"source": [
@@ -283,9 +651,21 @@
},
{
"cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [],
+ "execution_count": 19,
+ "id": "cad1b5b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(668, 223)"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# split the data into testing and training- this will give us indices\n",
"training_indices, validation_indices = training_indices, testing_indices = train_test_split(titanic.index, stratify = titanic_class, train_size=0.75, test_size=0.25)\n",
@@ -294,9 +674,25 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
+ "id": "bb29fe1c",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "77018f7b307d4184bc84729d4417703e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Optimization Progress: 0%| | 0/40 [00:00<?, ?pipeline/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# create the classifier and fit the model, reports the best pipeline\n",
"# Parameters within the TPOT Classifier can be changed to allow for longer run time across more models\n",
@@ -309,12 +705,13 @@
"id": "910d6e80",
"metadata": {},
"source": [
- "Issued warning of TPOT closed prematurely. I am increasing the max_time so tpot can completely run and the results are more accurate"
+ "Issued warning of TPOT closed prematurely. I am increasing the max_time_mins so TPOT can completely run and the results are more accurate"
]
},
{
"cell_type": "code",
"execution_count": 28,
+ "id": "e9f5c9af",
"metadata": {},
"outputs": [],
"source": [
@@ -325,6 +722,7 @@
{
"cell_type": "code",
"execution_count": 29,
+ "id": "61c375c3",
"metadata": {},
"outputs": [],
"source": [
@@ -335,6 +733,7 @@
{
"cell_type": "code",
"execution_count": 30,
+ "id": "f348eb12",
"metadata": {},
"outputs": [],
"source": [
@@ -346,6 +745,7 @@
{
"cell_type": "code",
"execution_count": 31,
+ "id": "64c48124",
"metadata": {},
"outputs": [],
"source": [
@@ -358,6 +758,7 @@
{
"cell_type": "code",
"execution_count": 32,
+ "id": "b051001d",
"metadata": {},
"outputs": [],
"source": [
@@ -369,6 +770,7 @@
{
"cell_type": "code",
"execution_count": 33,
+ "id": "e0b4c999",
"metadata": {},
"outputs": [],
"source": [
@@ -380,6 +782,7 @@
{
"cell_type": "code",
"execution_count": 34,
+ "id": "c931cf65",
"metadata": {},
"outputs": [],
"source": [
@@ -394,6 +797,7 @@
{
"cell_type": "code",
"execution_count": 35,
+ "id": "00d7bd45",
"metadata": {},
"outputs": [],
"source": [
@@ -404,6 +808,7 @@
{
"cell_type": "code",
"execution_count": 36,
+ "id": "db5c1909",
"metadata": {},
"outputs": [],
"source": [
@@ -413,6 +818,7 @@
{
"cell_type": "code",
"execution_count": 37,
+ "id": "3f2f4888",
"metadata": {},
"outputs": [],
"source": [
@@ -422,6 +828,7 @@
{
"cell_type": "code",
"execution_count": 38,
+ "id": "250d23c7",
"metadata": {},
"outputs": [],
"source": [
@@ -432,6 +839,7 @@
{
"cell_type": "code",
"execution_count": 40,
+ "id": "0132c44e",
"metadata": {},
"outputs": [],
"source": [
@@ -441,6 +849,7 @@
{
"cell_type": "code",
"execution_count": 41,
+ "id": "17714990",
"metadata": {},
"outputs": [],
"source": [
@@ -457,6 +866,7 @@
{
"cell_type": "code",
"execution_count": 42,
+ "id": "98bbe243",
"metadata": {},
"outputs": [],
"source": [
@@ -465,6 +875,7 @@
},
{
"cell_type": "markdown",
+ "id": "a275be3e",
"metadata": {},
"source": [
"### References\n",
@@ -475,7 +886,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.11 (default)",
"language": "python",
"name": "python3"
},
@@ -489,7 +900,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.4"
+ "version": "3.11.6"
}
},
"nbformat": 4,