From e2acda975d70617844c561aa42b677774464c03b Mon Sep 17 00:00:00 2001 From: brunosmaniotto <54648385+brunosmaniotto@users.noreply.github.com> Date: Sat, 1 Mar 2025 08:31:01 -0800 Subject: [PATCH] Updated issues with deprecated code List of issues described as an issue on github --- .vscode/launch.json | 15 + data/penguins_X_test.csv | 168 ++-- data/penguins_X_train.csv | 498 +++++------ data/penguins_y_test.csv | 154 ++-- data/penguins_y_train.csv | 454 +++++----- lessons/01_regression.ipynb | 937 ++++++++++++++++++-- lessons/02_regularization.ipynb | 506 ++++++++++- lessons/03_preprocessing.ipynb | 589 +++++++++++- lessons/04_classification.ipynb | 679 +++++++++++++- solutions/02_regularization_solutions.ipynb | 6 +- 10 files changed, 3206 insertions(+), 800 deletions(-) create mode 100644 .vscode/launch.json diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..6b76b4f --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,15 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python Debugger: Current File", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal" + } + ] +} \ No newline at end of file diff --git a/data/penguins_X_test.csv b/data/penguins_X_test.csv index 787f057..4708e34 100644 --- a/data/penguins_X_test.csv +++ b/data/penguins_X_test.csv @@ -1,85 +1,85 @@ ,Dream,Torgersen,Male,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g -0,1.0,0.0,1.0,1.1137063470051127,1.1966994432156142,-0.34260576201176546,-0.38521456736415055 -1,1.0,0.0,1.0,1.35500938885622,1.0424664404409307,-0.5528986989244922,-0.6928426214811282 -2,1.0,0.0,0.0,0.22274126940102307,-0.29421958360632183,-0.6930939901996434,-1.1850475080682923 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-300,Gentoo -325,Gentoo -63,Adelie -64,Adelie -288,Gentoo -338,Gentoo -258,Gentoo -297,Gentoo -265,Gentoo -53,Adelie -174,Chinstrap -119,Adelie -247,Gentoo -200,Chinstrap -150,Adelie -270,Gentoo -191,Chinstrap -123,Adelie -58,Adelie -199,Chinstrap -66,Adelie -186,Chinstrap -37,Adelie -17,Adelie -15,Adelie -92,Adelie -65,Adelie -25,Adelie -285,Gentoo -263,Gentoo -319,Gentoo +331,Gentoo +250,Gentoo +222,Gentoo +307,Gentoo +2,Adelie +321,Gentoo 274,Gentoo -106,Adelie +261,Gentoo +210,Chinstrap +213,Chinstrap +323,Gentoo +224,Gentoo +62,Adelie +151,Adelie +341,Gentoo +85,Adelie +64,Adelie +298,Gentoo +227,Gentoo +201,Chinstrap +43,Adelie +219,Chinstrap +29,Adelie +5,Adelie +80,Adelie +54,Adelie +205,Chinstrap +67,Adelie diff --git a/data/penguins_y_train.csv b/data/penguins_y_train.csv index f550b61..951cb82 100644 --- a/data/penguins_y_train.csv +++ b/data/penguins_y_train.csv @@ -1,250 +1,250 @@ ,species -168,Chinstrap -62,Adelie -284,Gentoo -135,Adelie -51,Adelie -233,Gentoo 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+334,Gentoo +87,Adelie 223,Gentoo -321,Gentoo -193,Chinstrap -305,Gentoo +173,Chinstrap +202,Chinstrap +75,Adelie +161,Chinstrap +101,Adelie +91,Adelie +136,Adelie +284,Gentoo +25,Adelie +169,Chinstrap +118,Adelie +237,Gentoo +301,Gentoo +119,Adelie +226,Gentoo +134,Adelie +14,Adelie +217,Chinstrap +196,Chinstrap +144,Adelie +163,Chinstrap +96,Adelie +132,Adelie +159,Chinstrap +77,Adelie +241,Gentoo +160,Chinstrap +332,Gentoo +133,Adelie 104,Adelie -44,Adelie -120,Adelie -152,Chinstrap -323,Gentoo -89,Adelie -335,Gentoo -129,Adelie -170,Chinstrap +259,Gentoo +218,Chinstrap +114,Adelie +143,Adelie +239,Gentoo +6,Adelie +198,Chinstrap +232,Gentoo +327,Gentoo +81,Adelie +242,Gentoo +90,Adelie +294,Gentoo +94,Adelie +15,Adelie +235,Gentoo +279,Gentoo +135,Adelie +72,Adelie +314,Gentoo +59,Adelie diff --git a/lessons/01_regression.ipynb b/lessons/01_regression.ipynb index b5631b0..f3d38b9 100644 --- a/lessons/01_regression.ipynb +++ b/lessons/01_regression.ipynb @@ -26,9 +26,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy in c:\\users\\bruno\\anaconda3\\lib\\site-packages (1.26.4)\n", + "Requirement already satisfied: pandas in c:\\users\\bruno\\anaconda3\\lib\\site-packages (2.2.3)\n", + "Requirement already satisfied: scikit-learn in c:\\users\\bruno\\anaconda3\\lib\\site-packages (1.5.1)\n", + "Requirement already satisfied: matplotlib in c:\\users\\bruno\\anaconda3\\lib\\site-packages (3.9.2)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from pandas) (2023.3)\n", + "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from scikit-learn) (1.12.0)\n", + "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from scikit-learn) (3.5.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from matplotlib) (4.51.0)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from matplotlib) (23.2)\n", + "Requirement already satisfied: pillow>=8 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from matplotlib) (10.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from matplotlib) (3.1.2)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\bruno\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n" + ] + } + ], "source": [ "# Run this cell if you don't have these packages installed\n", "!pip install numpy pandas scikit-learn matplotlib" @@ -36,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -55,9 +80,127 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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car namempgcylindersdisplacementhorsepowerweightaccelerationmodel yearorigin
0chevrolet chevelle malibu18.08307.0130350412.0701
1buick skylark 32015.08350.0165369311.5701
2plymouth satellite18.08318.0150343611.0701
3amc rebel sst16.08304.0150343312.0701
4ford torino17.08302.0140344910.5701
\n", + "
" + ], + "text/plain": [ + " car name mpg cylinders displacement horsepower \\\n", + "0 chevrolet chevelle malibu 18.0 8 307.0 130 \n", + "1 buick skylark 320 15.0 8 350.0 165 \n", + "2 plymouth satellite 18.0 8 318.0 150 \n", + "3 amc rebel sst 16.0 8 304.0 150 \n", + "4 ford torino 17.0 8 302.0 140 \n", + "\n", + " weight acceleration model year origin \n", + "0 3504 12.0 70 1 \n", + "1 3693 11.5 70 1 \n", + "2 3436 11.0 70 1 \n", + "3 3433 12.0 70 1 \n", + "4 3449 10.5 70 1 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = pd.read_csv('../data/auto-mpg.csv')\n", "# Check out the first few rows\n", @@ -96,9 +239,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(392, 9)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data.shape" ] @@ -114,9 +268,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = data['mpg'].hist(grid=False, bins=np.linspace(10, 50, 10))\n", "ax.set_xlabel('Miles per Gallon')\n", @@ -133,11 +298,162 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel yearorigin
mpg1.000000-0.777618-0.805127-0.778427-0.8322440.4233290.5805410.565209
cylinders-0.7776181.0000000.9508230.8429830.897527-0.504683-0.345647-0.568932
displacement-0.8051270.9508231.0000000.8972570.932994-0.543800-0.369855-0.614535
horsepower-0.7784270.8429830.8972571.0000000.864538-0.689196-0.416361-0.455171
weight-0.8322440.8975270.9329940.8645381.000000-0.416839-0.309120-0.585005
acceleration0.423329-0.504683-0.543800-0.689196-0.4168391.0000000.2903160.212746
model year0.580541-0.345647-0.369855-0.416361-0.3091200.2903161.0000000.181528
origin0.565209-0.568932-0.614535-0.455171-0.5850050.2127460.1815281.000000
\n", + "
" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight \\\n", + "mpg 1.000000 -0.777618 -0.805127 -0.778427 -0.832244 \n", + "cylinders -0.777618 1.000000 0.950823 0.842983 0.897527 \n", + "displacement -0.805127 0.950823 1.000000 0.897257 0.932994 \n", + "horsepower -0.778427 0.842983 0.897257 1.000000 0.864538 \n", + "weight -0.832244 0.897527 0.932994 0.864538 1.000000 \n", + "acceleration 0.423329 -0.504683 -0.543800 -0.689196 -0.416839 \n", + "model year 0.580541 -0.345647 -0.369855 -0.416361 -0.309120 \n", + "origin 0.565209 -0.568932 -0.614535 -0.455171 -0.585005 \n", + "\n", + " acceleration model year origin \n", + "mpg 0.423329 0.580541 0.565209 \n", + "cylinders -0.504683 -0.345647 -0.568932 \n", + "displacement -0.543800 -0.369855 -0.614535 \n", + "horsepower -0.689196 -0.416361 -0.455171 \n", + "weight -0.416839 -0.309120 -0.585005 \n", + "acceleration 1.000000 0.290316 0.212746 \n", + "model year 0.290316 1.000000 0.181528 \n", + "origin 0.212746 0.181528 1.000000 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "data.corr()" + "data.corr(numeric_only = True)" ] }, { @@ -181,9 +497,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(392, 7)\n", + "(392,)\n" + ] + } + ], "source": [ "# Remove the response variable and car name\n", "X = data.drop(columns=['car name', 'mpg'])\n", @@ -203,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -230,9 +555,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X train shape: (313, 7); y train shape: (313,)\n", + "X test shape: (79, 7); y test shape: (79,)\n" + ] + } + ], "source": [ "print(f'X train shape: {X_train.shape}; y train shape: {y_train.shape}')\n", "print(f'X test shape: {X_test.shape}; y test shape: {y_test.shape}')" @@ -328,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -338,7 +672,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -349,9 +683,427 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
LinearRegression()
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" + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Train the model using the fit function\n", "model.fit(X_train, y_train)" @@ -379,9 +1131,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training R^2: 0.8317921346660861\n" + ] + } + ], "source": [ "print(f'Training R^2: {model.score(X_train, y_train)}')" ] @@ -395,9 +1155,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test R^2: 0.7356060087557543\n" + ] + } + ], "source": [ "print(f'Test R^2: {model.score(X_test, y_test)}')" ] @@ -415,17 +1183,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# Import the mean squared error function\n", - "from sklearn.metrics import mean_squared_error" + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.metrics import root_mean_squared_error" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -436,20 +1205,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train RMSE: 10.66662748881793\n" + ] + } + ], "source": [ - "print(f'Train RMSE: {mean_squared_error(y_train, y_train_pred, squared=False)}')" + "print(f'Train RMSE: {mean_squared_error(y_train, y_train_pred)}')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test RMSE: 12.621614173039344\n" + ] + } + ], "source": [ - "print(f'Test RMSE: {mean_squared_error(y_test, y_test_pred, squared=False)}')" + "print(f'Test RMSE: {mean_squared_error(y_test, y_test_pred)}')" ] }, { @@ -461,12 +1246,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train MSE: 3.2659803258467326\n", + "Test MSE: 3.552691117032178\n" + ] + } + ], "source": [ - "print(f'Train MSE: {mean_squared_error(y_train, y_train_pred)}')\n", - "print(f'Test MSE: {mean_squared_error(y_test, y_test_pred)}')" + "print(f'Train MSE: {root_mean_squared_error(y_train, y_train_pred)}')\n", + "print(f'Test MSE: {root_mean_squared_error(y_test, y_test_pred)}')" ] }, { @@ -490,7 +1284,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -511,18 +1305,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.22757461, 0.01438493, -0.00937352, -0.00677882, 0.093271 ,\n", + " 0.80212989, 1.62181703])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.coef_" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration',\n", + " 'model year', 'origin'],\n", + " dtype='object')" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X.columns" ] @@ -588,7 +1407,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -604,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -623,15 +1442,27 @@ " knn_train_pred = knn_reg.predict(X_train)\n", " knn_test_pred = knn_reg.predict(X_test)\n", " # Print summary\n", - " print(f'K={K}: Train RMSE = {mean_squared_error(y_train, knn_train_pred, squared=False):0.4f}; '\n", - " f'Test RMSE: {mean_squared_error(y_test, knn_test_pred, squared=False):0.4f}')\n" + " print(f'K={K}: Train RMSE = {root_mean_squared_error(y_train, knn_train_pred):0.4f}; '\n", + " f'Test RMSE: {root_mean_squared_error(y_test, knn_test_pred):0.4f}')\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "K=2: Train RMSE = 2.6424; Test RMSE: 3.5303\n", + "K=3: Train RMSE = 3.1774; Test RMSE: 3.4507\n", + "K=4: Train RMSE = 3.3820; Test RMSE: 3.7358\n", + "K=5: Train RMSE = 3.5568; Test RMSE: 3.7338\n", + "K=6: Train RMSE = 3.6529; Test RMSE: 4.0213\n" + ] + } + ], "source": [ "# Example of hyperparameter tuning for the `k` neighbors value\n", "n_list = [2, 3, 4, 5, 6]\n", @@ -650,9 +1481,9 @@ "anaconda-cloud": {}, "hide_input": false, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -664,7 +1495,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.12.7" }, "toc": { "base_numbering": 1, diff --git a/lessons/02_regularization.ipynb b/lessons/02_regularization.ipynb index 93e2f44..6e554bf 100644 --- a/lessons/02_regularization.ipynb +++ b/lessons/02_regularization.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "32a8b441-f363-45ac-8abf-236df98f8612", "metadata": {}, "outputs": [], @@ -65,12 +65,13 @@ "import matplotlib.pyplot as plt\n", "\n", "from sklearn.metrics import mean_squared_error\n", + "from sklearn.metrics import root_mean_squared_error\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "dccf9b24-e59a-41e9-8a82-2c2054363427", "metadata": {}, "outputs": [], @@ -85,12 +86,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "6cecbfb1-0121-42f7-81ab-180c2acd805c", "metadata": {}, "outputs": [], "source": [ - "# YOUR CODE HERE\n" + "# YOUR CODE HERE" ] }, { @@ -141,10 +142,428 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "862640b7-4dee-487b-8308-d8c21aa61bae", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Ridge(alpha=10, random_state=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Ridge(alpha=10, random_state=1)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.linear_model import Ridge\n", "# Create models\n", @@ -158,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "fa21a66c-d694-410e-a198-ac73df50eb0f", "metadata": {}, "outputs": [], @@ -170,16 +589,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "a98614ab-de89-401f-b166-0eb216a2fe0f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training R^2: 0.8316874005583549\n", + "Test R^2: 0.7376542416455194\n", + "Train RMSE: 3.266996943903231\n", + "Test RMSE: 3.538903196495924\n" + ] + } + ], "source": [ "# Evaluate model\n", "print(f'Training R^2: {ridge.score(X_train, y_train)}')\n", "print(f'Test R^2: {ridge.score(X_test, y_test)}')\n", - "print(f'Train RMSE: {mean_squared_error(y_train, y_train_pred_ridge, squared=False)}')\n", - "print(f'Test RMSE: {mean_squared_error(y_test, y_test_pred_ridge, squared=False)}')" + "print(f'Train RMSE: {root_mean_squared_error(y_train, y_train_pred_ridge)}')\n", + "print(f'Test RMSE: {root_mean_squared_error(y_test, y_test_pred_ridge)}')" ] }, { @@ -197,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "d77e1c87-b0a7-44ef-ba8f-7246f97b57d5", "metadata": {}, "outputs": [], @@ -267,10 +697,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "1f632ebb-b5ae-45aa-a7de-2139454ca79e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8315700524898808\n", + "0.7385306965932485\n" + ] + } + ], "source": [ "from sklearn.linear_model import RidgeCV\n", "# Create ridge model, with CV\n", @@ -296,10 +735,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "f3237a9d-7083-4681-a1de-b39dc6457a53", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "15.199110829529348" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ridge_cv.alpha_" ] @@ -314,10 +764,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "85f569bf-38be-479d-8dea-5b8ef6e56ea6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.17639491, 0.01214292, -0.00699842, -0.0068124 , 0.094717 ,\n", + " 0.79833197, 1.42696367])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ridge_cv.coef_" ] @@ -357,7 +819,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "80ac5a53-31e1-4238-8314-ebedb5200079", "metadata": {}, "outputs": [], @@ -369,9 +831,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -383,7 +845,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/lessons/03_preprocessing.ipynb b/lessons/03_preprocessing.ipynb index 6465fb4..ff6dd81 100644 --- a/lessons/03_preprocessing.ipynb +++ b/lessons/03_preprocessing.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "f0142813-ac28-4ead-9996-39b2ada322ca", "metadata": {}, "outputs": [], @@ -57,12 +57,193 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "a612a6fb-fd37-4603-a430-2c018c5d7f29", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
0AdelieTorgersen39.118.7181.03750.0MALE
1AdelieTorgersen39.517.4186.03800.0FEMALE
2AdelieTorgersen40.318.0195.03250.0FEMALE
3AdelieTorgersenNaNNaNNaNNaNNaN
4AdelieTorgersen36.719.3193.03450.0FEMALE
........................
339GentooBiscoeNaNNaNNaNNaNNaN
340GentooBiscoe46.814.3215.04850.0FEMALE
341GentooBiscoe50.415.7222.05750.0MALE
342GentooBiscoe45.214.8212.05200.0FEMALE
343GentooBiscoe49.916.1213.05400.0MALE
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344 rows × 7 columns

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" + ], + "text/plain": [ + " species island culmen_length_mm culmen_depth_mm flipper_length_mm \\\n", + "0 Adelie Torgersen 39.1 18.7 181.0 \n", + "1 Adelie Torgersen 39.5 17.4 186.0 \n", + "2 Adelie Torgersen 40.3 18.0 195.0 \n", + "3 Adelie Torgersen NaN NaN NaN \n", + "4 Adelie Torgersen 36.7 19.3 193.0 \n", + ".. ... ... ... ... ... \n", + "339 Gentoo Biscoe NaN NaN NaN \n", + "340 Gentoo Biscoe 46.8 14.3 215.0 \n", + "341 Gentoo Biscoe 50.4 15.7 222.0 \n", + "342 Gentoo Biscoe 45.2 14.8 212.0 \n", + "343 Gentoo Biscoe 49.9 16.1 213.0 \n", + "\n", + " body_mass_g sex \n", + "0 3750.0 MALE \n", + "1 3800.0 FEMALE \n", + "2 3250.0 FEMALE \n", + "3 NaN NaN \n", + "4 3450.0 FEMALE \n", + ".. ... ... \n", + "339 NaN NaN \n", + "340 4850.0 FEMALE \n", + "341 5750.0 MALE \n", + "342 5200.0 FEMALE \n", + "343 5400.0 MALE \n", + "\n", + "[344 rows x 7 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = pd.read_csv('../data/penguins.csv')\n", "data" @@ -75,7 +256,7 @@ "source": [ "Below is the information for each of the columns:\n", "1. **species**: Species of penguin [Adelie, Chinstrap, Gentoo]\n", - "2. **island**: Island where the penguin was found [Torgersen, Biscoe]\n", + "2. **island**: Island where the penguin was found [Torgersen, Biscoe, Dream]\n", "3. **culmen_length_mm**: Length of upper part of penguin's bill (millimeters)\n", "4. **culmen_depth_mm**: Height of upper part of bill (millimeters)\n", "5. **flipper_length_mm**: Length of penguin flipper (millimeters)\n", @@ -103,12 +284,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "0fbb04bc-4a44-493f-85d6-739adb1c7d8d", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "species 0\n", + "island 0\n", + "culmen_length_mm 2\n", + "culmen_depth_mm 2\n", + "flipper_length_mm 2\n", + "body_mass_g 2\n", + "sex 10\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data.isnull().sum()" ] @@ -123,10 +322,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "2d613dce", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array(['MALE', 'FEMALE', nan, '.'], dtype=object)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data['sex'].unique()" ] @@ -141,10 +351,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "d980a391", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array(['MALE', 'FEMALE', nan], dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data.replace('.', np.nan, inplace=True)\n", "\n", @@ -167,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "af30fe06-eb35-48af-88a2-b4cbd74e1335", "metadata": {}, "outputs": [], @@ -190,12 +411,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "bc7157f2", - "metadata": { - "scrolled": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4201.75438596 200.91520468]\n", + " [4201.75438596 200.91520468]]\n" + ] + } + ], "source": [ "print(imputed[data[data['body_mass_g'].isna()].index])" ] @@ -218,10 +446,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "db11f7e0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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speciesislandculmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_gsex
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [species, island, culmen_length_mm, culmen_depth_mm, flipper_length_mm, body_mass_g, sex]\n", + "Index: []" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = data.dropna(subset='sex')\n", "\n", @@ -249,10 +523,79 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "3113d6a3-474c-4b57-9804-8040c38117a8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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islandsex
0TorgersenMALE
1TorgersenFEMALE
2TorgersenFEMALE
4TorgersenFEMALE
5TorgersenMALE
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" + ], + "text/plain": [ + " island sex\n", + "0 Torgersen MALE\n", + "1 Torgersen FEMALE\n", + "2 Torgersen FEMALE\n", + "4 Torgersen FEMALE\n", + "5 Torgersen MALE" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Define the variable names that are categorical for use later\n", "cat_var_names = ['island', 'sex']\n", @@ -284,20 +627,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "a9384a9e-453f-4b62-8bbf-7866b8ac441c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[array(['Biscoe', 'Dream', 'Torgersen'], dtype=object),\n", + " array(['FEMALE', 'MALE'], dtype=object)]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.preprocessing import OneHotEncoder\n", - "dummy_e = OneHotEncoder(categories='auto', drop='first', sparse=False)\n", + "dummy_e = OneHotEncoder(categories='auto', drop='first', sparse_output=False)\n", "dummy_e.fit(data_cat);\n", "dummy_e.categories_" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "e4091b24-0e57-47e3-a58a-d88826ab5c8b", "metadata": {}, "outputs": [], @@ -321,12 +676,93 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "06511352-4ba4-4bb5-8da4-82430ac080a9", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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culmen_length_mmculmen_depth_mmflipper_length_mmbody_mass_g
039.118.7181.03750.0
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240.318.0195.03250.0
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539.320.6190.03650.0
\n", + "
" + ], + "text/plain": [ + " culmen_length_mm culmen_depth_mm flipper_length_mm body_mass_g\n", + "0 39.1 18.7 181.0 3750.0\n", + "1 39.5 17.4 186.0 3800.0\n", + "2 40.3 18.0 195.0 3250.0\n", + "4 36.7 19.3 193.0 3450.0\n", + "5 39.3 20.6 190.0 3650.0" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data_num = data.drop(columns=cat_var_names + ['species'])\n", "data_num.head()" @@ -348,12 +784,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "19f872ea-59e4-46a6-b366-578f6d0716a7", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.84077154e-16, 6.40128591e-16, 2.13376197e-16, -1.70700958e-16])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "norm_e = StandardScaler()\n", @@ -372,10 +819,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "1ac3fe89", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean: [ 3.84077154e-16 6.40128591e-16 2.13376197e-16 -1.70700958e-16]\n", + "std: [1. 1. 1. 1.]\n" + ] + } + ], "source": [ "print('mean:',norm_e.fit_transform(data_num,).mean(axis=0))\n", "print('std:',norm_e.fit_transform(data_num,).std(axis=0))" @@ -424,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "4b097530", "metadata": {}, "outputs": [], @@ -436,10 +892,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "cea1cd98", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(249, 6)\n" + ] + } + ], "source": [ "# Perform the train-test split\n", "y = data['species']\n", @@ -466,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "af05022a-a041-4d01-b189-5ceb5e1e0468", "metadata": {}, "outputs": [], @@ -494,10 +958,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "c45d20a3-73b9-490c-9f81-23e37fc09a2d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "((249, 3), (84, 3))" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "warnings.filterwarnings('ignore')\n", "\n", @@ -520,10 +995,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "127c7fc4-fd8e-4deb-832a-8e02d82909d6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "((249, 4), (84, 4))" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Numerical feature standardization\n", "\n", @@ -551,10 +1037,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "5a97ace9-bd20-49c0-bae9-bd629a8b7a29", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "((249, 7), (84, 7))" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X_train = np.hstack((X_train_dummy, X_train_norm))\n", "X_test = np.hstack((X_test_dummy, X_test_norm))\n", @@ -590,7 +1087,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "d36e3bd7", "metadata": {}, "outputs": [], @@ -610,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "1f18fab4", "metadata": {}, "outputs": [], @@ -662,7 +1159,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "b0895317", "metadata": {}, "outputs": [], @@ -673,9 +1170,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -687,7 +1184,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/lessons/04_classification.ipynb b/lessons/04_classification.ipynb index 5b7b0b7..78f1233 100644 --- a/lessons/04_classification.ipynb +++ b/lessons/04_classification.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -96,11 +96,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "species\n", + "Adelie 0.550505\n", + "Gentoo 0.449495\n", + "Name: count, dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "y_train.value_counts('species')/sum(y_train.value_counts('species'))" ] @@ -130,11 +144,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sb.histplot(data=X_train.loc[y_train['species'].isin(['Adelie','Gentoo'])],\n", " x = 'body_mass_g',\n", @@ -153,9 +188,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sb.histplot(data=X_test.loc[y_test['species'].isin(['Gentoo','Adelie'])],\n", " x = 'body_mass_g',\n", @@ -218,9 +274,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training score: 0.909 Testing score: 0.91\n" + ] + } + ], "source": [ "#1) Initialize Model\n", "lr = LogisticRegression(max_iter=170)\n", @@ -232,7 +296,7 @@ "train_score = lr.score(X_train['body_mass_g'].values.reshape(-1, 1), y_train['species'])\n", "test_score = lr.score(X_test['body_mass_g'].values.reshape(-1, 1), y_test['species'])\n", "\n", - "print(\"Training score:\", train_score.round(3), \"Testing score:\", test_score.round(3))" + "print(\"Training score:\", round(train_score,3), \"Testing score:\", round(test_score,3))" ] }, { @@ -258,11 +322,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sb.scatterplot(data=X_train.loc[y_train['species'].isin(['Adelie','Gentoo'])],\n", " x = 'culmen_depth_mm',\n", @@ -279,9 +364,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training score = 1.0, testing score = 1.0\n" + ] + } + ], "source": [ "lr = LogisticRegression(max_iter=170)\n", "lr.fit(X_train[['body_mass_g','culmen_depth_mm']], y_train['species'])\n", @@ -289,7 +382,7 @@ "train_score = lr.score(X_train[['body_mass_g','culmen_depth_mm']], y_train['species'])\n", "test_score = lr.score(X_test[['body_mass_g','culmen_depth_mm']], y_test['species'])\n", "\n", - "print(\"Training score = {}, testing score = {}\".format(train_score.round(3), test_score.round(3)))" + "print(\"Training score = {}, testing score = {}\".format(round(train_score,3), round(test_score,3)))" ] }, { @@ -301,9 +394,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "culmen_depth_mm -2.766066\n", + "body_mass_g 2.473116\n", + "dtype: float64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\n", "coef = pd.Series(index=['body_mass_g','culmen_depth_mm'], data=lr.coef_[0])\n", @@ -384,9 +490,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Bruno\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but LogisticRegression was fitted without feature names\n", + " warnings.warn(\n" + ] + } + ], "source": [ "lr.fit(X_train['body_mass_g'].values.reshape(-1, 1), y_train['species'])\n", "preds = lr.predict(X_test[['body_mass_g']])" @@ -394,9 +509,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[34, 3],\n", + " [ 3, 27]], dtype=int64)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Pass y_test and preds into confusion_matrix\n", "confusion_matrix(y_test['species'], preds)" @@ -486,9 +613,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.9444444444444444" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Initialize model\n", "dt = DecisionTreeClassifier()\n", @@ -502,9 +640,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.8955223880597015" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Test score\n", "dt.score(X_test[['body_mass_g']], y_test['species'])" @@ -528,9 +677,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Our training score is 0.924 and our testing score is 0.896\n" + ] + } + ], "source": [ "# Initialize\n", "dt = DecisionTreeClassifier(max_depth=2)\n", @@ -541,7 +698,7 @@ "train_score = dt.score(X_train[['body_mass_g']], y_train['species'])\n", "test_score = dt.score(X_test[['body_mass_g']], y_test['species'])\n", "\n", - "print(\"Our training score is {} and our testing score is {}\".format(train_score.round(3), test_score.round(3)))" + "print(\"Our training score is {} and our testing score is {}\".format(round(train_score,3), round(test_score,3)))" ] }, { @@ -564,9 +721,427 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeClassifier(max_depth=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeClassifier(max_depth=2)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dt = DecisionTreeClassifier(max_depth=2)\n", "dt.fit(X_train[['body_mass_g']], y_train['species'])" @@ -581,9 +1156,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(28, 20))\n", "plot_tree(dt, feature_names=['body_mass_g'], class_names=[\"Adelie\", \"Chinstrap\",\"Gentoo\"], \n", @@ -622,9 +1208,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"None of [Index(['feature1', 'feature2'], dtype='object')] are in the [columns]\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[58], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m## YOUR CODE HERE\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msvm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SVC\n\u001b[1;32m----> 3\u001b[0m X_train_subset \u001b[38;5;241m=\u001b[39m X_train[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeature1\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeature2\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m 4\u001b[0m X_test_subset \u001b[38;5;241m=\u001b[39m X_test[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeature1\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeature2\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m 5\u001b[0m y_train_subset \u001b[38;5;241m=\u001b[39m y_train[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspecies\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m 4107\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 4108\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39m_get_indexer_strict(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m 4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m 6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6198\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6200\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[0;32m 6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m 6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m 6204\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m 6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[0;32m 6248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[1;32m-> 6249\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6251\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m 6252\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['feature1', 'feature2'], dtype='object')] are in the [columns]\"" + ] + } + ], "source": [ "## YOUR CODE HERE\n", "from sklearn.svm import SVC\n", @@ -652,9 +1253,9 @@ "anaconda-cloud": {}, "hide_input": false, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "python3" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -666,7 +1267,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.12.7" }, "toc": { "base_numbering": 1, diff --git a/solutions/02_regularization_solutions.ipynb b/solutions/02_regularization_solutions.ipynb index ad0cfda..afa4f94 100644 --- a/solutions/02_regularization_solutions.ipynb +++ b/solutions/02_regularization_solutions.ipynb @@ -185,9 +185,9 @@ ], "metadata": { "kernelspec": { - "display_name": "nlp", + "display_name": "Python [conda env:base] *", "language": "python", - "name": "nlp" + "name": "conda-base-py" }, "language_info": { "codemirror_mode": { @@ -199,7 +199,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.12.7" } }, "nbformat": 4,