{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ " $\"Open$ " ] }, { "cell_type": "markdown", "source": [ "#Классификатор - линейная регрессия (Ridge Regression)" ], "metadata": { "id": "JPp-AtY-y7fs" } }, { "cell_type": "markdown", "source": [ "Ridge Regression\n", "\n", "Ridge Regression is a type of Linear Regression in which the regularization term is added as a parameter. This regularization term is also known as L2 regularization. This is done so as to avoid overfitting. Overfitting is when the model performs excellently on training data but poorly on the test or unseen data. Regularization introduces penalties on higher terms so as to reduce loss as well as overfitting in the model. The cost formula for the Ridge Regression is as follows\n", "\n", "Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. It has been used in many fields including econometrics, chemistry, and engineering. Also known as **Tikhonov regularization**.\n", "\n", "The Ridge Classifier is a machine learning algorithm designed for multi-class classification tasks.\n", "\n", "Задача классификации, а так же расмотрим один из способов борьбы с неравномерностью данных в классах" ], "metadata": { "id": "-1-9503WCgi5" } }, { "cell_type": "markdown", "source": [ "![Снимок экрана 2024-03-11 в 16.56.19.png](data:image/png;base64,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)" ], "metadata": { "id": "yb8_8PCAzGMG" } }, { "cell_type": "markdown", "source": [ "![Снимок экрана 2024-03-11 в 16.56.26.png](data:image/png;base64,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)" ], "metadata": { "id": "Z9nZEnaqzx4d" } }, { "cell_type": "markdown", "source": [ "![Снимок экрана 2024-03-11 в 16.56.34.png](data:image/png;base64,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)" ], "metadata": { "id": "MAj9eomez1vI" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import itertools" ], "metadata": { "id": "UG6XLxqo1R4K" }, "execution_count": 1, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.metrics import classification_report\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn import linear_model" ], "metadata": { "id": "S78POQIE_Oxi" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "model = linear_model.RidgeClassifier()" ], "metadata": { "id": "WSx8kpFnLzOC" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "#model.fit()" ], "metadata": { "id": "tnl_JrVdL_ZL" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "#model.predict()" ], "metadata": { "id": "b3NCJur0L9fF" }, "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Пример задачи" ], "metadata": { "id": "nVyDsC1dPqEm" } }, { "cell_type": "code", "source": [ "df = pd.read_csv('oil_spill.csv')\n", "df.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WClkIYDAPsix", "outputId": "64533665-ab75-43a6-d428-f1bf503b74d9" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 937 entries, 0 to 936\n", "Data columns (total 50 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 f_1 937 non-null int64 \n", " 1 f_2 937 non-null int64 \n", " 2 f_3 937 non-null float64\n", " 3 f_4 937 non-null float64\n", " 4 f_5 937 non-null int64 \n", " 5 f_6 937 non-null int64 \n", " 6 f_7 937 non-null float64\n", " 7 f_8 937 non-null float64\n", " 8 f_9 937 non-null float64\n", " 9 f_10 937 non-null float64\n", " 10 f_11 937 non-null float64\n", " 11 f_12 937 non-null float64\n", " 12 f_13 937 non-null float64\n", " 13 f_14 937 non-null float64\n", " 14 f_15 937 non-null float64\n", " 15 f_16 937 non-null float64\n", " 16 f_17 937 non-null float64\n", " 17 f_18 937 non-null float64\n", " 18 f_19 937 non-null float64\n", " 19 f_20 937 non-null float64\n", " 20 f_21 937 non-null float64\n", " 21 f_22 937 non-null float64\n", " 22 f_23 937 non-null int64 \n", " 23 f_24 937 non-null float64\n", " 24 f_25 937 non-null float64\n", " 25 f_26 937 non-null float64\n", " 26 f_27 937 non-null float64\n", " 27 f_28 937 non-null float64\n", " 28 f_29 937 non-null float64\n", " 29 f_30 937 non-null float64\n", " 30 f_31 937 non-null float64\n", " 31 f_32 937 non-null float64\n", " 32 f_33 937 non-null float64\n", " 33 f_34 937 non-null float64\n", " 34 f_35 937 non-null int64 \n", " 35 f_36 937 non-null int64 \n", " 36 f_37 937 non-null float64\n", " 37 f_38 937 non-null float64\n", " 38 f_39 937 non-null int64 \n", " 39 f_40 937 non-null int64 \n", " 40 f_41 937 non-null float64\n", " 41 f_42 937 non-null float64\n", " 42 f_43 937 non-null float64\n", " 43 f_44 937 non-null float64\n", " 44 f_45 937 non-null float64\n", " 45 f_46 937 non-null int64 \n", " 46 f_47 937 non-null float64\n", " 47 f_48 937 non-null float64\n", " 48 f_49 937 non-null float64\n", " 49 target 937 non-null int64 \n", "dtypes: float64(39), int64(11)\n", "memory usage: 366.1 KB\n" ] } ] }, { "cell_type": "code", "source": [ "df.head(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 160 }, "id": "pDSxnBsKssyu", "outputId": "7c7b82a2-551d-4689-9620-cb6625de6429" }, "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " f_1 f_2 f_3 f_4 f_5 f_6 f_7 f_8 f_9 f_10 \\\n", "0 1 2558 1506.09 456.63 90 6395000 40.88 7.89 29780.0 0.19 \n", "1 2 22325 79.11 841.03 180 55812500 51.11 1.21 61900.0 0.02 \n", "\n", " ... f_41 f_42 f_43 f_44 f_45 f_46 f_47 f_48 f_49 \\\n", "0 ... 2850.0 1000.0 763.16 135.46 3.73 0 33243.19 65.74 7.95 \n", "1 ... 5750.0 11500.0 9593.48 1648.80 0.60 0 51572.04 65.73 6.26 \n", "\n", " target \n", "0 1 \n", "1 0 \n", "\n", "[2 rows x 50 columns]" ], "text/html": [ "\n", "

\n", "

\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "

	f_1	f_2	f_3	f_4	f_5	f_6	f_7	f_8	f_9	f_10	...	f_41	f_42	f_43	f_44	f_45	f_46	f_47	f_48	f_49	target
0	1	2558	1506.09	456.63	90	6395000	40.88	7.89	29780.0	0.19	...	2850.0	1000.0	763.16	135.46	3.73	0	33243.19	65.74	7.95	1
1	2	22325	79.11	841.03	180	55812500	51.11	1.21	61900.0	0.02	...	5750.0	11500.0	9593.48	1648.80	0.60	0	51572.04	65.73	6.26	0

\n", "

2 rows × 50 columns

\n", "

\n", "\n", "

\n", " \n", "\n", " \n", "\n", " \n", "

\n", "\n", "\n", "

\n", " \n", "\n", "\n", "\n", " \n", "

\n", "

\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df" } }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "code", "source": [ "df.groupby('target')['f_1'].count()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 176 }, "id": "XR9A-TnZUQxY", "outputId": "16fb4186-6ea1-43cd-e944-3b8807515bc4" }, "execution_count": 16, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "target\n", "0 896\n", "1 41\n", "Name: f_1, dtype: int64" ], "text/html": [ "

\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "

	f_1
target
0	896
1	41

\n", "

dtype: int64" ] }, "metadata": {}, "execution_count": 16 } ] }, { "cell_type": "code", "source": [ "Y = df['target']" ], "metadata": { "id": "MNo73I1eR4Fo" }, "execution_count": 17, "outputs": [] }, { "cell_type": "code", "source": [ "X = df.drop('target', axis=1)" ], "metadata": { "id": "XM40VddpSJBf" }, "execution_count": 18, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,\n", " random_state=42,\n", " test_size=0.2,\n", " stratify=Y)" ], "metadata": { "id": "MT5inG70RE3D" }, "execution_count": 19, "outputs": [] }, { "cell_type": "code", "source": [ "model = linear_model.RidgeClassifier(random_state=42)\n", "model.fit(X_train, Y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 128 }, "id": "n_hVx6DpSzYB", "outputId": "4c0306fd-8474-4f12-9828-2286c80cdfdd" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:200: LinAlgWarning: Ill-conditioned matrix (rcond=7.35131e-17): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "RidgeClassifier(random_state=42)" ], "text/html": [ "

RidgeClassifier(random_state=42)

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.

" ] }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "code", "source": [ "model.predict(X_test)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DaHe299jTUBF", "outputId": "7a93d42c-a8f2-4f05-dbd3-f4a2030c032b" }, "execution_count": 21, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "code", "source": [ "def plot_confusion_matrix(cm, classes,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " plt.xticks(tick_marks, classes, rotation=45)\n", " plt.yticks(tick_marks, classes)\n", "\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", " print(\"Normalized confusion matrix\")\n", " else:\n", " print('Confusion matrix, without normalization')\n", "\n", " print(cm)\n", "\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, cm[i, j],\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')" ], "metadata": { "id": "h7RdcLF0vutc" }, "execution_count": 22, "outputs": [] }, { "cell_type": "code", "source": [ "font = {'size' : 15}\n", "\n", "plt.rc('font', **font)\n", "\n", "cnf_matrix = confusion_matrix(Y_test, model.predict(X_test))\n", "plt.figure(figsize=(10, 8))\n", "plot_confusion_matrix(cnf_matrix, classes=['Non-churned', 'Churned'],\n", " title='Confusion matrix')\n", "plt.savefig(\"conf_matrix.png\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 868 }, "id": "VMrfe5Plvty3", "outputId": "cc844c34-70c4-42ee-8497-0de07d538f9c" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Confusion matrix, without normalization\n", "[[177 3]\n", " [ 6 2]]\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "

" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(classification_report(Y_train, model.predict(X_train)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nJ47I7loY07v", "outputId": "7c310f72-ba78-46fb-da86-a377b83f8373" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.97 1.00 0.99 716\n", " 1 1.00 0.42 0.60 33\n", "\n", " accuracy 0.97 749\n", " macro avg 0.99 0.71 0.79 749\n", "weighted avg 0.98 0.97 0.97 749\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "print(classification_report(Y_test, model.predict(X_test)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rqaozRcqTcDk", "outputId": "320130da-02e1-498a-a252-f298910fdf3c" }, "execution_count": 25, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.97 0.98 0.98 180\n", " 1 0.40 0.25 0.31 8\n", "\n", " accuracy 0.95 188\n", " macro avg 0.68 0.62 0.64 188\n", "weighted avg 0.94 0.95 0.95 188\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "from imblearn.under_sampling import RandomUnderSampler\n", "\n", "# Создание экземпляра RandomUnderSampler\n", "rus = RandomUnderSampler()\n", "\n", "# Применение уменьшения выборки к данным\n", "X_resampled, y_resampled = rus.fit_resample(X, Y)" ], "metadata": { "id": "8F27GozaXanH" }, "execution_count": 26, "outputs": [] }, { "cell_type": "code", "source": [ "y_resampled.sum(), len(y_resampled)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UIXCIjNWyEgT", "outputId": "11c715f5-da94-43e4-bf67-e601edf6cc58" }, "execution_count": 27, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(41, 82)" ] }, "metadata": {}, "execution_count": 27 } ] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, Y_train, Y_test = train_test_split(X_resampled, y_resampled,\n", " random_state=42,\n", " test_size=0.2,\n", " stratify=y_resampled)" ], "metadata": { "id": "fQhGZGwuX2qD" }, "execution_count": 28, "outputs": [] }, { "cell_type": "code", "source": [ "model = linear_model.RidgeClassifier(random_state=42)\n", "model.fit(X_train, Y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 74 }, "id": "fSUVfqcaYBI_", "outputId": "1a3d21f4-a81e-49aa-eacc-e5f0a0ba0bcf" }, "execution_count": 29, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "RidgeClassifier(random_state=42)" ], "text/html": [ "

RidgeClassifier(random_state=42)

" ] }, "metadata": {}, "execution_count": 29 } ] }, { "cell_type": "code", "source": [ "font = {'size' : 15}\n", "\n", "plt.rc('font', **font)\n", "\n", "cnf_matrix = confusion_matrix(Y_test, model.predict(X_test))\n", "plt.figure(figsize=(10, 8))\n", "plot_confusion_matrix(cnf_matrix, classes=['Non-churned', 'Churned'],\n", " title='Confusion matrix')\n", "plt.savefig(\"conf_matrix.png\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 868 }, "id": "iC-xHrIVyX-g", "outputId": "4cb8e59f-5659-49e7-92de-1690295038b3" }, "execution_count": 30, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Confusion matrix, without normalization\n", "[[8 1]\n", " [3 5]]\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "

" ], "image/png": "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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(classification_report(Y_train, model.predict(X_train)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mBnsE1yiYMvZ", "outputId": "debd0186-62c2-4009-af04-0850cb7e7cbb" }, "execution_count": 31, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.97 1.00 0.98 32\n", " 1 1.00 0.97 0.98 33\n", "\n", " accuracy 0.98 65\n", " macro avg 0.98 0.98 0.98 65\n", "weighted avg 0.99 0.98 0.98 65\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "print(classification_report(Y_test, model.predict(X_test)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hGlzqT1tYEJb", "outputId": "5692636c-1e6c-4694-fec7-5e28c07c7963" }, "execution_count": 32, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.73 0.89 0.80 9\n", " 1 0.83 0.62 0.71 8\n", "\n", " accuracy 0.76 17\n", " macro avg 0.78 0.76 0.76 17\n", "weighted avg 0.78 0.76 0.76 17\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "param_grid = {\n", " 'one_vs_all__loss': ['hinge', 'log', 'modified_huber',\n", " 'squared_hinge', 'perceptron'],\n", "\n", " 'one_vs_all__penalty': ['l1', 'l2', 'elasticnet'],\n", "\n", " 'one_vs_all__alpha': [0.001, 0.01, 0.1, 0.5, 1, 10]\n", "}" ], "metadata": { "id": "3s0upcuV5xm4" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import GridSearchCV" ], "metadata": { "id": "sG_cDgkG6EJ4" }, "execution_count": 34, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.linear_model import SGDClassifier\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "pipe_one_vs_all = Pipeline([\n", " (\"std_scaler\", StandardScaler()),\n", " (\"one_vs_all\", SGDClassifier(random_state=42))])\n", "\n", "pipe_one_vs_all.fit(X_train, Y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 124 }, "id": "_80ivQZ_68fv", "outputId": "01b86b31-dc12-4be6-990e-2a646ba435f1" }, "execution_count": 35, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Pipeline(steps=[('std_scaler', StandardScaler()),\n", " ('one_vs_all', SGDClassifier(random_state=42))])" ], "text/html": [ "

Pipeline(steps=[('std_scaler', StandardScaler()),\n",
              "                ('one_vs_all', SGDClassifier(random_state=42))])

" ] }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "source": [ "loss = ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']\n", "\n", "for i in loss:\n", " pipe_one_vs_all = Pipeline([(\"std_scaler\", StandardScaler()),\n", " (\"one_vs_all\", SGDClassifier(loss=i, random_state=42))])\n", "\n", " pipe_one_vs_all.fit(X_train, Y_train)\n", " print('-' * 5, i, '-' * 5)\n", " print(pipe_one_vs_all.classes_)\n", " print(classification_report(y_true = Y_test, y_pred = pipe_one_vs_all.predict(X_test),labels=[0, 1], digits=3))\n", " print('-' * 15)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 605 }, "id": "17SAgWk57KBo", "outputId": "4614d454-59b6-4909-9dd4-609c7f134c12" }, "execution_count": 36, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "----- hinge -----\n", "[0 1]\n", " precision recall f1-score support\n", "\n", " 0 1.000 0.667 0.800 9\n", " 1 0.727 1.000 0.842 8\n", "\n", " accuracy 0.824 17\n", " macro avg 0.864 0.833 0.821 17\n", "weighted avg 0.872 0.824 0.820 17\n", "\n", "---------------\n" ] }, { "output_type": "error", "ename": "InvalidParameterError", "evalue": "The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mInvalidParameterError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m (\"one_vs_all\", SGDClassifier(loss=i, random_state=42))])\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mpipe_one_vs_all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\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 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe_one_vs_all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1150\u001b[0m )\n\u001b[1;32m 1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 1153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1154\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"passthrough\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0mfit_params_last_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_params_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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;32m--> 427\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params_last_step\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 428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1144\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpartial_fit_and_fitted\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1145\u001b[0;31m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_params\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[1;32m 1146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1147\u001b[0m with config_context(\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_params\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 636\u001b[0m \u001b[0maccepted\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \"\"\"\n\u001b[0;32m--> 638\u001b[0;31m validate_parameter_constraints(\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameter_constraints\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mvalidate_parameter_constraints\u001b[0;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[1;32m 94\u001b[0m )\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m raise InvalidParameterError(\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;34mf\"The {param_name!r} parameter of {caller_name} must be\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;34mf\" {constraints_str}. Got {param_val!r} instead.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mInvalidParameterError\u001b[0m: The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead." ] } ] }, { "cell_type": "code", "source": [ "search_one_all = GridSearchCV(pipe_one_vs_all, param_grid, scoring='accuracy')\n", "search_one_all.fit(X_train, Y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "LWec1uSM63hh", "outputId": "f04ea0ee-08ab-4ce6-ebba-b467ec264537" }, "execution_count": 37, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n", "90 fits failed out of a total of 450.\n", "The score on these train-test partitions for these parameters will be set to nan.\n", "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", "\n", "Below are more details about the failures:\n", "--------------------------------------------------------------------------------\n", "90 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1152, in wrapper\n", " return fit_method(estimator, *args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\", line 427, in fit\n", " self._final_estimator.fit(Xt, y, **fit_params_last_step)\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1145, in wrapper\n", " estimator._validate_params()\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 638, in _validate_params\n", " validate_parameter_constraints(\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 96, in validate_parameter_constraints\n", " raise InvalidParameterError(\n", "sklearn.utils._param_validation.InvalidParameterError: The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead.\n", "\n", " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.78461538 0.83076923 0.75384615 nan nan nan\n", " 0.76923077 0.75384615 0.73846154 0.8 0.78461538 0.83076923\n", " 0.78461538 0.72307692 0.75384615 0.75384615 0.81538462 0.76923077\n", " nan nan nan 0.83076923 0.72307692 0.75384615\n", " 0.76923077 0.76923077 0.73846154 0.73846154 0.78461538 0.75384615\n", " 0.78461538 0.75384615 0.73846154 nan nan nan\n", " 0.78461538 0.8 0.8 0.78461538 0.72307692 0.72307692\n", " 0.70769231 0.76923077 0.73846154 0.61538462 0.72307692 0.76923077\n", " nan nan nan 0.83076923 0.75384615 0.8\n", " 0.70769231 0.63076923 0.66153846 0.49230769 0.75384615 0.53846154\n", " 0.49230769 0.64615385 0.70769231 nan nan nan\n", " 0.72307692 0.78461538 0.76923077 0.73846154 0.61538462 0.6\n", " 0.52307692 0.70769231 0.53846154 0.49230769 0.44615385 0.49230769\n", " nan nan nan 0.49230769 0.49230769 0.49230769\n", " 0.49230769 0.49230769 0.49230769 0.49230769 0.47692308 0.49230769]\n", " warnings.warn(\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "GridSearchCV(estimator=Pipeline(steps=[('std_scaler', StandardScaler()),\n", " ('one_vs_all',\n", " SGDClassifier(loss='log',\n", " random_state=42))]),\n", " param_grid={'one_vs_all__alpha': [0.001, 0.01, 0.1, 0.5, 1, 10],\n", " 'one_vs_all__loss': ['hinge', 'log', 'modified_huber',\n", " 'squared_hinge', 'perceptron'],\n", " 'one_vs_all__penalty': ['l1', 'l2', 'elasticnet']},\n", " scoring='accuracy')" ], "text/html": [ "

GridSearchCV(estimator=Pipeline(steps=[('std_scaler', StandardScaler()),\n",
              "                                       ('one_vs_all',\n",
              "                                        SGDClassifier(loss='log',\n",
              "                                                      random_state=42))]),\n",
              "             param_grid={'one_vs_all__alpha': [0.001, 0.01, 0.1, 0.5, 1, 10],\n",
              "                         'one_vs_all__loss': ['hinge', 'log', 'modified_huber',\n",
              "                                              'squared_hinge', 'perceptron'],\n",
              "                         'one_vs_all__penalty': ['l1', 'l2', 'elasticnet']},\n",
              "             scoring='accuracy')

" ] }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "code", "source": [ "search_one_all.best_params_" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RLIJGMK266LB", "outputId": "ebaabaf3-7908-47ac-87e2-9d793af706a2" }, "execution_count": 38, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'one_vs_all__alpha': 0.001,\n", " 'one_vs_all__loss': 'hinge',\n", " 'one_vs_all__penalty': 'l2'}" ] }, "metadata": {}, "execution_count": 38 } ] }, { "cell_type": "code", "source": [ "print(classification_report(y_true = Y_train, y_pred = search_one_all.predict(X_train)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s6gKQa1o9fU0", "outputId": "625a5578-98f6-4bbc-ae8c-49ad5a17408e" }, "execution_count": 39, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.93 0.88 0.90 32\n", " 1 0.89 0.94 0.91 33\n", "\n", " accuracy 0.91 65\n", " macro avg 0.91 0.91 0.91 65\n", "weighted avg 0.91 0.91 0.91 65\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "print(classification_report(y_true = Y_test, y_pred = search_one_all.predict(X_test),labels=[0, 1], digits=3))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hF1Ad3cw9rmr", "outputId": "99107c37-cd25-42ab-ea28-d54e98b89b2d" }, "execution_count": 40, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.833 0.556 0.667 9\n", " 1 0.636 0.875 0.737 8\n", "\n", " accuracy 0.706 17\n", " macro avg 0.735 0.715 0.702 17\n", "weighted avg 0.741 0.706 0.700 17\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "# пример 2 Банк" ], "metadata": { "id": "9TC9o2zUzjsW" } }, { "cell_type": "code", "source": [ "df = pd.read_csv('bankloans.csv')\n", "df.info()" ], "metadata": { "id": "2wrJp_Y4YHTg", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "64782ef4-8391-4af8-d1e8-73ef65beea79" }, "execution_count": 41, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "RangeIndex: 1150 entries, 0 to 1149\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 1150 non-null int64 \n", " 1 ed 1150 non-null int64 \n", " 2 employ 1150 non-null int64 \n", " 3 address 1150 non-null int64 \n", " 4 income 1150 non-null int64 \n", " 5 debtinc 1150 non-null float64\n", " 6 creddebt 1150 non-null float64\n", " 7 othdebt 1150 non-null float64\n", " 8 default 700 non-null float64\n", "dtypes: float64(4), int64(5)\n", "memory usage: 81.0 KB\n" ] } ] }, { "cell_type": "code", "source": [ "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "id": "vOzZeg2s_-jx", "outputId": "8cda1ce4-ad58-4267-cceb-0b56b545f3cc" }, "execution_count": 42, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " age ed employ address income debtinc creddebt othdebt default\n", "0 41 3 17 12 176 9.3 11.359392 5.008608 1.0\n", "1 27 1 10 6 31 17.3 1.362202 4.000798 0.0\n", "2 40 1 15 14 55 5.5 0.856075 2.168925 0.0\n", "3 41 1 15 14 120 2.9 2.658720 0.821280 0.0\n", "4 24 2 2 0 28 17.3 1.787436 3.056564 1.0" ], "text/html": [ "\n", "

\n", "

	age	ed	employ	address	income	debtinc	creddebt	othdebt	default
0	41	3	17	12	176	9.3	11.359392	5.008608	1.0
1	27	1	10	6	31	17.3	1.362202	4.000798	0.0
2	40	1	15	14	55	5.5	0.856075	2.168925	0.0
3	41	1	15	14	120	2.9	2.658720	0.821280	0.0
4	24	2	2	0	28	17.3	1.787436	3.056564	1.0

\n", "

\n", "\n", "

\n", " \n", "\n", " \n", "\n", " \n", "

\n", "\n", "\n", "

\n", " \n", "\n", "\n", "\n", " \n", "

\n", "

\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 1150,\n \"fields\": [\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 8,\n \"min\": 20,\n \"max\": 56,\n \"num_unique_values\": 37,\n \"samples\": [\n 26,\n 29,\n 39\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ed\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 5,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"employ\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 0,\n \"max\": 33,\n \"num_unique_values\": 33,\n \"samples\": [\n 28,\n 23,\n 21\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"address\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 0,\n \"max\": 34,\n \"num_unique_values\": 32,\n \"samples\": [\n 31,\n 26,\n 17\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"income\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 40,\n \"min\": 13,\n \"max\": 446,\n \"num_unique_values\": 129,\n \"samples\": [\n 73,\n 75,\n 52\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"debtinc\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6.584288388970545,\n \"min\": 0.1,\n \"max\": 41.3,\n \"num_unique_values\": 245,\n \"samples\": [\n 16.7,\n 3.6,\n 14.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"creddebt\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.1359670038262886,\n \"min\": 0.011696,\n \"max\": 20.56131,\n \"num_unique_values\": 842,\n \"samples\": [\n 1.19493,\n 1.300537,\n 2.863757\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"othdebt\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.5292730055400447,\n \"min\": 0.045584,\n \"max\": 35.1975,\n \"num_unique_values\": 848,\n \"samples\": [\n 2.663976,\n 0.738396,\n 1.45236\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"default\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.43972707660624494,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 42 } ] }, { "cell_type": "code", "source": [ "df = df[~(df['default'].isnull())]\n", "df.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fy0CP3WGAFtn", "outputId": "c32572a9-2f57-41fe-ba93-c994fe7bbd17" }, "execution_count": 43, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Index: 700 entries, 0 to 699\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 700 non-null int64 \n", " 1 ed 700 non-null int64 \n", " 2 employ 700 non-null int64 \n", " 3 address 700 non-null int64 \n", " 4 income 700 non-null int64 \n", " 5 debtinc 700 non-null float64\n", " 6 creddebt 700 non-null float64\n", " 7 othdebt 700 non-null float64\n", " 8 default 700 non-null float64\n", "dtypes: float64(4), int64(5)\n", "memory usage: 54.7 KB\n" ] } ] }, { "cell_type": "code", "source": [ "df['default'] = df['default'].astype('int')\n", "df.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hPdZpf8cAjhr", "outputId": "1bb64961-66d1-4660-d9fb-dff3e7458672" }, "execution_count": 44, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Index: 700 entries, 0 to 699\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 age 700 non-null int64 \n", " 1 ed 700 non-null int64 \n", " 2 employ 700 non-null int64 \n", " 3 address 700 non-null int64 \n", " 4 income 700 non-null int64 \n", " 5 debtinc 700 non-null float64\n", " 6 creddebt 700 non-null float64\n", " 7 othdebt 700 non-null float64\n", " 8 default 700 non-null int64 \n", "dtypes: float64(3), int64(6)\n", "memory usage: 54.7 KB\n" ] } ] }, { "cell_type": "code", "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,\n", " random_state=42,\n", " test_size=0.2,\n", " stratify=Y)" ], "metadata": { "id": "O9nDBDHDA5vK" }, "execution_count": 45, "outputs": [] }, { "cell_type": "code", "source": [ "pipe_one_vs_all = Pipeline([\n", " (\"std_scaler\", StandardScaler()),\n", " (\"one_vs_all\", SGDClassifier(random_state=42))])\n", "\n", "pipe_one_vs_all.fit(X_train, Y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 124 }, "id": "YL6bzHihAuQ-", "outputId": "b777646e-0de5-4efa-c135-a675250bce6c" }, "execution_count": 46, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Pipeline(steps=[('std_scaler', StandardScaler()),\n", " ('one_vs_all', SGDClassifier(random_state=42))])" ], "text/html": [ "

Pipeline(steps=[('std_scaler', StandardScaler()),\n",
              "                ('one_vs_all', SGDClassifier(random_state=42))])

" ] }, "metadata": {}, "execution_count": 46 } ] }, { "cell_type": "code", "source": [ "loss = ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']\n", "\n", "for i in loss:\n", " pipe_one_vs_all = Pipeline([(\"std_scaler\", StandardScaler()),\n", " (\"one_vs_all\", SGDClassifier(loss=i, random_state=42))])\n", "\n", " pipe_one_vs_all.fit(X_train, Y_train)\n", " print('-' * 5, i, '-' * 5)\n", " print(pipe_one_vs_all.classes_)\n", " print(classification_report(y_true = Y_test, y_pred = pipe_one_vs_all.predict(X_test),labels=[0, 1], digits=3))\n", " print('-' * 15)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 605 }, "id": "o-yAJAW1BAi9", "outputId": "368b97de-b1c7-45f7-c31e-cdf90c5bcc41" }, "execution_count": 47, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "----- hinge -----\n", "[0 1]\n", " precision recall f1-score support\n", "\n", " 0 0.978 0.972 0.975 180\n", " 1 0.444 0.500 0.471 8\n", "\n", " accuracy 0.952 188\n", " macro avg 0.711 0.736 0.723 188\n", "weighted avg 0.955 0.952 0.953 188\n", "\n", "---------------\n" ] }, { "output_type": "error", "ename": "InvalidParameterError", "evalue": "The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mInvalidParameterError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m (\"one_vs_all\", SGDClassifier(loss=i, random_state=42))])\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mpipe_one_vs_all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\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 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe_one_vs_all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1150\u001b[0m )\n\u001b[1;32m 1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 1153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1154\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"passthrough\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0mfit_params_last_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_params_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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;32m--> 427\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params_last_step\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 428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1144\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpartial_fit_and_fitted\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1145\u001b[0;31m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_params\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[1;32m 1146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1147\u001b[0m with config_context(\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_params\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 636\u001b[0m \u001b[0maccepted\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \"\"\"\n\u001b[0;32m--> 638\u001b[0;31m validate_parameter_constraints(\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameter_constraints\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mvalidate_parameter_constraints\u001b[0;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[1;32m 94\u001b[0m )\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m raise InvalidParameterError(\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;34mf\"The {param_name!r} parameter of {caller_name} must be\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;34mf\" {constraints_str}. Got {param_val!r} instead.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mInvalidParameterError\u001b[0m: The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead." ] } ] }, { "cell_type": "code", "source": [ "# Создание экземпляра RandomUnderSampler\n", "rus = RandomUnderSampler()\n", "\n", "# Применение уменьшения выборки к данным\n", "X_resampled, y_resampled = rus.fit_resample(X, Y)" ], "metadata": { "id": "4YT6J3rHBJmT" }, "execution_count": 48, "outputs": [] }, { "cell_type": "code", "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(X_resampled, y_resampled,\n", " random_state=42,\n", " test_size=0.2,\n", " stratify=y_resampled)" ], "metadata": { "id": "EZXzvtvJBqUV" }, "execution_count": 49, "outputs": [] }, { "cell_type": "code", "source": [ "pipe_one_vs_all.fit(X_train, Y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 333 }, "id": "OhsAOAIyBzZG", "outputId": "db0047be-909d-491b-c99e-05eb942e80a4" }, "execution_count": 50, "outputs": [ { "output_type": "error", "ename": "InvalidParameterError", "evalue": "The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mInvalidParameterError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpipe_one_vs_all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1150\u001b[0m )\n\u001b[1;32m 1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 1153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1154\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"passthrough\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0mfit_params_last_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_params_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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;32m--> 427\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params_last_step\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 428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1144\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpartial_fit_and_fitted\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1145\u001b[0;31m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_params\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[1;32m 1146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1147\u001b[0m with config_context(\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_params\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 636\u001b[0m \u001b[0maccepted\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \"\"\"\n\u001b[0;32m--> 638\u001b[0;31m validate_parameter_constraints(\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameter_constraints\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mvalidate_parameter_constraints\u001b[0;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[1;32m 94\u001b[0m )\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m raise InvalidParameterError(\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;34mf\"The {param_name!r} parameter of {caller_name} must be\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;34mf\" {constraints_str}. Got {param_val!r} instead.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mInvalidParameterError\u001b[0m: The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead." ] } ] }, { "cell_type": "code", "source": [ "for i in loss:\n", " pipe_one_vs_all = Pipeline([(\"std_scaler\", StandardScaler()),\n", " (\"one_vs_all\", SGDClassifier(loss=i, random_state=42))])\n", "\n", " pipe_one_vs_all.fit(X_train, Y_train)\n", " print('-' * 5, i, '-' * 5)\n", " print(pipe_one_vs_all.classes_)\n", " print(classification_report(y_true = Y_test, y_pred = pipe_one_vs_all.predict(X_test),labels=[0, 1], digits=3))\n", " print('-' * 15)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 605 }, "id": "za9R0hmbB3yd", "outputId": "cb2c99d5-07ac-414d-aaa7-e7ff6bf0d46b" }, "execution_count": 51, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "----- hinge -----\n", "[0 1]\n", " precision recall f1-score support\n", "\n", " 0 0.778 0.778 0.778 9\n", " 1 0.750 0.750 0.750 8\n", "\n", " accuracy 0.765 17\n", " macro avg 0.764 0.764 0.764 17\n", "weighted avg 0.765 0.765 0.765 17\n", "\n", "---------------\n" ] }, { "output_type": "error", "ename": "InvalidParameterError", "evalue": "The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead.", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mInvalidParameterError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m (\"one_vs_all\", SGDClassifier(loss=i, random_state=42))])\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mpipe_one_vs_all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\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 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-'\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe_one_vs_all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1150\u001b[0m )\n\u001b[1;32m 1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 1153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1154\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"passthrough\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0mfit_params_last_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_params_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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;32m--> 427\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params_last_step\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 428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1144\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mglobal_skip_validation\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpartial_fit_and_fitted\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1145\u001b[0;31m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_params\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[1;32m 1146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1147\u001b[0m with config_context(\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_params\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 636\u001b[0m \u001b[0maccepted\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \"\"\"\n\u001b[0;32m--> 638\u001b[0;31m validate_parameter_constraints(\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameter_constraints\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mvalidate_parameter_constraints\u001b[0;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[1;32m 94\u001b[0m )\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m raise InvalidParameterError(\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;34mf\"The {param_name!r} parameter of {caller_name} must be\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;34mf\" {constraints_str}. Got {param_val!r} instead.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mInvalidParameterError\u001b[0m: The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead." ] } ] }, { "cell_type": "code", "source": [ "search_one_all = GridSearchCV(pipe_one_vs_all, param_grid, scoring='accuracy')\n", "search_one_all.fit(X_train, Y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "liedbMzyB7xq", "outputId": "0330bdc8-3272-480f-f6fe-b73432aa39cc" }, "execution_count": 52, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:713: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n", "90 fits failed out of a total of 450.\n", "The score on these train-test partitions for these parameters will be set to nan.\n", "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", "\n", "Below are more details about the failures:\n", "--------------------------------------------------------------------------------\n", "90 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1152, in wrapper\n", " return fit_method(estimator, *args, **kwargs)\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\", line 427, in fit\n", " self._final_estimator.fit(Xt, y, **fit_params_last_step)\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 1145, in wrapper\n", " estimator._validate_params()\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 638, in _validate_params\n", " validate_parameter_constraints(\n", " File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 96, in validate_parameter_constraints\n", " raise InvalidParameterError(\n", "sklearn.utils._param_validation.InvalidParameterError: The 'loss' parameter of SGDClassifier must be a str among {'hinge', 'epsilon_insensitive', 'modified_huber', 'squared_hinge', 'perceptron', 'squared_error', 'huber', 'squared_epsilon_insensitive', 'log_loss'}. Got 'log' instead.\n", "\n", " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.83076923 0.8 0.8 nan nan nan\n", " 0.78461538 0.76923077 0.83076923 0.81538462 0.8 0.78461538\n", " 0.81538462 0.76923077 0.78461538 0.81538462 0.8 0.78461538\n", " nan nan nan 0.8 0.78461538 0.8\n", " 0.84615385 0.84615385 0.83076923 0.81538462 0.78461538 0.8\n", " 0.8 0.81538462 0.76923077 nan nan nan\n", " 0.8 0.78461538 0.83076923 0.84615385 0.8 0.76923077\n", " 0.61538462 0.86153846 0.8 0.78461538 0.76923077 0.76923077\n", " nan nan nan 0.78461538 0.86153846 0.78461538\n", " 0.81538462 0.72307692 0.75384615 0.47692308 0.72307692 0.73846154\n", " 0.49230769 0.78461538 0.75384615 nan nan nan\n", " 0.70769231 0.84615385 0.78461538 0.83076923 0.64615385 0.70769231\n", " 0.52307692 0.75384615 0.72307692 0.49230769 0.52307692 0.49230769\n", " nan nan nan 0.49230769 0.58461538 0.49230769\n", " 0.49230769 0.52307692 0.49230769 0.49230769 0.52307692 0.49230769]\n", " warnings.warn(\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "GridSearchCV(estimator=Pipeline(steps=[('std_scaler', StandardScaler()),\n", " ('one_vs_all',\n", " SGDClassifier(loss='log',\n", " random_state=42))]),\n", " param_grid={'one_vs_all__alpha': [0.001, 0.01, 0.1, 0.5, 1, 10],\n", " 'one_vs_all__loss': ['hinge', 'log', 'modified_huber',\n", " 'squared_hinge', 'perceptron'],\n", " 'one_vs_all__penalty': ['l1', 'l2', 'elasticnet']},\n", " scoring='accuracy')" ], "text/html": [ "

GridSearchCV(estimator=Pipeline(steps=[('std_scaler', StandardScaler()),\n",
              "                                       ('one_vs_all',\n",
              "                                        SGDClassifier(loss='log',\n",
              "                                                      random_state=42))]),\n",
              "             param_grid={'one_vs_all__alpha': [0.001, 0.01, 0.1, 0.5, 1, 10],\n",
              "                         'one_vs_all__loss': ['hinge', 'log', 'modified_huber',\n",
              "                                              'squared_hinge', 'perceptron'],\n",
              "                         'one_vs_all__penalty': ['l1', 'l2', 'elasticnet']},\n",
              "             scoring='accuracy')

" ] }, "metadata": {}, "execution_count": 52 } ] }, { "cell_type": "code", "source": [ "search_one_all.best_params_" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-SWepch9Ccwc", "outputId": "da1c867b-2ca3-44d6-c5c7-baaec908e841" }, "execution_count": 53, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'one_vs_all__alpha': 0.1,\n", " 'one_vs_all__loss': 'perceptron',\n", " 'one_vs_all__penalty': 'l2'}" ] }, "metadata": {}, "execution_count": 53 } ] }, { "cell_type": "code", "source": [ "print(classification_report(y_true = Y_train, y_pred = search_one_all.predict(X_train)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sMifedIFCjDx", "outputId": "58150872-b11e-42d3-92ed-d694f4a9e9c4" }, "execution_count": 54, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 32\n", " 1 1.00 1.00 1.00 33\n", "\n", " accuracy 1.00 65\n", " macro avg 1.00 1.00 1.00 65\n", "weighted avg 1.00 1.00 1.00 65\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "print(classification_report(y_true = Y_test, y_pred = search_one_all.predict(X_test)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WQhDTRpuCqKa", "outputId": "db2e9623-9095-4b05-f8ed-87d9ae5a6ff4" }, "execution_count": 55, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.78 0.78 0.78 9\n", " 1 0.75 0.75 0.75 8\n", "\n", " accuracy 0.76 17\n", " macro avg 0.76 0.76 0.76 17\n", "weighted avg 0.76 0.76 0.76 17\n", "\n" ] } ] } ] }