From 00c55c3d961fef089e11e0e64de37354ab455eeb Mon Sep 17 00:00:00 2001 From: Brian Caffo Date: Tue, 24 Oct 2023 10:42:41 -0400 Subject: [PATCH] Added some stuff --- .../ds4ph/24_regression_examples.ipynb | 178 +- .../ds4ph/24_regression_examples.slides.html | 15059 ++++++++++++++++ .../ds4ph/25_regression_interpretation.ipynb | 0 slides/ds4ph/readme.md | 1 + slides/ds4ph/slide_convert.ipynb | 20 +- 5 files changed, 15178 insertions(+), 80 deletions(-) rename book/regression_examples.ipynb => slides/ds4ph/24_regression_examples.ipynb (56%) create mode 100644 slides/ds4ph/24_regression_examples.slides.html rename book/regression_interpretation.ipynb => slides/ds4ph/25_regression_interpretation.ipynb (100%) diff --git a/book/regression_examples.ipynb b/slides/ds4ph/24_regression_examples.ipynb similarity index 56% rename from book/regression_examples.ipynb rename to slides/ds4ph/24_regression_examples.ipynb index c3c6d62..b1ee1d6 100644 --- a/book/regression_examples.ipynb +++ b/slides/ds4ph/24_regression_examples.ipynb @@ -3,27 +3,26 @@ { "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "view-in-github" + "slideshow": { + "slide_type": "slide" + }, + "tags": [] }, - "source": [ - "\"Open [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/smart-stats/ds4bio_book/HEAD)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ "# Linear models: a classic example" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", - "id": "oIimJROu2Hc_" + "id": "oIimJROu2Hc_", + "slideshow": { + "slide_type": "slide" + }, + "tags": [] }, "outputs": [], "source": [ @@ -35,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -43,7 +42,11 @@ }, "colab_type": "code", "id": "vsVJc6lS2Qov", - "outputId": "fc0b2fe3-eccd-4f2a-819f-084bc8e2602d" + "outputId": "fc0b2fe3-eccd-4f2a-819f-084bc8e2602d", + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] }, "outputs": [ { @@ -132,20 +135,23 @@ "" ], "text/plain": [ - " Region Fertility Agriculture ... Education Catholic Infant.Mortality\n", - "0 Courtelary 80.2 17.0 ... 12 9.96 22.2\n", - "1 Delemont 83.1 45.1 ... 9 84.84 22.2\n", - "2 Franches-Mnt 92.5 39.7 ... 5 93.40 20.2\n", - "3 Moutier 85.8 36.5 ... 7 33.77 20.3\n", - "4 Neuveville 76.9 43.5 ... 15 5.16 20.6\n", + " Region Fertility Agriculture Examination Education Catholic \\\n", + "0 Courtelary 80.2 17.0 15 12 9.96 \n", + "1 Delemont 83.1 45.1 6 9 84.84 \n", + "2 Franches-Mnt 92.5 39.7 5 5 93.40 \n", + "3 Moutier 85.8 36.5 12 7 33.77 \n", + "4 Neuveville 76.9 43.5 17 15 5.16 \n", "\n", - "[5 rows x 7 columns]" + " Infant.Mortality \n", + "0 22.2 \n", + "1 22.2 \n", + "2 20.2 \n", + "3 20.3 \n", + "4 20.6 " ] }, - "execution_count": 45, - "metadata": { - "tags": [] - }, + "execution_count": 3, + "metadata": {}, "output_type": "execute_result" } ], @@ -156,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -164,7 +170,11 @@ }, "colab_type": "code", "id": "T34j2o8u2cH_", - "outputId": "61309a3a-8ff6-489d-9dfd-c1ea40be99d8" + "outputId": "61309a3a-8ff6-489d-9dfd-c1ea40be99d8", + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] }, "outputs": [ { @@ -174,10 +184,8 @@ " array([-0.17211397, -0.25800824, -0.87094006, 0.10411533, 1.07704814])]" ] }, - "execution_count": 49, - "metadata": { - "tags": [] - }, + "execution_count": 4, + "metadata": {}, "output_type": "execute_result" } ], @@ -190,84 +198,99 @@ ] }, { - "cell_type": "code", - "execution_count": 50, + "cell_type": "markdown", "metadata": { - "colab": {}, - "colab_type": "code", - "id": "Dwova9_d5BKe" + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] }, - "outputs": [], "source": [ - "x2 = x\n", - "x2['Test'] = x2.Agriculture + x2.Examination\n", - "fit2 = LinearRegression().fit(x2, y)\n", - "yhat2 = fit2.predict(x2)" + "## Example of adjustment effect" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 10, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 283 + "slideshow": { + "slide_type": "subslide" }, - "colab_type": "code", - "id": "RAIz1jJs6YFg", - "outputId": "de9beb6e-d3e6-4033-eba9-27689bac6ff4" + "tags": [] }, "outputs": [ { "data": { "text/plain": [ - "[]" + "[60.304375228005725, array([0.19420175])]" ] }, - "execution_count": 51, - "metadata": { - "tags": [] - }, + "execution_count": 10, + "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" } ], "source": [ - "plt.plot(yhat, yhat2)" + "fit_marginal = LinearRegression().fit(x['Agriculture'].to_numpy().reshape(-1,1), y)\n", + "[fit_marginal.intercept_, fit_marginal.coef_]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Adding a useless regressor does what?" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Dwova9_d5BKe", + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "x2 = x\n", + "x2['Test'] = x2.Agriculture + x2.Examination\n", + "fit2 = LinearRegression().fit(x2, y)\n", + "yhat2 = fit2.predict(x2)" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 51, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "colab_type": "code", - "id": "626Z0Ql66qWx", - "outputId": "12617691-af4e-47d3-d080-10223101ec81" + "id": "RAIz1jJs6YFg", + "outputId": "de9beb6e-d3e6-4033-eba9-27689bac6ff4", + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] }, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 53, + "execution_count": 51, "metadata": { "tags": [] }, @@ -275,7 +298,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -288,10 +311,7 @@ } ], "source": [ - "x3 = x2.drop(['Agriculture'], axis = 1)\n", - "fit3 = LinearRegression().fit(x3, y)\n", - "yhat3 = fit3.predict(x3)\n", - "plt.plot(yhat, yhat3)\n" + "plt.plot(yhat, yhat2);" ] } ], @@ -303,9 +323,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python [conda env:.conda-ds4bio]", "language": "python", - "name": "python3" + "name": "conda-env-.conda-ds4bio-py" }, "language_info": { "codemirror_mode": { @@ -317,7 +337,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/slides/ds4ph/24_regression_examples.slides.html b/slides/ds4ph/24_regression_examples.slides.html new file mode 100644 index 0000000..23a77b7 --- /dev/null +++ b/slides/ds4ph/24_regression_examples.slides.html @@ -0,0 +1,15059 @@ + + + + + + + + + +24_regression_examples slides + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
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+ + + + + + + + + + + diff --git a/book/regression_interpretation.ipynb b/slides/ds4ph/25_regression_interpretation.ipynb similarity index 100% rename from book/regression_interpretation.ipynb rename to slides/ds4ph/25_regression_interpretation.ipynb diff --git a/slides/ds4ph/readme.md b/slides/ds4ph/readme.md index 59f9574..e04319f 100644 --- a/slides/ds4ph/readme.md +++ b/slides/ds4ph/readme.md @@ -20,3 +20,4 @@ + [Lecture 21, Maximum likelihood](https://smart-stats.github.io/ds4bio_book/slides/ds4ph/21_ml.slides.html) + [Lecture 22, Linear separable](https://smart-stats.github.io/ds4bio_book/slides/ds4ph/22_linear_separable.slides.html) + [Lecture 23, Linear separable interpretation](https://smart-stats.github.io/ds4bio_book/slides/ds4ph/23_linear_separable_smf.slides.html) ++ [Lecture 24, Regression example](https://smart-stats.github.io/ds4bio_book/slides/ds4ph/24_regression_examples.slides.html) \ No newline at end of file diff --git a/slides/ds4ph/slide_convert.ipynb b/slides/ds4ph/slide_convert.ipynb index 4ffbc58..fd2c26d 100644 --- a/slides/ds4ph/slide_convert.ipynb +++ b/slides/ds4ph/slide_convert.ipynb @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -75,6 +75,24 @@ "source": [ "!jupyter nbconvert 23_linear_separable_smf.ipynb --to slides --SlidesExporter.reveal_theme=solarized" ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[NbConvertApp] Converting notebook 23_linear_separable_smf.ipynb to slides\n", + "[NbConvertApp] Writing 590645 bytes to 23_linear_separable_smf.slides.html\n" + ] + } + ], + "source": [ + "!jupyter nbconvert 24_linear_separable_smf.ipynb --to slides --SlidesExporter.reveal_theme=solarized" + ] } ], "metadata": {