diff --git a/03_overfitting_and_cross_validation.ipynb b/03_overfitting_and_cross_validation.ipynb
index 14bb4dde5fb3582c46bee4826bb64e0963e097b9..26e78cb63f281545fe95fb18b07bee67447069dc 100644
--- a/03_overfitting_and_cross_validation.ipynb
+++ b/03_overfitting_and_cross_validation.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -103,15 +103,15 @@
" div#maintoolbar {display: none !important;}\n",
" /*\n",
"\n",
- " div#site {\n",
- " border-top: 20px solid #1F407A;\n",
- " border-right: 20px solid #1F407A;\n",
+ " div#site { \n",
+ " border-top: 20px solid #1F407A; \n",
+ " border-right: 20px solid #1F407A; \n",
" margin-bottom: 0;\n",
" padding-bottom: 0;\n",
" }\n",
- " div#toc-wrapper {\n",
- " border-left: 20px solid #1F407A;\n",
- " border-top: 20px solid #1F407A;\n",
+ " div#toc-wrapper { \n",
+ " border-left: 20px solid #1F407A; \n",
+ " border-top: 20px solid #1F407A; \n",
"\n",
" }\n",
"\n",
@@ -143,7 +143,7 @@
"<IPython.core.display.HTML object>"
]
},
- "execution_count": 25,
+ "execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@@ -836,9 +836,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The `cross_val_score` as used in the previous code example works as follows:\n",
+ "The `cross_val_score` as used in the previous code example works internally as follows:\n",
"\n",
- "0. split training data in four chunks\n",
+ "- split training data in four chunks\n",
"- learn `classifier` on chunk `1, 2, 3`, apply classifier to chunk `4` and compute score `s1`\n",
"- learn `classifier` on chunk `1, 2, 4`, apply classifier to chunk `3` and compute score `s2`\n",
"- learn `classifier` on chunk `1, 3, 4`, apply classifier to chunk `2` and compute score `s3`\n",
@@ -1216,30 +1216,96 @@
"source": [
"### Demonstration\n",
"\n",
- "We introduce the `train_test_split` function from `sklearn.model_selection` in the following example.\n",
- "\n",
- "It splits features and labels in a given proportion. Usually this is randomized, so that you get different results for every function invocation. To get the same result every time we use `random_state=..` (with arbitrary number) below:"
+ "We demonstrate what we explained before using the MNIST dataset."
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 54,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "digits data set shape: (1797, 8, 8)\n",
+ "feature matrix shape: (1797, 64)\n"
+ ]
+ }
+ ],
"source": [
- "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.datasets import load_digits\n",
+ "from sklearn.metrics import classification_report\n",
+ "from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split\n",
+ "from sklearn.svm import SVC\n",
"\n",
- "beer = pd.read_csv(\"data/beers.csv\")\n",
- "beer_eval = pd.read_csv(\"data/beers_eval.csv\")\n",
- "all_beer = pd.concat((beer, beer_eval))\n",
+ "digits = load_digits()\n",
"\n",
- "features = all_beer.iloc[:, :-1]\n",
- "labels = all_beer.iloc[:, -1]"
+ "print(\"digits data set shape:\", digits.images.shape)\n",
+ "\n",
+ "# flatten images of shape N_SAMPLES x 8 x 8\n",
+ "# to N_SAMPLES x 64:\n",
+ "labels = digits.target\n",
+ "n_samples = len(labels)\n",
+ "\n",
+ "features = digits.images.reshape((n_samples, -1))\n",
+ "print(\"feature matrix shape:\", features.shape)"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 864x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "plt.imshow(digits.images[0], cmap=\"gray\", )\n",
+ "plt.axis('off')\n",
+ "plt.title(f\"image for figure {labels[0]}\")\n",
+ "fig = plt.figure(figsize=(12, 4))\n",
+ "ax = plt.imshow(features[0][None, :], cmap=\"gray\")\n",
+ "plt.title(\"image flattened\")\n",
+ "plt.axis('off');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We introduce the `train_test_split` function from `sklearn.model_selection` in the following example.\n",
+ "\n",
+ "It splits features and labels in a given proportion. Usually this is randomized, so that you get different results for every function invocation. To get the same result every time we use `random_state=..` (with arbitrary number) below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
@@ -1247,16 +1313,44 @@
"output_type": "stream",
"text": [
"# Whole dataset \n",
- "number of all samples: 300\n",
- "proportion of yummy samples: 0.49666666666666665\n",
+ "number of all samples: 1797\n",
+ "proportion of images for class 0: 0.099\n",
+ "proportion of images for class 1: 0.101\n",
+ "proportion of images for class 2: 0.098\n",
+ "proportion of images for class 3: 0.102\n",
+ "proportion of images for class 4: 0.101\n",
+ "proportion of images for class 5: 0.101\n",
+ "proportion of images for class 6: 0.101\n",
+ "proportion of images for class 7: 0.1\n",
+ "proportion of images for class 8: 0.097\n",
+ "proportion of images for class 9: 0.1\n",
"\n",
"# Cross-validation dataset \n",
- "number of all samples: 240\n",
- "proportion of yummy samples: 0.49583333333333335\n",
+ "number of all samples: 1437\n",
+ "proportion of images for class 0: 0.099\n",
+ "proportion of images for class 1: 0.102\n",
+ "proportion of images for class 2: 0.099\n",
+ "proportion of images for class 3: 0.102\n",
+ "proportion of images for class 4: 0.101\n",
+ "proportion of images for class 5: 0.101\n",
+ "proportion of images for class 6: 0.101\n",
+ "proportion of images for class 7: 0.1\n",
+ "proportion of images for class 8: 0.097\n",
+ "proportion of images for class 9: 0.1\n",
"\n",
"# Validation dataset \n",
- "number of all samples: 60\n",
- "proportion of yummy samples: 0.5\n"
+ "number of all samples: 360\n",
+ "proportion of images for class 0: 0.1\n",
+ "proportion of images for class 1: 0.1\n",
+ "proportion of images for class 2: 0.097\n",
+ "proportion of images for class 3: 0.103\n",
+ "proportion of images for class 4: 0.1\n",
+ "proportion of images for class 5: 0.103\n",
+ "proportion of images for class 6: 0.1\n",
+ "proportion of images for class 7: 0.1\n",
+ "proportion of images for class 8: 0.097\n",
+ "proportion of images for class 9: 0.1\n",
+ "\n"
]
}
],
@@ -1274,39 +1368,85 @@
" labels_validation,\n",
") = train_test_split(features, labels, test_size=0.2, stratify=labels, random_state=42)\n",
"\n",
+ "def report(labels):\n",
+ " print(\"number of all samples:\", len(labels))\n",
+ " for number in range(10):\n",
+ " print(f\"proportion of images for class {number}:\", round(sum(labels == number) / len(labels), 3))\n",
+ " print()\n",
+ "\n",
"print(\"# Whole dataset \")\n",
- "print(\"number of all samples:\", len(labels))\n",
- "print(\"proportion of yummy samples:\", sum(labels == 1) / len(labels))\n",
- "print()\n",
+ "report(labels)\n",
"print(\"# Cross-validation dataset \")\n",
- "print(\"number of all samples:\", len(labels_crosseval))\n",
- "print(\n",
- " \"proportion of yummy samples:\", sum(labels_crosseval == 1) / len(labels_crosseval)\n",
- ")\n",
- "print()\n",
+ "report(labels_crosseval)\n",
"print(\"# Validation dataset \")\n",
- "print(\"number of all samples:\", len(labels_validation))\n",
- "print(\n",
- " \"proportion of yummy samples:\", sum(labels_validation == 1) / len(labels_validation)\n",
- ")"
+ "report(labels_validation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "As you can see the splits maintained the distribution of all classes `0` to `9`.\n",
+ "\n",
+ "\n",
+ "\n",
"Moreover, we introduce use of explicit speficiation of a cross-validation method: `StratifiedKFold` from `sklearn.model_selection`. \n",
"\n",
- "This allows us to spilt data during cross validation in the same way as we did with `train_test_split`, i.e. \n",
+ "`StratifiedKFold` allows us to splitt data during cross validation in the same way as we did with `train_test_split`, i.e. \n",
"\n",
- "a) with data shufflling before split, and \n",
+ "1. with data shufflling before split, and \n",
+ "2. **perserving class-proportions of samples**. \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "score = 0.103 +/- 0.011, C = 1.0e+01, gamma = 1.0e-01\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
"\n",
- "b) perserving class-proportions of samples, "
+ "cross_validator = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
+ "\n",
+ "\n",
+ "classifier = SVC(C=1, gamma=0.1)\n",
+ "test_scores = cross_val_score(\n",
+ " classifier,\n",
+ " features_crosseval,\n",
+ " labels_crosseval,\n",
+ " scoring=\"accuracy\",\n",
+ " cv=cross_validator,\n",
+ ") # cv arg is now different\n",
+ "print(\n",
+ " \"score = {:.3f} +/- {:.3f}, C = {:.1e}, gamma = {:.1e}\".format(\n",
+ " test_scores.mean(), test_scores.std(), C, gamma\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now try to use this approach to tune the **hyper-parameters** `C` and `gamma` of the `SVC` classifier.\n",
+ "\n",
+ "Remember:\n",
+ "1. A classifier learns parameters\n",
+ "2. Hyper-parameters control how a classifier learns."
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 58,
"metadata": {},
"outputs": [
{
@@ -1314,21 +1454,21 @@
"output_type": "stream",
"text": [
"OPTIMIZE HYPERPARAMETERS\n",
- "score = 0.725 +/- 0.086, C = 0.1, gamma = 0.1\n",
- "score = 0.796 +/- 0.096, C = 0.1, gamma = 1.0\n",
- "score = 0.867 +/- 0.064, C = 0.1, gamma = 10.0\n",
- "score = 0.512 +/- 0.037, C = 0.1, gamma = 100.0\n",
- "score = 0.842 +/- 0.083, C = 1.0, gamma = 0.1\n",
- "score = 0.908 +/- 0.055, C = 1.0, gamma = 1.0\n",
- "score = 0.892 +/- 0.046, C = 1.0, gamma = 10.0\n",
- "score = 0.754 +/- 0.057, C = 1.0, gamma = 100.0\n",
- "score = 0.938 +/- 0.038, C = 10.0, gamma = 0.1\n",
- "score = 0.950 +/- 0.036, C = 10.0, gamma = 1.0\n",
- "score = 0.925 +/- 0.041, C = 10.0, gamma = 10.0\n",
- "score = 0.754 +/- 0.057, C = 10.0, gamma = 100.0\n",
+ "score = 0.900 +/- 0.029, C = 1.0e-01, gamma = 1.0e-04\n",
+ "score = 0.962 +/- 0.012, C = 1.0e-01, gamma = 1.0e-03\n",
+ "score = 0.125 +/- 0.056, C = 1.0e-01, gamma = 1.0e-02\n",
+ "score = 0.099 +/- 0.003, C = 1.0e-01, gamma = 1.0e-01\n",
+ "score = 0.971 +/- 0.010, C = 1.0e+00, gamma = 1.0e-04\n",
+ "score = 0.991 +/- 0.007, C = 1.0e+00, gamma = 1.0e-03\n",
+ "score = 0.836 +/- 0.028, C = 1.0e+00, gamma = 1.0e-02\n",
+ "score = 0.103 +/- 0.011, C = 1.0e+00, gamma = 1.0e-01\n",
+ "score = 0.986 +/- 0.011, C = 1.0e+01, gamma = 1.0e-04\n",
+ "score = 0.990 +/- 0.006, C = 1.0e+01, gamma = 1.0e-03\n",
+ "score = 0.843 +/- 0.026, C = 1.0e+01, gamma = 1.0e-02\n",
+ "score = 0.104 +/- 0.014, C = 1.0e+01, gamma = 1.0e-01\n",
"\n",
"BEST RESULT CROSS VALIDATION\n",
- "score = 0.950 +/- 0.036, C = 10.0, gamma = 1.0\n"
+ "score = 0.991 +/- 0.007, C = 1.0e+00, gamma = 0.0\n"
]
}
],
@@ -1346,8 +1486,8 @@
"\n",
"print(\"OPTIMIZE HYPERPARAMETERS\")\n",
"# selected classifier hyperparameters to optimize\n",
- "SVC_C_values = (0.1, 1, 10)\n",
- "SVC_gamma_values = (0.1, 1, 10, 100)\n",
+ "SVC_C_values = (1e-1, 1, 10)\n",
+ "SVC_gamma_values = (0.0001, 0.001, 0.01, 0.1)\n",
"\n",
"for C in SVC_C_values:\n",
" for gamma in SVC_gamma_values:\n",
@@ -1360,7 +1500,7 @@
" cv=cross_validator,\n",
" ) # cv arg is now different\n",
" print(\n",
- " \"score = {:.3f} +/- {:.3f}, C = {:5.1f}, gamma = {:5.1f}\".format(\n",
+ " \"score = {:.3f} +/- {:.3f}, C = {:.1e}, gamma = {:.1e}\".format(\n",
" test_scores.mean(), test_scores.std(), C, gamma\n",
" )\n",
" )\n",
@@ -1375,15 +1515,22 @@
"print()\n",
"print(\"BEST RESULT CROSS VALIDATION\")\n",
"print(\n",
- " \"score = {:.3f} +/- {:.3f}, C = {:.1f}, gamma = {:.1f}\".format(\n",
+ " \"score = {:.3f} +/- {:.3f}, C = {:.1e}, gamma = {:.1f}\".format(\n",
" best_score_mean, best_score_std, best_C, best_gamma\n",
" )\n",
")"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally we evaluate our tuned classifier on the validation data set:"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 60,
"metadata": {},
"outputs": [
{
@@ -1391,7 +1538,7 @@
"output_type": "stream",
"text": [
"VALIDATION\n",
- "score = 0.967\n"
+ "score = 0.989\n"
]
}
],
@@ -1422,7 +1569,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 2,
"metadata": {
"tags": [
"solution"
@@ -1597,7 +1744,7 @@
"\n",
"2. Run cross-validation for the `SVC` classifier applied to the `\"data/spiral.csv\"` data set. Try different `C` and `gamma` values.\n",
"\n",
- "2. Optional exercise: implement same strategy for the iris data set introduced in script 1."
+ "3. Implement same strategy for the iris data set introduced in script 1."
]
},
{
@@ -1900,6 +2047,13 @@
"# Here, SVC is robust"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "markdown",
"metadata": {},