{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", "
\n", " \"Open\n", " \n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SETUP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll import a few common modules, check for python versions, setup the libraries and define some key helper functions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# To ensure Python ≥ 3.5 is used\n", "import sys\n", "assert sys.version_info >= (3, 5)\n", "\n", "#Check if it is running in Kaggle or Colab\n", "IS_COLAB = \"google.colab\" in sys.modules\n", "IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n", "\n", "#Scikit-Learn ≥0.20 is required\n", "import sklearn\n", "assert sklearn.__version__ >= \"0.20\"\n", "\n", "#common imports\n", "import numpy as np\n", "import os\n", "\n", "\n", "#for uniformity in output\n", "np.random.seed(42)\n", "\n", "\n", "#for our plots\n", "%matplotlib inline\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "mpl.rc(\"axes\", labelsize=14)\n", "mpl.rc(\"xtick\", labelsize=12)\n", "mpl.rc(\"ytick\", labelsize=12)\n", "\n", "\n", "#Where we save our figures\n", "PROJECT_ROOT_DIR = \".\"\n", "CHAPTER_ID = \"classification\"\n", "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n", "os.makedirs(IMAGES_PATH, exist_ok=True)\n", "\n", "\n", "#To handle saving of the figures\n", "def save_fig(fig_id, tight_layout=True, fig_extension =\"jpg\", resolution=300):\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n", " print(\"Saving figure\", fig_id)\n", " if tight_layout:\n", " plt.tight_layout()\n", " plt.savefig(path, format=fig_extension, dpi=resolution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MNIST DATASET" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this and keep the same code as in the book, we use `as_frame=False`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'details', 'categories', 'url'])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We'll use scikit-learn built-in popular datasets\n", "#Our version is not up to 0.24 (0.21.3)\n", "from sklearn.datasets import fetch_openml\n", "mnist = fetch_openml(\"mnist_784\", version=1) #as_frame=False \n", "mnist.keys()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(70000, 784)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Taking a look at the shape of the data\n", "X, y = mnist[\"data\"], mnist[\"target\"]\n", "X.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So there are 28 x 28 pixels intensities for each instance." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(70000,)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure some_digit_plot\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Let's have a look at some digit and plot the image using mpl\n", "\n", "some_digit = X[0]\n", "some_digit_image = some_digit.reshape(28,28) #We create a 28 x 28 array\n", "plt.imshow(some_digit_image, cmap=mpl.cm.binary) #Plotted in greyscale\n", "plt.axis(\"off\") # we don't want any axes to show\n", "\n", "save_fig(\"some_digit_plot\") #our custom func\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'5'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's confirm that this is a \"5\"\n", "y[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The target values are in strings, for speed and efficiency, we would prefer to work with ints\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "y = y.astype(np.uint8)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#Lets create a function for plotting the digits (instances)\n", "\n", "def plot_digit(data):\n", " \n", " \"\"\" a function for plotting the digits (instances) \"\"\"\n", " \n", " image = data.reshape(28,28)\n", " plt.imshow(image, cmap = mpl.cm.binary, interpolation=\"nearest\")\n", " plt.axis(\"off\")\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "## Another function for plotting multiple digits\n", "\n", "def plot_digits(instances, images_per_row=10, **options):\n", " \"\"\" a function for plotting multiple digits (instances) \"\"\"\n", " size = 28\n", " images_per_row = min(len(instances), images_per_row) #images per row at most 10\n", " \n", " # This is equivalent to n_rows = ceil(len(instances) / images_per_row):\n", " n_rows = ((len(instances)-1) // images_per_row) + 1\n", " \n", " #Append empty images to fill the end of the grid if needed:\n", " n_empty = n_rows * images_per_row - len(instances) \n", " padded_instances = np.concatenate([instances, np.zeros((n_empty, size * size))], axis = 0)\n", " \n", " #Reshape the array so it's organized as a grid containing 28x28 images:\n", " image_grid = padded_instances.reshape((n_rows, images_per_row, size, size))\n", " \n", " \n", " # Combine axes 0 and 2 (vertical image grid axis, and vertical image axis),\n", " # and axes 1 and 3 (horizontal axes). We first need to move the axes that we\n", " # want to combine next to each other, using transpose(), and only then we\n", " # can reshape:\n", " \n", " big_image = image_grid.transpose(0, 2, 1, 3).reshape(n_rows * size,\n", " images_per_row * size)\n", "\n", " #Now that we have a big image, we just show it:\n", " plt.imshow(big_image, cmap = mpl.cm.binary, **options)\n", " plt.axis(\"off\") \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure more_digits_plot\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Let's use our func\n", "\n", "plt.figure(figsize=(9,9))\n", "example_images = X[:100] #We will look at the first 100 images\n", "plot_digits(example_images, images_per_row=10)\n", "save_fig(\"more_digits_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that this is actually how the dataset is arranged, becuase y[0] is 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MNIST dataset is already split into training and test set for use first 60,000 is the train set and the last 10,000 is the test set." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Training a Binary Classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This binary classifier will classify 5 and not-5" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "y_train_5 = (y_train == 5)\n", "y_test_5 = (y_test == 5)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n", " early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n", " l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n", " max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n", " power_t=0.5, random_state=42, shuffle=True, tol=0.001,\n", " validation_fraction=0.1, verbose=0, warm_start=False)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import SGDClassifier\n", "\n", "sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42)\n", "sgd_clf.fit(X_train, y_train_5)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ True])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sgd_clf.predict([some_digit])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.96466961, 0.957 , 0.95516667, 0.95733333, 0.96733061])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We would perform a 3 fold Cross Validation\n", "from sklearn.model_selection import cross_val_score\n", "cross_val_score(sgd_clf, X_train, y_train_5, cv= 5, scoring=\"accuracy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Some Performance Measures**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Cross Validation" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9559\n", "0.9559\n", "0.96565\n" ] } ], "source": [ "#Making our own cross validator\n", "\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.base import clone\n", "\n", "skfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n", "\n", "for train_index, test_index in skfolds.split(X_train, y_train_5):\n", " clone_clf = clone(sgd_clf)\n", " X_train_folds = X_train[train_index]\n", " y_train_folds = y_train_5[train_index]\n", " X_test_fold = X_train[test_index]\n", " y_test_fold = y_train_5[test_index]\n", " \n", " #We fit for every other fold not the test index and evaluate on the test index\n", " clone_clf.fit(X_train_folds, y_train_folds)\n", " y_pred = clone_clf.predict(X_test_fold)\n", " n_correct = sum(y_pred == y_test_fold)\n", " print(n_correct / len(y_pred))\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "#Let us train a dumb classifier that predicts everything as not 5\n", "\n", "from sklearn.base import BaseEstimator\n", "class Never5Classifier(BaseEstimator):\n", " def fit(self, X, y=None):\n", " pass\n", " def predict(self, X):\n", " return np.zeros((len(X), 1), dtype=bool)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.91125, 0.90855, 0.90915])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "never_5_clf = Never5Classifier()\n", "cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows that normal cross validation is pretty useless" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Warning**: \n", "* first, Scikit-Learn and other libraries evolve, and algorithms get tweaked a bit, which may change the exact result you get. If you use the latest Scikit-Learn version (and in general, you really should), you probably won't be using the exact same version I used when I wrote the book or this notebook, hence the difference. I try to keep this notebook reasonably up to date, but I can't change the numbers on the pages in your copy of the book.\n", "* second, many training algorithms are stochastic, meaning they rely on randomness. In principle, it's possible to get consistent outputs from a random number generator by setting the seed from which it generates the pseudo-random numbers (which is why you will see `random_state=42` or `np.random.seed(42)` pretty often). However, sometimes this does not suffice due to the other factors listed here.\n", "* third, if the training algorithm runs across multiple threads (as do some algorithms implemented in C) or across multiple processes (e.g., when using the `n_jobs` argument), then the precise order in which operations will run is not always guaranteed, and thus the exact result may vary slightly.\n", "* lastly, other things may prevent perfect reproducibility, such as Python dicts and sets whose order is not guaranteed to be stable across sessions, or the order of files in a directory which is also not guaranteed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Confusion Matrix" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "#We will use cross_val_predict to get cross validated predictions\n", "\n", "from sklearn.model_selection import cross_val_predict\n", "\n", "y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=5)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[53677, 902],\n", " [ 1480, 3941]], dtype=int64)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "confusion_matrix(y_train_5, y_train_pred) \n", "\n", "# true negatives is C_{0,0}, false negatives is C_{1,0}\n", "# false positives is C_{0,1} and true positives is C_{1,1} \n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[54579, 0],\n", " [ 0, 5421]], dtype=int64)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's see what a perfect confusion matrix looks like\n", "y_train_perfect_predictions = y_train_5 \n", "confusion_matrix(y_train_5, y_train_perfect_predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Precision and Recall\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8137518067313648" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#these can be calculated from a confusion matrix \n", "\n", "from sklearn.metrics import precision_score, recall_score\n", "\n", "precision_score(y_train_5, y_train_pred)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8137518067313648" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Manually calculating it from a confusion matrix\n", "cm = confusion_matrix(y_train_5, y_train_pred) \n", "cm[1, 1] / (cm[0, 1] + cm[1, 1]) # TP/(FP + TP)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7269876406567054" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recall_score(y_train_5, y_train_pred) " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7269876406567054" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cm[1, 1] / (cm[1, 0] + cm[1, 1]) # TP/(FN + TP)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7679267342166797" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#A more compact metric is f1 score which is the harmonic mean of the two\n", "from sklearn.metrics import f1_score\n", "\n", "f1_score(y_train_5, y_train_pred) " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7679267342166797" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Calculating this manually\n", "cm[1, 1]/ (cm[1, 1] + (cm[1, 0] + cm[0, 1]) / 2) #2/((1/recall) + (1 / precision))\n", "\n", "# or TP/(TP + (FP + FN)/2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Precision/ Recall Trade-off" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2164.22030239])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We can get the scores for the prediction using this\n", "y_scores = sgd_clf.decision_function([some_digit])\n", "y_scores" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ True])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Based on this we can now run a prediction after setting a particular threshold\n", "threshold = 0\n", "y_some_digit_pred = (y_scores > threshold)\n", "y_some_digit_pred" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "#So we do this for the cross validator predict and get the prediction scores \n", "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n", " method=\"decision_function\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "#To get the optimum threshold value we need to plot some curves\n", "from sklearn.metrics import precision_recall_curve\n", "\n", "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure precision_recall_vs_threshold_plot\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Let's define a function to plot the precision and recall vs threshold and plot it\n", "\n", "def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):\n", " plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n", " plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n", " plt.legend(loc=\"center right\", fontsize=16)\n", " plt.xlabel(\"Threshold\", fontsize=16)\n", " plt.grid(True)\n", " plt.axis([-50000, 50000, 0, 1])\n", " \n", "#We would get the max arg i.e the first true value \n", "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n", "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n", "\n", "#We'll plot it\n", "plt.figure(figsize= (8, 4))\n", "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n", "plt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], \"r:\") # Not shown\n", "plt.plot([-50000, threshold_90_precision], [0.9, 0.9], \"r:\") # Not shown\n", "plt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], \"r:\")\n", "plt.plot([threshold_90_precision], [0.9], \"ro\") # Not shown\n", "plt.plot([threshold_90_precision], [recall_90_precision], \"ro\") # Not shown\n", "save_fig(\"precision_recall_vs_threshold_plot\") # Not shown\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's check that our previous prediction had scores above 0 \n", "(y_train_pred == (y_scores > 0)).all()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure precision_vs_recall_plot\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#For clarity too we shall plot precision vs Recall\n", "def plot_precision_vs_recall(precisions, recalls):\n", " plt.plot(recalls, precisions, \"b-\", linewidth=2)\n", " plt.xlabel(\"Recall\", fontsize=16)\n", " plt.ylabel(\"Precision\", fontsize=16)\n", " plt.axis([0, 1, 0, 1])\n", " plt.grid(True)\n", " \n", " \n", " #And we plot it\n", "plt.figure(figsize=(8, 6))\n", "plot_precision_vs_recall(precisions, recalls)\n", "plt.plot([recall_90_precision, recall_90_precision], [0., 0.9], \"r:\")\n", "plt.plot([0.0, recall_90_precision], [0.9, 0.9], \"r:\")\n", "plt.plot([recall_90_precision], [0.9], \"ro\")\n", "save_fig(\"precision_vs_recall_plot\")\n", "plt.show()\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3370.0194991439557" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's view our threshold value for a 90% prediction\n", "#threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n", "\n", "threshold_90_precision" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "#To make predictions we would just check for values above the threshold\n", "y_train_pred_90 = (y_scores >= threshold_90_precision)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9000345901072293" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's confirm that the precision is about 90%\n", "precision_score(y_train_5, y_train_pred_90)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4799852425751706" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's check for the recall score\n", "recall_score(y_train_5, y_train_pred_90)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. The ROC Curve" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "#another metric\n", "from sklearn.metrics import roc_curve\n", "\n", "fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure roc_curve_plot\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Lets create a function to plot the roc curve\n", "\n", "def plot_roc_curve(fpr, tpr, label= None):\n", " plt.plot(fpr, tpr, linewidth=2, label=label)\n", " plt.plot([0, 1], [0, 1], \"k--\") #dashed diagonal\n", " plt.axis([0, 1, 0, 1])\n", " plt.xlabel(\"False Positive Rate (Fall-Out)\", fontsize=16)\n", " plt.ylabel('True Positive Rate (Recall)', fontsize=16) \n", " plt.grid(True) \n", " \n", " \n", "#Then we plot it \n", "\n", "plt.figure(figsize=(8, 6))\n", "plot_roc_curve(fpr, tpr)\n", "\n", "#Getting the same metric to show on this plot\n", "fpr_90 = fpr[np.argmax(tpr >= recall_90_precision)]\n", "plt.plot([fpr_90, fpr_90], [0.0, recall_90_precision], \"r:\")\n", "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"g:\")\n", "plt.plot([fpr_90], [recall_90_precision], \"ro\")\n", "save_fig(\"roc_curve_plot\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9604938554008616" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We can get the score of the area under the roc curve\n", "#The closer to 1, the higher the better\n", "from sklearn.metrics import roc_auc_score\n", "\n", "roc_auc_score(y_train_5, y_scores)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "#Let's train a Random forest classifier to compare to our sgd binary classifier\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", "\n", "#we would get the probabilities for each prediction\n", "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n", " method=\"predict_proba\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "#We would use thes probabilities as scores\n", "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n", "\n", "#then we get the tpr and fpr values for the Random forest classifier\n", "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,\n", " y_scores_forest)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure roc_curve_comparison_plot\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# lets get the equivalent recall at 90% precision\n", "recall_for_forest = tpr_forest[np.argmax(fpr_forest >= fpr_90)]\n", "\n", "#We'll plot it's roc directly on top of the roc the sgd classifier\n", "plt.figure(figsize=(8, 6))\n", "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n", "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n", "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")\n", "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")\n", "plt.plot([fpr_90], [recall_90_precision], \"ro\")\n", "plt.plot([fpr_90, fpr_90], [0., recall_for_forest], \"r:\")\n", "plt.plot([fpr_90], [recall_for_forest], \"ro\")\n", "plt.grid(True)\n", "plt.legend(loc=\"lower right\", fontsize=16)\n", "save_fig(\"roc_curve_comparison_plot\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9983436731328145" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "roc_auc_score(y_train_5, y_scores_forest)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This classifier provides a roc auc score" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9905083315756169" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)\n", "precision_score(y_train_5, y_train_pred_forest)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8662608374838591" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recall_score(y_train_5, y_train_pred_forest)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And better much precision and recall scores !!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. TRAINING A MULTICLASS CLASSIFIER" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5], dtype=uint8)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Sci-kit learn performs OvO automatically on SVCs\n", "\n", "from sklearn.svm import SVC\n", "\n", "svm_clf = SVC(gamma=\"auto\", random_state=42)\n", "#We would just train on the prepartioned Train set \n", "# we are using the real labels and not the 5 versus all labels\n", "svm_clf.fit(X_train[:1000], y_train[:1000]) \n", "svm_clf.predict([some_digit])" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2.81585438, 7.09167958, 3.82972099, 0.79365551, 5.8885703 ,\n", " 9.29718395, 1.79862509, 8.10392157, -0.228207 , 4.83753243]])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "some_digit_scores = svm_clf.decision_function([some_digit])\n", "some_digit_scores" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#to check that it is actually the highest score that is chosen\n", "\n", "np.argmax(some_digit_scores)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#svm stores the classes\n", "svm_clf.classes_[5]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5], dtype=uint8)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#I'd force the SVC to use an OvA technique\n", "from sklearn.multiclass import OneVsRestClassifier\n", "ovr_clf = OneVsRestClassifier(SVC(gamma=\"auto\", random_state=42))\n", "ovr_clf.fit(X_train[:1000], y_train[:1000])\n", "ovr_clf.predict([some_digit])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#I'll check for the number of estimators\n", "len(ovr_clf.estimators_)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-0.9990256 , -0.99963766, -0.99949709, -0.99902667, -0.99986906,\n", " 0.10132159, -0.99976287, -0.99933311, -0.99943631, -0.99924045]])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ovr_digit_scores = ovr_clf.decision_function([some_digit])\n", "ovr_digit_scores" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3], dtype=uint8)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#The Sgd classifier defaults to OvA automatically\n", "sgd_clf.fit(X_train, y_train)\n", "sgd_clf.predict([some_digit])" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-31893.03095419, -34419.69069632, -9530.63950739,\n", " 1823.73154031, -22320.14822878, -1385.80478895,\n", " -26188.91070951, -16147.51323997, -4604.35491274,\n", " -12050.767298 ]])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's check the scores for the sgd classifier\n", "sgd_clf.decision_function([some_digit])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Warning** : The next two cells takes up to 30 minutes to run" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.87082583, 0.87089354, 0.88628294])" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's evaluate the sgd classifier\n", "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\envs\\myMLenv\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:561: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " ConvergenceWarning)\n" ] }, { "data": { "text/plain": [ "array([0.89957009, 0.89344467, 0.89963495])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Scaling the Input improves the accuracy\n", "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n", "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Error Analysis" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\envs\\myMLenv\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:561: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", " ConvergenceWarning)\n" ] }, { "data": { "text/plain": [ "array([[5576, 0, 21, 6, 9, 43, 37, 6, 224, 1],\n", " [ 0, 6398, 38, 23, 4, 44, 4, 8, 213, 10],\n", " [ 26, 27, 5242, 90, 71, 26, 62, 36, 371, 7],\n", " [ 24, 17, 117, 5220, 2, 208, 28, 40, 405, 70],\n", " [ 12, 14, 48, 10, 5192, 10, 36, 26, 330, 164],\n", " [ 28, 15, 33, 166, 55, 4437, 76, 14, 538, 59],\n", " [ 30, 14, 41, 2, 43, 95, 5560, 4, 128, 1],\n", " [ 21, 9, 52, 27, 51, 12, 3, 5693, 188, 209],\n", " [ 17, 63, 46, 90, 3, 125, 25, 10, 5429, 43],\n", " [ 23, 18, 31, 66, 116, 32, 1, 179, 377, 5106]],\n", " dtype=int64)" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#LEt's see the confusio matrix of the multiclass sgd classifier\n", "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n", "conf_mx = confusion_matrix(y_train, y_train_pred)\n", "conf_mx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our model is reasonably accurate, most of the values are along the diagonal (Correct prediction)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure confusion_matrix_plot\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Let's visualize the confusion matrix\n", "\n", "plt.matshow(conf_mx, cmap=plt.cm.gray)\n", "save_fig(\"confusion_matrix_plot\", tight_layout=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "#Plotting the confusion matrix in color\n", "# since sklearn 0.22, you can use sklearn.metrics.plot_confusion_matrix()\n", "def plot_confusion_matrix(matrix):\n", " \"\"\"If you prefer color and a colorbar\"\"\"\n", " fig = plt.figure(figsize=(8,8))\n", " ax = fig.add_subplot(111)\n", " cax = ax.matshow(matrix)\n", " fig.colorbar(cax)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure confusion_matrix_colour_plot\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_confusion_matrix(conf_mx)\n", "save_fig(\"confusion_matrix_colour_plot\", tight_layout=False)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "#Let's separate the classes and normalise per class\n", "#so that abundant classes don't look bad\n", "\n", "row_sums = conf_mx.sum(axis=1, keepdims=True)\n", "norm_conf_mx = conf_mx / row_sums" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure confusion_matrix_errors_plot\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Let's remove the correct classification (Diagonal)\n", "#And focus only on the errors\n", "#We'll fill the diagonal with zeros\n", "\n", "np.fill_diagonal(norm_conf_mx, 0)\n", "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n", "save_fig(\"confusion_matrix_errors_plot\", tight_layout=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure conf_matrix_errors_colour_plot\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_confusion_matrix(norm_conf_mx)\n", "save_fig(\"conf_matrix_errors_colour_plot\", tight_layout=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5 is badly misclassifed as an 8. Also 3 and 5 are confused for themselves (same colours for both meeting points)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "#Let's observe the mistakes of the classification off 3 and 5\n", "\n", "cl_a, cl_b = 3, 5\n", "#X_aa is 3 and is classified as 3\n", "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n", "\n", "#X_ab is 3 but is classifed as 5\n", "X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]\n", "\n", "#The same logic\n", "X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]\n", "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure error_analysis_digits_plot\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Let's plot these values to see the mistakes\n", "\n", "plt.figure(figsize=(8,8))\n", "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n", "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n", "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n", "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\n", "save_fig(\"error_analysis_digits_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that we would require a lot of preprocessing to clean this data and reduce errors, we may need to add more prediction steps and features (Probably try to centre the values more recognize loop (maybe with OpenCV) and then count these loops as a new feature)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MULTILABEL CLASSIFICATION" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n", " weights='uniform')" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "#Let's create two classes: For Large (7,8,9) and for odd numberes\n", "y_train_large = (y_train >= 7)\n", "y_train_odd = (y_train % 2 == 1)\n", "y_multilabel = np.c_[y_train_large, y_train_odd]\n", "\n", "knn_clf = KNeighborsClassifier()\n", "knn_clf.fit(X_train, y_multilabel) " ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[False, True]])" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn_clf.predict([some_digit])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5 is not a large number (it is less than 7,8 or 9) and it is an Odd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code takes hours to process depending on your hardware" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n", "\n", "#The following code assumes that both labels have the same weight \n", "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9778357403921755" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#To give a weighted f1_score\n", "f1_score(y_multilabel, y_train_knn_pred, average=\"weighted\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MULTIOUTPUT CLASSIFICATION" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# I want to simuate a multioutput-multiclass classification problem by creating a noisy image and \n", "# using a clean image as a target. (One lable per pixel and a pixel can have multiple intensities (classes))\n", "\n", "noise = np.random.randint(0, 100, (len(X_train), 784)) #We add noise\n", "X_train_mod = X_train + noise\n", "noise = np.random.randint(0, 100, (len(X_test), 784))\n", "X_test_mod = X_test + noise\n", "\n", "#The labels will be the clean image\n", "y_train_mod = X_train\n", "y_test_mod = X_test" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure noisy_digit_example_plot\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Let's view an example of the noisy image.\n", "#We would use the test set because we want to compare the \n", "#predicted image quickly. This is not ideal\n", "\n", "some_index = 0\n", "plt.subplot(121); plot_digit(X_test_mod[some_index])\n", "plt.subplot(122); plot_digit(y_test_mod[some_index])\n", "save_fig(\"noisy_digit_example_plot\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure cleaned_digit_example_plot\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Let's train a KNN classifier. \n", "#The pixel intensities are the classes and the different pixel positions\n", "#are the different labels per instance\n", "\n", "knn_clf.fit(X_train_mod, y_train_mod)\n", "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n", "plot_digit(clean_digit)\n", "save_fig(\"cleaned_digit_example_plot\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Extra Material" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. An MNIST Classifier With Over 97% Accuracy\n", "\n", "\n", "Hint: the KNeighborsClassifier works quite well for this task;\n", "you just need to find good hyperparameter values (try a grid search on the\n", "weights and n_neighbors hyperparameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Warning:* The next cell takes upto 6hrs to run on my hardware (i5 9300H and Nvidia 1600ti)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\envs\\myMLenv\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", " return f(*args, **kwds)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n", "[CV] n_neighbors=3, weights=uniform ..................................\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[CV] ...... n_neighbors=3, weights=uniform, score=0.972, total=23.5min\n", "[CV] n_neighbors=3, weights=uniform ..................................\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 23.5min remaining: 0.0s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[CV] ...... n_neighbors=3, weights=uniform, score=0.971, total=11.5min\n", "[CV] n_neighbors=3, weights=uniform ..................................\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 35.0min remaining: 0.0s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[CV] ...... n_neighbors=3, weights=uniform, score=0.969, total=11.7min\n", "[CV] n_neighbors=3, weights=uniform ..................................\n", "[CV] ...... n_neighbors=3, weights=uniform, score=0.969, total=11.5min\n", "[CV] n_neighbors=3, weights=uniform ..................................\n", "[CV] ...... n_neighbors=3, weights=uniform, score=0.970, total=11.4min\n", "[CV] n_neighbors=3, weights=distance .................................\n", "[CV] ..... n_neighbors=3, weights=distance, score=0.972, total=11.5min\n", "[CV] n_neighbors=3, weights=distance .................................\n", "[CV] ..... n_neighbors=3, weights=distance, score=0.972, total=11.5min\n", "[CV] n_neighbors=3, weights=distance .................................\n", "[CV] ..... n_neighbors=3, weights=distance, score=0.970, total=11.5min\n", "[CV] n_neighbors=3, weights=distance .................................\n", "[CV] ..... n_neighbors=3, weights=distance, score=0.970, total=11.5min\n", "[CV] n_neighbors=3, weights=distance .................................\n", "[CV] ..... n_neighbors=3, weights=distance, score=0.971, total=11.5min\n", "[CV] n_neighbors=4, weights=uniform ..................................\n", "[CV] ...... n_neighbors=4, weights=uniform, score=0.969, total=11.5min\n", "[CV] n_neighbors=4, weights=uniform ..................................\n", "[CV] ...... n_neighbors=4, weights=uniform, score=0.968, total=11.5min\n", "[CV] n_neighbors=4, weights=uniform ..................................\n", "[CV] ...... n_neighbors=4, weights=uniform, score=0.968, total=11.5min\n", "[CV] n_neighbors=4, weights=uniform ..................................\n", "[CV] ...... n_neighbors=4, weights=uniform, score=0.967, total=11.5min\n", "[CV] n_neighbors=4, weights=uniform ..................................\n", "[CV] ...... n_neighbors=4, weights=uniform, score=0.970, total=11.6min\n", "[CV] n_neighbors=4, weights=distance .................................\n", "[CV] ..... n_neighbors=4, weights=distance, score=0.973, total=11.4min\n", "[CV] n_neighbors=4, weights=distance .................................\n", "[CV] ..... n_neighbors=4, weights=distance, score=0.972, total=11.5min\n", "[CV] n_neighbors=4, weights=distance .................................\n", "[CV] ..... n_neighbors=4, weights=distance, score=0.970, total=11.5min\n", "[CV] n_neighbors=4, weights=distance .................................\n", "[CV] ..... n_neighbors=4, weights=distance, score=0.971, total=11.5min\n", "[CV] n_neighbors=4, weights=distance .................................\n", "[CV] ..... n_neighbors=4, weights=distance, score=0.972, total=11.5min\n", "[CV] n_neighbors=5, weights=uniform ..................................\n", "[CV] ...... n_neighbors=5, weights=uniform, score=0.970, total=11.5min\n", "[CV] n_neighbors=5, weights=uniform ..................................\n", "[CV] ...... n_neighbors=5, weights=uniform, score=0.970, total=11.5min\n", "[CV] n_neighbors=5, weights=uniform ..................................\n", "[CV] ...... n_neighbors=5, weights=uniform, score=0.969, total=11.5min\n", "[CV] n_neighbors=5, weights=uniform ..................................\n", "[CV] ...... n_neighbors=5, weights=uniform, score=0.968, total=11.5min\n", "[CV] n_neighbors=5, weights=uniform ..................................\n", "[CV] ...... n_neighbors=5, weights=uniform, score=0.969, total=11.5min\n", "[CV] n_neighbors=5, weights=distance .................................\n", "[CV] ..... n_neighbors=5, weights=distance, score=0.970, total=11.5min\n", "[CV] n_neighbors=5, weights=distance .................................\n", "[CV] ..... n_neighbors=5, weights=distance, score=0.971, total=11.5min\n", "[CV] n_neighbors=5, weights=distance .................................\n", "[CV] ..... n_neighbors=5, weights=distance, score=0.970, total=11.5min\n", "[CV] n_neighbors=5, weights=distance .................................\n", "[CV] ..... n_neighbors=5, weights=distance, score=0.969, total=11.5min\n", "[CV] n_neighbors=5, weights=distance .................................\n", "[CV] ..... n_neighbors=5, weights=distance, score=0.971, total=11.9min\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 30 out of 30 | elapsed: 357.3min finished\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise-deprecating',\n", " estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n", " metric='minkowski',\n", " metric_params=None, n_jobs=None,\n", " n_neighbors=5, p=2,\n", " weights='uniform'),\n", " iid='warn', n_jobs=None,\n", " param_grid=[{'n_neighbors': [3, 4, 5],\n", " 'weights': ['uniform', 'distance']}],\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", " scoring=None, verbose=3)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "param_grid = [{'weights': [\"uniform\", \"distance\"], 'n_neighbors': [3, 4, 5]}]\n", "\n", "knn_clf = KNeighborsClassifier()\n", "grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3)\n", "grid_search.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'n_neighbors': 4, 'weights': 'distance'}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_search.best_params_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9716166666666667" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_search.best_score_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9714" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Lets get the accuracy score\n", "\n", "from sklearn.metrics import accuracy_score\n", "\n", "y_pred = grid_search.predict(X_test)\n", "accuracy_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9715166824529755" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Let's get the Precision and Recall\n", "\n", "from sklearn.metrics import recall_score, precision_score, roc_auc_score\n", "\n", "#since the output is multiclass, we selectan average other than binary, weighted uses the weighted \n", "#mean of amount of data available for each class and their scores\n", "precision_score(y_test, y_pred, average = \"weighted\") \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9714" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Calculate metrics globally by counting the total true positives, false negatives and false positives\n", "recall_score(y_test, y_pred, average= \"micro\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#ROC AUC score\n", "\n", "#roc_auc_score(y_test, y_pred) #It is not supported for multiclass only for multilabel and binary classification\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.\n", "\n", "Write a function that can shift an MNIST image in any direction (left, right, up,\n", "or down) by one pixel. Then, for each image in the training set, create four shifted copies (one per direction) and add them to the training set. Finally, train your\n", "best model on this expanded training set and measure its accuracy on the test set.\n", "You should observe that your model performs even better now! This technique of\n", "artificially growing the training set is called data augmentation or training set\n", "expansion." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we will use the shift() function from the scipy.ndimage.interpolation module. For example, shift(image, [2, 1], cval=0) shifts the image 2 pixels down and 1 pixel to the right." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from scipy.ndimage.interpolation import shift" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#We use this function to create an image whose pixels are shifted by dy rows down\n", "#and dx columns right \n", "def shift_image(image, dx, dy):\n", " image = image.reshape((28, 28))\n", " shifted_image = shift(image, [dy, dx], cval=0, mode=\"constant\")\n", " return shifted_image.reshape([-1])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure Shifted digits\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "image = X_train[1000]\n", "shifted_image_down = shift_image(image, 0, 5)\n", "shifted_image_left = shift_image(image, -5, 0)\n", "\n", "plt.figure(figsize=(12,3))\n", "plt.subplot(131)\n", "plt.title(\"Original\", fontsize=14)\n", "plt.imshow(image.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n", "plt.subplot(132)\n", "plt.title(\"Shifted down\", fontsize=14)\n", "plt.imshow(shifted_image_down.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n", "plt.subplot(133)\n", "plt.title(\"Shifted left\", fontsize=14)\n", "plt.imshow(shifted_image_left.reshape(28, 28), interpolation=\"nearest\", cmap=\"Greys\")\n", "save_fig(\"Shifted digits\", fig_extension=\"png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "X_train_augmented = [image for image in X_train]\n", "y_train_augmented = [label for label in y_train]\n", "\n", "for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)): #We iterate through one pixel shift down, up, right and left for each image \n", " for image, label in zip(X_train, y_train): \n", " X_train_augmented.append(shift_image(image, dx, dy))\n", " y_train_augmented.append(label) #Label doesn't change\n", "\n", "X_train_augmented = np.array(X_train_augmented)\n", "y_train_augmented = np.array(y_train_augmented)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "shuffle_idx = np.random.permutation(len(X_train_augmented)) # returns a Permuted sequence or array range i.e a rearranged set\n", "X_train_augmented = X_train_augmented[shuffle_idx]\n", "y_train_augmented = y_train_augmented[shuffle_idx]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "##knn_clf = KNeighborsClassifier(**grid_search.best_params_) ##Use this when you run the whole notebook and this value is still in the namespace\n", "\n", "#In a previous run, we got the previous parameters as: {'n_neighbors': 4, 'weights': 'distance'}" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "knn_clf = KNeighborsClassifier(n_neighbors=4, weights='distance')\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=None, n_neighbors=4, p=2,\n", " weights='distance')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn_clf.fit(X_train_augmented, y_train_augmented)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Warning: The Following code take up to an hour to run" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "y_pred = knn_clf.predict(X_test)\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9763" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Evaluating the model\n", "from sklearn.metrics import accuracy_score\n", "\n", "#In binary classification, this function is equal to the jaccard_score function. \n", "#This function is sufficient for multilabel classifcation and does not have the problem of manually computing accuracy of a binary classifier\n", "\n", "#this function computes subset accuracy: the set of labels predicted for a sample \n", "#must exactly match the corresponding set of labels in y_true.\n", "accuracy_score(y_test, y_pred) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is very accurate because of the augmented data, the model preforms much better. By simply augmenting the data, we got a 0.5% accuracy boost. :)" ] } ], "metadata": { "interpreter": { "hash": "d18c3d99e326808c0ea9776033fedd1fe4d7964bdcf2f58ebb2d89bb2832f5d0" }, "kernelspec": { "display_name": "Python 3.7.9 64-bit ('myMLenv': conda)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }