"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# extra code – generates and saves Figure 4–21\n",
"\n",
"lim = 6\n",
"t = np.linspace(-lim, lim, 100)\n",
"sig = 1 / (1 + np.exp(-t))\n",
"\n",
"plt.figure(figsize=(8, 3))\n",
"plt.plot([-lim, lim], [0, 0], \"k-\")\n",
"plt.plot([-lim, lim], [0.5, 0.5], \"k:\")\n",
"plt.plot([-lim, lim], [1, 1], \"k:\")\n",
"plt.plot([0, 0], [-1.1, 1.1], \"k-\")\n",
"plt.plot(t, sig, \"b-\", linewidth=2, label=r\"$\\sigma(t) = \\dfrac{1}{1 + e^{-t}}$\")\n",
"plt.xlabel(\"t\")\n",
"plt.legend(loc=\"upper left\")\n",
"plt.axis([-lim, lim, -0.1, 1.1])\n",
"plt.gca().set_yticks([0, 0.25, 0.5, 0.75, 1])\n",
"plt.grid()\n",
"save_fig(\"logistic_function_plot\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decision Boundaries"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['data',\n",
" 'target',\n",
" 'frame',\n",
" 'target_names',\n",
" 'DESCR',\n",
" 'feature_names',\n",
" 'filename',\n",
" 'data_module']"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"\n",
"iris = load_iris(as_frame=True)\n",
"list(iris)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
".. _iris_dataset:\n",
"\n",
"Iris plants dataset\n",
"--------------------\n",
"\n",
"**Data Set Characteristics:**\n",
"\n",
" :Number of Instances: 150 (50 in each of three classes)\n",
" :Number of Attributes: 4 numeric, predictive attributes and the class\n",
" :Attribute Information:\n",
" - sepal length in cm\n",
" - sepal width in cm\n",
" - petal length in cm\n",
" - petal width in cm\n",
" - class:\n",
" - Iris-Setosa\n",
" - Iris-Versicolour\n",
" - Iris-Virginica\n",
" \n",
" :Summary Statistics:\n",
"\n",
" ============== ==== ==== ======= ===== ====================\n",
" Min Max Mean SD Class Correlation\n",
" ============== ==== ==== ======= ===== ====================\n",
" sepal length: 4.3 7.9 5.84 0.83 0.7826\n",
" sepal width: 2.0 4.4 3.05 0.43 -0.4194\n",
" petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n",
" petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n",
" ============== ==== ==== ======= ===== ====================\n",
"\n",
" :Missing Attribute Values: None\n",
" :Class Distribution: 33.3% for each of 3 classes.\n",
" :Creator: R.A. Fisher\n",
" :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
" :Date: July, 1988\n",
"\n",
"The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
"from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
"Machine Learning Repository, which has two wrong data points.\n",
"\n",
"This is perhaps the best known database to be found in the\n",
"pattern recognition literature. Fisher's paper is a classic in the field and\n",
"is referenced frequently to this day. (See Duda & Hart, for example.) The\n",
"data set contains 3 classes of 50 instances each, where each class refers to a\n",
"type of iris plant. One class is linearly separable from the other 2; the\n",
"latter are NOT linearly separable from each other.\n",
"\n",
".. topic:: References\n",
"\n",
" - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
" Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
" Mathematical Statistics\" (John Wiley, NY, 1950).\n",
" - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
" (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n",
" - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
" Structure and Classification Rule for Recognition in Partially Exposed\n",
" Environments\". IEEE Transactions on Pattern Analysis and Machine\n",
" Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
" - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n",
" on Information Theory, May 1972, 431-433.\n",
" - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n",
" conceptual clustering system finds 3 classes in the data.\n",
" - Many, many more ...\n"
]
}
],
"source": [
"print(iris.DESCR) # extra code – it's a bit too long"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
sepal length (cm)
\n",
"
sepal width (cm)
\n",
"
petal length (cm)
\n",
"
petal width (cm)
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
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"
5.1
\n",
"
3.5
\n",
"
1.4
\n",
"
0.2
\n",
"
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"
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"
1
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"
4.9
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"
3.0
\n",
"
1.4
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0.2
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"
\n",
"
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"
2
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"
4.7
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"
3.2
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"
1.3
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"
0.2
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"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"Name: target, dtype: int64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.target.head(3) # note that the instances are not shuffled"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['setosa', 'versicolor', 'virginica'], dtype='"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"X_new = np.linspace(0, 3, 1000).reshape(-1, 1) # reshape to get a column vector\n",
"y_proba = log_reg.predict_proba(X_new)\n",
"decision_boundary = X_new[y_proba[:, 1] >= 0.5][0, 0]\n",
"\n",
"plt.figure(figsize=(8, 3)) # extra code – not needed, just formatting\n",
"plt.plot(X_new, y_proba[:, 0], \"b--\", linewidth=2,\n",
" label=\"Not Iris virginica proba\")\n",
"plt.plot(X_new, y_proba[:, 1], \"g-\", linewidth=2, label=\"Iris virginica proba\")\n",
"plt.plot([decision_boundary, decision_boundary], [0, 1], \"k:\", linewidth=2,\n",
" label=\"Decision boundary\")\n",
"\n",
"# extra code – this section beautifies and saves Figure 4–21\n",
"plt.arrow(x=decision_boundary, y=0.08, dx=-0.3, dy=0,\n",
" head_width=0.05, head_length=0.1, fc=\"b\", ec=\"b\")\n",
"plt.arrow(x=decision_boundary, y=0.92, dx=0.3, dy=0,\n",
" head_width=0.05, head_length=0.1, fc=\"g\", ec=\"g\")\n",
"plt.plot(X_train[y_train == 0], y_train[y_train == 0], \"bs\")\n",
"plt.plot(X_train[y_train == 1], y_train[y_train == 1], \"g^\")\n",
"plt.xlabel(\"Petal width (cm)\")\n",
"plt.ylabel(\"Probability\")\n",
"plt.legend(loc=\"center left\")\n",
"plt.axis([0, 3, -0.02, 1.02])\n",
"plt.grid()\n",
"save_fig(\"logistic_regression_plot\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.6516516516516517"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"decision_boundary"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False])"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_reg.predict([[1.7], [1.5]])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# extra code – this cell generates and saves Figure 4–22\n",
"\n",
"X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
"y = iris.target_names[iris.target] == 'virginica'\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
"\n",
"log_reg = LogisticRegression(C=2, random_state=42)\n",
"log_reg.fit(X_train, y_train)\n",
"\n",
"# for the contour plot\n",
"x0, x1 = np.meshgrid(np.linspace(2.9, 7, 500).reshape(-1, 1),\n",
" np.linspace(0.8, 2.7, 200).reshape(-1, 1))\n",
"X_new = np.c_[x0.ravel(), x1.ravel()] # one instance per point on the figure\n",
"y_proba = log_reg.predict_proba(X_new)\n",
"zz = y_proba[:, 1].reshape(x0.shape)\n",
"\n",
"# for the decision boundary\n",
"left_right = np.array([2.9, 7])\n",
"boundary = -((log_reg.coef_[0, 0] * left_right + log_reg.intercept_[0])\n",
" / log_reg.coef_[0, 1])\n",
"\n",
"plt.figure(figsize=(10, 4))\n",
"plt.plot(X_train[y_train == 0, 0], X_train[y_train == 0, 1], \"bs\")\n",
"plt.plot(X_train[y_train == 1, 0], X_train[y_train == 1, 1], \"g^\")\n",
"contour = plt.contour(x0, x1, zz, cmap=plt.cm.brg)\n",
"plt.clabel(contour, inline=1)\n",
"plt.plot(left_right, boundary, \"k--\", linewidth=3)\n",
"plt.text(3.5, 1.27, \"Not Iris virginica\", color=\"b\", ha=\"center\")\n",
"plt.text(6.5, 2.3, \"Iris virginica\", color=\"g\", ha=\"center\")\n",
"plt.xlabel(\"Petal length\")\n",
"plt.ylabel(\"Petal width\")\n",
"plt.axis([2.9, 7, 0.8, 2.7])\n",
"plt.grid()\n",
"save_fig(\"logistic_regression_contour_plot\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Softmax Regression"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=30, random_state=42)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
"y = iris[\"target\"]\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
"\n",
"softmax_reg = LogisticRegression(C=30, random_state=42)\n",
"softmax_reg.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([2])"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"softmax_reg.predict([[5, 2]])"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0. , 0.04, 0.96]])"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"softmax_reg.predict_proba([[5, 2]]).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# extra code – this cell generates and saves Figure 4–23\n",
"\n",
"from matplotlib.colors import ListedColormap\n",
"\n",
"custom_cmap = ListedColormap([\"#fafab0\", \"#9898ff\", \"#a0faa0\"])\n",
"\n",
"x0, x1 = np.meshgrid(np.linspace(0, 8, 500).reshape(-1, 1),\n",
" np.linspace(0, 3.5, 200).reshape(-1, 1))\n",
"X_new = np.c_[x0.ravel(), x1.ravel()]\n",
"\n",
"y_proba = softmax_reg.predict_proba(X_new)\n",
"y_predict = softmax_reg.predict(X_new)\n",
"\n",
"zz1 = y_proba[:, 1].reshape(x0.shape)\n",
"zz = y_predict.reshape(x0.shape)\n",
"\n",
"plt.figure(figsize=(10, 4))\n",
"plt.plot(X[y == 2, 0], X[y == 2, 1], \"g^\", label=\"Iris virginica\")\n",
"plt.plot(X[y == 1, 0], X[y == 1, 1], \"bs\", label=\"Iris versicolor\")\n",
"plt.plot(X[y == 0, 0], X[y == 0, 1], \"yo\", label=\"Iris setosa\")\n",
"\n",
"plt.contourf(x0, x1, zz, cmap=custom_cmap)\n",
"contour = plt.contour(x0, x1, zz1, cmap=\"hot\")\n",
"plt.clabel(contour, inline=1)\n",
"plt.xlabel(\"Petal length\")\n",
"plt.ylabel(\"Petal width\")\n",
"plt.legend(loc=\"center left\")\n",
"plt.axis([0.5, 7, 0, 3.5])\n",
"plt.grid()\n",
"save_fig(\"softmax_regression_contour_plot\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise solutions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. to 11."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. If you have a training set with millions of features you can use Stochastic Gradient Descent or Mini-batch Gradient Descent, and perhaps Batch Gradient Descent if the training set fits in memory. But you cannot use the Normal Equation or the SVD approach because the computational complexity grows quickly (more than quadratically) with the number of features.\n",
"2. If the features in your training set have very different scales, the cost function will have the shape of an elongated bowl, so the Gradient Descent algorithms will take a long time to converge. To solve this you should scale the data before training the model. Note that the Normal Equation or SVD approach will work just fine without scaling. Moreover, regularized models may converge to a suboptimal solution if the features are not scaled: since regularization penalizes large weights, features with smaller values will tend to be ignored compared to features with larger values.\n",
"3. Gradient Descent cannot get stuck in a local minimum when training a Logistic Regression model because the cost function is convex. _Convex_ means that if you draw a straight line between any two points on the curve, the line never crosses the curve.\n",
"4. If the optimization problem is convex (such as Linear Regression or Logistic Regression), and assuming the learning rate is not too high, then all Gradient Descent algorithms will approach the global optimum and end up producing fairly similar models. However, unless you gradually reduce the learning rate, Stochastic GD and Mini-batch GD will never truly converge; instead, they will keep jumping back and forth around the global optimum. This means that even if you let them run for a very long time, these Gradient Descent algorithms will produce slightly different models.\n",
"5. If the validation error consistently goes up after every epoch, then one possibility is that the learning rate is too high and the algorithm is diverging. If the training error also goes up, then this is clearly the problem and you should reduce the learning rate. However, if the training error is not going up, then your model is overfitting the training set and you should stop training.\n",
"6. Due to their random nature, neither Stochastic Gradient Descent nor Mini-batch Gradient Descent is guaranteed to make progress at every single training iteration. So if you immediately stop training when the validation error goes up, you may stop much too early, before the optimum is reached. A better option is to save the model at regular intervals; then, when it has not improved for a long time (meaning it will probably never beat the record), you can revert to the best saved model.\n",
"7. Stochastic Gradient Descent has the fastest training iteration since it considers only one training instance at a time, so it is generally the first to reach the vicinity of the global optimum (or Mini-batch GD with a very small mini-batch size). However, only Batch Gradient Descent will actually converge, given enough training time. As mentioned, Stochastic GD and Mini-batch GD will bounce around the optimum, unless you gradually reduce the learning rate.\n",
"8. If the validation error is much higher than the training error, this is likely because your model is overfitting the training set. One way to try to fix this is to reduce the polynomial degree: a model with fewer degrees of freedom is less likely to overfit. Another thing you can try is to regularize the model—for example, by adding an ℓ₂ penalty (Ridge) or an ℓ₁ penalty (Lasso) to the cost function. This will also reduce the degrees of freedom of the model. Lastly, you can try to increase the size of the training set.\n",
"9. If both the training error and the validation error are almost equal and fairly high, the model is likely underfitting the training set, which means it has a high bias. You should try reducing the regularization hyperparameter _α_.\n",
"10. Let's see:\n",
" * A model with some regularization typically performs better than a model without any regularization, so you should generally prefer Ridge Regression over plain Linear Regression.\n",
" * Lasso Regression uses an ℓ₁ penalty, which tends to push the weights down to exactly zero. This leads to sparse models, where all weights are zero except for the most important weights. This is a way to perform feature selection automatically, which is good if you suspect that only a few features actually matter. When you are not sure, you should prefer Ridge Regression.\n",
" * Elastic Net is generally preferred over Lasso since Lasso may behave erratically in some cases (when several features are strongly correlated or when there are more features than training instances). However, it does add an extra hyperparameter to tune. If you want Lasso without the erratic behavior, you can just use Elastic Net with an `l1_ratio` close to 1.\n",
"11. If you want to classify pictures as outdoor/indoor and daytime/nighttime, since these are not exclusive classes (i.e., all four combinations are possible) you should train two Logistic Regression classifiers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 12. Batch Gradient Descent with early stopping for Softmax Regression\n",
"Exercise: _Implement Batch Gradient Descent with early stopping for Softmax Regression without using Scikit-Learn, only NumPy. Use it on a classification task such as the iris dataset._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by loading the data. We will just reuse the Iris dataset we loaded earlier."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
"y = iris[\"target\"].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to add the bias term for every instance ($x_0 = 1$). The easiest option to do this would be to use Scikit-Learn's `add_dummy_feature()` function, but the point of this exercise is to get a better understanding of the algorithms by implementing them manually. So here is one possible implementation:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"X_with_bias = np.c_[np.ones(len(X)), X]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The easiest option to split the dataset into a training set, a validation set and a test set would be to use Scikit-Learn's `train_test_split()` function, but again, we want to did this manually:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"test_ratio = 0.2\n",
"validation_ratio = 0.2\n",
"total_size = len(X_with_bias)\n",
"\n",
"test_size = int(total_size * test_ratio)\n",
"validation_size = int(total_size * validation_ratio)\n",
"train_size = total_size - test_size - validation_size\n",
"\n",
"np.random.seed(42)\n",
"rnd_indices = np.random.permutation(total_size)\n",
"\n",
"X_train = X_with_bias[rnd_indices[:train_size]]\n",
"y_train = y[rnd_indices[:train_size]]\n",
"X_valid = X_with_bias[rnd_indices[train_size:-test_size]]\n",
"y_valid = y[rnd_indices[train_size:-test_size]]\n",
"X_test = X_with_bias[rnd_indices[-test_size:]]\n",
"y_test = y[rnd_indices[-test_size:]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The targets are currently class indices (0, 1 or 2), but we need target class probabilities to train the Softmax Regression model. Each instance will have target class probabilities equal to 0.0 for all classes except for the target class which will have a probability of 1.0 (in other words, the vector of class probabilities for any given instance is a one-hot vector). Let's write a small function to convert the vector of class indices into a matrix containing a one-hot vector for each instance. To understand this code, you need to know that `np.diag(np.ones(n))` creates an n×n matrix full of 0s except for 1s on the main diagonal. Moreover, if `a` in a NumPy array, then `a[[1, 3, 2]]` returns an array with 3 rows equal to `a[1]`, `a[3]` and `a[2]` (this is [advanced NumPy indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html#advanced-indexing))."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"def to_one_hot(y):\n",
" return np.diag(np.ones(y.max() + 1))[y]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's test this function on the first 10 instances:"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1])"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train[:10]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 1., 0.],\n",
" [1., 0., 0.],\n",
" [0., 0., 1.],\n",
" [0., 1., 0.],\n",
" [0., 1., 0.],\n",
" [1., 0., 0.],\n",
" [0., 1., 0.],\n",
" [0., 0., 1.],\n",
" [0., 1., 0.],\n",
" [0., 1., 0.]])"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_one_hot(y_train[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks good, so let's create the target class probabilities matrix for the training set and the test set:"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"Y_train_one_hot = to_one_hot(y_train)\n",
"Y_valid_one_hot = to_one_hot(y_valid)\n",
"Y_test_one_hot = to_one_hot(y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's scale the inputs. We compute the mean and standard deviation of each feature on the training set (except for the bias feature), then we center and scale each feature in the training set, the validation set, and the test set:"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"mean = X_train[:, 1:].mean(axis=0)\n",
"std = X_train[:, 1:].std(axis=0)\n",
"X_train[:, 1:] = (X_train[:, 1:] - mean) / std\n",
"X_valid[:, 1:] = (X_valid[:, 1:] - mean) / std\n",
"X_test[:, 1:] = (X_test[:, 1:] - mean) / std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's implement the Softmax function. Recall that it is defined by the following equation:\n",
"\n",
"$\\sigma\\left(\\mathbf{s}(\\mathbf{x})\\right)_k = \\dfrac{\\exp\\left(s_k(\\mathbf{x})\\right)}{\\sum\\limits_{j=1}^{K}{\\exp\\left(s_j(\\mathbf{x})\\right)}}$"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"def softmax(logits):\n",
" exps = np.exp(logits)\n",
" exp_sums = exps.sum(axis=1, keepdims=True)\n",
" return exps / exp_sums"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are almost ready to start training. Let's define the number of inputs and outputs:"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"n_inputs = X_train.shape[1] # == 3 (2 features plus the bias term)\n",
"n_outputs = len(np.unique(y_train)) # == 3 (there are 3 iris classes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now here comes the hardest part: training! Theoretically, it's simple: it's just a matter of translating the math equations into Python code. But in practice, it can be quite tricky: in particular, it's easy to mix up the order of the terms, or the indices. You can even end up with code that looks like it's working but is actually not computing exactly the right thing. When unsure, you should write down the shape of each term in the equation and make sure the corresponding terms in your code match closely. It can also help to evaluate each term independently and print them out. The good news it that you won't have to do this everyday, since all this is well implemented by Scikit-Learn, but it will help you understand what's going on under the hood.\n",
"\n",
"So the equations we will need are the cost function:\n",
"\n",
"$J(\\mathbf{\\Theta}) =\n",
"- \\dfrac{1}{m}\\sum\\limits_{i=1}^{m}\\sum\\limits_{k=1}^{K}{y_k^{(i)}\\log\\left(\\hat{p}_k^{(i)}\\right)}$\n",
"\n",
"And the equation for the gradients:\n",
"\n",
"$\\nabla_{\\mathbf{\\theta}^{(k)}} \\, J(\\mathbf{\\Theta}) = \\dfrac{1}{m} \\sum\\limits_{i=1}^{m}{ \\left ( \\hat{p}^{(i)}_k - y_k^{(i)} \\right ) \\mathbf{x}^{(i)}}$\n",
"\n",
"Note that $\\log\\left(\\hat{p}_k^{(i)}\\right)$ may not be computable if $\\hat{p}_k^{(i)} = 0$. So we will add a tiny value $\\epsilon$ to $\\log\\left(\\hat{p}_k^{(i)}\\right)$ to avoid getting `nan` values."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 3.7085808486476917\n",
"1000 0.14519367480830644\n",
"2000 0.1301309575504088\n",
"3000 0.12009639326384539\n",
"4000 0.11372961364786884\n",
"5000 0.11002459532472425\n"
]
}
],
"source": [
"eta = 0.5\n",
"n_epochs = 5001\n",
"m = len(X_train)\n",
"epsilon = 1e-5\n",
"\n",
"np.random.seed(42)\n",
"Theta = np.random.randn(n_inputs, n_outputs)\n",
"\n",
"for epoch in range(n_epochs):\n",
" logits = X_train @ Theta\n",
" Y_proba = softmax(logits)\n",
" if epoch % 1000 == 0:\n",
" Y_proba_valid = softmax(X_valid @ Theta)\n",
" xentropy_losses = -(Y_valid_one_hot * np.log(Y_proba_valid + epsilon))\n",
" print(epoch, xentropy_losses.sum(axis=1).mean())\n",
" error = Y_proba - Y_train_one_hot\n",
" gradients = 1 / m * X_train.T @ error\n",
" Theta = Theta - eta * gradients"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And that's it! The Softmax model is trained. Let's look at the model parameters:"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.41931626, 6.11112089, -5.52429876],\n",
" [-6.53054533, -0.74608616, 8.33137102],\n",
" [-5.28115784, 0.25152675, 6.90680425]])"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Theta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make predictions for the validation set and check the accuracy score:"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9333333333333333"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logits = X_valid @ Theta\n",
"Y_proba = softmax(logits)\n",
"y_predict = Y_proba.argmax(axis=1)\n",
"\n",
"accuracy_score = (y_predict == y_valid).mean()\n",
"accuracy_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well, this model looks pretty ok. For the sake of the exercise, let's add a bit of $\\ell_2$ regularization. The following training code is similar to the one above, but the loss now has an additional $\\ell_2$ penalty, and the gradients have the proper additional term (note that we don't regularize the first element of `Theta` since this corresponds to the bias term). Also, let's try increasing the learning rate `eta`."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 3.7372\n",
"1000 0.3259\n",
"2000 0.3259\n",
"3000 0.3259\n",
"4000 0.3259\n",
"5000 0.3259\n"
]
}
],
"source": [
"eta = 0.5\n",
"n_epochs = 5001\n",
"m = len(X_train)\n",
"epsilon = 1e-5\n",
"alpha = 0.01 # regularization hyperparameter\n",
"\n",
"np.random.seed(42)\n",
"Theta = np.random.randn(n_inputs, n_outputs)\n",
"\n",
"for epoch in range(n_epochs):\n",
" logits = X_train @ Theta\n",
" Y_proba = softmax(logits)\n",
" if epoch % 1000 == 0:\n",
" Y_proba_valid = softmax(X_valid @ Theta)\n",
" xentropy_losses = -(Y_valid_one_hot * np.log(Y_proba_valid + epsilon))\n",
" l2_loss = 1 / 2 * (Theta[1:] ** 2).sum()\n",
" total_loss = xentropy_losses.sum(axis=1).mean() + alpha * l2_loss\n",
" print(epoch, total_loss.round(4))\n",
" error = Y_proba - Y_train_one_hot\n",
" gradients = 1 / m * X_train.T @ error\n",
" gradients += np.r_[np.zeros([1, n_outputs]), alpha * Theta[1:]]\n",
" Theta = Theta - eta * gradients"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because of the additional $\\ell_2$ penalty, the loss seems greater than earlier, but perhaps this model will perform better? Let's find out:"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9333333333333333"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logits = X_valid @ Theta\n",
"Y_proba = softmax(logits)\n",
"y_predict = Y_proba.argmax(axis=1)\n",
"\n",
"accuracy_score = (y_predict == y_valid).mean()\n",
"accuracy_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, the $\\ell_2$ penalty did not change the test accuracy. Perhaps try fine-tuning `alpha`?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's add early stopping. For this we just need to measure the loss on the validation set at every iteration and stop when the error starts growing."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 3.7372\n",
"281 0.3256\n",
"282 0.3256 early stopping!\n"
]
}
],
"source": [
"eta = 0.5\n",
"n_epochs = 50_001\n",
"m = len(X_train)\n",
"epsilon = 1e-5\n",
"C = 100 # regularization hyperparameter\n",
"best_loss = np.infty\n",
"\n",
"np.random.seed(42)\n",
"Theta = np.random.randn(n_inputs, n_outputs)\n",
"\n",
"for epoch in range(n_epochs):\n",
" logits = X_train @ Theta\n",
" Y_proba = softmax(logits)\n",
" Y_proba_valid = softmax(X_valid @ Theta)\n",
" xentropy_losses = -(Y_valid_one_hot * np.log(Y_proba_valid + epsilon))\n",
" l2_loss = 1 / 2 * (Theta[1:] ** 2).sum()\n",
" total_loss = xentropy_losses.sum(axis=1).mean() + 1 / C * l2_loss\n",
" if epoch % 1000 == 0:\n",
" print(epoch, total_loss.round(4))\n",
" if total_loss < best_loss:\n",
" best_loss = total_loss\n",
" else:\n",
" print(epoch - 1, best_loss.round(4))\n",
" print(epoch, total_loss.round(4), \"early stopping!\")\n",
" break\n",
" error = Y_proba - Y_train_one_hot\n",
" gradients = 1 / m * X_train.T @ error\n",
" gradients += np.r_[np.zeros([1, n_outputs]), 1 / C * Theta[1:]]\n",
" Theta = Theta - eta * gradients"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9333333333333333"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logits = X_valid @ Theta\n",
"Y_proba = softmax(logits)\n",
"y_predict = Y_proba.argmax(axis=1)\n",
"\n",
"accuracy_score = (y_predict == y_valid).mean()\n",
"accuracy_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Oh well, still no change in validation acccuracy, but at least early training shortened training a bit."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's plot the model's predictions on the whole dataset (remember to scale all features fed to the model):"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"custom_cmap = mpl.colors.ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
"\n",
"x0, x1 = np.meshgrid(np.linspace(0, 8, 500).reshape(-1, 1),\n",
" np.linspace(0, 3.5, 200).reshape(-1, 1))\n",
"X_new = np.c_[x0.ravel(), x1.ravel()]\n",
"X_new = (X_new - mean) / std\n",
"X_new_with_bias = np.c_[np.ones(len(X_new)), X_new]\n",
"\n",
"logits = X_new_with_bias @ Theta\n",
"Y_proba = softmax(logits)\n",
"y_predict = Y_proba.argmax(axis=1)\n",
"\n",
"zz1 = Y_proba[:, 1].reshape(x0.shape)\n",
"zz = y_predict.reshape(x0.shape)\n",
"\n",
"plt.figure(figsize=(10, 4))\n",
"plt.plot(X[y == 2, 0], X[y == 2, 1], \"g^\", label=\"Iris virginica\")\n",
"plt.plot(X[y == 1, 0], X[y == 1, 1], \"bs\", label=\"Iris versicolor\")\n",
"plt.plot(X[y == 0, 0], X[y == 0, 1], \"yo\", label=\"Iris setosa\")\n",
"\n",
"plt.contourf(x0, x1, zz, cmap=custom_cmap)\n",
"contour = plt.contour(x0, x1, zz1, cmap=\"hot\")\n",
"plt.clabel(contour, inline=1)\n",
"plt.xlabel(\"Petal length\")\n",
"plt.ylabel(\"Petal width\")\n",
"plt.legend(loc=\"upper left\")\n",
"plt.axis([0, 7, 0, 3.5])\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now let's measure the final model's accuracy on the test set:"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9666666666666667"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logits = X_test @ Theta\n",
"Y_proba = softmax(logits)\n",
"y_predict = Y_proba.argmax(axis=1)\n",
"\n",
"accuracy_score = (y_predict == y_test).mean()\n",
"accuracy_score"
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"Well we get even better performance on the test set. This variability is likely due to the very small size of the dataset: depending on how you sample the training set, validation set and the test set, you can get quite different results. Try changing the random seed and running the code again a few times, you will see that the results will vary."
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