SST unit improvements

ryanloney · Mar 22, 2019 · 2b0eb70 · 2b0eb70
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diff --git a/sst_02_hand_built_features.ipynb b/sst_02_hand_built_features.ipynb
@@ -41,6 +41,7 @@
     "  1. [Assessing BasicSGDClassifier](#Assessing-BasicSGDClassifier)\n",
     "  1. [Comparison with the baselines from Socher et al. 2013](#Comparison-with-the-baselines-from-Socher-et-al.-2013)\n",
     "  1. [A shallow neural network classifier](#A-shallow-neural-network-classifier)\n",
+    "  1. [A softmax classifier in PyTorch](#A-softmax-classifier-in-PyTorch)\n",
     "1. [Hyperparameter search](#Hyperparameter-search)\n",
     "  1. [utils.fit_classifier_with_crossvalidation](#utils.fit_classifier_with_crossvalidation)\n",
     "  1. [Example using LogisticRegression](#Example-using-LogisticRegression)\n",
@@ -94,6 +95,7 @@
     "from sklearn.linear_model import LogisticRegression\n",
     "import scipy.stats\n",
     "from np_sgd_classifier import BasicSGDClassifier\n",
+    "import torch.nn as nn\n",
     "from torch_shallow_neural_classifier import TorchShallowNeuralClassifier\n",
     "import sst\n",
     "import utils"
@@ -725,6 +727,80 @@
     "It looks like, with enough iterations (and perhaps some fiddling with the activation function and hidden dimensionality), this classifier would meet or exceed the baseline set up by `LogisticRegression`."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### A softmax classifier in PyTorch\n",
+    "\n",
+    "Our PyTorch modules should support easy modification. For example, to turn `TorchShallowNeuralClassifier` into a `TorchSoftmaxClassifier`, one need only write a new `define_graph` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TorchSoftmaxClassifier(TorchShallowNeuralClassifier):\n",
+    "    \n",
+    "    def define_graph(self):\n",
+    "        return nn.Linear(self.input_dim, self.n_classes_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fit_torch_softmax(X, y):\n",
+    "    mod = TorchSoftmaxClassifier(max_iter=100)\n",
+    "    mod.fit(X, y)\n",
+    "    return mod"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Finished epoch 100 of 100; error is 0.08181965257972479"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.780\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "    negative      0.773     0.765     0.769       995\n",
+      "    positive      0.786     0.794     0.790      1081\n",
+      "\n",
+      "   micro avg      0.780     0.780     0.780      2076\n",
+      "   macro avg      0.780     0.779     0.779      2076\n",
+      "weighted avg      0.780     0.780     0.780      2076\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = sst.experiment(\n",
+    "    SST_HOME,\n",
+    "    unigrams_phi, \n",
+    "    fit_torch_softmax, \n",
+    "    class_func=sst.binary_class_func)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -772,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -818,7 +894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -872,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -887,7 +963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -953,7 +1029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -996,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1009,7 +1085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {