model file

2bf3f559 · Zeynep Hakguder · f841c2fe · 2bf3f559
Commit 2bf3f559 authored 6 years ago by Zeynep Hakguder
--- a/ProgrammingAssignment_3/model.ipynb
+++ b/ProgrammingAssignment_3/model.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# JUPYTER NOTEBOOK TIPS\n",
+    "\n",
+    "Each rectangular box is called a cell. \n",
+    "* ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.\n",
+    "* alt+ENTER evaluates the current cell and adds a new cell below it.\n",
+    "* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two \"d\"s in a row) when the frame is blue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Supervised Learning Model Skeleton\n",
+    "\n",
+    "We'll use this skeleton for implementing different supervised learning algorithms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Model:\n",
+    "        \n",
+    "    def fit(self):\n",
+    "        \n",
+    "        raise NotImplementedError\n",
+    "    \n",
+    "    def predict(self, test_points):\n",
+    "        raise NotImplementedError"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## General supervised learning performance related functions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"conf_matrix\" function that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def conf_matrix(true, pred):\n",
+    "    '''\n",
+    "    Args:    \n",
+    "        true:  ndarray\n",
+    "            nx1 array of true labels for test set\n",
+    "        pred: ndarray \n",
+    "            nx1 array of predicted labels for test set\n",
+    "    Returns:\n",
+    "        ndarray\n",
+    "    '''\n",
+    "        \n",
+    "    tp = tn = fp = fn = 0\n",
+    "    # calculate true positives (tp), true negatives(tn)\n",
+    "    # false positives (fp) and false negatives (fn)\n",
+    "    \n",
+    "    size = len(true)\n",
+    "    for i in range(size):\n",
+    "        if true[i]==1:\n",
+    "            if pred[i] == 1:               \n",
+    "                tp += 1\n",
+    "            else: \n",
+    "                fn += 1\n",
+    "        else:\n",
+    "            if pred[i] == 0:\n",
+    "                tn += 1    \n",
+    "            else:\n",
+    "                fp += 1                            \n",
+    "    \n",
+    "    # returns the confusion matrix as numpy.ndarray\n",
+    "    return np.array([tp,fn, fp, tn])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:markdown id: tags:
+
+# JUPYTER NOTEBOOK TIPS
+
+Each rectangular box is called a cell.
+* ctrl+ENTER evaluates the current cell; if it contains Python code, it runs the code, if it contains Markdown, it returns rendered text.
+* alt+ENTER evaluates the current cell and adds a new cell below it.
+* If you click to the left of a cell, you'll notice the frame changes color to blue. You can erase a cell by hitting 'dd' (that's two "d"s in a row) when the frame is blue.
+
+%% Cell type:markdown id: tags:
+
+# Supervised Learning Model Skeleton
+
+We'll use this skeleton for implementing different supervised learning algorithms.
+
+%% Cell type:code id: tags:
+
+``` python
+class Model:
+
+    def fit(self):
+
+        raise NotImplementedError
+
+    def predict(self, test_points):
+        raise NotImplementedError
+```
+
+%% Cell type:markdown id: tags:
+
+## General supervised learning performance related functions
+
+%% Cell type:markdown id: tags:
+
+"conf_matrix" function that takes as input an array of true labels (*true*) and an array of predicted labels (*pred*).
+
+%% Cell type:code id: tags:
+
+``` python
+def conf_matrix(true, pred):
+    '''
+    Args:
+        true:  ndarray
+            nx1 array of true labels for test set
+        pred: ndarray
+            nx1 array of predicted labels for test set
+    Returns:
+        ndarray
+    '''
+
+    tp = tn = fp = fn = 0
+    # calculate true positives (tp), true negatives(tn)
+    # false positives (fp) and false negatives (fn)
+
+    size = len(true)
+    for i in range(size):
+        if true[i]==1:
+            if pred[i] == 1:
+                tp += 1
+            else:
+                fn += 1
+        else:
+            if pred[i] == 0:
+                tn += 1
+            else:
+                fp += 1
+
+    # returns the confusion matrix as numpy.ndarray
+    return np.array([tp,fn, fp, tn])
+```