Add iris example for snapshot creation in python script and jupyter n…

…otebook

examples/iris_logistic_regression.ipynb

@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import datmo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "config = { \"solver\": \"newton-cg\" } # extra line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_dataset = datasets.load_iris()\n",
+    "X, y = iris_dataset.data, iris_dataset.target\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = LogisticRegression(**config).fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.946428571429\n",
+      "0.973684210526\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_acc = model.score(X_train, y_train)\n",
+    "test_acc = model.score(X_test, y_test)\n",
+    "\n",
+    "print(train_acc)\n",
+    "print(test_acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'module' object has no attribute 'snapshot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-cab2e9246f30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mstats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m \u001b[0;34m\"train_accuracy\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtrain_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"test_accuracy\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtest_acc\u001b[0m \u001b[0;34m}\u001b[0m \u001b[0;31m# extra line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdatmo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msnapshot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# extra line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'snapshot'"
+     ]
+    }
+   ],
+   "source": [
+    "stats = { \"train_accuracy\": train_acc, \"test_accuracy\": test_acc } # extra line\n",
+    "datmo.snapshot.create(config=config, stats=stats) # extra line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/iris_logistic_regression.py

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+from sklearn import datasets
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+import datmo # extra line
+
+config = { "solver": "newton-cg" } # extra line
+iris_dataset = datasets.load_iris()
+X, y = iris_dataset.data, iris_dataset.target
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+model = LogisticRegression(**config).fit(X_train, y_train)
+
+train_acc = model.score(X_train, y_train)
+test_acc = model.score(X_test, y_test)
+
+print(train_acc)
+print(test_acc)
+
+stats = { "train_accuracy": train_acc, "test_accuracy": test_acc } # extra line
+datmo.snapshot.create(config=config, stats=stats) # extra line