Ch 8 Code pt 1

Author: JannesKlaas

7_marbles_test.py

@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import marbles.core\n",
+    "from marbles.mixins import mixins\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "class TimeSeriesTestCase(marbles.core.TestCase,mixins.MonotonicMixins):\n",
+    "    def setUp(self):\n",
+    "        self.df = pd.DataFrame({'dates':[datetime(2018,1,1),\n",
+    "                                         datetime(2018,2,1),\n",
+    "                                         datetime(2018,2,1)],\n",
+    "                                'ireland_unemployment':[6.2,6.1,6.0]})\n",
+    "        \n",
+    "    def tearDown(self):\n",
+    "        self.df = None\n",
+    "        \n",
+    "    def test_date_order(self):\n",
+    "        \n",
+    "        self.assertMonotonicIncreasing(sequence=self.df.dates,\n",
+    "                                  note = 'Dates need to increase monotonically')\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "F\n",
+      "======================================================================\n",
+      "FAIL: test_date_order (__main__.TimeSeriesTestCase)\n",
+      "----------------------------------------------------------------------\n",
+      "marbles.core.marbles.ContextualAssertionError: Elements in 0   2018-01-01\n",
+      "1   2018-02-01\n",
+      "2   2018-02-01\n",
+      "Name: dates, dtype: datetime64[ns] are not strictly monotonically increasing\n",
+      "\n",
+      "Source (<ipython-input-1-ebdbd8f0d69f>):\n",
+      "     19 \n",
+      " >   20 self.assertMonotonicIncreasing(sequence=self.df.dates,\n",
+      "     21                           note = 'Dates need to increase monotonically')\n",
+      "     22 \n",
+      "Locals:\n",
+      "\n",
+      "Note:\n",
+      "\tDates need to increase monotonically\n",
+      "\n",
+      "\n",
+      "----------------------------------------------------------------------\n",
+      "Ran 1 test in 0.007s\n",
+      "\n",
+      "FAILED (failures=1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == '__main__':\n",
+    "    marbles.core.main(argv=['first-arg-is-ignored'], exit=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "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": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

8.1 Unit Testing Data.ipynb

@@ -0,0 +1,48 @@
+
+# coding: utf-8
+
+# In[1]:
+
+
+from keras.datasets import mnist
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+
+# In[2]:
+
+
+from keras.models import Sequential
+from keras.layers import Dense
+
+x_train.shape = (60000, 28 * 28)
+
+
+x_test.shape = (10000, 28 * 28)
+
+
+x_train = x_train / 255
+x_test = x_test / 255
+
+model = Sequential()
+model.add(Dense(512,activation='relu',input_dim= 28*28))
+model.add(Dense(512,activation='relu'))
+model.add(Dense(512,activation='relu'))
+model.add(Dense(10,activation='softmax'))
+
+model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['acc'])
+
+from keras.callbacks import TensorBoard
+
+import tensorflow as tf
+from tensorflow.python import debug as tf_debug
+import keras
+
+keras.backend.set_session(
+    tf_debug.TensorBoardDebugWrapperSession(tf.Session(), "localhost:2018"))
+
+
+
+
+hist = model.fit(x_train*255,y_train,batch_size=128,epochs=5,validation_data=(x_test*255,y_test))
+