Added DTM example notebook

docs/notebooks/dtm_example.ipynb

@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DTM Example\n",
+    "\n",
+    "In this example we will present a sample usage of the DTM wrapper. \n",
+    "\n",
+    "The example dataset is already processed into list of tokens.\n",
+    "To see how to get a dataset to this stage please take a look at [Gensim Tutorials](https://radimrehurek.com/gensim/tut1.html#from-strings-to-vectors). The corpus contains 10 documents. The word dictionary is also already generated, but the wrapper will generate one if you supply a `None`.\n",
+    "\n",
+    "```python\n",
+    "  corpus = [[\"token1\",\"token3\"],[\"token1\",\"token2\"]]\n",
+    "```\n",
+    "\n",
+    "`time_seq` contains the time slice defentiion. For this case it is a list `[3,7]`, which states that the first time slice contains the first 3 documents of the corpus and the second timeslice contains the next 7 documents. I admit this is a bit weird, but ya well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import gensim\n",
+    "import os\n",
+    "from gensim import corpora, utils\n",
+    "from gensim.models.wrappers import dtmmodel\n",
+    "import numpy as np\n",
+    " \n",
+    "    \n",
+    "    # Load an alternate model if you wish\n",
+    "\n",
+    "\n",
+    "# So now we have to generate the path to DTM executable, here I have\n",
+    "# already set an ENV variable for the DTM_HOME\n",
+    "\n",
+    "dtm_home = os.environ.get(\n",
+    "    'DTM_HOME', \"C:/Users/arti/repos/TopicModels/dtm-master\")\n",
+    "dtm_path = os.path.join(dtm_home, 'bin', 'dtm') if dtm_home else None\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we wil setup logging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.DEBUG)\n",
+    "logging.debug(\"test\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A simple corups wrapper and load a premade corpus. You can exchange this with your own data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class DTMcorpus(corpora.textcorpus.TextCorpus):\n",
+    "\n",
+    "    def get_texts(self):\n",
+    "        return self.input\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.input)\n",
+    "corpus, time_seq = utils.unpickle('dtm_test')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So now we have to generate the path to DTM executable, here I have already set an ENV variable for the DTM_HOME"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dtm_home = os.environ.get(\n",
+    "    'DTM_HOME', \"C:/Users/arti/repos/TopicModels/dtm-master\")\n",
+    "dtm_path = os.path.join(dtm_home, 'bin', 'dtm') if dtm_home else None\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That is basically all we need to be able to invoke the Training. Currently there seems to be a bug, where \n",
+    "it still wants to initialize from and LDA model even though we tell it not to by default. So as workaround specify\n",
+    "\n",
+    "```python\n",
+    "  initialize_lda=True\n",
+    "```\n",
+    "I'll see why this is the case in the neart future, I promise.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "model = DtmModel(dtm_path,corpus,time_seq,num_topics=2,id2word=corpus.dictionary,initialize_lda=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If everything worked we should be able to print out the topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "topics = model.show_topics(topics=2,times=2, topn=10)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}