Data Science Team.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:681db335b17fd85c4c900f3c69a943bdabbfca1c1413b59ec99eb6f7fd1f548a"
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Building A Data Science Team"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "What NLP and Machine Learning can teach us"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We collected a few (14) data profiles on Linkedin, and labeled each one among 4 (unordered !) classes :\n",
      "- Class 0 : The machine learning god\n",
      "- Class 1 : The software development hero\n",
      "- Class 2 : The IT infrastructure guru\n",
      "- Class 3 : The data business rock star"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image \n",
      "Image(filename='C:/Users/c-bourguignat/Documents/perso/14-ML meetup/jazz_small.jpg') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "jpeg": 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b+67Wu9Wu3U/ZcDyLFRtU+Jxd0uazXktk+/Q+6T1ooor/ACrP1wKK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\noorhNgooooA//9k=\n",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.Image at 0x3c92e10>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Let's find the word that appears the most often in our corpus..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import Counter\n",
      "import io\n",
      "import sys\n",
      "\n",
      "cnt = Counter()\n",
      "\n",
      "with io.open('C:/Users/c-bourguignat/Documents/perso/14-ML meetup/train.csv', 'r', encoding='cp1252') as f:\n",
      "    for line in f:\n",
      "        for word in line.split():\n",
      "            if len(word) > 3:\n",
      "                cnt[word.lower()] += 1\n",
      "\n",
      "total_word_count = sum(cnt.values())\n",
      "for word, count in cnt.most_common(1):\n",
      "    print count, float(count) / float(total_word_count), word"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "145 0.0397369142231 data\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "... as expected, it's \"data\" ! "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Now do TF-IDF"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "from nltk.stem import WordNetLemmatizer\n",
      "from nltk import word_tokenize\n",
      "\n",
      "class LemmaTokenizer(object):\n",
      "    def __init__(self):\n",
      "        self.wnl = WordNetLemmatizer()\n",
      "    def __call__(self, doc):\n",
      "        return [self.wnl.lemmatize(t) for t in word_tokenize(doc) if len(t) > 2]\n",
      "\n",
      "traindata = pd.read_csv('C:/Users/c-bourguignat/Documents/perso/14-ML meetup/train.csv')\n",
      "\n",
      "X = traindata['text']\n",
      "y = traindata['target']\n",
      "\n",
      "vect = TfidfVectorizer(stop_words='english',encoding='cp1252',min_df=3,max_df=1.0,strip_accents='unicode',analyzer='word',\n",
      "                       ngram_range=(1,2),use_idf=1,smooth_idf=1,sublinear_tf=1, tokenizer=LemmaTokenizer())\n",
      "\n",
      "Xtfidf = np.array(vect.fit_transform(X).todense())\n",
      "featcolstfidf = vect.get_feature_names()\n",
      "print Xtfidf.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(15L, 183L)\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Find out wich terms are most typical of each class"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Most typical terms for class 0 : The machine learning god"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rows = list(y[y == 0].index)\n",
      "idx = np.argsort(np.mean(Xtfidf[rows,:], axis=0))[::-1]\n",
      "best0 = np.array(featcolstfidf)[idx][:20].tolist()\n",
      "print best0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'learning', u'statistical', u'machine', u'machine learning', u'python', u'science', u'software', u'web', u'data', u'technology', u'predictive', u'model', u'modeling', u'scikit-learn', u'analytics', u'search', u'network', u'algorithm', u'process', u'data science']\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Most typical terms for class 1 : the software development hero"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rows = list(y[y == 1].index)\n",
      "idx = np.argsort(np.mean(Xtfidf[rows,:], axis=0))[::-1]\n",
      "best1 = np.array(featcolstfidf)[idx][:20].tolist()\n",
      "print best1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'python', u'development', u'java', u'application', u'library', u'software', u'implementation', u'open', u'data', u'engineer', u'structure', u'team', u'risk', u'javascript', u'algorithm', u'platform', u'architecture', u'project', u'source', u'open source']\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Most typical terms for class 2 : the IT infrastructure guru"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rows = list(y[y == 2].index)\n",
      "idx = np.argsort(np.mean(Xtfidf[rows,:], axis=0))[::-1]\n",
      "best2 = np.array(featcolstfidf)[idx][:20].tolist()\n",
      "print best2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'architecture', u'user', u'integration', u'data', u'project', u'architect', u'engine', u'warehouse', u'study', u'server', u'ibm', u'france', u'manager', u'customer', u'solution', u'management', u'development', u'search', u'year', u'design']\n"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Most typical terms for class 3 : the data business rock star"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rows = list(y[y == 3].index)\n",
      "idx = np.argsort(np.mean(Xtfidf[rows,:], axis=0))[::-1]\n",
      "best3 = np.array(featcolstfidf)[idx][:20].tolist()\n",
      "print best3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'big data', u'big', u'data', u'value', u'sale', u'business', u'lead', u'service', u'microsoft', u'best', u'solution', u'data science', u'consultant', u'scientist', u'sa', u'practice', u'customer', u'analytics', u'management', u'financial']\n"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Comment : only this class uses the \"big data\" buzz world.... ;)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Term intersections between classes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inter = set(best0)&set(best1)&set(best2)&set(best3)\n",
      "print inter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "set([u'data'])\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "\"Data\" is the common term between all classes ! And here are the pairwise common terms"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"intersection classes 0 - 1\", set(best0)&set(best1)\n",
      "print \"intersection classes 0 - 2\", set(best0)&set(best2)\n",
      "print \"intersection classes 0 - 3\", set(best0)&set(best3)\n",
      "print \"intersection classes 1 - 2\", set(best1)&set(best2)\n",
      "print \"intersection classes 1 - 3\", set(best1)&set(best3)\n",
      "print \"intersection classes 2 - 3\", set(best2)&set(best3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "intersection classes 0 - 1 set([u'python', u'data', u'algorithm', u'software'])\n",
        "intersection classes 0 - 2 set([u'search', u'data'])\n",
        "intersection classes 0 - 3 set([u'analytics', u'data science', u'data'])\n",
        "intersection classes 1 - 2 set([u'project', u'development', u'data', u'architecture'])\n",
        "intersection classes 1 - 3 set([u'data'])\n",
        "intersection classes 2 - 3 set([u'customer', u'management', u'data', u'solution'])\n"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Developer and business guy have very few in common. \n",
      "Idem for ML practitioner and IT architect."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image \n",
      "Image(filename='C:/Users/c-bourguignat/Documents/perso/14-ML meetup/words_small.jpg') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "jpeg": 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HhLxB4ahsvB+naXe6E/iC2iurmaHVo59QtYZorp1R7q0ScRy+YqgTqGaMgquN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\nTWLxp4livLe6udS0yN1/tu/g0y/vhcRRWLXFlHMLaeUTmBU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/i/8AaOcdH+L+o4/DS9LU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\nUYdWFo+uX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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "<IPython.core.display.Image at 0x1aa61a58>"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Being descriptive is cool. Being predictive is even cooler ..."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      ".... Now build a machine learning model"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Train the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import RandomForestClassifier\n",
      "\n",
      "model = RandomForestClassifier(n_estimators=200, max_depth=15)\n",
      "model.fit(Xtfidf, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "RandomForestClassifier(bootstrap=True, compute_importances=None,\n",
        "            criterion='gini', max_depth=15, max_features='auto',\n",
        "            max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n",
        "            min_samples_split=2, n_estimators=200, n_jobs=1,\n",
        "            oob_score=False, random_state=None, verbose=0)"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Find out the most important terms used by the model to discriminate classes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "idx = np.argsort(model.feature_importances_)\n",
      "for i in range(10):\n",
      "    print featcolstfidf[idx[-i-1]], \" : \", model.feature_importances_[idx[-i-1]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "statistical  :  0.0560929159389\n",
        "project  :  0.0343596375795\n",
        "development  :  0.0275559820415\n",
        "library  :  0.0238504862066\n",
        "big  :  0.0206673791634\n",
        "open  :  0.020571391534\n",
        "machine  :  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0205285856389\n",
        "engine  :  0.0203463046416\n",
        "learning  :  0.0197008269102\n",
        "engineer  :  0.0186815063962\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Now play with the model !"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Ouch !... Cannot try with celebrities like Doug Cutting, Andrew Ng, Sean Owen... Their Linkedin profile is so poor and incomplete !!"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Anyway, find other guinea pigs ...."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Yann LeCun, Director of AI Research at Facebook. What a ML god ! "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test = pd.read_csv('C:/Users/c-bourguignat/Documents/perso/14-ML meetup/lecun.csv')\n",
      "Xtest = vect.transform(test['text']).todense()\n",
      "model.predict_proba(Xtest)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "array([[ 0.57,  0.24,  0.13,  0.06]])"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Guido van Rossum, creator of the Python Language. Indeed he knows a bit about coding"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test = pd.read_csv('C:/Users/c-bourguignat/Documents/perso/14-ML meetup/rossum.csv')\n",
      "Xtest = vect.transform(test['text']).todense()\n",
      "model.predict_proba(Xtest)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "array([[ 0.105,  0.535,  0.28 ,  0.08 ]])"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Marco Cecconi, building Stack Overflow network, as part of the core team. He makes things scale"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test = pd.read_csv('C:/Users/c-bourguignat/Documents/perso/14-ML meetup/marcocecconi.csv')\n",
      "Xtest = vect.transform(test['text']).todense()\n",
      "model.predict_proba(Xtest)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "array([[ 0.105,  0.34 ,  0.385,  0.17 ]])"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Remark : strong class 2 (IT) people are also strong at class 1 (software). These two classes are difficult to separate"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Joel Horwitz, head of marketing at 0xdata. Indeed a business rock star !"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test = pd.read_csv('C:/Users/c-bourguignat/Documents/perso/14-ML meetup/horwitz.csv')\n",
      "Xtest = vect.transform(test['text']).todense()\n",
      "model.predict_proba(Xtest)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "array([[ 0.16 ,  0.175,  0.325,  0.34 ]])"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Remark : class 2 (IT) people and class 3 (business) are also sometimes difficult to separate"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      ".... and myself ! Balanced !"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test = pd.read_csv('C:/Users/c-bourguignat/Documents/perso/14-ML meetup/bourguignat.csv')\n",
      "Xtest = vect.transform(test['text']).todense()\n",
      "model.predict_proba(Xtest)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "array([[ 0.215,  0.265,  0.22 ,  0.3  ]])"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Improvements suggestions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Use much more Linkedin profiles to train the model !\n",
      "- Cross-validate to find the best model and its meta-parameters\n",
      "- Make an API to allow anybody to try with its own profile"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Lessons learned"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* With a quite simple model, it\u2019s possible to cluster the 4 profiles\n",
      "\n",
      "* Typical terms for each profile are aligned with our intuition\n",
      "\n",
      "* \u201cIT architecture\u201d only, or \u201cBusiness\u201d only profiles are uncommon. They are often mixed with an other profile\n",
      "\n",
      "* Some profiles are difficult to discriminate, as they are in-between (also in line with intuition) :\n",
      "  * IT architecture and software development\n",
      "  * IT architecture and business"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The bottom line"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- My twitter account : [@chris_bour](https://twitter.com/chris_bour)\n",
      "- My [Linkedin Profile](http://fr.linkedin.com/in/christophebourguignat/)\n",
      "- I also write [on Medium](https://medium.com/@chris_bour)\n",
      "- You can find this Notebook, and other ones on [my GitHub](https://github.com/christophebourguignat)\n",
      "- And [my decks](https://speakerdeck.com/kriss)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}