notebooks/ocbio.extract usage notes.ipynb

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     "source": [
      "##### Gavin Gray 18th June\n",
      "\n",
      "The `ocbio.extract` code can be used to quickly extract features and execute arbitrary scripts while doing so.\n",
      "It's designed to keep track of and update many different data sources.\n",
      "The primary way it does this is through the _data source table file_.\n",
      "\n",
      "## Data source table\n",
      "\n",
      "This table is of the following form and provides the information about where to find the data file to be processed and where to store the output database.\n",
      "\n",
      "| Data source directory | Output database directory | Options |\n",
      "| --------- | --------- | ------- |\n",
      "| `/relative/path/to/data` | `/relative/path/to/output/database` | protindexes=1,3;valindex=4;script=`/path/to/script`;csvdelim=`\\t` |\n",
      "| ... | ... | ... |\n",
      "\n",
      "The options affect how the data source will be parsed to produce the output file.\n",
      "An important note about the data source directory is that this must be the directory the script _produces_ not the directory the script processes _if a script is specified_.\n",
      "This is because the data source directory is expected in a form such as:\n",
      "\n",
      "| Spam | Protein 1 | Spam |  Protein 2 | Lovely | Value |\n",
      "| --- | ---- | --------- | --------- | ------- | --- |\n",
      "| Spam | 1243 | Wonderful | 3214 | Spam | 123.4 |\n",
      "\n",
      "The parsing done by the actual code is limited to selecting certain columns; two for protein identifiers and one for the value of interest.\n",
      "The rest of the processing is expected to be done by the chosen script.\n",
      "\n",
      "The output of the script is a ProteinPairDB, which is written as a child class from the database produced by shelve.open (Dbfilenameshelf), modifying the `__getitem__` and `__setitem__` methods.\n",
      "\n",
      "### Options\n",
      "\n",
      "The available options to place in the table as above separated by `;` are:\n",
      "\n",
      "* protindexes\n",
      "    * the column indexes of the protein indexes\n",
      "    * __starting from 0__ and given in brackets\n",
      "    * defaults are (0,1)\n",
      "* valindexes\n",
      "    - the column indexes of the values to be used as features \n",
      "    - as above starting from zero and given in brackets\n",
      "    - default is (2)\n",
      "* script\n",
      "    - __relative__ path to script\n",
      "* csvdelim\n",
      "    - delimiter used in data file csv\n",
      "    - default is tab (ie `\\t`)\n",
      "* ignoreheader\n",
      "    - ignore the first line of the csv\n",
      "    - either 1 (ignore) or 0 (don't ignore)\n",
      "    - default is 0\n",
      "\n",
      "### Location\n",
      "\n",
      "The data source table must be placed at the top directory containing the data sources.\n",
      "It assumes that the paths given to data and output files in the table are _relative_ to that position.\n",
      "So, first we navigate to this position:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd /home/gavin/Documents/MRes/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/gavin/Documents/MRes\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At the time of writing there are three extracted features which we can add to this table.\n",
      "Note that it is _tab delimited_:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"datasource.tab\", \"w\")\n",
      "# the HIPPIE feature\n",
      "f.write(\"HIPPIE/hippie_current.txt\"+\"\\t\"+\"HIPPIE/feature.HIPPIE.db\"+\"\\t\"+\"protindexes=(1,3);valindexes=(4)\"+\"\\n\")\n",
      "# the abundance feature\n",
      "f.write(\"forGAVIN/pulldown_data/dataset/ppi_ab_entrez.csv\"+\"\\t\"+\"forGAVIN/pulldown_data/dataset/abundance.Entrez.db\"+\"\\t\"+\"ignoreheader=1\"+\"\\n\")\n",
      "# the affinity feature\n",
      "f.write(\"affinityresults/results2/unique_data_ppi_coor_C2S_entrez.csv\"+\"\\t\"+\"affinityresults/results2/affinity.Entrez.db\"+\"\\t\"+\"\")\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Adding the module to path\n",
      "\n",
      "To load the module it will have to be in Python's path.\n",
      "So the opencast-bio directory must be added to Python's path:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.append(\"/home/gavin/Documents/MRes/opencast-bio/\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import ocbio.extract"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(ocbio.extract)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<module 'ocbio.extract' from '/home/gavin/Documents/MRes/opencast-bio/ocbio/extract.pyc'>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Running the extraction\n",
      "\n",
      "To run the extraction we first need to initialise the `FeatureVectorAssembler` with the source table defined above.\n",
      "\n",
      "### Initialisation\n",
      "\n",
      "The full path to the source table should be supplied:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assembler = ocbio.extract.FeatureVectorAssembler(\"/home/gavin/Documents/MRes/datasource.tab\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Regeneration\n",
      "\n",
      "Generating the protein pair database files at the locations defined in the data source table can be done in two ways.\n",
      "Soft regeneration involves checking to see if the pre-processed files are newer than the processed files.\n",
      "If they are the data source is processed again to regenerate the database.\n",
      "This is the default and will be run if the regenerate method is used:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assembler.regenerate()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this case the data files are older than the processed files so it does nothing.\n",
      "We can force it to regenerate the databases with the `force=True` option:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assembler.regenerate(force=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, this can take a long time to complete as it regenerates __all__ databases.\n",
      "To force regeneration of a single database either delete the current output file or change the path for the output file in the data source table and re-initialise the assembler."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Assembly\n",
      "\n",
      "Assembling a feature vector file can be done using the `assemble` method of the `FeatureVectorAssembler` instance.\n",
      "As input this takes a file containing a list of protein pairs and produces a file containing feature vectors corresponding to each protein pair in that file.\n",
      "An output file must also be specified."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assembler.assemble(\"DIP/human/training.nolabel.positive.Entrez.txt\", \"features/training.nolabel.positive.Entrez.vectors.txt\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looking at this output file we can see that each row is a feature vector.\n",
      "Missing values are given by the `missinglabel` option, which defaults to the string \"missing\"."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "head features/training.nolabel.positive.Entrez.vectors.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.86\tmissing\tmissing\r\n",
        "0.97\tmissing\tmissing\r\n",
        "0.9\tmissing\tmissing\r\n",
        "0.62\tmissing\tmissing\r\n",
        "missing\tmissing\tmissing\r\n",
        "missing\tmissing\tmissing\r\n",
        "missing\tmissing\tmissing\r\n",
        "0.96\tmissing\tmissing\r\n",
        "missing\tmissing\t-0.70339\r\n",
        "0.7\tmissing\tmissing\r\n"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This can be changed to any string required:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assembler.assemble(\"DIP/human/training.nolabel.positive.Entrez.txt\",\n",
      "                   \"features/training.nolabel.positive.Entrez.vectors.txt\", missinglabel=\"any string required\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "head features/training.nolabel.positive.Entrez.vectors.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.86\tany string required\tany string required\r\n",
        "0.97\tany string required\tany string required\r\n",
        "0.9\tany string required\tany string required\r\n",
        "0.62\tany string required\tany string required\r\n",
        "any string required\tany string required\tany string required\r\n",
        "any string required\tany string required\tany string required\r\n",
        "any string required\tany string required\tany string required\r\n",
        "0.96\tany string required\tany string required\r\n",
        "any string required\tany string required\t-0.70339\r\n",
        "0.7\tany string required\tany string required\r\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Each row _corresponds_ to a row in the protein pair file supplied (in this case `training.nolabel.positive.Entrez.txt`).\n",
      "However, if you would like to have these inside this file then the `pairlabels` option can be used:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assembler.assemble(\"DIP/human/training.nolabel.positive.Entrez.txt\",\n",
      "                   \"features/training.nolabel.positive.Entrez.vectors.txt\",\n",
      "                   missinglabel=\"any string required\",\n",
      "                   pairlabels=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "head features/training.nolabel.positive.Entrez.vectors.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4084\t207\t0.86\tany string required\tany string required\r\n",
        "8360\t8356\t0.97\tany string required\tany string required\r\n",
        "5914\t9612\t0.9\tany string required\tany string required\r\n",
        "79833\t6634\t0.62\tany string required\tany string required\r\n",
        "29102\t4090\tany string required\tany string required\tany string required\r\n",
        "7074\t6382\tany string required\tany string required\tany string required\r\n",
        "7159\t22059\tany string required\tany string required\tany string required\r\n",
        "1869\t7029\t0.96\tany string required\tany string required\r\n",
        "801\t817\tany string required\tany string required\t-0.70339\r\n",
        "207\t1786\t0.7\tany string required\tany string required\r\n"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Verbose output\n",
      "\n",
      "Finally, adding the `verbose=True` option to the methods of `FeatureVectorAssembler` will activate a lot of print statements telling you what is happening."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assembler.assemble(\"DIP/human/training.nolabel.positive.Entrez.txt\",\n",
      "                   \"features/training.nolabel.positive.Entrez.vectors.txt\", verbose=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Reading pairfile: DIP/human/training.nolabel.positive.Entrez.txt\n",
        "Opening databases:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t /home/gavin/Documents/MRes/HIPPIE/feature.HIPPIE.db open\n",
        "\t /home/gavin/Documents/MRes/forGAVIN/pulldown_data/dataset/abundance.Entrez.db open"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t /home/gavin/Documents/MRes/affinityresults/results2/affinity.Entrez.db open\n",
        "Checking feature sizes:\n",
        "\t Database /home/gavin/Documents/MRes/HIPPIE/feature.HIPPIE.db contains features of size 1."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t Database /home/gavin/Documents/MRes/forGAVIN/pulldown_data/dataset/abundance.Entrez.db contains features of size 1."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t Database /home/gavin/Documents/MRes/affinityresults/results2/affinity.Entrez.db contains features of size 1."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Writing feature vectors."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Wrote 4857 vectors.\n",
        "Matched 60.08 % of protein pairs in DIP/human/training.nolabel.positive.Entrez.txt to /home/gavin/Documents/MRes/HIPPIE/feature.HIPPIE.db\n",
        "Matched 0.29 % of protein pairs in DIP/human/training.nolabel.positive.Entrez.txt to /home/gavin/Documents/MRes/forGAVIN/pulldown_data/dataset/abundance.Entrez.db\n",
        "Matched 3.69 % of protein pairs in DIP/human/training.nolabel.positive.Entrez.txt to /home/gavin/Documents/MRes/affinityresults/results2/affinity.Entrez.db\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assembler.regenerate(force=True, verbose=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Regenerating parsers:\n",
        "\t parser 0\n",
        "Forcing regeneration of database /home/gavin/Documents/MRes/HIPPIE/feature.HIPPIE.db from data file /home/gavin/Documents/MRes/HIPPIE/hippie_current.txt.\n",
        "Filling database"
       ]
      },
      {
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        "..."
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