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# 6. Saving and managing sampling results

## 6.1. Accessing Sampled Data

The recommended way to access data from an MCMC run, irrespective of the database backend, is to use the `trace` method:

    >>> from pymc.examples import disaster_model
    >>> from pymc import MCMC
    >>> M = MCMC(disaster_model)
    >>> M.sample(10)
    Sampling: 100% [00000000000000000000000000000000000000000000000000] Iterations: 10
    >>> M.trace('early_mean')[:]
    array([ 2.28320992,  2.28320992,  2.28320992,  2.28320992,  2.28320992,
            2.36982455,  2.36982455,  3.1669422 ,  3.1669422 ,  3.14499489])

`M.trace('early_mean')` returns a copy of the `Trace` instance associated with the tallyable object early_mean:

    >>> M.trace('early_mean')
    <pymc.database.ram.Trace object at 0x7fa4877a8b50>

Particular subsamples from the trace are obtained using the slice notation `[]`, similar to NumPy arrays. By default, `trace` returns the samples from the last chain. To return the samples from all the chains, use `chain=None`:

    >>> M.sample(5)
    Sampling: 100% [000000000000000000000000000000000000000000000000000] Iterations: 5
    >>> M.trace('early_mean', chain=None)[:]
    array([ 2.28320992,  2.28320992,  2.28320992,  2.28320992,  2.28320992,
          2.36982455,  2.36982455,  3.1669422 ,  3.1669422 ,  3.14499489,
          3.14499489,  3.14499489,  3.14499489,  2.94672454,  3.10767686])

### 6.1.1. Output Summaries

PyMC samplers include a couple of methods that are useful for obtaining summaries of the model, or particular member nodes, rather than the entire trace. The `summary` method can be used to generate a pretty-printed summary of posterior quantities. For example, if we want a statistical snapshot of the `early_mean` node:

    >>> M.early_mean.summary()

    early_mean:

        Mean             SD               MC Error        95% HPD interval
        ------------------------------------------------------------------
        3.075            0.287            0.01             [ 2.594  3.722]

        Posterior quantiles:

        2.5             25              50              75             97.5
         |---------------|===============|===============|---------------|
        2.531            2.876           3.069          3.255         3.671

A method of the same name exists for the sampler, which yields summaries for every node in the model.

Alternatively, we may wish to write posterior statistics to a file, where they may be imported into a spreadsheet or plotting package. In that case, `write_csv` may be called to generate a comma-separated values (csv) file containing all available statistics for each node:

    M.write_csv("disasters.csv", variables=["early_mean", "late_mean", "switchpoint"])

Summary statistics of stochastics from the `disaster_model` example, shown in a spreadsheet.

`write_csv` is called with a single mandatory argument, the name of the output file for the summary statistics, and several optional arguments, including a list of parameters for which summaries are desired (if not given, all model nodes are summarized) and an alpha level for calculating credible intervalse (defaults to 0.05).

## 6.2. Saving Data to Disk

By default, the database backend selected by the `MCMC` sampler is the `ram` backend, which simply holds the data in memory. Now, we will create a sampler that instead will write data to a pickle file:

    >>> M = MCMC(disaster_model, db='pickle', dbname='Disaster.pickle')
    >>> M.db
    <pymc.database.pickle.Database object at 0x7fa486623d90>
    >>> M.sample(10)
    >>> M.db.close()

Note that in this particular case, no data is written to disk before the call to `db.close`. The `close` method will flush data to disk and prepare the database for a potential session exit. Closing a Python session without calling `close` beforehand is likely to corrupt the database, making the data irretrievable. To simply flush data to disk without closing the database, use the `commit` method.

Some backends not only have the ability to store the traces, but also the state of the step methods at the end of sampling. This is particularly useful when long warm-up periods are needed to tune the jump parameters. When the database is loaded in a new session, the step methods query the database to fetch the state they were in at the end of the last trace.

Check that you `close()` the database before closing the Python session.

## 6.3. Reloading a Database

To load a file created in a previous session, use the `load` function from the backend:

    >>> db = pymc.database.pickle.load('Disaster.pickle')
    >>> len(db.trace('early_mean')[:])
    10

The `db` object also has a `trace` method identical to that of `Sampler`. You can hence inspect the results of a model, even when you don’t have the model around.

To add a new trace to this file, we need to create an MCMC instance. This time, instead of setting `db='pickle'`, we will pass the existing `Database` instance and sample as usual. A new trace will be appended to the first:

    >>> M = MCMC(disaster_model, db=db)
    >>> M.sample(5)
    Sampling: 100% [000000000000000000000000000000000000000000000000000] Iterations: 5
    >>> len(M.trace('early_model', chain=None)[:])
    15
    >>> M.db.close()

### 6.3.1. The `ram` backend

Used by default, this backend simply holds a copy in memory, with no output written to disk. This is useful for short runs or testing. For long runs generating large amount of data, using this backend may fill the available memory, forcing the OS to store data in the cache, slowing down all other applications.

### 6.3.2. The `no_trace` backend

This backend simply does not store the trace. This may be useful for testing purposes.

### 6.3.3. The txt backend

With the `txt` backend, the data is written to disk in ASCII files. More precisely, the `dbname` argument is used to create a top directory into which chain directories, called `Chain_<#>`, are going to be created each time `sample` is called:

    dbname/
      Chain_0/
        <object0 name>.txt
        <object1 name>.txt
        ...
      Chain_1/
        <object0 name>.txt
        <object1 name>.txt
        ...
      ...

In each one of these chain directories, files named `<variable name>.txt` are created, storing the values of the variable as rows of text:

    # Variable: e
    # Sample shape: (5,)
    # Date: 2008-11-18 17:19:13.554188
    3.033672373807017486e+00
    3.033672373807017486e+00
    ...

While the txt backend makes it easy to load data using other applications and programming languages, it is not optimized for speed nor memory efficiency. If you plan on generating and handling large datasets, read on for better options.

### 6.3.4. The `pickle` backend

The `pickle` database relies on the `cPickle` module to save the traces. Use of this backend is appropriate for small-scale, short-lived projects. For longer term or larger projects, the `pickle` backend should be avoided since generated files might be unreadable across different Python versions. The pickled file is a simple dump of a dictionary containing the NumPy arrays storing the traces, as well as the state of the `Sampler`‘s step methods.

### 6.3.5. The `sqlite` backend

The `sqlite` backend is based on the python module sqlite3 (built-in to Python versions greater than 2.4) . It opens an SQL database named `dbname`, and creates one table per tallyable objects. The rows of this table store a key, the chain index and the values of the objects:

    key (INTT), trace (INT),  v1 (FLOAT), v2 (FLOAT), v3 (FLOAT) ...

The key is autoincremented each time a new row is added to the table, that is, each time `tally` is called by the sampler. Note that the `savestate` feature is not implemented, that is, the state of the step methods is not stored internally in the database.

### 6.3.6. The `hdf5` backend

The `hdf5` backend uses pyTables to save data in binary HDF5 format. The `hdf5` database is fast and can store huge traces, far larger than the available RAM. Data can be compressed and decompressed on the fly to reduce the disk footprint. Another feature of this backends is that it can store arbitrary objects. Whereas the other backends are limited to numerical values, `hdf5` can tally any object that can be pickled, opening the door for powerful and exotic applications (see `pymc.gp`).

The internal structure of an HDF5 file storing both numerical values and arbitrary objects is as follows:

    / (root)
      /chain0/ (Group) 'Chain #0'
        /chain0/PyMCSamples (Table(N,)) 'PyMC Samples'
        /chain0/group0 (Group) 'Group storing objects.'
          /chain0/group0/<object0 name> (VLArray(N,)) '<object0 name> samples.'
          /chain0/group0/<object1 name> (VLArray(N,)) '<object1 name> samples.'
          ...
      /chain1/ (Group) 'Chain #1'
        ...

All standard numerical values are stored in a `Table`, while `objects` are stored in individual `VLArrays`.

The `hdf5` Database takes the following parameters:

- `dbname` (string) Name of the hdf5 file.
- `dbmode` (string) File mode: `a`: append, `w`: overwrite, `r`: read-only.
- `dbcomplevel` (int (0-9)) Compression level, 0: no compression.
- `dbcomplib` (string) Compression library (`zlib`, `bzip2`, `lzo`)

According the the pyTables manual, zlib ([Roelofs2010]) has a fast decompression, relatively slow compression, and a good compression ratio. LZO ([Oberhumer2008]) has a fast compression, but a low compression ratio. bzip2 ([Seward2007]) has an excellent compression ratio but requires more CPU. Note that some of these compression algorithms require additional software to work (see the pyTables manual).

## 6.4. Writing a New Backend

It is relatively easy to write a new backend for `PyMC`. The first step is to look at the `database.base` module, which defines barebone `Database` and `Trace` classes. This module contains documentation on the methods that should be defined to get a working backend.

Testing your new backend should be trivial, since the `test_database` module contains a generic test class that can easily be subclassed to check that the basic features required of all backends are implemented and working properly.

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