Module EnrichPlot
source code
Command line tool to construct an enrichment plot from saved composite models
Usage: EnrichPlot [optional args] -d dbname -t tablename <models>
Required Arguments:
-d "dbName": the name of the database for screening
-t "tablename": provide the name of the table with the data to be screened
<models>: file name(s) of pickled composite model(s).
If the -p argument is also provided (see below), this argument is ignored.
Optional Arguments:
- -a "list": the list of result codes to be considered active. This will be
eval'ed, so be sure that it evaluates as a list or sequence of
integers. For example, -a "[1,2]" will consider activity values 1 and 2
to be active
- --enrich "list": identical to the -a argument above.
- --thresh: sets a threshold for the plot. If the confidence falls below
this value, picking will be terminated
- -H: screen only the hold out set (works only if a version of
BuildComposite more recent than 1.2.2 was used).
- -T: screen only the training set (works only if a version of
BuildComposite more recent than 1.2.2 was used).
- -S: shuffle activity values before screening
- -R: randomize activity values before screening
- -F *filter frac*: filters the data before training to change the
distribution of activity values in the training set. *filter frac*
is the fraction of the training set that should have the target value.
**See note in BuildComposite help about data filtering**
- -v *filter value*: filters the data before training to change the
distribution of activity values in the training set. *filter value*
is the target value to use in filtering.
**See note in BuildComposite help about data filtering**
- -p "tableName": provides the name of a db table containing the
models to be screened. If you use this argument, you should also
use the -N argument (below) to specify a note value.
- -N "note": provides a note to be used to pull models from a db table.
- --plotFile "filename": writes the data to an output text file (filename.dat)
and creates a gnuplot input file (filename.gnu) to plot it
- --showPlot: causes the gnuplot plot constructed using --plotFile to be
displayed in gnuplot.
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message(msg,
noRet=0,
dest=sys.stderr)
emits messages to _sys.stderr_
override this in modules which import this one to redirect output |
source code
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error(msg,
dest=sys.stderr)
emits messages to _sys.stderr_
override this in modules which import this one to redirect output |
source code
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ScreenModel(mdl,
descs,
data,
picking=[1],
indices=[],
errorEstimate=0)
collects the results of screening an individual composite model that match
a particular value |
source code
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MakePlot(details,
final,
counts,
pickVects,
nModels,
nTrueActs=-1) |
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__VERSION_STRING = "2.4.0"
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Imports:
RDConfig,
numpy,
copy,
cPickle,
DataUtils,
SplitData,
Stats,
DbConnect,
DataStructs,
CompositeRun,
sys,
os,
types,
cmp
emits messages to _sys.stderr_
override this in modules which import this one to redirect output
**Arguments**
- msg: the string to be displayed
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emits messages to _sys.stderr_
override this in modules which import this one to redirect output
**Arguments**
- msg: the string to be displayed
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ScreenModel(mdl,
descs,
data,
picking=[1],
indices=[],
errorEstimate=0)
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collects the results of screening an individual composite model that match
a particular value
**Arguments**
- mdl: the composite model
- descs: a list of descriptor names corresponding to the data set
- data: the data set, a list of points to be screened.
- picking: (Optional) a list of values that are to be collected.
For examples, if you want an enrichment plot for picking the values
1 and 2, you'd having picking=[1,2].
**Returns**
a list of 4-tuples containing:
- the id of the point
- the true result (from the data set)
- the predicted result
- the confidence value for the prediction
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AccumulateCounts(predictions,
thresh=0,
sortIt=1)
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Accumulates the data for the enrichment plot for a single model
**Arguments**
- predictions: a list of 3-tuples (as returned by _ScreenModels_)
- thresh: a threshold for the confidence level. Anything below
this threshold will not be considered
- sortIt: toggles sorting on confidence levels
**Returns**
- a list of 3-tuples:
- the id of the active picked here
- num actives found so far
- number of picks made so far
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