def __init__(self):
from sklearn.base import ClusterMixin
from htmd.clustering.kcenters import KCenter
from moleculekit.projections.projection import Projection
super().__init__()
self._arg(
'datapath', 'str',
'The directory in which the completed simulations are stored',
'data', val.String())
self._arg('filter', 'bool',
'Enable or disable filtering of trajectories.', True,
val.Boolean())
self._arg('filtersel', 'str', 'Filtering atom selection', 'not water',
val.String())
self._arg(
'filteredpath', 'str',
'The directory in which the filtered simulations will be stored',
'filtered', val.String())
self._arg(
'projection',
':class:`Projection <moleculekit.projections.projection.Projection>` object',
'A Projection class object or a list of objects which will be used to project the simulation '
'data before constructing a Markov model',
None,
val.Object(Projection),
nargs='+')
self._arg(
'truncation', 'str',
'Method for truncating the prob distribution (None, \'cumsum\', \'statecut\'',
None, val.String())
self._arg(
'statetype',
'str',
'What states (cluster, micro, macro) to use for calculations.',
'micro',
val.String(),
valid_values=('micro', 'cluster', 'macro'))
self._arg('macronum', 'int', 'The number of macrostates to produce', 8,
val.Number(int, 'POS'))
self._arg(
'skip', 'int',
'Allows skipping of simulation frames to reduce data. i.e. skip=3 will only keep every third frame',
1, val.Number(int, 'POS'))
self._arg('lag', 'int', 'The lagtime used to create the Markov model',
1, val.Number(int, 'POS'))
self._arg(
'clustmethod',
':class:`ClusterMixin <sklearn.base.ClusterMixin>` class',
'Clustering algorithm used to cluster the contacts or distances',
KCenter, val.Class(ClusterMixin))
self._arg(
'method', 'str',
'Criteria used for choosing from which state to respawn from',
'1/Mc', val.String())
self._arg(
'ticalag', 'int',
'Lagtime to use for TICA in frames. When using `skip` remember to change this accordinly.',
20, val.Number(int, '0POS'))
self._arg(
'ticadim', 'int',
'Number of TICA dimensions to use. When set to 0 it disables TICA',
3, val.Number(int, '0POS'))
self._arg('contactsym', 'str', 'Contact symmetry', None, val.String())
self._arg('save', 'bool', 'Save the model generated', False,
val.Boolean())
def __init__(self):
from sklearn.base import ClusterMixin
from moleculekit.projections.projection import Projection
super().__init__()
self._arg('datapath', 'str', 'The directory in which the completed simulations are stored', 'data', val.String())
self._arg('filter', 'bool', 'Enable or disable filtering of trajectories.', True, val.Boolean())
self._arg('filtersel', 'str', 'Filtering atom selection', 'not water', val.String())
self._arg('filteredpath', 'str', 'The directory in which the filtered simulations will be stored', 'filtered', val.String())
self._arg('projection', ':class:`Projection <moleculekit.projections.projection.Projection>` object',
'A Projection class object or a list of objects which will be used to project the simulation '
'data before constructing a Markov model', None, val.Object(Projection), nargs='+')
self._arg('goalfunction', 'function',
'This function will be used to convert the goal-projected simulation data to a ranking which'
'can be used for the directed component of FAST.', None, val.Function(), nargs='any')
self._arg('reward_method', 'str', 'The reward method', 'max', val.String())
self._arg('skip', 'int', 'Allows skipping of simulation frames to reduce data. i.e. skip=3 will only keep every third frame', 1, val.Number(int, 'POS'))
self._arg('lag', 'int', 'The lagtime used to create the Markov model. Units are in frames.', 1, val.Number(int, 'POS'))
self._arg('exploration', 'float', 'Exploration is the coefficient used in UCB algorithm to weight the exploration value', 0.5, val.Number(float, 'OPOS'))
self._arg('temperature', 'int', 'Temperature used to compute the free energy', 300, val.Number(int, 'POS'))
self._arg('ticalag', 'int', 'Lagtime to use for TICA in frames. When using `skip` remember to change this accordinly.', 20, val.Number(int, '0POS'))
self._arg('ticadim', 'int', 'Number of TICA dimensions to use. When set to 0 it disables TICA', 3, val.Number(int, '0POS'))
self._arg('clustmethod', ':class:`ClusterMixin <sklearn.base.ClusterMixin>` class', 'Clustering algorithm used to cluster the contacts or distances', MiniBatchKMeans, val.Class(ClusterMixin))
self._arg('macronum', 'int', 'The number of macrostates to produce', 8, val.Number(int, 'POS'))
self._arg('save', 'bool', 'Save the model generated', False, val.Boolean())
self._arg('save_qval', 'bool', 'Save the Q(a) and N values for every epoch', False, val.Boolean())
self._arg('actionspace', 'str', 'The action space', 'metric', val.String())
self._arg('recluster', 'bool', 'If to recluster the action space.', False, val.Boolean())
self._arg('reclusterMethod', '', 'Clustering method for reclustering.', MiniBatchKMeans)
self._arg('random', 'bool', 'Random decision mode for baseline.', False, val.Boolean())
self._arg('reward_mode', 'str', '(parent, frame)', 'parent', val.String())
self._arg('reward_window', 'int', 'The reward window', None, val.Number(int, 'POS'))
self._arg('pucb', 'bool', 'If True, it uses PUCB algorithm using the provided goal function as a prior', False, val.Boolean())
self._arg('goal_init', 'float', 'The proportional ratio of goal initialization compared to max frames set by nframes', 0.3, val.Number(float, 'POS'))
self._arg('goal_preprocess', 'function',
'This function will be used to preprocess goal data after it has been computed for all frames.', None, val.Function(), nargs='any')
self._arg('actionpool', 'int', 'The number of top scoring actions used to randomly select respawning simulations', 0, val.Number(int, 'OPOS'))
def __init__(self):
from sklearn.base import ClusterMixin
from htmd.clustering.kcenters import KCenter
from moleculekit.projections.projection import Projection
super().__init__()
self._arg(
"datapath",
"str",
"The directory in which the completed simulations are stored",
"data",
val.String(),
)
self._arg(
"filter",
"bool",
"Enable or disable filtering of trajectories.",
True,
val.Boolean(),
)
self._arg("filtersel", "str", "Filtering atom selection", "not water",
val.String())
self._arg(
"filteredpath",
"str",
"The directory in which the filtered simulations will be stored",
"filtered",
val.String(),
)
self._arg(
"projection",
":class:`Projection <moleculekit.projections.projection.Projection>` object",
"A Projection class object or a list of objects which will be used to project the simulation "
"data before constructing a Markov model",
None,
val.Object(Projection),
nargs="+",
)
self._arg(
"truncation",
"str",
"Method for truncating the prob distribution (None, 'cumsum', 'statecut'",
None,
val.String(),
)
self._arg(
"statetype",
"str",
"What states (cluster, micro, macro) to use for calculations.",
"micro",
val.String(),
valid_values=("micro", "cluster", "macro"),
)
self._arg(
"macronum",
"int",
"The number of macrostates to produce",
8,
val.Number(int, "POS"),
)
self._arg(
"skip",
"int",
"Allows skipping of simulation frames to reduce data. i.e. skip=3 will only keep every third frame",
1,
val.Number(int, "POS"),
)
self._arg(
"lag",
"int",
"The lagtime used to create the Markov model",
1,
val.Number(int, "POS"),
)
self._arg(
"clustmethod",
":class:`ClusterMixin <sklearn.base.ClusterMixin>` class",
"Clustering algorithm used to cluster the contacts or distances",
KCenter,
val.Class(ClusterMixin),
)
self._arg(
"method",
"str",
"Criteria used for choosing from which state to respawn from",
"1/Mc",
val.String(),
)
self._arg(
"ticalag",
"int",
"Lagtime to use for TICA in frames. When using `skip` remember to change this accordinly.",
20,
val.Number(int, "0POS"),
)
self._arg(
"ticadim",
"int",
"Number of TICA dimensions to use. When set to 0 it disables TICA",
3,
val.Number(int, "0POS"),
)
self._arg("contactsym", "str", "Contact symmetry", None, val.String())
self._arg("save", "bool", "Save the model generated", False,
val.Boolean())