def __init__(self, parent, stype='st'):
if stype not in ['dm15', 'st']:
raise ValueError, "This model only supports the dm15 and st parameter"
model.__init__(self, parent)
self.rbs = ['u', 'B', 'V', 'g', 'r', 'i', 'Y', 'J', 'H']
self.parameters = {
'DM': None,
stype: None,
'EBVhost': None,
'Tmax': None
}
self.errors = {'DM': 0, stype: 0, 'EBVhost': 0, 'Tmax': 0}
if stype == 'dm15':
self.template = NIR_ubertemp.template()
else:
self.template = NIR_ubertemp.stemplate()
self.stype = stype
if stype in ['st']:
self.a, self.ea, self.b, self.eb, self.c, self.ec, self.Rv_host, self.eRv_host, self.sigSN = read_table(
os.path.join(base, 'st_calibration2.dat'))
else:
self.a, self.ea, self.b, self.eb, self.c, self.ec, self.Rv_host, self.eRv_host, self.sigSN = read_table(
os.path.join(base, 'dm15_calibration2.dat'))
self.do_Robs = 0
self.Robs = {}
def __init__ (self, vpath):
model.__init__(self, vpath)
##
## General settings
##
self.buildable = True
self.quantitative = True
self.confidential = False
self.identity = True
self.SDFileName = 'name'
self.SDFileActivity = 'activity'
##
## Normalization settings
##
self.norm = True
self.normStand = True
self.normNeutr = False
self.normNeutrMethod = 'moka'
self.normNeutr_pH = 7.4
self.norm3D = True
##
## Molecular descriptor settings
##
self.MD = 'adriana' # 'padel'|'pentacle'|'adriana'
self.padelMD = ['-2d','-3d'] # '-2d'|'-3d'
self.padelMaxRuntime = None
self.padelDescriptor = None
self.pentacleProbes = ['DRY','O','N1','TIP']
self.pentacleOthers = []
##
## Modeling settings
##
self.model = 'pls'
self.modelLV = 2
self.modelAutoscaling = True
self.modelCutoff = 'auto'
self.selVar = True
#self.selVarMethod = GOLPE
self.selVarLV = 2
#self.selVarCV = 'LOO'
self.selVarRun = 2
self.selVarMask = None
##
## Path to external programs
##
self.mokaPath = '/opt/blabber/blabber4eTOX/'
self.padelPath = '/opt/padel/padel218ws/'
self.padelURL = 'http://localhost:9000/computedescriptors?params='
self.pentaclePath = '/opt/pentacle/pentacle106eTOX/'
self.adrianaPath = '/opt/AdrianaCode/AdrianaCode226/'
self.corinaPath = '/opt/corina/corina3494/'
self.javaPath = '/usr/java/jdk1.7.0_51/'
self.RPath = '/opt/R/R-3.0.2/'
self.standardiserPath = '/opt/standardiser/standardise20140206/'
def __init__(self, isTest):
model.__init__(self, 'RNN', 'RNN network')
Conf = Configuration_RNN()
Conf.Return()
self.input_layer_input_w, self.input_layer_output_w, self.input_layer_output_b, self.core_layer_input_w, self.core_layer_output_w, self.core_layer_output_b, self.action_layer_input_w, self.action_layer_output_w, self.action_layer_output_b, self.batch_size, self.epoch, self.learning_rate, self.checkpoint_path = Conf.Return(
)
self.repeat, self.transfer_layer_input_w = Conf.Return_RNN()
if isTest:
self.epoch = 1
np.random.seed(1337)
tf.set_random_seed(1337)
def __init__(self, isTest=5):
model.__init__(self, 'FC', 'Fully connective network')
Conf = Configuration_FC()
Conf.Return()
self.input_layer_input_w, self.input_layer_output_w, self.input_layer_output_b, self.core_layer_input_w, self.core_layer_output_w, self.core_layer_output_b, self.action_layer_input_w, self.action_layer_output_w, self.action_layer_output_b, self.batch_size, self.epoch, self.learning_rate, self.checkpoint_path = Conf.Return(
)
if isTest:
self.epoch = 1
np.random.seed(1337)
tf.set_random_seed(1337)
def __init__(self, isTest=5):
model.__init__(self, 'RandGlimpse', 'RandGlimpse network')
Conf = Configuration_RandGlimpse()
Conf.Return()
self.input_layer_input_w, self.input_layer_output_w, self.input_layer_output_b, self.core_layer_input_w, self.core_layer_output_w, self.core_layer_output_b, self.action_layer_input_w, self.action_layer_output_w, self.action_layer_output_b, self.batch_size, self.epoch, self.learning_rate, self.checkpoint_path = Conf.Return(
)
self.repeat, self.transfer_layer_input_w = Conf.Return_RNN()
self.sensorX, self.sensorY, self.depth, self.hg_size, self.hl_size, self.glimpse_size = Conf.Return_RandGlimpse(
)
if isTest:
self.epoch = 1
np.random.seed(1337)
tf.set_random_seed(1337)
def __init__(self, isTest=5):
model.__init__(self, 'CNN', 'CNN network')
Conf = Configuration_CNN()
Conf.Return()
Conf.Return_CNN()
self.input_layer_input_w, self.input_layer_output_w, self.input_layer_output_b, self.core_layer_input_w, self.core_layer_output_w, self.core_layer_output_b, self.action_layer_input_w, self.action_layer_output_w, self.action_layer_output_b, self.batch_size, self.epoch, self.learning_rate, self.checkpoint_path = Conf.Return(
)
self.filter_depth, self.filter_height, self.filter_width, self.in_channels, self.out_channels, self.stride = Conf.Return_CNN(
)
if isTest:
self.epoch = 1
np.random.seed(1337)
tf.set_random_seed(1337)
def __init__(self, vpath):
model.__init__(self, vpath)
##########################################################################################################
##
## General settings
##
## General characteristics of the model
##
##########################################################################################################
self.buildable = True # True if the model needs to be trained (built) before it can be used
# for prediction
self.quantitative = True # True for quantitative dependent variables, False for qualitative
# (e.g. 1 or 0) endpoints
self.confidential = True # True for building the model without storing any information about
# the training series stuctures
self.identity = False # If set to True, the structure of any query compound is compared with
# those in the training series, returning the annotated value
self.experimental = False # If set to True, and the input file contains a field with the label
# described by 'SDFileExperimental' this value is returned
##########################################################################################################
##
## Input setting
##
## Labels of SDFile fields recognized by the program in the input file. These have the following
## synthax. For example, if the SDFilenName field is 'name', the input file will contain this string
## as shown:
##
## > <name>
## aspirin
##
##########################################################################################################
self.SDFileName = 'name' # Label of the SDFile field in the input file containing the name of
# the molecule
self.SDFileActivity = 'activity' # Label of the SDFile field in the input file containing the value of
# the dependent variable. Leave blank for non-supervided models (PCA)
self.SDFileExperimental = '' # Label of the SDFile field in the input file containing the value to
# be returned, typically an experimentally determined value
self.SDFileMetadata = [
] # Label of any SDFile field that must be passed throug the normalization
# filters. Typically they contain information that is used by models
##########################################################################################################
##
## Normalization settings
##
## Define how the input structures are normalized and transformed prior to being processed
##
## These settings apply both to the stucture of the training series and query structures, which are
## submitted to exactly the same normalization workflow
##
## * Licenses: the program moka is third party software and requires a license activation
##
##########################################################################################################
self.norm = False # If True the structures are normalized. When set to False the values
# of all the following settings are ignored
self.normStand = True # If True the structures are submitted to the structural normalization
# program 'standardizer'
self.normNeutr = True # If True the ionization of the structures is adjusted to the pH
# detailed by 'normNeutr_pH' using the program defined below
self.normNeutrMethod = 'moka' # Name of the program used to adjust the ionization of the structures
# when the value of 'normNeutr' is set to True
self.normNeutr_pH = 7.4 # Value of the pH used to adjust the ionization of the structures
# when the value of 'normNeutr' is set to True
self.norm3D = True # If True the structures are converted to 3D using the program 'corina'
# irrespectivelly if the input structure is 2D or already 3D
##########################################################################################################
##
## Molecular descriptor settings
##
## Define the program used to calculate the molecular descriptors (MD) from the input structures and
## diverse parameters defining how these programs will carry out the molecular descriptor computation
##
## * Licenses: the program pentacle is third party software and requires a license activation
##
##########################################################################################################
self.MD = 'padel' # Program used to calculate the MD. Must be one of the following
# values: 'padel', 'pentacle', 'adriana' or 'external'
# padel relevant settings
self.padelMD = [
'-2d'
] # Type of padel MD to be calculared. Must be a subset of the values
# in the following list, separated by a comma: '-2d' or '-3d'
self.padelMaxRuntime = None # Timeout (in miliseconds) used by padel to compute a single molecule.
# If the computation time excedes this value the MD are undefined
self.padelDescriptor = None # Name of a xml formated filed containing the names of the MD computed
# by padel
# pentacle relevant settings
self.pentacleProbes = [
'DRY', 'O', 'N1', 'TIP'
] # Name of the molecular probes used by program pentacle to compute
# the molecular descriptors. Must be a subset of the values in the
# following list, separated by a comma: 'DRY', 'O', 'N1' or 'TIP'
self.pentacleOthers = [
] # Include here any command line parameter to be sent to the program
# pentacle
# external relevant settings
self.MDexternalFile = None # Name of a TSV with MD. The first colum must be a text string
# used to associate the MD with a given compound
self.MDexternalID = None # Name of the SDField containing the text string used in the TSV
# file for associating the MD with the compound
self.MDexternalField = None # Name of a SDFiled with a tab separated line containing MD for this
# molecule. This could be used to pass MD for predicted compound, not
# present in the TSV file used at model building time
##########################################################################################################
##
## Modeling settings
##
## Define the modeling method used to build the predictive model and diverse parameters defining
## how this method will work
##
##########################################################################################################
self.model = 'pls' # Name of the modeling method used to build the model. Must be one of
# the following values: 'pls', 'pca', 'RF'
# pls relevant settings
self.modelLV = 2 # Number of latent variables used to build the model
self.modelAutoscaling = True # If true, the molecular descriptors will be normalized to unit
# variance by dividing each value by the variable variance
self.modelCutoff = 'auto' # Applicable only for PLS-DA ('quantitative' is False). The cutoff
# value used to asign 1 or 0 to the predictions.
# When set to 'auto' the value is assigned automatically as the one
# producing the best compromise between sensitivity and specificity
self.selVar = False # If True the program will run the variable selection method
# defined by 'selVarMethod'. This setting can extend significantly
# the time required to build a model (by many hours or even days)
# variable selection relevant settings
self.selVarMethod = 'golpe' # Name of the variable selection method. Must be one the following
# values: 'golpe' (to be extended in following versions)
self.selVarLV = 2 # Number of latent variables used to build the reduced models used
# by the variable selection. Could be diferent than 'modelLV'
self.selVarCV = 'LOO' # Name of the cross-validation method used during the variable
# selection. Must be one the following values: 'LOO' (to be extended
# in following versions)
self.selVarRun = 2 # Number of maximumm sequential runs of the GOLPE algorithm to run
# before stop. The algorithm will run only until the model predictive
# ability (assesed by cross-validation) will no longer improve
self.selVarMask = None # Name of a file containing a previously computed mask of selected and
# non selected variables
# RF relevant settings
self.RFestimators = 100 # number of trees in the forest
self.RFfeatures = 'sqrt' # The number of features to consider when looking for the best split.
# Acceptable values are 'sqrt', 'log2' and 'none'
# - 'sqrt': then max_features=sqrt(n_features)
# - 'log2': then max_features=log2(n_features)
# - 'none': then max_features=n_features
self.RFclass_weight = 'balanced' # None or Balanced
# class_weight : dict, list of dicts, “balanced”
# The “balanced” mode uses the values of y to automatically adjust weights inversely
# proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))
self.RFtune = True # If true optimizes the values of RFestimators and RFfeatures and generates a
# diagnostic plot
self.RFrandom = False # If True a random seed is used for boostrapping
# RF: Qualitative and Quantitative
# Only valid for Qualitative Models # If True a random seed is used as aleatory, if False, random seed is fixed to 1226.
# int seed, RandomState instance, or None (default)
# The seed of the pseudo random number generator to use when shuffling the d.ta for
# probability estimation.
## Model Validation Settings
self.ModelValidationCV = 'loo' ## ('kfold', 'gkfold', 'stkfold', 'logo', 'lpgo', 'loo', 'lpo', 'shufsplit', 'gshufplit', 'stshufsplit', 'psplit', 'tsplit')
self.ModelValidationN = 2 ## int, Only for n_splits or n_groups
self.ModelValidationP = 1 ## int, Only for n_samples e.g. LeavePOut(p)
## kfold = KFold(n_splits=2, random_state=self.random_state, shuffle=False) ### K-Folds cross-validator
## gkfold = GroupKFold(n_splits=2) ### K-fold iterator variant with non-overlapping groups.
## stkfold = StratifiedKFold(n_splits=2, random_state=self.random_state, shuffle=False) ### Stratified K-Folds cross-validator
## logo = LeaveOneGroupOut() ### Leave One Group Out cross-validator
## lpgo = LeavePGroupsOut(n_groups=2) ### Leave P Group(s) Out cross-validator
## loo = LeaveOneOut() ### Leave-One-Out cross-validator
## lpo = LeavePOut(2) ### Leave-P-Out cross-validator
## shufsplit = ShuffleSplit(n_splits=3, random_state=0, test_size=0.25, train_size=None) ### Random permutation cross-validator
## gshufplit = GroupShuffleSplit(test_size=10, n_splits=100) ### Shuffle-Group(s)-Out cross-validation iterator
## stshufsplit = StratifiedShuffleSplit(n_splits=3, test_size=0.5, random_state=0) ### Stratified ShuffleSplit cross-validator
## psplit = PredefinedSplit(test_fold=array([ 0, 1, -1, 1])) ### Predefined split cross-validator
## tssplit = TimeSeriesSplit(n_splits=3) ### Time Series cross-validator
self.ModelValidationLC = False ## Plot learning curve
##########################################################################################################
##
## View settings
##
## Define the type of visual representation to be generated as well as some properties of the generated
## graphics, like the color, shape and dimension of the markers
##
## Available graphic types:
##
## 'pca' builds a PCA model with the training series of the selected model and the same MDs. Then
## represents the scores of the PC1 and PC2 in the horizontal and vertical axes
##
## 'property' represents the training series of the selected model using the log P for the X axis and
## the molecular weight (MW) for the Y axis, as computed using RDKit
##
## 'project' projects the training series of the selected model on a reference dataset for which
## it must have been generated a PCA model. The markers of each compound can be assigned
## colors according to the distance to model (DModX)
##
##
##########################################################################################################
self.viewType = 'property' # Type of graphic to generate. Must be one of the following
# values: 'pca', 'property' or 'project'
self.viewBackground = False # If True, the selected model is represented together with another
# dataset, shown as reference, defined by the two following settings
self.viewReferenceEndpoint = None # Name of the reference dataset (the endpoint label)
self.viewReferenceVersion = 0 # Version of the reference dataset
self.plotPCAColor = 'red' # Color of the marker
self.plotPCAMarkerShape = 'D' # Shape of the marker (see 'matplotlib.markers' document for a list)
self.plotPCAMarkerSize = 40 # Size of the marker
self.plotPCAMarkerLine = 0 # Thickness of the marker border
self.plotPRPColor = 'red' # Color of the marker
self.plotPRPMarkerShape = 'D' # Shape of the marker (see 'matplotlib.markers' document for a list)
self.plotPRPMarkerSize = 40 # Size of the marker
self.plotPRPMarkerLine = 0 # Thickness of the marker border
self.plotPRJColor = 'DModX' # Color of the marker or 'DModX' to assign a color according to
# the distance to model (DModX) of every compound
self.plotPRJMarkerShape = 'o' # Shape of the marker (see 'matplotlib.markers' document for a list)
self.plotPRJMarkerSize = 50 # Size of the marker
self.plotPRJMarkerLine = 1 # Thickness of the marker border
self.plotBGColor = '#aaaaaa' # Color of the marker
self.plotBGMarkerShape = 'o' # Shape of the marker (see 'matplotlib.markers' document for a list)
self.plotBGMarkerSize = 20 # Size of the marker
self.plotBGMarkerLine = 0 # Thickness of the marker border
##########################################################################################################
##
## Path to external programs
##
## Define the absolute path to diverse external programs used by the program. If the software is
## updated, the correct path must be introduced here
##
##########################################################################################################
self.mokaPath = '/opt/blabber/blabber4eTOX/'
self.padelPath = '/opt/padel/padel218ws/'
self.padelURL = 'http://localhost:9000/computedescriptors?params='
self.pentaclePath = '/opt/pentacle/pentacle106eTOX/'
self.adrianaPath = '/opt/AdrianaCode/AdrianaCode226/'
self.corinaPath = '/opt/corina/corina3494/'
self.javaPath = '/usr/java/jdk1.7.0_51/'
self.RPath = '/opt/R/R-3.0.2/'
self.standardiserPath = '/opt/standardiser/standardise20140206/'
def __init__(self):
model.__init__(self)
def __init__ (self, vpath):
model.__init__(self, vpath)
##########################################################################################################
##
## General settings
##
## General characteristics of the model
##
##########################################################################################################
self.buildable = True # True if the model needs to be trained (built) before it can be used
# for prediction
self.quantitative = True # True for quantitative dependent variables, False for qualitative
# (e.g. 1 or 0) endpoints
self.confidential = False # True for building the model without storing any information about
# the training series stuctures
self.identity = False # If set to True, the structure of any query compound is compared with
# those in the training series, returning the annotated value
self.experimental = False # If set to True, and the input file contains a field with the label
# described by 'SDFileExperimental' this value is returned
##########################################################################################################
##
## Input setting
##
## Labels of SDFile fields recognized by the program in the input file. These have the following
## synthax. For example, if the SDFilenName field is 'name', the input file will contain this string
## as shown:
##
## > <name>
## aspirin
##
##########################################################################################################
self.SDFileName = 'name' # Label of the SDFile field in the input file containing the name of
# the molecule
self.SDFileActivity = 'activity' # Label of the SDFile field in the input file containing the value of
# the dependent variable. Leave blank for non-supervided models (PCA)
self.SDFileExperimental = '' # Label of the SDFile field in the input file containing the value to
# be returned, typically an experimentally determined value
self.SDFileMetadata = [] # Label of any SDFile field that must be passed throug the normalization
# filters. Typically they contain information that is used by models
##########################################################################################################
##
## Normalization settings
##
## Define how the input structures are normalized and transformed prior to being processed
##
## These settings apply both to the stucture of the training series and query structures, which are
## submitted to exactly the same normalization workflow
##
## * Licenses: the program moka is third party software and requires a license activation
##
##########################################################################################################
self.norm = False # If True the structures are normalized. When set to False the values
# of all the following settings are ignored
self.normStand = True # If True the structures are submitted to the structural normalization
# program 'standardizer'
self.normNeutr = True # If True the ionization of the structures is adjusted to the pH
# detailed by 'normNeutr_pH' using the program defined below
self.normNeutrMethod = 'moka' # Name of the program used to adjust the ionization of the structures
# when the value of 'normNeutr' is set to True
self.normNeutr_pH = 7.4 # Value of the pH used to adjust the ionization of the structures
# when the value of 'normNeutr' is set to True
self.norm3D = True # If True the structures are converted to 3D using the program 'corina'
# irrespectivelly if the input structure is 2D or already 3D
##########################################################################################################
##
## Molecular descriptor settings
##
## Define the program used to calculate the molecular descriptors (MD) from the input structures and
## diverse parameters defining how these programs will carry out the molecular descriptor computation
##
## * Licenses: the program pentacle is third party software and requires a license activation
##
##########################################################################################################
self.MD = 'padel' # Program used to calculate the MD. Must be one of the following
# values: 'padel', 'pentacle', 'adriana' or 'external'
# padel relevant settings
self.padelMD = ['-2d'] # Type of padel MD to be calculared. Must be a subset of the values
# in the following list, separated by a comma: '-2d' or '-3d'
self.padelMaxRuntime = None # Timeout (in miliseconds) used by padel to compute a single molecule.
# If the computation time excedes this value the MD are undefined
self.padelDescriptor = None # Name of a xml formated filed containing the names of the MD computed
# by padel
# pentacle relevant settings
self.pentacleProbes = ['DRY','O',
'N1','TIP'] # Name of the molecular probes used by program pentacle to compute
# the molecular descriptors. Must be a subset of the values in the
# following list, separated by a comma: 'DRY', 'O', 'N1' or 'TIP'
self.pentacleOthers = [] # Include here any command line parameter to be sent to the program
# pentacle
# external relevant settings
self.MDexternalFile = None # Name of a TSV with MD. The first colum must be a text string
# used to associate the MD with a given compound
self.MDexternalID = None # Name of the SDField containing the text string used in the TSV
# file for associating the MD with the compound
self.MDexternalField = None # Name of a SDFiled with a tab separated line containing MD for this
# molecule. This could be used to pass MD for predicted compound, not
# present in the TSV file used at model building time
##########################################################################################################
##
## Modeling settings
##
## Define the modeling method used to build the predictive model and diverse parameters defining
## how this method will work
##
##########################################################################################################
self.model = 'pls' # Name of the modeling method used to build the model. Must be one of
# the following values: 'pls', 'pca', 'RF'
# pls relevant settings
self.modelLV = 2 # Number of latent variables used to build the model
self.modelAutoscaling = True # If true, the molecular descriptors will be normalized to unit
# variance by dividing each value by the variable variance
self.modelCutoff = 'auto' # Applicable only for PLS-DA ('quantitative' is False). The cutoff
# value used to asign 1 or 0 to the predictions.
# When set to 'auto' the value is assigned automatically as the one
# producing the best compromise between sensitivity and specificity
self.selVar = False # If True the program will run the variable selection method
# defined by 'selVarMethod'. This setting can extend significantly
# the time required to build a model (by many hours or even days)
# variable selection relevant settings
self.selVarMethod = 'golpe' # Name of the variable selection method. Must be one the following
# values: 'golpe' (to be extended in following versions)
self.selVarLV = 2 # Number of latent variables used to build the reduced models used
# by the variable selection. Could be diferent than 'modelLV'
self.selVarCV = 'LOO' # Name of the cross-validation method used during the variable
# selection. Must be one the following values: 'LOO' (to be extended
# in following versions)
self.selVarRun = 2 # Number of maximumm sequential runs of the GOLPE algorithm to run
# before stop. The algorithm will run only until the model predictive
# ability (assesed by cross-validation) will no longer improve
self.selVarMask = None # Name of a file containing a previously computed mask of selected and
# non selected variables
# RF relevant settings
self.RFestimators = 100 # number of trees in the forest
self.RFfeatures = 'sqrt' # The number of features to consider when looking for the best split.
# Acceptable values are 'sqrt', 'log2' and 'none'
# - 'sqrt': then max_features=sqrt(n_features)
# - 'log2': then max_features=log2(n_features)
# - 'none': then max_features=n_features
self.RFclass_weight = 'balanced' # None or Balanced
# class_weight : dict, list of dicts, “balanced”
# The “balanced” mode uses the values of y to automatically adjust weights inversely
# proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))
self.RFtune = True # If true optimizes the values of RFestimators and RFfeatures and generates a
# diagnostic plot
self.RFrandom = False # If True a random seed is used for boostrapping
# RF: Qualitative and Quantitative
# Only valid for Qualitative Models # If True a random seed is used as aleatory, if False, random seed is fixed to 1226.
# int seed, RandomState instance, or None (default)
# The seed of the pseudo random number generator to use when shuffling the d.ta for
# probability estimation.
## Model Validation Settings
self.ModelValidationCV = 'loo' ## ('kfold', 'gkfold', 'stkfold', 'logo', 'lpgo', 'loo', 'lpo', 'shufsplit', 'gshufplit', 'stshufsplit', 'psplit', 'tsplit')
self.ModelValidationN = 2 ## int, Only for n_splits or n_groups
self.ModelValidationP = 1 ## int, Only for n_samples e.g. LeavePOut(p)
## kfold = KFold(n_splits=2, random_state=self.random_state, shuffle=False) ### K-Folds cross-validator
## gkfold = GroupKFold(n_splits=2) ### K-fold iterator variant with non-overlapping groups.
## stkfold = StratifiedKFold(n_splits=2, random_state=self.random_state, shuffle=False) ### Stratified K-Folds cross-validator
## logo = LeaveOneGroupOut() ### Leave One Group Out cross-validator
## lpgo = LeavePGroupsOut(n_groups=2) ### Leave P Group(s) Out cross-validator
## loo = LeaveOneOut() ### Leave-One-Out cross-validator
## lpo = LeavePOut(2) ### Leave-P-Out cross-validator
## shufsplit = ShuffleSplit(n_splits=3, random_state=0, test_size=0.25, train_size=None) ### Random permutation cross-validator
## gshufplit = GroupShuffleSplit(test_size=10, n_splits=100) ### Shuffle-Group(s)-Out cross-validation iterator
## stshufsplit = StratifiedShuffleSplit(n_splits=3, test_size=0.5, random_state=0) ### Stratified ShuffleSplit cross-validator
## psplit = PredefinedSplit(test_fold=array([ 0, 1, -1, 1])) ### Predefined split cross-validator
## tssplit = TimeSeriesSplit(n_splits=3) ### Time Series cross-validator
self.ModelValidationLC = False ## Plot learning curve
##########################################################################################################
##
## View settings
##
## Define the type of visual representation to be generated as well as some properties of the generated
## graphics, like the color, shape and dimension of the markers
##
## Available graphic types:
##
## 'pca' builds a PCA model with the training series of the selected model and the same MDs. Then
## represents the scores of the PC1 and PC2 in the horizontal and vertical axes
##
## 'property' represents the training series of the selected model using the log P for the X axis and
## the molecular weight (MW) for the Y axis, as computed using RDKit
##
## 'project' projects the training series of the selected model on a reference dataset for which
## it must have been generated a PCA model. The markers of each compound can be assigned
## colors according to the distance to model (DModX)
##
##
##########################################################################################################
self.viewType = 'property' # Type of graphic to generate. Must be one of the following
# values: 'pca', 'property' or 'project'
self.viewBackground = False # If True, the selected model is represented together with another
# dataset, shown as reference, defined by the two following settings
self.viewReferenceEndpoint = None # Name of the reference dataset (the endpoint label)
self.viewReferenceVersion = 0 # Version of the reference dataset
self.plotPCAColor = 'red' # Color of the marker
self.plotPCAMarkerShape = 'D' # Shape of the marker (see 'matplotlib.markers' document for a list)
self.plotPCAMarkerSize = 40 # Size of the marker
self.plotPCAMarkerLine = 0 # Thickness of the marker border
self.plotPRPColor = 'red' # Color of the marker
self.plotPRPMarkerShape = 'D' # Shape of the marker (see 'matplotlib.markers' document for a list)
self.plotPRPMarkerSize = 40 # Size of the marker
self.plotPRPMarkerLine = 0 # Thickness of the marker border
self.plotPRJColor = 'DModX' # Color of the marker or 'DModX' to assign a color according to
# the distance to model (DModX) of every compound
self.plotPRJMarkerShape = 'o' # Shape of the marker (see 'matplotlib.markers' document for a list)
self.plotPRJMarkerSize = 50 # Size of the marker
self.plotPRJMarkerLine = 1 # Thickness of the marker border
self.plotBGColor = '#aaaaaa' # Color of the marker
self.plotBGMarkerShape = 'o' # Shape of the marker (see 'matplotlib.markers' document for a list)
self.plotBGMarkerSize = 20 # Size of the marker
self.plotBGMarkerLine = 0 # Thickness of the marker border
##########################################################################################################
##
## Path to external programs
##
## Define the absolute path to diverse external programs used by the program. If the software is
## updated, the correct path must be introduced here
##
##########################################################################################################
self.mokaPath = '/opt/blabber/blabber4eTOX/'
self.padelPath = '/opt/padel/padel218ws/'
self.padelURL = 'http://localhost:9000/computedescriptors?params='
self.pentaclePath = '/opt/pentacle/pentacle106eTOX/'
self.adrianaPath = '/opt/AdrianaCode/AdrianaCode226/'
self.corinaPath = '/opt/corina/corina3494/'
self.javaPath = '/usr/java/jdk1.7.0_51/'
self.RPath = '/opt/R/R-3.0.2/'
self.standardiserPath = '/opt/standardiser/standardise20140206/'
def __init__(self, parent, stype='dm15'):
if stype != 'dm15':
raise ValueError, "This model only supports the dm15 parameter"
model.__init__(self, parent)
self.rbs = [
'u', 'B', 'V', 'g', 'r', 'i', 'Y', 'J', 'H', 'K', 'Bs', 'Vs', 'Rs',
'Is', 'J_K', 'H_K'
]
self.parameters = {
'DM': None,
'dm15': None,
'EBVhost': None,
'Tmax': None
}
self.errors = {'DM': 0, 'dm15': 0, 'EBVhost': 0, 'Tmax': 0}
self.template = NIR_ubertemp.template()
# R_V as a function of which calibration fit number (see Folatelli et
# al. (2009) table 9
self.Rv_host = {1: 0, 2: 3.10, 3: 1.50, 4: 1.46, 5: 1.46, 6: 1.01}
self.dRv_host = {1: 0, 2: 0, 3: 0.11, 4: 0.10, 5: 0.33, 6: 0.31}
self.M0 = {
1: -19.07,
2: -19.39,
3: -19.15,
4: -19.13,
5: -19.16,
6: -19.11
}
self.dM0 = {1: 0.01, 2: 0.02, 3: 0.02, 4: 0.01, 5: 0.03, 6: 0.02}
self.b = {1: 1.03, 2: 0.98, 3: 0.94, 4: 1.05, 5: 0.94, 6: 1.08}
self.db = {1: 0.25, 2: 0.41, 3: 0.11, 4: 0.11, 5: 0.12, 6: 0.11}
# B-X pseudo-colors from Folatelli et al. (2009) table 3
self.colors = {
'u': -0.32,
'B': 0.0,
'V': -0.02,
'g': 0.05,
'r': -0.09,
'i': -0.63,
'Y': -0.69,
'J': -0.65,
'H': -0.79,
'K': -0.61,
'J_K': -0.65,
'H_K': -0.79
}
self.dcolors = {
'u': 0.04,
'B': 0,
'V': 0.01,
'g': 0.02,
'r': 0.02,
'i': 0.02,
'Y': 0.03,
'J': 0.02,
'H': 0.03,
'K': 0.05,
'J_K': 0.02,
'H_K': 0.03,
'Bs': 0,
'Vs': 0,
'Rs': 0,
'Is': 0
}
self.color_slopes = {
'u': -0.47,
'B': 0.0,
'V': 0.12,
'g': 0.05,
'r': 0.29,
'i': 0.39,
'Y': 0.63,
'J': 0.67,
'H': 0.66,
'K': 0.26,
'J_K': 0.67,
'H_K': 0.66
}
self.dcolor_slopes = {
'u': 0.25,
'B': 0,
'V': 0.05,
'g': 0.06,
'r': 0.07,
'i': 0.08,
'Y': 0.17,
'J': 0.10,
'H': 0.11,
'K': 0.18,
'J_K': 0.10,
'H_K': 0.11,
'Bs': 0,
'Vs': 0,
'Rs': 0,
'Is': 0
}
self.do_Robs = 0
self.Robs = {}