qsprpred.data.descriptors package
Submodules
qsprpred.data.descriptors.fingerprints module
Fingerprint classes.
- class qsprpred.data.descriptors.fingerprints.AtomPairFP(nBits=2048, **kwargs)[source]
Bases:
FingerprintAtom pair fingerprint.
- Variables:
Initialize the atom pair fingerprint.
- Parameters:
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the atom pair fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.AvalonFP(nBits=1024, **kwargs)[source]
Bases:
FingerprintAvalon fingerprint.
- Variables:
Initialize the Avalon fingerprint.
- Parameters:
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the Avalon fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.Fingerprint(used_bits: list[int] | None = None)[source]
Bases:
DescriptorSet,ABCBase class for calculation of binary fingerprints.
- Variables:
Initialize the fingerprint.
- Parameters:
used_bits (list) – list of bits of the fingerprint currently being used
- property dtype
Data type of the descriptor.
- abstract getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray
Method to calculate descriptors for a list of molecules.
This method should use molecules as they are without any preparation. Any preparation steps should be defined in the
DescriptorSet.prepMolsmethod., which is picked up by the mainDescriptorSet.__call__.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.LayeredFP(minPath=1, maxPath=7, nBits=2048, **kwargs)[source]
Bases:
FingerprintLayered fingerprint.
- Variables:
Initialize the layered fingerprint.
- Parameters:
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the layered fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.MaccsFP(nBits=167, **kwargs)[source]
Bases:
FingerprintMACCS keys fingerprint.
- Variables:
Initialize the MACCS keys fingerprint.
- Parameters:
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the MACCS keys fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.MorganFP(radius=2, nBits=2048, **kwargs)[source]
Bases:
FingerprintMorgan fingerprint.
- Variables:
Initialize the Morgan fingerprint.
- Parameters:
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the Morgan fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.PatternFP(nBits=2048, **kwargs)[source]
Bases:
FingerprintPattern fingerprint.
- Variables:
Initialize the pattern fingerprint.
- Parameters:
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the pattern fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.RDKitFP(minPath=1, maxPath=7, nBits=2048, **kwargs)[source]
Bases:
FingerprintRDKit fingerprint.
This is a wrapper around RDKit’s RDKFingerprint function.
- Variables:
Initialize the RDKit fingerprint.
- Parameters:
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the RDKit fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.RDKitMACCSFP(used_bits: list[int] | None = None)[source]
Bases:
FingerprintRDKits implementation of MACCS keys fingerprint.
Initialize the fingerprint.
- Parameters:
used_bits (list) – list of bits of the fingerprint currently being used
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the MACCS keys fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.fingerprints.TopologicalFP(nBits=2048, **kwargs)[source]
Bases:
FingerprintTopological torsion fingerprint.
- Variables:
Initialize the topological torsion fingerprint.
- Parameters:
- property dtype
Data type of the descriptor.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the topological torsion fingerprints for the input molecules.
- property isFP
Whether the descriptor is a fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
qsprpred.data.descriptors.sets module
Descriptorset: a collection of descriptors that can be calculated for a molecule. To add a new descriptor or fingerprint calculator: * Add a descriptor subclass for your descriptor calculator * Add a function to retrieve your descriptor by name to the descriptor retriever class
- class qsprpred.data.descriptors.sets.DataFrameDescriptorSet(df: DataFrame, joining_cols: list[str] | None = None, suffix: str = '', source_is_multi_index: bool = False)[source]
Bases:
DescriptorSetDescriptorSetthat uses apandas.DataFrameof precalculated descriptors.- Variables:
Initialize the descriptor set with a dataframe of descriptors.
- Parameters:
df – dataframe of descriptors
joining_cols – list of columns to use as joining index, properties of the same name must exist in the data set this descriptor is added to
suffix – suffix to add to the descriptor name
source_is_multi_index – assume that a multi-index is already present in the supplied dataframe. If
True, thejoining_colsargument must also be specified to indicate which properties should be used to create the multi-index in the destination.
- property dtype
Return the data type of the descriptor values.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Return the descriptors for the input molecules. It simply searches for descriptor values in the data frame using the
idPropas index.- Parameters:
mols – list of SMILES or RDKit molecules
props – dictionary of properties
*args – positional arguments
**kwargs – keyword arguments
- Returns:
descriptor values of shape (n_mols, n_descriptors)
- Return type:
(np.ndarray)
- property isFP
Return True if descriptor set is a binary fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- prepMols(mols: list[str | Mol]) list[Mol]
Prepare the molecules for descriptor calculation.
- Parameters:
mols – list of SMILES or RDKit molecules
- Returns:
list of RDKit molecules
- Return type:
(list[Mol])
- static setIndex(df: DataFrame, cols: list[str]) DataFrame[source]
Create a multi-index from several columns of the data set.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.sets.DescriptorSet(id_prop: str | None = 'ID')[source]
Bases:
JSONSerializable,MolProcessorWithID,ABCMolProcessorWithIDthat calculates descriptors for a molecule.- Variables:
Initialize the processor with the name of the property that contains the molecule’s unique identifier.
- Parameters:
id_prop (str) – Name of the property that contains the molecule’s unique identifier. Defaults to “QSPRID”.
- property dtype
Return the data type of the descriptor values.
- abstract getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Method to calculate descriptors for a list of molecules.
This method should use molecules as they are without any preparation. Any preparation steps should be defined in the
DescriptorSet.prepMolsmethod., which is picked up by the mainDescriptorSet.__call__.
- property isFP
Return True if descriptor set is a binary fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None][source]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]][source]
Parse the properties and molecules passed to the descriptor set.
- prepMols(mols: list[str | Mol]) list[Mol][source]
Prepare the molecules for descriptor calculation.
- Parameters:
mols – list of SMILES or RDKit molecules
- Returns:
list of RDKit molecules
- Return type:
(list[Mol])
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- class qsprpred.data.descriptors.sets.DrugExPhyschem(physchem_props: list[str] | None = None)[source]
Bases:
DescriptorSetVarious properties used for scoring in DrugEx.
- Variables:
Initialize the descriptorset with Property arguments (a list of properties to calculate) to select a subset.
- Parameters:
physchem_props – list of properties to calculate
- property dtype
Return the data type of the descriptor values.
- getDescriptors(mols, props, *args, **kwargs)[source]
Calculate the DrugEx properties for a molecule.
- Parameters:
mols – list of SMILES or RDKit molecules
props – dictionary of properties for the passed molecules
args – positional arguments
kwargs – keyword arguments
- Returns:
array of descriptor values of shape (n_mols, n_descriptors)
- Return type:
np.ndarray
- static getPropDict() dict[str, callable][source]
Return a dictionary of DrugEx properties and their corresponding functions.
- property isFP
Return True if descriptor set is a binary fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- prepMols(mols: list[str | Mol]) list[Mol]
Prepare the molecules for descriptor calculation.
- Parameters:
mols – list of SMILES or RDKit molecules
- Returns:
list of RDKit molecules
- Return type:
(list[Mol])
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.sets.PredictorDesc(model: Type[ForwardRef('QSPRModel')] | str)[source]
Bases:
DescriptorSetMoleculeDescriptorSet that uses a Predictor object to calculate descriptors from a molecule.
- Variables:
Initialize the descriptorset with a
QSPRModelobject.- Parameters:
model (QSPRModel) – a fitted model instance or a path to the model’s meta file
- property descriptors
Return the descriptors names.
- property dtype
Return the data type of the descriptor values.
- getDescriptors(mols: list[str | Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the descriptor for a list of molecules.
- property isFP
Return True if descriptor set is a binary fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- prepMols(mols: list[str | Mol]) list[Mol]
Prepare the molecules for descriptor calculation.
- Parameters:
mols – list of SMILES or RDKit molecules
- Returns:
list of RDKit molecules
- Return type:
(list[Mol])
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.sets.RDKitDescs(rdkit_descriptors: list[str] | None = None, include_3d: bool = False)[source]
Bases:
DescriptorSetCalculate RDkit descriptors.
- Variables:
Initialize the descriptorset with a list of RDKit descriptors to calculate.
- Parameters:
- property dtype
Return the data type of the descriptor values.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the RDKit descriptors for a molecule.
- property isFP
Return True if descriptor set is a binary fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- prepMols(mols: list[str | Mol]) list[Mol]
Prepare the molecules for descriptor calculation.
- Parameters:
mols – list of SMILES or RDKit molecules
- Returns:
list of RDKit molecules
- Return type:
(list[Mol])
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.sets.RandomDescs(n: int = 10, missing: float | int | None = None, seed: int | None = None)[source]
Bases:
DescriptorSet,RandomizedDescriptorset of a set of random numbers as descriptors.
Note. that when setting the randomState the seed of random number generator is based on the idProp of the dataset. Therefore, if the id of the molecules changes the results will be different, however if the order of the molecules is shuffled the descriptors per molecule will remain the same.
- Variables:
Initialize the descriptorset with a number of random descriptors.
- Parameters:
- property dtype
Return the data type of the descriptor values.
- getDescriptors(mols: list[str | Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the descriptor for a list of molecules.
- property isFP
Return True if descriptor set is a binary fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- prepMols(mols: list[str | Mol]) list[Mol]
Prepare the molecules for descriptor calculation.
- Parameters:
mols – list of SMILES or RDKit molecules
- Returns:
list of RDKit molecules
- Return type:
(list[Mol])
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
- class qsprpred.data.descriptors.sets.SmilesDesc(id_prop: str | None = 'ID')[source]
Bases:
DescriptorSetDescriptorset that calculates descriptors from a SMILES sequence.
- Variables:
descriptors (list) – list of descriptor
Initialize the processor with the name of the property that contains the molecule’s unique identifier.
- Parameters:
id_prop (str) – Name of the property that contains the molecule’s unique identifier. Defaults to “QSPRID”.
- property dtype
Return the data type of the descriptor values.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Return smiles as descriptors.
- property isFP
Return True if descriptor set is a binary fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- prepMols(mols: list[str | Mol]) list[Mol]
Prepare the molecules for descriptor calculation.
- Parameters:
mols – list of SMILES or RDKit molecules
- Returns:
list of RDKit molecules
- Return type:
(list[Mol])
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- class qsprpred.data.descriptors.sets.TanimotoDistances(list_of_smiles: list[str], fingerprint_type: Type[ForwardRef('Fingerprint')], *args, **kwargs)[source]
Bases:
DescriptorSetCalculate Tanimoto distances to a list of SMILES sequences.
- Variables:
fingerprintType (Fingerprint) – fingerprint type to use.
descriptors (list) – list of descriptor names
_descriptors (list) – list of SMILES sequences to calculate the distances.
_args –
fingerprintarguments_kwargs –
fingerprintkeyword arguments, should contain fingerprint_type
Initialize the descriptorset with a list of SMILES sequences and a fingerprint type.
- Parameters:
list_of_smiles (list of strings) – list of SMILES to calculate the distances.
fingerprint_type (Fingerprint) – fingerprint type to use.
*args –
fingerprintarguments**kwargs –
fingerprintkeyword arguments, should contain fingerprint_type
- calculate_fingerprints(list_of_smiles: list[str]) list[ExplicitBitVect][source]
Calculate the fingerprints for the list of SMILES sequences
- property dtype
Return the data type of the descriptor values.
- getDescriptors(mols: list[Mol], props: dict[str, list[Any]], *args, **kwargs) ndarray[source]
Calculate the Tanimoto distances to the list of SMILES sequences.
- Parameters:
- property isFP
Return True if descriptor set is a binary fingerprint.
- static iterMols(mols: list[str | Mol], to_list=False) list[Mol] | Generator[Mol, None, None]
Create a molecule generator or list from RDKit molecules or SMILES.
- iterMolsAndIDs(mols, props: dict[str, list] | None)
Iterate over molecules and their corresponding IDs regardless of the input molecule format. This is just a helper function that will detect the input and yield the molecule and its ID.
- parsePropsAndMols(mols: list[str | Mol], props: dict[str, list[Any]] | None) tuple[list[Mol], dict[str, list[Any]]]
Parse the properties and molecules passed to the descriptor set.
- prepMols(mols: list[str | Mol]) list[Mol]
Prepare the molecules for descriptor calculation.
- Parameters:
mols – list of SMILES or RDKit molecules
- Returns:
list of RDKit molecules
- Return type:
(list[Mol])
- property requiredProps: list[str]
The properties required by the processor. This is to inform the caller that the processor requires certain properties to be passed to the
__call__method or via thepropsattribute ofStoredMolinstances.
- toFile(filename: str) str
Serialize object to a JSON file. This JSON file should contain all data necessary to reconstruct the object.
- toJSON() str
- Serialize object to a JSON string. This JSON string should
contain all data necessary to reconstruct the object.
- Returns:
JSON string of the object
- Return type:
json (str)
- transformToFeatureNames() list[str]
Transform the descriptor names to feature names by adding the descriptor set name as a prefix.
- static treatInfs(df: DataFrame) DataFrame
Replace infinite values by NaNs.
- Parameters:
df (pd.DataFrame) – dataframe to treat
- Returns:
dataframe with infinite values replaced by NaNs
- Return type:
(pd.DataFrame)
qsprpred.data.descriptors.tests module
- class qsprpred.data.descriptors.tests.TestDescriptorCalculation(methodName='runTest')[source]
Bases:
DataSetsPathMixIn,QSPRTestCaseTest the calculation of descriptors.
Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.
- classmethod addClassCleanup(function, /, *args, **kwargs)
Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).
- addCleanup(function, /, *args, **kwargs)
Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.
Cleanup items are called even if setUp fails (unlike tearDown).
- addTypeEqualityFunc(typeobj, function)
Add a type specific assertEqual style function to compare a type.
This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.
- Parameters:
typeobj – The data type to call this function on when both values are of the same type in assertEqual().
function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.
- assertAlmostEqual(first, second, places=None, msg=None, delta=None)
Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.
Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).
If the two objects compare equal then they will automatically compare almost equal.
- assertCountEqual(first, second, msg=None)
Asserts that two iterables have the same elements, the same number of times, without regard to order.
- self.assertEqual(Counter(list(first)),
Counter(list(second)))
- Example:
[0, 1, 1] and [1, 0, 1] compare equal.
[0, 0, 1] and [0, 1] compare unequal.
- assertDictEqual(d1, d2, msg=None)
- assertEndsWith(s, suffix, msg=None)
- assertEqual(first, second, msg=None)
Fail if the two objects are unequal as determined by the ‘==’ operator.
- assertFalse(expr, msg=None)
Check that the expression is false.
- assertGreater(a, b, msg=None)
Just like self.assertTrue(a > b), but with a nicer default message.
- assertGreaterEqual(a, b, msg=None)
Just like self.assertTrue(a >= b), but with a nicer default message.
- assertHasAttr(obj, name, msg=None)
- assertIn(member, container, msg=None)
Just like self.assertTrue(a in b), but with a nicer default message.
- assertIs(expr1, expr2, msg=None)
Just like self.assertTrue(a is b), but with a nicer default message.
- assertIsInstance(obj, cls, msg=None)
Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.
- assertIsNone(obj, msg=None)
Same as self.assertTrue(obj is None), with a nicer default message.
- assertIsNot(expr1, expr2, msg=None)
Just like self.assertTrue(a is not b), but with a nicer default message.
- assertIsNotNone(obj, msg=None)
Included for symmetry with assertIsNone.
- assertIsSubclass(cls, superclass, msg=None)
- assertLess(a, b, msg=None)
Just like self.assertTrue(a < b), but with a nicer default message.
- assertLessEqual(a, b, msg=None)
Just like self.assertTrue(a <= b), but with a nicer default message.
- assertListEqual(list1, list2, msg=None)
A list-specific equality assertion.
- Parameters:
list1 – The first list to compare.
list2 – The second list to compare.
msg – Optional message to use on failure instead of a list of differences.
- assertLogs(logger=None, level=None)
Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.
This method must be used as a context manager, and will yield a recording object with two attributes:
outputandrecords. At the end of the context manager, theoutputattribute will be a list of the matching formatted log messages and therecordsattribute will be a list of the corresponding LogRecord objects.Example:
with self.assertLogs('foo', level='INFO') as cm: logging.getLogger('foo').info('first message') logging.getLogger('foo.bar').error('second message') self.assertEqual(cm.output, ['INFO:foo:first message', 'ERROR:foo.bar:second message'])
- assertMultiLineEqual(first, second, msg=None)
Assert that two multi-line strings are equal.
- assertNoLogs(logger=None, level=None)
Fail unless no log messages of level level or higher are emitted on logger_name or its children.
This method must be used as a context manager.
- assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)
Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.
Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).
Objects that are equal automatically fail.
- assertNotEndsWith(s, suffix, msg=None)
- assertNotEqual(first, second, msg=None)
Fail if the two objects are equal as determined by the ‘!=’ operator.
- assertNotHasAttr(obj, name, msg=None)
- assertNotIn(member, container, msg=None)
Just like self.assertTrue(a not in b), but with a nicer default message.
- assertNotIsInstance(obj, cls, msg=None)
Included for symmetry with assertIsInstance.
- assertNotIsSubclass(cls, superclass, msg=None)
- assertNotRegex(text, unexpected_regex, msg=None)
Fail the test if the text matches the regular expression.
- assertNotStartsWith(s, prefix, msg=None)
- assertRaises(expected_exception, *args, **kwargs)
Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.
If called with the callable and arguments omitted, will return a context object used like this:
with self.assertRaises(SomeException): do_something()
An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.
The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:
with self.assertRaises(SomeException) as cm: do_something() the_exception = cm.exception self.assertEqual(the_exception.error_code, 3)
- assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)
Asserts that the message in a raised exception matches a regex.
- Parameters:
expected_exception – Exception class expected to be raised.
expected_regex – Regex (re.Pattern object or string) expected to be found in error message.
args – Function to be called and extra positional args.
kwargs – Extra kwargs.
msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.
- assertRegex(text, expected_regex, msg=None)
Fail the test unless the text matches the regular expression.
- assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)
An equality assertion for ordered sequences (like lists and tuples).
For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.
- Parameters:
seq1 – The first sequence to compare.
seq2 – The second sequence to compare.
seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.
msg – Optional message to use on failure instead of a list of differences.
- assertSetEqual(set1, set2, msg=None)
A set-specific equality assertion.
- Parameters:
set1 – The first set to compare.
set2 – The second set to compare.
msg – Optional message to use on failure instead of a list of differences.
assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).
- assertStartsWith(s, prefix, msg=None)
- assertTrue(expr, msg=None)
Check that the expression is true.
- assertTupleEqual(tuple1, tuple2, msg=None)
A tuple-specific equality assertion.
- Parameters:
tuple1 – The first tuple to compare.
tuple2 – The second tuple to compare.
msg – Optional message to use on failure instead of a list of differences.
- assertWarns(expected_warning, *args, **kwargs)
Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.
If called with the callable and arguments omitted, will return a context object used like this:
with self.assertWarns(SomeWarning): do_something()
An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.
The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:
with self.assertWarns(SomeWarning) as cm: do_something() the_warning = cm.warning self.assertEqual(the_warning.some_attribute, 147)
- assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)
Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.
- Parameters:
expected_warning – Warning class expected to be triggered.
expected_regex – Regex (re.Pattern object or string) expected to be found in error message.
args – Function to be called and extra positional args.
kwargs – Extra kwargs.
msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.
- clearGenerated()
Remove the directories that are used for testing.
- countTestCases()
- createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': TargetTasks.MULTICLASS, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': TargetTasks.REGRESSION}], random_state=42)
Create a large dataset for testing purposes.
- Parameters:
- Returns:
a
QSPRDataSetobject- Return type:
- createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': TargetTasks.REGRESSION}], random_state=42, n_jobs=1, chunk_size=None, drop_empty_target_props=True)
Create a large dataset for testing purposes.
- Parameters:
- Returns:
a
QSPRDataSetobject- Return type:
- createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': TargetTasks.REGRESSION}], random_state=42, drop_empty_target_props=True)
Create a small dataset for testing purposes.
- Parameters:
- Returns:
a
QSPRDataSetobject- Return type:
- createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': TargetTasks.REGRESSION}], random_state=None, n_jobs=1, chunk_size=None, drop_empty_target_props=True)
Create a dataset for testing purposes from the given data frame.
- Parameters:
df (pd.DataFrame) – data frame containing the dataset
name (str) – name of the dataset
target_props (List of dicts or TargetProperty) – list of target properties
random_state (int) – random state to use for splitting and shuffling
prep (dict) – dictionary containing preparation settings
n_jobs (int) – number of jobs to use for parallel processing
chunk_size (int) – size of chunks to use per job in parallel processing
- Returns:
a
QSPRDataSetobject- Return type:
- debug()
Run the test without collecting errors in a TestResult
- defaultTestResult()
- classmethod doClassCleanups()
Execute all class cleanup functions. Normally called for you after tearDownClass.
- doCleanups()
Execute all cleanup functions. Normally called for you after tearDown.
- classmethod enterClassContext(cm)
Same as enterContext, but class-wide.
- enterContext(cm)
Enters the supplied context manager.
If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.
- fail(msg=None)
Fail immediately, with the given message.
- failureException
alias of
AssertionError
- classmethod getAllDescriptorSets()
Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.
TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.
- getBigDF()
Get a large data frame for testing purposes.
- Returns:
a
pandas.DataFramecontaining the dataset- Return type:
pd.DataFrame
- classmethod getDataPrepGrid()
Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.
- Returns:
a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)
- Return type:
grid
- classmethod getDefaultCalculatorCombo()
Makes a list of default descriptor calculators that can be used in tests.
It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.
- static getDefaultPrep(add_imputer=None)
Return a dictionary with default preparation settings.
- classmethod getPrepCombos()
Return a list of all possible preparation combinations as generated by
getDataPrepGridas well as their names. The generated list can be used to parameterize tests with the given named combinations.
- getSmallDF()
Get a small data frame for testing purposes.
- Returns:
a
pandas.DataFramecontaining the dataset- Return type:
pd.DataFrame
- getStorage(df, name, n_jobs=1, chunk_size=None)
- id()
- longMessage = True
- maxDiff = 640
- run(result=None)
- classmethod setUpClass()
Hook method for setting up class fixture before running tests in the class.
- setUpPaths()
Create the directories that are used for testing.
- shortDescription()
Returns a one-line description of the test, or None if no description has been provided.
The default implementation of this method returns the first line of the specified test method’s docstring.
- skipTest(reason)
Skip this test.
- subTest(msg=<object object>, **params)
Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.
- tearDown()
Remove all files and directories that are used for testing.
- classmethod tearDownClass()
Hook method for deconstructing the class fixture after running all tests in the class.
- testSwitching = None
- testSwitching_0(**kw)
Test if the feature calculator can be switched to a new dataset [with n_cpu=1, chunk_size=None].
- testSwitching_1(**kw)
Test if the feature calculator can be switched to a new dataset [with n_cpu=2, chunk_size=None].
- testSwitching_2(**kw)
Test if the feature calculator can be switched to a new dataset [with n_cpu=1, chunk_size=17].
- testSwitching_3(**kw)
Test if the feature calculator can be switched to a new dataset [with n_cpu=2, chunk_size=17].
- class qsprpred.data.descriptors.tests.TestDescriptorSets(methodName='runTest')[source]
Bases:
DataSetsPathMixIn,QSPRTestCaseTest the descriptor sets.
Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.
- classmethod addClassCleanup(function, /, *args, **kwargs)
Same as addCleanup, except the cleanup items are called even if setUpClass fails (unlike tearDownClass).
- addCleanup(function, /, *args, **kwargs)
Add a function, with arguments, to be called when the test is completed. Functions added are called on a LIFO basis and are called after tearDown on test failure or success.
Cleanup items are called even if setUp fails (unlike tearDown).
- addTypeEqualityFunc(typeobj, function)
Add a type specific assertEqual style function to compare a type.
This method is for use by TestCase subclasses that need to register their own type equality functions to provide nicer error messages.
- Parameters:
typeobj – The data type to call this function on when both values are of the same type in assertEqual().
function – The callable taking two arguments and an optional msg= argument that raises self.failureException with a useful error message when the two arguments are not equal.
- assertAlmostEqual(first, second, places=None, msg=None, delta=None)
Fail if the two objects are unequal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is more than the given delta.
Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).
If the two objects compare equal then they will automatically compare almost equal.
- assertCountEqual(first, second, msg=None)
Asserts that two iterables have the same elements, the same number of times, without regard to order.
- self.assertEqual(Counter(list(first)),
Counter(list(second)))
- Example:
[0, 1, 1] and [1, 0, 1] compare equal.
[0, 0, 1] and [0, 1] compare unequal.
- assertDictEqual(d1, d2, msg=None)
- assertEndsWith(s, suffix, msg=None)
- assertEqual(first, second, msg=None)
Fail if the two objects are unequal as determined by the ‘==’ operator.
- assertFalse(expr, msg=None)
Check that the expression is false.
- assertGreater(a, b, msg=None)
Just like self.assertTrue(a > b), but with a nicer default message.
- assertGreaterEqual(a, b, msg=None)
Just like self.assertTrue(a >= b), but with a nicer default message.
- assertHasAttr(obj, name, msg=None)
- assertIn(member, container, msg=None)
Just like self.assertTrue(a in b), but with a nicer default message.
- assertIs(expr1, expr2, msg=None)
Just like self.assertTrue(a is b), but with a nicer default message.
- assertIsInstance(obj, cls, msg=None)
Same as self.assertTrue(isinstance(obj, cls)), with a nicer default message.
- assertIsNone(obj, msg=None)
Same as self.assertTrue(obj is None), with a nicer default message.
- assertIsNot(expr1, expr2, msg=None)
Just like self.assertTrue(a is not b), but with a nicer default message.
- assertIsNotNone(obj, msg=None)
Included for symmetry with assertIsNone.
- assertIsSubclass(cls, superclass, msg=None)
- assertLess(a, b, msg=None)
Just like self.assertTrue(a < b), but with a nicer default message.
- assertLessEqual(a, b, msg=None)
Just like self.assertTrue(a <= b), but with a nicer default message.
- assertListEqual(list1, list2, msg=None)
A list-specific equality assertion.
- Parameters:
list1 – The first list to compare.
list2 – The second list to compare.
msg – Optional message to use on failure instead of a list of differences.
- assertLogs(logger=None, level=None)
Fail unless a log message of level level or higher is emitted on logger_name or its children. If omitted, level defaults to INFO and logger defaults to the root logger.
This method must be used as a context manager, and will yield a recording object with two attributes:
outputandrecords. At the end of the context manager, theoutputattribute will be a list of the matching formatted log messages and therecordsattribute will be a list of the corresponding LogRecord objects.Example:
with self.assertLogs('foo', level='INFO') as cm: logging.getLogger('foo').info('first message') logging.getLogger('foo.bar').error('second message') self.assertEqual(cm.output, ['INFO:foo:first message', 'ERROR:foo.bar:second message'])
- assertMultiLineEqual(first, second, msg=None)
Assert that two multi-line strings are equal.
- assertNoLogs(logger=None, level=None)
Fail unless no log messages of level level or higher are emitted on logger_name or its children.
This method must be used as a context manager.
- assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)
Fail if the two objects are equal as determined by their difference rounded to the given number of decimal places (default 7) and comparing to zero, or by comparing that the difference between the two objects is less than the given delta.
Note that decimal places (from zero) are usually not the same as significant digits (measured from the most significant digit).
Objects that are equal automatically fail.
- assertNotEndsWith(s, suffix, msg=None)
- assertNotEqual(first, second, msg=None)
Fail if the two objects are equal as determined by the ‘!=’ operator.
- assertNotHasAttr(obj, name, msg=None)
- assertNotIn(member, container, msg=None)
Just like self.assertTrue(a not in b), but with a nicer default message.
- assertNotIsInstance(obj, cls, msg=None)
Included for symmetry with assertIsInstance.
- assertNotIsSubclass(cls, superclass, msg=None)
- assertNotRegex(text, unexpected_regex, msg=None)
Fail the test if the text matches the regular expression.
- assertNotStartsWith(s, prefix, msg=None)
- assertRaises(expected_exception, *args, **kwargs)
Fail unless an exception of class expected_exception is raised by the callable when invoked with specified positional and keyword arguments. If a different type of exception is raised, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception.
If called with the callable and arguments omitted, will return a context object used like this:
with self.assertRaises(SomeException): do_something()
An optional keyword argument ‘msg’ can be provided when assertRaises is used as a context object.
The context manager keeps a reference to the exception as the ‘exception’ attribute. This allows you to inspect the exception after the assertion:
with self.assertRaises(SomeException) as cm: do_something() the_exception = cm.exception self.assertEqual(the_exception.error_code, 3)
- assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)
Asserts that the message in a raised exception matches a regex.
- Parameters:
expected_exception – Exception class expected to be raised.
expected_regex – Regex (re.Pattern object or string) expected to be found in error message.
args – Function to be called and extra positional args.
kwargs – Extra kwargs.
msg – Optional message used in case of failure. Can only be used when assertRaisesRegex is used as a context manager.
- assertRegex(text, expected_regex, msg=None)
Fail the test unless the text matches the regular expression.
- assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)
An equality assertion for ordered sequences (like lists and tuples).
For the purposes of this function, a valid ordered sequence type is one which can be indexed, has a length, and has an equality operator.
- Parameters:
seq1 – The first sequence to compare.
seq2 – The second sequence to compare.
seq_type – The expected datatype of the sequences, or None if no datatype should be enforced.
msg – Optional message to use on failure instead of a list of differences.
- assertSetEqual(set1, set2, msg=None)
A set-specific equality assertion.
- Parameters:
set1 – The first set to compare.
set2 – The second set to compare.
msg – Optional message to use on failure instead of a list of differences.
assertSetEqual uses ducktyping to support different types of sets, and is optimized for sets specifically (parameters must support a difference method).
- assertStartsWith(s, prefix, msg=None)
- assertTrue(expr, msg=None)
Check that the expression is true.
- assertTupleEqual(tuple1, tuple2, msg=None)
A tuple-specific equality assertion.
- Parameters:
tuple1 – The first tuple to compare.
tuple2 – The second tuple to compare.
msg – Optional message to use on failure instead of a list of differences.
- assertWarns(expected_warning, *args, **kwargs)
Fail unless a warning of class warnClass is triggered by the callable when invoked with specified positional and keyword arguments. If a different type of warning is triggered, it will not be handled: depending on the other warning filtering rules in effect, it might be silenced, printed out, or raised as an exception.
If called with the callable and arguments omitted, will return a context object used like this:
with self.assertWarns(SomeWarning): do_something()
An optional keyword argument ‘msg’ can be provided when assertWarns is used as a context object.
The context manager keeps a reference to the first matching warning as the ‘warning’ attribute; similarly, the ‘filename’ and ‘lineno’ attributes give you information about the line of Python code from which the warning was triggered. This allows you to inspect the warning after the assertion:
with self.assertWarns(SomeWarning) as cm: do_something() the_warning = cm.warning self.assertEqual(the_warning.some_attribute, 147)
- assertWarnsRegex(expected_warning, expected_regex, *args, **kwargs)
Asserts that the message in a triggered warning matches a regexp. Basic functioning is similar to assertWarns() with the addition that only warnings whose messages also match the regular expression are considered successful matches.
- Parameters:
expected_warning – Warning class expected to be triggered.
expected_regex – Regex (re.Pattern object or string) expected to be found in error message.
args – Function to be called and extra positional args.
kwargs – Extra kwargs.
msg – Optional message used in case of failure. Can only be used when assertWarnsRegex is used as a context manager.
- clearGenerated()
Remove the directories that are used for testing.
- countTestCases()
- createLargeMultitaskDataSet(name='QSPRDataset_multi_test', target_props=[{'name': 'HBD', 'task': TargetTasks.MULTICLASS, 'th': [-1, 1, 2, 100]}, {'name': 'CL', 'task': TargetTasks.REGRESSION}], random_state=42)
Create a large dataset for testing purposes.
- Parameters:
- Returns:
a
QSPRDataSetobject- Return type:
- createLargeTestDataSet(name='QSPRDataset_test_large', target_props=[{'name': 'CL', 'task': TargetTasks.REGRESSION}], random_state=42, n_jobs=1, chunk_size=None, drop_empty_target_props=True)
Create a large dataset for testing purposes.
- Parameters:
- Returns:
a
QSPRDataSetobject- Return type:
- createSmallTestDataSet(name='QSPRDataset_test_small', target_props=[{'name': 'CL', 'task': TargetTasks.REGRESSION}], random_state=42, drop_empty_target_props=True)
Create a small dataset for testing purposes.
- Parameters:
- Returns:
a
QSPRDataSetobject- Return type:
- createTestDataSetFromFrame(df, name='QSPRDataset_test', target_props=[{'name': 'CL', 'task': TargetTasks.REGRESSION}], random_state=None, n_jobs=1, chunk_size=None, drop_empty_target_props=True)
Create a dataset for testing purposes from the given data frame.
- Parameters:
df (pd.DataFrame) – data frame containing the dataset
name (str) – name of the dataset
target_props (List of dicts or TargetProperty) – list of target properties
random_state (int) – random state to use for splitting and shuffling
prep (dict) – dictionary containing preparation settings
n_jobs (int) – number of jobs to use for parallel processing
chunk_size (int) – size of chunks to use per job in parallel processing
- Returns:
a
QSPRDataSetobject- Return type:
- debug()
Run the test without collecting errors in a TestResult
- defaultTestResult()
- classmethod doClassCleanups()
Execute all class cleanup functions. Normally called for you after tearDownClass.
- doCleanups()
Execute all cleanup functions. Normally called for you after tearDown.
- classmethod enterClassContext(cm)
Same as enterContext, but class-wide.
- enterContext(cm)
Enters the supplied context manager.
If successful, also adds its __exit__ method as a cleanup function and returns the result of the __enter__ method.
- fail(msg=None)
Fail immediately, with the given message.
- failureException
alias of
AssertionError
- classmethod getAllDescriptorSets()
Return a list of (ideally) all available descriptor sets. For now they need to be added manually to the list below.
TODO: would be nice to create the list automatically by implementing a descriptor set registry that would hold all installed descriptor sets.
- getBigDF()
Get a large data frame for testing purposes.
- Returns:
a
pandas.DataFramecontaining the dataset- Return type:
pd.DataFrame
- classmethod getDataPrepGrid()
Return a list of many possible combinations of descriptor calculators, splits, feature standardizers, feature filters and data filters. Again, this is not exhaustive, but should cover a lot of cases.
- Returns:
a generator that yields tuples of all possible combinations as stated above, each tuple is defined as: (descriptor_calculator, split, feature_standardizer, feature_filters, data_filters)
- Return type:
grid
- classmethod getDefaultCalculatorCombo()
Makes a list of default descriptor calculators that can be used in tests.
It creates a calculator with only morgan fingerprints and rdkit descriptors, but also one with them both to test behaviour with multiple descriptor sets. Override this method if you want to test with other descriptor sets and calculator combinations.
- static getDefaultPrep(add_imputer=None)
Return a dictionary with default preparation settings.
- classmethod getPrepCombos()
Return a list of all possible preparation combinations as generated by
getDataPrepGridas well as their names. The generated list can be used to parameterize tests with the given named combinations.
- getSmallDF()
Get a small data frame for testing purposes.
- Returns:
a
pandas.DataFramecontaining the dataset- Return type:
pd.DataFrame
- getStorage(df, name, n_jobs=1, chunk_size=None)
- id()
- longMessage = True
- maxDiff = 640
- run(result=None)
- classmethod setUpClass()
Hook method for setting up class fixture before running tests in the class.
- setUpPaths()
Create the directories that are used for testing.
- shortDescription()
Returns a one-line description of the test, or None if no description has been provided.
The default implementation of this method returns the first line of the specified test method’s docstring.
- skipTest(reason)
Skip this test.
- subTest(msg=<object object>, **params)
Return a context manager that will return the enclosed block of code in a subtest identified by the optional message and keyword parameters. A failure in the subtest marks the test case as failed but resumes execution at the end of the enclosed block, allowing further test code to be executed.
- tearDown()
Remove all files and directories that are used for testing.
- classmethod tearDownClass()
Hook method for deconstructing the class fixture after running all tests in the class.