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import numpy as np
import joblib
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.feature_selection import VarianceThreshold
from rdkit import Chem
import pandas as pd
from rdkit.Chem import Descriptors
from tqdm import tqdm
MAX_NA = 0.2
class NanFilter(object):
def __init__(self):
self._name = "nan_filter"
def fit(self, X):
max_na = int((1 - MAX_NA) * X.shape[0])
idxs = []
for j in range(X.shape[1]):
c = np.sum(np.isnan(X[:, j]))
if c > max_na:
continue
else:
idxs += [j]
self.col_idxs = idxs
def transform(self, X):
return X[:, self.col_idxs]
def save(self, file_name):
joblib.dump(self, file_name)
def load(self, file_name):
return joblib.load(file_name)
class Imputer(object):
def __init__(self):
self._name = "imputer"
self._fallback = 0
def fit(self, X):
ms = []
for j in range(X.shape[1]):
vals = X[:, j]
mask = ~np.isnan(vals)
vals = vals[mask]
if len(vals) == 0:
m = self._fallback
else:
m = np.median(vals)
ms += [m]
self.impute_values = np.array(ms)
def transform(self, X):
for j in range(X.shape[1]):
mask = np.isnan(X[:, j])
X[mask, j] = self.impute_values[j]
return X
def save(self, file_name):
joblib.dump(self, file_name)
def load(self, file_name):
return joblib.load(file_name)
class VarianceFilter(object):
def __init__(self):
self._name = "variance_filter"
def fit(self, X):
self.sel = VarianceThreshold()
self.sel.fit(X)
self.col_idxs = self.sel.transform([[i for i in range(X.shape[1])]]).ravel()
def transform(self, X):
return self.sel.transform(X)
def save(self, file_name):
joblib.dump(self, file_name)
def load(self, file_name):
return joblib.load(file_name)
def physchem_featurizer(smiles_list):
R = []
for smiles in tqdm(smiles_list):
mol = Chem.MolFromSmiles(smiles)
descriptors = []
for _, descr_calc_fn in Descriptors._descList:
descriptors.append(descr_calc_fn(mol))
R += [np.array(descriptors)]
return np.array(R)
def physchem_featurizer_as_dataframe(smiles_list):
R = []
for smiles in tqdm(smiles_list):
mol = Chem.MolFromSmiles(smiles)
descriptors = []
for _, descr_calc_fn in Descriptors._descList:
descriptors.append(descr_calc_fn(mol))
R += [np.array(descriptors)]
return pd.DataFrame(np.array(R), columns=[x[0] for x in Descriptors._descList])
class PhyschemDescriptor(object):
def __init__(self, discretize=True):
self.nan_filter = NanFilter()
self.imputer = Imputer()
self.variance_filter = VarianceFilter()
self.discretizer = KBinsDiscretizer(
n_bins=5, encode="ordinal", strategy="quantile"
)
self.discretize = discretize
def fit(self, smiles):
R = physchem_featurizer(smiles)
X = np.array(R, dtype=np.float32)
self.nan_filter.fit(X)
X = self.nan_filter.transform(X)
self.imputer.fit(X)
X = self.imputer.transform(X)
self.variance_filter.fit(X)
X = self.variance_filter.transform(X)
self.discretizer.fit(X)
def transform(self, smiles):
df = physchem_featurizer_as_dataframe(smiles)
X = np.array(df, dtype=np.float32)
X = self.nan_filter.transform(X)
X = self.imputer.transform(X)
X = self.variance_filter.transform(X)
X = self.discretizer.transform(X)
return np.array(X, dtype=int)
class PhyschemDescriptorWithFeatures(object):
def __init__(self, discretize=True):
self.nan_filter = NanFilter()
self.imputer = Imputer()
self.variance_filter = VarianceFilter()
self.discretizer = KBinsDiscretizer(
n_bins=5, encode="ordinal", strategy="quantile"
)
self.discretize = discretize
def fit(self, smiles):
df = physchem_featurizer_as_dataframe(smiles)
X = np.array(df, dtype=np.float32)
self.nan_filter.fit(X)
X = self.nan_filter.transform(X)
self.imputer.fit(X)
X = self.imputer.transform(X)
self.variance_filter.fit(X)
X = self.variance_filter.transform(X)
if self.discretize:
self.discretizer.fit(X)
col_idxs = self.variance_filter.col_idxs
feature_names = list(df.columns)
self.feature_names = [feature_names[i] for i in col_idxs]
def transform(self, smiles):
df = physchem_featurizer_as_dataframe(smiles)
X = np.array(df, dtype=np.float32)
X = self.nan_filter.transform(X)
X = self.imputer.transform(X)
X = self.variance_filter.transform(X)
if self.discretize:
X = self.discretizer.transform(X)
return np.array(X, dtype=int)
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