def mapperfunc(mol):
fig, weight = SimilarityMaps.GetSimilarityMapForModel(
mol,
SimilarityMaps.GetMorganFingerprint,
lambda x: getProba(x, model.predict_proba),
colorMap=cm.bwr)
cls_result = model.predict(calc_fp_arr(mol))
fname = uuid.uuid1().hex + ".png"
fig.savefig("static/tempfig/" + fname, bbox_inches="tight")
return cls_result, fname
def _map(x):
mol = molf.mol_method(x)
fig, _ = SimilarityMaps.GetSimilarityMapForModel(
mol,
SimilarityMaps.GetMorganFingerprint,
lambda y: _get_proba(y, model.predict_proba),
linewidths=0,
)
b64 = b64_fig(fig, dpi=72)
img_src = '<img src="data:image/png;base64,{}" alt="Map" />'.format(
b64)
return img_src
def mapperfunc( mol ):
fig, weight = SimilarityMaps.GetSimilarityMapForModel( mol, SimilarityMaps.GetMorganFingerprint, lambda x: getProba( x, cls.predict_proba), colorMap=cm.bwr )
fp = AllChem.GetMorganFingerprintAsBitVect( mol, 2 )
print(fp)
arr = np.zeros((1,))
DataStructs.ConvertToNumpyArray( fp, arr )
print(arr)
res = cls.predict( arr )
smi = Chem.MolToSmiles( mol )
print(smi)
if res[0] == 1:
fig.savefig( "res/act_"+smi+"_.png", bbox_inches = "tight" )
else:
fig.savefig("res/nonact_"+smi+"_.png", bbox_inches = "tight" )
def pred_frag(mol, model, radius=3, nBits=2048):
def getProba(fp, probabilityfunc):
return probabilityfunc(fp)[0][1]
def fpFunction(mol,atomId=-1):
fp = SimilarityMaps.GetMorganFingerprint(mol,
atomId=atomId,
radius=radius,
nBits=nBits)
return fp
fig = SimilarityMaps.GetSimilarityMapForModel(mol,
fpFunction,
lambda x: getProba((x,),
model.predict_proba),
colorMap=cm.PiYG_r)
def sim_map(mol_list,
model,
id_prop=None,
interact=False,
highlight=None,
show_hidden=False,
order=None,
size=300):
"""Parameters:
mol_list (Mol_List): List of RDKit molecules
highlight (dict): Dict of properties (special: *all*) and values to highlight cells,
e.g. {"activity": "< 50"}
show_hidden (bool): Whether to show hidden properties (name starts with _) or not.
Defaults to *False*.
link (str): column used for linking out
target (str): column used as link target
order (list): A list of substrings to match with the field names for ordering in the table header
img_dir (str): if None, the molecule images are embedded in the HTML doc.
Otherwise the images will be stored in img_dir and linked in the doc.
Returns:
HTML table as TEXT to embed in IPython or a web page."""
time_stamp = time.strftime("%y%m%d%H%M%S")
td_opt = {"style": "text-align: center;"}
header_opt = {"bgcolor": "#94CAEF", "style": "text-align: center;"}
table_list = []
prop_list = tools.list_fields(mol_list)
if isinstance(order, list):
for k in reversed(order):
prop_list.sort(key=lambda x: k.lower() in x.lower(), reverse=True)
if id_prop is None:
guessed_id = tools.guess_id_prop(prop_list)
else:
guessed_id = id_prop
if interact and guessed_id is not None:
table_list.append(
tools.TBL_JAVASCRIPT.format(ts=time_stamp, bgcolor="transparent"))
if id_prop is not None:
if id_prop not in prop_list:
raise LookupError(
"Id property {} not found in data set.".format(id_prop))
if len(mol_list) > 5:
pb = nbt.ProgressbarJS()
if guessed_id:
# make sure that the id_prop (or the guessed id prop) is first:
prop_list.pop(prop_list.index(guessed_id))
tmp_list = [guessed_id]
tmp_list.extend(prop_list)
prop_list = tmp_list
cells = html.td(html.b("#"), header_opt)
cells.extend(html.td(html.b("Molecule"), header_opt))
cells.extend(html.td(html.b("SimMap"), header_opt))
for prop in prop_list:
cells.extend(html.td(html.b(prop), header_opt))
rows = html.tr(cells)
list_len = len(mol_list)
for idx, mol in enumerate(mol_list):
if len(mol_list) > 5:
pb.update(100 * (idx + 1) / list_len)
cells = []
mol_props = mol.GetPropNames()
if guessed_id:
id_prop_val = mol.GetProp(guessed_id)
img_id = id_prop_val
cell_opt = {"id": "{}_{}".format(id_prop_val, time_stamp)}
else:
img_id = idx
cell_opt = {"id": str(idx)}
cell = html.td(str(idx), cell_opt)
cells.extend(cell)
if not mol:
cells.extend(html.td("no structure"))
else:
b64 = tools.b64_img(mol, size * 2)
img_src = "data:image/png;base64,{}".format(b64)
cell_opt = {}
if interact and guessed_id is not None:
img_opt = {
"title": "Click to select / unselect",
"onclick": "toggleCpd('{}')".format(id_prop_val)
}
else:
img_opt = {"title": str(img_id)}
# img_opt["width"] = size
# img_opt["height"] = size
img_opt[
"style"] = 'max-width: {}px; max-height: {}px; display: block; margin: auto;'.format(
size, size)
cell = html.img(img_src, img_opt)
cells.extend(html.td(cell, cell_opt))
fig, _ = SimilarityMaps.GetSimilarityMapForModel(
mol, SimilarityMaps.GetMorganFingerprint,
lambda x: _get_proba(x, model.predict_proba))
b64 = b64_fig(fig, dpi=72)
img_src = "data:image/png;base64,{}".format(b64)
cell_opt = {}
img_opt[
"style"] = 'max-width: {}px; max-height: {}px; display: block; margin: auto;'.format(
size, size)
cell = html.img(img_src, img_opt)
cells.extend(html.td(cell, cell_opt))
for prop in prop_list:
td_opt = {"style": "text-align: center;"}
if prop in mol_props:
if not show_hidden and prop.startswith("_"): continue
td_opt["title"] = prop
prop_val = mol.GetProp(prop)
if highlight:
eval_str = None
if "*all*" in highlight:
if not guessed_id or (guessed_id
and prop != guessed_id):
eval_str = " ".join([prop_val, highlight["*all*"]])
else:
if prop in highlight:
eval_str = " ".join([prop_val, highlight[prop]])
if eval_str and eval(eval_str):
td_opt["bgcolor"] = "#99ff99"
cells.extend(html.td(prop_val, td_opt))
else:
cells.extend(html.td("", td_opt))
rows.extend(html.tr(cells))
table_list.extend(html.table(rows))
if interact and guessed_id is not None:
table_list.append(tools.ID_LIST.format(ts=time_stamp))
if len(mol_list) > 5:
pb.done()
return "".join(table_list)