def plot_dataset_dist_distr(dataset, feat_type, dist_metric, task_name, **metric_kwargs):
"""
Generate a density plot showing the distribution of distances between dataset feature
vectors, using the specified feature type and distance metric.
"""
log = logging.getLogger('ATOM')
num_cmpds = dataset.X.shape[0]
if num_cmpds > 50000:
log.warning("Dataset has %d compounds, too big to calculate distance matrix" % num_cmpds)
return
log.warning("Starting distance matrix calculation for %d compounds" % num_cmpds)
dists = cd.calc_dist_diskdataset(feat_type, dist_metric, dataset, calc_type='all', **metric_kwargs)
log.warning("Finished calculation of %d distances" % len(dists))
if len(dists) > ndist_max:
# Sample a subset of the distances so KDE doesn't take so long
dist_sample = np.random.choice(dists, size=ndist_max)
else:
dist_sample = dists
dist_pdf = gaussian_kde(dist_sample)
x_plt = np.linspace(min(dist_sample), max(dist_sample), 500)
y_plt = dist_pdf(x_plt)
fig, ax = plt.subplots(figsize=(8.0,8.0))
ax.plot(x_plt, y_plt, color='forestgreen')
ax.set_xlabel('%s distance' % dist_metric)
ax.set_ylabel('Density')
ax.set_title("%s dataset\nDistribution of %s distances between %s feature vectors" % (
task_name, dist_metric, feat_type))
return dists
def plot_dataset_dist_distr(dataset, feat_type, dist_metric, task_name,
**metric_kwargs):
"""
Generate a density plot showing the distribution of distances between dataset feature
vectors, using the specified feature type and distance metric.
Args:
dataset (deepchem.Dataset): A dataset object. At minimum, it should contain a 2D numpy array 'X' of feature vectors.
feat_type (str): Type of features ('ECFP' or 'descriptors').
dist_metric (str): Name of metric to be used to compute distances; can be anything supported by scipy.spatial.distance.pdist.
task_name (str): Abbreviated name to describe dataset in plot title.
metric_kwargs: Additional arguments to pass to metric.
Returns:
np.ndarray: Distance matrix.
"""
log = logging.getLogger('ATOM')
num_cmpds = dataset.X.shape[0]
if num_cmpds > 50000:
log.warning(
"Dataset has %d compounds, too big to calculate distance matrix" %
num_cmpds)
return
log.warning("Starting distance matrix calculation for %d compounds" %
num_cmpds)
dists = cd.calc_dist_diskdataset(feat_type,
dist_metric,
dataset,
calc_type='all',
**metric_kwargs)
log.warning("Finished calculation of %d distances" % len(dists))
if len(dists) > ndist_max:
# Sample a subset of the distances so KDE doesn't take so long
dist_sample = np.random.choice(dists, size=ndist_max)
else:
dist_sample = dists
dist_pdf = gaussian_kde(dist_sample)
x_plt = np.linspace(min(dist_sample), max(dist_sample), 500)
y_plt = dist_pdf(x_plt)
fig, ax = plt.subplots(figsize=(8.0, 8.0))
ax.plot(x_plt, y_plt, color='forestgreen')
ax.set_xlabel('%s distance' % dist_metric)
ax.set_ylabel('Density')
ax.set_title(
"%s dataset\nDistribution of %s distances between %s feature vectors" %
(task_name, dist_metric, feat_type))
return dists
def get_dset_diversity(dset_key, ds_client, bucket='gsk_ml', feat_type='descriptors', dist_metric='cosine',
**metric_kwargs):
"""
Load datasets from datastore, featurize them, and plot distributions of their inter-compound
distances.
"""
log = logging.getLogger('ATOM')
dset_df = dsf.retrieve_dataset_by_datasetkey(dset_key, bucket, ds_client)
if feat_type == 'descriptors':
params = parse.wrapper(dict(
dataset_key=dset_key,
bucket=bucket,
descriptor_key='/ds/projdata/gsk_data/GSK_Descriptors/GSK_2D_3D_MOE_Descriptors_By_Variant_ID_With_Base_RDKit_SMILES.feather',
descriptor_type='moe',
featurizer='descriptors',
system='twintron-blue',
datastore=True,
transformers=True))
elif feat_type == 'ECFP':
params = parse.wrapper(dict(
dataset_key=dset_key,
bucket=bucket,
featurizer='ECFP',
system='twintron-blue',
datastore=True,
ecfp_radius=2,
ecfp_size=1024,
transformers=True))
else:
log.error("Feature type %s not supported" % feat_type)
return
metadata = dsf.get_keyval(dataset_key=dset_key, bucket=bucket)
if 'id_col' in metadata.keys():
params.id_col = metadata['id_col']
if 'param' in metadata.keys():
params.response_cols = [metadata['param']]
elif 'response_col' in metadata.keys():
params.response_cols = [metadata['response_col']]
elif 'response_cols' in metadata.keys():
params.response_cols = metadata['response_cols']
if 'smiles_col' in metadata.keys():
params.smiles_col = metadata['smiles_col']
if 'class_number' in metadata.keys():
params.class_number = metadata['class_number']
params.dataset_name = dset_key.split('/')[-1].rstrip('.csv')
log.warning("Featurizing data with %s featurizer" % feat_type)
featurization = feat.create_featurization(params)
model_dataset = md.MinimalDataset(params, featurization)
model_dataset.get_featurized_data(dset_df)
num_cmpds = model_dataset.dataset.X.shape[0]
if num_cmpds > 50000:
log.warning("Too many compounds to compute distance matrix: %d" % num_cmpds)
return
# plot_dataset_dist_distr(model_dataset.dataset, feat_type, dist_metric, params.response_cols, **metric_kwargs)
dists = cd.calc_dist_diskdataset('descriptors', dist_metric, model_dataset.dataset, calc_type='all')
import scipy
dists = scipy.spatial.distance.squareform(dists)
res_dir = '/ds/projdata/gsk_data/model_analysis/'
plt_dir = '%s/Plots' % res_dir
file_prefix = dset_key.split('/')[-1].rstrip('.csv')
mcs_linkage = linkage(dists, method='complete')
pdf_path = '%s/%s_mcs_clustermap.pdf' % (plt_dir, file_prefix)
pdf = PdfPages(pdf_path)
g = sns.clustermap(dists, row_linkage=mcs_linkage, col_linkage=mcs_linkage, figsize=(12, 12), cmap='plasma')
if plt_dir is not None:
pdf.savefig(g.fig)
pdf.close()
return dists