def re_cluster(self, gdf, new_figerprints=None, new_chembl_ids=None):
if gdf.shape[0] == 0:
return None
# Before reclustering remove all columns that may interfere
ids = gdf['id']
chembl_ids = gdf['chembl_id']
gdf.drop(['x', 'y', 'cluster', 'id', 'chembl_id'], inplace=True)
if new_figerprints is not None and new_chembl_ids is not None:
# Add new figerprints and chEmblIds before reclustering
fp_df = cudf.DataFrame(new_figerprints, columns=gdf.columns)
gdf = gdf.append(fp_df, ignore_index=True)
chembl_ids = chembl_ids.append(
cudf.Series(new_chembl_ids), ignore_index=True)
kmeans_float = KMeans(n_clusters=self.n_clusters)
kmeans_float.fit(gdf)
Xt = self.umap.fit_transform(gdf)
# Add back the column required for plotting and to correlating data
# between re-clustering
gdf.add_column('x', Xt[0].to_array())
gdf.add_column('y', Xt[1].to_array())
gdf.add_column('id', gdf.index)
gdf.add_column('chembl_id', chembl_ids)
gdf.add_column('cluster', kmeans_float.labels_.to_array())
return gdf
def kmeans_fit(X):
alg = KMeans(n_clusters=params.n_clusters,
tol=params.tol,
max_iter=params.maxiter,
init=X_init,
max_samples_per_batch=params.samples_per_batch)
alg.fit(X)
return alg
def _cluster(self, embedding):
logger.info('Computing cluster...')
embedding = embedding.reset_index()
n_molecules = embedding.shape[0]
# Before reclustering remove all columns that may interfere
embedding, prop_series = self._remove_non_numerics(embedding)
with MetricsLogger('random_proj', n_molecules) as ml:
srp = self.srp_embedding.fit_transform(embedding.values)
ml.metric_name = 'spearman_rho'
ml.metric_func = self._compute_spearman_rho
ml.metric_func_args = (embedding, embedding, srp)
with MetricsLogger('kmeans', n_molecules) as ml:
kmeans_cuml = KMeans(n_clusters=self.n_clusters)
kmeans_cuml.fit(srp)
kmeans_labels = kmeans_cuml.predict(srp)
ml.metric_name = 'silhouette_score'
ml.metric_func = batched_silhouette_scores
ml.metric_func_kwargs = {}
ml.metric_func_args = (None, None)
if self.context.is_benchmark:
(srp_sample,
kmeans_labels_sample), _ = self._random_sample_from_arrays(
srp, kmeans_labels, n_samples=self.n_silhouette)
ml.metric_func_args = (srp_sample, kmeans_labels_sample)
# Add back the column required for plotting and to correlating data
# between re-clustering
srp = self.rand_jitter(srp)
embedding['cluster'] = kmeans_labels
embedding['x'] = srp[:, 0]
embedding['y'] = srp[:, 1]
# Add back the prop columns
for col in prop_series.keys():
embedding[col] = prop_series[col]
return embedding
y = np.asarray(
[[1.0, 2.0], [1.0, 4.0], [1.0, 0.0], [4.0, 2.0], [4.0, 4.0], [4.0, 0.0]],
dtype=np.float32)
x = np2cudf(y)
q = np.asarray([[0, 0], [4, 4]], dtype=np.float32)
p = np2cudf(q)
a = np.asarray([[1.0, 1.0], [1.0, 2.0], [3.0, 2.0], [4.0, 3.0]],
dtype=np.float32)
b = np2cudf(a)
print("input:")
print(b)
print("\nCalling fit")
kmeans_float = KMeans(n_clusters=2, n_gpu=-1)
kmeans_float.fit(b)
print("labels:")
print(kmeans_float.labels_)
print("cluster_centers:")
print(kmeans_float.cluster_centers_)
'''
print("\nCalling Predict")
print("labels:")
print(kmeans_float.predict(p))
print("cluster_centers:")
print(kmeans_float.cluster_centers_)
'''
print("\nCalling fit_predict")
kmeans_float2 = KMeans(n_clusters=2, n_gpu=-1)
print("labels:")
count+=1
if count>max:
break
logger.info('Initializing Morgan fingerprints...')
results = db.from_sequence(smiles_list).map(MorganFromSmiles).compute()
np_array_fingerprints = np.stack(results).astype(np.float32)
# take np.array shape (n_mols, nBits) for GPU DataFrame
gdf = np2cudf(np_array_fingerprints)
# prepare one set of clusters
n_clusters = 7
kmeans_float = KMeans(n_clusters=n_clusters)
kmeans_float.fit(gdf)
# UMAP
umap = UMAP(n_neighbors=100,
a=1.0,
b=1.0,
learning_rate=1.0)
Xt = umap.fit_transform(gdf)
gdf.add_column('x', Xt[0].to_array())
gdf.add_column('y', Xt[1].to_array())
gdf.add_column('cluster', kmeans_float.labels_)
# start dash
v = chemvisualize.ChemVisualization(
gdf.copy(), n_clusters, chemblID_list)