def test_create_model_wrapper():
"""
Args:
params (Namespace) : Parameters passed to the model pipeline
featurizer (Featurization): Object managing the featurization of compounds
ds_client (DatastoreClient): Interface to the file datastore
Returns:
model (pipeline.Model): Wrapper for DeepChem, sklearn or other model.
Raises:
ValueError: Only params.model_type = 'NN' or 'RF' is supported.
Dependencies:
None
Calls:
DCNNModelWrapper, DCRFModelWrapper
"""
inp_params = parse.wrapper(general_params)
featurization = feat.create_featurization(inp_params)
mdl = model_wrapper.create_model_wrapper(inp_params, featurization)
mdl.setup_model_dirs()
# testing for correct attribute initialization with model_type == "NN"
test = []
test.append(mdl.params.model_type == 'NN')
test.append(isinstance(mdl.featurization, feat.DynamicFeaturization))
test.append(mdl.output_dir == inp_params.output_dir)
test.append(mdl.model_dir == inp_params.output_dir + '/' + 'model')
test.append(mdl.best_model_dir == inp_params.output_dir + '/' +
'best_model')
test.append(mdl.baseline_model_dir == inp_params.output_dir + '/' +
'baseline_epoch_model')
test.append(mdl.transformers == [])
test.append(mdl.transformers_x == [])
test.append(isinstance(mdl, model_wrapper.DCNNModelWrapper))
# testing for correct attribute initialization with model_type == "RF"
temp_params = copy.deepcopy(inp_params)
temp_params.model_type = 'RF'
featurization = feat.create_featurization(temp_params)
mdl_RF = model_wrapper.create_model_wrapper(temp_params, featurization)
test.append(isinstance(mdl_RF, MP.model_wrapper.DCRFModelWrapper))
test.append(mdl_RF.params.model_type == 'RF')
# assertion for all tests
assert all(test)
#testing for Exception with model_type not in ['NN','RF']
with pytest.raises(ValueError):
temp_params.model_type = 'wrong'
mdl_wrong = model_wrapper.create_model_wrapper(temp_params,
featurization)
def uncurated_objects(y=["VALUE_NUM"]):
params_from_ds = parse.wrapper(currentdir + '/config_uncurated_bp.json')
params_from_ds.response_cols = y
featurization = feat.create_featurization(params_from_ds)
data = model_dataset.create_model_dataset(params_from_ds, featurization)
uncurated_df = data.load_full_dataset()
return params_from_ds, data, uncurated_df
def datastore_objects(y=["PIC50"]):
params_from_ds = parse.wrapper(currentdir +
'/config_datastore_dset_cav12.json')
params_from_ds.response_cols = y
featurization = feat.create_featurization(params_from_ds)
data = model_dataset.create_model_dataset(params_from_ds, featurization)
dset_df = data.load_full_dataset()
data.get_featurized_data()
data.split_dataset()
return params_from_ds, data, dset_df
def split_and_save_dataset(self, assay_params):
self.get_dataset_metadata(assay_params)
#TODO: check usage with defaults
namespace_params = parse.wrapper(assay_params)
#TODO: Don't want to recreate each time
featurization = feat.create_featurization(namespace_params)
data = model_datasets.create_model_dataset(namespace_params,
featurization)
data.get_featurized_data()
data.split_dataset()
data.save_split_dataset()
assay_params['previously_split'] = True
assay_params['split_uuid'] = data.split_uuid
def test_super_transform_dataset():
"""
Args:
dataset: The DeepChem DiskDataset that contains a dataset
Returns:
transformed_dataset
Raises:
None
Dependencies:
model_dataset.create_transformers
Calls:
None
"""
#set up for a model wrapper with regression and NN.
inp_params = parse.wrapper(general_params)
featurization = feat.create_featurization(inp_params)
data_obj_ecfp = model_dataset.create_model_dataset(inp_params,
featurization,
ds_client=None)
df_delaney = data_obj_ecfp.load_full_dataset()
data_obj_ecfp.get_dataset_tasks(df_delaney)
data_obj_ecfp.check_task_columns(df_delaney)
data_obj_ecfp.get_featurized_data()
mdl = model_wrapper.create_model_wrapper(inp_params,
data_obj_ecfp.featurization)
mdl.setup_model_dirs()
mdl.create_transformers(data_obj_ecfp)
dataset = mdl.transform_dataset(data_obj_ecfp.dataset)
test = []
# checking that the dataset is the correct type
test.append(isinstance(dataset, DD))
# since this is not descriptor featurization, the X values for the datasets should be the same
test.append((dataset.X == data_obj_ecfp.dataset.X).all())
# and the response values should be the same length:
test.append(len(dataset.y) == len(data_obj_ecfp.dataset.y))
test.append(len(dataset.y) == len(dataset.ids))
assert all(test)
def delaney_objects(y=["measured log solubility in mols per litre"],
featurizer="ecfp",
split_strategy="train_valid_test",
splitter="random",
split_uuid=None):
delaney_inp_file = currentdir + '/config_delaney.json'
inp_params = parse.wrapper(delaney_inp_file)
inp_params.response_cols = y
inp_params.featurizer = featurizer
inp_params.split_strategy = split_strategy
inp_params.splitter = splitter
if split_uuid is not None:
inp_params.previously_split = True
inp_params.split_uuid = split_uuid
featurization = feat.create_featurization(inp_params)
mdl = model_dataset.create_model_dataset(inp_params,
featurization,
ds_client=None)
delaney_df = mdl.load_full_dataset()
mdl.get_featurized_data()
mdl.split_dataset()
return inp_params, mdl, delaney_df
def moe_descriptors(datastore=False):
if datastore == True:
params_ds = parse.wrapper(currentdir +
"/config_MAOA_moe_descriptors_ds.json")
else:
params_file = parse.wrapper(currentdir +
"/config_MAOA_moe_descriptors.json")
# if not os.path.isfile(params_file.dataset_key):
# os.makedirs('pytest/config_MAOA_moe_descriptors/moe_descriptors', exist_ok=True)
# copyfile(params_ds.dataset_key, params_file.dataset_key)
if datastore == True:
params_desc = params_ds
else:
params_desc = params_file
featurization = feat.create_featurization(params_desc)
dataset_obj_for_desc = model_dataset.create_model_dataset(params_desc,
featurization,
ds_client=None)
df = dataset_obj_for_desc.load_full_dataset()
return params_desc, dataset_obj_for_desc, df
def split_and_save_dataset(self, assay_params):
"""
Splits a given dataset, saves it, and sets the split_uuid in the metadata
Args:
assay_params: Dataset metadata
Returns:
None
"""
self.get_dataset_metadata(assay_params)
# TODO: check usage with defaults
namespace_params = parse.wrapper(assay_params)
# TODO: Don't want to recreate each time
featurization = feat.create_featurization(namespace_params)
data = model_datasets.create_model_dataset(namespace_params, featurization)
data.get_featurized_data()
data.split_dataset()
data.save_split_dataset()
assay_params['previously_split'] = True
assay_params['split_uuid'] = data.split_uuid
def test_split_dataset_kfold_scaffold_from_pipeline(caplog):
#Testing for correct type and length of dataset for k-fold splitting with a scaffold splitter
#Testing a 3-fold split first for uniqueness of all validation and training sets.
#mp.model_wrapper = model_wrapper.create_model_wrapper(mp.params, mp.featurization, mp.ds_client)
#mp.model_wrapper.setup_model_dirs()
mp = utils.delaney_pipeline(featurizer="ecfp",
split_strategy="k_fold_cv",
splitter="scaffold")
mp.featurization = feat.create_featurization(mp.params)
mp.data = model_datasets.create_model_dataset(mp.params, mp.featurization,
mp.ds_client)
mp.data.get_featurized_data()
mp.data.split_dataset()
splitter_k_fold_scaffold = mp.data.splitting
splitter_k_fold_scaffold.num_folds = 3
nf = splitter_k_fold_scaffold.num_folds
#mp.model_wrapper.create_transformers(self.data)
#mp.data.dataset = mp.model_wrapper.transform_dataset(self.data.dataset)
data_obj_k_fold_scaffold = mp.data
data_obj_k_fold_scaffold.split_dataset()
train_valid, test, train_valid_attr, test_attr = splitter_k_fold_scaffold.split_dataset(
data_obj_k_fold_scaffold.dataset, data_obj_k_fold_scaffold.attr,
data_obj_k_fold_scaffold.params.smiles_col)
#assert no overlap of the k-fold validation sets between each other
test_list = []
for kfoldindex in range(0, nf):
test_list.append(
(data_obj_k_fold_scaffold.train_valid_dsets[kfoldindex][0].X ==
train_valid[kfoldindex][0].X).all())
test_list.append(
(data_obj_k_fold_scaffold.train_valid_dsets[kfoldindex][1].X ==
train_valid[kfoldindex][1].X).all())
test_list.append(
(data_obj_k_fold_scaffold.train_valid_dsets[kfoldindex][1].ids ==
train_valid[kfoldindex][1].ids).all())
test_list.append(
(data_obj_k_fold_scaffold.train_valid_dsets[kfoldindex][1].ids ==
train_valid[kfoldindex][1].ids).all())
test_list.append(train_valid_attr[kfoldindex][0].equals(
data_obj_k_fold_scaffold.train_valid_attr[kfoldindex][0]))
test_list.append(train_valid_attr[kfoldindex][1].equals(
data_obj_k_fold_scaffold.train_valid_attr[kfoldindex][1]))
assert all(test_list)
test_list = []
concat_valid = [x[1].ids.tolist() for x in train_valid]
concat_valid = sum(concat_valid, [])
test_list.append(len(concat_valid) == len(set(concat_valid)))
assert all(test_list)
tv_split = []
test_list = []
#asserting that each k-fold split has no internal overlap.
for kfoldindex in range(0, nf):
current_tv_split = train_valid[kfoldindex][0].ids.tolist(
) + train_valid[kfoldindex][1].ids.tolist()
test_list.append(
len(train_valid[kfoldindex][0].ids) == len(train_valid[kfoldindex]
[0].y))
test_list.append(
len(train_valid[kfoldindex][1].ids) == len(train_valid[kfoldindex]
[1].y))
current_full_dataset = sum([current_tv_split, test.ids.tolist()], [])
test_list.append(
len(current_full_dataset) == len(set(current_full_dataset)))
test_list.append(
set(train_valid[kfoldindex][0].ids.tolist()) == set(
train_valid_attr[kfoldindex][0].index.tolist()))
test_list.append(
set(train_valid[kfoldindex][1].ids.tolist()) == set(
train_valid_attr[kfoldindex][1].index.tolist()))
#checking length of the validation set (should be length of the kv set/num_folds +/- 1)
len_valid = round(len(current_tv_split) / nf)
test_list.append(
len_valid - 1 <= len(train_valid[kfoldindex][1]) <= len_valid + 1)
tv_split.append(current_tv_split)
#asserting that all k-fold train valid sets are equivalent
test_list.append(
set.intersection(*[set(l) for l in tv_split]) == set(tv_split[0]))
#aasserting that the test and test_attrs have the same index:
test_list.append(set(test.ids.tolist()) == set(test_attr.index.tolist()))
test_list.append(len(test.y) == len(test.ids))
assert all(test_list)
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
def return_split_uuid(self,
dataset_key,
bucket=None,
splitter=None,
split_combo=None,
retry_time=60):
"""
Loads a dataset, splits it, saves it, and returns the split_uuid
Args:
dataset_key: key for dataset to split
bucket: datastore-specific user group bucket
splitter: Type of splitter to use to split the dataset
split_combo: tuple of form (split_valid_frac, split_test_frac)
Returns:
"""
if bucket is None:
bucket = self.params.bucket
if splitter is None:
splitter = self.params.splitter
if split_combo is None:
split_valid_frac = self.params.split_valid_frac
split_test_frac = self.params.split_test_frac
else:
split_valid_frac = split_combo[0]
split_test_frac = split_combo[1]
retry = True
i = 0
#TODO: need to catch if dataset doesn't exist versus 500 failure
while retry:
try:
metadata = dsf.get_keyval(dataset_key=dataset_key,
bucket=bucket)
retry = False
except Exception as e:
if i < 5:
print(
"Could not get metadata from datastore for dataset %s because of exception %s, sleeping..."
% (dataset_key, e))
time.sleep(retry_time)
i += 1
else:
print(
"Could not get metadata from datastore for dataset %s because of exception %s, exiting"
% (dataset_key, e))
return None
assay_params = {
'dataset_key': dataset_key,
'bucket': bucket,
'splitter': splitter,
'split_valid_frac': split_valid_frac,
'split_test_frac': split_test_frac
}
#Need a featurizer type to split dataset, but since we only care about getting the split_uuid, does not matter which featurizer you use
if type(self.params.featurizer) == list:
assay_params['featurizer'] = self.params.featurizer[0]
else:
assay_params['featurizer'] = self.params.featurizer
if 'id_col' in metadata.keys():
assay_params['id_col'] = metadata['id_col']
if 'response_cols' not in assay_params or assay_params[
'response_cols'] is None:
if 'param' in metadata.keys():
assay_params['response_cols'] = [metadata['param']]
if 'response_col' in metadata.keys():
assay_params['response_cols'] = [metadata['response_col']]
if 'response_cols' in metadata.keys():
assay_params['response_cols'] = metadata['response_cols']
if 'smiles_col' in metadata.keys():
assay_params['smiles_col'] = metadata['smiles_col']
if 'class_name' in metadata.keys():
assay_params['class_name'] = metadata['class_name']
if 'class_number' in metadata.keys():
assay_params['class_number'] = metadata['class_number']
assay_params['dataset_name'] = assay_params['dataset_key'].split(
'/')[-1].rstrip('.csv')
assay_params['datastore'] = True
assay_params[
'previously_featurized'] = self.params.previously_featurized
try:
assay_params['descriptor_key'] = self.params.descriptor_key
assay_params['descriptor_bucket'] = self.params.descriptor_bucket
except:
print("")
#TODO: check usage with defaults
namespace_params = parse.wrapper(assay_params)
# TODO: Don't want to recreate each time
featurization = feat.create_featurization(namespace_params)
data = model_datasets.create_model_dataset(namespace_params,
featurization)
retry = True
i = 0
while retry:
try:
data.get_featurized_data()
data.split_dataset()
data.save_split_dataset()
return data.split_uuid
except Exception as e:
if i < 5:
print(
"Could not get metadata from datastore for dataset %s because of exception %s, sleeping"
% (dataset_key, e))
time.sleep(retry_time)
i += 1
else:
print(
"Could not save split dataset for dataset %s because of exception %s"
% (dataset_key, e))
return None
def analyze_split(params,
id_col='compound_id',
smiles_col='rdkit_smiles',
active_col='active'):
"""
Evaluate the AVE bias for the training/validation and training/test set splits of the given dataset.
Also show the active frequencies in each subset and for the dataset as a whole.
id_col, smiles_col and active_col are defaults to be used in case they aren't found in the dataset metadata; if found
the metadata values are used instead.
Args:
params (argparse.Namespace): Pipeline parameters.
id_col (str): Dataset column containing compound IDs.
smiles_col (str): Dataset column containing SMILES strings.
active_col (str): Dataset column containing binary classifications.
Returns:
:obj:`pandas.DataFrame`: Table of split subsets showing sizes, numbers and fractions of active compounds
"""
dset_key = params.dataset_key
bucket = params.bucket
split_uuid = params.split_uuid
ds_client = dsf.config_client()
try:
split_metadata = dsf.search_datasets_by_key_value('split_dataset_uuid',
split_uuid,
ds_client,
operator='in',
bucket=bucket)
split_oid = split_metadata['dataset_oid'].values[0]
split_df = dsf.retrieve_dataset_by_dataset_oid(split_oid,
client=ds_client)
except Exception as e:
print("Error when loading split file:\n%s" % str(e))
raise
try:
dataset_df = dsf.retrieve_dataset_by_datasetkey(dset_key,
bucket,
client=ds_client)
dataset_meta = dsf.retrieve_dataset_by_datasetkey(dset_key,
bucket,
client=ds_client,
return_metadata=True)
except Exception as e:
print("Error when loading dataset:\n%s" % str(e))
raise
kv_dict = dsf.get_key_val(dataset_meta['metadata'])
id_col = kv_dict.get('id_col', id_col)
smiles_col = kv_dict.get('smiles_col', smiles_col)
active_col = kv_dict.get('response_col', active_col)
try:
print('Dataset has %d unique compound IDs' %
len(set(dataset_df[id_col].values)))
print('Split table has %d unique compound IDs' %
len(set(split_df.cmpd_id.values)))
dset_df = dataset_df.merge(split_df,
how='inner',
left_on=id_col,
right_on='cmpd_id').drop('cmpd_id', axis=1)
except Exception as e:
print("Error when joining dataset with split dataset:\n%s" % str(e))
raise
featurization = feat.create_featurization(params)
data = md.create_model_dataset(params, featurization, ds_client)
data.get_featurized_data()
feat_arr = data.dataset.X
# TODO: impute missing values if necessary
y = data.dataset.y.flatten()
if len(set(y) - set([0, 1])) > 0:
raise ValueError(
'AVEMinSplitter only works on binary classification datasets')
ids = data.dataset.ids
active_ind = np.where(y == 1)[0]
inactive_ind = np.where(y == 0)[0]
active_feat = feat_arr[active_ind, :]
inactive_feat = feat_arr[inactive_ind, :]
num_active = len(active_ind)
num_inactive = len(inactive_ind)
active_ids = ids[active_ind]
inactive_ids = ids[inactive_ind]
active_id_ind = dict(zip(active_ids, range(len(active_ids))))
inactive_id_ind = dict(zip(inactive_ids, range(len(inactive_ids))))
if params.featurizer == 'ecfp':
metric = 'jaccard'
elif params.featurizer == 'graphconv':
raise ValueError(
"ave_min splitter dopesn't support graphconv features")
else:
metric = 'euclidean'
# Calculate distance thresholds where nearest neighborfunction should be evaluated
if metric == 'jaccard':
max_nn_dist = 1.0
else:
nan_mat = np.isnan(feat_arr)
nnan = np.sum(nan_mat)
if nnan > 0:
log.info('Input feature matrix has %d NaN elements' % nnan)
not_nan = ~nan_mat
for i in range(feat_arr.shape[1]):
feat_arr[nan_mat[:, i], i] = np.mean(feat_arr[not_nan[:, i],
i])
nn_dist = np.sort(squareform(pdist(feat_arr, metric)))[:, 1]
med_nn_dist = np.median(nn_dist)
max_nn_dist = 3.0 * med_nn_dist
ndist = 100
dist_thresh = np.linspace(0.0, max_nn_dist, ndist)
# Compute distance matrices between subsets
num_workers = 1
aa_dist = _calc_dist_mat(active_feat, active_feat, metric, None,
num_workers)
ii_dist = _calc_dist_mat(inactive_feat, inactive_feat, metric, None,
num_workers)
ai_dist = _calc_dist_mat(active_feat, inactive_feat, metric, None,
num_workers)
ia_dist = ai_dist.transpose()
subsets = sorted(set(dset_df.subset.values))
subset_active_ind = {}
subset_inactive_ind = {}
if 'train' in subsets:
# this is a TVT split
subsets = ['train', 'valid', 'test']
for subset in subsets:
subset_df = dset_df[dset_df.subset == subset]
active_df = subset_df[subset_df[active_col] == 1]
inactive_df = subset_df[subset_df[active_col] == 0]
subset_active_ids = active_df[id_col].values
subset_inactive_ids = inactive_df[id_col].values
subset_active_ind[subset] = [
active_id_ind[id] for id in subset_active_ids
]
subset_inactive_ind[subset] = [
inactive_id_ind[id] for id in subset_inactive_ids
]
taI = subset_active_ind['train']
tiI = subset_inactive_ind['train']
print("Results for %s split with %s %s features:" %
(params.splitter, params.descriptor_type, params.featurizer))
for valid_set in ['valid', 'test']:
vaI = subset_active_ind[valid_set]
viI = subset_inactive_ind[valid_set]
split_params = ((vaI, viI, taI, tiI), aa_dist, ii_dist, ai_dist,
ia_dist, dist_thresh)
_plot_nn_dist_distr(split_params)
bias = _plot_bias(split_params, niter=0)
print("For train/%s split: AVE bias = %.5f" % (valid_set, bias))
else:
# TODO: deal with k-fold splits later
print('k-fold CV splits not supported yet')
return
# Tabulate the fractions of actives in the full dataset and each subset
subset_list = []
size_list = []
frac_list = []
active_frac_list = []
dset_size = data.dataset.X.shape[0]
dset_active = sum(data.dataset.y)
subset_list.append('full dataset')
size_list.append(dset_size)
frac_list.append(1.0)
active_frac_list.append(dset_active / dset_size)
for subset in subsets:
active_size = len(subset_active_ind[subset])
inactive_size = len(subset_inactive_ind[subset])
subset_size = active_size + inactive_size
active_frac = active_size / subset_size
subset_list.append(subset)
size_list.append(subset_size)
frac_list.append(subset_size / dset_size)
active_frac_list.append(active_frac)
frac_df = pd.DataFrame(
dict(subset=subset_list,
size=size_list,
fraction=frac_list,
active_frac=active_frac_list))
print('\nSplit subsets:')
print(frac_df)
return frac_df
def test_train_NN_graphconv_scaffold_inputs():
"""
Args:
pipeline (ModelPipeline): The ModelPipeline instance for this model run.
Dependencies:
ModelPipeline creation
featurization creation
creation of model_wrapper
mp.load_featurize_data
Calls:
create_perf_data
perf_data.accumulate_preds
perf_data.comput_perf_metrics
data.combined_training-data()
self._copy_model
"""
# checking that the layers, dropouts, and learning rate are properly added to the deepchem graphconv model
general_params['featurizer'] = 'graphconv'
general_params['layer_sizes'] = '100,100,10'
general_params['dropouts'] = '0.3,0.3,0.1'
general_params['uncertainty'] = False
inp_params = parse.wrapper(general_params)
mp = MP.ModelPipeline(inp_params)
mp.featurization = feat.create_featurization(inp_params)
mp.model_wrapper = model_wrapper.create_model_wrapper(
inp_params, mp.featurization, mp.ds_client)
# asserting that the correct model is created with the correct layer sizes, dropouts, model_dir, and mode by default
test1 = []
test1.append(mp.model_wrapper.params.layer_sizes == [100, 100, 10])
test1.append(mp.model_wrapper.params.dropouts == [0.3, 0.3, 0.1])
# checking that parameters are properly passed to the deepchem model object
test1.append(isinstance(mp.model_wrapper.model, GraphConvModel))
test1.append(
mp.model_wrapper.model.model_dir == mp.model_wrapper.model_dir)
test1.append(
[i.out_channel
for i in mp.model_wrapper.model.model.graph_convs] == [100, 100])
test1.append(
[i.rate
for i in mp.model_wrapper.model.model.dropouts] == [0.3, 0.3, 0.1])
test1.append(mp.model_wrapper.model.mode == 'regression')
test1.append(mp.model_wrapper.model.model.dense.units == 10)
assert all(test1)
#***********************************************************************************
def test_super_get_train_valid_pred_results():
"""
Args:
perf_data: A PerfData object that stores the predicted values and metrics
Returns:
dict: A dictionary of the prediction results
Raises:
None
Dependencies:
create_perf_data
Calls:
perf_data.get_prediction_results()
"""
pass
# should be tested in perf_data.get_prediction_results()
# should still be called to make sure that the function is callable
#***********************************************************************************
def test_super_get_test_perf_data():
"""
Args:
model_dir (str): Directory where the saved model is stored
model_dataset (DiskDataset): Stores the current dataset and related methods
Returns:
perf_data: PerfData object containing the predicted values and metrics for the current test dataset
Raises:
None
Dependencies:
A model must be in model_dir
model_dataset.test_dset must exist
Calls:
create_perf_data
self.generate_predictions
perf_data.accumulate_preds
"""
pass
# mostly tested in accumulate_preds, but should be tested to ensure taht the predictions are properly being called
#***********************************************************************************
def test_super_get_test_pred_results():
"""
Args:
model_dir (str): Directory where the saved model is stored
model_dataset (DiskDataset): Stores the current dataset and related methods
Returns:
dict: A dictionary containing the prediction values and metrics for the current dataset.
Raises:
None
Dependencies:
A model must be in model_dir
model_dataset.test_dset must exist
Calls:
self.get_test_perf_data
perf_data.get_prediction_results
"""
pass
#mostly tested in perf_data.get_prediction_results
#***********************************************************************************
def test_super_get_full_dataset_perf_data():
"""
Args:
model_dataset (DiskDataset): Stores the current dataset and related methods
Returns:
perf_data: PerfData object containing the predicted values and metrics for the current full dataset
Raises:
None
Dependencies:
A model must already be trained
Calls:
create_perf_data
self.generate_predictions
self.accumulate_preds
"""
pass
#***********************************************************************************
def test_super_get_full_dataset_pred_results():
"""
Args:
model_dataset (DiskDataset): Stores the current dataset and related methods
Returns:
dict: A dictionary containing predicted values and metrics for the current full dataset
Raises:
None
Dependencies:
A model was already be trained.
Calls:
get_full_dataset_perf_data
self.get_prediction_results()
"""
pass
def test_super_create_transformers():
"""
Args:
model_dataset: The ModelDataset object that handles the current dataset
Returns:
self.transformers
self.transformers_x
self.params.transformer_key
self.params.transformer_oid (if datastore)
Raises:
Exception when failing to save to the datastore
Dependencies:
create_featurization
create_model_dataset
model_dataset.load_full_dataset
model_dataset.get_dataset_tasks
model_dataset.check_task_columns
model_dataset.get_featurized_data
Requires (self.params.prediction_type == 'regression' and self.params.transformers == True) or len(self.transformers) > 0
Calls:
self.featurization.create_feature_transformer
dsf.upload_pickle_to_DS
"""
#set up for a model wrapper with regression and NN.
inp_params = parse.wrapper(general_params)
featurization = feat.create_featurization(inp_params)
data_obj_ecfp = model_dataset.create_model_dataset(inp_params,
featurization,
ds_client=None)
df_delaney = data_obj_ecfp.load_full_dataset()
data_obj_ecfp.get_dataset_tasks(df_delaney)
data_obj_ecfp.check_task_columns(df_delaney)
data_obj_ecfp.get_featurized_data()
mdl = model_wrapper.create_model_wrapper(inp_params,
data_obj_ecfp.featurization)
mdl.setup_model_dirs()
#testing correct model_wrapper build with regression and NN
test = []
test.append(mdl.params.prediction_type == 'regression')
test.append(mdl.params.model_type == 'NN')
mdl.create_transformers(data_obj_ecfp)
test.append(
isinstance(mdl.transformers[0],
dc.trans.transformers.NormalizationTransformer))
test.append(mdl.transformers_x == [])
#testing saving of transformer to correct location:
transformer_path = os.path.join(mdl.output_dir, 'transformers.pkl')
test.append(os.path.isfile(transformer_path))
# TODO: test proper saving of the transformer to the datastore
# TODO: test when transformers is False:
inp_params.prediction_type = 'classification'
mdl = model_wrapper.create_model_wrapper(inp_params, featurization)
test.append(mdl.transformers == [])
test.append(mdl.transformers_x == [])
assert all(test)
