def test_downloadAllModels(self):
"""Test if all models are downloaded locally properly"""
modelOptions = cancerscope.getmodelsdict()
assert len(modelOptions.keys()) == 5
scope_ensemble_obj = cancerscope.scope()
#my_downloaded_models = cancerscope.get_models.getmodel() ## This should retrieve all models
#print(my_downloaded_models)
my_downloaded_models = scope_ensemble_obj.downloaded_models_dict
assert len(my_downloaded_models.keys()) == 5
for k_model in my_downloaded_models.keys():
modelname_address = my_downloaded_models[k_model]
"""For each model, test if model dir exists, then set up the model once"""
self.assertTrue(os.path.isdir(modelname_address))
self.assertTrue(
os.path.exists("".join(
[modelname_address, "/lasagne_bestparams.npz"])))
"""TO BE FIXED: THEN SET UP MODEL (memory issues in travis (3 GB RAM there)"""
def localtest_samplefilereading(self):
my_test_file = "/".join([
os.path.dirname(sys.modules["cancerscope"].__file__),
"../tests/data/test_tcga.txt"
]) #read_ensg_input.txt"])
"""Test if a testX.txt file can be read in and mapped to the correct gene names"""
scope_ensemble_obj = cancerscope.scope()
test_X = scope_ensemble_obj.load_data(
my_test_file) # X, samples, features_test, in_genecode
self.assertEqual(test_X[-2][-1], "ZRANB1_ENSG00000019995")
## Process input file and get predictions from all 5 models
preds_df_from_file = scope_ensemble_obj.get_predictions_from_file(
my_test_file)
### Compare to getting output from the x data object itself
test_x, test_samples, test_features, test_genecode = scope_ensemble_obj.load_data(
my_test_file) #
preds_df_from_xdat = scope_ensemble_obj.predict(
X=test_x,
x_features=test_features,
x_features_genecode=test_genecode,
x_sample_names=test_samples)
self.assertEqual(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].label.tolist(), ['BLCA_TS', 'ESCA_EAC_TS'])
self.assertEqual(
preds_df_from_xdat[preds_df_from_xdat["rank_pred"] ==
1].label.tolist(), ['BLCA_TS', 'ESCA_EAC_TS'])
self.assertEqual(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].pred.tolist()[0], 0.26819298484099996)
self.assertEqual(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].pred.tolist()[1], 0.562124497548)
self.assertEqual(
preds_df_from_xdat[preds_df_from_xdat["rank_pred"] ==
1].pred.tolist()[1], 0.562124497548)
self.assertEqual(
preds_df_from_xdat[preds_df_from_xdat["rank_pred"] ==
1].pred.tolist()[0], 0.26819298484099996)
def test_rm500dropout(self):
"""Test if all models can be downloaded correctly"""
my_test_file = "/".join([
os.path.dirname(sys.modules["cancerscope"].__file__),
"../tests/data/test_tcga.txt"
])
scope_ensemble_obj = cancerscope.scope()
test_X = scope_ensemble_obj.load_data(
my_test_file) # X, samples, features_test, in_genecode
## Get the model of interest
model_name = "v1_rm500dropout"
model_in = ""
query_localdirs = cancerscope.get_models.findmodel(
os.path.dirname(cancerscope.__file__), model_name)
if query_localdirs is not None:
model_in = query_localdirs[model_name]
else:
model_in = cancerscope.get_models.downloadmodel(
model_label=model_name)
self.assertTrue(os.path.isdir(model_in))
self.assertTrue(
os.path.exists("".join([model_in, "/lasagne_bestparams.npz"])))
"""Test if model can be setup correctly"""
lmodel = cancerscope.scopemodel(model_in)
lmodel.fit()
self.assertEqual(len(lmodel.features), 17688)
x_input = lmodel.prepare_input_featorders(
X=test_X[0], x_features_genecode=test_X[3], x_features=test_X[2])
"""Test if it predicts properly"""
allpreds_names = lmodel.predict(x_input,
get_all_predictions=True,
get_numeric=False,
get_predictions_dict=False)
allpreds_values = lmodel.predict(x_input,
get_all_predictions=True,
get_numeric=True,
get_predictions_dict=False)
toppreds_names = lmodel.predict(x_input,
get_all_predictions=False,
get_numeric=False,
get_predictions_dict=False)
toppreds_values = lmodel.predict(x_input,
get_all_predictions=False,
get_numeric=True,
get_predictions_dict=False)
toppreds_df = lmodel.predict(x_input,
get_all_predictions=True,
get_numeric=False,
get_predictions_dict=True)
self.assertEqual(len(allpreds_names[0]), 66)
self.assertEqual(len(allpreds_names[1]), 66)
self.assertEqual(allpreds_values.shape[1], 66)
self.assertEqual(round(allpreds_values[0][1], 12),
round(0.001936952939, 12))
self.assertEqual(toppreds_names[0], "TFRI_GBM_NCL_TS")
self.assertEqual(toppreds_names[1], "TFRI_GBM_NCL_TS")
self.assertEqual(round(toppreds_values[0], 12),
round(0.576824542222, 12))
self.assertEqual(round(toppreds_values[1], 12),
round(0.516448831323, 12))
self.assertEqual(toppreds_df[0][0][0], toppreds_names[0])
self.assertEqual(round(float(toppreds_df[0][0][1]), 12),
round(toppreds_values[0], 12))
self.assertEqual(toppreds_df[1][0][0], toppreds_names[1])
self.assertEqual(round(float(toppreds_df[1][0][1]), 12),
round(toppreds_values[1], 12))
def test_ensemblewith2models(self):
my_test_file = "/".join([
os.path.dirname(sys.modules["cancerscope"].__file__),
"../tests/data/test_tcga.txt"
])
my_models = ["v1_rm500", "v1_rm500dropout"]
scope_ensemble_obj = cancerscope.scope()
test_X = scope_ensemble_obj.load_data(
my_test_file) # X, samples, features_test, in_genecode
self.assertEqual(test_X[-2][-1], "ZRANB1_ENSG00000019995")
## Process input file and get predictions from the selected 2 models
preds_df_from_file = scope_ensemble_obj.get_predictions_from_file(
my_test_file, modelnames=my_models)
### Compare to getting output from the x data object itself
test_x, test_samples, test_features, test_genecode = scope_ensemble_obj.load_data(
my_test_file)
preds_df_from_xdat = scope_ensemble_obj.predict(
X=test_x,
x_features=test_features,
x_features_genecode=test_genecode,
x_sample_names=test_samples,
modelnames=my_models)
print(preds_df_from_xdat)
print(
round(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].pred.tolist()[1], 12))
self.assertEqual(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].label.tolist(),
['TFRI_GBM_NCL_TS', 'TFRI_GBM_NCL_TS'])
self.assertEqual(
preds_df_from_xdat[preds_df_from_xdat["rank_pred"] ==
1].label.tolist(),
['TFRI_GBM_NCL_TS', 'TFRI_GBM_NCL_TS'])
self.assertEqual(
round(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].pred.tolist()[0], 12), 0.576824542222)
self.assertEqual(
round(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].pred.tolist()[1], 12), 0.516448831323)
self.assertEqual(
round(
preds_df_from_xdat[preds_df_from_xdat["rank_pred"] ==
1].pred.tolist()[1], 12), 0.516448831323)
self.assertEqual(
round(
preds_df_from_xdat[preds_df_from_xdat["rank_pred"] ==
1].pred.tolist()[0], 12), 0.576824542222)
self.assertEqual(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].pred.tolist()[0],
preds_df_from_xdat[preds_df_from_xdat["rank_pred"] ==
1].pred.tolist()[0])
self.assertEqual(
preds_df_from_file[preds_df_from_file["rank_pred"] ==
1].pred.tolist()[1],
preds_df_from_xdat[preds_df_from_xdat["rank_pred"] ==
1].pred.tolist()[1])
### Plot models
plotdir = tempfile.mkdtemp()
preds_df_plotted = scope_ensemble_obj.predict(
X=test_x,
x_features=test_features,
x_features_genecode=test_genecode,
x_sample_names=test_samples,
modelnames=my_models,
outdir=plotdir)
self.assertEqual(
glob.glob(plotdir + "/SCOPE_allPredictions.txt")[0],
plotdir + "/SCOPE_allPredictions.txt")
self.assertEqual(
glob.glob(plotdir + "/SCOPE_topPredictions.txt")[0],
plotdir + "/SCOPE_topPredictions.txt")
preds_df_from_plots = pd.read_csv(
glob.glob(plotdir + "/SCOPE_topPredictions.txt")[0],
delimiter="\t")
self.assertTrue(preds_df_from_xdat[[
'sample_ix', 'label', 'freq', 'models', 'rank_pred', 'sample_name'
]].equals(preds_df_from_plots[[
'sample_ix', 'label', 'freq', 'models', 'rank_pred', 'sample_name'
]]))
self.assertTrue(preds_df_from_file[[
'sample_ix', 'label', 'freq', 'models', 'rank_pred', 'sample_name'
]].equals(preds_df_from_plots[[
'sample_ix', 'label', 'freq', 'models', 'rank_pred', 'sample_name'
]]))
self.assertEqual(len(glob.glob(plotdir + "/SCOPE_sample*")), 2)
def test_rm500(self):
"""Test if all models can be downloaded correctly"""
my_test_file = "/".join([
os.path.dirname(sys.modules["cancerscope"].__file__),
"../tests/data/test_tcga.txt"
])
scope_ensemble_obj = cancerscope.scope()
test_X = scope_ensemble_obj.load_data(
my_test_file) # X, samples, features_test, in_genecode
## Get the model of interest
model_name = "v1_rm500"
model_in = ""
query_localdirs = cancerscope.get_models.findmodel(
os.path.dirname(cancerscope.__file__), model_name)
if query_localdirs is not None:
model_in = query_localdirs[model_name]
else:
model_in = cancerscope.get_models.downloadmodel(
model_label=model_name)
self.assertTrue(os.path.isdir(model_in))
self.assertTrue(
os.path.exists("".join([model_in, "/lasagne_bestparams.npz"])))
"""Test if model can be setup correctly"""
lmodel = cancerscope.scopemodel(model_in)
lmodel.fit()
self.assertEqual(len(lmodel.features), 17688)
x_input = lmodel.prepare_input_featorders(
X=test_X[0], x_features_genecode=test_X[3], x_features=test_X[2])
"""Test if it predicts properly"""
allpreds_names = lmodel.predict(x_input,
get_all_predictions=True,
get_numeric=False,
get_predictions_dict=False)
allpreds_values = lmodel.predict(x_input,
get_all_predictions=True,
get_numeric=True,
get_predictions_dict=False)
toppreds_names = lmodel.predict(x_input,
get_all_predictions=False,
get_numeric=False,
get_predictions_dict=False)
toppreds_values = lmodel.predict(x_input,
get_all_predictions=False,
get_numeric=True,
get_predictions_dict=False)
toppreds_df = lmodel.predict(x_input,
get_all_predictions=True,
get_numeric=False,
get_predictions_dict=True)
self.assertEqual(len(allpreds_names[0]), 66)
self.assertEqual(len(allpreds_names[1]), 66)
self.assertEqual(allpreds_values.shape[1], 66)
self.assertAlmostEqual(allpreds_values[0][1], 0.003065253372039)
self.assertEqual(toppreds_names[0], "PAAD_TS")
self.assertEqual(toppreds_names[1], "HNSC_TS")
self.assertAlmostEqual(toppreds_values[0], 0.20889836023919614, 6,
0.000001)
self.assertAlmostEqual(toppreds_values[1], 0.44416348623870444, 6,
0.000001)
#self.assertEqual(round(toppreds_values[0],12), round(0.208874390780809,12)); self.assertEqual(round(toppreds_values[1],12), round(0.444162763077693,12))
self.assertEqual(toppreds_df[0][0][0], toppreds_names[0])
self.assertAlmostEqual(float(toppreds_df[0][0][1]), toppreds_values[0])
self.assertEqual(toppreds_df[1][0][0], toppreds_names[1])
self.assertAlmostEqual(float(toppreds_df[1][0][1]), toppreds_values[1])