def _compare_mleap_pyspark(mleap_prediction, spark_prediction):
import pandas
spark_pandas = spark_prediction.toPandas()
mleap_pandas = mleap_prediction.toPandas()
spark_predicted_labels = spark_pandas.prediction.values
mleap_predicted_labels = mleap_pandas.prediction.values
msg = compare_outputs(spark_predicted_labels, mleap_predicted_labels, decimal=5)
if msg:
raise OnnxRuntimeAssertionError("Predictions in mleap and spark do not match")
spark_probability = spark_pandas.probability.apply(lambda x: pandas.Series(x.toArray())).values
mleap_probability = mleap_pandas.probability.apply(lambda x: pandas.Series(x.toArray())).values
msg = compare_outputs(spark_probability, mleap_probability, decimal=5)
if msg:
raise OnnxRuntimeAssertionError("Probabilities in mleap and spark do not match")
def compare_results(expected, output, decimal=5):
tested = 0
if isinstance(expected, list):
if isinstance(output, list):
if len(expected) != len(output):
raise OnnxRuntimeAssertionError(
"Unexpected number of outputs: expected={0}, got={1}".format(len(expected), len(output)))
for exp, out in zip(expected, output):
compare_results(exp, out, decimal=decimal)
tested += 1
else:
raise OnnxRuntimeAssertionError(
"Type mismatch: output type is {0}".format(type(output)))
elif isinstance(expected, dict):
if not isinstance(output, dict):
raise OnnxRuntimeAssertionError("Type mismatch fo")
for k, v in output.items():
if k not in expected:
continue
msg = compare_outputs(expected[k], v, decimal=decimal)
if msg:
raise OnnxRuntimeAssertionError("Unexpected output '{0}': \n{2}".format(k, msg))
tested += 1
elif isinstance(expected, numpy.ndarray):
if isinstance(output, list):
if expected.shape[0] == len(output) and isinstance(output[0], dict):
import pandas
output = pandas.DataFrame(output)
output = output[list(sorted(output.columns))]
output = output.values
if isinstance(output, (dict, list)):
if len(output) != 1:
ex = str(output)
if len(ex) > 70:
ex = ex[:70] + "..."
raise OnnxRuntimeAssertionError(
"More than one output when 1 is expected\n{0}".format(ex))
output = output[-1]
if not isinstance(output, numpy.ndarray):
raise OnnxRuntimeAssertionError(
"output must be an array not {0}".format(type(output)))
msg = compare_outputs(expected, output, decimal=decimal)
if isinstance(msg, ExpectedAssertionError):
raise msg
if msg:
raise OnnxRuntimeAssertionError("Unexpected output\n{1}".format(msg))
tested += 1
else:
from scipy.sparse.csr import csr_matrix
if isinstance(expected, csr_matrix):
# DictVectorizer
one_array = numpy.array(output)
msg = compare_outputs(expected.todense(), one_array, decimal=decimal)
if msg:
raise OnnxRuntimeAssertionError("Unexpected output\n{1}".format(msg))
tested += 1
else:
raise OnnxRuntimeAssertionError(
"Unexpected type for expected output ({0})".format(type(expected)))
if tested == 0:
raise OnnxRuntimeAssertionError("No test for model")