def test_convert_predict_list_of_array(self):
tf.reset_default_graph()
sc = init_nncontext()
sqlcontext = SQLContext(sc)
rdd = sc.parallelize([(1, 2, 3), (4, 5, 6), (7, 8, 9)])
df = rdd.toDF(["feature", "label", "c"])
predict_rdd = df.rdd.map(lambda row: [np.array([1, 2]), np.array(0)])
resultDF = convert_predict_to_dataframe(df, predict_rdd)
resultDF.printSchema()
print(resultDF.collect()[0])
predict_rdd = df.rdd.map(lambda row: np.array(1))
resultDF = convert_predict_to_dataframe(df, predict_rdd)
resultDF.printSchema()
print(resultDF.collect()[0])
def predict(self,
data,
batch_size=4,
feature_cols=None,
hard_code_batch_size=False):
if isinstance(data, DataFrame):
assert feature_cols is not None, \
"feature columns is None; it should not be None in prediction"
dataset = to_dataset(data,
batch_size=-1,
batch_per_thread=batch_size,
validation_data=None,
feature_cols=feature_cols,
labels_cols=None,
hard_code_batch_size=hard_code_batch_size,
sequential_order=True,
shuffle=False)
predicted_rdd = self.model.predict(dataset, batch_size)
if isinstance(data, DataFrame):
return convert_predict_to_dataframe(data, predicted_rdd)
else:
return predicted_rdd
def predict(self,
data,
batch_size=4,
feature_cols=None,
hard_code_batch_size=False):
assert self.outputs is not None, \
"output is None, it should not be None in prediction"
if isinstance(data, DataFrame):
assert feature_cols is not None, \
"feature columns is None; it should not be None in prediction"
dataset = to_dataset(data,
batch_size=-1,
batch_per_thread=batch_size,
validation_data=None,
feature_cols=feature_cols,
labels_cols=None,
hard_code_batch_size=hard_code_batch_size,
sequential_order=True,
shuffle=False)
flat_inputs = nest.flatten(self.inputs)
flat_outputs = nest.flatten(self.outputs)
tfnet = TFNet.from_session(sess=self.sess,
inputs=flat_inputs,
outputs=flat_outputs)
predicted_rdd = tfnet.predict(dataset)
if isinstance(data, DataFrame):
return convert_predict_to_dataframe(data, predicted_rdd)
else:
return predicted_rdd
