def predict(self, data, batch_size=32, feature_cols=None, profile=False):
"""
Using this PyTorch model to make predictions on the data.
:param data: An instance of SparkXShards or a Spark DataFrame
:param batch_size: The number of samples per batch for each worker. Default is 32.
:param profile: Boolean. Whether to return time stats for the training procedure.
Default is False.
:param feature_cols: feature column names if data is a Spark DataFrame.
:return: A SparkXShards that contains the predictions with key "prediction" in each shard
"""
from bigdl.orca.data import SparkXShards
param = dict(batch_size=batch_size, profile=profile)
from pyspark.sql import DataFrame
if isinstance(data, DataFrame):
xshards, _ = dataframe_to_xshards(data,
validation_data=None,
feature_cols=feature_cols,
label_cols=None,
mode="predict")
pred_shards = self._predict_spark_xshards(xshards, param)
result = convert_predict_xshards_to_dataframe(data, pred_shards)
elif isinstance(data, SparkXShards):
if data._get_class_name() == 'pandas.core.frame.DataFrame':
data = process_xshards_of_pandas_dataframe(data, feature_cols)
pred_shards = self._predict_spark_xshards(data, param)
result = update_predict_xshards(data, pred_shards)
else:
raise ValueError(
"Only xshards or Spark DataFrame is supported for predict")
return result
def test_convert_predict_xshards_to_dataframe_multi_output(self):
rdd = self.sc.range(0, 100)
df = rdd.map(lambda x: ([float(x)] * 50,
[int(np.random.randint(0, 2, size=()))])).toDF(
["feature", "label"])
pred_shards = _dataframe_to_xshards(
df, feature_cols=["feature"]).transform_shard(
lambda x: {"prediction": [x["x"][:, :25], x["x"][:, 25:]]})
result_df = convert_predict_xshards_to_dataframe(df, pred_shards)
expr = "sum(cast(feature <> flatten(prediction) as int)) as error"
assert result_df.selectExpr(expr).first()["error"] == 0
def predict(self,
data,
batch_size=None,
verbose=1,
steps=None,
callbacks=None,
data_config=None,
feature_cols=None):
"""
Predict the input data
:param data: predict input data. It can be XShards or Spark DataFrame.
If data is XShards, each partition can be a Pandas DataFrame or a dictionary of
{'x': feature}, where feature is a numpy array or a tuple of numpy arrays.
:param batch_size: Batch size used for inference. Default: None.
:param verbose: Prints output of one model if true.
:param steps: Total number of steps (batches of samples) before declaring the prediction
round finished. Ignored with the default value of None.
:param callbacks: List of Keras compatible callbacks to apply during prediction.
:param data_config: An optional dictionary that can be passed to data creator function.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame or an XShards of Pandas DataFrame. Default: None.
:return:
"""
logger.info("Starting predict step.")
params = dict(
verbose=verbose,
batch_size=batch_size,
steps=steps,
callbacks=callbacks,
data_config=data_config,
)
from bigdl.orca.data import SparkXShards
from pyspark.sql import DataFrame
if isinstance(data, DataFrame):
xshards, _ = dataframe_to_xshards(data,
validation_data=None,
feature_cols=feature_cols,
label_cols=None,
mode="predict",
accept_str_col=True)
pred_shards = self._predict_spark_xshards(xshards, params)
result = convert_predict_xshards_to_dataframe(data, pred_shards)
elif isinstance(data, SparkXShards):
if data._get_class_name() == 'pandas.core.frame.DataFrame':
data = process_xshards_of_pandas_dataframe(data, feature_cols)
pred_shards = self._predict_spark_xshards(data, params)
result = update_predict_xshards(data, pred_shards)
else:
raise ValueError(
"Only xshards or Spark DataFrame is supported for predict")
return result
def predict(self,
data,
batch_size=None,
verbose=1,
steps=None,
callbacks=None,
data_config=None,
feature_cols=None):
"""
Predict the input data
:param data: predict input data. It can be XShards or Spark DataFrame.
If data is XShards, each partition can be a Pandas DataFrame or a dictionary of
{'x': feature}, where feature is a numpy array or a tuple of numpy arrays.
:param batch_size: Batch size used for inference. Default: None.
:param verbose: Prints output of one model if true.
:param steps: Total number of steps (batches of samples) before declaring the prediction
round finished. Ignored with the default value of None.
:param callbacks: List of Keras compatible callbacks to apply during prediction.
:param data_config: An optional dictionary that can be passed to data creator function.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame or an XShards of Pandas DataFrame. Default: None.
:return:
"""
logger.info("Starting predict step.")
sc = OrcaContext.get_spark_context()
if self.model_weights:
weights = sc.broadcast(self.model_weights)
else:
weights = None
init_params = dict(model_creator=self.model_creator,
compile_args_creator=self.compile_args_creator,
config=self.config,
verbose=self.verbose,
size=self.num_workers,
model_weights=weights,
mode="predict",
cluster_info=None)
params = dict(verbose=verbose,
batch_size=batch_size,
steps=steps,
callbacks=callbacks,
data_config=data_config)
if isinstance(data, DataFrame):
data = data.repartition(self.num_workers)
xshards, _ = dataframe_to_xshards(data,
validation_data=None,
feature_cols=feature_cols,
label_cols=None,
mode="predict",
accept_str_col=True)
def transform_func(iter, init_param, param):
partition_data = list(iter)
# res = combine_in_partition(partition_data)
param["data_creator"] = make_data_creator(partition_data)
return SparkRunner(**init_param).predict(**param)
pred_shards = SparkXShards(xshards.rdd.repartition(self.num_workers) \
.mapPartitions(
lambda iter: transform_func(iter, init_params, params)))
result = convert_predict_xshards_to_dataframe(data, pred_shards)
else:
raise ValueError(
"Only xshards or Spark DataFrame is supported for predict")
return result