def pred(datasource,
estimator_string,
select,
result_table,
feature_columns,
feature_column_names,
feature_column_names_map,
train_label_name,
result_col_name,
feature_metas={},
model_params={},
pred_params={},
save="",
batch_size=1,
pai_table=""):
estimator = import_model(estimator_string)
model_params.update(feature_columns)
is_estimator = is_tf_estimator(estimator)
if pai_table != "":
conn = PaiIOConnection.from_table(pai_table)
selected_cols = db.selected_cols(conn, None)
predict_generator = db.db_generator(conn, None)
else:
conn = db.connect_with_data_source(datasource)
selected_cols = db.selected_cols(conn, select)
predict_generator = db.db_generator(conn, select)
pop_optimizer_and_loss(model_params)
if pred_params is None:
extra_result_cols = []
else:
extra_result_cols = pred_params.get("extra_outputs", "")
extra_result_cols = [
c.strip() for c in extra_result_cols.split(",") if c.strip()
]
if not is_estimator:
if not issubclass(estimator, tf.keras.Model):
# functional model need field_metas parameter
model_params["field_metas"] = feature_metas
print("Start predicting using keras model...")
keras_predict(estimator, model_params, save, result_table,
feature_column_names, feature_metas, train_label_name,
result_col_name, conn, predict_generator, selected_cols,
extra_result_cols)
else:
# TODO(sneaxiy): support extra_result_cols for estimator
model_params['model_dir'] = save
print("Start predicting using estimator model...")
estimator_predict(result_table, feature_column_names, feature_metas,
train_label_name, result_col_name, conn,
predict_generator, selected_cols)
print("Done predicting. Predict table : %s" % result_table)
def pai_download_table_data_worker(dname, feature_metas, feature_column_names,
label_meta, pai_table, slice_id,
slice_count, feature_column_code,
raw_data_dir):
import runtime.xgboost as xgboost_extended
if isinstance(feature_column_code, dict):
# NOTE(typhoonzero): feature_column_code is a dict of
# runtime.feature.column in refactored step code.
feature_column_transformers = compile_ir_feature_columns(
feature_column_code, EstimatorType.XGBOOST)
transform_fn = \
xgboost_extended.feature_column.ComposedColumnTransformer(
feature_column_names,
*feature_column_transformers["feature_columns"])
else:
feature_column_transformers = eval('[{}]'.format(feature_column_code))
transform_fn = \
xgboost_extended.feature_column.ComposedColumnTransformer(
feature_column_names, *feature_column_transformers)
conn = PaiIOConnection.from_table(pai_table, slice_id, slice_count)
gen = db.db_generator(conn, None, label_meta=label_meta)()
selected_cols = db.selected_cols(conn, None)
filename = "{}/{}.txt".format(dname, slice_id)
dump_dmatrix(filename,
gen,
feature_column_names,
feature_metas,
label_meta,
selected_cols,
transform_fn=transform_fn,
raw_data_dir=raw_data_dir)
def pred(datasource,
select,
feature_metas,
feature_column_names,
train_label_meta,
pred_label_meta,
result_table,
is_pai=False,
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass="",
pai_table="",
model_params=None,
train_params=None,
transform_fn=None,
feature_column_code=""):
if not is_pai:
conn = db.connect_with_data_source(datasource)
else:
conn = None
dpred = xgb_dataset(
datasource=datasource,
fn='predict.txt',
dataset_sql=select,
feature_metas=feature_metas,
feature_column_names=feature_column_names,
label_meta=None,
is_pai=is_pai,
pai_table=pai_table,
pai_single_file=True,
cache=True,
batch_size=DEFAULT_PREDICT_BATCH_SIZE,
transform_fn=transform_fn,
feature_column_code=feature_column_code,
raw_data_dir="predict.raw.dir") # NOTE: default to use external memory
bst = xgb.Booster({'nthread': 4}) # init model
bst.load_model("my_model") # load data
print("Start predicting XGBoost model...")
if is_pai:
pai_table = "odps://{}/tables/{}".format(*pai_table.split("."))
selected_cols = db.pai_selected_cols(pai_table)
else:
selected_cols = db.selected_cols(conn, select)
feature_file_id = 0
train_label_name = train_label_meta["feature_name"]
pred_label_name = pred_label_meta["feature_name"]
for pred_dmatrix in dpred:
predict_and_store_result(bst, pred_dmatrix, feature_file_id,
model_params, selected_cols, train_label_name,
pred_label_name, feature_column_names,
feature_metas, is_pai, conn, result_table,
hdfs_namenode_addr, hive_location, hdfs_user,
hdfs_pass)
feature_file_id += 1
print("Done predicting. Predict table : %s" % result_table)
def input_fn(select,
datasource,
feature_column_names,
feature_metas,
label_meta,
is_pai=False,
pai_table="",
num_workers=1,
worker_id=0):
feature_types = []
shapes = []
for name in feature_column_names:
# NOTE: vector columns like 23,21,3,2,0,0 should use shape None
if feature_metas[name]["is_sparse"]:
if feature_metas[name]["delimiter_kv"]:
# extract two features from generator data.
feature_types.append(
(get_dtype(feature_metas[name]["dtype"]),
get_dtype(feature_metas[name]["dtype_weight"]), tf.int64))
shapes.append((None, None, None))
else:
feature_types.append((tf.int64, tf.int32, tf.int64))
shapes.append((None, None, None))
else:
feature_types.append(get_dtype(feature_metas[name]["dtype"]))
shapes.append(feature_metas[name]["shape"])
if is_pai:
pai_table = "odps://{}/tables/{}".format(*pai_table.split("."))
return pai_dataset(pai_table,
feature_column_names,
label_meta,
feature_metas,
slice_id=worker_id,
slice_count=num_workers)
else:
conn = db.connect_with_data_source(datasource)
gen = db.db_generator(conn, select, label_meta)
selected_cols = db.selected_cols(conn, select)
gen = tf_generator(gen, selected_cols, feature_column_names, feature_metas)
# Clustering model do not have label
if not label_meta or label_meta["feature_name"] == "":
dataset = tf.data.Dataset.from_generator(gen, (tuple(feature_types), ),
(tuple(shapes), ))
ds_mapper = functools.partial(
parse_sparse_feature_predict,
feature_column_names=feature_column_names,
feature_metas=feature_metas)
else:
dataset = tf.data.Dataset.from_generator(
gen, (tuple(feature_types), eval("tf.%s" % label_meta["dtype"])),
(tuple(shapes), label_meta["shape"]))
ds_mapper = functools.partial(
parse_sparse_feature,
feature_column_names=feature_column_names,
feature_metas=feature_metas)
return dataset.map(ds_mapper)
def pred_imp(datasource,
select,
feature_metas,
feature_column_names,
train_label_meta,
pred_label_meta,
result_table,
is_pai=False,
pai_table="",
model_params=None,
train_params=None,
transform_fn=None,
feature_column_code="",
rank=0,
nworkers=1):
print("rank={} nworkers={}".format(rank, nworkers))
if not is_pai:
conn = db.connect_with_data_source(datasource)
else:
conn = PaiIOConnection.from_table(pai_table)
dpred = xgb_dataset(
datasource=datasource,
fn='predict.txt',
dataset_sql=select,
feature_metas=feature_metas,
feature_column_names=feature_column_names,
label_meta=None,
is_pai=is_pai,
pai_table=pai_table,
pai_single_file=True,
cache=True,
batch_size=DEFAULT_PREDICT_BATCH_SIZE,
rank=rank,
nworkers=nworkers,
transform_fn=transform_fn,
feature_column_code=feature_column_code,
raw_data_dir="predict.raw.dir") # NOTE: default to use external memory
bst = xgb.Booster({'nthread': 4}) # init model
bst.load_model("my_model") # load data
print("{} Start predicting XGBoost model...".format(datetime.now()))
if not model_params:
model_params = load_metadata("model_meta.json")["attributes"]
selected_cols = db.selected_cols(conn, select)
feature_file_id = 0
train_label_name = train_label_meta["feature_name"]
pred_label_name = pred_label_meta["feature_name"]
for pred_dmatrix in dpred:
predict_and_store_result(bst, pred_dmatrix, feature_file_id,
model_params, selected_cols, train_label_name,
pred_label_name, feature_column_names,
feature_metas, is_pai, conn, result_table,
rank)
feature_file_id += 1
print("{} Done predicting. Predict table: {}".format(
datetime.now(), result_table))
def pred(datasource,
estimator_string,
select,
result_table,
feature_columns,
feature_column_names,
feature_column_names_map,
train_label_name,
result_col_name,
feature_metas={},
model_params={},
save="",
batch_size=1,
hdfs_namenode_addr="",
hive_location="",
hdfs_user="",
hdfs_pass=""):
estimator = import_model(estimator_string)
model_params.update(feature_columns)
is_estimator = is_tf_estimator(estimator)
conn = db.connect_with_data_source(datasource)
driver = conn.driver
predict_generator = db.db_generator(conn, select)
selected_cols = db.selected_cols(conn, select)
if not is_estimator:
if not issubclass(estimator, tf.keras.Model):
# functional model need field_metas parameter
model_params["field_metas"] = feature_metas
print("Start predicting using keras model...")
keras_predict(estimator, model_params, save, result_table,
feature_column_names, feature_metas, train_label_name,
result_col_name, driver, conn, predict_generator,
selected_cols, hdfs_namenode_addr, hive_location,
hdfs_user, hdfs_pass)
else:
model_params['model_dir'] = save
print("Start predicting using estimator model...")
estimator_predict(estimator, model_params, save, result_table,
feature_column_names, feature_column_names_map,
feature_columns, feature_metas, train_label_name,
result_col_name, driver, conn, predict_generator,
selected_cols, hdfs_namenode_addr, hive_location,
hdfs_user, hdfs_pass)
print("Done predicting. Predict table : %s" % result_table)
def pai_download_table_data_worker(dname, feature_metas, feature_column_names,
label_meta, pai_table, slice_id,
slice_count, feature_column_code,
raw_data_dir):
import runtime.xgboost as xgboost_extended
feature_column_transformers = eval('[{}]'.format(feature_column_code))
transform_fn = xgboost_extended.feature_column.ComposedColumnTransformer(
feature_column_names, *feature_column_transformers)
conn = PaiIOConnection.from_table(pai_table, slice_id, slice_count)
gen = db.db_generator(conn, None, label_meta=label_meta)()
selected_cols = db.selected_cols(conn, None)
filename = "{}/{}.txt".format(dname, slice_id)
dump_dmatrix(filename,
gen,
feature_column_names,
feature_metas,
label_meta,
selected_cols,
transform_fn=transform_fn,
raw_data_dir=raw_data_dir)
def _predict(datasource,
estimator_string,
select,
result_table,
feature_columns,
feature_column_names,
feature_column_names_map,
train_label_name,
result_col_name,
feature_metas={},
model_params={},
save="",
batch_size=1,
pai_table=""):
estimator = import_model(estimator_string)
model_params.update(feature_columns)
is_estimator = is_tf_estimator(estimator)
conn = PaiIOConnection.from_table(pai_table)
selected_cols = db.selected_cols(conn, None)
predict_generator = db.db_generator(conn, None)
pop_optimizer_and_loss(model_params)
if not is_estimator:
if not issubclass(estimator, tf.keras.Model):
# functional model need field_metas parameter
model_params["field_metas"] = feature_metas
print("Start predicting using keras model...")
keras_predict(estimator, model_params, save, result_table,
feature_column_names, feature_metas, train_label_name,
result_col_name, conn, predict_generator, selected_cols)
else:
model_params['model_dir'] = save
print("Start predicting using estimator model...")
estimator_predict(result_table, feature_column_names, feature_metas,
train_label_name, result_col_name, conn,
predict_generator, selected_cols)
print("Done predicting. Predict table : %s" % result_table)
def xgb_shap_dataset(datasource,
select,
feature_column_names,
label_meta,
feature_metas,
is_pai,
pai_explain_table,
transform_fn=None,
feature_column_code=""):
if is_pai:
# (TODO: lhw) we may specify pai_explain_table in datasoure
# and discard the condition statement here
conn = PaiIOConnection.from_table(pai_explain_table)
stream = db.db_generator(conn, None, label_meta)
else:
conn = db.connect_with_data_source(datasource)
stream = db.db_generator(conn, select, label_meta)
selected_cols = db.selected_cols(conn, select)
if transform_fn:
feature_names = transform_fn.get_feature_column_names()
else:
feature_names = feature_column_names
xs = None
dtypes = []
sizes = []
offsets = []
i = 0
for row, label in stream():
features = db.read_features_from_row(row,
selected_cols,
feature_column_names,
feature_metas,
is_xgboost=True)
if transform_fn:
features = transform_fn(features)
flatten_features = []
for j, feature in enumerate(features):
if len(feature) == 3: # convert sparse to dense
col_indices, values, dense_shape = feature
size = int(np.prod(dense_shape))
row_indices = np.zeros(shape=[col_indices.size])
sparse_matrix = scipy.sparse.csr_matrix(
(values, (row_indices, col_indices)), shape=[1, size])
values = sparse_matrix.toarray()
else:
values = feature[0]
if isinstance(values, np.ndarray):
flatten_features.extend(values.flatten().tolist())
if i == 0:
sizes.append(values.size)
dtypes.append(infer_dtype(values))
else:
flatten_features.append(values)
if i == 0:
sizes.append(1)
dtypes.append(infer_dtype(values))
# Create the column name according to the feature number
# of each column.
#
# If the column "c" contains only 1 feature, the result
# column name would be "c" too.
#
# If the column "c" contains 3 features,
# the result column name would be "c_0", "c_1" and "c_2"
if i == 0:
offsets = np.cumsum([0] + sizes)
column_names = []
for j in six.moves.range(len(offsets) - 1):
start = offsets[j]
end = offsets[j + 1]
if end - start == 1:
column_names.append(feature_names[j])
else:
for k in six.moves.range(start, end):
column_names.append('{}_{}'.format(
feature_names[j], k))
xs = pd.DataFrame(columns=column_names)
xs.loc[i] = flatten_features
i += 1
# NOTE(typhoonzero): set dtype to the feature's actual type, or the dtype
# may be "object". Use below code to reproduce:
# import pandas as pd
# feature_column_names=["a", "b"]
# xs = pd.DataFrame(columns=feature_column_names)
# for i in range(10):
# xs.loc[i] = [int(j) for j in range(2)]
# print(xs.dtypes)
columns = xs.columns
for i, dtype in enumerate(dtypes):
for j in six.moves.range(offsets[i], offsets[i + 1]):
xs[columns[j]] = xs[columns[j]].astype(dtype)
return xs
def xgb_dataset(datasource,
fn,
dataset_sql,
feature_metas,
feature_column_names,
label_meta,
is_pai=False,
pai_table="",
pai_single_file=False,
cache=False,
batch_size=None,
epoch=1,
rank=0,
nworkers=1,
transform_fn=None,
feature_column_code="",
raw_data_dir=None):
if raw_data_dir:
# raw_data_dir is needed when predicting. Because we
# should write the raw data from the source db into
# the dest db, instead of the transformed data after
# `transform_fn(features)` . If raw_data_dir is not
# None, the raw data from the source db would be written
# into another file.
if os.path.exists(raw_data_dir):
shutil.rmtree(raw_data_dir, ignore_errors=True)
os.mkdir(raw_data_dir)
if is_pai:
for dmatrix in pai_dataset(fn,
feature_metas,
feature_column_names,
label_meta,
pai_table,
pai_single_file,
cache,
rank,
nworkers,
batch_size=batch_size,
feature_column_code=feature_column_code,
raw_data_dir=raw_data_dir):
yield dmatrix
return
conn = db.connect_with_data_source(datasource)
gen = db.db_generator(conn, dataset_sql, label_meta)()
selected_cols = db.selected_cols(conn, dataset_sql)
for _ in six.moves.range(epoch):
step = 0
# the filename per batch is [filename]_[step]
step_file_name = "%s_%d" % (fn, step)
written_rows = dump_dmatrix(step_file_name,
gen,
feature_column_names,
feature_metas,
label_meta,
selected_cols,
transform_fn=transform_fn,
raw_data_dir=raw_data_dir)
while written_rows > 0:
yield load_dmatrix('{0}#{0}.cache'.format(step_file_name)
if cache else step_file_name)
os.remove(step_file_name)
step += 1
step_file_name = "%s_%d" % (fn, step)
written_rows = dump_dmatrix(step_file_name,
gen,
feature_column_names,
feature_metas,
label_meta,
selected_cols,
transform_fn=transform_fn,
raw_data_dir=raw_data_dir)
def explain(datasource, select, explainer, model_params, result_table, model):
"""
Do explanation to a trained TensorFlow model.
Args:
datasource (str): the database connection string.
select (str): the input data to predict.
explainer (str): the explainer to explain the model.
Not used in TensorFlow models.
model_params (dict): the parameters for evaluation.
result_table (str): the output data table.
model (Model|str): the model object or where to load the model.
Returns:
None.
"""
if isinstance(model, six.string_types):
model = Model.load_from_db(datasource, model)
else:
assert isinstance(model,
Model), "not supported model type %s" % type(model)
plot_type = model_params.get("summary.plot_type", "bar")
train_attributes = model.get_meta("attributes")
train_fc_map = model.get_meta("features")
train_label_desc = model.get_meta("label").get_field_desc()[0]
estimator_string = model.get_meta("class_name")
save = "model_save"
field_descs = get_ordered_field_descs(train_fc_map)
feature_column_names = [fd.name for fd in field_descs]
feature_metas = dict([(fd.name, fd.to_dict(dtype_to_string=True))
for fd in field_descs])
feature_columns = compile_ir_feature_columns(train_fc_map,
model.get_type())
label_name = model_params.get("label_col", train_label_desc.name)
train_label_desc.name = label_name
label_meta = train_label_desc.to_dict(dtype_to_string=True)
if result_table:
conn = db.connect_with_data_source(datasource)
if estimator_string.startswith("BoostedTrees"):
column_defs = [
"feature %s" %
DataType.to_db_field_type(conn.driver, DataType.STRING),
"dfc %s" %
DataType.to_db_field_type(conn.driver, DataType.FLOAT32),
"gain %s" %
DataType.to_db_field_type(conn.driver, DataType.FLOAT32),
]
else:
selected_cols = db.selected_cols(conn, select)
if label_name in selected_cols:
selected_cols.remove(label_name)
name_to_shape = dict([(fd.name, fd.shape) for fd in field_descs])
column_defs = []
float_field_type = DataType.to_db_field_type(
conn.driver, DataType.FLOAT32)
for name in selected_cols:
shape = name_to_shape.get(name, None)
if shape is None:
raise ValueError("cannot find column %s" % name)
size = int(np.prod(shape))
if size == 1:
column_def = "%s %s" % (name, float_field_type)
column_defs.append(column_def)
else:
for i in six.moves.range(size):
column_def = "%s_%d %s" % (name, i, float_field_type)
column_defs.append(column_def)
drop_sql = "DROP TABLE IF EXISTS %s;" % result_table
create_sql = "CREATE TABLE %s (%s);" % (result_table,
",".join(column_defs))
conn.execute(drop_sql)
conn.execute(create_sql)
conn.close()
_explain(datasource=datasource,
estimator_string=estimator_string,
select=select,
feature_columns=feature_columns,
feature_column_names=feature_column_names,
feature_metas=feature_metas,
label_meta=label_meta,
model_params=train_attributes,
save=save,
plot_type=plot_type,
result_table=result_table)
with open('summary.png', 'rb') as f:
img = f.read()
img = base64.b64encode(img)
if six.PY3:
img = img.decode('utf-8')
img = "<div align='center'><img src='data:image/png;base64,%s' /></div>" \
% img
print(img)
def keras_predict(estimator, model_params, save, result_table, is_pai,
pai_table, feature_column_names, feature_metas,
train_label_name, result_col_name, datasource, select,
hdfs_namenode_addr, hive_location, hdfs_user, hdfs_pass):
classifier = init_model_with_feature_column(estimator, model_params)
classifier_pkg = sys.modules[estimator.__module__]
conn = None
if is_pai:
driver = "pai_maxcompute"
else:
conn = db.connect_with_data_source(datasource)
driver = conn.driver
if is_pai:
pai_table_parts = pai_table.split(".")
formatted_pai_table = "odps://%s/tables/%s" % (pai_table_parts[0],
pai_table_parts[1])
gen = db.pai_maxcompute_db_generator(formatted_pai_table)
selected_cols = feature_column_names
else:
gen = db.db_generator(conn, select)
selected_cols = db.selected_cols(conn, select)
def eval_input_fn(batch_size, cache=False):
feature_types = []
for name in feature_column_names:
# NOTE: vector columns like 23,21,3,2,0,0 should use shape None
if feature_metas[name]["is_sparse"]:
feature_types.append((tf.int64, tf.int32, tf.int64))
else:
feature_types.append(get_dtype(feature_metas[name]["dtype"]))
tf_gen = tf_generator(gen, selected_cols, feature_column_names,
feature_metas)
dataset = tf.data.Dataset.from_generator(tf_gen,
(tuple(feature_types), ))
ds_mapper = functools.partial(
parse_sparse_feature_predict,
feature_column_names=feature_column_names,
feature_metas=feature_metas)
dataset = dataset.map(ds_mapper).batch(batch_size)
if cache:
dataset = dataset.cache()
return dataset
# NOTE: always use batch_size=1 when predicting to get the pairs of
# features and predict results to insert into result table.
pred_dataset = eval_input_fn(1)
one_batch = next(iter(pred_dataset))
# NOTE: must run predict one batch to initialize parameters
# see: https://www.tensorflow.org/alpha/guide/keras/saving_and_serializing#saving_subclassed_models
classifier.predict_on_batch(one_batch)
classifier.load_weights(save)
pred_dataset = eval_input_fn(1, cache=True).make_one_shot_iterator()
column_names = selected_cols[:]
train_label_index = selected_cols.index(train_label_name)
if train_label_index != -1:
del column_names[train_label_index]
column_names.append(result_col_name)
with db.buffered_db_writer(driver, conn, result_table, column_names, 100,
hdfs_namenode_addr, hive_location, hdfs_user,
hdfs_pass) as w:
for features in pred_dataset:
result = classifier.predict_on_batch(features)
# FIXME(typhoonzero): determine the predict result is classification by
# adding the prediction result together to see if it is close to 1.0.
if len(result[0]) == 1: # regression result
result = result[0][0]
else:
sum = 0
for i in result[0]:
sum += i
if np.isclose(sum, 1.0): # classification result
result = result[0].argmax(axis=-1)
else:
result = result[0] # multiple regression result
row = []
for idx, name in enumerate(feature_column_names):
val = features[name].numpy()[0][0]
row.append(str(val))
if isinstance(result, np.ndarray):
if len(result) > 1:
# NOTE(typhoonzero): if the output dimension > 1, format output tensor
# using a comma separated string. Only available for keras models.
row.append(",".join([str(i) for i in result]))
else:
row.append(str(result[0]))
else:
row.append(str(result))
w.write(row)
del pred_dataset
def estimator_predict(estimator, model_params, save, result_table,
feature_column_names, feature_column_names_map,
feature_columns, feature_metas, train_label_name,
result_col_name, datasource, select, hdfs_namenode_addr,
hive_location, hdfs_user, hdfs_pass, is_pai, pai_table):
if not is_pai:
conn = db.connect_with_data_source(datasource)
if is_pai:
driver = "pai_maxcompute"
conn = None
pai_table_parts = pai_table.split(".")
formatted_pai_table = "odps://%s/tables/%s" % (pai_table_parts[0],
pai_table_parts[1])
selected_cols = db.pai_selected_cols(formatted_pai_table)
predict_generator = db.pai_maxcompute_db_generator(
formatted_pai_table)()
else:
driver = conn.driver
# bypass all selected cols to the prediction result table
selected_cols = db.selected_cols(conn, select)
predict_generator = db.db_generator(conn, select)()
write_cols = selected_cols[:]
try:
train_label_index = selected_cols.index(train_label_name)
except ValueError:
train_label_index = -1
if train_label_index != -1:
del write_cols[train_label_index]
write_cols.append(result_col_name)
# load from the exported model
with open("exported_path", "r") as fn:
export_path = fn.read()
if tf_is_version2():
imported = tf.saved_model.load(export_path)
else:
imported = tf.saved_model.load_v2(export_path)
def add_to_example(example, x, i):
feature_name = feature_column_names[i]
dtype_str = feature_metas[feature_name]["dtype"]
if feature_metas[feature_name]["delimiter"] != "":
if feature_metas[feature_name]["is_sparse"]:
# NOTE(typhoonzero): sparse feature will get (indices,values,shape) here, use indices only
values = x[0][i][0].flatten()
else:
values = x[0][i].flatten()
if dtype_str == "float32" or dtype_str == "float64":
example.features.feature[feature_name].float_list.value.extend(
list(values))
elif dtype_str == "int32" or dtype_str == "int64":
example.features.feature[feature_name].int64_list.value.extend(
list(values))
else:
if "feature_columns" in feature_columns:
idx = feature_column_names.index(feature_name)
fc = feature_columns["feature_columns"][idx]
else:
# DNNLinearCombinedXXX have dnn_feature_columns and linear_feature_columns param.
idx = -1
try:
idx = feature_column_names_map[
"dnn_feature_columns"].index(feature_name)
fc = feature_columns["dnn_feature_columns"][idx]
except:
try:
idx = feature_column_names_map[
"linear_feature_columns"].index(feature_name)
fc = feature_columns["linear_feature_columns"][idx]
except:
pass
if idx == -1:
raise ValueError(
"can not found feature %s in all feature columns")
if dtype_str == "float32" or dtype_str == "float64":
# need to pass a tuple(float, )
example.features.feature[feature_name].float_list.value.extend(
(float(x[0][i][0]), ))
elif dtype_str == "int32" or dtype_str == "int64":
numeric_type = type(tf.feature_column.numeric_column("tmp"))
if type(fc) == numeric_type:
example.features.feature[
feature_name].float_list.value.extend(
(float(x[0][i][0]), ))
else:
example.features.feature[
feature_name].int64_list.value.extend(
(int(x[0][i][0]), ))
elif dtype_str == "string":
example.features.feature[feature_name].bytes_list.value.extend(
x[0][i])
def predict(x):
example = tf.train.Example()
for i in range(len(feature_column_names)):
add_to_example(example, x, i)
return imported.signatures["predict"](
examples=tf.constant([example.SerializeToString()]))
with db.buffered_db_writer(driver, conn, result_table, write_cols, 100,
hdfs_namenode_addr, hive_location, hdfs_user,
hdfs_pass) as w:
for row, _ in predict_generator:
features = db.read_features_from_row(row, selected_cols,
feature_column_names,
feature_metas)
result = predict((features, ))
if train_label_index != -1 and len(row) > train_label_index:
del row[train_label_index]
if "class_ids" in result:
row.append(str(result["class_ids"].numpy()[0][0]))
else:
# regression predictions
row.append(str(result["predictions"].numpy()[0][0]))
w.write(row)
def load_db_data_to_data_frame(datasource,
select=None,
odps_table=None,
load_schema_only=False):
if odps_table is None:
conn = db.connect_with_data_source(datasource)
selected_cols = db.selected_cols(conn, select)
if load_schema_only:
return pd.DataFrame(columns=selected_cols)
generator = db.db_generator(conn, select)
else:
project, table = odps_table.split('.')
conn = db.connect_with_data_source(datasource)
schema = conn.get_table(table).schema
selected_cols = [column.name for column in schema]
if load_schema_only:
return pd.DataFrame(columns=selected_cols)
select_sql = "SELECT * FROM {}".format(table)
instance = conn.execute_sql(select_sql)
if not instance.is_successful():
raise ValueError('cannot get data from table {}.{}'.format(
project, table))
def generator_func():
from odps import tunnel
compress = tunnel.CompressOption.CompressAlgorithm.ODPS_ZLIB
with instance.open_reader(tunnel=False,
compress=compress) as reader:
for record in reader:
row_value = [
record[i] for i in six.moves.range(len(selected_cols))
]
yield row_value, None
generator = generator_func
dtypes = [None] * len(selected_cols)
values = [[] for _ in six.moves.range(len(selected_cols))]
for row_value, _ in generator():
for i, item in enumerate(row_value):
if dtypes[i] == np.str:
values[i].append(item)
continue
float_value = None
try:
float_value = float(item)
except:
pass
if float_value is None: # cannot convert to float value
dtypes[i] = np.str
else:
item = float_value
int_value = long(item) if six.PY2 else int(item)
if int_value != item:
dtypes[i] = np.float64
values[i].append(item)
numpy_dict = collections.OrderedDict()
for col_name, dtype, value in six.moves.zip(selected_cols, dtypes, values):
if dtype is None:
dtype = np.int64
numpy_dict[col_name] = np.array(value, dtype=dtype)
df = pd.DataFrame(data=numpy_dict)
return df
