def xgb_shap_dataset(datasource, select, feature_column_names, label_spec,
feature_specs, is_pai, pai_explain_table):
label_column_name = label_spec["feature_name"]
if is_pai:
pai_table_parts = pai_explain_table.split(".")
formated_pai_table = "odps://%s/tables/%s" % (pai_table_parts[0],
pai_table_parts[1])
stream = db.pai_maxcompute_db_generator(formated_pai_table,
feature_column_names,
label_column_name,
feature_specs)
else:
conn = db.connect_with_data_source(datasource)
stream = db.db_generator(conn.driver, conn, select,
feature_column_names, label_spec,
feature_specs)
xs = pd.DataFrame(columns=feature_column_names)
i = 0
for row in stream():
xs.loc[i] = [item[0] for item in row[0]]
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)
for fname in feature_column_names:
dtype = feature_specs[fname]["dtype"]
xs[fname] = xs[fname].astype(dtype)
return xs
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"]))
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,
feature_column_names, None,
feature_metas)
else:
gen = db.db_generator(conn.driver, conn, select,
feature_column_names, None, feature_metas)
dataset = tf.data.Dataset.from_generator(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
def pai_download_table_data_worker(dname, feature_specs, feature_column_names,
label_spec, pai_table, slice_id,
slice_count, feature_column_code,
raw_data_dir):
import sqlflow_submitter.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)
label_column_name = label_spec['feature_name'] if label_spec else None
gen = db.pai_maxcompute_db_generator(pai_table,
feature_column_names,
label_column_name,
feature_specs,
slice_id=slice_id,
slice_count=slice_count)()
selected_cols = db.pai_selected_cols(pai_table)
filename = "{}/{}.txt".format(dname, slice_id)
dump_dmatrix(filename,
gen,
feature_column_names,
feature_specs,
label_spec,
selected_cols,
transform_fn=transform_fn,
raw_data_dir=raw_data_dir)
def pai_download_table_data_worker(dname, feature_specs, feature_column_names,
label_spec, pai_table, slice_id):
label_column_name = label_spec['feature_name'] if label_spec else None
gen = db.pai_maxcompute_db_generator(pai_table,
feature_column_names,
label_column_name,
feature_specs,
slice_id=slice_id,
slice_count=SLICE_NUM)()
filename = "{}/{}.txt".format(dname, slice_id)
dump_dmatrix(filename, gen, feature_column_names, feature_specs,
label_spec)
def estimator_predict(estimator, model_params, save, result_table,
feature_column_names, feature_column_names_map,
feature_columns, feature_metas, 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)
column_names = feature_column_names[:]
column_names.append(result_col_name)
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])
predict_generator = db.pai_maxcompute_db_generator(
formatted_pai_table, feature_column_names, None, feature_metas)()
else:
driver = conn.driver
predict_generator = db.db_generator(conn.driver, conn, select,
feature_column_names, None,
feature_metas)()
# 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, column_names, 100,
hdfs_namenode_addr, hive_location, hdfs_user,
hdfs_pass) as w:
for features in predict_generator:
result = predict(features)
row = []
for idx, _ in enumerate(feature_column_names):
per_feature = features[0][idx]
if isinstance(per_feature, tuple) or isinstance(
per_feature, list):
# is sparse feature: tuple (indices, values, shape) or scalar
val = per_feature[0]
elif isinstance(per_feature, np.ndarray):
val = per_feature
# val = features[0][idx][0]
row.append(str(val))
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 estimator_predict(estimator, model_params, save, result_table,
feature_column_names, feature_metas, 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)
column_names = feature_column_names[:]
column_names.append(result_col_name)
if is_pai:
driver = "pai_maxcompute"
conn = None
pai_table_parts = pai_table.split(".")
formated_pai_table = "odps://%s/tables/%s" % (pai_table_parts[0],
pai_table_parts[1])
predict_generator = db.pai_maxcompute_db_generator(
formated_pai_table, feature_column_names, None, feature_metas)()
else:
driver = conn.driver
predict_generator = db.db_generator(conn.driver, conn, select,
feature_column_names, None,
feature_metas)()
# load from the exported model
if save.startswith("oss://"):
with open("exported_path", "r") as fn:
export_path = fn.read()
parts = save.split("?")
export_path_oss = parts[0] + export_path
if TF_VERSION_2:
imported = tf.saved_model.load(export_path_oss)
else:
imported = tf.saved_model.load_v2(export_path_oss)
else:
with open("exported_path", "r") as fn:
export_path = fn.read()
if TF_VERSION_2:
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 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":
# FIXME(typhoonzero): figure out why int64 features need to convert to float
example.features.feature[feature_name].float_list.value.extend(
(float(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, column_names, 100,
hdfs_namenode_addr, hive_location, hdfs_user,
hdfs_pass) as w:
for features in predict_generator:
result = predict(features)
row = []
for idx, _ in enumerate(feature_column_names):
per_feature = features[0][idx]
if isinstance(per_feature, tuple) or isinstance(
per_feature, list):
# is sparse feature: tuple (indices, values, shape) or scalar
val = per_feature[0]
elif isinstance(per_feature, np.ndarray):
val = per_feature
# val = features[0][idx][0]
row.append(str(val))
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 xgb_shap_dataset(datasource,
select,
feature_column_names,
label_spec,
feature_specs,
is_pai,
pai_explain_table,
transform_fn=None,
feature_column_code=""):
label_column_name = label_spec["feature_name"]
if is_pai:
pai_table_parts = pai_explain_table.split(".")
formatted_pai_table = "odps://%s/tables/%s" % (pai_table_parts[0],
pai_table_parts[1])
stream = db.pai_maxcompute_db_generator(formatted_pai_table,
feature_column_names,
label_column_name,
feature_specs)
selected_cols = db.pai_selected_cols(formatted_pai_table)
else:
conn = db.connect_with_data_source(datasource)
stream = db.db_generator(conn.driver, conn, select,
feature_column_names, label_spec,
feature_specs)
selected_cols = db.selected_cols(conn.driver, conn, select)
if transform_fn:
column_names = transform_fn.get_column_names()
else:
column_names = feature_column_names
# NOTE(sneaxiy): pandas.DataFrame does not support Tensor whose rank is larger than 2.
# But `INDICATOR` would generate one hot vector for each element, and pandas.DataFrame
# would not accept `INDICATOR` results as its input. In a word, we do not support
# `TO EXPLAIN` when using `INDICATOR`.
xs = pd.DataFrame(columns=column_names)
dtypes = []
i = 0
for row, label in stream():
features = db.read_features_from_row(row, selected_cols,
feature_column_names,
feature_specs)
if transform_fn:
features = transform_fn(features)
# TODO(sneaxiy): support sparse features in `TO EXPLAIN`
features = [item[0] for item in features]
xs.loc[i] = features
if i == 0:
for f in features:
if isinstance(f, np.ndarray):
if f.dtype == np.float32 or f.dtype == np.float64:
dtypes.append('float32')
elif f.dtype == np.int32 or f.dtype == np.int64:
dtypes.append('int64')
else:
raise ValueError('Not supported data type {}'.format(
f.dtype))
elif isinstance(f, (np.float32, np.float64, float)):
dtypes.append('float32')
elif isinstance(f, (np.int32, np.int64, six.integer_types)):
dtypes.append('int64')
else:
raise ValueError('Not supported data type {}'.format(
type(f)))
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)
for dtype, name in zip(dtypes, column_names):
xs[name] = xs[name].astype(dtype)
return xs