def reader():
for row, label in gen():
features = db.read_features_from_row(row, selected_cols,
feature_column_names,
feature_metas)
if label is None:
yield (features, )
else:
yield (features, label)
def test_generator(self):
driver = os.environ.get('SQLFLOW_TEST_DB')
if driver == "mysql":
database = "iris"
user, password, host, port, database = testing_mysql_cfg()
conn = connect(driver,
database,
user=user,
password=password,
host=host,
port=int(port))
# prepare test data
execute(driver, conn, self.drop_statement)
execute(driver, conn, self.create_statement)
execute(driver, conn, self.insert_statement)
column_name_to_type = {
"features": {
"feature_name": "features",
"delimiter": "",
"dtype": "float32",
"is_sparse": False,
"shape": []
}
}
label_spec = {
"feature_name": "label",
"shape": [],
"delimiter": ""
}
gen = db_generator(driver, conn,
"SELECT * FROM test_table_float_fea",
["features"], label_spec, column_name_to_type)
idx = 0
for row, label in gen():
features = read_features_from_row(row, ["features"],
["features"],
column_name_to_type)
d = (features, label)
if idx == 0:
self.assertEqual(d, (((1.0, ), ), 0))
elif idx == 1:
self.assertEqual(d, (((2.0, ), ), 1))
idx += 1
self.assertEqual(idx, 2)
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(".")
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 = feature_column_names[:]
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)
xs = pd.DataFrame(columns=feature_column_names)
i = 0
for row, label in stream():
features = db.read_features_from_row(row, selected_cols,
feature_column_names,
feature_specs)
xs.loc[i] = [item[0] for item in 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)
for fname in feature_column_names:
dtype = feature_specs[fname]["dtype"]
xs[fname] = xs[fname].astype(dtype)
return xs
def dump_dmatrix(filename,
generator,
feature_column_names,
feature_specs,
has_label,
selected_cols,
batch_size=None):
# TODO(yancey1989): generate group and weight text file if necessary
row_id = 0
with open(filename, 'a') as f:
for row, label in generator:
features = db.read_features_from_row(row, selected_cols,
feature_column_names,
feature_specs)
row_data = []
for i, v in enumerate(features):
fname = feature_column_names[i]
dtype = feature_specs[fname]["dtype"]
if dtype == "int32" or dtype == "int64":
row_data.append("%d:%d" % (i, v[0] or 0))
elif dtype == "float32" or dtype == "float64":
row_data.append("%d:%f" % (i, v[0] or 0))
else:
raise ValueError(
"not supported columnt dtype %s for xgboost" % dtype)
if has_label:
row_data = [str(label)] + row_data
f.write("\t".join(row_data) + "\n")
row_id += 1
# batch_size == None meas use all data in generator
if batch_size == None:
continue
if row_id >= batch_size:
break
# return rows written
return row_id
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
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])
selected_cols = db.pai_selected_cols(formatted_pai_table)
predict_generator = db.pai_maxcompute_db_generator(
formatted_pai_table, feature_column_names, None, feature_metas)()
else:
driver = conn.driver
# bypass all selected cols to the prediction result table
selected_cols = db.selected_cols(conn.driver, conn, select)
predict_generator = db.db_generator(conn.driver, conn, select,
feature_column_names, None,
feature_metas)()
write_cols, target_col_index = write_cols_from_selected(
result_col_name, selected_cols)
# 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 target_col_index != -1:
del row[target_col_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 dump_dmatrix(filename,
generator,
feature_column_names,
feature_specs,
has_label,
selected_cols,
batch_size=None,
transform_fn=None,
raw_data_dir=None):
# TODO(yancey1989): generate group and weight text file if necessary
row_id = 0
if raw_data_dir:
index = filename.rindex('/') + 1 if '/' in filename else 0
raw_data_fid = open(os.path.join(raw_data_dir, filename[index:]), 'a')
else:
raw_data_fid = None
with open(filename, 'a') as f:
for row, label in generator:
features = db.read_features_from_row(row, selected_cols,
feature_column_names,
feature_specs)
if raw_data_fid is not None:
row_data = ["{}:{}".format(i, r) for i, r in enumerate(row)]
raw_data_fid.write("\t".join(row_data) + "\n")
if transform_fn:
features = transform_fn(features)
row_data = []
offset = 0
for i, v in enumerate(features):
if len(v) == 1: # dense feature
value = v[0]
if isinstance(value, np.ndarray):
value = value.reshape((-1, ))
row_data.extend([
"{}:{}".format(i + offset, item)
for i, item in enumerate(value)
])
offset += value.size
else:
row_data.append("{}:{}".format(offset, value))
offset += 1
else: # sparse feature
indices = v[0]
value = v[1].reshape((-1))
dense_size = np.prod(v[2])
row_data.extend([
"{}:{}".format(i + offset, item)
for i, item in six.moves.zip(indices, value)
])
offset += dense_size
if has_label:
row_data = [str(label)] + row_data
f.write("\t".join(row_data) + "\n")
row_id += 1
# batch_size == None meas use all data in generator
if batch_size == None:
continue
if row_id >= batch_size:
break
# return rows written
if raw_data_fid is not None:
raw_data_fid.close()
return row_id