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Posted to commits@madlib.apache.org by ok...@apache.org on 2022/06/16 17:31:13 UTC
[madlib] branch master updated (bdf8cae0 -> ac22af57)
This is an automated email from the ASF dual-hosted git repository.
okislal pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/madlib.git
from bdf8cae0 Doc: Update online examples for various model selection modules
new 011bddf9 Update gitignore to exclude /cmake-build-debug
new 9f1c5029 Add users for docker commands
new 4a98bc47 Fix jenkins
new ac22af57 XGBoost: Add new module xgboost
The 4 revisions listed above as "new" are entirely new to this
repository and will be described in separate emails. The revisions
listed as "add" were already present in the repository and have only
been added to this reference.
Summary of changes:
.gitignore | 1 +
CMakeLists.txt | 1 -
doc/CMakeLists.txt | 6 +
.../example/madlib_xgboost_example.sql | 121 +++-
doc/mainpage.dox.in | 1 +
src/config/Modules.yml | 2 +
.../postgres/modules/mxgboost}/__init__.py_in | 0
.../postgres/modules/mxgboost/madlib_xgboost.py_in | 601 +++++++++++++++++
.../modules/mxgboost/madlib_xgboost.sql_in | 720 +++++++++++++++++++++
.../modules/mxgboost/test/madlib_xgboost.sql_in | 119 +++-
tool/docker_start.sh | 4 +-
tool/jenkins/jenkins_build.sh | 4 +-
12 files changed, 1513 insertions(+), 67 deletions(-)
copy methods/sketch/src/pg_gp/sql/mfv.sql_in => doc/example/madlib_xgboost_example.sql (98%)
copy {methods/sketch/src/pg_gp => src/ports/postgres/modules/mxgboost}/__init__.py_in (100%)
create mode 100644 src/ports/postgres/modules/mxgboost/madlib_xgboost.py_in
create mode 100644 src/ports/postgres/modules/mxgboost/madlib_xgboost.sql_in
copy methods/sketch/src/pg_gp/sql/mfv.sql_in => src/ports/postgres/modules/mxgboost/test/madlib_xgboost.sql_in (98%)
[madlib] 04/04: XGBoost: Add new module xgboost
Posted by ok...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
okislal pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/madlib.git
commit ac22af57a65a3e5082bedb3cb9bc064388ee79db
Author: Orhan Kislal <ok...@apache.org>
AuthorDate: Fri May 6 15:05:28 2022 +0300
XGBoost: Add new module xgboost
JIRA: MADLIB-1425, MADLIB-1490
This commit adds a new module to use XGBoost library via MADlib function
calls. Parallel grid search of parameters is supported.
Co-authored-by: Bhuvnesh Chaudhary <bc...@pivotal.io>
Co-authored-by: Srivatsan Ramanujam <va...@gmail.com>
Co-authored-by: Ian Pytlarz <ip...@purdue.edu>
---
CMakeLists.txt | 1 -
doc/CMakeLists.txt | 6 +
doc/example/madlib_xgboost_example.sql | 4287 ++++++++++++++++++++
doc/mainpage.dox.in | 1 +
src/config/Modules.yml | 2 +
src/ports/postgres/modules/mxgboost/__init__.py_in | 0
.../postgres/modules/mxgboost/madlib_xgboost.py_in | 601 +++
.../modules/mxgboost/madlib_xgboost.sql_in | 720 ++++
.../modules/mxgboost/test/madlib_xgboost.sql_in | 4285 +++++++++++++++++++
9 files changed, 9902 insertions(+), 1 deletion(-)
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 1d9199e2..06634561 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -382,4 +382,3 @@ install(CODE "
${CMAKE_MADLIB_ROOT}/doc
)
")
-
diff --git a/doc/CMakeLists.txt b/doc/CMakeLists.txt
index f1a71d95..e3bebda4 100644
--- a/doc/CMakeLists.txt
+++ b/doc/CMakeLists.txt
@@ -233,3 +233,9 @@ endif(FLEX_FOUND AND BISON_FOUND AND DOXYGEN_FOUND)
# -- Add subdirectories --------------------------------------------------------
add_subdirectory(design)
+
+file(
+ COPY
+ example
+ DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/user/html/"
+)
diff --git a/doc/example/madlib_xgboost_example.sql b/doc/example/madlib_xgboost_example.sql
new file mode 100644
index 00000000..457d4206
--- /dev/null
+++ b/doc/example/madlib_xgboost_example.sql
@@ -0,0 +1,4287 @@
+/* ----------------------------------------------------------------------- *//**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ *
+ *//* ----------------------------------------------------------------------- */
+
+
+DROP TABLE IF EXISTS abalone;
+CREATE TABLE abalone (
+ id integer,
+ sex char(1),
+ length float,
+ diameter float,
+ height float,
+ whole_weight float,
+ shucked_weight float,
+ viscera_weight float,
+ shell_weight float,
+ rings integer
+ );
+
+
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+(4105,'F',0.71,0.545,0.175,1.907,0.8725,0.4565,0.475,11),
+(4106,'F',0.715,0.565,0.18,1.79,0.844,0.3535,0.5385,9),
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+(4114,'I',0.56,0.42,0.14,0.837,0.414,0.214,0.2,8),
+(4115,'F',0.57,0.45,0.15,0.9645,0.531,0.189,0.209,9),
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+(4170,'M',0.52,0.385,0.165,0.791,0.375,0.18,0.1815,10),
+(4171,'M',0.55,0.43,0.13,0.8395,0.3155,0.1955,0.2405,10),
+(4172,'M',0.56,0.43,0.155,0.8675,0.4,0.172,0.229,8),
+(4173,'F',0.565,0.45,0.165,0.887,0.37,0.239,0.249,11),
+(4174,'M',0.59,0.44,0.135,0.966,0.439,0.2145,0.2605,10),
+(4175,'M',0.6,0.475,0.205,1.176,0.5255,0.2875,0.308,9),
+(4176,'F',0.625,0.485,0.15,1.0945,0.531,0.261,0.296,10),
+(4177,'M',0.71,0.555,0.195,1.9485,0.9455,0.3765,0.495,12);
+
+DROP TABLE IF EXISTS xgb_out, xgb_out_summary;
+SELECT madlib.xgboost(
+ 'abalone', -- Training table
+ 'id', -- Id column
+ 'sex', -- Class label column
+ '*', -- Independent variables
+ NULL, -- Columns to exclude from features
+ $$
+ {
+ 'learning_rate': [0.01], #Regularization on weights (eta). For smaller values, increase n_estimators
+ 'max_depth': [9],#Larger values could lead to overfitting
+ 'subsample': [0.85],#introduce randomness in samples picked to prevent overfitting
+ 'colsample_bytree': [0.85],#introduce randomness in features picked to prevent overfitting
+ 'min_child_weight': [10],#larger values will prevent over-fitting
+ 'n_estimators':[100] #More estimators, lesser variance (better fit on test set)
+ }
+ $$, -- XGBoost grid search parameters
+ 'xgb_out', -- Grid search results table.
+ '', -- Class weights
+ 0.8, -- Training set size ratio
+ NULL -- Variable used to do the test/train split.
+);
+
+SELECT * FROM xgb_out_summary;
+
+DROP TABLE IF EXISTS xgb_score_out, xgb_score_out_metrics, xgb_score_out_roc_curve;
+SELECT madlib.xgboost_predict(
+ 'abalone', -- test_table
+ 'xgb_out', -- model_table
+ 'xgb_score_out', -- predict_output_table
+ 'id', -- id_column
+ 'sex' -- class_label
+);
+SELECT * FROM xgb_score_out LIMIT 10;
+SELECT * FROM xgb_score_out_roc_curve limit 10;
+SELECT * FROM xgb_score_out_metrics;
+
+DROP TABLE IF EXISTS xgb_out, xgb_out_summary;
+SELECT madlib.xgboost(
+ 'abalone', -- Training table
+ 'id', -- Id column
+ 'sex', -- Class label column
+ '*', -- Independent variables
+ NULL, -- Columns to exclude from features
+ $$
+ {
+ 'learning_rate': [0.01,0.1], #Regularization on weights (eta). For smaller values, increase n_estimators
+ 'max_depth': [9,12],#Larger values could lead to overfitting
+ 'subsample': [0.85],#introduce randomness in samples picked to prevent overfitting
+ 'colsample_bytree': [0.85],#introduce randomness in features picked to prevent overfitting
+ 'min_child_weight': [10],#larger values will prevent over-fitting
+ 'n_estimators':[100] #More estimators, lesser variance (better fit on test set)
+ }
+ $$, -- XGBoost grid search parameters
+ 'xgb_out', -- Grid search results table.
+ '', -- Class weights
+ 0.8, -- Training set size ratio
+ NULL -- Variable used to do the test/train split.
+);
+SELECT * FROM xgb_out_summary;
+
+DROP TABLE IF EXISTS xgb_score_out, xgb_score_out_metrics, xgb_score_out_roc_curve;
+SELECT madlib.xgboost_predict(
+ 'abalone', -- test_table
+ 'xgb_out', -- model_table
+ 'xgb_score_out', -- predict_output_table
+ 'id', -- id_column
+ 'sex', -- class_label
+ 2 -- model_filters
+);
+SELECT * FROM xgb_score_out LIMIT 10;
diff --git a/doc/mainpage.dox.in b/doc/mainpage.dox.in
index ba601625..009ad290 100644
--- a/doc/mainpage.dox.in
+++ b/doc/mainpage.dox.in
@@ -361,6 +361,7 @@ Interface and implementation are subject to change.
@defgroup grp_dbscan DBSCAN
@defgroup grp_bayes Naive Bayes Classification
@defgroup grp_sample Random Sampling
+ @defgroup grp_xgboost XGBoost
@}
@defgroup grp_deprecated Deprecated Modules
diff --git a/src/config/Modules.yml b/src/config/Modules.yml
index cbf099f4..77041de4 100644
--- a/src/config/Modules.yml
+++ b/src/config/Modules.yml
@@ -29,6 +29,8 @@ modules:
- name: linalg
- name: linear_systems
depends: ['utilities']
+ - name: mxgboost
+ depends: ['utilities']
- name: pca
depends: ['linalg', 'convex']
- name: pmml
diff --git a/src/ports/postgres/modules/mxgboost/__init__.py_in b/src/ports/postgres/modules/mxgboost/__init__.py_in
new file mode 100644
index 00000000..e69de29b
diff --git a/src/ports/postgres/modules/mxgboost/madlib_xgboost.py_in b/src/ports/postgres/modules/mxgboost/madlib_xgboost.py_in
new file mode 100644
index 00000000..bd4d5d28
--- /dev/null
+++ b/src/ports/postgres/modules/mxgboost/madlib_xgboost.py_in
@@ -0,0 +1,601 @@
+# coding=utf-8
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import plpy
+import re
+import pandas as pd
+import xgboost as xgb
+import numpy
+import cPickle as pickle
+import zlib
+import ast
+import collections
+import itertools
+from bisect import bisect_left
+from operator import itemgetter
+
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import precision_recall_fscore_support
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import roc_auc_score
+from sklearn.metrics import roc_curve
+
+from utilities.utilities import _assert
+from utilities.utilities import add_postfix
+from utilities.utilities import unique_string
+from utilities.validate_args import get_cols
+from utilities.validate_args import input_tbl_valid
+from utilities.validate_args import output_tbl_valid
+from utilities.validate_args import cols_in_tbl_valid
+
+def serialize_pandas_dframe_as_bytea(schema_madlib, source_table, id_column,
+ class_label, features):
+ """
+ Load the data from database and compress it to be stored in a single cell.
+ """
+
+ mdl_train_sql = """
+ SELECT
+ {id_column},
+ {features},
+ {class_label}
+ FROM
+ {source_table}
+ """.format(**locals())
+
+ result = plpy.execute(mdl_train_sql)
+ df = pd.DataFrame.from_records(result)
+ df_filtered = df.dropna(axis=1, how='all')
+
+ compressed = zlib.compress(pickle.dumps(df_filtered))
+ return compressed
+
+def print_prec_rec_fscore_support(mat, metric_labels, class_label, class_values):
+ """
+ pretty print precision, recall, fscore & support using pandas dataframe
+ """
+ tbl = pd.DataFrame(mat, columns=metric_labels)
+ tbl[class_label] = class_values
+ tbl = tbl[[class_label]+metric_labels]
+ return tbl
+
+def takeClosest(myList, myNumber):
+ """
+ Assumes myList is sorted. Returns closest value to myNumber.
+ If two numbers are equally close, return the smallest number.
+ """
+ pos = bisect_left(myList, myNumber)
+ if pos == 0:
+ return myList[0]
+ if pos == len(myList):
+ return myList[-1]
+ before = myList[pos - 1]
+ after = myList[pos]
+ if after - myNumber < myNumber - before:
+ return after
+ else:
+ return before
+
+def expand_grid(params):
+ #Expand the params to run-grid search
+ params_list = []
+ for key, val in params.items():
+ #If supplied param is a list of values, expand it out
+ if(val and isinstance(val, collections.Iterable)):
+ r = ["""{k}={v}""".format(k=key,v=v) for v in val]
+ else:
+ r = ["""{k}={v}""".format(k=key,v=val)]
+ params_list.append(r)
+
+ params_grid = [l for l in itertools.product(*params_list)]
+ return params_grid
+
+def xgboost_train(schema_madlib, dframe, features_all, class_label, params,
+ class_weights, train_set_size, id_column, train_set_split_var):
+ """
+ Run a single xgboost workload.
+ - Load the data
+ - Split train and test data for scoring
+ - Train xgboost
+ - Calculate metrics to report
+ """
+
+ df = pickle.loads(zlib.decompress(dframe))
+ features_all.append(id_column)
+ features = filter(lambda x: x in df.columns, features_all)
+ X = df[features].as_matrix()
+ y = df[class_label]
+ class_list = numpy.unique(y).tolist()
+
+ if not train_set_split_var or train_set_split_var == 'None':
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=float(1-train_set_size))
+ #We don't actually want the test set size to change. We want it to be constant as we change train set size so we can compare apples to apples
+ #so lets lock it at 20% (only less if the train size is > 80%)
+ test_set_size = min((1-train_set_size),0.2)
+ X_test = X_test[range(0,int(len(y)*test_set_size)),]
+ y_test = y_test.head(int(len(y)*test_set_size))
+ else:
+ split_indx = numpy.WHERE(features == train_set_split_var)[0]
+ X = numpy.delete(X,split_indx,1)
+ X_train = X[numpy.array(df[train_set_split_var]==1),]
+ X_test = X[numpy.array(df[train_set_split_var]==0),]
+ y_train = y[numpy.array(df[train_set_split_var]==1)]
+ y_test = y[numpy.array(df[train_set_split_var]==0)]
+ #save off and remove the id_column for later output. Make sure to get rid of id_column from features!
+ test_ids = X_test [:,len(features)-1]
+ X_train = numpy.delete(X_train,len(features)-1,1)
+ X_test = numpy.delete(X_test,len(features)-1,1)
+ features = features[0:len(features)-1]
+
+ class_list_y_train = numpy.unique(y_train).tolist()
+ class_list_y_test = numpy.unique(y_test).tolist()
+ if (class_list != class_list_y_train) or (class_list != class_list_y_test):
+ plpy.error("Train test split caused a subset with missing classes.")
+
+ #run weights
+ sample_representation = y_train.value_counts()
+ total_samples = sum(sample_representation)
+ sample_weights = None
+ if not class_weights:
+ sample_weights = map(
+ lambda s: total_samples*1.0/sample_representation[s]
+ /
+ sum([total_samples*1.0/sample_representation[c] for c in sample_representation.keys()])
+ ,
+ y_train
+ )
+ else:
+ #User-supplied class-weights
+ class_weights_dict = ast.literal_eval(re.sub("[\\t]","",class_weights).strip())
+ sample_weights = map(lambda s: class_weights_dict[s], y_train)
+
+ #Train gradient boosted trees
+ p_list = [p.split('=') for p in ast.literal_eval(re.sub("[\\t]","",params).strip())]
+ params_dict = dict([(k, ast.literal_eval(v.strip())) for k,v in p_list])
+ gbm = xgb.XGBClassifier(**params_dict)
+
+ #Fit model
+ gbm.fit(
+ X_train,
+ y_train,
+ eval_metric = 'auc',
+ sample_weight = sample_weights
+ )
+ #Compute and return model metrics score
+ y_pred_train = gbm.predict(X_train)
+ y_pred_test = gbm.predict(X_test)
+ cmat_train = confusion_matrix(y_train, y_pred_train)
+ cmat_test = confusion_matrix(y_test, y_pred_test)
+ scores = numpy.array(precision_recall_fscore_support(y_test, y_pred_test)).transpose()
+
+ metric_labels = ['precision', 'recall', 'fscore', 'support']
+ model_metrics = print_prec_rec_fscore_support(scores, metric_labels, class_label, gbm.classes_)
+
+ #Calculate feature importance scores
+ importance = gbm._Booster.get_fscore()
+ if len(importance) == 0:
+ plpy.error("No importance found for any feature")
+ fnames_importances = sorted(
+ [(features[int(k.replace('f',''))], importance[k]) for k in importance],
+ key=itemgetter(1),
+ reverse=True
+ )
+ fnames, f_importance_scores = zip(*fnames_importances)
+ important_features = pd.DataFrame(fnames_importances)
+
+ test_ids = [int(x) for x in test_ids]
+ return (features, pickle.dumps(gbm), params, fnames, f_importance_scores,
+ model_metrics.iloc[:,1].values.tolist(), model_metrics.iloc[:,2].values.tolist(),
+ model_metrics.iloc[:,3].values.tolist(),model_metrics.iloc[:,4].values.tolist(),
+ test_ids)
+
+def xgboost_grid_search(schema_madlib, source_table, id_column, class_label,
+ list_of_features, list_of_features_to_exclude,
+ params_str, grid_search_results_tbl, class_weights,
+ train_set_size, train_set_split_var):
+ """
+ Run multiple xgboost workloads in parallel via grid search.
+ """
+
+ input_tbl_valid(source_table, 'XGBoost')
+ cols_in_tbl_valid(source_table, [id_column, class_label], 'XGBoost')
+ if train_set_split_var is not None:
+ cols_in_tbl_valid(source_table, train_set_split_var, 'XGBoost')
+
+ output_tbl_valid(grid_search_results_tbl, 'XGBoost')
+ grid_search_results_tbl_summary = add_postfix(grid_search_results_tbl, '_summary')
+ output_tbl_valid(grid_search_results_tbl_summary, 'XGBoost')
+
+ if list_of_features.strip() == '*':
+ #Extract feature names from information_schema
+ if list_of_features_to_exclude is None:
+ list_of_features_to_exclude = []
+ discard_features = list_of_features_to_exclude + [class_label, id_column]
+ features = [col for col in get_cols(source_table) if col not in discard_features]
+ list_of_features = ','.join(features)
+ else:
+ features = [f.strip() for f in list_of_features.split(',')]
+ cols_in_tbl_valid(source_table, features, 'XGBoost')
+
+ class_weights = '' if class_weights is None else class_weights
+
+ if not params_str:
+ params_str = """
+ {
+ 'learning_rate': [0.3], #Regularization on weights (eta). For smaller values, increase n_estimators
+ 'max_depth': [6],#Larger values could lead to overfitting
+ 'n_estimators':[100] #More estimators, lesser variance (better fit on test set)
+ }
+ """
+
+ params = ast.literal_eval(re.sub("[\\t]","",params_str).strip())
+ params_grid = expand_grid(params)
+ #Save each parameter list in the grid as a row in a distributed table
+ grid_search_params_temp_tbl = unique_string('grid_params')
+ grid_search_params_temp_tbl_df = unique_string('grid_params_df')
+ dist_str = "m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY (params_index)')"
+ sql = """
+ CREATE TEMP TABLE {grid_search_params_temp_tbl}
+ (
+ params_index int,
+ params text
+ ) {dist_str}
+ """.format(**locals())
+ plpy.execute(sql)
+ sql = """
+ INSERT INTO {grid_search_params_temp_tbl}
+ VALUES ({params_index}, $X${val}$X$);
+ """
+ for indx, val in enumerate(params_grid):
+ plpy.execute(
+ sql.format(
+ val=val,
+ params_index = indx+1, #postgres indices start from 1, so keeping it consistent
+ grid_search_params_temp_tbl=grid_search_params_temp_tbl
+ )
+ )
+
+
+ grid_size = len(params_grid)
+ sql = """
+ CREATE TEMP TABLE {grid_search_params_temp_tbl_df}
+ AS
+ (
+ SELECT
+ df,
+ generate_series(1, {grid_size}) AS params_index
+ FROM
+ (
+ SELECT
+ {schema_madlib}.__serialize_pandas_dframe_as_bytea__(
+ '{source_table}',
+ '{id_column}',
+ '{class_label}',
+ '{list_of_features}'
+ ) AS df
+ )q
+ ) {dist_str}
+ """.format(**locals())
+ plpy.execute(sql)
+
+ #Invoke XGBoost's train by passing each row from parameter list table. This will run in parallel.
+ grid_search_results_temp_tbl = unique_string('results_tbl')
+ features_str = features = str(features).replace('[','').replace(']','').replace(',',',\n')
+ sql = """
+ CREATE TEMP TABLE {grid_search_results_temp_tbl}
+ AS
+ (
+ SELECT
+ {schema_madlib}.__xgboost_train_parallel__(
+ df,
+ ARRAY[
+ {features_str}
+ ],
+ '{class_label}',
+ params,
+ $CW${class_weights}$CW$,
+ {train_set_size},
+ '{id_column}',
+ '{train_set_split_var}'
+ ) AS mdl_results,
+ t1.params_index
+ FROM
+ {grid_search_params_temp_tbl} t1,
+ {grid_search_params_temp_tbl_df} t2
+ WHERE
+ t1.params_index = t2.params_index
+ ) {dist_str}
+ """.format(**locals())
+ plpy.execute(sql)
+
+ sql = """
+ CREATE TABLE {grid_search_results_tbl_summary}
+ AS
+ (
+ SELECT
+ now() AS mdl_train_ts,
+ '{source_table}'||'_xgboost' AS mdl_name,
+ (mdl_results).features,
+ (mdl_results).params,
+ (mdl_results).fnames,
+ (mdl_results).importance,
+ (mdl_results).precision,
+ (mdl_results).recall,
+ (mdl_results).fscore,
+ (mdl_results).support,
+ (mdl_results).test_ids::INTEGER[],
+ params_index
+ FROM
+ {grid_search_results_temp_tbl}
+
+ ) {dist_str}
+ """.format(**locals())
+ plpy.execute(sql)
+
+ sql = """
+ CREATE TABLE {grid_search_results_tbl}
+ AS
+ (
+ SELECT
+ (mdl_results).mdl AS model,
+ (mdl_results).features,
+ params_index
+ FROM
+ {grid_search_results_temp_tbl}
+
+ ) {dist_str}
+ """.format(**locals())
+ plpy.execute(sql)
+ plpy.execute("""
+ DROP TABLE {grid_search_params_temp_tbl};
+ DROP TABLE {grid_search_params_temp_tbl_df};
+ DROP TABLE {grid_search_results_temp_tbl};
+ """.format(**locals()))
+
+def xgboost_predict(schema_madlib, scoring_tbl, mdl_table, mdl_output_tbl,
+ id_column, class_label, params_index):
+ """
+ Predict using an xgboost model. Also generate metrics and roc curve tables.
+ """
+
+ input_tbl_valid(scoring_tbl, 'XGBoost')
+ cols_in_tbl_valid(scoring_tbl, [id_column, class_label], 'XGBoost')
+ input_tbl_valid(mdl_table, 'XGBoost')
+ output_tbl_valid(mdl_output_tbl, 'XGBoost')
+ mdl_output_tbl_metrics = add_postfix(mdl_output_tbl, '_metrics')
+ mdl_output_tbl_roc_curve = add_postfix(mdl_output_tbl, '_roc_curve')
+ output_tbl_valid(mdl_output_tbl_metrics, 'XGBoost')
+ output_tbl_valid(mdl_output_tbl_roc_curve, 'XGBoost')
+
+ #Load the serialized XGBoost model from the table
+ mdl_sql = """
+ SELECT
+ model,
+ features
+ FROM
+ {mdl_table}
+ WHERE params_index = {params_index}
+ """.format(**locals())
+ result = plpy.execute(mdl_sql)
+ model = result[0]['model']
+ features = result[0]['features']
+ #Train gradient boosted trees
+ gbm = pickle.loads(model)
+
+ #Fetch features from test dataset for scoring
+ plpy.info(features)
+ if isinstance(features, list):
+ features_str = ','.join(features)
+ else:
+ features_str = features
+ features = [features]
+ comma_class_label = ', {0}'.format(class_label) if class_label else ''
+ plpy.info(features_str)
+ mdl_score_sql = """
+ SELECT
+ {id_column},
+ {features_str}
+ {comma_class_label}
+ FROM
+ {scoring_tbl}
+ """.format(**locals())
+ result = plpy.execute(mdl_score_sql)
+
+ df = pd.DataFrame.from_records(result)
+ X_test = df[features]
+ y_test = df[class_label] if class_label else None
+
+ #Score the test set
+ y_pred_test = gbm.predict(X_test.as_matrix())
+ y_pred_proba_test = gbm.predict_proba(X_test.as_matrix())
+ if(class_label):
+ cmat_test = confusion_matrix(y_test, y_pred_test)
+ scores = numpy.array(precision_recall_fscore_support(y_test, y_pred_test)).transpose()
+ metric_labels = ['precision', 'recall', 'fscore', 'support']
+ model_metrics = print_prec_rec_fscore_support(scores, metric_labels, class_label, gbm.classes_)
+ else:
+ model_metrics = 'NA'
+
+ predicted_class_label = class_label+'_predicted' if class_label else 'class_label_predicted'
+ predicted_class_proba_label = class_label+'_proba_predicted' if class_label else 'class_label_proba_predicted'
+ pred = pd.Series(y_pred_test, index = X_test.index).to_frame(predicted_class_label)
+ num_unique_classes = pd.DataFrame(data=pred[predicted_class_label]).apply(lambda x: len(x.unique()))
+ if (range(num_unique_classes) == [0]):
+ plpy.error('XGBoost: Every prediction is of the same class.')
+
+ pred_proba = pd.DataFrame(data=y_pred_proba_test,
+ index = X_test.index,
+ columns = range(num_unique_classes) #if not class_label else class_label
+ )
+
+ res_df = pd.concat([df[id_column],pred,pred_proba],axis=1).set_index(X_test.index)
+
+ #create a combined column list for all the proba values
+ res_df['all_class_probas'] = '{' + res_df[0].map(str)
+ for class_col in range(1,num_unique_classes):
+ res_df['all_class_probas'] = res_df['all_class_probas'] + ',' + res_df[class_col].map(str)
+ res_df['all_class_probas'] = res_df['all_class_probas'] + '}'
+
+ #Feature importance scores
+ importance = gbm._Booster.get_fscore()
+
+ if len(importance) > 1:
+ fnames_importances = sorted(
+ [(features[int(k.replace('f',''))], importance[k]) for k in importance],
+ key=itemgetter(1),
+ reverse=True
+ )
+ fnames, f_importances = zip(*fnames_importances)
+ fnames = str(fnames).replace('(','{').replace(')','}').replace('\'','\"')
+ f_importances = str(f_importances).replace('(','{').replace(')','}').replace('\'','\"')
+ else:
+ fnames = "{{{0}}}".format(features)
+ f_importances = "{{{0}}}".format(importance['f0'])
+
+ ret_dict = res_df.to_dict('records')
+ ret_result = (
+ (
+ r[id_column],
+ r[predicted_class_label],
+ r['all_class_probas']
+ )
+ for r in ret_dict
+ )
+
+ #Create a ROC Curve if testing on a set with class label
+ if (class_label):
+ class_list = numpy.unique(y_test)
+ roc_auc_scores, fpr, tpr, thresholds = [],[],[],[]
+ for index, classname in enumerate(class_list):
+
+ roc_auc_scores.append(roc_auc_score(numpy.array(y_test)==classname,y_pred_proba_test[:,index]))
+ t_fpr, t_tpr, t_thresholds = roc_curve(numpy.array(y_test),y_pred_proba_test[:,index],pos_label=classname)
+ fpr.append(t_fpr)
+ tpr.append(t_tpr)
+ thresholds.append(t_thresholds)
+ fpr_df = pd.DataFrame(fpr).transpose()
+ tpr_df = pd.DataFrame(tpr).transpose()
+ thresholds_df = pd.DataFrame(thresholds).transpose()
+ else:
+ roc_auc_scores = [0]
+
+ #Create a table to hold the unit-level results
+ sql = """
+ CREATE TABLE {mdl_output_tbl}
+ (
+ {id_column} INTEGER,
+ {predicted_class_label} TEXT,
+ {predicted_class_proba_label} FLOAT8[]
+ )
+ m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED BY ({id_column})');
+ """.format(**locals())
+ plpy.execute(sql)
+ sql = """
+ INSERT INTO {mdl_output_tbl} VALUES
+
+ """.format(**locals())
+ for row in ret_result:
+ sql = sql + """
+ {0},""".format(row)
+ sql = sql[:-1]
+ plpy.execute(sql)
+
+ #Create a table for holding the metrics and feature importances
+ sql = """
+ CREATE TABLE {mdl_output_tbl_metrics}
+ (
+ precision DOUBLE PRECISION[],
+ recall DOUBLE PRECISION[],
+ fscore DOUBLE PRECISION[],
+ support DOUBLE PRECISION[],
+ roc_auc_scores DOUBLE PRECISION[],
+ feature_names TEXT[],
+ feature_importance_scores DOUBLE PRECISION[]
+ )
+ m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED RANDOMLY');
+ """.format(**locals())
+ plpy.execute(sql)
+
+ #generate metrics for output
+ if(class_label):
+ precision = str(model_metrics.iloc[:,1].values.tolist()).replace('[','{').replace(']','}').replace('\'','\"')
+ recall = str(model_metrics.iloc[:,2].values.tolist()).replace('[','{').replace(']','}').replace('\'','\"')
+ fscore = str(model_metrics.iloc[:,3].values.tolist()).replace('[','{').replace(']','}').replace('\'','\"')
+ support = str(model_metrics.iloc[:,4].values.tolist()).replace('[','{').replace(']','}').replace('\'','\"')
+ roc_auc_scores = str([round(elem,5) for elem in roc_auc_scores]).replace('[','{').replace(']','}').replace('\'','\"')
+ else:
+ precision = '{NULL}'
+ recall = '{NULL}'
+ fscore = '{NULL}'
+ support = '{NULL}'
+ roc_auc_scores = '{NULL}'
+
+ sql = """
+ INSERT INTO {mdl_output_tbl_metrics}
+ VALUES (
+ $X${precision}$X$,
+ $X${recall}$X$,
+ $X${fscore}$X$,
+ $X${support}$X$,
+ $X${roc_auc_scores}$X$,
+ $X${fnames}$X$,
+ $X${f_importances}$X$
+ );
+ """.format(**locals())
+
+ plpy.execute(sql)
+
+ #If a class label was used, create a third output table for roc curves
+ if (class_label):
+ #calculate 10% of the data points to save, evenly spaced. We don't need to wait for 100k+ rows to be written to make a good looking curve
+ output_length = 1000#round(len(thresholds_df)*0.1,0)
+ numbers = list(range(output_length))
+ numbers = [100.0*p/output_length for p in numbers]
+ thresh_list = sorted(thresholds_df.iloc[:,0].values.tolist())
+ thresh_nums = []
+ for x in numbers:
+ thresh_nums.append(takeClosest(thresh_list,numpy.percentile(thresh_list,x)))
+
+ thresh_index = []
+ for x in thresh_nums:
+ thresh_index.append(thresh_list.index(x))
+
+ sql = """
+ CREATE TABLE {mdl_output_tbl_roc_curve}
+ (
+ fpr text[],
+ tpr text[],
+ thresholds text[]
+ )
+ m4_ifdef(`__POSTGRESQL__', `', `DISTRIBUTED RANDOMLY');
+ """.format(**locals())
+ plpy.execute(sql)
+
+ sql = """
+ INSERT INTO {mdl_output_tbl_roc_curve} VALUES
+
+ """.format(**locals())
+ for x in thresh_index:
+ sql = sql + """
+ ($X${fpr}$X$,$X${tpr}$X$,$X${thresholds}$X$),""".format(
+ fpr = str(['%.5f' % round(elem,5) for elem in fpr_df.iloc[x].values.tolist()]).replace('[','{').replace(']','}').replace('\'','\"'),
+ tpr = str(['%.5f' % round(elem,5) for elem in tpr_df.iloc[x].values.tolist()]).replace('[','{').replace(']','}').replace('\'','\"'),
+ thresholds = str(['%.5f' % round(elem,5) for elem in thresholds_df.iloc[x].values.tolist()]).replace('[','{').replace(']','}').replace('\'','\"'))
+ sql = sql[:-1]
+ plpy.execute(sql)
diff --git a/src/ports/postgres/modules/mxgboost/madlib_xgboost.sql_in b/src/ports/postgres/modules/mxgboost/madlib_xgboost.sql_in
new file mode 100644
index 00000000..83be72dc
--- /dev/null
+++ b/src/ports/postgres/modules/mxgboost/madlib_xgboost.sql_in
@@ -0,0 +1,720 @@
+/* ----------------------------------------------------------------------- *//**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ *
+ * @file madlib_xgboost.sql_in
+ * @brief SQL functions for xgboost
+ * @date 05/12/2022
+ *
+ */
+ /* ----------------------------------------------------------------------- */
+
+
+/**
+@addtogroup grp_xgboost
+
+@brief
+This module allows you to use SQL to build gradient boosted tree
+models designed in XGBoost [1].
+
+<div class="toc"><b>Contents</b>
+<ul>
+<li><a href="#train">Training Function</a></li>
+<li><a href="#predict">Prediction Function</a></li>
+<li><a href="#examples">Examples</a></li>
+</ul>
+</div>
+
+XGBoost is an optimized distributed gradient boosting library. The MADlib
+implementation is designed to train multiple models with different parameters
+to achieve parallelism on Greenplum systems (PostgreSQL is also supported.)
+
+The main use case supported is classification. Regression is not currently
+supported.
+
+Please note that the XGBoost implementation has the following limitations:
+- All of the dataset is collected in a single segment, compressed by zlib and
+stored in a single cells (one column of one row). PostgreSQL has a 1 GB limit
+for each cell which means the supported dataset size is limited.
+- The collected data is passed to the XGBoost in a single segment. This means,
+without grid search, the XGBoost is using only a single segment which might
+increase the run time.
+
+XGBoost requires the following python libraries available in every host machine:
+- pandas v0.42
+- scikit-learn v0.19
+- xgboost v0.82
+
+Versions are provided to ensure a python2 supported libraries can be obtained.
+Other versions of these libraries might work just as well.
+
+@anchor train
+@par Training Function
+<pre class="syntax">
+SELECT xgboost(
+ source_table,
+ output_table,
+ id_column,
+ dependent_variable,
+ list_of_features,
+ list_of_features_to_exclude,
+ params_str,
+ sample_weights,
+ train_set_size,
+ train_set_split_var
+)
+</pre>
+
+\b Arguments
+<dl class="arglist">
+ <dt>source_table</dt>
+ <dd>TEXT. Name of the table containing the training data.</dd>
+
+ <dt>output_table</dt>
+ <dd>TEXT. Name of the generated table containing the model.
+ If a table with the same name already exists, an
+ error will be returned. A summary table
+ named <em>\<output_table\>_summary</em> is also
+ created.
+ </DD>
+
+ <DT>id_column</DT>
+ <DD>TEXT. Name of the column containing id information in the training data.
+ This is a mandatory argument and the values are expected to be unique for each
+ row.
+ </DD>
+
+ <DT>dependent_variable</DT>
+ <DD>TEXT. Name of the column that contains the output (response) for
+ training. Boolean, integer and text types are accepted for classification.
+ </DD>
+
+ <DT>list_of_features</DT>
+ <DD>TEXT. Comma-separated string of column names or expressions to use as predictors.
+ Can also be a '*' implying all columns are to be used as predictors (except for the
+ ones included in the next argument that lists exclusions).
+
+ <DT>list_of_features_to_exclude (optional)</DT>
+ <DD>TEXT[]. Text array of column names to exclude from the predictors
+ list. The names in this parameter should be identical to the names used in the table and
+ quoted appropriately. The id_column and dependent_variable are excluded by default.
+ </DD>
+
+ <DT>params_str (optional)</DT>
+ <DD>TEXT. Comma-separated string of key-value pairs used for initializing
+ XGBClassifier. Each parameter can be assigned an array of values. These values
+ are expanded into multiple parameter sets for grid search.
+
+ Note that these key-value pairs are passed to the xgboost as is and xgboost
+ accepts any parameter thanks to kwargs. If there is a typo in the list, it
+ might get ignored by xgboost.
+
+ Default values set by MADlib:
+ learning_rate: 0.3
+ max_depth: 6
+ n_estimators: 100
+
+ </DD>
+
+ <DT>sample_weights (optional)</DT>
+ <DD>TEXT. Column name containing numerical weights for sample_weights parameter of
+ XGBClassifier fit function.
+ </DD>
+
+ <DT>train_set_size (optional)</DT>
+ <DD>DOUBLE PRECISION. The proportion of the dataset to be used in training.
+ </DD>
+
+ <DT>train_set_split_var (optional)</DT>
+ <DD>TEXT. Column name containing information on whether the associated row will be used for training or testing. If this parameter is set, train_set_size will be ignored.
+ </DD>
+</DL>
+
+\b Output
+<dl class="arglist">
+<DD>
+ The model table produced by the training function contains the following columns:
+
+ <table class="output">
+ <tr>
+ <th>model</th>
+ <td>BYTEA8. Trained XGBoost model stored in binary
+ format (not human readable).</td>
+ </tr>
+ <tr>
+ <th>features</th>
+ <td>TEXT[]. Ordered levels (values) of categorical variables
+ corresponding to the categorical features. Used to help
+ interpret the trained model.</td>
+ </tr>
+ <tr>
+ <th>params_index</th>
+ <td>INTEGER. The index of the model. During grid search, each parameter
+ set is given an index to distinguish different models.
+ </td>
+ </tr>
+
+ </table>
+
+ A summary table named <em>\<output_table\>_summary</em> is also created at
+ the same time, which has the following columns:
+
+ <table class="output">
+ <tr>
+ <th>mdl_train_ts</th>
+ <td>TEXT. The timestamp for the initiation of the xgboost command.</td>
+ </tr>
+ <tr>
+ <th>mdl_name</th>
+ <td>TEXT. The name of the model set to <source_table>_xgboost.</td>
+ </tr>
+ <tr>
+ <th>features</th>
+ <td>TEXT[]. The list of features inputed to the XGBoost.</td>
+ </tr>
+ <tr>
+ <th>params</th>
+ <td>TEXT. The XGBClassifier parameters used for this particular model.</td>
+ </tr>
+ <tr>
+ <th>fnames</th>
+ <td>TEXT. The list of features in sorted order. This column is used for importance</td>
+ </tr>
+ <tr>
+ <th>importance</th>
+ <td>DOUBLE PRECISION[]. Importance values for each feature in the sorted order.</td>
+ </tr>
+ <tr>
+ <th>recall</th>
+ <td>DOUBLE PRECISION[]. Recall values for each class.</td>
+ </tr>
+ <tr>
+ <th>fscore</th>
+ <td>DOUBLE PRECISION[]. fscore values for each class.</td>
+ </tr>
+ <tr>
+ <th>support</th>
+ <td>DOUBLE PRECISION[]. support values for each class.</td>
+ </tr>
+ <tr>
+ <th>test_ids</th>
+ <td>INTEGER[]. The ids of the rows used for this particular model.</td>
+ </tr>
+ <tr>
+ <th>params_index</th>
+ <td>INTEGER. The index of the parameter set used for this model.</td>
+ </tr>
+ </table>
+
+ </DD>
+</DL>
+
+@anchor predict
+@par Prediction Function
+
+<pre class="syntax">
+SELECT xgboost_predict(
+ test_table,
+ model_table,
+ predict_output_table,
+ id_column,
+ class_label,
+ model_filters
+);
+</pre>
+
+\b Arguments
+<dl class="arglist">
+ <dt>test_table</dt>
+ <dd>TEXT. Name of the table containing the test data.</dd>
+
+ <dt>model_table</dt>
+ <dd>TEXT. Name of the table containing the xgboost train output.</dd>
+
+ <dt>predict_output_table</dt>
+ <dd>TEXT. Name of the generated table containing the prediction.
+ If a table with the same name already exists, an
+ error will be returned. A metrics table named
+ <em>\<output_table_name\>_metrics</em> and an roc curve table named
+ <em>\<output_table_name\>_roc_curve</em> are also created if class_label is
+ not NULL.
+ </DD>
+
+ <DT>id_column</DT>
+ <DD>TEXT. Name of the column containing id information in the test data.
+ This is a mandatory argument and the values are expected to be unique for each
+ row.
+ </DD>
+
+ <DT>class_label (optional)</DT>
+ <DD>TEXT. Name of the column containing class_label for the metrics and
+ roc_curve calculations.
+ </DD>
+
+ <DT>model_filters (optional)</DT>
+ <DD>TEXT. The filter for the model_table in case the user wants to use a
+ subset of the models from the grid search.
+ </DD>
+</DL>
+
+\b Output
+
+XGBoost prediction function creates three tables: <predict_output_table>,
+<predict_output_table>_metrics and <predict_output_table>_roc_curve.
+Some fields of the metrics table as well as the roc_curve table are available
+only if a class_label column is provided.
+
+<predict_output_table>
+<dl class="arglist">
+<DD>
+<table class="output">
+ <tr>
+ <th>id_column</th>
+ <td>INTEGER. Name of the column containing id information in the test data.</td>
+ </tr>
+ <tr>
+ <th>class_label_predicted</th>
+ <td>TEXT. The predicted value for the given data id.</td>
+ </tr>
+ <tr>
+ <th>class_label_proba_predicted</th>
+ <td>DOUBLE PRECISION[]. The prediction probabilities for the given data id.</td>
+ </tr>
+</table>
+
+<predict_output_table>_metrics
+<table class="output">
+ <tr>
+ <th>precision</th>
+ <td>DOUBLE PRECISION[]. Precision values for each class in the sorted order.</td>
+ </tr>
+ <tr>
+ <th>recall</th>
+ <td>DOUBLE PRECISION[]. Recall values for each class.</td>
+ </tr>
+ <tr>
+ <th>fscore</th>
+ <td>DOUBLE PRECISION[]. fscore values for each class.</td>
+ </tr>
+ <tr>
+ <th>support</th>
+ <td>DOUBLE PRECISION[]. support values for each class.</td>
+ </tr>
+ <tr>
+ <th>roc_auc_scores</th>
+ <td>DOUBLE PRECISION[]. roc_auc scores for each class.</td>
+ </tr>
+ <tr>
+ <th>feature_names</th>
+ <td>TEXT[]. The list of features in sorted order. This column is used for importance</td>
+ </tr>
+ <tr>
+ <th>feature_importance_scores</th>
+ <td>DOUBLE PRECISION[]. Importance values for each feature in the sorted order.</td>
+ </tr>
+</table>
+
+<predict_output_table>_roc_curve
+<table class="output">
+ <tr>
+ <th>fpr</th>
+ <td>DOUBLE PRECISION[]. False positive rate for the roc curve.</td>
+ </tr>
+ <tr>
+ <th>tpr</th>
+ <td>DOUBLE PRECISION[]. True positive rate for the roc curve.</td>
+ </tr>
+ <tr>
+ <th>thresholds</th>
+ <td>DOUBLE PRECISION[]. Threshold values for the roc curve.</td>
+ </tr>
+</table>
+
+@anchor examples
+@par Examples
+
+<a href="example/madlib_xgboost_example.sql">Download the example sql file here.</a>
+
+-# Show the input data set.
+<pre class="example">
+SELECT * FROM abalone LIMIT 10;
+</pre>
+<pre class="result">
+ id | sex | length | diameter | height | whole_weight | shucked_weight | viscera_weight | shell_weight | rings
+----+-----+--------+----------+--------+--------------+----------------+----------------+--------------+-------
+ 1 | M | 0.455 | 0.365 | 0.095 | 0.514 | 0.2245 | 0.101 | 0.15 | 15
+ 12 | M | 0.43 | 0.35 | 0.11 | 0.406 | 0.1675 | 0.081 | 0.135 | 10
+ 15 | F | 0.47 | 0.355 | 0.1 | 0.4755 | 0.1675 | 0.0805 | 0.185 | 10
+ 20 | M | 0.45 | 0.32 | 0.1 | 0.381 | 0.1705 | 0.075 | 0.115 | 9
+ 23 | F | 0.565 | 0.44 | 0.155 | 0.9395 | 0.4275 | 0.214 | 0.27 | 12
+ 26 | F | 0.56 | 0.44 | 0.14 | 0.9285 | 0.3825 | 0.188 | 0.3 | 11
+ 30 | M | 0.575 | 0.425 | 0.14 | 0.8635 | 0.393 | 0.227 | 0.2 | 11
+ 31 | M | 0.58 | 0.47 | 0.165 | 0.9975 | 0.3935 | 0.242 | 0.33 | 10
+ 35 | F | 0.705 | 0.55 | 0.2 | 1.7095 | 0.633 | 0.4115 | 0.49 | 13
+ 36 | M | 0.465 | 0.355 | 0.105 | 0.4795 | 0.227 | 0.124 | 0.125 | 8
+</pre>
+
+-# Run XGBoost for a single parameter set and show the summary table
+<pre class="example">
+DROP TABLE IF EXISTS xgb_out, xgb_out_summary;
+SELECT xgboost(
+ 'abalone', -- Training table
+ 'id', -- Id column
+ 'sex', -- Class label column
+ '*', -- Independent variables
+ NULL, -- Columns to exclude from features
+ $$
+ {
+ 'learning_rate': [0.01], #Regularization on weights (eta). For smaller values, increase n_estimators
+ 'max_depth': [9],#Larger values could lead to overfitting
+ 'subsample': [0.85],#introduce randomness in samples picked to prevent overfitting
+ 'colsample_bytree': [0.85],#introduce randomness in features picked to prevent overfitting
+ 'min_child_weight': [10],#larger values will prevent over-fitting
+ 'n_estimators':[100] #More estimators, lesser variance (better fit on test set)
+ }
+ $$, -- XGBoost grid search parameters
+ 'xgb_out', -- Grid search results table.
+ '', -- Class weights
+ 0.8, -- Training set size ratio
+ NULL -- Variable used to do the test/train split.
+);
+\\x on
+SELECT * FROM xgb_out_summary;
+</pre>
+<pre class="result">
+-[ RECORD 1 ]+---------------------------------------------
+mdl_train_ts | 2022-05-16 17:42:45.033789+03
+mdl_name | abalone_xgboost
+features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings}
+params | ('colsample_bytree=0.85', 'learning_rate=0.01', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=9')
+fnames | {viscera_weight,whole_weight,shucked_weight,shell_weight,length,rings,diameter,height}
+importance | {1206,1189,1184,867,766,622,589,526}
+precision | {0.4295774647887324,0.6858006042296072,0.4318181818181818}
+recall | {0.46387832699619774,0.8021201413427562,0.328719723183391}
+fscore | {0.4460694698354662,0.739413680781759,0.3732809430255403}
+support | {263.0,283.0,289.0}
+test_ids | {486,3627,432,2766,132,2397,3313,2346...}
+</pre>
+
+-# Run XGBoost prediction. For this example we are using the same abalone table for prediction as well.
+<pre class="example">
+DROP TABLE IF EXISTS xgb_score_out, xgb_score_out_metrics, xgb_score_out_roc_curve;
+SELECT xgboost_predict(
+ 'abalone', -- test_table
+ 'xgb_out', -- model_table
+ 'xgb_score_out', -- predict_output_table
+ 'id', -- id_column
+ 'sex' -- class_label
+);
+\\x off
+SELECT * FROM xgb_score_out LIMIT 10;
+</pre>
+<pre class="result">
+ id | sex_predicted | sex_proba_predicted
+----+---------------+------------------------------------------------
+ 5 | I | {0.168330997229,0.632858633995,0.198810324073}
+ 6 | I | {0.202547758818,0.574552714825,0.222899526358}
+ 9 | I | {0.255484640598,0.44379144907,0.300723910332}
+ 10 | M | {0.347418963909,0.242429286242,0.410151779652}
+ 11 | F | {0.4157371521,0.316571623087,0.267691165209}
+ 13 | F | {0.338543832302,0.32665386796,0.334802359343}
+ 14 | F | {0.400314897299,0.293721526861,0.305963635445}
+ 17 | I | {0.175603896379,0.608917593956,0.215478509665}
+ 21 | M | {0.280931055546,0.333337903023,0.385731071234}
+ 25 | F | {0.498989373446,0.185877665877,0.315133005381}
+</pre>
+
+-# Show roc curve and metrics tables
+<pre class="example">
+SELECT * FROM xgb_score_out_roc_curve limit 10;</pre>
+<pre class="result">
+ fpr | tpr | thresholds
+---------------------------+---------------------------+---------------------------
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+ {0.00000,0.00000,0.00000} | {0.00077,0.00075,0.00065} | {0.54791,0.65354,0.49352}
+</pre>
+<pre class="example">
+\\x on
+SELECT * FROM xgb_score_out_metrics;
+</pre>
+<pre class="result">
+-[ RECORD 1 ]-------------+---------------------------------------------------------------------------------------
+precision | {0.549047282992237,0.70062893081761,0.598290598290598}
+recall | {0.595256312165264,0.830104321907601,0.458115183246073}
+fscore | {0.571218795888399,0.759890859481583,0.518902891030393}
+support | {1307,1342,1528}
+roc_auc_scores | {0.77659,0.9091,0.74816}
+feature_names | {viscera_weight,whole_weight,shucked_weight,shell_weight,length,rings,diameter,height}
+feature_importance_scores | {1206,1189,1184,867,766,622,589,526}
+</pre>
+
+-# Run XGBoost grid search with parameter options
+<pre class="example">
+DROP TABLE IF EXISTS xgb_out, xgb_out_summary;
+SELECT xgboost(
+ 'abalone', -- Training table
+ 'id', -- Id column
+ 'sex', -- Class label column
+ '*', -- Independent variables
+ NULL, -- Columns to exclude from features
+ $$
+ {
+ 'learning_rate': [0.01,0.1], #Regularization on weights (eta). For smaller values, increase n_estimators
+ 'max_depth': [9,12],#Larger values could lead to overfitting
+ 'subsample': [0.85],#introduce randomness in samples picked to prevent overfitting
+ 'colsample_bytree': [0.85],#introduce randomness in features picked to prevent overfitting
+ 'min_child_weight': [10],#larger values will prevent over-fitting
+ 'n_estimators':[100] #More estimators, lesser variance (better fit on test set)
+ }
+ $$, -- XGBoost grid search parameters
+ 'xgb_out', -- Grid search results table.
+ '', -- Class weights
+ 0.8, -- Training set size ratio
+ NULL -- Variable used to do the test/train split.
+);
+\\x on
+SELECT * FROM xgb_out_summary;
+</pre>
+<pre class="result">
+-[ RECORD 1 ]+---------------------------------------------
+mdl_train_ts | 2022-05-16 17:56:11.488767+03
+mdl_name | abalone_xgboost
+features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings}
+params | ('colsample_bytree=0.85', 'learning_rate=0.01', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=12')
+fnames | {viscera_weight,whole_weight,shucked_weight,shell_weight,length,rings,diameter,height}
+importance | {1276,1205,1200,906,864,822,580,415}
+precision | {0.4090909090909091,0.752411575562701,0.5}
+recall | {0.45188284518828453,0.8153310104529616,0.42071197411003236}
+fscore | {0.42942345924453285,0.782608695652174,0.45694200351493847}
+support | {239.0,287.0,309.0}
+test_ids | {3975,35,1759,1469,3437,3951...}
+-[ RECORD 2 ]+---------------------------------------------
+mdl_train_ts | 2022-05-16 17:56:11.488767+03
+mdl_name | abalone_xgboost
+features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings}
+params | ('colsample_bytree=0.85', 'learning_rate=0.01', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=9')
+fnames | {viscera_weight,whole_weight,shucked_weight,shell_weight,length,rings,diameter,height}
+importance | {1268,1196,1182,860,832,668,595,461}
+precision | {0.46096654275092935,0.6566265060240963,0.5213675213675214}
+recall | {0.4901185770750988,0.8288973384030418,0.3824451410658307}
+fscore | {0.475095785440613,0.7327731092436974,0.4412296564195299}
+support | {253.0,263.0,319.0}
+test_ids | {2988,2303,2034,3085,2465,2887...}
+-[ RECORD 3 ]+---------------------------------------------
+mdl_train_ts | 2022-05-16 17:56:11.488767+03
+mdl_name | abalone_xgboost
+features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings}
+params | ('colsample_bytree=0.85', 'learning_rate=0.1', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=9')
+fnames | {shucked_weight,whole_weight,viscera_weight,shell_weight,length,diameter,height,rings}
+importance | {998,948,862,765,629,489,441,383}
+precision | {0.4635036496350365,0.6986301369863014,0.45724907063197023}
+recall | {0.4847328244274809,0.75,0.40863787375415284}
+fscore | {0.47388059701492535,0.7234042553191489,0.43157894736842106}
+support | {262.0,272.0,301.0}
+test_ids | {396,2150,809,2846,1108,1841...}
+-[ RECORD 3 ]+---------------------------------------------
+mdl_train_ts | 2022-05-16 17:56:11.488767+03
+mdl_name | abalone_xgboost
+features | {length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings}
+params | ('colsample_bytree=0.85', 'learning_rate=0.1', 'min_child_weight=10', 'n_estimators=100', 'subsample=0.85', 'max_depth=12')
+fnames | {shucked_weight,viscera_weight,whole_weight,shell_weight,length,diameter,height,rings}
+importance | {1101,1056,1045,958,680,621,458,458}
+precision | {0.40484429065743943,0.7016949152542373,0.43824701195219123}
+recall | {0.4517374517374517,0.75,0.36666666666666664}
+fscore | {0.42700729927007297,0.7250437828371278,0.3992740471869329}
+support | {259.0,276.0,300.0}
+test_ids | {1740,2777,1907,581,3525,1022...}
+</pre>
+
+-# Run XGBoost prediction using params_index=2
+<pre class="example">
+DROP TABLE IF EXISTS xgb_score_out, xgb_score_out_metrics, xgb_score_out_roc_curve;
+SELECT xgboost_predict(
+ 'abalone', -- test_table
+ 'xgb_out', -- model_table
+ 'xgb_score_out', -- predict_output_table
+ 'id', -- id_column
+ 'sex', -- class_label
+ 2 -- params_index
+);
+\\x off
+SELECT * FROM xgb_score_out LIMIT 10;
+</pre>
+<pre class="result">
+ id | sex_predicted | sex_proba_predicted
+----+---------------+------------------------------------------------
+ 5 | I | {0.178420484066,0.599636971951,0.221942588687}
+ 6 | I | {0.185853347182,0.602131128311,0.212015494704}
+ 9 | I | {0.253592431545,0.440728843212,0.30567869544}
+ 10 | M | {0.374555230141,0.249226689339,0.376218110323}
+ 11 | F | {0.402999937534,0.336779236794,0.260220855474}
+ 13 | I | {0.338803291321,0.36541134119,0.295785397291}
+ 14 | F | {0.377499818802,0.301990658045,0.320509523153}
+ 17 | I | {0.179169252515,0.602536559105,0.218294218183}
+ 21 | M | {0.27216938138,0.33275142312,0.395079195499}
+ 25 | F | {0.449239164591,0.187060594559,0.36370024085}
+</pre>
+
+
+
+
+@anchor literature
+@literature
+
+[1] Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System.
+In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge
+Discovery and Data Mining (pp. 785–794). New York, NY, USA: ACM.
+https://doi.org/10.1145/2939672.2939785
+
+*/
+
+m4_include(`SQLCommon.m4')
+
+DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__serialize_pandas_dframe_as_bytea__(
+ TEXT,
+ TEXT,
+ TEXT,
+ TEXT[]
+);
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__serialize_pandas_dframe_as_bytea__(
+ source_table TEXT,
+ id_column TEXT,
+ class_label TEXT,
+ features TEXT
+)
+RETURNS BYTEA
+AS
+$$
+ PythonFunctionBodyOnly(mxgboost, madlib_xgboost)
+ with AOControl(False):
+ return madlib_xgboost.serialize_pandas_dframe_as_bytea(schema_madlib, source_table, id_column, class_label, features)
+$$ LANGUAGE plpythonu VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
+
+
+DROP TYPE IF EXISTS MADLIB_SCHEMA.xgb_gridsearch_train_results_type CASCADE;
+CREATE TYPE MADLIB_SCHEMA.xgb_gridsearch_train_results_type
+AS
+(
+ features TEXT[],
+ mdl BYTEA,
+ params TEXT,
+ fnames TEXT[],
+ importance TEXT[],
+ precision TEXT[],
+ recall TEXT[],
+ fscore TEXT[],
+ support TEXT[],
+ test_ids INTEGER[]
+);
+
+DROP FUNCTION IF EXISTS MADLIB_SCHEMA.__xgboost_train_parallel__(
+ BYTEA,
+ TEXT[],
+ TEXT,
+ TEXT,
+ TEXT,
+ NUMERIC
+);
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.__xgboost_train_parallel__(
+ dframe BYTEA,
+ features_all TEXT[],
+ class_label TEXT,
+ params TEXT,
+ class_weights TEXT,
+ train_set_size NUMERIC,
+ id_column TEXT,
+ train_set_split_var TEXT
+)
+RETURNS MADLIB_SCHEMA.xgb_gridsearch_train_results_type
+AS
+$$
+ PythonFunctionBodyOnly(mxgboost, madlib_xgboost)
+ with AOControl(False):
+ return madlib_xgboost.xgboost_train(schema_madlib, dframe, features_all, class_label, params, class_weights, train_set_size, id_column, train_set_split_var)
+$$ LANGUAGE plpythonu VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
+DROP FUNCTION IF EXISTS MADLIB_SCHEMA.xgboost(
+ TEXT,
+ TEXT,
+ TEXT,
+ TEXT[],
+ TEXT,
+ TEXT,
+ TEXT,
+ TEXT,
+ NUMERIC,
+ TEXT
+);
+
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost(
+ source_table TEXT,
+ grid_search_results_tbl TEXT,
+ id_column TEXT,
+ class_label TEXT,
+ list_of_features TEXT,
+ list_of_features_to_exclude TEXT[] DEFAULT NULL,
+ params_str TEXT DEFAULT NULL,
+ class_weights TEXT DEFAULT NULL,
+ train_set_size NUMERIC DEFAULT 0.8,
+ train_set_split_var TEXT DEFAULT NULL
+)
+RETURNS VOID
+AS
+$$
+ PythonFunctionBodyOnly(mxgboost, madlib_xgboost)
+ with AOControl(False):
+ madlib_xgboost.xgboost_grid_search(schema_madlib, source_table, id_column, class_label, list_of_features, list_of_features_to_exclude, params_str, grid_search_results_tbl, class_weights, train_set_size, train_set_split_var)
+$$ LANGUAGE plpythonu VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
+
+DROP FUNCTION IF EXISTS MADLIB_SCHEMA.xgboost_predict(
+ TEXT,
+ TEXT,
+ TEXT,
+ TEXT,
+ TEXT,
+ INTEGER);
+CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.xgboost_predict(
+ scoring_tbl TEXT,
+ mdl_table TEXT,
+ predict_output_table TEXT,
+ id_column TEXT,
+ class_label TEXT DEFAULT NULL,
+ params_index INTEGER DEFAULT 1
+)
+RETURNS VOID
+AS
+$$
+ PythonFunctionBodyOnly(mxgboost, madlib_xgboost)
+ with AOControl(False):
+ madlib_xgboost.xgboost_predict(schema_madlib, scoring_tbl, mdl_table, predict_output_table, id_column, class_label, params_index)
+$$ LANGUAGE plpythonu VOLATILE
+m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
diff --git a/src/ports/postgres/modules/mxgboost/test/madlib_xgboost.sql_in b/src/ports/postgres/modules/mxgboost/test/madlib_xgboost.sql_in
new file mode 100644
index 00000000..345129e1
--- /dev/null
+++ b/src/ports/postgres/modules/mxgboost/test/madlib_xgboost.sql_in
@@ -0,0 +1,4285 @@
+/* ----------------------------------------------------------------------- *//**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ *
+ *//* ----------------------------------------------------------------------- */
+
+
+DROP TABLE IF EXISTS abalone;
+CREATE TABLE abalone (
+ id integer,
+ sex char(1),
+ length float,
+ diameter float,
+ height float,
+ whole_weight float,
+ shucked_weight float,
+ viscera_weight float,
+ shell_weight float,
+ rings integer
+ );
+
+
+INSERT INTO abalone VALUES
+(1,'M',0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15),
+(2,'M',0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7),
+(3,'F',0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9),
+(4,'M',0.44,0.365,0.125,0.516,0.2155,0.114,0.155,10),
+(5,'I',0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7),
+(6,'I',0.425,0.3,0.095,0.3515,0.141,0.0775,0.12,8),
+(7,'F',0.53,0.415,0.15,0.7775,0.237,0.1415,0.33,20),
+(8,'F',0.545,0.425,0.125,0.768,0.294,0.1495,0.26,16),
+(9,'M',0.475,0.37,0.125,0.5095,0.2165,0.1125,0.165,9),
+(10,'F',0.55,0.44,0.15,0.8945,0.3145,0.151,0.32,19),
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+(23,'F',0.565,0.44,0.155,0.9395,0.4275,0.214,0.27,12),
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+(26,'F',0.56,0.44,0.14,0.9285,0.3825,0.188,0.3,11),
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+(36,'M',0.465,0.355,0.105,0.4795,0.227,0.124,0.125,8),
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+(4090,'M',0.61,0.365,0.155,1.0765,0.488,0.249,0.27,9),
+(4091,'M',0.615,0.475,0.205,1.337,0.5995,0.2815,0.37,11),
+(4092,'M',0.625,0.5,0.18,1.3705,0.645,0.303,0.3705,12),
+(4093,'F',0.625,0.49,0.19,1.7015,0.7465,0.4105,0.3855,11),
+(4094,'M',0.63,0.485,0.18,1.2435,0.5175,0.308,0.37,11),
+(4095,'M',0.63,0.53,0.175,1.4135,0.667,0.2945,0.3555,13),
+(4096,'F',0.635,0.485,0.155,1.073,0.467,0.1975,0.35,11),
+(4097,'F',0.635,0.5,0.175,1.477,0.684,0.3005,0.39,12),
+(4098,'M',0.635,0.5,0.18,1.2915,0.594,0.2695,0.37,9),
+(4099,'F',0.65,0.495,0.16,1.3105,0.577,0.3315,0.355,9),
+(4100,'M',0.67,0.525,0.18,1.4915,0.728,0.343,0.381,9),
+(4101,'F',0.675,0.52,0.175,1.494,0.7365,0.3055,0.37,9),
+(4102,'F',0.675,0.51,0.15,1.1965,0.475,0.304,0.386,11),
+(4103,'M',0.68,0.545,0.185,1.672,0.7075,0.364,0.48,11),
+(4104,'M',0.7,0.545,0.215,1.9125,0.8825,0.4385,0.506,10),
+(4105,'F',0.71,0.545,0.175,1.907,0.8725,0.4565,0.475,11),
+(4106,'F',0.715,0.565,0.18,1.79,0.844,0.3535,0.5385,9),
+(4107,'F',0.72,0.59,0.205,1.7495,0.7755,0.4225,0.48,11),
+(4108,'I',0.42,0.305,0.1,0.3415,0.1645,0.0775,0.086,7),
+(4109,'I',0.48,0.35,0.1,0.519,0.2365,0.1275,0.126,7),
+(4110,'M',0.48,0.365,0.13,0.5305,0.2405,0.127,0.139,8),
+(4111,'M',0.51,0.41,0.155,1.2825,0.569,0.291,0.3795,9),
+(4112,'I',0.515,0.4,0.14,0.7165,0.3495,0.1595,0.1785,8),
+(4113,'F',0.56,0.42,0.18,1.6645,0.7755,0.35,0.4525,9),
+(4114,'I',0.56,0.42,0.14,0.837,0.414,0.214,0.2,8),
+(4115,'F',0.57,0.45,0.15,0.9645,0.531,0.189,0.209,9),
+(4116,'F',0.605,0.465,0.155,1.1,0.547,0.2665,0.2585,10),
+(4117,'M',0.625,0.48,0.16,1.2415,0.6575,0.2625,0.2785,9),
+(4118,'F',0.64,0.505,0.175,1.3185,0.6185,0.302,0.3315,9),
+(4119,'M',0.65,0.525,0.185,1.3455,0.586,0.278,0.3865,9),
+(4120,'I',0.3,0.215,0.05,0.1185,0.048,0.0225,0.042,4),
+(4121,'M',0.35,0.265,0.09,0.197,0.073,0.0365,0.077,7),
+(4122,'I',0.455,0.35,0.13,0.4725,0.215,0.0745,0.15,9),
+(4123,'I',0.46,0.365,0.11,0.4495,0.1755,0.102,0.15,8),
+(4124,'I',0.49,0.375,0.115,0.557,0.2275,0.1335,0.1765,8),
+(4125,'I',0.5,0.385,0.12,0.516,0.197,0.1305,0.165,8),
+(4126,'I',0.54,0.415,0.135,0.709,0.3195,0.174,0.185,9),
+(4127,'M',0.55,0.42,0.145,0.7385,0.321,0.1485,0.252,11),
+(4128,'I',0.55,0.445,0.11,0.7935,0.378,0.142,0.26,10),
+(4129,'M',0.555,0.435,0.145,0.9205,0.404,0.2275,0.255,8),
+(4130,'I',0.57,0.425,0.14,0.7655,0.331,0.14,0.24,10),
+(4131,'M',0.58,0.45,0.14,0.824,0.3465,0.1765,0.263,10),
+(4132,'I',0.58,0.425,0.145,0.83,0.379,0.1605,0.2575,11),
+(4133,'I',0.585,0.47,0.17,0.985,0.3695,0.2395,0.315,10),
+(4134,'M',0.585,0.45,0.15,0.997,0.4055,0.283,0.251,11),
+(4135,'F',0.595,0.455,0.14,0.914,0.3895,0.2225,0.271,9),
+(4136,'F',0.6,0.5,0.17,1.13,0.4405,0.267,0.335,11),
+(4137,'F',0.615,0.495,0.155,1.0805,0.52,0.19,0.32,9),
+(4138,'M',0.63,0.505,0.155,1.105,0.492,0.226,0.325,11),
+(4139,'M',0.63,0.49,0.155,1.229,0.535,0.29,0.335,11),
+(4140,'F',0.635,0.495,0.175,1.2355,0.5205,0.3085,0.347,10),
+(4141,'F',0.645,0.535,0.19,1.2395,0.468,0.2385,0.424,10),
+(4142,'F',0.65,0.505,0.165,1.357,0.5725,0.281,0.43,11),
+(4143,'M',0.655,0.525,0.18,1.402,0.624,0.2935,0.365,13),
+(4144,'F',0.655,0.5,0.22,1.359,0.642,0.3255,0.405,13),
+(4145,'M',0.67,0.535,0.19,1.669,0.7465,0.2935,0.508,11),
+(4146,'M',0.67,0.525,0.2,1.7405,0.6205,0.297,0.657,11),
+(4147,'M',0.695,0.53,0.21,1.51,0.664,0.4095,0.385,10),
+(4148,'M',0.695,0.55,0.195,1.6645,0.727,0.36,0.445,11),
+(4149,'M',0.77,0.605,0.175,2.0505,0.8005,0.526,0.355,11),
+(4150,'I',0.28,0.215,0.07,0.124,0.063,0.0215,0.03,6),
+(4151,'I',0.33,0.23,0.08,0.14,0.0565,0.0365,0.046,7),
+(4152,'I',0.35,0.25,0.075,0.1695,0.0835,0.0355,0.041,6),
+(4153,'I',0.37,0.28,0.09,0.218,0.0995,0.0545,0.0615,7),
+(4154,'I',0.43,0.315,0.115,0.384,0.1885,0.0715,0.11,8),
+(4155,'I',0.435,0.33,0.095,0.393,0.219,0.075,0.0885,6),
+(4156,'I',0.44,0.35,0.11,0.3805,0.1575,0.0895,0.115,6),
+(4157,'M',0.475,0.37,0.11,0.4895,0.2185,0.107,0.146,8),
+(4158,'M',0.475,0.36,0.14,0.5135,0.241,0.1045,0.155,8),
+(4159,'I',0.48,0.355,0.11,0.4495,0.201,0.089,0.14,8),
+(4160,'F',0.56,0.44,0.135,0.8025,0.35,0.1615,0.259,9),
+(4161,'F',0.585,0.475,0.165,1.053,0.458,0.217,0.3,11),
+(4162,'F',0.585,0.455,0.17,0.9945,0.4255,0.263,0.2845,11),
+(4163,'M',0.385,0.255,0.1,0.3175,0.137,0.068,0.092,8),
+(4164,'I',0.39,0.31,0.085,0.344,0.181,0.0695,0.079,7),
+(4165,'I',0.39,0.29,0.1,0.2845,0.1255,0.0635,0.081,7),
+(4166,'I',0.405,0.3,0.085,0.3035,0.15,0.0505,0.088,7),
+(4167,'I',0.475,0.365,0.115,0.499,0.232,0.0885,0.156,10),
+(4168,'M',0.5,0.38,0.125,0.577,0.269,0.1265,0.1535,9),
+(4169,'F',0.515,0.4,0.125,0.615,0.2865,0.123,0.1765,8),
+(4170,'M',0.52,0.385,0.165,0.791,0.375,0.18,0.1815,10),
+(4171,'M',0.55,0.43,0.13,0.8395,0.3155,0.1955,0.2405,10),
+(4172,'M',0.56,0.43,0.155,0.8675,0.4,0.172,0.229,8),
+(4173,'F',0.565,0.45,0.165,0.887,0.37,0.239,0.249,11),
+(4174,'M',0.59,0.44,0.135,0.966,0.439,0.2145,0.2605,10),
+(4175,'M',0.6,0.475,0.205,1.176,0.5255,0.2875,0.308,9),
+(4176,'F',0.625,0.485,0.15,1.0945,0.531,0.261,0.296,10),
+(4177,'M',0.71,0.555,0.195,1.9485,0.9455,0.3765,0.495,12);
+
+SELECT xgboost(
+ 'abalone', -- Training table
+ 'xgb_single_out', -- Grid search results table.
+ 'id', -- Id column
+ 'sex', -- Class label column
+ 'rings', -- Independent variables
+ NULL, -- Columns to exclude from features
+ $$
+ {
+ 'learning_rate': [0.01], #Regularization on weights (eta). For smaller values, increase n_estimators
+ 'max_depth': [9],#Larger values could lead to overfitting
+ 'subsample': [0.85],#introduce randomness in samples picked to prevent overfitting
+ 'colsample_bytree': [0.85],#introduce randomness in features picked to prevent overfitting
+ 'min_child_weight': [10],#larger values will prevent over-fitting
+ 'n_estimators':[100] #More estimators, lesser variance (better fit on test set)
+ }
+ $$, -- XGBoost grid search parameters
+ '', -- Class weights
+ 0.8, -- Training set size ratio
+ NULL -- Variable used to do the test/train split.
+);
+
+SELECT xgboost_predict(
+ 'abalone', -- test_table
+ 'xgb_single_out', -- model_table
+ 'xgb_single_score_out', -- predict_output_table
+ 'id', -- id_column
+ 'sex', -- class_label
+ 1 -- model_filters
+);
+
+SELECT xgboost(
+ 'abalone', -- Training table
+ 'xgb_grid_out', -- Grid search results table.
+ 'id', -- Id column
+ 'sex', -- Class label column
+ '*', -- Independent variables
+ NULL, -- Columns to exclude from features
+ $$
+ {
+ 'learning_rate': [0.01,0.1], #Regularization on weights (eta). For smaller values, increase n_estimators
+ 'max_depth': [9,12],#Larger values could lead to overfitting
+ 'subsample': [0.85],#introduce randomness in samples picked to prevent overfitting
+ 'colsample_bytree': [0.85],#introduce randomness in features picked to prevent overfitting
+ 'min_child_weight': [10],#larger values will prevent over-fitting
+ 'n_estimators':[100] #More estimators, lesser variance (better fit on test set)
+ }
+ $$, -- XGBoost grid search parameters
+ '', -- Class weights
+ 0.8, -- Training set size ratio
+ NULL -- Variable used to do the test/train split.
+);
+
+SELECT xgboost_predict(
+ 'abalone', -- test_table
+ 'xgb_grid_out', -- model_table
+ 'xgb_grid_score_out', -- predict_output_table
+ 'id', -- id_column
+ 'sex', -- class_label
+ 2 -- model_filters
+);
+
+SELECT xgboost(
+ 'abalone',
+ 'xgb_default_out',
+ 'id',
+ 'sex',
+ '*'
+);
[madlib] 03/04: Fix jenkins
Posted by ok...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
okislal pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/madlib.git
commit 4a98bc47a73d729e5fbe9f1ccb19acb9bdb8f17d
Author: Orhan Kislal <ok...@apache.org>
AuthorDate: Wed May 18 15:21:43 2022 +0300
Fix jenkins
---
tool/jenkins/jenkins_build.sh | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/tool/jenkins/jenkins_build.sh b/tool/jenkins/jenkins_build.sh
index 6a1c0b86..9e486085 100755
--- a/tool/jenkins/jenkins_build.sh
+++ b/tool/jenkins/jenkins_build.sh
@@ -60,9 +60,9 @@ sleep 15
echo "---------- Install pip, and mock -----------"
# cmake, make, make install, and make package
cat <<EOF
-docker exec madlib bash -c 'apt-get update; apt-get install -y python-pip; pip install mock' | tee $workdir/logs/madlib_compile.log
+docker exec madlib bash -c 'apt-get update; apt-get install -y python-pip; pip install mock pandas numpy xgboost scikit-learn' | tee $workdir/logs/madlib_compile.log
EOF
-docker exec madlib bash -c 'apt-get update; apt-get install -y python-pip; pip install mock' | tee $workdir/logs/madlib_compile.log
+docker exec madlib bash -c 'apt-get update; apt-get install -y python-pip; pip install mock pandas numpy xgboost scikit-learn' | tee $workdir/logs/madlib_compile.log
echo "---------- Building package -----------"
# cmake, make, make install, and make package
[madlib] 01/04: Update gitignore to exclude /cmake-build-debug
Posted by ok...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
okislal pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/madlib.git
commit 011bddf96b7a015ce0235f89a615e76fbedaee14
Author: Orhan Kislal <ok...@apache.org>
AuthorDate: Thu May 12 16:39:08 2022 +0300
Update gitignore to exclude /cmake-build-debug
---
.gitignore | 1 +
1 file changed, 1 insertion(+)
diff --git a/.gitignore b/.gitignore
index 2cbb50bb..fb590136 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
/build*
/build-docker*
/src/ports/postgres/modules/dbscan/test/perf/blobs
+/cmake-build-debug/
# Ignore generated code files
*.so
[madlib] 02/04: Add users for docker commands
Posted by ok...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
okislal pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/madlib.git
commit 9f1c50291846a6710261873d1475204c95a5ccd3
Author: Orhan Kislal <ok...@apache.org>
AuthorDate: Tue May 17 16:03:26 2022 +0300
Add users for docker commands
---
tool/docker_start.sh | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/tool/docker_start.sh b/tool/docker_start.sh
index bd193e43..bb7bd7f6 100755
--- a/tool/docker_start.sh
+++ b/tool/docker_start.sh
@@ -83,13 +83,13 @@ docker exec "${CONTAINER_NAME}" bash -c "rm -rf /madlib/build_docker; \
echo "---------- Installing and running install-check --------------------"
# Install MADlib and run install check
-docker exec "${CONTAINER_NAME}" bash -c "PATH=$PATH:/usr/lib/postgresql/10/bin \
+docker exec --user=postgres "${CONTAINER_NAME}" bash -c "PATH=$PATH:/usr/lib/postgresql/10/bin \
/madlib/build_docker/src/bin/madpack -p postgres \
-c postgres/postgres@localhost:5432/postgres \
install" \
| tee "${workdir}/build_docker_logs/madlib_install.log"
-docker exec "${CONTAINER_NAME}" bash -c "/madlib/build_docker/src/bin/madpack -p postgres \
+docker exec --user=postgres "${CONTAINER_NAME}" bash -c "/madlib/build_docker/src/bin/madpack -p postgres \
-c postgres/postgres@localhost:5432/postgres \
install-check" \
| tee "${workdir}/build_docker_logs/madlib_install_check.log"